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Group for Research in APplied Economics

Warszawa, Polska
Joined August 2013
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GRAPE retweeted
France’s 35-hour workweek didn’t generate work-sharing: absent tax cuts, employment would have fallen 3.8%, concentrated among lower-skill workers. TFP rose 2%, output fell 1%, from @PaulineCarry, @cmontialoux, @ElioNimier, Alexandra Roulet, and @ninaroussille nber.org/papers/w35917
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Today: California billionaire wealth tax prop 40. Throwback: One of the papers I most enjoyed writing some 15 years ago (with Farhi and later Sleet, Yeltekin) was on Wealth taxation, nonlinear in a political economy with no commitment. Let me explain what we did. I am not trying to take political sides really or advocate one thing or another. Enough of that and it is good to think of the economic logic in a basic way. As you see, the paper is more subtle. The paper modeled a situation where you (e.g., voters, on average) want to do a "one time" wealth tax and promise never to do it again. The incentive to do so is increasing in the amount of wealth inequality. You have nonlinear taxes on wealth and income, so you have rich tools. However, you can't bind future politics or policies. Every now and then you reevaluate (vote). Sounds familiar? Indeed, various basics of the real world tradeoffs are present. Obvious: this is a time-consistency problem that leads to a 3rd best (third world?) outcome (not your standard optimal tax one, where you assume you can commit). Well known and studied. At its most basic, the temptation is confiscation (slogans: robin hood, socialism), but the expectation of this happening in future is of course a problem (slogans: capitalism, property rights). Less obvious: what it is the best you can do today if you with your tax tools, given the situation and constraints. AGAIN: you are not allowed to say "I promise never to tax again". ALSO NOTE: the options are also not necessarily just a choice between taxing wealth now or never doing so. (As the debate on this oversimplify.) Logically: taxing never may just not be feasible/credible. Equally important: you may come up with a different more intermediate plan. Let's see... Indeed, the model spits out something subtle. You actually declare a more moderate but progressive nonlinear tax on wealth that will be in place permanently. It is progressive with rising marginal tax rates: indeed, you subsidize savings for the poor (!) but tax the rich. In fact, you want to promise this tax today for the future, even if inequality is zero today. Why? The credibility problem depends on the amount of wealth inequality. That is what affects the incentive to renege on promises and tax away wealth. The solution tries to deal with this by creating an environment that will lower or keep inequality in check, to avoid a temptation to fall into the time inconsistency revision. A progressive tax goes in that direction both mechanically and in terms of saving incentives. (BTW, the fact that the tax comes out progressive is very clean and surprising, given that the optimal tax literature rarely obtains such results.) Insight: By keeping inequality in check, it makes policy more credible, avoiding falling into a much worse 3rd best. At the time, we called it a "compassionate conservative" solution: even if you want zero taxes, you compromise and do some progressive taxation, to avoid more radical taxes later. Metaphorically, the best way to deal with a tide is not by ignoring it, denying it or swimming frantically, you may have to float with it, adapt and swim moderately finding the right moves. Links: (older ungated): crei.cat/wp-content/uploads/… published: academic.oup.com/restud/arti… Footnote: We titled it capital taxation, but in reality it is more exactly about wealth taxation, e.g. it holds even in a world without capital, where people hold assets against each other etc.
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W ramach @GRAPE_ORG ostatnie kilka miesięcy ślęczałem nad danymi, aby dowiedzieć się o ubóstwie w Polsce jak najwięcej. W nitce przedstawię kilka ciekawych (przynajmniej taką mam nadzieję) ustaleń, do jakich udało mi się dotrzeć w trakcie mojej pracy. (1/10)
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W podcaście GRAPE gadam zaskakująco długo o ubóstwie w Polsce. Jak ktoś chce posłuchać przemyśleń do jakich doszedłem po gapieniu się w dane BBGD przez wiele miesięcy to gorąco zachęcam!
