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Running Head: ENDOGENOUS PREDICTORS OF LEISURE-TIME PHYSICAL1

Endogenous Predictors of Leisure-Time Physical Activity and Okun’s Law

Johnny Wright

Southern New Hampshire University

ENDOGENOUS PREDICTORS OF LEISURE-TIME PHYSICAL ACTIVITY2

Abstract

Physical activity is a necessity to the health and welfare of not only people, but entire nations

aggregately. The main failing is the evidence of public choice in the matter, when it comes to

engaging in leisure-time physical activity. The U.S. Department of Health and Human Services

is considering an initiative to increase leisure physical activity for its health and economic

benefits. The paper researches the endogenous determinants of physical activity as it relates to a

proper diet, unemployment, and socioeconomic status, more specifically, race. Models are

developed that adjust for race. To further test the predictor variables, Okun’s law, traditionally

used to measure economic growth, is used to get a better fit of the data to the model. Empirical

testing found that Hispanics were the least likely to engage in leisure time physical activity due

to their lack of participation in the market, indicated by their per-capita GDP. The Black/African

American population, while ranking high in meeting federal LPTA standards, showed the highest

unemployment, a variable highly correlated with LPTA. The Asian population ranked the lowest

in the consumption of farm produced foods, a variable both the unadjusted and Okun’s law

predicted models show is correlated to LPTA. The goal of the paper is to identify opportunities

for business that the U.S. Department of Health and Human Services can use to increase LPTA

and add economic value aggregately.


Endogenous Predictors of Leisure-Time Physical Activity and Okun’s Law

Health care is a topic of much debate in the United States of America. Citizens and

politicians alike constantly discuss the appropriate level and type of focus that is needed to

ensure the long-term well-being of the nation. Some citizens believe that it is the government’s

job to provide efficiency in the market for quality of life; while politicians and government

organizations, such as the U.S. Department of Health and Human Services, believe that the

people must take an active role prolonging their health. This causes a disjunction between the

role of the people and the role of the government as it pertains to this issue and how that

responsibility should be distributed between the public and private sectors. Included in the core

responsibilities is the appropriate behavior of the citizens. The role of the government is to help

encourage its citizens to produce in the long-term and correct for any problems that arise when

inefficiency in production occurs (De Jasay, 2006). This correction does not imply a continuous

subsidy, but a mutually beneficial principle-agent scenario for both parties (“Principal-Agent

Problem Definition,” 2010).

Introduction

Taking an unbiased view of the government, push policies that are made are ultimately

for the betterment of the whole. Federal leaders see that, over time, accounting for those in the

country that engage in myopic health behavior (i.e. consumption of unhealthy food, inactivity,

tobacco smoking) causes externalities not specific to just the person engaging in the behavior.

Externalities to correct include prescription costs, unintended myopic cognitive behavior transfer

to other citizens, and overall decreased level of perceived national health. Proof of this can be

seen in the American obesity rate, which says that 66% of Americans are likely to be considered

overweight or obese, by standards of body mass index (“Overweight and Obesity Statistics,”


2012). Cigarette smoking, which has a myriad of externalities in addition to its own by-products,

is another public health failure that government expenditures are expected to account for. It is

estimated that 18% of legal age citizens smoke cigarettes in the United States (“Current Cigarette

Smoking Among Adults in the United States,” 2015). Behavior and realities such as these are the

reason why the government places such high importance on citizens doing their duty to keep

themselves healthy as much as possible. Encouraging this behavior can cost citizens tax money

and lead to health problems for those not involved in the activity. In order to maximize

consumption on merit goods with respect to aggregate physical health, it is important to keep in

mind the constraint of public choice (“Merit Goods,” 2015).

