Data

The Goldin Nobel

James BaileyLeave a comment

This week the Nobel Foundation recognized Claudia Goldin “for having advanced our understanding of women’s labour market outcomes”. If you follow our blog you probably already know that each year Marginal Revolution quickly puts up a great explanation of the work that won the economics Prize. This year they kept things brief with a sort of victory lap pointing to their previous posts on Goldin and the videos and podcast they had recorded with her, along with a pointer to her latest paper. You might also remember our own review of her latest book, Career and Family.

But you may not know that Kevin Bryan at A Fine Theorem does a more thorough, and typically more theory-based explanation of the Nobel work most years; here is his main take from this year’s post on Goldin:

Goldin’s work helps us understand whose wages will rise, will fall, will equalize going forward. Not entirely unfairly, she will be described in much of today’s coverage as an economist who studies the gender gap. This description misses two critical pieces. The question of female wages is a direct implication of her earlier work on the return to different skills as the structure of the economy changes, and that structure is the subject of her earliest work on the development of the American economy. Further, her diagnosis of the gender gap is much more optimistic, and more subtle, than the majority of popular discourse on the topic.

He described my favorite Goldin paper, which calculates gender wage gaps by industry and shows that pharmacists moved from having one of the highest gaps to one of the lowest as one key feature of the job changed:

Alongside Larry Katz, Goldin gives the canonical example of the pharmacist, whose gender gap is smaller than almost every other high-wage profession. Why? Wages are largely “linear in hours”. Today, though not historically, pharmacists generally work in teams at offices where they can substitute for each other. No one is always “on call”. Hence a pharmacist who wants to work late nights while young, then shorter hours with a young kid at home, then a longer worker day when older can do so. If pharmacies were structured as independent contractors working for themselves, as they were historically, the marginal productivity of a worker who wanted this type of flexibility would be lower. The structure of the profession affects marginal productivity, hence wages and the gender gap, particularly given the different demand for steady and shorter hours among women. Now, not all jobs can be turned from ones with convex wages for long and unsteady hours to ones with linear wages, but as Goldin points out, it’s not at all obvious that academia or law or other high-wage professions can’t make this shift. Where these changes can be made, we all benefit from high-skilled women remaining in high-productivity jobs: Goldin calls this “the last chapter” of gender convergence.

Source: A Grand Gender Convergence: Its Last Chapter

There is much more to the post, particularly on economic history; it concludes:

When evaluating her work, I can think of no stronger commendation than that I have no idea what Goldin will show me when I begin reading a paper; rather, she is always thoughtful, follows the data, rectifies what she finds with theory, and feels no compunction about sacrificing some golden goose – again, the legacy of 1970s Chicago rears its head. Especially on a topic as politically loaded as gender, this intellectual honesty is the source of her influence and a delight to the reader trying to understand such an important topic.

This year also saw a great summary from Alice Evans, who to my eyes (admittedly as someone who doesn’t work in the subfield) seems like the next Claudia Goldin, the one taking her work worldwide:

That is the story of “Why Women Won”.

Claudia Goldin has now done it all. With empirical rigor, she has theorised every major change in American women’s lives over the twentieth century. These dynamics are not necessarily true worldwide, but Goldin has provided the foundations.

I’ve seen two lines of criticism for this prize. One is the usual critique, generally from the left, that the Econ Nobel shouldn’t exist (or doesn’t exist), to which I say:

The critique from the right is that Goldin studied unimportant subjects and only got the prize because they were politically fashionable. But labor markets make up most of GDP, and women now make up almost half the labor force; this seems obviously important to me. Goldin has clearly been the dominant researcher on the topic, being recognized as a citation laureate in 2020 (i.e. someone likely to win a Nobel because of their citations). At most politics could explain why this was a solo prize (the first in Econ since Thaler in 2017), but even here this seems about as reasonable as the last few solo prizes. David Henderson writes a longer argument in the Wall Street Journal for why Claudia Goldin Deserves that Nobel Prize.

