essay of rate of unemployment

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AP®︎/College Macroeconomics

Course: ap®︎/college macroeconomics > unit 2.

Unemployment rate primer
Natural, cyclical, structural, and frictional unemployment rates
Worked free response question on unemployment

Lesson summary: Unemployment

Unemployment
Types of unemployment and the natural rate of unemployment

Lesson overview

Key takeaways, the labor force participation rate (lfpr), limitations of the unemployment rate, three types of unemployment, the natural rate of unemployment, changes in the natural rate of unemployment (nru), key equations, the labor force:, the unemployment rate (ur), common misperceptions.

Not everyone who is out of work is unemployed. In order to be counted as unemployed you have to be out of work, looking for work, and able to accept a job if one is offered to you. If you are out of work and not looking, then you are considered “not in the labor force” rather than unemployed.
We tend to think of unemployment as an undesirable thing, but a certain amount of unemployment is actually part of a healthy economy. Structural unemployment occurs when new industries are created and old industries become obsolete. For example, when we moved from using horses and buggies to using cars to get around, this put a lot of buggy makers in the structurally unemployed category.
Frictional unemployment might not seem very fun, but consider what it means to have zero unemployment—nobody ever looks for a job, they just remain in whatever job they are given! In fact, a number of dystopian novels have been written in which everyone in a society is automatically assigned a fixed career (such as the Divergent series). Those societies have zero frictional unemployment, but they are also quite unpleasant if you are unhappy with that career!
A decrease in the unemployment rate isn’t necessarily a sign of an improving economy. When people stop looking for jobs and drop out of the labor force as discouraged workers, the unemployment rate will decrease even though the true employment situation hasn’t gotten any better. This is why it is important to look at both changes in the unemployment rate and changes in the labor force participation rate. Looking at both changes let’s you get a more complete idea about changes in the employment situation.

Discussion Questions:

An inventor in Burginville developed a fantastic new dictation machine that perfectly records speech and turns it into a typed document. Unfortunately, that meant that unemployment increased among typists working in offices. Which type of unemployment is this? Explain. Solution, please. This is structural unemployment because typists skills are no longer desired. The changing structure of office work has resulted in people losing their jobs.
The nation of Fitlandia has 120 , 000 ‍ people. Of these, 20 , 000 ‍ are children under the age of 16, 72,000 ‍ have jobs, 8,000 ‍ don’t have jobs and are looking for work, and 20,000 ‍ people are retired. Assuming that these are all noninstitutionalized civilians, calculate the labor force participation rate and the unemployment rate. I think I got it. Can I check my work? L F = # Employed + # Unemployed = 72,000 + 8,000 = 80,000 L F P R = L F Eligible Population × 100 % = 80,000 100,000 × 100 % = 80 % U R = # Unemployed # Labor Force × 100 % = 8,000 80,000 × 100 % = 10 % ‍
Explain why a decrease in the unemployment rate can actually signal a tough job market.

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Home — Essay Samples — Economics — Unemployment — Unemployment: Causes, Effects, and Solutions

Unemployment: Causes, Effects, and Solutions

Categories: Unemployment

About this sample

Words: 685 |

Published: Jan 30, 2024

Words: 685 | Pages: 2 | 4 min read

Introduction, causes of unemployment, effects of unemployment, solutions to combat unemployment, a. economic factors.

Global recession: During economic downturns, companies may reduce employment to cut costs and remain competitive.
Automation and technological advancements: The use of machines and technology may replace human workers in some industries, leading to job losses.
Outsourcing of jobs: Companies may choose to outsource jobs to other countries where labor costs are lower, leaving domestic workers unemployed.

B. Societal Factors

Lack of education and skills: Individuals without proper education and job training may be ill-equipped to meet the demands of a constantly evolving job market.
Discrimination in hiring processes: Certain groups, such as women, minorities, and older workers, may face barriers in securing employment due to discrimination.
Dependency on welfare programs: Some individuals may choose to remain on welfare programs due to a lack of incentive to enter the workforce or because they cannot find suitable employment.

A. Economic Effects

Reduction in consumer spending: Without a steady income, unemployed individuals may have less money to spend, resulting in a decline in consumer spending.
Decline in government revenue: With fewer people working, the government may see a decline in tax revenue, which can impact its ability to provide necessary services and promote economic growth.
Increase in social welfare expenses: The government may need to allocate more funds toward social welfare programs, such as unemployment benefits and food assistance, to support those who are unemployed.

B. Social Effects

Increase in crime rates: Individuals who are unemployed may resort to criminal activities to make ends meet, leading to a rise in crime rates.
Mental health issues: Unemployment can cause stress, anxiety, and depression, which can negatively impact an individual's mental health.
Strained relationships and family instability: Unemployment may cause financial strain and tension within families, leading to relationship problems and instability.

A. Economic Solutions

Encouraging entrepreneurship and small business development: Providing resources and support for individuals to start their own businesses can lead to job creation and economic growth.
Promoting vocational training and skill development programs: Ensuring that individuals have access to education and training programs can increase their job readiness and competitiveness in the job market.
Implementing balanced trade policies: Creating policies that promote fair trade and reduce job outsourcing can protect domestic jobs and promote job growth.

B. Social Solutions

Addressing educational disparities and providing access to quality education: Providing quality education to disadvantaged communities can improve their job readiness and reduce unemployment rates.
Combating discrimination in the workplace: Enforcing anti-discrimination laws and promoting diversity and inclusion in the workplace can reduce barriers to employment for certain groups.
Strengthening social safety net programs: Ensuring that social welfare programs are designed to incentivize work and provide support to those in need can promote economic stability and reduce poverty.
Bureau of Labor Statistics. (2021, October 8). Employment Situation Summary. https://www.bls.gov/news.release/empsit.nr0.htm

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What does the unemployment rate measure?

Subscribe to the hutchins roundup and newsletter, stephanie aaronson stephanie aaronson senior associate director, division of research and statistics - federal reserve board.

February 18, 2021

13 min read

This explainer builds off of a May 2020 post, “ Making Sense of the Monthly Jobs Report During the COVID-19 Pandemic .”

The unemployment rate soared from a 50-year low of 3.5 percent to 14.8 percent in April 2020 at the beginning of the COVID-19 pandemic, and then fell faster than many forecasters anticipated, to 6.3 percent in January 2021. But the labor market is far from healthy: for instance, the Bureau of Labor Statistics (BLS) counted 4.5 million more people as unemployed in January than were unemployed before the pandemic—and many more people weren’t counted as unemployed because they’d stopped looking for work. Here’s a guide to various measures of the health of the labor market.

What does the unemployment rate measure?

The headline unemployment rate (known as U-3) measures the percentage of people over the age of 16 who aren’t working but are available and actively looking for work.

Where do the data on unemployment come from?

Data on unemployment are collected every month in the Current Population Survey (CPS), a survey of about 60,000 households , conducted by the Census and the BLS every month, which includes roughly 105,000 people ages 16 and older . The questions about unemployment refer to what people were doing during the week that includes the 12th of the month, known as the “reference week”—so the survey to be released on Friday, March 4, 2021 will cover the week of February 8, 2021. The CPS is referred to as the household survey, to distinguish it from the establishment survey, which counts the number of people on employer payrolls. (The latest tally of payrolls showed that as of January 2021, we have recovered 9.9 million of the 22.4 million jobs lost since the beginning of 2020.)

How are individuals in the monthly survey identified as being unemployed ?

Respondents to the survey are first asked whether they worked during the week that includes the 12th of the month. Individuals are counted as employed if they did any work at all as a paid employee, if they worked in their own business, or worked without pay for at least 15 hours in a family business. People are also counted as employed if they were temporarily absent from work as a result of sickness, bad weather, vacation, a strike, or personal reasons. Such workers are classified as employed but absent from work.

Respondents who are not employed then are asked if they have looked for work in the previous four weeks and are available to work. If so, they are counted as unemployed. Respondents who did not work but are on temporary layoff from a job with the expectation that they will be recalled—as many furloughed employees are today—are counted as unemployed whether they looked for a job or not.

What’s the difference between being unemployed and being out of the labor force?

People who are not working and who don’t meet the criteria to be counted as unemployed are said to be out of the labor force. This category includes students, retirees, and those who stay at home to take care of family members. In addition, people who report wanting a job but who have not looked for work in the most recent four weeks are also considered out of the labor force. Between February 2020 and January 2021, 5.5 million people dropped out of the labor force, on net. At the same time, the number of people out of the labor force who said they wanted a job rose by 1.9 million.

Why is it hard to determine if people are unemployed in the COVID-19 pandemic?

While the survey questions are the same as always, the nature of the COVID-19 economy means that people’s behavior, and hence the data, may not follow the same pattern that we usually see when the economy is turning down. For instance, early in the pandemic, the share of workers reporting themselves employed but not at work skyrocketed. While some of this increase was due to a rise in illness or childcare responsibilities, the number of people reporting themselves “not at work for other reasons” rose from around 600,000 prior to the pandemic to over 8 million. In conducting the survey, the BLS has attempted to categorize those employed but absent from work due to “pandemic-related business closures or cutbacks” as unemployed. Despite this effort, an unusually large number of workers in this situation have still been counted as “not being at work for other reasons,” which the BLS views as misclassification . Following its typical procedure, the BLS has categorized individuals reported as “not being at work for other reasons” as employed rather than unemployed. While this classification is in keeping with their standard practice, it has the effect of depressing the unemployment rate relative to a case where these workers are categorized as unemployed. That said, this misclassification is certainly less severe now than it was early in the recession: in January 2021, the number was 1.7 million. In addition, the BLS has publicly stated its estimate of the size of this mismeasurement. In January, the BLS reported that if misclassified workers had been counted as unemployed, the unemployment rate would have been 0.6 percentage points higher, or 6.9 percent. It also noted that this was an upper-bound estimate.

Among those who have lost jobs, the typical behavior would be to transition from employment into unemployment rather than to transition out of the labor force. However, early in the pandemic, with stay-at-home orders in place and nonessential businesses closed in many communities, people who left employment were much less likely to seek work than would typically be the case. In addition, schools closed in many places, which meant that many people who lost their jobs had child-care responsibilities that prevented them from seeking or accepting a new job. Even now, nearly a year into the pandemic, many of these same dynamics are in place. As a result, relative to a typical downturn, we expect the headline unemployment rate to be relatively lower than in a typical recession and the percent of those out of the labor force to be relatively higher, especially the percent of those who say they want a job but aren’t looking.

Indeed, we saw evidence of this in March 2020 , when, relative to the prior trend, an additional 1.2 million people moved from employment to out of the labor force, and the number of people categorized as out of the labor force but wanting a job rose by 500,000. The resulting decline in the labor force participation rate was much larger than would be expected given the rise in the unemployment rate, and it remains unusually low.

Given the limitations of the unemployment rate as a measure of labor market slack, what are some alternatives?

The BLS releases six measures of labor market slack in the monthly jobs report. These include the official unemployment rate (U-3), discussed above, as well as more narrow definitions, called U-1 and U-2, which respectively include only those unemployed at least 15 weeks (the long-term unemployed) or for less than a month (the short-term unemployed). The BLS also publishes broader definitions of slack. The U-6 rate, for instance, counts all those who are technically unemployed plus those are who are working part-time but would prefer full time work, and those “marginally attached to the labor force,” that is, people who say they want either a full-time or part-time job, have not looked for work in the most recent four weeks, but have looked for a job sometime in the past 12 months. When adults classified as “marginally attached” report that they did not recently seek work because they do not believe jobs are available for them, they are classified as “discouraged workers.” The U-4 counts the unemployed and discouraged workers, while U-5 adds in other marginally attached workers. In January, the broadest of these measures, U-6, stood at 11.1 percent, 4.8 percentage points higher than the official unemployment rate.

Alternate Measures of Unemployment, U1-U6

Given the measurement problems during the pandemic, are there alternatives to the BLS indicators of slack to gauge the health of the labor market?

Some economists have offered their own estimates of labor market slack trying to account for the misclassification and unusual movements in labor force participation during the pandemic. For example, Jason Furman and Wilson Powell III at the Peterson Institute for International Economics calculate what they call the “ realistic unemployment rate .” Their realistic unemployment rate was 8.3 percent in January , two percentage points higher than the official unemployment rate. Furman and Powell’s realistic unemployment rate differs from the official in two ways. First, they estimate the number of workers misclassified as being “not at work for other reasons” and count them as unemployed. Second, they try to estimate the excess decline in labor force participation beyond what would be expected given the rise in unemployment, and add those people to the unemployment rate as well. While this estimate is dependent on the specific modeling assumptions, it is nonetheless a useful attempt to reveal the extent to which we underestimate the true disruption to people’s livelihoods if we fail to account for the unusually large drop in labor force participation.

In a recent speech , Fed Chair Jerome Powell outlined his own method for adjusting the unemployment rate to capture the unusual features of the pandemic labor market. First, like Furman and Powell, he adds to the count of the unemployed an estimate of the misclassified workers. In addition, he adds in the number of people who have left the labor force since last February. This measure does not attempt to account for the fact that more people dropped out of the labor force than usual, and it does not account for structural reasons that individuals may have dropped out of the labor force, such as retirement. At the same time, it also isn’t sensitive to the specific modeling assumptions regarding the behavior of the participation rate. His calculations boost the unemployment rate to close to 10 percent in January .

