DOI: 10.7910/dvn/smqfgs
title: Replication data for: Job-to-Job Mobility and Inflation

description: Replication files for "Job-to-Job Mobility and Inflation"
Authors: Renato Faccini and Leonardo Melosi
Review of Economics and Statistics
Date: February 2, 2023
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ORDERS OF TOPICS

.Section 1. We explain the code to replicate all the figures in the paper (except Figure 6)
.Section 2. We explain how Figure 6 is constructed
.Section 3. We explain how the data are constructed

SECTION 1
Replication_Main.m is used to reproduce all the figures of the paper except Figure 6. All the primitive variables are defined in the code and all the steps are commented in code to facilitate the replication of our results.

Replication_Main.m, should be run in Matlab. The authors tested it on a DELL XPS 15 7590 laptop wih the follwoing characteristics:

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Processor Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz 2.40 GHz
Installed RAM 64.0 GB
System type 64-bit operating system, x64-based processor
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It took 2 minutes and 57 seconds for this machine to construct Figures 1, 2, 3, 4a, 4b, 5, 7a, and 7b. The following version of Matlab and Matlab toolboxes has been used for the test:

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MATLAB Version: 9.7.0.1190202 (R2019b)
MATLAB License Number: 363305
Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 19045)
Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
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MATLAB Version 9.7 (R2019b)
Financial Toolbox Version 5.14 (R2019b)
Optimization Toolbox Version 8.4 (R2019b)
Statistics and Machine Learning Toolbox Version 11.6 (R2019b)
Symbolic Math Toolbox Version 8.4 (R2019b)
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The replication code uses auxiliary files and save the pictures in various subfolders:

\JL_models: It contains the equations describing the model including the observation equations and routine used to solve the model. To do so, the routine in this folder calls other routines located in some fo the subfolders below.

\gensystoama: It contains a set of codes that allow us to solve linear rational expectations models. We use the AMA solver. More information are provided in the file AMASOLVE.m. The codes in this subfolder have been developed by Alejandro Justiniano.

\filters: it contains the Kalman filter augmented with a routine to make sure that the zero lower bound constraint for the nominal interest rate is satisfied in every period in our sample.

\SteadyStateSolver: It contains a set of routines that are used to solved the steady state of the model numerically.

\NLEquations: It contains some of the equations of the model that are log-linearized using the symbolic toolbox of matlab.

\NberDates: It contains a set of routines that allows to add shaded area to graphs to denote NBER recessions.

\Graphics: It contains useful codes enabling features to construct some of the graphs in the paper.

\Data: it contains the data set used in the paper.

\Params: It contains a spreadsheet with the values attributes to the model parameters.

\VAR_Estimation: It contains the forecasts implied by the Bayesian VAR model of Section 2.

The output of Replication_Main.m are the figures of the paper that are stored in the subfolder \Figures



SECTION 2
The Excel file "Figure-6.xlsx" is used to create the charts in Figure 6. 
All three panels of the charts (A, B, and C) plot a measure of unexpected wage inflation against the unemployment rate, 
then fits separate linear regressions for the periods 1960-1985,1986-2007, and 2008-2009. 
Unexpected wage inflation is given by the difference between wage growth and a measure of expected wage growth. 
In all three panels, the unemployment rate used is the civilian unemployment rate (UNRATE), seasonally adjusted, from the BLS. 

The sheet "Panel A" uses quarterly manufacturing sector average hourly earnings growth data, seasonally adjusted (CES3000000008), from the Bureau of Labor Statistics (BLS) Employment Situation 
report as the measure of wage inflation. The unexpected wage inflation is given by the difference between earnings growth at time t and the 
average of earnings growth across the previous four months. 
Growth rates are annualized quarterly values.

The sheet "Panel B" uses quarterly Nonfarm Business Sector Compensation Per Hour, seasonally adjusted (COMPNFB), from the BLS Productivity and Costs report as its measure of wage inflation. 
As in Panel A, expected wage inflation is given by the average wage inflation over the previous four quarters.
Growth rates are annualized quarterly values.

The sheet "Panel C" uses semiannual manufacturing average weekly earnings data (CES3000000030) as the wage measure. 
Expectations are taken from the Livingstone Survey of forecasts of average weekly earnings in manufacturing 
(http://www.phil.frb.org/research-and-data/real-time-center/livingston-survey/historical-data/). 
Growth rates are annualized semiannual values. See the Excel file for more detail. 

SECTION 3
In this section, we show how to reproduce data used in the paper. In the subfolder Data Replication, see the "Data" tab of "Data Replication.xlsx"
1) The unemployment rate series and labor force participation series come from FRED
 (see the descriptions in the Excel sheet for more details)
2) The employment-to-employment flow rate ("E2E/J2J flow rate") comes from the Current Population Survey. 
 Pre-1996 observations use a method of job-to-job flows calculated by Jason Faberman of the Chicago Fed. 
 After 1996, we use the data from Fujita, Moscarini and Postel-Vinay. The series is seasonally adjusted.
 (See the Excel sheet for more details). 
3) The job-finding rate (JFR-CPS) comes from the Current Population Survey. 
 Pre-1996 observations use the method of Robert Shimer. 
 Post-1996 observations are job-to-unemployment flows calculated by Jason Faberman of the Chicago Fed.
 The series is seasonally adjusted. 
 (See the Excel sheet for more details). 

Seasonal adjustment is carried out in the folders:
-- "Create E2E from Fujita"
-- "Create JFR from CPS flows"
We use the "seasonal" package in R, which uses the X11 seasonal adjustment algorithm. Shimer's job-finding rate is calculated in the subfolder "Shimer JFR".

The data used in the VAR estimation are explained in details in Appendix D.