MMays-Complete-Independent-Study

Innovation Through Technical Change: The Differential Effects Technology
Expenditures Have on the Wages of Employees Within the Oil and Gas Industry
By. Michael Mays
Submitted in Partial Fulfillment of the Requirements of Senior
Independent Study for the Department of Business Economics at the
College of Wooster
Advised by
Dr. Philip Mellizo, PhD
Department of Economics
March 28th
, 2016

ii
Acknowledgements
I owe a very special thank you to a role model of mine Dr. Philip Melizo. Dr. Melizo has
offered nothing but tremendous support and continuous help throughout the entire
Independent Study Process. He has pushed me above and beyond, teaching me to be a
better student and individual outside the classroom. I would also like to offer a special
thank you to all of the staff in the economics department who has helped prepare me over
my time here at The College of Wooster. To my family and friends, I could not have
completed this process without your continuous love and support, for that I thank you. To
Andy Pfeuffer, we agreed to push one other and stick to a set schedule everyday for this
entire process. I want say thank you for the continuous support, commitment, and great
friendship we developed along the way. Congratulations on the completion of your study
my friend.

iii
Abstract:
This thesis analyzes the effect of technical expenditures on the demand for labor in the oil
and gas extraction industry. The findings suggest that increases in technological
expenditures have an adverse effect on wages for laborers employed within the industry.
The broader implications are increased technologies expenditures increase the wages for
employees of higher skill, but negatively affect wages of those with lesser human capital
stock.

V
Table of Contents
Chapter 1: Introduction:.................................................................................................. 6
Chapter 2: Theory Chapter:............................................................................................ 8
Section 2.1: Differential Effects of Wages with Introduction of Technology:........ 9
Section 2.1.2 The Hiring Decision in the Long Run: ......................................... 11
Section 2.2: Profit Maximizing Equation Using Skilled and Unskilled Laborers:
........................................................................................................................................... 13
Section 2.3 The Possibility of Technology Locking In Low Earnings................... 16
Section 2.4: Summary of Theory and Predictions For the Empirical Model: ..... 22
Chapter 3: Literature Review Chapter: ....................................................................... 23
Section 3.1: Persefoni V. Tsaliki Exploring Technical Change and Deskillzation:
........................................................................................................................................... 24
Section 3.2: Support of Differential Effects on Wages:.......................................... 27
Section 3.3: Effects on Laborers With Limited Human Capital:.......................... 34
Section 3.4: Technology, Trade, and Outsourcing’s Potential Affect on Wages: 39
Section 3.5: Evidence of Structural Unemployment:.............................................. 42
Chapter 4.0: Data & Descriptive Statistics: ................................................................. 47
Section 4.1: Variables and Summary Statistics...................................................... 47

V
Section 4.2 Fixed-Effect vs. Random-Effect Regression: ....................................... 50
Section 4.3 Empirical Fixed-Effect Regression Model:.......................................... 53
Section 4.4 Econometric Problems: Heteroskedasticity and Serial Correlation: 54
Section 4.5 Robust Standard Errors Cluster Fixed-Effect Estimation Results:.. 56
Chapter: 5 Concluding Remarks and Discussion ........................................................ 62
Section 5.1: In Depth-Analysis of Results:............................................................... 62
Section 5.1: Final Thoughts: ...................................................................................... 64
Bibliography:................................................................................................................... 66
Appendices: ..................................................................................................................... 68
Appendix A-1: Categorization of Job Occupations In the Oil and Gas Industry..... 69
Appendix A-2: Summary Statics of the Data/Variables: ............................................ 69
Appendix B-1: Breush Pagan Test for Random Effects: ............................................ 71
Appendix B-2: Hausman Test for Fixed Effects:......................................................... 71
Appendix C-1: Modified Wald Test for Heteroskedasticity:...................................... 71
Appendix C-2: Wooldridge Test for Autocorrelation in Panel Data:........................ 72
Appendix C-3: S.E. Robust Correction for Heteroskedasticity & Serial Correlation:
........................................................................................................................................... 72

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Chapter 1: Introduction:

The oil and gas industry plays an important role in the United States economy and
world oil market. The US Energy and Information Administration has reported that oil
and gas production was at it largest in the year 2014 (EIA – Independent Statistics and
Analysis, 2016). In the five-year period between 2007-2012, the administration reported
that growth was consecutive (EIA – Independent Statistics and Analysis, 2013).
Growth in the oil and gas industry continued to increase after oil prices began to fall in
the 2009. Despite dropping oil price, employment opportunities have continued to
increase for many laborers seeking employment in the United States. With other key
competitors in the world oil market (OPEC, Russia, etc.), global competition puts
pressure on firms to continuously innovate production methods that economize on costs
(Whorton 2014). When firms adopt new capital, there can be a differential affect on the
laborers working in the industry. For some laborers the technology adoption can affect
their wages positively, and for others negatively. The purpose of this thesis is to analyze
the differential effects of technological adoption for workers in different occupations
within the oil and gas industry.
Technical change results from the change in relationship between the inputs and
outputs of the labor process through the introduction of a technological good in the form
of capital that will increase the profits of firm (Bowles 2005). Technological adoption
affects the type of skills an employee must have to meet the demand of the firm. For
example, firms may invest in a technology that makes the labor process simpler (e.g.
drilling equipment), increasing demand for low-skilled employees. Similarly, a new

7
technology (e.g. diagnostic computer equipment) could increase the demand for high-
skilled labor, but lowers the demand for low-skilled laborers.
The following I.S. is organized as follow: In Chapter 2, I use microeconomic
theory to explain how technology can have differential effects on wages of employees in
the oil and gas industry. I will model the differential effects illustrating the dominating
effects of the individual substitution and scale effects that are consequences of increased
technological expenditures. Chapter 2 continues illustrating the potential for a low-skilled
employee to get locked into low earnings through the use of the human capital theory. I
will summarize the theory chapter providing my predictions for how I think an increase
in technological expenditures will affect laborers in the oil and gas industry.
In Chapter 3, I provide an analysis and critique of the five pieces of literature that
was used to help develop the foundation of the I.S. Much of the literature compliments
the affects that theory in Chapter 2 associates with technical change. However, each piece
of literature provides alternative factors (e.g. International Trade and Outsourcing) that
create effects identical to that of technology. The purpose of this chapter is to focus on
the material that was used in guiding me to develop the specifications of my model.
In Chapter 4, I begin by providing an in-depth explanation of my data and how it
was obtained. The data used in the I.S. is categorized as panel data in which a fixed-effect
regression was used to obtain the results. When using panel data there is the potential to
run into econometric problems (heteroskedasticity and serial correlation). For each
problem, I will explain their consequences and the ways to correct for them. Chapter 4
will conclude through the illustration and explanation of the results obtained after running
the model.

8
In chapter 5, I begin by discussing an in-depth analysis of my results. I will then
continue by reflecting on how my results faired in comparison to my predictions,
providing my thoughts as to what the real world implications of my results are. The
conclusion of the I.S begins with Section 5.2, providing a critique that can improve this
study in the future, finishing with the relevance of this study to modern economics.
Chapter 2: Theory Chapter:
The purpose of this chapter is to build a theoretical model on how the adoption of
technology can change the demand for laborers in the oil and gas extraction industry.
The US Energy and Information Administration have reported that oil and gas production
was at it largest in the year 2014 (EIA – Independent Statistics and Analysis, 2016).
The administration shows roughly a five-year increase in oil production from 2007-2012
(EIA – Independent Statistics and Analysis, 2013). To achieve consecutive years of
increased oil production, firms had to increase their units of inputs (capital, technology,
and laborers) used in the production process (Whorton 2014).
I have hypothesized that as technology increases in the form of capital in an
industry, the demand for high and low skilled laborers will change. I theorize that
technology increases wages for high-skilled employee occupational groups (e.g.
business/management occupations) and decrease the ages for low-skilled occupational
groups (e.g. ground crews) within the oil and gas industry.
The chapter is organized as follow: In Section 2.1 I model the differential effects
of technology on wages by occupation. Section 2.2 continues by illustrating the
differential effects of technology, when unskilled and skilled laborers are included
together in the profit-maximization equation. Section 2.3 uses the human capital theory to

9
explain how low-skilled employees are disadvantaged compared to employees of higher
skill in terms of earnings. Section 2.4 concludes by summarizing the theory in the
previous sections to develop my predictions for how I think an increase in technological
expenditures will affect laborers in the oil and gas industry.
Section 2.1: Differential Effects of Wages with Introduction of Technology:

In this section I model the two different effects technology can have on wages.
These effects are called the substitution and scale effects. To develop an understanding of
these effects, I begin this section briefly explaining the production process for firms in
the oil and gas industry. I will continue the section explaining the Marginal Revenue
Product of Labor (MRPL) and illustrating how firm incorporate laborers into their profit-
maximizing equation. The section will end with a detailed illustration of the scale and
substitution effect within the oil and gas industry when a new piece of technology is
introduced.
The oil and gas industry is comprised of many firms, where each firm produces a
level of output (Q) using a combination of inputs, capital (K) and labor (L),
Q=f(K,L).
Intuitively, firms must choose a level of output to produce given market demands and
then choose which combination of K and L is the cheapest to produce this output. The
amount of labor that is demanded by the firm is dependent on the cost of capital relative
to the cost of labor, product demand, and the technology that is available within the
industry (Ehrenberg 2014).
In Figure 1, Graph A and Graph B show the effects of a decrease in the price of
technological capital goods have on labor demand. Notice that there are two different

