Every September, caregivers and kids alike prepare for one big change: the start of a new school year. OUPblog - Academic insights for the thinking world.
Every September, caregivers and kids alike prepare for one big change: the start of a new school year. As the weeks of summer draw to a close, families are cramming in the last moments of summer fun while simultaneously gearing up for school drops offs and new classroom schedules. While it can be an incredibly exciting time, filled with first day of school outfits and new school gear, it can also be incredibly stressful. This can be particularly true for teenagers who, compared to younger kids, are facing higher academic demands and social pressure while experiencing the major physical and developmental changes that come during adolescence. On top of that, a 2023 Center of Disease Control report showed that teens of today have higher rates of mental health concerns, such as anxiety and depression, and that suicidal thoughts and behaviors are increasing. This can make the return to school daunting for teens, as well as parents who are worried about how their child will manage the transition and demands of the year.
Fortunately, there are several tools that parents and caregivers can use to prepare kids and teens for the first few weeks in September. This includes setting clear expectations, skills to encourage helpful behavior, and strategies that help kids feel supported by their parents.
Setting expectations
While many kids prefer to keep their heads in the sand when it comes to a new academic cycle, it can be incredibly helpful to set expectations for the school year a few weeks in advance. The most basic version of this includes outlining differences between summer versus school schedules, such as changes to sleep and wake times, limits to screens, or daily responsibilities. This preview can help kids’ brains prepare for the upcoming shifts in their daily lives and make the transition a little smoother. It’s also a great idea to talk to kids about how the upcoming school year might be different than the last one. This could include providing information on class size, the structure of the day, or increased expectations. The goal is not to scare your kids about everything coming their way, but rather to provide them with simple clear information in a manner that builds excitement. For example, “It’s so fun that you get to go to go off-campus for lunch this year. I bet it will make the day feel way more interesting!” Or, “I know high school is bigger than middle school. It may feel a little overwhelming, but it’s also such a great time for you to see how capable you are.”
Encouraging positive behaviors
Once expectations have been set, parents can also work to encourage brave or skillful behavior. This may include things like taking more responsibility (e.g., managing their own communication with teachers and coaches), growing outside of their comfort zone (e.g., joining a new club or social circle), or challenging themselves with new opportunities or roles (e.g., a first job or harder courseload). This most effective way to do this is through a skill called “labeled praise.”
Labeled praise is when you show appreciation for a specific behavior or characteristic your child is demonstrating. When it comes to a new school year, parents can look for opportunities to praise preparation, flexibility, and bravery. For example, “I know you really loved your teachers last year, and I appreciate how openminded you are about your new schedule.” Another parent may say, “Great call on getting to bed a little earlier this week. It’ll make the start of school so much easier!” For teens who haven’t mastered brave or skillful choices, parents can offer cheerleading and encouragement. Phrases like “I know you’re going to do a beautiful job making friends because you’ve done it before!” or “10th grade is tough, and I have total confidence that you’re going to find a way to balance everything” send a message that they really believe in their kid. This can go a long way towards encouraging positive behaviors.
Providing validation
When you do notice your child having a hard time, whether it’s nerves, low mood, or difficulty organizing themselves for a new semester, it’s always a great idea to offer validation. Validation is a skill used to show somebody that you can see their perspective or understand where they are coming from. Validation can be a tricky skill to master for caregivers because it is sometimes hard to put yourself in your child’s shoes, or you are eager to get them to see a new perspective. For example, when your child complains about their new math teacher who they have heard is a hard grader, it’s tempting to say “Nah! I’m sure it’ll be fine!” This may work for some kids. However, it can come off as dismissive and hard to believe for a teen whose anxiety or stress is high. Instead, try validation: “It makes sense that you’re nervous based on what you’ve heard!” While you aren’t agreeing with your child’s worries, you are acknowledging them, and that can help increase a sense of connection and communication. Once your child feels understood, they’ll be better able to think clearly about the situation and problem solve as needed.
As you navigate another year of permission slips, homework, and extracurricular activities, remember that you have a handful of tools in your pocket to help ease the way. With a little bit of preparation, encouragement, and support, you and child can start the school year off on a great foot.