Using the goal of merit good maximization constrained to public choice, a solution to

encourage the consumption of goods that promote health can be formed. One way this is done is

through initiatives to educate citizens to decrease consumption of demerit goods (i.e. cigarettes

and unhealthy food). The U.S. Department of Health and Human Services should consider the

consumption of goods that are related to leisure-time physical activity (LPTA). LPTA discounts

physical activity related to every day necessities, such as that needed for job functions. Its goal is

to decrease the amount of time used on unproductive, myopic behavior, in favor for physical

activity. As of 2013, employed Americans use almost 3 times the amount of hours engaging in

relaxation and television watching than participating in a physical or sports activity (“Charts by

Topic: Leisure and sports activities,” 2014). The results of the survey show that Americans need

activities that either decrease consumption of television watching or increase production of

leisure-time physical activities. Considering that employment was a qualifier for those statistics,

a hypothesis can be formed that looks into how employment and other economic indicators

impact physical activity consumption


Literature Review

Many studies have been done on the correlation between economic indicators and

physical activity. The data is usually categorized further into different factors that can contribute

to aggregate physical activity, such as demographics, income and work performance, to evaluate

the link between the response variable (physical activity) and the predictors.

Physical Activity and Personal Health

Assuming that age reduces the type of activity that one can perform, Arvidson et al.

(2013) created a model to predict work ability based on reported LPTA. Using a sample size of

2783, the study found a direct correlation between work ability and physical activity. Those who

scored lower on the work ability index (WAI) were reported to have lower amounts of leisure

time physical activity. They also found that physical activity can be used as a predictive tool for

future performance as it demonstrates the employee’s balanced lifestyle. This information is

important to businesses looking to increase employee morale and corporate culture unity due to

common interests.

Since a balanced lifestyle was correlated with better work ability, past data can be used to

predict future performance. Casual observation suggests that people learn their lifestyle habits at

an early age, usually by adolescence. Wichstrøm, Soest, & Kvalem, (2012) evaluated the

difference between adolescent and adult activity. A 3-session survey was conducted on a sample

of 3251 kids in Norway collected information on their physical activity and amount of activity

during their adolescent years and tracked them to adulthood. They found that those who were

associated with sports clubs or had a positive image of physical activity throughout childhood

continued on with leisure physical activities into adulthood. Those with characteristics that

suppressed the activity in childhood were seen as having lower levels of leisure activity. The


information here seems to indicate a cognitive aspect to leisure time activity, usually measured

by education.

Consumer Confidence and Public Health. In addition to ability to perform at work and

precognition, aggregate unemployment can be used to show public health. Janlert (2009) studied

the effects of unemployment on aggregate public health. Using mental health as response, Janlert

found that economic fluctuations influenced the number of people treated for mental issues. In

addition to the correlation, the data showed that recessions correlated with negative mental health

and increases in psychiatric patients, while expansions showed the opposite effect. Since market

activity (Gross Domestic Product or GDP) is a reflection of consumer confidence, lower

confidence is associated with a negative impact on overall health (Shields & Shooshtari, 2001).

This finding indicates that the wealth and substitution effects are present and highlights the need

for an understanding of the principle-agent problem between citizen and government.

While the indicators do influence aggregate health, decisions on how to handle the

problem should not be solely linked to them for short-term results. Ashraf, Lester, & Weil (2008)

studied the subsequent impact on GDP before and after the African malaria outbreak in 2005 to

determine whether or not sickness influenced the direction of GDP. This was done by evaluating

macroeconomic indicators (such as per capita income) in relation to overall continental health.

There was a claim in Africa that since malaria broke out, the continent’s GDP decreased by 1.3%

in 2005, prompting claims that GDP would have been higher if not for the malaria outbreak. The

null hypothesis that sickness that sickness correlated with health was upheld due to short-term

GDP on the continent showing no affect after the outbreak; however, an effect on long-term

GDP and per capita income was found. It gives reason to infer that health improvement projects


are best used as humanitarian and not economic arguments; however, economic indicators

should be used a guide for policy creation.

