This week on The Learning Curve co-hosts U-Arkansas Prof. Albert Cheng and Dr. Jocelyn Chadwick interview Cheryl Brown Henderson, daughter of the lead plaintiff in the landmark U.S. Supreme Court decision, Brown v. Board of Education. She explores her family’s pivotal role in the Brown case, detailing her father’s part within the NAACP’s wider legal strategy. Cheryl discusses the influence of religious faith on the Civil Rights Movement, as well as the impact of segregation on her family, and their courageous decision to confront the legal barriers to racial equality in K-12 education. She emphasizes the ongoing need for comprehensive school reform leadership that will address the racial disparities still found across American public education.
Stories of the Week: Albert shared an article from Chalkbeat discussing the Newark Public Schools’ new AI tutor; Jocelyn discussed a piece in The Washington Post on how to help struggling third graders read.
Guest:
Cheryl Brown Henderson is one of the three daughters of the late Rev. Oliver L. Brown, who, with NAACP attorneys, filed suit against the local Board of Education. Their case was appealed to the U.S. Supreme Court on May 17, 1954, and became known as the landmark decision, Brown v. Board of Education of Topeka, Kansas. Cheryl is the Founding President of the Brown Foundation for Educational Equity, Excellence and Research, and owner of Brown & Associates, an educational consulting firm. She has an extensive background in education, business, and civic leadership, having served on and chaired various local, state, and national boards. She earned a bachelor’s degree in Elementary Education, minor in Mathematics from Baker University, Baldwin City, Kansas, a master’s degree in Guidance and Counseling from Emporia State University, Emporia, Kansas, and honorary doctorates from Washburn University and the University of South Florida.
Tweet of the Week:
Click here to read a transcript
Hubwonk: Examining Diversity’s Dividends: Can Studies Survive Contact with Peer Review
[00:00:00] Joe Selvaggi: This is Hubwonk. I’m Joe Selvaggi. Welcome to Hubwonk, a podcast of Pioneer Institute, a think tank in Boston. Does diversity pay? Beyond the laudable goals of assembling the best talent pool, irrespective of race or ethnicity, does actively seeking diversity in executive leadership truly redound to a company’s financial success?
[00:00:23] McKinsey, a highly regarded global consulting firm, asserts confidently that data from their four studies conducted in 2016 to 2023 unequivocally asserts it does. Their findings have effectively endorsed diversity, equity, and inclusion initiatives worldwide, lending these programs the weight of scientific validation.
[00:00:43] However, such resolute claims must warrant a critical examination by fellow data scientists who are bound to scrutinize the quality of the methodologies and data employed to arrive at these conclusions. To their credit, McKinsey has been transparent with their research methodology, having only anonymized the firms studied to safeguard client confidentiality.
[00:01:05] Given this transparency and the firmness of their research findings, how do their assertions fare under the scrutiny of peer review? Joining us today is Dr. Jeremiah Green, an Associate Professor of Data Analytics at Texas A&M Mays Business School, and co-author of the recently published research paper, Does Greater Diversity in Executive Race Ethnicity Reliably Predict Better Future Firm Financial Performance?
[00:01:33] Dr. Green and his co-authors delved into McKinsey’s research to examine the extent and causality of the purported financial benefits associated with diverse executive leadership. He will share with us what prompted his team to scrutinize McKinsey’s findings. and unveil the outcomes of their efforts to replicate the results of these four studies that undergird the business case for diversity, equity, and inclusion programs worldwide.
[00:01:58] When I return, I’ll be joined by Texas A& M Associate Professor, Dr. Jeremiah Green. Okay, we’re back. This is Hubwonk. I’m Joe Selvaggi, and I’m now pleased to be joined by Texas A& M Associate Professor, Dr. Jeremiah Green. Welcome to Hubwonk, Professor Green.
[00:02:16] Jeremiah Green: Thanks. Great to be here.
[00:02:17] Joe Selvaggi: All right. It’s a pleasure to have you.
[00:02:19] This is your first time on Hubwonk. We’re going to talk about a paper you recently co-authored titled, Does Greater Diversity in Executive Race Ethnicity Reliably Predict Better Future Firm Financial Performance? It’s a research paper that examines the consulting firm McKinsey’s widely accepted claim that diversity delivers to firms bottom line.
[00:02:41] We’re going to go deep on this, but before we do, I want our listeners to, learn a little bit about you and the science of using accounting data for business analysis. You teach at Texas A& M, you teach data analytics, and you use it in your work. What does this field of data analytics do in the business community?
[00:03:00] Jeremiah Green: Okay, yeah, so in terms of data analytics for accounting, that’s also a little more specific than just data analytics in general. Data analytics for accounting typically means we’re using statistical approaches to analyzing financial reports. If you think about a newspaper article that says, apples earnings per share was, X dollars this year, it’s looking at things like that, what they provide to analysts, what they provide to investors, and trying to understand what that means for the company, for investors, for other groups that are interested in looking at those financial reports.
[00:03:41] Joe Selvaggi: So, you, you examine, differences in how financial data, again, you’re looking at publicly traded companies, I assume. All of that should be, available to, someone who wants to analyze it. And you’re trying to relate how different features of a, of a firm’s balance sheet, either explain current success or perhaps, predict future success.
[00:03:59] let’s see an example. We’re going to talk about your analysis of McKinsey’s analysis in this area. What’s an example of where your work in, maybe a different topic might help our listeners understand, the contours of your work?
[00:04:12] Jeremiah Green: Yeah. So, an example of thinking about how financial information that companies report either explains their performance now or predicts their future performance, we might think about an example could be a paper by Mark Solomon.
[00:04:28] He is a professor at University of Southern California. What he does is look at ratios from the financial statements, so profitability or efficiency, and tries to see whether that predicts future performance. What is he? He tries to predict earnings and future returns, stock returns, and that’s the type of thing we might do.
[00:04:52] Some of my research is looking at predicting stock returns also, so we try and use those sorts of pieces from the financial statements to say, what can we learn from them,
[00:05:02] Joe Selvaggi: So, you polished crystal balls to help people who are potentially inventors try to understand what might come in the future.
[00:05:09] We all know, everybody would love such a wonderful device, but it’s challenging, and that’s why you need rigorous analysis and your best effort. So, I want to then talk, rather than the general concept, let’s dive at least a little bit into your paper, the recent paper. You wrote about, the relationship between the diversity, in the leadership positions of a firm and their, probability of being profitable.
[00:05:35] We’re going to talk about that in depth. What was your interest in writing about? Of all the features of a firm, why did you want to examine why diversity might be, among all the other, measures you could use in a company, one that might predict future profitability?
[00:05:50] Jeremiah Green: Yeah, that’s a really good question.
[00:05:52] I, I think it’s probably worth putting in context what got us into that, and that is, we’ve heard lots of claims in, by journalists and by, McKinsey, trying to, that, that say that diversity in various forms improves financial performance. And so, from an academic standpoint, I think we tend to be pretty skeptical of bold claims.
[00:06:16] So that’s one aspect. and the other pa the other issue is if it is the case that diversity improves performance, then that’s something as an accounting person that I want to know so I can tell companies or students that if you increase diversity it’s going to improve your performance and this should be a, a top level concern for something we might want to focus on.
[00:06:41] Joe Selvaggi: So, you either want to challenge a bold claim, see if it’s valid, but you also, if it’s valid, you want to incorporate it into your analysis for the future, right? You want to, as a new, yardstick to add to the other yardsticks used when predicting future performance. But for, I want to provide some background for our listeners.
[00:06:55] We’re going to be looking at a study or several studies, four different studies by the same firm, McKinsey. This is a firm that’s very well regarded in the, a global consulting world. This is the gold standard of consulting. So when you’re talking about challenging or examining, studies or four studies by a very well regarded, accounting, consulting firm.
[00:07:14] These are, you’re taking on a Goliath, if you will. They’ve been around for about 100 years, I think. They’re two years away from 100. so that the relevance when you’re taking on somebody like, a firm like McKinsey is to say that what they’ve put out is almost regarded, I want to use, religious terms, but almost as gospel.
[00:07:31] It essentially is almost presented as it ought to be unquestioning. when we’re talking about you as an academic versus, a firm like McKinsey, which is consulting, Aren’t you more or less, when you’re advising firms or, analyzing what works in businesses, aren’t you in the same business, analyzing the same kinds of data?
[00:07:49] Jeremiah Green: Yeah, okay. Yes. I guess the easiest answer is yes, we are doing, using the same data. We’re trying to make, understand, and make similar conclusions. Can I push back just a little bit in that I would like to not say that we’re trying to take on McKinsey or challenge McKinsey, but, from my viewpoint, if we see research or conclusions based on research that are not, justified by the evidence, then that is something that, as academics, we should be involved in. In terms of what we do with the evidence or decisions we make outside of that isn’t really something I can say anything about, but yes, so in general, we’re doing the same sort of thing and we’re, we have the same sort of, interests. We may not have the same sort of use of it after we make these claims, or whatever they are.
[00:08:45] Joe Selvaggi: I didn’t mean to, I appreciate you pushing back, and I didn’t mean to imply that it was some sort of hostility towards their, the firm. Rather, you’re an academic, and all academics, we hope, present evidence, and then invite their critics. Their colleagues or peers, other, data analysts to scrutinize, meaning you get better.
[00:09:03] You, you don’t want to keep a mistake a secret. You want others to scrutinize your work so that they can point out your mistakes. Every paper, I think, since the dawn of time, has mistakes. And you invite other very intelligent, let’s say colleagues, be they academics or consultants, to scrutinize your work.
[00:09:19] You, what I meant to say, you’re taking on, you’re scrutinizing McKinsey’s work. Can we make that stipulation?
[00:09:26] Jeremiah Green: Yeah, that seems fair enough, yeah.
[00:09:28] Joe Selvaggi: Okay. All right. So, let’s just, before we get into the details, I mentioned there’s four studies by McKinsey. I don’t have the dates right in front of me, but I think the first was 2016.
[00:09:38] The other just came out in 2023. They had similar, conclusions. They became more confident as they went on. For our listeners, just at the high level, how would you summarize the conclusions McKinsey, makes about diversity? In high level the board and, c-suite leadership and, profitability.
[00:10:01] Jeremiah Green: Yeah. Let me, let me caveat it, or, try and split that into two pieces. I think the first piece is pretty clear about what they do in mechanically and what they say within their papers. when I go to make the step about what conclusions they make. those are slightly different than how they describe what they do.
[00:10:23] In terms of the general conclusions, they say that, diverse, having more diversity, like executive diversity, increases company performance. That’s the general claim, even though they are more nuanced when they get into the discussion, I think.
[00:10:39] Joe Selvaggi: Yeah, I, again, I went into the research, looked at the data, not the way you did, but more generally, but also I looked at the overarching, summary and in the most recent report, I want to quote for our listeners, just to add color to this conversation, how bold McKinsey describes, how boldly they described the observations they made in this most recent paper.
[00:10:58] I’m going to report from, this is a quote from McKinsey’s work. For almost a decade through our Diversity Matters series of reports, McKinsey’s has delivered a comprehensive global perspective on the relationship between leadership diversity and company performance. This year, the business case is the strongest it has ever been since we’ve been tracking, and for the first time in some areas, equitable representation is in sight.
[00:11:22] Further, a striking new finding is that leadership diversity is also convincingly associated with holistic growth, ambitions, greater social impact, and more satisfied workforces. Unquote. So, these are pretty bold claims, based on what they claim is decades of research and four studies. So that’s where I want to start.
[00:11:39] Let’s examine by defining terms. In that passage, McKinsey uses the term diversity. How does McKinsey define diversity?
[00:11:48] Jeremiah Green: Yeah, okay, so broadly, I guess let’s do a, simple explanation. What they really are trying to measure is how, not concentrated their executive team is in any, small or single group or ethnicity.
[00:12:03] They’re looking at ethnicity, different ethnicities, they group it into a few different categories, and then they try and say if it’s, So if it’s all concentrated in one ethnicity, then that’s not diverse. If it’s concentrated, or if it’s spread out across different ethnicities, then it’s diverse.
[00:12:23] Joe Selvaggi: So, for the benefit of our listeners, I thought about this myself. I’m like, okay, what would make something a perfect score? And what would make a firm a, Terrible score. So, a perfect diversity score would be whatever number of diversities Let’s say there’s eight or five. I think those are the two different measures.
[00:12:40] If you have five, one of each, and you have five board members, that’s perfect score. If they’re all white, or for that matter, all black, or Hispanic, or pick it, Asian, that would be a bad score. So, what we’re looking at is how numerous, how many different ethnicities are represented, and if there’s any sort of particular concentration. Is that a fair characterization?
[00:13:00] Jeremiah Green: Yeah, exactly. That’s, I think that’s what they’re doing. Yeah.
[00:13:03] Joe Selvaggi: Okay. And of course, now that the other variable is how does that relate to performance? Again, if diversity is hard to define, so also is performance. But, what yardstick did McKinsey use in this, in these studies?
[00:13:17] Jeremiah Green: Yeah, they used, earnings before interest and taxes divided by revenues. So, it’s a measure of profitability, how, given the revenues that they generate, how profitable are those revenues in generating sort of a bottom-line profitability measure.
[00:13:34] Joe Selvaggi: So, this is your expertise, they’re consultants and they, they have their criteria. As an expert yourself, is that a fair measure? Should you use that profitability score as a measure of performance?
[00:13:48] Jeremiah Green: Yeah, I think it’s okay. You could argue for other measures, right? We might try and measure, return to investors or something like that, but it’s an important part of performance and probably a decent place to start.
[00:14:02] Joe Selvaggi: So, we’ve got two scores. We’ve got diversity and we’ve got performance. And again, this is a layperson, analysis of their work. As I see it, what they did was say they looked at all firms. They didn’t make clear which firms they were looking at. But all the firms, I guess almost 2,000 firms, and they ranked them in quartiles.
[00:14:20] They said, okay, we’ve got bottom performing, bottom middle, upper middle, and top performing. And they compared the diversity score of those in the Yeah, the profitability of those in the top, I reverse this, they had, diversity scores from a bottom quintile, top quintile, and they compared the performance in the top quintile of diversity versus the performance in the bottom quintile of performance, and then looked at the difference between the two. Share with our listeners. How did that look?
[00:14:49] Jeremiah Green: Yeah, we’ll stick with the general explanation, like you’re talking about, diversity and performance, rather than get into the details of that right now, but they’re talking, by performance, they’re talking about outperformance or underperformance.
[00:15:04] They’re comparing average profitability, relative to their industry. So If you have, if a company were to have Profitability that’s higher than their industry average, those are outperformers. If it’s below their industry average, they’re underperformers. So that’s the Specific profitability part. In terms of the diversity, what they do is rank companies and in the top 25%, They compare those in the top 25 percent of diversity to those in the lowest 25 percent of diversity.
[00:15:39] And then they look and see how much more likely is the top diversity group than the bottom diversity group to outperform their industry. So, they find something, like around, they’re like 30 percent more likely to outperform their industry if they’re in the top, diversity group.
[00:15:59] Joe Selvaggi: So that’s the crux of the claim, which is to say the more diversity, the firms with higher diversity scores were more likely to outperform their average in their industry, and those on the low end of the diversity score were more likely to underperform, and they compared those two, to really, arrive at whether it’s a, a trivial difference or a substantial, when we’re looking at difference between signal and noise, all kinds of things can show up when we do analysis.
[00:16:23] The larger that difference, the more confident they were in their conclusions. I’m not sure I don’t have the numbers right in front of me, but by my analysis, there was a substantial difference in the likelihood of profitability for more diverse firms than less diverse firms, giving them, I would say, again, reflected in the summary of their paper, high levels of confidence that diversity and performance are related, profoundly related. So, say more about. How confident their observations were and why they would be confident.
[00:16:55] Jeremiah Green: Yeah, I think you said two things there. So, one is the magnitude of how much they’re likely to outperform. So, something like a 30 percent likelihood, more likely to outperform is a huge effect. I, I don’t know of hardly anything else that is that magnitude in terms of how important it would be, right? You can’t like, say, if you, let’s say implement a new information system in your company, you’re 30 percent more likely to outperform. That just doesn’t exist, right? Maybe 1 percent or something. that’s in terms of the magnitude of the effect. The other part is the statistical significance, and they report p values that are less than 5% that would mean highly statistically significant.
[00:17:41] Joe Selvaggi: Again, for our listeners who are not statisticians, I’m going to I’m going to take a crack at explaining P values. I’m dusting off my, graduate degree here and say, what we know about P values is we want to be sure that our observation, or we want to be, measure the confidence that our observations weren’t randomly generated.
[00:17:58] Taking any given set of numbers, All kinds of things can happen. the larger the result that you observe, the more likely that it isn’t random. it’s likely to actually be measuring something real rather than a statistical artifact. Is that fair?
[00:18:14] Jeremiah Green: Yeah, that’s, I think that’s a reasonably good way to, state it loosely. I would add one more piece that, it’s about how the results in the sample that you’re using, how likely is that to happen again in another sample? And so, if we have a really small p value, we would expect, that says it’s very unlikely that what we see in our sample is a statistical anomaly. If we go to another sample, we expect to see the, a very similar result. So low P value, highly confident that this is a reliable claim.
[00:18:52] Joe Selvaggi: So, we imagine a large salami, every slice is going to be the same. If we’re low P value, wherever we look, we’re going to find a similar result rather than, some next, the next result to be the opposite. We’re fairly confident that this is a consistent outcome.
[00:19:05] All right. So, then I wanted to talk about one more dimension, which is we’ve established that at least McKinsey asserts that there’s a relationship between, diversity and performance. But there’s another step, right? We have to observe that in their case, there’s a causational relationship, which is to say, as you say, it’s both big, but it also has to, one has to come before the other.
[00:19:28] The diversity has to happen and then the performance has to happen for it to be causal. I hope I’m saying that Share with our listeners. In their analysis, does the way they measure and when they measure diversity and performance, does it really show a causational relationship? You get, you are diverse, then you outperform. Is that what this study really shows?
[00:19:50] Jeremiah Green: Short answer, no. before getting too critical about what McKinsey says, they explain this in their papers, right? They acknowledge that this is a problem. When you read their conclusions, that’s a very different, they don’t match their conclusions to what they’re saying, but they, let’s take the first study for an example. They rank diversity in 2017, and then the performance, the profitability measure that they use is before the diversity ranking. So, they’re, just with their design, it’s impossible for diversity to increase performance because performance happens before the diversity happens.
