Last week, I focused on creating the presentation, balancing aspects of engagement, comprehension, and rigor. This week, I finished writing my paper. Unlike the presentation, I no longer faced the same time limit restraints or the need to simplify. Thus, in each section, I focused on giving as much relevant detail as possible.

Data

The data section was split into three sections: unemployment data, AI exposure indices, and summary statistics.

Unemployment Data

In the first section, unemployment data, I introduced the variables and data that I obtained from IPUMS. These include the data for the primary analysis, EMPSTAT, YEAR, MONTH, and COUNTY. This also includes the variables for the robustness tests and heterogeneous treatment effect analysis, like OCC2010, SEX, EDUC, and AGE.

AI Exposure Indices

In the second section, AI exposure indices, I introduce the variables that I used to measure AI exposure. Most importantly, this contains the AI exposure indices developed by Felten et al. (2021). I briefly explained how it was calculated; first, using a public dataset, each task was given an AI application-ability relatedness score. Then, using a weighted sum of task importance and prevalence within each occupation, the AI Occupation Exposure (AIOE) score was calculated. Finally, after using O*NET data, the AIOE scores were successfully mapped onto individual counties, creating the AI Geographic Exposure (AIGE) score. In addition to the AIGE score, I explained how I would use the Local Estimates of Internet Adoption (LEIA) by the UC Census Bureau as a robustness test.

In the last section, I gave a brief description and a table for the summary statistics of my dataset, including averages, standard deviations, quartiles, and number of observations.

Methodology

Like the data section, the methodology section was also split into three sections: identification strategy, robustness checks, and heterogeneity analysis.

Identification Strategy

In the first section, I talked about the methodology I used in my primary analysis. I introduced the methodologies proposed by Callaway et al. (2024) as an extension of the traditional differences-in-differences (DiD) design. I briefly explained the problems with TWFE as demonstrated in the paper and weighed the pros and cons of using multi-valued discrete DiD as opposed to continuous DiD. After choosing my design, I explained the exact specification I used, binning AIGE scores into deciles with the lowest exposure decile as the baseline. I also explained the assumptions of parallel trends and strong parallel trends.

Robustness Checks

In the second section, I introduced the alternative specifications I used for my robustness checks. I justified the need to utilize alternative specifications and introduced the three robustness checks I would analyze. The first was identical to the primary identification strategy, except AIGE scores were binned into quintiles instead of deciles. The second was identical to the first robustness check, but using LEIA estimates instead of AIGE scores. The last was also similar to the primary identification strategy, except that, instead of county-level data, occupation-level data was used, with AIOE being split into deciles.

Heterogeneous Treatment Effects

In the last section, I introduced the models I would use to analyze heterogeneous treatment effects (HTEs). I used a generalized model that could be applied to all three specifications for my HTE analysis. The three analyses focused on different treatment effects between sexes, educational attainment levels, and ages.

Results

The results section was split into the same three sections as the methodology section: primary analysis, robustness checks, and HTE analysis. In each of these sections, I start by explaining the results of the parallel trends tests, then explain the regression estimates.

Primary Analysis

In my primary analysis, the parallel trends tests passed after removing data from 2020. However, when analyzing the data, there were no statistically significant estimates, indicating that AI has little to no effect on the unemployment rate.

Robustness Checks

In my robustness checks, the first regression I analyzed is the AIGE quintile analysis. I found that parallel trends once again hold after removing data from 2020. However, once again, there were no statistically significant estimates, indicating that AI has no significant effect on unemployment.

The second regression I analyzed is the LEIA quintile analysis. I found that parallel trends hold only after removing data from 2020 and 2021. However, this time, the estimates showed that AI seemingly increased unemployment; however, I also mentioned that since LEIA is less direct a measurement for AI exposure, it may provide biased estimates due to measurement error. The results of the LEIA quintile regression demonstrate why robustness checks are so important; if I had only analyzed the results of the LEIA quintile regression, I would have concluded that AI causes unemployment, something that does not show up in the other regression estimates.

The last regression I examined is the occupation-level decile analysis. However, even after removing data from 2020 and 2021, the parallel trends assumption was not plausibly satisfied. Thus, I could not perform the DiD regression.

Heterogeneous Treatment Effects

In my HTE analysis, the first variable I analyzed was sex. The parallel trends tests were plausibly satisfied without the need to remove any data. However, the regression estimates did not show different treatment effects between males and females.

The second variable I analyzed was education. The parallel trends tests were plausibly satisfied only after removing data from both 2020 and 2021. The regression estimates did show statistically significant differences between the baseline no degree group and the high school degree group. However, I elaborated that due to labor force calculations not counting high schoolers without jobs in school, it was the likely cause for the statistically significant difference, not because of HTEs.

Similarly, in the third variable I analyzed, age, similar results showed up. Parallel trends passed without the need to remove any data, but the regression estimates only showed statistically significant differences between 18-24 year olds and 25-34 year olds. A similar reasoning can be used to justify the differences, as many undergraduates and high school seniors are in school, distorting labor force calculations.

Conclusion

In the conclusion, I had two sections: discussion and limitations.

Discussion

In the discussion section, I interpreted my results. Most interestingly, my null results indicated that AI did not significantly affect the unemployment rate, and it did not particularly affect any group different from other groups, or at least to an interesting degree. I provided two potential explanations for this phenomenon. The first was the repeat of the Solow paradox, where implementation lags and learning adjustments increased costs temporarily as novel technologies were introduced, preventing significant changes in productivity and thus employment. The second was a clash between automation and augmentation, as suggested by Hampole et al. (2025). Automation effects of AI substituted physical labor, increasing unemployment; however, augmentation effects of AI increased productivity, decreasing unemployment. These two effects work against each other, leading to minimal changes in the unemployment rate.

Limitations

In the limitations section, I introduced three potential avenues for improvement. The first was data; by incorporating other, alternative AI indices that directly measured AI exposure, new estimates could be derived that may paint a better picture of the full story. Additionally, waiting for newer data to be released in the upcoming years could lead to analysis of the long-term treatment effect as the implementation lags, learning adjustments, and automation effects become less prevalent.

The second avenue of improvement was analyzing causal mechanisms. I was able to estimate the effect of AI on unemployment, but not provide empirical evidence to back the “why”. This could be improved in future studies by using empirical or mathematical models that analyze the two potential explanations I proposed.

The last avenue of improvement was using alternative methodologies. I relied on using ChatGPT as an exogenous shock that significantly affected the use of AI in the industry. However, developments in AI over time, both before and after the release of ChatGPT, may be better analyzed using other methods, like instrumental variable analysis, albeit with their own tradeoffs.

Introduction

Looping back to the introduction, I began my discussion on my paper by briefly discussing the public opinion on artificial intelligence. Using a poll by the Pew Research Center, I cited growing concerns about AI becoming more commonplace in our daily lives. Then, I transitioned into the literature review, explaining existing research on the effects of AI on the economy.

Funneling into more labor market-specific papers, I briefly explain their estimates of AI’s impact on employment and provide my own explanation for how their methodologies had room for improvement. Interestingly, many papers did not use causal inference, or at least used causal inference methodologies that suffered from endogeneity issues. Additionally, I also explain how very few studies have analyzed HTEs. Thus, I was able to explain how I would improve upon the literature by using a new, more accurate empirical strategy and explore the HTEs of AI on unemployment.