Demographic and food consumption information as a Health Predictor. Public and

private health is influenced by cognitive development as well a consumer confidence. Cognitive

development is related to life-long production of LPTA. Self-esteem, indicated by market

participation, can be seen by the types of goods and services being sold as well. Both related

predictors can be traced back to food consumption choices based on demographics. Observation

suggests that physical activity and weight-loss is related to pre-existing eating patterns and those

successful in healthy style changes accompany that with increased physical activity. There is also

reason to believe that citizen profile has a role to play in individual quality of health, with the

summation of those individuals showing the aggregate quality. Brennan and Singh (2012)

studied the effects of socioeconomic indicators and self-reported health statistics on general

health that accounted for dietary intake. The study looked at a sample of 444 citizens of

Adelaide, Australia, via survey to collect data on their gender, age, socioeconomic status, daily

diet, and oral problems. The results found a correlation among socioeconomic status, daily

dietary intake, and general health status. Gallant and Dorn (2000) found lifestyle choices varied

based on demographics. The research sampled 1266 older adults to collect information on

preventative behaviors as a function of baseline characteristics and found that preventative

methods varied by health behavior, race and gender. It gives reason to infer that not only

economic indicators (Shields & Shooshtari, 2001) as well as categorical data, such as race having

an influence on general health, thus causing LPTA to respond accordingly.

Costs associated with increasing LPTA. It is known that a poor diet can lead to poor

health. A good diet has a cost that some citizens cannot afford (Brennan and Singh, 2012).


Because of this knowledge, there exists a skew in the types of foods being consumed to favor

that which gives citizens more utility in the short-run, based on cost and income. Using a cost

benefit analysis, the substitution effect would be more powerful of an influence as citizens want

the most amount of health per dollar (in other words, to be healthy) at the lowest cost and

producers want to provide it at the highest cost (Adetunji, 2009). A market failure in public

health is now created (Normad, 1991), influencing all areas of public choice, including the

choice to engage in leisure-time physical activity.

Data Collection and Descriptive Statistics

The increase in presence of alternative cheaper alternative food choices as well as the

rising costs of farm-produced food give reason to suspect that activity may be linked to the

consumption of those products. In addition to high food prices, consumer confidence in the

aggregate market, indicated by per capita GDP, and unemployment was evaluated as standard

economic indicators. 10 yearly observations across the 4 major ethic groups in the United States

were used to test the 3 different predictors against the response. The objective is to test whether

or not the prevailing factors found in the research are endogenous to activity in order to assist the

Department of Health and Human Service identify opportunities at the state and national levels

to increase participation in leisure time physical activity.

To empirically test how physical activity responds to the important predictors, data was

collected from the Center for Disease Control (CDC) regarding meaningful participation in

LPTA across the four major racial groups in the United States since 2003. The model looks at

how reported physical activity that meets federal standards relates to variables such as race,

unemployment, and farm-produced food consumption as a percentage of per-capita GDP. The

data collected was time organized and captures specific data across racial groups. As discussed


in the research (Janlert, 2009; Shields & Shooshtari, 2001; and Brennan and Singh, 2012), a

hypothesis of the response in LPTA would correlate positively with the three indicator variables.

In addition to the data collected on farm-produced food consumption, an Okun’s law relationship

was used against each unemployment rate. Okun’s law, which equates the difference of potential

and actual market activity to the change in unemployment against the natural rate of

unemployment, lagged 2%, was used to test the predicted farm-produced food consumption

correlation against its non-adjusted counterpart. Mathematically, Okun’s law is expressed as:

2 (u−un )=∆ YY

where u represents unemployment, un represents the natural rate of unemployment (given as

mean unemployment for the 2003-2012 decade), Y represents potential GDP, and ΔY

representing the difference between the actual activity and potential activity (GDP). It is

commonly used to forecast economic growth in its base form (Fladlein, 2009). To test correlation

with respect to farm produced food consumption and LPTA, Okun’s law will be rearranged as

follows:

Y=−Y a

(2u−2un−1)

Appendix A shows descriptive statistics of each variable across the 4 different races

across the 2003-2012 decade. The mean percentage of people who reported meeting federal

standards across all four major racial groups is 15.2%. The lowest reported percentage that

contributed to the mean came from the Hispanic/Latino population at 12.11%. Appendix B

shows the frequency distribution of LPTA meeting standards. The White/Caucasian population

had the highest volatility through the years, though its data yielded a more uniform population of

scores. With n = 10, none of the groups fit a normal distribution model. Appendices C and D


show normal distribution fits of farm-produced food consumption and unemployment,

respectively. The white/Caucasian population showed the second lowest mean unemployment

rate and the highest amount of farm-produced food consumption expenditures, at over 9 times

aggregate per-capita GDP. The Asian population, with the lowest unemployment rate (almost

equal to the natural rate for the decade), corresponds with the 3rd highest frequency of people

reporting meeting federal LPTA standards and the lowest expenditures on farm-produced food.