10
effects. The substitution effect refers to the decline in labor demand owing to decrease in
the price of capital relative to labor. This means that tech can lead to capital being
cheaper relative to labor, thus giving firms an incentive to substitute labor with capital.
This effect is shown in Figure 1, Graph A, by an inward shift in the labor demand. The
scale effect can do the opposite of the substitution effect and increase demand for
laborers when the relative price of capital falls. The reason being that a fall in the price of
capital means that the production process is now cheaper overall. The cheaper production
process now means that a firm can afford to hire more labor than before, increasing labor
demand (shown by Figure 1, Graph B).
A profit-maximizing firm will hire labor until the Marginal Revenue Product of
Labor (MRPL) is equalt to the Marginal Expense of Labor (MEL). The MRPL is
generated when the Marginal Revenue (MR) of the product being sold multiplied by the
Marginal Product of Labor (MPL). In a perfectly competitive economy, the MEL is equal
The Number of Laborers Employed
WagesForLaborers
Graph A: The Substitution Effect
LD When Price of K Decreases
LD When K is High
The Number of Laborers Employed
WagesForLaborers
Graph B: The Scale Effect
LD When Price of K Decreases
LD When K is High
Figure 1: Substitution vs. Scale Effect

11
to the market wage. To show MRPL=MEL in terms of physical quantities, it can be
rearranged to look as follow:
MPL= W/P, where W/P is the real wage.
The real wage represents the purchasing power laborers have symbolizing the amount of
physical quantities that can be bought at that wage. This is shown graphically with Figure
3. In the figure, the labor demand for an individual firm is shown in real wages. At W/Po,
the firms profit maximize where the MR=MC by employing at E0.
If the firm previously employed at E1, they would have an incentive to add another unit
of labor because their MC would be less than marginal product paying the real wage
W/Po. At point E2, firms are paying a marginal cost that is greater than their marginal
product and would be inclined to reduce employment if paying at the real wage W/Po.
2.1.2 The Hiring Decision in the Long Run:

Firms adjust their long-run demand for their inputs (capital and labor) using the
profit maximizing condition
!
!"#
=
!
!"#
. This states that the most cost-effective method of
Level of Laborers Employed
RealWagesW/P
W/Po
E1 E0 E2
Figure 2: Real Wages

12
production is when the combinations of capital and labor are adjusted so the marginal
cost of producing an extra unit of output generated through capital (
!
!"#
) is equal to the
marginal cost of producing the same unit of output using labor (
!
!"#
). If the
!
!"#
𝑖𝑠 >
!
!"#
, a firm is not profit maximizing because it is paying a higher marginal cost for
output with the use of labor than the marginal cost of output using capital. The firm
would want to adjust their units of input by substituting capital for labor to reach their
profit maximizing equilibrium. They will continue to increase capital and decrease labor
until
!
!"#
=
!
!"#
(Ehrenberg 2014).
To illustrate the long-run profit maximizing equation, I will use Figure 3. The
curve labeled Q* is an isoquant curve which represents the desired the level of output by
a firm in the oil and gas industry that can be obtained using any combination of labor and
capital along the curve. The slope of the isoquant is equal to marginal rate of technical
substitution (-MPL/MPk). Understanding the slope allows us to see how changes to MPL
0
Labor in Units of Hours
CapitalinPhysicalUnits
X
Y
Z
B
B'
D
D'
Kz
Lz
Figure 3: Proﬁt-Maximizing Firms

13
or MPK allow firms to move along the isoquant curve to produce Q* with different
combinations of K and L inputs (e.g. points X, Y, and Z). At each point, capital and labor
still present costs to the firm. Due to the limited available income a firm has, the firm is
faced with a budget in which they have to spend on the production process and this
budget is illustrated by the budget constraint line BB’. The slope of the budget constraint
is the negative ratio of the cost of labor relative to the cost of capital (-W/C). Similar to
the isoquant curve, any point along BB’ is a combination of labor and capital that can be
used costing the firm the same amount of money at every point.
A profit-maximizing firm will choose not to produce at points X and Y because
the cost associated with producing at DD’ is greater than the cost associated with the
production along isoexpenditure BB’. The firm will choose to profit maximize at point Z
where the isoquant Q* is tangent to the isoexpenditure BB’. At point Z, the slope of the
isoquant (−
!"#
!"#
) is equal to the slope of the isoexpenditure (−
!
!
). The equation
−
!"#
!"#
= −
!
!
. can be rearranged by cross multiplying and dividing to derive the long-run
profit maximization equation
!
!"#
=
!
!"#
(Ehrenberg 2014). Using this information I will
now illustrate and elaborate the differential effects of technology at the industry level
using the profit-maximizing equations.
Section 2.2: Profit Maximizing Equation Using Skilled and Unskilled Laborers:

In my model, I am analyzing the differential effects that technology has on wages
of occupational groups within the oil and gas industry. For example, laborers under the
occupational group called the Construction/Extraction Occupations are laborers with
limited experience whom have recently began working categorizing them as a low-skilled

14
laborer within that occupation. On the other hand, there can be laborers with multiple
years experience, additional schooling, and additional training making them a high-
skilled employee within the occupational group. The marginal cost of producing an extra
unit of output generated through unskilled laborers is represented by,
!"
!"#$
.
!"
!"#$
,
represents the marginal cost of producing an additional unit of output generated through
skilled-labor. A firm’s profit-maximization equation will now look as follow:
!"
!"#$
=
!
!"#
=
!"
!"#$
.
In Figure 4, there are two graphs (Unskilled-Labor and Skilled-Labor) that
represent the high and low-skilled laborers of the Construction/Extraction Occupational
group. For the purpose of this example, a new piece of technology (e.g. oil extraction
drill bit) has been adapted that makes the extraction of oil and gas more efficient. Before
the adaptation of this technology, neither the unskilled or skilled laborers differ in terms
of MPL. Initially, labors of both skill-sets within the construction/extraction occupational
group are paid the same real wage (
!
!
𝑢,
!
!
𝑠), making the demand and levels of
employment for each type of laborer equal.
The drill bit that has been acquired allows one high-skilled laborer to
produce the same output as five low-skilled laborers combined. The new profit
maximizing equation looks as follow:
!"
!"#$
>
!
!"#
=
!"
!"#$
because of the following
reasons. First we would see an increase in the cost of capital but it will be offset by the
increase in the MPK through the use of skilled-laborers allowing
!
!"#
to remain
unchanged. Second, the unskilled-laborers do not have the skills needed to operate or
understand the new technology being implemented decreasing their MPL (MPLU). As

15
the labor demand is equal to MPL, the demand for unskilled-laborers will decrease from
Du to Du’. At the new demand, the wage of the unskilled-laborers
!
!
𝑢 𝑖𝑠 > 𝑀𝑃𝐿
incentivizing the firm to decrease the wages of unskilled-laborers to
!
!
′𝑢.
Since the marginal cost of producing of an additional unit of output using
unskilled labor is greater than the marginal cost of producing that unit with capital, the
labor lost (Lu-L’u) is now substituted with capital. Capital will continue to replace the
unskilled-laborers until point B is reached and the profit-maximizing equation is back
into equilibrium
!"
!"#$
=
!
!"#
=
!"
!"#$
. The introduction of the technologically innovated
drill bit resulted in a substitution effect where units of capital were substituted for
unskilled-laborers.
As capital increased to substitute for the unskilled-laborers, the marginal cost of
producing an extra unit of output using capital is now greater than the marginal cost of
producing that same additional unit of output using skilled-laborers changing the profit-
Su
Du
Ss
DsD'u
D's
W/Pu
W/P'u
Real Wages W/P
W/Ps
W/P's
Real Wages W/P
EuE'u Es E's
Employment of Unskilled Extraction Laborers
Employment of Skilled Extraction Laborers
Figure 4: Unskilled vs. Skilled Construction/Extraction Laborers
A
B
C
D

16
maximizing equation to
!"
!"#$
=
!
!"!
>
!"
!"#$
. Knowing that the new technological
program allows skilled-laborers to increase their MPL to five times of that of a unskilled-
laborers, the demand for skilled-laborers will increase from Ds to D’s. In order to get
back to the profit-maximizing equilibrium condition, the firm must increase the number
of skilled-laborers from Ls to L’s. The firm will attract high-skilled
construction/extraction laborers incentivizing laborers by offering higher real wages. The
real wage must increase from
!
!
𝑠 𝑡𝑜
!
!
′𝑠. Firms will continue to adjust wages until
enough laborers have been hired until the marginal cost of producing with a high-skilled
construction/extraction laborer is equal to the marginal cost of producing an additional
unit generated through capital. This new point of equilibrium for skilled-laborers is
shown by point D. Initially, Graph A illustrated how an increase in capital was used to
substitute for low-skilled laborers. After the capital was substituted, the firm increased
the amount of high-skilled laborers to maximize the benefits of adopting the technology,
a scale-effect has been observed.
Theory supports that the introduction of newer or more technologies in the form
of capital can have a differential effect of wages. In the previous example, wages changed
within an occupation, increasing the demand for employees with higher skills. In the next
section I will provide support as to why technologies benefited the high-skilled laborers
in terms of wages, but present a disadvantage to low-skilled workers. Using the human
capital theory, I will explain how the use of technology in the oil and gas industry can
lock in low-skilled workers to a lower earning potential.
Section 2.3 The Possibility of Technology Locking In Low Earnings

17
In Section 2.1, the scale and substitution effects were used to show that
technologies used in the production process have the potential to affect the wages of
laborers differently. To begin the section I will highlight why individuals may choose to
invest their time in the oil and gas industry rather than investing in a college degree. I will
continue with the human capital theory using the theory to illustrate how technologies
can lock in low-skilled employees of the oil and gas industry. In conclusion of this
section, I will offer an explanation as to why firms within the oil and gas industry may
choose not to invest in the on-the-job training for their employees and elaborate on the
effect it can have on these employees. I will conclude the chapter with Section 2.4,
summarizing my theory to develop the predictions for my model.
In section 2.1 I illustrated the substitution effect for low-skilled laborers in the
Construction/Extraction Occupations Group when the firm adapted new technology. The
results supported that low-skilled employees within these occupations were demanded
less, earning a lower real wage as a consequence. The low-skilled laborers who were
affected from the adoption of technology can choose to look for a new job, or invest in
more human capital. In Figure 5, there are two graphs (Oil Market and Coal Market) that
show the demand for laborers within the oil and coal market. The new adoption of
technology drops demand for low-skilled employees in the oil industry, decreasing the
demand and wages for these employees. These employees could potentially migrate to
the coal market, which offers similar type of work in the energy sector. Firms within the
coal market face of a shortage of laborers. The labor demanded by firms is greater than
the labor being supplied in the coal market. To meet the increase in demand (Dc-Dc’)
firms in the coal market increase wages that will signal low-skilled laborers affected in

