Affordable Care Act and Labor Market

Transcription

Affordable Care Act and Labor Market
Hanming Fang
University of Pennsylvania
CEAR/Huebner Summer Risk Institute
July 28-29, 2014
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Outline
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Patient Protection and Affordable Care Act (ACA): A Brief
Introduction
US Employment-Based Health Insurance System and Medical
Expenditure
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Would the Troubles in the Initial Roll Out of HealthCare.gov last?
And Does the Form of Individual Mandate Penalty Matter?
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Fang and Gavazza (2011, AER): Dynamic Inefficiencies in an
Employment Based Health Insurance System: Theory and Evidence
Scheuer and Smetters (2014, NBER Working Paper): Could a Website
Really Have Doomed the Health Insurance Exchange? Multiple
Equilibria, Initial Conditions and the Constructionof the Fine.
Equilibrium Labor Market and Health Insurance Reform
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Aizawa and Fang (2013): Equilibrium Labor Market Search and Helath
Insurance Reform
Fang and Shephard (WIP): Joint Household Labor Supply and Health
Care Reform
Fang, Shephard and Tilly (WIP): Equilibrium Labor Market Search
with Endogenous Technology Choice and Health Insurance
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I: Patient Protection and Affordable Care Act (ACA): A Brief
Introduction
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Health Insurance Coverage of the Total Population in the
US (2011-12)
Source
Percentage (%)
Employer
48
Other Private
5
Medicaid
16
Medicare
14
Other Public
1
Uninsured
15
Total
100
Source: Kaiser Family Foundation
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National Health Expenditure as a Share of GDP:
1961-2011
Share of National Health Expenditures in GDP, 1961-2011
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
1961
1965
1970
1975
1980
1985
1990
1995
2000
2005
2011
NHE as a Share of GDP
SOURCE: Kaiser Family Foundation calculations using NHE data from Centers for Medicare and Medicaid Services, Office of
the Actuary, National Health Statistics Group, at http://www.cms.hhs.gov/NationalHealthExpendData/ (see Historical; National
Health Expenditures by type of service and source of funds, CY 1960-2011; file nhe2011.zip). Gross Domestic Product data from
Bureau of Economic Analysis, at http://bea.gov/national/index.htm#gdp (file gdplev.xls).
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0
2.4
11.0
10.9
9.1
9.0
Brazil
8.5
Turkey
Indonesia
India
China
Russian Fed.
4.2
6.9
Korea
5.4
6.1
6.4
Poland
Estonia
Mexico
7.4
7.4
7.0
Hungary
7.9
7.8
Israel
Luxembourg ³
8.4
8.2
Chile
Czech Rep.
8.5
Japan
South Africa
8.7
Slovak Rep.
Australia
9.3
9.2
Finland
Total
Slovenia
9.5
9.5
9.5
Italy
Ireland
17.4
% of GDP
Spain
9.6
9.6
9.6
OECD
9.7
Iceland
Norway
Greece
10.0
9.8
United Kingdom
10.1
Sweden
10.3
Portugal
New Zealand
Belgium ²
Austria
11.4
11.4
Canada
12.0
Switzerland
2
11.8
4
11.6
4.6
6
11.5
8
France
10
Denmark
12
Germany
Netherlands ¹
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United States
National Health Expenditure as a Share of GDP in 2011 In
Various Countries
20
Residual
16
14
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ACA on Access: Title I: Quality, Affordable Health Care for
All Americans
prohibits insurance companies from denying coverage based on
preexisting conditions
Caps out of pocket expenditures
Extends dependent coverage for young adults to age 26
Requires full coverage for preventive care and immunization
creates individual and small-business insurance exchanges
establishes tax subsidies for individuals up to 400% of the federal
poverty level (FPL) and employers
establishes individual and employer mandates and penalties
requires minimum medical loss ratio for insurance companies
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ACA on Access: Title II: The Role of Public Programs
Expands Medicaid to 133% of FPL (with an across-the-board 5%
income disregard)
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The federal government would pay 100% of the cost of Medicaid
expansion from 2014-2016, declining to 95% (2017), 94% (2018), 93%
(2019) and 90% (2020 and beyond)
Ruled unconstitutional by the Supreme Court, allowing States to opt
out
Currently, 26 States and DC have expanded Medicaid; 20 decided not
to expand; and 4 are still undecided
The expanded Medicaid program is supposed to offer a package that
fulfills the requirements of the “essential benefits” that is the basis of
the health insurance options in the exchange - this is not as
comprehensive as the traditional Medicaid benefits.
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Where Do States Stand on Medicaid Expansion?
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Health Insurance Exchange
Title I, Section 1311-1312: States are permitted to create two
health insurance exchanges, one for individuals and one for small
businesses of up to 100 employees (called SHOP exchanges, and
could be opened to larger companies after 2017).
Structure of Marketplace:
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part of an existing state agency or office (Operated by State);
an independent public agency (Quasi-governmental);
a non-profit entity (Non-profit).
Title I, Section 1333: ACA permits groups of several states to
affiliate and form a regional exchange instead of each individual state
operating its own exchange (in 2014, no such regional exchange was
formed).
Beginning in 2016, states can allow insurance companies from
another state to offer plans in their state, but not on the exchange.
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Health Insurance Plans in the Exchange
Four different tiers: bronze, silver, gold and platinum, representing
different levels of coverage and costs:
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bronze: cover about 60% of total predicted health care costs, with a
limit on out-of-pocket expense set at $6,250 for individuals and
$12,500 for families. The bronze plan quality as meeting the minimum
essential coverage necessary for satisfying individual mandate.
Silver: cover 70%;
Gold: 80%;
Platinum: 90%.
Catastrophic coverage option: available only to people up to 30 years
of age or those exempt from the individual mandate because they can
not afford their health plan
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Premium Setting of the Health Insurance Plans in the
Exchange
Premiums for the different levels of plans will vary by state (including
geographic rating area within a state), the way the plans are
designed, and the networks and prices insurance companies negotiated
with hospitals and physicians;
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also by age curve and smoking status (Age: 3:1; Smoking status: 1.5:1)
DHHS’ guidance on age curve (Feb. 25, 2013):
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Children: A single age band for children ages 0 through 20.
Adults: One-year age bands for adults ages 21 through 63.
Older adults: A single age band for adults ages 64 and older.
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Subsidies
Two types of subsidies (available only to those who purchase
insurance from HIX)
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premium subsidy:refundable and advanceable tax credits to helps
people whose income is between 100% and 400% of the FPL to pay for
the insurance premium
cost sharing subsidy:help poorer individuals and families to pay for the
out-of-pocket expenses associated with insurance, such as deductibles
and co-pays.
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Premium Subsidy (Tax Credits)
Eligibility for premium subsidy:
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Citizens and legal residents in families with incomes between 100% and
400% of poverty who purchase coverage through a health insurance
exchange are eligible for a tax credit to reduce the cost of coverage.
People eligible for public coverage are not eligible for premium
assistance in exchanges. In states without expanded Medicaid
coverage, people with incomes less than 100% of poverty will not be
eligible for exchange subsidies, while those with incomes at or above
poverty will be.
People offered coverage through an employer are also not eligible
for premium tax creditsunless the employer plan does not have an
actuarial value of at least 60% or unless the person’s share of the
premium for employer-sponsored insurance exceeds 9.5% of income.
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Level of premium subsidy:
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based on the premium for the second lowest cost silver plan in the
exchange and area where the person is eligible to purchase coverage.
A silver plan is a plan that provides the essential benefits and has an
actuarial value of 70%.
A person who wants to purchase a plan that is more expensive would
have to pay the full difference between the cost of the second lowest
cost silver plan and the plan that they wish to purchase.
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The amount of the tax credit varies with income such that the
premium that the premium a person would have to pay for the second
lowest cost silver plan would not exceed a specified percentage of
their income (adjusted for family size):
Income Level
Up to 133% FPL
133-150% FPL
150-200% FPL
200-250% FPL
250-300% FPL
300-400% FPL
Premium as a Percent of Income
2% of income
3% of income
4% of income
6.3% of income
8.05% of income
9.5% of income
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Cost-Sharing Subsidy
Cost-sharing subsidies protect lower income people with health
insurance from high out-of-pocket costs at the point of service for
essential benefits.
Generally, the limits are based on the maximum out-of-pocket limits
for Health Savings Account-qualified health plans ($5,950 for single
coverage and $11,900 for family coverage in 2010), which will be
indexed to the change in the Consumer Price Index until 2014 when
the provision takes effect.
After 2014, the limits will be indexed to the change in the cost of
health insurance.
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Cost-Sharing Subsidy
People with incomes at or below 400% of poverty have their
out-of-pocket liability capped at lower levels, as follows:
Income Level
100-200% FPL
200-300% FPL
300-400% FPL
Reduction in Out-of-Pocket Liability
Two-thirds of the maximum
One-half of the maximum
One-third of the maximum
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States’ Choices of Own Exchange or Federal Exchange
State Health Insurance Marketplace Decisions, 2014 VT WA ND MT OR WI SD ID WY UT* CA AZ CO NM IL KS MO OK TX HI MI PA OH IN WV KY AR AL VA CT NJ DE MD MA RI DC NC TN MS AK NY IA NE NV ME NH MN SC GA LA FL State-‐based Marketplace (16 states and DC) Partnership Marketplace (7 states) Federally-‐facilitated Marketplace (27 states) * In Utah, the federal government will run the marketplace for individuals while the state will run the small business, or
SHOP, marketplace.
SOURCE: State Decisions For Creating Health Insurance Marketplaces, 2014, KFF State Health Facts:
h"p://kﬀ.org/health-‐reform/state-‐indicator/health-‐insurance-‐exchanges/.
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HIX Competition by State
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Health Insurance Exchanges: Individual market competition
Now that open enrollment has begun and Qualified Health Plans (QHP) application and rate filing deadlines have passed, details surrounding the competitive
landscape of each state’s health insurance exchange (HIX) have emerged. Here’s an analysis on the number of medical carriers competing in states’ individual HIX
markets in 2014.
Projected number of medical carriers in the individual market per state and breakdown of
state-based, state-partnership, and federally-facilitated exchanges as of October 2, 2013
WA (8)
ME (2)
MT (3)
ND (3)
VT (2)
MN
(5)
OR (11)
ID (4)
NH (1)
WI
(13)
SD (3)
MI
(9)
IA (4)
NE (4)
NV (4)
IL
(5)
UT (6)
CA
(12)
MA (10)
RI (2)
NY (16)
WY (2)
CO (10)
KS (3)
OK (4)
NM (5)
NJ
CT (3)
NJ (3)
DE (2)
VA
(5)
WV (1)
MO
(3)
KY
(5)
MD (4)
DC (3)
NC (2)
TN (4)
AZ (8)
PA (8)
OH
(12)
IN
(4)
SC
(4)
AR
(3)
MS
(2)
AL
(2)
GA
(5)
TX (11)
LA (4)
AK
(2)
FL
(9)
HI (2)
Legend:
1-3 carriers
4-6 carriers
State-based exchange
7-9 carriers
10-12 carriers
State-partnership exchange
13+ carriers
Federally-facilitated exchange
Additional Notes:
• Unless otherwise noted, data represents number of applicants based on QHP or rate filing submissions that are
publically available. These are subject to changes and approval by federal and/or state regulators.
• Products offered under subsidiaries of the same company are counted as one carrier
• National multi-state plans (MSP) BCBS products and state BCBS products are counted as separate carriers
(including in NM, MI, KS, and AK. 31 states are expected to have MSP plans but has not released the list.
• UT: individual exchange will be facilitated by the federal government; SHOP will be state-based
• NM and ID: federal government will help run individual markets in 2014. States will continue to maintain plan
management and consumer assistance functions; HHS will operate the IT systems. SHOP will be state-based
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Regulations on HIX
Minimum Essential Coverage for Qualified Plans: 10 categories
of service are specified in the ACA but the States are given discretion
over the specific services in each category
ACA only permits qualified health plans to be offered in the exchange.
