Economics Rules - Dani Rodrik

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Here is a summary of key points about economics from the introduction:

• John Maynard Keynes and Harry Dexter White helped design the post-World War II international economic order known as the Bretton Woods system. This system promoted economic growth and stability for decades.

• The Bretton Woods system established principles like fixed exchange rates, liberalized trade but not capital flows, empowered national monetary and fiscal policies, and created the IMF and World Bank to enable cooperation.

• In 1952, economist William Vickrey proposed “congestion pricing” for the New York City subway system. This involved variable fares based on traffic levels to spread out passenger volume over time.

• Singapore was the first to implement congestion pricing for roads in 1975, charging drivers tolls to enter the central business district. This reduced traffic and improved transport efficiency.

• The introduction highlights how economic ideas and models, even when initially considered radical, can provide practical solutions and framework for policy when applied to real-world problems and institutions. It establishes economics as both an analytical framework and a tool for policymaking.

• In 1997, Santiago Levy proposed overhauling Mexico’s antipoverty programs from food subsidies to direct cash transfers with conditions. This became the Progresa/Oportunidades program.

• Levy argued that direct cash grants are more effective at helping the poor than subsidies on specific goods. He also added conditions - mothers had to ensure their children attended school and received healthcare to receive the cash grants.

• Progresa was the first major conditional cash transfer program in a developing country. Levy designed it to be rigorously evaluated, allowing a clear assessment of its impacts.

• Results showed positive impacts, and the program became a model replicated in over a dozen Latin American countries and even New York City. It represented a successful application of economic principles to antipoverty policy.

• In 1998, Singapore replaced paper tickets with electronic tolling for its roads. This allowed variable toll rates based on traffic conditions. It successfully reduced congestion, increased public transport use, lowered emissions, and generated government revenue. Other cities emulated this with adaptations.

• These examples show how economists have successfully applied frameworks like incentives, conditions and market-based solutions to address real-world problems in areas like transport, poverty alleviation and more. Simple economic models transformed parts of our world for the better when applied rigorously.

Economic models serve an important purpose in simplifying and abstracting aspects of the real world to better understand social and economic mechanisms. Simple models isolate key variables and causal relationships that may not be apparent otherwise. Critics argue models are too simplistic and abstract, but supporters counter that simplicity is a virtue that allows insights.

There is no single universal model - different contexts require different models. Economists get in trouble when they favor one universal model over selecting the right model for the situation. When chosen carefully, models provide valuable insights, but dogmatic use leads to mistakes.

The essay describes three common economic models - supply and demand, prisoners’ dilemma, coordination failure - to illustrate how different valid models exist capturing different market structures and outcomes. The supply/demand model shows competitive market efficiency, while prisoners’ dilemma models inefficiency in an oligopoly setting. Coordination failure models multiple equilibria arising from interdependencies rather than just number of firms.

In short, the real value of models lies in their diversity and appropriate selection based on context, not a singular universal model. Simplicity allows modeling key mechanisms, but diversity is needed to represent real world complexity.

• Economists use models as analytical tools to understand economic phenomena, not because they think markets always provide the right answer.

• Economic models are like fables or simplified stories that illustrate key theoretical points. They make stylized assumptions to be clear and concise rather than realistic.

• Different models can provide different answers to the same question depending on their underlying assumptions. The assumptions capture critical real-world contextual factors.

• Examples given show how answers to questions like minimum wage impacts, effects of capital flows, and fiscal policy depend on assumptions around competition, growth constraints, and monetary/currency policies.

• Models highlight the relevant contextual factors that determine outcomes and show how outcomes depend on those factors, even if standard conclusions don’t always hold.

• Judgment is needed to select the most applicable model based on a situation’s specific contextual features, though evidence can provide some guidance. Models are like experiments that test hypotheses within insulated conceptual frameworks.

So in summary, economic models are simplified stories or thought experiments, not claims of universal truth, but useful analytical tools to understand how outcomes depend on key contextual factors when their assumptions align with reality.

• Economic models necessarily involve unrealistic simplifying assumptions to abstract key mechanisms from reality. However, the realism of assumptions does matter to some degree for a model to have predictive power.

• Critical assumptions are those where changing the assumption in a more realistic direction would substantially alter the model’s conclusions. The realism of critical assumptions needs to pass a “realism filter” for the model to be useful.

• Whether an assumption is critical depends on what question the model is being used to address. For example, assumptions about market competition are more critical for models examining the effects of price controls than models looking at tax impacts.

• While simplification is necessary, overreliance on assumptions that grossly violate reality can undermine a model’s applicability. The solution is to build alternative models with more appropriate assumptions, not abandon modeling altogether.