Best of all, Goldin maintains a page to share datasets she helped create here.

Is the Job Growth Driven by Part-Time Workers?

Jeremy Horpedahl1 Comment

A few weeks ago I wrote about several measures of the labor market, and whether the labor market was actually doing well. It’s a good idea to look beyond the headline unemployment rate, but even looking at alternative unemployment rates, labor force participation, employment rates, and unemployment insurance claims, I concluded in that post that the labor market is still looking healthy.

Lately I have heard another objection to the job growth numbers: part-time employment. I’ve seen this pop-up a few times on Twitter lately and just yesterday my co-blogger Scott Buchanan (in a post primarily about excess savings) stated that “much of the jobs creation this year has been in the part-time category.”

So is the jobs recovery mostly about part-time jobs? What is going on?

First things first: most of the data on part-time employment is from the household survey. There’s already a lot of noise in the household survey, due to the sample size, and part-time workers are a small share of the workforce, so expect it to be even noisier. In short, don’t trust one-month fluctuations too much. Furthermore, most of the data folks look at is seasonally adjusted. That’s generally good practice! But again, for a small number in a small sample, the seasonal adjustment factors won’t be perfect. Don’t read too much into one or a few months of data.

Let’s get the big picture first. How much of the labor force in the US is usually working part-time (defined in most data as less than 35 hours per week)? As usual FRED is the best place to go for graphing BLS data:

Continue reading →

Monetary and Fiscal Policy Is Still Easy

James Bailey4 Comments

The last post where I attempted a macro prescription was in April 2022, when I said the Fed was still under-reacting to inflation. That turned out right; since then the Fed has raised rates a full 500 basis points (5 percentage points) to fight inflation. So I’ll try my luck again here.

Headline annual CPI inflation has fallen from its high of 9% at the peak last year to 3.7% today. Core PCE, the measure more closely watched by the Fed, is at a similar 3.9%. Way better than last year, but still well above the Fed’s target of 2%. Are these set to fall to 2% on the current policy path, or does the Fed still need to do more?

The Fed’s own projections suggest one more rate hike this year, followed by cuts next year. They expect inflation to remain a bit elevated next year (2.5%), and that it will take until 2026 to get all the way back to 2.0%. They expect steady GDP growth with no recession.

What do market-based indicators say? The yield curve is still inverted (usually a signal of recession), though long rates are rising rapidly. The TIPS spread suggests an average inflation rate of 2.18% of the next 5 years, indicating a belief the Fed will get inflation under control fairly quickly. Markets suggest the Fed might not raise rates any more this year, and that if they do it will only be once. All this suggests that the Fed is doing fine, and that a potential recession is a bigger worry than inflation.

Some of my other favorite indicators muddy this picture. The NGDP gap suggests things are running way too hot:

M2 shrank in the last month of data, but has mostly leveled off since May, whereas a year ago it seemed like it could be in for a major drop. I wonder if the Fed’s intervention to stop a banking crisis in the Spring caused this. Judging by the Fed’s balance sheet, their buying in March undid 6 months of tightening, and I think that underestimates its impact (banks will behave more aggressively knowing they could bring their long term Treasuries to the Fed at par, but for the most part they won’t have to actually take the Fed up on the offer).

The level of M2 is still well above its pre-Covid trend:

Before I started looking at all this data, I was getting worried about a recession. Financial markets are down, high rates might start causing more things to break, the UAW strike drags on, student loan repayments are starting, one government shutdown was averted but another one in November seems likely. After looking at the data though, I think inflation is still the bigger worry. People think that monetary policy is tight because interest rates have risen rapidly, but interest rates alone don’t tell you the stance of policy.

