US Unemployment Research Articles

Modeling and evaluating the heterogeneous impacts of the COVID-19 on US unemployment

Modeling and evaluating the heterogeneous impacts of the COVID-19 on US unemployment

Spillover Effects of Currency Interactions Across Economic Cycles: A Quantile-VAR Analysis

This study employs Markov-Switching Regression (MS-Regression) to model four macroeconomic indicators—US GDP, CPI, interest rate, and unemployment rate—to identify economic crisis cycles, while all indicators provide some level of insight into these cycles, the unemployment rate offers the closest alignment with the actual patterns of economic cycles. Based on the regime identification derived from the unemployment rate, we delineate the time series for expansion and recession periods. Subsequently, we apply the Quantile Vector Autoregression (Quantile-VAR) model to analyze three sets of time series: the entire dataset, the expansion period, and the recession period. Our findings reveal that, under normal conditions, the US dollar exerts the greatest influence on and is most influenced by other currencies, whereas the Australian dollar has the least impact on others. In the extreme lower and upper tails, the mutual influence among the currencies of different countries intensifies, concurrently diminishing the relative influence of the US dollar. Notably, the spillover effects under extreme lower and upper tail conditions are not consistent, as the occurrence of extreme values does not coincide, suggesting an asymmetry in the spillover effects at these quantiles.

Regional determinants in origin–destination remittance flows from the US to Mexico: evidence from the COVID-19 pandemic

ABSTRACT Despite expectations of a decline in remittances due to rising US unemployment, total remittances to Mexico increased during the COVID-19 pandemic, although with regional disparities. Using a spatial gravity econometric model with state-level origin–destination data, we analysed these variations, considering regional economic and health conditions and demand for digital remittance services. The study focused on the second quarter of 2020, marked by lockdowns and economic disruptions. Findings suggest a strong link between employment changes and digital service demand, influencing US remittance flows. Results for Mexico support the significant impact of digital services over the altruistic hypothesis, highlighting the need for a regional approach to studying remittances.

Diagnostic analystics in the Bayesian vector autoregressive model

The vector autoregressive model is extensively employed in macroeconomics, finance, and the natural sciences. However, it often encounters the issue of over-parametrization, resulting in undesirable behavior within the model. In this paper, we utilize the Bayesian vector autoregressive model as a tool to tackle this problem. Additionally, we employ the Bayesian local influence method to detect possible extreme observations under the model. We use posterior inference to estimate related parameters and construct Bayesian perturbation schemes for priors, variance, and data perturbations. Bayesian local influence is conducted based on three objective functions: Bayes factor, ϕ divergence, and posterior mean. To demonstrate the effectiveness of the diagnostics, we conduct numerical simulations. In the real data example, we use US inflation, unemployment, and interest rate data to verify this method's effectiveness.

Asymptotic optimality of the nonnegative garrote estimator in threshold models

In this paper, it is aimed to broad Xiong's scope of analysis to the threshold models and develop an adjusted Mallows criterion to select the garrote parameter vector. Compared with other penalized least-squares methods, the nonnegative garrote (NG) method has a natural penalty and tuning parameter. Furthermore, we show the asymptotic optimality of the NG estimator by referring to the idea of the asymptotic optimality of the model averaging estimator under some regular conditions. Our investigation of finite-sample performance demonstrates that the proposed method exhibits very favorable properties with respect to the ratio of the correct number of zero estimated components (RCNZ) and the root mean square error (RMSE) compared to the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) penalized regression methods. The proposed method is applied to the analysis of body fat data, soil respiration data and the US unemployment rate data and performs well.

Labour market mismatches in G7 countries: a fractional integration approach

ABSTRACT This paper examines the G7 labour market, analysing unemployment, job vacancies and the spread of both in terms of time series persistence from January 2002 to October 2023. Using fractional integration, we observe the series show long memory and persistence in all G7 countries. These findings differ slightly depending on the specification of the error term. If it is white noise, no evidence of mean reversion is found in any scenario except for US unemployment. With autocorrelated disturbances, mean reversion is found in unemployment rates in Canada, Germany, and the US. In France, this is the case for job vacancies, and in France and Italy, for spread. The UK is the only country that does not display any degree of reversion to the mean in the three series examined. Our results show evidence of a downward trend for unemployment and an upward trend for job vacancies in all G7 countries. Consequently, the reduction of the imbalance unemployment-vacancies seems permanent, which is a positive outcome for advanced economies.

