Tag: economics

  • Read Smarter in 2025

    Read Smarter in 2025

    My top 10 book picks from 2024, to help you build your 2025 reading list

    This year, for some reason, I found myself reading a lot more than usual — 52 books in total — without even pushing myself.

    I think it happened naturally because I bought many books that genuinely interested me (I also tackled some really short ones, to be fair).

    Here are some of the best books I read this year to help you build your reading list for 2025.

    But before we dive in, you might be wondering why bother with this. Why create a reading list or read books at all? Well, reading is one of the best things you can do for yourself. There’s so much knowledge out there from experts in various fields — Nobel laureates, Harvard PhDs, and more. Books are affordable, and most of them are really enjoyable to read.

    And why books? Why not just read blog posts?

    A good book is like a painting: the author invests a lot of time, does extensive research, and works tirelessly to distill their ideas into the pages. That effort really shows.

    Good books have layers and depth. Re-reading them reveals new insights each time.

    This applies to both fiction and non-fiction. These days, self-development books are selling really well, partly because their titles are so straightforward: “How to Win Friends and Influence People,” “How to Talk to Anyone,” “Think and Grow Rich”.

    There’s nothing wrong with that, but remember that many of those lessons can also be found in fiction, presented in a more friendly and subtle way.

    Plus, reading doesn’t have to be just for learning — it can also be purely for fun!

    Personally, I like to mix things up. This year, I read a lot of fiction, economics, and data science books.

    Now, onto the list.

    Less Technical Stuff

    Build

    Tony Fadell’s memoir and practical guide for entrepreneurs offers insights from his experiences designing iconic products like the iPod and Nest.

    It’s a rare book written by someone who has actually built things. It covers everything from HR to marketing to legal issues and walks you through the different stages of building a business — from working on a product with a small team to managing an organization of over 400 people.

    The Capitalist Manifesto

    This book argues in favor of capitalism as the ultimate system for freedom, innovation, and wealth creation.

    Contrary to popular belief, global free-market capitalism has been the main driver of prosperity, reduced inequality, and fostered innovation over the past few centuries.

    While some of the author’s claims come across as naive and heavily biased toward capitalism (for example, suggesting capitalism has a net positive impact on the environment), most arguments are solid and backed by strong data. Absolutely worth reading.

    The Chronicles of Narnia

    A classic fantasy series by C.S. Lewis about children discovering a magical world full of adventure, talking animals, and profound moral lessons.

    The seven books are an allegory of biblical stories, spanning from the creation of Narnia to its end. Remember Aslan, the talking lion from the movies? He symbolizes God, which is made very clear in the books, as he serves as a benevolent and just king/father figure.

    Though I’m not religious, I found it fascinating to see the lessons built into the narrative. Regardless of your beliefs, many of these lessons are universal, and reading these books with your children can be a great way to pass those values on.

    Educated

    Tara Westover’s memoir chronicles her journey from an isolated, fundamentalist upbringing to pursuing education and self-discovery. I couldn’t put it down.

    Tara’s parents were extreme conspiracy theorists who refused to send their kids to school or take them to the hospital, believing these institutions were part of a larger scheme to control people. While their worldview might seem absurd at first, it’s heartbreaking to see its impact on their children.

    Despite this, Tara managed to escape that environment and eventually earned a doctorate from the University of Cambridge. Safe to say, she turned out okay.

    The Power of Creative Destruction

    This book explores how innovation drives economic growth and progress by disrupting and replacing outdated systems.

    For me, the main takeaway is that demonizing either free-market competition or state intervention doesn’t make sense. Both are necessary, and the book does a great job explaining when government intervention is helpful and when it can cause more harm than good.

    Factfulness

    This book illustrates global progress by plotting GDP per capita against life expectancy and categorizing countries into four development levels along this axis.

    Interestingly, most countries fall in the middle, with only a few being extremely poor or very rich. Almost all countries, however, are moving in the right direction.

