The share of law students who graduated in 2023 and were looking for work was * percent. This was a slight decrease when compared the previous year, with a slightly lower amount of graduates in 2023.

This statistic illustrates the unemployment rate of legal professionals in the United states in the second quarter of 2017. In that period, some *** percent of the lawyers were not employed in the U.S.

As of April 2024, almost ** percent of law students who graduated in 2023 in the United States were employed in law firm positions, while **** percent were working for the government.

Dataset

This dataset was created by VasL

Contents

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

Okun's law is an empirical relationship that measures the correlation between the deviation of the unemployment rate from its natural rate and the deviation of output growth from its potential. In this paper, we estimate Okun's coefficients for each U.S. state and examine the potential factors that explain the heterogeneity of the estimated Okun relationships. We find that indicators of more flexible labor markets (higher levels of education achievement in the population, lower rate of unionization, and a higher share of nonmanufacturing employment) are important determinants of the differences in Okun's coefficient across states. Finally, we show that Okun's relationship is not stable across specifications, which can lead to inaccurate estimates of the potential determinants of Okun's coefficient.

Context

This dataset is created via OECD datasource which is consisted of 2000 between 2019. https://data.oecd.org/unemp/unemployment-rate.htm

Content

The unemployed are people of working age who are without work, are available for work, and have taken specific steps to find work. The uniform application of this definition results in estimates of unemployment rates that are more internationally comparable than estimates based on national definitions of unemployment. This indicator is measured in numbers of unemployed people as a percentage of the labour force and it is seasonally adjusted. The labour force is defined as the total number of unemployed people plus those in employment. Data are based on labour force surveys (LFS). For European Union countries where monthly LFS information is not available, the monthly unemployed figures are estimated by Eurostat.

The number of law graduates in the United States steadily ******** between 2013 and 2019. Between 2020 and 2022 there was a slight ******** in the number of graduates, however this number fell again in 2023. In 2024, this figure increased again and reached almost ******. The share of unemployed law graduates in the United States followed approximately the same trend: the percentage of law students who did not find a job after graduating in 2019 was roughly half the share recorded in 2013, before increasing again in 2020, and falling in the following years. Career opportunities Law school graduates can undertake many career paths. Legal occupations can be primarily distinguished between lawyers, judges, and judicial workers on one hand, and legal support workers, such as paralegals and legal assistants, on the other. In 2024, the outright majority of professionals employed in legal occupations in the United States were lawyers. According to the same study, lawyers were also the highest-paid workers in the sector, followed by judges and magistrates. Leading law firms The United States are home to some of the most renowned law firms in the world. In 2024, Wachtell was the leading law firms in the country in terms of revenue per lawyer, as Kirkland & Ellis generated the highest gross revenue. Baker Mckenzie was the company in the United States with the highest number of lawyers employed. In fact, the multinational firm headquartered in Chicago employed roughly 500 more lawyers than DLA Piper, who were second in the rankings.

Financial overview and grant giving statistics of Unemployment Law Project

With this panel data from 106 countries from 2000 until 2016, I tried to analyze the nexus between compulsory military service (CMS) laws and labor market performance as measured by employment rates and unemployment rates. For the main variable of interest, which is CMS, I used mainly the Economic Freedom of the World Index (EFW). Besides some governmental sources for some countries in the MENA region that EFW did not include. I went through the text of laws that governs military recruitment in some countries to show whether CMS exists in these countries, and then followed the same pattern of EFW. The theoretical literature on the topic had consensus on the costs affiliated with compulsory military service laws. However, the empirical literature did not share the same consensus as the empirical results were mixed with respect to the effect of compulsory military service law on different economic outcomes. I found that compulsory military service laws were likely to significantly reduce employment rates. Additionally, the regression results showed that the longer the length of compulsory military service the more significant the effect.

Context

The importance of Okun's Law at the economic level especially when we talk about the problem of Unemployment, this model helps us to know how to remove or reduce the problem of unemployment. further, it can give us more information about the type of unemployment. However, for this version of Data, I based on the several countries to know the special elements with them when we apply the same model. that's why I choose annual frequentists because we do have not the same volume of data for all countries that I had to choose it.

