1

BEST INTEREST: A NEW LOOK AT THE “TRICKLE-DOWN EFFECT” IN THE

UNITED STATES

by

ALEX MOZELL

JERRY GRAY, Advisor

Senior Thesis Department of Economics

Willamette University

4/28/11

2

Introduction

This thesis deals with the “trickle-down effect” in the United States. A boost to

the incomes of those at the top of the income distribution is hypothesized to raise the

long-term incomes of the poorest Americans. I assume in this paper that this would be

the case, but qualify this assumption. What would happen if retained earnings for the

rich were instead transferred to the poor? Would the poor spend their newfound transfer

on endeavors that would actually raise their long-term incomes more than if the money

had been retained by the wealthy? Or would they spend it on things that have almost

nothing to do with raising their incomes? My central question is: do the poor in the

United States spend transfer income on education and healthcare?

To do so, I research the spending habits of the poor, specifically on human

capital. I look at expenditures on education and healthcare, comparing individuals

earning similar before-transfer incomes, but who vary in how much transfer they receive.

I then run several regressions to better determine if transfer recipients are likely to spend

on human capital.

The first section explains the most generally accepted theory of the “trickle-down

effect” and two complimentary theories. It also reviews literature that tested the validity

of the “trickle-down effect.” Another section of the literature review relates existing

literature to the poor and their spending on human capital.

The next section outlines the justification for my research. It states the theory of

human capital investment and its place in my research. It then outlines a matrix model,

which models an economy over time, and shows that it is possible for a transfer to the

poor to raise the incomes for the poor more than retained earnings for the rich if several

3

parameters are assumed. Following my analysis of data I assess my findings, explaining

how they relate to my central question, and make suggestions for further research.

Theory and Literature Review

“Trickle-Down” Theory

The trickle-down effect is a phrase, popularized by political debate and adopted in

economic academia. It describes the mechanism through which income, wealth, or other

forms of economic wellbeing for the rich make their way to the poor, ultimately

benefiting all. There is no unified theory that alone describes the “trickle-down effect,”

but the most popular theory deals with the marginal propensity to consume.

The poor tend to consume a greater share of their wealth than the rich do, and

consequently save and invest a smaller share. This is because the poor struggle to meet

basic needs such as food, healthcare, and household utilities, and so the marginal utility

gained from devoting dollars into these realms is far greater than saving or investing for a

less tangible future. The rich, on the other hand, easily meet their “needs” with what they

earn, and so they have money left over to save and invest.

The dollars that are saved and invested are transformed into capital through the

financial markets. Savings make financial markets more liquid, thus creating loanable

funds for borrowers, who use the funds to improve or start their businesses. Money that

4

is directly invested into businesses creates capital through direct purchases of productive

assets, such as computers or assembly lines.

This newly created capital has three primary “trickle-down” effects. First, it is

paired with labor, creating new jobs. The poorest, who almost by identity make up the

unemployed, may be able to find a job after the creation of this capital. Second, it creates

scarcity in the labor market, driving up the wages for those who are employed. A smaller

pool of unemployed means that firms must compete for workers, usually through wages

and benefits. This might not help the poorest of the poor as much. These workers, as

minimum wage is well above the market wage, generally do not see firms competing for

them through wages. However, it would help the group of earners just above them.

Third, the capital is paired with workers who already have jobs. This raises their

productivity. Some of the gains from this productivity go to the owners of the firms, but

some goes to the workers in the form of higher wages. Thus, the capital that the rich

create by saving and investing their income ultimately trickles down to the poor.

Considerations About the Theory

The primary theory of the “trickle-down effect” bears some considerations. The

theory is not perfectly robust; there are counterarguments to be made. First, to say that

increasing the incomes of the rich raises incomes for the poor lacks meaning because it

fails to take into account the opportunity cost. Retained earnings for the rich must be set

against an alternative, a transfer of those retained earnings to some other group, be it

5

government, nonprofits, or the poor, themselves. It is fine to say that a wealthier wealthy

raise incomes for the poor, but do they do so more than the best alternative?

For instance, suppose a transfer payment were made from the wealthy to the poor.

Naturally, a proportion of this transfer would no longer be saved or invested, equal to the

rich class’s marginal propensity to save and invest. However, after this transfer, the poor

would invest some of the money in the form of human capital. The poor spend some of

their income on education and healthcare, two primary forms of human capital. This

raises their productivity, and thus raises their long-term wages. Whether this investment

by the poor in their own human capital is more effective at raising their wages than the

rich investing and saving through financial markets is yet to be seen, but it is important to

consider. In The Model and Results section of this paper, I analyze whether the poor

would be likely to invest a substantial proportion of transfer income in human capital.

Second, the “trickle-down effect” may not work equally well at all times. For

instance, during recessions firms suffer because there is not enough demand to meet their

productive capacity. Financial institutions are less willing to loan out saved money, and

the rich are less willing to directly invest. This means that the rich contribute more to

money being horded rather than spent to boost badly needed demand.

The poor, on the other hand, spend a greater portion of their incomes, and are

therefore more likely to boost demand. Additionally, the money spent by the poor is

income for others, who spend this money and create even more income: the multiplier

effect. Thus, when firms need a great boost in spending in order to survive, the “trickle-

down effect” may give way to a stronger “trickle-up effect.”

6

Two other qualities of recessions help make consumption better able to raise

incomes. The first is that large changes in the demand for money do not change the

interest rate too much. Thus, when incomes start rising because a cash transfer to the

poor increases consumption, and thus when people demand more money as a result of

this rise in income, the interest rate will not rise by much relative to boom years. This

determines how much private investment is crowded out. Additionally, and second,

because investment prospects look dim, private investment does not change by much

relative to changes in the interest rate. Thus, private investment is crowded out even less

with any given rise in the interest rate relative to boom years. Therefore, because of

consumption’s potency to end recessions, and because of the high propensity for the poor

to consume, giving cash transfers to the poor might be the best way to induce growth

during recessions, which doubtlessly induces growth for the bottom, as well. (Froyen,

2009).

This demand-side effect, outlined in the previous few paragraphs, may actually

work well in all times, not just recessions. With stimulated demand comes expansion in

the productive capacity of firms, more hiring, and ultimately more output and higher

wages. This paper primarily deals with supply-side effects, meaning that giving the poor

transfers will actually allow them to invest in themselves, raising their productive

capacity. However, it is important to remember that the demand-side effects are still

present.

A third consideration is that capital may not be a compliment in consumption to

labor, but rather a substitute. This means that rather than capital raising the marginal

productivity of another worker, it may actually replace the work of laborers. Likely,

7

however, things are more complicated than this. A piece of machinery may replace the

job of a very low skilled worker, but raise the productivity of a semiskilled worker who

operates it. The worker who loses his or her job is likely to be poorer than the one who

sees his or her productivity rise. One can see how this would complicate the notion of the

“trickle down effect.”

A fourth consideration is that perhaps increased income for the wealthy does

create capital, and perhaps capital does create jobs, but maybe those jobs are created

outside of the United States. This could be the case if foreign investment opportunities,

facilitated by the globalization of finance, are more attractive than domestic investment

opportunities. Thus, in order to maximize their returns, the rich invest their money in

foreign ventures, creating jobs oversees but leaving the poor in the United States unaided.

A fifth consideration is that even if jobs are being created domestically, maybe

they are not going to the poorest Americans. One possibility, as mentioned earlier, is that

capital may favor the skilled and disfavor the unskilled. Another possibility is that there

may simply be more demand for skilled workers in the economy than the unskilled, and

so when businesses expand they hire more from the former category.

