Why Remote Work Stuck: Structural Analysis of Remote Work Persistence

Author: Mitchell Valdes-Bobes Institution: University of Wisconsin-Madison Status: Working Paper Data Coverage: 2013-2025

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Research Overview

This project investigates why remote work adoption increased dramatically during COVID-19 and then stabilized at levels significantly higher than pre-pandemic, rather than returning to baseline. Using a structural search-and-matching model with worker heterogeneity in remote work preferences and job heterogeneity in teleworkability, I decompose the persistence of remote work into: (1) technology improvements, (2) preference shifts, and (3) sorting mechanisms.

Key Finding

Remote work persistence is primarily driven by technology improvements in teleworkability (60%) rather than preference changes (30%) or improved sorting (10%). This suggests policy interventions targeting workplace infrastructure may be more effective than those targeting worker preferences.

Methods

• Statistical Matching: ML-based imputation to merge SIPP remote work intensity with CPS earnings data
• Structural Estimation: Simulated Method of Moments (SMM) with genetic algorithm optimization
• Data Pipeline: Integrated processing of 73GB multi-source data (SIPP, CPS, ATUS, O*NET)
• High-Performance Computing: Parallel estimation using 128 cores, 512 population GA

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Technical Highlights

1. Large-Scale Data Engineering (73GB Pipeline)

Data Sources Integrated:

• SIPP (Survey of Income and Program Participation): 131MB processed, remote work intensity measures
• CPS (Current Population Survey): 65GB processed, main analysis sample with earnings
• ATUS (American Time Use Survey): Pre-2022 telework validation
• O*NET: Occupation-level teleworkability scores

Statistical Matching:

• LightGBM-based machine learning imputation
• Harmonized variables across datasets (education, age, occupation, geography)
• Cross-validation: R² = 0.68 for remote work intensity prediction

2. Structural Model Estimation

Model Features:

• Search and matching framework with worker-job heterogeneity
• Workers differ in remote work preferences (z ~ LogNormal)
• Jobs differ in teleworkability (ψ ~ distribution)
• Optimal choice of remote work intensity (α ∈ [0,1])

Estimation Method:

• Genetic algorithm optimization (100 generations, 512 population)
• Simulated Method of Moments with bootstrap variance weighting
• 11 empirical moments matched (wage distribution, remote work shares, sorting patterns)
• ~30 minutes per estimation run on HPC cluster

3. Code Architecture

Languages Used:

• Julia: Structural model, optimization, moment computation (47 files)
• Python: Data processing, statistical matching, ML imputation (20 files)
• Stata: Wage regressions, robustness checks (18 files)

Key Modules:

• src/structural_model/: Economic model implementation (9 modules)
• src/optimization/: GA and multi-start local search
• src/empirical/: Moments estimation and statistical matching
• src/data/: Multi-source data acquisition and processing

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Repository Structure

why_remote_work_stuck/
│
├── docs/
│   ├── technical/
│   │   ├── EMPIRICAL_PIPELINE.md      # Complete data processing documentation
│   │   ├── DATA_DICTIONARY.md         # Variable definitions and sources
│   │   └── STRUCTURAL_MODEL.md        # Model specification and estimation
│   ├── development_logs/
│   │   └── optimization_oct2025.md    # Parameter estimation session notes
│   └── outputs/
│       ├── main.pdf                   # Latest paper
│       └── slides.pdf                 # Latest presentation
│
├── src/
│   ├── data/                          # Data acquisition & processing (Python)
│   │   ├── sipp_process.py            # SIPP remote work metrics
│   │   ├── ipums_process.py           # CPS/ATUS processing
│   │   └── get_fred_q_theta.py        # Labor market tightness
│   ├── empirical/                     # Moments & statistical matching
│   │   ├── stat_matching/             # SIPP→CPS ML imputation
│   │   ├── data_moments_core.jl       # Moment computation
│   │   └── wfh_wage_facts/            # Wage-remote work regressions
│   ├── structural_model/              # Economic model (Julia)
│   │   ├── ModelSetup.jl              # Parameter initialization
│   │   ├── ModelSolver.jl             # Equilibrium computation
│   │   ├── ModelEconomics.jl          # Production & matching
│   │   └── GeneticAlgorithm.jl        # Custom GA implementation
│   ├── optimization/                  # Parameter estimation
│   │   ├── OptimizationObjective.jl   # SMM objective function
│   │   └── multi_start_local_search.jl
│   └── reporting/                     # Results & decomposition
│
├── data/                              # 73GB processed data (see below)
│   ├── processed/
│   │   ├── empirical/                 # Estimation-ready datasets
│   │   ├── cps/                       # CPS with imputed remote work
│   │   ├── sipp/                      # SIPP person-year files
│   │   └── harmonized/                # Cross-dataset harmonization
│   └── aux/                           # Crosswalks and auxiliary data
│
├── results/
│   ├── global_optimization/           # GA estimation outputs
│   │   └── final/
│   │       ├── *_3144287_best.yaml    # Best parameters (2019)
│   │       └── *_3144287.json         # Full optimization history
│   └── bootstrap_moments/             # Bootstrap variance estimates
│
├── manuscript/
│   └── final_document/
│       ├── main.pdf                   # Compiled paper
│       ├── main.tex                   # LaTeX source
│       └── figures/                   # Publication figures
│
└── presentations/
    └── QMW_10202025/
        ├── slides.pdf                 # Queen Mary Workshop slides
        └── slides.tex                 # Beamer source

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Quick Access

📄 Paper PDF - Latest manuscript version 📊 Slides PDF - Queen Mary Workshop presentation 📖 Technical Documentation - Pipeline and data documentation 💾 Estimation Results - Parameter estimates and diagnostics

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Data Availability

Processed data (73GB) is included in this repository at data/processed/. This ensures full reproducibility without re-running expensive data acquisition steps.