One shortcoming of both these approaches is that they implicitly or explicitly make an assumption about what share of the individuals who are out of the labor force would be unemployed in a more normal recession. In addition, by counting individuals who are out of the labor force as unemployed, these measures would seem to assume that such individuals will act like the unemployed once the economy recovers. But typically, people who are out of the labor force are less likely to become employed than are those who are unemployed. One measure of the unemployment rate that includes individuals out of the labor force but also accounts for this variation in the propensity to return to work is the Hornstein-Kudlyak-Lange non-employment index , which was 9.3 percent in January.

A simpler measure is the employment-to-population-ratio (EPOP), a ratio of the number of people employed to the number of people in the population. The EPOP , which stood at 61.1 percent on the eve of the pandemic, declined by 9.8 percentage points between February and April—the largest decline since the series began in January 1948. Although the EPOP has recovered somewhat, to 57.5 percent, it still stands at its lowest level since the early 1980s, a time when far fewer women were in the labor force. This large drop is direct evidence of the unprecedented toll that the pandemic has taken on the labor market and people’s livelihoods.

What are “initial claims” for unemployment insurance?

When people first file for unemployment insurance (UI), they are counted as an “initial claim.” So when unemployment increases, initial claims tend to rise. Because initial claims are reported weekly , they are often used as an early indicator of the overall unemployment rate.

What is the relationship between initial claims and the unemployment rate — and why might it be different now?

The number of people receiving UI and the number counted as unemployed do tend to move in the same direction, but there is no formal link between the two. The only criteria for being counted as unemployed (and hence included in the unemployment rate) are that you are without a job and that you have actively searched for work or are on temporary layoff. You don’t need to be collecting unemployment insurance to be counted as unemployed. And some people are eligible to collect partial unemployment insurance benefits if they are working but have been assigned a schedule that is far below their usual weekly hours.

Many people who become unemployed do not apply for UI benefits, either because they are not eligible or because they choose not to apply. So initial claims typically understate the number of people becoming unemployed in a given week. That said, there are people who file an initial claim and are not counted as unemployed in the CPS. This could happen if a person doesn’t meet the CPS criteria for being unemployed—for instance, if they file for UI because their work schedule was reduced, or if the person has a very short spell of unemployment which is not captured in the CPS (for example, a person who becomes unemployed and finds a job in between survey reference weeks).

Furthermore, many people who are unemployed and do file an initial claim do not end up receiving unemployment insurance benefits, either because they are not covered by the program, because they have not accumulated enough working hours to be eligible for benefits, or because they don’t satisfy the job search requirements. In February 2020, before the pandemic, the number of people unemployed was about 5.8 million while the number of people receiving UI benefits averaged only about 1.7 million.

What is the payroll survey? And why is it likely to be less useful than usual?

The payroll (or establishment) survey is a survey of 145,000 businesses—employing about one third of all workers on nonfarm payrolls. The payroll survey tends to have difficulty when the economy is at a turning point, as is the case now. To create the sample to be surveyed, the BLS picks firms from the universe of firms that have unemployment insurance tax accounts. However, new firms do not enter the BLS sample universe right away, and the BLS can have difficulty distinguishing non-response from a firm closure in real time. Since the net contribution of jobs created at new firms and jobs destroyed at closing firms is typically small, the BLS assumes that nonresponding firms have the same change in employment as occurred at firms that responded. It then uses a model, called the net birth-death model, to forecast the residual between that imputation and the actual data. This model tends to overestimate employment growth when the economy is weakening and underestimate it when the economy is improving. And while the model error is typically small, it can, on occasion, be large.

We know about these forecast errors because the BLS revises the data based on more complete information. In most years the benchmark is small, with the level of employment revising up or down by less than 0.2 percentage points. However, strikingly, when the establishment survey data for March 2009—the depths of the Great Recession—were benchmarked, the level of payroll employment was revised down by over 900,000 jobs, or 0.7 percentage points—meaning that employers had shed 75,000 more jobs each month between April 2008 and March 2009 than previously estimated.

If, as a result of the pandemic, an unusually large number of firms are closing and few are opening, it seems possible that even the dramatic decline in employment that we are likely to see will underestimate the true extent of job loss.

Why might the historical link between unemployment and poverty be a misleading way to look at today’s numbers?

When people become unemployed, they lose an important (and sometimes their only) source of income and are at risk of falling into poverty. Of course, the more generous unemployment insurance is, the less likely it is for someone who loses a job to become poor. But unemployment insurance has typically replaced only about 40 percent of lost wages, on average, over the past 20 years, with a lot of variation in generosity across the states.

The federal response to the pandemic changed that. The CARES Act, for instance, added $600 a week to weekly unemployment insurance benefits through the end of July 2020, preventing many families from falling into poverty, and the December extension provided for an additional $300. And, of course, Congress provided two rounds of one-time payments for most families—$1,200 per adult and $500 per dependent child in the spring of 2020 and another $600 per individual in December, with payments phasing out for higher earners. Estimates suggest that about 13 million people were prevented from falling into poverty by these efforts.

The author thanks Francisca Alba for research assistance and Becca Portman for graphic design.

Labor & Unemployment

Economic Studies

The Hutchins Center on Fiscal and Monetary Policy

David Wessel

May 22, 2024

Joseph Parilla, Glencora Haskins, Mark Muro

May 21, 2024

Robert Maxim

The Reporter

Long-Run Trends and the Natural Rate of Unemployment

Starting with Milton Friedman and Edmund Phelps, academics and policymakers have endeavored to measure a sustainable level of unemployment and the implications that deviations from this level have for inflation of prices and wages. This natural rate of unemployment, u *, is broadly defined as the unemployment rate at which, controlling for supply shocks, inflation remains stable.

Long-run trends in the labor market and changes in inflation expectations make it hard to pin down this natural rate of unemployment. Specifically, the dramatic trend decline in unemployment and the concurrent anchoring of inflation expectations since the 1980s have triggered extensive discussions in policy and academic circles. My recent work focuses on using detailed data on labor market flows and inflation expectations to estimate the natural rate of unemployment.

In this report, I first focus on the drivers of the trend decline in unemployment and review my work that connected this decline to two prominent long-run trends in the economy: the grand gender convergence and the dual aging of workers and firms. Then I summarize my work and discuss a unified framework that I have developed with Richard Crump, Stefano Eusepi, and Marc Giannoni for estimating the natural rate of unemployment. While I mostly focus on the period before the COVID-19 pandemic, I end with a discussion of the effect of the pandemic on the natural rate of unemployment.

Trend Decline in the Unemployment Rate

A useful insight from my research with Michael Elsby and Bart Hobijn is that the flow origins of unemployment rate movements provide useful information about the underlying drivers of unemployment fluctuations and trends. 1 The idea is simple: the unemployment rate increases either because more workers become unemployed (inflows increase) or it becomes harder for the unemployed to leave unemployment (outflows decrease). Visual examination of inflow and outflow rates in Figure 1 shows that the inflow rate is characterized by sharp, short-lived spikes during recessions, while the outflow rate from unemployment is strongly procyclical with persistent downswings during recessions. The figure also shows the secular trends in these flow rates, estimated using flow data by detailed demographics with a state-space method that I developed with Crump, Eusepi, and Giannoni. 2 The two flows that shape the evolution of the unemployment rate over time exhibit differential long-run trends. The inflow rate has a striking downward trend declining gradually to 0.02, with half of its level preceding the twin recessions of the early 1980s. In contrast, there is no evident trending behavior in the outflow rate.

This stark decline in the rate at which workers become unemployed caused about a 1 percentage point decline from the 1980s to the 1990s and another 1.5 percentage point decline from the 1990s to 2020 in the long-term trend rate of unemployment. Interestingly, this downward trend continued even after the dramatic job losses of the Great Recession, underscoring the importance of secular trends in the labor market. My research has focused on explaining this declining incidence of unemployment.

Grand Gender Convergence

The United States experienced grand gender convergence in the 20th century, with female labor force participation, the fraction of all women who are in the labor force, increasing from around 47 percent in 1976 to approximately 60 percent in 2000. 3 The main driver of the rise in the female labor force participation rate was the increase in participation of married women with children. Women started to work longer into their pregnancies and to work sooner after childbirth than their counterparts in the 1960s, likely due to changes in social norms, more widespread availability of maternity leave, which facilitated return to women’s previous jobs, and advances in maternal health and child care. As labor market interruptions declined, women’s labor force attachment gradually increased. Having stretches of uninterrupted employment allowed women to build more stable employment relationships. Stefania Albanesi and I found that this reduced frictional unemployment through a decline in the incidence of job loss and the incidence of unemployment during reentry into the labor force. 4 Figure 2 shows the unemployment inflow rate by gender.

During the 1980s and 1990s, the unemployment inflow rate for women, which had been higher than that for men, converged to men’s rate, driving down the secular trend of unemployment. The importance of gender convergence was relatively minor after 2000. This is when another prominent trend — dual aging — took over.

The US economy has been experiencing a striking shift toward older workers and older firms since the mid-1990s. While the change in worker demographics is directly attributable to the drastic increase in births following World War II, the emphasis on aging of firms is relatively new, as data have only recently become available. Benjamin Pugsley and I show that declining births of firms almost fully account for the shift of employment toward older firms. 5 Moreover, in joint work with Fatih Karahan, Pugsley and I find that the origin of the decline in firm entry is the decline in labor supply growth arising from the aging of the baby boom cohort and the flattening out of the female labor force participation rate. 6 We establish a clear link from worker to firm demographics.

The aging pattern is stark. Around 18 percent of the labor force consisted of workers between 16 and 24 years old ( young workers in Figure 3) in 1987. By 2017, the number had declined to 10 percent. The employment share of firms less than five years old also followed a similar pattern, with their share declining from around 20 percent to 10 percent. On the flip side, in 1987, firms 11 or more years old —mature firms — employed about two-thirds of the workers in the economy. By 2017, the number of workers in mature firms had increased to 80 percent. [Figure 3]

Younger workers are four times more likely to become unemployed than prime-age workers. Similarly, firms aged between one and five years old are twice as likely to eliminate jobs as their older counterparts. These patterns suggest that declines in unemployment and job destruction are direct consequences of dual aging. While the shift in worker and firm age composition falls short of accounting for the decline in the inflow rate, aging also affects the economy by affecting age-specific outcomes. Put differently, in economies with older workers and firms, unemployment and job destruction are lower for all workers. Using state-level variation and an instrumental variables approach, Crump, Eusepi, Giannoni and I showed that a 1 percentage point increase in mature firms’ share lowers the job destruction rate by 0.60 percentage points for younger firms. 7

While grand gender convergence was important in accounting for the secular decline in the unemployment rate until 2000, dual aging stands out as an important driver of the decline since then.

Natural Rate of Unemployment

Grand gender convergence and dual aging together have reduced the overall incidence of unemployment, and consequently the secular trend of unemployment. Concurrently, inflation expectations became better anchored following the Volcker disinflation. 8 Estimating the natural rate of unemployment requires recognizing these prominent changes in the macroeconomy.

Crump, Eusepi, Giannoni and I calculate this in our recent paper. 9 We employ a forward-looking Phillips curve linking inflation to expected inflation and the unemployment gap (the difference between the actual and natural rates of unemployment). We utilize survey-based expectations of inflation at different horizons to provide noisy signals of true inflation expectations and impose that the secular trend of unemployment we derive from the flow dynamics acts as an anchor for the natural rate, while accommodating the possibility of persistent deviations.

Figure 4 shows the evolution of u* since 1960. In the first decade of the sample, the natural rate hovers slightly below 6 percent and starts rising in the early 1970s, easily exceeding 7 percent by the late 1970s before falling to about 7 percent in 1983. The natural rate then declines throughout the 1980s falling consistently below the median of the secular trend of the unemployment rate (black solid line). The period from the 1990s to the Great Recession is characterized by a fairly stable natural rate of unemployment, which remains range-bound between 4.5 and 5.5 percent. Finally, during the prerecession years 2005–06, the natural rate of unemployment begins increasing toward its long-run trend. In the aftermath of the Great Recession, the natural rate of unemployment gradually declines roughly in line with its secular trend. The natural rate toward the end of 2018 was 3.8 percent, with a confidence interval between 3.4 and 4.5 percent, consistent with the unemployment gap being around zero before the pandemic.

While some recent work argues for a change in the unemployment-inflation trade-off, our work emphasizes the role of inflation expectations. This is illustrated in Table 1, which compares the early 1980s with the late 2000s. In the early 1980s, we estimate an unemployment gap (the difference between the actual and the natural rate of unemployment) of 3.5 percentage points. During this time period, average core CPI inflation fell from 9.1 percent in 1978–79 to 4.6 percent in 1982–83. Following the Great Recession, which displays the largest unemployment gap in the sample, at around 4 percentage points, price inflation declined only modestly, from 2.5 percent in 2006–07 to 1.2 percent in 2009–10. The key determinant is the behavior of inflation expectations, which dropped much more sharply in the early 1980s than in the aftermath of the Great Recession. The comparison of the early 1980s with the Great Recession period demonstrates the importance of accounting for inflation expectations in explaining the behavior of inflation and the unemployment gap.

