Writing Sample - Honors Thesis

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The Impact of Industry on Healthcare Offerings for Employees and

Perceptions of the Affordable Care Act

Jonathan Chernov

Advisor: Dr. Carolyn Moehling

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Introduction

Healthcare reform is a significant, yet contentious issue in the United States. While the

private sector, including employers and insurance firms, is responsible for insuring a good

portion of the country, the government has had to step in and enact major programs such as

Medicare and Medicaid in order to help many people deal with poverty as a result of factors such

as high costs over the decades. However, there is still a significant portion of the population that

remains uninsured. Furthermore health insurance coverage remains uneven even among the

employed, with some industries having a higher percentage of insured workers compared to

others. In order to attempt to cover the rest of those that are uninsured the Obama administration

developed the Patient Protection and Affordable Care Act, more commonly known as the

Affordable Care Act (ACA). The purpose of the ACA was to increase the accessibility and

quality of health insurance and expand public and private coverage. These would be

accomplished by reducing costs and introducing new mechanisms such as subsidies and

insurance exchanges. One such mandate would require insurance companies to cover everyone

with the same rates regardless of characteristics such as pre-existing conditions (“Pre-existing

Conditions”). Another mandate, the Employer Mandate, would target certain businesses and

force them to cover their employees.

While proponents lauded the bill for increasing coverage, opponents have claimed,

among other things, that businesses will be negatively affected by numerous provisions in the

legislation. Arguments against these policies range from increased operating costs to religious

exemptions. Private firms have always been wary of government regulation and lobby against

such mandates often, including, but not limited to, environmental regulations, increased taxes,

and restrictions on products. When these new rules are passed businesses generally have methods

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of maintaining a stable bottom line. In the case of providing health insurance firms primarily do

this by shifting the costs to their employees, specifically in raising their deductibles that they

must pay out of pocket before their insurance takes over. From 2006 to 2015, the average

deductible “has more than tripled from $303 to $1,077” whiles wages have only “increased 1.9%

between April 2014 and April 2015” (Levey, “Healthcare Costs Rise”). In economic terms this

acts similarly to a tax: when legislatures pass new taxes on firms or increase current ones, firms

offset the increased costs by shifting them to consumers in the form of increased prices. Other

ways firms might deal with the increased costs include reducing employee hours or laying them

off.

In the case of mandated health insurance the ACA affects businesses differently

depending on their size. Self-employed individuals must have basic health insurance; if not, they

either have to qualify for an exemption or pay a fee. Employers with up to 50 full-time

employees (FTE), or those working 30 or more hours a week, will not have penalties applied to

them, but they can purchase plans through the Small Business Health Options Program (SHOP).

Employers with 100 or more FTEs are affected by the Employer Shared Responsibility

Provisions; firms with more than 50 employees are subject to these rules after 2015. The

Employer Shared Responsibility Provisions state that firms must pay penalties if they do not

offer health insurance or coverage that is not affordable to their FTEs (Healthcare). However,

because the focus of the mandate is on size and not on industry, it is unclear whether or not the

ACA will affect the imbalance in health insurance coverage among the employed based on

different industries.

The motivation for this paper lies not in analyzing the impact of the ACA, but in

analyzing health care coverage differences across industries and the reasons for these

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differences. In 2013, prior to the implementation of the ACA, 42 million Americans (13.4% of

the population) were uninsured (Census Bureau, Figure 2). From a worker’s perspective being

sick while uninsured can snowball from going on unpaid sick leave and missing long periods of

work, depending on the severity of the ailment, to being unable to pay for medical expenses and

becoming unemployed due to an inability to work. Businesses, on the other hand, experience

decreased productivity because of a reduced employee base, resulting in a decline of profits.

Costs then go up because these businesses need to invest in hiring a replacement. The point in

these examples is that, given the state of the current system, there are significant gaps and flaws

that need to be addressed that would substantially boost economic productivity and prosperity in

the United States. A healthier population leads to healthier workers that are able to produce

more, reducing unemployment and increasing GDP in the process. So far the ACA has partially

accomplished its goals; in 2014 the number of uninsured had fallen by 25%, or 8-11 million

Americans, through Medicaid expansion (Sanger-Katz, “Has the Percentage”). However, the

Employee Mandate portion of the ACA had been scheduled to go in effect in 2015/2016, so we

have yet to see the effects of the legislation that affects businesses.

This study will examine two areas. First I will look at the differences in health care plan

offerings between industries. I will then study firms’ perceptions of the ACA based on industry.

While an employee’s position and full-time status in a company is the most significant indicator

of their wages and fringe benefits, there exist differences in these offerings depending on

industry. The retail industry for example hires many part-time workers; generally speaking, part-

time workers are seldom offered fringe benefits like health insurance. Meanwhile, workers in

professional service jobs are most likely FTEs and will have fringe benefit packages. Looking at

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the differences in health care offerings between industries is an important first step as the data

might shed new light on this view.

Whether or not these firms offer health insurance plans depending on industry might also

play a role in how these firms think of the new rules. Even though the regulations focus primarily

on the size of the firms, it is worth analyzing how the type of industry might affect how these

firms think about the ACA. If most of the firms in a certain industry already offer health

insurance plans maybe they will not think much of the new rules, especially if they think that the

ACA makes it easier to change policies like employee scheduling. Using a survey of businesses

this paper will look at if businesses respond differently to questions about their employee health

care offerings based on industry. Responses to relevant health care questions will be considered,

in addition to responses to a question regarding the ACA that will reflect the firms’ outlook on

the law’s possible effects.

Overall this paper will look at how firms in different industries choose employee health

care plans and whether or not choice of industry affects these firms’ outlook on the ACA. We

will review the literature on interindustry differences in wages and fringe benefits. We will then

focus on the description of the data and eventually move on to the model and estimation method.

We will then run the regressions and estimate the results. Finally we will discuss these results

and their implications in the final portion. As for my hypothesis, I predict that businesses in the

healthcare, professional services, and education industries will be more likely to offer health

insurance compared to businesses in the manufacturing, services, and construction industries.

Furthermore, I believe the analysis will show that the majority of firms, regardless of industry,

believe that the ACA will affect them in a significant manner.

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Literature Review

While the ACA has only been in effect for a few years there have been several studies on

how firms choose fringe benefits for their employees. However, they seldom focus on

interindustry differences between employee benefits and how these differences affect firms’

choice in fringe benefits. Although some of these studies may not be directly related to

legislative issues regarding health insurance, it is important to delve into other important factors

that affect interindustry differences and firms’ decision-making such as the size of firms or

geographic location. In addition, while the research methods and empirical analysis performed in

these papers might not directly relate to what I will do, the information they provide on aspects

like fringe benefits provide useful context for my research.

