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Mostly Harmless Econometrics: An Empiricist's Companion

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The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages?Mostly Harmless Econometricsshows how the basic tools of applied econometrics allow the data to speak.In addition to econometric essentials,Mostly Harmless Econometricscovers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and J rn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science.An irreverent review of econometric essentialsA focus on tools that applied researchers use mostChapters on regression-discontinuity designs, quantile regression, and standard errorsMany empirical examplesA clear and concise resource with wide applications.
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... To investigate this difference more profoundly, we explore the levels at which the gains of education were realised through FPE for our full sample, and for the poor and wealthy subgroups, separately. Given that our IV-estimates resemble local average treatment effects (LATE), this exercise informs us about the sub-population of "compliers" driving our results (see Angrist and Pischke 2009). Figure 3 depicts these shifts visually, plotting the difference in the conditional probability (on the y-axis) of having completed at least a given school grade (on the x-axis) for women that were just able to benefit from the policy change (13 years and 28 younger), compared to women who too old to benefit from the removal of fees (14 years and older). ...
... Note that these changes are conditional CDF changes, including the usual covariates and weighing with the sample weights. Further, the changes of the three different samples are normalised by their respective first-stages as outlined inAngrist and Pischke (2009), giving us the contribution (weight) of the respective schooling level change towards the average causal response over all educational levels. ...
... Finally, ivt is iid error term. To avoid the "bad control" problem, I refrain from including any other individual and household level covariates in my baseline specifications 6 (Angrist & Pischke 2008;Dell et al. 2014;Desbureaux & Rodella 2019). My coefficient of interest, 1 , measures the impact of an additional degree-day of temperature above a threshold on different labour market outcomes. ...
... The random nature of my temperature anomaly variable ensures that my results can be interpreted as causal. As such, I refrain from including additional variables in my model to avoid the problem of too many controls or the 'bad control' problem (Angrist & Pischke 2008). However, for the sake of empirical rigour, I check the sensitivity of the results to the inclusion of additional individual and household level covariates (Table H1). ...
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... We identified the effect of child benefit reform on household saving using a difference-in-differences approach (Angrist & Pischke, 2008;Cunningham, 2021). We estimated the following equation: ...
... Finally, to check whether the effect of child benefit on savings was similar across different social groups of the treated households, we tested for heterogeneity of our results. We estimated the following equation (e.g., Angrist & Pischke, 2008): ...
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... While logistic models are typically used to predict binary outcomes, we used a LPM with mixed effects because the interpretation of coefficients is more intuitive [61]. This has led many researchers to suggest using LPMs [2], especially because they are typically as good as logistic models at predicting dichotomous variables, and their p-values are highly correlated [28]. ...
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This paper estimates effects of long-term care (LTC) benefits on utilization of primary and secondary healthcare in Catalonia (Spain). Identification comes from plausibly exogenous variation in the leniency of LTC needs assessment. We estimate that receiving LTC benefits worth 365 euros per month, on average, reduces the probability of avoidable hospital admissions by 66%, and has no significant effect on planned hospitalisations nor on hospitalisation for any reason. Receiving LTC benefits is estimated to reduce unscheduled primary care visits by 44% and has no significant effect on scheduled visits. These findings have important policy implications suggesting that allocating resources to LTC may not only increase the welfare of LTC beneficiaries but also reduce avoidable and unscheduled utilisation of healthcare.
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