18
the oil industry to migrate to the coal industry. However, the available jobs in the coal
industry require a higher skill-set then what is being supplied by the individuals leaving
the oil industry. There is now and oversupply of low-skilled workers in each industry.
The mismatch in the skills required for the job and the skills being supplied is called
structural unemployment (Ehrenberg 2014).
As the problem worsens, the oversupply of low-skilled laborers puts downward
pressure on wages through competition. The supply for high-skilled laborers will remain
constant initially, allowing them more bargaining power putting upward pressure on
wages for laborers of their skill-set. It seems rational for an individual of low skill to
want to increase their human capital stock. Human capital categorizes workers as having
a set of skills that can be rented out to employers. Individuals can increase the value of
this stock through the investment of higher education or training, migration, or search for
new jobs. The idea to find a new job, go back to school, or participate in more training
Wages
So Sc
D'o
Do
Dc
D'c
EoE'o
W'o
Wo W'c
Wc
Ec E'c
Wages
Employment in Oil Market Employment in Coal Market
Figure 5: Structural Unemployment Between Industry
Oil Industry
Coal Industry

19
seems simple, but the underlying cost to the individual or employer can outweigh the
potential benefits received from the investment in present time.
When choosing to invest, individuals have the option of looking at their
investment through the present value method. The present value method specifies a value
for the discount rate, r, then determining the present value of benefits (PV) compared to
the cost (c). The equation looks as follow:
PV= B1/(1+r) + B2/(1+r)^2+ BT/(1+r)^T>C (Ehrenberg 2014)(Eq. 9.6)
B1 is the expected value of benefits from the additional year of investment and r
is the interest rate. The smaller the interest rate, the better the return of benefits for the
individual. Therefore, the individual will choose to invest as long as the present value of
benefits (PV) continues remain better than the costs (C) assuming individuals are utility
maximizes (Ehrenberg 2014).
The decision to invest in human capital comes when the marginal benefits of the
investment exceeds the marginal cost of the investment. Figure 6 on the next page shows
the optimal acquisition of human capital that an individual would want to acquire in
terms of marginal costs and marginal benefits. The marginal costs line assumes that
investments are going to stay constant over time and these cost include the direct/out of
pocket expenses, forgone earnings, and psychic cost. As individuals age, they are left
with less time to return on their investments, shown by the marginal benefit line. Where
the marginal benefit and marginal cost curve intersect is the desire acquisition of human
capital. In figure 6, each graph represents a different individual (Individual A and
Individual B). Individual A is a young low-skilled worker who suffered a loss in wages
when new technology was adopted. Individual B is an older gentleman who has been

20
working in the gas industry for forty years. Individual A is already in the labor market,
the cost of leaving his family without his share of support for income pushes the level of
human capital he desires backwards compared to if he was a single individual out of high
school. Individual B is older decreasing the benefits received from the investment
because there is potential he will exit the labor force before maximizing his return.
Therefore, individual B is likely to desire less human capital compared to if he chose to
enter the market at a younger age (Ehrenberg 2014).
Figure 7, will be used to illustrate how firms in the oil and gas industry view an
investment in on-the-job training. This figure will be used to explain why a firm in the oil
and gas industry would substitute a low-skill extraction laborer (e.g. in Section 2.2) rather
than investing in on the job training to develop the skills needed to operate the new
technology. The likelihood for a firm to invest in on-the-job training is higher for
individuals of younger ages. Younger individuals have more time to available to generate
the maximum benefits that can be received from the training. On-the-job training requires
MB MB
MB'
Marginal Costs
Marginal Benefits &
Marginal Costs
Marginal Benefits &
Units of Human Capital Units of Human Capital
MC
MC'
MC
HC*HC' HC*HC''
Figure 6: Cost Benefit of Human Capital Analysis

21
depreciation of wages during the training period in order to receive higher wages
afterwards (Ehrenberg 2014).
In figure 7, a low-skilled extraction laborer begins earning with his or her current
stock of human capital at Es. If the firm decided to invest in on-the-job training for this
individual, the individual would begin to earn along line Ea, which is the amount the
extraction laborer, would receive after subtracting the cost of the investment. The cost of
the investment to the firm is the decrease in the overall productivity during training time;
Resources such as other laborers are used to train the trainee, the time used to train the
trainee could otherwise been used in the production process. This investment for the firm
is represent by the region between the earning potential (Ep) and Ea lines. From the
firm’s perspective, rather than training the low-skilled extraction laborer, the age or
current level of human capital stock an individual has makes the cost of training the
individual too costly.
Theory shows in a competitive market that technology adoption may more
beneficial for firms in comparison to investing in their employees. As theory supports,
Earnings
Es
Ea
Ep
Es
Ao A*
Age
Actual Earnings
Between Ep-Ea
Figure 7: Firm's Decision to Invest in Training For Employees

22
once an individual is in the labor market, it is hard to make the decision to give up
earnings in the workforce and choose to invest in human capital alone. As structural
unemployment arises, the competition between low-skilled laborers increases the cost of
forgoing earnings making the human capital investment less likely. I will now continue
by summarizing the theory as a whole, developing my predictions used for the empirical.
Section 2.4: Summary of Theory and Predictions For the Empirical Model:

Employees of different skill-sets can be affected differently as supported by
theory. With the example of jobs within the oil and gas industry, the adoption of
technology can benefit laborers of one skill simultaneously putting laborers with a
different level of skills at much larger disadvantage. Once working in the labor force it
the present value benefits of obtaining more education do not outweigh the cost of
forgoing current earnings. Firm’s also have the potential to increase output to meet
demands by increasing the productivity of high-skilled employees through the adoption
of technology. This same technology may be used to substitute for low-skilled laborers
who are unable to operate the advancements within the firm.
Moving forward with my research, I predict that the introduction of technology
will change the demand for occupational groups within the oil and gas industry.
Occupational groups that would be considered low-skilled such as Build/Grounds Crew
Occupations, Security and Protective Services, and Maintenance Occupations will see
wages lower and demand decrease as technology is introduced. I am predicting as
expenditures increase on technology that the low-skilled laborers will be dominated by
the substitution effect. Conversely, I am predicting laborers of high skilled such as
Management, IT Services, Finance, Architecture Occupations etc., will benefit from a

23
increase in technological expenditures. For these high-skilled laborers I am predicting
that the scale effect will dominate and we see an increase in wages for employees under
these occupational groups.
Chapter 3: Literature Review Chapter:

The purpose of this chapter is to make a connection between the theories
discussed in Chapter 2 with published literature that focuses on the differential effects on
the workforce created through technology. I provide critiques of five pieces of literature
that helped me develop my empirical model. Each article compliments the notion that
technology has differential effects on wages. With each critique I will explain how the
authors independent of one another view technological effects differently.
I have hypothesized that as technology increases in the form of capital in an
industry, the demand for high and low skilled laborers will change. I have also theorized
that technology increases wages for high-skilled employee occupational groups and
decrease the ages for low-skilled occupational groups within the oil and gas industry.
With each literature review, I show support for my hypothesis but also offer altercations
that can be made. The different approaches used by authors allows for a clearer
understanding of the multiple effects, some positive, some negative, that technology has
on the production process.
The chapter is organized as follow: In Section 3.1 I critique the work of Persefoni
V. Tsaliki whom wrote "Economic Development, Human Capital, and Technical Change:
The Question of Machinery Revisited." Section 3.2 provides evidence of technical
change having an effect on employee’s wages through the work of Timonthy Dunne and
James Schmitz. Section 3.3 supports that low-skilled workers have the potential to lock-

24
in low earning through use of the human capital theory by Jacob Mincer. Section 3.4 is a
critique to the work of Catherin Morrison Paul and Donald Seigal whom used
technology, international trade, outsourcing, and product demand as group of factors that
affected wages. Section 3.5 provides evidence of skill-biased technical change at the
industry level through the critique of an article wrote by Bernardo S. Blum. Section 3.6
concludes the chapter with an explanation on how I used each critique to develop my
empirical model.
Section 3.1: Persefoni V. Tsaliki Exploring Technical Change and Deskillzation:
The article, “Economic development, human capital, and technical change: the
question of machinery revisited” by Persefoni V. Tsaliki explores consequences of the
introduction of technological capital goods. Understanding that it can increase economic
growth, the question becomes does the technical change cause more harm than good.
Tsaliki introduces the article explaining Say’s Law used by early economist to show the
advantages of technical change. He concludes with neo-classical theory and where the
process of deskillization of workers begins.
Initially technical change was looked at through the theoretical lenses of Say’s
Law. Say’s law states that the employment lost from the introduction of laborsaving
technological goods is only a temporary consequence. This consequences is considered
temporary, because the profits received through higher through increased production of
goods should increase employment opportunities elsewhere (Tsaliki 2008). Tsaliki
continues by showing the change in economic thought by explaining the change in the
stance of Economists David Ricardo’s beliefs of technical change. Initially, Ricardo
agreed with Say’s Law but changed his stance once he realized the profits made from the

25
labor saving technologies could be re-invested in the form of fixed capital. Fixed capital
becomes an issue because it will create permanent unemployment (Tsaliki 2008).
What gives entrepreneurs the incentive to invest in the introduction of
technological goods? Tsaliki notes entrepreneurs engage in technical change because it
gives them the competitive advantage in the market. The advantage is gained because the
production process becomes more efficient; lowering the unit cost of production while
simultaneously producing more goods. The increase in production allows for the
produced goods to be sold a cheaper price. This cheaper price is often preferred by the
consumer increasing demand (Tsaliki 2008). Continuing, Tsaliki states this is where the
deskillization or specialization among laborers takes place.
Using neo-classical theory, Tsaliki better explained his thought process on the
deskilling of laborers. When workers are deskilled, they are required to have fewer skills
to enter the market because the machinery allows for a simpler production process. In
order to receive a higher position in the company, laborers would have to specialize in a
skill that is required to operate the more difficult forms of capital accumulated by the
firm (Tsaliki 2008). The neo-classical theory Tsaliki uses, explains that as physical
capital receives investment, the members in society should invest in the same amount of
human capital. As my theory suggests, it is by no means simple to invest in human
capital. Since every member of society is not able to invest in human capital, the need to
deskill becomes important to the firms. Why? As Tslaki states, a higher skilled worker
offers more benefits to a company compared to a worker with fewer skills. If technology
can take away the skills needed for a large portion of production, firms are able to invest
more in high skilled workers to further increase efficiency.