The considerations mentioned are: the theory often fails to account for an

opportunity cost, demand-side stimulus may work better than a supply-side stimulus

under certain conditions, capital may be more of a substitute than a compliment for low-

skill labor, capital may be being created outside of the United States, and that the jobs

being created may not be going to the poorest Americans. These considerations are

important, and together they represent a formidable challenge to proponents of the

“trickle-down effect.” This paper aims to study primarily an opportunity cost of retained

8

incomes for the wealthy, that being transfers to the poor. If money was transferred from

the wealthy to the poor, would the poor create human capital with it?

Alternative Theory

i)

The “trickle-down effect”, as mentioned, does not enjoy a unified theory in

Economic literature. The term was coined in popular culture, and it has been applied to a

medley of phenomena. Two alternative theories for how the “trickle-down effect” works

are discussed here. These theories are only tangentially related to my independent

research, but I feel that their inclusion better presents the theory in its entirety, a necessity

for a paper claiming to study the “trickle-down effect.”

Philippe Aghion and Patrick Bolton (1997), model an economy in which there are

wealthy individuals and less wealthy individuals. The wealthy pass their income down from

generation to generation, while the poor have very little money inherited from their parents.

There are two ways to use one’s wealth to create income: save it in the financial market or

use it to start a business. Due to how the risk structure is set up for starting one’s own

business, the wealthy generally prefer to save their wealth. This helps create liquidity in the

financial markets. Due to the rate of return on a smaller investment, poorer individuals may

decide to use their wealth as collateral for a loan, with which they will start their own

businesses. However, loanable funds would not be available for poorer individuals if it were

not for the high savings rate of the rich. The net risk-adjusted rate of return on investment in

9

a small business is well above zero. In this way, a wealthier upper class raises the incomes

of poorer individuals.

MS Nabi (2009) points out an important consideration regarding this mechanism.

He agrees that in order to be eligible for a loan, one must put down collateral. However, he

states that this is possible only for those who already own wealth, and the poor cannot put

down the collateral to do so. He flips the above equation on its head, concluding that the

wealthy, facing a much lower interest rate (due to their collateral), are much more likely to

take out loans and start their own businesses than the poor. The poor, on the other hand, are

left to save their income. As mentioned, business profits have a much higher net risk-

adjusted rate of return than the interest rate on savings. Thus, the poor create liquidity in the

financial market for the rich to borrow and invest. The result exacerbates inequality.

The true nature of this mechanism may lie between these two models. Aghion and

Bolton (1997) assume that the rich face a risk structure such that investing their wealth in a

small business is not worth the potential gains, so rather they choose to save it. Conversely

the poor use their meager wealth as collateral in order to take out loans and start their own

businesses. Nabi (2009) states that in order to get a loan, one must own a substantial

amount of money, an amount difficult for the poor to assemble. He states that the poor save,

and the rich start businesses.

The difference is that the two sets of authors are likely not talking about the same

rich and the same poor. Aghion and Bolton (1997) assume the rich prefer to save because

investing such large sums of money in a business would be too great a risk. The rich here

are likely the very wealthy, owning millions of dollars in assets. They are likely sitting on a

vast sum of money, but do not want to use it to start their own business. This could be for

10

two reasons. The first is that a wealthy individual might have earned his or her money by

working at a high-wage job. If this were the case, the return from continuing working at this

job is probably greater than the return if the individual started his or her business with

personal wealth. At any rate, it would be much less risky. Otherwise, this person inherited

his or her wealth, and lacks the entrepreneurial skills necessary to manage a business worth

his or her wealth. The “poor” in this article likely represent the middle class, for whom

using a few tens of thousands of dollars in personal assets to use as collateral, and then

starting a restaurant or coffee shop, seems practical. The “trickle-down effect” uses the

wealth of the superrich to finance the entrepreneurs of the middle class.

Nabi (2009) maintains that the poor are unable to borrow funds because they cannot

muster the necessary collateral to take out a loan. The rich, however, are able to borrow

(due to personal assets) and willing to invest it (due to the risk-incentive structure).

However, the “poor” in this article likely represent what most would refer to as the “working

class”, those whose assets amount to very little, and who may live more or less a subsistence

living. The “rich”, in this article, likely represent the middle class, who use a sum of

personal assets to get a loan and start their own businesses. This article outlines how the

meager savings of the poor are used to finance the projects of the middle class.

Thus, these two articles set their variables differently, and use a similar model to

come to different conclusions. However, I would argue that these two models represent two

sides of the same economy. The former shows how the wealth of the rich finances the

projects of the middle class, and the second shows how a wealth-strapped poor cannot get

loans but the middle class can. Thus, there is an inflow of savings from both sides of the

income distribution, from which the middle class borrows and creates small businesses.

11

Thus, when we understand these two articles together, the “trickle-down effect” is

successful in helping the less well-off, but within bounds, as it comes short of helping the

poorest to start small businesses.

ii)

The second alternative theory about how the “trickle-down effect” functions has

to do with industry moving to an area. To explain this theory, Bender, Green, and

Campbell argue that as industry moves to an area, the well-off, skilled workers get job

offers. This is the phase of the “trickle-down effect” in which the wealthy benefit.

However, after this phase, the relatively worse-off, unskilled workers are promoted to the

positions the former group previously held. They naturally experience pay and benefits

raises. Additionally, the unskilled workers who were not lucky enough to get promoted may

still enjoy pay raises due to the subsequent shortage of unskilled labor in the area. Thus,

industry directly helps the skilled, and through this it indirectly helps the unskilled.

The authors point out an important consideration to be made regarding this theory,

however. It is that the coming of industry also induces an immigration of laborers to fill its

labor demand. These immigrants are likely skilled workers because that is what the demand

directly calls for. Thus, the new jobs that industry brings are filled without much

displacement in the old skilled jobs. Also, there is very little promotion from the pool of

unskilled workers, and there is likewise little shortage created amid that pool.

However, although there is likely immigration, washing out much of the local effects

on wages, there is emigration of labor from other areas. This creates a shortage, in this case

of skilled workers, and subsequent promotion from and shortage amid the unskilled pool in

12

other areas. Thus, the effects of this “trickle-down effect” are likely shared, part of it

concentrated in the area to which industry moves, and part of it dispersed among

surrounding areas.

Furthermore, though the authors might not consider it relevant specifically to their

question, is that industry improves lives in more way than one. It is the embodiment of

capital creation, raising productivity given an arbitrary amount of labor, which raises

incomes overall. The nature of the product determines which demographic of consumer will

most benefit, and the distribution of revenue determines what group of laborers or owners

will most benefit. But with the creation of capital comes the improvement of productivity

and living standards. In the short-term, labor-replacing capital should be supplemented with

policies supporting laid off workers, but in the long-term the standard of living should rise

more or less uniformly.

Literature Review – “Trickle-Down Effect”

i)

This section outlines three papers that directly study the “trickle-down effect,” the

latter two empirically testing it using United States data. I outline their arguments, state

my thoughts, and then end with a description of these works’ relation to mine.

Shu-Chun Susan Yang (2008) used mathematical models to study the “trickle-

down effect.” In her paper, she rejects the Marxist notion that greater earnings for the

elite actually lessen the long-term earning potential for the poor. She instead accepts that

13

the “trickle-down effect” functions as it is laid out in the “Trickle-Down” Theory section

of this paper.

However, she points out an important consideration: an opportunity cost comes

with retained earnings for the wealthy. The author explains that if the government cuts

taxes for the elite, it must account for the lost revenue through cutting current spending

projects. The author does not consider issuing debt.