🇵🇱 JAK SIĘ W POLSCE WALCZY Z UBÓSTWEM? [🎙 PODCAST] Czy trzeba być bogatym krajem, by nie mieć ubóstwa? Czy w dwudziestej gospodarce świata wypada mówić, że nie wychodzi nam eliminacja tego zjawiska? Czyją twarz ma ubóstwo w Polsce? I dlaczego wśród rodzin wielopokoleniowych ryzyko ubóstwa jest tak wysokie? Z danych tłoczą: 🎙 @AdamSuraj14 (@GRAPE_ORG ) 🎙 @P_Maciejewicz (@PolitykaInsight) 🎙 Joanna Tyrowicz (@GRAPE_ORG). Posłuchaj: 🟢 Spotify: tinyurl.com/grape-s08e04-sp 🟣 Apple Podcast: tinyurl.com/grape-s08e04-ap 🔴 YouTube: tinyurl.com/grape-s08e04-yt W tym odcinku: 🔎 Czy istnieje jedna obiektywna granica ubóstwa? 🔎 Co mierzą różne wskaźniki ubóstwa? 🔎 Kto jest najbardziej narażony na ubóstwo w Polsce? 🔎 Dlaczego rodziny wielopokoleniowe wyróżniają się w danych? 🔎 Co wydatki na żywność, transport, mieszkanie i łączność mówią o warunkach życia? 🔎 Jak wprowadzenie 500+, jego rozszerzenie i waloryzacja wpłynęły na mierzone ubóstwo? 🔎 Dlaczego powszechne świadczenia pieniężne mogą przynosić inne rezultaty niż pomoc rzeczowa? 🔎 Czy rosnące koszty ogrzewania mogą stworzyć nową grupę osób zagrożonych ubóstwem? Zapraszamy! 😎
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GRAPE ma tylko 15 lat i jest dziełem talentów organizacyjnych jednej osoby, a już w rankingach publikacji naukowych wyprzedza większość polskich uczelni i instytucji ekonomicznych. Niesamowite osiągniecie!
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Wielkie gratulacje także dla prof. Joanny Tyrowicz, która sama od podstaw zbudowała @GRAPE_ORG - instytucję, której współpracownicy zdobywają takie honory i publikują w najlepszych czasopismach ekonomicznych na świecie 1/2
🏆 PIOTR DWORCZAK ELECTED FELLOW OF THE @ECONOMETRICSOC. The Fellowship is one of the highest honors in economics, recognizing outstanding contributions to the field. Piotr is one of just 30 researchers selected this year. Since the Society was founded in 1930, only 1 284 economists have received this distinction. Among them are 90 Nobel laureates in Economic Sciences. Piotr is a researcher at @GRAPE_ORG. His research focuses on economic theory, especially market design and information economics. He is the first – and so far the only – researcher in Poland to receive a @ERC_Research grant in economics. He is also a professor at the Adenauer School of Government at @UniCologne and at @NorthwesternU. His work has been published in leading journals, including Econometrica, the Quarterly Journal of Economics and the Journal of Political Economy. Piotr, congratulations! 👏
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Bardzo dziękuję @pzoch5 i @P_Maciejewicz za sympatyczne spotkanie przy mikrofonie. Rozmawiamy o tym jak redystrybuuje polityka pieniężna i fiskalna. Nie tylko bezpośrednio (np przez podatki lub stopy procentowe), ale też pośrednio (np poprzez inflację czy bezrobocie)
💰 KTO PŁACI ZA INFLACJĘ? [🎙 PODCAST] Jak inflacja, stopy procentowe i decyzje banku centralnego tworzą niewidzialne transfery? Kto na nich zyskuje: młodzi kredytobiorcy, starsi oszczędzający, pracownicy czy przedsiębiorcy? Z danych tłoczą: 🎙 @BrzozaBrzezina (@SGHWarsaw) 🎙 @P_Maciejewicz (@PolitykaInsight) 🎙 @pzoch5 (@GRAPE_ORG) Posłuchaj: 🟢 Spotify: tinyurl.com/grape-s08e03-sp 🟣 Apple Podcast: tinyurl.com/grape-s08e03-ap 🔴 YouTube: tinyurl.com/grape-s08e03-yt W najnowszym odcinku podcast GRAPE | Tłoczone z danych rozmawiamy o tym, jak modele makroekonomiczne pozwalają zobaczyć to, czego nie widać w prostych statystykach: kto naprawdę płaci za inflację, komu pomaga luźna polityka pieniężna i dlaczego brak decyzji banku centralnego też może być decyzją. To rozmowa o tym, jak działa polityka pieniężna, jak globalne szoki gospodarcze wpływają na dobrobyt i dlaczego w ekonomii nawet cisza potrafi redystrybuować. To odcinek o tym: ✅ jak niewidzialne transfery powstają bez nowych ustaw, podatków i oficjalnych przelewów, ✅ dlaczego polityka pieniężna nie jest neutralna dla kredytobiorców, oszczędzających, pracujących i emerytów, ✅ kto zyskuje, a kto traci, gdy bank centralny obniża stopy procentowe albo zwleka z reakcją na inflację, ✅ jak modele makroekonomiczne symulują strefę euro i pokazują ukryte efekty decyzji gospodarczych, ✅ dlaczego szoki podażowe po pandemii i wzrost inflacji mogły inaczej dotknąć młodych, biednych, starszych i bogatych, ✅ czy bank centralny powinien brać pod uwagę redystrybucję dochodu, czy trzymać się przede wszystkim celu inflacyjnego. Do posłuchania! 👋