The financialization of Western economies has unfolded as a prolonged systemic failure. What began as a mechanism to support productive enterprise has evolved into a structural dominance of finance over the real economy. Through deregulation, the proliferation of speculative activity, and successive asset bubbles, the sector has prioritized short-term gains over long-term investment. The 2008 financial crisis underscored these dynamics, transferring the burdens of systemic risk to the broader public while financial institutions were largely shielded from the consequences. This trajectory has entrenched income inequality and contributed to the political capture of regulatory institutions, inhibiting meaningful reform.
In contrast, China presents a divergent model. Its state-led financialization exemplifies a proactive deployment of financial mechanisms in service of national industrial objectives. Unlike the market-driven financialization typical of advanced Western economies, China’s approach is characterized by strategic state intervention and institutional design. The government not only participates in markets but reconfigures them—mobilizing state-owned enterprises as venture capital vehicles, directing bank lending toward emerging technologies, and leveraging local government financing platforms to support innovation. This model represents a deliberate recalibration of financial systems to prioritize long-term technological development over immediate capital returns.
State-owned enterprises (SOEs): from asset managers to venture capitalists
Chinese SOEs have increasingly transitioned from passive asset holders to active financial agents, functioning as quasi–venture capital entities with a targeted focus on high-technology sectors such as artificial intelligence, semiconductors, and advanced manufacturing. This transformation is rooted in the 2013 reforms under Xi Jinping, which marked a shift in state asset governance from a model of “managing assets” to one of “managing capital.” Central to this new framework are state-owned capital investment and operation companies (SCIOCs)—market-oriented entities tasked with allocating state capital in alignment with national strategic objectives.
Prominent SCIOCs such as Guoxin and Chengtong exemplify this model, channeling investments into key technological domains while retaining mechanisms of state oversight. Notably, their investment strategies increasingly resemble those of global institutional investors like BlackRock, characterized by portfolio diversification and minority equity stakes across a wide range of publicly listed firms. Over time, both Guoxin and Chengtong have reduced the size of their individual holdings while broadening the scope of their portfolios, mirroring BlackRock’s index-based approach. However, unlike BlackRock, whose investment logic is primarily driven by market signals and shareholder value maximization, these Chinese entities operate within a state-directed paradigm. Their capital allocation decisions are subordinated to broader industrial policy objectives, underscoring a distinctive model of “state-capital hybridization” wherein global financial practices are repurposed to advance national technological priorities.
Banks: from conservative lenders to investment partners
China’s banking sector has undergone a significant transformation from a traditionally conservative, loan-centric model—once governed by the “separation principle” that delineated clear boundaries between lending and investment—toward a more integrated, market-oriented system. Since 2015, mechanisms such as “investment and loan linkage” have enabled commercial banks to engage in equity-related activities, particularly in support of high-technology enterprises. Institutions like the Bank of China have introduced “green channel” loans that prioritize lending to startups with venture capital backing, and in some cases have experimented with convertible instruments such as “stock option models,” allowing for the conversion of debt into equity.
This evolution has been further institutionalized through the establishment of bank wealth management companies (BWMCs), which are permitted to make direct equity investments in high-tech firms. As of the end of 2022, the China Banking and Insurance Regulatory Commission (CBIRC) had approved 29 such entities. One notable example is BOCOM International, affiliated with the Bank of Communications, which manages the BOCOM Science and Technology Innovation Fund—an investment vehicle explicitly oriented toward advancing technological innovation. These developments underscore a broader trend of financial re-engineering within the Chinese banking system, as state-affiliated financial institutions adopt quasi-investor roles to support national strategic priorities, reinforcing the architecture of state-led financialization.
Local governments: trading land speculation for innovation funding
In recent years, Chinese local governments have transitioned away from reliance on Local Government Financing Vehicles (LGFVs), traditionally used to support land-based urban development, toward the deployment of Government Guidance Funds (GGFs). This strategic reorientation marks a shift from speculative real estate-driven financing to a model of purposeful financialization aimed at fostering technological innovation. Rather than leveraging land assets to finance urban expansion, local authorities are increasingly channeling capital into science and technology sectors through state-backed investment vehicles.
A prominent example is the National Integrated Circuit Industry Investment Fund (NICIIF), with a targeted fund size of approximately USD 95.8 billion, which supports enterprises in strategically vital sectors such as semiconductors. These funds operate not merely as instruments of capital allocation but as policy tools through which local governments execute central industrial strategies. According to the Zero2IPO database, as of 2023, there were 2,086 active GGFs across China, collectively managing assets exceeding USD 1.8 trillion. This proliferation underscores a broader recalibration of subnational fiscal behavior, whereby the objectives of economic development and industrial policy are fused within a state-directed financial architecture oriented toward national technological advancement.
