I. INTRODUCTION

This page includes all the Compilations of the generosity comparisons for the 124 colleges analyzed in this website for the current academic year. My Archive page includes my Compilations for the six previous years.

I outlined my methodology on the previous page, but you should be aware of five important facts now:

A. All of these examples are for comparison purposes only. The input data for our sample families will not exactly match your family's economic data even if you have that income, so your Net Price Calculator results will not be the same as the results shown. As always, do your own data to get truly accurate results for your family.

B. The standard of error for NPC's is 3%, or about $1,500 per year. This means that you should think of schools with results that are within that standard of error as equally generous.

C. Think of these Compilations as "forty-nine state" versions, where all Tuition and Fee data are for non-resident applicants. So, these examples are as accurate as I can make them for schools located in all the states of the Union EXCEPT YOUR HOME STATE. The results for publicly-funded schools - and sometimes some private schools - in your home state will probably be much better than shown, and I give some glaring examples below.

D. Unless merit-based aid is noted in the "Total Grants" column, all aid shown is need-based. This means that those colleges assume all admitted students to be "equally meritorious", and all aid is based on financial need only.

E. Remember that student loans aren't financial aid. They're a way to defer the payment of a present cost to a later time while paying an interest fee for that privilege. So, no loans are shown as aid anywhere in the American College Generosity website, and the Total Grants shown in these Compilations are absolutely loan-free.

Finally, the $60K Compilations are based on a family with an Adjusted Gross Income of $60,000 per year, about the same as the current median household income of the United States (where the District of Columbia and twenty states have median household incomes above $60,000 per year, and thirty states have MHIs below $60K/year). And the $40K Compilations are based on a family with an Adjusted Gross Income of $40,000 per year, about the same as the median household incomes for a very large number of American cities.

This page includes all the Compilations of the generosity comparisons for the 124 colleges analyzed in this website for the current academic year. My Archive page includes my Compilations for the six previous years.

I outlined my methodology on the previous page, but you should be aware of five important facts now:

A. All of these examples are for comparison purposes only. The input data for our sample families will not exactly match your family's economic data even if you have that income, so your Net Price Calculator results will not be the same as the results shown. As always, do your own data to get truly accurate results for your family.

B. The standard of error for NPC's is 3%, or about $1,500 per year. This means that you should think of schools with results that are within that standard of error as equally generous.

C. Think of these Compilations as "forty-nine state" versions, where all Tuition and Fee data are for non-resident applicants. So, these examples are as accurate as I can make them for schools located in all the states of the Union EXCEPT YOUR HOME STATE. The results for publicly-funded schools - and sometimes some private schools - in your home state will probably be much better than shown, and I give some glaring examples below.

D. Unless merit-based aid is noted in the "Total Grants" column, all aid shown is need-based. This means that those colleges assume all admitted students to be "equally meritorious", and all aid is based on financial need only.

E. Remember that student loans aren't financial aid. They're a way to defer the payment of a present cost to a later time while paying an interest fee for that privilege. So, no loans are shown as aid anywhere in the American College Generosity website, and the Total Grants shown in these Compilations are absolutely loan-free.

Finally, the $60K Compilations are based on a family with an Adjusted Gross Income of $60,000 per year, about the same as the current median household income of the United States (where the District of Columbia and twenty states have median household incomes above $60,000 per year, and thirty states have MHIs below $60K/year). And the $40K Compilations are based on a family with an Adjusted Gross Income of $40,000 per year, about the same as the median household incomes for a very large number of American cities.

II. ACG 2019/2020 COMPILATIONS

These are my Compilations for the 2019/2020 school year. You will see that, as in the three previous years, there are two types of Compilations this year. In both types, the name of the school is in the left-hand column, and what you could call the "target value" is in the right-hand column. In the first type, the target value is the Stabilized Net Cost, the leftover amount for Mom and Dad to pay or for the student and parents to borrow. While in the next type of Compilation, the target value is the Expected Annual Loans required at each of those schools this year.