Dickens and Katz (1986) used covariance analysis to study interindustry wage

differences for nonunion workers and found that in their aggregate model, industry effects

account for at least 6.7% of inter-personal wage variation even after controlling for individual

characteristics and geography. In other words, a worker’s choice of industry is the most

significant factor involved in individual wage variation. Additionally, the authors cite Dunlop

(1985) in saying that differences in fringe benefits only seem to expand wage differences across

industries. The most significant limitation with this paper is that it was published in 1986. While

interindustry differences might still be a factor in wage differentials there have been a plethora of

changes in the world and U.S. industries since then, such as the growth of newer industries like

consumer electronics, the decline of manufacturing, and new legislat ion like the North American

Free Trade Agreement.

Linnan et al. (2008) analyzed the worksite health promotion programs, policies, and

services of a cross-sectional, nationally representative sample of U.S. firms using logistic

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regression models and found that only 6.9% of respondents offered a comprehensive workplace

health promotion program. Additionally, the results showed that larger worksites (e.g. those with

>750 employees) consistently offered more programs and the like than did smaller ones. Most

importantly, however, was the fact that worksites in the agricultural, mining, and financial

services industries were much less likely to offer such programs compared to those in other

sectors like manufacturing and business. It is also important to note that there were few observed

differences in the programs themselves between industries. There are two limitations with this

paper that involve the respondents themselves. First off the survey was conducted with

respondents that were identified as “being ‘directly responsible for health promotion or wellness’

or as having an ‘in-depth knowledge of these types of programs at the worksite,’” meaning that

the respondents consisted of those in management. The study assumes that the opinions of the

management aligned with their employees, which means that caution should be exercised as

employees’ perceptions regarding access to and participation in these programs may be distinctly

different than those of the employer. Secondly the respondents only answered questions based on

their own worksites, meaning that their responses may not reflect the situations of other

worksites or programs given by a particular company.

Bernstein (2002) focuses on several factors such as firm size and demographic variables

and their effects on the availability of fringe benefits, specifically pensions and health insurance.

Using a logit model he determined that 24% of sole proprietors provide health insurance

benefits, while 70% of more complex firms (e.g. corporations, LLCs, etc.) offered plans.

Additionally, the firm was more likely to offer health care coverage if the owner was not a

minority. Education was also an important variable in determining coverage, as those with higher

education offered more benefits. However, the database being studied did not contain

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information on, among other variables, eligibility requirements or coverage rates for employees

that work part-time for businesses that offer benefits, so the study lacks information in specific

areas.

In the Handbook of Health Economics, Gruber (2000) looks at the impact of health

insurance on the labor market. He reviews existing literature on the subject and finds, among

other things, that there is a strong negative relationship between fringe benefit costs and wages.

When health insurance costs increase, workers’ wages decrease. One such study he cites looks at

health benefits of New York school districts workers from 1972-1977 and finds that, after

controlling for worker and district characteristics, 83% of health cost increases across districts

were reflected in decreased wages. Another relevant study examines mandated comprehensive

health insurance coverage for childbirth. In 1978 federal law outlawed insurance companies from

severely reducing coverage for childbirth compared to other services. Gruber found that there

was a full shifting of these increased costs to wages, with married 20-40 year old women

absorbing the most impact. This reinforces the idea of the earlier deductible discussion, where

firms nowadays increase employee deductibles to make up for rising health insurance costs. The

main issue with this source is that it is itself a literature review of past papers, some of which use

data from 30 years ago. While some of the information is still useful, more recent research on the

topic would not only be more relevant but also more helpful.

Because of the similarities between the ACA and Massachusetts’ own healthcare reform

years ago, Dillender et al. (2015) use data from Massachusetts in order to estimate the possible

effects that the ACA might have nationwide. Their concern revolves around firms avoiding the

mandate by changing staff arrangements: either by using more temporary workers, reducing the

amount of employees below 50, or hiring more part-time workers. In their initial analysis, the

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researchers estimated a model for analyzing the reform’s effect on part-time employment in

Massachusetts based on education. Prior to reform, 68% of FTEs (in this case, people working

35+ hours per week) with college degrees had insurance through their employers. Meanwhile

51% of FTEs without college degrees had employer-sponsored health insurance. After the

reform, FTEs without college degrees were 1.9% more likely to work part-time hours. This

represents a 9.8% increase in part-time work. On the other hand, those with college degrees

experienced no effects. In other words, there was no increased likelihood that they would work

part-time hours. Despite the lack of focus on interindustry differences, the focus on studying the

effects of reform similar to that of the ACA

There are several limitations worth noting with this paper. First, while the reforms

instituted by the ACA and Massachusetts are similar, there are still notable differences in some

areas. From a punishment standpoint, the penalties for not abiding by ACA guidelines are larger

than the ones under the Massachusetts reform. Moreover, because this paper looks at one state or

region, it is limited in scope. The similarities in legislation are apparent, but sentiments regarding

healthcare reform are significantly different between people in the Northeast and people in the

South. For example, while people in Massachusetts might tend to be more supportive of this

model of reform those in Texas might be staunchly opposed. Education is another disparity in

this model; about 40% of those living in Massachusetts have a college as opposed to less than

30% of the rest of the U.S.

While previous literature mostly looks at interindustry differences in wages and

differences in fringe benefits based on non-industry characteristics like firm size, this paper will

examine how much of an influence the firm’s industry has in providing health insurance to their

workers while also looking at the role it plays in perceptions of the ACA. Currently, due to the

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delayed implementation of the Employer Mandate and the limited time of its existence, there is a

distinct dearth of research materials on the effects of the ACA on businesses and, by extension,

on employees. Furthermore, with the lack of research on interindustry differences in employer-

mandated health insurance, this paper will attempt to fill the gaps in previous works that did not

examine these areas of interest. I am also looking to develop a more modern perspective on

health care in the business world in context of the new health care legislation as other literature

on the subject might not be as current. With the advent of the ACA, we will see if industry

differentials play a role in firms’ perception of the new regulations.

Data Description

This paper will analyze Employer Perspectives on the Health Insurance Market: A

Survey of Businesses in the United States, 2014. This survey was conducted by the Associated

Press-NORC Center for Public Affairs Research from August 19-October 8, 2014. The total

number of observations includes 1,061 firms from across the United States and the world, albeit

only 6 firms are international. The geographic coverage only specifies census regions (Northeast,

Midwest, West, and South). The primary focus of the survey was analyzing firms’ perspectives

on the health insurance market based on firm size.