Three types of projects may be cut: public investment (i.e. education and

healthcare), transfer payments to the poor, and public “services” (i.e. the spending

variable in the model that does not create earning potential for anyone). If the

government cuts public investment, long-term incomes for the poor suffer because these

programs fund education and healthcare for the poor, which help them attain higher

earnings. If the government cuts transfer payments to the poor, then the poor suffer in

future earnings because they cannot take steps to increase their incomes. Only if the

government cuts “services,” which are here defined as having no effect on the incomes of

anyone, does the income of the poor rise (due to the “trickle-down effect”) more than it

declines caused by cuts in government spending.

This is related to my research because the author considers the opportunity cost

for retained earnings for the poor. She considers two important costs, public investment

(including education and healthcare), and transfers to the poor. The opportunity cost I

will consider is transfers to the poor, with special consideration to their own investments

in human capital (specifically education and healthcare). More will be discussed in The

Model and Results section of this paper.

14

ii)

Yuexing Lan and Charles Hegji (2009) empirically test the “trickle-down effect”

in the United States. They regress wage income, small business profits and corporate

profits onto the inverse gini coefficient (a measure of equality). The authors assume that

the profit incomes are generally earned by the wealthier, and the wage income variable

generally goes to the poorer. The authors find that there is a positive correlation between

wage income and equality, but no significant correlation between small business profits

or corporate profits and equality. The authors conclude, therefore, that there is no

evidence for the existence of the “trickle-down effect” in the United States.

Although this article determines the relationship between income for the rich

versus equality and income for the poor versus equality, it does not explore the

relationship between income for the rich and absolute income for the poor. It is possible

that income for the poor rises as a result of a rise in income for the rich, but that income

for the rich proportionally rises by even more. In this scenario, the “trickle-down effect”

would be functioning (by one definition of the term), but inequality would increase, as

well. If this scenario were in effect for the duration of the period of this study, then the

existence of the “trickle-down effect” would not be captured in these results.

This article is valuable to my research because it addresses the issue of income for

the poor having an endogenous effect on their own long-term incomes. Thus, the authors

consider the effect of a rise in incomes for the poor on a proxy for benefits for the poor:

equality. So too, in my research, I consider the effect that a lump-sum transfer to the

poor has on their own long-term incomes, to be discussed later in The Model and Results

section of this paper.

15

iii)

Nozar Hashemazaeh and Wayne Saubert explore the “trickle-down effect” by

studying tax rates for different income demographics. They regress the marginal tax rate

and average tax rate among different income demographics onto economic growth. The

authors do not find any significant correlation between lower taxes for the rich and

growth in the economy, so they conclude that there is no significant evidence for the

existence of the “trickle-down effect.”

This article does well to test the “trickle-down effect” in the context of taxes. It

tests based on a broad, empirical approach. My research tests on a narrower scale, both

in the scope of the question and the region analyzed. Thus, it is possible that the “trickle-

down effect,” as I am defining it, may still function. The scope of my question includes

how the United States poor spend their money, with an emphasis on human capital.

Again, this will be iterated in depth in The Model and Results.

Literature Review – The Poor and Human Capital

This section outlines several authors’ ideas on how the poor spend on human capital.

I relate the findings in some of the articles to how the poor would likely spend transfers. It

is more directly pertinent to my research, as it does not directly address the broader

theoretical basis of the “trickle-down effect.” Rather, it addresses the question of whether

the poor are likely to spend transfer payments on human capital.

Two articles conclude that spending on human capital is a good investment when

comparing its rate of return to the interest rate. Hesitation of the poor to invest in this is

16

likely due to their personal discount rate rather than an inability to turn human capital into

increased earnings. Carolyn Heinrich and John Karl Scholz (2008) find that public safety

nets designed to promote training for the poor raise their long-term incomes much more than

safety nets aimed at putting the poor back to work immediately. According to this research,

the poor find it easy to turn human capital into increased earnings, and so the argument that

the poor are not investing because they struggle to turn human capital into earnings probably

does not explain the lack of investment. Training programs pay off, and thus human capital

spending for the poor is an investment worth making, the personal discount rate aside.

Because her research is so related to mine, I feel it is pertinent to include a brief

description of her methods. She cited previous literature which found a meaningful rise in

incomes of individuals who attended work-first programs. She then tracked women leaving

welfare over time, and she found that they remained, for the most part, well below the

poverty line.

It is not just investing in oneself that is important, but spending on children’s human

capital is crucial to raise their future earnings. Janet Currie (2009) finds that the

socioeconomic status of the parents is positively correlated with the children’s health status,

which is positively correlated with children’s future earnings. Thus, health during childhood

is related to future earnings. Also, wealthier parents raise healthier children than poorer

parents. Casual conjecture would lead one to the conclusion that the poor are no less able to

improve their children’s health status, dollar for dollar. So, the observed difference in their

willingness to invest in their children is probably due more to their personal discount rate

than their ability to turn human capital into earnings. Admittedly, the ability for poor versus

rich children to turn health into earnings is debatable.

17

However, three other articles conclude that the poor are unlikely to spend transfers

on human capital. César Martinelli and Susan W. Parker (2008) found that once families

received subsidies for their children’s education, those families actually increased their

spending on human capital. Thus, an increase stock of human capital seems to boost human

capital spending, at least for low levels of income.

This could occur for two reasons. First, there is increasing marginal rate of return at

low levels of human capital stock. This goes against mainstream theory, but it would make

sense if there is a significant amount of setup necessary in order to capitalize on more

investment, but the setup itself does not yield as much return. The second possibility is if

there is a degree of increasing marginal utility for parents in seeing their children as

academically successful. In other words, once children are in school and doing well, parents

begin thinking of them as driven academically, and are more excited to further that process.

Either way, some sort of marginal return is increasing at low levels of human capital, which

may not sit well with mainstream theory, but explains what we observe in this article.

One might argue that the income effect is taking hold here, and that the transfer

increases a family’s effective income, thereby prompting them to spend more on human

capital. My counterargument is that all of the transfer was spent on human capital, and so

we would expect to see a sharp drop in human capital spending as this consumer now has

much more human capital in his or her consumption bundle. Casual inference suggests that

this drop should outweigh the income effect.

The implication of this research is that cash transfers may not increase spending on

human capital as much as they could. The poor, if faced with this increasing marginal

benefit curve, are likely stuck spending very little on human capital. Thus, a cash transfer is

18

likely to be spent on a similar proportion of human capital as was spent on it before.

However, if the poor gain access to better education, then they may spend more on human

capital than they presently do.

Larry D. Singell, Jr. and Joe A. Stone (2002) find that the poor are less likely to

attend a university when receiving merit-based aid than the rich, even after controlling for

ability and other variables. These other variables include race, gender, age, whether the

student received admission through academic exceptions, and ability (measured by GPA

and SAT scores). The authors controlled for high school quality by including the number of

AP courses taken by the student (indicating the number offered by the high school), the

number of Scholastic Aptitude Tests sent to the university, and whether the high school was

private. Finally, income was measured by looking at the average income of the student’s

parents’ zip code (based on the 1990 census).

There is a probable reason that the rich are more likely to attend the university with

more merit-based aid. This is that the poor, valuing immediate money over return much

more than the rich, take absolute tuition rates into account much more than how much they

would save with a merit-based scholarship. This means that the rich view a scholarship as

saved money, while the poor see what is left to pay, which may still be very high. The poor

may also have a very low threshold for how much they are willing to spend than the rich,

meaning that they will not attend college unless they can find one that is very inexpensive.