The longest labor market expansion in postwar US history came to an abrupt end with the emergence of the COVID-19 pandemic in March 2020. The unemployment rate jumped from its historically low level of 3.5 percent in February to 14.8 percent in April. This sharp increase was followed by a steep decline; the unemployment rate retreated to 6.9 percent by October. Murat Tasci, Jin Yan, and I show that this was due to the unprecedented rise in unemployment inflows, which was mostly driven by workers on temporary layoffs. 10 Applying the methodology I developed with Crump, Eusepi, and Giannoni, we find that during the pandemic recession, increased from 3.8 percent to a range of 4.0 to 4.5 percent, suggesting that the unemployment gap as of May 2021 stood between 1.3 and 1.8 percentage points.

Researchers

Unemployment Essay

500+ words essay on unemployment.

Unemployment is a serious problem among young people. There are thousands of people who do not have any work to do and cannot find work for themselves. Unemployment refers to the situation where a person wants to work but cannot find employment in the labour market. One of the major reasons that contribute to unemployment is the large population of India and the limited availability of resources. In this essay on unemployment, we will discuss all these issues responsible for unemployment in India and how we can overcome this problem. Students must go through this unemployment essay to get ideas on how to write an effective essay on the topic related to unemployment. Also, they can practice more CBSE essays on different topics to boost their writing skills.

Unemployment is measured by the unemployment rate, defined as the number of people actively looking for a job as a percentage of the labour force. The unemployment rate for the year 2013-14 in rural India was 4.7%, whereas it was 5.5% for urban India. In the short term, unemployment significantly reduces a person’s income and, in the long term, it reduces their ability to save for retirement and other goals. Unemployment is a loss of valuable productive resources to the economy. The impact of job loss in rural and regional areas flows through the local community, damaging businesses.

Reason for Unemployment

An unemployed person is one who is an active member of the labour force and is seeking work but is unable to find any work for himself. There are multiple reasons behind the unemployment of a person. One of them is the slow economic growth, due to which jobs in adequate numbers are not created. Excessive dependence on agriculture and slow growth of non-farm activities also limit employment generation. Unemployment in urban areas is mainly the result of substantial rural migration to urban areas. This has also resulted in a labour workforce in cities. The lack of technology and proper machinery has also contributed to unemployment.

The present educational system is based on theoretical knowledge instead of practical work. Thus, it lacks the development of aptitude and technical qualifications required for various types of work among job seekers. This has created a mismatch between the need and availability of relevant skills and training. This results in unemployment, especially among the youth and educated people with high degrees and qualifications. Apart from it, the lack of investment and infrastructure has led to inadequate employment opportunities in different sectors.

Steps to Eliminate Unemployment

Various strategies and proposals have been implemented to generate employment. Many Employment programmes and policies have been introduced and undertaken to boost self-employment and help unemployed people engage in public works. The Government of India has taken several policy measures to fight the problem of unemployment. Some of the measures are the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), National Skill Development Mission, Swarna Jayanti Shahari Rozgar Yojana (SJSRY), Regional Rural Banks (RRBs).

Despite the measures taken by the government, India remains a country experiencing severe unemployment problems. It can be resolved by imparting education in such a way that youth get the necessary skills so as to get employment easily. Setting up various vocational training and vocational courses for undergraduate and postgraduate students will help in finding employment for youth. The government needs to emphasise these courses at the primary level and make them a compulsory part of the curriculum to make students proficient in their early stages of life. Career counselling should be provided within schools and colleges so that students can choose a better career option based on their interests and ability. Government should create more job opportunities for the youth and graduates.

India is a fast-growing economy. There is an enormous scope for improvement in the unemployment sector. The various measures and steps taken by the government to increase the employment rate have succeeded to a great extent. The widespread skill development programmes have gained popularity across the nation. With better enforcement of the strategies, the employment level can be significantly improved. Although, we have to go a long way before we can say that all the people in India will get employment.

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Frequently Asked Questions on Unemployment Essay

Is unemployment still an existing problem in india.

Yes, unemployment is still a serious issue in our country. Steps need to be taken by the government and also by the youngsters in India to improve this situation.

Is it necessary for schoolchildren to be informed about unemployment?

Students at this young age should definitely be informed about this topic as it will motivate them to study and aim for higher scores in exams.

What points are to be added to an essay topic on Unemployment?

Add details about different age groups of people suffering from this state of employment. You can focus on the fact that poverty is an indirect reason for unemployment and vice-versa. Then, suggest steps that can be taken to bring about an improvement in education and increase the percentage of literacy.

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Recessions and the Trend in the US Unemployment Rate

Kurt G. Lunsford

The unemployment rate in the United States falls slowly in expansions, and it may not reach its previous low point before the next recession begins. Based on this feature, I document that the frequent recessions prior to 1983 are associated with an upward trend in the unemployment rate. In contrast, the long expansions beginning in 1983 are associated with a downward trend. I then estimate a two-variable vector autoregression (VAR) that includes the unemployment rate and a recession indicator. Long-horizon forecasts from this VAR conditioned on no future recessions project that the unemployment rate will go to 3.6 percent after a long period with no recessions.

The views authors express in Economic Commentary are theirs and not necessarily those of the Federal Reserve Bank of Cleveland or the Board of Governors of the Federal Reserve System. The series editor is Tasia Hane. This paper and its data are subject to revision; please visit clevelandfed.org for updates.

When it comes to analyzing economic indicators to predict where the US economy is headed, the unemployment rate is arguably the variable familiar to most people. It receives attention from academics, policymakers, business economists, and politicians, but also the public at large. An appealing feature of the unemployment rate is its perceived ease of interpretation. A high or rising unemployment rate is a signal of macroeconomic slack or contraction, and a low or falling unemployment rate is a signal of macroeconomic health or expansion.

One issue that can confound this simple interpretation is that the unemployment rate may have a slow-moving trend that changes over time. If the trend is not static, then it is hard to know how far the current or forecasted unemployment rates are from the underlying trend. Figure 1 highlights this issue. It shows the monthly unemployment rate from January 1948 to October 2020 along with a line intended to estimate the unemployment rate trend. I compute this line with the statistical technique in Hodrick and Prescott (1997) (HP). 1 The trend line shows substantial variation, falling below 5 percent in the 1960s and 1990s and rising above 7 percent in the 1980s and 2010s. Because of this changing trend, an unemployment rate of 6 percent may be viewed as indicating macroeconomic slack in some periods but macroeconomic health in other periods, making it difficult for economists, policymakers, and the public at large to know where the economy stands. 2

Notes: Trend computed using a Hodrick and Prescott (1997) filter. Gray bars indicate recession periods. Sources: US Bureau of Labor Statistics, Unemployment Rate [UNRATE], retrieved from FRED, Federal Reserve Bank of St. Louis ( https://fred.stlouisfed.org/series/UNRATE ), and author’s calculations.

Researchers and policymakers often acknowledge the trend in the unemployment rate. Researchers typically remove a time-varying trend from the unemployment rate before studying its business cycle properties. 3 Policymakers on the Federal Reserve’s Federal Open Market Committee (FOMC) note in their Statement on Longer-Run Goals and Monetary Policy Strategy that the maximum level of employment “changes over time.” 4 In fact, the longer-run projections of the unemployment rate in the FOMC’s Summary of Economic Projections have drifted down since 2012. 5

Research has attributed much of the trend in the unemployment rate to demographic changes. 6 In this Commentary , I suggest an additional, previously unrecognized source of the trend: the frequency of recessions. Because the unemployment rate rises quickly in recessions but falls slowly in expansions, it may not fall to its previous low point if a recession cuts an expansion short. 7 Hence, frequent recessions can cause the unemployment rate to trend up over time. Figure 1 shows that this happened in the 1950s and the 1970s. Since 1983, recessions have been less frequent and expansions have been longer, causing the unemployment rate to regularly fall below its previous low point and generating a downward trend in the unemployment rate. 8 In February 2020, the unemployment rate fell to 3.5 percent, its lowest level since 1969.

I also estimate the relationship between recessions and the unemployment rate with a statistical model called a vector autoregression (VAR). I use the VAR to make forecasts of the unemployment rate under the hypothetical scenario that there will be no recessions in the future. I intend for this hypothetical scenario to match the spirit of the FOMC’s longer-run projections of the unemployment rate, which are made “in the absence of further shocks to the economy.” 9 My forecasts project that the unemployment rate will go to 3.6 percent after a long period with no recessions.

Recessions and Unemployment Rate Trends

Figure 2 depicts a series of computations that result in a view of the alignment between recessions and the unemployment rate. This view of the alignment (panel D) highlights the intuition that frequent recessions, separated by short expansions, are associated with upward drift in the unemployment rate, while infrequent recessions, separated by long expansions, are associated with downward drift.

Note: Gray bars indicate recession periods. Sources: US Bureau of Labor Statistics, Unemployment Rate [UNRATE], retrieved from FRED, Federal Reserve Bank of St. Louis ( https://fred.stlouisfed.org/series/UNRATE ), NBER-based Recession Indicators for the United States from the Period following the Peak through the Trough [USREC], retrieved from FRED, Federal Reserve Bank of St. Louis ( https://fred.stlouisfed.org/series/USREC ), and author’s calculations.

Panel A of figure 2 shows the cumulative sum of the National Bureau of Economic Research’s (NBER’s) recession months from January 1948 to October 2020. I define a recession as starting in the month following the NBER peak and ending in the month of an NBER trough. For the current period, the NBER announced a business cycle peak in February 2020 but has not announced a subsequent trough. In figure 2, I treat March and April 2020 as recession months. 10

In panel B of figure 2, I fit a linear time trend to the cumulative sum of the NBER recession months with ordinary least squares. This time trend gives an estimate of how quickly recessions accumulate on average. Then in panel C, I remove the linear time trend from the cumulative sum and show a detrended cumulative sum of NBER recession months. This detrended cumulative sum shows when recessions have accumulated more quickly and less quickly than average.

The detrended cumulative sum in panel C rises at a constant rate in every recessionary month and falls at a constant but slower rate in every expansionary month. This structure implies that this variable may not fall to its previous low point if a recession cuts an expansion short. As a result, frequent recessions, separated by short expansions, can cause this detrended cumulative sum to drift up over time. This upward drift occurs with the four recessions that begin in 1948, 1953, 1957, and 1960 and again with the four recessions that begin in 1970, 1973, 1980, and 1981. In other words, both 1948 to 1960 and 1970 to 1982 are 13-year periods where recessions accumulated more quickly than average. In contrast, recessions accumulated less quickly than average during the long expansions that occur mostly since 1983 and also in the 1960s. During these periods, the detrended cumulative sum falls below its low point from previous expansions, creating downward drifts in the series.

The periods of rapid recession accumulation, 1948 to 1960 and 1970 to 1982, are also periods when the unemployment rate trend rises in figure 1. In contrast, periods when recessions accumulate less quickly than average, the 1960s, 1983 to 2000, and the 2010s, are all periods when the unemployment rate trend falls in figure 1. To make this comparison between the accumulation of recessionary months and the unemployment rate more explicit, panel D shows the detrended cumulative sum of NBER recession months (left axis) along with the unemployment rate (right axis). The two series move closely together and have a correlation of about 0.7, even including the unusually large spike in the unemployment rate in April 2020.

A positive correlation between the frequency of recessionary months and the unemployment rate is not surprising. The NBER’s Business Cycle Dating Committee uses labor market variables when assigning business cycle peaks and troughs. 11 However, what is surprising about panel D is how closely the unemployment rate follows the detrended cumulative sum of recessionary months for such a long time—from 1948 to 2020. 12 This is surprising because the US labor market has been driven by a variety of economic shocks along with changing government policies, labor market regulations, and demographics; yet, the unemployment rate closely tracks the stable and linear structure of the detrended cumulative sum of recessionary months. As with the detrended cumulative sum of recessionary months, the unemployment rate rises quickly in recessions but falls slowly in expansions, and these features cause the unemployment rate to trend up with frequent recessions and trend down with infrequent recessions.

Longer-Run Unemployment Rate Projections

The results in the previous section show that the unemployment rate trend is aligned closely with how quickly recessionary months accumulate. Consequently, the unemployment rate trend may not be easily separated from the business cycle with statistical techniques that estimate slow-moving trends, such as in Hodrick and Prescott (1997), to offer just one example. This is because the unemployment rate’s trend is itself related to business cycles. 13 Instead, I model the unemployment rate and the NBER recession indicator, which has a value of zero in expansion months and a value of one in recession months, together with a statistical tool known as a VAR.

Using this VAR, I can produce longer-run projections of the unemployment rate in the spirit of the FOMC’s Summary of Economic Projections, which assumes that there will be no shocks to the economy in the future. I do this by producing forecasts of the unemployment rate while imposing that the recession indicator has a value of zero in all future periods. 14

There are two important steps for computing the forecasts. First, I use data from January 1948 to February 2020 to estimate the parameters of the VAR. These parameters establish the statistical relationship between the unemployment rate and the recession indicator, allowing me to predict how the unemployment rate will move in the future under the hypothetical scenario of no future recessions. Second, I choose the initial conditions as a starting point for my forecasts. 15 For example, I need to decide if I want to start my forecasts from a high unemployment rate or a low unemployment rate. Forecasters often use the most recent data as their starting points. However, they may also choose older data to check how accurate their projections would have been in the past.

