What are the best tools for analyzing labor market data?

Job-posting websites are the most powerful sources of labour market intelligence nowadays. They contain the most up-to-date data on in-demand skills, labour demand, and supply. The major problem that exists in this context is the standardisation of the available data and, of course, the data’s availability itself, given that it is the intellectual property of job-posting sites.

Online job postings (OJPs) data is an excellent resource for analyzing labour markets. It offers the most up-to-date information on labour trends, as well as sought-after skills. The extensive collection of specific, localized and up-to-date?data points assist individuals in understanding sector shifts, forecasting trends, and matching their approach to market conditions. Crucially, each job listing provides a comprehensive rundown of the employer's requirements, also highlighting occupation-specific and regional patterns, along with necessary qualifications/skills.

Labour market statistics usually lag the present time by about one quarter of the year, due to the time it takes to accurately collect and encode the data, using best-practice random sampling techniques. This lag makes it difficult to quickly adapt policy to meet the present needs of the labour market, especially because labour markets in modern times can change quickly when shocks happen. Therefore, economists often look for alternative, unofficial but correlated indicators of economic activity, or practice so-called nowcasting forecasts of the present time or near future. Acdemically-driven surveys can also provide excellent complementary sources of labour market data, especially where official longitudinal data has poor methodology.

Programming languages allow research to be reviewed and checked. Statistical programming languages allow one to handle big datasets (even ones that are millions to hundreds of millions of rows long), whereas Excel cannot handle such datasets. Descriptive statistics run much faster with programming software compared to analysis in Excel. R, with its ggplot2 package, is very well designed for precise, statistical data visualization, and the language is natural to use for this purpose compared to Stata. R even supports a web-app building framework (Shiny) that one can use to present data. Data visualization tools such as Tableau, Power BI and Google Data Studio are well-suited for business intelligence in a corporation, hiding technicalities.

When thinking about labor market, it is always important to consider what you will not observe in the data. How many unemployed workers are not looking for a job and missing in unemployment numbers? Is self-employment important? How do you control for unobserved characteristics of both workers and firms? Having an answer to these questions will help you make better and more subtle interpretations than taking results at face value.

Research on labour markets is published in working papers and journal articles, and researchers in most cases do this within a research unit. Often, these research projects stem from within an academic setting. Economists often write papers using LaTeX, a word processing language that gives the writer a lot of control over the presentation and in many ways ensures consistency in the typography. When researchers analyse numerical or visual findings in a paper, it is common to describe the results literally, especially identifying broad trends. A secondary step is to try to understand why the results are the way they are; we look elsewhere for a "mechanism" that drives behaviour or a movement, as spurious conclusions are to be avoided.

When working with large datasets stored of labor market data, SQL can be considered, as it might help in dealing with complex data manipulations and aggregations.

The data used to determine the Index is obtained from the Kronos system The vast amount of information (about 30 million requests per year) is stored in a SQL database. Since Mathematica has built-in support for many databases, including MySQL, Open Database Connectivity (ODBC), and HSQLDB, Yerex said it was easy for him to find the data. He then analyzed the data with Mathematica. Time series analysis Labor demand in the labor market is highly seasonal, and analysis of economically related time series requires specialized forms of seasonal adjustment. Yerex used Mathematica for time series analysis and to remove seasonality.

Hiring managers need regional and sectorial data to adjust their recruitment strategy on markets getting tighter. Those data can be provided by a third party on request obvsiouly, but since it is a recurrent need, Labour Market intelligence tools add great value to their work.