To summarize the results of the survey, there are five major findings to take away from it

all. First, the majority of employers do think that the ACA will indeed impact their decision-

making about healthcare benefits for employees. However opinions of its effects vary as some

say it will make scaling benefits back easier, some say it will make it harder, and some say it will

have little effect. Secondly, 20% of firms claim that they are examining the design of health

insurance exchange plans in preparation for updating or changing the benefits that they offer.

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Third, out of the firms that offer health benefits to employees 60% believe that quality ratings of

the plans are important; however, 90% of them are unfamiliar with objective quality metrics. In

other words, while they think that quality is a significant aspect of selecting a plan most of these

firms are unable to discern the quality of these plans themselves. Fourth, firms take two costs

into consideration when selecting plans: the cost to the firm and the cost to their employees, with

the former taking precedence. Finally, out of the all the firms that offer plans with 100+

employees, only 4% plan to change scheduling in order to reduce the number of FTEs to comply

with ACA regulations.

There are a plethora of advantages to using this source. For starters, the survey covers a

wide range of categories and numbers for multiple variables. All major regions of the country are

accounted for, along with six specific industries including manufacturing, health care, service

and retail, professional services, education, and construction. In addition the survey includes

responses from small and large firms with both part-time and FTEs, so the spectrum of

businesses examined is fairly comprehensive. The survey itself is also extensive as it goes over

many different questions regarding healthcare policy choices that each firm has made. These

questions serve to obtain a better understanding of what plans each firm chose and why.

While this is a solid source of data, there are significant limitations to this survey. The

survey has a small sample size, so it might not be entirely representative of the nation as a whole.

In the case of firm size, the survey oversampled large businesses in order to ensure sufficient

sample size for analysis. According to the NORC and the U.S. Census Bureau 96% of employers

are small businesses with fewer than 50 employees, yet these firms only account for 28% of

workers. Medium- and large-sized businesses are only 4% of employers but are responsible for

employing 72% of workers. In the survey, small businesses accounted for 92.9848% of

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respondents, while medium and large businesses comprised the rest with 7.0152%. For the most

part the survey does a decent job of representing U.S. firms by firm size.

Unfortunately, with respect to industry, it is difficult to compare the sample percentages

to population percentages due to the differences in categories. The table below shows the

different sectors analyzed by the NORC and by the U.S. Census Bureau.

Table 2A: Comparisons by Sector

Survey Sectors U.S. Census Bureau Sectors Manufacturing Manufacturing

Construction Construction

Professional Services Services

Services, Wholesale, Retail Wholesale Trade

Education Retail Trade

Healthcare Agriculture, Forestry, Fishing

Other Mining

Finance, Insurance, Real Estate

Transportation, Communication, Public Utilities

As we can see, the Census Bureau separates the economy into more sectors while the NORC

groups multiple sectors into one category. One caveat to note is that the Census Bureau data

includes only the private sector, while the NORC counts firms from the private and public

sectors. The table below shows the distributions of firms by sector for each source.

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Table 2B: Distribution by Sector

Survey Distribution (%) Census Distribution (%) Manufacturing 16 4

Construction 3 7

Services, Wholesale, Retail 41 71

Professional Services 12 8

Other 26 6

Total 98 96

In this table I combined several categories with each other in order to make it easier for

comparison. For the survey section I combined Education and Healthcare with Other; for the

Census section I grouped Agriculture, Forestry, Fishing, Mining, Transportation,

Communication, and Public Utilities into an Other category. Additionally I used Finance,

Insurance, and Real Estate as a proxy for Professional Services. The reason I grouped the

categories in this manner is because there are significantly more distinctions in the census data

compared to the survey. It is easier to reduce the number of categories through consolidation

because the industries that the survey examines are much more limited compared to those in the

census data. The survey does not specify anything about industries related to Agriculture,

Forestry, etc. so in the context of the survey these categories would go under Other. This is also

the case for the financial industries, but they can be considered related enough to one another

that they can all be combined under the category of Professional Services. Additionally, as is the

case with law and medicine, finance is also considered a “professional services” type industry.

As we can see, there are major discrepancies between the distributions. While Services make up

the majority of the firms for both sources, the Census calculated a much higher percentage of

services firms. The other two sectors that see the largest differences are Manufacturing and

Other, while the rest of the sectors are fairly similar. Based on these distributions the survey does

a poor job of representing U.S. firms by industry. It is important to note, however, that the

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primary focus of the survey was on firm size and not industry. Furthermore, the differences in

categories between the two sources make it difficult to compare them overall.

In addition to representation discrepancies, not all firms answered every question.

Granted, some questions did not pertain to every firm but the number of observations becomes

increasingly limited as a result. Finally, because this is a survey, the data is all self-reported. The

firms do not have to necessarily back up their numbers or responses.

Methodology

For this paper I propose two sets of models for analysis. The NORC study concentrated

on multiple aspects of business health care markets, including employers’ knowledge of health

insurance plans and their quality metrics, considerations of costs to the firm and employees, and

the ACA’s potential impact on their businesses, mostly with respect to firm size. While the ACA

is tailored to affect firms based on their size, I am interested to see whether industry plays a role.

The models in this paper specifically look to study how the industries of these firms affect two

areas: how significant of a reason was the ACA in the firm’s consideration of offering health

insurance and what they think of the ACA’s impact on their businesses. The data will be

analyzed in context of industries and see how significant they are in relation to these areas.

The first set of linear probability models will measure the impact of a firm’s industry on

whether or not their firm provides health insurance plans to their employees. In order to decide

on which variables to include, we must first look at the basic economic model for the question

that we are answering. For a firm to offer health insurance to employees the perceived benefits

must outweigh the perceived costs, and the variables selected need to reflect what affects these

benefits and costs. In order to examine the employer mindset in the realm of health insurance and

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the ACA, it would be helpful to examine the sentiments of the business community in regards to

the ACA prior to its implementation. To do this, I read articles in business-oriented publications

such as Forbes and other periodicals that related to why businesses offer health insurance to

employees and business owners’ attitudes over the ACA.