Figure 3 uses this latter approach. Each panel shows the unemployment rate for the 7 most recent NBER expansions. 16 In addition, it shows the forecasts from the VAR under the hypothetical scenario of no recessions. I use the months before each expansion started as the starting point for the forecasts.

Note: Forecasts are computed under the hypothetical scenario of no future recessions. Sources: US Bureau of Labor Statistics, Unemployment Rate [UNRATE], retrieved from FRED, Federal Reserve Bank of St. Louis ( https://fred.stlouisfed.org/series/UNRATE ), and author’s calculations.

While these forecasts do not perfectly track the unemployment rate over the course of an expansion, they generally match the downward trend of the unemployment rate in expansions. These forecasts also demonstrate relative accuracy in predicting where the unemployment rate will fall at the end of expansions. For the long expansion from 1991 to early 2001, the forecast predicts almost perfectly where the unemployment rate fell. For the other two longest expansions—1961 through 1969 and 2009 to early 2020—the forecast overpredicts where the unemployment rate fell to by about 0.6 percentage points.

The longest expansion shown in figure 3 (panel G)—July 2009 to February 2020—lasted 10 years and 8 months. In order to compute unemployment rate forecasts in the spirit of the FOMC’s Summary of Economic Projections, which assumes that there will be no shocks to the economy in the future, I next consider how low the unemployment rate could fall in expansions that last much longer than 10 years and 8 months. Specifically, I produce forecasts by imposing no recessions for 20 years. 17 In addition, I study the importance of the starting points for the forecasts by considering three different starting points. The first two are November 1982 and June 2009. These are the same starting points that I used in panels D and G of figure 3 and they coincide with the ends of the deepest recessions since 1948. The third starting point is February 2020, which is the last month in my estimation sample.

Figure 4 shows the 20-year forecasts. When using the November 1982 and June 2009 starting points, the forecasts start with the unemployment rate at high levels. This is natural as both of these starting points coincide with the end of recessions. In contrast, the forecasts generated with the February 2020 starting point start with a low level of the unemployment rate, a measure which is consistent with the healthy labor market at the start of 2020.

Note: The three lines correspond to three starting points: November 1982, June 2009, and December 2019. Source: Author’s calculations.

The forecasts generated with the November 1982 and June 2009 starting points move down over time. The forecasts generated from the February 2020 starting point rise very slightly before falling again. The forecasts with all three initial conditions become very similar at about 20 years, showing that the starting point does not affect how low the VAR projects the unemployment rate will fall as long as expansions are sufficiently long. For each of the three starting points, the VAR projects that the unemployment rate will be about 3.6 percent after 20 years without a recession. 18 That is, if one were to use this VAR to make a longer-run projection of the unemployment rate in the absence of further shocks to the economy as done in the FOMC’s Summary of Economic Projections, then one would project 3.6 percent. 19

The value of the projections in figure 4 is that they provide an answer to where the unemployment rate could fall in a hypothetical world in which recessions do not occur, which I approximate with 20-year forecasts with no recessions. Of course, other choices of forecast length are possible. With shorter forecasts, the starting point of the forecast still matters. As shown in figure 4, the forecasts generated with the November 1982 and June 2009 starting points are not all the way down to 3.6 percent until about 20 years. I have also considered longer forecasts; however, I do not show these forecasts here because they also yield unemployment rates at 3.6 percent.

To check the robustness of my results, I drop early portions of my estimation sample and recompute the long-run forecasts under the assumption of no future recessions. Using samples of January 1958 to February 2020, January 1968 to February 2020, and January 1978 to February 2020, I compute the long-run forecasts of the unemployment rate to be 3.6 percent, 3.8 percent, and 3.6 percent, respectively. These values indicate that the early portions of my sample do not have a big impact on the results. Alternatively, if I use January 1948 to June 2009 as my sample, the long-run forecast is 3.9 percent. This sample choice shows that the 2010s, which were part of the longest expansion in US history, have only a small impact on the results. Overall, the findings suggest that how low the unemployment rate can fall in an expansion appears to be quite stable over a variety of sample periods.

Finally, to highlight how the assumption of no future recessions affects the forecast of the unemployment rate, I also compute the 20-year forecast of the unemployment rate without this assumption. This unconditional forecast of the unemployment rate is 5.7 percent, more than 2 percentage points above the forecast that assumes no future recessions. Clearly, when making longer-run projections of the unemployment rate, such as those in the FOMC’s Summary of Economic Projections, conditioning upon future recessions can make large changes in the projection.

Conclusions

The unemployment rate in the United States falls slowly in expansions, and it may not reach its previous low point before the next recession begins. This feature suggests that the unemployment rate trends up with frequent recessions and trends down when recessions are infrequent. In this Commentary , I show that the US unemployment rate indeed trended up with the rapid accumulation of recessions prior to 1983 and then trended down again with the slow accumulation of recessions after 1983. In addition, I estimate the relationship between recessions and the unemployment rate with a VAR. Long-run forecasts from this VAR under the scenario of no future recessions can be used to produce longer-run projections of the unemployment rate in the spirit of the FOMC’s Summary of Economic Projections. I find that the unemployment rate moves to 3.6 percent in the absence of future recessions.

I use a tuning parameter of 106 for the Hodrick and Prescott (1997) filter. This parameter is higher than what academic researchers often use for monthly data. I choose this higher value to highlight the lower frequency variation in the data. Using the filter from Müller and Watson (2015) with frequencies of 12 years or longer produces a similar picture. Return to 1
For example, Weiner (1993) argues that the natural rate of unemployment was about 6.25 percent in 1993 but about 6.7 percent in 1980. An important point is that Weiner (1993) accounts for inflation in his estimate of the natural rate of unemployment. However, the HP filter in figure 1 does not account for inflation, nor do I throughout this Commentary . Return to 2
For example, see the handbook chapter of Rogerson and Shimer (2011), which like many other studies, uses the Hodrick and Prescott (1997) filter to separate the trend and cycle components of the unemployment rate. Return to 3
See https://www.federalreserve.gov/monetarypolicy/files/FOMC_LongerRunGoals.pdf . Return to 4
For the April 2012 FOMC meeting, the range of longer-run unemployment rate projections was 4.9 percent to 6.0 percent. For the September 2020 FOMC meeting, this longer-run unemployment rate range had fallen to 3.5 percent to 4.7 percent. Return to 5
Weiner (1993), also discussed in footnote 2, emphasizes demographic change. See Crump, Eusepi, Giannoni, and Șahin (2019) for more recent analysis and discussion of demographics and the unemployment rate. Return to 6
Neftçi (1984) and Sichel (1993) have previously documented that the unemployment rate changes asymmetrically over the business cycle, rising quickly in recessions and falling slowly in expansions. This Commentary draws out the implication that this asymmetry can affect the longer-run trend in the unemployment rate. Return to 7
Consistent with the slower accumulation of recession months, 1983 roughly corresponds to beginning of the “Great Moderation,” a period in US history in which many economic variables became less volatile (Kim and Nelson, 1999; McConnell and Perez-Quirós, 2000; Stock and Watson, 2002). The results in this Commentary link the Great Moderation to theoretical models of unemployment rate asymmetry, such as Dupraz, Nakamura, and Steinsson (2019) and Lepetit (2020), who find that more stable economic environments imply lower average unemployment rates. That is, the general downward trend in the unemployment rate after 1983 is consistent with theoretical models that show that the average unemployment rate can fall with a reduction in economic volatility. Return to 8
See the notes to table 1 of https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20200916.pdf . Return to 9
See https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions for a list NBER peaks and troughs. Return to 10
The NBER’s Business Cycle Dating Committee also uses gross domestic product, gross domestic income, personal consumption expenditures, and personal income less transfers when assigning business cycle peaks and troughs. For an example of the Business Cycle Dating Committee’s reasoning, see https://www.nber.org/news/business-cycle-dating-committee-announcement-june-8-2020 . Return to 11
Consistent with this finding, Hall and Kudlyak (2020) document that the pace of reduction of the unemployment rate in expansions has been roughly stable for 70 years. Additionally, figure 2 implies that the pace of unemployment rate increases in recessions has also been roughly stable for 70 years. Return to 12
Separating trends and business cycles is challenging for a wide variety of economic indicators. For example, Coibion, Gorodnichenko, and Ulate (2018) show that the Congressional Budget Office has historically made large changes to its measure of potential output around recessions. Return to 13
I use the conditional forecasting approach in Doan, Litterman, and Sims (1983). I provide details of the VAR and the conditional forecasting exercise in a supplemental appendix. Return to 14
Mathematically, these initial conditions are the values of the right-hand side variables in the VAR that are needed to produce the forecasts. See the supplemental appendix for additional details. Return to 15
I exclude the expansion that began in August 1980 because this expansion lasted only one year. Return to 16
I choose 20 years because that is the horizon at which forecasts appear to converge for all of the initial conditions in figure 4. If I only use a 15-year forecast horizon, the November 1982 and June 2009 initial conditions yield forecasts of the unemployment that fall to 3.8 percent, but it does not fall all the way to 3.6 percent as shown in figure 4. If I use horizons of 25 or 30 years, then the unemployment rate falls to 3.6 percent for all the initial conditions shown in figure 4, but it does not fall further. Return to 17
This finding uses a VAR with 6 lags and is sensitive to the number of lags included in the VAR. I have checked lag lengths from 1 to 13 and found that the 20-year unemployment rate projection varies from a low of about 2.9 percent (with 13 lags) to a high of about 3.7 percent (with 1 lag). Hence, the results that I provide are conservative in the sense that different lag choices may yield materially lower unemployment rate projections, but none yields materially higher unemployment rate projections. Return to 18
This long-run projection of 3.6 percent is very similar to Hall and Kudlyak’s (2020) steady-state unemployment rate of 3.5 percent. Hence, my results provide empirical support for Hall and Kudlyak’s choice of a steady state. In contrast, my long-run projection is materially above the steady-state of Dupraz, Nakamura, and Steinsson (2019), which is 4.6 percent. In addition, the conditional forecasts that I produce can provide data moments that may help researchers estimate labor market congestion functions as in Section 5.3 of Hall and Kudlyak (2020). Return to 19
Coibion, Olivier, Yuriy Gorodnichenko, and Mauricio Ulate. 2018. “The Cyclical Sensitivity in Estimates of Potential Output.” Brookings Papers on Economic Activity , Fall: 343–411. https://www.doi.org/10.1353/eca.2018.0020 .
Crump, Richard K., Stefano Eusepi, Marc Giannoni, and Ayşegül Șahin. 2019. “A Unified Approach to Measuring u*.” Brookings Papers on Economic Activity , Spring: 143–214. https://www.doi.org/10.1353/eca.2019.0002 .
Doan, Thomas, Robert Litterman, and Christopher A. Sims. 1983. “Forecasting and Conditional Projection Using Realistic Prior Distributions.” NBER Working Paper, No. 1202. https://www.doi.org/10.3386/w1202 .
Dupraz, Stéphane, Emi Nakamura, and Jón Steinsson. 2019. “A Plucking Model of Business Cycles.” NBER Working Paper, No. 26351. https://www.doi.org/10.3386/w26351 .
Hall, Robert E., and Marianna Kudlyak. 2020. “Why Has the US Economy Recovered So Consistently from Every Recession in the Past 70 Years?” NBER Working Paper No. 27234. https://www.doi.org/10.3386/w27234 .
Hodrick, Robert J., and Edward C. Prescott. 1997. “Postwar US Business Cycles: An Empirical Investigation.” Journal of Money, Credit, and Banking , 29(1): 1–16. https://www.doi.org/10.2307/2953682 .
Kim, Chang-Jin, and Charles R. Nelson. 1999. “Has the US Economy Become More Stable? A Bayesian Approach Based on a Markov
Switching Model of the Business Cycle.” Review of Economics and Statistics , 81(4): 608–616. https://www.doi.org/10.1162/003465399558472 .
Lepetit, Antoine, 2020. “Asymmetric Unemployment Fluctuations and Monetary Policy Trade-Offs.” Review of Economic Dynamic s, 36: 29–45. https://doi.org/10.1016/j.red.2019.07.005 .
McConnell, Margaret M., and Gabriel Perez-Quirós. 2000. “Output Fluctuations in the United States: What Has Changed since the Early 1980s?” American Economic Review , 90(5): 1464–1476. https://doi.org/10.1257/aer.90.5.1464 .
Müller, Ulrich K. and Mark W. Watson, 2015. “Low-Frequency Econometrics.” NBER Working Paper, No. 21564. https://www.doi.org/10.3386/w21564 .
Neftçi, Salih N. 1984. “Are Economic Time Series Asymmetric over the Business Cycle?” Journal of Political Economy , 92(2): 307–328. https://www.doi.org/10.1086/261226 .
Rogerson, Richard, and Robert Shimer. 2011. “Search in Macroeconomic Models of the Labor Market.” Handbook of Labor Economics , Volume 4a, Chapter 7, 619-700. https://www.doi.org/10.1016/S0169-7218(11)00413-8 .
Sichel, Daniel E. 1993. “Business Cycle Asymmetry: A Deeper Look.” Economic Inquiry , 31(2): 224–236. https://www.doi.org/10.1111/j.1465-7295.1993.tb00879.x .
Stock, James H., and Mark W. Watson. 2002. “Has the Business Cycle Changed and Why?” NBER Macroeconomics Annual , 17: 159–218. https://www.doi.org/10.1086/ma.17.3585284 .
Weiner, Stuart E. 1993. “New Estimates of the Natural Rate of Unemployment.” Federal Reserve Bank of Kansas City, Economic Review , Fourth Quarter: 53–63. https://ideas.repec.org/a/fip/fedker/y1993iqivp53-69nv.78no.4.html .

