[Maarten Buis]

I don't understand your question. You already have the combined effects, why would you want to make them again?

[Chris Boulis]

Hi Maarten Buis. Sorry of course, educp* gives the combined effects of education in couples, which is great thank you. When I run a regression using educp* Stata gives results for 1# and 2# for each category (e.g. educp2-educp7 - educp1 is the reference category). Can you clarify what educp* [1] and [2] represent?

Regarding my question in #15, I believe I am not after the combined effects again, but rather I want to add the value labels that represents the respective levels of education for each in a couple to see if these can be explained by some demographic factor (family values or a residential location). Does that make sense?

[Chris Boulis]

Actually, Maarten Buis. I'd like to check that my understanding of how to interpret the results from the regressions is correct. I've copied my results for two different model specifications to check to see if my interpretation is correct.

Using the individual education measures for partners in couples: 
Education (male partner)


M1
M2

[1] up to 12 years


ref
ref

[2] 13-14 years (Cert, Dip)


52,031***
25,470

[3] 15 + years (under/post-grad)


310,268***
176,504***

Education (female partner)





[1] up to 12 years


ref
ref

[2] 13-14 years (Cert, Dip)


95,028***
36,573*

[3] 15 + years (under/post-grad)


185,379***
102,007***

Here the benefit to individual partners given there level of education made in comparison to 'up to 12 Years of education'. As such, one could add the relevant values for a given couple. For example, in M1, if within a couple, the male had 13-14 years of educ and the female had 17-25 years of educ, then (on average) the couple would have an extra $237,410 in assets than a couple whose education was 'up to 12 years'.

The combined measure (as per #7) - entered in regression as 'i.educp* (educp1: ref group)':    
Education
			educp2=0


0
0

educp2=1


115,352***
67,898***

educp2=2


307,609***
221,578***

educp3=0


0
0

educp3=1


93,013***
56,127***

educp3=2


163,247***
87,861**

educp4=0


0
0

educp4=1


167,016***
95,708***

educp4=2


348,986***
272,192***

educp5=0


0
0

educp5=1


197,472***
100,502***

educp5=2


532,235***
368,695***

educp6=0


0
0

educp6=1


281,982***
164,605***

educp6=2


613,779***
371,909***

educp7=0


0
0

educp7=1


403,780***
244,528***

educp7=2


947,388***
592,660***

"= 0" means neither partner has this level (zero appears to be redundant). "= 1" means if one partner has this level of education. "=2" is if both partners have this level. As such, in the case when only one partner has a certain level, then this can be added to the level of the other partner. For example, in M1, a couple where one partner is educp2 ($115,352) and the other is educp5 ($197,472) would, on average, have this combined amount in assets ($312,824) above a couple of educp1 - is my interpretation correct?

The combined measure (as per #7) - entered in regression as 'educp*' (educp1: ref group):    
Education (combined couple measure)


M1
M2

educp1 (up to 11 Years)


ref
ref

educp2 (12 Years)


127,844***
83,173***

educp3 (Cert 3/Cert 4)


83,862***
45,314***

educp4 (Diploma/adv dip)


164,628***
101,340***

educp3 (Bachelor/honours)


229,637***
143,966***

educp3 (Grad dip/cert)


280,006***
160,626***

educp3 (Master/Doctorate)


425,742***
258,398***

I'm not sure how to interpret this, but think it is better to include educp* as i.educp* in the regression. Is that correct?

Stata v.15.1. Using panel data.

[Barc]

Hi Maarten Buis. I've returned to investigating the effect of a couple's level of education on assets and other financial variables. I ran your code in #7 which appears to combine the effects in creating 'educp`i' (in which you drop educp2 to create a reference group) and obtained the following output:

. xtreg asset educp*

Random-effects GLS regression Number of obs = 21,473
Group variable: id Number of groups = 8,212

R-sq: Obs per group:
within = 0.0227 min = 1
between = 0.0867 avg = 2.6
overall = 0.0746 max = 5

Wald chi2(6) = 1087.34
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

------------------------------------------------------------------------------
asset | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educp1 | -6235.733 25474.09 -0.24 0.807 -56164.04 43692.58
educp3 | 74522.69 26269.58 2.84 0.005 23035.26 126010.1
educp4 | 356952.6 32289.92 11.05 0.000 293665.5 420239.7
educp5 | 338992.3 28246.98 12.00 0.000 283629.2 394355.4
educp6 | 579156.9 36669.78 15.79 0.000 507285.4 651028.4
educp7 | 796663 37092.34 21.48 0.000 723963.4 869362.7
_cons | 609103.8 41067.81 14.83 0.000 528612.3 689595.2
-------------+----------------------------------------------------------------
sigma_u | 944832.83
sigma_e | 847984.18
rho | .55386319 (fraction of variance due to u_i)
------------------------------------------------------------------------------

The results appear to make sense - in that there appears a positive relationship between assets and education.