Interestingly enough one of the biggest reasons for why firms offer health insurance to

employees has a basis in World War II. During the war the government instituted wage controls

which prevented employees from enticing workers with higher pay. Businesses found a way

around this in the form of nonmonetary compensation, e.g. by offering health insurance instead

of money (Akst, “On the Contrary”). As we know, this practice exists to this day. Furthermore a

popular economic theory suggests that “employers are willing to arrange health insurance plans

for workers because workers are willing to ‘buy’ that health insurance through wages reduced by

the amount of the cost of the insurance” (O’Brien, 5), although the empirical results for this

theory are quite weak. Alternatively there is another theory that states that employers might

profit more from offering both wages and benefits as opposed to offering wages only. Providing

health insurance would allow businesses to recruit and retain high-quality workers, improve

workers’ health, increase productivity, and reduce absenteeism and turnover (6). However,

empirical research has only shown some support for the ideas of “lower turnover, improved

access to care, healthier and more productive workers, and fewer disability claims”, with

inconclusive evidence in other areas (34). Even with this kind of analysis it is still difficult to

ascertain employers’ motivations for offering insurance (35).

Unsurprisingly one of the biggest reasons over firms’ apprehension over the ACA is

increased costs coming from the “Cadillac tax,” a “40% excise on the cost of health care

coverage above $10,200 for an individual and $27,500 for a family” (Howell Jr., “Obamacare

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‘Cadillac tax’”). It is estimated that “26% of employers would owe at least some Cadillac tax in

2018, and 42% by 2028” (Hiltzik, “Do we need Obamacare’s”), which consists of a significant

portion of businesses in the United States. Small and large businesses alike also voiced

disapproval over a “2013 ruling from the IRS that imposes steep penalties on employers who

offer tax-free reimbursement to their employees to help them purchase individual health

insurance plans” (Sullivan, “Small-business owners”), showing that firms of all sizes are

primarily wary of the legislation’s effect on costs and penalties. However, not all businesses

might be negatively affected. With a quarter of small business owners in the U.S. being

uninsured, the ACA could increase coverage for 83% of currently uninsured owners while also

allowing those “who currently buy their own individual healthcare coverage in the private

market…to take advantage of new cost savings” (Lorenzen, “Is the Affordable Care Act”).

Businesses might potentially be positively or negatively affected by the ACA, but if one thing is

for certain it is that costs and penalties are the most significant concerns of firms in regards to the

new laws.

With this information in mind, I can better understand the kinds of factors involved in

employers’ decision-making over providing healthcare coverage. Looking at the survey in

particular we can see several variables that capture what firms perceive as some of the benefits

and costs, many of which were discussed earlier in O’Brien’s paper. For instance, from a benefit

standpoint, a number of employers offer health insurance as a way of recruiting and retaining

employees. On the other hand, some employers offer health insurance to avoid costs due to fines

and decreased production from employee absenteeism.

In the first model the dependent variable will be Insurance, or whether or not the firm

offers health insurance coverage. The key independent variables will be a set of categorical

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variables indicating the industries being examined – manufacturing, healthcare, services,

professional services, education, construction, and other. I plan to include several other variables

as controls, including some such as Region, Profit, and Size. Region will include locations like

the Northeast, Midwest, South, West, and International. Profit will look at whether the firm is

for-profit or not, while Size indicates the number of FTEs in a firm (the equivalent of one person

working full-time) which will be separated into small, medium, and large firms. These are

characteristics that I think will affect each firm’s reasons for providing or not providing health

insurance coverage. States across the U.S. might have different laws or mechanisms dealing with

health insurance, such as state exchanges. The industry that each firm works in might influence

fringe benefit provisions to employees. For example, retail businesses are more likely to have a

greater proportion of part-time employees and less likely to offer those employees health

insurance compared to firms working in professional services.

The profit goals of the firms might also play a role. Since for-profit businesses rely more

on making a profit (by selling a product or service, etc.) to survive compared to something like a

charity which would depend mostly on other source of income like donations, the for-profit

businesses might change employees’ benefit packages or change their staff arrangements in order

to make ends meet. That being said, non-profit firms can engage in the same actions in case they

are losing money so I have to see if there are any significant differences between these two types

of organizations. The number of FTEs and size of the firm are also very relevant as the ACA

takes those two factors into account when deciding which firms are ultimately affected by the

changes. Finally, the coverage offered by the firm is important because businesses that currently

offer health insurance to their employees would probably be the most affected by the ACA. If the

ACA does make it more difficult to offer benefits, then businesses that do not offer them will

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likely not change their minds. However, businesses that offer health insurance coverage might

also not change their provisions regardless of the negative effects.

Delving further into the employer mindset, I will also examine the connection between

industry and the reasons why firms offer health insurance. As stated above, firms can perceive a

multitude of reasons for offering plans. It will be interesting to see whether firms in specific

industries are more likely to offer insurance than those in others and their reasons for doing so.

Discovering the reasons for why this would be the case is beyond the scope of this paper, but it

could have important implications for healthcare reform in terms of analyzing why firms in

certain industries are more likely to offer coverage and how this could be translated to firms in

other industries. The survey captured several of these possibilities by asking the firms 12

different questions as to why they offer plans. Because these questions only look at firms that

offer plans, firms that do not offer plans will be dropped from the observations.

There will be 12 different models looking at each of the reasons given in the survey as to

why firms offer insurance. These reasons will be the dependent variables, while the independent

variables from the first model will stay the same. The dependent variables include Rec, Comp,

Abs, Prod, Dem, Med, TaxInc, TaxDed, Right, Law, and Fine. Rec looks at employee

recruitment, while Comp refers to competitors also offering insurance. Abs refers to reducing

employee absenteeism and Prod refers to increasing productivity. Dem stands for employee

demand or expectations of insurance, while Med refers to firms offering insurance due to one or

more employees having medical issues. TaxInc refers to firms having insurance because it is not

included in taxable income for employees, while TaxDed is because offering insurance is tax

deductible for the firm. Right represents the belief that offering insurance is the right thing to do.

Law refers to offering insurance because it is law under the ACA, while Fine refers to offering

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insurance because otherwise the firm will be fined. Each of these variables explains why a

company would offer insurance to employees and whether it was a major/minor reason or not for

doing so.

The second set of model will also be a linear probability model. This one will measure

the firms’ perceptions of the ACA’s effect on their ability to change their health benefit plans.

