
Better Answers to Our Biggest Problems
Abhijit V. Banerjee and Esther Duflo · 2019 · Politics & Society
Original summary · AI-drafted, human-published · added by Library
Banerjee and Duflo, both trained in rigorous empirical economics, take on the era's most divisive policy fights—immigration, trade, growth, automation, inequality, and climate change—and argue that both free-market orthodoxy and populist backlash rest on bad evidence. They show what careful data actually says, which is usually messier, more local, and less ideological than either side admits, and argue this matters because policy built on wrong assumptions has real human costs.
Pick a finish date and Genius lays out the days — the plan shows today's target and keeps you honest.
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- A voter trying to sort real evidence from political talking points on trade, immigration, or welfare - A policy professional or student who wants the empirical state of play on major economic debates, not slogans - A reader unsettled by 2016-era populism who wants to know what mainstream economics got wrong and right
Economics lost public trust not because its core findings are wrong but because economists oversold certainty on contested questions like trade and immigration.
Standard economic models assume workers move toward better wages, but in practice people stay put even amid steep, sustained economic decline because migration carries psychological and social costs that dwarf the financial upside.
Trade liberalization produces real aggregate gains, but those gains are diffuse while the losses are concentrated and durable, meaning 'trade is good on average' is compatible with specific communities being devastated for decades.
Economists do not actually have a reliable theory of what makes economies grow, so policies justified by promised growth effects, like large tax cuts for high earners, rest on far weaker evidence than politicians claim.
The narrative of mass job destruction by automation is overstated relative to measured effects so far, but tax and subsidy policy needlessly accelerates capital substituting for labor.
Opposition to redistribution is driven less by rational self-interest than by deep-seated beliefs about who deserves help, beliefs shaped by narratives of effort and identity rather than economic calculation.
Unconditional cash transfers work better than skeptics feared, but a full universal basic income may undermine the social meaning people attach to work, making targeted, work-compatible programs a better fit than either extreme.
Standard cost-benefit climate models understate the case for urgent action because they treat catastrophic, irreversible risk as an ordinary discounted-value tradeoff.
Economists should act less like theorists proclaiming universal laws and more like plumbers fixing specific, local problems through tested, incremental interventions, since institutions and behavior vary too much for one-size-fits-all prescriptions.
Abhijit Banerjee and Esther Duflo are economists at MIT and a married couple. Along with Michael Kremer, they won the 2019 Nobel Prize in Economic Sciences for pioneering randomized controlled trials in development economics. Duflo is the youngest person and second woman to win the prize. Both have spent careers testing anti-poverty programs in the field rather than theorizing from armchairs.