[00:20:28] Joe Selvaggi: So, the proverbial cart before the horse, or this is something that academics in your field wrestle with all the time, right? Correlation is not causation. I might use a, I’ll use something silly and say, umbrella use, and rainy days seem to correlate, but it doesn’t mean umbrellas cause it to rain. You know, for our listeners, just briefly explain why, what a data scientist has to do to establish causation rather than mere correlation.
[00:20:54] Jeremiah Green: Yeah, what do we have to do to establish causation? That’s difficult. the best-case scenario would be we’re in a lab where we can randomly assign Something to happen, and then we see that the effect happens. a couple of things we can do. One is the direction that you’re taught, that we had just talked about, right?
[00:21:14] It, the thing that causes something else should happen beforehand. the other part is we have to deal with other, what we call correlated omitted variables, something else that could explain the results that, we were trying to claim are causal. So, you use the example of the umbrella. Another typical one that I find easy to conceptualize is, ice cream sales and shark attacks are correlated. And we don’t think ice cream sales drive shark attacks. Because we think something else is driving both of those, and that’s warmer temperatures. And we have to deal with those. I guess two things. We have to go through the logic of, is this plausible? And then we also have to try and deal with other things that could cause that. In the case of ice cream sales, we would need to find a way to control for warm temperatures. If we found, for example, that, using diversity, that, if we have some 3rd variable, but we can find that it happens outside of when that third variable could affect it, then okay, we’re, at least more confident that there’s a cause and effect happening.
[00:22:29] Joe Selvaggi: Yeah. and so they acknowledge that I’d say in the footnotes in this study, they do acknowledge the challenge with establishing causation. So, I thought, the, people who are doing the analysis Unlike the people who write the headline for McKinsey, acknowledge that the data does not support a causational, but rather just a correlational.
[00:22:45] I want to caveat that if we’re going to criticize McKinsey for something. Is that fair? Meaning that the, the people doing the research acknowledge this limitation?
[00:22:53] Jeremiah Green: Yeah, perfectly fair. And I have no problem with their research at all. I have a problem with the connections of the, like you said, the headlines that they’re putting out there don’t match their research, right? And so that’s right. I think they’re pretty fair when you actually read inside the research there. I think they’re pretty straightforward about it.
[00:23:16] Joe Selvaggi: All right, so we’ve set up this whole sort of conversation. Let’s get to it. okay, you say you, connect more with the, the research of McKinsey rather than the headlines of McKinsey, but let’s get into the research. Was McKinsey’s report, were you able to essentially get their data sets and do analysis of the simple math, or was that something that’s, difficult to, for them that they’re reluctant to share or they were unclear about?
[00:23:39] Jeremiah Green: Yeah, okay, so a few things, in terms of the method they use, they’re very clear. So that, it’s not hard to go in and, if you have the data, to do what they did. The piece that they don’t provide is the companies that they’re using. we don’t have access to, their sample is something like, 300 or 500 companies, nobody but them, they’re the only ones that know what those companies are. And being able to perfectly replicate what they do is not possible unless they give us their companies. But other than that, the rest is very straightforward. You can just copy and do exactly what they do.
[00:24:19] Joe Selvaggi: And of course they didn’t share which companies, not because they’re trying to keep it a secret, but it’s discretion. They don’t want to, in a sense, out their clients, right? They, that’s reasonable. They have a responsibility to keep that confidential. You did find your own companies to analyze. As we mentioned earlier, the P value is very low, suggesting that you’re going to find this result more or less everywhere you look. How did you develop, again, if you’re trying to replicate without the actual list of companies, you’re just replicating the methodology rather than the actual dataset, where did you go to find companies to analyze?
[00:24:50] Jeremiah Green: Yeah, sure. We tried a few things. It’s, this is really the biggest challenge, I think, in terms of trying to replicate their research. We there are so many companies out there. If we look globally, it’s hard to know where to start, but we know that they consult with a bunch of the largest companies, S&P 500 is a pretty common company.
[00:25:15] Benchmark for all companies. So that’s where we started. So, S&P 500, it’s about the same size as the, their sample. And we know that they consult with many of them. And so, to us, that was as close as we could get. as a side note, we also did a random sample, we thought, of U.S. company, sorry, public companies, and tried the same thing.
[00:25:38] And no matter what we did, we get similar results, but it, I don’t know, is that good enough? We tried the S& P500, and we tried a random sample, to try and approximate what we think they did.
[00:25:50] Joe Selvaggi: Yes, and of course, if their, observations would be valid, and your observations would be valid, you, they couldn’t, in a sense, Use selection bias to craft their results if they did. They would say, among those companies, among the companies we looked at, this is true, but who knows about any other company? If that were the case, then no assertion would be generalizably true. It would be a worthless observation. In order to have their observations have any weight, they have to be generalizable your analysis and their analysis, though they’re different companies, in theory, should show the same approximate result. Is that fair?
[00:26:22] Jeremiah Green: Yeah, yeah, I would say even more than in theory. Their P values aren’t valid if Their sample is not a random sample, and so what their P value should be is a reflection of what we should see elsewhere. And yeah, if we can get a sample that is meant to approximate the same types of companies that they’re looking at, we should see something similar.
[00:26:45] Joe Selvaggi: So, the companies used, provided it’s a large enough sample, are virtually irrelevant to an observation like this. and you replicated their methodology, which you say they were very candid about how they went about evaluating. So we’ve buried the lead enough. Given all your research, you had two co-authors and a substantial amount of time to analyze the data, did your research see a correlation, not, we’re not going to get to causation yet, between diversity and performance?
[00:27:15] Jeremiah Green: I just want the short answer.
[00:27:16] Joe Selvaggi: No.
[00:27:18] Jeremiah Green: No.
[00:27:18] Joe Selvaggi: Okay. I don’t know if our listeners were anticipating that answer. It’s the shortest of all the answers you’ve offered. So I want to go, a little bit further and say, okay, we established early on that what we’re talking about diversity, we’re talking about diversity in the higher level ranks. We’re talking about a large signal that they saw that a large result of confidence in their results. You found no such, relationship between How diverse a firm was and how profitable a firm was. Did you use, the same exact, let’s say, I think it was earnings before interest and taxes. did you use, did you try to tweak the numbers in any way to look at different metrics that maybe you were missing some, some variable that they accounted for?
[00:27:55] Jeremiah Green: Yeah, let me give a little, more than you would see in the paper. So, in the paper we tried, I can’t remember, six or something different measures. Outside of that, I, we were convinced there’s got to be something to this. So, I tried every Measure that I could think of, so I don’t know what it was 25, 30 measures. There’s got to be something there was my thinking and no, no matter what we did, we couldn’t find it. and this is replicating their approach. We mentioned that they measured diversity and then looked at performance prior to that. And so, I tried that with every measure, we tried it with every measure we could think of.
[00:28:36] No association at all. And then in another paper, so this paper that we’ve been talking about is with John Hand. He’s at North Carolina. We also have another paper with Sekou Burmes, who is a strategy professor at North Carolina, and we flipped the direction. So, then we tried to say, what if we measure diversity, let’s say, in 2017 and looked at performance after 2017.
[00:29:02] And, maybe we could get this sort of the right direction, and then we would find results. Nothing. So, no matter what we did, and this is You know, you don’t see it all in there, but we spent an enormous amount of time trying to find any association that we could and just can’t do it.
[00:29:22] Joe Selvaggi: So, we’re not going to even talk about causation because where there’s no correlation, of course, there could be no causation in either direction that this is an extraordinary revelation in my view. It’s an extraordinary revelation. one would think even randomness might generate some sort of result, you could draw a line in the middle of a room and say, did the people on the left hand in the room have anything different than the people on the right-hand room? You’re going to find something somewhere. Guys named Joe may be different than guys, everybody else, you didn’t find anything.
[00:29:49] Jeremiah Green: I tried, I guess the only thing I didn’t try, we did the S& P 500 and the random sample. I guess another thing we could have tried is, just keep shifting around the companies and see if in any sample we could happen to find it, right?
[00:30:06] If it was just, you’ve looked through the data too much and randomly you should find a result. That, I guess that’s the other thing we could try, but yeah, everything we tried, there’s just nothing there. Oh, we also expanded it when we started, we did it with one year, just like a single one of their studies, and then we expanded it to many years, and no matter what, we just can’t find it.
[00:30:28] Joe Selvaggi: This is pretty powerful. When one finds no results, one can’t say there isn’t some relationship, but it’s just not measurable, particularly given the data and the methodology. This is big news, because I think McKinsey has a, their influence is powerful. corporate boards and, C suite people use that as I won’t say gospel, but essentially as a north star for many of their decisions. To make the assertion that they do, that it essentially is almost. not almost. It is proved that diversity leads to greater profitability. If I’m a leader of a firm or a member of a board, I have a fiduciary responsibility to my shareholders, right? I don’t own the company. I may be the president, but I don’t own the company. I work for the shareholders. It’s my job. I have to make choices for their benefit. If it’s true, what McKinsey asserts, then I would be, violating my oath as a fiduciary to not make my firm more diverse. Without this evidence, in a sense, now we can’t privilege diversity over others, let’s say, of course, we want to make the firm profitable.
[00:31:29] We’re not saying that this is morally or ethically a bad idea, diversity, we’re not going there. We’re just saying as a measure of profitability, as a head of a firm, it’s either imperative, it’s a financial imperative that I use diversity, but without that evidence, no such imperative exists. We can essentially use Pro more traditional, profitability criteria when making our decisions. I don’t want to go too bold here, but I want to say if this is, if this, data is not valid or observation is not valid, I’ll say maybe the data is, would this, upset the entire, diversity push in financial markets?
[00:32:10] Jeremiah Green: Yeah, let me, be careful in how I state this here. I think I agree that, if that were the case, and I think this is how what you were saying, has been used that way. If it is the case that increasing diversity increases performance, managers must increase diversity because that is going to improve returns to their shareholders. Fantastic, right? It’s a good story. If it works, then the path forward is clear. let me try and claw back some space for McKinsey here.
[00:32:44] In the last study, I, the one that just came out in 2023, so that was actually after we had, looked at the first three studies, they add other things that, that diversity might influence. for example, employee satisfaction. It’s completely believable that diversity could influence employee satisfaction.
[00:33:09] You could think of a lot of reasons that might be the case in terms of that, great, right? that may be something that companies should care about. It may be something that employees care about, and if a company decides to make decisions based on that, then no problem. The challenge is, though, they can’t rely on this evidence to say, it’s going to increase performance because that’s not the case. Let me give the other side of that is we don’t find that it decreases performance either. So, it, it doesn’t seem to hurt or help in terms of bottom-line performance. It could be as benefits in other ways, but just not, the sort of bottom-line profitability approach, or type of outcome.
[00:34:00] Joe Selvaggi: I’m glad you point out the double-edged sword of finding no results, which is diversity, when pushed doesn’t help, of course, but diversity when pushed doesn’t hurt it maybe is a Rorschach test to say which, which, support you want to, observe. that, that’s fair, and I’m glad you did point that out. Again, no signal gives us all latitude to, to look for other things. And as you say, employee satisfaction. But of course, if it improved employee, being or satisfaction or something like that, that theoretically, we want that. Of course, it’s nice, it’s inherently a good idea, but we would also think that would improve Thank you.
[00:34:31] Profitability. Again, if you want to signal, employee satisfaction isn’t correlated with profitability. It’s good, but it’s not a, a financial imperative. so what I really want to, again, I’m going to ask you to go on a limb here and say, okay, McKinsey has produced these four reports with glowing, summaries, and which are touted, when there’s legendary slide decks to, to C suite executives and boards, why would McKinsey, with, has built a reputation, I think they hire the best, smartest people coming out of the best schools, why would, McKinsey, make claims that their own data really wouldn’t support?
[00:35:07] It seems, or at least it is impossible to reproduce. What do you see as a sort of incentive for a firm to, say, jeopardize their reputation for data, quality data and integrity?
[00:35:20] Jeremiah Green: Yeah, I’m glad you said you’re going to ask me to go out on a limb because this is just speculation, right? I don’t know. Although we have examples in academic peer reviewed research where, researchers do things that would have bad consequences in terms of their reputation. So This does happen everywhere, so I can’t, claim that, we’re peer reviewed, so we’re so much better, but in terms of, I think, the incentives, we do have, some amount of, we are different, and I think the big difference, if I were to take a I don’t want to take a view like they were purposefully trying to, misstate what they are saying, but I think they, they want to be able to tell companies you should do this, and we don’t want to do that as academics, we just want to evaluate the evidence, and in the jump from them. Here’s what we found should. I think that’s where the misinterpretation of their evidence came from. That’s my guess. if you’re trying to tell a company you should do this, but then you tell them, if you have diversity, you previously would have performed better, that isn’t very compelling to get them to do something, I guess.
[00:36:37] Joe Selvaggi: There’s so many ways to interpret it. Again, we’re not inside the minds of McKinsey consultants, but of course, we all respond to incentives. Academics, hopefully academics’ reputation for integrity and accuracy is the sort of coin of the realm. In consultancy, perhaps, There is an incentive to tell your client what they want to hear, or I’ll say, again, I’m going to relate it to many topics we cover here on Hubwonk is, unfortunately, this sounds like a political, an aspirationally political position, which, and my, it’s going to go on our, my tombstone, perhaps, is the unfortunate fact that when you mix science and politics, you get politics.
[00:37:13] This seems to be just one more instance where a revered institution has Perhaps, wandered off the path and compromised integrity for, results that people prefer. you know, do you see this sort of perhaps as a potential victim of either group thinking or motivated reasoning?
[00:37:31] Jeremiah Green: Yeah, I hesitate to make any judgment about the moral or ethical good or bad, is this good or bad? It does seem, I like your statement of if you mix science with politics, you get politics. That seems pretty accurate to me. So, what I do think is that we should be aware of when we’re interpreting what research says, and maybe particularly when it comes from, people like consulting firms or groups like consulting firms that we need to be careful of how it’s being interpreted. And I think, we, I hope we’ve shown that, at least in these studies, you can’t make the conclusions that McKinsey has been making, those are just not, they’re just not correct, they’re not what is shown in the data.
[00:38:23] Joe Selvaggi: And that’s fair, and one can understand why McKinsey, having found nothing in the data, that, the headline cannot read, we found nothing they took the ball and ran with it. I appreciate you taking your valuable time with us today, Professor Green, it’s a dry subject, it’s a difficult subject, but I think peer reviews, scrutiny of, academics with integrity is absolutely essential. We’re not asserting whether diversity has a moral benefit.
[00:38:44] We’re just examining whether it has the. the financial benefit that the study claims. So at least in this regard, you’re shedding some light in what seems to be a fairly, dark or cloudy area of analysis. Where can our listeners, find either your study or your paper? I was able to access it, there were no, paywalls or anything. Where might, are, and you’ve published quite a bit, where could our listeners read more about your work?
[00:39:10] Jeremiah Green: Yeah, a few places. If you just search Jeremiah Green at Texas A&MI pop up so you can find my webpage and my CV there. if you go to econ journal, watch, or Journal of Economics Race and Policy, that’s where one of our other ones is. Or you can look up some of the co-authors, John Hand at UNC or Kuber at UNC. I think it’s pretty easy on academics to find that stuff.
[00:39:36] Joe Selvaggi: Wonderful. I appreciate your time and thank you again for joining me today on Hubwonk. Professor Green, you really, I think shed some light on a difficult topic.
[00:39:43] Thank you. for having me.
This has been another episode of Hubwonk. If you enjoyed today’s show, there are several ways to support Hubwonk and Pioneer Institute. It would be easier for you and better for us if you subscribe to Hubwonk on your iTunes podcatcher. It would make it far easier for others to find Hubwonk if you offer a five star rating or a favorable review.
[00:40:03] We’re of course grateful if you share Hubwonk with friends. If you have ideas or comments or suggestions for me about future episode topics, you’re welcome to email me at hubwonk@pioneerinstitute. org. Please join me next week for a new episode of Hubwonk.
Joe Selvaggi talks with business data scientist Dr. Jeremiah Green about his peer review work examining consulting firm McKinsey’s studies on the measurable financial benefits of diversity in corporate executive leadership.
Guest:
Jeremiah Green is an Associate Professor of Accounting and holds the Ernst & Young Professorship of Accounting at the Mays School of Business at Texas A&M University. He does research on executive race/ethnicity in US public companies, and capital markets research that focuses on the use of accounting information. Dr. Green also studies hedge funds, equity and debt analysts, auditors, managers, the business press, and equity trading strategies. His teaching centers on data analytics and analytics for financial reporting.
Commentary On The Senate Ways And Means Committee FY2025 Budget
The Senate Ways and Means Committee (SWM) released its FY2025 budget on May 7th. This spending plan totals $57.9 billion, an increase of $1.8 billion over the FY2024 General Appropriations Act (GAA). Like the Governor’s and House’s versions of the budget, the SWM budget is based on the consensus revenue estimate of $41.5 billion in tax revenue – a decrease of $208 million from last year’s consensus figure. Given the volatility of revenue collections for the current fiscal year – shortfalls for the first nine months and then more than a $1.5 surplus over anticipated revenues in April – there is a lot of uncertainty around the revenues upon which FY2025 budget proposals are based and this uncertainty warrants a cautious approach to spending.
The Senate, going last in the budget process, has the advantage of reliance on more current revenue numbers when formulating its annual operating budget. Perhaps, the latest revenue collections made them cautiously optimistic, even while acknowledging the tightening fiscal environment, allowing them to spend more freely on priority areas than they otherwise would.
The budget’s executive summary states that the SWM Committee adhered to a disciplined and responsible fiscal stewardship. As proof, it indicates that the budget does not include tax increases, or withdrawals from the Stabilization Fund (the state’s reserves) or the Transitional Escrow Account (federal money that has not been appropriated yet). SWM deserves high praise for both of these strategies, but that is not the entire fiscal picture.
Where the SWM budget goes astray is in its use of more than $1 billion in one-time revenues to fund both new and ongoing expenses. The Senate is not alone in the practice, nor is it the worst offender, as the House relies on $1.19 billion in one-time and new revenue sources and the Governor proposed $1.25 billion in new taxes and one-time revenue sources. In fact, the following sources of one-time revenue are common to all three versions of the budget:
While one can argue that the trust fund resources were dedicated for the purposes for which they are being used, and therefore, a reasonable use of funds, that money will be unavailable next year and thus is not a sustainable way to pay for operating expenses.