I’ll repeat the exercise with the Bernanke version of the Taylor Rule I did in April 2022. Back then, the Fed Funds rate was under 0.5% when the Taylor Rule suggested it should be at 9%- so policy was way too loose. Today, the Taylor Rule (using core PCE and the Fed’s estimate of the output gap) suggests:

3.9% + 0.5*(2.1%-1.8%) + 0.5%*(3.9%-2%) + 2% = 7%

This suggests the Fed is still over 1.5% below where they need to be. Much better than being 9% below like last April, but not good. The Taylor rule isn’t perfect- among other issues it is backward-looking- but it tends to be at least directionally right and I think that’s the case here. Monetary policy is still too easy. Fiscal policy is still way too easy. If current policy continues and we don’t get huge supply shocks, I think a mild “inflationary boom” is more likely than either stagflation or a deflationary recession.

Pinball Prices (Not Adjusted for Inflation)

Jeremy HorpedahlLeave a comment

Last weekend I had the opportunity to visit an arcade, but not one of those modern fancy arcades with virtual reality, laser tag, etc. This arcade specializes in having old-school games, primarily pinball, but also early video arcade games. You pay a cover charge ($5 for kids, $10 for adults), and then you use quarters to play the games. But here’s the cool part: the price of the games is the same as it was when the games were first released.

As an economist, of course, I was very interested in the prices.

They had pinball machines that dated back the 1960s, and video games from the late 1970s. Most video arcade games were around 50 cents for the early games (late 1970s and early 1980s). But the pinball machines started out at 25 cents, with the earliest game they had being a Bally Blue Ribbon machine, manufactured in 1965 (interestingly, some of the earlier machines had slots for both dimes and quarters — I assume the price was adjustable mechanically). Notably, you also got to play 5 balls for this price (3 balls seems to be standard later on).

How should we think about that 25 cents? A standard reaction is to adjust the number for inflation. Using the CPI-U as the inflation index, that means the 25 cents from 1965 is “worth” about $2.40 now. That’s interesting, but I don’t think it really provides the relevance that we want today.

An alternative is to calculate the “time price” of playing the game. Using the average hourly wage of $2.67 in December 1965, we can calculate that it would take about 5.5 minutes of work to pay for that game — a game which probably only lasts about 5.5 minutes, unless you are really good at it!

Another comparison we could do is with the cost of video games today compared with wages today. But that’s not really a fair comparison — video games are much more advanced today. We would need to do some sort of quality adjustment, which is overly complicated.

But, at least in my case, there is no need to do the quality adjustment — I can play the exact same game as 1965. In fact, I did (several times). There was also that $10 cover charge that I mentioned, and if I spread that fixed cost over 40 games, it cost me about 50 cents per play (including the 25 cents to start the machine) to play the 1965 Bally’s Blue Ribbon Pinball machine. At the average wage today of $29 per hour, it takes about 1 minute to afford a play of that same game. In other words, my Blue-Ribbon-Pinball standard of living is about 5.5 times greater than in 1965.

Now this isn’t to say we are 5.5 times better off overall than 1965. Prices don’t stay constant for most goods! But hopefully it is a useful way to think about that 25 cent price tag from the past, and how to compare it to today.

New Center for the Restoration of Economic Data

James Bailey1 Comment

Regular readers will know that we love not only economics, but also history and data. We especially love it when “data heroes” take data that was difficult or impossible to access and make it easily available to everyone. The Federal Reserve Bank of Philadelphia just announced a project that brings together all of these things we love, their new Center for the Restoration of Economic Data:

Our mission is to advance research in topics related to regional economics and consumer finance by making economic data available in readily accessible, digital form. CREED combines state-of-the-art machine learning technology with deep subject matter expertise to convert natively unstructured data (information in books, images, and other undigitized formats) into readily accessible digital data.

The CREED research team shares the original analog or unstructured data as well as the code used to recover and clean these data, which are aggregated for use in novel economic research. Our collection features volumes of old, often overlooked, and frequently inaccessible data, which have been mined, restored, and converted into unstructured digital and analytically usable formats.

Their first project is to map all of the racially restrictive covenants in the city of Philadelphia. Until the U.S. Supreme Court declared such covenants to be unenforceable in 1948, they often barred properties from being sold to non-whites or non-citizens. After 1948 redlining took different forms, some of which may still persist today.