Assuming that the rich in this study spend more on college because it yields the best

return, accounting for the real interest rate, we again see a difference in the personal

discount rate between the rich and the poor. The difference in willingness to invest is, again,

probably not due to less capacity of the poor to transfer a college education into earnings

19

because the authors controlled for ability. Thus, this means that the poor are less likely to

invest in human capital, rate of return constant, than the rich. Thus, a transfer to them is less

likely to yield as much investment in human capital.

Amparo Castelló-Climent and Rafael Doménech model a poverty trap, the premise

being that the poor have less funds to make themselves healthier, so they live shorter lives,

and so because of a decreased return on human capital (because they will not live long

enough to earn all they could), they chose to invest less in human capital.

The implication is that the poor are less likely to invest much of a transfer in human

capital. The reason is that they are poorer, cannot invest in their health, and thus live shorter

lives and choose not to invest in other forms of human capital that raise their incomes in a

per-year fashion. The poor do not get to experience the fruits of their labors for as long in

retirement. This study supports my conclusion that the poor are likely to spend little of a

cash transfer on human capital.

The Model and Results

The Model

i) – Human Capital

Human capital mainly refers to education and healthcare, but it is anything that

raises one’s future long-term income. It is a form of capital because it increases

productive capacity given a fixed amount of other inputs. Theoretically, a person

20

chooses to invest in his or her own human capital based on the net present value of the

investment. The net present value is the expected return on the investment, adjusted for

the discount rate. The discount rate adjusts for the interest rate (the next best alternative

for the investment) and for the personal discount rate (one’s preference for spending the

money on something now rather than on something in the future).

It would seem that everyone would spend similar amounts on human capital

because the interest rate is similar for most people, even across income demographics,

and because the rate of return is seemingly the same. However, two things should be

considered. First, the rate of return is not the same for all people. Some people are

endowed with a natural ability to turn an arbitrary investment in human capital into

increased earnings. Age is one obvious example of this, but so is intelligence. To the

extent that intelligence may be positively correlated with income, the poor may therefore

be less willing to invest in human capital. This reason would suggest that a transfer to the

poor might not increase their incomes by very much, as they would be less willing to

spend the transfer on endeavors that would increase their earnings.

Another reason that the poor may be less willing to invest in human capital would

be that they have a much higher discount rate than the rich. The poor suffer from a

chronic lack of basic needs, such as household utilities, adequate transportation, and at

times nourishment. It makes sense that they would value the immediate alleviation of

this discomfort much more than some future increase in earnings, leading to a higher

discount rate. If this were the case, a transfer to the poor might actually induce them to

invest more in human capital, as they might be on the border at which the marginal utility

for basic needs diminishes substantially. When all one’s needs are met, he or she might

21

then worry about his or her future earnings. Because of the theoretical ambiguity as to

whether a transfer to the poor would significantly raise their future earnings, my research

has its place.

ii) – The Model

To help justify why my research question is important, I used linear algebra. This

involved a dynamic matrix equation. It is important to note that I did not plug in values

that resembled reality into the matrices. I only attempted to form a relationship to justify

why my research question is important. Based on this model, there may be a certain

relationship among how much the poor gain in income from investment in their human

capital, how much they gain in income from physical capital investment, the proportion

that the rich invest in physical capital, the proportion that the poor invest in physical

capital, and the proportion that the poor invest in human capital. If this relationship

holds, then the “trickle-down effect” may not be as effective as it could be, based on my

conjecture involving the opportunity cost. A step-by-step guide to how I arrived at the

relationship is presented here.

* aa= (1)

* aa= (2)

RC MC PC

RI MI PI

0 0 PHC

RIV

MIV

PIV

CSV

ISV

HCSV

DR DR + IR DR

DM DM + IM DM

DP DP + IP DP + HCP

CSV

ISV

HCSV

RIV (2)

MIV (2)

PIV (2)

22

Consider equation (1), and first I will explain the meaning of the first matrix

shown. This matrix is the spending matrix. Each column represents a different

socioeconomic group. The first column represents the rich, the second represents the

middle class, and the third represents the poor. The rows represent how what proportion

of income is spent on consumption, physical capital investment, and investment in human

capital for the poor, from top to bottom. Two of the entries are 0 because I am assuming

that the rich and the middle class spend a negligible amount of their incomes on human

capital for the poor.

The next matrix over, which is a vector and multiplied by the spending matrix, is

called the income vector. From top to bottom, the entries represent the absolute income

that the rich, middle class, and poor earn. The product of these matrices represents the

spending vector, which is last in equation (1). From top to bottom, the entries in this

vector represent the total absolute amount spent on consumption, physical capital

investment, and human capital investment for the poor.

Now please consider equation (2). The first matrix is the income matrix. Each

column in this matrix represents a different avenue of spending (consumption, capital

investment, and human capital investment for the poor, from left to right). Each row

represents how much the socioeconomic groups stand to earn from each avenue of

spending, (rich, middle class, and poor, from top to bottom). These groups stand to earn

their previous share of income plus some growth factor based on each avenue of

spending. They earn their previous share of income because, growth aside, I did not want

to postulate the share of income from various avenues of expenditure.

23

The next matrix over is the spending vector from equation (1). The income

matrix multiplies by the spending vector to yield the income vector [2]. The income

vector [2] is the absolute incomes of the rich, middle class and poor (from top to bottom)

in the next time period.

RC, RI The proportion of their income the rich spend on consumption and

investment

DR, DM, DP Proportion of income earned by the rich, middle class, and poor in the

same time period (derived from income vector)

IR, IM, IP Proportion that the rich, middle class, and poor grow in income given

some amount of physical capital investment; for simplicity, I will not

define this value too narrowly, as it is unnecessary for these purposes

RIV, MIV, PIV The absolute income of the rich, middle class, and poor, respectively

CSV, ISV,

HCSV

The absolute expenditure in consumption, physical capital investment,

and human capital investment for the poor, respectively

Now, please follow the logic until I arrive at my relationship that justifies this type of

research. First, see that the following few equations make sense given the matrices. We

are interested in finding what the poor earn in period 2 given the initial state in period 1.

PIV[2] = DP(CSV) + (DP + IP)ISV + (DP + HCP)HSV (3)

CSV = (RIV)(RC) + (MIV)(MC) + (PIV)(PC) (4a)

ISV = (RIV)(RI) + (MIV)(MI) + (PIV)(PI) (4b)

HCSV = (PIV)(PHC) (4c)

24

One can see that plugging in the values in equations (4) into equation (3), we get a new

equality.

PIV[2] = ((RIV)(RC) + (MIV)(MC) + (PIV)(PC)) (DP)

+ ((RIV)(RI) + (MIV)(MI) + (PIV)(PI)) (DP + IP)

+ ((PIV)(PHC)) (DP + HCP) (5)

Now, for simplicity, I normalize income to total 1, and allocate the share of income to be

equal among the rich, middle class and poor (meaning each socioeconomic group earns

1/3). For simplicity, let us call RIV, MIV, and PIV simply IV. At first glance, it may seem

odd to allocate the same income among the different groups, but when we remember that

population is still variable, it makes sense. Further, it is acceptable to distribute income

equally in this way because we begin at an arbitrary period, meaning that we look at the

outcome of that period given an equal distribution of income among the groups.

However, it is important to note that this model has not been tested for different

allocations of income. So, we arrive at a new equation, derived from equation (5).

PIV[2] = IV(RC + MC + PC) (DP)

+ IV(RI + MI + PI) (DP + IP)

+ IV(PHC) (DP + HCP) (6a)

25

Now, dividing each side by IV we get a new equality. I will still call PIV[2] from the

previous equation its same notation, instead of 3(PIV[2] ) because the actual values are

arbitrary anyway. It will make sense why this is acceptable once we get to equation (7a).

