P. David Boll

Work in Progress

Career ladders and the skill structure of the labor market
2026

Publications

Testing and correcting for spatial unit roots in regression analysis
2026

with Sascha O. Becker, Hans-Joachim Voth

| The Stata Journal
DOIOpen access Software

Spatial unit roots can lead to spurious regression results. We present an overview of the methods developed in Müller and Watson (2024, Econometrica 92: 1661-1695) to test and correct for spatial unit roots and introduce a suite of commands (spur) implementing these techniques. Our commands exactly replicate results in Müller and Watson (2024) using the same data as Chetty et al. (2014, Quarterly Journal of Economics 129: 1553-1623). As a guide for applied researchers, we provide a practical algorithm for regression analysis using these methods and a simulated illustration in Stata.
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Becker, Sascha O., P. David Boll, and Hans-Joachim Voth. 2026. “Testing and correcting for spatial unit roots in regression analysis.” The Stata Journal 26(2): 177-202. https://doi.org/10.1177/1536867X261449932.
BibTeX
```
@article{bbv2026,
  author = {Becker, Sascha O. and Boll, P. David and Voth, Hans-Joachim},
  title = {Testing and correcting for spatial unit roots in regression analysis},
  journal = {The Stata Journal},
  year = {2026},
  volume = {26},
  number = {2},
  pages = {177--202},
  doi = {10.1177/1536867X261449932},
  url = {https://doi.org/10.1177/1536867X261449932},
}
```

Pre-PhD

Cyclical signals from the labor market
2022

with Tino Berger, James Morley, Benjamin Wong

| Oxford Open Economics
DOIOpen access Working paper

We consider which labor market variables are the most informative for estimating and nowcasting the US output gap using a multivariate trend-cycle decomposition. Although the unemployment rate clearly contains important cyclical information, it also appears to reflect more persistent movements related to labor force participation that could distort inferences about the output gap. Instead, we show that the alternative U-2 unemployment rate (job losers as a percentage of the labor force) provides a more purely cyclical indicator of labor market conditions. To a lesser extent, but consistent with a link of the output gap to real labor costs in a New Keynesian setting, we also find that average hourly earnings are informative about the output gap.
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Berger, Tino, P. David Boll, James Morley, and Benjamin Wong. 2022. “Cyclical signals from the labor market.” Oxford Open Economics 1: odab002. DOI: 10.1093/ooec/odab002.
BibTeX
```
@article{BBMW2022,
  title = {Cyclical signals from the labor market},
  author = {Berger, Tino and Boll, Paul David and Morley, James and Wong, Benjamin},
  year = {2022},
  journal = {Oxford Open Economics},
  volume = {1},
  number = {},
  pages = {odab002},
  doi = {10.1093/ooec/odab002},
  url = {https://doi.org/10.1093/ooec/odab002}
}
```
The Gender Pay Gap in University Student Employment
2022

with Lukas Mergele, Larissa Zierow

| Empirical Economics
DOIOpen access Working paper Coverage (FAZ)Coverage (Ökonomenstimme)

Gender pay gaps are commonly studied in populations with already completed educational careers. We focus on an earlier stage by investigating the gender pay gap among university students working alongside their studies. With data from five cohorts of a large-scale student survey from Germany, we use regression and wage decomposition techniques to describe gender pay gaps and potential explanations. We find that female students earn about 6% less on average than male students, which reduces to 4.1% when accounting for a rich set of explanatory variables. The largest explanatory factor is the type of jobs male and female students pursue.
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Boll, P. David, Lukas Mergele, and Larissa Zierow. 2022. “The Gender Pay Gap in University Student Employment.” Empirical Economics 63: 2253-2313. DOI: 10.1007/s00181-021-02194-1.
BibTeX
```
@article{BMZ2022,
  title = {The Gender Pay Gap in University Student Employment},
  author = {Boll, P David and Mergele, Lukas and Zierow, Larissa},
  year = {2022},
  journal = {Empirical Economics},
  volume = {63},
  number = {},
  pages = {2253-2313},
  doi = {10.1007/s00181-021-02194-1},
  url = {https://doi.org/10.1007/s00181-021-02194-1}
}
```

EC9A2 Advanced Macroeconomic Analysis, Term 1
PhD-level course, 2023 – present

EC226 Econometrics 1
Undergraduate course, 2022 – 2023

Spatial Econometrics

SPUR and SCPC Packages

Stata, Python, and R packages implementing techniques for spatial inference developed by Müller and Watson.