These 124 schools represent only 5% of the 2500 four-year colleges in America. Also, these Compilations only include cost and aid data as established by the colleges' own Net Price Calculator Programs for my sample families - not your family - and the data are for non-resident or out-of-state students only. The improvement for in-state students at publicly funded in-state schools - and at private ones in some states - is typically huge. As examples, the Stabilized Net Cost for California residents at the $60K income level at UCLA is about $5,000 - or one seventh the amount for out-of-staters, and the Stabilized Net Cost for Washington residents at that income level at the University of Washington is about the same $5,000 - or one eighth the amount for out-of-staters. So, the in-state Stabilized Net Costs of $5,000 for California kids at UCLA and Washington kids at the U-Dub are the rough equivalents of their Expected Family Contributions at that income, making them no-loan offers. But you will see that the Stabilized Net Costs for out-of-staters at both schools will likely result in extremely high loans. These examples show the value of doing your own data, a phrase you will only read about twenty more times in this site.

A. SOLVING FOR THE "STABILIZED NET COST"

*Note: Nine schools at the $60K income level, and twenty-six schools at the $40K income level have Stabilized Net Costs which are below zero. Negative Stabilized Net Costs typically occur when highly generous schools have a lower expectation of Student Work than the standard used in the ACG website, and these amounts are not refunded to parents or students.*

Now, here's the Compilation for our $60K data set solving for the Stabilized Net Cost:

These are my Compilations for the 2019/2020 school year. You will see that, as in the three previous years, there are two types of Compilations this year. In both types, the name of the school is in the left-hand column, and what you could call the "target value" is in the right-hand column. In the first type, the target value is the Stabilized Net Cost, the leftover amount for Mom and Dad to pay or for the student and parents to borrow. While in the next type of Compilation, the target value is the Expected Annual Loans required at each of those schools this year.

These 124 schools represent only 5% of the 2500 four-year colleges in America. Also, these Compilations only include cost and aid data as established by the colleges' own Net Price Calculator Programs for my sample families - not your family - and the data are for non-resident or out-of-state students only. The improvement for in-state students at publicly funded in-state schools - and at private ones in some states - is typically huge. As examples, the Stabilized Net Cost for California residents at the $60K income level at UCLA is about $5,000 - or one seventh the amount for out-of-staters, and the Stabilized Net Cost for Washington residents at that income level at the University of Washington is about the same $5,000 - or one eighth the amount for out-of-staters. So, the in-state Stabilized Net Costs of $5,000 for California kids at UCLA and Washington kids at the U-Dub are the rough equivalents of their Expected Family Contributions at that income, making them no-loan offers. But you will see that the Stabilized Net Costs for out-of-staters at both schools will likely result in extremely high loans. These examples show the value of doing your own data, a phrase you will only read about twenty more times in this site.

A. SOLVING FOR THE "STABILIZED NET COST"

Now, here's the Compilation for our $60K data set solving for the Stabilized Net Cost:

acg-2019-_60k-stabilized_net_cost_compilation-3.pdf | |

File Size: | 463 kb |

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And here's the Compilation for our $40K data set solving for the Stabilized Net Cost:

acg-2019-_40k-stabilized_net_cost_compilation-3.pdf | |

File Size: | 463 kb |

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B. SOLVING FOR "EXPECTED ANNUAL LOANS"

*(*__Where the Stabilized Net Cost minus the Expected Family Contribution equals the Expected Annual Loans.__)

*Note: You will see in these two Compilations that families should expect no-loan offers at 30 schools in the $60K Compilation and at 28 schools in the $40K Compilation, but that doesn't make the Stabilized Net Cost - the Mom and Dad column - the same for all the no-loan schools. Between the schools where no loans are expected, there is still a $7,711 range in the annual Stabilized Net Cost at $60K and a $4,178 range at $40K. Additionally, although there is a great deal of commonality in the schools with expected no-loan offers on the $60K and $40K lists, that does not mean that you should expect no-loan offers from those schools at all income levels. As always, the only way to find out your own results is to do your own data.*

Now, here's the Compilation for our $60K data set solving for Expected Annual Loans:

Now, here's the Compilation for our $60K data set solving for Expected Annual Loans:

acg-2019-_60k-expected_annual_loan_compilation-3.pdf | |

File Size: | 450 kb |

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And here's the Compilation for our $40K data set solving for Expected Annual Loans:

acg-2019-_40k-expected_annual_loan_compilation-3.pdf | |

File Size: | 454 kb |

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I. INTRODUCTION

This section includes all the Compilations of my generosity comparisons for the 130 or so colleges analyzed in this website for the six academic years back from 2018/2019 through 2013/2014. My Compilations for the most current year, 2019/2020 are included on my Results page. I list a number of caveats in Part I of that page that apply here as well, so be sure to read it.