The dependent variable will be ACA, or the firms’ perceptions of the ACA. It will look at

whether firms think that the ACA makes it easier for employers to scale back their own plans,

harder to scale back, or have no effect. This model will only take into account firms that

currently offer health insurance plans in order to eliminate possible endogeneity issues. The key

independent variables will again be the industry variables. I plan to include similar variables as

before for controls, such as Size, Region, and Profit, because I think they will also all play a role

in determining the impact of the new variable. Other variables that I will take into consideration

focus more on the impact of health insurance plans and the ACA on the firms themselves,

particularly their costs. One of the biggest concerns over the effects of the ACA is that

employers might have to change scheduling, forcing FTEs to work part-time in order to reduce

costs for providing health insurance. Regional sentiments over the ACA might influence firms’

opinions as well as differences in law and regulations between states. For-profit firms might be

more concerned about lowering costs as well. Firms might also want to comply with the new

ACA regulations and avoid paying substantial fines, which is where Fine comes in. Keeping

costs low is always an important consideration for firms, so CostE will be used to see how

important the cost of the plans would be to the firms. Exch1 refers to whether firms are

“examining the design of exchange plans as [they] think about updating or changing the

insurance benefits [the firm] offers.” I feel this is relevant to the model as with the

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implementation of the ACA, state and federal exchanges have been instituted in order to help the

population with obtaining health insurance; this includes businesses, specifically small

businesses. It will look to see whether these exchanges were important to the firms’ decision-

making.

There are several challenges with implementing these models, the most significant being

data limitation issues. The responses to the survey are all either categorical or binary, with no

continuous numbers. Additionally, as I explained earlier, the sample data is not completely

representative of the population.

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Descriptive Statistics

Variable Observations Mean Standard Deviation

Insurance 1059 0.5872 0.0286

Manufacturing 1061 0.0969 0.0154

Healthcare 1061 0.1712 0.0217

Services 1061 0.6028 0.0282

Professional Services 1061 0.1237 0.0195

Education 1061 0.0158 0.0086

Construction 1061 0.0304 0.0099

Other 1061 0.0562 0.0133

Other2 (Other+Educ+HC)

1061 0.2431 0.0249

Profit 1054 0.8604 0.0190

Nonprofit 1054 0.1396 0.0190

Northeast 1055 0.2024 0.0223

Midwest 1055 0.2255 0.0200

South 1055 0.3414 0.0271

West 1055 0.2301 0.0279

International 1055 0.0007 0.0006

Small 1057 0.9417 0.0087

Medium 1057 0.0514 0.0083

Large 1057 0.0070 0.0027

Recruiting 878 0.7566 0.0332

Competition 876 0.6415 0.0356

Retention 878 0.8148 0.0302

Absenteeism 875 0.7107 0.0339

Productivity 875 1.8516 0.0599

Demand 878 0.6598 0.0342

Medical 876 0.4062 0.0348

Taxable Income 874 0.5258 0.0358

Tax Deductible 869 0.6319 0.0342

Right 874 0.8694 0.0263

Law 870 0.5121 0.0358

Fine 875 0.4279 0.0350

Cost Importance 874 0.9640 0.0156

Exchange 1029 0.2030 0.0225

ACA 1025 0.3562 0.0277

Chernov 22

Empirical Results

Table 3A: Linear Probability Model Results for the Provision of Insurance

Insurance Coefficient Std.

Error

t-value P>t 95% Confidence Interval

Manufacturing 0.0606 0.0308 1.97 0.049 0.0002 0.1211 Healthcare -0.0701 0.0646 -1.09 0.278 -0.1968 0.0566

Services -0.0912 0.0600 -1.52 0.129 -0.2089 0.0266 Professional Services -0.0065 0.0650 -0.10 0.921 -0.1340

0.1211

Education -0.0810 0.0787 -1.03 0.304 -0.2355 0.0734 Other -0.0680 0.0745 -0.91 0.362 -0.2142 0.0782

Nonprofit 0.0331 0.0309 1.07 0.284 -0.0275 0.0937 Northeast 0.0045 0.2394 0.02 0.985 -0.4653 0.4743

Midwest -0.0424 0.2390 -0.18 0.859 -0.5115 0.4266 South -0.0608 0.2394 -0.25 0.799 -0.5305 0.4088

West -0.0167 0.2403 -0.07 0.945 -0.4882 0.4549 Small -0.3410 0.0273 -12.5 0 -0.3945 -0.2875

Medium -0.0386 0.0285 -1.36 0.176 -0.0946 0.0173 Constant 1.0829 0.2471 4.38 0 0.5981 1.5678

Table 3B: Linear Probability Model Results for the Provision of Insurance – Recruiting

Employees

Recruiting Coefficient Std.

Error


Manufacturing 0.0195 0.0276 0.71 0.48 -0.0347 0.0737

Healthcare -0.0228 0.0570 -0.40 0.689 -0.1346 0.0890 Services 0.0091 0.0525 0.17 0.863 -0.0940 0.1121

Professional Services 0.0009 0.0566 0.02 0.987 -0.1102

0.1120

Education -0.0826 0.0692 -1.19 0.233 -0.2184 0.0532

Other -0.0284 0.0658 -0.43 0.666 -0.1576 0.1007 Nonprofit 0.0415 0.0286 1.45 0.147 -0.0146 0.0975

Northeast 0.0198 0.1989 0.10 0.921 -0.3707 0.4102 Midwest -0.0176 0.1986 -0.09 0.929 -0.4075 0.3722

South -0.0539 0.1999 -0.27 0.787 -0.4444 0.3366 West -0.0466 0.1999 -0.23 0.816 -0.4391 0.3458

Small -0.1553 0.0246 -6.32 0 -0.2036 -0.1071 Medium -0.0262 0.0239 -1.10 0.273 -0.0730 0.0207

Constant 0.9866 0.2059 4.79 0 0.5825 1.3908

Chernov 23

Table 3C: Linear Probability Model Results for the Provision of Insurance – Competitors Offer

It

Competition Coefficient Std. Error t-value P>t 95% Confidence Interval

Manufacturing -0.0317 0.0387 -0.82 0.413 -0.1077 0.0443 Healthcare 0.0632 0.0798 0.79 0.429 -0.0934 0.2198

Services 0.1168 0.0735 1.59 0.113 -0.0276 0.2611 Professional Services 0.1312 0.0793 1.66 0.098 -0.0244

0.2869

Education 0.0445 0.0969 0.46 0.646 -0.1456 0.2347 Other 0.1320 0.0921 1.43 0.152 -0.0489 0.3129

Nonprofit -0.0230 0.0400 -0.57 0.566 -0.1015 0.0556 Northeast -0.1358 0.2787 -0.49 0.626 -0.6828 0.4112

Midwest -0.1291 0.2782 -0.46 0.643 -0.6752 0.4170 South -0.1958 0.2787 -0.70 0.483 -0.7428 0.3513

