Many factors influence the way a child grows and develops, but it is clear that, when it comes to adult outcomes, neighborhoods matter.1 In income-segregated cities, neighborhoods vary widely by school quality, amenities, and demographics. As a result, parents in these cities must spend more money to access better neighborhoods for their children’s development. A related factor is income inequality, which has increased substantially in the United States over the past 40 years. High-income parents have more money to spend on their children, and the return on these investments has increased.2 We examined the relationship between city-level income segregation and income inequality and found it has changed over time. In 1980, cities with more income inequality did not exhibit higher income segregation. But by 2015, that pattern had changed: Cities with higher levels of income inequality now also have higher levels of income segregation.
{"title":"Income Segregation and Income Inequality","authors":"Hannah Rubinton, M. Isaacson","doi":"10.20955/es.2023.5","DOIUrl":"https://doi.org/10.20955/es.2023.5","url":null,"abstract":"Many factors influence the way a child grows and develops, but it is clear that, when it comes to adult outcomes, neighborhoods matter.1 In income-segregated cities, neighborhoods vary widely by school quality, amenities, and demographics. As a result, parents in these cities must spend more money to access better neighborhoods for their children’s development. A related factor is income inequality, which has increased substantially in the United States over the past 40 years. High-income parents have more money to spend on their children, and the return on these investments has increased.2 We examined the relationship between city-level income segregation and income inequality and found it has changed over time. In 1980, cities with more income inequality did not exhibit higher income segregation. But by 2015, that pattern had changed: Cities with higher levels of income inequality now also have higher levels of income segregation.","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82946626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
enced deposit runs that were extraordinarily fast and large by historical standards. To explain these historically unprecedented developments, banking regulators have focused on three factors: (i) changes in technology that have enabled faster withdrawals, (ii) social media that facilitated information dissemination and coordination among depositors, and (iii) uninsured deposits that were concentrated among bank customers with connections to each other (Federal Reserve, 2023a and 2023b; FDIC, 2023a and 2023b; New York DFS, 2023; California DFPI, 2023). This essay provides historical comparisons to help elucidate how these factors may have increased the severity of recent runs relative to other severe runs that took place in 1984 and 2008—the most severe runs in US history since the Great Depression and until recently. It appears that technological improvements can explain some of the increase in speed, but large increases in speed likely only apply to household and small business depositors. Major corporations, which were the predominant source of deposit withdrawals in prior run episodes at the Understanding the Speed and Size of Bank Runs in Historical Comparison
{"title":"Understanding the Speed and Size of Bank Runs in Historical Comparison","authors":"J. Rose","doi":"10.20955/es.2023.12","DOIUrl":"https://doi.org/10.20955/es.2023.12","url":null,"abstract":"enced deposit runs that were extraordinarily fast and large by historical standards. To explain these historically unprecedented developments, banking regulators have focused on three factors: (i) changes in technology that have enabled faster withdrawals, (ii) social media that facilitated information dissemination and coordination among depositors, and (iii) uninsured deposits that were concentrated among bank customers with connections to each other (Federal Reserve, 2023a and 2023b; FDIC, 2023a and 2023b; New York DFS, 2023; California DFPI, 2023). This essay provides historical comparisons to help elucidate how these factors may have increased the severity of recent runs relative to other severe runs that took place in 1984 and 2008—the most severe runs in US history since the Great Depression and until recently. It appears that technological improvements can explain some of the increase in speed, but large increases in speed likely only apply to household and small business depositors. Major corporations, which were the predominant source of deposit withdrawals in prior run episodes at the Understanding the Speed and Size of Bank Runs in Historical Comparison","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75489817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
and development, connecting countries and businesses across the world.1 While trade barriers have consistently decreased in recent decades, leading to the unprecedented growth of trade linkages, significant barriers remain across countries: The US is no exception. Barriers hinder the free flow of goods and services between countries and hurt economies and consumers alike. In this essay, we document the prevalence of barriers to international trade in the US. One challenge when characterizing the extent of barriers in a given country is the variety of policies through which countries may limit trade. Here, we take a comprehensive look at trade barriers in the US by partitioning policies into two types: tariffs and non-tariff measures (NTMs).2 Tariffs are taxes on imports expressed as a percentage of the total value imported, effectively raising the price of imports and making domestic substitutes more attractive. Non-tariff measures encompass a broad range of policies and regulations that can also limit the free flow of goods, such as quotas, licenses, and technical standards. US Barriers to International Trade of Goods: Tariffs and Non-Tariff Measures