• Economic models incorporate formal mathematical notation, which provides clarity but can also act as a barrier to comprehension and contribute to perceptions of economists living in abstraction. Modeling necessarily involves both rigor and practical relevance.

• The flagship journals of economics (AER) and political science (APSR) differ in their accessibility to outsiders. Economics relies more heavily on mathematical formalism which makes it harder to understand without graduate training.

• Math is used instrumentally in economic models for clarity and consistency, not sophistication. It ensures all assumptions and results are clearly specified and internally consistent. This prevents ambiguity and incorrect reasoning.

• Some of the best economic thinkers like Arthur Lewis effectively used verbal models without math. But math can help make the logical steps in complex verbal arguments more transparent.

• Excessive formalism and math for its own sake is a problem in some areas of economics. The focus should be on substantive insights, not abstract technical prowess.

• Highly mathematical models have produced useful real-world applications, like auction theory and market design models. So abstract formal work is not necessarily useless.

• Economic models tend to be simple by design to be solvable, but this simplicity requires omitting real-world complexity. The strategic choices of what to include or exclude still matter.

So in summary, math plays an instrumental role in economic modeling for clarity and rigor, but too much formalism can be unhelpful, and verbal models are also effective when properly specified. Both simplicity and realism are important considerations for model design.

The passage discusses the tension between simplicity and complexity in models. While complexity seems appealing given the multifaceted nature of issues like the economy, purely complex models have limitations.

Two developments have increased complexity - rising computing power enabling larger computational models, and big data revealing intricate patterns. However, purely complex models remain unintelligible without linking to simpler, theoretical frameworks.

While complex models can provide estimations of effects, their results are only credible if motivated by transparent, intuitive smaller models. Outcomes emphasized by complexity like tipping points originally come from simpler models.

Simplicity is not a limitation but an asset, as models teach through simplification. Relevance does not require complexity, and complexity may impede understanding. Plural simple models remain indispensable compared to purely complex maps that become useless. Overall, the passage argues for complexity combined with interpretability through linking to clear theoretical notions from simpler models.

Here is a summary of the key points about economic models and the science of economics from the passage:

• Models clarify hypotheses by making explicit their logic and assumptions, which helps refine intuitions. They can also uncover counterintuitive possibilities.

• Models expand the set of explanations for social phenomena, allowing knowledge to accumulate over time like a library expanding its collection.

• Models imply an empirical method by suggesting how hypotheses can be applied to the real world, enabling hypotheses to be judged as right or wrong based on evidence.

• Models establish shared professional standards for evaluating economic work, rather than relying on personal hierarchies. The quality of work depends on the model/analysis itself.

• A famous example is the First Fundamental Theorem of Welfare Economics, which uses a model to show that competitive markets can achieve efficient outcomes under certain assumptions, via the “invisible hand.” This clarified Adam Smith’s initially intuitive argument.

• Models advance scientific understanding in economics, though the field seeks to understand society rather than discover fundamental natural laws like in physics. Humility is needed given social complexity.

• The passage discusses two foundational economic models: the Arrow-Debreu model and the Ricardo model of comparative advantage.

• The Arrow-Debreu model proved the “invisible hand hypothesis” - that under certain assumptions, free markets can achieve economic efficiency. However, it relies on many strict assumptions like perfect competition, no externalities, etc.

• The Ricardo model showed how comparative advantage allows gains from trade even when one country is more efficient in all goods. This clarified that trade benefits are not dependent on absolute efficiency differences.

• Both models use mathematical modeling to make economic hypotheses more precise and explicit. They reveal the exact assumptions needed for effects like efficiency or gains from trade.

• The models clarify arguments that seem counterintuitive, like how all countries can benefit from trade. They also show limitations, like the conditions where trade may cause losses.

• In general, economic models aim to make hypotheses and evidence clearer by making the underlying reasoning and assumptions transparent. This helps interpret and extrapolate evidence properly across contexts.

Here are the key points about general equilibrium analysis compared to partial equilibrium analysis:

• General equilibrium analysis takes into account feedback effects across different markets, not just what happens within a single market. It follows the chain of effects across labor markets, goods markets, capital markets, etc.

• This interlinked approach often qualifies or even reverses the conclusions of partial equilibrium models that look at one market in isolation.

• Examples given include analyzing the effects of immigration on wages by considering broader impacts like migration of local workers, increased investment, technology adoption, and increased demand from immigrant consumers.

• Another example looks at trade impacts on skilled workers - partial analysis may suggest negative effects in certain industries, but general equilibrium captures new opportunities in export sectors.

• Unexpected results can also come from “second best” scenarios, where addressing distortions in one market may not help if other related markets remain restricted.