Additionally, please note that, negative "Remaining Balances" typically occur when highly generous schools have an expectation of student earnings that is lower than the $5,000 annual amount we use as a guide. Negative Remaining Balances are not refunded to students or their parents.

This section includes all the Compilations of my generosity comparisons for the 130 or so colleges analyzed in this website for the six academic years back from 2018/2019 through 2013/2014. My Compilations for the most current year, 2019/2020 are included on my Results page. I list a number of caveats in Part I of that page that apply here as well, so be sure to read it.

Additionally, please note that, negative "Remaining Balances" typically occur when highly generous schools have an expectation of student earnings that is lower than the $5,000 annual amount we use as a guide. Negative Remaining Balances are not refunded to students or their parents.

II. 2018/2019 COMPILATIONS

A. SOLVING FOR THE REMAINING BALANCE

Here is the Compilation for our $60K data set solving for the Remaining Balance:

Here is the Compilation for our $60K data set solving for the Remaining Balance:

acg-2018-_60k-compilation-2.pdf | |

File Size: | 389 kb |

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And here is the Compilation for our $40K data set solving for the Remaining Balance:

acg-2018-_40k-compilation-2.pdf | |

File Size: | 389 kb |

File Type: |

B. SOLVING FOR EXPECTED ANNUAL LOANS

*Where the Remaining Balance minus the Expected Family Contribution equals the Expected Annual Loans.*

Here is the Compilation for our $60K data set solving for Expected Annual Loans:

Here is the Compilation for our $60K data set solving for Expected Annual Loans:

acg-2018-_60k-expected_loan_compilation-2.pdf | |

File Size: | 374 kb |

File Type: |

And here is the Compilation for our $40K data set solving for Expected Annual Loans:

acg-2018-_40k-expected_loan_compilation-2.pdf | |

File Size: | 374 kb |

File Type: |

III. 2017/2018 COMPILATIONS

A. SOLVING FOR THE REMAINING BALANCE

Here is the Compilation for our $60K data set solving for the Remaining Balance:

Here is the Compilation for our $60K data set solving for the Remaining Balance:

acg-2017-_60k-compilation.pdf | |

File Size: | 428 kb |

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And here is the Compilation for our $40K data set solving for the Remaining Balance:

acg-2017-_40k-compilation.pdf | |

File Size: | 428 kb |

File Type: |

B. SOLVING FOR EXPECTED ANNUAL LOANS

*Where the Remaining Balance minus the Expected Family Contribution equals the Expected Annual Loans.*

Here is the Compilation for our $60K data set solving for Expected Annual Loans:

Here is the Compilation for our $60K data set solving for Expected Annual Loans:

acg-2017-_60k-expected_loan_compilation.pdf | |

File Size: | 417 kb |

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And here is the Compilation for our $40K data set solving for Expected Annual Loans:

acg-2017-_40k-expected_loan_compilation.pdf | |

File Size: | 459 kb |

File Type: |

IV. 2016/2017 COMPILATIONS

A. SOLVING FOR THE REMAINING BALANCE

Here is the Compilation for our $60K data set solving for the Remaining Balance:

Here is the Compilation for our $60K data set solving for the Remaining Balance:

2016-_60k-compilation.pdf | |

File Size: | 437 kb |

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And here is the Compilation for our $40K data set solving for the Remaining Balance:

2016-_40k-compilation.pdf | |

File Size: | 437 kb |

File Type: |

B. SOLVING FOR EXPECTED ANNUAL LOANS

*Where the Remaining Balance minus the Expected Family Contribution equals the Expected Annual Loans.*

Here is the Compilation for our $60K data set solving for Expected Annual Loans:

Here is the Compilation for our $60K data set solving for Expected Annual Loans:

2016-_60k-expected_loan_compilation-2.pdf | |

File Size: | 418 kb |

File Type: |

And here is the Compilation for our $40K data set solving for Expected Annual Loans:

2016-_40k-expected_loan_compilation-2.pdf | |

File Size: | 417 kb |

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C. SOLVING FOR EXPECTED ANNUAL LOAN DIFFERENTIALS

The third Compilation in my loan series includes data on the differential between annual loan amounts for our middle and lower income sample families, and I want to offer a few preliminary comments before you review it.