26
The process of technical change will continue to increase wage differentials, as
the innovation of new technological capital goods is almost constant. This is because as
the skills required entering the labor force for a high paying job continue to increase, so
does the amount of time people are willing to spend on their education. As levels of
education increase, so will levels of technical change. As more people are put through
school, a more skilled labor force becomes prevalent in the market. Overtime, technical
change will cause those with more schooling to remain in the low-skilled worker
category. Technical change either advances to specializes ones skills or decrease the
skills needed to do a job. This creates a gap for those who have continued schooling but
chose not to specialize. The process of technical change leaves a gap for those who would
be considered “middle-skilled” workers.
Tsaliki concludes the article by noting that technical change will leave more
people in the market unemployed if the market has a higher number of high skilled
workers. This is because technical change encourages the use of unskilled labor making it
easier to find work initially. The decreased training times, competition between workers,
and increased efficiency without the worker knowing allow for lower wages to be paid
for low-skilled workers furthering the income distribution gap of low and high skilled
workers (Tsaliki 2008).
I chose this article because it gave different economists views on the subject of
technical change. The article supports my research on technical change. Factors involving
the deskilling or specialization of laborers in the workforce is something I need to
research further. On the other hand, it would be beneficial if Tsaliki included empirical
work to better support his theory on technical change. I am curious as to how he would

27
apply his research to measure the change of skills and wages for employees in the labor
market.
Section 3.2: Support of Differential Effects on Wages:
In the article, “Wages, Employment Structure and Employer Size-Wage Premia:
Their Relationship to Advanced-Technology Usage at US Manufacturing
Establishments,” authors Timothy Dunne and James Schmitz ask whether manufacturing
plants that are equipped with technology as capital require a skilled workforce. The
authors introduce the article explaining that a growing theme in literature is the use of
advanced technology in the production process requires a more educated and skilled
workforce. Dunne and Schmitz label this as the theory of skill-biased technical change.
The two authors continue by developing three exercises based off of predictions
generated by using the SMT that can be studied using cross-section wage regressions and
production-worker share regressions.
Dunne and Schmitz’s begin their article with the following prediction: if
advanced production methods require more skills, then plants that employ these methods
should pay higher wages. The authors use their first regression to test whether plants
using more advanced technology than other plants pay higher wages to both production
and non-production workers than those of smaller and less advanced technological
manufacturing firms? Second, Dunne and Schmitz predict that more non-production
workers will exist in firms that have more advanced technology than firms with less
technological capital goods because non-production workers are considered to have more
skills than that of a production worker. Therefore exercise two asks, do firms with more
advanced technology demand more non-production workers? Third, larger firms are

28
expected to be more efficient with the use of their technologies by employing a greater
number of skilled workers for their technologies than those of smaller firms. Hence, the
third exercise asks if the employer size-wage premia is reduced by using controls for the
use of computer-based machines and other measures of production.
Before conducting these exercises, Dunne and Schmitz give a description of their
data to better understand the cross-section wage and production-worker share regressions.
The authors use two different sets of data in their study: the 1988 Survey of

29
Manufacturing Technology (SMT) and the 1987 Census of Manufactures (CM).
Analyzing the two data sets, the authors were able to compile data for 6,909 total
manufacturing plants. Using Table 1 will allow me to better explain the SMT.
The SMT includes information from a sample drawn in 1988 that is constructed
of manufacturing plants that employ twenty or more employees in a two-digit
manufacturing industry. As shown in the table above these two-digit industries include:
Fabricated Metal Products (34), Non-Electrical Machinery (35), Electric and Electrical
Equipment (36), Transportation Equipment (37), and Instruments & Related Products
(38). The SMT accounts for 17 different computer based machines that can be used in the
production process. Table 1 above shows the percentage of usage of each of these
machines within the five two-digit industries. In order to label a firm as having a more
advanced technological process than another, a manufacturing plant (Plant A) that uses a
higher percentage of computer-based machines than another manufacturing plant (Plant
B), then Plant A has a more advanced production process than Plant B.
The second data set used, the Census of Manufactures (CM), provides information about
employment and wages for both production and non-production workers at each plant in
the year 1987. Table 2 below will allow me to go into better detail on the relation
between plant wages and the author’s measures of how goods are produced. The first row
of the table: 0 techs used, 1-2 techs used, etc., shows how goods are produced: the
number of technologies (computer-based machines) owned by the plant. The first column
in the table represents the number of employees at a manufacturing plant. If we were to
look at the plants that had 0 techs used and < 100 employees, the first number represents
the average production worker hourly wages. The second number which is in parenthesis

30
represents the standard deviation of the average plant wages while the third number
shows that there are 346 plants that have less then one hundred employees and use a total
of zero technologies.
In Dunne and Schmitz’s next section, they develop a empirical model of
employment structure and plant wages. They assume that the employment share and
mean of plant wages can be explained linear function that looks as follow:
PWi, NPWi, PWSi = f (industry,region, (method of production)i, Sizei),

31
where PWi is equal to the production worker wage in plant i, NPWi is equal to the non-
production worker wage in plant i, and PWSi measures the production worker total share
of employment in plant i. Table 3 below includes a summary of statistics
for the dummy variables for 149 4-digit industries, nine Census regions, which include
plant attributes known as indicator variables.Tech1, Tech3, and Tech6 is an indicator
variable for the number of machines used within the plant. Price2-Price6 indicates the
average price of most products while MP2-MP3 indicates the type of manufacturing

32
production process employed at the plant. Plant age (Age2-Age4), Plant Size in terms of
employment (Size2-Size6), multi-plant firm or single plant firm (MU) and the number of

33
7 digit SIC products produced at the plant (Np2-Np3) are also used as indicator variable
within table 3.
Table Four on the previous page shows the basic regression for production-
workers wages, non production-worker wages, and production-worker share in total
employment. The first column of table four shows the logarithm of the average annual
hourly wages in dollars for production workers at a plant. When comparing wages
between a firm that have obtained six technologies (tech 6), pay a wage that is about 14%
higher than that of firms that no technologies (tech 0). Plants that produce products of
higher price (Price 6) also show a higher wage for their non-production workers as
compared to a manufacturing plant that produced goods of a lower price (Price 1).
Throughout the article, a higher wage has been correlated with more skill. This is
potential issue within the article. Just because a product cost more to produce, does it
actually require a higher skill set among it’s employees? Could the materials used to
produce the good cost more but the skill set stay the same as producing another good?
Moving onto the second column of Table Four, we see the logarithm of annual hourly
wages for non-production workers within a manufacturing plant. Most results are similar
to those of production workers such as that the wage-premia increases for non-production
workers as the price of the products (Price2-Price6) increase, along with increasing wage-
premia as the size of firms increase (Size2-Size6). In contrast, the wage-premia for non-
production does not increase as significantly for non-production workers when the
number of technologies (Tech0, Tech3), Tech6 increases among plants. For example, the
wages between firms that have no technologies (tech0) and firms that use three-five
technologies (tech3) are essentially the same. There is however, an increase in wages for

34
non-production workers as firms acquire more the six technologies (tech 6) for their
production processes.
This comparison makes me ask myself what does technology do that requires
such an increase in wages for production workers but not non-production workers? Does
more accumulation of technologies increase wages for production workers because it
requires more training for the production worker than compared to that of the non-
production worker?
Authors Dunne and Schmitz results support their earlier predictions that skill
requirements of the workforce change as production methods vary between plants. The
way they obtained and categorized their data offers suggestions to arrange the data I
obtain. Their empirical model offers a way to measure skill-sets needed in the workforce
in correlation to wages. However, there are some critiques to the article that I would like
to add. Dunne and Schmitz did not break done how the technologies were used among
the employees. Could it be more beneficial to know how the percentage of production
and non-production workers that participate with technologies change as more
technologies are produced? The authors also suggest that an increase in wages is because
of increase in skills needed for the production process. Other variables such as material
cost and reducing shirking behavior among employees could explain the difference in
wages rather than the skills needed to produce a particular good. Next, I will be analyzing
a article that shows how technology in a new industry affects those that need to migrate
from collapsing industry.
Section 3.3: Effects on Laborers With Limited Human Capital:

35
In the article “Technology and the Labor Market,” author Jacob Mincer is
studying the effects the introduction of technological goods as a form of capital has on
fluctuations in human capital attainment, skill-biased wage structure, the technological
cycle for wages and unemployment, and why the human capital trend has shifted from
the 1970’s to a consistently upward trend. Mincer’s empirical work shows that the
technological cycle produces a lag that shapes the composition of the labor force. Mincer
predicts this lag is due to the acceleration of the technological cycle.
The introduction of the article explains that between the time period of 1950 up
until 1970, the demand side of the labor market (technology) was matched by the supply
side of the labor market (education). In the late 1970’s, the influx of baby boomers that
attained an education caused an oversupply of educated workers in the labor force,
narrowing the skill-wage structure. Referring to Figure 1 above, the rates of human