Content

First of all, this data is available for everyone, just install wbdata package in your notebook. So this data has 4 features that are really important to build Okun's Law model. GDP growth: Gross Domestic Products growth (annual %) Unemployment_TLF: Unemployment, total (% of total labor force) (modeled ILO estimate) Unemployment_AEF :: Unemployment with advanced education, female (% of female labor force with advanced education) Unemployment_AEM: Unemployment with advanced education, male (% of male labor force with advanced education) Unemployment_AET : Unemployment with advanced education (% of total labor force with advanced education)

Acknowledgements

Okun's Law model is like this :

Unemployment = beta * GDP Growth + alpha

Inspiration

As you know the data is not enough, so we need to use the Bayesian approach to estimate these coefficients.

The quotation above expresses a common, if not dominant, view of the genesis of inflationary pressure in an economy. The story goes something like this: High GDP growth eventually places excessive strain on a nation’s resources. This strain can become particularly acute in labor markets, where it is manifested as low unemployment. The labor market tightness associated with this low unemployment ultimately leads to higher prices.

Portugal's graduate unemployment landscape between 2020 and 2024 reveals a striking imbalance across fields of study. Business sciences, administration, and law graduates faced the highest unemployment rate at 25.7 percent, while information and communication technologies (ICT) graduates experienced the lowest at 1.8 percent. The social sciences, journalism, and information field and arts and humanities presented the second and third-highest shares of unemployed graduates registered in employment centers, with 18 and 15.7 percent, respectively. Rising graduate numbers, persistent gender gap  The number of higher education graduates in Portugal has more than doubled since the late 1990s, reaching over 95,600 in the 2022/2023 academic year. Women consistently outnumbered men among graduates, with nearly 56,000 female graduates compared to 40,000 male graduates in the most recent year. However, this gender gap reversed in science, technology, engineering, and mathematics (STEM) fields, where men accounted for 65 percent of graduates across all study cycles during the 2022/2023 academic year. Growing higher education enrollment  Despite the increasing number of graduates, the unemployment rate for the youth has been decreasing slowly since the end of 2023. The positive trend occurred as higher education enrollment continues to grow, with over 446,000 students in the 2022/2023 academic year. Universities attract more students than polytechnic institutes across all regions, with Greater Lisbon hosting the largest student population of over 147,000, despite not being the country’s region with the highest number of higher education establishments.

Relation between sentence in court and employment status. Seriousness of delict ( theft ), influence of alcohol, drugs / dismissal of charge / temporary custody / sentence demanded / report by expert / sentence / appeal to higher court, final sentence / recidivism, number of previous convictions. Background variables: basic characteristics/ residence/ occupation/employment

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels. Disclaimer: For information regarding future updates or preliminary/final data releases, please refer to the Bureau of Labor Statistics Release Calendar: https://www.bls.gov/cew/release-calendar.htm

Dataset

This dataset was created by JasonL

Contents

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.

CS Disco: A Case for (LAW) Growth

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

The Canada Survey of Giving, Volunteering and Participating is the latest iteration of a series of surveys that began with the National Survey of Giving, Volunteering and participating. It was conducted by Statistics Canada in 1997 as a supplement to the Labour Force Survey, and was repeated in the fall of 2000. In 2001, the federal government provided funding to establish a permanent survey program on charitable giving, volunteering and participating within Statistics Canada. The survey itself was renamed the Canada Survey of Giving, Volunteering and Participating (CSGVP). The CSGVP was developed through a partnership of federal government departments and voluntary sector organizations. These include Canadian Heritage, Health Canada, Human Resources and Social Development Canada, Imagine Canada, the Public Health Agency of Canada, Statistics Canada and Volunteer Canada. Because the 2004 CSGVP employs a different survey approach and because it uses a somewhat different questionnaire than did the previous surveys, it is not appropriate to compare results from the 2004 CSGVP with the two previous surveys. There are two data files for the 2004 Canada Survey of Giving, Volunteering and Participating (CSGVP): the main answer file (MAIN.TXT), and the giving file (GS.TXT). To link between the MAIN and GS Public Use Microdata Files use the variable PUMFID. This is the giving or charitable donation answer file. It contains one or more records for each person who made a financial donation: one record for each of up to 10 charitable organizations to which the respondent donated, over the 12 month reference period, in response to a particular solicitation method. For each of the 13 methods of solicitation itemized in the questionnaire, a donor may therefore have up to 10 records, each containing information regarding the type of organization, as well as the total value of all donations made to that organization in response to that method of solicitation. In cases where the respondent donated to more than 10 organizations in response to a given method of solicitation, the total value of all donations made to the remaining organizations is present on the 10th record as derived variable GS1D08.