With regards to your comments in #10 regarding the composition of educ levels:

. tab educ1 (male partner educ)

educ1 | Freq. Percent Cum.
-------------------------------------+-----------------------------------
[1] up to 11 years (up to Year 11) | 19,841 23.08 23.08
[2] 12 years (Year 12) | 9,007 10.48 33.56
[3] 13 years (Cert 3/4) | 26,584 30.93 64.49
[4] 14 years (Diploma/Adv Dip) | 8,505 9.89 74.38
[5] 17-19 years (Bachelor/Honours) | 11,969 13.92 88.30
[6] 19-20 years (Grad dip/grad cert) | 4,871 5.67 93.97
[7] 20-25 years (Master, Doctorate) | 5,184 6.03 100.00
-------------------------------------+-----------------------------------
Total | 85,961 100.00

. tab educ2 (female partner educ)

educ2 | Freq. Percent Cum.
-------------------------------------+-----------------------------------
[1] up to 11 years (up to Year 11) | 28,232 31.66 31.66
[2] 12 years (Year 12) | 12,337 13.84 45.50
[3] 13 years (Cert 3/4) | 14,220 15.95 61.45
[4] 14 years (Diploma/Adv Dip) | 9,163 10.28 71.73
[5] 17-19 years (Bachelor/Honours) | 14,667 16.45 88.18
[6] 19-20 years (Grad dip/grad cert) | 6,363 7.14 95.32
[7] 20-25 years (Master, Doctorate) | 4,177 4.68 100.00
-------------------------------------+-----------------------------------
Total | 89,159 100.00

After further consideration of how to test the effect of each partner's level of education on asset wealth, I want to know if I can sum the level of education of both partners (by summing the value labels of educ1 and educ2), e.g. the highest level in a couple would be 14. I would then like to interact this 'new' educ variable with demographic factors, such as race or religion, etc to see if these explain education levels in couples. Do you know how I could code such a variable?

* Example generated by -dataex-. To install: ssc install dataex
clear
input float couple byte(wave educ1 educ2 values) float asset
60 18 1 1 1 37208
10753 18 2 2 1 43730
3344 18 1 1 1 18282
7962 18 1 2 1 2061050
10620 18 1 1 1 1418426
9252 18 1 1 1 452
11873 18 2 2 1 9001
373 2 3 1 2 30000
4738 10 1 1 2 225202
6489 10 2 2 2 10150
3666 10 2 2 2 55862
9129 10 1 2 2 130358
987 14 2 2 2 38182
10419 14 1 3 2 1115950
4435 14 3 2 2 970679
603 6 3 2 3 228998
1320 6 3 2 3 21700
2067 6 1 1 3 905
4340 6 2 3 3 4530
7826 10 2 2 3 772293
8200 14 2 2 3 197823
9066 14 3 1 4 13744
10648 18 2 2 4 18529
6315 10 1 5 7 98675
7492 10 1 2 7 35280
1657 10 1 1 7 33137
5016 6 1 2 8 21870
453 6 1 1 8 4030
760 14 1 3 8 7003
9880 18 2 2 8 22890
3579 10 2 2 9 32975
9524 10 2 2 9 123373
5504 14 2 3 9 2300
492 18 3 2 9 9997
7786 18 2 2 9 264234
2018 18 2 2 9 357362
11531 18 1 1 9 410774
1472 10 1 2 10 12720
435 10 2 2 10 31819
6355 10 1 1 10 451
7683 10 1 1 10 24506
2662 18 1 3 10 119371
10916 18 1 1 10 821803
8615 18 1 1 10 73488
10973 18 3 4 10 11494
1822 2 2 2 11 48650
6977 6 3 2 11 42626
499 6 2 3 11 62018
9267 10 2 5 11 9306
2507 10 3 2 11 15197
10186 14 2 3 11 2365820
10050 14 1 3 11 4020
end

Stata v15.1. I am using panel data.

Thank you so much for these recommendations! Thanks for the tips. I have been writing a personal application for admission to the residency for several days now. I turned for help to a third party because I want this document to be written in good quality and it increased my chances of admission.