36
capital fell in the late 1970’s so fewer individuals chose to attend college. Since
education attainment remained stagnant, the skill-biased demand increased in the 1980’s
causing a increase in rates of return to human capital attainment.
As presented by Mincer, the Human Capital Decomposition of Wage Inequality
Regression:
lnWij=riKij=ri(Si+Kpji),
measures the returns to and individuals investment in human capital, where i denotes the
individual, j is equal to the working age, kij is equal to schooling in (Si) time units, Kpji
is equal to post-school investments, and ri= individual rate of return. This equation helps
explain the lag in the technological cycle. Referring back to Figure 1, we can see that in
the 1970’s college enrollments in college declined due to the decrease in the wage-premia
received by graduates. This caused education attainment in the 1980’s to remain stagnant.
Due to technological advances, the rates of returns began to increase in the 1980’s
producing a strong increase in enrollments leading to growth attainment in the 1990’s.
The lag is the time from high school graduation until after a few years of work
experience after an individual’s college graduation and is estimated by the years it takes
to maximize the statistical fit between enrollment and attainment. Table 2 below shows
the empirical evidence on the response of investment (enrollments) to the education
wage-premia is shown in the results of three regressions. The three regressions are
percentage of HS students enrolled during October of graduation (column 1), percent of
HS graduates enrolled at ages 18-24 (column 2), and enrollment as percent of population
between ages 18-24 (column 3). The table shows that investments in human capital
respond positively to the predicated rates of return for that investment. Human capital

37
also responds positively to parental income since college attainment is not a cheap
endeavor in the United States.
Mincer continued his article by explaining that if technology was able to widen
the skill-biased demand for labor, then it should increase the ration of unemployed
unskilled laborers to that of skilled laborers. Using data from the Panel Study of Income
Dynamics for micro data, Mincer was able to distinguish 38 2 digit industry sectors. For
each of the 38 sectors, two technology indexes, Total Factor Productivity (TFP) and
Computer per Worker (CIW) are used. Using the data, the following regression equation
is produced to measure the affect the independent variable technology combined with the
remaining independent variables to find the effect on unemployment:
P(U)= (Tech, X, X^2, Ed, NW/Mar, Nu, Eg, Union),

38
where X is equivalent to work experience, as Ed is years of schooling, NW is race, Mar is
marital status, Nu is the national unemployment rate, Eg is growth within sector, and
Union is union membership.
Table 5 supports that technology affects unemployment on the basis of skill over time.
Unskilled workers are three to four times more likely be unemployed compared to those
skilled workers. The unskilled worker having less then 12 years of schooling and the
skilled worker having greater than 12.

39
I chose this article because it supports to my hypothesis that technology cause a
change in demand for the skillset of employees in the labor force. Mincer’s article was
able to offer an empirical aspect that I have not yet saw, which is the measuring
unemployment on the basis of skill due to the introduction of technological goods.
Mincer had great findings in his study but as he approached the 1990’s Mincer noted the
upward trend in the demand for human capital. This makes me wonder if the lag in
human capital among the labor force is even relevant anymore? How does the labor force
and high school graduates react to a labor market that is constantly innovating requiring
more skills? Can a society keep up with that pace in the face of recessions?
Section 3.4: Technology, Trade, and Outsourcing’s Potential Affect on Wages:

In the article, The Impacts of Technology, Trade and Outsourcing on Employment
and Labor Composition by Catherine Morrison Paul and Donald Siegel find evidence to
support the theory of skill-biased technical change. They hypothesize that the
introduction of technology along with influences from international trade and
outsourcing, affect the skills required to participate in the labor force. Rather then looking
at skill-biased technical change through a cost or production framework, Paul and Siegel
focus on variables such as input, labor demand, and composition to support their
research. Using data from the US manufacturing sector between 1959-1989, the two
authors develop seven-equation system based on a dynamic variable cost function to test
their hypothesis.
Paul and Siegel’s model dynamic cost function looks as follow:
C=G(p,Y,x,deltax,T)+Sum(kXkPk).
The vector of J variable input prices is p, the output is Y, the vector of K quasi-fixed

40
inputs is x, delta x shows the cost adjustments made for x inputs, and T is the vector of N
external technological trade factors. There are outside factors that can affect a firms cost
function, which the firm has no control over and these factors are labeled as Tn variables
in the author’s model. These factors include: technological change, trade/openness factor,
state of technology, and the cost share of purchased services.
Before sharing the results from their tested dynamic cost functions it is important to
note Paul and Siegel collected their data. Between the years 1958-1989, data from 450
four-digit SIC level manufacturing manufacturing industries was collected using the
National Bureau of Economic Research’s productivity file. The data contained
information on the annual measures of both outputs and inputs in constant and current
dollars. Five inputs along with their costs and quantities are provided within the data.
These five inputs include: capital, production labor, non-production labor, energy, and
materials. Data for the Tn variables mentioned previously were collected from Feenstra,
Bureau of Economics Analysis, National Science Foundation, Bureau of Labor Statistics,
and the Current Population Survey.
Looking below at Table 2, Tn and Ln variables are paired to see the reduction in
demand for a worker with categorized skill set. Ln variables, in order from L1 to L4
include, no high school, high school, some college, and college degree. When looking at
the table, individuals with no high school or only a high school diploma are affected
negatively in terms of demand for their skill set when computers and R&D are present in
the work force. However, individuals with some college experiences or a college degree
show a positive increase in demand for their skill set when computers and R&D are
prevalent. Outsourcing and trade showed a decrease in demand for workers of L1, L2,

41
and L3 but a positive increase in demand for L4 workers.
Paul and Siegel’s model supported their predictions throughout the paper.
Technology is the driving force in demanding more educated/skilled workers. Factors of
trade and outsourcing have little impact on the demand for laborers but used with
technology can actually increase the effects of skill-biased technical change.

42
Researching this article has offered useful information on how to collect data for
more than variable. An example would be the different sources used to obtain data from
the Tn variables alone. The authors’ model does a nice job expanding the basic cost
functions used in other literature. Including indirect effects provides a more concretes
analysis compared to literature that uses assumptions rather than data on indirect effects.
This makes me wonder if a factor such as consumer demand for products causes a
fluctuation in demand for laborers or the need to accumulate more technology as capital.
Section 3.5: Evidence of Structural Unemployment:

In the article “ Trade, technology, and the rise of the service sector: The effects on
US wage equality,” Bernardo S. Blum uses a multi-sector version of the Ricardo-Viner
Model to empirically test factors that affect the wage premium. Bloom has hypothesized
international trade, skill-biased technical change, and the reallocation of capital between
sectors has influenced a rising wage disparity between laborers in the United States labor
market. As my theory chapter suggests, higher wages are likely correlated with more
skills. Unlike other models used in previous literature, the general equilibrium model
Blum uses allows him to test his hypothesis allowing mobility for prices of goods and
technical skill change.
Using the Hecksher-Ohlin model, Blum notes that changes in the price of tradable
goods due to international trade cause firms to outsource to countries that provide
cheaper labor costs. In return, if low-skilled jobs are being outsourced to other countries,
the remaining jobs in the economy will increase the skills demanded to produce more
intensive skill produced products. Using support from an article produced by M.J
Slaughter, Blum states more skills are demanded because the price of the high skilled

43
produced tradable goods will increase more than that of low-skill produced goods after
outsourcing has occurred (Blum 2007).
An additional explanation explained by Blum is the mechanism of skill-biased
technical change. Skill biased technical change has its largest impact on growing wage
disparities within industry. The wage disparities grow most within industry because the
technology introduced raises the demand for more skilled workers. Literature from
Berman, Bound, and Gliches support Blum’s research by saying half of the change of the
relative earnings of high skilled workers changed within industry (Blum 2007).
Blum continues to explain changes in wage premiums using a mechanism not yet
researched by other economists. This mechanism is the reallocation of capital goods from
manufacturing sectors to service/non-trade sectors of the economy. Blum begins
explaining this mechanism as starting in 1979, which is the same time the city of
Pittsburgh had began experiencing structural changes in their steel industry. Suggesting
skills are complimentary to capital accumulation, the shift of capital to service/non-trade
sectors, also increase the need for the skills demanded within those sectors. Inversely,
with less capital being accumulated in the manufacturing sector, the lesser skills
demanded by employers for their production process (Blum 2007).
In order to test international trade, technical change, and reallocation of capital,
Blum used a multi-sector general equilibrium model. This model is a generalized
Ricardo-Viner Model. This generalized model allowed for the testing of the above
variables in both the tradable and non-tradable sectors. The framework set by the
generalized RV model accounts for changes in labor supply, skills demanded by
employers, capital accumulation, and trade. This model was adapted from Jones’ model

44
of 1965 to make capital goods fixed and allow two factors to become mobile: prices of
goods and skill-biased technical change (Blum 2007).
When using the general equilibrium model, it was assumed that compared to cross
variation prices, the labor demanded by employers is more elastic than own-price
variations. Therefore, holding this condition constant the increase in supply of skilled
workers will decrease wage premium as and increase supply of unskilled workers will
increase the wage premium as a determinant of wage inequality (Blum 2007).
Blum notes there are two channels of international trade that can affect the
determinants of the wage inequality. The First channel is what Blum calls trade literature:
foreign-supply sources compete, lowering the price of tradable goods, affecting the
wages of the employees who produce tradable goods. The effect on the wage premium
from trade literature is as follow: if the product price in the sector were to decrease, the
wage premium would decrease if the demand for low skilled laborers were less elastic
than that of high skilled laborers and vice versa. The second channel Blum observes is
outsourcing. As firms outsource and send their low-skilled jobs over seas, the remaining
jobs in the economy require a higher skill set then the jobs that have left the country. This
affects those employees who lost the low-skilled jobs from outsourcing that are not able
to acquire the new skills to work (Blum 2007).
Blum continues to note there are two types of technical change that will affect the
wage premium received by employees. The first type is called Hicks-neutral sectorial
productivity improvements. This technical change has the same affect on the wage
premium as the first channel of international trade has above. The second type of
technical change is called factor-biased technical change that creates an abundance of

45
low-skilled workers that cannot become employed due to the sector demanding more
high-skilled workers (Blum 2007). The affect of this theory results in a larger wage
disparity as it drives down the wage premium for less skilled laborers but increases the
wage premium for high-skilled workers due to factors of demand.
To test his theories, Blum gathered data for the manufacturing sectors from the
NBER Manufacturing Productivity Data Base. He also obtained data for the retail trade,
wholesale trade, and services sectors for the time periods between 1964-1996 from his
online appendix. This data set excludes healthcare and legal services (Blum 2007).
Table one shows the demand elasticities for capital accumulation between
manufacturing and non-manufacturing sectors. This table supports Blum’s research in the
following ways. One, as capital increases in the manufacturing sector, so does the need
for low skilled employees to operate that capital.

