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The model In a ne report, there is a finding hich states that getting married can help o

increase one s income. In this model, it ill foc s more on describing h marriage can

ha e a positi e income on earnings. The reports incl ded reasons like the presence of

positi e females, happiness at home.

Belo are the ass mptions of this empirical model are that married athletes do

make more mone than a single athlete. The statistical data is abo t 44%, e en after

controlling for ed cation, e perience, and a n mber of children. From the modeling

ass mption here are the competing ass mptions, in hich each and e er one ma be

partl tr e.

Ass mption one is h man capital. It increases male income b making men more

prod cti e orkers. There co ld be a possibilit that marriage makes athletes more

engage more in sports acti ities than the e athletes ho are not married. The increase

in prod cti it is estimated to be aro nd 44%. The second ass mption here is signaling,

married athletes ill be paid ell definitel ith a ero-ca se effect on prod cti it .

Assumption Causal Effect

on productivity

Effect on

Premium

wage

Employers

Believe

about

Productivity

Ability Bias no no No

Human Capital Yes es es

Signaling no es Yes

When sing the statistical e periment of premarital conception as a potentiall

e ogeno s ca se of marriage in the athlete comm nit .

Here ill se a regression approach of the marriage premi m since in this data set

there are a lot of ariables that can one a or the other has a ariable that co ld affect

a pla er s performance. This eq ation of the regression here ill be as follo s.

Log (EEF) = ​0​ + ​ ​1​ age ​+ ​ ​2 ​ ea p o + ​ ​3 ​ ea p o ​2 ​+ ​ ​4 ​heigh ​+ ​ ​5 ​g a d + ​ ​6 ​fo a d +

​7 ​in e na ional + ​ ​9 ​child en.

This eq ation is based on the data set belo .

As mentioned earlier, EFF is the best estimator of pla ers performance compared

to other inde es. I ha e decided t se a logarithm model other than an other model

beca se of three good reasons. The first reason that log has the ad antage of being

narro er and closer to a normal distrib tion than that of a standard distrib tion. The

second reason is simpl beca se of the efficienc as positi e al es. The third reason

here is that the real res lts are going to displa the effect in percentage. This happens

to be more realistic to the dependent and independent ariables.

In this model, married is regarded as a d mm ariable that in one a or the

other recei es a al e of 1. If in case the pla er is married, he or she is gi en a one and

a ero of the pla er is not et married. In the h pothesis, this al e ass med to be

positi e and significant. In children, it basicall means the total n mber of children the

pla er has, this ariable is ass med to be positi e.

There are other e tra ariables despite these t o important ariables. The athlete

age and ears of e perience are er important dimensions in the a the are in the

NBA leag e. Ho e er, it sho ld be kno n that s ch regression demonstrated that

b ers of e perience affects performance in man a s, both negati e and positi e

a s. The good thing is that the e pected effect of the performance of ariables can be

re ersed. In o r model regression, it is most likel to affect the performance in a positi e

a .

There is a necessit to incl de t o additional ariables that are incl ded hich are

age and ears of e perience sq ared. In the final regression eq ation, a decision as

made to incl de a sq ared ariable for ears of e perience. The independent ariable

here is height and ariables for the pla er s gro p position hich can be regarded as

d mm ariables. In the g ard and for ard positions, if incase bot of the ariable

recei e 0, the position is the center).

When it comes to originalit , international ill get a one hile Americans ill get a

0. In the anal sis, so that determination co ld occ r has a significant difference in the

marriage premi m bet een different pop lations of pla ers. Belo is an additional

regression eq ation.

Log (EEF) = ​b ​0​ + b ​1​ age ​+ ​b ​2 ​ ea p o + b ​3 ​ ea p o ​2 ​ + b ​4 ​heigh ​+ ​b ​5 ​ g a d + b ​6 ​fo a d

+ b ​7 ​in e na ional + b​8 ​ma ied + b ​9 ​child en + b​10 ​child en * in e na ional.