    It’s remarkable how life has improved globally over the past 100 years. What’s even more surprising is how wrong people often are about the current state of the world. The author surveyed people worldwide, asking specific questions about statistics like vaccination rates, and the results showed widespread pessimism.

    This negativity is partly due to the media’s tendency to focus on bad news. While the world isn’t perfect, things are steadily improving, and this book is a great reminder of that.

    More Technical Stuff

    Fundamentals of Software Architecture

    Especially with the rise of generative AI, we’re often asked to build tools that don’t require much data science — just smartly calling APIs and wrapping them in a Streamlit interface.

    This calls for a better understanding of software architecture.

    All data scientists can benefit from learning software architecture principles. We tend to focus heavily on coding without understanding how our work fits into larger systems. This book offers a comprehensive guide to designing better systems.

    Clean Code

    A practical guide to writing clean, maintainable, and efficient code, this is a classic in the field.

    It’s particularly useful for data scientists like me, who learned coding through Jupyter notebooks and picked up some bad habits along the way. Trust me, clean code matters — it improves readability and reduces bugs.

    While it’s a great book, much of it could be distilled into a list of dos and don’ts (which I might create in a future story). However, keep in mind it’s very Java-specific.

    System Design Interview

    This preparation guide for system design interviews explains frameworks and best practices in a clear, concise way, making it easy to understand.

    It’s a great starting point for learning system design concepts, regardless of whether you actually have an interview coming or not.

    Causal Inference in Python

    This hands-on guide shows how to apply causal inference methods using Python for real-world data science problems.

    It borrows heavily from econometrics, so it’s an excellent resource if you come from that background. If your prior exposure to causality has been through machine-learning-focused sources, this book provides a refreshing new perspective.

  • AI Is Not Taking Our Jobs

    A quantitative exploration of the relationship between technology, labor and wealth

    I see many people say AI will take our jobs any time now, based on the following narrative: the better AI becomes, the less companies will need us, therefore we will be replaced.

    It’s an interesting chain of thought, but does it have the empirical basis to back it up? In other words, does reality match the story? Well, it doesn’t seem so.

    It’s common to engage in this sort of discussion with well-thought arguments, based purely on conjectures, without looking at the existing body of scientific work nor at the data. I propose we take a tour of those two dimensions, to see if we can learn a thing or two from the empirical evidence.

    Before we start, there is one link we need to establish: technology increases productivity. Here, we are talking from an economics perspective. Don’t think “when I have my cellphone I can’t work as much”. Here we are looking more at “the more technology in the world, the more we can produce with the same amount of work”.

    This phenomenon is explained by the ability of technology to automate tasks, streamline processes, and facilitate the creation of new products and services. The intrinsic connection between technology and productivity is fundamental to understanding everything else you will read here.

    With that out of the way, let’s see what science and data have to say about the impact of AI on our jobs.

    Scientific work

    Will AI take our jobs? This question can be seen as a specific case of the broader, more strucured question “does technology increase unemployment?”.

    Unsurprisingly, this question has been asked by researchers many times before: a meta-analysis from 2022 that looked at 127 studies concluded that there is more evidence suggesting that technology creates net jobs than the other way around [1]. Their analysis specifically focuses on industrialized economies, to capture technological change at the frontier. They have also explored how this effect can be different depending on how we look at technology, but we will talk more about this later.

    Some of the fear of AI comes from the narrative that, since AI makes us more productive, companies will need less of us to do the same job and, therefore, they will hire less of us. So, another study, from the European Central Bank [2], looked at the more general question “does productivity growth threaten employment?”. It turns out the answer is no. Even though some industries with higher productivity have seen fewer jobs, overall, the growth in productivity hasn’t really harmed employment. The study shows that one industry becoming more productive doesn’t automatically mean it will hire more people. However, it suggests that the positive impacts of productivity in one area can still create more jobs in other parts of the economy, offsetting any job losses in sectors with productivity gains. So, in the big picture, productivity growth has actually led to more jobs across various sectors. This study also concluded that current technology advances might bring a positive contribution to net jobs:

    […] the source of productivity growth matters for its aggregate employment consequences. Given that service sector productivity growth appears to have relatively strong employment spillovers, our findings suggest that the productivity growth spurred by the spread of (ultimately) general-purpose technologies such as robotics from heavy industry and into services may prove a boon for employment growth.