The Senate budget differs from the Governor’s and House’s budgets in two noteworthy ways. The Senate does not propose tax law changes (elimination of various deductions) or additional sources of one-time revenue, (such as using Gaming Funds, allowing online lottery sales, or providing a tax amnesty period) to support this increased spending like its budget counterparts. While Pioneer Institute supports this no new tax approach, it also recognizes that these sizable new investments introduced by the Senate will grow the bottom line of the state budget without any revenue offsets at a time of slow revenue growth.
Examples of noteworthy new investments or increases include:
These spending items will either require new taxes in subsequent years or budget cuts elsewhere to sustain these program expansions over time.
Pioneer Institute appreciates the ebbs and flows of tax revenue collections and the need to smooth out spending cuts over time, but the reliance on one-time revenues seems unnecessary given duplicative or overly generous spending in several key areas. A sampling of these items include:
“She’s a good girl. She helps me a lot. She has big, big dreams. I don’t have the money, but she has big dreams. I hope she’s going to get everything, but she works so hard. She works so hard in school.”
“Our family is needing to make some really big sacrifices because we believe this is important, and so, we’re basically going to do whatever it takes… Sometimes we look at each other and go ‘I don’t know if I can do it again another month…’”
“A lot of the families have to sacrifice and work multiple jobs… And just scraping together enough money to just make tuition, just the basics.”
“It is discriminatory, that parents who want to choose an alternative to public school for their children, would not in any way receive any compensation for that, whether it be tax credit, whether it be a voucher…”
In the 1840s, nativist movement leaders formed official political parties and local chapters of the national Native American Party (later the American Party), although they continued to be commonly known as the Know-Nothing Party. Politicians sought to insert provisions into state constitutions against Catholics who refused to renounce the pope. The Know-Nothing movement brought bigotry and hatred to a new level of violence and organization.
The party’s legacy endured in the post-Civil War era, with laws and constitutional amendments it supported, still today severely limiting parents’ educational choices. A federal constitutional amendment was proposed by Speaker of the House James Blaine prohibiting money raised by taxation in any State to be under the control of any religious sect; nor shall any money so raised or lands so devoted be divided between religious sects or denominations. These were then named the Blaine Amendments of 1875.
in recent decades, often in response to challenges to school choice programs, the U.S. Supreme Court has demonstrated great interest in examining the issues of educational alternatives and attempts limit parental options. Massachusetts plays a key role in this debate. The Bay State was a key center of the Know-Nothing movement and has the oldest version of Anti-Aid Amendments in the nation, as well as a second such amendment approved in 1917. Two-fifths of Massachusetts residents are Catholic, and its Catholic schools outperform the state’s public schools, which are the best in the nation.
This week on The Learning Curve co-hosts U-Arkansas Prof. Albert Cheng and Dr. Jocelyn Chadwick interview Cheryl Brown Henderson, daughter of the lead plaintiff in the landmark U.S. Supreme Court decision, Brown v. Board of Education. She explores her family’s pivotal role in the Brown case, detailing her father’s part within the NAACP’s wider legal strategy. Cheryl discusses the influence of religious faith on the Civil Rights Movement, as well as the impact of segregation on her family, and their courageous decision to confront the legal barriers to racial equality in K-12 education. She emphasizes the ongoing need for comprehensive school reform leadership that will address the racial disparities still found across American public education.
Stories of the Week: Albert shared an article from Chalkbeat discussing the Newark Public Schools’ new AI tutor; Jocelyn discussed a piece in The Washington Post on how to help struggling third graders read.
Guest:
Cheryl Brown Henderson is one of the three daughters of the late Rev. Oliver L. Brown, who, with NAACP attorneys, filed suit against the local Board of Education. Their case was appealed to the U.S. Supreme Court on May 17, 1954, and became known as the landmark decision, Brown v. Board of Education of Topeka, Kansas. Cheryl is the Founding President of the Brown Foundation for Educational Equity, Excellence and Research, and owner of Brown & Associates, an educational consulting firm. She has an extensive background in education, business, and civic leadership, having served on and chaired various local, state, and national boards. She earned a bachelor’s degree in Elementary Education, minor in Mathematics from Baker University, Baldwin City, Kansas, a master’s degree in Guidance and Counseling from Emporia State University, Emporia, Kansas, and honorary doctorates from Washburn University and the University of South Florida.
Tweet of the Week:
Click here to read a transcript
Hubwonk: Examining Diversity’s Dividends: Can Studies Survive Contact with Peer Review
[00:00:00] Joe Selvaggi: This is Hubwonk. I’m Joe Selvaggi. Welcome to Hubwonk, a podcast of Pioneer Institute, a think tank in Boston. Does diversity pay? Beyond the laudable goals of assembling the best talent pool, irrespective of race or ethnicity, does actively seeking diversity in executive leadership truly redound to a company’s financial success?
[00:00:23] McKinsey, a highly regarded global consulting firm, asserts confidently that data from their four studies conducted in 2016 to 2023 unequivocally asserts it does. Their findings have effectively endorsed diversity, equity, and inclusion initiatives worldwide, lending these programs the weight of scientific validation.
[00:00:43] However, such resolute claims must warrant a critical examination by fellow data scientists who are bound to scrutinize the quality of the methodologies and data employed to arrive at these conclusions. To their credit, McKinsey has been transparent with their research methodology, having only anonymized the firms studied to safeguard client confidentiality.
[00:01:05] Given this transparency and the firmness of their research findings, how do their assertions fare under the scrutiny of peer review? Joining us today is Dr. Jeremiah Green, an Associate Professor of Data Analytics at Texas A&M Mays Business School, and co-author of the recently published research paper, Does Greater Diversity in Executive Race Ethnicity Reliably Predict Better Future Firm Financial Performance?
[00:01:33] Dr. Green and his co-authors delved into McKinsey’s research to examine the extent and causality of the purported financial benefits associated with diverse executive leadership. He will share with us what prompted his team to scrutinize McKinsey’s findings. and unveil the outcomes of their efforts to replicate the results of these four studies that undergird the business case for diversity, equity, and inclusion programs worldwide.
[00:01:58] When I return, I’ll be joined by Texas A& M Associate Professor, Dr. Jeremiah Green. Okay, we’re back. This is Hubwonk. I’m Joe Selvaggi, and I’m now pleased to be joined by Texas A& M Associate Professor, Dr. Jeremiah Green. Welcome to Hubwonk, Professor Green.
[00:02:16] Jeremiah Green: Thanks. Great to be here.
[00:02:17] Joe Selvaggi: All right. It’s a pleasure to have you.
[00:02:19] This is your first time on Hubwonk. We’re going to talk about a paper you recently co-authored titled, Does Greater Diversity in Executive Race Ethnicity Reliably Predict Better Future Firm Financial Performance? It’s a research paper that examines the consulting firm McKinsey’s widely accepted claim that diversity delivers to firms bottom line.
[00:02:41] We’re going to go deep on this, but before we do, I want our listeners to, learn a little bit about you and the science of using accounting data for business analysis. You teach at Texas A& M, you teach data analytics, and you use it in your work. What does this field of data analytics do in the business community?
[00:03:00] Jeremiah Green: Okay, yeah, so in terms of data analytics for accounting, that’s also a little more specific than just data analytics in general. Data analytics for accounting typically means we’re using statistical approaches to analyzing financial reports. If you think about a newspaper article that says, apples earnings per share was, X dollars this year, it’s looking at things like that, what they provide to analysts, what they provide to investors, and trying to understand what that means for the company, for investors, for other groups that are interested in looking at those financial reports.
[00:03:41] Joe Selvaggi: So, you, you examine, differences in how financial data, again, you’re looking at publicly traded companies, I assume. All of that should be, available to, someone who wants to analyze it. And you’re trying to relate how different features of a, of a firm’s balance sheet, either explain current success or perhaps, predict future success.
[00:03:59] let’s see an example. We’re going to talk about your analysis of McKinsey’s analysis in this area. What’s an example of where your work in, maybe a different topic might help our listeners understand, the contours of your work?
[00:04:12] Jeremiah Green: Yeah. So, an example of thinking about how financial information that companies report either explains their performance now or predicts their future performance, we might think about an example could be a paper by Mark Solomon.
[00:04:28] He is a professor at University of Southern California. What he does is look at ratios from the financial statements, so profitability or efficiency, and tries to see whether that predicts future performance. What is he? He tries to predict earnings and future returns, stock returns, and that’s the type of thing we might do.
[00:04:52] Some of my research is looking at predicting stock returns also, so we try and use those sorts of pieces from the financial statements to say, what can we learn from them,
[00:05:02] Joe Selvaggi: So, you polished crystal balls to help people who are potentially inventors try to understand what might come in the future.
[00:05:09] We all know, everybody would love such a wonderful device, but it’s challenging, and that’s why you need rigorous analysis and your best effort. So, I want to then talk, rather than the general concept, let’s dive at least a little bit into your paper, the recent paper. You wrote about, the relationship between the diversity, in the leadership positions of a firm and their, probability of being profitable.
[00:05:35] We’re going to talk about that in depth. What was your interest in writing about? Of all the features of a firm, why did you want to examine why diversity might be, among all the other, measures you could use in a company, one that might predict future profitability?
[00:05:50] Jeremiah Green: Yeah, that’s a really good question.
[00:05:52] I, I think it’s probably worth putting in context what got us into that, and that is, we’ve heard lots of claims in, by journalists and by, McKinsey, trying to, that, that say that diversity in various forms improves financial performance. And so, from an academic standpoint, I think we tend to be pretty skeptical of bold claims.
[00:06:16] So that’s one aspect. and the other pa the other issue is if it is the case that diversity improves performance, then that’s something as an accounting person that I want to know so I can tell companies or students that if you increase diversity it’s going to improve your performance and this should be a, a top level concern for something we might want to focus on.
[00:06:41] Joe Selvaggi: So, you either want to challenge a bold claim, see if it’s valid, but you also, if it’s valid, you want to incorporate it into your analysis for the future, right? You want to, as a new, yardstick to add to the other yardsticks used when predicting future performance. But for, I want to provide some background for our listeners.
[00:06:55] We’re going to be looking at a study or several studies, four different studies by the same firm, McKinsey. This is a firm that’s very well regarded in the, a global consulting world. This is the gold standard of consulting. So when you’re talking about challenging or examining, studies or four studies by a very well regarded, accounting, consulting firm.
[00:07:14] These are, you’re taking on a Goliath, if you will. They’ve been around for about 100 years, I think. They’re two years away from 100. so that the relevance when you’re taking on somebody like, a firm like McKinsey is to say that what they’ve put out is almost regarded, I want to use, religious terms, but almost as gospel.
[00:07:31] It essentially is almost presented as it ought to be unquestioning. when we’re talking about you as an academic versus, a firm like McKinsey, which is consulting, Aren’t you more or less, when you’re advising firms or, analyzing what works in businesses, aren’t you in the same business, analyzing the same kinds of data?
[00:07:49] Jeremiah Green: Yeah, okay. Yes. I guess the easiest answer is yes, we are doing, using the same data. We’re trying to make, understand, and make similar conclusions. Can I push back just a little bit in that I would like to not say that we’re trying to take on McKinsey or challenge McKinsey, but, from my viewpoint, if we see research or conclusions based on research that are not, justified by the evidence, then that is something that, as academics, we should be involved in. In terms of what we do with the evidence or decisions we make outside of that isn’t really something I can say anything about, but yes, so in general, we’re doing the same sort of thing and we’re, we have the same sort of, interests. We may not have the same sort of use of it after we make these claims, or whatever they are.
[00:08:45] Joe Selvaggi: I didn’t mean to, I appreciate you pushing back, and I didn’t mean to imply that it was some sort of hostility towards their, the firm. Rather, you’re an academic, and all academics, we hope, present evidence, and then invite their critics. Their colleagues or peers, other, data analysts to scrutinize, meaning you get better.
[00:09:03] You, you don’t want to keep a mistake a secret. You want others to scrutinize your work so that they can point out your mistakes. Every paper, I think, since the dawn of time, has mistakes. And you invite other very intelligent, let’s say colleagues, be they academics or consultants, to scrutinize your work.
[00:09:19] You, what I meant to say, you’re taking on, you’re scrutinizing McKinsey’s work. Can we make that stipulation?
[00:09:26] Jeremiah Green: Yeah, that seems fair enough, yeah.
[00:09:28] Joe Selvaggi: Okay. All right. So, let’s just, before we get into the details, I mentioned there’s four studies by McKinsey. I don’t have the dates right in front of me, but I think the first was 2016.
[00:09:38] The other just came out in 2023. They had similar, conclusions. They became more confident as they went on. For our listeners, just at the high level, how would you summarize the conclusions McKinsey, makes about diversity? In high level the board and, c-suite leadership and, profitability.
[00:10:01] Jeremiah Green: Yeah. Let me, let me caveat it, or, try and split that into two pieces. I think the first piece is pretty clear about what they do in mechanically and what they say within their papers. when I go to make the step about what conclusions they make. those are slightly different than how they describe what they do.
[00:10:23] In terms of the general conclusions, they say that, diverse, having more diversity, like executive diversity, increases company performance. That’s the general claim, even though they are more nuanced when they get into the discussion, I think.
[00:10:39] Joe Selvaggi: Yeah, I, again, I went into the research, looked at the data, not the way you did, but more generally, but also I looked at the overarching, summary and in the most recent report, I want to quote for our listeners, just to add color to this conversation, how bold McKinsey describes, how boldly they described the observations they made in this most recent paper.
[00:10:58] I’m going to report from, this is a quote from McKinsey’s work. For almost a decade through our Diversity Matters series of reports, McKinsey’s has delivered a comprehensive global perspective on the relationship between leadership diversity and company performance. This year, the business case is the strongest it has ever been since we’ve been tracking, and for the first time in some areas, equitable representation is in sight.
[00:11:22] Further, a striking new finding is that leadership diversity is also convincingly associated with holistic growth, ambitions, greater social impact, and more satisfied workforces. Unquote. So, these are pretty bold claims, based on what they claim is decades of research and four studies. So that’s where I want to start.
[00:11:39] Let’s examine by defining terms. In that passage, McKinsey uses the term diversity. How does McKinsey define diversity?
[00:11:48] Jeremiah Green: Yeah, okay, so broadly, I guess let’s do a, simple explanation. What they really are trying to measure is how, not concentrated their executive team is in any, small or single group or ethnicity.
[00:12:03] They’re looking at ethnicity, different ethnicities, they group it into a few different categories, and then they try and say if it’s, So if it’s all concentrated in one ethnicity, then that’s not diverse. If it’s concentrated, or if it’s spread out across different ethnicities, then it’s diverse.
[00:12:23] Joe Selvaggi: So, for the benefit of our listeners, I thought about this myself. I’m like, okay, what would make something a perfect score? And what would make a firm a, Terrible score. So, a perfect diversity score would be whatever number of diversities Let’s say there’s eight or five. I think those are the two different measures.
[00:12:40] If you have five, one of each, and you have five board members, that’s perfect score. If they’re all white, or for that matter, all black, or Hispanic, or pick it, Asian, that would be a bad score. So, what we’re looking at is how numerous, how many different ethnicities are represented, and if there’s any sort of particular concentration. Is that a fair characterization?
[00:13:00] Jeremiah Green: Yeah, exactly. That’s, I think that’s what they’re doing. Yeah.
[00:13:03] Joe Selvaggi: Okay. And of course, now that the other variable is how does that relate to performance? Again, if diversity is hard to define, so also is performance. But, what yardstick did McKinsey use in this, in these studies?
[00:13:17] Jeremiah Green: Yeah, they used, earnings before interest and taxes divided by revenues. So, it’s a measure of profitability, how, given the revenues that they generate, how profitable are those revenues in generating sort of a bottom-line profitability measure.
[00:13:34] Joe Selvaggi: So, this is your expertise, they’re consultants and they, they have their criteria. As an expert yourself, is that a fair measure? Should you use that profitability score as a measure of performance?
[00:13:48] Jeremiah Green: Yeah, I think it’s okay. You could argue for other measures, right? We might try and measure, return to investors or something like that, but it’s an important part of performance and probably a decent place to start.
[00:14:02] Joe Selvaggi: So, we’ve got two scores. We’ve got diversity and we’ve got performance. And again, this is a layperson, analysis of their work. As I see it, what they did was say they looked at all firms. They didn’t make clear which firms they were looking at. But all the firms, I guess almost 2,000 firms, and they ranked them in quartiles.
[00:14:20] They said, okay, we’ve got bottom performing, bottom middle, upper middle, and top performing. And they compared the diversity score of those in the Yeah, the profitability of those in the top, I reverse this, they had, diversity scores from a bottom quintile, top quintile, and they compared the performance in the top quintile of diversity versus the performance in the bottom quintile of performance, and then looked at the difference between the two. Share with our listeners. How did that look?
[00:14:49] Jeremiah Green: Yeah, we’ll stick with the general explanation, like you’re talking about, diversity and performance, rather than get into the details of that right now, but they’re talking, by performance, they’re talking about outperformance or underperformance.
[00:15:04] They’re comparing average profitability, relative to their industry. So If you have, if a company were to have Profitability that’s higher than their industry average, those are outperformers. If it’s below their industry average, they’re underperformers. So that’s the Specific profitability part. In terms of the diversity, what they do is rank companies and in the top 25%, They compare those in the top 25 percent of diversity to those in the lowest 25 percent of diversity.
[00:15:39] And then they look and see how much more likely is the top diversity group than the bottom diversity group to outperform their industry. So, they find something, like around, they’re like 30 percent more likely to outperform their industry if they’re in the top, diversity group.
[00:15:59] Joe Selvaggi: So that’s the crux of the claim, which is to say the more diversity, the firms with higher diversity scores were more likely to outperform their average in their industry, and those on the low end of the diversity score were more likely to underperform, and they compared those two, to really, arrive at whether it’s a, a trivial difference or a substantial, when we’re looking at difference between signal and noise, all kinds of things can show up when we do analysis.
[00:16:23] The larger that difference, the more confident they were in their conclusions. I’m not sure I don’t have the numbers right in front of me, but by my analysis, there was a substantial difference in the likelihood of profitability for more diverse firms than less diverse firms, giving them, I would say, again, reflected in the summary of their paper, high levels of confidence that diversity and performance are related, profoundly related. So, say more about. How confident their observations were and why they would be confident.
