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 legislation 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 BureauSectors
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
Nonprofit 0.0363 0.0388 0.94 0.349 -0.0398 0.1125
Northeast -0.1253 0.2690 -0.47 0.642 -0.6534 0.4027
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
Nonprofit 0.0649 0.0415 1.56 0.118 -0.0166 0.1465
Northeast -0.1380 0.2893 -0.48 0.633 -0.7058 0.4298
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
Nonprofit 0.0404 0.0513 0.79 0.43 -0.0602 0.1411
Northeast -0.5266 0.3566 -1.48 0.14 -1.2265 0.1732
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
Manufacturing -0.0423 0.0492 -0.87 0.386 -0.1391 0.0539
Healthcare 0.1540 0.1013 1.52 0.129 -0.0449 0.3529
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
Nonprofit -0.0615 0.0509 -1.21 0.228 -0.1614 0.0385
Northeast -0.4526 0.3538 -1.28 0.201 -1.1470 0.2418
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
Manufacturing -0.0793 0.0495 -1.6 0.109 -0.1764 0.0178
Healthcare 0.1791 0.1020 1.76 0.079 -0.0210 0.3793
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
Nonprofit -0.0006 0.0512 -0.01 0.991 -0.1010 0.0998
Northeast -0.4900 0.3562 -1.38 0.169 -1.1891 0.2091
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
Akst, Daniel. “On the Contrary; Why Do Employers Pay for Health Insurance, Anyway?”
nytimes.com. The New York Times. Web. 6 April 2016.
Bernstein, David. “Fringe Benefits and Small Businesses: Evidence from the Federal Reserve
Board Small Business Survey.” Applied Economics 34 (2002): 2063-2067.
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.
Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.
Dunlop, John T. “Industrial Relations and Economics: The Common Frontier of Wage
Determination.” IRRA Proceedings 1984, 1985.
Employer Perspectives on the Health Insurance Market: A Survey of Businesses in the United

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States, 2014. ICPSR version. Chicago: Associated Press-NORC [producer], 2014. Ann
Arbor, MI: Inter-university Consortium for Political and Social Research [distributor],
2014. Web. 15 October 2015.
Gruber, Jonathan. “Health Insurance and the Labor Market.” Handbook of Health Economics.
Eds. Culyer, A.J. and J.P. Newhouse. 1st Ed. Elsevier Science B.V., 2000. Print.
Healthcare: U.S. Small Business Administration. The U.S. Small Business Administration. Web.
30 November 2015.
Hiltzik, Michael. “Do We Need Obamacare’s ‘Cadillac tax’? Yes—and No.” Latimes.com. Los
Angeles Times. Web. 15 March 2016.
Howell Jr., Tom. “Obamacare ‘Cadillac tax’ to Hit 1 in 4 Employers that Offer Health Care
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.”
latimes.com. Los Angeles Times. Web. 6 April 2016.
Linnan, Laura et al. “Results of the 2004 National Worksite Health Promotion
Survey.” American Journal of Public Health 98.8 (2008): 1503–1509. PMC. Web. 28
December 2015.
Lorenzen, Richard. “Is the Affordable Care Act Really Bad for Business?” Forbes.com. Forbes.
Web. 15 March 2016.
O’Brien, Ellen. “Employers’ Benefits from Workers’ Health Insurance.” The Milbank Quarterly
81.8 (2003): 5-43. PMC. Web. 6 April 2016.
“Pre-existing Conditions.” HHS.gov. U.S. Department of Health and Human Services. Web. 30
November 2015.
Sanger-Katz, Margot. “Has the Percentage of Uninsured People Been Reduced?” nytimes.com.

Chernov 34
The New York Times. Web. 4 April 2016.
Sullivan, Peter. “Small-Business Owners Swarm DC to Oppose ObamaCare Fines.” Thehill.com.
The Hill. Web. 15 March 2016
United States. Census Bureau. “Figure 2: Uninsured Rate Using the American Community
Survey: 2008 to 2013.” Health Insurance Coverage in the United States: 2013.
Washington: US Census Bureau, Sept. 2014. Web. 4 April 2016.
<http://www.census.gov/content/dam/Census/library/publications/2014/demo/p60-
250.pdf>

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