Estimating the Impact of the National Minimum Wage on Voting Behavior in the UK Using Double Machine Learning for Static Panel Models with Fixed Effects

Основные понятия

The introduction of the National Minimum Wage in the UK had a positive impact on voting for conservative political parties.

Аннотация

This paper revisits a study by Fazio and Reggiani (2023) on the impact of the introduction of the National Minimum Wage (NMW) in the UK on voting behavior. The authors use a partially linear panel regression model with individual fixed effects to estimate the causal effect, employing a double machine learning (DML) approach.

Key highlights:

• The authors extend the partially linear regression model of Robinson (1988) to panel data with fixed effects, allowing for non-linear and high-dimensional confounding effects.
• They propose three DML estimation procedures to handle the fixed effects problem: correlated random effects (CRE), first-difference (FD), and within-group (WG) transformations.
• A simulation study shows that DML with flexible learners like LASSO can outperform ordinary least squares when the data generating process is highly non-linear, while ensemble learning strategies are crucial for robust inference.
• In the empirical application, the authors find evidence that the introduction of the NMW had a positive impact on voting for conservative parties in the UK, confirming the findings of the original study.

Статистика

"Workers who were paid the minimum wage rate were more likely to support conservative parties."
"The introduction of the National Minimum Wage in the UK had a positive impact on voting for conservative political parties."

Цитаты

"We find evidence that the approaches, based on linear models, are appropriate, and confirm the conclusion from our simulation study that ensemble learning strategies are crucial to the reporting of robust results that readers can rely on."

Ключевые выводы из

Double Machine Learning for Static Panel Models with Fixed Effects

by Paul Clarke,... в arxiv.org 09-10-2024

https://arxiv.org/pdf/2312.08174.pdf

Дополнительные вопросы

How would the results change if the analysis accounted for potential spillover effects of the minimum wage policy on non-treated individuals?

Incorporating potential spillover effects of the minimum wage policy on non-treated individuals could significantly alter the results of the analysis. Spillover effects refer to the indirect impact of a treatment on individuals who are not directly subjected to it. In the context of the minimum wage policy, non-treated individuals may experience changes in their voting behavior due to shifts in the labor market, such as increased competition for jobs or changes in local economic conditions.
If the analysis were to account for these spillover effects, it would likely require a more complex modeling approach, potentially incorporating spatial econometrics or network analysis to capture the interactions between treated and non-treated individuals. This could lead to a more nuanced understanding of the policy's impact, revealing that the minimum wage may not only affect those directly impacted by the wage increase but also influence broader voting patterns within the community.
Moreover, the estimated treatment effects could be diluted or amplified depending on the nature of the spillover effects. For instance, if non-treated individuals perceive the minimum wage as beneficial to the local economy, they may be more inclined to vote for conservative parties, thereby increasing the estimated treatment effect. Conversely, if they view the policy as detrimental, it could lead to a decrease in support for conservative parties, thus complicating the interpretation of the results.

What are the potential limitations of the causal assumptions underlying the partially linear panel regression model in this context?

The partially linear panel regression (PLPR) model relies on several causal assumptions that may pose limitations in the context of analyzing the effects of the minimum wage policy on voting behavior. Key assumptions include:

No Feedback to Predictors: This assumption posits that the treatment (minimum wage) does not influence the predictors (voting behavior) in a way that would bias the results. However, if the introduction of the minimum wage alters the political landscape or public sentiment, this assumption may be violated.

Static Panel: The assumption of static relationships may not hold if the effects of the minimum wage evolve over time. For instance, initial support for conservative parties may change as individuals adjust to the new wage environment, leading to time-varying treatment effects that the model does not capture.

Selection on Observables: The assumption that treatment selection is ignorable given the observed covariates may not hold if there are unobserved confounders influencing both the treatment and the outcome. For example, if individuals with certain political leanings are more likely to be affected by the minimum wage, this could bias the estimated treatment effects.

Homogeneity of Treatment Effects: The assumption that the treatment effect is constant across individuals may not be valid. Different demographic or socioeconomic groups may respond differently to the minimum wage, leading to heterogeneous effects that the model fails to capture.

These limitations suggest that while the PLPR model provides a useful framework for causal inference, careful consideration of the underlying assumptions is crucial. Violations of these assumptions could lead to biased estimates and misinterpretation of the policy's impact on voting behavior.

Could the analysis be extended to examine heterogeneous effects of the minimum wage policy on voting behavior across different demographic or socioeconomic groups?

Yes, the analysis could be extended to examine heterogeneous effects of the minimum wage policy on voting behavior across different demographic or socioeconomic groups. This extension would involve modifying the partially linear panel regression model to allow for varying treatment effects based on individual characteristics such as age, gender, income level, education, and employment status.
To implement this, the model could incorporate interaction terms between the treatment variable (minimum wage) and demographic variables. For instance, the model could be specified as follows:
[ V oteit = NMWitθ + l(xit) + h(xit) \cdot NMWit + αi + uit ]
where ( h(xit) ) represents the interaction between the treatment and demographic characteristics. This approach would enable the analysis to capture how the effects of the minimum wage policy differ among various groups, providing insights into which segments of the population are most influenced by the policy.
Additionally, employing machine learning techniques, such as causal forests or generalized random forests, could enhance the analysis by allowing for flexible modeling of heterogeneous treatment effects without the need for strict parametric assumptions. This would facilitate a more comprehensive understanding of the policy's impact, revealing potential disparities in voting behavior that could inform future policy decisions and political strategies.
Overall, examining heterogeneous effects would enrich the analysis, providing a more detailed picture of how the minimum wage policy influences voting behavior across diverse segments of the population.