Analysis and Results

Due to limitations in available data for years prior to 2003 and post 2012, two ordinary

least sum of squares models (OLS) were used: one model uses the unadjusted consumption of

farm-produced food to get a base understanding of how the correlates with LPTA reporting; the

second model uses Okun’s law-adjusted consumption. In both cases, the generalized model and

race-adjusted models are considered. Estimates for LPTA reporting are presented in both models

and assumed the following hypothesis:

H0: β1= β2= β3=0; besides what has already been researched, there is no additional

correlation between the chosen predictors;

H1: At least one of the predictors (β1, β2, or β3) is not zero, indicating further correlation

under the assumption that rejection of the null hypothesis means that the variables are

endogenous to each other.

Model 1: Unadjusted consumption and LPTA

A multiple regression on Minitab revealed that while the data did not perfectly fit the

normal distribution curve, each variable was statistically significant at the 95% level. In the

unadjusted consumption model, each of the 3 predictor variables explained almost 80% of the

model. As expected, unemployment contributed largely to the fit of the model; however, farm-


produced food consumption added a small amount of fit of the model. The generalized race

unadjusted model in this case yielded the following regression equation:

0.82+0.000365 X 1+0.3487 X2+68.01 X3

where X1 is per-capita GDP, X2 is farm-produced food expenditures as a percentage of per-capita

GDP, and X3 is the unemployment rate per race. The generalized model shows that LPTA

heavily responded to unemployment and responded weakly to per-capita GDP. Farm-produced

food expenditures showed a weakly positive correlation, but one above per capita income. The

model predicts that a 1% rise in farm-produced food consumption would prompt a .35%

response in meeting LPTA standards. Due to the R2 value of almost 90%, the model is said to fit

data well, assuming a 95% level of confidence. The model was significant at the 95% level.

Because at least one correlation was larger than zero, the null hypothesis is rejected, favoring the

alternative hypothesis that at our predictor variables correlate with LPTA.

Appendix E shows the race-adjusted models of unadjusted consumption. Each equation

represents the four major racial groups. These models suggest that the likelihood of meeting

LPTA standards, given by the intercept of the equation, does change per race and is impacted by

each of the predictor variables. In this model, while no one predictor stands out as a leading

cause, it does show that the activities of each variable having a significant correlation to LPTA.

Model 2: Okun’s Law Adjusted Consumption. Studied in the first model, farm-

produced food expenditures did have a significant correlation in both race unadjusted and

adjusted models, since the coefficient for the variable is greater than zero. Using Okun’s law

predictions with respect to food expenditures, each race was predicted to have 20% more

consumption on farm-produced food on average. The largest difference came from the

Black/African American population, which had a 35% difference in its predicted value from its


actual value. The generalized (race unadjusted) prediction model using adjusted consumption

yields the following equation:

−0.60+0.000363 X1+0.3437 X2+65.32 X3

where X1, X2, and X3 retain the same identification as in Model 1. In this case, farm-produced

food still retained its significance, but differed by 1%. Each of the other predictors varied slightly

from the consumption unadjusted models. One key difference between the two is the equation

intercepts. The Okun’s Law adjusted model shows that, under ceteris paribus, LPTA is

inherently down by .6%, as opposed to up .8% in model 1. Shown in Appendix G, while

correlation strength was traded when using Okun’s Law, the model did achieve a greater fit than

its unadjusted counterpart, thus explaining more of the variation in the response of LPTA.

Adjusting this model for race, the predictor variables for each race decreased slightly.

Similarly to the race unadjusted model, the race adjusted model under Okun’s Law achieved a

larger amount of fit than the consumption unadjusted race adjusted model. It shows that Okun’s

Law can accurately predict the growth or decline of LPTA given inputs for unemployment and

specific expenditures, showing a higher likelihood across the four races for engaging in and

meeting LPTA minimum standards. Appendix H shows the model equations using Okun’s law

consumption and race adjustment.