• Strategic interactions between economic actors can also produce counterintuitive outcomes compared to analyses without considering strategic behavior.

So in summary, general equilibrium analysis provides a more holistic view of how economic changes ripple through an interlinked system, often leading to different conclusions than partial analysis of individual markets in isolation. It better captures real-world complexities.

The interaction between today’s self and future self can create problems due to lack of commitment to desirable long-term behaviors. Today’s self may be tempted to under-save for retirement or engage in inflationary monetary policies that harm the future.

The solution is precommitment - restricting one’s future options in a way that binds today’s self to the course of action that benefits the future. For example, delegating monetary policy to an independent central bank focused on price stability, or setting up automatic deductions from paychecks to a retirement fund.

While limiting choices seems paradoxical according to standard economic theory, it can actually make one better off through precommitment. The paradox is illusory when viewed through different theoretical models.

Scientific progress in economics happens gradually through the development of new models, not by rejecting old ones. Models often respond to empirical phenomena rather than being directly tested. They address different contexts rather than replace each other. Over time, models have expanded the set of market conditions considered, from perfectly competitive to imperfect competition, asymmetric information, behavioral factors, and more. This horizontal expansion improves economics’ ability to explain diverse real-world outcomes.

• Economic models continue to become more sophisticated over time as economists develop new insights, but don’t render older models irrelevant. Older, simpler models remain useful for answering many real-world questions.

• Progress in economics involves expanding the body of models and cases available, rather than discovering fixed laws of nature. Models highlight the assumptions needed to generate their conclusions and identify appropriate contexts for application.

• Having multiple models is a strength, not a weakness, as long as empirical evidence guides choosing the most applicable model for a given situation. Models clarify the sources of disagreement in debates.

• Examples are given of how debates between models play out, such as around fiscal policy, and how evidence can build to favor one model over time. Research helps shift debates to domains outside of economics’ expertise.

• A controversy around a 2010 paper by Reinhart and Rogoff is discussed. While their work was politically seized upon, the eventual scrutiny and critique of their analysis through replication represents a salutary process of refining economic research.

• Reinhart and Rogoff were criticized by Herndon, Ash, and Pollin for errors and selective reporting in their analysis of the relationship between high debt and low growth. Specifically, they were accused of data errors, selective exclusion of data, and inappropriate weighting of summary statistics.

• Alternative processing of the data by Herndon, Ash, and Pollin yielded different results than Reinhart and Rogoff, showing a weaker relationship between high debt and low growth.

• Ultimately, Reinhart and Rogoff did not rigidly assert a 90% debt threshold for low growth, and agreed the relationship could have different interpretations. Their critics also did not fully disagree on the evidence or policy implications.

• The episode showed that economics can progress through open criticism and reanalysis of evidence according to the principles of science. Both sides shared a common approach to evidence and resolving disagreements, even if their political views differed.

• While portrayed as established professors brought down by a graduate student, the real significance was that any economist could point out weaknesses in research according to the field’s standards, not just based on status or affiliations. The authority comes from the internal quality of the work, not external attributes of the researcher.

• Economists are confronted with choosing among multiple models when tackling practical problems like advising governments on economic growth strategies. However, graduate training provides little guidance on model selection.

• The mainstream view is that economics progresses by continually improving models through testing, but this view doesn’t account for the need to choose applicable models for specific contexts.

• To formulate a good growth strategy, economists had to diagnose which models best highlighted the dominant causal mechanisms in each country’s economy. Different models pointed to different priority reforms.

• Models like neoclassical, endogenous growth, institutional quality, and dual economy each provide a distinct lens and identify different bottleneck obstacles holding back growth. Choosing reforms required targeting the largest obstacles as identified by the most relevant model for that context.

• A laundry list approach to reforms doesn’t work as well as prioritizing a narrow set of strategic reforms targeting the core impediments as diagnosed by the applicable theoretical framework for that economy. This calls for carefully selecting the right model lens through which to view the problem.

This passage summarizes that when analyzing different policy models to address an economic issue, it is important to verify assumptions, mechanisms, implications and incidental outcomes of each model against real-world evidence. Specifically:

• The discussion emphasizes verifying the critical assumptions of each model - the assumptions that would lead to substantially different outcomes if altered. This involves checking how realistic the key assumptions are in reflecting the actual context.

• It also involves verifying that the mechanisms posited by each model are in fact operating as the model describes.

• Another step is verifying the direct implications of each model by seeing if outcomes match what the model predicts.

• Finally, incidental implications the model generates should also be broadly consistent with observed outcomes, to further vet the applicability of the model.