CREED shares the underlying data used to build the map here, and they say much more is one the way. I love it when economic historians (and regular historians) digitize old paper records and share the resulting data, and hope to see more examples like this to share in the coming years.

Disclaimer: I am a visiting scholar at the Federal Reserve Bank of Philadelphia but I was not involved with this project

Who is the Wealthiest Generation? Mid-2023 Update

Jeremy Horpedahl1 Comment

The Federal Reserve has released the latest update to their Distributional Financial Accounts data, which the data underlying several of my past posts on generational wealth. With that recent data, I have updated the chart of wealth for Baby Boomers, Generation X, and Millennials.

The data is shown on a log scale to better show growth rates and allow for easier visual comparisons. But if you are interested in the more precise numbers, in the most recent quarter (2023q2) Generation X has, on average about $620,000 in net wealth, which compares favorably with Baby Boomers at about the same age (in 2006) with about $539,000 in net wealth per person. That’s about 20 percent more.

Millennials have about $115,000 in net wealth on average, which also compares favorably with Baby Boomers, who had slightly more at about the same age (in 1990) with $121,000 in net wealth on average. Given the uncertainties of all the data that goes into this, I’d say those are roughly equal. Gen X had a bit more around the same age (in 2007) with $149,000, but that fell significantly the next two years during the Great Recession.

(For more detail on my approach to creating the chart, see the linked post above, but in short I’m using the Fed DFA data for wealth, Census Bureau data by single year of age for population, and the Personal Consumption Expenditures price index for inflation adjustments (I also have a chart with the CPI-U — it’s not much different). Wealth data is for the 2nd quarter in each year (to match 2023), except for 1989 since the 3rd quarter is the first available.)

Given how much wealth can fluctuate based on housing values (see above for Gen X from 2007-2009), it might be useful to look at the data with housing. Housing is also a weird kind of wealth — for the most part, you can’t access it without selling (other than certain home equity loans), and when you do sell, unless your home appreciated more than average, you just have to move to another home that also appreciated.

Here’s the chart excluding housing value and mortgage debt:

The chart… doesn’t change much. The values are all lower, of course, but the comparisons across generations look pretty similar. Gen X right now is 17 percent wealthier than Boomers at the same age. And if we look at all three generations around the median age of 35, they are pretty close: Gen X with $123,000 (but slipping over the next few years), Boomers with $99,000, and Millennials with $90,000.

Median Family Income in US States, 2022

Jeremy HorpedahlLeave a comment

Last week I wrote about median income in the US, and how it had declined since 2019 and 2021 through 2022 (inflation adjusted, of course). The big story is that median income (both for households and families) has been falling in recent years. While there are some silver linings when looking at subgroups, such as Black families, the overall data isn’t good.

But while that is true for the US overall, it’s not true for every state. In fact, it’s not even true for most states! From 2019 to 2022, there were 29 states that saw their median family incomes rise! That’s adjusted for inflation (I’m using the C-CPI-U, which is Census’s preferred inflation measure for this data). The income data in this post all comes from the Census ACS 1-year estimates.

Here’s a map showing the states that had increases in median family income (green) and those that had decreases (in red). (This is my first time experimenting with Datawrapper maps, feedback appreciated!)

Some states had pretty robust growth, with New Mexico and Arizona leading the way with around 5 percent growth. There is substantial variation across US states, including with big declines like Wyoming at -5 percent, and Oklahoma and Illinois are -3 percent.

A few weeks ago I also wrote about the richest and poorest MSAs in the US. But what about the richest and poorest states in the US? The following map shows that data.

The immediate fact which will jump out at you is that the lowest income US states are almost all located in the South. This will probably not surprise most of us, although it probably is a bit surprising since the data is adjusted for differences in the cost of living (using the BEA RPP data). Even after making these adjustments, the South is still clearly the poorest region (and it definitely was the poorest without the adjustments).