Data Description: Pa I

The q estion of hether there is a relationship bet een marriage and the le el of

income has become a common research q estion. Some research has sho n a

positi e relationship hile some ha e sho n no relationship bet een the t o ariables.

A research cond cted b (Bardasi & Ta lor, 2005) has associated marriage ith

increased ages for men. Ho e er, there is the q estion of- Does marriage itself

makes men more prod cti e and therefore increasing their earnings? Or, does marital

stat s affect prod cti it ? If there is no effect in prod cti it in prod cti it d e to

increase marital stat s, then the composition of the orkforce ill ha e no economic

impact on the o tp t. This research ill in estigate the presence and ca ses of the

age difference bet een married and nmarried indi id als.

The role of marriage in shaping the attit des to ards salar and general

de elopment is a s bject that req ires e tensi e research. Lack of attention to marital

stat s and other famil characteristics ma reflect a more pressing interest in research

to identif the effect of job and organi ation feat res being faced b managers to

change. St dies s ggest that there is a relationship bet een earnings beha ior and

marriage (Gorman, 2000). According to Stimpson, Wilson, & Peek, (2012), there is a

gro ing interest in ho social ties infl ence economic acti ities and age in general.

Often, marriage is associated ith some e penses that ere not there before. For

instance, a married person ill be req ired to in est in health ins rance, b a ho se for

the famil , ed cation plans, etc. This st d led to the de elopment of the follo ing

h pothesis that;

H0: There is no difference bet een the ages of indi id als ith indi id al incomes of

married people and nmarried ones.

H1: Marred indi id als earn more than indi id als that are separated, di orced,

ido ed and single indi id als.

H2: There is a significant difference bet een the indi id al incomes of married people

than the nmarried ones.

Part II

In research cond cted b Balca ar, (2019), marriage is a ell established social

instit tion. The researcher st died the relationship bet een the t o ariables to

establish hether one infl enced the other. Among other e isting e planations, there is

a grad al rise in cohabitation as a religio s belief. The infl ence of religion on marital

stat s ith regard to decisions being made b people to get married. In a st d

cond cted b Balca ar, (2019) s ggest that there is an increase in people postponing

plans to get married or marr in the arg ment that the need to be financiall stable.

While marriage is held on top of social frame orks that introd ce se al intimac , legal

nion, and gro nds for economic agreement. Ho e er, the q estion of the infl ence of

marriage on the economic frame ork has increased. St dies ha e sho n that married

co ples income s rpasses the net orth and incomes of ad lts ith similar ages that

ha e di orced, ido ed or ne er married. Thro gho t the lifespan, an indi id al s

marriage stat s helps them increase their economic ad antage. Balca ar, (2019) linked

marriage ith emotional s pport therefore likel to ha e mental here ith to make

economic ad antage (Madalo o, 2008). This is also linked ith enhancing c rrent job

sit ations as ell as increasing f t re prospects.

Ass ming that married people are able to adj st to societ d e to an increase in

social s pport, the ill ha e an honorable interaction ith the la and polic , th s

being promoted or so ght at ork compared to the nmarried ones.

Table 1: Ma iage Wage

Correlation

0.1581166

Linear Regression

Intercept

0.3303300, age 0.0000787

Econometric Model In this t pe of model, it is er rare that o r data fits e actl the e periment e test

for. In this section, e are going to anal e and form late the econometric model of the

premi m age of athletes. In this partic lar econometric model, e can ha e single and

m ltiple ariables, there is also a single eq ation ers s sim ltaneo s eq ations. In this

model, there are three main t pes of data of hich the are not necessaril m t all

e cl si e.

The first one being cross-sectional data

The second one being the panel (longit dinal) data.

Time series of data.

All of the data mentioned abo e do req ire different anal sis methods to ens re

that the econometric model is acq ired. This the econometric model ill star ith (OLS)

model hich is ordinar least sq ares ith rob st standard errors, there ill also be a

random-effects (RE) model, and lastl , a median regression model ill be introd ced.