PIV[2] = (RC + MC + PC) (DP)

+ (RI + MI + PI) (DP + IP)

+ (PHC) (DP + HCP) (6b)

The following equation is not a direct derivation from the previous. It is only based on it.

Here, I set up a relation to see whether a marginal dollar to the rich or to the poor raises

the income of the poor more. As you can see, this is directly related to my research. If

the first part of the relation is greater than the second, then the “trickle-down effect” may

not work given the opportunity cost. The first part of the relation is how much the poor

induce their own growth in income, and the second part is how much the rich induce

growth in income for the poor. The first part represents the proportion of income the

poor spend on their own human capital times how much income it creates for them in

period 2, how much they invest in physical capital times how much income physical

capital induces in time period 2, and so on. The second part represents how much the

rich spend on physical capital times how much income it induces for the poor in time

period 2, and similarly for consumption.

PHC(DP + HCP) + PI(DP + IP) + (PC)(DP) >

RI(DP + IP) + (RC)(DP) (7a)

26

Because we assumed that the rich, middle class, and poor all earned equal shares, we may

set DP = 1/3.

PHC((1/3) + HCP) + PI((1/3) + IP) + (PC)(1/3) >

RI((1/3) + IP) + (RC)(1/3) (7b)

Now, we multiply out the previous equation.

(1/3)PHC + (PHC)(HCP) + (1/3)PI + (PI)(IP) + (1/3)PC >

(1/3)RI + (RI)(IP) + (1/3)(RC) (7c)

Recall that PHC + PI + PC = RI + RC = 1. This is because these represent columns of a

stochastic matrix, so the entries summed equal 1. So, it is evident that we can continue

simplifying the equation.

(1/3) + (PHC)(HCP) + (PI)(IP) >

(1/3) + (RI)(IP) (7d)

27

Subtract 1/3 from each side.

(PHC)(HCP) + (PI)(IP) > (8a)

(RI)(IP)

(PHC)(HCP) > (RI)(IP) – (PI)(IP) (8b)

(PHC)(HCP) > IP (RI – PI) (8c)

HCP > (IP (RI – PI)) / PHC (9a)

PHC > (IP (RI – PI)) / HCP (9b)

Equations (9a) and (9b) are equivalent. Please see the table to understand what this

relation means. If this relation holds, based on this rudimentary model, then a dollar

given to the poor raises their income more than the rich, and further investigation, for

which I play a small part, is appropriate.

28

Independent Research

i) – Descriptive Statistics

To try to understand if transfers are used to purchase human capital, I used the

Consumer Expenditure Survey (CES). These data are from 2003, and track income and

expenditure information on 2645 households. I began looking at the descriptive statistics

in order to form hypotheses for how to interpret the data.

I created several new variables from the information given. I created a “before

transfer income” variable, of the summed total of “wages/salaries,” “proprietor’s profits,”

“farm income,” “rental income,” “dividends,” “interest,” “pensions,” “social security

income,” and “supplemental secutity income.” I created a “poor transfer income”

variable by summing “welfare income” and “food stamps income”. I included food

stamps income because food stamps free up household resources, even though the

stamps, themselves, may only be spent on food. I created a “health human capital

spending” variable by summing “drug preparations,” “ophthalmic products and

orthopedic appliances,” “physicians, dentists, and other medical professionals,”

“hospitals” and “health insurance”. I included “ophthalmic products and orthopedic

appliances” because being able to see clearly is important for reading and other skills

associated with work. I excluded nursing homes from this variable because, though they

provide the benefit of physical wellbeing, they ultimately lack the investment component

because a person who lives in a nursing home probably does not expect to raise his or her

future income by doing so. I created an “education human capital spending” variable by

summing “higher education,” “nursery and elementary education,” and “other education.”

29

I created a “human capital spending” variable by summing my “health human capital

spending” and “education human capital spending” variables. I also looked at family size

for each data point.

My Variable Sum Of…

Before Transfer Income Wages/Salaries, Proprietor’s Profits, Farm

Income, Rental Income, Dividends,

Interest, Pensions, Social Security Income,

Supplemental Security Income

Poor Transfer Income Welfare Income, Food Stamps Income

Health Human Capital Spending Drug Preparations, Ophthalmic Products

and Orthopedic Appliances, Physicians

Dentists and Other Medical Professionals,

Hospitals, Health Insurance

Education Human Capital Spending Higher Education, Nursery and Elementary

Education, Other Education

Human Capital Spending Health Human Capital Spending,

Education Human Capital Spending

I then took a sample of the larger sample. This sample was the bottom 25th

percentile of everyone who reported a positive “before transfer income”. This was the

sample that I studied the most intensively. I then divided this sample into two groups: the

first being those who received transfers, the second being those who did not receive

transfers. Thus, I first studied two groups of people, with similar incomes and

presumably other similar attributes, the main difference being that one of the groups

received transfers. The numbers listed are rounded, usually to the nearest five dollars.

I found that the “no-transfer average income” was $8,765, while “yes-transfer

average income” was $9,470. The average size of a transfer was $1,610. It is interesting

to note that transfer recipient households earned more than households not receiving

assistance. My hypothesis is that income, in this case, is positively correlated with

30

earners in a household, which is also positively correlated with family size. Larger

families receive more benefits, and so those receiving benefits may actually earn more.

Indeed, the average family size of households receiving assistance was 2.68 people, while

the average family size of households not receiving assistance was only 1.71 people. On

average, households receiving assistance were almost an entire person larger than those

without it.

Moving on to human capital spending, households not receiving transfer

payments spent much more on human capital than their transfer-receiving counterparts.

The former group spent an average of $1,610 on “health human capital,” $290 on

“education human capital,” and $1,905 on “total human capital.” The latter group spent

$405 on “health human capital,” $45 on “education human capital,” and $435 on “total

human capital.” Those without transfer income outpaced those with it in health spending

by 300%, in education by 540%, and in total human capital by 340%.

Income Transfer Health

Spending

Education

Spending

HC

Spending

Family

Size

Yes-

Transfer

$9,470 $1,610 $405 $45 $435 2.68

No-

Transfer

$8,765 $0 $1,610 $290 $1,905 1.71

One hypothesis that I formed to explain this difference is that households with

more members find it difficult to devote their resources to purchasing human capital for

all of them. Human capital is more of a luxury than other things, such as food and

clothing, which must be purchased first. Even with an average of about 25% more

31

income (including transfer payments), transfer recipients might not afford to spend nearly

as much as non-transfer recipients on human capital, perhaps because they have more

people to take care of. Health may be more necessary than education, but even so both of

them are severely cut, perhaps because of family size.

There is another possibility for why those who received transfers spent so much

less on human capital, and why they had larger families than those not receiving

transfers. This is that both lack of human capital investment and larger families are

associated with people who make “poor” decisions in life. Larger families would then be

associated with larger transfers because people with children are better eligible for these

programs.

It is here important to not what I mean by “luxury” and “necessity” in this paper.

The formal definition for a luxury is a good for which the percent change in consumption

in response to the change in one’s income is greater than the percent change in income.

A necessity is a good for which the percent change in consumption is less than the

percent change in income. I use the more colloquial definition, being that a necessity is

something that is much prioritized over a luxury when deciding what to buy. The

definition is inexact as I use the terms to describe general phenomena.

ii) – Regressions

To test these competing hypotheses, I ran several regressions. I will state up front

several failings in these regressions. The first is that the sample size was quite small, as I

studied only a fraction of the data in the entire Consumer Expenditure Survey sample.