Summary, Conclusion, and Future Study

Identified in the research, the prevailing factors that help determine engagement in LPTA

are unemployment, diet, and socioeconomic status. Using data on each variable, statistical

significance was found on each predictor identified. Market demand for the health, determined

by the summation of the individual demands, can then be used by the U.S. Department of Health

and Human Services to create programs that effectively reduce market failures in this area. One


major finding is that race does have an impact on reported LPTA, in particular, for the

Hispanic/Latino population which reported the lowest likelihood to engage in physical activity.

Coupled with their low ranking in per-capita GDP, states that have large populations of

Hispanics would be better served to encourage participation of physical activity through

education. Indicated by Arvidson et al. (2013), stimulating a balanced life outside of work for

them would be crucial to increasing their expenditures in the market, and, ultimately, their

LPTA.

While the results aligned with expectations based on the research and intuitive by nature,

there are three findings that could be considered for future study. First, based on predicted

models, it can be statistically seen that farm-produced foods is endogenous with physical

activity. A way this can be further researched is to look at the consumer price index for food

products over the years. Due to the limitation of available data for physical activity, a more

robust model will be difficult to create; however, changing food prices only strengthens the

substitution effect on consumer choices. It is possible that food prices have a larger correlation

with activity and can be looked into. If that is the case, business and nonprofits specializing in

community outreach can focus their efforts into emphasizing the positive view of diet and

exercise, as opposed to the normative view. Based on the significance of the data at the 95%

level, the normative view will not influence public choice as it is merely an opinion rather than

fact. Second, Okun’s law can be used as a way to forecast leisure time physical activity. By only

capturing the specific consumption to study and using an unadjusted race model, a higher degree

of confidence was attained. Firms that specialize in physical activity would be best served to

open in areas that feature the Hispanic population. Also, firms that specialize in farm-produced

foods should consider partnering with Asian restaurants and predominantly Asian areas. Their


mean consumption on proper food was only half of their per-capita consumption, compared to

double for the Black and Hispanic populations and almost 9 times for the White/Caucasian

population. Lastly, unemployment has a large significance in leisure-time physical activity. The

highest unemployment rate was seen in the Black/African American populations. This is an area

where gyms and fitness services could take advantage of as it would serve the purpose of

reducing unemployment for this group as well as increase aggregate LPTA. Areas that have a

significantly large population of African American unemployment would benefit most from this

due to being endogenous to physical activity.


References

Adetunji, H. (2009). Economics of health. In Key concepts in public health. London, United

Kingdom: Sage UK

Arvidson, E., Börjesson, M., Ahlborg, G., Lindegård, A., & Jonsdottir, I. (2013). The level of

leisure time physical activity is associated with work ability-a cross sectional and

prospective study of health care workers. BMC Public Health, (13), 855-855.

Ashraf, Q., Lester, A., & Weil, D. (2008). When Does Improving Health Raise GDP? NBER

Macroeconomics Annual, (144), 157-204.

Brennan, D. S., & Singh, K. A. (2012). Dietary, self-reported oral health and socio-demographic

predictors of general health status among older adults. The Journal of Nutrition, Health

& Aging, 16(5), 437-41. doi:http://dx.doi.org/10.1007/s12603-012-0006-3

Charts by Topic: Leisure and sports activities. (2014, September 30). Retrieved April 15, 2015,

from http://www.bls.gov/TUS/CHARTS/LEISURE.HTM

Consumer Expenditure Survey - U.S. Bureau of Labor Statistics. (2015, April 2). Retrieved April

19, 2015, from http://www.bls.gov/cex/#data

Current Cigarette Smoking Among Adults in the United States. (2015, January 23). Retrieved

April 15, 2015, from http://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/

cig_smoking/

De Jasay, A. (2006, October 2). The Failure of Market Failure. Part I. The Problem of Contract

Enforcement. Retrieved April 15, 2015, from

http://econlib.org/library/Columns/y2006/Jasayfailure.html

Fladlein, M. (2009, June 17). Mikeroeconomics. Retrieved April 18, 2015, from

http://mikeroeconomics.blogspot.com/2009/06/growth-rate-form-of-okuns-law.html

http://mikeroeconomics.blogspot.com/2009/06/growth-rate-form-of-okuns-law.html

http://econlib.org/library/Columns/y2006/Jasayfailure.html

http://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/

http://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/

http://www.bls.gov/cex/#data

http://www.bls.gov/TUS/CHARTS/LEISURE.HTM


Janlert, U. (2009). Economic crisis, unemployment and public health. Scandinavian Journal of

Public Health, (37), 783-784.

