How to Digitize a Dataset Using Your Phone

This summer and fall, I decided that I wanted to digitize 600+ tables from the 1923-1936 Ministry of Labour Gazette, a British government publication with detailed monthly data on unemployment in different industries during the interwar period. While many, many academic papers have used aggregated or partial data based on this series, to digitize the entire dataset by hand would be an overwhelming (perhaps impossible) task. But there are some dimensions of interwar unemployment that we can only really understand with a complete series, including the different experiences of men and women under the unemployment insurance system, the scale and nature of short-time work, and the distinct patterns of unemployment across specific industries. Motivated by these questions and my optimism about the power of new technology, I set out to find a method of digitizing this data that was time efficient, cheap, and accurate. Here’s what I came up with!…

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Takeaways from my MPhil dissertation

The last academic year was a whirlwind dominated by researching, writing, editing, and submitting my MPhil dissertation, "Normal Science and Anomaly: Responding to Unemployment in Economics in the 1920s." My motivation for the work was to explore the relationship between empirical evidence and economic theory, which is important for understanding how academic economic research relates to the "real world" -- a perennially interesting question.

To get at these bigger questions, I did a case study of a specific historical episode: persistently high unemployment in Britain in the 1920s. Unlike the United States and some other European countries, Britain experienced high unemployment (on average 12%) for the years preceding the early 1930s Great Depression…

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Using regular expressions to clean and process OCR data

This is a write-up of a script I wrote for my RA work, demonstrating how regular expressions in Python can be used to clean and process OCR text with many errors in order to generate a workable dataset. The goal in this specific example is to clean US Senate testimony to make a dataset listing the speaker in one column with their testimony in the next column. I also show how to categorize the comments by the section of testimony they are in and how to give an index for those sections. The script is available on GitHub.

The script as written requires an input file called "V1". In this case, the file is OCRd text of Senate testimony from 1913. The text delineates the speaker at the start of each comment (e.g. "Senator Gallinger") and then gives the comment. There are also various section breaks (e.g. "TESTIMONY OF TRUMAN G. PALMER—Continued."). These comments and section breaks are the text data we are interested in...

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