A Congestion Theory of Unemployment Fluctuations

We propose a theory of unemployment fluctuations in which new hires and incumbent workers are imperfect substitutes. Hence, attempts to hire away the unemployed during recessions diminish the marginal product of new hires, discouraging job creation. This single feature achieves a tenfold increase in the volatility of hiring in an otherwise standard search model, produces a realistic Beveridge curve despite countercyclical separations, and explains 30–40 percent of US unemployment fluctuations. Additionally, it explains the excess procyclicality of new hires’ wages, the cyclical labor wedge, countercyclical earnings losses from job displacement, and the limited steady-state effects of unemployment insurance. (JEL E24, E32, J22, J23, J31, J63)

Прогноз мировых цен нефти на основе монетарной политики Соединённых Штатов Америки

This study explores the critical realm of forecasting oil prices within the global energy resources market. The research is particularly timely as it provides new insights into the future growth prospects of Brent crude oil prices, which are increasingly influenced by the indicators of the monetary policy set forth by the USA Federal Reserve. The primary objective of this scientific endeavor is to predict Brent crude oil prices in response to shifts in the indicators of the US Federal Reserve's monetary policy. To fulfill this objective, the study systematically tackled several key tasks. These tasks encompassed a thorough examination of the theoretical foundations underpinning oil price forecasting, the meticulous compilation of statistical data related to the target indicators, and the application of specialized economic methodologies. Mul[1]tiple linear regression equations were formulated to establish the relationship be[1]tween Brent crude oil prices and the indicators of the US Federal Reserve's monetary policy. Furthermore, the study ventured into the prediction of these Federal Reserve indicators and arrived at substantive conclusions. The research methodology employed a range of general and specialized scientific techniques, encompassing analysis, synthesis, and statistical analysis. Notably, the scientific innovation of this study lies in the construction of multiple linear regres[1]sion equations to forecast Brent crude oil prices, hinging on key indicators of the USA Federal Reserve's monetary policy, including the effective federal funds rate, the M2 monetary aggregate, the US dollar index, the core personal consumption expenditure price index, and the US unemployment rate. Additionally, this study cal[1]culated an increasing trend equation for the M2 monetary aggregate in the United States. The practical implications of this research are profound, offering valuable insights for investors looking to craft effective trading strategies within the global energy resources market.

Analysis of the Effect of COVID-19 Pandemic to US Unemployment Rate

On April 2020, one year after the first reported case of COVID-19 in 2019, the United States reached its highest unemployment rate in the recent 20 years. The effects of it on the US citizens, therefore, raise the interest of many academics. This essay analyzes the impact from three aspects: the labor market structural change, physical burden and psychological burden. All these will accentuate the existing inequality, and unequal access to resources, opportunities, and wealth, creating a cycle of poverty. In order to address these problems, the suggestions also separated into three areas, including promoting remote work as a path to reduce income inequality; policy measures such as expanding healthcare insurance coverage for addressing health inequality; policy intervention such as expanding unemployment benefits to address mental health inequalities. The suggestions can provide policymakers insight into the current social situation under the pandemic, thus making adequate decisions to mitigate the effects of COVID-19.