The process focuses on moving between candidate models and real-world data, to systematically evaluate which model most accurately captures the relevant mechanisms driving the issues in that particular economic setting.

• Models make assumptions that are critical to their conclusions but may be unstated. Failing to scrutinize these assumptions can lead to problems in applying the models in practice.

• A key assumption in models is the mechanisms or causal relationships they rely on. These must be plausible and supported by real-world evidence for the models to provide useful insights.

• Examples of important mechanisms include the relationship between supply and price in the oil industry models, and the link between exchange rates and manufacturing in the Dutch disease model.

• Models should also be evaluated based on whether their direct implications match observed phenomena. Some macroeconomic models made unrealistic assumptions that produced conclusions not very relevant to real economic problems like booms, recessions, and unemployment.

• Verifying mechanisms and implications is important for determining if a model’s key working parts are reasonable and if it can shed light on the phenomenon being examined. This helps economists choose models that truly apply to the situation rather than just being mathematically elegant.

• Economists often build models based on assumptions of rationality and well-functioning markets that do not always match reality. They are reluctant to abandon these models even when predictions fail.

• A story is told of game theorist Barry Nalebuff who got in trouble using rational modeling in a real-world scenario with a taxi driver in Israel. His assumptions failed and led to an unpleasant outcome.

• Experiments, both in labs and real-world field experiments, have helped economists learn more about human behavior and refine their models. Behavior is more complex than just selfish rationality.

• One of the earliest large-scale field experiments was on Mexico’s conditional cash transfer program PROGRESA/Oportunidades, which was phased in randomly to test effects. It showed positive impacts on poverty, schooling, and health.

• Many other field experiments have tested policies and models, providing useful evidence while still having limitations in applicability to macro questions. Natural experiments also provide opportunities to test models without researcher interference.

• Experiments have led to refining economic models to better reflect real-world complexities like fairness, social norms, imperfect information, etc. But some economists remain skeptical of lab experiments and field experiments have limited scope.

• Economists employ models rather than theories, as models make more modest claims about context-specific causal explanations rather than universal validity.

• Models answer “what if” questions by isolating the effects of particular causes, while theories aim to provide overarching explanations.

• Testing models involves looking for implications beyond the initial observations, to see if those implications are borne out empirically. This process helps discriminate among alternative models.

• The author’s research on public spending and trade exposure demonstrated this approach, deriving and confirming additional implications from the proposed compensation-for-risk model.

• Growth diagnostics work also systematically analyzed tangential implications to evaluate proposed bottlenecks and constraints.

• Ultimately, model validity depends on subjective judgments of similarity between the model setting and reality. While empirical testing helps, model selection involves unavoidable elements of analogy and craft.

The key point is that models provide tentative, context-specific explanations for causal effects, while theories aim for broader explanatory power, but model implications can be empirically evaluated to help discriminate among alternatives. Both empirical testing and analogical reasoning are involved in assessing model validity.

• Economic theories aim to explain fundamental questions like what determines value and income distribution in an economy.

• Classical economists like Smith, Ricardo and Marx argued costs of production determine value based on a “labor theory of value.” But they had limited explanation of demand factors and fluctuations.

• The marginalist revolution introduced supply and demand analysis, showing prices are set at the margin where marginal cost equals marginal benefit/utility. This established modern value theory accounting for both production costs and consumer preferences.

• Marginalist theory also provided an account of income distribution, explaining how factors of production like labor and capital earn their marginal products and how national income is functionally distributed.

• However, these theories are based on unobserved concepts like marginal utility/product that require strong assumptions. They also depend on perfect competition that may not exist.

• While neoclassical theory clarified pricing and provided a distribution framework, its concepts require further empirical testing and contextual analysis before being useful for real-world explanation and measurement.

• The passage discusses theories of distribution, primarily the neoclassical marginal productivity theory which says wages are determined by workers’ marginal productivity.

• It raises some issues with the theory, noting concepts like capital and utility are not directly observable. Firm abilities and consumer preferences also vary, complicating the theory.

• Empirically, the theory fits cross-country wage differences reasonably well, but has more difficulty explaining trends within the US since 2000 as productivity growth outpaced wage growth.

• Alternative distribution theories emphasize bargaining between employers/employees, social norms around pay inequality, and “efficiency wages” paid above market rates.

• The passage then discusses theories of business cycles and unemployment. Classical economists saw markets and macroeconomy as self-stabilizing, while Keynes argued governments should intervene to boost demand and employment during downturns.

• Early macroeconomic models tried to formalize Keynes’ ideas but the quest for a unified theory of business cycles has so far been unsuccessful. Theories provide some explanations but require context-specific details. Grand theories deliver less than promised.