46
Second, as capital accumulates more in the service and trade sectors so will the
need for the more high skilled laborers. This supports his theory of structural change
which states capital and skill are compliments. Therefore, for those moving to the non-
manufacturing sector beginning in 1979 should have seen a higher wage premium than
those in the manufacturing sector (Blum 2007). Table 2 shows the impact that impact that
prices of international trade have on wage premiums elasticity in responses to change in
product prices of the manufacturing sector. As shown by Table 2, trade has almost no
affect on wages in the manufacturing sector.
Analyzing Blum’s article has allowed me to get an understanding of an the
generalized Ricardo-Viner Model that I could potentially modify moving forward. I feel
like his idea behind international trade was useful but could have been approach by how
it changed the local economy structurally. I feel like minimum wage laws or union wage
restrictions could better represent a factor influencing growing wage disparities between
sectors.

47
Chapter 4.0: Data & Descriptive Statistics:
For the use of this paper, data from two-industry industry level data sets were
used. The first data set I acquired is called The National Industry-Specific Wage and
Employment Estimates created by The United States Department of Labor Bureau of
Labor Statistics. Data was additionally drawn from The United States Census Bureau
whom produced The Annual Expenditure Survey. For each data set, information was
used between the years 2003 and 2012.
The chapter is organized as follow: Section 4.1 explains the variables,
occupational groups, and provides a summary of statistics for each variable. Section 4.2
explains why I chose the fixed-effect regression model and how I tested for it. In Section
4.3 I show the steps used in creating my fixed-effect regression estimation equation.
Section 4.4 explains and corrects for the problems associated with the use of a fixed-
effect regression model. Section 4.5 concludes the chapter, providing results for the
regression ran in the I.S.
Section 4.1: Variables and Summary Statistics

Twenty different occupations within the oil and gas industry are used for the
purpose of this paper. The twenty occupations within the industry are labeled through the
National American Industry Classification System (NAICS). The Oil and Gas Extraction
Industry is labeled as NAICS 211000, and in the table below, Table 1, shows each
occupation used within the NAICS 211000. Each occupation is labeled by a six digit
code, in which I have labeled each occupation with a jobcode. For each of the twenty
occupations listed in the table, The National Industry-Specific Wage and Employment

48
Estimates allowed me to create three variables used in my model. Using data that shows
the average hourly wage of the individuals within each occupation, I created the variable,
meanhw, which stands for the mean hourly wage. In correlation to wages, I was able to
create a variable called, anearn, which are the annual earnings of the individuals within
each occupation. The last variable generated from the data set is called, employment,
which is the total number of individuals included within each occupation. It is important
to note that the data set includes information for each of these variables between the years
of 2003-2012.
Table 1: NAICS 211000 Job-Occupations
Job Code: Occupation Title:
000000 All Occupations
110000 Management Occupations
130000 Business/Financial Occupations
150000 Computer/Mathematical Occupations
170000 Architecture/Engineering Occupations
190000 Life/Physical/Social Science Occupations
230000 Legal Occupations
250000 Education/Training/Library Occupations
270000 Arts/Design/Entertainment/Sports Occupations
290000 Healthcare/Technical Occupations
330000 Protective Services Occupations
350000 Food Prep/Serving Occupations
370000 Building/Grounds Cleaning Crew
410000 Sales/Related Occupations
430000 Office Administration Occupations
450000 Farming/Fishing/Forestry Occupations
470000 Construction/Extraction Occupations
490000 Installation/Maintenance/ Repair Occupations
510000 Production Occupations
530000 Transportation/Material Moving Occupations
To generate my variable on technology, I used Annual Expenditure Survey
produced for each year between 2002-2012. Under the expenditure survey, it reports how
much money has been spent of capital goods. For the use of my technology variable
(tech_expend) I looked at how much money was spent on equipment within the oil and

49
gas industry. According to the definitions provided through The United States Census
Bureau, equipment expenditures include the purchasing of both new and used machinery,
computers, and vehicles, along with furniture/furniture fixture expenses. I have assumed
that technology is any capital good or add on (software for computer) that can make the
job for the individual that is designed for easier. In terms of the NAICS 211000, a piece
of machinery called the drill bit can be purchased to allow for deeper, more efficient, and
quicker extraction of oil. I consider this a technological good because it makes it easier
on the individual who no longer has to dig by hand, increases the extraction speed, and
reduces waste of the oil being extracted. Computers are in of itself a piece of technology.
Upgrading or purchasing more computers will allow more monitoring of well sites, more
efficient work through the employees that have more access the good, and allow for more
information to spread across industry. Vehicles can also be considered a technological
good because as technology advances in the world market, vehicles use this technology
that will reduce the risk of oil spills by the oil tanker trucks, a larger towing capacity on
the rigs used to haul drilling equipment, and provide a easier reliable connection with
Bluetooth communication and other sharing features.
The last variable that is used within my regressions is called oilprice. The oilprice
variable contains data on the oil price for each individual year between 2003-2012. This
data was collected through the U.S Energy and Information Administration. I have
included the data on oil, because the price of the commodity being produced in the
market correlates with the demand the industry has for employees used to extract and sell
the product. In my theory section, I mentioned a substitution and scale effect. If the price
of oil were to rise, demand for laborers within the industry will increase to produce more

50
of the good that is demanded in the market. Industries can react by hiring more laborers
or increase the efficiency of the current labor force through technological capital goods.
One issue I have with The Annual Expenditure Survey is that it does not provide
individual specifications on the equipment purchased. One potential problem with the
data set is it does not separate machinery, computers, vehicles, and furniture expenses
one by one. The expense of all four is grouped under the equipment expenditure. To
defend this issue, within the industry, the total number of expenditures each year is a
relatively high number. Assuming that the firms inside the industry are profit-maximizing
firms, it is more realistic to assume the majority of these expenditures are on
“technological goods (machinery, computers, and vehicles),” because these goods offer a
better chance of turning a profit than compared to furniture on the well site as an
example. Further in the chapter I will show the results of my regressions, as my results
are relatively robust, a small number of expenditures subtracted for furniture purchases
should not have an impact on the results or significance of the tech_expend variable. The
summary statistics of each variable are available in Appendix A-2.
Section 4.2 Fixed-Effect vs. Random-Effect Regression:

The data I have selected includes data of time-series intervals and cross-sectional
entities. Combing the two different types of data will allow observations of the same
variables that in the sample are also the same cross sectional from more than two
different periods (Studenmund 2011). In my model this cross-sectional identifier is called
jobcode, which identifies the effect on wages for each occupational group from the
independent variables in the model between the years 2003-2012. When using panel
data, I have the option to use a fixed-effects or random-effects regression model.

51
Before testing whether or not to use the fixed-effects or random-effects model, I
generate the log of each variable in the model. Generating the log of each variable creates
elasticities among all variables to measure them in percentage terms. For example
purposes, if my results in a regression show the natural log of technological expenditures
(ltech_expend) is significant and has a coefficient value of .234, I can interpret the results
as, with a one unit increase in the technological expenditures, the mean average wages
(lmeanhw) will increase by .234%.
After generating the log of each variable, I can use the Breusch Pagan to test
whether or not the random effect model is necessary. When performing the Breusch
Pagan test, the null hypothesis states there are no differences across the units being tested.
If the prob > chi2 is less than .05, the null is to be rejected suggesting that the random-
effects model should be used for regression purposes. In Table 2 below, the results of my
Breusch-Pagan Test (Prob>Chi2 =.0000) suggest that I reject the null and use the
random-effects model.
Table 2: Breusch and Pagan Lagrangian multiplier test for random effects
lmeanhw[jobcode,t] = Xb + u[jobcode] + e[jobcode,t]
Estimated results:
| Var sd = sqrt(Var)
---------+-----------------------------
lmeanhw | .2469428 .4969334
e | .0095201 .0975709
u | .2153359 .464043
Test: Var(u) = 0
chibar2(01) = 671.29
Prob > chibar2 = 0.0000
An additional that can be ran when choosing between fixed-effect and random-
effect models is called the Hausman Test. In order to run a Hausman Test, both a fixed
and random-effect regression must be ran and stored. Once each regression results are
stored, the STATA will allow the test to be performed. The null hypothesis of the test

52
states that error terms and regressors are uncorrelated and the fixed effects model should
be used. Therefore if the Prob >Chi2 is .05 or less, we reject the null and conclude the use
of random-effects model.
Looking at Table 3 below, we can see that the Hausman Test suggests that I
accept the null and use a random-effects model. However, I will still choose to run a
fixed-effect regression. I will explain why I will continue with the fixed-effect model by
further analyzing my variables in comparison the methodology behind the uses of both
the random and fixed effects models.
Table 3: hausman fixed random
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed random Difference S.E.
-------------+----------------------------------------------------------------
ltech_expend | .2336807 .2256427 .008038 .0007805
lemployment | .0052869 .0291499 -.023863 .0122288
loilprice | -.0441677 -.0424637 -.0017041 .
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 3.79
Prob>chi2 = 0.2845
(V_b-V_B is not positive definite)
Within each job occupation, I assume that an individual such as an accountant
will have the same responsibilities across the oil and gas industry. To further elaborate,
an individual laborer labeled as a robust, will have the same responsibilities of
maintaining the oil/gas wells as all robust laborers across the entire industry. Each
occupation is considered fixed because the responsibilities of employees within each
occupation will remain the same over time. The fixed-effect model is able to eliminate
the omitted variable bias by allowing each cross-sectional unit to have their own intercept
(Studenmund 2011). If I were to use the random-effects model, I would encounter the
risk of omitted variable bias, because each intercept is selected from random cross-

