This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS). This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The monthly inflation rate and unemployment rate in Belgium, France, Germany, Italy, Poland, and Spain from 2020 to 2021. I simply put the data in one file since you need to download them separately if you get them from the link. These economics indicator might help for the Kaggle competition Tabular Playground Series September 2022.

Here are the data sources for the inflation rate and the unemployment rate

Graph and download economic data for Noncyclical Rate of Unemployment (NROU) from Q1 1949 to Q4 2035 about NAIRU, long-term, projection, unemployment, rate, and USA.

Abstract

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS)

Documentation

Update frequency: Monthly Dataset source: U.S. Bureau of Labor Statistics Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/bls-public-data/bureau-of-labor-statistics

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Ireland IE: NAIRU: Equilibrium Unemployment Rate data was reported at 7.152 % in 2022. This records an increase from the previous number of 7.136 % for 2021. Ireland IE: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 8.407 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 12.280 % in 1990 and a record low of 7.136 % in 2021. Ireland IE: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Ireland – Table IE.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

The Volcker Shock was a period of historically high interest rates precipitated by Federal Reserve Chairperson Paul Volcker's decision to raise the central bank's key interest rate, the Fed funds effective rate, during the first three years of his term. Volcker was appointed chairperson of the Fed in August 1979 by President Jimmy Carter, as replacement for William Miller, who Carter had made his treasury secretary. Volcker was one of the most hawkish (supportive of tighter monetary policy to stem inflation) members of the Federal Reserve's committee, and quickly set about changing the course of monetary policy in the U.S. in order to quell inflation. The Volcker Shock is remembered for bringing an end to over a decade of high inflation in the United States, prompting a deep recession and high unemployment, and for spurring on debt defaults among developing countries in Latin America who had borrowed in U.S. dollars.

Monetary tightening and the recessions of the early '80s

Beginning in October 1979, Volcker's Fed tightened monetary policy by raising interest rates. This decision had the effect of depressing demand and slowing down the U.S. economy, as credit became more expensive for households and businesses. The Fed funds rate, the key overnight rate at which banks lend their excess reserves to each other, rose as high as 17.6 percent in early 1980. The rate was allowed to fall back below 10 percent following this first peak, however, due to worries that inflation was not falling fast enough, a second cycle of monetary tightening was embarked upon starting in August of 1980. The rate would reach its all-time peak in June of 1981, at 19.1 percent. The second recession sparked by these hikes was far deeper than the 1980 recession, with unemployment peaking at 10.8 percent in December 1980, the highest level since The Great Depression. This recession would drive inflation to a low point during Volcker's terms of 2.5 percent in August 1983.

The legacy of the Volcker Shock

By the end of Volcker's terms as Fed Chair, inflation was at a manageable rate of around four percent, while unemployment had fallen under six percent, as the economy grew and business confidence returned. While supporters of Volcker's actions point to these numbers as proof of the efficacy of his actions, critics have claimed that there were less harmful ways that inflation could have been brought under control. The recessions of the early 1980s are cited as accelerating deindustrialization in the U.S., as manufacturing jobs lost in 'rust belt' states such as Michigan, Ohio, and Pennsylvania never returned during the years of recovery. The Volcker Shock was also a driving factor behind the Latin American debt crises of the 1980s, as governments in the region defaulted on debts which they had incurred in U.S. dollars. Debates about the validity of using interest rate hikes to get inflation under control have recently re-emerged due to the inflationary pressures facing the U.S. following the Coronavirus pandemic and the Federal Reserve's subsequent decision to embark on a course of monetary tightening.

Graph and download economic data for Natural Rate of Unemployment (Short-Term) (DISCONTINUED) (NROUST) from Q1 1949 to Q4 2031 about NAIRU, short-term, projection, unemployment, rate, and USA.

South Korea NAIRU: Equilibrium Unemployment Rate data was reported at 3.542 % in 2022. This records an increase from the previous number of 3.535 % for 2021. South Korea NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 3.437 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 4.236 % in 1998 and a record low of 2.793 % in 1991. South Korea NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

Norway NO: NAIRU: Equilibrium Unemployment Rate data was reported at 3.514 % in 2022. This records an increase from the previous number of 3.480 % for 2021. Norway NO: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 3.462 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 4.744 % in 1993 and a record low of 2.775 % in 1985. Norway NO: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What is the relationship between unemployment and inflation?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

These are the replication files for Oraby (JCRE, 2022). The paper aims to replicate Svensson (American Economic Journal: Macroeconomics, 2015).