Merit Goods. (2015, January 1). Retrieved April 15, 2015, from

http://www.economicsonline.co.uk/Market_failures/Merit_goods.html

Normand, C. (1991). Economics, health, and the economics of health. BMJ, 303(6817), 1572-

1577.

Overweight and Obesity Statistics. (2012, October 1). Retrieved April 15, 2015, from

http://www.niddk.nih.gov/health-information/health-statistics/Pages/overweight-obesity-

statistics.aspx

Principal-Agent Problem Definition | Investopedia. (2010, November 29). Retrieved April 16,

2015, from http://www.investopedia.com/terms/p/principal-agent-problem.asp

Shields, M., & Shooshtari, S. (2001). Determinants of self-perceived health. Health Reports,

13(1), 35-52. Retrieved from

http://ezproxy.snhu.edu/login?url=http://search.proquest.com/docview/207491544?

accountid=3783

Table A-2. Employment status of the civilian population by race, sex, and age - Bureau of Labor

Statistics. (2015, April 3). Retrieved April 19, 2015, from

http://www.bls.gov/news.release/empsit.t02.htmhttp://www.bls.gov/news.release/

empsit.t02.htm

Table 68. Participation in leisure-time aerobic and muscle-strengthening activities that meet the

federal 2008 Physical Activity Guidelines for Americans among adults aged 18 and over,

by selected characteristics: United States, selected years 1998-2012 - Centers for Disease

http://www.bls.gov/news.release/empsit.t02.htmhttp://www.bls.gov/news.release/empsit.t02.htm

http://www.bls.gov/news.release/empsit.t02.htmhttp://www.bls.gov/news.release/empsit.t02.htm

http://ezproxy.snhu.edu/login?url=http://search.proquest.com/docview/207491544?accountid=3783

http://ezproxy.snhu.edu/login?url=http://search.proquest.com/docview/207491544?accountid=3783

http://www.investopedia.com/terms/p/principal-agent-problem.asp

http://www.niddk.nih.gov/health-information/health-statistics/Pages/overweight-obesity-statistics.aspx

http://www.niddk.nih.gov/health-information/health-statistics/Pages/overweight-obesity-statistics.aspx

http://www.economicsonline.co.uk/Market_failures/Merit_goods.html


Control and Prevention. (2015, January 6). Retrieved April 19, 2015, from

http://www.cdc.gov/nchs/hus/contents2013.htm#068

Wichstrøm, L., Soest, T., & Kvalem, I. (2012). Predictors of growth and decline in leisure time

physical activity from adolescence to adulthood. Health Psychology, 775-784

http://www.cdc.gov/nchs/hus/contents2013.htm#068


Column1Appendix A:

Descriptive StatisticsColumn

2Column

3

Variable Race Mean StDev

Dependent VariablePercent meeting Federal LPTA minimum

Asian 14.81 2.007Black/African American 14.98 2.225Hispanic/Latino 12.11 2.286White 18.92 2.051

Explanatory Variables

Farm-Prod Food ConsumptionAsian 0.5251 0.1229Black/African American 2.1036 0.2308Hispanic/Latino 2.977 0.593White 9.332 0.841

Farm-Prod Food Consumption (Okun's Prediction)

Asian 0.5301 0.1251Black/African American 2.458 0.423Hispanic/Latino 3.235 0.805White 9.541 1.149

UnemploymentAsian 0.05534 0.00397Black/African American 0.11882 0.02911Hispanic/Latino 0.08547 0.02787White 0.0601 0.01871

Per Capita GDPAsian 31120 1478Black/African American 19508 552Hispanic/Latino 16889 640White 34111 553