[00:16:55] Jeremiah Green: Yeah, I think you said two things there. So, one is the magnitude of how much they’re likely to outperform. So, something like a 30 percent likelihood, more likely to outperform is a huge effect. I, I don’t know of hardly anything else that is that magnitude in terms of how important it would be, right? You can’t like, say, if you, let’s say implement a new information system in your company, you’re 30 percent more likely to outperform. That just doesn’t exist, right? Maybe 1 percent or something. that’s in terms of the magnitude of the effect. The other part is the statistical significance, and they report p values that are less than 5% that would mean highly statistically significant.
[00:17:41] Joe Selvaggi: Again, for our listeners who are not statisticians, I’m going to I’m going to take a crack at explaining P values. I’m dusting off my, graduate degree here and say, what we know about P values is we want to be sure that our observation, or we want to be, measure the confidence that our observations weren’t randomly generated.
[00:17:58] Taking any given set of numbers, All kinds of things can happen. the larger the result that you observe, the more likely that it isn’t random. it’s likely to actually be measuring something real rather than a statistical artifact. Is that fair?
[00:18:14] Jeremiah Green: Yeah, that’s, I think that’s a reasonably good way to, state it loosely. I would add one more piece that, it’s about how the results in the sample that you’re using, how likely is that to happen again in another sample? And so, if we have a really small p value, we would expect, that says it’s very unlikely that what we see in our sample is a statistical anomaly. If we go to another sample, we expect to see the, a very similar result. So low P value, highly confident that this is a reliable claim.
[00:18:52] Joe Selvaggi: So, we imagine a large salami, every slice is going to be the same. If we’re low P value, wherever we look, we’re going to find a similar result rather than, some next, the next result to be the opposite. We’re fairly confident that this is a consistent outcome.
[00:19:05] All right. So, then I wanted to talk about one more dimension, which is we’ve established that at least McKinsey asserts that there’s a relationship between, diversity and performance. But there’s another step, right? We have to observe that in their case, there’s a causational relationship, which is to say, as you say, it’s both big, but it also has to, one has to come before the other.
[00:19:28] The diversity has to happen and then the performance has to happen for it to be causal. I hope I’m saying that Share with our listeners. In their analysis, does the way they measure and when they measure diversity and performance, does it really show a causational relationship? You get, you are diverse, then you outperform. Is that what this study really shows?
[00:19:50] Jeremiah Green: Short answer, no. before getting too critical about what McKinsey says, they explain this in their papers, right? They acknowledge that this is a problem. When you read their conclusions, that’s a very different, they don’t match their conclusions to what they’re saying, but they, let’s take the first study for an example. They rank diversity in 2017, and then the performance, the profitability measure that they use is before the diversity ranking. So, they’re, just with their design, it’s impossible for diversity to increase performance because performance happens before the diversity happens.
[00:20:28] Joe Selvaggi: So, the proverbial cart before the horse, or this is something that academics in your field wrestle with all the time, right? Correlation is not causation. I might use a, I’ll use something silly and say, umbrella use, and rainy days seem to correlate, but it doesn’t mean umbrellas cause it to rain. You know, for our listeners, just briefly explain why, what a data scientist has to do to establish causation rather than mere correlation.
[00:20:54] Jeremiah Green: Yeah, what do we have to do to establish causation? That’s difficult. the best-case scenario would be we’re in a lab where we can randomly assign Something to happen, and then we see that the effect happens. a couple of things we can do. One is the direction that you’re taught, that we had just talked about, right?
[00:21:14] It, the thing that causes something else should happen beforehand. the other part is we have to deal with other, what we call correlated omitted variables, something else that could explain the results that, we were trying to claim are causal. So, you use the example of the umbrella. Another typical one that I find easy to conceptualize is, ice cream sales and shark attacks are correlated. And we don’t think ice cream sales drive shark attacks. Because we think something else is driving both of those, and that’s warmer temperatures. And we have to deal with those. I guess two things. We have to go through the logic of, is this plausible? And then we also have to try and deal with other things that could cause that. In the case of ice cream sales, we would need to find a way to control for warm temperatures. If we found, for example, that, using diversity, that, if we have some 3rd variable, but we can find that it happens outside of when that third variable could affect it, then okay, we’re, at least more confident that there’s a cause and effect happening.
[00:22:29] Joe Selvaggi: Yeah. and so they acknowledge that I’d say in the footnotes in this study, they do acknowledge the challenge with establishing causation. So, I thought, the, people who are doing the analysis Unlike the people who write the headline for McKinsey, acknowledge that the data does not support a causational, but rather just a correlational.
[00:22:45] I want to caveat that if we’re going to criticize McKinsey for something. Is that fair? Meaning that the, the people doing the research acknowledge this limitation?
[00:22:53] Jeremiah Green: Yeah, perfectly fair. And I have no problem with their research at all. I have a problem with the connections of the, like you said, the headlines that they’re putting out there don’t match their research, right? And so that’s right. I think they’re pretty fair when you actually read inside the research there. I think they’re pretty straightforward about it.
[00:23:16] Joe Selvaggi: All right, so we’ve set up this whole sort of conversation. Let’s get to it. okay, you say you, connect more with the, the research of McKinsey rather than the headlines of McKinsey, but let’s get into the research. Was McKinsey’s report, were you able to essentially get their data sets and do analysis of the simple math, or was that something that’s, difficult to, for them that they’re reluctant to share or they were unclear about?
[00:23:39] Jeremiah Green: Yeah, okay, so a few things, in terms of the method they use, they’re very clear. So that, it’s not hard to go in and, if you have the data, to do what they did. The piece that they don’t provide is the companies that they’re using. we don’t have access to, their sample is something like, 300 or 500 companies, nobody but them, they’re the only ones that know what those companies are. And being able to perfectly replicate what they do is not possible unless they give us their companies. But other than that, the rest is very straightforward. You can just copy and do exactly what they do.
[00:24:19] Joe Selvaggi: And of course they didn’t share which companies, not because they’re trying to keep it a secret, but it’s discretion. They don’t want to, in a sense, out their clients, right? They, that’s reasonable. They have a responsibility to keep that confidential. You did find your own companies to analyze. As we mentioned earlier, the P value is very low, suggesting that you’re going to find this result more or less everywhere you look. How did you develop, again, if you’re trying to replicate without the actual list of companies, you’re just replicating the methodology rather than the actual dataset, where did you go to find companies to analyze?
[00:24:50] Jeremiah Green: Yeah, sure. We tried a few things. It’s, this is really the biggest challenge, I think, in terms of trying to replicate their research. We there are so many companies out there. If we look globally, it’s hard to know where to start, but we know that they consult with a bunch of the largest companies, S&P 500 is a pretty common company.
[00:25:15] Benchmark for all companies. So that’s where we started. So, S&P 500, it’s about the same size as the, their sample. And we know that they consult with many of them. And so, to us, that was as close as we could get. as a side note, we also did a random sample, we thought, of U.S. company, sorry, public companies, and tried the same thing.
[00:25:38] And no matter what we did, we get similar results, but it, I don’t know, is that good enough? We tried the S& P500, and we tried a random sample, to try and approximate what we think they did.
[00:25:50] Joe Selvaggi: Yes, and of course, if their, observations would be valid, and your observations would be valid, you, they couldn’t, in a sense, Use selection bias to craft their results if they did. They would say, among those companies, among the companies we looked at, this is true, but who knows about any other company? If that were the case, then no assertion would be generalizably true. It would be a worthless observation. In order to have their observations have any weight, they have to be generalizable your analysis and their analysis, though they’re different companies, in theory, should show the same approximate result. Is that fair?
[00:26:22] Jeremiah Green: Yeah, yeah, I would say even more than in theory. Their P values aren’t valid if Their sample is not a random sample, and so what their P value should be is a reflection of what we should see elsewhere. And yeah, if we can get a sample that is meant to approximate the same types of companies that they’re looking at, we should see something similar.
[00:26:45] Joe Selvaggi: So, the companies used, provided it’s a large enough sample, are virtually irrelevant to an observation like this. and you replicated their methodology, which you say they were very candid about how they went about evaluating. So we’ve buried the lead enough. Given all your research, you had two co-authors and a substantial amount of time to analyze the data, did your research see a correlation, not, we’re not going to get to causation yet, between diversity and performance?
[00:27:15] Jeremiah Green: I just want the short answer.
[00:27:16] Joe Selvaggi: No.
[00:27:18] Jeremiah Green: No.
[00:27:18] Joe Selvaggi: Okay. I don’t know if our listeners were anticipating that answer. It’s the shortest of all the answers you’ve offered. So I want to go, a little bit further and say, okay, we established early on that what we’re talking about diversity, we’re talking about diversity in the higher level ranks. We’re talking about a large signal that they saw that a large result of confidence in their results. You found no such, relationship between How diverse a firm was and how profitable a firm was. Did you use, the same exact, let’s say, I think it was earnings before interest and taxes. did you use, did you try to tweak the numbers in any way to look at different metrics that maybe you were missing some, some variable that they accounted for?
[00:27:55] Jeremiah Green: Yeah, let me give a little, more than you would see in the paper. So, in the paper we tried, I can’t remember, six or something different measures. Outside of that, I, we were convinced there’s got to be something to this. So, I tried every Measure that I could think of, so I don’t know what it was 25, 30 measures. There’s got to be something there was my thinking and no, no matter what we did, we couldn’t find it. and this is replicating their approach. We mentioned that they measured diversity and then looked at performance prior to that. And so, I tried that with every measure, we tried it with every measure we could think of.
[00:28:36] No association at all. And then in another paper, so this paper that we’ve been talking about is with John Hand. He’s at North Carolina. We also have another paper with Sekou Burmes, who is a strategy professor at North Carolina, and we flipped the direction. So, then we tried to say, what if we measure diversity, let’s say, in 2017 and looked at performance after 2017.
[00:29:02] And, maybe we could get this sort of the right direction, and then we would find results. Nothing. So, no matter what we did, and this is You know, you don’t see it all in there, but we spent an enormous amount of time trying to find any association that we could and just can’t do it.
[00:29:22] Joe Selvaggi: So, we’re not going to even talk about causation because where there’s no correlation, of course, there could be no causation in either direction that this is an extraordinary revelation in my view. It’s an extraordinary revelation. one would think even randomness might generate some sort of result, you could draw a line in the middle of a room and say, did the people on the left hand in the room have anything different than the people on the right-hand room? You’re going to find something somewhere. Guys named Joe may be different than guys, everybody else, you didn’t find anything.
[00:29:49] Jeremiah Green: I tried, I guess the only thing I didn’t try, we did the S& P 500 and the random sample. I guess another thing we could have tried is, just keep shifting around the companies and see if in any sample we could happen to find it, right?
[00:30:06] If it was just, you’ve looked through the data too much and randomly you should find a result. That, I guess that’s the other thing we could try, but yeah, everything we tried, there’s just nothing there. Oh, we also expanded it when we started, we did it with one year, just like a single one of their studies, and then we expanded it to many years, and no matter what, we just can’t find it.
[00:30:28] Joe Selvaggi: This is pretty powerful. When one finds no results, one can’t say there isn’t some relationship, but it’s just not measurable, particularly given the data and the methodology. This is big news, because I think McKinsey has a, their influence is powerful. corporate boards and, C suite people use that as I won’t say gospel, but essentially as a north star for many of their decisions. To make the assertion that they do, that it essentially is almost. not almost. It is proved that diversity leads to greater profitability. If I’m a leader of a firm or a member of a board, I have a fiduciary responsibility to my shareholders, right? I don’t own the company. I may be the president, but I don’t own the company. I work for the shareholders. It’s my job. I have to make choices for their benefit. If it’s true, what McKinsey asserts, then I would be, violating my oath as a fiduciary to not make my firm more diverse. Without this evidence, in a sense, now we can’t privilege diversity over others, let’s say, of course, we want to make the firm profitable.
[00:31:29] We’re not saying that this is morally or ethically a bad idea, diversity, we’re not going there. We’re just saying as a measure of profitability, as a head of a firm, it’s either imperative, it’s a financial imperative that I use diversity, but without that evidence, no such imperative exists. We can essentially use Pro more traditional, profitability criteria when making our decisions. I don’t want to go too bold here, but I want to say if this is, if this, data is not valid or observation is not valid, I’ll say maybe the data is, would this, upset the entire, diversity push in financial markets?
[00:32:10] Jeremiah Green: Yeah, let me, be careful in how I state this here. I think I agree that, if that were the case, and I think this is how what you were saying, has been used that way. If it is the case that increasing diversity increases performance, managers must increase diversity because that is going to improve returns to their shareholders. Fantastic, right? It’s a good story. If it works, then the path forward is clear. let me try and claw back some space for McKinsey here.
[00:32:44] In the last study, I, the one that just came out in 2023, so that was actually after we had, looked at the first three studies, they add other things that, that diversity might influence. for example, employee satisfaction. It’s completely believable that diversity could influence employee satisfaction.
[00:33:09] You could think of a lot of reasons that might be the case in terms of that, great, right? that may be something that companies should care about. It may be something that employees care about, and if a company decides to make decisions based on that, then no problem. The challenge is, though, they can’t rely on this evidence to say, it’s going to increase performance because that’s not the case. Let me give the other side of that is we don’t find that it decreases performance either. So, it, it doesn’t seem to hurt or help in terms of bottom-line performance. It could be as benefits in other ways, but just not, the sort of bottom-line profitability approach, or type of outcome.
[00:34:00] Joe Selvaggi: I’m glad you point out the double-edged sword of finding no results, which is diversity, when pushed doesn’t help, of course, but diversity when pushed doesn’t hurt it maybe is a Rorschach test to say which, which, support you want to, observe. that, that’s fair, and I’m glad you did point that out. Again, no signal gives us all latitude to, to look for other things. And as you say, employee satisfaction. But of course, if it improved employee, being or satisfaction or something like that, that theoretically, we want that. Of course, it’s nice, it’s inherently a good idea, but we would also think that would improve Thank you.
[00:34:31] Profitability. Again, if you want to signal, employee satisfaction isn’t correlated with profitability. It’s good, but it’s not a, a financial imperative. so what I really want to, again, I’m going to ask you to go on a limb here and say, okay, McKinsey has produced these four reports with glowing, summaries, and which are touted, when there’s legendary slide decks to, to C suite executives and boards, why would McKinsey, with, has built a reputation, I think they hire the best, smartest people coming out of the best schools, why would, McKinsey, make claims that their own data really wouldn’t support?
[00:35:07] It seems, or at least it is impossible to reproduce. What do you see as a sort of incentive for a firm to, say, jeopardize their reputation for data, quality data and integrity?
[00:35:20] Jeremiah Green: Yeah, I’m glad you said you’re going to ask me to go out on a limb because this is just speculation, right? I don’t know. Although we have examples in academic peer reviewed research where, researchers do things that would have bad consequences in terms of their reputation. So This does happen everywhere, so I can’t, claim that, we’re peer reviewed, so we’re so much better, but in terms of, I think, the incentives, we do have, some amount of, we are different, and I think the big difference, if I were to take a I don’t want to take a view like they were purposefully trying to, misstate what they are saying, but I think they, they want to be able to tell companies you should do this, and we don’t want to do that as academics, we just want to evaluate the evidence, and in the jump from them. Here’s what we found should. I think that’s where the misinterpretation of their evidence came from. That’s my guess. if you’re trying to tell a company you should do this, but then you tell them, if you have diversity, you previously would have performed better, that isn’t very compelling to get them to do something, I guess.
[00:36:37] Joe Selvaggi: There’s so many ways to interpret it. Again, we’re not inside the minds of McKinsey consultants, but of course, we all respond to incentives. Academics, hopefully academics’ reputation for integrity and accuracy is the sort of coin of the realm. In consultancy, perhaps, There is an incentive to tell your client what they want to hear, or I’ll say, again, I’m going to relate it to many topics we cover here on Hubwonk is, unfortunately, this sounds like a political, an aspirationally political position, which, and my, it’s going to go on our, my tombstone, perhaps, is the unfortunate fact that when you mix science and politics, you get politics.
[00:37:13] This seems to be just one more instance where a revered institution has Perhaps, wandered off the path and compromised integrity for, results that people prefer. you know, do you see this sort of perhaps as a potential victim of either group thinking or motivated reasoning?
[00:37:31] Jeremiah Green: Yeah, I hesitate to make any judgment about the moral or ethical good or bad, is this good or bad? It does seem, I like your statement of if you mix science with politics, you get politics. That seems pretty accurate to me. So, what I do think is that we should be aware of when we’re interpreting what research says, and maybe particularly when it comes from, people like consulting firms or groups like consulting firms that we need to be careful of how it’s being interpreted. And I think, we, I hope we’ve shown that, at least in these studies, you can’t make the conclusions that McKinsey has been making, those are just not, they’re just not correct, they’re not what is shown in the data.
[00:38:23] Joe Selvaggi: And that’s fair, and one can understand why McKinsey, having found nothing in the data, that, the headline cannot read, we found nothing they took the ball and ran with it. I appreciate you taking your valuable time with us today, Professor Green, it’s a dry subject, it’s a difficult subject, but I think peer reviews, scrutiny of, academics with integrity is absolutely essential. We’re not asserting whether diversity has a moral benefit.
[00:38:44] We’re just examining whether it has the. the financial benefit that the study claims. So at least in this regard, you’re shedding some light in what seems to be a fairly, dark or cloudy area of analysis. Where can our listeners, find either your study or your paper? I was able to access it, there were no, paywalls or anything. Where might, are, and you’ve published quite a bit, where could our listeners read more about your work?
[00:39:10] Jeremiah Green: Yeah, a few places. If you just search Jeremiah Green at Texas A&MI pop up so you can find my webpage and my CV there. if you go to econ journal, watch, or Journal of Economics Race and Policy, that’s where one of our other ones is. Or you can look up some of the co-authors, John Hand at UNC or Kuber at UNC. I think it’s pretty easy on academics to find that stuff.
[00:39:36] Joe Selvaggi: Wonderful. I appreciate your time and thank you again for joining me today on Hubwonk. Professor Green, you really, I think shed some light on a difficult topic.
[00:39:43] Thank you. for having me.