As I put together both of the Expected Annual Loan Compilations, I noted that the quality of the news they represented varied a lot. The grant aid at some schools was generous enough to result in no expected loans for either sample family, which was wonderful. Loans were expected at the majority of the schools analyzed, not an unexpected result, but one fact took me by surprise. The expected annual loan amounts predicted by the NPC's at a noticeably large proportion of those schools were significantly higher for lower income students than they were for middle income students. As I analyzed all the data on loan differentials, I noticed that they naturally divided into four distinct categories with each interesting and important in its own way, and let's go through them one by one.

The amount of the Remaining Balance was less than or equal to the Expected Family Contribution at 29 of the 126 schools analyzed, representing 23% of the total. That meant that no loans were expected for either sample family at those schools each year, and that resulted in no loan amount differential. Loans were expected at the remaining 97 schools analyzed.

At 20 of the 126 schools analyzed, representing about 16% of the total, expected loan amounts for the lower income sample family were less than for the middle income sample family. That appears to be a gratifying result, and I am not the guy to argue against increased college generosity, but that advantage for lower income families isn't really logical. Obviously, students from lower income families should not be penalized with higher loans than middle income students, but the ability to repay a student loan is dependent on the income of the students, not their parents. So, I think that an equivalence of loan amounts for both sample families is the most logical result, but I can see why colleges might want to err on the generous side to avoid the potential problems discussed below.

31 of the 126 schools analyzed, about 25%, had loan amount differentials which were functionally equal to zero, and I find this the most interesting segment of this Compilation. Tracking big breaks in the data and realizing that zero might be a difficult outcome to reliably hit, I defined "functionally equal to zero" as values between +$250 and -$250 per year. This meant that this segment included 501 potential outcomes, not forgetting zero as a potential outcome. Being very used to the even spray of outcomes in my other Compilations over the last four years , I expected these 31 outcomes to include the whole 501 unit range with no - or almost no - duplication, but that was not the case at all. Only four numeric outcomes covered 23 of the 31 schools, meaning 74% of the segment; and there were only eight solo outcomes. It took me a while to figure it out, and this is my best guess at what's going on here: all 31 schools with functionally zero loan differentials are doing their best to approximate that worthy goal; to do this, 8 schools are using their own algorithms, and 23 schools are using publicly available algorithms. Take a look at the data and think about the vastly different schools achieving exactly the same results, and see if you don't agree with me. I mean, when you've got 13 schools at the plus $18 outcome, and those schools range from Sweet Briar to Berkeley and UCLA, I think they have to be using the same algorithm.

Sadly, the last segment in this Compilation is also the largest. At 46 of the 126 schools analyzed, about 37% of the total, expected annual loans as predicted using the school's own Net Price Calculator programs for the sample family with an AGI of $40,000 per year were more than the expected annual loans for the sample family with an AGI of $60,000 per year. The dollar differential ran from $527 to $5,872 in additional expected loans per year. I find that ethically and morally unacceptable, and I think most Americans would agree. Additionally, the potential practical consequences of this differential for these schools are vast. The list of identified schools includes a large number of otherwise admirable institutions, my own*alma mater *is one of them, and they should all fix this problem.

The third Compilation in my loan series includes data on the differential between annual loan amounts for our middle and lower income sample families, and I want to offer a few preliminary comments before you review it.

As I put together both of the Expected Annual Loan Compilations, I noted that the quality of the news they represented varied a lot. The grant aid at some schools was generous enough to result in no expected loans for either sample family, which was wonderful. Loans were expected at the majority of the schools analyzed, not an unexpected result, but one fact took me by surprise. The expected annual loan amounts predicted by the NPC's at a noticeably large proportion of those schools were significantly higher for lower income students than they were for middle income students. As I analyzed all the data on loan differentials, I noticed that they naturally divided into four distinct categories with each interesting and important in its own way, and let's go through them one by one.

The amount of the Remaining Balance was less than or equal to the Expected Family Contribution at 29 of the 126 schools analyzed, representing 23% of the total. That meant that no loans were expected for either sample family at those schools each year, and that resulted in no loan amount differential. Loans were expected at the remaining 97 schools analyzed.