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O luce czynszowej do posłuchania. A jeśli ktoś chciałby się obczytać to zachęcam: doi.org/10.1080/02673037.202… doi.org/10.14746/rpeis.2025.… P.S. już nie długo aktualizacja danych i kilka nowych przeliczeń pozwalających rzucić lepsze światło na strukturę luki czynszowej w Polsce. #staytuned
🏡 CZY KLASA ŚREDNIA W POLSCE MA GDZIE MIESZKAĆ? [🎙 PODCAST] Czy w Polsce naprawdę brakuje mieszkań, czy raczej nie umiemy ich dobrze dopasować? I dlaczego problem dotyczy nie tylko najbiedniejszych, ale też klasy średniej? Z danych tłoczy @AdamCzerniak (@SGHWarsaw). Zapraszają: @P_Maciejewicz (@PolitykaInsight) i Joanna Tyrowicz (@GRAPE_ORG). Posłuchaj: 🟢 Spotify: tinyurl.com/grape-s08e02-sp 🟣 Apple Podcast: tinyurl.com/grape-s08e02-ap 🔴 YouTube: tinyurl.com/grape-s08e02-yt W najnowszym odcinku podcast GRAPE | Tłoczone z danych rozmawiamy o jednym z najgłośniejszych pojęć w debacie o mieszkalnictwie: luce czynszowej. Adama Czerniaka pytamy, dlaczego wpada w nią aż 35% gospodarstw domowych w Polsce? Czemu najbardziej narażeni są samodzielni rodzice i rodziny wielodzietne? Czy praca i regularne dochody wystarczą na zaspokojenie potrzeb mieszkaniowych? Jak mają się do tego rosnące ceny mieszkań, czynszów i energii? Sprawdzamy, co o właściwie oznaczają „minimalne potrzeby mieszkaniowe”? Ile zależy od tego, jak skonstruowany jest rynek mieszkaniowy, od przyjętych norm społecznych i tego, jak mierzymy problem? Co jeśli część kryzysu wynika z tego, że zaczęliśmy inaczej definiować „godne warunki życia”? Dowiesz się także, jakie polityki mieszkaniowe naprawdę działają, a które tylko brzmią dobrze? To odcinek o tym: ✅ co mierzy luka czynszowa i czym się różni od ubóstwa mieszkaniowego; ✅ dlaczego luka czynszowa to nie tylko problem biedy, ale też struktury rynku mieszkaniowego; ✅ dlaczego kawalerka dla jednej osoby może być uznana za przeludnioną; ✅ kto najczęściej wpada w lukę mieszkaniową; ✅ jak średnie krajowe ceny mieszkań mogą zniekształcać obraz; ✅ jak różne modele mieszkalnictwa w Europie wpływają na dostępność mieszkań; ✅ dlaczego „średnie dane” mogą zniekształcać realny obraz rynku. Do posłuchania! 👋
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Fifth question on AI. AI is currently viewed as a centralizing technology – with ever-larger models, trained on much of the world’s knowledge, running on massive, concentrated compute, overseen by a few companies, and aiming to control and deploy all of that knowledge But many early computer scientists and pioneers thought of digital technologies as powerfully decentralizing, as Simon Johnson and I discuss in our book Power and Progress (hachettebookgroup.com/titles…). One such pioneer, Lee Felsenstein, wrote “The industrial approach is grim and doesn’t work: the design motto is Designed by Geniuses for Use by Idiots.” In the words of one of his collaborators, Bob Marsh, “We wanted to make the microcomputer accessible to all human beings.” And this wasn’t just an aspiration; they believed it was both feasible and natural that computers would empower individuals. The first newsletter of People’s Computer Company also captures this optimism: “Computers are mostly; used against people instead of for people; used to control people instead of to free them; time to change all that – we need a…PEOPLE’S COMPUTER COMPANY”. Should we accept centralizing AI? Or is there a way in which the promise of artificial intelligence can be realized while data, capabilities and control are much more decentralized both across domains and across people? Wouldn’t decentralized AI be more consistent with human agency and control? (Some of the ways in which greater decentralization can happen are discussed in my first post in this series.)