To be qualified, a health plan must be accredited and show contracts
with hospitals, physicians and other providers, as well as financial
resources to operate. Moreover,
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guaranteed issue: no one should be rejected for preexisting conditions
and premium can not depend on preexisting conditions
guaranteed renewability
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Mandates: Individual Mandate
Individual Mandate: individuals who do not purchase insurance that
meets the minimum essential benefits at the bronze level of coverage
will face a “tax”.
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The ”tax” will be phased in: $95 per person in 2014; by 2016, it will
be $695 per year per person or 2.5% of the household income, with a
max limit of $2,085. The numbers will the indexed to cost of living
after 2016.
Certain exemptions to individual mandate: certain religious groups, and
American Indians
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Mandates: Employer Mandate
Employer Mandate: the idea is to discourage employers from
discontinuing their employees’ health insurance
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employers with an average of 50 or more full time workers during the
preceding year (or the current year if employer is new) are required to
offer ESHI or face a penalty of $2,000 per worker (with an allowance of
30 workers in the calculation of the penalty).
IRS regulations point out that:
Any group of companies under ”common control”are to be treated as a
single company.
Common control is defined as the same five or fewer people owning at
least 80 percent of the companies
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Other Important Elements of ACA
Cost Control:
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Reducing health care prices (e.g. change the payment rates for
Medicare Advantage Plans; reduce the annual increase in hospital
payments; reduce Disproportionate Share Hosptial Payments;
competitive bidding for durable medical equipment (DME)
Reducing Health Care Utilization: Accountable Care Organization
(ACO) instead of fee-for-service.
Independent Payment Advisory Board (IPAB)
Quality of Care: (electronic health records, etc)
Prevention and Health Promotion
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II: US Employment-Based Health Insurance System and Medical
Expenditure
Fang and Gavazza (2011, AER): Dynamic Inefficiencies in an
Employment Based Health Insurance System: Theory and Evidence
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Fang and Gavazza (2011): Introduction
The health care system in the United States differs in at least two
stark ways from those of other industrialized countries.
The first is its institutional organization:
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The United States is unique among industrialized nations in that it
lacks a national health insurance system.
The U.S. health insurance system is a mixture of private and public
insurances, with private insurance playing a much more important role
than in other industrialized countries.
In particular, in the U.S., a private, employment-based system provides
insurance to most of the working-age population, while a public
program—i.e., Medicare—provides insurance to almost all individuals
aged 65 and over.
The second difference is its costs:
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The U.S. spends more than twice as much on health care as a fraction
of G.D.P. as other developed countries.
For example, in 2005, the U.S. and the U.K. spent about 17 and eight
percent of their GDP, respectively, on health care.
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Introduction
This paper investigates the effects of the institutional settings of the
U.S. health care system on individuals’ life-cycle medical expenditures.
Our premise is that health is a form of general human capital and
that health investment—medical expenditures in
particular—determines the stock of health.
Hence, like all other forms of human capital, health increases labor
productivity, thereby affecting the surplus generated in the
employment relationship.
Thus, current health expenditures are an investment that affects the
current and future surplus of the employment relationship.
We embed this link between health investment and employment
surplus in a frictional labor market, and derive the implications of
employee turnover on the employer-employee pair’s incentives to
invest in the employee’s health.
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Motivating Figure
Ave.$Medical$Expenditure$
Workers$in$industries$
$with$Above$Median$Annual$$
Turnover$Rate$
Workers$in$industries$
$with$Below$Median$Annual$$
Turnover$Rate$
Age$
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Explaining the Economic Mechanism
We show that employee turnover leads to an inefficiently low level of
investment in the employees’ health, and that investment is lower and
inefficiencies larger when employee turnover is higher.
The reason is that frictions in the labor market imply that part of the
surplus from the current investment in the employee’s health accrues
to a future employer.
Hence, the employer-employee pair does not internalize the full social
surplus created by the current investment in the employee’s health.
As a result, the pair under-invests in health capital.
Further, we show that this inefficiently low level of medical
expenditures during the working years increases medical expenditures
during retirement, possibly increasing the overall expenditures.
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Summary of Empirical Methods and Findings
We provide extensive empirical evidence consistent with the
predictions of the model using two datasets, the Medical Expenditure
Panel Survey (MEPS) and the Health and Retirement Study (HRS).
Our empirical model is designed to deal explicitly with two issues that
may hinder the identification of the effect of job turnover.
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The first is selection: Workers may select into different jobs due to
unobserved characteristics, such as ability, discount factor, risk
aversion, etc. that could potentially be correlated with their job
turnover.
The second is reverse causality: Workers’ health outcome and health
expenditures could affect their job turnover.
We deal with these issues using panel data to control for fixed and
persistent unobservables that could affect selection into different jobs,
along with demand-side instruments—i.e., plant closures—that
arguably are not affected by reverse causality.
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Summary of Empirical Methods and Findings
We find that workers with shorter job tenures spend less on health
care.
However, we find a stark reversal of expenditures among the elderly:
Retirees who had longer job tenures spend less on health.
The magnitude of our results is considerable.
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Workers with job tenures that are one standard deviation longer have
medical expenditures about $660 higher per year.
Individuals over 65 whose tenure at their main job is one standard
deviation spend about $4,700 less per year on health care.
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Magnitude of The Effects: A Back-of-Envelope Calculation
Consider the lifetime medical expenditures of two workers whose only
difference is their job tenures.
Suppose that both individuals work 45 years and then retire for 15
years before dying, but the first individual’s job tenure is one standard
deviation longer than the second individual’s.
According to our estimates, during their working years, the first
individual spent approximately $29,700 more on health care than the
second individual did.
During retirement, the first individual’s health expenditures are
approximately $70,500 lower than the second individual’s.
The total difference is around $40,000.
This calculation suggests that one additional dollar of health
expenditures during working years may lead to about 2.5 dollars
of savings in retirement.
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Health and Productivity: Literature
A key premise of our model is that health is a productive general
human capital (Grossman 1972).
Several papers establish that increased life expectancy and reduced
morbidity increase productivity and output.
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Fogel (1991, 1994) shows how improvements in health affect living
standards over time in Europe and in the United States.
Several empirical studies document that health has a significant and
positive effect on economic growth (e.g., Barro and Sala-i-Martin,
1995; Knowles and Owen, 1995; Bloom, Canning and Sevilla, 2001).
At the individual level, many papers find that less healthy individuals
are less productive, broadly defined.
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Haveman et al. (1994) finds that prior health limitations negatively
affect work-time and have a significant negative effect on wages.
Berkovec and Stern (1991) finds that bad health decreases labor
market participation among the elderly.
Stern (1996) finds that health limitations on the ability to work have
larger effects on individual labor supply than on labor demand.
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Health and Productivity: Literature
A few recent studies focus on even more-detailed micro-evidence to
study the effects of health on worker productivity.
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Nicholson et al. (2006) uses survey data from a sample of
establishments and provides direct evidence that the cost or
productivity loss associated with missed work is higher than the wage.
Davis et al. (2005), using a survey, finds that: “labor time lost due to
health reasons represents lost economic output totaling $260 billion per
year.”
More broadly, an individual’s current health investment can affect his
future health costs, and the individual and his current or future
employer will need to pay for these future costs.
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In an interesting study of diabetes management, Beaulieu et al. (2007)
finds that improved diabetes care affords economic benefits to health
plans, as well as valuable benefits to people with diabetes.
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A Simple Model
The model is adapted from Acemoglu and Pischke (1999) frictional
labor market.
There are two periods with no discounting.
Health is a form of general human capital and thus is an input in the
production function of the worker.
Assume that health is the only input in the production function f (h) ,
where f (·) is increasing, differentiable and concave.
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A Simple Model: Timing
All workers are risk neutral, are endowed with an initial stock of
health h1 and can invest m1 in health at a unit cost p.
Health evolves according to
h2 = k (h1 , m1 )
where k is increasing in the stock of health h1 and in the investment
in health m1 .
In period 2, the worker either stays with the firm at wage w2 (h2 ) or
decides to quit and obtains an outside wage of v (h2 ) .
The key assumption is that v 0 (h) < f 0 (h) , i.e. wage compression.
Exogenous Turnover Rate: With probability q the firm and the
worker receives an adverse shock and decide to separate. With
probability (1 − q) the continue the productive relationship.
Assume that firms compete in the first period by offering a pair of
wage and medical consumption {w1 , m1 } to workers, and in
equilibrium they make zero profits.
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Equilibrium
Frictional labor markets imply that the worker receives a lower wage
than his marginal productivity, i.e. v (h2 ) < f (h2 ) .
Hence, the worker and current employer can share the surplus
f (h2 ) − v (h2 ) if they continue the employment relationship.
We assume that the surplus is divided according to the Nash
Bargaining solution, i.e. the wage w2 (h2 ) is equal to
w2 (h2 ) = (1 − β) v (h2 ) + βf (h2 )
where β is the bargaining power of the worker and the outside option
of the firm has been normalized to 0.
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Equilibrium
Firm expected profits in period 2 are
π 2 (h2 ) = (1 − q) [f (h2 ) − w2 (h2 )]
= (1 − q) (1 − β) [f (h2 ) − v (h2 )]
and in the first period are
π 1 (h1 ) = f (h1 ) − w1 − pm1 .
So firm maximize the sum of profits
π 1 (h1 ) + π 2 (h2 ) = f (h1 ) − w1 − pm1
+ (1 − q) (1 − β) [f (h2 ) − v (h2 )]
by choosing m1 and w1 , subject to the constraint that workers receive
as much utility as that offered by other firms U.
Competition ensures that U is high enough such that
π 1 (h1 ) + π 2 (h2 ) = 0.
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Equilibrium
The first order condition implies that the optimal m∗1 solves
0
∂h2
qv (h2 (m∗1 )) + (1 − q) f 0 (h2 (m∗1 ))
= p.
∂m1
(1)
Proposition
A decrease in the turnover rate q increases medical expenditure m∗1 .
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Dynamics of Health Expenditure
Now add a third period in which the individual is retired.
In this third period, an individual receives utility d (h3 ) from his
health with d0 (·) > 0.
h3 evolves according to:
h3 = min k (h2 , m2 ) , h3 (h2 ) .
m2 is free for simplicity (think of Medicare).
All individuals choose medical expenditures m∗2 so that their health
reaches h3 (h2 ):
k (h2 , m∗2 ) = h3 (h2 ) .
(2)
Proposition
If ∂h3 /∂h2 < ∂k/∂h2 , then workers in jobs with lower turnover rates have:
(i) a higher medical expenditures m∗1 while working; and
(ii) a lower medical expenditures m∗2 and better health during retirement.
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Data
US: 4 sources of data:
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MEPS 1996-2006;
HRS (1996-2002) for retirees (RAND version);
Statistics of U.S. Businesses (SUSB);
Employment Protection Laws.
UK: British Household Panel Survey (BHPS): 1995-2002.
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MEPS
MEPS: large-scale national survey of health care use, expenditures,
sources of payment, and insurance coverage.
Two components: the Household Component (HC) and the Insurance
Component (IC).
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HC: demographic characteristics, health conditions, health status, use
of medical services, charges and source of payments, access to care,
satisfaction with care, health insurance coverage, income, employment
with 3-digit industry code.
IC: health plans from a sample of private and public employers.
Number and types of private insurance plans offered (if any),
premiums, contributions by employers and employees, and benefits
associated with these plans.
We use HC.
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HRS
HRS: We use waves 1996-2002 (focus on retirees).
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individual employment history.
We can reconstruct the tenure at longest reported job with 3-digit
industry codes.
total medical expenditure in each waves (RAND computes sums for us).
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Statistics of US Businesses
The Statistics of U.S. Businesses (SUSB) is a dataset extracted from
the Business Register, a file of all known single- and
multi-establishment employer companies maintained and updated by
the U.S. Census Bureau.