{"title":"US Barriers to International Trade of Goods: Tariffs and Non-Tariff Measures","authors":"Fernando Leibovici, Jason Dunn","doi":"10.20955/es.2023.9","DOIUrl":"https://doi.org/10.20955/es.2023.9","url":null,"abstract":"and development, connecting countries and businesses across the world.1 While trade barriers have consistently decreased in recent decades, leading to the unprecedented growth of trade linkages, significant barriers remain across countries: The US is no exception. Barriers hinder the free flow of goods and services between countries and hurt economies and consumers alike. In this essay, we document the prevalence of barriers to international trade in the US. One challenge when characterizing the extent of barriers in a given country is the variety of policies through which countries may limit trade. Here, we take a comprehensive look at trade barriers in the US by partitioning policies into two types: tariffs and non-tariff measures (NTMs).2 Tariffs are taxes on imports expressed as a percentage of the total value imported, effectively raising the price of imports and making domestic substitutes more attractive. Non-tariff measures encompass a broad range of policies and regulations that can also limit the free flow of goods, such as quotas, licenses, and technical standards. US Barriers to International Trade of Goods: Tariffs and Non-Tariff Measures","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81338630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Mechanics of Fed Balance Sheet Normalization","authors":"Amalia Estenssoro, Kevin L. Kliesen","doi":"10.20955/es.2023.18","DOIUrl":"https://doi.org/10.20955/es.2023.18","url":null,"abstract":"","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85876022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Immigrants make up significant proportions of the population and labor force in many developed economies, but they face considerable challenges in navigating those labor markets, such as occupational regulations and licensing, lack of destination-specific skills, or outright discrimination. In our recent working paper,1 we studied how labor market barriers affect immigrant workers collectively and by occupation groups across the US and other destination countries. In a recent blog post, we summarized cross-country empirical evidence from our paper of employment differences between immigrant and native workers across four occupation groups—non-routine cognitive, non-routine manual, routine cognitive, and routine manual.2 Nonroutine cognitive occupations include management and professional occupations; non-routine manual occupations include jobs like health aides and food workers; routine cognitive occupations include jobs like retail sales and The Allocation of Immigrant Talent Across Countries: Earnings Gaps
{"title":"The Allocation of Immigrant Talent Across Countries: Earnings Gaps","authors":"S. Birinci, Fernando Leibovici, K. See","doi":"10.20955/es.2023.2","DOIUrl":"https://doi.org/10.20955/es.2023.2","url":null,"abstract":"Immigrants make up significant proportions of the population and labor force in many developed economies, but they face considerable challenges in navigating those labor markets, such as occupational regulations and licensing, lack of destination-specific skills, or outright discrimination. In our recent working paper,1 we studied how labor market barriers affect immigrant workers collectively and by occupation groups across the US and other destination countries. In a recent blog post, we summarized cross-country empirical evidence from our paper of employment differences between immigrant and native workers across four occupation groups—non-routine cognitive, non-routine manual, routine cognitive, and routine manual.2 Nonroutine cognitive occupations include management and professional occupations; non-routine manual occupations include jobs like health aides and food workers; routine cognitive occupations include jobs like retail sales and The Allocation of Immigrant Talent Across Countries: Earnings Gaps","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88536807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
future by looking at possible changes in the economy, including income, the labor market, and interest rates. They may also seek guidance from professional forecasters. Currently, there is much debate as to how the U.S. economy will perform in 2023. But the general consensus of professional forecasters and FOMC participants is that economic growth will be slow and inflation will come down from its elevated 2022 rates. Forecasters also expect that the hot labor market from the past few years will begin to cool. However, there are many risks to the outlook: elevated global inflation, slower growth in China, and a cooling domestic housing market, to name a few. The historical track record of forecasters indicates these economic projections are rarely fully correct, but they do contain useful information beyond what can be gained by simple projections. So how likely is it that the economy in 2023 will be in line with professional forecasts? And if they are wrong, as they have been in the past, how wrong will they be? I look at the one-year-ahead projections reported in the December Blue Chip survey of about fifty professional economic forecasters. Although these projections are updated monthly, I use data in the December report for Professional Forecasters’ Outlook for 2023 and Caveats Based on Past Performance
{"title":"Professional Forecasters’ Outlook for 2023 and Caveats Based on Past Performance","authors":"Charles S. Gascon","doi":"10.20955/es.2023.1","DOIUrl":"https://doi.org/10.20955/es.2023.1","url":null,"abstract":"future by looking at possible changes in the economy, including income, the labor market, and interest rates. They may also seek guidance from professional forecasters. Currently, there is much debate as to how the U.S. economy will perform in 2023. But the general consensus of professional forecasters and FOMC participants is that economic growth will be slow and inflation will come down from its elevated 2022 rates. Forecasters also expect that the hot labor market from the past few years will begin to cool. However, there are many risks to the outlook: elevated global inflation, slower growth in China, and a cooling domestic housing market, to name a few. The historical track record of forecasters indicates these economic projections are rarely fully correct, but they do contain useful information beyond what can be gained by simple projections. So how likely is it that the economy in 2023 will be in line with professional forecasts? And if they are wrong, as they have been in the past, how wrong will they be? I look at the one-year-ahead projections reported in the December Blue Chip survey of about fifty professional economic forecasters. Although these projections are updated monthly, I use data in the December report for Professional Forecasters’ Outlook for 2023 and Caveats Based on Past