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

6

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–IndependentStatisticsand

Analysis,2016). In the five-year period between 2007-2012, the administration reported

that growth was consecutive (EIA–IndependentStatisticsandAnalysis,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–IndependentStatisticsandAnalysis,2016).

The administration shows roughly a five-year increase in oil production from 2007-2012

(EIA–IndependentStatisticsandAnalysis,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

Wag

es F

or L

abor

ers

Graph A: The Substitution Effect

LD When Price of K Decreases

LD When K is High

The Number of Laborers Employed

Wag

es F

or L

abor

ers

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

Rea

l Wag

es W

/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

Cap

ital i

n Ph

ysic

al U

nits

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 LaborersEmployment 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

Wag

es

So Sc

D'oDo

DcD'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 MBMB'

Marginal CostsMarginal Benefits &

Marginal CostsMarginal 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,

Earn

ings

Es

Ea

Ep

Es

Ao A*Age

Actual EarningsBetween 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),

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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),

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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-IndependentStatisticsandAnalysis,U.(2013,August8).Oilandgasindustry

employmentgrowingmuchfasterthantotalprivatesectoremployment.

RetrievedMarch8,2016,from

https://www.eia.gov/todayinenergy/detail.cfm?id=12451

67

EIA - Independent Statistics and Analysis, U. (2016). Petroleum and Other Liquids (Data

Set). Retrieved From:

http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RWTC&f=D

EIA-IndependentStatisticsandAnalysis,U.(2016,March25).U.S.petroleum

productexportscontinuetoincrease.RetrievedMarch25,2016,from

https://www.eia.gov/todayinenergy/detail.cfm?id=25532

Mincer, Jacob. "Technology and the Labor Market." Review of Economics of the

Household (2003): 249-72. Print.

Morrison Paul, Catherine, and Donald Siegel. "The Impacts of Technology, Trade and

Outsourcing on Employment and Labor Composition." Scand. J. of Economics

103.2 (2001): 241-64. Blackwell Publishers. Web. 10 Sept. 2015.

Studenmund, A. H. Using Econometrics: A Practical Guide. Boston: Addison-Wesley,

2011. Print.

Tsaliki, Persefoni. "Economic Development, Human Capital, and Technical Change: The

Question of Machinery Revisited." Int Rev Econ 55 (2008): 363-71. Print.

United States Department of Labor Bureau Statistics (2003-2012). National Industry-

Specific Occupational Employment and Wage Estimates (NAICS 211000 - Oil

and Gas Extraction Data Sets) Retrieved From:

http://www.bls.gov/oes/may/naics3_211000.htm

United States Census Bureau (2003-2012). Annual Capital Expenditures Survey (Data

Sets). Retrieved From: http://www.census.gov/econ/aces/xls/

68

Whorton, J.C., Jr., and J.C. Whorton. "Reinventing the West: The Role of Its Natural

Resources in the 21st Century." Journal of Energy Development 2nd ser. 39.1

(2014): 119-43. EBSCOhost. Web. 12 Jan. 2016.

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Appendices:

Appendix A-1: Categorization of Job Occupations In the Oil and Gas Industry

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

Appendix A-2: Summary Statics of the Data/Variables: -> year = 2003 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 24.51 14.81239 0 56.81 employment | 17 13831.18 27545.85 0 117970 tech_expend | 20 14152 0 14152 14152 oilprice | 20 31.08 0 31.08 31.08 anearn | 19 54912.63 27164.64 15580 118150 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2004 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 23.918 14.14136 0 53.44 employment | 20 11920 26025.45 0 119200 tech_expend | 20 13931 0 13931 13931 oilprice | 20 41.51 0 41.51 41.51 anearn | 18 55275.56 25365.2 16850 111150 -----------------------------------------------------------------------------------------------------------------------------------

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-> year = 2005 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 24.538 15.24368 0 53.82 employment | 20 12226 26750.31 0 122360 tech_expend | 20 16689 0 16689 16689 oilprice | 20 56.64 0 56.64 56.64 anearn | 17 60045.29 24884.74 18340 111950 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2006 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 25.3965 14.36006 0 54.29 employment | 19 13572.63 28786.42 0 129000 tech_expend | 20 20313 0 20313 20313 oilprice | 20 66.05 0 66.05 66.05 anearn | 18 58695.56 25146.64 17410 112920 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2007 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 26.745 16.46731 0 56.96 employment | 20 14018 30631.41 0 140250 tech_expend | 20 35502 0 35502 35502 oilprice | 20 72.34 0 72.34 72.34 anearn | 17 65447.06 26654.59 18060 118480 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2008 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 27.81 15.72333 0 58.3 employment | 20 14974.5 32738.54 0 149800 tech_expend | 20 40297 0 40297 40297 oilprice | 20 99.67 0 99.67 99.67 anearn | 18 64271.67 27531.48 19140 121260 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2009 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 30.2645 15.56566 0 62.42 employment | 20 15951.5 35020.91 0 159750 tech_expend | 20 28850 0 28850 28850 oilprice | 20 61.95 0 61.95 61.95 anearn | 19 66262.11 29574.09 20350 129830 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2010 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 30.0745 17.90031 0 66.57 employment | 20 15494 34043.83 0 154980 tech_expend | 20 30276 0 30276 30276 oilprice | 20 79.48 0 79.48 79.48 anearn | 18 69505 32213.57 22650 138470 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2011

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Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 33.2105 19.3146 0 73.38 employment | 20 16255 35657.12 0 162570 tech_expend | 20 41213 0 41213 41213 oilprice | 20 94.88 0 94.88 94.88 anearn | 19 72713.68 37745.08 22210 152620 ----------------------------------------------------------------------------------------------------------------------------------- -> year = 2012 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- meanhw | 20 35.4845 18.94032 10.58 74.34 employment | 20 18158 39818.94 30 181580 tech_expend | 20 45945 0 45945 45945 oilprice | 20 94.05 0 94.05 94.05 anearn | 20 73784 39388.41 22010 154630

Appendix B-1: Breush Pagan Test for Random Effects: 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

Appendix B-2: Hausman Test for Fixed Effects: 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)

Appendix C-1: Modified Wald Test for Heteroskedasticity:

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Table 4: xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (20) = 819.69 Prob>chi2 = 0.0000

Appendix C-2: Wooldridge Test for Autocorrelation in Panel 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

Appendix C-3: S.E. Robust Correction for Heteroskedasticity & Serial Correlation: 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