It provides national and sub-national data on the distribution of
economic data by size and industry, reporting the number of
establishments, employment, and annual payroll for each
geographic-industry-size cell.
More importantly, SUSB reports the number of establishments and
corresponding employment change for births, deaths, expansions, and
contractions by employment size of enterprise, industry, and state.
We use data on establishment deaths to construct our instruments for
job turnover in the empirical analysis.
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Employment Protection Laws
During the 1970s and 1980s, the majority of U.S. state courts adopted
one or more common-law exceptions to the employment-at-will
doctrine that limited employers’ ability to fire employees.
Autor, Donohue, and Schwab (2006) presents a detailed dataset of
these wrongful-discharge laws prevailing in each state and year for the
period from 1972 to 1999, and investigates the effects of these
employment protection laws on the labor market.
We use data on one protection—i.e., the implied contract
exception—to construct our instruments for job turnover in the
empirical analysis for retirees.
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British Household Panel Survey (BHPS)
The British Household Panel Survey (BHPS) is an annual panel
survey beginning in 1991, following about 5,500 households and
10,300 individuals drawn from 250 areas of Great Britain.
It is a data set with rich individual-level demographic, social and
economic variables, as well as detailed information on health-related
issues such as number of doctor visits and self-perceived health status.
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Summary Statistics
MEPS(1995-2005)
HRS(1996-2002)
Variable
Mean
Std. Dev.
Mean
Std. Dev.
Medical Expenditure
BHPS(1995-2002)
Mean
Std. Dev.
1,814
1,574
8,327
24,707
...
...
Job Tenure
6.7
4.3
...
...
6.1
7.2
Longest Job Tenure
...
...
23.8
12.7
...
...
Age
38.9
11.6
75.1
6.8
41.1
12.3
Yrs. of Education
12.9
1.3
11.9
3.2
11.8
2.4
$31,403
$11,317
...
...
£ 28,934
£ 22,129
...
...
3.34
8.12
...
...
0.51
0.50
0.51
0.50
0.46
0.49
Income
Total Assets/10,000
Male
White
0.80
0.10
0.85
0.35
0.96
0.18
Black
0.14
0.10
0.13
0.33
...
...
Married
0.59
0.20
0.59
0.49
0.60
0.48
Family (Household) Size
3.23
0.55
1.94
0.93
3.00
1.32
Union
0.13
0.14
...
...
0.96
0.29
47 / 137
An Illustrative Comparison
Simple illustrative patterns comparing the average medical
expenditures of individuals across two one-digit
industries—manufacturing and retailing—that exhibit substantial
differences in average job tenure.
Variable
Medical Expenditure
Job Tenure
Longest Job Tenure
MEPS (1995-2005)
Manu.
Retail.
HRS (1998-2004)
Manu.
Retail.
1,684
1,580
7,363
8,389
8.65
4.91
...
...
...
...
25.58
18.40
48 / 137
Synthetic Panel Methods
We use MEPS data to construct synthetic panels.
As in all papers that use synthetic panels, the definition of a cohort is
arbitrary.
In our case, we are constrained by the sample size of each MEPS
survey and by the limited geographic and industry information
available in the public version of the MEPS.
As a result, we choose to define cohorts by grouping people by sex,
decade of birth, one-digit industry, and Census Region.
49 / 137
Synthetic Panel Methods
We write the cohort-version of the empirical model as:
yjt = β 0 + β T Job Tenurejt + β X Xjt + η rt + ζ j + jt
∆yjt = ρ∆yjt−1 + β∆Zjt − ρβ∆Zjt−1 + ∆η rt − ρ∆η rt−1 + ∆ν jt ,
I
I
I
I
I
I
I
I
j now denotes a cohort, for which industry i and region r are fixed over
time.
yjt is one of the outcomes of interest for cohort j in year t.
Job Tenurejt is the average number of years individuals in cohort j
have been employed in their current firm.
Xjt is now the cohort-average of a large set of control variables.
η rt is, as before, a year fixed effect for each region r.
ζ j is now a fixed effect for cohort j.
jt is an unobservable, autoregressive component with innovation
ν jt —i.e., jt = ρjt−1 + ν jt .
We use the (lagged) number and the rate of deaths of establishments
in industry i and region r, and the number and the rate of workers that
lost their jobs due to establishment deaths in industry i and region r
from SUSB data as instruments for Job Tenure.
50 / 137
The Relationship Between Workers’ Job Tenure and
Medical Expenditures and Doctor Visits
Log (Job Tenure)
ρ
# Obs
Panels
A: Log Med. Exp.
(1)
(2)
***
0.686***
0.698
(0.256)
(0.248)
0.244***
...
(0.030)
4652
3930
620
586
B: Fraction Not Visiting Doc.
(3)
(4)
**
-0.108
-0.111**
(0.057)
(0.057)
0.100***
...
(0.031)
4652
3930
620
586
51 / 137
The Relationship Between Retirees’ Past Job Tenure and
Medical Expenditures (A) and Health Status (B)
Log (Job Tenure)
# Obs
Panels
A: Log Medical Expenditure
(1)
-0.746**
B: Health Status
(2)
-0.430***
(0.356)
27,229
10,395
(0.063)
27,229
10,395
52 / 137
Magnitude of the Effects
Consider the lifetime expenditures of two workers A and B whose only
difference is their job tenures.
Suppose that both individuals work for 45 years and then retire for 15
years before dying.
Individual A works in a job in which mobility is high, while individual
B works in a job in which mobility is low.
Let us assume that individual B’s job tenure is one standard deviation
higher than that of individual A.
53 / 137
In MEPS, one standard deviation of log tenure is equal to .52.
Multiplying it by the coefficient of log tenure in the MEPS
regressions, we obtain .7*.52=36 percent. At the average of MEPS
medical expenditures ($1,814), this implies that individual A has
expenditures lower than B’s by approximately $660 per year.
Now consider both individuals’ medical expenditures during
retirement.
One standard deviation of log tenure in the HRS data is equal to .77.
Multiplying it by the coefficient of log tenure in the HRS regressions,
we obtain .74*.77=56 percent. At the average of HRS medical
expenditure ($8,327), this implies that individual A has expenditures
higher than B’s by approximately $4,700 per year.
54 / 137
Thus, if individuals A and B work for 45 years and then retire for 15
years, non-discounting their expenditures, we have that, during their
working years, individual A’s health expenditures are approximately
$29,700 lower than individual B’s.
During retirement, individual A’s health expenditures are
approximately $70,500 higher than individual B’s.
he total difference is around $40,000, a rather large difference.
This calculation suggests that one additional dollar of health
expenditures during the working years may lead to about 2.5 dollars
of savings in retirement.
55 / 137
Doctor Visits in the U.K.
Log (Job Tenure)
ρ
# Obs
Panels
IV with
FE
(1)
-0.011
(0.019)
IV with
First Diff.
(2)
-0.021
(0.021)
AB with
iid Res.
(3)
-0.023
(0.078)
...
...
...
93,709
15,931
95,955
15,760
94,015
16,237
AB with
AR(1) Res.
(4)
0.111
(0.101)
0.130***
(0.007)
75,955
15,760
56 / 137
Using ASVP as Exogenous Proxy for Job Turnover:
Workers
A: Total Med. Exp.
Variables
Level
Log
(1)
(2)
∗∗
ASVP
Obs.
199.8
(92.2)
13,459
0.22∗∗∗
(0.06)
13,459
B: Doctor Visits
Office-Based
Physician
Visits
Visits
(3)
(4)
∗
0.046
0.053**
(0.026)
13,459
(0.022)
13,459
57 / 137
Using ASVP as Exogenous Proxy for Job Turnover:
Retirees
Variables
ASVP
No. of Obs.
Total Med. Exp.
(1)
-1,012.5*
Perceived Health Status
(2)
-0.045**
(537.3)
5,583
(0.020)
6,730
58 / 137
Magnitude of the Estimates: ASVP Results
Suppose that both individuals A and B work for 45 years and then
retire for 15 years before dying.
Suppose that individual A’s ASVP is one unit lower than individual
B’s.
The coefficient of ASVP in the regressions from MEPS data implies
that individual A’s expenditures are lower than B’s by $113 per year.
The coefficient of ASVP in the regressions from HRS data implies
that individual A has higher medical expenditures than individual B by
$1,037 per year.
Individual A has approximately $5,000 less in medical expenditures
per year than individual B when working, but approximately $15,000
more in medical expenditures when retired.
Thus one additional dollar of medical expenditures during the working
years may lead to about three dollars of savings during retirement.
59 / 137
Falsification Test: Relationship Between Industry ASVP
and Doctor Visits and Perceived Health Status for U.K.
Workers
Variables
ASVP
No. of Obs.
Doctor
Visits
(1)
-0.007
(0.031)
4,926
Perceived
Health Status
(2)
-0.035
(0.028)
4,928
60 / 137
Alternative Hypotheses
Good jobs versus bad jobs
Several papers document true wage differentials across industries and
jobs and a negative correlation between wage differentials and quit
rates (e.g., Pencavel, 1970; Krueger and Summers, 1988; Gibbons
and Katz, 1992).
If workers are less likely to leave “good jobs” than “bad jobs”, then
differences between good jobs and bad jobs could imply a positive
correlation between job attachment and health expenditures.
But our empirical model on workers’ medical expenditures is designed
to precisely control for fixed and for persistent unobserved effects that
may induce different workers to select into different jobs/industries.
The instruments that we employed in the empirical analysis on
workers’ medical expenditures exploit demand-side (i.e., firms)
variation in turnover across regions and industries, precluding any
reverse causality hypothesis based on supply-side (i.e., workers)
variation in quits.
61 / 137
Is health more important in jobs that have also higher
attachment?
The empirical relationship between job attachment and health
expenditures could simply be due to the fact that health is more
important in industries that have also higher job attachment.
However, the evidence from U.K. workers does not substantiate this
claim.
This difference in health care utilization between U.K. and U.S. is also
in stark contrast with many labor-market patterns—i.e., wages and
inequality—that are remarkably similar in the two countries.
62 / 137
Selection Based on Discounting?
It may be that in high turnover industries wage profiles are flatter
(high earlier, lower later) and more myopic people go into these
industries, attracted by the higher initial wage. These people are
likely to have a different intertemporal discount, i.e. value today
much more than tomorrow.
However, this explanation is in contrast with current theories of
human capital. General human capital steepens wage-tenure profiles
because workers must pay, in the form of lower wages, for any
training that is general and thus transferable across employers.
Conversely, any type of specific human capital flattens wage-tenure
profiles because the firm makes a specific investment, but recoups its
investment later once the workers are locked in.
Crocker and Moran (2003) indeed found that wage profile in high
turnover (low specific human capital) industries are steeper.
63 / 137
What about job lock? Selection based on health status.
There is a large established literature showing that employment-based
health insurance provides inefficiently low separation between
mismatched workers and firms.
To take this job lock hypothesis to a dynamic setting, we would
expect to see that industries with high ASVP, because they are more
likely to offer health insurance and more likely to offer better health
insurance contracts should be more attractive to workers with worse
health: after all, healthy workers benefit less from generous health
insurance.
In a steady state, then job lock dynamics should lead to a negative
relationship between workers’ health and ASVP.
However, from the retirees’ regression, we saw that healthier workers
were working in low turnover industries.
64 / 137
Could it be a pure wealth effect?
If wages are higher in low turnover industries, then wealth effect
explains why health expenditure is higher in low turnover industries.
Hall and Jones (2006) argue that the growth of health spending is a
rational response to the growth of per capita income.
Our explanation and Hall and Jones’ are not mutually exclusive. Hall
and Jones focus on the growth of expenditure in the last 20 years, we
focus on the intertemporal profile of expenditure.
However, we believe that the wealth effect cannot fully explain a
number of our cross-sectional results:
I
I
our regressions on MEPS data include individuals’ current income and
the best proxy for permanent income, i.e. education.
in the regressions using HRS data we find exactly the opposite: we find
that wealthier retired individuals spend less in health.