Suggested Citation

Lunsford, Kurt G. 2021. “Recessions and the Trend in the US Unemployment Rate.” Federal Reserve Bank of Cleveland, Economic Commentary 2021-01. https://doi.org/10.26509/frbc-ec-202101

Essay on Unemployment Rate

Introduction

The essay is focused on examining the issue of unemployment among people in various societies and countries around the world, the reasons for unemployment, types of unemployment, ways to overcome the issue of unemployment and examine the costs associated with unemployment. Unemployment is a major social issue among people in the economic market today, there are several reasons for unemployment in economics depending on the type of unemployment available, these can be directed to government, employer, supply of labor, among others. The types of unemployment can be directed to frictional unemployment, structural unemployment, cyclical unemployment, and seasonal unemployment. There is a need to solve the unemployment problem in the social economies, this can be done in several ways including enhancing political stability, promoting education, controlling the growth of population, among others. The issue of unemployment in the social economies has several costs ranging from loss of income among individuals, low standard of living among unemployed people, low production, and economic growth, among other costs.

Unemployment and reasons for unemployment

Unemployment is a major problem in various countries around the world, this is a situation whereby people do not have any work that can generate income and are actively searching for a job (Hauser & Burrows, 2018). Individuals in this situation are mostly employable and are actively searching for jobs. This can also include people who are working in jobs that are below their qualifications, experience, and satisfaction. The situation where there is a high level of unemployment will result in several economic distress and problems. The situation of unemployment hinders people to engage in productive work to facilitate development in the country. This will result in a decrease in economic productivity and development (Hauser & Burrows, 2018).

The high growth of the population can result in unemployment challenges when the country is not developing and when fewer job opportunities are in the country. Also, people who depend on agriculture production work can become unemployed when the season is not favorable for plant growth. A decrease in industrialization and investment can account for unemployment in several countries that are not developing, the situation where there are few industries and investments are not available will have a severe influence on the employment of people in a developing country. Improper economic planning can also result in unemployment challenges, situations, where there is a gap between supply and demand of labor, which will result in unemployment.

Various types of unemployment

Hauser & Burrows, (2018) Types of unemployment can include frictional, cyclical, structural, institutional, and seasonal unemployment. These types of unemployment are based on voluntary and involuntary unemployment. Frictional unemployment is a situation where a person voluntarily moves from one job to another in a country. The time spent to get another job can be linked to unemployment, it may take some time before the person gets another job. Some people move to another location to search for a new job which can take some time before getting the desired job. When fresh graduate students’ transit to search for a job, the time spent before getting a job is also linked to frictional unemployment. The time can be very short or longer depending on the availability of the job the applicant is seeking (Hwang, 2019).

Hwang, (2019) cyclical unemployment is another type of unemployment where the decrease in production and demand for products results in a decrease in employment of new workers. The situation where a company is not getting enough purchases of goods from the market will decrease income level and ability to pay workers. This can lead to a decrease in salary, and an increase in savings. People will not be able to buy enough goods when their salary is decreased and savings are increased. The situation is cyclical in nature but can be resolved when economic growth and investment are facilitated in the country.

Structural unemployment is when there is a new change in the operation of a business, technology, and production. The situation whereby a company adapts to a new technological system or process will result in laying off workers who do not have the relevant skills for the new changes. There will be a need for retraining of staffs or workers to understand how to use new systems in the company (Wolcott et al., 2020).

Institutional unemployment occurs when there are changes in policies, institutional incentives, and interference of the free market condition in a country. The change in employment policies and strategies can result in laying off some workers in order to regulate the new policies.

Seasonal unemployment is a situation whereby workers that depend on jobs that operate in different seasons are not having jobs due to the unfavorable season or weather conditions to engage in such jobs. An example is farming, among others.

Various ways to overcome unemployment

Education is very important in solving the problem of unemployment, educating people will help increase their skills and ability to be employed and start their own businesses. Education will enable workers to advance their skills and employability level. The education system has to be improved in order to enhance the admission of students into higher studies in colleges and universities. The availability and improvement of vocational institutions can also facilitate the development of skills among students in the higher level of education. Students from these training institutions can start their own businesses after completion and reduce the unemployment rate in the country. The industrial technique can be enhanced to identify the employment needs of the country and improve labor-intensive technology in order to employ more people instead of using machines and other fully technological systems (Sari, 2019).

There is a need to design a policy to solve seasonal unemployment issues, mostly in the agriculture sector, the provision of alternative job opportunities for workers who depend on seasonal jobs will help reduce the unemployment rate during unfavorable seasons. Also, the use of multiple cropping can be encouraged in order to facilitate the production of crops favorable in any given season. The addition of varied plantations, animal husbandry, food processing, and other farming practices will enhance employment.

The use of communication and employment exchanges can be provided to enhance the dissemination of information regarding job opportunities available anytime. This will alert the public in order to enhance the employment rate and reduce frictional unemployment. There is a need to assist self-employed individuals in order to facilitate their work and production. This can be in a form of financial support, technical training, and raw material support in order to improve self-employed businesses.

The development and implementation of policies that encourage the development of more jobs in a country will help reduce unemployment. There is a need to increase development of jobs in a country and increase employment of labor in order to help them gain benefits and contribute towards societal development. There can also, be enhanced employment program facilitation, this is done by setting plans to develop infrastructure, power supply, roads, and other facilities that encourage production (Sari, 2019).

Costs associated with unemployment

There are some costs associated with unemployment which include individual and societal costs; the major cost to unemployed individuals is income lost, this is the situation where a person does not get any income due to unemployment. Unemployment affects people’s ability to earn money. The situation can hinder people’s ability to finance and take good care of themselves. The well-being of people becomes disrupted due to a lack of income to support themselves. The cost to society is a reduction in productivity through a decrease in goods and services, the people do not have money to purchase goods and services, this also affects the performance of companies and organizations in the society. The situation also causes waste of resources including labor, since the economy does not utilize these resources efficiently. The situation, when people take up jobs below their qualifications, brings about inefficient use of labor. This also results in the loss of skills and labor capital in the economy (Alrasheedy, 2019).

The situation of unemployment can affect the health of people in the country, this includes the mental and physical wellbeing of individuals in the country. There is an incidence of family and social unrest, an increase in the crime rate, and other bad practices in society. The situation of unemployment can cause disruption on confidence and self-esteem among people who are unemployed (Alrasheedy, 2019). The situation can drastically deplete the skills, motivation, and well-being of people in the country. The situation of unemployment also causes loss of tax and revenue to the government, development of the country will be slowed and hindered. The expenditure incurred by the government increases as a result of providing support to unemployed people in the country. The situation of a high rate of unemployment will bring about severe problems to individuals, the situation where the unemployment problem takes longer duration, the country resort to borrowing from other countries and becoming indebted to those countries.

Unemployment is a major social issue in many countries in the world, the situation of unemployment is a challenge to the government, employers, and individuals in the society. The types of unemployment can be directed to frictional unemployment, structural unemployment, cyclical unemployment, and seasonal unemployment. The situation of unemployment can be identified when a person is actively seeking a job but does not get any available. This can also include people who are working in jobs that are below their qualifications, experience, and satisfaction. The situation where there is a high level of unemployment will result in several economic distress and problems. The situation of unemployment hinders people to engage in productive work to facilitate development in the country. This will result in a decrease in economic productivity and development.

The two categories of unemployment are voluntary and involuntary unemployment, the situation where one moves out from a job to search for another is known as voluntary unemployment, and a situation where a person is laid off and now looking for a job is known as involuntary unemployment. There are several causes of unemployment, including population growth, slow economic development, people who depend on seasonal jobs such as agriculture production work can become unemployed. A decrease in industrialization and investment can account for unemployment, improper economic planning can also result in unemployment challenges. The situation of unemployment can result in social unrest, an increase in the crime rate, and other bad practices in society. The situation of unemployment can cause disruption on confidence and self-esteem among people who are unemployed. Unemployment can be solved when there is education, development of new jobs, investment, and support of self-employed workers.

Alrasheedy, A. (2019). The cost of unemployment in Saudi Arabia. International Journal of Economics and Finance, 11 (11), 1-30.

Hauser, M. M., & Burrows, P. (2018). The Economics of Unemployment Insurance (1st ed.). London: Routledge.

Hwang, G. J. (2019). How fair are unemployment benefits? The experience of East Asia. International Social Security Review, 72 (2), pp.49-73.

Sari, A. I. (2019). Social Entrepreneurs and Innovation for the Unemployment. International Journal of Economics and Management, 1 (3), pp.72-79.

Wolcott, E., Ochse, M. G., Kudlyak, M., & Kouchekinia, N. A. (2020). Temporary Layoffs and Unemployment in the Pandemic. FRBSF Economic Letter , p.34.

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Unemployment Essay for Students and Children

500+ words essay on unemployment.

Unemployment is a very serious issue not only in India but in the whole world. There are hundreds and thousands of people out there who do not have employment . Besides, the problems of unemployment are very severe in India because of the growing population and demand for jobs. Moreover, if we neglect this problem then it will be going to become the reason for the doom of the nation.

What is Unemployment?

Unemployment refers to a situation in which a skilled and talented people wanted to do a job. But cannot find a proper job due to several reasons.

Types of Unemployment

Now we know what is unemployment but unemployment does not only mean that the person does not have a job. Likewise, unemployment also includes people working in areas out of their expertise.

The various types of unemployment include disguised unemployment, seasonal unemployment, open unemployment, technological unemployment, structural unemployment. Besides, some other unemployment is cyclic unemployment, educated unemployment, underemployment, frictional unemployment, chronic unemployment, and casual unemployment.

Above all, seasonal unemployment, under unemployment, and disguised unemployment are the most common unemployment that is found in India.

Reasons for Unemployment

In a country like India, there is much reason for a large section of the population for being unemployed. Some of these factors are population growth, slow economic growth , seasonal occupation, slow growth of the economic sector, and fall in the cottage industry.

Moreover, these are the major reason for unemployment in India. Also, the situation has become so drastic that highly educated people are ready to do the job of a sweeper. Besides, the government is not doing his work seriously.

Apart from all these, a large portion of the population is engaged in the agricultural sector and the sector only provides employment in harvest or plantation time.

In addition, the biggest reason of unemployment in India is its vast population which demands a large number of jobs every year which the government and authorities are unable to provide.

Consequences of Unemployment

If things will go on like the current scenario then unemployment will become a major issue. Apart from this, the following things happen in an economy which is an increase in poverty, an increase in crime rate, exploitation of labor, political instability, mental health, and loss of skills. As a result, all this will eventually lead to the demise of the nation.

Get the huge list of more than 500 Essay Topics and Ideas

Initiative by Government

The government has taken the problem very seriously and have taken measures to slowly reduce unemployment. Some of these schemes includes IRDP (Integrated Rural Development Programme), DPAP (Drought Prone Area Programme), Jawahar Rozgar Yojana, Employment Assurance Scheme, NRY (Nehru Rozgar Yojana), Training for self-Employment, PMIUPEP (Prime Minister’s Integrated Urban Poverty Eradication Program), employment exchange, Employment Guarantee Scheme, development of organized sector, small and cottage industries, employment in forging countries, and Jawahar Gram Samridhi Yojana and few more.

Besides, these schemes the government also make some rules flexible, so that employment can be created in the private sector also.

To conclude, we can say that the problem of unemployment in India has reached a critical stage. But, now the government and local authorities have taken the problem seriously and working on it to reduce unemployment. Also, to completely solve the issue of unemployment we have to tackle the main issue of unemployment that is the vast population of India.

FAQs about Unemployment

Q.1 Why there is a problem of unemployment in India? A.1 Due to overpopulation and lack of proper skills there is a problem of unemployment in India.

Q.2 Define Disguised unemployment? A.2 Disguised unemployment refers to a form of employment in which more than the required numbers of people work in industry or factory. And removing some employee will not affect productivity.

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Unemployment and Policy Trade-Offs (Revision Essay Plan)

Last updated 4 May 2019

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Here is a revision essay plan on this title: “Evaluate the view that falling unemployment inevitably has trade-offs with other macroeconomic objectives. Discuss with reference to a country or countries of your choice.”

KAA Point 1

Falling U – may cause an acceleration in wage inflation in labour market – leads to a rise in cost-push and demand-pull inflationary pressures – this is a trade-off suggested by the short-run Phillips Curve analysis. Bargaining power of workers rises + skilled labour shortages & increased demand for raw materials and components can all drive variable costs higher – inward shift of SRAS, leading to a higher rate of inflation.

EVAL Point 1

Not automatic that inflation will rise. Improved supply-side flexibility of the labour market might have caused a fall in the NAIRU (non-accelerating inflation rate of unemployment) – this means that U can fall further without threatening rising wage inflation. External factors (e.g. lower commodity prices, impact of global competition) can also act as off-setting factors even if wages are rising more quickly.