A coordinated push for tech supremacy
This evolving model of state-led financialization reflects a deliberate integration of financial instruments with industrial policy, positioning the state as what we termed as “financial entrepreneur.” In this capacity, the state assumes a dual function: both as a strategic investor in capital markets and as a fund manager whose objectives are shaped through a hybrid of administrative directive and market logic. The recalibration of incentives across state institutions—ranging from banks and SOEs to local governments—facilitates the targeted allocation of financial resources toward sectors deemed essential for national technological leadership.
This coordinated mobilization contrasts sharply with earlier phases of development finance in China, which were heavily reliant on infrastructure-led investment through Local Government Financing Vehicles (LGFVs). The current financial architecture instead orients capital toward innovation and industrial upgrading. As illustrated in the accompanying figure, this shift embodies a paradigmatic change in the underlying logic of state intervention. The empirical results are notable: according to a 2023 report by the Australian Strategic Policy Institute (ASPI), China now leads globally in 37 out of 44 critical technologies, including advanced batteries, quantum sensing, and 5G communications.
State–finance relationship through GGFs. Figure 8, “Mapping the investor state: state-led financialization in accelerating technological innovation in China,” Socio-Economic Review, 18 June 2025.
A growing network of state agencies in innovation finance ecosystem is to ensure ideological alignment and managerial oversight, forming a core feature of China’s model of state-led financialization. This system also serves as a reminder of the original rationale behind China’s economic reform process where the boundaries between public and private sectors, and between liberal market coordination and socialist planning, become increasingly blurred. Notwithstanding its strategic coherence, China’s model of state-led financialization faces a series of structural and operational challenges. One key risk lies in the emergence of overcapacity within state-targeted sectors such as photovoltaics and electric vehicles. In the absence of commensurate demand, excessive production may generate inefficiencies, underutilized assets, and financial losses. Furthermore, the expansive use of mechanisms like GGFs has the potential to inflate asset bubbles, as state-directed capital may push valuations beyond sustainable levels, raising concerns over long-term financial stability.
The persistence of so-called “zombie firms”—enterprises maintained through state support despite chronic unprofitability—also continues to divert capital from more productive uses, undermining allocative efficiency. Tensions emerge from the dual imperative to stimulate market-based innovation while retaining centralized Party and state control over capital flows. These competing logics often complicate investment decisions and diminish the responsiveness of the financial system. Additionally, fragmented coordination across state entities and growing international scrutiny or resistance to China’s state-capitalist practices further limit the replicability and effectiveness of this model.
For Western economies, the implications are profound. Initiatives such as the U.S. Stargate Project—reportedly valued at $500 billion over four years to support AI and semiconductor infrastructure—and the European Commission’s InvestAI scheme, backed by €20 billion in guarantees, signal a renewed policy interest in public–private coordination. However, these efforts remain constrained by political fragmentation and a reliance on market-led frameworks. China’s approach is characterized by a level of centralized state capacity and institutional discipline that would be difficult to replicate without foundational political transformation in the West.
Should China succeed in sustaining this model without triggering systemic instability, the result would extend beyond technological leadership. It would represent a paradigmatic shift in the global political economy—one that challenges prevailing liberal capitalist orthodoxy and compels a fundamental reconsideration of the relationship between the state, capital, and innovation. In this sense, China is not merely competing within existing rules but reshaping the terrain on which economic competition is conducted.
‘Where do you want to go today?’ served as the tagline for software giant Microsoft’s global marketing campaign running through the mid-1990s. The accompanying advertisements were replete with flashy images of people around the world of all ages, ethnicities, and backgrounds engaging in a diverse range of activities, including business, education, video games, artistic expression, socializing, and research, to name some of the most prominent examples. The slogan ‘Where do you want to go today?’ implied that people were largely free to travel where they wished, but, of course, Microsoft was selling the power of its software to facilitate the free flow of information and communication, and by extension greater connectivity and collaboration, among people around the world, rather than the actual movement of people.
Yet combined with rapid advances in hardware and software, the tagline captured something of a popular mood of the time. Within many Western societies, the end of the Cold War, the continued liberalization of international trade and travel through a variety of supranational institutions and international agreements, and the growing clout of transnational corporations and nongovernment organizations heralded the coming of a borderless world. The prospect of unprecedented, unfettered mobility and connectivity for an ever-growing number of people seemed imminent.
Looking back thirty years later, those expectations were overly optimistic. It is impossible to deny the truly remarkable technological advances—personal computers, the internet, mobile phones, and wireless communications—that compress space and bridge territories. Yet far from a borderless world, the first decades of the twenty-first century have witnessed a resurgence of borders with impacts on a variety of political, socioeconomic, environmental, technological, and human rights issues.
In fact, borders have been central to two of the most significant events of the 2020s, namely the COVID-19 pandemic and Russia’s invasion of Ukraine in 2022. The COVID-19 pandemic saw governments, with varying degrees of severity and effectiveness, impose border controls, restrict domestic and international travel, and implement systems of confinement and quarantine. These measures disrupted global supply chains and confined millions of people to their homes as their freedom to attend school, go to work, gather for worship, or even simply shop for daily essentials was restricted. Russia’s invasion of Ukraine has also disrupted global trade networks, while ravaging large swathes of Ukrainian territory, displacing millions of civilians, and prompting massive increases in defense spending far beyond the direct combatants.
Unfortunately, there is no shortage of international and civil conflicts roiling the international scene. The attacks by Hamas militants from the Gaza Strip into Israel in 2023 prompted Israeli retaliatory attacks and eventually a full-scale invasion into Gaza. This, in turn, gave rise to a series of broader, overlapping regional conflicts involving dozens of state and non-state combatants, including Hezbollah and Houthi militants in Lebanon and Yemen respectively and Iranian and Israeli attacks and counterattacks. That turmoil provided at least proximate triggers for the rapid collapse of the Assad regime in Syria in 2024, leaving that country divided among a mixture of forces representing a provisional government, various sectarian militias with unclear allegiances, and remnants of Islamic State forces. Syrian territory also hosts American, Russian, and Turkish armed forces, in some ways resembling the proxy conflicts of the Cold War.
While the war in Ukraine and tensions in the Middle East have dominated headlines, other armed struggles have flared and persisted across the North African, Sahel, South Asian, and Central Asian regions. Afghanistan, Congo, India, Myanmar, Pakistan, Somalia, Sudan, South Sudan, and Yemen remain gripped, at least in part, by civil strife and border disputes stretching back years, if not decades. Beyond the battlefield death and destruction, these conflicts have broader consequences, including refugee flows, economic dislocation and poverty, and malnutrition and hunger, among other problems.
Looming menacingly in the background is the specter of renewed great power competition, primarily between the United States with its global alliance system and the burgeoning partnerships between China, Iran, North Korea, and Russia, as well as other like-minded authoritarian regimes. After years of forging economic interdependencies, China has been increasingly assertive in projecting power across the Indo-Pacific realm, especially regarding its claims over Taiwan, the South China Sea, and the Himalayas. The United States has responded with calls to ‘pivot to Asia’ based on targeted sanctions and a general decoupling from China’s economy, strengthening alliances stretching from East Asia through Southeast Asia and Oceania into the Indian basin, and more robust and forward military deployments across the region. Ramifications of great power conflict across the Indo-Pacific realm would greatly exceed the calamities of other ongoing wars.
This blog has summarized, admittedly in broad strokes, the shift from relative optimism in the 1990s—characterized by aspirations for a more collaborative and interconnected global community—to a world confronted by profound challenges in which borders will play central roles through the coming decades. Beyond this focus on larger-scale geopolitics and hard international power, borders are central to a variety of other issues across multiple scales, including debates about trade and tariffs, citizenship and immigration, crime, surveillance and privacy, and cultural change and human rights, to name a few. Headlines on any day offer striking examples of issues and events involving borders.
Given the salience of borders to such an array of pressing issues, Oxford University Press has launched Oxford Intersections: Borders to provide the latest border research, highlighting this field’s broad relevance. Borders are shown to be simultaneously positive and negative, often in the same place and at the same time to different people. Borders remain a prime modality of defining and enacting power across multiple scales. This collection seeks to reveal how, where, why, by whom, and to what effect that power and aspiration of territorial control is exercised. We hope readers will engage Oxford Intersections: Borders to encounter new perspectives on a topic that is elemental to human experience and foundational to the form and function of power.
The advent of the World Wide Web in the turn of the last century completely transformed the way most people find and absorb information. Rather than a world in which information is stored in books or housed in libraries, we have a world where all of the information in the world is accessible to everyone via computers, and in the last decade or so, via their handheld mobile device. The young people currently in university or in school grew up in a world where information is not privileged and immediate access to all of it is taken for granted. In this age of immediate and readily accessible information on any subject, we must ask: What is the role of academic institutions in teaching? If anyone can find out anything at any time, why learn anything? Is there any value to knowledge in its own right?
The answer is that of course teaching and learning are still important, but they must change to reflect the way information is accessed. The fact is that information on its own is useless without a contextual framework. It may be possible to easily find a detailed account of all of the units and commanders that participated in the Battle of Regensburg in 1809, but if the reader has no understanding of military history, and no background on the politics leading to the Napoleonic wars, this information is no different from a shopping list. Similarly, it may be possible to find detailed information on the excretory system of annelid worms, but without an understanding of what excretory systems are and what their role is in the organism, and without a knowledge of the biology and evolution of annelid worms, this information is no more than a list of incoherent technical terms.
These two very different examples serve to highlight the difference between information and knowledge. Possessing knowledge about a subject means being able to place information into a broad framework and context. People who are knowledgeable about the Napoleonic wars do not necessarily know the names of every commander of every unit in the Battle of Regensburg, but if they need this information, they can access it and use it better than someone with no knowledge. A comparative zoologist may not know all the details about annelid excretory systems, but when needed, they will know what to look for.
With this distinction in mind, I suggest that teaching and textbooks need to shift their focus from transferring information to transferring knowledge. No textbook can compete with the wealth of information available at the students’ fingertips. No course can ever impart all that there is to know about a subject. However, a good teacher and a well-written textbook can provide a much better framework for knowledge and understanding than a search engine will ever be able to. Indeed, a course or module that overburdens the students with numerous bits of information is not only a misuse of resources, it is ultimately counter-productive, as the student will always be able to challenge the teacher with a new bit of information not included in the course.
Teaching in the age of information should focus on providing a working vocabulary of a subject and on building a robust framework of knowledge. Detailed examples can be used to demonstrate principles, but this should be done sparingly. The curious students can then fill in the details on their own, taking advantage of the information at their fingertips.
I have been following these principles in my teaching of evolution and organismic biology for as long as I have been a university professor. My frustration at the details-heavy zoology textbooks led me to write a new textbook, focusing on principles and on providing a conceptual framework to organismic biology, rather than on details. For example, I have written a chapter on excretory systems that outlines what the roles and functions of this system are, and gives a few demonstrative examples of how these functions are manifested in a small number of organisms. I have included similar chapters on other systems interspersed with chapters on individual animal phyla, which give an overview of the phylum and its diversity, and present the specific variations within each of the organ systems, and how these are adapted to the life history of members of the phylum.
As we and our students continue to have easier and more readily available access to information, this new approach will provide a more successful framework for students to continue to grow and learn as they step out into the world. Hopefully this approach will be picked up by authors of additional textbooks to provide a new generation of teaching resources, more suitable for the age of information.
Economists analyze data. Machine learning (ML) offers a powerful set of tools for doing just that. But while econometrics and ML share a foundation in statistics, their aims and philosophies often diverge. The questions they ask and the tools they prioritize can differ dramatically. To clarify these differences—and the reasons economists might ultimately use ML—it helps to begin by deliberately sharpening the contrast between the two.
Quantifying vs. predicting
At its core, econometrics is about explanation. The typical economist is interested in quantifying the effect of a specific variable, often within a framework of causal inference. For example: What is the effect of raising the minimum wage on employment? Do peers influence students’ academic achievements? What is the average wage gap between men and women? These questions focus on estimating one or a few key parameters, with great attention to the rigor of the identification strategy. The emphasis is on the assumptions under which we can identify the parameters and on inference—constructing confidence intervals, testing hypotheses, and, above all, establishing causality.
This approach gained prominence in recent decades, culminating in Nobel Prizes for economists like Joshua Angrist, Guido Imbens, David Card, and Esther Duflo, whose work emphasizes empirical strategies to identify causal effects in natural, field, or experimental settings.
Machine learning, by contrast, is largely concerned with prediction. The primary goal is to develop models—or more precisely, algorithms—that deliver accurate predictions for new data points. Whether it’s recommending movies, classifying elements of an image, translating text, or matching job-seekers with firms, ML prioritizes predictive performance under computational constraints. Rather than focusing on a particular parameter, the goal is to learn complex patterns from data, often using highly flexible (sometimes opaque) models.
That said, forecasting is one area where econometrics and ML converge. Econometric forecasting often imposes structure on messy data to reduce noise, while ML emphasizes complexity and flexibility. Nevertheless, many traditional econometric tasks can be reframed as prediction (sub)problems or built upon them. Estimating a treatment effect, for example, involves building a counterfactual and is inherently a predictive exercise: being able to credibly predict what would have happened to this individual had they not been treated?
Models and assumptions
Econometric models tend to be simple, theory-driven, and interpretable. They often rest on strong assumptions—like linearity or exogeneity—that are difficult to verify but motivated by behavioral or economic theory. These models aim to isolate the effect of a particular variable, not to simulate the entire system.
In ML, simplicity is often sacrificed for performance. Black-box models, such as deep neural networks, are acceptable (and even preferred) if they generate more accurate predictions. A battery of performance metrics—like precision and recall—guide model selection, depending on the stakes. For instance, in fraud detection, a model with high precision ensures that flagged cases are likely real; in cancer screening, high recall ensures few real cases are missed.
Nevertheless, within a particular defined problem, ML offers algorithms whose predictive performance often surpass the standard (non)parametric toolkit in data-rich environments. For example, when selecting a model, they allow modeling complex interactions between variables or being robust to possibly high-dimensional nuisance parameters. The issue is that the theoretical behavior of these tools is often intractable, making them difficult to use within the classic econometric framework. Fortunately, over the past fifteen years, econometric theory has advanced to incorporate ML techniques in a way that enables statistical inference—allowing researchers to understand the working assumptions and their limits, construct tests, and build confidence intervals using ML-powered estimators.
Data and deployment
Another important divergence lies in how data is used. Econometric models are typically built on a single dataset, intended for a specific study. Replication is possible, but each new dataset generally leads to a different model. The focus is on understanding a particular phenomenon using the data at hand.
In ML, models are developed to be deployed in production, where they will continuously generate predictions as new data becomes available. This makes it crucial to guard against overfitting—when a model performs well on training data but poorly on unseen data. This risk is mitigated by techniques like cross-validation, and by splitting data into training and test sets. Modern ML even grapples with new phenomena like “double descent” where larger models trained on more data can paradoxically generalize better.
Complex data, new frontiers
ML’s rise is partly fueled by its success in handling complex, unstructured data—images, text, audio—that traditional statistical approaches struggle to process. These data types don’t fit neatly into rows and columns, and extracting meaningful features from them requires sophisticated techniques from computer science. ML excels in these domains, often matching human-level performance on tasks like facial recognition or language translation. As such, ML is the key ingredient to compress or extract information from such unstructured datasets, unlocking new possibilities.
Think about it:
classifying the sentiment of an internet review on a numerical scale to enter a regression model,
compressing a product image into a fixed-size vector (an embedding) to analyze consumer behavior,
measuring the tone of a central banker’s speech.
Text data is undoubtedly one of the richest sources of economic information that largely remains out-of-reach for traditional econometric approaches.
A two-way street
The distinctions above are real, but they are not absolute. Economists have long used prediction tools, and ML researchers are increasingly concerned with issues that economists know well: fairness, bias, and explainability. Recent public controversies—from racial bias in criminal risk algorithms (e.g., the COMPAS tool) to gender stereotypes in language models—have underscored the social consequences of automated decision-making.
Likewise, econometrics is not immune to methodological pitfalls. The replication crisis, “p-hacking,” and specification searching can be seen as forms of overfitting problems that ML addresses through careful validation practices. Techniques like pre-analysis plans (committing to a set of statistical tests before receiving the data in order to reduce false positives) have been adopted by economists to mitigate these risks. However, possible solutions can draw inspiration from ML’s train/test split approach.
Bridging the divide
So, should economists learn machine learning? Absolutely. ML extends the standard econometric toolkit with methods that improve predictive performance, extract insights from text and images, and enhance robustness in estimation. For economists looking to stay at the frontier of empirical research—especially in a data-rich world—ML is not just useful. It’s essential.