    Anothery interesting study, from the OECD [3], spanning 13 countries over two decades, investigates the relationship between productivity, employment, and wages. It found a positive correlation between productivity growth and increased employment and wages, both at the firm and aggregate levels:

    At the more aggregate level, the role of reallocation and links across industries becomes more evident. Yet also here, results confirm that productivity growth is, overall, associated with positive changes in employment and wages. Increasing employment among expanding firms tends to outweigh decreasing employment in shrinking or exiting firms. Furthermore, productivity gains at the industry level contribute to stronger employment growth in downstream industries through value chains.

    Looking at AI more specifically, a panel study from 2023 shows evidence that it decreases the level of unemployment, at least in high-tech developed countries [4]. The study investigated how AI affects unemployment in 24 high-tech developed countries from 2005 to 2021, using Google Trend Index data related to AI and unemployment rates.

    Data

    Now, for some extra context, let’s take the time to explore some of the data ourselves, to answer some broeader questions. If technology is evolving (and I don’t think anyone questions that), and this is not decreasing the number of jobs available, then what is it doing for us?

    We are not necessarily “less employed”

    To contribute to the body of evidence that we are not being replaced by technology, let us look at the employment rates in the world since 1950:

    Image by author

    I put China and India separately because their data was not available for every year and, given their populations, this had a big impact on the variability of the indicator, specially since the 1990s.

    We can see that, despite the impressive technological advances over the last 70 years, employment rates do not seem to be a bigger issue now than they were back then.

    We are working less

    We are, on the other hand, working much less than our ancestors:

    Image by author

    This global trend, however, is not the same everywhere:

    Image by author

    Richer countries are reducing their working hours at higher rate than the rest. I cherry-picked some specific countries that are representative of different trends:

    • Germany is an example of a rich country where people worked much more than the rest of the world, and has drastically decreased since then;
    • The US has become substantially richer since the 1950’s, yet its work load have not decreased significantly;
    • China and India seem to be going in the opposite direction of the rest of the world, working longer hours.

    We are richer than ever

    I hope this does not come as a surprise, but we have never been richer:

    Image by author

    So, even though the world is working less, pretty much every country got richer.

    Of course, the distribution of that wealth was not the same across the globe. Pretty much every country got richer, but some got richer than others (again, no surprise):

    Image by author

    But how can we work less and make more money? What allowed that to happen? Well, we happen to have become more productive over time thanks, in part, to technology.

    We are more productive

    Technology, education and solid institutions contribute to productivity, and we can see the results over the years:

    Image by author

    Unfortunately, I couldn’t find data older from before the 2000s but, given the decrease in number of hours worked and the increase in GDP per capita, we can see the trend is there and is pretty much strong.

    This constant productivity increase over the time is what allows us to work less and make more money.

    Things will still change

    So, everything will stay the same? Well, no. Of course AI will have an impact in the job market. But the change might be more related to jobs migrating from one industry to another, and from one region to another.

    The European Central Bank study [2] suggests that increases in productivity move jobs in two main ways:

    1. less manufacturing jobs, more services jobs
    2. less low-skill jobs, more high-skill jobs

    More specifically, we expect more jobs in health, education, and services, and less jobs in utilities, mining, and construction.

    Also, in the meta-analysis we saw earlier [1], they identify five key categories of technology measures in the literature, with different impacts on job markets:

    1. Information and communication technology: more high-skillnon-routine, and service jobs;
    2. Robots: the negative impact on employment is generally offset by new jobs related to their production, operation, and maintenance;
    3. Innovation: product innovation seems to create jobs, but the evidence for process innovation remains mixed;
    4. Productivity: job gains were mostly favorable for non-productionhigh-skill, and service jobs. Nonetheless, the net employment effects observed in these studies are rather negative than positive;
    5. Other: other/indirect measures of technology indicate net job creation effects, particularly for non-production labour, but also for lowly skilled workers, particularly in service jobs.