At 20 of the 126 schools analyzed, representing about 16% of the total, expected loan amounts for the lower income sample family were less than for the middle income sample family. That appears to be a gratifying result, and I am not the guy to argue against increased college generosity, but that advantage for lower income families isn't really logical. Obviously, students from lower income families should not be penalized with higher loans than middle income students, but the ability to repay a student loan is dependent on the income of the students, not their parents. So, I think that an equivalence of loan amounts for both sample families is the most logical result, but I can see why colleges might want to err on the generous side to avoid the potential problems discussed below.

31 of the 126 schools analyzed, about 25%, had loan amount differentials which were functionally equal to zero, and I find this the most interesting segment of this Compilation. Tracking big breaks in the data and realizing that zero might be a difficult outcome to reliably hit, I defined "functionally equal to zero" as values between +$250 and -$250 per year. This meant that this segment included 501 potential outcomes, not forgetting zero as a potential outcome. Being very used to the even spray of outcomes in my other Compilations over the last four years , I expected these 31 outcomes to include the whole 501 unit range with no - or almost no - duplication, but that was not the case at all. Only four numeric outcomes covered 23 of the 31 schools, meaning 74% of the segment; and there were only eight solo outcomes. It took me a while to figure it out, and this is my best guess at what's going on here: all 31 schools with functionally zero loan differentials are doing their best to approximate that worthy goal; to do this, 8 schools are using their own algorithms, and 23 schools are using publicly available algorithms. Take a look at the data and think about the vastly different schools achieving exactly the same results, and see if you don't agree with me. I mean, when you've got 13 schools at the plus $18 outcome, and those schools range from Sweet Briar to Berkeley and UCLA, I think they have to be using the same algorithm.

Sadly, the last segment in this Compilation is also the largest. At 46 of the 126 schools analyzed, about 37% of the total, expected annual loans as predicted using the school's own Net Price Calculator programs for the sample family with an AGI of $40,000 per year were more than the expected annual loans for the sample family with an AGI of $60,000 per year. The dollar differential ran from $527 to $5,872 in additional expected loans per year. I find that ethically and morally unacceptable, and I think most Americans would agree. Additionally, the potential practical consequences of this differential for these schools are vast. The list of identified schools includes a large number of otherwise admirable institutions, my own

2016_compilation_of_expected_annual_loan_differentials-2.pdf | |

File Size: | 408 kb |

File Type: |

V. 2015/2016 COMPILATIONS

A. Here is the Compilation for our $60K data set solving for the Remaining Balance:

A. Here is the Compilation for our $60K data set solving for the Remaining Balance:

2015-_60k-compilation.pdf | |

File Size: | 433 kb |

File Type: |

B. And here is the Compilation for our $40K data set solving for the Remaining Balance:

2015-_40k-compilation.pdf | |

File Size: | 435 kb |

File Type: |

VI. 2014/2015 COMPILATIONS

A. Here is the Compilation for our $60K data set solving for the Remaining Balance:

A. Here is the Compilation for our $60K data set solving for the Remaining Balance:

2014-_60k-compilation-6.pdf | |

File Size: | 384 kb |

File Type: |

B. And here is the Compilation for our $40K data set solving for the Remaining Balance:

2014-_40k-compilation-3.pdf | |

File Size: | 438 kb |

File Type: |

VII. 2013/2014 COMPILATIONS

A. Here is the Compilation for our $60K data set solving for the Remaining Balance:

A. Here is the Compilation for our $60K data set solving for the Remaining Balance:

2013__60k_compilation.pdf | |

File Size: | 344 kb |

File Type: |

B. And here is the Compilation for our $40K data set solving for the Remaining Balance:

2013__40k_compilation.pdf | |

File Size: | 330 kb |

File Type: |

Copyright 2020, Mark Warns, All Rights Reserved

Here again are Word versions of my Apple-to-Apples templates for your own college costs and expected loans along with a Word version of my blank data input pages that you can download for use by your own family:

acg_apples-to-apples_stabilized_net_cost_comparison.docx | |

File Size: | 16 kb |

File Type: | docx |

acg_apples-to-apples_expected_annual_loan_template.docx | |

File Size: | 16 kb |

File Type: | docx |

acg_2019-2020_family_npc_input_data.docx | |

File Size: | 17 kb |

File Type: | docx |

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