32

There were only 422 households that I studied, and an example of a further failing is that

only 55 of them received any sort of transfer. Another failing is that I set the significance

level at p < .1. This is because, probably due to my small sample, if it were lower then

very few things would be significant. Finally, I ran six regressions for which the

dependent variable was some form of expenditure on human capital, so this further

creates a possibility for erroneous significance. The reason I ran so many is because I

wanted data on human capital expenditure per person. With these rather large failings

comes an admission that anything I conclude is not by any standard for sure; it is merely

a means to begin forming hypotheses. Further research is certainly necessary for any

meaningful degree of certainty. I would like to mention also that I attempt to explain the

significant variables to the best of my ability, and determine what line of reasoning each

supports, but these are my own opinions, and may not actually reflect reality.

First, see the table that explains the variables I used. The indicator I refers to an

independent variable, and a D refers to a dependent variable.

33

HC D “Human Capital Expenditure,” as defined previously

H. HC D “Health Human Capital Expenditure,” as defined previously

E. HC D “Education Human Capital Expenditure,” as defined previously

HC/Per D “Human Capital Expenditure per Person,” this is equal to HC/FamSize

H.

HC/Per

D “Health Human Capital Expenditure per Person,” this is equal to

H. HC/FamSize

E.

HC/Per

D “Education Human Capital Expenditure per Person,” this is equal to

E. HC/FamSize

NT

Income

I “Net Income,” this is equal to “Before Transfer Income” (as defined

previously) + Poor Trans

Poor

Trans

I “Transfers,” as defined previously

FamSize I,

D

“Family Size,” which equals the number of people in the consumer unit

Smoker? I “Smoker,” this is a dummy equaling 1 if expenditure on tobacco was

positive

Nec Ind I “Necessity Indicator,” this was a measure of spending on necessities,

measured by “Rent” + “Electricity” + “Gas” + “Water and Sanitary

Services” + “Fuel Oil and Coal”

I used the measure HC because my central question pertains to overall human

capital expenditure. I used H. HC and E. HC because I realize that expenditure on these

two forms of human capital may differ form each other, which is exacerbated by my use

of such a small data set. Hence, even if there is a similar trend in how one would spend

on each of these, if the difference were too great, I might not get a significant result when

adding the two. I used the per person measure of these variables because I wanted to see

how the cut in expenditure on human capital per person is correlated with my

independent variables. This is particularly interesting against family size, as I see how

much a person sacrifices per person by adding a new family member.

I included NT Income because I wanted to see the differences in human capital

expenditure holding income constant. If I did not hold income constant, it is possible that

I could get a spurious correlation if there is collinearity between income and some other

variable. Poor Trans was included because I wanted to see if there was an independent

34

correlation with human capital expenditure from transfers to the poor, Net Income

constant. I included FamSize to see if increasing family size meant a decrease in human

capital expenditure, as this pertains to my first hypotheses from descriptive statistics. I

included Smoker? because I felt it was important to control for, and also to help see if

different “poor” decisions are made by similar people. Finally, I included Nec Ind to see

if, when one has to pay more in basic necessities, he or she cuts back on something which

is more of a luxury, here human capital and education. This may help determine one of

my hypotheses: that the poor are unlikely to invest in human capital because it is too

much of a luxury.

I will go through each regression, mention interesting associations (or lack of

them), provide hypotheses to explain them, and try to provide some analysis. The first

regression, which had HC as the dependent variable, found no significant results. This, I

hypothesize, is due to a difference in the way people invest in education and healthcare,

and adding the two of them confused the data. Regardless, it was important to include, as

it is directly pertinent to my research.

The second regression I ran had H. HC as the dependent variable. Transfers was

significant, with a coefficient of -.32 and a p-value of .015. This is interesting as it shows

that receiving transfers had a negative effect on how much healthcare a head of

household provides for his or her family. It is important to note that total income was

held constant, meaning that there is likely some reason that receiving transfers means a

person spends less to keep his or her family healthy. There are two possibilities that I

will put forth. First, going with my second hypothesis above, people who receive

transfers are people who make bad choices, and so they choose to spend less. Maybe

35

they are also lazy, so they are more likely to apply for transfers, while the more

conscientious provide proper care for their families and choose the industrious route,

providing their own assistance. The second possibility, supporting my first hypothesis, is

that there is an overlooked variable associated with receiving transfers. This variable

may be a measure of desperation not measured by these data. If one has a high

“desperation” measure, perhaps he or she is more likely to receive transfers, and

subsequently less likely to invest in healthcare because he or she has more important

things to worry about.

If there is a smoker in the household, less is spent on human capital. The variable

Smoker? has a coefficient of -442 and a p-value of .085. This was an interesting result. I

partly expected the sign to be positive, as smokers are less healthy and more in need of

healthcare. However, the fact that it is negative may lend support for the hypothesis that

poor decisions are made by similar people. Those who choose to put their health in

jeopardy by smoking, and who may disregard science and health professionals, may also

be less willing to keep healthy by trusting healthcare services. Family size was

insignificant; the p-value was .846, so unfortunately I cannot comment meaningfully on

this.

The third regression I ran had E. HC as the dependent variable. Here, only the

Necessity Indicator was significant, with a coefficient of -.036 and a p-value of .070.

This means that for every extra dollar that one spends on these necessities, one spends a

few cents less on education. Thus, though significant, this result is not very meaningful.

Family size was insignificant here, as well, so unfortunately I cannot comment on that,

again.

36

The fourth regression I ran had HC/Per as the dependent variable. Here, as one

would expect, family size was significant, with a coefficient of -266 and a p-value of

.000. This means that substitutions are likely being made. Indeed, my fifth regression,

with H. HC/Per as the dependent variable, had a significant sign on family size. The

coefficient was -204 and the p-value was .001. Thus, it seems that with larger families,

people have to cut back on healthcare for each member. This gives credence to the

hypothesis that if the poor have larger families, then human capital takes a hit. However,

it is expected for any good to decline in expenditure per person with an increase in family

size, all else constant. Something else may further account for the fact that larger

families lead to less healthcare spending. This is that healthcare often experiences

economies of scale, meaning that it is less than twice as expensive to provide healthcare,

especially insurance, for two people than it is for one, and so on. If there was a smoker in

the household, H. HC/Per declined by -314, with a p-value of .065.

The sixth regression, with E. HC/Per as the dependent variable, saw the necessity

indicator as the only significant variable, with a coefficient of -.032 and a p-value of .092.

Not even family size was significant. I attribute this lack of significance to too small a

sample size, as there were only 80 individuals reporting positive education spending.

The seventh regression I ran did not have a measure of human capital as the

dependent variable. Instead, its function was to test to see if people who make some poor

decisions tend to make other poor decisions, too. Family size was the dependent

variable, and the smoker dummy, transfer amount, and net income were the independent

variables. I found that receiving transfers was significant at p = .000. I took this to be

evidence of the fact that larger families help one receive transfer payments. If there was

37

a smoker in the household, family size increased by .25, with a p-value of .081. This is a

sizable coefficient, which may be evidence that people who make the “poor” decision to

have a larger family also make the “poor” decision to smoke tobacco. Another

possibility is that larger families are more stressful, so people with more children find

smoking to be a stress-reducing outlet.