Key Processed Files

File	Size	Description
data/processed/empirical/simulation_scaffolding_all_years.feather	5.3GB	Estimation-ready simulation data
data/processed/cps/cps_processed.csv	65GB	CPS with imputed ALPHA (remote work intensity)
data/processed/sipp/sipp_py_B.csv.gz	131MB	SIPP person-year (worked-mass weights)
data/processed/empirical/cps_mi_ready.dta	-	Stata-ready CPS for analysis
data/aux/fred_q_theta.csv	-	Quarterly labor market tightness

Note: For GitHub deployment, consider using Git LFS or hosting data separately (Zenodo, institutional server). See docs/DATA_AVAILABILITY.md for options.

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Estimation Results Summary

2019 Baseline Parameters (Job 3144287)

Preference Parameters:

• μ_z = -1.926 (mean taste for remote work, LogNormal)
• σ_z = 0.744 (preference dispersion)

Technology Parameters:

• ψ₀ = 0.772 (baseline teleworkability)
• A₁ = 24.60 (skill productivity)

Model Fit:

• mean_alpha: 0.0697 vs data 0.0675 (3% error) ✅
• in-person share: 0.9187 vs data 0.8867
• Objective value: 6.97 (SMM weighted distance)

Estimation Details:

• Method: Genetic algorithm (100 generations, 512 population)
• Runtime: 30 minutes on 128 cores
• Convergence: 511/512 individuals per generation

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Requirements

Software Dependencies

Python (3.10+):

pip install -r requirements/python_requirements.txt
# Key packages: polars, pandas, scikit-learn, lightgbm

Julia (1.8+):

using Pkg
Pkg.activate(".")
Pkg.instantiate()
# Key packages: DataFrames, Arrow, Optim, StatsBase

Stata (17+):

• See requirements/stata_requirements.txt for user-written packages
• reghdfe, estout, gtools

System Requirements

• Storage: 73GB for processed data
• Memory: 32GB RAM recommended for full sample analysis
• Compute: HPC cluster recommended for estimation (uses SLURM)

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Quick Start

1. Explore Processed Data

using Arrow, DataFrames

# Load estimation-ready simulation data
scaff = Arrow.Table("data/processed/simulation_scaffolding_all_years.feather") |> DataFrame

# Check remote work shares by year
by_year = combine(groupby(scaff, :year), :alpha => mean, :remote => mean)

2. View Estimation Results

# View best parameters from optimization
cat results/global_optimization/final/global_optimization_modified_2019_3144287_best.yaml

3. Run Model with Estimated Parameters

# Load model and estimated parameters
include("src/structural_model/ModelInterface.jl")

# Quick model test
include("test_model_quick.jl")

4. Reproduce Moments

# Compute empirical moments
include("src/empirical/data_moments_core.jl")

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Skills Demonstrated

Data Engineering

• Large-scale data pipeline (73GB multi-source integration)
• Statistical matching across incompatible surveys
• ETL workflows with data validation
• Efficient storage formats (Arrow/Feather, compressed CSV)

Statistical & Econometric Methods

• Simulated Method of Moments estimation
• Bootstrap variance estimation
• Machine learning imputation (LightGBM)
• Regression analysis with high-dimensional fixed effects

Computational Methods

• Genetic algorithm implementation
• Parallel computing (multi-core optimization)
• High-performance Julia programming
• SLURM job scheduling and monitoring

Software Engineering

• Modular code architecture
• Version control (Git)
• Reproducible research practices
• Comprehensive documentation

Economic Modeling

• Search and matching models
• Worker-job heterogeneity
• Equilibrium computation
• Counterfactual analysis

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Project Timeline

• 2023-Q1: Data acquisition and processing pipeline
• 2023-Q2: Statistical matching methodology development
• 2023-Q3: Structural model specification
• 2024-Q1: Initial parameter estimation
• 2024-Q2: Model refinement and robustness checks
• 2024-Q3: Decomposition analysis
• 2024-Q4: Manuscript preparation
• 2025-Q1: Optimization refinement (latest)

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Citation

If you use this code or data, please cite:

@unpublished{valdesbobes2025remote,
  author = {Valdes-Bobes, Mitchell},
  title = {Why Remote Work Stuck: A Structural Analysis of Remote Work Persistence},
  institution = {University of Wisconsin-Madison},
  year = {2025},
  note = {Working Paper}
}

Or use the included CITATION.cff file.

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License

• Code: MIT License (see LICENSE)
• Data: Original data sources have separate licenses (IPUMS, Census Bureau)
• Paper: All rights reserved

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Contact

Mitchell Valdes-Bobes Department of Economics University of Wisconsin-Madison

For questions about the code or data, please open an issue in this repository.

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Acknowledgments

This research uses data from:

• IPUMS CPS (Flood et al. 2024)
• Survey of Income and Program Participation (U.S. Census Bureau)
• O*NET (U.S. Department of Labor)
• FRED (Federal Reserve Economic Data)

Computational resources provided by the Center for High Throughput Computing at UW-Madison.

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Version: 1.0 Last Updated: November 2, 2025 Repository: why_remote_work_stuck