In summary, we believe that the wealth effect cannot explain the
intertemporal patterns in health expenditure that we document.
65 / 137
Conclusion
The paper provides a strong link between the institutional features of
the U.S. health care market, the incentives to invest in health, and
health outcomes.
Employment-based health insurance system leads to dynamic
inefficiency in health investment over life-cycle due to hold up
problem. Single payer system would do better in internalizing the
dynamic externalities.
We believe that the interaction between private and public provision
of medical care in the U.S. might be particularly subject to the
dynamic externality we consider.
66 / 137
III. Would the Troubles in the Initial Roll Out of HealthCare.gov
last? And Does the Form of Individual Mandate Penalty Matter?
Scheuer and Smetters (2014, NBER Working Paper): Could a
Website Really Have Doomed the Health Insurance Exchange?
Multiple Equilibria, Initial Conditions and the Constructionof the Fine.
67 / 137
Health Insurance Exchange Roll Out
Launch of the federally run healthcare.gov website was not smooth
Would this have long-run consequences for the functioning of the
HIX?
Massachusetts: individual mandate penalty is half of the lowest priced
Commonwealth Care enrollee premium that could be charged to an
individual at the corresponding income level
ACA: individual mandate penalty is $695 per year or 2.5% of income,
whichever is higher.
Will the way the penalty is structured make a difference on how HIX
operates?
Scheuer and Smetters: ”Could a Website Really Have Doomed the
Health Exchanges? Multiple Equilibria, Initial Conditions and the
Construction of the Fine” (NBER WP)
68 / 137
Model
Suppose that there is a unit measure of consumers with wealth w > 0
and face a potential loss of size 0 < l < w.
Consumer differ in the probability π ∈ [0, 1] of the loss, distributed
according to CDF H (·) .
Consumers are risk averse with concave utility function u (c) .
Consider the Akerlof’s model of the insurance market where
consumers choose between (i) not purchasing insurance; and (ii)
purchase an insurance with a premium p (to be determined in
equilibrium) that covers the loss l if it were to occur.
The competitive equilibrium consists of a premium p∗ and a critical
risk type π ∗ such that
u (w − p∗ ) = π ∗ u (w − l) + (1 − π ∗ ) u (w)
Z 1
∗
∗
[1 − H (π )] p = l
πdH (π)
π∗
69 / 137
Equilibrium via Demand and Supply Curves
Supply for insurance: Write
Γ (π) ≡ lE [Π|Π ≥ π] =
l
R1
π̃dH (π̃)
1 − H (π)
π
as the average cost of insuring everyone with risk equal to or greater
than π.
Demand for insurance: Write the willingness to pay for type-π
consumer for insurance Ω (π) as the solution to
u (w − Ω) ≡ πu (w − l) + (1 − π) u (w)
Ω (π) can be interpreted as an inverse demand curve, and Ω−1 (p) is
the marginal buyer when the premium is p.
70 / 137
Equilibrium via Demand and Supply Curves
Equilibrium is characterized by any π ∗ such that
Ω (π ∗ ) = Γ (π ∗ )
(= p∗ )
Properties of Γ (π) and Ω (π) :
I
I
Γ (·) is continuous and increasing in π; Γ (0) = E [Π] l, Γ (1) = l;
Ω (·) is continuous and decreasing in π; Ω (0) = 0 and Ω (1) = l.
71 / 137
Pareto-Ranked Multiple Equilibria
Figure 2: Multiple Competitive Equilibria
average cost and demand curves are upward sloping, but their shapes are otherwise unrestricted. A simple example is depicted in Figure 3. There are three competitive equilibria
in total, namely the one with unraveling located at π ∗ = 1 as well as two additional
∗
∗
72 / 137
Dynamics
Suppose that at time t, due to whatever reason, π t is the marginal
type of buyer in the insurance market;
In period t + 1, insurance companies will set pt+1 = Γ (π t ) ;
Thus in period t + 1, the new marginal buyer type will be
π t+1 = Ω−1 (pt+1 ) = Ω−1 (Γ (π t )) .
73 / 137
Dynamics
Figure 3: Dynamics and Equilibrium Stability
π1∗ and to the right of π2∗ , whereas the dynamics imply falling premiums and more indienrolling otherwise.
dynamics
have
been documented in website
states
If viduals
the problems
in theEvidence
initial for
rollsuch
out
of the
HealthCare.gov
∗ , then
which,
before
the
ACA,
which
placed
restrictions
on
adjusting
premiums
based
on
has induced only the most risky types to enroll, i.e., if π 0 > πage
2
and preexisting conditions. Writing about the New Jersey Individual Health Coverage
the
above
dynamics
will
lead
to
unraveling
of
the
HIX.
Program (IHCP) started in 1993, Monheit et al. (2004) found dynamics similar to those
74 / 137
Empirical Implications
In order to test whether the disruptions in the first year of the
operation of the HIX will lead to unravelling of the market, we will
need to create the empirical analogs of the average cost curve and the
inverse demand curve as in the above figure.
The analysis can be generalized to account for richer consumer
heterogeneity:
I
loss, if occurs, is drawn from distribution Fl , for example
The difficulty is how to empirically separate the risk type of an
individual from the random loss?
Do insurers cross subsidize different age groups? They are not forced
to because DHHS allows age bracket to be each age from 21-64 in
the premium setting.
In a competitive equilibrium, cross-subsidization across age groups
could not occur. So then, why the common perception that somehow
18-34 years old males signing up in HIX is crucial for the success of
ObamaCare?
75 / 137
Do the Forms of Mandate Penalty Matter?
ACA absolute fine: fixed fine f if not having insurance and subsidy
s if purchasing insurance.
The supply side average cost function Γ (π) is not affected by f and s;
The demand side is now characterized by Ω̂ (π; s, f ) which is
implicitly defined by:
u w − Ω̂ + s = πu (w − f − l) + (1 − π) u (w − f )
76 / 137
Relative to Ω (·) , Ω̂ shifts up in a parallel manner with s, and shifts
up by f.
Figure 7: Enforcement of a Mandate through a Fine f
this outcome may be both inefficient and politically challenging. Indeed, the peculiar na77 / 137
MA relative fine:
u w − Ω̃
= πu (w − l − kΓ (π)) + (1 − π) u (w − kΓ (π))
where kΓ (π) = kp is the fine proportional to premium
If f under ACA fine is set to be equal to kΓ (π̂ ∗1 ) , then Ω̃ (·) is
counter-clockwise rotation of Ω̂ (·) pivoted at π̂ ∗1 .
78 / 137
Figure 8: Relative versus absolute fine with kΓ(π̂1∗ ) = f
by
(w −
Ω̃) = πu
(w − l − kΓ(πfrom
)) + (1 −
π )u(w −
kΓ(π )).19
(7) best
The interval uof
initial
conditions
which
convergence
to the
equilibrium
occurs
the
fine
wider
than demand
under for
the
The benefit of the
relativeunder
fine is that
therelative
fine value —
and,is
hence,
consumers’
insurance —
automatically increase as the market unravels towards a bad stable equilibabsolute
fine.
rium. This outcome occurs even if we choose k such that kΓ(π̂ ∗ ) = f , so the relative and
79 / 137
IV: Equilibrium Labor Market and Health Insurance Reform
Aizawa and Fang (2013): Equilibrium Labor Market Search and
Helath Insurance Reform
Fang and Shephard (WIP): Joint Household Labor Supply and Health
Care Reform
Fang, Shephard and Tilly (WIP): Equilibrium Labor Market Search
with Endogenous Technology Choice and Health Insurance
80 / 137
Aizawa and Fang (2013): Introduction
Affordable Care Act represents the most significant reforms to the
U.S. health insurance and health care market since the establishment
of Medicare in 1965.
There are many provisions in the ACA; some of the most significant
changes started taking effects from January 2014.
81 / 137
Major Components of ACA
(Individual Mandate) All individuals must hold health insurance or
face a penalty of $695 or 2.5 percent of income, whichever is higher;
(Employer Mandate) Employers with more than 50 employees must
provide health insurance or pay a fine of $2,000 per worker each year
if they do not offer health insurance.
(Insurance Exchanges) State-based health insurance exchanges will
be established where the uninsured and those employed without
insurance can purchase insurance from the exchange where premium
will be based on community rating.
(Premium Subsidies) Subsidies will be provided to individuals and
families whose income is between the 133% and 400% of the federal
poverty level. Individuals with income below 133% will receive
Medicaid.
82 / 137
Goal of This Paper
... is to understand how the health care reform will affect the health
insurance and labor markets.
Would the ACA significantly reduce the uninsured rate?
Would more employers be offering health insurance to their
employees?
How would the reform affect wage, health, productivity, employment,
and employer size distributions?
What is the impact on total health expenditures and on government
budget?
83 / 137
Goal of the Paper
We are also interested in several counterfactual policies, e.g.,
I
I
I
How would the remainder of the ACA perform, had the individual
mandate been struck down by the Supreme Court?
What would happen if the current tax exemption status of
employer-provided insurance premium is eliminated?
Can we identify alternative reforms that can improve welfare relative to
the ACA?
84 / 137
Labor Market and Health Insurance Market
To address these questions, it is important to have an equilibrium
model that integrates the labor and health insurance market (e.g.,
Dey and Flinn 2005).
The U.S. is unique among industrialized nations in that it lacks a
national health insurance system and most of the working age
populations obtain health insurance coverage through their employers.
There have been many well-documented connections between firm
sizes, wages, health insurance offerings and worker turnovers:
employer size
annual wage
annual worker separation rate
all employers
with HI
w/o HI
24.43
$25,863.72
0.163
33.89
$29,077.49
0.158
8.83
$20,560.4
0.173
Workers in firms that offer health insurance also tend to have better
self-reported health: 95.36% (HI) vs. 93.89% (No HI) are Healthy in
our data.
85 / 137
In this paper ...
We present and empirically implement an equilibrium labor search
model where employers make decisions to offer health insurance.
I
I
we incorporate health and health insurance to Burdett and Mortensen’s
(1998) model with heterogenous firm productivity.
wage, insurance provision, employment, employer size, and worker’s
health status are endogenously determined.
Use structural estimates to assess the impact the ACA on health
insurance and labor market outcomes.
86 / 137
The Model: Worker
Ex ante homogenous except health.
Preference: risk averse.
Health status: {healthy, unhealthy}
Health insurance status: {uninsured, insured}
Health insurance has two effects:
1
2
insure medical expenditure shocks.
affect the law of motion for health status.
Health insurance is only available through employers.
Given the offer distribution of compensation (wage, health
insurance provision), both unemployed and employed individuals
decide whether to accept a new offer, if any.
87 / 137
The Model: Employer
Ex ante heterogenous with respect to productivity p.
Production technology:
I
I
linear with labor inputs;
an unhealthy worker produces d fraction of output, d ≤ 1.
Choose wage and health insurance coverage to maximize the
steady state profit flow subject to the constraint that all workers in
the same firm are equally treated (HIPAA).
In each firm that offers health insurance, the health insurance
premium is set to cover the total expected medical expenditure by its
workforce, plus a fixed administrative cost C.
88 / 137
Worker’s Preference and Health
Utility function: u(c) = − exp(−γc).
Health status: h ∈ {H, U }; Health insurance status: x ∈ {0, 1}.
Medical Expenditure:
I
prob of a medical shock:
Pr(m > 0|h, x) = Φ(α0 + β 0 1 {h = U } + γ 0 x),
I
(3)
conditional on a medical shock, medical expenditure is drawn from:
m| (h, x) ∼ exp (αm + β m 1 {h = U } + γ m x + hx ) ,
where hx ∼ N (0, σ 2hx ) and iid across time periods.
Health status follows Markov process, which depends on insurance
status x:
πx =
π xHH
π xHU
π xU H
π xU U
,
where π xU H = 1 − π xHH and π xHU = 1 − π xU U .
