Skip to content


Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Replicating Oswald (2019)

Unit Tests Status (click): Build Status

This is the replication kit for The Effect of Homeownership on the Option Value of Regional Migration, forthcoming in Quantitative Economics.

Software Requirements

  1. julia version 0.6.x (not the latest version of julia) is needed to replicate all results from section 3 onwards in the paper.
  2. R at least version 3.5 for results in chapter 2 and if you want to rebuild the data inputs to the julia package.
  3. git (optional, but useful. In general ;-))

Installation Instructions

Installing julia Package mig

All results from the structural model are produced with the package mig, which is a sub-folder of this repository. Here is how to install it.

  1. In order to use the code, you should clone this repository to your computer by typing in your terminal

    # I refer to where you want this on your computer as MIGJL
    # `MIGJL` is only an example. 
    cd MIGJL  
    git clone

    or you manually download the source code as a zip folder from here.

  2. Go to where you cloned the package to, e.g. MIGJL.

  3. We'll call julia6 the julia v0.6.x executable you installed above, in order to comply with the software requirements. Type this in your terminal:

    julia6 mig/install.jl

    to download all dependencies and precompile the package.

  4. Given the non-standard structure of this repo, the above script tries to sym link directory MIGJL/mig/ into your standard julia package directory at ~/.julia/v0.6 with unix command ln -s. This may not work on a windows machine, but I found this for you to try yourself.

  5. Run julia6 -e 'Pkg.test("mig")'; to run the unit tests on your computer.

  6. Observe that the (green!) badge on top of this README indicates that the same tests run on MacOS and Linux on Travis-CI.

Installing R package migR

The easiest way to deal with dependencies is to directly install from github with this command issued in an R session:

library(devtools)  # install `devtools` if not installed.

Alternatively, you can install the package using RStudio, but you need to make sure yourself that all dependencies are installed. Download the migR_1.0.tar.gz package and install as described.


Replication Instructions

This section describes how to replicate all results in the paper.

Replication Hierarchy

Results in the main text are numbered in standard latin (1,2,...) whereas results in the online appendix are prefixed by a section letter (A.1, A.2,...). There are several levels at which a replication exericse could start, related to how much of the input data one wants to take as given. The structure of this could be illustrated in the following way:

    |                                   |
    |          migR R package           |
    |                                   |
                 |    |    |
                 |    |    |
              uses and processes
                 |    |    |
                 |    |    |
        <--------+    v    +--------->

SIPP micro data   FHFA Indices    BEA Income Series       Level 1
   Motivating Results       Data Moments                  Level 2
     chapter 2                      |
                       |    mig julia package    |
                            Results chapters 3+           Level 3

As it is probably most relevant for many users, I will start at the highest Level 3, taking all input data as given, then move down the chain.

Level 3 Replication

This level uses the pre-built input datasets in MIGJL/mig/in to run the structural model. The simplest interface is to use the run file which you can launch on your command line in directory MIGJL/mig as follows:

> cd MIGJL/mig
> julia6 run.jl --help

The Effect of Homeownership on the Option
Value of Regional Migration (Oswald, 2019)

    | Welcome to the run file of my paper. Please
    | see below how to run the code. 
    | Thanks,
    | Florian

    run.jl -h | --help
    run.jl --version
    run.jl estim (bgp|grad|slices|stderrors) [--nworkers=<nw>] [--maxiter=<maxit>][--npoints=<npts>]  
    run.jl test 
    run.jl experiment (elasticity|ownersWTP|ownersWTP2|moneyMC|decomp) [--nworkers=<nw>] [--shock=<sh>] [--nosave]  [--neg]
    run.jl experiment moversWTP [--nworkers=<nw>] [--nosave] [--region=<reg>] 
    run.jl experiment noMove [--yshock=<ys>] [--pshock=<ps>] [--nosave] [--nworkers=<nw>] 

    -h --help           Show this screen.
    --nworkers=<nw>     use <nw> of workers for task. [default: 1]
    --npoints=<npts>    number of points to use [default: 10].
    --maxiter=<maxit>   max number of iterations in estimation [default: 500].
    --region=<reg>      in which region to run experiment [default: 1].
    --nosave            don't save experiment output. If you set it, it doesn't save. 
    --neg               perform negative elasticity shock. If you set it, it does negative.
    --shock=<sh>        type of shock to apply [default: q]
    --yshock=<ys>       shock applied to regional income [default: 1.0]
    --pshock=<ps>       shock applied to regional price [default: 1.0]
    --version           show version