Abstract: This paper replicates the main analysis of Svensson (2015) with some expansion to the original analysis, mainly for the United States. Overall, the replication exercise successfully confirms the conclusions of Svensson (2015). In both Sweden and the United States, empirical evidence supports the existence of a non-vertical long run Phillips curve. The slope of the long run Phillips curve recorded -0.75 in Sweden and -0.23 in the United States. While the average inflation rate in the United States was very close to its targeted level, the average inflation rate in Sweden was 0.6 percentage points below its targeted level over the sample period. The deviation of inflation rate from its targeted level in Sweden resulted in an unemployment cost equivalent to 0.8 percentage points over the sample period where the average unemployment rate recorded 7.4 percent compared with an estimated 6.6 percent had the average inflation rate been at its targeted level.

Lithuania LT: NAIRU: Equilibrium Unemployment Rate data was reported at 6.680 % in 2022. This records a decrease from the previous number of 6.683 % for 2021. Lithuania LT: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 7.322 % from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 9.571 % in 2011 and a record low of 5.965 % in 2002. Lithuania LT: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

Analysis of ‘Inflation and Unemployment’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harisonmwangi/inflation-and-unemployment on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Annual data on inflation and unemployment for countries and regions in the world.

Content

Annual data on inflation and unemployment for countries and regions in the world. Retrieved from World Development Indicators data bank.

Inspiration

The main aim of the data is to explore the relationship between inflation and unemployment rates. Does the Phillips curve in economics still exist, or is it some spurious correlation of data in the '60s?

--- Original source retains full ownership of the source dataset ---

Australia NAIRU: Equilibrium Unemployment Rate data was reported at 5.413 % in 2022. This records an increase from the previous number of 5.345 % for 2021. Australia NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 5.759 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 8.377 % in 1993 and a record low of 4.951 % in 2007. Australia NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

Using the statistical technique of fuzzy clustering, regimes of inflation and unemployment are explored for the United States, the United Kingdom and Germany between 1871 and 2009. We identify for each country three distinct regimes in inflation/unemployment space. There is considerable similarity across the countries in both the regimes themselves and in the timings of the transitions between regimes. However, the typical rates of inflation and unemployment experienced in the regimes are substantially different. Further, even within a given regime, the results of the clustering show persistent fluctuations in the degree of attachment to that regime of inflation/unemployment observations over time. The economic implications of the results are that, first, the inflation/unemployment relationship experiences from time to time major shifts. Second, that it is also inherently unstable even in the short run. It is likely that the factors which govern the inflation/unemployment trade off are so multi-dimensional that it is hard to see that there is a way of identifying periods of short run Phillips curves which can be assigned to particular historical periods with any degree of accuracy or predictability. The short run may be so short as to be meaningless. The analysis shows that reliance on any kind of trade off between inflation and unemployment for policy purposes is entirely misplaced.

The misery index is an economic indicator that combines the unemployment rate and the inflation rate. Although it is rare for both unemployment and inflation to be high at the same time, there have been instances of this occurring, such as during episodes of stagflation in the 1970s. Due to high levels of inflation since late 2021, the misery index in March 2023 is at a relatively high rate of 8.49 percent.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

Background: This study investigates the influence of regional minimum wages (RMW), gross domestic product (GDP), and inflation on Indonesia's unemployment rates from 2012 to 2020. Methods: Multiple linear regression analysis examines the relationships between these economic variables. Findings: The findings reveal that RMW significantly negatively affects unemployment rates, indicating that a 1% increase in the minimum wage leads to a 3.951% decrease in unemployment, ceteris paribus. GDP also exhibits a significant negative influence, aligning with Okun's law, which suggests an inverse relationship between economic growth and unemployment. In contrast, inflation does not significantly impact unemployment rates during the studied period. Collectively, the three variables positively and significantly affect Indonesia's unemployment rate, with an adjusted R-squared value of 0.749. This implies that 74.9% of the variation in unemployment can be explained by GDP, inflation, and minimum wages, while other factors account for the remaining 25.1%. Conclusion: The study highlights the complex interplay between these macroeconomic indicators and unemployment, providing insights for policymakers to develop effective strategies for managing employment challenges in Indonesia. Novelty/Originality of this article: This empirical analysis reveals the dynamic relationship between RMW, GDP, inflation, and unemployment in Indonesia (2012—2020). The findings provide an evidence-based basis for formulating more effective and responsive employment and economic policies for Indonesia's labour market conditions.

This scatter chart displays unemployment (% of total labor force) against inflation (annual %) in Central America. The data is about countries.