89 / 137
Worker’s Problem: Expected Flow Utility
A worker’s flow utility with income y and insurance status x is:
u (T (y))
if x = 1
vh (y, x) =
Em̃0 [u T (y) − m̃0h ] if x = 0,
h
where T (y) is after tax income:
T (y) = τ 0 + τ 1
y (1+τ 2 )
,
1 + τ2
where τ 0 > 0, τ 1 > 0, τ 2 < 0.
90 / 137
Unemployed Worker’s Problem
Let F (w, x) be the job offer distribution that each worker faces. It is
endogenously determined in an equilibrium.
The value function of the unemployed with health status h, Uh , is:
Uh
= vh (b, 0)
1−ρ
Z
+βEh0 |(h,0) λu max{Vh0 (w, x), Uh0 }dF (w, x) + (1 − λu )Uh0
Job acceptance decision: let wxh be
Vh (wxh , x) = Uh .
An unemployed worker accepts an job offer (w, x) if w ≥ wxh .
91 / 137
Employed Worker’s Problem
The value function of the employed:
Vh (w, x)
= vh (w, x)
1−ρ
R
(1 − δ)Eh0 |(h,x) R
max{Vh0 (w̃, x̃), Vh0 (w, x), Uh0 }dF(w̃, x̃)
+βλe
+δEh0 |(h,x) max {Uh0 , Vh0 (w̃, x̃)} dF (w̃, x̃)
(1 − δ)Eh0 |(h,x) [max {Uh0 , Vh0 (w, x)}]
+β(1 − λe )
.
+δEh0 |(h,x) [Uh0 ]
92 / 137
Employed Worker’s Problem
Job-to-job switching decision: sxh (·, ·) ;
Job quitting decision: q xh .
93 / 137
No HI
HI
wage
Unemployment
Steady State Condition
Worker distribution is characterized by (uh , exh , Gxh (w)) where
I
I
I
uh is the measure of unemployed workers with health status h;
exh is the measure of employed workers with health status h and health
insurance status x;
Gxh (w) is the fraction of employed workers with health status h
working on jobs with insurance status x and wage below w; ghx (w) is
the associated density.
We require that worker distribution must satisfy the steady state
conditions: Given F (w, x),
1
2
3
the inflow and outflow of uh are equalized.
the
of exh ghx (w) are equalized.
P inflow and outflow
0
1
h∈{U,H} (uh + eh + eh ) = M.
94 / 137
Employer’s Problem
An employer draws the health insurance offering preference shock
σ f , which is persistent over time.
max{Π0 (p), Π1 (p) + σ f },
max (p − w0 ) nH (w0 , 0) + (pd − w0 ) nU (w0 , 0)
w0
p − w1 − m1H n H (w1 , 1)
− C.
Π1 (p) = max Π (w1 , 1) ≡
+ pd − w1 − m1U nU (w1 , 1)
{w1 }
Π0 (p)
=
Assuming that follows i.i.d. Type-I extreme value distribution, the
fraction of employers offering health insurance among those with
productivity p is
∆ (p) =
exp( Πσ1 (p)
)
f
exp( Πσ1 (p)
) + exp( Πσ0 (p)
)
f
f
.
(4)
95 / 137
The Definition of Equilibrium
A
is
Dsteady state equilibrium
E
x x
x
x
wh , sh (·, ·) , q h , (uh , eh , Gxh (w)) , (wx (p) , ∆ (p)) , F (w, x) s. t.
(Worker Optimization) Given
F (w, x) , for
each
x x
x
(h, x) ∈ {U, H} × {0, 1} , wh , sh (·, ·) , q h solves worker’s optimization
problem.
(Steady State Worker Distribution) Given wxh , sxh (·, ·) , q xh and
F (w, x) , (uh , exh , Gxh (w)) satisfies the steady state flow conditions.
(employer Optimization) Given F (w, x) and the steady state employee
sizes implied by (uh , exh , Gxh (w)), (w0 (p) , w1 (p) , ∆ (p)) solves employer’s
optimization problem.
(Equilibrium Consistency) F (w, x) must satisfy:
Z p
1(w1 (p) < w)∆(p)dΓ(p),
F (w, 1) =
p
Z
F (w, 0)
=
p
p
1(w0 (p) < w) [1 − ∆(p)] dΓ(p).
96 / 137
Why Are Large Firms More Likely to Offer Health
Insurance?
Statistics
Frac. of Unhealthy in SS
Frac. of Unhealthy (New Hires)
One-Period Ahead
Nine-Period Ahead
J-to-J Transition for Healthy
J-to-J Transition for Unhealthy
Low-Prod. Firms
High-Prod Firms
HI
No HI
HI
No HI
0.0494
0.096
0.037
0.107
Adverse Selection Effect
0.080
0.074
0.051
0.050
Health Insurance Effect on Health
0.067
0.084
0.046
0.067
0.038
0.109
0.037
0.107
Retention Effect
0.109
0.126
8.29E-9 4.03E-14
0.104
0.126
8.29E-9 5.91E-5
97 / 137
Data Sets
Worker-side Data
I
I
1996 Panel of Survey of Income and Program Participation
(SIPP 1996): labor market dynamics, wage, health insurance, and
health variables.
1997-1999 Panels of Medical Expenditure Panel Survey (MEPS
1997-1999): medical expenditure, health, and health insurance.
Employer-side Data:
I
1997 Robert Wood Johnson Foundation Employer Health
Insurance Survey (RWJ-EHI 1997): employer size distribution, health
insurance coverage, and wage.
98 / 137
Sample Selection for SIPP and MEPS
We restrict the samples which satisfy the following criteria:
I
I
I
I
I
I
Men, aged between 26-46
at most high school graduates
do not attend in school, military service, and any government welfare
program (AFDC, WIC, Food Stamps)
do not work as a self-employed or in public agency.
are not covered by other sources (Medicaid, individual insurance, and
spouse insurance).
wage is between 3-97 percentiles.
Sample size for SIPP is 5,309.
Sample size for MEPS 1997-1999 are 4,815.
99 / 137
Sample Selection for RWJ-EHI 1997
Sample selection:
I
I
belong to private sector.
at least 3 workers.
The sample size is 19,089.
100 / 137
Summary Statistics: SIPP
Variable
Fraction of Insured Among Employed Workers
Average (4-Month) Wages for Employed Workers
... for insured employees
... for uninsured employees
Fraction of Unemployed Workers
Fraction of Healthy Workers
... among insured workers
... among uninsured workers
Mean
0.7619
0.8538
0.9240
0.6187
0.0318
0.9511
0.9536
0.9389
Std. Dev.
0.4260
0.3532
0.3462
0.2750
0.1758
0.2177
0.2103
0.2398
101 / 137
Summary Statistics: RWJ-EHI
Variable Name
Average Establishment Size
... for those that Offer Health Insurance
... for those that Do Not Offer Health Insurance
Health Insurance Coverage Rate
... for those with less than 50 workers
... for those with 50 or more workers
Average Annual Wage Compensation, in $10,000
... for those that Offer Health Insurance
... for those that Do Not Offer Health Insurance
Mean
19.92
30.08
6.95
0.56
0.53
0.95
2.53
2.92
2.03
Std. Dev.
133.40
177.24
11.03
0.50
0.50
0.23
2.44
2.50
2.27
102 / 137
Two-Step Estimation Strategy
In First Step, we estimate parameters of the medical expenditure
distributions hα0 , β 0 , γ 0 , αm , β m , γ m , σ hx i, as well as the health
transitions π without explicitly using the model.
In Second Step, we estimate the remaining parameters by Generalized
Method of Moments (Imbens and Lancaster, 1994), where moments
are constructed from:
I
I
likelihood of worker-side labor market transitions.
firm-side characteristics (size distribution, coverage rate...).
103 / 137
First Step
We estimate the parameters in medical expenditure distributions
hα0 , β 0 , γ 0 , αm , β m , γ m , σ hx i by GMM using the MEPS.
We estimate the parameters in health transition matrix, π 1HH , π 1U U ,
π 0HH , and π 0U U , using SIPP 1996 based on maximum likelihood.
We calibrate some of other parameters:
I
I
I
discount factor β = 0.99;
exogenous retirement rate ρ = 0.001 (from mortality rate);
(1+τ 2 )
Parameterization of after-tax income, T (y) = τ 0 + τ 1 y1+τ 2 (from
Kaplan’s 2011).
104 / 137
Second Step
Estimate θ = [θ1 θ2 ] where θ1 = (λu , λe , δ, γ, µ, b) and θ2 = (C,
d, M, µp , σ p , σ f ) by minimum distance estimation:
min g(θ)0 Ωg(θ)
where
"
g(θ) =
I
I
P
i
∂ log(Li (θ))
∂θ
s − E[s; θ]
#
,
L(θ1 ) is likelihood of workers’ labor market transitions.
E[s; θ] is other firm-side moments.
105 / 137
Firm-Side Moments
Mean establishment size;
Fraction of firms less than 50 workers;
Mean size of establishments that offer health insurance;
Mean size of establishment that do not offer health insurance;
Health insurance coverage rate;
Health insurance coverage rate among employers with more than 50
workers;
Health insurance coverage rate among employers with less than 50
workers;
Average wages of firms with less than 50 workers;
Average wages of firms with more than 50 workers.
106 / 137
Likelihood Components from Worker-Side Labor Market
Transitions
An unemployed workers can transition to a job (w̃, x) after l periods;
An employed workers at job (w, x) can experienece one of the four
job transitions after l periods,
I
I
I
I
[Event “Job Loss”] the individual experienced a job loss at period l + 1;
[Event “Switch 1”] the individual transitioned to a job (w̃, x0 ) such
that x0 = x and the accepted wage is w̃ > w;
that x0 = x and the accepted wage is w̃ < w;
that x0 6= x and the accepted wage is w̃.
107 / 137
Details in the Second Step
1
Initialize a guess of θ;
2
Given the guess, solve equilibrium numerically, by using the numerical
algorithm. Obtain the offer distribution F̂ (w, x) from the equilibrium.
3
We will then use F̂ (w, x) and other parameters to evaluate the
moments.
108 / 137
First-Step Estimates
109 / 137
Parameter
Estimate
Std. Err.
Panel A: Medical Expenditure Parameters in Eq. (4)
α0
-1.0909
(0.0446)
β0
0.5247
(0.0723)
γ0
0.5787
(0.0747)
αm
-4.4222
(0.3099)
βm
1.6262
(0.3268)
γm
0.7227
(0.3867)
αp
-1.0909
(0.0446)
σ H1
1.4783
(0.0662)
σ H0
1.9895
(0.1235)
σU 1
1.3584
(0.0919)
σU 0
1.3193
(0.0173)
Panel B: Health Transition Parameters in Eq. (5)
π 1HH
π 0HH
π 1U U
π 0U U
0.9865
(0.0023)
0.9689
(0.0058)
0.7294
(0.0310)
0.7587
(0.0365)
Table 7: Parameter Estimate from Step 1.
Note: Standard errors are in parentheses. The unit of medical expenditure is $10,000.
99.30%.
Panel B reports our estimates of parameters θ2 ≡ (C, d, M, µp , σ p ). We find that the productivity of a
worker in bad health, d, is only 0.3386, implying that there is a significant amount of productivity loss from
bad health. This seems plausible because we categorize only those whose self-reported health is “Poor” or
“Fair” as unhealthy. Moreover, we find that the fixed administration cost of offering health insurance is
about $730 per four month, (equivalent to about $2,190 per year).
In order to fit the average firm size, our estimate of M, the ratio between workers and firms, is about
18.92. This estimate is smaller than the average establishment size of 19.92 reported in Table 5 because
in our model some low-productivity firms do not attract any workers in equilibrium. We also estimated
that the scale and shape parameters of the lognormal productivity distribution are respectively -0.5860
and 0.4043, which implies that the mean (4-month) productivity of firms is about 0.6149 (i.e. $6149). The
fact that the mean accepted four-month wage in our sample is 0.8538 (see Table 3) is largely due to the
fact more productive firms attract more workers in the steady state as our model implies, but also due to
the fact that a fraction of the low-productivity firms are not able to attract any workers. in equilibrium
(i.e. they are inactive.)39
39
One advantage of postulating a parametric productivity distribution is that it allows us to potentially capture how
counterfactual policies might have affected the set of active firms. If we estimated firms’ productivity non-parametrically from
35
Parameter
Estimate
Std. Err.