Apart from a few individual studies the labor market and it’s segregation, the relation between supply and demand, employment structure, and unemployment is until today not analyzed in its historical dimension. The same applies to the history of labor market policy. The present study’s aim is to fill this gap by describing the most important elements of the labor market policy: the employment service, the job creation, and the unemployment benefit. The researcher addresses one of the most important problems of the modern, on the intense division of labor based economy: the labor market coverage with highly skilled workers. The reason for the complexity of this task lies in the strong segmentation of the labor market (numerous sub-markets and sectors with different requirements on qualifications) and – since the industrialization – the fact, that the labor market is in a process of constant, sometimes short term changes. To manage this situation, market transparency to the largest possible extent is necessary, which is a central field of the employment service’s responsibility. To this basic function (the supply of the labor market with adequate skilled workers, called by Anselm Faust ‘market function’) further important functions are attached, for example the prevention of unemployment. The not commercially labor service was expanded in Germany to an inherent part of modern labor market policy in a period between 30 and 40 years. The labor service was in the end of the 19th century insignificant and both institutionally and in terms of a policy of interests fragmented. But in 1927 it was integrated by the law about labor service and unemployment insurance into a system of coordinated public institutions and public policies. The purpose of this new implemented law is to balance and to influence the labor market, the employment policy, and to ensure a basic social care of the unemployed. The goals of the labor market policy, developed in a long historical process til today, can be summarized as follows: - to influence quantity, composition and qualification of possible and actual labor force in direction to an optimal structure and development; - to induce the best possible adaption between available labor force and working places; - to use the labor force productively, fully and continuously to enable the individual and public increase in welfare or benefit; - to protect the economically active population from the consequences of unemployment. The preset study addresses the most important elements in historical view and in policy terms, i.e. the labor service, job creation, and unemployment compensation. The labor market is the place to meet demand and supply. Therefore, labor service is the organized market process and the contact point, where supply and demand for labor does coincide. The history of labor service, job creation, and unemployment compensation in Germany between 1890 and 1918 is analyzed in terms of: - its social and economical preconditions: the structure of the labor market, the development and social meaning of employment and unemployment; - the theoretical and ideological subsumption as well as the social interests derived from the social and economic conditions. - the function of labor market policy within the German ‘Kaiserreich’s’ (German Royal empire’s) conflicts of interests and the political importance of labor market policy, resulting from the conflicts of interests; - the strategies for solving the labor market conflicts, the actions and investments and their organizational arrangement; - the government’s part by solving conflicts and organizing the labor market; - the relevance of labor market instruments to organize labor market processes and to protect the unemployed. (see: Faust, A., 1986: Arbeitsmarktpolitik im Deutschen Kaiserreich. Arbeitsvermittlung, Arbeitsbeschaffung und Arbeitslosenunter¬stützung 1890-1918. Stuttgart: Franz Steiner, S. 2f, S. 10).

Datatables in the search- and downloadsystem HISTAT (Topic: Erwerbstätigkeit (=employment) ) Annotation: HISTAT is offered in German.

A. Arbeitslosigkeit (=Unemployment)

A.01 Arbeitsgesuche auf 100 offene Stellen (1907-1918) (number of applications to 100 vacancies) A.02 Die Arbeitslosenquote in den Gewerkschaften (1904-1918) (unemployment rate in lobor unions) A.03 Die Arbeitslosenquoten in den Gewerkschaftsverbänden (1904-1918) (unemployment rates in trade union associations) A.04 die geschlechtsspezifische Arbeitslosenqu...

Dataset

This dataset was created by Abdul Qoyyuum Haji Abdul Kadir

Released under Data files © Original Authors

Contents