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Data pull script for 'Uncertainty and Labor Market Fluctuations' by Jo and Lee (2019)

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U.S. Labor State and Flow Big Data

This repository contains a series of API calls that pull and aggregate labor market time series data from the public sources using julia programming language. The script builds a big data frame used in my co-authored research paper 'Uncertainty and Labor Market Fluctuations' by Jo and Lee (2019), Federal Reserve Bank of Dallas Working Paper. Other research papers citing our research is documented by the Google Scholar. The dataframe is best used to evaluate the U.S. labor market in the application of macroeconomics and machine learning.

Use of this repository requires a citation of this repository and a note of proper authorships of myself and the cited papers noted in the code.

Application: Data overview used in Jo and Lee (2019)

The metrics calculated in the code use snippets translated from the MATLAB codes provided by the authors' research note below. All optimization processes uses julia's non-linear solver instead of MATLAB's fsolve.

Data Description Data Source Relevant Literature
Unemployment by duration and by reason Unemployment by duration (weeks) by reasons BLS -
Claims of unemployment insurance Initial and continuous claims of unemployment insurance BLS/FRED -
Employment: Part-time by reason Part-timer employment statistics by reason (economic conditions) BLS/FRED -
Hours worked Average hours worked for non-agricultural sector BLS/FRED -
Job finding statistics Job finding and separation rate CPS/JOLTS/FRED Elsby et al. (2009)
Flow hazard rate Flow of labor between different states BLS/Elsby et al. (2009) Elsby et al. (2009)
Transition probability Probability of changing employment state/status BLS/Borowczyk-Martins and Lalé (2019, 2020) Borowczyk-Martins and Lalé (2019, 2020)

Unemployment by duration and by reason

Data Source Data Pull Method Aggregation
Unemployed less than 5 weeks BLS/FRED julia - FRED API (level: UEMPLT5 / rate: LNS13008397)
Unemployed for 5 to 14 weeks BLS/FRED julia - FRED API (level: UEMP5TO14 / rate: LNS13025701)
Unemployed for 15 weeks and over BLS/FRED julia - FRED API (level: UEMP15OV / rate: U1RATE)
Average duration of unemployment BLS/FRED julia - FRED API (level: UEMPMEAN)

For those with specific reasons, as a percent of total unemployed,

Data Source Data Pull Method Aggregation
Job losers on layoff BLS/FRED julia - FRED API (rate: LNS13023654)
Job losers not on layoff BLS/FRED julia - FRED API (rate: LNS13026511)
Job leaver BLS/FRED julia - FRED API (rate: LNS13023706)
Re-entrants BLS/FRED julia - FRED API (rate: LNS13023558)
New entrants BLS/FRED julia - FRED API (rate: LNS13023570)

For those with unemployed less than 5 weeks, specifically,

Data Source Data Pull Method Aggregation
Initial Claims BLS/FRED julia - FRED API (rate: ICSA)
Continuous Claims BLS/FRED julia - FRED API (rate: CCSA)
Job loser BLS julia - BLS API (level: LNU03023633)
Job leaver BLS julia - BLS API (level: LNU03023717)
Re-entrants BLS julia - BLS API (level: LNU03023581)
New entrants BLS julia - BLS API (level: LNU03023585)

The BLS series of unemployment by reasons are also broken down by different durations of unemployed.

Employment: Part-time by reason

Part-time employment count in non-agricultural sector, for,

Data Source Data Pull Method Aggregation
Economic reason BLS/FRED julia - FRED API (level: LNS12032197)
Non-Economic reasons BLS/FRED julia - FRED API (level: LNS12033182)

Hours worked for part-time workers

Data Source Data Pull Method Aggregation
Average hours worked BLS julia - BLS API (level: LNU02033235)

Future Applications

With the collection of labor time series data, there are numerous applications to investigate the historical and real-time labor market and macroeconomic dynamics. Here is a growing list to explore in future research.

Hidden Markov Model for transition probability estimation

  • State-space model implementation
  • Recession predictor and space estimation of labor flow

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Data pull script for 'Uncertainty and Labor Market Fluctuations' by Jo and Lee (2019)

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