α0
-1.0909
(0.0446)
β0
0.5247
(0.0723)
γ0
0.5787
(0.0747)
αm
-4.4222
(0.3099)
βm
1.6262
(0.3268)
γm
0.7227
(0.3867)
αp
-1.0909
(0.0446)
σ H1
1.4783
(0.0662)
σ H0
1.9895
(0.1235)
σU 1
1.3584
(0.0919)
σU 0
1.3193
(0.0173)
π 1HH
π 0HH
π 1U U
π 0U U
0.9865
(0.0023)
0.9689
(0.0058)
0.7294
(0.0310)
0.7587
(0.0365)
99.30%.
39
35
Parameter
Estimate
Std. Err.
α0
-1.0909
(0.0446)
β0
0.5247
(0.0723)
γ0
0.5787
(0.0747)
αm
-4.4222
(0.3099)
βm
1.6262
(0.3268)
γm
0.7227
(0.3867)
αp
-1.0909
(0.0446)
σ H1
1.4783
(0.0662)
σ H0
1.9895
(0.1235)
σU 1
1.3584
(0.0919)
σU 0
1.3193
(0.0173)
π 1HH
π 0HH
π 1U U
π 0U U
0.9865
(0.0023)
0.9689
(0.0058)
0.7294
(0.0310)
0.7587
(0.0365)
99.30%.
39
35
Second-Step Estimates
110 / 137
Parameter
Estimates
Std. Err.
Panel A: Parameters in θ1 ≡ (λu , λe , δ, γ, µ, b)
CARA Coefficient (γ)
0.4915
(0.0051)
Unemployment Income (b)
0.0137
(0.0002)
Offer Arrival Rate for the Unemployed (λu )
0.4340
(0.0112)
Offer Arrival Rate for the Employed (λe )
0.2680
(0.0038)
Probability of Exogenous Match Destruction (δ)
0.0179
(0.0003)
Fraction of New Born Workers that are Healthy (µH )
0.9930
(0.0156)
Productivity of a Worker in Bad Health (d)
0.3386
(0.0063)
Fixed Administrative Cost of Insurance in $10,000 (C)
0.0730
(0.0063)
Panel B: Parameters in θ2 ≡ (C, d, M, µp , σ p , σ f )
Total Measure of Workers Relative to Firms (M )
18.8920
(8.7940)
-0.5680
(0.0031)
Shape Parameter of Firms’ Lognormal Productivity Distribution (σ p )
0.4043
(0.0036)
Scale Parameter of Choice Specific Shock to EHI offering (σ f )
0.2397
(0.0025)
Scale Parameter of Firms’ Lognormal Productivity Distribution µp
Table 8: Parameter Estimate from Step 2
7.2
Within-Sample Goodness of Fit
In this section, we examine the within-sample goodness of fit of our estimates by simulating the equilibrium of our estimated model and compare with the model predictions to their data counterparts.
Worker-Side Goodness of Fit.
Table 9 reports the model fits for medical expenditure in the first step.
It shows that our parameter estimates fit the data on the means (in Panel A) and variances (in Panel B)
of medical expenditure conditional on health and health insurance status very well; moreover, in Panel C
we show that we accurately replicate the fraction of individuals with zero medical expenditures conditional
on health and health insurance status.
Table 10 reports the model fit for the worker-side moments. It shows that the model fit really well for
cross section worker distribution in terms of health, health status, health insurance, wage, and employment
distribution.
Figure 1 plots the distribution of workers’ accepted wages by health insurance status. It shows that
our model is able to capture the overall patterns reasonably well, but it predicts a much more concentrated
wage distribution than what is in the data, especially among workers who have health insurance from their
employers.
Employer-Side Goodness of Fit. Table 11 compares the model’s predictions of the targetted employerside moments listed in Section 6.2.2 with those in the data. With the exception of the average wage of
workers’ productivity, we would not have been able to examine this margin.
36
Parameter
Estimates
Std. Err.
0.4915
(0.0051)
0.0137
(0.0002)
0.4340
(0.0112)
0.2680
(0.0038)
0.0179
(0.0003)
0.9930
(0.0156)
0.3386
(0.0063)
0.0730
(0.0063)
18.8920
(8.7940)
-0.5680
(0.0031)
0.4043
(0.0036)
0.2397
(0.0025)
7.2
distribution.
employers.
36
Parameter
Estimates
Std. Err.
0.4915
(0.0051)
0.0137
(0.0002)
0.4340
(0.0112)
0.2680
(0.0038)
0.0179
(0.0003)
0.9930
(0.0156)
0.3386
(0.0063)
0.0730
(0.0063)
18.8920
(8.7940)
-0.5680
(0.0031)
0.4043
(0.0036)
0.2397
(0.0025)
7.2
distribution.
employers.
36
Parameter
Estimates
Std. Err.
0.4915
(0.0051)
0.0137
(0.0002)
0.4340
(0.0112)
0.2680
(0.0038)
0.0179
(0.0003)
0.9930
(0.0156)
0.3386
(0.0063)
0.0730
(0.0063)
18.8920
(8.7940)
-0.5680
(0.0031)
0.4043
(0.0036)
0.2397
(0.0025)
7.2
distribution.
employers.
36
Parameter
Estimates
Std. Err.
0.4915
(0.0051)
0.0137
(0.0002)
0.4340
(0.0112)
0.2680
(0.0038)
0.0179
(0.0003)
0.9930
(0.0156)
0.3386
(0.0063)
0.0730
(0.0063)
18.8920
(8.7940)
-0.5680
(0.0031)
0.4043
(0.0036)
0.2397
(0.0025)
7.2
distribution.
employers.
36
Parameter
Estimates
Std. Err.
0.4915
(0.0051)
0.0137
(0.0002)
0.4340
(0.0112)
0.2680
(0.0038)
0.0179
(0.0003)
0.9930
(0.0156)
0.3386
(0.0063)
0.0730
(0.0063)
18.8920
(8.7940)
-0.5680
(0.0031)
0.4043
(0.0036)
0.2397
(0.0025)
7.2
distribution.
employers.
36
Data
Model
Panel A: Mean Annual Medical Expenditure
Healthy & insured
0.0672
0.0673
Healthy & uninsured
0.0365
0.0359
Unhealthy & insured
0.4804
0.4794
Unhealthy & uninsured
0.1249
0.1249
Panel B: Variance of Annual Medical Expenditure
Healthy & insured
0.0393
0.0392
Healthy & uninsured
0.1601
0.1601
Unhealthy & insured
0.8084
0.8084
0.0856
0.0856
Panel C: Fraction with Zero Medical Expenditure
Healthy & insured
0.3324
0.3368
Healthy & uninsured
0.6458
0.6413
Unhealthy & insured
0.1290
0.1213
0.3600
0.3646
Table 9: Fit for Medical Expenditure Distributions: Model vs. Data.
Moments
Data
Model
Fraction of individuals who are unemployed and healthy
0.0314
0.0301
Fraction of individuals who are unemployed and unhealthy
0.0040
0.0021
Fraction of individuals who are employed, healthy and have health insurance
0.7009
0.7667
Fraction of individuals who are employed, unhealthy and have health insurance
0.0340
0.0319
Fraction of individuals who are employed, healthy and do not have health insurance
0.2156
0.1525
Fraction of individuals who are employed, unhealthy and do not have health insurance
0.0140
0.0167
Mean wage ($10,000)
0.8538
0.8501
Mean wage with health insurance ($10,000)
0.9240
0.8986
Mean wage without health insurance ($10,000)
0.6187
0.6211
Mean medical expenditure ($10,000)
0.0266
0.0253
Table 10: Worker-Side Moments in the Labor Market: Model vs. Data.
37
Data
Model
Panel A: Mean Annual Medical Expenditure
Healthy & insured
0.0672
0.0673
Healthy & uninsured
0.0365
0.0359
Unhealthy & insured
0.4804
0.4794
0.1249
0.1249
Panel B: Variance of Annual Medical Expenditure
Healthy & insured
0.0393
0.0392
Healthy & uninsured
0.1601
0.1601
Unhealthy & insured
0.8084
0.8084
0.0856
0.0856
Panel C: Fraction with Zero Medical Expenditure
Healthy & insured
0.3324
0.3368
Healthy & uninsured
0.6458
0.6413
Unhealthy & insured
0.1290
0.1213
0.3600
0.3646
Table 9: Fit for Medical Expenditure Distributions: Model vs. Data.
Moments
Data
Model
Fraction of individuals who are unemployed and healthy
0.0314
0.0301
Fraction of individuals who are unemployed and unhealthy
0.0040
0.0021
Fraction of individuals who are employed, healthy and have health insurance
0.7009
0.7667
Fraction of individuals who are employed, unhealthy and have health insurance
0.0340
0.0319
Fraction of individuals who are employed, healthy and do not have health insurance
0.2156
0.1525
Fraction of individuals who are employed, unhealthy and do not have health insurance
0.0140
0.0167
Mean wage ($10,000)
0.8538
0.8501
Mean wage with health insurance ($10,000)
0.9240
0.8986
Mean wage without health insurance ($10,000)
0.6187
0.6211
Mean medical expenditure ($10,000)
0.0266
0.0253
Table 10: Worker-Side Moments in the Labor Market: Model vs. Data.
37
Moments
Data
Model
Mean establishment size
19.92
18.5239
Fraction of firms less than 50 workers
0.93
0.9026
Mean size of establishments that offer health insurance
30.08
27.0368
Mean size of establishment that do not offer health insurance
6.95
7.2363
Health insurance coverage rate
0.56
0.5581
Health insurance coverage rate among employers with less than 50 workers
0.53
0.5200
Health insurance coverage rate among employers with more than 50 workers
0.95
0.9113
Average wages of firms with less than 50 workers
0.84
0.4129
Average wages of firms with more than 50 workers
0.92
0.9563
Table 11: Employer-Side Moments: Model vs. Data.
8
Counterfactual Experiments
In this section, we use our estimated model to conduct counterfactual policy experiments and evaluate
the impact of the Affordable Care Act and its various components. For the ACA, we consider a stylized
version which incorporates its main components as mentioned in the introduction: first, all individuals are
required to have health insurance or have to pay a penalty; second, all firms with more than 50 workers are
required to offer health insurance, or have to pay a penalty; third, we introduce a health insurance exchange
where individuals can purchase health insurance at community rated premium; fourth, the participants in
health insurance exchange can obtain income-based subsidies.
The introduction of health insurance exchange where individuals can purchase health insurance if
they are unemployed or if their employers do not offer them represents a substantial departure from our
benchmark model because premium in exchange will be endogenously determined. As a result, we will first
describe how we extend and analyze our benchmark model to incorporate the health insurance exchange.
8.1
Model for the Counterfactual Experiments
We provide a brief explanation of the main changes in the economic environment, as well as the
definition of equilibrium, for the model used in our counterfactual experiments.
The Main Change in Individuals’ Environment.
We now assume individuals who are not offered
health insurance by their employers and those who are unemployed can purchase individual insurance from
the health insurance exchange. We assume that the insurance purchased from the exchange is similar to
those offered by the employers in that it also fully insures medical expenditure risk. Thus in the extended
39
Wage distribution of workers with HI
1.4
Data
Model
1.2
1
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
wage
1
1.2
1.4
Wage distribution of workers without HI
1.5
Data
Model
1
0.5
0
0
0.2
0.4
0.6
0.8
wage
1
1.2
1.4
Size distribution of establishment
0.035
Data
Model
0.03
0.025
0.02
0.015
0.01
0.005
0
0
50
100
150
200
250
employer size
300
350
400
Size distribution of establishment offering HI
0.03
Data
Model
0.025
0.02
0.015
0.01
0.005
0
0
50
100
150
200
250
employer size
300
350
400
Size distribution of establishment NOT offering HI
0.04
Data
Model
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
0
50
100
150
200
250
employer size
300
350
400
Counterfactual for ACA
Insurance Exchange (EX):
I
I
I
Health insurance effect on health between ESHI and individual health
insurance is the same.