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Fourth question on AI. One of the great promises of AI is the discovery of new drugs and cures, so that we can live longer and healthier lives. If and when this becomes a reality, it would indeed be a big achievement. These potential health benefits are often invoked to justify current investments (and lack of regulations). Some even argue that slowing down AI would harm humanity by delaying these benefits. (See a16z.com/the-techno-optimist…). This raises another uncomfortable question: can we justify rapid and large AI investments based on health benefits? Here is why I think this is an uncomfortable question. First, despite significant effort, AI has so far produced few gains in drug discovery or new cures. A fascinating new paper by Ryan Hill and Carolyn Stein documents significant (downstream) scientific work on proteins whose structures AlphaFold predicted. But the authors conclude “we find no evidence so far that more applied, early-stage drug development is targeting these proteins.” (See their paper here papers.ssrn.com/sol3/papers.…). This thought-provoking post by Daphne Koller explains the difficulties that AI is currently facing in drug discovery. Koller emphasizes that AI-based research is focused on the last layer of drug development (creating new molecular entities for already well-understood therapeutic modalities), rather than targeting new disease mechanisms (see deepphenotype.substack.com/p…). Understanding disease mechanisms is harder because there is much less data on it, and this endeavor requires more innovative approaches. It remains an open question whether current AI models are capable of doing this. Second and more importantly, if you want to improve life expectancy and health in the United States, there is plenty of low hanging fruit and diverting some of the huge investments going to AI for this purpose would likely do much more for health outcomes. The United States has the lowest life expectancy at birth among large rich economies. Americans live about five years less, in expectation, than the citizens of Switzerland, Sweden, Japan, Italy, South Korea and several other industrialized countries, and most of the gap comes from deaths before age of 70 due to chronic diseases, overdoses and other preventable causes (see, for example, healthsystemtracker.org/char…). These largely reflect failures in US public health: preventable problems, unhealthy diets, insufficient immunization rates, poor access to primary care; and poor information about health. The country spends much more than other peer economies on healthcare, but too little and too ineffectively on public health (here is a reference to my own work on this: aeaweb.org/articles?id=10.12…). Could we, and should we, divert some of the massive investments in AI towards public health if what we want is better health and longer lives for Americans? (And yes, new drugs will benefit citizens of other countries as well, but they can also invest more in public health and the bulk of global AI spending is currently in the United States).
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First question on AI. Is the problem superintelligence or distorted intelligence? This is a question I tried to address in this piece. I can add the following additional comment here. I believe the debate is being obfuscated by equating goal-driven, autonomous behavior with intelligence. Plants and insects have goal-driven, largely autonomous behaviour. But this isn’t what we mean by intelligence in general. Intelligence is general-purpose and holistic, and is typically well-adapted to its environment. There is no doubt that current AI models have impressive capabilities. But in attempting to achieve these capabilities, AI labs may be creating distorted intelligence rather than superintelligence, as I explain in this article. This matters because the dangers we should watch out for, the kinds of policies and regulations we should adopt, and the hope we should hold depend on the answer to this question.