• Keynes argued that saving and investment must equal in the economy, but they could become imbalanced in the short run, leading to unemployment. He believed investment is driven by “animal spirits” rather than interest rates, unlike classical economists.

• If investment is fixed but saving rises, unemployment rises as output falls to bring saving in line with investment. Price adjustments like interest rates would be too slow to remedy the imbalance, per Keynes.

• This focus on aggregate demand and autonomous changes therein formed the basis for Keynesian macroeconomic models through the 1970s.

• Stagflation in the 1970s challenged demand-side models. Robert Lucas then introduced “microfoundations,” modeling individual decision-making and rational expectations, which quickly gained acceptance.

• Lucas argued Keynesian models took too mechanical a view of behavior. His approach implied weaker government influence over the economy via policy.

• The new classical models became dominant in academia despite empirical debates, as the economy saw growth and stability in the 1980s-1990s.

• However, the models provided little help in the 2008 crisis, when Keynesian stimulus was widely supported even by Lucas initially to remedy recession. Beyond liquidity measures, the new classical framework offered little guidance.

• Classical economists suggested restraint and caution rather than active policies like quantitative easing. They warned about inflation risks and urged the Fed to tighten policies even as unemployment remained high.

• They argued the economy would recover on its own without fiscal stimulus, claiming the slug: gish recovery was due to uncertainty from potential tax hikes and other government interventions.

• Keynesians like Paul Krugman argued the stimulus was inadequate and withdrawn too soon, prolonging high unemployment. Others argued stimulus would pay for itself by boosting recovery.

• The debate centered on whether problems were from lack of demand or supply constraints. Keynesians pointed to lack of inflation pressures, while others cited evidence policy uncertainty rose.

• Theories provide useful perspectives but fail as universal explanations. Specific intermediate theories are more appropriate, like those analyzing the rise in U.S. inequality since the 1970s.

• Globalization via trade and offshoring, as well as technological changes rewarding skilled labor more, helped explain rising inequality and the widening skill premium between high- and low-wage workers.

Here are the key points about the rise in the skill premium and doubts that emerged about skill-biased technological change (SBTC) theory:

• SBTC theory posited that new technologies like computers increased demand for skilled/highly educated workers, raising their wages relative to unskilled workers and increasing inequality.

• Unlike trade theories, SBTC was consistent with skill upgrading within firms/industries as they adopted new technologies.

• Employers hired more skilled workers due to automation/greater computer use, pushing up the skill premium globally as these tech trends spread worldwide.

• By the late 1990s, most economists agreed SBTC was the primary driver of rising wage inequality, with trade playing a smaller 10-20% role.

• However, doubts emerged as the skill premium stabilized in the 1990s even as new tech continued. Wage inequality also grew within skill categories.

• Upgrading of jobs/high-skill occupations predated computerization trends. Globalization may have stimulated new tech adoption.

• Rising inequality also stemmed from growth in top 1% incomes from capital/stocks rather than wages.

• No single theory could fully explain post-1970s US inequality trends, and different theories likely contributed through various channels.

• Economists work with many models that can point to contradictory conclusions, depending on the assumptions. However, their views often converge in ways that cannot be justified by evidence alone. This creates a paradox of “uniformity amid diversity.”

• When economists confuse a specific model for reality, it can lead to two types of errors: errors of omission and errors of commission.

• Errors of omission occur when economists fail to anticipate problems due to being fixated on one model/view of the world. Most economists failed to foresee the 2008 financial crisis, as they overlooked issues in housing and finance.

• Errors of commission happen when economists endorse policies based on a specific model, even when its assumptions may not apply. Advocacy of policies like financial globalization fell into this category.

• The financial crisis had elements that were well-explained by existing economic models like bubbles, bank runs, principal-agent problems, and global financial contagion. However, economists relied too heavily on some models and ignored warning signs.

• Economists should be more cautious about endorsing any one model’s conclusions as universally applicable, given the diversity of models and real-world complexity. Overconfidence in models can lead to failures of analysis and flawed policy advice.

• The “Washington Consensus” referred to a set of economic policy prescriptions promoted by Washington-based institutions like the IMF and World Bank for developing countries in Latin America in the 1980s. It advocated for liberalizing economies through privatization, deregulation, and trade openness.

• Proponents argued it reflected sound economics of free markets and competition. However, critics said it represented an overzealous agenda for textbook free markets without consideration of local context or institutions.

• In practice, it did not achieve the intended growth effects and often backfired. It underestimated the importance of institutions, presented a one-size-fits-all approach without prioritization, and failed to account for second-best complications in imperfect markets.