KAA Point 2

Falling U – leads to rising real wages – increased household incomes – causes surge in demand for imports of goods and services – especially if the marginal propensity to import is high – in the absence of offsetting factors, a rise in M will lead to a worsening of the current account of the BoP. E.g. unemployment in UK now less than 4%, in 2016 the UK ran a record current account deficit of more than 5% of GDP.

EVAL Point 2

Falling U might be result of improved supply-side performance e.g. increased labour productivity which makes UK export industries more competitive. Or export sales might have grown because of a depreciation of the exchange rate (e.g. 2016) which (assuming Marshall-Lerner condition holds) will improve the net trade balance whilst also stimulating output and employment in sectors such as cars and tourism.

FINAL CONCLUSION

Phillips Curve for the UK seems to have flattened i.e. improving trade-off between unemployment and inflation. UK might be able to get close to full-employment and stay within the 2% inflation target. But weak productivity and low investment (17% of GDP) mean that strong GDP growth often associated with a worsening of the external trade accounts.

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Unemployment

Definitions of Unemployment

Unemployment Rate. This is the % of people in the labour force without a job but registered as being willing and available for work
Labour Force Those people holding a job or registered as willing and able to work Therefore this is the number employed plus number unemployed (but actively seeking work)
Participation Rate The % of the population of working age declaring themselves to be in the Labour Force. This will exclude groups such as long-term disabled, women bringing up children and students

The participation rate is a similar concept to that of ‘economically active’.

UK Unemployment stats

Latest UK unemployment stats
EU unemployment

Revision notes on unemployment

Costs of Unemployment
Measuring Unemployment
Causes of Unemployment
Policies to reduce unemployment
Phillips Curve
Natural Rate of Unemployment

Causes of unemployment

See: Causes of Unemployment

Unemployment Essays

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Russia Unemployment Rate

Russia's unemployment rate fell to a new record low of 2.8% in february 2024, down from 2.9% in the previous month and compared with market expectations of 2.9%. this is primarily due to labor shortages of skilled workers caused by the emigration and mobilization of men for the war in ukraine. the number of unemployed individuals declined by 41 thousand from the previous month to an all-time low of 2.109 million, but the officially registered unemployed increased to 0.481 million in february from 0.480 million the month before. in february 2023, the jobless rate was higher at 3.5%. source: federal state statistics service, unemployment rate in russia decreased to 2.70 percent in march from 2.80 percent in february of 2024. unemployment rate in russia averaged 6.96 percent from 1992 until 2024, reaching an all time high of 14.60 percent in february of 1999 and a record low of 2.70 percent in march of 2024. this page provides - russia unemployment rate - actual values, historical data, forecast, chart, statistics, economic calendar and news. russia unemployment rate - data, historical chart, forecasts and calendar of releases - was last updated on may of 2024., unemployment rate in russia decreased to 2.70 percent in march from 2.80 percent in february of 2024. unemployment rate in russia is expected to be 3.20 percent by the end of this quarter, according to trading economics global macro models and analysts expectations. in the long-term, the russia unemployment rate is projected to trend around 3.10 percent in 2025 and 3.20 percent in 2026, according to our econometric models., markets, gdp, labour, prices, money, trade, government, business, consumer, housing, taxes, climate.

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As job growth in California falls back, unemployment rate remains highest in the country

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California posted another month of anemic job growth in April, keeping the state’s unemployment rate the highest in the country, 5.3%, the government reported Friday.

Statewide, employers added a net of just 5,200 jobs in April, down from 18,200 in March, according to California’s Employment Development Department .

Nationwide, employers added 175,000 jobs in April and 315,000 in March. The U.S. unemployment rate in April was 3.9% .

Major sectors of California’s economy — including manufacturing, information and professional and business services — showed job losses last month, and job opportunities aren’t as plentiful as before, even as the number of unemployed workers in the state has risen by 164,000 over the last 12 months.

In California, there were 140 unemployed workers for every 100 job openings in March, according to federal statistics released Friday. Less than two years ago, there were about two openings for every jobless person.

Carol Jackson, an unemployed worker in South Los Angeles, says she has been pounding the pavement for months, hoping to make use of her recently minted associate degree in web management and database administration. But despite sending her resume to at least 100 employers, she has not had a single interview.

“I can tell you that California is pretty brutal now,” said Jackson, 57.

Hiring in California has been lagging behind national trends, with one notable exception. The state’s healthcare and social assistance sector added 10,100 jobs last month, bringing the gains over the last 12 months to about 155,000. That’s 75% of all new jobs added since April 2023.

Hospitals and doctors’ offices have been bulking up, but the fastest growth has been at outpatient centers, home healthcare firms, nursing facilities and, especially, social assistance, which includes vocational rehabilitation and child day-care services.

“Healthcare is the big gorilla in the room; it dominates everything,” said Mark Schniepp, director of the California Economic Forecast in Santa Barbara, adding that it’s likely to keep growing robustly with new and expanded medical facilities across the state.

Leisure and hospitality businesses added 3,100 jobs last month. The gains included employment at hotels and restaurants — despite the added stress employers are feeling from a minimum wage increase to $20 an hour for fast-food workers that went into effect April 1.

While there are fears of layoffs as the food industry adopts technology to replace workers, California’s restaurants are getting a lift from a pickup in tourism. The leisure sector overall is close to fully recovering from the deep losses caused by the COVID-19 pandemic.

Public-sector payrolls also held up well last month, increasing by 2,600. Thus far, state and local government jobs seem to be showing little effects from California’s massive budget deficits .

“But clearly that will be another factor,” said Sung Won Sohn, economics professor at Loyola Marymount University in Los Angeles.

Sohn and other economists worry that there are national, cyclical and state-specific threats to California’s employment and broader economic outlook.

Key pillars of the state’s economy continue to struggle.

Motion picture producers and other employers in the information sector show few signs of breaking out of the hiring doldrums, despite the film industry’s resolution of labor strikes last fall. Los Angeles’ motion picture and recording studio industries were down by 13,400 employees, or 12%, in April compared with the same month a year earlier. And many workers in the industry say conditions do not appear to be improving.

Large parts of the farm economy in the Central Valley remain sluggish, in part due to rising costs, tighter financial conditions and ongoing climate challenges .

Despite strong investments in artificial intelligence , layoffs have persisted at high-tech firms in the Bay Area and elsewhere. Scientific and technical companies shed jobs last month, and employment at computer systems design work and related services has been gradually declining.

Nationally, economists expect job growth to slow in the coming months, the result of persistently high interest rates and an expected pullback from consumers. The outlook is particularly dim in California.

“On the ground, there are several signs of even more slowdowns,” said Michael Bernick, an employment lawyer at Duane Morris in San Francisco and former director of the state’s EDD. Among them, he said, “small businesses continue to struggle statewide with higher prices and tightened consumer spending.”

He and other experts have a similar refrain about what ails the state: high costs, excessive regulation and unaffordable home prices, among other factors.

“We just have real challenges here in California that other states don’t face,” said Renee Ward, founder of Seniors4Hire.org, a Huntington Beach-based organization that helps older workers find employment.

She said the number of job seekers registered with her service has jumped 26% so far in 2024 from a year ago.

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SAN FRANCISCO, CALIFORNIA - JUNE 03: A job seeker meets with a recruiter during the Healthcare Academy career and training fair outside of the Chase Center on June 03, 2022 in San Francisco, California. The Healthcare Academy held a career and training fair as employers added 390,000 jobs in May, according to the Bureau of Labor Statistics. The national unemployment rate remains at 3.6 percent. (Photo by Justin Sullivan/Getty Images)

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Don Lee writes economic stories out of Washington, D.C. Since joining the Los Angeles Times in 1992, he has served as the Shanghai bureau chief and in various editing and reporting roles in California. Lee previously worked at the Kansas City Star. He is a native of Seoul, Korea, and graduated from the University of Chicago.

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LOS ANGELES, CA - OCTOBER 08: Rideshare driver Jorge Vargas raises his No on 22" sign in support as app based gig workers held a driving demonstration with 60-70 vehicles blocking Spring Street in front of Los Angeles City Hall urging voters to vote no on Proposition 22, a November ballot measure that would classify app-based drivers as independent contractors and not employees or agents, providing them with an exemption from California's AB 5. The action is part of a call for stronger workers' rights organized by the Mobile Workers Alliance with 19,000 drivers in Southern California and over 40,000 in all of California. Los Angeles on Thursday, Oct. 8, 2020 in Los Angeles, CA. (Al Seib / Los Angeles Times

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Australia's unemployment rate rises to 4.1 per cent in April

Australia's headline unemployment rate rose to 4.1 per cent in April, up 0.2 percentage points from March.

The number of people counted as officially unemployed increased by 30,300 last month, while the number of employed people increased by 38,500.

With employment and unemployment both rising, the participation rate rose by 0.1 percentage points, to 66.7 per cent, which is relatively high.

Bureau of Statistics data show the employment-to-population ratio remained steady at 64 per cent, indicating that recent employment growth is broadly keeping pace with population growth.

"This suggests that the labour market remains tight, though less tight than late 2022 and early 2023," said Bjorn Jarvis, ABS head of labour statistics.

"Unemployment" can grow at the same time as "employment" as more people join the labour force to officially look for work but they don't find work immediately.

Hours worked remained unchanged last month, despite the steady 38,500 increase in employment.

That's because more people took leave around Easter than they did last year, with work patterns aligning more broadly to pre-COVID times.

Economists say it's another sign labour markets are returning to more normal seasonal patterns, and becoming looser overall.

"The labour market is expected to slacken over 2024," said Sean Langcake, head of macroeconomic forecasting for Oxford Economics Australia.

"The economy is slowing, and forward indicators of labour demand are weakening," he said.

"Today's data are consistent with that easing being underway, although the data are likely to look stronger again next month as job attached workers flow into employment," he said.

The unemployment data comes a day after ABS figures showed the Wage Price Index rose at an annual pace of 4.1 per cent in the March quarter, down from 4.2 per cent in the December quarter.

Economists said the decline in the index suggested wages growth may have peaked in Australia, which could help the Reserve Bank of Australia (RBA) to control inflation.

Marcel Thieliant, from Capital Economics, said the continued rise in the unemployment rate last month further diminished the likelihood the RBA would deliver another interest rate hike.

He noted the under-utilisation rate jumped from 10.3 per cent to 10.7 per cent, in seasonally adjusted terms, and overall hours worked have actually declined by 15 million during the past 12 months, to be 0.8 per cent lower than this time last year.

"The RBA has argued that most of the adjustment in the labour market will come via falling working hours and a decline in job vacancies," he said.

"The bank will therefore be pleased that overall hours worked fell by 0.8 per cent year-on-year, the first annual decline since early 2021.

"Looking ahead, the labour market is set to loosen further as GDP growth is set to remain below trend.

"Indeed, job vacancies continue to fall and employment surveys point to employment rising by around 20,000 per month, well below the average 34,000 monthly rise in the labour force over the last six months.

"We expect the unemployment rate to rise to 5 per cent by 2026," he said.

Customers spending less, revenue down, resumes pouring in

Ben Neumann says with cost-of-living pressures biting, people are spending less on drinks and food at his bar, Ellora, in St Kilda, Melbourne.

He says revenue is down and he can't give staff as many hours as they want.

"People are going out a bit less, people are spending less, and they're not ordering as much," he told the ABC.

"It has limited the amount of shifts we have for casual staff … shifts might be a bit shorter, we might close early on a certain day, we might not be able to keep an extra one or two [staff] on that we otherwise might, or when people were out and spending more," he said.

Mr Neumann says in the immediate post-COVID era it was hard to find staff, but it was a different story today.

"I've never received as many resumes as we have at the moment," he said.

"I can see there's a lot of people out there looking for more part-time work and more work in the gig economy, and I see that perhaps there won't be anything in the near future in terms of being able to give people the amount of shifts they need."

He said to survive, his bar had been looking for new ways to raise revenue.

"We've had to be clever and valuate our (events) packages and value-add our offerings, so that we can bring more people in," he said.

According to the ABS, since October last year, full-time employment has increased by 56,608 people while part-time employment has increased by 104,299 people.

Westpac economists Ryan Wells and Pat Bustamante said the total number of hours worked had been softening over the last year, and that softness continued into April.

"Average number of hours worked have returned to pre-pandemic levels," they said.

"These dynamics suggest that the labour market went through a significant adjustment in the second half of 2023 and has now started to normalise."

However, they said the labour market was still, generally, in robust health, with signs of emerging slack "being gradual at best".

"The extent to which labour demand will continue to cool over the near-term will critically depend on the interplay between headcount and hours," they said.

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Unemployment Issue in the United States Essay

Introduction: unemployment rates remain dangerously high, major cause: increasing deficiency in the number of jobs, leading second cause: downward revisions, other reasons: long-term unemployment and part-time jobs, vacancies deficiency and its impact on economy, people and the lack of vacancies: spurring competition, reference list.

Despite a rapid increase of SMEs and large entrepreneurships evolution enhanced by the globalization process and the opportunities that it provides, unemployment remains a major issue in a range of states, the USA not being an exception. While the situation regarding unemployment rates is comparatively steady at present, the current rates leave much to be desired. The progress is obvious, yet the economic growth of the state must occur at a faster pace, and the unemployment rates hinder the growth considerably. Though a shrinkage in the number of vacancies and the rapid increase in the number of part-time jobs have had their toll on the unemployment rates, the lack of vacancies still remains the key cause of unemployment in the U.S.