Conclusion

Analysis

i) – Hypotheses

The two competing hypotheses for why transfer recipients spent less on human

capital are first, that human capital is a luxury among the poor. When family size

increases, these families are more eligible for transfers, but less able to provide human

capital now that their family is so large. The second hypothesis is that some people

simply make bad decisions, and other people make better decisions. Those who choose

to have larger families may do so because they do not plan for the future. These people

subsequently apply and qualify to be a transfer recipient. I will outline the evidence that I

gathered for each hypothesis.

ii) – Evidence

First is the hypothesis that human capital is a luxury, and larger families mean

that earners are hard-pressed to provide human capital for them. It is true that for every

38

three dollars a person earned in transfers, he or she spent one dollar less on human capital

for his or her family, but perhaps there is a measure of “desperation” that is not accounted

for in my data. Perhaps the civil servants in charge of allocating transfers can identify

this desperation, and are more willing to provide assistance. This same desperation,

however, may cause this individual to spend less on human capital as he or she is

consumed with other obligations.

A stronger piece of evidence is that expenditure on education human capital was

negatively correlated with expenditure on necessities. I took this to mean that as one’s

utilities and rent became more expensive, one cut back on his or her family’s education.

Only a few cents is cut per dollar more spent on these basic necessities, but I did not

include all basic necessities. Thus, basic needs take precedence over human capital,

meaning that other basic needs associated with increasing the size of a family may cause

a decline in human capital expenditure, also. However, it is important to note that the

size of this decline should be taken into account. All else constant, it is expected that a

rise in the price of any good to diminish the expenditure on other goods. Thus, it is

important to be cautious of placing too much weight on this piece of evidence.

Finally, how much a person spent on health human capital per person in his or her

family was negatively correlated with family size. About $200 dollars a year was cut per

extra family member. Thus, it seems likely that people need to make adjustments with

growing families, and cutting healthcare for each individual is a way to go about doing

this. The strength of this piece of evidence is dubious, however, as we would expect to

see a decline in any good per person with an increase in family size. The amount $200

per person lacks meaning as I have nothing against which to compare it.

39

I feel like a stronger case can be made for some people making “worse” decisions

than others, and for these people to spend less on human capital and receive more in

transfers. First, spending on healthcare was negatively associated with transfer receipts.

As mentioned, for every three dollars earned in transfers, about one dollar less was spent

on healthcare. A possible reason for the reason for this is because those who make bad

decisions, for whatever reason, tend to end up receiving transfers. They continue making

bad decisions even after they receive the transfer, which means spending less on their

family’s healthcare.

Another piece of evidence for the second hypothesis is that smokers spent about

$442 less per year on their family’s healthcare than non-smokers. This seems odd, as

smoking is related to many health related problems, including asthma for children in the

household, and cancer for the smoker. The fact that smokers spent less on healthcare

may mean that these people have some quality, a “disregard for health,” which prompts

them to smoke.

Finally, and related to my last point, smokers tended to have larger families than

non-smokers, by about a quarter of a person. This may mean that bad decisions are made

by the same people; if a person decides to disregard birth control, he or she may disregard

science’s warnings of cancer, and may ultimately disregard the need to maintain his or

her family’s health through healthcare spending.

iii) – A Broader Look

This applies to the question of the “trickle-down effect” because it addresses the

question of whether the poor would raise their future incomes more if they were given

40

transfers, rather than the rich retaining their income. If the poor spent much of their

income on human capital, then it could be argued that transfers to the poor, not retained

earnings for the rich, would increase their income more.

My data suggests that the aggregate “poor” is made up of different people, each

with different characteristics and ways he or she goes about human capital investment.

The way the transfer system is currently structured means that transfers to the poor are

unlikely to stimulate human capital investment, meaning that retained earnings for the

rich have more of a chance of boosting incomes of the poor, strictly speaking supply side.

If the transfer system was set up differently, which I will discuss momentarily, there is a

greater chance that the poor would be able to help themselves out of hardship.

I will make some suggestions as to how to most effectively boost human capital

based on each hypothesis. For instance, human capital may not be a luxury everyone can

well afford. If this were the case, it seems that government should try to meet the basic

needs of the poor so they may then begin spending on human capital. This goes along

with the human capital expenditure model, where tending to immediate needs lowers the

personal discount rate, thus allowing for more investment.

Looking at the second hypothesis, non-smokers spent about $442 more per year

on their family’s healthcare. If they did not receive transfers, they further increased the

amount they spent on healthcare. Also, family size was positively correlated with

amount received in transfers and tobacco consumption. So, these data suggest that

certain types of people are likely to spend more on human capital. Thus, if a government

was intending to boost the incomes of the poor through some sort of program advocating

41

human capital expenditure, be it related to transfers or else training, then perhaps it

should make an effort to identify who is likely to benefit from it most.

Thus, if my first hypothesis is the case, then trying to meet the basic needs of the

poor should be the first priority. If my second hypothesis is the case, then trying to

identify “good decision makers” and reward them by boosting their human capital stock

is the way to go. It is likely that each hypothesis has some bearing, as the world is very

complex and questions like this rarely have a single answer. So, to maximize the

effectiveness of government programs, it would probably be beneficial to combine these

policy prescriptions.

iv) – Problems

There are several problems with my research. I will first start with the descriptive

statistics. I obviously was unable to conclude with certainty that family size was the

reason for varying amounts being spent on human capital. It was purely a look at

descriptive statistics, an interesting association, for which further investigation was

called.

I will restate the problems with my empirical analysis. First, I have, in my

opinion, too small a sample size to yield very meaningful results, especially with regard

to the variables of transfers received and education spending. Second, I have a very high

p-value necessary to count a sign as significant, which may have led me to incorrect

conclusions regarding the significance of some variables. Third, I ran six regressions for

which some form of human capital spending was the dependent variable, and seven

regressions in all, so a sign may have been significant when it is really not.

42

I cannot tell with certainty how much bearing my hypotheses hold. For instance, I

cite correlations in the data, but I have no way of knowing for sure what these

correlations mean. I create stories to explain what they might mean, but I have no way of

telling whether these explanations are correct, or if I am overlooking a more accurate

account. I will say that my evidence seems to support the second hypothesis, but there is

always the possibility that I am interpreting it incorrectly.

Relating to the broader question, I cannot know certainly whether the poor are

likely to spend enough on human capital to counteract the physical capital in which the

rich would invest. Furthermore, I do not know the return to the poor of human capital

versus physical capital. All I can say is that if my hypotheses are correct, then certain

types of transfers will be more likely to induce human capital investment for the poor.

v) – Opportunities

I will be the first to say that further research is necessary to come to definitive

conclusions. The first thing I would suggest is for similar regressions to be made with

more data. I feel that more variables would be significant and more meaning could be

inferred if there were more data. For instance, spending on education per person against

family size was not significant, while I would guess it should be. This suggests to me

that more data points would yield better results.

Second, it would be good to judge the return to human capital investment for the

poor. This has been done to some extent with education, but less so with healthcare. For

instance, one could look at the number of people out of work due to a preventable

disease, and then look at how much it would have cost to cure it. Then, the return for this

43

could be calculated. Admittedly, this may not calculate the effectiveness of the marginal

dollar for all healthcare spending. Also, one could look at how many days of work are

missed due to illness versus some types of healthcare spending. These days may be

unpaid, or they may stand in the way of promotion.

As for education, research has been done to determine the average return for

different sorts of investments. However, it would be useful to determine the return on

these investments given certain qualities that the poor have. Measuring the return on

these investments given changes in intelligence, for instance, would be useful. This

return given changes in family size would further be informative.

One finding of my data was that more transfers meant less healthcare spending. I

attributed this either to poor decision makers are more likely to get transfers, or else that

very desperate people are more likely to get transfers. I want to test which omitted

variable is more meaningful, if either possibility is even true. If one could find a proxy

for “desperation,” and include it in the regression, one could see if the absolute value of

the transfers received coefficient shrinks.