Among the higher income states, they are distributed pretty well across the rest of the non-South. There are 16 states (plus DC) that have median family incomes over $100,000 (again, cost of living adjusted), and while many of these are in New England and the Mid-Atlantic, there area still a few in the Midwest, Great Plains, and the West. Utah and New Jersey have similar incomes, as do Virginia and Rhode Island.

The highest income states are Massachusetts and Connecticut, with over $112,000 in median family income, while the lowest are Mississippi and West Virginia, both under $78,000. Median family income in Massachusetts is 46 percent higher than Mississippi. And that’s after adjusting for differences in the cost of living.

Median Income Is Down Again. Are There Any Silver Linings in the Data?

Jeremy Horpedahl1 Comment

This week the Census Bureau released their annual update on “Income, Poverty and Health Insurance Coverage in the United States.” This release is always exciting for researchers, because it involves as massive release of data based on a fairly large (75,000 household) sample with detailed questions about income and related matters. For non-specialists, it also generates some of the most commonly used national data on income and poverty. Have you heard of the poverty rate? It’s from this data. How about median household income? Also from this data.

I’ll focus on income data in this post, though there is a lot you could say about poverty and health insurance too. The headline result on median income is, once again, a dismal one. Whether you look at median household income (very commonly reported, even though I don’t like this measure) or median family income (which I prefer), both are down from 2021 to 2022 when adjusted for inflation. Both are still down noticeably from the pre-pandemic high in 2019 (though both are also above 2018 — we aren’t quite back to the Great Depression or Dark Ages, folks!).

These headline results are bad. There is no way to sugarcoat or “on the other hand” those results. And these results are probably more robust and representative than other measures of average or median earnings, since they aren’t subject to “composition effects” — when those with zero wages in one period don’t show up in the data. I will note that these results are for 2022, and we are highly likely to see a turnaround when we get the 2023 data in about a year (inflation has slowed to less than wage growth in 2023).

But given that obviously bad headline result, was there any good data? As I mentioned above, a ton of data, sliced many different ways, is released with this report. Some of it also gives us consistent data back decades, in some cases to the 1940s. What else can we learn from this data release?

Median Income by Race

When we look at median income by race, there are a few silver linings. The headline data from Census tells us that only the drop in household income for White, Non-Hispanics was statistically significant. For other races and ethnicities, the changes were not statistically significant from 2021 to 2022 — and some of those changes were actually positive. We shouldn’t dismiss White, Non-Hispanics — they are the largest racial/ethnic group! — but it is useful to look at others.

Black household and families are the most interesting to look at in more detail, especially because they are the poorest large racial group in the US. Black household and family income increased from 2021 to 2022, although the increase was small enough that we can’t say it is statistically significant (remember, this is a sample, not the universe of the decennial Census).

But what’s more important is that median Black household income is now at the highest level it has ever been (adjusted for inflation, as always). Median Black household income is about $1,000, or around 2 percent higher than in 2019 — the peak date for overall median income. Two percent growth over 3 years is nothing to shout from the rooftops, but it is very different from White, Non-Hispanic households, which are down over 6 percent since 2019.

Median black family income is roughly flat since 2019, but it is up about 1.5 percent in the past year — not quite as robust, but still better than the overall numbers.

Historical Income Data

The other silver lining I always like to mention is the long-run historical data. This data often gets overlooked in the obsessive focus on the most recent changes, so it’s useful to sit back and look at how far we have come. Let’s start where we just left off, with Black families. I wrote a post back in February about Black family income, which had data current through 2021, but it’s useful once again to look at the data with another year (plus they have updated the inflation adjustments for 2000 onward).

