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This edition was written by Elijah Asdourian, Georgia Nabors, Lorae Stojanovic, and David Wessel.
Married couples in the U.S. often pay a different amount in income tax than if they file separately. Using earnings data from 1998 to 2019 and a model that simulates households’ tax liabilities, Janet Holtzblatt of the Urban Institute and co-authors find that Black couples are more likely than white couples to face this marriage penalty. Marriage penalties are higher for households where earners have similar incomes, and members of Black couples have more equal incomes than members of white households. In addition, couples were less likely to qualify for the Child Tax Credit (CTC) or Earned-Income Tax Credit (EITC) over this period because the income limits for married couples were less than double the limits for single filers (this was changed in 2017 for the CTC, but not the EITC). The reduction in eligibility for the EITC and CTC affected Black couples more because they are more likely to have dependents. The authors note that while a higher proportion of Black couples face marriage penalties, a higher proportion of white tax units are married, so policies to reduce marriage penalties would affect a higher percentage of white households than Black households.
Using voter surveys and congressional roll call data, Ilyana Kuziemko of Princeton and Nicolas Longuet Marx and Suresh Naidu of Columbia find that changes in the Democratic Party’s economic policies contributed significantly to the partisan realignment of the 1970s and 1980s, when less-educated voters began to leave the party after decades of support for Democrats. They find that less-educated Americans disproportionately support “predistribution” policies, such as raising the minimum wage, which aim to preempt the market and make the distribution of income more equal. The more educated, by contrast, favor redistribution policies, which redress existing inequality through taxes and transfers. Roll call votes in the House of Representatives on predistribution policies have declined since 1970, suggesting that elected Democrats have shifted attention away from the preferences of less educated voters. This coincides with the emergence of “New Democrats,” a more economically conservative faction of the party. New Democrats have seen growing financial support from more-educated out-of-district donors in primaries compared to Republicans. The authors conclude that about half of partisan realignment can be explained by growing distaste for the Democratic Party’s economic policy position among less-educated voters.
Viral Acharya of New York University and co-authors document how supply chain pressures led to broad-based inflation in the euro area. Initially, supply chain pressures increased prices on select goods, which sparked a rise in consumer inflation expectations through an “experience effect” from encountering higher prices firsthand and an “information effect” from seeing media coverage of supply chain bottlenecks. Rapid price changes made it challenging to compare prices across firms, and the expectations of widespread inflation diminished the perceived benefit of doing so, the authors hypothesize. This meant that consumers were more likely to accept broad-sweeping inflation on all goods. Consequently, firms with high pricing power were well-equipped to increase margins amidst high consumer demand without fearing declines in sales. High pricing power firms were also able to sustain or increase markups even after supply chain pressures waned. The authors warn that conventional monetary policy approaches that “see through” temporary supply chain shocks may be ineffective if household inflation expectations become unanchored and firms wield strong pricing power.
“The adoption of AI raises certain risks, which fall into three broad categories… In the first category, the opacity of certain AI models can create challenges in explaining how the technology produces its output. This could produce, and possibly mask, biased or inaccurate results that could, in turn, implicate consumer protection issues such as fair lending… In the second category, the use of flawed internal models can cause significant model risk management issues. As the financial crisis of 2007-09 and other failures of financial institutions, like Long-Term Capital Management, have shown, the overreliance on faulty risk models can have financial stability implications…” says Graham Steele, Assistant Secretary for Financial Institutions at the U.S. Department of the Treasury.
“Finally, in the third category, the high volumes and wide range of data consumed by AI, particularly generative AI, makes controls around data quality, suitability, and security and privacy vital for ensuring that AI is sound. As referenced in the national cyber strategy, responsibility must be placed on the stakeholders most capable of taking action to prevent bad outcomes, not on the end-users that often bear the consequences of insecure software nor on the open-source developer of a component that is integrated into a commercial product. [Cloud service providers] that play a major role in storage and processing of big data today can and should lead the effort to develop and establish necessary controls.”
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