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René Chalom, Fatih Karahan, Brendan Moore, and Giorgio Topa
The growth rate of hourly earnings is a widely used indicator to assess the economic progress of U.S. workers, as well as the health of the labor market. It is also a measure of wage pressures that could potentially spill over into inflationary pressures in a tightening labor market. Hourly earnings growth, on average, has gradually risen over the course of the current expansion, under way since the end of the Great Recession. But how have different groups of workers fared in this regard? Have hourly earnings risen uniformly at all points of the wage distribution, or have some segments of the workforce been left behind? In this post, we take a close look at earnings growth over the past two decades at different points of the wage distribution and for various demographic groups. Our goal is to examine whether there are any significant patterns in the evolution of the distribution of earnings, as opposed to just looking at the behavior of aggregate earnings growth. We focus primarily on hourly earnings growth, although our findings apply to total earnings as well.
Technological change and globalization have caused a massive transformation in the U.S. economy. While creating new opportunities for many workers, these forces have eliminated millions of good-paying jobs, particularly routine jobs in the manufacturing sector. Indeed, a great deal of attention
has focused on the consequences
of the loss of blue-collar production jobs
for prime‑age men. What is often overlooked, however, is that
women have also been hit hard
by the loss of routine jobs, particularly administrative support jobs—a type of routine work that has historically been largely performed by women. In this post, we show that the combined loss of production and administrative support jobs since 2000 is actually more than three times as large for prime-age women than prime-age men.
While average outcomes serve as important yardsticks for how the economy is doing, understanding heterogeneity—how outcomes vary across a population—is key to understanding both the whole picture and the implications of any given policy. Following our six-part look at heterogeneity in October 2019, we now turn our focus to heterogeneity in the labor market—the subject of four posts set for release tomorrow morning. Average labor market statistics mask a lot of underlying variability—disparities that factor into labor market dynamics. While we have written about labor market heterogeneity before, this series is an attempt to pull together in a cohesive way new insights on the labor market and highlight details that are not immediately obvious when we study aggregate labor market statistics.
In recent years there has been a lot of interest in the effect of income inequality (heterogeneity) on the economy, from both academics and policymakers. Researchers have developed Heterogeneous Agent New Keynesian (HANK) models that incorporate heterogeneity and uninsurable idiosyncratic risk into the New Keynesian models that have become a cornerstone of monetary policy analysis. This research has argued that heterogeneity and idiosyncratic risk change many features of New Keynesian models – the
transmission of conventional monetary policy, the forward guidance puzzle, fiscal multipliers, the efficacy of targeted transfers and automatic stabilizers, among others. However, the source of the difference between HANK and representative agent New Keynesian (RANK) models remains unclear. This is because HANK models are typically not analytically tractable, leaving it unclear what exactly is driving the results. To shed light on the macroeconomic consequences of heterogeneity, we develop a stylized HANK model that contains key features present in more complicated HANK models.
Health is an integral part of well-being. The United Nations Human Development Index uses life expectancy (together with GDP per capita and literacy) as one of three key indicators of human welfare across the world. In this post, I discuss the state of life expectancy inequality in the United States and examine some of the underlying factors in its evolution over the past several decades.
Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw
The rising cost of a college education has become an important topic of discussion among both policymakers and practitioners. At least eleven states have recently introduced programs to make public two-year education tuition free, including New York, which is rolling out its Excelsior Scholarship to provide tuition-free four-year college education to low-income students across the SUNY and CUNY systems. Prior to these new initiatives, New York, had already instituted merit scholarship programs that subsidize the cost of college conditional on academic performance and in-state attendance. Given the rising cost of college and the increased prevalence of tuition-subsidy programs, it’s important for us to understand the effects of such programs on students, and whether these effects vary by income and race. While a rich body of work has studied the effects of merit scholarship programs on educational attainment, the same is not true for the effects on financial outcomes of students, such as debt and repayment. This blog post reports preliminary findings from ongoing work, which is one of the first research initiatives to understand such effects.
Andrew F. Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw
Student loans are increasingly a focus of discourseamongpoliticians, policymakers, and the news media, resulting in a range of new ideas to address the swelling aggregate debt. Evaluating student loan policy proposals requires understanding the challenges faced by student borrowers. In this post, we explore the substantial variation in the experiences of borrowers and consider the distributional effects of various policy options.
Workers in the United States experience vast differences in lifetime earnings. Individuals in the 90th percentile earn around seven times more than those in the 10th percentile, and those in the top percentile earn almost twenty times more. A large share of these differences arise over the course of people’s careers. What accounts for these vastly different outcomes in the labor market? Why do some individuals experience much steeper earnings profiles than others? Previous research has shown that the “job ladder”—in which workers obtain large pay increases when they switch to better jobs or when firms want to poach them—is important for wage growth. In this post, we investigate how job ladders differ across workers.
Economic inequality in the United States is much more pronounced in some parts of the country than others. In this post, we examine the geography of wage inequality, drawing on our recent Economic Policy Review article. We find that the most unequal places tend to be large urban areas with strong economies where wage growth has been particularly strong for those at the top of the wage distribution. The least unequal places, on the other hand, tend to have relatively sluggish economies that deliver slower wage growth for high, middle, and lower wage earners alike. Many of the least unequal places are concentrated in the Rust Belt. These differences in the degree of wage inequality are tied to powerful economic forces arising from technological change and globalization, which have pushed up wages strongly for high-skilled workers in locations that have become the most unequal. Yet those same forces have kept wage growth compressed within a fairly narrow range for workers in places that are the least unequal.
Economic analysis is often geared toward understanding the average effects of a given policy or program. Likewise, economic policies frequently target the average person or firm. While averages are undoubtedly useful reference points for researchers and policymakers, they don’t tell the whole story: it is vital to understand how the effects of economic trends and government policies vary across geographic, demographic, and socioeconomic boundaries. It is also important to assess the underlying causes of the various inequalities we observe around us, whether they are related to income, health, or any other set of indicators. Starting today, we are running a series of six blog posts (apart from this introductory post), each of which focuses on an interesting case of heterogeneity in the United States.
Liberty Street Economics features insight and analysis from New York Fed economists working at the intersection of research and policy. Launched in 2011, the blog takes its name from the Bank’s headquarters at 33 Liberty Street in Manhattan’s Financial District.
The editors are Michael Fleming, Andrew Haughwout, Thomas Klitgaard, and Asani Sarkar, all economists in the Bank’s Research Group.
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