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Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables

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1,744 characters · 8 sections · 7 citation commands

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\subfile{titlepage}

Introduction

\subfile{0_introduction}

Identification results

\subfile{1_identification}

Test for detecting violations of LiM

\subfile{2_lim_test}

Estimation of the CC-ACR

\subfile{3_estimation}

Empirical applications

The proposed methodology is broadly applicable to various studies involving nonbinary, ordered treatments and multiple instruments, including studies like attanasio2013community. My results further extend to studies using Mendelian randomization, which employs genetic variants as instruments and has found applications in social sciences and economics (see, for example, dixon2020mendelian, scholder2013child). To provide another example, my results are relevant to applications that instrument for child BMI using the BMI of biological relatives (see, for example, cawley2004impact, lindeboom2010assessing, kline2008wages).

To demonstrate the results of the preceding sections, I consider two empirical applications. First, I revisit card1995geographic's (card1995geographic) seminal study on the returns to education. Second, I apply the novel methodology to angrist1996children's (angrist1996children) data to study the effect of an additional child on female labor market outcomes.

The causal effect of schooling on wage

\subfile{4_results_card}

The causal effect of additional child on female labor market outcomes

\subfile{4_results_twins}

Conclusion

\subfile{6_discussion}