United States: Unemployment rate from 2004 to 2014.

At present, the people seeking for work in the United States suffer from the lack of vacancies. According to the official data, the U.S. government cut jobs by 13,000 positions in May, 2012 (Rushe, 2012). The specified strategy did not seem to work well for the state’s economy, since the solution, which seemed reasonable two years ago, resulted in a major lack of job opportunities and the unemployment rates hitting 6.2% in 2014 (Rushe, 2014). Even the introduction of 209,000 new jobs in 2014 did not seem to have a tangible effect on the issue: the unemployment rates among certain U.S. demographics, teenagers (20% (Rushe, 2014)) and African American people (11.4% (Rushe, 2014)) in particular. While the specified social strata are affected by unique range of factors, including cultural and social ones, the overall unemployment rate, though admittedly lower than in 2010, still raises a lot of concern.

Not only has the number of jobs available ceased to grow, but also experienced a major drop in 2012, which the changes applied to the U.S. economy in 2014 did not affect in the slightest (Rushe, 2014). True, the U.S. economy has admittedly passed its pre-recession peak and is slowly recovering, yet the effects in massive job cuts, which the American population experienced in 2012, are drastic: The specified issue is partially related to the increased competition rates among candidates, which, in its turn, is the result of peaking immigration rates (Harding, 2014).

Psychologically, long-term unemployment (LTU) is obviously the most depressing factor that keeps unemployment rates rising (Bureau of Labor Statistics, 2014). In 2014, the rates of LTU reached 37.7% and have been growing since (Bureau of Labor Statistics, 2014). Indeed, when being rejected by a range of companies, one is most likely to lose their spirit and abandon the attempts to get a job. This, in its turn, may lead to a major depression. It is remarkable that such depressive attitudes among parents are quickly acquired by their children; as a result, unemployment becomes a family issue.

The abundance of part-time jobs has also contributed to the evolution of unemployment in the U.S. (Rushe, 2012). While often viewed as a reasonable alternative, working part time leads to loss of outstanding professional qualities and becoming a rather mediocre employee, who can hardly become a member of the full-time staff (Rushe, 2012).

As a rule, with a growth in unemployment rates, the money flow process is slackened greatly (Schmidt, 2013). Without the curricular flow of finances, the economy of the state becomes stagnant. Another significant effect of unemployment concerns wealth drainage. Carving money out of the budget to support the unemployed, the government sacrifices a lot of alluring prospects (Schmidt, 2013). Finally, the increase in debts in public spending must be mentioned. The unavailability of funds leads to a major economic recession (Schmidt, 2013).

As it has been stressed above, the unavailability of certain jobs affects people’s personal and professional growth, reducing their self-esteem and depriving them of belief in their success. Naturally, the rates of economic and financial well-being of the people, who are affected by unemployment, drop significantly as well. Unless the measures for terminating unemployment or at least bringing unemployment rates a few notches down are introduced into the affected field, poverty rates are most likely to start rising increasingly.

In search for the jobs that are still available, people affected by unemployment and poverty are likely to resort to illegal means of money-making. The rates of crime peak in the area, where unemployment is an issue, thus, tearing the moral fabric of the community apart. It would be wrong to claim that such crimes are restricted to thefts and armed robberies; even such types of illegal business as drug peddling may possibly emerge, not to mention money laundering and tax dodging (Andersen, 2012).

Andersen, S. H. (2012). Unemployment and crime: Experimental evidence of the causal effects of intensified ALMPs on crime rates among unemployed individuals . Odense: University Press of Southern Denmark.

Bureau of Labor Statistics. (2014). Long-term unemployed account for 37.7 percent of all unemployed persons in December 2013. U.S. Department of Labor . Web.

Harding, R. (2014). US employment passes pre-recession peak. Financial Times . Web.

Rushe, D. (2012). US unemployment: six reasons May figures show a troubled recovery. The Guardian . Web.

Rushe, D. (2014). US adds 209,000 jobs in July as unemployment rate rises to 6.2%. The Guardian . Web.

Schmidt, J. (2013). Why does the minimum wage have no discernible effect on employment? Washington, DC: Center for Economic and Policy Research.

The Statistics Portal. (2014). United States: Unemployment rate from 2004 to 2014. Statista: The Statistics Portal. Web.

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Canada unexpectedly added 90,000 jobs in April, though unemployment stayed flat at 6.1%

Statscan's monthly labour force survey shows employment rate steady after 6 months of declines.

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The Canadian economy gained 90,000 jobs in April, much higher than the average of 20,000 jobs many economists were predicting for the latest Labour Force Survey numbers from Statistics Canada.

The federal agency pinned the increases in employment on part-time work, with more than 50,000 more of those types of positions. There were more jobs in the professional, scientific and technical services industries.

As well, employment for those aged 15 to 24 went up by 40,000 in April, the first monthly increase for that demographic since December 2022.

However, the unemployment rate was unchanged from the month before, staying at 6.1 per cent. This is higher than a year ago.

It's even harder for young people to find jobs, and the unemployment rate proves it
Canada's job numbers almost unchanged in March, while unemployment rose to 6.1%

"Within these numbers is strong employment growth but also still strong population growth," CIBC senior economist Andrew Grantham told CBC News, explaining why the unemployment rate was stable despite higher job growth.

More people are also actively employed or looking for work in Canada, with April's 0.1 per cent increase the first since June 2023.

That matches the experience of Dan Hong, owner of Ah-So Fine Foods in Toronto, who told CBC News his company is growing these days — and struggling to hire enough staff.

An East Asian man in a blue jacket stands inside a food retailer.

"We're willing to train, but unfortunately, it's been very challenging to find people to work," said Hong, whose company prepares and delivers fresh sushi to retailers in Ontario.

"We could use 20 to 30 people at any given time," he added, because his business operates in cities such as London, Toronto and Ottawa.

Statistics Canada said private sector employment went up in April after four months of little change. Grantham noted that while employment growth over the past year has been at least partly driven by the public sector, April's numbers show an encouraging move.

"This time around we did see quite a significant increase in private sector hiring, which was good news from an economic point of view," said the economist.

The employment rate, or the percentage of the population that is employed, was also steady at 61.4 per cent, which StatCan pointed out comes after six consecutive months of drops. That rate was also nearly one per cent lower in April 2024 than the year before, as population growth in Canada was higher than employment growth.

Wage growth down compared to March

These numbers come after a loss of 2,200 jobs in March, with that month's unemployment rate showing the largest increase since summer 2022.

Grantham said for the majority of people, this data is "good news" because the country is adding jobs, but measures that contribute to inflation, such as wage growth, are starting to come down.

Canada added 90,000 jobs in April, unemployment rate unchanged

Average hourly wages went up to $34.95, a 4.7 per cent increase compared to April 2023. However, that is a lower increase than March, which saw wages jump 5.1 per cent.

The Bank of Canada will be taking this report into account as it determines whether it will change interest rates in a decision next month.

CIBC's Grantham said the central bank could still cut rates in June, even with the job growth being stronger than expected, though economists from other institutions such as Citi are projecting a July rate cut.

BMO's Doug Porter wrote in an emailed note to subscribers, "Today's showy headline jobs increase will give the Bank of Canada some pause" when it comes to interest rate cuts, but that a June cut could be a "toss-up."

Regardless of the specific month, economists widely expect the bank to lower its trend-setting policy rate sometime this summer, though April's consumer price index data measuring inflation will heavily influence that decision.

ABOUT THE AUTHOR

Senior Reporter

Anis Heydari is a senior business reporter at CBC News. Prior to that, he was on the founding team of CBC Radio's "The Cost of Living" and has also reported for NPR's "The Indicator from Planet Money." He's lived and worked in Edmonton, Edinburgh, southwestern Ontario and Toronto, and is currently based in Calgary. Email him at [email protected].

Follow Anis on Twitter

With files from Nisha Patel and Laura MacNaughton

Press Release Massachusetts Unemployment and Job Estimates for April 2024

Media contact for massachusetts unemployment and job estimates for april 2024, matthew kitsos, deputy chief of staff & director of communications.

BOSTON — The state’s April total unemployment rate was 2.9 percent, unchanged from the revised March estimate of 2.9 percent , the Executive Office of Labor and Workforce Development announced Friday. The Massachusetts unemployment rate was 1.0 percentage points lower than the national rate of 3.9 percent reported by the Bureau of Labor Statistics (BLS). Over-the-year, the state’s seasonally adjusted unemployment rate was down by 0.3 percentage points.

The labor force increased by an estimated 12,200 from the revised estimate of 3,757,000 in March, with 10,100 residents more employed and 1,900 more residents unemployed over-the-month. The state’s labor force participation rate – the total number of residents 16 or older who worked or were unemployed and actively sought work in the last four weeks – increased 0.2 percentage points over-the-month, to 65.1 percent. Compared to April 2023, the labor force participation rate was unchanged.

The BLS preliminary job estimates indicate Massachusetts lost 500 jobs in April . This follows March’s revised loss of 4,100 jobs. The largest over-the-month private sector job gains were in Education and Health Services, Professional, Scientific, and Business Services, and Trade, Transportation, and Utilities. Employment now stands at 3,733,000. Massachusetts gained 669,500 jobs since the employment low in April 2020.

From April 2023 to April 2024, BLS estimates Massachusetts gained 21,800 jobs. The largest over-the-year gains occurred in Education and Health Services, Leisure and Hospitality, and Government.

April 2024 Employment Overview

Education and Health Services gained 1,900 jobs over-the-month. Over-the-year, 17,900 were added.

Professional, Scientific, and Business Services gained 800 jobs over-the-month. Over-the-year, 5,500 were lost.

Trade, Transportation, and Utilities gained 600 jobs over-the-month. Over-the-year, 2,100 were lost.

Construction gained 200 jobs over-the-month. Over-the-year, 4,000 were added.

Government gained 200 jobs over-the-month. Over-the-year, 5,100 were added.

Information lost 200 jobs over-the-month. Over-the-year, 4,400 were lost.

Financial Activities lost 500 jobs over-the-month. Over-the-year, 200 were added.

Manufacturing lost 600 jobs over-the-month. Over-the-year, 3,500 were lost.

Other Services lost 900 jobs over-the-month. Over-the-year, 3,300 were added.

Leisure and Hospitality lost 1,900 jobs over-the-month. Over-the-year, 6,800 were added.

Labor Force Overview

The April estimates show 3,659,900 Massachusetts residents were employed and 109,200 were unemployed, for a total labor force of 3,769,200. The unemployment rate at 2.9 percent was unchanged from the revised March rate of 2.9 percent. Over-the-month, the April labor force increased by 12,200 from 3,757,000 in March, with 10,100 more residents employed and 1,900 more residents unemployed. The labor force participation rate, the share of the working age population employed and unemployed, increased by 0.2 percentage points to 65.1 percent. The labor force was up 21,300 from the April 2023 estimate of 3,747,900 following the annual revision, with 33,600 more employed residents, and 12,400 fewer unemployed residents.

The unemployment rate is based on a monthly sample of households. The job estimates are derived from a monthly sample survey of employers. As a result, the two statistics may exhibit different monthly trends.

The labor force is the sum of the numbers of employed residents and those unemployed, that is, residents not working but actively seeking work in the last four weeks. Estimates may not add up to the total labor force due to rounding.

For further information on seasonal adjustment methodology, please refer to the Bureau of Labor Statistics website https://www.bls.gov .

The preliminary May 2024 and revised April 2024 unemployment rate, labor force, and job estimates for Massachusetts will be released on Friday, June 21, 2024. See the 2024 Media Advisory annual schedule for a complete list of release dates.

Detailed labor market information is available at www.mass.gov/economicresearch .

Executive Office of Labor and Workforce Development

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Rent Is Harder to Handle and Inflation Is a Burden, a Fed Financial Survey Finds

The Federal Reserve’s 2023 survey on household financial well-being found Americans excelling in the job market but struggling with prices.

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By Jeanna Smialek

American households struggled to cover some day-to-day expenses in 2023, including rent, and many remained glum about inflation even as price increases slowed.

That’s one of several takeaways from a new Federal Reserve report on the financial well-being of American households. The report suggested that American households remained in similar financial shape to 2022 — but its details also provided a split screen view of the U.S. economy.

On the one hand, households feel good about their job and wage growth prospects and are saving for retirement, evidence that the benefits of very low unemployment and rapid hiring are tangible. And about 72 percent of adults reported either doing OK or living comfortably financially, in line with 73 percent the year before.

But that optimistic share is down from 78 percent in 2021, when households had just benefited from repeated pandemic stimulus checks. And signs of financial stress tied to higher prices lingered, and in some cases intensified, just under the report's surface.

Inflation cooled notably over the course of 2023, falling to 3.4 percent at the end of the year from 6.5 percent going into the year. Yet 65 percent of adults said that price changes had made their financial situation worse. People with lower income were much more likely to report that strain: Ninety-six percent of people making less than $25,000 said that their situations had been made worse.

Renters also reported increasing challenges in keeping up with their bills. The report showed that 19 percent of renters reported being behind on their rent at some point in the year, up two percentage points from 2022.