Finally, I have some suggestions for a larger scale. These are very broad topics,

and I do not have as specific of means to test them as I had for my previous suggestions.

As I mentioned in a previous section of this paper, one could measure how much of the

investment of the rich becomes physical capital, how much of it stays in the United

States, if the poor are the ones that actually get the jobs capital creates, or if capital even

creates jobs for the poor in the first place (if it is a substitute for labor).

Answering these questions would help to analyze the question of whether a dollar

transferred from the rich to the poor raises the incomes of the poor by more than it would

44

have if retained by the rich. The “trickle-down effect” is an important question, with

endless complexities. It is, in my opinion, time this political debate turned from rhetoric

and speculation to numbers and research.

45

Appendix – Regression Data

Regression Analysis: HC The regression equation is

HC = 2012 - 0.0049 NT Income - 0.330 Poor Trans - 31.7 FamSize - 579 Smoker?

+ 0.0072 Nec Ind

Predictor Coef SE Coef T P

Constant 2011.7 301.1 6.68 0.000

NT Income -0.00487 0.02598 -0.19 0.851

Poor Trans -0.3297 0.1417 -2.33 0.020

FamSize -31.70 94.93 -0.33 0.739

Smoker? -579.1 274.7 -2.11 0.036

Nec Ind 0.00717 0.04178 0.17 0.864

S = 2381.50 R-Sq = 3.2% R-Sq(adj) = 2.1%

Regression Analysis: Health HC The regression equation is

Health HC = 1414 + 0.0078 NT Income - 0.323 Poor Trans + 15.2 FamSize

- 442 Smoker? + 0.0431 Nec Ind

Predictor Coef SE Coef T P

Constant 1414.1 280.7 5.04 0.000

NT Income 0.00785 0.02421 0.32 0.746

Poor Trans -0.3230 0.1321 -2.44 0.015

FamSize 15.19 88.49 0.17 0.864

Smoker? -441.9 256.1 -1.73 0.085

Nec Ind 0.04307 0.03894 1.11 0.269

S = 2219.92 R-Sq = 2.8% R-Sq(adj) = 1.6%

46

Regression Analysis: Edu HC The regression equation is

Edu HC = 597 - 0.0126 NT Income - 0.0067 Poor Trans - 47.2 FamSize - 138

Smoker?

- 0.0357 Nec Ind

Predictor Coef SE Coef T P

Constant 597.1 141.9 4.21 0.000

NT Income -0.01262 0.01224 -1.03 0.303

Poor Trans -0.00671 0.06678 -0.10 0.920

FamSize -47.15 44.73 -1.05 0.292

Smoker? -137.9 129.4 -1.07 0.287

Nec Ind -0.03570 0.01969 -1.81 0.070

S = 1122.23 R-Sq = 2.3% R-Sq(adj) = 1.1%

Regression Analysis: HC/Per The regression equation is

HC/Per = 1750 + 0.0122 NT Income - 0.106 Poor Trans - 266 FamSize - 410 Smoker?

- 0.0006 Nec Ind

Predictor Coef SE Coef T P

Constant 1750.5 217.6 8.05 0.000

NT Income 0.01221 0.01877 0.65 0.516

Poor Trans -0.1055 0.1024 -1.03 0.303

FamSize -265.95 68.60 -3.88 0.000

Smoker? -410.1 198.5 -2.07 0.039

Nec Ind -0.00065 0.03019 -0.02 0.983

S = 1720.89 R-Sq = 6.6% R-Sq(adj) = 5.4%

Regression Analysis: H. HC/Per The regression equation is

H. HC/Per = 1192 + 0.0240 NT Income - 0.0966 Poor Trans - 204 FamSize

- 314 Smoker? + 0.0314 Nec Ind

Predictor Coef SE Coef T P

Constant 1192.2 186.0 6.41 0.000

NT Income 0.02395 0.01605 1.49 0.136

Poor Trans -0.09655 0.08756 -1.10 0.271

FamSize -203.73 58.65 -3.47 0.001

Smoker? -314.2 169.7 -1.85 0.065

Nec Ind 0.03137 0.02581 1.22 0.225

S = 1471.46 R-Sq = 5.8% R-Sq(adj) = 4.7%

47

Regression Analysis: E. HC/Per The regression equation is

E. HC/Per = 558 - 0.0116 NT Income - 0.0090 Poor Trans - 62.5 FamSize

- 97 Smoker? - 0.0318 Nec Ind

Predictor Coef SE Coef T P

Constant 557.7 135.9 4.10 0.000

NT Income -0.01164 0.01172 -0.99 0.321

Poor Trans -0.00897 0.06395 -0.14 0.889

FamSize -62.48 42.84 -1.46 0.145

Smoker? -96.5 124.0 -0.78 0.437

Nec Ind -0.03182 0.01885 -1.69 0.092

S = 1074.69 R-Sq = 2.3% R-Sq(adj) = 1.1%

Regression Analysis: FamSize The regression equation is

FamSize = 1.52 + 0.247 Smoker? + 0.000018 NT Income + 0.000455 Poor Trans

Predictor Coef SE Coef T P

Constant 1.5220 0.1341 11.35 0.000

Smoker? 0.2475 0.1415 1.75 0.081

NT Income 0.00001797 0.00001329 1.35 0.177

Poor Trans 0.00045520 0.00007007 6.50 0.000

S = 1.23549 R-Sq = 11.5% R-Sq(adj) = 10.8%

48

Works Cited

Aghion, Philippe and Bolton, Patrick. “A theory of Trickle-Down Growth and

Development.” The Review of Economic Studies 64 no. 2 (April 1997): 151-172.

Bender, and Green, and Campbell. “Trickle-down and Leakage and the War on

Poverty.” Growth and Change 2, no. 4 (October 1971): 24 – 41.

Castelló-Climent, Amparo and Doménech, Rafael. “Human Capital Inequality, Life

Expectancy, and Economic Growth.” The Economic Journal 118 (April 2008):

653-677.

Currie, Janet. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in

Childhood, and Human Capital Development.” Journal of Economic Literature

47:1 (2009): 87-122.

Froyen, Richard T. Macroeconomics: Theories and Policies. 9 ed. Chris Rogers.

Upper Saddle River, NJ: Pearson Education, 2009.

Hashemzadeh, Nozar and Saubert, Wayne. "The Efects of Bush's Tax Cuts on Income

Distribution and Economic Growth in the United States." Problems and

Perspectives in Management 3, (2004): 111-120.

Heinrich, Carolyn and Scholz, John Karl. “Making the Work-Based Safety Net Work

Better.” Institute for Research on Poverty, (September 6-7, 2007): 1-35.

Lan, Yuexing and Hegji, Charles. "A new look at the trickle-down effect in the united

states economy." Economics Bulletin 29, no. 3 (2009): 1743-1748.

Martinelli, César and Parker, Susan W. “Do School Subsidies Promote Human Capital

Investment among the Poor?” The Scandinavian Journal of Economics 110:2

(2008): 261-276.

Nabi, MS. “Financial Imperfections, Inequality and Capital Accumulation.” Economics

Bulliten 29, no. 3 (2009): 2388 – 2403.

Singell, Larry D. Jr. and Stone, Joe A. “The Good, the Poor and the Wealthy: Who

Responds Most to College Financial Aid?” Bulletin of Economic Research 54:4

(2002): 0307-3378.

Yang, Shu-Chun Susan. "Do Capital Income Tax Cuts Trickle Down?." National Tax

Journal LX, no. 3 (2008): 551-567.