Section 5.3: Parameter Estimates and Moments

Table 8: Parameter Estimates

You perform estimation of the model by running

julia6 run.jl estim grad

optionally specifying the number of workers desired. Standard errors are subsequently obtained by running

julia6 run.jl estim stderrors

Figures 4 and D.1 and tables D.1 and D.2

The fit of the model is illustrated by comparing model and data moments. You can write a table to this extent to disk by running in a julia6 session

> julia6
   _       _ _(_)_     |  A fresh approach to technical computing
  (_)     | (_) (_)    |  Documentation:
   _ _   _| |_  __ _   |  Type "?help" for help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 0.6.2 (2017-12-13 18:08 UTC)
 _/ |\__'_|_|_|\__'_|  |  Official release
|__/                   |  x86_64-apple-darwin14.5.0

julia> using mig

julia> runObj(printm = true)   

The figures are then created with function plot_moment_fit in R package migR.

Section 6.1: Elasticities with respect to Regional Shocks

Tables 9 and E.1

julia6 --color=yes run.jl experiment elasticity --nworkers=9
julia6 --color=yes run.jl experiment elasticity --nworkers=9 --shock=p

Users may find it helpful to refer to script MIGJL/ to see how I ran this on an AWS cluster built with cfncluster.

Section 6.2: Why Do Owners Move Less?

Table 10 is created by running

julia6 --color=yes run.jl experiment decomp

Section 6.3: Owner Regret

Table 12 is created by running:

julia6 --color=yes run.jl experiment ownersWTP2 --nworkers=9

Section 6.4: The value of Migration

Table 11 is created by running:

julia6 --color=yes run.jl experiment noMove --nworkers=9

Version 2 and 3 of that experiment, i.e. tables E.11 and E.12 in the online appendix are similarly created with

julia6 --color=yes run.jl experiment noMove --nworkers=9 --yshock=0.99 --pshock=0.99
julia6 --color=yes run.jl experiment noMove --nworkers=9 --yshock=0.95 --pshock=0.9

Figures 5 and 6, along with several other figures, can be created, after running the above experiments, by doing in a julia6 session

julia> using mig

julia> n = mig.read_noMove()   # reads baseline version (table 11)

julia> mig.plot_noMove(n)  

Section E.2 Comparative Statics of a Regional Price Shock

The tables in that section can be reproduced by running

julia6 run.jl experiment scenarios --nworkers=9

Level 2 Replication

This section is concerned with code in a different repository at

This replication level is taking as given the input data in folder data/ of the migR package. The package help manual with function reference is available as a pdf at the root of the package or online as a searchable website.

You can obtain all code and prebuilt data on the commandline with

git clone

or by downloading the precompiled package as described above

Chapter 2 Tables and Figures

To replicate, you would invoke R and load the package with

R version 3.5.1 (2018-07-02) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(migR)


Table 2

With the package loaded as above, you get table 2 with function CPS.distance

Table 3

Function Sipp.SumStats.

Table 4

Function SippProbitMove.

Figure 1

Function PlotSippMigrationRates.

Figure 2

Function correlograms.

Section 5.1: Estimation of Exogenous Processes and Moment Generation

To parametrize the structural model, several processes need to be estimated first. This section describes the functions in charge of this task.

Tables 5, B.4 and Figures 3, B.3, B.1 and B.2

This is produced by function Export.VAR

Table 6

This is produced by function both_prices_output

Income Process

Table and figure C.1 are produced by function Export.IncomeProcess

Estimation of Movers' Copula

Figures C.2 and C.3 as well as Table C.2 is produced by Sipp.wage_residual_copulas

Creation of Moments

Function Sipp.moments returns a data.table with data moments.

Exporting All Inputs for julia

Function Export.Julia writes all relevant data to disk in .rda format.

Level 1 Replication

SIPP Download

This is the lowest level i.e. it starts with SIPP data acquisition. I used code previously published at ajdamico/asdfree, but it has since evolved. I have a fork the previous version, however, and so the process starts with this:

  1. git clone
  2. cd asdfree/SIPP
  3. uncommenting as warranted, execute scripts downxxxx.R.

SIPP Extraction

Once the SIPP database is created locally, we can extract data from it. The function is Extract.wrap.

FHFA and BEA download

Functions get_BEA_persincome and download.FHFA get the macro data series. Notice that get_BEA_persincome depends on external package EconData


Replication Kit for Oswald, Quantitative Economics (2019)






No packages published