The chart shows the percent of Black families that are in three income groups, using total money income data. The data is adjusted for inflation. The progress is dramatic. In 1967, the first year available, half of Black families had incomes under $35,000. By 2022 that number had been cut in half to just one quarter of families (the 2022 number is the lowest on record, even beating 2019). Twenty-five percent is still very high, especially when compared to White, Non-Hispanics (it’s about 12 percent), but it’s still massive progress. It’s even a 10-percentage point drop from just 10 years ago. And Black families haven’t just moved up a little bit: the “middle class” group (between $35,000 and $100,000) has been pretty stable in the mid-40 percentages, while the number of rich (over $100,000) Black families has grown dramatically, from just 5 percent to over 30 percent.

We saw earlier that progress for White, Non-Hispanics has stumbled in the past 3 years, but the long run data is much more optimistic (this data starts in 1972).

The progress here should be evident too, but let me highlight one thing for emphasis: as far back as 1999, the largest of these three groups was the “rich” (over $100,000 group). And since 2017, the upper income group has been the majority, with median White Non-Hispanic family income surpassing $100,000 in 2017, up from $70,000 at the beginning of the series in the early 1970s (all inflation adjusted, of course).

The next question I often get with this historical data is: How much of this increase is due to the rise of two-income households. Well, this same data release allows us to look at that data too! This final chart shows median family income for families with either one or two earners (there are families with zero earners or more than two, but these two categories make up the bulk of families). This data is pretty cool because it goes all the way back to 1947.

This chart doesn’t look so good for one-earner families. After growing along with two-earner families in the 1950s and 1960s, it basically stagnates from the early 1970s until the late 2010s. Then you get a little growth. Not good!

I think more investigation is needed here, but the share of families that have two earners has grown dramatically, from 26 percent of families in 1947 to 42 percent in 2022. Single earner families shrunk from 59 percent to 31 percent, and dual-income families have been the most common family type since the late 1960s. There are some important compositional differences here in what types of families only have one earner. If we imagine some alternate history where, by law, only one spouse was allowed to work, certainly the single earner line would have risen more. And many of the single earner families today are single mothers, who for a variety of reasons have much lower earning potential than the fathers heading married couples in the 1950s and 1960s. So the numbers aren’t perfectly comparable.

Still, even for single earner families, real median income has more than doubled since 1947 — though most of that growth had happened by the early 1970s.

As we make our way through a challenging economic time following the pandemic and 2 years of unusually high inflation, hopefully we can look forward to a future of resuming the upward trajectory of incomes for all kinds of families.

Generative AI Nano-Tutorial

Scott Buchanan4 Comments

Everyone who has not been living under a rock this year has heard the buzz around ChatGPT and generative AI. However, not everyone may have clear definitions in mind, or understanding of how this stuff works.

Artificial intelligence (AI) has been around in one form or another for decades. Computers have long been used to analyze information and come up with actionable answers. Classically, computer output has been in the form of numbers or graphical representation of numbers. Or perhaps in the form of chess moves, beating all human opponents since about 2000.

Generative AI is able to “generate” a variety of novel content, such as images, video, music, speech, text, software code and product designs, with quality which is difficult to distinguish from human-produced content. This mimicry of human content creation is enabled by having the AI programs analyze reams and reams of existing content (“training data”), using enormous computing power.

I wanted to excerpt here a fine article I just saw which is informative on this subject. Among other things, it lists some examples of gen-AI products, and describes the “transformer” model that underpins many of these products. I skipped the section of the article that discusses the potential dangers of gen-AI (e.g., problems with false “hallucinations”), since that topic has been treated already in this blog.

Between this article and the Wikipedia article on Generative artificial intelligence , you should be able to hold your own, or at least ask intelligent questions, when the subject next comes up in your professional life (which it likely will, sooner or later).

One technical point for data nerds is the distinction between “generative” and “discriminative” approaches in modeling. This is not treated in the article below, but see here.

All text below the line of asterisks is from Generative AI Defined: How it Works, Benefits and Dangers, by Owen Hughes, Aug 7, 2023.

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What is generative AI in simple terms?

Generative AI is a type of artificial intelligence technology that broadly describes machine learning systems capable of generating text, images, code or other types of content, often in response to a prompt entered by a user.