• For example, trade liberalization led manufacturing sectors to shrink but failed to facilitate efficient reallocation of resources to new competitive export sectors due to issues like labor market rigidities, weak capital markets, currency overvaluation, coordination failures, and lack of government support. Overall productivity suffered in many countries.

• The Washington Consensus reflected overconfidence in simple economic models without consideration of real-world complexities and underestimation of what was required to transition developing economies successfully.

• The passage discusses some shortcomings of the Washington Consensus policies promoted by economists in Latin America and Africa in contrast to the experience of Asian countries like South Korea, Taiwan, and China.

• It notes that Asian countries pursued more interventionist strategies like directly subsidizing domestic manufacturing and maintaining competitive currencies, rather than immediately liberalizing imports as advocated by the Washington Consensus.

• Another area where economists pushed the Washington Consensus too far was in promoting full financial globalization and free capital flows. While this could increase efficiency, it also increased instability, as seen in many emerging market financial crises.

• Advocates of financial globalization underestimated second-best complications and overestimated countries’ ability to improve regulation and macroeconomic management to reduce risks from free capital flows. In reality, free capital flows often stimulated consumption over investment and appreciated currencies, hurting growth.

• The lesson is that a universal one-size-fits-all approach was excessive and reforms need to be tailored to specific country circumstances, with no single set of policies appropriate for all. Common blueprints are out and model selection based on context is in.

• Economists believe they understand how markets work better than the general public. They are aware of market failures but think public concerns about markets are often exaggerated or unjustified.

• Promoting markets has become a professional obligation for economists in public debates. They are more likely to defend free markets even when private discussions acknowledge shortcomings.

• This leads to the view that those wanting market restrictions are “barbarians” while those advocating freer markets have good intentions, even if mistaken.

• Economists’ training makes them overconfident in models and received wisdom. Their insular guild mentality discounts outside criticism.

• Empirical work is also problematic as researchers often leave models unspecified, overstating how widely findings apply.

• Economists’ power comes from presenting work as scientific knowledge and crafting narratives that align with political ideologies. This can lead to treating some models as universal explanations.

• Economics needs more “foxes” who apply different frameworks based on context, not “hedgehogs” stuck on a single dominant view like free markets. Greater recognition of alternative models is needed.

Here is a summary of the key points from the blog post:

• The post argues that economics is often criticized for its models being too simplistic or making unrealistic assumptions. However, the author states that some level of simplicity and abstraction is necessary for analysis and building models.

• Critics accuse economics of neglecting social/cultural factors, but the author says economics can and does incorporate these influences through models of social interactions and identity formation.

• Economists are seen as biased toward free markets, but the author says the field features many counterexamples and the problem is more one of public perception than the content of economics itself.

• Empirical testing of economic models is difficult given economies are complex social systems, not natural phenomena. Advancing economics is more about expanding the theoretical models used rather than definitively testing any one model.

Overall, the post seeks to push back on common criticisms of economics by arguing the field is more diverse in its approaches than critics acknowledge, and the challenges of modeling social systems mean criticisms around realism, testing, etc. may miss their mark or overstate what can reasonably be expected from economics.

• Economics is often criticized for failing to predict future events like financial crises. However, no social science can truly predict the direction of complex social systems, where many interacting factors are at play. At best, economics can make conditional predictions about how specific changes might affect outcomes, assuming other factors are held constant.

• Economics models typically assume individuals behave selfishly to maximize their own outcomes. While a reasonable simplifying assumption, this is not intended as a value judgment. Self-interested behavior provides a useful benchmark for understanding interactions in markets.

• Some argue economics promotes selfishness through these assumptions. However, evidence suggests economics attracts more self-interested students rather than changing student values. And alternative models incorporating other motivations like altruism can also produce valid insights.

• Economists’ reliance on incentive-based policy solutions like carbon taxes is critiqued as turning moral issues into cost-benefit analyses. However, economists view this through an empirical lens of determining effective policies, not taking a normative stance on the issues themselves. Their role is to engineer solutions based on perceptions of real-world constraints like human selfishness.

• An Israeli daycare imposed a fee for late pickup of children which economists would expect to reduce tardiness. However, tardiness actually increased as parents now felt it was acceptable to be late since there was a fee. This showed how material incentives can sometimes “crowd out” moral behaviors.

• Economists need richer models of human behavior that account for factors beyond simply costs and benefits. While efficiency is important, it is not the only consideration.

• Markets are often defended solely on grounds of efficiency but they raise issues of fairness, justice and ethics that economists have no special expertise in evaluating.

• While efficiency is a valid consideration, it is not the only value and sometimes other values like equity may argue against market solutions. Economists should acknowledge these limits to their analysis.