Recent security breaches suggest that AI capabilities are developing rapidly while being put in the service of imperfect quantitative metrics that ultimately distort the behavior of the most advanced models, @DAcemogluMIT writes. project-syndicate.org/commen…
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Second question on AI. We are told repeatedly that AI is going to transform every aspect of our lives – jobs, productivity, inequality, science, communication, daily activities, social order, and politics, among others. But this promise (or threat) is coupled with the rhetoric that such an important technology, with all of the risks and competitive pressures that it entails, should be left to experts or to “technocracy” (perhaps construed broadly to include some regulators). These two statements are hard to reconcile in a democratic society. If anything is half as important as AI is said to be (and I agree, AI is potentially very important and transformative), then involving democratic voice is essential. If something will shape our future in a democratic society, then its direction is for democratic institutions to decide. My instinct is that democratic voice is essential, and relying too much on technocracy could be both dangerous and counterproductive. The counterargument that AI’s direction can and should be entrusted to technocracy would go something along the following lines. First, democratic decision-making has become imperiled in our age of polarization. Second, AI is sufficiently complex that most citizens won’t have a deep enough understanding to meaningfully contribute to the debate (and even to the question of what we want from AI). Third, competition between different labs, and perhaps competition between the US and China, creates enough discipline for a socially beneficial direction of AI to be adopted. Fourth, today’s AI leaders are enlightened and ethical enough that within the framework created by competition, they can be broadly trusted. There are many aspects of this counterargument that I do not find convincing. Taking them in order: polarization can be overcome, and big decisions and challenges sometimes bring societies together; in fact, delegating key decisions to technocracy without democratic input may diminish trust in institutions and experts, and may worsen polarization. Second, democratic voice does not require citizens to write code or design new models; the debate should be informative enough that citizens can weigh in about what type of future they want and how they trade off the costs and benefits of different options. Third, competition doesn’t seem to be a good disciplining framework; on the contrary, competition sometimes brings the worst out of both organizations and people. Fourth, if three decades of work on political economy and institutions has taught me anything, it is that we should not bank on the ethical grounding of unconstrained leaders. But, still, I do not mean to immediately dismiss the technocracy option if there are more compelling arguments for it. The question is, then, whether there are any circumstances under which such important decisions can be delegated to AI experts and technocracy. One final secondary question: even if we managed to get democratic input in the United States or even in Europe, AI will shape the lives of everyone on this planet. How do we ensure that the voice of nearly 6 billion people who don’t live in the US, Europe and China also contributes to the debates on AI?
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Third question on AI. A question that also remains unasked is whether the AI boom can continue without leading to a massive increase in inequality. A recent paper by Stijn Van Nieuwerburgh runs the numbers on how much revenue the AI industry needs to generate to recover its massive investment (summary and a link to the paper can be found here: brookings.edu/articles/finan…). Van Nieuwerburgh’s arithmetic should make us more concerned. AI investments will average about 3.6% of GDP annually between 2025 and 2032. Van Nieuwerburgh calculates that, using a 10% rate of return, the industry would need to generate annual revenues of about $3.7 trillion by 2032 to recover these costs (growing from its current levels of about $200 billion or so). That is significantly more than 10% of current US national income, and will likely remain around 10% of national income by 2032, even if GDP growth rose from its current level. A large fraction of this revenue will go to capital income. That means a massive increase in the share of capital in national income, which has already risen substantially over the last 25 years or so – now standing at an all-time high of about 47% (bls.gov/news.release/pdf/pro…). Capital income is much more unequally distributed than labor income, so a massive increase in the capital share of national income will translate into a very sizable surge in inequality. The rise in inequality may not stop with the capital share. My work with Pascual Restrepo documents that (automation-driven) increases in the capital share of national income are typically associated with rising labor income inequality as well (see, for example, economics.mit.edu/sites/defa…). The same may happen in the next several years, boosting inequality further. What is missing from our current debate is any discussion of a fundamental dilemma these numbers pose: can the AI boom avoid both an economically costly crash and a huge increase in inequality? If the industry reaches these revenues, inequality surges. If the industry does not become profitable, a crash, with substantial costs in terms of lost output and jobs, becomes likely. My assessment would be that the industry is unlikely to reach levels of revenue Van Nieuwerburgh calculates. First, diffusion has been and will likely continue to be slow. Second, competition from open-weight models, which are getting better, will limit how much proprietary models can charge. Third, despite important advances, I still believe that AI models will not be able to automate entire occupations anytime soon, thus limiting their value to businesses as cost-saving devices. Whether this leads to a crash or not is more complicated and will depend on whether various AI companies are bailed out and what kind of support they receive. Nevertheless, even if revenues fall short of these gargantuan amounts and we avoid a dramatic surge in inequality, I expect that the diffusion of AI will push up inequality between capital and labor and within labor. If inequality does surge, a further question becomes central: can our democracy survive such astronomical levels of inequality?