The repetitions ill be from a t ne of abo t 150 to 200 repetitions. These res lts ill

then be compared to those that are obtained of ordinar least sq ares models ell as

RE- and MR-estimation. Despite all these fe of the coefficients remain constant.

lnPAY = 0 + 1 AGE + 2 AGE2 + 3 GPL + 4 CGP + 5 CGP2 + 6 CGP3 +

7 IAL+ 8 IAL2 + 9 IAL3 + 10 IAP + 11 IAP2 + 12 IAP3 + 13 GSL + 14 CGS +

15 CGS2 + 16 CGS3 + 17 TEN + 18 CAP + 19 FDA + 20 PD + 21 RD + 22

TD + 23 YD +

herein this scenario

Age e AGE: athlete age

GPL: N mber of appearances in NBA last season

CGP: N mber of careers appearances in NBA

IAL: International appearances last season

IAP: International appearances in career

GSL: N mber goal scored in B ndesliga

CGS: Career goals scored in NBA

CAP: Captain of the team (0 = no; 1 = es)

FDA: Pre io s team in first di ision abroad (0 = no; 1 = es)

PD: Vector of position d mmies (ref.: G ard)

RD: Vector of the region of birth d mmies (ref.: America)

To ens re there is a minimal error as possible, e did a bootstrap ith 200

replications. This is to boost efficienc .

Res lts

Concl sion

Any empirical paper should roughly follow the format outlined below.

Introduction/Motivation Here is the place to lay out explicitly:

1) The question you are trying to address (stating the hypothesis to be tested directly is a good way to do this)

2) Why we should care about this question (Is it an unproven theoretical result? An important policy question?). This is not the place to do a long literature review. If, e.g., there has been a debate in the literature about this question, just briefly describe the uncertainty. For example, you may want to point out the range of previous results.

3) What is your contribution? How are you answering the question? You should state whether you are testing a model, evaluating a program or a change in policy, and what data you are using.

4) What are your main results? Explain briefly how your findings differ from previous work and what the implications of these findings are. If your analysis is inconclusive (which is fine!) be upfront about this and very briefly state why.

Literature Review This section should basically consist of two parts (both of which should be brief).

1) The first section should discuss previous research that is directly relevant to your paper (not every single paper written on the topic). The review need not only be topical, but can include research that employs the same methods you are using, analyzes a similar model, uses the same dataset, etc.

2) The second section should explain your contribution in more detail. You should discuss how your approach is different from what has been done before: Is it new data? A new model? A new identification strategy? Are you answering a question more broadly/specifically? Specifically comparing how you are improving on a previous paper is useful. You should think creatively in this section about issues of external validity: Are your findings relevant for a population/institutional environment that is different from previous work, and could this be the reason your findings differ?

The Model In this section you want to discuss the basic behavioral, informational and institutional assumptions you are making. It is important to be explicit about which assumptions may be driving the results. You should discuss how the results are sensitive to changes in parameters.

1) Not every paper will require the development of a full theoretical model. If it is possible to explain the assumptions, the mechanisms at work and the results clearly, then a model could be redundant. Every paper, however, should include the empirical model that will tested (see below).

2) If the question you wish to address requires (or even benefits from) a formal model, you should start with the simplest model you can think of. You can complicate things later if you need to (i.e. if you find that you cannot make

any of the predictions you would like to test). Think carefully about functional form and continuous vs. discrete time (again, go for simplicity but be open about the assumptions and how they might drive the results). There are two important things to keep in mind here:

a. The model should be simple and help illustrate the question you will be testing but should not be ad hoc. That is, don’t use a two period model because it is simple and gives you the result you want, if adding one more period to the model would negate these results. The form of the model should be appropriate to the problem. The easiest way to achieve this is to build on previous models that have been used to test this or related topics.

b. In the end, you need an empirical model, so the theoretical model you develop must lead somehow to what you are testing. You will need to address how, say, the regression you are running is a linear form of an equation from your optimization problem. Be explicit about how the empirical model differs from the theoretical model, e.g. if you are unable to estimate certain parameters or if you need to assume a particular functional form.