Premium in the insurance exchange is determined via community
rating;
Set loading factor ξ to 25% (ACA stipulates that the medical loss ratio
should at least 80%).
Individual Mandate (IM):
ACA
PW
(y) = max {0.025 × (y − TFT 2011) , $695} ,
(5)
111 / 137
Counterfactual for ACA
Employer Mandate (EM): If n ≥ 50,
PEACA (n) = (n − 30) × $2, 000.
(6)
Premium Subsidies (Sub):
I
I
I
I
If an individual’s income is at 133% of the FPL, his contribution to the
premium is equal to 3.5% of his income;
When an individual’s income is at FPL400, his premium contribution is
set to be 9.5% of the income.
When his income is below FPL133, he will receive insurance with zero
premium contribution.
If his income is above FPL400, he is no longer eligible for premium
subsidies.
112 / 137
Main Results from Counterfactual Experiments
113 / 137
Benchmark
(1)
Frac. of firms offering HI
...if firm size is less than 50
...if firm size is 50 or more
Frac. of firms with less than 50 workers
Average labor productivity
Firm’s profit
Frac. of firms in operation
0.5581
0.5200
0.9113
0.9026
1.1300
0.4717
0.9872
Uninsured rate
Frac. of emp. workers with HI from ESHI
Frac. of emp. workers with HI from EX
Average wage
... with health insurance
... without health insurance
Unemployment rate
Frac. of healthy workers
... among uninsured
... among insured through ESHI
... among insured through EX
Frac. of emp. workers in firms with 50+ workers
Average worker utility (CEV)
0.2012
0.8253
0.8501
0.8986
0.6211
0.0322
0.9494
0.9017
0.9600
0.6142
0.6152
Average tax expenditure to ESHI
Subsidies to exchange purchases
Revenue from penalties
Average health expenditure
Average premium in ESHI
Premium in exchange
0.0084
0.0253
0.0306
-
ACA
EX+Sub+EM
EX+Sub+IM
(2)
(3)
(4)
Panel A: Effects on the Firm Side
0.5486
0.5494
0.5531
0.5039
0.5012
0.5111
0.9993
0.9988
0.9506
0.9097
0.9031
0.9043
1.1299
1.1309
1.1349
0.4937
0.4809
0.4765
0.9872
0.9872
0.9872
Panel B: Effects on the Worker Side
0.0727
0.1218
0.0644
0.8284
0.8259
0.8364
0.0965
0.0482
0.0971
0.8449
0.8482
0.8526
0.8934
0.9002
0.9019
0.6132
0.6021
0.6014
0.0320
0.0322
0.0322
0.9592
0.9558
0.9598
1.0000
1.0000
1.0000
0.9636
0.9628
0.9636
0.8890
0.7170
0.8984
0.5940
0.6129
0.6096
0.6133
0.6164
0.6184
Panel C: Effects on Expenditures
0.0083
0.0083
0.0084
0.0034
0.0038
0.0032
0.0010
0.00002
0.0009
0.0273
0.0272
0.0273
0.0301
0.0302
0.0300
0.0439
0.0595
0.0427
Table 12: Counterfactual Policy Experiments: Evaluation of the ACA and its Two Variations.
38
Benchmark
(1)
Firm’s profit
0.5581
0.5200
0.9113
0.9026
1.1300
0.4717
0.9872
Uninsured rate
Average wage
Unemployment rate
... among uninsured
0.2012
0.8253
0.8501
0.8986
0.6211
0.0322
0.9494
0.9017
0.9600
0.6142
0.6152
Premium in exchange
0.0084
0.0253
0.0306
-
ACA
EX+Sub+EM
EX+Sub+IM
(2)
(3)
(4)
0.5486
0.5494
0.5531
0.5039
0.5012
0.5111
0.9993
0.9988
0.9506
0.9097
0.9031
0.9043
1.1299
1.1309
1.1349
0.4937
0.4809
0.4765
0.9872
0.9872
0.9872
0.0727
0.1218
0.0644
0.8284
0.8259
0.8364
0.0965
0.0482
0.0971
0.8449
0.8482
0.8526
0.8934
0.9002
0.9019
0.6132
0.6021
0.6014
0.0320
0.0322
0.0322
0.9592
0.9558
0.9598
1.0000
1.0000
1.0000
0.9636
0.9628
0.9636
0.8890
0.7170
0.8984
0.5940
0.6129
0.6096
0.6133
0.6164
0.6184
0.0083
0.0083
0.0084
0.0034
0.0038
0.0032
0.0010
0.00002
0.0009
0.0273
0.0272
0.0273
0.0301
0.0302
0.0300
0.0439
0.0595
0.0427
38
Benchmark
(1)
Firm’s profit
0.5581
0.5200
0.9113
0.9026
1.1300
0.4717
0.9872
Uninsured rate
Average wage
Unemployment rate
... among uninsured
0.2012
0.8253
0.8501
0.8986
0.6211
0.0322
0.9494
0.9017
0.9600
0.6142
0.6152
Premium in exchange
0.0084
0.0253
0.0306
-
ACA
EX+Sub+EM
EX+Sub+IM
(2)
(3)
(4)
0.5486
0.5494
0.5531
0.5039
0.5012
0.5111
0.9993
0.9988
0.9506
0.9097
0.9031
0.9043
1.1299
1.1309
1.1349
0.4937
0.4809
0.4765
0.9872
0.9872
0.9872
0.0727
0.1218
0.0644
0.8284
0.8259
0.8364
0.0965
0.0482
0.0971
0.8449
0.8482
0.8526
0.8934
0.9002
0.9019
0.6132
0.6021
0.6014
0.0320
0.0322
0.0322
0.9592
0.9558
0.9598
1.0000
1.0000
1.0000
0.9636
0.9628
0.9636
0.8890
0.7170
0.8984
0.5940
0.6129
0.6096
0.6133
0.6164
0.6184
0.0083
0.0083
0.0084
0.0034
0.0038
0.0032
0.0010
0.00002
0.0009
0.0273
0.0272
0.0273
0.0301
0.0302
0.0300
0.0439
0.0595
0.0427
38
Firm’s profit
Uninsured rate
Average wage
Unemployment rate
... among uninsured
... among insured through exchange
Tax expenditure to ESHI
Tax revenue from penalties
Premium in Exchange
Benchmark
ACA
Exempt No Exempt
Exempt No Exempt
(1)
(2)
(3)
(4)
0.5581
0.5419
0.5486
0.5290
0.5200
0.5053
0.5039
0.4889
0.9113
0.8834
0.9993
0.9837
0.9026
0.9031
0.9097
0.9188
1.1300
1.1287
1.1299
1.1133
0.4717
0.4692
0.4937
0.4995
0.9872
0.9872
0.9872
0.9919
0.2012
0.2339
0.0727
0.0915
0.8253
0.7916
0.8284
0.7871
0.0965
0.1170
0.8501
0.8510
0.8449
0.8401
0.8986
0.9072
0.8934
0.8983
0.6211
0.6374
0.6132
0.6336
0.0322
0.0320
0.0320
0.0353
0.9494
0.9470
0.9592
0.9557
0.9017
0.9007
1.0000
1.0000
0.9600
0.9597
0.9636
0.9627
0.8890
0.8798
0.6142
0.6127
0.5940
0.5698
0.6152
0.6077
0.6133
0.6028
0.0084
0.0083
0.0034
0.0044
0.0010
0.0014
0.0253
0.0249
0.0273
0.0275
0.0306
0.0307
0.0301
0.0302
0.0439
0.0466
Table 14: Counterfactual Policy Experiments: Evaluating the Effects of Eliminating the Tax Exemption
for EHI Premium under the Benchmark and the ACA.
44
EX+
IM1
(1)
Firm’s profit
0.5293
0.5071
0.7400
0.9047
1.1388
0.4689
0.9919
Uninsured rate
Average wage
Unemployment rate
... among uninsured
0.0000
0.6948
0.3052
0.8633
0.9019
0.7755
0.0318
0.9644
0.9643
0.9644
0.6093
0.6175
Premium in exchange
0.0070
0.00000
0.0273
0.0304
0.0341
No ESHI
Sub2 +EX
MA
Sub+EX
Sub+EX
3
+EM
Reform
+IM
+IM4
(2)
(3)
(4)
(5)
0.5286
0.5509
0.4779
0.5083
0.9972
0.9529
0.9025
0.9041
0.5192
0.6958
1.1212
1.1280
1.2185
1.2169
0.4995
0.4893
3.0564
1.6809
0.9898
0.9872
0.2065
0.3264
0.0752
0.0529
0.5896
0.0000
0.8061
0.8470
0.1162
0.0983
0.2929
1.0000
0.8426
0.8449
0.8179
0.8909
0.9006
0.8949
0.6121
0.5787
0.8909
0.0320
0.0322
0.1662
0.1090
0.9591
0.9606
0.9229
0.9644
1.0000
1.0000
1.0000
0.9636
0.9638
0.8994
0.9106
0.7146
0.9643
0.6150
0.6103
0.7827
0.7080
0.6142
0.6146
0.4866
0.5630
0.0081
0.0085
0.0051
0.0029
0.0179
0.0057
0.00004
0.0011
0.0096
0.00000
0.0273
0.0273
0.0269
0.0273
0.0300
0.0300
0.0429
0.0411
0.0603
0.0342
Table 15: Counterfactual Policy Experiments: Evaluation of Alternative Policy Arrangements.
Notes: (1). Individual mandate penalty in Column (1) is set to be 15 times as large as the $695 specified in the ACA; (2).
In Column (2), the individual mandate is eliminated, but besides the income based premium subsidies as specified under the
ACA, any employed worker who chooses to purchase health insurance from the exchange receives a $135 flat subsidy; (3). In
Column (3) we assume that the individual mandate penalty is the same as that in the ACA; the rest follows the MA reform
rules; (4). In Column (5), the individual mandate penalty is assumed to be 2.5% of income, or $1,390, whichever is higher.
46
Conclusion
We presented a structural estimation of an equilibrium labor market
search model with endogenous health insurance provision.
The implementation of the full version of the ACA would significantly
reduced the uninsured rate from 20.12% in the benchmark economy
to 7.27%.
This large reduction of the uninsured rate is mainly driven by
low-wage workers participating in the insurance exchange with their
premium supported by the income-based subsidies.
114 / 137
Conclusion
We find that the ACA would also have achieved significant reduction
in the uninsured rate even if its individual mandate component were
removed: the uninsured rate would be 12.18%.
If the subsidies were removed from the ACA, the insurance exchange
will suffer from severe adverse selection problem, resulting in a much
more modest reduction in the uninsured rate to 17.14 − 17.28%.
Interestingly, we find that the current version of ACA without
employer mandate ismore efficient than the one with employer
mandate.
We also find that eliminating the tax exemption for
employer-sponsored health insurance (ESHI) premium both under the
benchmark and under the ACA would increase uninsured rates both
under the benchmark and under the ACA, but quite modestly.
115 / 137
Major Changes in the Current Revision
Include female workers in the integrated labor market (though not
joint with spouses)
Introduce worker heterogeneity in value from unemployment
Include three health states (Poor/Fair, Good/Very Good, Excellent)
116 / 137
Fang and Shephard (WIP): Introduction
In the United States, most of the workers obtained health insurance
from either their own employers, or if they are married, from their
spouses’ employers.
An important, yet under explored aspect of the employer-sponsored
health insurance system is that employers typically offer insurance not
only to their own employees, but also the employees’ spouses (and
dependent children as well).
117 / 137
Introduction
To the best of our understanding, the spousal health insurance benefit
is not required by any existing law.