This has been another episode of Hubwonk. If you enjoyed today’s show, there are several ways to support Hubwonk and Pioneer Institute. It would be easier for you and better for us if you subscribe to Hubwonk on your iTunes podcatcher. It would make it far easier for others to find Hubwonk if you offer a five star rating or a favorable review.
[00:40:03] We’re of course grateful if you share Hubwonk with friends. If you have ideas or comments or suggestions for me about future episode topics, you’re welcome to email me at hubwonk@pioneerinstitute. org. Please join me next week for a new episode of Hubwonk.
Joe Selvaggi talks with business data scientist Dr. Jeremiah Green about his peer review work examining consulting firm McKinsey’s studies on the measurable financial benefits of diversity in corporate executive leadership.
Guest:
Jeremiah Green is an Associate Professor of Accounting and holds the Ernst & Young Professorship of Accounting at the Mays School of Business at Texas A&M University. He does research on executive race/ethnicity in US public companies, and capital markets research that focuses on the use of accounting information. Dr. Green also studies hedge funds, equity and debt analysts, auditors, managers, the business press, and equity trading strategies. His teaching centers on data analytics and analytics for financial reporting.
Commentary On The Senate Ways And Means Committee FY2025 Budget
The Senate Ways and Means Committee (SWM) released its FY2025 budget on May 7th. This spending plan totals $57.9 billion, an increase of $1.8 billion over the FY2024 General Appropriations Act (GAA). Like the Governor’s and House’s versions of the budget, the SWM budget is based on the consensus revenue estimate of $41.5 billion in tax revenue – a decrease of $208 million from last year’s consensus figure. Given the volatility of revenue collections for the current fiscal year – shortfalls for the first nine months and then more than a $1.5 surplus over anticipated revenues in April – there is a lot of uncertainty around the revenues upon which FY2025 budget proposals are based and this uncertainty warrants a cautious approach to spending.
The Senate, going last in the budget process, has the advantage of reliance on more current revenue numbers when formulating its annual operating budget. Perhaps, the latest revenue collections made them cautiously optimistic, even while acknowledging the tightening fiscal environment, allowing them to spend more freely on priority areas than they otherwise would.
The budget’s executive summary states that the SWM Committee adhered to a disciplined and responsible fiscal stewardship. As proof, it indicates that the budget does not include tax increases, or withdrawals from the Stabilization Fund (the state’s reserves) or the Transitional Escrow Account (federal money that has not been appropriated yet). SWM deserves high praise for both of these strategies, but that is not the entire fiscal picture.
Where the SWM budget goes astray is in its use of more than $1 billion in one-time revenues to fund both new and ongoing expenses. The Senate is not alone in the practice, nor is it the worst offender, as the House relies on $1.19 billion in one-time and new revenue sources and the Governor proposed $1.25 billion in new taxes and one-time revenue sources. In fact, the following sources of one-time revenue are common to all three versions of the budget:
While one can argue that the trust fund resources were dedicated for the purposes for which they are being used, and therefore, a reasonable use of funds, that money will be unavailable next year and thus is not a sustainable way to pay for operating expenses.
The Senate budget differs from the Governor’s and House’s budgets in two noteworthy ways. The Senate does not propose tax law changes (elimination of various deductions) or additional sources of one-time revenue, (such as using Gaming Funds, allowing online lottery sales, or providing a tax amnesty period) to support this increased spending like its budget counterparts. While Pioneer Institute supports this no new tax approach, it also recognizes that these sizable new investments introduced by the Senate will grow the bottom line of the state budget without any revenue offsets at a time of slow revenue growth.
Examples of noteworthy new investments or increases include:
These spending items will either require new taxes in subsequent years or budget cuts elsewhere to sustain these program expansions over time.
Pioneer Institute appreciates the ebbs and flows of tax revenue collections and the need to smooth out spending cuts over time, but the reliance on one-time revenues seems unnecessary given duplicative or overly generous spending in several key areas. A sampling of these items include:
Cheryl Brown Henderson on the 70th Anniversary of Brown v. Board of Education
/in Education, Featured, Learning Curve, News, Podcast /by Editorial StaffRead a transcript
TheLearningCurve_CherylBrownHenderson
[00:00:25] Albert Cheng: Hello, everybody. Welcome to another episode of the Learning Curve podcast. I’m one of your hosts this week, Albert Cheng from the University of Arkansas. And hosting with me, co-hosting with me this week is Jocelyn Chadwick. Jocelyn, good to see you again.
[00:00:41] Jocelyn Chadwick: It’s good to see you too, albert. I’m glad to be here today.
[00:00:44] Albert Cheng: Yeah. I still think about your reading of Langston Hughes last time you were on.
[00:00:47] Jocelyn Chadwick: Ah, yes. That was very interesting. And I’m really glad that we were able to do that.
[00:00:54] Albert Cheng: Yeah. we’ve got a great program this week. First, I want to give a shout out. It is National Charter Schools Week. If you’re paying attention to that or didn’t know, check that out. National Charter Schools Week has started. It’s this week. But also, this Friday is the 70th anniversary. of key Supreme Court case, Brown v. Board of Education. To commemorate that, we’re going to have Cheryl Brown Henderson on, and I’ll introduce her later, but she’s one of the daughters of the late Reverend Oliver Brown, who is the plaintiff in this case. really excited to get into this week’s show. Jocelyn, before we do all that, though, let’s do some news. Do you have a story that you’d like to share?
[00:01:31] Jocelyn Chadwick: Oh, yes. I have one I would love to share. It’s Opinion, How to Help Third Graders Struggling to Read, Don’t Socially Promote Them by Mitch Daniels. And I must say, as I read this op ed piece, I have never seen, nor read, nor heard the word of the author Educrat.
[00:01:53] And I thought, that’s a first for me. And I went to the Oxford English Dictionary, and the Oxford English Dictionary says that it is a word that is only used here in the United States. And it denotes a tyrant who happens to be a teacher. And I really took umbrage with that. So I did the op ed piece.
[00:02:12] Objectively, because I do that, that’s just what I’m supposed to do. And while I understand the position about social promotion, it seems to me that in this one, in this particular article, the writer decides, Mr. Daniels decides that he’s going to paint all educators and administrators, as he calls them, with a broad brush of educrats, establishment of unions and career administrations.
[00:02:39] I would like to say just, against it first, that not all teachers are members of unions, not all unions are bad, and not all administrators are career administrators who don’t care, which is what he says. That is his primary thesis here, aside from We should not socially promote students who cannot read.
[00:03:01] The focus of it is on reading, but Mr. Daniels doesn’t seem to want to focus on that. But I want to focus on his point about it. Third grade, if a student hasn’t learned to read by third grade, we’re really in trouble. So where does it begin and who’s responsible? And so, my response to this Op ed piece is, number one, reading begins in utero.
[00:03:25] If parents read to their children and let them listen to music and all of that, it does have an impact, as well as parents playing a key role in having reading at home. Out loud, storytelling, reading books, papers, everything, reading in the grocery stores, reading billboards. It’s just reading, and parents are with them 24 7.
[00:03:49] Teachers are to teach. We teach reading, especially English language arts, but in all of our classes, in all core content areas, there’s reading. And administrators, I know some amazing administrators who work with their teachers and who, even when I’m teaching those classes around the country from fourth grade through 12, and I’m guest teaching.
[00:04:08] Lecturing with them or teaching. Sometimes the administrators are in those classes, and they become learners and exemplars for their students. And the other, of course, is the community and the community libraries and so forth. All of those things combine to help students learn how to read. This idea of calling all teachers and administrators educrats, I think, is specious. And I would love to have a conversation with Mr. Daniels one of these days.
[00:04:38] Albert Cheng: Yeah, and just to even speak a little bit more on this, the issue. here in Arkansas, we’re making a pretty great, big effort to ensure reading readiness by third grade. And there’s some policy levers that are being pulled, teacher training and some work on curriculum and pedagogy. But, I got to agree with you too, as well, that it takes a lot of folks, both inside the school, outside the school, to get our kids to read. Hopefully I’ll have some really good news to share come out of Arkansas in a few years, once these new programs and policies get implemented and running.
[00:05:07] And I know a lot of states across the U. S. are also engaging in some of these efforts. So yeah, hopefully we can make some progress here. It’s an important issue.
[00:05:15] Jocelyn Chadwick: Absolutely. Yeah.
[00:05:17] Albert Cheng: Speaking of remediation, Jocelyn, I found a news article this week about something that Newark Public Schools in New Jersey is doing.
[00:05:25] They’re going to use Khanmigo. I don’t know if you’ve Played with this is Khan Academy’s AI tutor. And so again, speaking of remediation, this is, its old news, but important news where, we’ve had a lot of loss of learning progress since the pandemic. And so, lots of school districts are trying to figure out some interventions and ways to catch kids up and address learning gaps.
[00:05:48] And so Newark Public Schools is going to start using Khanmigo to help tutor their students. You know what’s it is, what excites me about this? I. I’m, I wouldn’t say I’m bullish about AI, I don’t know what to think, if you ask me to predict if it’s going to be wildly successful or not, I’m moderate, but, what I appreciate about this news article is that, and what Newark’s doing is that they mentioned a call for more research, and they’re actually going to do an evaluation of this program. And so, we’re going to be able to learn with some data and evidence about the effectiveness of this initiative. And, hopefully this research will have something to teach, not just Newark Public Schools, but all districts across the country, all schools, public, private as well. So hopefully it works, but we’ll see.
[00:06:32] Jocelyn Chadwick: I totally agree with that. I, again, like the idea of AI. This is the 21st century and Gen Z. there is, it’s not either or. I know that folks think that you cannot read a book unless you’re actually reading the book. My iPad goes with me everywhere, and I’m reading and notating books. And I get the idea that. I like what they’re trying to do. The idea of just investigating and exploring and trying to address our students where they are and bring those of us who are teachers from a long time ago into the future.
[00:07:02] Albert Cheng: Yeah. If there’s one thing that’s going, there’s plenty of research out there suggesting that tutoring is a pretty good intervention for catching kids up. We’ll see how it goes when we do it. Shift from a human-to-human tutoring arrangement to an AI to student. So anyway, let’s stay tuned to see what happens there. Stick with us because coming up after the break, we’re going to have Cheryl Brown Henderson, who’s going to talk to us about Brown v. Board of Education and her own experience with it. Stay tuned.
[00:07:49] Cheryl Brown Henderson is one of the three daughters of the late Reverend Oliver L. Brown, who, with NAACP attorneys, filed suit against the local Board of Education. Their case was appealed to the U. S. Supreme Court on May 17, 1954, and became known as the landmark decision Brown v. Board of Education of Topeka, Kansas.
[00:08:12] Cheryl is the founding president of the Brown Foundation for Educational Equity, Excellence, and Research, and owner of Brown and Associates, an educational consulting firm. She has an extensive background in education, business, and civic leadership, having served on and chaired various local, state, and national boards. She earned a bachelor’s degree in elementary education, minor in mathematics from Baker University, Baldwin City, Kansas, a master’s degree in guidance and counseling from Emporia State University in Emporia, Kansas, and honorary doctorates from Washburn University and the University of South Florida. Cheryl, I want to welcome you to the show. It’s really great to have you here with us.
[00:08:53] Cheryl Brown Henderson: Thank you. It’s a pleasure to be here. This is a very busy time with the anniversary of Brown v. Board coming up, one of the major anniversaries. I welcome the opportunity.
[00:09:02] Albert Cheng: Great. Yes, it is. As for our listeners who are tuning in, it is the 70th anniversary of that landmark ruling. So, Cheryl, let’s talk about the case and just some of the background in it. Particularly, a lot of the folks that are involved, many of them very well. So, can we start with your father? Would you share with our listeners more about Reverend Oliver Brown and just your family’s experiences growing up in Kansas in the 1950s?
[00:09:28] Cheryl Brown Henderson: That’s a tall order, because Brown v. Board is very complex and multifaceted, as people are coming to learn, and a lot of people, I believe the general public, may have a sense that it’s something that happened somewhat in isolation. However, it was a long campaign, and those of you in Massachusetts know full well that it was about 105 years in the making, between, the first case documented in Boston, Roberts versus the city of Boston, all the way to Brown v. Board in 1954, 105 years. And in that ensuing time, however, there were multiple school desegregation cases in my home state of Kansas. And as a native Kansan, I’m always thrilled to be able to educate people about the significance of the state. Kansas just happened to be an extremely consequential state.
[00:10:20] With respect to the civil rights in this country, the civil rights movement, what happened post-Civil War, all of that. And I think all of that fed into the fact that Kansas was a hotbed of activity around litigating school desegregation cases starting in the 1800s. So, from 1881 all the way to 1949, 11 school desegregation cases.
[00:10:44] In my home state, they were litigated in the state Supreme Court. So, Brown v. Board in Kansas was not an anomaly, was not unique, and it was not unexpected that there would be an additional attempt at integrating public schools. The sea change, however, was rather than state Supreme Court. Brown v. Board was taken to federal district court. And that’s what really opened the doors for what we are experiencing, what happened after Brown v. Board, was putting it in the lap of the federal government, the part of our country that had the interpretation of the Constitution as a responsibility. And that made all the difference in the world in moving this along.
[00:11:27] Albert Cheng: Let’s continue talking about the case a bit more. What was your father’s experience carrying out the Topeka NAACP strategy of having African American parents attempt to enroll their children in white only elementary schools?
[00:11:42] Cheryl Brown Henderson: My father was one of these people at the time he was studying for the ministry, and he was a very family oriented, a very loyal person in terms of friendships, and it just so happened that one of the attorneys for the NAACP, working on organizing this litigation, had been a boyhood friend of my dad’s.
[00:12:04] His name was Charles Scott, and ironically, both Charles Scott and his brother John Scott were attorneys for the NAACP along with Charles Bledsoe. So, Charles Scott called on friendship. He came to our home and asked my dad if he would be willing to join this litigation they were organizing. To desegregate elementary schools into people, because in our family, three girls, my mom was expecting me, so I guess I won’t say three girls, two girls and a baby on the way.
[00:12:33] And so Charles wanted to know if dad would do this. My oldest sister Linda was the oldest in our family and the only one in school at the time. Charles had claimed to my dad that they were recruiting all over the city, going to churches, talking to other friends and neighbors who were African American to solicit, getting them to sign on, because in the 1950s, perhaps not so much in Kansas, but around the country, particularly in the South, it was a risk, it was risky to take a public stand against Jim Crow laws, which is what, in essence, he was asking them to do.
[00:13:06] So my father contemplated and did not say yes immediately. There were nine people on the roster when they came to our home when Charles came, and nine of those were all women, married women, and homemakers, but women, nonetheless. So, one of my dad’s questions and reluctance was, are you planning to recruit other dads? Will there be other fathers involved as plaintiffs, and Charles assured him that they were still recruiting. Of course, he couldn’t deny that. Answer beyond that. it really took some convincing on the part of my mother, and I can be very persuasive, and explaining to my dad that as someone studying to become a pastor, someone that would soon be leading a congregation, someone who by virtue of that role would be seen as a community leader, that this was the right thing to do, not just for our family, but for the community and for his eventual congregation.
[00:14:00] Charles came back in a couple of weeks, and Dad agreed that he would join the roster. just that simply is how my father got involved in the first place. And then, once they met, in the fall of 1915, they ended up with 13 families that had agreed to sign on as litigants. They were instructed by the NAACP to locate a white school near your home, and take your child or children and a witness, and they did. And it came to enroll, because that was the evidentiary part of this. They needed that documentation of being refused the right to enroll their children. It was very technical. It wasn’t high drama, perhaps as it was in South Carolina or Virginia, places in the South. It was very matter of fact and very. My father and his fellow 12 plaintiffs spread out across the city attempting to enroll their children in white schools close to home. We lived in integrated neighborhoods.
[00:15:00] Cheryl Brown Henderson: These schools were in our neighborhoods. It was just that based on race, you were assigned to a school that was segregated for African American children and could not go to the neighborhood school unless it was a segregated African American school. And the one in our neighborhood was not.
[00:15:17] Albert Cheng: So, you’ve told us a bit about how your father got involved and how the lawsuit was actually filed. I’m curious to hear more about that if you have anything else to say, but I also want to hear about the 12 other families that were involved.
[00:15:29] Cheryl Brown Henderson: Yeah, I’m glad you asked that.
[00:15:32] His fellow plaintiffs, or those that signed on as well, and actually, as I said, many of them signed on before my dad. These were moms. These were married women, homemakers back then. The traditional families, I’ll put it that way, really wanted something better for their children. They wanted them not to have to walk blocks and then ride many more blocks to attend school when there were schools down the block.
[00:15:57] They would often talk to their white neighbors, the moms would talk, and the kids played together after school, played together all summer, and the moms would step outside often and talk about how This made no sense, that the only difference was when the school bell rang. My child goes one way, your child goes another way, then they come back together in the evening.
[00:16:18] It just makes no sense. Some of the women were from small towns and had moved to Topeka, the big city, the capital city, as young women, prior to being married. So, they had already lived in integrated settings and attended integrated schools. Because Kansas Only allowed segregated elementary schools in cities of 15,000 or larger.
[00:16:41] Mrs. Todd, for example, from Little Oak, Litchfield, Kansas, small town, gone to integrated schools, gone to Pittsburgh State University, integrated college. Mrs. Henderson from Oakley, Kansas, way out west near the Colorado border. Moved to Topeka and all of a sudden, her rights, as she says, I didn’t have as many rights as I came here with. So a lot of these women, I think because of their experiences in small towns, wouldn’t stand for, what they were witnessing in Topeka after having lived under different circumstances.
[00:17:13] Albert Cheng: So, I want to ask you a bit more about your father, just to shift back to him. You mentioned that when this all got started, he was about to start his role as a clergyman. And so this is, he’s similar to a lot of the other civil rights leaders that are familiar to us. Dr. Martin Luther King, Fred Shuttlesworth, Reverend Fred Shuttlesworth, all of them were clergymen. So could you discuss the central role that faith and religion played in your family’s decision to be plaintiffs on this case?
[00:17:40] Cheryl Brown Henderson: Yeah, I think that’s quite telling, the litany, the men’s names that you just read a minute ago, quite telling. The African American church is always the gathering place in, within our communities. It’s a safe space, to borrow from some of the current terminology, and a lot of the strategy and strategic sessions around protest marches, and obviously Brown v. Board as well, it took place in those hallowed halls. I always talk about the Black church or African American church as being the first social service agency. It was a place you could go if you needed help of any kind. It was a place you could go, if you needed counseling and support. It was always Looked at as not just a place of respect and spirituality, but a place of safety and security and guidance, and I think that’s why, when you look at the men you named, why the Church is front and center.