West -0.1571 0.2801 -0.56 0.575 -0.7068 0.3927

Small -0.2201 0.0345 -6.39 0 -0.2878 -0.1525 Medium -0.0417 0.0335 -1.25 0.213 -0.1074 0.0240

Constant 0.9523 0.2885 3.30 0.001 0.3861 1.5184

Table 3D: Linear Probability Model Results for the Provision of Insurance – Employee

Retention

Retention Coefficient Std. Error t-value P>t 95% Confidence Interval

Manufacturing -0.0132 0.0267 -0.49 0.621 -0.0656 0.0392 Healthcare -0.0059 0.0550 -0.11 0.915 -0.1139 0.1021

Services 0.0326 0.0507 0.64 0.521 -0.0670 0.1321 Professional Services 0.0012 0.0547 0.02 0.982 -0.1061

0.1086

Education -0.1065 0.0668 -1.59 0.111 -0.2377 0.0247 Other 0.0377 0.0636 0.59 0.554 -0.0871 0.1624

Nonprofit 0.0336 0.0276 1.22 0.224 -0.0206 0.0878 Northeast -0.0131 0.1922 -0.07 0.946 -0.3904 0.3641

Midwest -0.0156 0.1919 -0.08 0.935 -0.3923 0.3610 South -0.0463 0.1922 -0.24 0.81 -0.4236 0.3310

West -0.0382 0.1932 -0.2 0.843 -0.4173 0.3410 Small -0.1091 0.0238 -4.59 0 -0.1557 -0.0624

Medium -0.0095 0.0231 -0.41 0.68 -0.0548 0.0358 Constant 0.9668 0.1990 4.86 0 0.5763 1.3573

Chernov 24

Table 3E: Linear Probability Model Results for the Provision of Insurance – Reducing

Absenteeism

Absenteeism Coefficient Std.

Error


Manufacturing 0.0372 0.0375 0.99 0.321 -0.0363 0.1108 Healthcare 0.0836 0.0770 1.09 0.278 -0.0676 0.2348

Services -0.0241 0.0710 -0.34 0.734 -0.1635 0.1152 Professional Services 0.0247 0.0765 0.32 0.747 -0.1255 0.1749

Education -0.0704 0.0936 -0.75 0.452 -0.2541 0.1132 Other 0.0097 0.0890 0.11 0.913 -0.1650 0.1843


Midwest -0.1579 0.2686 -0.59 0.557 -0.6851 0.3693 South -0.2065 0.2691 -0.77 0.443 -0.7347 0.3216

West -0.1222 0.2704 -0.45 0.652 -0.6528 0.4085 Small -0.1504 0.0333 -4.51 0 -0.2158 -0.0850

Medium -0.0448 0.0324 -1.38 0.167 -0.1083 0.0188 Constant 1.0321 0.2785 3.71 0 0.4855 1.5787

Table 3F: Linear Probability Model Results for the Provision of Insurance – Increasing

Productivity

Productivity Coefficient Std.

Error


Manufacturing -0.0500 0.0681 -0.73 0.463 -0.1836 0.0837

Healthcare -0.0718 0.1418 -0.51 0.613 -0.3501 0.2065 Services 0.0616 0.1310 0.47 0.639 -0.1956 0.3188

Professional Services 0.0016 0.1409 0.01 0.991 -0.2750 0.2782 Education 0.0256 0.1714 0.15 0.881 -0.3108 0.3620

Other -0.0621 0.1632 -0.38 0.704 -0.3824 0.2582 Nonprofit -0.0955 0.0702 -1.36 0.174 -0.2333 0.0423

Northeast 0.4540 0.4889 0.93 0.353 -0.5057 1.4137

Midwest 0.5782 0.4882 1.18 0.237 -0.3799 1.5363 South 0.5177 0.4890 1.06 0.29 -0.4421 1.4776

West 0.4886 0.4914 0.99 0.32 -0.4758 1.4531 Small 0.3188 0.0606 5.26 0 0.1998 0.4377

Medium 0.0816 0.0588 1.39 0.165 -0.0338 0.1971 Constant 0.9295 0.5066 1.83 0.067 -0.0649 1.9239

Chernov 25

Table 3G: Linear Probability Model Results for the Provision of Insurance – Employee Demand

Demand Coefficient Std.

Error


Manufacturing 0.0227 0.0402 0.57 0.572 -0.0561 0.1015 Healthcare 0.1661 0.0828 2.01 0.045 0.0035 0.3287

Services 0.2521 0.0763 3.3 0.001 0.1023 0.4019 Professional Services 0.2725 0.0823 3.31 0.001 0.1110 0.4341

Education 0.1932 0.1006 1.92 0.055 -0.0042 0.3906 Other 0.1986 0.0957 2.08 0.038 0.0108 0.3863


Midwest -0.1340 0.2888 -0.46 0.643 -0.7009 0.4329

South -0.1498 0.2893 -0.52 0.605 -0.7177 0.4180 West -0.0984 0.2907 -0.34 0.735 -0.6690 0.4722

Small -0.1503 0.0358 -4.2 0 -0.2205 -0.0801 Medium -0.0334 0.0347 -0.96 0.336 -0.1015 0.0347

Constant 0.7375 0.2994 2.46 0.014 0.1497 1.3252

Table 3H: Linear Probability Model Results for the Provision of Insurance – Employee Medical

Issues

Medical Coefficient Std.

Error


Manufacturing 0.0511 0.0495 1.03 0.302 -0.0461 0.1482 Healthcare 0.0250 0.1022 0.24 0.807 -0.1756 0.2256

Services -0.0001 0.0941 0 0.999 -0.1847 0.1845 Professional Services 0.0677 0.1014 0.67 0.505 -0.1314 0.2668

Education 0.0831 0.1240 0.67 0.503 -0.1603 0.3265 Other 0.0565 0.1182 0.48 0.633 -0.1755 0.2885


Midwest -0.4763 0.3560 -1.34 0.181 -1.1750 0.2224 South -0.5009 0.3566 -1.4 0.16 -1.2008 0.1990

West -0.4649 0.3583 -1.3 0.195 -1.1682 0.2384 Small -0.0986 0.0441 -2.24 0.026 -0.1851 -0.0121

Medium -0.0266 0.0428 -0.62 0.535 -0.1106 0.0575 Constant 0.9809 0.3691 2.66 0.008 0.2566 1.7053

Chernov 26

Table 3I: Linear Probability Model Results for the Provision of Insurance – Insurance Not

Counted as Taxable Income

Taxable

Income

Coefficient Std.