24211815129

40

30

20

10

0

24211815129

40

30

20

10

0

Mean 14.81StDev 2.007N 10

Asian


Black/African American


Hispanic/Latino


White

Asian

Percent meeting Leisure time PA

Perc

ent


Hispanic/Latino White

Normal

Panel variable: Race

Appendix B: Frequency Distribution of LPTA reporting by Race

100

0

100

0

Mean 0.5251StDev 0.1229N 10

Asian

Mean 2.104StDev 0.2308N 10


Mean 2.977StDev 0.5927N 10

Hispanic/Latino

Mean 9.332StDev 0.8414N 10

White

Asian

Farm-Prod Food Consumption (GDP

Perc

ent



Normal


Appendix C: Distribution of Farm-Produced Food


18.00%15.00%12.00%9.00%6.00%3.00%

100

75

50

25

0

18.00%15.00%12.00%9.00%6.00%3.00%

100

75

50

25

0

Mean 0.05534StDev 0.003968N 10

Asian

Mean 0.1188StDev 0.02911N 10


Mean 0.08547StDev 0.02787N 10

Hispanic/Latino

Mean 0.0601StDev 0.01871N 10

White

Asian

Unemployment

Perc

ent



Normal


Appendix D: Distribution of Unemployment

Percent mee = -0.82 + 0.000365 X1 + 0.3487 X2 + 68.01 X3

Step Change Step P Final P

3

2

1

Add X3

Add X1

Add X2

0.000

0.011

0.000

0.000

0.000

0.000

1007550250

R-Squared(adjusted) %

Unemployment

Farm-Prod Fo

Per Capita I

3020100

Increase in R-Squared %

Unemployment

Farm-Prod Fo

Per Capita I

100500

R-Squared %

X1: Per Capita I X2: Farm-Prod Fo X3: Unemployment

Final Model Equation

Model Building SequenceDisplays the order in which terms were added or removed.

Incremental Impact of X VariablesLong bars represent Xs that contribute the most new

information to the model.

Each X Regressed on All Other XsGray bars represent Xs that do not help explain

additional variation in Y.

A gray bar represents an X variable not in the model.

Appendix E: Unadjusted Consumption Model (race unadjusted)Model Building Report


Asian Percent meeting = 35.88 - 0.000764 X1 + 1.518 X2 + 34.6 X3

Black/African Americ Percent meeting = 22.58 - 0.000764 X1 + 1.518 X2 + 34.6 X3

Hispanic/Latino Percent meeting = 17.54 - 0.000764 X1 + 1.518 X2 + 34.6 X3

White Percent meeting = 28.74 - 0.000764 X1 + 1.518 X2 + 34.6 X3

Race

X1: Per Capita I X2: Farm-Prod Fo X3: Unemployment X4: Race

Final Equations

Appendix F: Unadjusted Consumption Model (race adjusted)Model Equations Report

statistically significant (p < 0.10).The relationship between Y and the X variables in the model is

Yes No

0 0.1 > 0.5

P < 0.001

model.79.63% of the variation in Y can be explained by the regression

Low High

0% 100%

R-sq = 79.63%

range of values for Percent meet.settings for the X variables that correspond to a desired value orPercent meet for specific values of the X variables, or find theIf the model fits the data well, this equation can be used to predict X3: UnemploymentX2: Okuns law on farm prod food conX1: Per Capita Incomerelationship between Y and the X variables:The following terms are in the fitted equation that models the

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Per Capita I Okuns law on Unemployment

Is there a relationship between Y and the X variables? Comments

Percent meet vs X Variables

not in the model.represents an X variableA gray background

Appendix G: Okun's Adjusted Model (race unadjusted)Summary Report

% of variation explained by the model


Asian Percent meeting = 36.86 - 0.000776 X1 + 1.264 X2 + 25.5 X3

Black/African Americ Percent meeting = 23.97 - 0.000776 X1 + 1.264 X2 + 25.5 X3

Hispanic/Latino Percent meeting = 18.94 - 0.000776 X1 + 1.264 X2 + 25.5 X3

White Percent meeting = 31.78 - 0.000776 X1 + 1.264 X2 + 25.5 X3

Race

X1: Per Capita I X2: Okuns law on X3: Unemployment X4: Race

Final Equations

Appendix H: Okun's Law Consumption (race adjusted)Model Equations Report

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