[00:18:35] Dr. King, as a young man, become, pastor at Dexter Avenue Baptist Church, where Berta Johns, before him had been pastor and very much an activist, and left in part because of that activism which led to the bombing. And ironically, Bertha Johns niece, Barbara Johns, was a young woman who organized the strike in Farmville, Virginia, that ended up being one of the cases in Brown v. Board of Education. So, a lot of this activism emanating from the Black church.
[00:19:04] Cheryl Brown Henderson: Where we were taught, for public speaking, we were taught leadership. We were taught there was responsibility to uplift our race, ourselves, to always represent in a positive way. I think it was reflective throughout the country when you look at the other movements.
[00:19:19] Albert Cheng: Let’s pivot a little bit and get into the nitty gritty of the case. Earlier in the previous question, you were mentioning how school segregation was an issue everywhere, just across the whole country, it wasn’t just a Kansas thing. And the term separate but equal. Dates all the way back to 1849, so this is, years and decades before Brown v. Board. In a court case, Roberts v. City of Boston, northeast city, that was a case that sought to end racial discrimination in Boston public schools. Could you catch listeners up to what was going on in other districts and other places like the northeast and the Midwest as the case was developing?
[00:19:54] Cheryl Brown Henderson: Yeah, after Plessy v. Fergus in 1896, which gave us that doctrine of separate but equal. It spread like wildfire, to be honest. We often talk about the Civil War, and my thinking on that is always that the South won the physical war, but they won the war of ideology. Because the ideology of keeping African Americans and whites separate, the ideology of the Civil Whose children do we invest in and second-class citizenship and everything that the Civil Wars seem to represent seem to be a sentiment shared in many other places. And so not only in the Northeast, when you talk about Roberts v. the City of Boston, but also as far away as California, Asian desegregation cases, Latino, Mendez versus Westminster. So, this was all about in my view. Built in, or baked in, disadvantage. creating disadvantage for certain populations.
[00:20:50] If we talk about a level playing field, we know full well, by looking at the record, the historic record, that it was very much intentional that certain groups of people would be disadvantaged in this country by not being Afforded equal educational opportunity, not being afforded, up to date textbooks and facilities that would accommodate children without leaky roofs and broken windows.
[00:21:13] And we can’t pretend that all of what we’ve seen and all of it we’re talking about now that was not just in the South was not intentional. It was very intentional. And the post reconstruction amendments, 13th, 14th, and 15th, that sought to make a difference after the civil war. We all know Reconstruction was short lived.
[00:21:35] Jim Crow and Black Codes and that sentiment, became the law of the land, even though it wasn’t law in the true sense. But other parts of the country were no different. And even though Kansas had been a free state, not a slave state, even though Kansas offered certain we’ve had levels of integration in neighborhoods and some workplaces, but still there was that issue of education. That’s where we fell in line with the southern sentiment of racial segregation.
[00:22:06] Jocelyn Chadwick: Cheryl, this is Jocelyn, and I just first want to thank you. Hello! I first want to thank you. Your parents certainly blazed the trail for people such as myself coming after, after Brown v. Board. So, thank you. And listening to you talk about your parents, just, I’m just sitting here smiling and I’m really happy about that.
[00:22:25] So let’s continue. Last week on our podcast, we hosted the biographer of Justice John Marshall Harlan, author of the famous dissenting opinion in the notorious 1896 U.S. Supreme Court decision Plessy v. Ferguson. Will you talk with us? about why people should know about and remember the Plessy decision and Jim Crow segregation.
[00:22:50] Cheryl Brown Henderson: Obviously, Plessy v. Ferguson is what set all of this into motion. And Plessy v. Ferguson is what made a Brown v. Board necessary. And as we know, beyond Brown, it made the civil rights movement necessary. It made the legislation that came after necessary. John Marshall Ferland, I think people should know more about him because he spoke in ways that depicted the constitution as a document that applied to all.
[00:23:17] He talked about being colorblind, a document that intended for citizens, regardless of ethnicity, gender, disability. Any other characteristic to be treated fairly and freely and equitably and our country in Brown I believe is a reckoning against Plessy v. Ferguson, a reckoning that we were anything but adhering to the documents talking about equality, like the founding documents and the 15th Amendment to the Constitution. So, I think young people in particular need to know the genesis of why a Brown v. Board was needed and Plessy v. Ferguson. It is, in fact, that genesis.
[00:23:58] Jocelyn Chadwick: You are so right. As you were speaking, the phrase kept coming into my mind when Harlan says that there would remain a power in the states by sinister legislation. And that is just, you’ve just explained that so well. to continue on this, the NAACP’s chief legal counsel, Thurgood Marshall, called John Marshall Harlan’s Plessy dissent his Bible and legal roadmap to overturning segregation in Brown v. Board of Education. Would you share with us your family’s interactions with the NAACP, Thurgood Marshall, and the legal preparation for this landmark decision?
[00:24:38] Cheryl Brown Henderson: Yeah, I, first of all, let me say that I, Justice Marshall is somebody I greatly admire, and I have his photo from the postage stamp hanging here in my office. One of the things I believe he meant, just, is that the law was really the foundation for social change. If you change the law, it makes social change possible.
[00:24:58] And that was in fact true, because once Brown v. Board was announced, it made possible the movement towards social change that we saw after. Now, Justice Marshall was not someone our family interacted with, again, one of those misnomers and there’s so much out there and social media and the internet certainly doesn’t help our cause because people come up with things they believe must have happened without actually knowing.
[00:25:24] And but my father never met Thurgood Marshall. My mother heard him speak when he came to Topeka to speak to an audience of people about all of the activity of the NAACP and in particular the cases. That’s it. That were being organized, but she was one in the audience, didn’t interact with him. He did stay in the home of Lucinda Todd, who was the secretary for the NAACP.
[00:25:47] And Lucinda Todd was, in fact, the first plaintiff in Brown v. Board of Education. Because she was at the meeting when they came up with the strategy and immediately volunteered. not like the internet would have you believe. We did not have a personal relationship, nor did my parents ever actually meet Thurgood Marshall.
[00:26:08] Jocelyn Chadwick: I thank you for that clarification, but as you said, his influence and his impact affected all of us, and that is special. to continue, In May 1954, the U. S. Supreme Court issued its unanimous 9 0 decision in favor of plaintiffs in five cases they consolidated from Delaware, Kansas, South Carolina, Virginia, and Washington, D.C., ruling that separate educational facilities are inherently unequal, quote, unquote, and therefore laws that impose them violate the Equal Protection Clause. of the 14th Amendment of the U.S. Constitution. Please share your perspective of this decision and how this historic event in U.S. history still resonates with you personally.
[00:27:01] Cheryl Brown Henderson: I think the biggest takeaway is that Supreme Court decisions end up being simply a beginning of a conversation. People on the winning side are happy, excited, and they speak of change and implementation. People on the losing side are Disgruntled and will do everything they can to obstruct, deny, reverse, ignore, refuse to comply.
[00:27:30] And Brown v. Board is a quintessential example of that, because on the one side, the NAACP, even though it was measured enthusiasm, and that was true in our community and in our church and NAACP meetings, it was really cautious. Optimism that in fact, this would move forward in a forthwith manner, but on the other side of that conversation, we have people across the South who were gearing up for pushback.
[00:28:00] We have people in the United States Congress who were gearing up for pushback. The Southern Manifesto emerged out of the United States Congress, about a hundred elected officials from the South. Saying they were going to work to reverse the court’s decision, saying that it was judicial overreach, saying they were going to do everything in their power, and that any school district planning to defy, basically, they would support that.
[00:28:24] Massive resistance across the South within three years after the decision was announced. I’m saying all this to say that very few school districts immediately Complied. Now, Kansas was one of those states. Topeka was a city that immediately complied. May 54, court announced its opinion. In the fall of 54, schools desegregated in Topeka. And even some of the African American educators were moved into formerly all-white schools. The only thing that happened was principals for one year, if you had a black teacher in your building for the first time, were charged to call all white parents at that grade level to make certain that they were okay with having an African American teacher for their children.
[00:29:08] It was only something that they did for one semester because no one said no. So, we’re living in the aftermath of this conversation because those that really wanted to push back on Brown are still pushing back on Brown. Those who believed it was the way forward are still trying their best to make it the way forward and to bring us all together around the intent of the decision very complex, very difficult, not a straight line. This was not a linear decision where we decide and then going forward in a straight line, all these things happen. Far from it. And I think we’re still there.
[00:29:48] Jocelyn Chadwick: You are so right. And I just have to tell you, I was in that generation, as I said, that benefited later from what your father and others did. And, in Texas. So, I was in the South. And you’re right, it was unequal. some. Some schools did have that integration and tried, others didn’t, so I’m glad that you took your time to explain that and make it very clear that we’re still in the midst of all of this shift and strum and drum. Thank you for that. Let’s move on. The court’s Brown decision’s 14 pages did not spell out a method for ending racial segregation in schools. And the court’s second decision in Brown 2 Ordered states to desegregate, quote, with deliberate speed, unquote. Would you tell us more about Brown 2 and states resistance to the Brown decision? And that’s where you were going when you were just finishing up the previous one, so you can continue on.
[00:30:45] Cheryl Brown Henderson: Yeah, when you think about Brown 2 with all deliberate speed, that the courts giving the implementation decision, if you will. Disappointing, and you almost wonder if it’s laden with some sort of message, because the word deliberate is an odd choice. Deliberate, if you bother looking up the definition in any dictionary, it is a word that means slow.it doesn’t mean deliver from the standpoint that you go out there and you get it done right now, which is what I would say. People that don’t understand the definition think it means, it doesn’t mean that at all.
[00:31:24] So when you talk about anything with the little ration, if you think about the derivative of the word, it is painstaking, slow, methodical, all of that. But to Southern heirs, I believe it meant. Don’t hurry, don’t bother. So it’s interesting to me that particular choice of words, and of course we have no way of knowing why the court, the Oral War and Supreme Court, decided that was the word that would be the lynchpin that would make the difference, because that was the word that I believe Emboldened the Southern Manifesto and emboldened massive resistance in Virginia and everything that we saw after that.
[00:32:05] There are many other words that may have put us in a better position. When you talk about the part of the decision that Chief Justice Earl Warren read, he actually read a much longer part of the decision. But for whatever reason, and here we go with the media yet again, they picked up on That last part of what he read, which is, in the field of public education, the doctrine of separate but equal, and then he goes on to say that separating African American children caused a badge of inferiority in ways likely never to be undone.
[00:32:41] And it bothers me that is the part of the decision that was picked up and repeated decade after decade, when in fact, the earlier part of that very paragraph talked about the significance of education. That education is the most important act of state and local government, and that no child can be expected to succeed in life without the benefit of an education, and where it is provided.
[00:33:06] It is a right. They use the word right, RAGHT, that must be provided in unequal terms. I personally believe that may have moved us forward a little faster had we focused on the significance of education and then taken a look inside of what was happening with African American children. Why was it they were not getting, that world class education that schools are supposed to deliver, yet white children were?
[00:33:34] It may have been a whole different dialogue about how do we correct that error. But when we got into the mode of court order desegregations, because people were not complying, we got into the mode of busing, we got into the mode of counting heads, who’s sitting next to who, and that became problematic. And I think it’s still problematic because it opened the door to white flight. It opened the door to schools remaining segregated, largely because in counting those heads, white parents did not want their children to be in certain settings. The onus, sadly, then was placed on African American children and families. For this big implementation with all delivered speed.
[00:34:20] Jocelyn Chadwick: Oh, this is a really prescient conversation. We move to the final question. You’ve supported various successful school reform models in traditional school districts, charter public schools, and METCO style desegregation efforts that have delivered. Excellent results, 70 years after Brown, how would you like to see politicians, the courts, and policymakers address the racial inequities still found across American K-12 education?
[00:34:52] Cheryl Brown Henderson: First, I think they need to come clean. There needs to be an honest understanding and an honest conversation about what are the inequities.
[00:35:00] And the inequities we hear now are about outcomes. The inequities we hear now are about failing underfunded public schools, and sadly we have accepted the rhetoric of failure without challenging or pushing back and asking to explain how is that possible that a public school can be underfunded? How is it possible?
[00:35:25] That a public school, supported by all of us, can be a failing entity. So I think that we have, let me just say this, propaganda is the most powerful tool of any government at every level. More powerful than any weapon, if you can convince people of certain things. And I believe we have been convinced that our public school system is in fact failing.
[00:35:51] Now, rather than really asking the hard questions and demanding accountability, yes, I think that everything you mentioned, the other modes of education that have emerged, although school choice was a method of pushback after Brown, I’ll mean something different now, but I think every method that has emerged is all about trying to address.
[00:36:12] Something that we shouldn’t be accepting in the first place. We shouldn’t be accepting any idea that public schools can be legitimately considered failing and underfunded. But I will say, we cannot keep losing generations of children, which is why there is support for these alternative methods that are non-traditional to traditional public schools have been more accepted. But I still think the goal, the prize is education, and we need to redouble our efforts to define that and to make sure that all of our children are actually receiving that, a genuine education, and where there are disparities based on dollars, those disparities need to be equalized. Where there are disparities based on quality, that can be addressed by additional teacher training or shifting teachers around that need a little more support.
[00:37:10] This is not rocket science, yet we pretend it is. And we act in ways that we convince people that, oh my good, this is just not something we have any control over, not something we can do, when in fact that is 100 percent not true. And it should not matter the color of the child in the classroom. Because the goal is education, and children, teachers, and administrators, and school districts educate the children that show up where they show up.
[00:37:41] And I know in instances where that has been problematic, what you mentioned earlier has come in. In order to make sure that we don’t continue losing other generations, but boy, we need to roll up our sleeves, have a heavy conversation, and face reality. Because I think that we cannot afford to destroy public education.
[00:38:02] We just can’t afford that. Because it’s where the majority of our children live. And where the majority of us were educated. And then, the last thing, people also seem so willing to believe this rhetoric, because they believe that public schools are predominated by children of color, when they are not. If you look at the U.S. Department of Education website, and you look at the demographics around school children, I think African American children are 15 percent of public education students.
[00:38:34] Cheryl Brown Henderson: It’s not a high number, but the rhetoric again, the propaganda again, that leads you to believe otherwise and makes people more willing to accept a lackluster performance on the part of how we educate our children.
[00:38:49] Jocelyn Chadwick: Sadly, I agree with you. And it is, it’s for me, it’s a nightmare that I keep having every now and then. I’m thinking, what are we going to do to stop the skid? And you and I are definitely on the same page there. Thank you so much. I appreciate that.
[00:39:04] Cheryl Brown Henderson: You’re very welcome. And let me do say this, though. I will congratulate what Massachusetts has been doing. Until people step up, in the public arena, then, yeah, things have to be done. And don’t get me wrong, I congratulate the successes that they’ve had, but it just really saddens me. And it makes me angry, really, when we know that we can and should be doing better.
[00:39:28] Jocelyn Chadwick: I agree, and I was thinking about Massachusetts when I gave the last question about METCO, because METCO is such an amazing program. And in so many ways, people who are native to Massachusetts accept it. But for someone such as myself, coming from outside the state, seeing how METCO works, I am always in awe that in this day, it seems that there’s something that works.
[00:39:54] It’s not that it can’t be made better. Everything can be made better. But METCO is this jewel that I never even conceived could happen. It takes me back to all of those field trips and access and equity that Texas did at one time have in its educational system. And yeah, I, thank you for talking about Massachusetts. I wasn’t going to say it, but since you opened the door, I thought I’d go there.
[00:40:23] Cheryl Brown Henderson: I’m always happy to talk about these issues, and we can continue to, it shouldn’t just be a dream, it should at some point be. An opportunity for the entire nation. And my big soapbox issue is how we appoint secretaries of education. We hope could set an education agenda, but they’re never there long enough to really see it through. When you look at every four to eight years, we’re changing the guard. And then on the ground, school districts are expected to shift to the new platform. So someday we’ll wake up to the realities that we’re not doing our children any favor.
[00:41:00] Albert Cheng: Cheryl, that was great. Thanks for being on the show with us.
[00:41:03] Cheryl Brown Henderson: Oh, you’re very welcome.
[00:41:16] Albert Cheng: That takes us to the end of our show, but before we close out, I want to give the Tweet of the Week. This week’s Tweet of the Week comes from Ed Surge. It reads, At T Mobile’s Project 10 million initiative provides free internet to US K-12 students to help bridge the digital divide. Learn more about how eligible families and teachers can sign up here.
[00:41:39] And there is a link on that tweet, Jocelyn, I don’t know if you have thoughts about this, but it seems a neat initiative that T Mobile is doing to offer broadband internet to folks that don’t quite have access to it.
[00:41:50] Jocelyn Chadwick: Oh, I think it’s critical. I’m really glad that they’re doing that.
[00:41:53] Albert Cheng: Yeah. Yeah. So, if you know somebody that might benefit from this, refer them to this and there’s an opportunity to apply. Also, if you’re an educator or a school leader that’s listening. I think I saw on the website there a link specifically for you folks, how you might be able to get your schools involved, so check that out.
[00:42:12] Jocelyn, thanks for being on this show, it’s great to co-host with you.
[00:42:16] Jocelyn Chadwick: I really enjoy it, and with this particular topic, it is close to my heart, and so thank you for having me, it’s always an honor.
[00:42:25] Albert Cheng: yeah. And coming up next week, Jocelyn Chadwick’s going to be on a panel that the Pioneer Institute is hosting. It’s a webinar entitled, Civic Virtue and the Language of Democracy. That’s going to be Thursday, May 23rd at 2 p. m. I believe there should be a link for you to sign up and RSVP to that. Stay tuned for that and hear more from Dr. Chadwick and other guests on this important topic. Before we end, the last thing I want to say is that we’re I hope you enjoyed this week’s episode.
[00:42:54] Come back next week. We’re going to have Kimberly Stedman, who is the Network Co Director for the Brooke Charter Schools Network in Boston. So, hope to see you then, and until then, I wish you well. Have a great day.