• Early philosophers argued markets would promote more ethical behavior by channeling passions into profit-seeking and cooperation. However, today markets are often associated with moral corrosion instead. Both extremes overlook limitations.

So in summary, the passage discusses how economists’ focus on efficiency is too narrow and they need to acknowledge non-economic values and the limits of markets in addressing issues of ethics, fairness and human behavior.

• Students at Manchester University criticized economics education for being too narrow and focused on standard economic models that promote market ideology. They called for more pluralism and consideration of perspectives from ethics, history, and politics.

• Mankiw dismissed the critics as “poorly informed.” He argued that economics is just a method to analyze issues objectively, not an ideology.

• However, intro-level economics courses often focus heavily on benchmark market models, giving little sense of diversity in the field. This can be perceived as ideological. While economics allows pluralism in conclusions, diversity in methods is more limited.

• Still, economics has become more empirical and pluralistic over time. New areas like behavioral economics, randomized trials, and institutional economics have flourished due to influence from outside fields like psychology, medicine, and history. Overall, economics continues to evolve and incorporate new approaches.

• Randomized controlled trials (RCTs) represent a major shift toward empirical research in economics. They allow for clearer identification of causal relationships by randomly assigning participants to treatment and control groups. This has been particularly useful for evaluating interventions in developing countries.

• While insightful, RCTs only study specific communities and interventions. New research on institutional development took a broader, more macro view focusing on how institutions like property rights, rule of law, and democracy influenced economic development over the long run.

• Leading this work was Acemoglu and Robinson, who used innovative empirical analyses to argue colonial institutions influenced long-term development paths. Their work revived interest in comparative history and political economy.

• These new areas incorporated insights from other fields, expanded the scope of economics, and challenged the view of it as an insular discipline. However, they did not produce definitive answers and have not transformed economics entirely.

• The key is that economics encompasses multiple models and perspectives rather than a single framework. Successful economists like Tirole exemplify navigating among models modestly rather than making categorical claims. This approach makes economics appropriately humble while still allowing exploration of important issues.

• Economists use models to gain insights into how the world works, but models are simplifications and do not perfectly represent reality. They isolate specific causes and mechanisms.

• Unrealistic assumptions in models are acceptable if they do not undermine the critical assumptions being tested. Models are often “second best” representations of the real world.

• Applying models to real-world policy requires bridging the gap between the model and empirical evidence through explicit diagnosis. There are rarely definitive answers from economic models alone.

• Economists’ views are formed through debating different models in seminars, not from claiming any one model represents certainty. They may communicate differently in public versus academic settings.

• Noneconomists should not criticize a model solely based on its assumptions, but rather assess how sensitive the results are to problematic assumptions. They should also ask economists to justify how well their models apply in specific policy contexts.

Here is a summary of the key points from “On Exactitude in Science” by Jorge Luis Borges:

• The story describes an imaginary variant of the Chinese Empire where cartographers create minutely detailed maps that cover the empire completely to its smallest details.

• Over time, these maps grow in size and detail to match the empire itself. Successive generations create larger and more detailed maps to represent the empire perfectly.

• Eventually, map scale becomes so large that the map itself becomes equal in size to the territory it represents and is essentially useless.

• Borges uses this fictional story as a metaphor for the limits of scientific precision and representation. No model or theory can perfectly represent reality in its entirety due to its infinite complexity.

• There will always be some loss of accuracy or abstraction in any scientific representation of reality. While science aims for exactitude, a perfect one-to-one mapping is impossible. Models and theories can only approximate reality, not capture it fully.

• Borges highlights the diminishing returns of increasingly precise representation and questions where to draw the line between usefulness and perfect accuracy in scientific endeavor. The story comments on the relationship between maps/models and what they represent.

Here is a summary of the key points from the selected sources:

• There is disagreement among economists about the causes of the financial crisis and the appropriate policy response. While many supported additional stimulus spending, others like Thomas Sargent warned against too much debt.

• Economists generally agree on core macroeconomic theories like supply and demand but disagree on specific policy applications. There is no single “consensus” view.

• Robert Shiller warned of an unsustainable housing bubble beforehand while Eugene Fama promoted the efficient market hypothesis. The crisis challenged some economic models.

• Alan Greenspan later admitted to underestimating risks in deregulated financial markets. More regulation may have helped prevent the crisis.

• Globalization increased inequality in some countries as skilled labor gained relative to unskilled workers. Trade adjustments were difficult for some affected groups.

• Reforms in developing economies varied widely in their approaches. Uniform “Washington Consensus” policies were too simplistic. Successful countries adapted policies to their own circumstances.

• Economic models and theories have limitations and may require revisions in light of real-world events and new evidence. Humility is important to avoid false certainty.