Generative AI models are increasingly being incorporated into online tools and chatbots that allow users to type questions or instructions into an input field, upon which the AI model will generate a human-like response.

How does generative AI work?

Generative AI models use a complex computing process known as deep learning to analyze common patterns and arrangements in large sets of data and then use this information to create new, convincing outputs. The models do this by incorporating machine learning techniques known as neural networks, which are loosely inspired by the way the human brain processes and interprets information and then learns from it over time.

To give an example, by feeding a generative AI model vast amounts of fiction writing, over time the model would be capable of identifying and reproducing the elements of a story, such as plot structure, characters, themes, narrative devices and so on.

……

Examples of generative AI

…There are a variety of generative AI tools out there, though text and image generation models are arguably the most well-known. Generative AI models typically rely on a user feeding it a prompt that guides it towards producing a desired output, be it text, an image, a video or a piece of music, though this isn’t always the case.

Examples of generative AI models include:

  • ChatGPT: An AI language model developed by OpenAI that can answer questions and generate human-like responses from text prompts.
  • DALL-E 2: Another AI model by OpenAI that can create images and artwork from text prompts.
  • Google Bard: Google’s generative AI chatbot and rival to ChatGPT. It’s trained on the PaLM large language model and can answer questions and generate text from prompts.
  • Midjourney: Developed by San Francisco-based research lab Midjourney Inc., this gen AI model interprets text prompts to produce images and artwork, similar to DALL-E 2.
  • GitHub Copilot: An AI-powered coding tool that suggests code completions within the Visual Studio, Neovim and JetBrains development environments.
  • Llama 2: Meta’s open-source large language model can be used to create conversational AI models for chatbots and virtual assistants, similar to GPT-4.
  • xAI: After funding OpenAI, Elon Musk left the project in July 2023 and announced this new generative AI venture. Little is currently known about it.

Types of generative AI models

There are various types of generative AI models, each designed for specific challenges and tasks. These can broadly be categorized into the following types.

Transformer-based models

Transformer-based models are trained on large sets of data to understand the relationships between sequential information, such as words and sentences. Underpinned by deep learning, these AI models tend to be adept at NLP [natural language processing] and understanding the structure and context of language, making them well suited for text-generation tasks. ChatGPT-3 and Google Bard are examples of transformer-based generative AI models.

Generative adversarial networks

GANs are made up of two neural networks known as a generator and a discriminator, which essentially work against each other to create authentic-looking data. As the name implies, the generator’s role is to generate convincing output such as an image based on a prompt, while the discriminator works to evaluate the authenticity of said image. Over time, each component gets better at their respective roles, resulting in more convincing outputs. Both DALL-E and Midjourney are examples of GAN-based generative AI models…

Multimodal models

Multimodal models can understand and process multiple types of data simultaneously, such as text, images and audio, allowing them to create more sophisticated outputs. An example might be an AI model capable of generating an image based on a text prompt, as well as a text description of an image prompt. DALL-E 2 and OpenAI’s GPT-4 are examples of multimodal models.

What is ChatGPT?

ChatGPT is an AI chatbot developed by OpenAI. It’s a large language model that uses transformer architecture — specifically, the “generative pretrained transformer”, hence GPT — to understand and generate human-like text.

What is Google Bard?

Google Bard is another example of an LLM based on transformer architecture. Similar to ChatGPT, Bard is a generative AI chatbot that generates responses to user prompts.

Google launched Bard in the U.S. in March 2023 in response to OpenAI’s ChatGPT and Microsoft’s Copilot AI tool. In July 2023, Google Bard was launched in Europe and Brazil.

…….

Benefits of generative AI

For businesses, efficiency is arguably the most compelling benefit of generative AI because it can enable enterprises to automate specific tasks and focus their time, energy and resources on more important strategic objectives. This can result in lower labor costs, greater operational efficiency and new insights into how well certain business processes are — or are not — performing.