This week on The Learning Curve co-hosts U-Arkansas Prof. Albert Cheng and Dr. Jocelyn Chadwick interview Cheryl Brown Henderson, daughter of the lead plaintiff in the landmark U.S. Supreme Court decision, Brown v. Board of Education. She explores her family’s pivotal role in the Brown case, detailing her father’s part within the NAACP’s wider legal strategy. Cheryl discusses the influence of religious faith on the Civil Rights Movement, as well as the impact of segregation on her family, and their courageous decision to confront the legal barriers to racial equality in K-12 education. She emphasizes the ongoing need for comprehensive school reform leadership that will address the racial disparities still found across American public education.
Stories of the Week: Albert shared an article from Chalkbeat discussing the Newark Public Schools’ new AI tutor; Jocelyn discussed a piece in The Washington Post on how to help struggling third graders read.
Guest:
Tweet of the Week:
https://x.com/EdSurge/status/1790035570124583421
Examining Diversity’s Dividends: Can Studies Survive Contact with Peer Review
/in Featured, News, Podcast Hubwonk /by Editorial StaffClick here to read a transcript
Hubwonk: Examining Diversity’s Dividends: Can Studies Survive Contact with Peer Review
[00:00:00] Joe Selvaggi: This is Hubwonk. I’m Joe Selvaggi. Welcome to Hubwonk, a podcast of Pioneer Institute, a think tank in Boston. Does diversity pay? Beyond the laudable goals of assembling the best talent pool, irrespective of race or ethnicity, does actively seeking diversity in executive leadership truly redound to a company’s financial success?
[00:00:23] McKinsey, a highly regarded global consulting firm, asserts confidently that data from their four studies conducted in 2016 to 2023 unequivocally asserts it does. Their findings have effectively endorsed diversity, equity, and inclusion initiatives worldwide, lending these programs the weight of scientific validation.
[00:00:43] However, such resolute claims must warrant a critical examination by fellow data scientists who are bound to scrutinize the quality of the methodologies and data employed to arrive at these conclusions. To their credit, McKinsey has been transparent with their research methodology, having only anonymized the firms studied to safeguard client confidentiality.
[00:01:05] Given this transparency and the firmness of their research findings, how do their assertions fare under the scrutiny of peer review? Joining us today is Dr. Jeremiah Green, an Associate Professor of Data Analytics at Texas A&M Mays Business School, and co-author of the recently published research paper, Does Greater Diversity in Executive Race Ethnicity Reliably Predict Better Future Firm Financial Performance?
[00:01:33] Dr. Green and his co-authors delved into McKinsey’s research to examine the extent and causality of the purported financial benefits associated with diverse executive leadership. He will share with us what prompted his team to scrutinize McKinsey’s findings. and unveil the outcomes of their efforts to replicate the results of these four studies that undergird the business case for diversity, equity, and inclusion programs worldwide.
[00:01:58] When I return, I’ll be joined by Texas A& M Associate Professor, Dr. Jeremiah Green. Okay, we’re back. This is Hubwonk. I’m Joe Selvaggi, and I’m now pleased to be joined by Texas A& M Associate Professor, Dr. Jeremiah Green. Welcome to Hubwonk, Professor Green.
[00:02:16] Jeremiah Green: Thanks. Great to be here.
[00:02:17] Joe Selvaggi: All right. It’s a pleasure to have you.
[00:02:19] This is your first time on Hubwonk. We’re going to talk about a paper you recently co-authored titled, Does Greater Diversity in Executive Race Ethnicity Reliably Predict Better Future Firm Financial Performance? It’s a research paper that examines the consulting firm McKinsey’s widely accepted claim that diversity delivers to firms bottom line.
[00:02:41] We’re going to go deep on this, but before we do, I want our listeners to, learn a little bit about you and the science of using accounting data for business analysis. You teach at Texas A& M, you teach data analytics, and you use it in your work. What does this field of data analytics do in the business community?
[00:03:00] Jeremiah Green: Okay, yeah, so in terms of data analytics for accounting, that’s also a little more specific than just data analytics in general. Data analytics for accounting typically means we’re using statistical approaches to analyzing financial reports. If you think about a newspaper article that says, apples earnings per share was, X dollars this year, it’s looking at things like that, what they provide to analysts, what they provide to investors, and trying to understand what that means for the company, for investors, for other groups that are interested in looking at those financial reports.
[00:03:41] Joe Selvaggi: So, you, you examine, differences in how financial data, again, you’re looking at publicly traded companies, I assume. All of that should be, available to, someone who wants to analyze it. And you’re trying to relate how different features of a, of a firm’s balance sheet, either explain current success or perhaps, predict future success.
[00:03:59] let’s see an example. We’re going to talk about your analysis of McKinsey’s analysis in this area. What’s an example of where your work in, maybe a different topic might help our listeners understand, the contours of your work?
[00:04:12] Jeremiah Green: Yeah. So, an example of thinking about how financial information that companies report either explains their performance now or predicts their future performance, we might think about an example could be a paper by Mark Solomon.
[00:04:28] He is a professor at University of Southern California. What he does is look at ratios from the financial statements, so profitability or efficiency, and tries to see whether that predicts future performance. What is he? He tries to predict earnings and future returns, stock returns, and that’s the type of thing we might do.
[00:04:52] Some of my research is looking at predicting stock returns also, so we try and use those sorts of pieces from the financial statements to say, what can we learn from them,
[00:05:02] Joe Selvaggi: So, you polished crystal balls to help people who are potentially inventors try to understand what might come in the future.
[00:05:09] We all know, everybody would love such a wonderful device, but it’s challenging, and that’s why you need rigorous analysis and your best effort. So, I want to then talk, rather than the general concept, let’s dive at least a little bit into your paper, the recent paper. You wrote about, the relationship between the diversity, in the leadership positions of a firm and their, probability of being profitable.
[00:05:35] We’re going to talk about that in depth. What was your interest in writing about? Of all the features of a firm, why did you want to examine why diversity might be, among all the other, measures you could use in a company, one that might predict future profitability?
[00:05:50] Jeremiah Green: Yeah, that’s a really good question.
[00:05:52] I, I think it’s probably worth putting in context what got us into that, and that is, we’ve heard lots of claims in, by journalists and by, McKinsey, trying to, that, that say that diversity in various forms improves financial performance. And so, from an academic standpoint, I think we tend to be pretty skeptical of bold claims.
[00:06:16] So that’s one aspect. and the other pa the other issue is if it is the case that diversity improves performance, then that’s something as an accounting person that I want to know so I can tell companies or students that if you increase diversity it’s going to improve your performance and this should be a, a top level concern for something we might want to focus on.
[00:06:41] Joe Selvaggi: So, you either want to challenge a bold claim, see if it’s valid, but you also, if it’s valid, you want to incorporate it into your analysis for the future, right? You want to, as a new, yardstick to add to the other yardsticks used when predicting future performance. But for, I want to provide some background for our listeners.
[00:06:55] We’re going to be looking at a study or several studies, four different studies by the same firm, McKinsey. This is a firm that’s very well regarded in the, a global consulting world. This is the gold standard of consulting. So when you’re talking about challenging or examining, studies or four studies by a very well regarded, accounting, consulting firm.
[00:07:14] These are, you’re taking on a Goliath, if you will. They’ve been around for about 100 years, I think. They’re two years away from 100. so that the relevance when you’re taking on somebody like, a firm like McKinsey is to say that what they’ve put out is almost regarded, I want to use, religious terms, but almost as gospel.
[00:07:31] It essentially is almost presented as it ought to be unquestioning. when we’re talking about you as an academic versus, a firm like McKinsey, which is consulting, Aren’t you more or less, when you’re advising firms or, analyzing what works in businesses, aren’t you in the same business, analyzing the same kinds of data?
[00:07:49] Jeremiah Green: Yeah, okay. Yes. I guess the easiest answer is yes, we are doing, using the same data. We’re trying to make, understand, and make similar conclusions. Can I push back just a little bit in that I would like to not say that we’re trying to take on McKinsey or challenge McKinsey, but, from my viewpoint, if we see research or conclusions based on research that are not, justified by the evidence, then that is something that, as academics, we should be involved in. In terms of what we do with the evidence or decisions we make outside of that isn’t really something I can say anything about, but yes, so in general, we’re doing the same sort of thing and we’re, we have the same sort of, interests. We may not have the same sort of use of it after we make these claims, or whatever they are.
[00:08:45] Joe Selvaggi: I didn’t mean to, I appreciate you pushing back, and I didn’t mean to imply that it was some sort of hostility towards their, the firm. Rather, you’re an academic, and all academics, we hope, present evidence, and then invite their critics. Their colleagues or peers, other, data analysts to scrutinize, meaning you get better.
[00:09:03] You, you don’t want to keep a mistake a secret. You want others to scrutinize your work so that they can point out your mistakes. Every paper, I think, since the dawn of time, has mistakes. And you invite other very intelligent, let’s say colleagues, be they academics or consultants, to scrutinize your work.
[00:09:19] You, what I meant to say, you’re taking on, you’re scrutinizing McKinsey’s work. Can we make that stipulation?
[00:09:26] Jeremiah Green: Yeah, that seems fair enough, yeah.
[00:09:28] Joe Selvaggi: Okay. All right. So, let’s just, before we get into the details, I mentioned there’s four studies by McKinsey. I don’t have the dates right in front of me, but I think the first was 2016.
[00:09:38] The other just came out in 2023. They had similar, conclusions. They became more confident as they went on. For our listeners, just at the high level, how would you summarize the conclusions McKinsey, makes about diversity? In high level the board and, c-suite leadership and, profitability.
[00:10:01] Jeremiah Green: Yeah. Let me, let me caveat it, or, try and split that into two pieces. I think the first piece is pretty clear about what they do in mechanically and what they say within their papers. when I go to make the step about what conclusions they make. those are slightly different than how they describe what they do.
[00:10:23] In terms of the general conclusions, they say that, diverse, having more diversity, like executive diversity, increases company performance. That’s the general claim, even though they are more nuanced when they get into the discussion, I think.
[00:10:39] Joe Selvaggi: Yeah, I, again, I went into the research, looked at the data, not the way you did, but more generally, but also I looked at the overarching, summary and in the most recent report, I want to quote for our listeners, just to add color to this conversation, how bold McKinsey describes, how boldly they described the observations they made in this most recent paper.
[00:10:58] I’m going to report from, this is a quote from McKinsey’s work. For almost a decade through our Diversity Matters series of reports, McKinsey’s has delivered a comprehensive global perspective on the relationship between leadership diversity and company performance. This year, the business case is the strongest it has ever been since we’ve been tracking, and for the first time in some areas, equitable representation is in sight.
[00:11:22] Further, a striking new finding is that leadership diversity is also convincingly associated with holistic growth, ambitions, greater social impact, and more satisfied workforces. Unquote. So, these are pretty bold claims, based on what they claim is decades of research and four studies. So that’s where I want to start.
[00:11:39] Let’s examine by defining terms. In that passage, McKinsey uses the term diversity. How does McKinsey define diversity?
[00:11:48] Jeremiah Green: Yeah, okay, so broadly, I guess let’s do a, simple explanation. What they really are trying to measure is how, not concentrated their executive team is in any, small or single group or ethnicity.
[00:12:03] They’re looking at ethnicity, different ethnicities, they group it into a few different categories, and then they try and say if it’s, So if it’s all concentrated in one ethnicity, then that’s not diverse. If it’s concentrated, or if it’s spread out across different ethnicities, then it’s diverse.
[00:12:23] Joe Selvaggi: So, for the benefit of our listeners, I thought about this myself. I’m like, okay, what would make something a perfect score? And what would make a firm a, Terrible score. So, a perfect diversity score would be whatever number of diversities Let’s say there’s eight or five. I think those are the two different measures.
[00:12:40] If you have five, one of each, and you have five board members, that’s perfect score. If they’re all white, or for that matter, all black, or Hispanic, or pick it, Asian, that would be a bad score. So, what we’re looking at is how numerous, how many different ethnicities are represented, and if there’s any sort of particular concentration. Is that a fair characterization?
[00:13:00] Jeremiah Green: Yeah, exactly. That’s, I think that’s what they’re doing. Yeah.
[00:13:03] Joe Selvaggi: Okay. And of course, now that the other variable is how does that relate to performance? Again, if diversity is hard to define, so also is performance. But, what yardstick did McKinsey use in this, in these studies?
[00:13:17] Jeremiah Green: Yeah, they used, earnings before interest and taxes divided by revenues. So, it’s a measure of profitability, how, given the revenues that they generate, how profitable are those revenues in generating sort of a bottom-line profitability measure.
[00:13:34] Joe Selvaggi: So, this is your expertise, they’re consultants and they, they have their criteria. As an expert yourself, is that a fair measure? Should you use that profitability score as a measure of performance?
[00:13:48] Jeremiah Green: Yeah, I think it’s okay. You could argue for other measures, right? We might try and measure, return to investors or something like that, but it’s an important part of performance and probably a decent place to start.
[00:14:02] Joe Selvaggi: So, we’ve got two scores. We’ve got diversity and we’ve got performance. And again, this is a layperson, analysis of their work. As I see it, what they did was say they looked at all firms. They didn’t make clear which firms they were looking at. But all the firms, I guess almost 2,000 firms, and they ranked them in quartiles.
[00:14:20] They said, okay, we’ve got bottom performing, bottom middle, upper middle, and top performing. And they compared the diversity score of those in the Yeah, the profitability of those in the top, I reverse this, they had, diversity scores from a bottom quintile, top quintile, and they compared the performance in the top quintile of diversity versus the performance in the bottom quintile of performance, and then looked at the difference between the two. Share with our listeners. How did that look?
[00:14:49] Jeremiah Green: Yeah, we’ll stick with the general explanation, like you’re talking about, diversity and performance, rather than get into the details of that right now, but they’re talking, by performance, they’re talking about outperformance or underperformance.
[00:15:04] They’re comparing average profitability, relative to their industry. So If you have, if a company were to have Profitability that’s higher than their industry average, those are outperformers. If it’s below their industry average, they’re underperformers. So that’s the Specific profitability part. In terms of the diversity, what they do is rank companies and in the top 25%, They compare those in the top 25 percent of diversity to those in the lowest 25 percent of diversity.
[00:15:39] And then they look and see how much more likely is the top diversity group than the bottom diversity group to outperform their industry. So, they find something, like around, they’re like 30 percent more likely to outperform their industry if they’re in the top, diversity group.
[00:15:59] Joe Selvaggi: So that’s the crux of the claim, which is to say the more diversity, the firms with higher diversity scores were more likely to outperform their average in their industry, and those on the low end of the diversity score were more likely to underperform, and they compared those two, to really, arrive at whether it’s a, a trivial difference or a substantial, when we’re looking at difference between signal and noise, all kinds of things can show up when we do analysis.
[00:16:23] The larger that difference, the more confident they were in their conclusions. I’m not sure I don’t have the numbers right in front of me, but by my analysis, there was a substantial difference in the likelihood of profitability for more diverse firms than less diverse firms, giving them, I would say, again, reflected in the summary of their paper, high levels of confidence that diversity and performance are related, profoundly related. So, say more about. How confident their observations were and why they would be confident.
[00:16:55] Jeremiah Green: Yeah, I think you said two things there. So, one is the magnitude of how much they’re likely to outperform. So, something like a 30 percent likelihood, more likely to outperform is a huge effect. I, I don’t know of hardly anything else that is that magnitude in terms of how important it would be, right? You can’t like, say, if you, let’s say implement a new information system in your company, you’re 30 percent more likely to outperform. That just doesn’t exist, right? Maybe 1 percent or something. that’s in terms of the magnitude of the effect. The other part is the statistical significance, and they report p values that are less than 5% that would mean highly statistically significant.
[00:17:41] Joe Selvaggi: Again, for our listeners who are not statisticians, I’m going to I’m going to take a crack at explaining P values. I’m dusting off my, graduate degree here and say, what we know about P values is we want to be sure that our observation, or we want to be, measure the confidence that our observations weren’t randomly generated.
[00:17:58] Taking any given set of numbers, All kinds of things can happen. the larger the result that you observe, the more likely that it isn’t random. it’s likely to actually be measuring something real rather than a statistical artifact. Is that fair?
[00:18:14] Jeremiah Green: Yeah, that’s, I think that’s a reasonably good way to, state it loosely. I would add one more piece that, it’s about how the results in the sample that you’re using, how likely is that to happen again in another sample? And so, if we have a really small p value, we would expect, that says it’s very unlikely that what we see in our sample is a statistical anomaly. If we go to another sample, we expect to see the, a very similar result. So low P value, highly confident that this is a reliable claim.
[00:18:52] Joe Selvaggi: So, we imagine a large salami, every slice is going to be the same. If we’re low P value, wherever we look, we’re going to find a similar result rather than, some next, the next result to be the opposite. We’re fairly confident that this is a consistent outcome.
[00:19:05] All right. So, then I wanted to talk about one more dimension, which is we’ve established that at least McKinsey asserts that there’s a relationship between, diversity and performance. But there’s another step, right? We have to observe that in their case, there’s a causational relationship, which is to say, as you say, it’s both big, but it also has to, one has to come before the other.
[00:19:28] The diversity has to happen and then the performance has to happen for it to be causal. I hope I’m saying that Share with our listeners. In their analysis, does the way they measure and when they measure diversity and performance, does it really show a causational relationship? You get, you are diverse, then you outperform. Is that what this study really shows?
[00:19:50] Jeremiah Green: Short answer, no. before getting too critical about what McKinsey says, they explain this in their papers, right? They acknowledge that this is a problem. When you read their conclusions, that’s a very different, they don’t match their conclusions to what they’re saying, but they, let’s take the first study for an example. They rank diversity in 2017, and then the performance, the profitability measure that they use is before the diversity ranking. So, they’re, just with their design, it’s impossible for diversity to increase performance because performance happens before the diversity happens.
[00:20:28] Joe Selvaggi: So, the proverbial cart before the horse, or this is something that academics in your field wrestle with all the time, right? Correlation is not causation. I might use a, I’ll use something silly and say, umbrella use, and rainy days seem to correlate, but it doesn’t mean umbrellas cause it to rain. You know, for our listeners, just briefly explain why, what a data scientist has to do to establish causation rather than mere correlation.
[00:20:54] Jeremiah Green: Yeah, what do we have to do to establish causation? That’s difficult. the best-case scenario would be we’re in a lab where we can randomly assign Something to happen, and then we see that the effect happens. a couple of things we can do. One is the direction that you’re taught, that we had just talked about, right?