That covers the key substantive points discussed across the selected sources regarding economists’ views and disagreements related to the financial crisis and globalization. Let me know if you need any part of the summary expanded upon.

Here are summaries of the key papers and sources referenced in the prompt:

• Madrian and Shea (2000): Study that finds people exhibit inertia when enrolling in 401(k) retirement savings plans. People tend to stick with the default option they are given, whether that is to opt in or opt out of enrollment. This illustrates how defaults and subtle nudges can significantly impact behavior.

• Liebman and Zeckhauser (2008): Analyzes how human decision-making deviates from strict rational choice models. Discusses the implications of behavioral factors like status quo bias, present bias, complexity aversion, etc. for insurance policy design and subsidies.

• Duflo, Kremer, and Robinson (2009): Field experiment on nudging farmer fertilizer use in Kenya. Found simple informational prompts increased fertilizer purchase and use. Demonstrates how subtle nudges based on behavioral economics can change real world behaviors and outcomes.

• Deaton (2009): Supports use of randomized controlled trials (RCTs) to rigorously evaluate development interventions in the global south. Cites their ability to isolate causal effects from confounding factors.

• Acemoglu et al. (2001): Influential study finding former European colonization had long-lasting impacts on modern economic institutions and outcomes. Countries with more extractive colonial institutions have lower modern incomes.

• Acemoglu and Robinson (2012): Synthesis of research finding economic institutions are a fundamental determinant of long-run economic success. Traces how different institutions induce different incentives and political outcomes.

• Appelbaum (2014): Interview with Nobel Prize winner Jean Tirole discussing his work on market power, regulation, and other issues. Highlights implications of issues like network effects.

Here is a summary of the key points about models from the passage:

• Models are used to represent aspects of the real world in a simplified way to better understand causal relationships and make predictions. They involve critical assumptions.

• Comparative advantage models show how trade can benefit all parties even if one country is more productive in all goods.

• General equilibrium models consider interactions between different parts of the economy.

• Imperfect competition models relax the assumption of perfect competition.

• Endogenous growth models explore sources of economic growth from within the system rather than external factors.

• Keynesian models view business cycles and recessions as resulting from inadequate aggregate demand.

• Diagnostic growth models aim to identify the most binding constraints on economic growth in specific countries.

• Experiments and field experiments can test economic models by observing outcomes in real-world settings.

• Models are judged based on internal consistency, coherence, clarity of argument, critical assumptions, empirical fit, ability to make correct predictions, and whether they provide insight beyond common sense. Causal interpretation requires caution.

• Markets are complex and models provide simplified representations, so their implications and usefulness are limited and depend on context. Multiple models may be needed to understand different aspects of the economy.

Here is a summary of the key points about economic models from the passage:

• Economic models use mathematics and assume things like rationality to simplify reality and make predictions. They come in many varieties from perfectly competitive markets to strategic interactions.

• Models have been useful for scientific understanding, but always involve simplifying assumptions that may not reflect the real world. Simplicity is valued for tractability but can limit applicability.

• Verification is important for selection of useful models through testing predictions against evidence. Randomized experiments have helped evaluate policies and theories.

• General equilibrium analysis looks at interactions between markets, while partial equilibrium focuses on a single market. Both have insights but general equilibrium is more challenging.

• New classical and real business cycle models emphasized microfoundations and rational expectations. Later theories introduced imperfect competition, game theory concepts, and time-inconsistent preferences.

• Trade models illustrate concepts like comparative advantage and gains from trade. Applied models help evaluate policies in areas like taxes, competition, and globalization.

• Pluralism in economics is valuable as different theories provide alternative perspectives, but simplicity allows clearer analysis of mechanisms even if assumptions are unrealistic.

• Hal, an economist, is mentioned discussing wages and the relationship between productivity and wages. Specifically, wages have not kept up with productivity gains over the long term.

• William Vickrey is credited with pioneering work on auction theory and mechanism design. He developed new auction formats.

• Vietnam and the Vietnam War are discussed in relation to the economy and economic impacts.

• A paper by Gordon and Dahl studying surveys of economists and finding professional consensus is referenced.

• Social choice theory and its implications for voting are mentioned.

• Behavioral economics and its relevance to understanding wage behaviors is noted.

• Immigration impacts on wages are briefly covered.

• The labor theory of value and its perspectives on wages are discussed.

• The Washington Consensus, an IMF/World Bank led set of recommendations for emerging market countries, is analyzed critically. It spanned fiscal discipline, tax reform, interest rates, trade, and more.

• Citations are provided for further readings on topics ranging from lemons markets to stimulus impacts to dissent among economists.