53
sectional unit that is distributed around a mean. This allows for observable heterogeneity
because random factors such as employees race, gender, etc., will be included
(Studenmund 2011).
Section 4.3 Empirical Fixed-Effect Regression Model:

Using the book Using Econometrics: A Practical guide by A.H Studenmond, I
have built my model off of equation 16.5 (Studenmund 2011). that has allowed me set up
my fixed-effect regressions as follow:
lmeanhwit= βltech_expendit +µit, fe (4.21)
lmeanhwit= βltech_expendit +βlemploymentit + µit, fe(4.22)
lmeanhwit= βltech_expendit + βlemploymentit + βloilpriceit +µit, fe (4.23)
lmeanhwit= βltech_expendit + βlemploymentit + βloilpriceit + βg1-g19 + µit, fe (4.24), where
i is each occupation in the industry, t can represent any time period between 2003-2012,
lmeanhwit represents the natural log of the average wages earned hourly for job i in
period t, ltech_expendit corresponds to the total amount of expenditures on technology in
period t, lemploymentit shows the total number of individuals employed for job i in period
t, loilpriceit correlates to the price of oil in period of t, and g1-g19 are the dummy
variables created for each job occupation within the oil and gas industry. In each
regression estimation equation µit the error term. My null hypothesis states that
tech_expenditures have no effect on the demand for laborers within the oil and gas
industry:
H0≤0,
Ha>0.

54
To measure demand I will look at how the wages for reach occupation change when a
one-unit increase of technological expenditures is applied.
Section 4.4 Econometric Problems: Heteroskedasticity and Serial Correlation:

Choosing to use the fixed-effect model, there is potential to encounter the
problem of heteroskedasticity and serial correlation. Failing to test for these econometric
problems can lead to biasedness of the results if either problem is present. I will be
explaining each econometric problem, how to test for each problem, and correct for each
(in some cases both) econometric problem if present in the model.
The first econometric test that I tested for was heteroskedasticity. If
heteroskedasticity is present, it can be assumed that the standard errors and t-cores of the
regressions are underestimated. Since the error term is no longer considered to have the
property of minimum variance, the hypothesis testing is no longer reliable (Studenmund
2011). Heteroskedasticity violates Classical Assumption V that states the variances
associated with the distribution that is used to create the error term are not constant. A
potential cause of heteroskedasticity is a variable that has been omitted (Studenmund
2011). To test for heteroskedasticity, I have used the Modified Wald Test For
Heteroskedasticity in Fixed Effect Regression Model provided by STATA. The null
hypothesis of the Wald Test states that heteroskedasticity is not present in the model.
Once the test is performed, if the Prob>Chi2 is less than .05, the null is rejected
heteroskedasticity is concluded to exist.
Table 4: xttest3
Modified Wald test for groupwise heteroskedasticity
in fixed effect regression model
H0: sigma(i)^2 = sigma^2 for all i

55
chi2 (20) = 819.69
Prob>chi2 = 0.0000
The results in the Table 4 above show that I would in fact reject null and conclude
my data is heteroskedastic. To correct for heteroskedasticity, I perform the Standard
Errors Robust regression in STATA. This test will increase the standard errors, making it
harder for each independent variable to become significant. This corrects for the
underestimation of standard errors cause by the heteroskedasticity.
Serial correlation occurs when the value of an error term in time period t, is
influenced by the value of another error term in a different time period. This violates the
Classical Assumption IV, which states that error terms from different time periods are
uncorrelated with one another (Studenmund 2011). Serial correlation leads to unreliable
hypothesis testing because the coefficients of the stand errors are considered biased.
Knowing that the t-scores are correlated with the standard errors, the t-scores are also
considered biased and the t-scores become insignificant (Studenmund 2011). To test for
serial correlation, I again use a test provided by STATA called the xtserial test. When
using this test, I check the value of the reported f-statistic to see if I will reject the null.
The null hypothesis of the xtserial test states that serial correlation is not present in the
model. In Table 5 below, my f-statistic shows that I would reject the null and conclude
serial correlation within my data.
Table 5: xtserial lmeanhw ltech_expend lemployment loilprice
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1, 18) = 25.985
Prob > F = 0.0001

56
To correct for serial correlation, a generalized least squares estimation can be ran to
restore the minimum variance property to the estimation by making sure the error term is
not serial correlated (Studenmund 2011).
After testing for each econometric problem, both econometric problems of
heteroskedasticity and serial correlation are present within my fixed-effect regression
estimations. Rather than correcting for one at a time, I will run a Robust Standard Error
Cluster fixed-effect regression. This regression uses the same robust function mentioned
previously in combination with clustering the variables around the cross-sectional
identifier (jobcode) as shown below in Table 6 (Burnell 2014).
Table 6: xtreg lmeanhw ltech_expend lemployment loilprice, fe robust cluster(jobcode)
------------------------------------------------------------------------------
| Robust
lmeanhw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ltech_expend | .2336807 .0457032 5.11 0.000 .1380227 .3293387
lemployment | .0052869 .0645705 0.08 0.936 -.1298608 .1404346
loilprice | -.0441677 .0399233 -1.11 0.282 -.1277282 .0393928
_cons | 1.097136 .4602673 2.38 0.028 .1337852 2.060486
This changes the regression equations presented in the empirical section of the chapter to:
lmeanhwit= βltech_expendit +µit, fe robust cluster (jobcode) (4.21a)
lmeanhwit= βltech_expendit +βlemploymentit + µit, fe robust cluster (jobcode) (4.22b)
lmeanhwit= βltech_expendit + βlemploymentit + βloilpriceit +µit, fe robust cluster
(jobcode) (4.23c)
lmeanhwit= βltech_expendit + βlemploymentit + βloilpriceit + βg1-g19 + µit, robust cluster
(jobcode) (4.24d)
4.5 Robust Standard Errors Cluster Fixed-Effect Estimation Results:

The results in the table below show the estimations for the four equations
presented in the previous section. The first regression estimation is shown in column one
of the table representing the results from running regression 4.21a. This regression
measures the effect that the technological expenditures (ltech_expend) have on the
average hourly wages received by employees (lmeanhw) in the entire oil and gas

57
industry. My results show that the ltech_expend variable is significant with a p-value of
.0000. Looking at the first column in Table 7, this significance mean that for every one
percent increase in technology expenditures on capital, wages will increase by .204%.
Table 7: Regression On Average Hourly Wages
Tech Expenditure Impact on Average Hourly Wages
------------------------------------------------------------------------------------
(1) (2) (3) (4)
lmeanhw lmeanhw lmeanhw lmeanhw
------------------------------------------------------------------------------------
ltech_expend 0.204*** 0.228*** 0.234*** 0.234***
(0.023) (0.037) (0.046) (0.048)
loilprice -0.031 -0.044 -0.044
(0.035) (0.040) (0.042)
lemployment 0.005 0.005
(0.065) (0.068)
All Occupations 0.454***
(0.163)
Management 1.055***
(0.008)
Business/Financial 0.494***
(0.007)
Computer/Mathemati~l 0.555***
(0.076)
Architecture/Engin~g 0.876***
(0.019)
Life/Physical/Soci~e 0.786***
(0.014)
legal 0.774***
(0.124)
Education/Training~y 0.137
(0.425)
Arts/Design/Media/~/ 0.395
(0.307)
Healthcare Practit~c 0.524**
(0.222)
Protective Services -0.107
(0.348)
Food Preparation/S~g -0.500*
(0.274)
Building/GroundsCl~c -0.698***
(0.266)
Sales/Related 0.483***
(0.135)
Office/Administrat~t -0.187***
(0.029)
Farming/Fishing/Fo~y -0.687**
(0.345)

58
Contrsuction/Extra~n 0.012
(0.032)
Installation/Maint~r 0.137**
(0.063)
Production 0.239***
(0.002)
Constant 1.235*** 1.125*** 1.097** 0.805
(0.234) (0.278) (0.460) (0.556)
------------------------------------------------------------------------------------
Observations 182 182 176 176
------------------------------------------------------------------------------------
OLS Estimates;
* p<0.10, ** p<0.05, *** p<0.01.
In columns two and three, regressions 4.22b and 4.23c are ran. With the addition
of the loilprice and lemployment variable, we can see that ltech_expend variable remains
significant. However, both the loilprice and lemployment variables are insignificant not
having an impact on the wages received by the employees within the industry. Each
regression does provide valuable information to how the ltech_expend variable effects
average hourly wages. Looking at the results from 4.22b, we see that a one percent
increase in ltech_expend increase the average hourly wages of employees by .228%
(compared to .204% in 4.21a). The results from 4.23c show that one percent increase in
ltech_expend results in a .234% increase in average hourly wages of all employees
(compared to .222% in 4.22b). The results explain that the coefficient of the ltech_expend
variable is underestimated when the variables of lemployment and loilprice are excluded.
The fourth column represents the estimation results from regression 4.24d. This
regression allows me to observe the effect that the ltech_expend (only significant
variable) has on the individual occupations in the oil and gas industry. Overall, the results
show that that majority of occupations benefit from an increase in ltech_expend, as the
average wages of All Occupations increase by .454% with a one percent increase in
ltech_expend. Looking at the food and protective services, a one percent increase in the

59
ltech_expend has a -.107% effect on wages. This result is interesting because the tech
expenditure could have gone towards a camera and that camera essentially replaces the
need for a security guard. As mentioned in theory, this is an example of a substitution
effect. This effect was also felt by a few other occupations: Food/Services,
Buildings/Grounds-Crew, Office Administration, and Farming/Fishery occupations. The
increase in technological expenditures has the largest negative effect on the
Farming/Fishery Occupations, as a one percent increase in ltech_expend, decreases
wages for these employees by -.678%. Equipment can be purchased that allows faster
seed planting, more efficient pesticide procedures, or quicker crop irrigation times in
which this equipment will replace the laborers to perform the tasks. The next table shows
the results for when the regression is ran with the annual earnings (lanearn) being run as
the dependent variable.
Table 8: Regression On Annual Earnings
Tech Expenditures on Annual Earnings
------------------------------------------------------------------------------------
(1) (2) (3) (4)
lanearn lanearn lanearn lanearn
------------------------------------------------------------------------------------
ltech_expend 0.212*** 0.218*** 0.234*** 0.234***
(0.022) (0.040) (0.046) (0.048)
loilprice -0.008 -0.044 -0.044
(0.041) (0.040) (0.042)
lemployment 0.005 0.005
(0.065) (0.068)
All Occupations 0.455***
(0.163)
Management 1.055***
(0.008)
Business/Financial 0.494***
(0.007)
Computer/Mathemati~l 0.555***
(0.076)
Architecture/Engin~g 0.877***
(0.019)
Life/Physical/Soci~e 0.786***
(0.014)
legal 0.774***
(0.124)

60
Education/Training~y 0.136
(0.425)
Arts/Design/Media/~/ 0.395
(0.307)
Healthcare Practit~c 0.523**
(0.222)
Protective Services -0.108
(0.347)
Food Preparation/S~g -0.500*
(0.274)
Building/GroundsCl~c -0.699***
(0.266)
Sales/Related 0.483***
(0.135)
Office/Administrat~t -0.187***
(0.029)
Farming/Fishing/Fo~y -0.688**
(0.345)
Contrsuction/Extra~n 0.012
(0.032)
Installation/Maint~r 0.137**
(0.063)
Production 0.239***
(0.002)
Constant 8.790*** 8.761*** 8.739*** 8.448***
(0.225) (0.278) (0.460) (0.556)
------------------------------------------------------------------------------------
Observations 183 183 176 176
------------------------------------------------------------------------------------
OLS Estimates;
* p<0.10, ** p<0.05, *** p<0.01.
The results in Table 8 above show identical results when the regression is ran with
lmeanhw as its dependent variable. It would be expected to see these results because
wages are and indicator of earnings. Comparing the fourth regression with that of the first
and second, we see that the effect ltech_expend has on lanearn is underestimated when
the variables of loilprice and lemployment are not included.
Similar to the regressions ran for the Tech Expenditures for Average Hourly
Wages, my results in Table 8 show that the ltech_expend variable is significant with a p-
value of .0000. Looking at the first column in Table 8, this significance mean that for
every one percent increase in technology expenditures on capital, annual earnings will

61
increase by .212%, roughly .008% larger increase for annual earnings than hourly wages.
In columns two and three, regressions 4.22b and 4.23c are ran. With the addition of the
loilprice and lemployment variable, we can see that ltech_expend variable remains
significant. However, both the loilprice and lemployment variables are insignificant not
having an impact on the wages received by the employees within the industry. Each
regression does provide valuable information to how the ltech_expend variable affects
annual earnings. Looking at the results from 4.22b, we see that a one percent increase in
ltech_expend increase the average annual earnings of employees by .218% (compared to
.212% in 4.21a). The results from 4.23c show that one unit increase in ltech_expend
results in a .234% increase in annual earnings of all employees (compared to .218% in
4.22b). The results explain that the coefficient of the ltech_expend variable is
underestimated when the variables of lemployment and loilprice are excluded.
The fourth column represents the estimation results from regression 4.24d. This
regression allows me to observe the effect that the ltech_expend (only significant
variable) has on the individual occupations in the oil and gas industry. Overall, the results
show that that majority of occupations benefit from an increase in ltech_expend, as the
annual earnings of All Occupations increase by .455% with a one percent increase in
ltech_expend. Looking at the food and protective services, a one percent increase in the
ltech_expend has a -.608% effect on annual earnings. This result is interesting because
the tech expenditure could have gone towards a camera and that camera essentially
replaces the need for a security guard. As mentioned in theory, this is an example of a
substitution effect. This effect was also felt by a few other occupations: Food/Services,
Buildings/Grounds-Crew, Office Administration, and Farming/Fishery occupations. The

62
increase in technological expenditures has the largest negative effect on the
Farming/Fishery Occupations, as a one percent increase in ltech_expend, decreases the
annual earnings for these employees by -.688%. Equipment can be purchased that allows
faster seed planting, more efficient pesticide procedures, or quicker crop irrigation times
in which this equipment will replace the laborers to perform the tasks. The next chapter
will provide an in-depth analysis the results and apply the results real world applications.
Chapter: 5 Concluding Remarks and Discussion

The purpose of this chapter is to conclude and summarize the findings of my I.S. I
will begin the chapter with Section 5.1, discussing the implications of the results and how
they can contribute to research. I will conclude the chapter with Section 5.2 where I
discuss what I could have differently and what my results can mean for the entire labor
market.
Section 5.1: In Depth-Analysis of Results:

Within the oil and gas industry, the results show that technological expenditures
are significant at the 99% level. As technical goods are introduced, the impact on the
demand for laborers is relatively high. Oil price and employment levels showed that they
have no impact on labor demand in this analysis. The idea of technical change is
supported as we saw a decrease in demand for laborers in occupations like security and
protective services but an increase in demand for those in sales and management when
the expenditures on technology increase by 1%.

63
The results agree with my prediction that technology can cause differential effects
on wages. Occupations that have laborers with higher skills (e.g. legal, architecture, &
management) experienced a scale effect. As technology expenditures increased, these
occupations experienced an increase in their wages, also increasing the demand for
individuals in those occupations. Other occupation groups (farming, building crews, &
office administration) felt the consequences of the substitution effect. The results show as
technological expenditures were increased overtime, employees of these occupations
experienced a decrease in their wages and demand for these laborers fell.
Overall, the technological expenditures increased the overall demand for laborers
in the oil and gas industry. This idea is supported by looking at All Occupations in which
a one percent increases in technological expenditures increase the overall demand by
.454%. These results show us that technology can have a positive impact on job growth
in the oil and gas industry. However, the local economy must be able to supply laborers
with the skills needed to meet the requirements for the new jobs that are now being
demanded.
Some econometric problems that arose during the analysis are heteroskedasticity
and serial correlation. However, using the robust standard error cluster function in
STATA, the regression was able to correct for both. In addition, if each problem were
corrected for individually, the data is robust enough that there is no change in the level of
significance or coefficient of the ltech_expend variable. Furthermore, the data in the
paper does not directly indicate what individual type of technology is bought.
Understanding that a technological good increases efficiency making the job easier for

64
the individual performing the task, allows almost all characteristics of the expenditure to
fall under the technology category.
Section 5.1: Final Thoughts:

The results from the regressions show that expenditures on technological goods
can cause both a substitution and scale effect on the demand for laborers within the oil
and gas extraction industry. This makes me wonder what happens to every industry
within an economy as companies acquire more technology? If the answer to question is
similar to the results of this study, technology can increase low-skilled laborers at a rapid
rate.
I studied a ten-year period within one industry (oil and gas) and saw roughly a 1%
(-.687%) decrease in demand for some occupations (farming/fishery occupations).
Theory in Chapter 2 supports a change in market demand can also be shown by a change
in wages. Therefore, occupational groups like the farming/fishing occupations also saw
their wages decrease by 1%. However, some occupations (management occupations)
experienced the opposite, a 1% (1.055%) gain in demand for laborers within those
positions. The average hourly wages for these laborers would have also increased by 1%.
The difference in these numbers show how technology can attribute to an increasing
wage-gap within society. If technology is to continue to advance, are those who are
unable to obtain more human capital at a larger risk to become unemployed and earned
less?
The first critique I have for my model comes from the variables used in the
regressions. I would use the same variables, but consider other factors mentioned by Paul
and Siegal that use international trade and outsourcing as other factors that affect wages.

65
It would be interesting to see technology expenditures effects on hourly wages decreased
or changed significance in the presence of these other variables that were significant in
Paul and Siegal study.
I should also note the number of observations I obtained for this study were
relatively low in comparison to other studies that also used panel data. The data in model
was balanced, providing more observations over a longer period of time could have
allowed for more accurate results.
In conclusion, I feel that it is important to look for technologies that can decrease
or eliminate the negative consequences associated with the substitution effect. The results
show that technology is capable of creating a scale effect for high-skilled laborers. There
is the potential for technology to have a scale effect on all types of laborers, but I do not
know at what cost the employers. Although the study concentrates on the oil and gas
industry, the results of these studies can be used to predict what would happen in other
industries. I also believe these results can be used to think of ways to innovate
classrooms, allowing young individuals to obtain the maximum level of human capital
stock.

66
Bibliography:

Beeson, Patrica, and Frederick Tannery. "The Impact of Industrial Restructuring on
Earnings Inequality: The Decline of Steel and Earnings in Pittsburgh." Growth
and Change 35.1 (2004): 21-44. Print.
Blum, Bernardo. "Trade, Technology, and the Rise of the Service Sector: The Effects on
US Wage Inequality." Journal of International Economics 74 (2007): 441-58.
Print.
Bowles, Samuel, Richard Edwards, and Frank Roosevelt. Understanding Capitalism. 3rd
ed. New York: Oxford UP, 2005. 283-339. Print.
Burnell, James D. Notes Provided From A In Class Handout in the Applied Regression
Course. Fall 2014.
Dunne, Timonthy, and James Schmitz Jr. "Wages, Employment Structureand Employer
Size-Wage Premia: Their Relationship to Advanced-technology Usage at US
Manufacturing Establishments." Economica 62 (1993): 89-107. The London
School of Economics and Political Science. Web. 20 Sept. 2016.
Ehrenberg, Ronald G., and Robert Stewart Smith. Modern Labor Economics: Theory and
Public Policy. Twelfth ed. Upper Saddle River: Prentice Hall, 2014. 282-522.
Print.
EIA - Independent Statistics and Analysis, U. (2013, August 8). Oil and gas industry
employment growing much faster than total private sector employment.
Retrieved March 8, 2016, from
https://www.eia.gov/todayinenergy/detail.cfm?id=12451

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