Data Description This section should be in two parts.

1) The first should simply describe the name and source of the data you are using and the period it covers. Describe whether you have a panel, cross section or time series, what the unit of observation is and how many observations you have. Discuss limitations of the data such as missing variables, missing observations, survey response, small number of observations, etc. You may want to highlight the important limitations (e.g. those that you might address in a falsification or robustness check later) in the body of the paper and put the rest in a footnote. It is useful to think about what the ideal dataset would be for the hypothesis you want to test and compare your data to it.

2) The second section should present (relevant) descriptive statistics of the data. You should have a couple of tables with means and standard deviations for the variables you will be using in the analysis (all of the outcomes, independent variables and controls). You may want to present these descriptive statistics for different subgroups (e.g. treatment vs. control; attriters vs. non-attriters; pre vs. post, etc.). The names of the variables should be clear to the reader.

Econometric Model You should write out the basic econometric specification first and explain each of the variables and the parameters of interest. Why is this the correct specification for the question you wish to address? Was it derived from theory and has it been used in previous empirical work? Why are certain variables included and others not? Discuss whether you are using basic OLS, IV, etc. and why this is appropriate. You should be very clear about where identification is coming from and what assumptions you need to make in order to interpret the parameters as you wish to

interpret them (e.g. discussing exclusion restrictions if you wish to interpret certain parameters as causal). After discussing the basic specification, write out any elaborations or additional tests you will perform and why.

Results Here are some basic things to guide you in presenting your results:

1) You should present results in a way that develops your argument step-by-step. For example, you may want to present your main results first, then break those results down by subgroups and then perform robustness checks.

2) Any tables with parameter estimates should clearly state which dependent variable you are using, which control variables are included and which specification you are testing. Just discuss the most interesting and important estimates in your discussion of the table. Make sure you report standard errors with your estimates. Just look at some economics journals for a good table format.

3) Interpret the magnitude of your parameter estimates in an economically meaningful way. For example: “we find that b=0.003, so that increasing X by one unit increases y by 0.003. The implied elasticity is…”. This is particularly important if you are not estimating a simple OLS regression. And even with OLS it is useful, especially when the magnitudes of the variables are not immediately apparent, for example when x is in logs.

4) Make sure you give your parameters the smell test. Are they a reasonable sign and magnitude?

5) Graphs are worth a thousand words. Think about the most illustrative way of presenting the results in a graph…this is a very convincing way to show your reader that you have found something real.

6) Discuss whether the parameter estimates are statistically significant. If you don’t get significance, why? Do you have enough data? Is your test strong enough to detect effects below a certain magnitude (power tests are great for this sort of thing)? Are the results still suggestive even if they are not estimated precisely?

7) Compare your results to what others have found. You don’t need to worry if you don’t find anything significant as long as your methods are sound and you have interpreted the results well. Discuss why your results may differ from past research.

Conclusion Summarize your findings and point out limitations of the results and possible extensions. This is a good place to speculate in a more casual manner about the implications of your results. In general, the conclusion should not contain any new results.

Other General Comments o Substantiate general claims that are not proved in the paper either by providing

relevant data or citing studies o Make an effort to write concisely and to the point (try to keep the text under 25

pages). Try to make the paper as short as possible and not to repeat arguments. Digressions and marginal comments belong in footnotes and not in the main text.

o Long proofs, details on data collection and other secondary documents belong in the appendices of the paper.

o It is not important whether or not you find “find results”. Rather, what is important is that you are as careful as possible in your methods so you can be confident about your conclusions. Problems you encounter should not be dismissed, hidden, etc. but rather you should try to address them. If you cannot address them than you should acknowledge the limitations of your approach, perhaps in the discussion section.

o You should document what you do. In principle, another person should be able to read your paper and be able to reproduce your results. Some of these descriptions may belong in the appendix if they are very long.

o Before you write, try to make an outline of the arguments and results that you want to present.

o Remember that it is an economics thesis so it is important that you try to discuss your question and your results in that context.

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