Forecasting USA Unemployment Rate Base on ARIMA Model

This paper presents a detailed analysis of unemployment rate forecasting, a critical subject for various stakeholders including policymakers, businesses, and individuals. Amid significant economic events such as the global financial crisis and COVID-19 pandemic, the need for precise unemployment forecasts has become crucial. The research utilizes an Autoregressive Integrated Moving Average (ARIMA) model to analyze US unemployment rate data from 2000 to 2023, sourced from the Federal Reserve Economic Data (FRED). The paper identifies seasonality patterns, executes appropriate data transformations, and incorporates the Box-Jenkins methodology for ARIMA model identification. The findings reveal the model's resilience, demonstrating accurate forecasts despite significant disruptions. These insights offer valuable contributions in understanding labor market dynamics, facilitating informed decision-making and strategic planning. The paper highlights the robustness of ARIMA models, and their potential to adapt to rapid changes in the economic landscape, thereby proving invaluable in forecasting unemployment rates.

The Impact of CPI and Unemployment Rate on Economic Growth: Based on TVP-VAR Model

Inflation has a significant impact on economic growth and monetary policy. In recent times, the US economy is facing the threat of high inflation, and the Federal Reserve has been continuously adopting interest rate hikes, which have an impact on the economy and finance. And the relationship between inflation and unemployment rate is a trade-off, so this article studies the impact of CPI and unemployment rate on the US economy. This artic le uses the US CPI and unemployment rate from 1948 to 2009 to construct a VAR model, predicts the final ten periods, and compares them with actual values. Using US CPI, unemployment rate, and GDP data from 1948 to 2009, construct a TVP-VAR model to analyze the impact of inflation and unemployment rate on economic growth.

Intelligent Identification of Trend Components in Singular Spectrum Analysis

Singular spectrum analysis (SSA) is a non-parametric adaptive technique used for time series analysis. It allows solving various problems related to time series without the need to define a model. In this study, we focus on the problem of trend extraction. To extract trends using SSA, a grouping of elementary components is required. However, automating this process is challenging due to the nonparametric nature of SSA. Although there are some known approaches to automated grouping in SSA, they do not work well when the signal components are mixed. In this paper, a novel approach that combines automated identification of trend components with separability improvement is proposed. We also consider a new method called EOSSA for separability improvement, along with other known methods. The automated modifications are numerically compared and applied to real-life time series. The proposed approach demonstrated its advantage in extracting trends when dealing with mixed signal components. The separability-improving method EOSSA proved to be the most accurate when the signal rank is properly detected or slightly exceeded. The automated SSA was very successfully applied to US Unemployment data to separate an annual trend from seasonal effects. The proposed approach has shown its capability to automatically extract trends without the need to determine their parametric form.

Large motherhood penalties in US administrative microdata

Whereas previous research has described motherhood penalties in US survey data, we leverage administrative data on 811,000 quarterly earnings histories from the US Unemployment Insurance program. We analyze contexts where smaller motherhood penalties might be expected: couples where the woman outearns her male partner prior to childbearing, at firms that are headed by women, and at firms that are predominantly women. Our startling result is that none of these propitious contexts appear to diminish the motherhood penalty, and indeed, the gap often increases in magnitude over time following childbearing. We estimate one of the largest motherhood penalties in "female-breadwinner" families, where higher-earning women experience a 60% drop from their prechildbirth earnings relative to their male partners. Turning to proximate mechanisms, women are less likely to switch to a higher-paying firm postchildbearing than men and are substantially more likely to quit the labor force. On the whole, our findings are discouraging relative even to existing research on motherhood penalties.

A long-run approach to money, unemployment, and equity prices

We propose a search and matching model that can account for a long-run joint relationship between inflation, unemployment, and equity prices. The model predicts the following long-run joint relationship: (i) a positive relationship between inflation and unemployment; (ii) a negative relationship between unemployment and equity prices; and (iii) a negative relationship between inflation and equity prices. Empirical evidence for this trivariate relationship in the post-WWII US data has been documented in previous studies. We reconfirm this evidence with expanded dataset. Furthermore, our calibration exercises show that the model results driven solely by US monetary policy can account for 62.9% and 29.8% of variations of the long-term trends of US unemployment rate and real equity prices, respectively.

Race, employment, and the pandemic: An exploration of covariate explanations of COVID-19 case fatality rate variance.