    So we could expect either:

    1. relocation of jobs from manufacturing countries towards service-intensive countries;
    2. a change in the sectorial structure within countries, with an increase of service jobs when compared to manufacturing;
    3. a mix of both.

    I imagine these changes could increase inequality due to:

    1. the migration towards high-skill jobs, particularly if education doesn’t keep up;
    2. more money going to capital owners: there is already evidence [2] that the share of labor on national income is decreasing.

    We could be wrong, but how?

    Of course this whole analysis could be wrong. Let’s try and gather some evidence that goes in the opposite direction, or at least understand in what ways the evidence we saw before could be inadequate.

    There are some theoretical attempts to model the Marxist thesis of “labor immiseration”, looking at different axes:

    1. Inter-generational market failure: quick advancements in technology benefit skilled workers and those who own capital in the short term. However, over time, it leads to hardships for people who cannot invest in physical or human capital;
    2. Task encroachment: two opposing economic forces shape how much income goes to labor: technology advancement which replaces ‘old’ tasks, decreasing labor’s share of output, potentially lowering real wages; and internal technological progress that creates new tasks requiring labor. The interaction of these forces may result in a unbalanced growth path;
    3. New tasks might not be created “fast enough”: the number of automated tasks could grow at a higher rate than the new tasks created by automation, leading to a reduction in the number of tasks that can be performed by humans;

    These models are theoretical, though: they are not evidence that the net job losses will happen, they just outline scenarios where it could.

    There is some evidence showing that this time could be different, and that this relationship between productivity and unemployment might be shifting: recent decades have witnessed more negative own-sector effects of productivity growth, especially in manufacturing, and less positive external effects on other-sector employment, possibly due to increased trade openness. However, this pattern has been seen before, in the 1980s, and it’s not new [2].

    Another study, by the National Bureau of Economic Research [5], looks at the impact of increased industrial robot usage in the United States from 1990 to 2007 on local labor markets. The researchers find that the rise in robot usage leads to a significant negative effect on employment and wages across commuting zones. They support their findings by showing that areas most exposed to robots post-1990 did not display different trends before that period. The impact of robots is distinct from other factors like imports from China, decline of routine jobs, offshoring, and various types of IT capital. The conclusion suggests that for every additional robot per thousand workers, the employment to population ratio decreases by about 0.18–0.34 percentage points, and wages decrease by 0.25–0.5 percent. The study focuses specifically on industrial robots in certain local labor markets, which can’t account for the spillover effects on other regions or markets.

    Maybe this time it’s different, and AI is such a disruptive technology that it will behave in a different way. Maybe, its relationship with productivity is different than other technologies.

    Maybe. But the empirical evidence suggests otherwise, so I wouldn’t bet on it.

    References

    1. Somers, M., Theodorakopoulos, A., & Hötte, K. (2022). “The fear of technology-driven unemployment and its empirical base.”
    2. Autor, D., & Salomons, A. (2017, June 19). “Does Productivity Growth Threaten Employment?”
    3. Calligaris, S., Calvino, F., Reinhard, M., & Verlhac, R. (OECD). (2023). “Is there a trade-off between productivity and employment? A cross-country micro-to-macro study.” OECD Science, Technology and Industry Policy Papers, №157.
    4. Guliyev, H. (2023). “Artificial intelligence and unemployment in high-tech developed countries: New insights from dynamic panel data model.” Economic Research Center of Turkish World, Azerbaijan State Economic University.
    5. Acemoglu, Daron and Pascual Restrepo. (2017). “Robots and Jobs: Evidence from U.S. Labor Markets.” NBER Working Paper №23285, March.