Indeed, as have been reported in the news several large employers,
e.g. the United Parcel Services and the University of Virginia, have
decided to modify their health insurance plans not to automatically
include spousal benefits in response to the Affordable Act Act.
In this project, we aim to present and implement an equilibrium
model where household members jointly search for employment
opportunities in the labor market and firms make decisions regarding
wage and health insurance offerings.
We will use the estimated model to evaluate the equilibrium responses
to labor market responses to the Affordable Care Act, with a
particular focus on how the ACA would impact married couples’
employment options, decisions and welfare.
118 / 137
Coverage Status and Sources of Health Insurance for
Married Couples
MALE
MALE
Panel A: Both Employed
FEMALE
Own ESHI Spousal ESHI Other Ins.
0.247
0.364
0.017
0.178
0.004
0.000
0.018
0.001
0.047
0.020
0.002
0.003
Uncovered
0.016
0.001
0.002
0.082
Panel B: Husband Employed, Wife Not Employed
FEMALE
Own ESHI
0.026
0.528
0.031
Spousal ESHI 0.006
0.006
0.000
Other Ins.
0.002
0.000
0.067
Uncovered
0.001
0.002
0.008
Uncovered
0.062
0.002
0.004
0.255
Own ESHI
Spousal ESHI
Other Ins.
Uncovered
119 / 137
Coverage Status and Sources of Health Insurance for
Married Couples
MALE
MALE
Panel C: Husband Not Employed, Wife Employed
FEMALE
Own ESHI
0.043
0.039
0.003
Spousal ESHI 0.357
0.006
0.000
Other Ins.
0.066
0.008
0.113
Uncovered
0.072
0.008
0.012
Uncovered
0.003
0.002
0.013
0.257
Panel D: Both Not Employed
FEMALE
Own ESHI
0.006
0.084
0.014
Spousal ESHI 0.017
0.003
0.000
Other Ins.
0.006
0.000
0.181
Uncovered
0.006
0.008
0.030
Uncovered
0.021
0.003
0.011
0.610
120 / 137
ACA and Firms’ Incentives to Offer Spousal Health
Insurance Benefits
One reason is related to a provision in the ACA that, from 2014,
employers are required to pay an annual fee of about $65 per covered
life.
The second, and potentially more important reason, is related to how
the ACA specifies the tax credits if individuals purchase health
insurance from the health insurance exchange.
121 / 137
Insurance Benefits
First of all, eligible individuals can receive tax credit only if they
purchase health insurance from their states’ health insurance
marketplace.
Importantly, however, the eligibility for the health insurance tax
credits depends on two criterion. The first is income. Only individuals
and families who make between 138 percent and 400 percent of the
federal poverty level (FPL) are eligible for a tax credit.
The second criterion is that the individual does not have access to
affordable health insurance through their employer or another
government program.
To the extent that, net of tax credits, the spouse of an employee can
get similar insurance from the health insurance exchange less than the
full cost of spousal insurance offered by the employer, the spouse
would have preferred that the employer did not offer spousal
insurance benefits.
The same could even happen for the employees themselves.
122 / 137
Insurance Benefits
A third reason that ACA can fundamentally change firms’ decisions to
offer spousal health insurance benefits to their employees is simply
because of the availability of health insurance from a regulated health
insurance exchange.
Prior to ACA, the individual private insurance market was
dysfunctional due to adverse selection, and as a result employers have
strong incentives to provide spousal insurance benefits to their
workers because otherwise it would be close to impossible for the
non-working spouses to obtain insurance elsewhere.
123 / 137
Insurance Benefits
If health insurance exchange established under the ACA operates as
well as it is intended, the employees would no longer value the
spousal insurance benefits as much.
Firms benefit from improving the health of their employees because
healthy workers are more productive, so they may still have incentive
to offer health insurance to their workers; but they know they do not
directly benefit from the improved health of the spouses of their
employees, especially if the mobility decisions of their employees are
now less dependent on whether spousal insurance benefits are offered.
124 / 137
Survey Evidence
There has already been some early evidence that employers are
responding to the ACA in their offerings of benefit plans.
According to a survey conducted by Towers Watson National Business
Group on Health titled “Employer Survey on Purchasing Value in
Health Care” (2013):
I
I
I
18 percent of surveyed firms either have already or are planning to
require spouses to purchase health insurance through their employer
plan before enrolling in their health plan;
12 percent of the respondent firms either have already or are planning
to exclude spouses from enrolling in their health plan when similar
coverage is available through their own employer;
and 5 percent are planning to completely eliminate spousal coverage
(page 19).
125 / 137
Our Research Question
Do these short-run responses by the firms represent a longer-term
trend?
Our proposed research will help uncovering the mechanisms
underlying firms’ decisions regarding the offerings of their health
insurance benefits packages.
We plan to estimate the model using MEPS and SIPP data and use
the estimated model to conduct counterfactual experiments to
evaluate the new labor market equilibrium under the ACA, as well as
variations to the ACA.
A structurally estimated model of joint household labor market search
would also allow us to evaluate the job lock hypothesis, particularly
how much spousal health insurance benefits affect job lock.
126 / 137
Model: Overview
In the equilibrium model that we develop to study health care reform,
we consider a frictional labour market where workers receive job offers
when both employed and non-employed.
These job offers are characterized by a wage rate, together with a
vector of insurance offerings that the worker is able to select from.
The vector of insurance offers corresponds to the different
combinations of employee and spousal coverage, and the associated
insurance premiums.
The worker side of the economy is populated by both singles and
couples, both of whom differ in observed and unobserved dimensions.
These risk averse households make a series of job mobility and
insurance take-up decisions.
In the context of couple households, the acceptance and insurance
decision of any one adult will in general depend upon the state of
their spouse, thus generating potentially rich behavior and dynamics.
127 / 137
Model: Overview
Health and health insurance impact the labour market through several
channels.
Workers recognize that their decision to purchase insurance (when
available) not only insures their household against medical
expenditure risk, but also influences how their health statuses evolve.
There is also a direct impact on the demand side of the market, since
health affects how productive workers are in their job.
The provision of health insurance (and the structure of the
compensation package more generally) is an equilibrium object, that
emerges as an outcome of a non-cooperative game between
heterogeneous firms.
The reforms to the health care market that we study here have a
direct impact on the incentive for firms to provide health insurance,
and also change the value that households place on different
compensation packages.
128 / 137
Modeling Firms’ Insurance Offering Options
1
No health insurance (I = 0). Workers therefore receive pre-tax
monetary compensation equal to the wage w. A worker in such a job
may still be insured if they are covered by their spouse’s insurance.
2
Employee only insurance (I = 1). Insurance is offered, but it does not
extend coverage to spouses. Workers decide whether to decline
insurance (i = 0) and receive pre-tax monetary compensation w
(since r(0; w, I) = 0), or to purchase the insurance (i = 1) at the
premium r(1; w, 1) (which is a pre-tax deduction).
3
Employee and spouse insurance (I = 2). Insurance is offered, and
made available to both the employee and their spouse. Again, workers
decide whether to decline insurance (i = 0) and receive w, to
purchase insurance for the employee only (i = 1) at premium
r(1; w, 2), or to purchase insurance for the employee and spouse
(i = 2) at premium r(2; w, 2).
129 / 137
Qualifying Events and Open Enrollment Period
Employees are not able to change insurance coverage options freely
during a job spell.
In particular, workers are not able to change coverage in response to
a changes in health.
There are two ways that coverage may be changed.
1
2
it may be changed in response to a qualifying event, which (given the
absence of family transitions in our model) is associated with either
adult starting a new job, or entering the non-employment pool.
coverage may be changed when an open enrollment event occurs.
We model an open enrollment period by assuming it takes place at
some exogenous rate η > 0, which then allows the household to
reoptimize over the set of insurance options.
130 / 137
Fang, Shephard, and Tilly (WIP): ACA and Firm Behavior
So far, we have limited firms’ responses to ACA to their designs of
compensation packages;
ACA may also change firms’ incentives in their choice of production
technology
Would firms decide to use more skill-biased technology in response to
ACA, which may be interpreted as a regulation on the labor market
that can have differential impact on the cost of hiring different types
of labor?
This paper aims to understand the important determinants of the
strength of firm response in this dimension.
131 / 137
Model
Firms produce output with a production function that is multiplicative
in the firm’s productivity type and the effective units of labor
employed.
The effectiveness of a given worker depends on the worker’s skill type,
k ∈ {H, L}, the worker’s endogenous health status q ∈ {q1 , . . . , qQ },
and the firm’s costly technology choice, j ∈ {1, 2}.
We use subscripts for characteristics related to the worker, and
superscripts for characteristics related to the firm.
132 / 137
Model: Technology and Insurance Choice
The column vector Yqj describes the effectiveness of labour employed
when workers are of health status q and employed in a firm with
technology j, and is defined as:
"
#
j
Y
Hq
Yqj =
.
j
YLq
At some cost, firms also make a health insurance offering decision
I ∈ {IN S, IN S}.
This does not affect productively, as given by Yqj , but impacts both
the steady state size and composition of its workforce.
Without loss of generality, we refer to the sector of the firm, as
comprising its joint technology and insurance offering decision, and
index this by s ∈ {[j = 1, I = IN S], [j = 1, I = IN S], [j = 2, I =
IN S], [j = 2, I = IN S]}.
133 / 137
Model: Firms
The measure of workers of H and L workers with health status q in
sector s employed by a firm is given by the column vector Lsq :
s
s ) LHq (wH
s
s
Lq (w ) =
.
s)
LsLq (wL
s , w s ]0 are the wage offers (which, by assumption, do
where ws = [wH
L
not vary with current health status q).
Excluding any potential fixed sector and insurance offering costs, the
profit flow of a productivity p firm that is active in sector s is
X
0
π s (ws , p) =
pYqs − ws − msq Lsq (ws )
q
where msq = [msHq , msLq ]0 is the vector of expected medical
expenditure (as faced by the firm) per-worker. For sectors s such that
I = IN S, all elements of this vector are zero, with workers facing the
full cost.
134 / 137
Model: Labor Market
The labor market is subject to search frictions.
There is search off and on the job.
The total rate at which unemployed and employed workers receive job
offers depends on their current labor force status (u or e), their skill
type (H or L), and their health status q. These are respectively
denoted λeHq , λuHq , λeLq and λuLq .
The rate at which workers meet firms in a particular sector are given
by these arrival rates multiplied by the fraction of firms active in a
particular sector.
The rates at which jobs are exogenously destroyed is given by δ sHq
and δ sLq . The offer arrival and destruction rates for type L workers are
defined analogously.
135 / 137
Firms’ Problem
Firms are exogenously endowed with a productivity type p that is
distributed according to a distribution denoted Γ(p), with lower
bound p and upper bound p̄.
A sector s firm takes the aggregate labor supply functions Lsq (ws ) as
given. The sector s firm chooses a pair of wages ws to maximize its
steady state payoffs. The maximization problem of a sector s firm is
as follows:
+
*
X
s
s
s 0 s
s
s
(7)
π (p) = max
pYq − w − mq Lq (w )
s
w
q
subject to Lsq (ws ) being described by the flow equations below, and
expected medical expenditure per-worker given by:
Z
0
Z
msq =
[m − z s (m)] · dMHq (m), [m − z s (m)] · dMLq (m)
136 / 137
Firms’ Problem
Firm choose to be active in the sector that leads to higher steady
state profits.
They are choosing amongst four possible sectors conditional upon the
(persistent) sector specific shocks.
The flow cost in sector s is given by cs + cs , where cs is common to
all firms, and cs is distributed independently across firms and sectors
according to a Type-I extreme value distribution.
The probability that a productivity p firm will be active in sector s is
therefore given by:
i
h s
s
exp π (p)−c
σc
h s0
Pr(p firm in sector s) = P
0 i.
π (p)−cs
exp
s0
σc
where σ c is the scale parameter.
137 / 137

Affordable Care Act and Labor Market

Transcription

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