For professionals and content creators, generative AI tools can help with idea creation, content planning and scheduling, search engine optimization, marketing, audience engagement, research and editing and potentially more. Again, the key proposed advantage is efficiency because generative AI tools can help users reduce the time they spend on certain tasks so they can invest their energy elsewhere. That said, manual oversight and scrutiny of generative AI models remains highly important.

Use cases of generative AI

Generative AI has found a foothold in a number of industry sectors and is rapidly expanding throughout commercial and consumer markets. McKinsey estimates that, by 2030, activities that currently account for around 30% of U.S. work hours could be automated, prompted by the acceleration of generative AI.

In customer support, AI-driven chatbots and virtual assistants help businesses reduce response times and quickly deal with common customer queries, reducing the burden on staff. In software development, generative AI tools help developers code more cleanly and efficiently by reviewing code, highlighting bugs and suggesting potential fixes before they become bigger issues. Meanwhile, writers can use generative AI tools to plan, draft and review essays, articles and other written work — though often with mixed results.

The use of generative AI varies from industry to industry and is more established in some than in others. Current and proposed use cases include the following:

  • Healthcare: Generative AI is being explored as a tool for accelerating drug discovery, while tools such as AWS HealthScribe allow clinicians to transcribe patient consultations and upload important information into their electronic health record.
  • Digital marketing: Advertisers, salespeople and commerce teams can use generative AI to craft personalized campaigns and adapt content to consumers’ preferences, especially when combined with customer relationship management data.
  • Education: Some educational tools are beginning to incorporate generative AI to develop customized learning materials that cater to students’ individual learning styles.
  • Finance: Generative AI is one of the many tools within complex financial systems to analyze market patterns and anticipate stock market trends, and it’s used alongside other forecasting methods to assist financial analysts.
  • Environment: In environmental science, researchers use generative AI models to predict weather patterns and simulate the effects of climate change

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Generative AI vs. machine learning

As described earlier, generative AI is a subfield of artificial intelligence. Generative AI models use machine learning techniques to process and generate data. Broadly, AI refers to the concept of computers capable of performing tasks that would otherwise require human intelligence, such as decision making and NLP.

Machine learning is the foundational component of AI and refers to the application of computer algorithms to data for the purposes of teaching a computer to perform a specific task. Machine learning is the process that enables AI systems to make informed decisions or predictions based on the patterns they have learned.

( Again, to make sure credit goes where it is due, the text below the line of asterisks above was excerpted from Generative AI Defined: How it Works, Benefits and Dangers, by Owen Hughes).

Cool the Schools

James BaileyLeave a comment

Short post today because I’m busy watching my kids, who had their school canceled because of excessive heat, like many schools in Rhode Island today.

I thought this was a ridiculous decision until my son told me he heard from his teacher that his elementary school is the only one in town that has air conditioning for every classroom. Given that, the decision to cancel given the circumstances is at least reasonable, but the lack of AC is not.

It’s not just that hot classrooms are unpleasant for students and staff, or that sudden cancellations like this are a major burden for parents. Several economics papers have found that air conditioning significantly improves students’ learning as measured by test scores (though some find not). Park et al. (2020 AEJ: EP) find that:

Student fixed effects models using 10 million students who retook the PSATs show that hotter school days in the years before the test was taken reduce scores, with extreme heat being particularly damaging. Weekend and summer temperatures have little impact, suggesting heat directly disrupts learning time. New nationwide, school-level measures of air conditioning penetration suggest patterns consistent with such infrastructure largely offsetting heat’s effects. Without air conditioning, a 1°F hotter school year reduces that year’s learning by 1 percent.

This can actually be a bigger issue in somewhat Northern places like Rhode Island- we’re South enough to get some quite hot days, but North enough that AC is not ubiquitous. Data from the Park paper shows that New York and New England are actually some of the worst places for hot schools:

This is because of the lack of AC in the North:

The days are only getting hotter…. it’s time to cool the schools.