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Kto korzysta na podatku liniowym dla przedsiębiorców i ile zyskałby budżet, gdyby go zlikwidować? Nowa analiza (z Leszkiem Morawskim, dane MF): ta preferencja podatkowa jest warta 19 mld zł rocznie 71% trafia do górnych 10%, a górny 1% zyskuje średnio 1,1 mln zł na osobę. 🧵
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Wazny temat i znakomity ekspert. Warto posłuchać. – at Piastów, Polska
CZY ROZWÓJ AI MOŻE NAS ZABIĆ? [🎧 PODCAST] Rozwój technologiczny miał dawać nam więcej dobrobytu, ale może też odebrać nam kontrolę nad przyszłością. Czy sztuczna inteligencja stanie się kolejnym wielkim skokiem cywilizacyjnym, czy technologią, której nie będziemy już w stanie zatrzymać? 🎙 Z danych tłoczy Jakub Growiec (@SGHWarsaw). Zapraszają: @P_Maciejewicz (@PolitykaInsight) i Piotr Żoch @pzoch5 (@grape_org). Do posłuchania tutaj: 🟢 Spotify: bit.ly/grape-s08e01-sp 🟣 Apple Podcast: bit.ly/grape-s08e01-ap 🔴 YouTube: bit.ly/grape-s08e01-yt Przez 200 tysięcy lat ludzkość przechodziła kolejne rewolucje: poznawczą, rolniczą, przemysłową i cyfrową. Każda zwiększała nasze możliwości, ale każda przynosiła też skutki uboczne, których wcześniej nie potrafiliśmy przewidzieć. Paradoks polega na tym, że problemem AI może być nie tyle utrata miejsc pracy, ile utrata ludzkiej kontroli nad decyzjami. Jak działa rozwój gospodarczy i dlaczego tempo zmian technologicznych może być dziś bezprecedensowe? Co oznacza AI alignment i czym różni się wspieranie człowieka od zastępowania jego decyzji? I co się stanie, jeśli powstanie AGI zdolna nie tylko do rozwiązywania problemów, ale także do samodzielnego realizowania celów? Gościem podcastu GRAPE | Tłoczone z danych jest Jakub Growiec ze Szkoły Głównej Handlowej. Rozmowa prowadzi od ekonomii wzrostu i pełnej automatyzacji aż po najbardziej niepokojące scenariusze: nierówności, wyścig AI między USA i Chinami, samodoskonalenie modeli oraz ryzyko utraty kontroli. Dowiesz się, dlaczego maksymalizowanie wzrostu gospodarczego niekoniecznie jest racjonalne, jeśli ceną może być katastrofa. To odcinek o tym: ✅ dlaczego kolejne rewolucje technologiczne coraz mocniej przyspieszają rozwój gospodarczy; ✅ dlaczego AI może być inna niż wcześniejsze technologie - zamiast pracy fizycznej zaczyna zastępować ludzką pracę umysłową i podejmowanie decyzji; ✅ czym jest AI alignment i dlaczego zgodność celów sztucznej inteligencji z ludzkimi wartościami jest tak trudna; ✅ co może oznaczać AGI, samodoskonalenie modeli i utrata kontroli nad coraz bardziej sprawczą AI; ✅ czy pełna automatyzacja przyniesie powszechny dobrobyt, czy raczej rekordowe nierówności ekonomiczne; ✅ jak pogodzić wzrost gospodarczy i bezpieczeństwo, gdy wyścig technologiczny nie daje uczestnikom dobrego powodu, by zwolnić. Do posłuchania! 😘
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GRAPE retweeted
Join us online for the next session of the Virtual Seminar on Monetary Economics. @ludwigstraub @Harvard will present the paper "Exchange Rates and Monetary Policy with Heterogeneous Agents: Sizing up the Real Income Channel". 1 October 2026 | 17:00 CEST 🔗 ow.ly/4Yms50Z2zqW
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Treasury Yield, R*, government funding advantage (did depend on banking era not Bretton Woods), expectation hypothesis holds except for 1960-90, ... with Bill Dudley & Jonathan Payne | Markus Academy youtu.be/ZseTYM_A-60?si=IZia… via @YouTube
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New Danmarks Nationalbank working paper, out today, with @SimoneMBonin and Andreas Kuchler. We match Denmark's official firm IT survey to the monthly wage register, identify the firms that first used AI in 2023, and follow them month by month.
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