Error



Services 0.1069 0.0934 1.14 0.253 -0.0764 0.2901 Professional Services 0.2167 0.1007 2.15 0.032 0.0192 0.4143

Education 0.1679 0.1230 1.36 0.173 -0.0736 0.4094 Other 0.0337 0.1173 0.29 0.774 -0.1965 0.2639


Midwest -0.4530 0.3532 -1.28 0.2 -1.1463 0.2403 South -0.5070 0.3538 -1.43 0.152 -1.2014 0.1875

West -0.4077 0.3556 -1.15 0.252 -1.1056 0.2901 Small 0.0102 0.0439 0.23 0.815 -0.0758 0.0963

Medium 0.0506 0.0426 1.19 0.234 -0.0329 0.1341 Constant 0.8945 0.3662 2.44 0.015 0.1758 1.6133

Table 3J: Linear Probability Model Results for the Provision of Insurance – Insurance Tax

Deductible for Employer

Tax

Deductible

Coefficient Std.

Error


Manufacturing -0.0429 0.0462 -0.93 0.353 -0.1336 0.0477

Healthcare 0.0739 0.0949 0.78 0.436 -0.1124 0.2603 Services 0.1899 0.0873 2.18 0.03 0.0185 0.3612

Professional Services 0.1671 0.0942 1.77 0.076 -0.0177 0.3520 Education -0.0056 0.1150 -0.05 0.961 -0.2314 0.2202

Other -0.1011 0.1094 -0.92 0.356 -0.3158 0.1136 Nonprofit -0.0860 0.0476 -1.81 0.071 -0.1796 0.0075

Northeast -0.2760 0.3307 -0.83 0.404 -0.9251 0.3731

Midwest -0.3501 0.3302 -1.06 0.289 -0.9982 0.2979 South -0.3369 0.3308 -1.02 0.309 -0.9861 0.3124

West -0.2772 0.3324 -0.83 0.404 -0.9296 0.3751 Small -0.1167 0.0409 -2.85 0.004 -0.1971 -0.0364

Medium -0.0636 0.0399 -1.6 0.111 -0.1419 0.0146 Constant 0.9383 0.3423 2.74 0.006 0.2664 1.6102

Chernov 27

Table 3K: Linear Probability Model Results for the Provision of Insurance – It is the Right Thing

to Do

Right Coefficient Std.

Error


Manufacturing 0.0376 0.0251 1.5 0.134 -0.0116 0.0868 Healthcare -0.0489 0.0516 -0.95 0.343 -0.1502 0.0524

Services -0.0225 0.0475 -0.47 0.636 -0.1158 0.0708 Professional Services 0.0233 0.0513 0.45 0.65 -0.0774 0.1239

Education -0.1065 0.0627 -1.7 0.09 -0.2294 0.0165 Other 0.0017 0.0597 0.03 0.978 -0.1156 0.1189

Nonprofit 0.0656 0.0259 2.53 0.011 0.0148 0.1165 Northeast -0.0189 0.1802 -0.1 0.917 -0.3726 0.3348

Midwest -0.0497 0.1799 -0.28 0.782 -0.4028 0.3034 South -0.0362 0.1802 -0.2 0.841 -0.3899 0.3175

West -0.0403 0.1811 -0.22 0.824 -0.3957 0.3151 Small -0.0270 0.0223 -1.21 0.226 -0.0707 0.0168

Medium -0.0121 0.0217 -0.56 0.578 -0.0546 0.0305 Constant 0.9830 0.1865 5.27 0 0.6169 1.3490

Table 3L: Linear Probability Model Results for the Provision of Insurance – It is Law under the

ACA

Law

Coefficient Std.

Error


Manufacturing -0.0392 0.0496 -0.79 0.43 -0.1365 0.0581

Healthcare 0.0140 0.1019 0.14 0.891 -0.1861 0.2140 Services 0.0936 0.0938 1 0.319 -0.0905 0.2776

Professional Services 0.0676 0.1012 0.67 0.504 -0.1310 0.2663 Education 0.0155 0.1236 0.13 0.9 -0.2271 0.2580

Other 0.0496 0.1175 0.42 0.673 -0.1810 0.2803 Nonprofit 0.0779 0.0512 1.52 0.129 -0.0226 0.1783

Northeast -0.3918 0.3554 -1.1 0.271 -1.0894 0.3057

Midwest -0.3343 0.3548 -0.94 0.346 -1.0307 0.3621 South -0.4094 0.3554 -1.15 0.25 -1.1070 0.2881

West -0.3879 0.3571 -1.09 0.278 -1.0889 0.3131 Small -0.0081 0.0441 -0.18 0.854 -0.0946 0.0783

Medium 0.0493 0.0428 1.15 0.25 -0.0348 0.1334 Constant 0.8378 0.3679 2.28 0.023 0.11582 1.5599

Chernov 28

Table 3M: Linear Probability Model Results for the Provision of Insurance – Employer Fined if

No Insurance Offered

Fine Coefficient Std.

Error



Services 0.2203 0.0940 2.34 0.019 0.0359 0.4048 Professional Services 0.1366 0.1014 1.35 0.178 -0.0625 0.3357

Education 0.0845 0.1238 0.68 0.495 -0.1586 0.3276 Other 0.1104 0.1178 0.94 0.349 -0.1208 0.3416


Midwest -0.4239 0.3556 -1.19 0.234 -1.1218 0.2741 South -0.4872 0.3562 -1.37 0.172 -1.1863 0.2119

West -0.4200 0.3579 -1.17 0.241 -1.1226 0.2825 Small -0.0770 0.0440 -1.75 0.081 -0.1634 0.0095

Medium -0.0256 0.0428 -0.6 0.55 -0.1095 0.0584 Constant 0.8452 0.3687 2.29 0.022 0.1216 1.5688

Table 4: Linear Probability Model Results for the Perception of the ACA’s Effects on Firms

ACA Coefficient Std.

Error


Manufacturing -0.0548 0.0499 -1.1 0.272 -0.1527 0.0430 Healthcare -0.0386 0.1036 -0.37 0.71 -0.2419 0.1648

Services 0.0326 0.0961 0.34 0.735 -0.1560 0.2212 Professional Services -0.0301 0.1031 -0.29 0.771 -0.2326 0.1724

Education 0.0482 0.1249 0.39 0.7 -0.1970 0.2933 Other 0.0830 0.1215 0.68 0.495 -0.1555 0.3215

Nonprofit -0.0149 0.0511 -0.29 0.771 -0.1151 0.0854 Northwest 0.3529 0.4877 0.72 0.47 -0.6045 1.3103

Midwest 0.3312 0.4871 0.68 0.497 -0.6249 1.2874 South 0.3254 0.4878 0.67 0.505 -0.6322 1.2829

West 0.3370 0.4887 0.69 0.491 -0.6222 1.2962 Small 0.0230 0.0437 0.53 0.598 -0.0627 0.1088

Medium -0.0088 0.0427 -0.21 0.837 -0.0925 0.0750 Fine 0.0361 0.0345 1.05 0.295 -0.0316 0.1037

Exchange 0.0453 0.0392 1.16 0.247 -0.0315 0.1222 Cost Importance 0.0652 0.1554 0.42 0.675 -0.2398 0.3702

Constant -0.0703 0.5214 -0.13 0.893 -1.0939 0.9533

Chernov 29

Note: The excluded variables include Cons, Profit, Int, and Large for all models to avoid

collinearity.

Interpretation of Results

Given the nature of the sample and the discrepancies of the survey, I will use 10% as my

significance level. Looking at the results there seems to be a few, if any, general patterns.

Industry variables, along with the other variables, were, more often than not, insignificant when

explaining why firms offer health insurance. Industry looks to be irrelevant in whether or not

firms offer health insurance. However, there were some instances where an industry variable

showed significance. Healthcare, professional services, services, education, and services firms

tested significant in several LPMs, including the ones for competition, employee demand, tax

deductibility, and ACA. In most of these industries health insurance is a standard part of an

employee’s package, less so in services firms which contain retail businesses. Because offering

health benefits is considered the norm, employees at these firms expect to be offered such

benefits. People in professions like lawyers, doctors, and teachers are likely to expect insurance

as part of their benefits package, especially with teachers given union representation. The

services firms’ coefficient is a bit of an oddity as retail firms like Walmart generally do not offer

any kinds of benefits, but the services firms in the survey probably do not consist solely of retail

firms. Furthermore several services jobs are unionized, which might explain employee demand

for health insurance in this case.

In the case of competition professional firms such as law firms and financial institutions

need to compete for the most educated, qualified employees and offering fringe benefits is

generally one way these firms persuade candidates to join them. Tax deductibility serves as a

Chernov 30

cost-saving measure, but it is not entirely certain why these industries or firms place more

importance on this reason compared to other firms. Additionally, since insurance companies are

directly involved in the implementation of the ACA, for them not to follow the regulations and

suffer penalties as a result would probably be detrimental to their reputation and role in

healthcare. Small firms are not affected by the new regulations as they are not required to offer

insurance to their employees, so they will not have penalties levied against them.

The only variable that was consistent throughout a majority of the models was Small. In

almost every case, small firms tested significant and negative. Intuitively this makes sense as

smaller firms generally have to pay more proportionally to offer fringe benefits since their pool

of employees is smaller and the premiums they pay are higher. Except for rare exceptions, this

was the case for most, if not all, of the models. This helps explain why size was the primary

focus of the Employer Mandate rather than any other characteristic.

Finally, the LPM for the perceptions of the ACA’s effects shows insignificance for all

variables, meaning that none of these variables play a role in what firms think of the ACA’s

impact on their plans. While effects on cost should probably be the most important concern for

firms in the changes to health insurance policy, industry should not play much of a role in this

case. Intuitively speaking industry should not have any importance as to whether a firm might or

might not have difficulty in changing their health insurance plans, especially in regards to the

ACA.

Conclusion

Overall I find that industry plays a minor role, if any, in most instances regarding offering

insurance. The LPM for the provision of insurance showed that manufacturing was the only

Chernov 31

industry that showed significance and the coefficient was relatively small. Because the ACA was

designed to focus on firm size rather than any other aspect, it makes sense as to why small firms

was the only variable whose significance was constant throughout most of the models. This is

particularly intuitive because, as I mentioned before, small firms are not as likely to provide

insurance compared to larger firms due to higher costs having a larger effect on them, relatively

speaking. Larger firms do not have to worry about the costs of providing fringe benefits as much

as small firms because they have a larger pool of employees that can contribute to payments,

along with the firms being able to bargain for lower premiums due to their size. In the rest of the

LPMs for the provision of health insurance, the role industry played was inconsistent. There

were some patterns, such as professional services and services being significant and positive in

several cases, but most of the time industry variables were insignificant. This is especially the

case in the LPM for the perceptions of the ACA’s effects, where all of the industry variables

were insignificant.

It is hard for me to evaluate my earlier hypotheses given the inconsistencies in the survey

data and its effect on my results, but if I were to do so I would say that I, more or less, accept my

first hypothesis. In the models where there were significant variables, healthcare, professional

services, and education collectively had more positive coefficients compared to manufacturing

and services. There are some caveats though. Because construction was excluded from all

models it is more difficult to make the comparison in my original hypothesis. Additionally the

widespread insignificance in most of the models does not lend much credibility to the claim as

the comparisons are harder to measure. I also reject my second hypothesis as the data showed

that none of the characteristics had any effect on what firms perceived to be the effects of the

ACA.

Chernov 32

For further research there are several different areas worth looking at in the future. For

instance running similar models with continuous variables would provide much better results,

possibly more accurate as well since we can run probit or ordered models. Additionally it would

be interesting to examine the effects of the ACA on businesses a few years down the line, as the

regulations affecting firms have only just been implemented. Analyzing the effects of other

variables would be something else worth considering, possibly variables relating to a firm’s

financials like revenue or market capitalization.

Works Cited

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Dickens, William T. and Lawrence F. Katz. “Interindustry Wage Differences and Industry

Differences.” Working Paper 2014. Sept. 1986. National Bureau of Economic Research.

Web. 2 January 2016.

Dillender, Marcus, Carolyn J. Heinrich, and Susan N. Houseman. The Potential Effects of

Federal Health Insurance Reforms on Employment Arrangements and Compensation.

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Chernov 33

States, 2014. ICPSR version. Chicago: Associated Press-NORC [producer], 2014. Ann

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Benefits.” Washingtontimes.com. The Washington Times. Web. 15 March 2016.

Levey, Noam M. “Healthcare Costs Rise Again, and the Burden Continues to Shift to Workers.”

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The New York Times. Web. 4 April 2016.

Sullivan, Peter. “Small-Business Owners Swarm DC to Oppose ObamaCare Fines.” Thehill.com.

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<http://www.census.gov/content/dam/Census/library/publications/2014/demo/p60-

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