Interestingly, slightly fewer households were taking action — like switching to cheaper products or delaying big purchases — to defray their higher costs compared with 2022. Still, about 79 percent of households indicated that they had done something to offset climbing costs, suggesting that Americans have not yet broadly accepted high prices as an unavoidable reality of life.

The Fed’s annual checkup on household finances is particularly relevant this year. Consumer confidence has been depressed even though the job market is booming and inflation is cooling notably, a mystery that has befuddled analysts and bedeviled the White House.

Polls show that President Biden is suffering as Americans take a dim view of the economy under his administration. Donald J. Trump, the presumptive Republican nominee for November’s presidential election, has been hammering Mr. Biden’s economic record.

The report underscores that even though inflation is cooling, it remains a major concern for many Americans, one that may be a big enough worry to take the shine away from an economy that is growing quickly and adding jobs.

Part of the continued concern, many economists speculate, is because households pay more attention to price levels — which are sharply higher than they were as recently as 2020 — than to price changes, which is what statisticians mean when they talk about inflation. To use an example, a person may focus on the fact that his or her latte now costs $5 instead of $3, rather than the fact that it is no longer climbing in price as quickly as it was last year.

“When I talk to folks, they all tell me that they want interest rates to be lower and they also tell me that prices are too high,” Raphael Bostic, the president of the Federal Reserve Bank of Atlanta, said in an interview with reporters on Tuesday morning. “People remember where prices used to be, and they remember that they didn’t have to talk about inflation, and that was a very comfortable place.”

The Fed has raised interest rates to 5.3 percent from near zero as recently at 2022 in a bid to cool the economy and stamp out rapid price increases. While that, too, is painful for many households — placing home-buying further out of reach and making credit card balances painfully expensive — officials like Mr. Bostic emphasize that the policy is necessary.

“We’ve got to get inflation back to 2 percent as quickly as we can,” Mr. Bostic said, referring to the inflation rate that was roughly normal before the pandemic and that is the Fed’s goal.

Jeanna Smialek covers the Federal Reserve and the economy for The Times from Washington. More about Jeanna Smialek

The State of Development Journals 2024: Quality, Acceptance Rates, Review Times, and What’s New

David mckenzie.

This is the eighth in my annual series of efforts to put together data on development economics journals that is not otherwise publicly available or easy to access (see 2017 , 2018 , 2019 , 2020 , 2021 , 2022 , 2023 for the previous editions). I once again thank all the journal editors and editorial staff who graciously shared their statistics with me.

Journal Quality

The most well-known metric of journal quality is its impact factor . The standard impact factor is the mean number of citations in the last year of papers published in the journal in the past 2 years, while the 5-year is the mean number of cites in the last year of papers published in the last 5. As noted in previous years, the distribution of citations are highly skewed, and while the mean number of citations differs across journals, there is substantial overlap in the distributions – most of the variation in citations is within, rather than across journals. We continue to see growth in these impact factors at many journals. The big news this year is that they have decided that you really don’t need three decimal places any more in the impact factors. I compliment these stats with RePec’s journal rankings which take into account article downloads and abstract views in addition to citations.

Table 3 then shows two additional metrics, taken from Scimago , which uses information from the Scopus database. The first is the SJR (SCImago Journal Rank), which is a prestige-weighted citation metric – which works like Google PageRank, giving more weight to citations in sources with a relatively high SJR. I’ve included some of the top general journals in economics for comparison. Scimago also provides an H-index which is the number of papers published by a journal in any year that were cited at least h times in the reference year – so this captures how many papers continue to be influential but as a result, favors more established journals, and ones that publish more articles, that have a larger body of articles to draw upon.

How many submissions are received, and what are the chances of getting accepted?

Table 4 shows the number of submissions received each year. See previous years posts for statistics before 2019. The total submissions in the 11 journals tracked is almost 10,000 papers (note I received no data from the Review of Development Economics this year so have excluded it). Total submissions in these journals are up 7.7% over last year, although not quite at the 2020 peak.

At most journals the number of submissions has either leveled off or fallen since a peak in 2020-21. World Development had the largest 2020 peak when they had a special call for a variety of short papers on COVID-19, but perhaps the combination of people sending off lots of papers during the pandemic and then being a little slower to start new projects has halted the rapid growth somewhat.

· The newish World Development Perspectives already received 532 submissions last year, more than many long established development journals.

· The Review of Development Economics has seen very rapid growth in submissions. I only started collecting stats for it last year, but the editors note that in 2015 they received about 450 submissions, and this has now grown to more than 1,500 last year.

Table 5 shows the total number of papers published in each journal. 782 papers were published in 2023, so that’s a lot of development research (even though less than 1 in 10 of the submitted papers and down slightly on the 811 papers published in 2022). I’ve noted in previous years that some of the journals have been able to flexibly increase the number of articles published as their submission numbers have risen, reducing publication lags as well.

The ratio of the number of papers published to those submitted is approximately the acceptance rate. Of course papers are often published in a different year from when they are submitted, and so journals calculate acceptance rates by trying to match up the timing. Each journal does this in somewhat different ways. Hence Economia-Lacea reports a 0% acceptance rate for 2023 since none of the papers submitted in 2023 have yet been accepted, although some are still under review. Table 6 shows the acceptance rates at different journals as reported by these journals. Of course the number and quality of submissions varies across journals, and so comparing acceptance rates across journals does not tell you what the chances are of your particular paper getting accepted is at these different journals.

How long does it take papers to get refereed?

In addition to wanting to publish in a high quality outlet, and having a decent chance of publication, authors also care a lot about how efficient the process is. Table 7 provides data on the review process (see the previous years’ posts for historic data). The first column shows the desk rejection rate, which averages 73%. Column 2 uses the desk rejection rates and acceptance rates to estimate the acceptance rate conditional on you making it past the desk rejection stage. On average, about one in three papers that gets sent to referees gets accepted, with this varying from 12% to 63% across journals.

The remaining columns give some numbers on how long it takes to get a first-round decision. The statistics “Unconditional on going to referees” includes all the desk rejections, which typically don’t take that many days. The average conditional on going to referees is in the 3-5 month range. The last two columns then show that at most journals, almost all papers have a decision within 6 months – so in my opinion, you should feel free to send an enquiry if your paper takes longer than that.

Do revisions typically get sent back to the referees or handled by the editor?

Another factor that can make a big difference in how long it takes to publish a paper is whether editors send revised papers back to referees, or instead reads the response letter and revision themselves and just makes a decision on this basis. This is something that the AER and AEJ Applied have been trying to do more and more, with only 25% of revisions at the AEJ Applied going back to referees. In my own editing at WBE, I send fewer than 5% of revisions back to referees. This year I asked the different journals what their approaches were. Many do not systematically track this, but offered some approximations:

· Journal of Development Economics: approximately 60% of revisions go back to referees, although 0% for the short papers (see below)

· Development Policy Review: only 10% of revisions go back to referees.

· Journal of Development Effectiveness: 7.7% were sent back to referees

· Journal of Development Studies: not tracked, but less than 20% go back to referees

· Journal of African Economies: 52% are sent back to referees

· Economia: 70% go back to referees.

· EDCC: does not track this, but first revisions are usually sent back to referees.

· World Development, World Development Perspectives, WBRO, and WBER do not track this, and results may vary a lot by editor.

Updates on the JDE Short Paper and Registered Report Tracks

The Journal of Development Economics has two other categories of papers that differ from other development journals:

· The short paper format has proved popular. There were 148 submissions in 2023 (about 8% of total submissions), and 21 short papers were accepted. These papers follow the model of AER Insights, ReStat, etc in which papers are either conditionally accepted or rejected, and so any revisions are minor and are not sent back to referees.

· The JDE registered reports had 19 stage 1 acceptances in 2023, and 1 stage 2 acceptance, reflecting a lag from COVID when there were not many new submissions. They have a website jdepreresults.org which tracks the stage 1 and stage 2 registered reports, but some of the data was lost when transitioning the website, so if you have a registered report accepted that is not listed there, please let the journal know.

Other Development Journal News

Finally, I asked the journals if they had any other major news or changes to report. Here are what they wanted to share:

· At EDCC, Prashant Bharadwaj has replaced Marcel Fafchamps as editor. Thanks to Marcel for 10 years at the helm. The journal is one of the few development journals with a submission fee ($50), but offers a fee waiver to referees who have submitted a timely report in the year prior to submission.

· Other editorial changes are Ganeshan Wignaraja replacing Colin Kirkpatrick as co-editor at Development Policy Review, and Marie Gardner and Ashu Handa taking over from Manny Jimenez at the Journal of Development Effectiveness.

· The Journal of Development Effectiveness notes they are implementing a set of actions to raise awareness about transparency, ethics and equity in research, and to address power imbalances among HIC-L&MIC research teams. The editors note they are particularly concerned with research involving primary data collection in an L&MIC where there is no author from an institution in that country. For articles submitted to JDEff that fall into this category, they will require the authors to complete a short author reflexivity statement that will be published along with the article. The statement will explain the contribution of each author per Taylor & Francis authorship criteria, which are consistent with the criteria established by the International Committee of Medical Journal Editors. Authors will be asked to explain why there is no contributing author from the study location, specifically, whether any team member based in the study location made a ’significant contribution to conception, study design, execution or acquisition of data,’ and if so, why they were not subsequently invited to review the manuscript and take responsibility for its contents. And for work involving randomized controlled trials or interviews with vulnerable groups, authors will also be asked to answer a set of questions about research ethics. Final manuscript acceptance and publication in JDEff will be based on the scientific quality of the work as well as an assessment of whether the work was conducted in an equitable, inclusive and ethical manner.

Finally, thanks again to all the editors for all the time and effort they devote to improving the quality and visibility of development research. As you can see, they have a lot to deal with!

Lead Economist, Development Research Group, World Bank

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Psychologically, long-term unemployment (LTU) is obviously the most depressing factor that keeps unemployment rates rising (Bureau of Labor Statistics, 2014). In 2014, the rates of LTU reached 37.7% and have been growing since (Bureau of Labor Statistics, 2014). Indeed, when being rejected by a range of companies, one is most likely to lose ...
Canada unexpectedly added 90,000 jobs in April, though unemployment
Canada added 90,000 jobs in April, unemployment rate unchanged. A sign displays a vacant position in Halifax. Average hourly wages went up to $34.95, a 4.7 per cent increase compared to April 2023 ...
Massachusetts Unemployment and Job Estimates for April 2024
The April estimates show 3,659,900 Massachusetts residents were employed and 109,200 were unemployed, for a total labor force of 3,769,200. The unemployment rate at 2.9 percent was unchanged from the revised March rate of 2.9 percent. Over-the-month, the April labor force increased by 12,200 from 3,757,000 in March, with 10,100 more residents ...
Rent Is Harder to Handle and Inflation Is a Burden, a Fed Financial
Inflation cooled notably over the course of 2023, falling to 3.4 percent at the end of the year from 6.5 percent going into the year. Yet 65 percent of adults said that price changes had made ...
The State of Development Journals 2024: Quality, Acceptance Rates
The first column shows the desk rejection rate, which averages 73%. Column 2 uses the desk rejection rates and acceptance rates to estimate the acceptance rate conditional on you making it past the desk rejection stage. On average, about one in three papers that gets sent to referees gets accepted, with this varying from 12% to 63% across journals.
Economic Developments
Our May forecast maintains our previous outlook for Q4/Q4 real Gross Domestic Product (GDP) growth of 1.8 percent in 2024 and 1.9 percent in 2025. We expect payroll growth to decelerate and the unemployment rate to drift upward to 4 percent by the end of the year. Longer-term interest rates, including mortgage rates, have been volatile the past ...

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Lesson summary: Unemployment

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Unemployment: Causes, Effects, and Solutions

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B. Societal Factors

A. Economic Effects

B. Social Effects

A. Economic Solutions

B. Social Solutions

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What does the unemployment rate measure?

What does the unemployment rate measure?

Where do the data on unemployment come from?

How are individuals in the monthly survey identified as being unemployed ?

What’s the difference between being unemployed and being out of the labor force?

Why is it hard to determine if people are unemployed in the COVID-19 pandemic?

Given the limitations of the unemployment rate as a measure of labor market slack, what are some alternatives?

Given the measurement problems during the pandemic, are there alternatives to the BLS indicators of slack to gauge the health of the labor market?

What are “initial claims” for unemployment insurance?

What is the relationship between initial claims and the unemployment rate — and why might it be different now?

What is the payroll survey? And why is it likely to be less useful than usual?

Why might the historical link between unemployment and poverty be a misleading way to look at today’s numbers?

Long-Run Trends and the Natural Rate of Unemployment

Trend Decline in the Unemployment Rate

Grand Gender Convergence

Natural Rate of Unemployment

Researchers

Unemployment Essay

Reason for Unemployment

Steps to Eliminate Unemployment

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Recessions and the Trend in the US Unemployment Rate

Recessions and Unemployment Rate Trends

Longer-Run Unemployment Rate Projections

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Essay on Unemployment Rate

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Unemployment Essay for Students and Children

What is Unemployment?

Types of Unemployment

Reasons for Unemployment

Consequences of Unemployment

Initiative by Government

FAQs about Unemployment

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Unemployment and Policy Trade-Offs (Revision Essay Plan)

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EVAL Point 1

KAA Point 2

EVAL Point 2

FINAL CONCLUSION

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Unemployment

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