Performance","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79967657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Where Are the Workers? A Look into the Decline in Immigration","authors":"Hannah Rubinton, Cassie Marks","doi":"10.20955/es.2023.19","DOIUrl":"https://doi.org/10.20955/es.2023.19","url":null,"abstract":"","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135495518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advent of the information and communications technology revolution in the 1980s introduced significant improvements in search technologies, changing the way unemployed workers look for jobs. Previously, workers had to go door-to-door to look for a job, but they can now easily access information on a job’s requirements, offered wages and benefits, and work environment through online jobsearch platforms. They can also apply to many jobs in a short period of time. How have these revolutionary changes in the way workers search and apply for jobs affected jobfinding rates over time? In Job Applications and Labor Market Flows, we use data from the Employment Opportunities Pilot Projects (EOPP) and the Federal Reserve Bank of New York’s Survey of Consumer Expectations (SCE) to empirically document the evolution of job applications and outcomes since the 1980s.1 The EOPP was designed to analyze the impacts of an intensive job search and a work-and-training program. It captures unemployment spells and job-search activities of unemployed workers between 1979 and 1980. Meanwhile, the SCE captures respondents’ labor market activities and outcomes of unemployed workers between 2013 and 2019. Our sample from both datasets consists of unemployed individuals 25 to 65 years of age who submitted at least one job application during their unemployment spell.2
{"title":"The Evolution of Job Applications and Job-Finding Rates since the 1980s","authors":"S. Birinci, K. See, Shu Lin Wee","doi":"10.20955/es.2023.4","DOIUrl":"https://doi.org/10.20955/es.2023.4","url":null,"abstract":"The advent of the information and communications technology revolution in the 1980s introduced significant improvements in search technologies, changing the way unemployed workers look for jobs. Previously, workers had to go door-to-door to look for a job, but they can now easily access information on a job’s requirements, offered wages and benefits, and work environment through online jobsearch platforms. They can also apply to many jobs in a short period of time. How have these revolutionary changes in the way workers search and apply for jobs affected jobfinding rates over time? In Job Applications and Labor Market Flows, we use data from the Employment Opportunities Pilot Projects (EOPP) and the Federal Reserve Bank of New York’s Survey of Consumer Expectations (SCE) to empirically document the evolution of job applications and outcomes since the 1980s.1 The EOPP was designed to analyze the impacts of an intensive job search and a work-and-training program. It captures unemployment spells and job-search activities of unemployed workers between 1979 and 1980. Meanwhile, the SCE captures respondents’ labor market activities and outcomes of unemployed workers between 2013 and 2019. Our sample from both datasets consists of unemployed individuals 25 to 65 years of age who submitted at least one job application during their unemployment spell.2","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82688821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
(MNEs) to expand their operations to reach new markets across the globe. However, one drawback of globalization is that MNEs can easily shift their profits to lower-tax countries. Profit shifting is a practice in which MNEs move their profits from high-tax countries to low-tax countries to lower their overall tax burden. One of the most common forms of this practice is the transfer of intellectual property (IP), including patents, trademarks, copyrights, and trade secrets. It’s a popular option for MNEs looking to reduce their tax burdens, as IP is transferred across borders easily without a need for significant capital investment or even a physical presence. Several notable papers, including 2022 studies by Torslov, Wier, and Zucman and by Guvenen, Mataloni, Rassier, and Ruhl, have argued that the movement of IP is the main channel for profit shifting. However, empirical evidence on the prevalence of this practice has been scarce. This essay uses IP transactions data from the platform ktMINE to compute the amount of patent transfers flowing from the US to two groups of countries: tax havens and non-tax havens.1 Global Profit Shifting through Intellectual Property and the Impact of US Tax Reforms
{"title":"Global Profit Shifting through Intellectual Property and the Impact of US Tax Reforms","authors":"Ana Maria Santacreu, J. LaBelle","doi":"10.20955/es.2023.10","DOIUrl":"https://doi.org/10.20955/es.2023.10","url":null,"abstract":"(MNEs) to expand their operations to reach new markets across the globe. However, one drawback of globalization is that MNEs can easily shift their profits to lower-tax countries. Profit shifting is a practice in which MNEs move their profits from high-tax countries to low-tax countries to lower their overall tax burden. One of the most common forms of this practice is the transfer of intellectual property (IP), including patents, trademarks, copyrights, and trade secrets. It’s a popular option for MNEs looking to reduce their tax burdens, as IP is transferred across borders easily without a need for significant capital investment or even a physical presence. Several notable papers, including 2022 studies by Torslov, Wier, and Zucman and by Guvenen, Mataloni, Rassier, and Ruhl, have argued that the movement of IP is the main channel for profit shifting. However, empirical evidence on the prevalence of this practice has been scarce. This essay uses IP transactions data from the platform ktMINE to compute the amount of patent transfers flowing from the US to two groups of countries: tax havens and non-tax havens.1 Global Profit Shifting through Intellectual Property and the Impact of US Tax Reforms","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80789842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Rise and Fall of M2","authors":"Christopher J. Neely","doi":"10.20955/es.2023.11","DOIUrl":"https://doi.org/10.20955/es.2023.11","url":null,"abstract":"","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80127147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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