We derive a simple asymptotic approximation for the long-run case fatality rate of COVID-19 (alpha and delta variants) and show that these estimations are highly correlated to the interaction between US State median age and projected US unemployment rate (Adj. r2 = 60%). We contrast this to the high level of correlation between point (instantaneous) estimates of per state case fatality rates and the interaction of median age, population density and current unemployment rates (Adj. r2 = 50.2%). To determine whether this is caused by a "race effect," we then analyze unemployment, race, median age and population density across US states and show that adding the interaction of African American population and unemployment explains 53.5% of the variance in COVID case fatality rates for the alpha and delta variants when considering instantaneous case fatality rate. Interestingly, when the asymptotic case fatality rate is used, the dependence on the African American population disappears, which is consistent with the fact that in the long-run COVID does not discriminate on race, but may discriminate on access to medical care which is highly correlated to employment in the US. The results provide further evidence of the impact inequality can have on case fatality rates in COVID-19 and the impact complex social, health and economic factors can have on patient survival.

Oil price shocks and US unemployment: evidence from disentangling the duration of unemployment spells in the labor market

Oil price shocks and US unemployment: evidence from disentangling the duration of unemployment spells in the labor market

The effects of conventional and unconventional monetary policy on the unemployment rate in the US

PurposeThe purpose of this paper is to investigate the impact of the Federal Reserve's conventional and unconventional monetary policy shocks on the US unemployment rate.Design/methodology/approachThe authors employ a unified time-varying framework to an extensive data set from 1960 to 2019.FindingsThe authors find that both conventional and unconventional monetary policy influence the unemployment rate, but the effects of unconventional monetary policy vary greatly during the first, second and third rounds of quantitative easing (dubbed QE1, QE2 and QE3, respectively). It significantly influenced the unemployment rate in QE3. However, the effects are less persistent than the effects of conventional monetary policy shocks. The impact of unconventional monetary policy transmits to the real economy through conventional interest rates, exchange rates and asset price channels. The responses of unemployment rate are smaller during QE1 and QE2 due to the rise in inflation uncertainty and economic policy uncertainty.Originality/valueThe impact of the Fed's unconventional monetary policy shocks on the US unemployment rate during QE1, QE2 and QE3 is time-varying. It is explained by inflation uncertainty and real option channels.

Business Cycles and Low-Frequency Fluctuations in the US Unemployment Rate

Business Cycles and Low-Frequency Fluctuations in the US Unemployment Rate

These Caps Spilleth Over: Equilibrium Effects of Unemployment Insurance

The design of US unemployment insurance (UI) policy--which features benefits assigned as a percentage of past wages up to a cap--engenders tests for spillovers from policy variation to workers who are not directly treated. We test for and find a pattern of spillovers from state-level UI policy changes that cannot be neatly reconciled with workhorse or cutting-edge models of UI spillovers. Instead, we show that the documented pattern conforms with the predictions of a canonical model of information frictions: wage posting with random search. Taken together, our results provide novel evidence of quantitatively- and policy-relevant information frictions in this market. Moreover, our estimates suggest that aggregate unemployment of insured individuals would decrease if the replacement rate were increased while holding the cap constant.

Mexico: Remittances, Organized Crime and US Drug Overdose Crisis in Borderlands (2015-2021)

The present article aims at shedding some light to the question whether Mexico´s boom in remittances between 2015 and 2021 was the result of low rates of unemployment in the USA or of higher revenues of drug trafficking made by Mexican Criminal Groups. We found that Mexican migration to the USA took off up 2019 and accelerated with Covid-19 pandemic. Since higher rates of unemployment matched with larger number of remittances and a spike in drug overdose deaths in the USA, a hypothesis aroused stating that Mexican remittances could be related to drug trafficking revenues. An Exploratory Data Analysis (EDA) found a normal negative correlation (not causation) between US-unemployment rate and remittances from 2015 to 2019 but an abnormal negative correlation from 2020 to 2021. We conclude that the record level in Mexican remittances between 2020 and 2021 could be the result of an increase in Mexican migration but also from a windfall in drug trafficking earnings mirrored in a spike of drug overdose deaths in the USA.