[00:21:14] It, the thing that causes something else should happen beforehand. the other part is we have to deal with other, what we call correlated omitted variables, something else that could explain the results that, we were trying to claim are causal. So, you use the example of the umbrella. Another typical one that I find easy to conceptualize is, ice cream sales and shark attacks are correlated. And we don’t think ice cream sales drive shark attacks. Because we think something else is driving both of those, and that’s warmer temperatures. And we have to deal with those. I guess two things. We have to go through the logic of, is this plausible? And then we also have to try and deal with other things that could cause that. In the case of ice cream sales, we would need to find a way to control for warm temperatures. If we found, for example, that, using diversity, that, if we have some 3rd variable, but we can find that it happens outside of when that third variable could affect it, then okay, we’re, at least more confident that there’s a cause and effect happening.
[00:22:29] Joe Selvaggi: Yeah. and so they acknowledge that I’d say in the footnotes in this study, they do acknowledge the challenge with establishing causation. So, I thought, the, people who are doing the analysis Unlike the people who write the headline for McKinsey, acknowledge that the data does not support a causational, but rather just a correlational.
[00:22:45] I want to caveat that if we’re going to criticize McKinsey for something. Is that fair? Meaning that the, the people doing the research acknowledge this limitation?
[00:22:53] Jeremiah Green: Yeah, perfectly fair. And I have no problem with their research at all. I have a problem with the connections of the, like you said, the headlines that they’re putting out there don’t match their research, right? And so that’s right. I think they’re pretty fair when you actually read inside the research there. I think they’re pretty straightforward about it.
[00:23:16] Joe Selvaggi: All right, so we’ve set up this whole sort of conversation. Let’s get to it. okay, you say you, connect more with the, the research of McKinsey rather than the headlines of McKinsey, but let’s get into the research. Was McKinsey’s report, were you able to essentially get their data sets and do analysis of the simple math, or was that something that’s, difficult to, for them that they’re reluctant to share or they were unclear about?
[00:23:39] Jeremiah Green: Yeah, okay, so a few things, in terms of the method they use, they’re very clear. So that, it’s not hard to go in and, if you have the data, to do what they did. The piece that they don’t provide is the companies that they’re using. we don’t have access to, their sample is something like, 300 or 500 companies, nobody but them, they’re the only ones that know what those companies are. And being able to perfectly replicate what they do is not possible unless they give us their companies. But other than that, the rest is very straightforward. You can just copy and do exactly what they do.
[00:24:19] Joe Selvaggi: And of course they didn’t share which companies, not because they’re trying to keep it a secret, but it’s discretion. They don’t want to, in a sense, out their clients, right? They, that’s reasonable. They have a responsibility to keep that confidential. You did find your own companies to analyze. As we mentioned earlier, the P value is very low, suggesting that you’re going to find this result more or less everywhere you look. How did you develop, again, if you’re trying to replicate without the actual list of companies, you’re just replicating the methodology rather than the actual dataset, where did you go to find companies to analyze?
[00:24:50] Jeremiah Green: Yeah, sure. We tried a few things. It’s, this is really the biggest challenge, I think, in terms of trying to replicate their research. We there are so many companies out there. If we look globally, it’s hard to know where to start, but we know that they consult with a bunch of the largest companies, S&P 500 is a pretty common company.
[00:25:15] Benchmark for all companies. So that’s where we started. So, S&P 500, it’s about the same size as the, their sample. And we know that they consult with many of them. And so, to us, that was as close as we could get. as a side note, we also did a random sample, we thought, of U.S. company, sorry, public companies, and tried the same thing.
[00:25:38] And no matter what we did, we get similar results, but it, I don’t know, is that good enough? We tried the S& P500, and we tried a random sample, to try and approximate what we think they did.
[00:25:50] Joe Selvaggi: Yes, and of course, if their, observations would be valid, and your observations would be valid, you, they couldn’t, in a sense, Use selection bias to craft their results if they did. They would say, among those companies, among the companies we looked at, this is true, but who knows about any other company? If that were the case, then no assertion would be generalizably true. It would be a worthless observation. In order to have their observations have any weight, they have to be generalizable your analysis and their analysis, though they’re different companies, in theory, should show the same approximate result. Is that fair?
[00:26:22] Jeremiah Green: Yeah, yeah, I would say even more than in theory. Their P values aren’t valid if Their sample is not a random sample, and so what their P value should be is a reflection of what we should see elsewhere. And yeah, if we can get a sample that is meant to approximate the same types of companies that they’re looking at, we should see something similar.
[00:26:45] Joe Selvaggi: So, the companies used, provided it’s a large enough sample, are virtually irrelevant to an observation like this. and you replicated their methodology, which you say they were very candid about how they went about evaluating. So we’ve buried the lead enough. Given all your research, you had two co-authors and a substantial amount of time to analyze the data, did your research see a correlation, not, we’re not going to get to causation yet, between diversity and performance?
[00:27:15] Jeremiah Green: I just want the short answer.
[00:27:16] Joe Selvaggi: No.
[00:27:18] Jeremiah Green: No.
[00:27:18] Joe Selvaggi: Okay. I don’t know if our listeners were anticipating that answer. It’s the shortest of all the answers you’ve offered. So I want to go, a little bit further and say, okay, we established early on that what we’re talking about diversity, we’re talking about diversity in the higher level ranks. We’re talking about a large signal that they saw that a large result of confidence in their results. You found no such, relationship between How diverse a firm was and how profitable a firm was. Did you use, the same exact, let’s say, I think it was earnings before interest and taxes. did you use, did you try to tweak the numbers in any way to look at different metrics that maybe you were missing some, some variable that they accounted for?
[00:27:55] Jeremiah Green: Yeah, let me give a little, more than you would see in the paper. So, in the paper we tried, I can’t remember, six or something different measures. Outside of that, I, we were convinced there’s got to be something to this. So, I tried every Measure that I could think of, so I don’t know what it was 25, 30 measures. There’s got to be something there was my thinking and no, no matter what we did, we couldn’t find it. and this is replicating their approach. We mentioned that they measured diversity and then looked at performance prior to that. And so, I tried that with every measure, we tried it with every measure we could think of.
[00:28:36] No association at all. And then in another paper, so this paper that we’ve been talking about is with John Hand. He’s at North Carolina. We also have another paper with Sekou Burmes, who is a strategy professor at North Carolina, and we flipped the direction. So, then we tried to say, what if we measure diversity, let’s say, in 2017 and looked at performance after 2017.
[00:29:02] And, maybe we could get this sort of the right direction, and then we would find results. Nothing. So, no matter what we did, and this is You know, you don’t see it all in there, but we spent an enormous amount of time trying to find any association that we could and just can’t do it.
[00:29:22] Joe Selvaggi: So, we’re not going to even talk about causation because where there’s no correlation, of course, there could be no causation in either direction that this is an extraordinary revelation in my view. It’s an extraordinary revelation. one would think even randomness might generate some sort of result, you could draw a line in the middle of a room and say, did the people on the left hand in the room have anything different than the people on the right-hand room? You’re going to find something somewhere. Guys named Joe may be different than guys, everybody else, you didn’t find anything.
[00:29:49] Jeremiah Green: I tried, I guess the only thing I didn’t try, we did the S& P 500 and the random sample. I guess another thing we could have tried is, just keep shifting around the companies and see if in any sample we could happen to find it, right?
[00:30:06] If it was just, you’ve looked through the data too much and randomly you should find a result. That, I guess that’s the other thing we could try, but yeah, everything we tried, there’s just nothing there. Oh, we also expanded it when we started, we did it with one year, just like a single one of their studies, and then we expanded it to many years, and no matter what, we just can’t find it.
[00:30:28] Joe Selvaggi: This is pretty powerful. When one finds no results, one can’t say there isn’t some relationship, but it’s just not measurable, particularly given the data and the methodology. This is big news, because I think McKinsey has a, their influence is powerful. corporate boards and, C suite people use that as I won’t say gospel, but essentially as a north star for many of their decisions. To make the assertion that they do, that it essentially is almost. not almost. It is proved that diversity leads to greater profitability. If I’m a leader of a firm or a member of a board, I have a fiduciary responsibility to my shareholders, right? I don’t own the company. I may be the president, but I don’t own the company. I work for the shareholders. It’s my job. I have to make choices for their benefit. If it’s true, what McKinsey asserts, then I would be, violating my oath as a fiduciary to not make my firm more diverse. Without this evidence, in a sense, now we can’t privilege diversity over others, let’s say, of course, we want to make the firm profitable.
[00:31:29] We’re not saying that this is morally or ethically a bad idea, diversity, we’re not going there. We’re just saying as a measure of profitability, as a head of a firm, it’s either imperative, it’s a financial imperative that I use diversity, but without that evidence, no such imperative exists. We can essentially use Pro more traditional, profitability criteria when making our decisions. I don’t want to go too bold here, but I want to say if this is, if this, data is not valid or observation is not valid, I’ll say maybe the data is, would this, upset the entire, diversity push in financial markets?
[00:32:10] Jeremiah Green: Yeah, let me, be careful in how I state this here. I think I agree that, if that were the case, and I think this is how what you were saying, has been used that way. If it is the case that increasing diversity increases performance, managers must increase diversity because that is going to improve returns to their shareholders. Fantastic, right? It’s a good story. If it works, then the path forward is clear. let me try and claw back some space for McKinsey here.
[00:32:44] In the last study, I, the one that just came out in 2023, so that was actually after we had, looked at the first three studies, they add other things that, that diversity might influence. for example, employee satisfaction. It’s completely believable that diversity could influence employee satisfaction.
[00:33:09] You could think of a lot of reasons that might be the case in terms of that, great, right? that may be something that companies should care about. It may be something that employees care about, and if a company decides to make decisions based on that, then no problem. The challenge is, though, they can’t rely on this evidence to say, it’s going to increase performance because that’s not the case. Let me give the other side of that is we don’t find that it decreases performance either. So, it, it doesn’t seem to hurt or help in terms of bottom-line performance. It could be as benefits in other ways, but just not, the sort of bottom-line profitability approach, or type of outcome.
[00:34:00] Joe Selvaggi: I’m glad you point out the double-edged sword of finding no results, which is diversity, when pushed doesn’t help, of course, but diversity when pushed doesn’t hurt it maybe is a Rorschach test to say which, which, support you want to, observe. that, that’s fair, and I’m glad you did point that out. Again, no signal gives us all latitude to, to look for other things. And as you say, employee satisfaction. But of course, if it improved employee, being or satisfaction or something like that, that theoretically, we want that. Of course, it’s nice, it’s inherently a good idea, but we would also think that would improve Thank you.
[00:34:31] Profitability. Again, if you want to signal, employee satisfaction isn’t correlated with profitability. It’s good, but it’s not a, a financial imperative. so what I really want to, again, I’m going to ask you to go on a limb here and say, okay, McKinsey has produced these four reports with glowing, summaries, and which are touted, when there’s legendary slide decks to, to C suite executives and boards, why would McKinsey, with, has built a reputation, I think they hire the best, smartest people coming out of the best schools, why would, McKinsey, make claims that their own data really wouldn’t support?
[00:35:07] It seems, or at least it is impossible to reproduce. What do you see as a sort of incentive for a firm to, say, jeopardize their reputation for data, quality data and integrity?
[00:35:20] Jeremiah Green: Yeah, I’m glad you said you’re going to ask me to go out on a limb because this is just speculation, right? I don’t know. Although we have examples in academic peer reviewed research where, researchers do things that would have bad consequences in terms of their reputation. So This does happen everywhere, so I can’t, claim that, we’re peer reviewed, so we’re so much better, but in terms of, I think, the incentives, we do have, some amount of, we are different, and I think the big difference, if I were to take a I don’t want to take a view like they were purposefully trying to, misstate what they are saying, but I think they, they want to be able to tell companies you should do this, and we don’t want to do that as academics, we just want to evaluate the evidence, and in the jump from them. Here’s what we found should. I think that’s where the misinterpretation of their evidence came from. That’s my guess. if you’re trying to tell a company you should do this, but then you tell them, if you have diversity, you previously would have performed better, that isn’t very compelling to get them to do something, I guess.
[00:36:37] Joe Selvaggi: There’s so many ways to interpret it. Again, we’re not inside the minds of McKinsey consultants, but of course, we all respond to incentives. Academics, hopefully academics’ reputation for integrity and accuracy is the sort of coin of the realm. In consultancy, perhaps, There is an incentive to tell your client what they want to hear, or I’ll say, again, I’m going to relate it to many topics we cover here on Hubwonk is, unfortunately, this sounds like a political, an aspirationally political position, which, and my, it’s going to go on our, my tombstone, perhaps, is the unfortunate fact that when you mix science and politics, you get politics.
[00:37:13] This seems to be just one more instance where a revered institution has Perhaps, wandered off the path and compromised integrity for, results that people prefer. you know, do you see this sort of perhaps as a potential victim of either group thinking or motivated reasoning?
[00:37:31] Jeremiah Green: Yeah, I hesitate to make any judgment about the moral or ethical good or bad, is this good or bad? It does seem, I like your statement of if you mix science with politics, you get politics. That seems pretty accurate to me. So, what I do think is that we should be aware of when we’re interpreting what research says, and maybe particularly when it comes from, people like consulting firms or groups like consulting firms that we need to be careful of how it’s being interpreted. And I think, we, I hope we’ve shown that, at least in these studies, you can’t make the conclusions that McKinsey has been making, those are just not, they’re just not correct, they’re not what is shown in the data.
[00:38:23] Joe Selvaggi: And that’s fair, and one can understand why McKinsey, having found nothing in the data, that, the headline cannot read, we found nothing they took the ball and ran with it. I appreciate you taking your valuable time with us today, Professor Green, it’s a dry subject, it’s a difficult subject, but I think peer reviews, scrutiny of, academics with integrity is absolutely essential. We’re not asserting whether diversity has a moral benefit.
[00:38:44] We’re just examining whether it has the. the financial benefit that the study claims. So at least in this regard, you’re shedding some light in what seems to be a fairly, dark or cloudy area of analysis. Where can our listeners, find either your study or your paper? I was able to access it, there were no, paywalls or anything. Where might, are, and you’ve published quite a bit, where could our listeners read more about your work?
[00:39:10] Jeremiah Green: Yeah, a few places. If you just search Jeremiah Green at Texas A&MI pop up so you can find my webpage and my CV there. if you go to econ journal, watch, or Journal of Economics Race and Policy, that’s where one of our other ones is. Or you can look up some of the co-authors, John Hand at UNC or Kuber at UNC. I think it’s pretty easy on academics to find that stuff.
[00:39:36] Joe Selvaggi: Wonderful. I appreciate your time and thank you again for joining me today on Hubwonk. Professor Green, you really, I think shed some light on a difficult topic.
[00:39:43] Thank you. for having me.
This has been another episode of Hubwonk. If you enjoyed today’s show, there are several ways to support Hubwonk and Pioneer Institute. It would be easier for you and better for us if you subscribe to Hubwonk on your iTunes podcatcher. It would make it far easier for others to find Hubwonk if you offer a five star rating or a favorable review.
[00:40:03] We’re of course grateful if you share Hubwonk with friends. If you have ideas or comments or suggestions for me about future episode topics, you’re welcome to email me at hubwonk@pioneerinstitute. org. Please join me next week for a new episode of Hubwonk.
Joe Selvaggi talks with business data scientist Dr. Jeremiah Green about his peer review work examining consulting firm McKinsey’s studies on the measurable financial benefits of diversity in corporate executive leadership.
Guest:
Commentary On The Senate Ways And Means Committee FY2025 Budget
/in Blog, Blog: Economy, Economic Opportunity, Economic Opportunity, Featured, News /by Eileen McAnnenyCommentary On The Senate Ways And Means Committee FY2025 Budget
The Senate Ways and Means Committee (SWM) released its FY2025 budget on May 7th. This spending plan totals $57.9 billion, an increase of $1.8 billion over the FY2024 General Appropriations Act (GAA). Like the Governor’s and House’s versions of the budget, the SWM budget is based on the consensus revenue estimate of $41.5 billion in tax revenue – a decrease of $208 million from last year’s consensus figure. Given the volatility of revenue collections for the current fiscal year – shortfalls for the first nine months and then more than a $1.5 surplus over anticipated revenues in April – there is a lot of uncertainty around the revenues upon which FY2025 budget proposals are based and this uncertainty warrants a cautious approach to spending.
The Senate, going last in the budget process, has the advantage of reliance on more current revenue numbers when formulating its annual operating budget. Perhaps, the latest revenue collections made them cautiously optimistic, even while acknowledging the tightening fiscal environment, allowing them to spend more freely on priority areas than they otherwise would.
The budget’s executive summary states that the SWM Committee adhered to a disciplined and responsible fiscal stewardship. As proof, it indicates that the budget does not include tax increases, or withdrawals from the Stabilization Fund (the state’s reserves) or the Transitional Escrow Account (federal money that has not been appropriated yet). SWM deserves high praise for both of these strategies, but that is not the entire fiscal picture.
Where the SWM budget goes astray is in its use of more than $1 billion in one-time revenues to fund both new and ongoing expenses. The Senate is not alone in the practice, nor is it the worst offender, as the House relies on $1.19 billion in one-time and new revenue sources and the Governor proposed $1.25 billion in new taxes and one-time revenue sources. In fact, the following sources of one-time revenue are common to all three versions of the budget:
While one can argue that the trust fund resources were dedicated for the purposes for which they are being used, and therefore, a reasonable use of funds, that money will be unavailable next year and thus is not a sustainable way to pay for operating expenses.
The Senate budget differs from the Governor’s and House’s budgets in two noteworthy ways. The Senate does not propose tax law changes (elimination of various deductions) or additional sources of one-time revenue, (such as using Gaming Funds, allowing online lottery sales, or providing a tax amnesty period) to support this increased spending like its budget counterparts. While Pioneer Institute supports this no new tax approach, it also recognizes that these sizable new investments introduced by the Senate will grow the bottom line of the state budget without any revenue offsets at a time of slow revenue growth.
Examples of noteworthy new investments or increases include:
These spending items will either require new taxes in subsequent years or budget cuts elsewhere to sustain these program expansions over time.
Pioneer Institute appreciates the ebbs and flows of tax revenue collections and the need to smooth out spending cuts over time, but the reliance on one-time revenues seems unnecessary given duplicative or overly generous spending in several key areas. A sampling of these items include: