Podcast
Questions and Answers
In the context of field experiments on discrimination using name randomization on resumes, what critical assumption must hold true for the treatment effect $\gamma$ to be a valid estimate of discrimination?
In the context of field experiments on discrimination using name randomization on resumes, what critical assumption must hold true for the treatment effect $\gamma$ to be a valid estimate of discrimination?
- The only systematic difference between the two groups of resumes (Black-sounding vs. White-sounding names) is the perceived ethnicity conveyed by the names, and this perception directly influences employer callback decisions without interacting with other resume attributes. (correct)
- Employers must be completely unaware that a name manipulation is taking place and treat all applications as if they were genuinely representative of different racial demographics.
- The distribution of unobserved characteristics affecting callback rates must be identical for both resumes with Black-sounding names and resumes with White-sounding names _before_ name assignment.
- The selection bias arising from differential resume quality must be explicitly modeled and regressed out of the observed callback rate disparity.
According to the callback rate equations provided, $E[Y_{0i}] = \alpha_i$ and $E[Y_{1i}] = \alpha_i + \gamma N_i$, under what condition would a researcher be justified in concluding that $\gamma$ represents the causal effect of perceived race on callback rates, assuming $N_i = 1$ indicates treatment with a Black-sounding name?
According to the callback rate equations provided, $E[Y_{0i}] = \alpha_i$ and $E[Y_{1i}] = \alpha_i + \gamma N_i$, under what condition would a researcher be justified in concluding that $\gamma$ represents the causal effect of perceived race on callback rates, assuming $N_i = 1$ indicates treatment with a Black-sounding name?
- Only when the effect of name perception ($\gamma$) is statistically insignificant, indicating no impact of perceived race on callback rates.
- If the researcher can demonstrate that resumes with Black-sounding names are, on average, of lower quality than those with White-sounding names, thus necessitating a bias adjustment.
- Given that $\alpha_i$ represents a random error term and, therefore, any observed difference in callback rates must be attributed to the name manipulation.
- When $\alpha_i$ perfectly captures all non-racial determinants of callback rates, and there are no interactions between name-based perceptions and resume content. (correct)
In the context of the field experiments on discrimination, the equation $E[Y_{1i} - Y_{0i} | N_i = 1] = E[Y_{1i} - Y_{0i} | N_i = 1] + E[Y_{0i} | N_i = 1] - E[Y_{0i} | N_i = 0]$ is presented. What does the term $E[Y_{0i} | N_i = 1] - E[Y_{0i} | N_i = 0]$ represent, and why does it ideally vanish in a well-executed randomized experiment?
In the context of the field experiments on discrimination, the equation $E[Y_{1i} - Y_{0i} | N_i = 1] = E[Y_{1i} - Y_{0i} | N_i = 1] + E[Y_{0i} | N_i = 1] - E[Y_{0i} | N_i = 0]$ is presented. What does the term $E[Y_{0i} | N_i = 1] - E[Y_{0i} | N_i = 0]$ represent, and why does it ideally vanish in a well-executed randomized experiment?
- It denotes the systematic difference in potential callback rates that would exist even if the resumes with Black-sounding names had White-sounding names instead (or vice versa), and its disappearance signifies successful randomization. (correct)
- It represents the component of callback rate disparity attributable to inherent differences in skill distribution across racial demographics, ideally vanishing due to statistical control.
- It quantifies the selection bias arising from observable differences in resume characteristics and vanishes because resumes are meticulously matched on all attributes.
- It captures the bias stemming from employers' subjective assessments of resume quality, eliminated by blinding employers to applicant names.
Consider a scenario where an audit study aims to measure gender discrimination in hiring. Researchers send matched resumes that are identical except for the applicant's name, where one set of names is identifiably female and the other set is identifiably male. However, unbeknownst to the researchers, employers in a specific industry sub-sector systematically favor candidates with extracurricular activities that are statistically more common among male applicants. What econometric challenge does this introduce, and how could researchers attempt to mitigate it?
Consider a scenario where an audit study aims to measure gender discrimination in hiring. Researchers send matched resumes that are identical except for the applicant's name, where one set of names is identifiably female and the other set is identifiably male. However, unbeknownst to the researchers, employers in a specific industry sub-sector systematically favor candidates with extracurricular activities that are statistically more common among male applicants. What econometric challenge does this introduce, and how could researchers attempt to mitigate it?
In Soetevent's (2005) field experiment on anonymity in giving within Dutch Baptist churches, what critical assumption underlies the causal inference regarding the effect of anonymity on monetary contributions, and how might unobservable violations of this assumption compromise the study's validity?
In Soetevent's (2005) field experiment on anonymity in giving within Dutch Baptist churches, what critical assumption underlies the causal inference regarding the effect of anonymity on monetary contributions, and how might unobservable violations of this assumption compromise the study's validity?
In the context of experimental design, differentiate between the Hawthorne and John Henry effects by identifying the group primarily influenced and the nature of behavioral change exhibited.
In the context of experimental design, differentiate between the Hawthorne and John Henry effects by identifying the group primarily influenced and the nature of behavioral change exhibited.
A researcher conducts a study on a new teaching method, using volunteer participants. While internal validity is maintained, what primary concern arises regarding the generalizability of the study's findings to the broader student population?
A researcher conducts a study on a new teaching method, using volunteer participants. While internal validity is maintained, what primary concern arises regarding the generalizability of the study's findings to the broader student population?
In the context of scaling up a small-scale educational voucher experiment to a nationwide program, what potential general equilibrium effect should policymakers anticipate that was not evident in the initial experiment?
In the context of scaling up a small-scale educational voucher experiment to a nationwide program, what potential general equilibrium effect should policymakers anticipate that was not evident in the initial experiment?
Consider a randomized controlled trial assessing the impact of a novel therapeutic intervention. If the control group, upon realizing their status, actively seeks supplementary treatments or modifies their health behaviors, which threat to external validity is most pertinent?
Consider a randomized controlled trial assessing the impact of a novel therapeutic intervention. If the control group, upon realizing their status, actively seeks supplementary treatments or modifies their health behaviors, which threat to external validity is most pertinent?
A research team is evaluating the efficacy of a new organizational management strategy within a single department of a large corporation. Employees are aware of the study. Which of the following biases is most likely to confound the study's findings?
A research team is evaluating the efficacy of a new organizational management strategy within a single department of a large corporation. Employees are aware of the study. Which of the following biases is most likely to confound the study's findings?
In the original Hawthorne factory studies, productivity increased regardless of whether lighting was increased or decreased. What key insight regarding experimental design does this illustrate concerning external validity?
In the original Hawthorne factory studies, productivity increased regardless of whether lighting was increased or decreased. What key insight regarding experimental design does this illustrate concerning external validity?
An economist is evaluating a microfinance intervention in a small village. The intervention shows remarkable success in the controlled setting. However, policymakers hesitate to scale up the program nationwide. What represents the most significant concern regarding the potential for general equilibrium effects?
An economist is evaluating a microfinance intervention in a small village. The intervention shows remarkable success in the controlled setting. However, policymakers hesitate to scale up the program nationwide. What represents the most significant concern regarding the potential for general equilibrium effects?
A pharmaceutical company conducts a clinical trial for a novel drug, where participants exhibit a noticeable improvement, irrespective of whether they receive the active drug or a placebo. Recognizing the influence of the Hawthorne effect, what refined methodological approach should be implemented to isolate and ascertain treatment-specific efficacy?
A pharmaceutical company conducts a clinical trial for a novel drug, where participants exhibit a noticeable improvement, irrespective of whether they receive the active drug or a placebo. Recognizing the influence of the Hawthorne effect, what refined methodological approach should be implemented to isolate and ascertain treatment-specific efficacy?
In the context of educational interventions, the John Henry effect manifests when students in the control group, aware of their status, exert extraordinary effort to outperform the treatment group. Which advanced statistical technique can researchers employ to disentangle the true intervention effect from the confounding influence of this compensatory behavior?
In the context of educational interventions, the John Henry effect manifests when students in the control group, aware of their status, exert extraordinary effort to outperform the treatment group. Which advanced statistical technique can researchers employ to disentangle the true intervention effect from the confounding influence of this compensatory behavior?
A non-profit organization implements a new job training program in a small community. After one year, the program appears highly successful, with a significant increase in employment rates among participants. However, when policymakers attempt to replicate the program on a national scale, the observed employment gains are substantially smaller. Which of the following is the most plausible explanation for this discrepancy, considering general equilibrium effects?
A non-profit organization implements a new job training program in a small community. After one year, the program appears highly successful, with a significant increase in employment rates among participants. However, when policymakers attempt to replicate the program on a national scale, the observed employment gains are substantially smaller. Which of the following is the most plausible explanation for this discrepancy, considering general equilibrium effects?
In a randomized experiment with partial compliance, if policymakers are primarily interested in the impact of offering a treatment rather than the treatment's effect itself, which estimate is most relevant, considering real-world policy implementation?
In a randomized experiment with partial compliance, if policymakers are primarily interested in the impact of offering a treatment rather than the treatment's effect itself, which estimate is most relevant, considering real-world policy implementation?
An experiment evaluating a new educational intervention exhibits differential attrition: students in the treatment group, who initially showed lower performance, are more likely to drop out. Which statistical method would BEST address the potential bias introduced by this non-random attrition, in order to estimate the true treatment effect?
An experiment evaluating a new educational intervention exhibits differential attrition: students in the treatment group, who initially showed lower performance, are more likely to drop out. Which statistical method would BEST address the potential bias introduced by this non-random attrition, in order to estimate the true treatment effect?
In a clinical trial for a novel drug, researchers observe that attrition rates differ significantly between the treatment and control groups. Specifically, a higher proportion of participants in the treatment group discontinue the trial due to reported side effects, while more participants in the control group withdraw due to a lack of perceived benefit. Which statistical method would you use to obtain the LEAST biased treatment effect?
In a clinical trial for a novel drug, researchers observe that attrition rates differ significantly between the treatment and control groups. Specifically, a higher proportion of participants in the treatment group discontinue the trial due to reported side effects, while more participants in the control group withdraw due to a lack of perceived benefit. Which statistical method would you use to obtain the LEAST biased treatment effect?
Consider a large-scale social experiment offering job training to unemployed individuals. Participation is encouraged but not mandatory. Suppose an external economic shock disproportionately affects the control group, leading to higher unemployment rates specifically among those not receiving the job training. How would this event MOST likely impact the interpretation of the experiment's internal validity in estimating the causal effect of job training?
Consider a large-scale social experiment offering job training to unemployed individuals. Participation is encouraged but not mandatory. Suppose an external economic shock disproportionately affects the control group, leading to higher unemployment rates specifically among those not receiving the job training. How would this event MOST likely impact the interpretation of the experiment's internal validity in estimating the causal effect of job training?
In a randomized controlled trial (RCT) evaluating the effectiveness of a new mindfulness app on reducing anxiety, participants in the treatment group are given access to the app, while those in the control group receive standard care. However, a significant subset of participants in the treatment group do not consistently use the app, and some participants in the control group independently start using other mindfulness resources. Which of the following statistical approaches would MOST accurately estimate the effect of actually engaging with the mindfulness app intervention?
In a randomized controlled trial (RCT) evaluating the effectiveness of a new mindfulness app on reducing anxiety, participants in the treatment group are given access to the app, while those in the control group receive standard care. However, a significant subset of participants in the treatment group do not consistently use the app, and some participants in the control group independently start using other mindfulness resources. Which of the following statistical approaches would MOST accurately estimate the effect of actually engaging with the mindfulness app intervention?
In a study evaluating the effectiveness of a new weight-loss drug, a researcher discovers that participants in the treatment group who experience early positive results are significantly more likely to remain in the study, while those who do not experience such results tend to drop out. Conversely, participants in the control group who perceive their weight as stable or decreasing are more likely to stay in the study, while those whose weight increases are more likely to drop out. This attrition process is MOST likely to:
In a study evaluating the effectiveness of a new weight-loss drug, a researcher discovers that participants in the treatment group who experience early positive results are significantly more likely to remain in the study, while those who do not experience such results tend to drop out. Conversely, participants in the control group who perceive their weight as stable or decreasing are more likely to stay in the study, while those whose weight increases are more likely to drop out. This attrition process is MOST likely to:
A researcher conducts a randomized experiment to assess digital literacy training on employment outcomes, but finds that due to a concurrent government initiative promoting digital skills, many individuals in the control group independently seek and receive similar training. How does this MOST directly threaten the study's ability to accurately estimate the causal impact?
A researcher conducts a randomized experiment to assess digital literacy training on employment outcomes, but finds that due to a concurrent government initiative promoting digital skills, many individuals in the control group independently seek and receive similar training. How does this MOST directly threaten the study's ability to accurately estimate the causal impact?
In a randomized experiment evaluating a new therapy for depression, the research team discovers that a significant portion of participants in the treatment group are also receiving concurrent treatment from outside providers, which the researchers are unable to fully track. Simultaneously, some participants in the control group independently seek alternative treatments, creating a 'treatment contamination' effect. What is the MOST appropriate statistical approach to address this complex scenario and estimate the therapy's true effect?
In a randomized experiment evaluating a new therapy for depression, the research team discovers that a significant portion of participants in the treatment group are also receiving concurrent treatment from outside providers, which the researchers are unable to fully track. Simultaneously, some participants in the control group independently seek alternative treatments, creating a 'treatment contamination' effect. What is the MOST appropriate statistical approach to address this complex scenario and estimate the therapy's true effect?
A researcher aims to assess how a new policy affects small businesses, but the policy implementation varies significantly across different regions due to local adaptations and pre-existing regulations. Some regions fully adopt the policy, others only partially implement it, and some barely change at all. How does this differential implementation MOST directly affect any assessment of causal impact?
A researcher aims to assess how a new policy affects small businesses, but the policy implementation varies significantly across different regions due to local adaptations and pre-existing regulations. Some regions fully adopt the policy, others only partially implement it, and some barely change at all. How does this differential implementation MOST directly affect any assessment of causal impact?
In the context of voucher programs and their impact on public schools, which of the following scenarios best exemplifies a general equilibrium effect that is virtually impossible to capture in a small-scale experiment, yet critically influences the large-scale program outcome?
In the context of voucher programs and their impact on public schools, which of the following scenarios best exemplifies a general equilibrium effect that is virtually impossible to capture in a small-scale experiment, yet critically influences the large-scale program outcome?
Consider a field experiment designed to evaluate the efficacy of a novel educational intervention. The intervention shows promising results in a controlled setting. However, after scaling, the outcomes plateau. Which type of validity is most threatened by this plateau, and why?
Consider a field experiment designed to evaluate the efficacy of a novel educational intervention. The intervention shows promising results in a controlled setting. However, after scaling, the outcomes plateau. Which type of validity is most threatened by this plateau, and why?
What critical limitation is inherent in relying solely on earnings regressions with race dummy variables and control variables to estimate the effect of race on labor market outcomes, as highlighted by Bertrand and Mullainathan's work?
What critical limitation is inherent in relying solely on earnings regressions with race dummy variables and control variables to estimate the effect of race on labor market outcomes, as highlighted by Bertrand and Mullainathan's work?
In the context of field experiments on labor market discrimination, what is the most significant advantage of the methodology employed by Bertrand and Mullainathan (sending out 'fake' job applications) compared to traditional audit studies utilizing actors?
In the context of field experiments on labor market discrimination, what is the most significant advantage of the methodology employed by Bertrand and Mullainathan (sending out 'fake' job applications) compared to traditional audit studies utilizing actors?
How does Equation 2, $E[Y_{1i} - Y_{0i} | B_i = 1]$, address the fundamental problem of causal inference in the context of measuring labor market discrimination?
How does Equation 2, $E[Y_{1i} - Y_{0i} | B_i = 1]$, address the fundamental problem of causal inference in the context of measuring labor market discrimination?
What is the most critical assumption required for Equation 2, $E[Y_{1i} - Y_{0i} | B_i = 1]$, to provide an unbiased estimate of the treatment effect in measuring discrimination?
What is the most critical assumption required for Equation 2, $E[Y_{1i} - Y_{0i} | B_i = 1]$, to provide an unbiased estimate of the treatment effect in measuring discrimination?
Consider a scenario where a researcher aims to replicate Bertrand and Mullainathan's field experiment in a different country with significantly different cultural norms and labor market structures. What adjustments to the experimental design would be most crucial to ensure the validity and relevance of the findings?
Consider a scenario where a researcher aims to replicate Bertrand and Mullainathan's field experiment in a different country with significantly different cultural norms and labor market structures. What adjustments to the experimental design would be most crucial to ensure the validity and relevance of the findings?
In the context of incidental experiments, such as draft lotteries or random assignment of judges, what potential threat to internal validity must be carefully considered when drawing causal inferences?
In the context of incidental experiments, such as draft lotteries or random assignment of judges, what potential threat to internal validity must be carefully considered when drawing causal inferences?
A researcher aims to study the impact of a new microfinance program on poverty reduction using a field experiment. After two years, the researcher finds a statistically significant, but small, effect on income. However, qualitative data reveals that many participants used the loans primarily for consumption smoothing rather than investment. What type of validity is most directly challenged by this finding?
A researcher aims to study the impact of a new microfinance program on poverty reduction using a field experiment. After two years, the researcher finds a statistically significant, but small, effect on income. However, qualitative data reveals that many participants used the loans primarily for consumption smoothing rather than investment. What type of validity is most directly challenged by this finding?
What methodological challenge arises when attempting to extrapolate the findings from a lab experiment involving trust games to real-world economic behavior, and how might researchers mitigate this challenge?
What methodological challenge arises when attempting to extrapolate the findings from a lab experiment involving trust games to real-world economic behavior, and how might researchers mitigate this challenge?
In a perfectly randomized experiment, the difference-in-means estimator, formulated as $\frac{\sum_{i=1}^{n} D_{i} Y_{i}}{\sum_{i=1}^{n} D_{i}} - \frac{\sum_{i=1}^{n} (1 - D_{i}) Y_{i}}{\sum_{i=1}^{n} (1 - D_{i})}$, provides an unbiased estimate of which causal parameter under conditions of treatment effect homogeneity?
In a perfectly randomized experiment, the difference-in-means estimator, formulated as $\frac{\sum_{i=1}^{n} D_{i} Y_{i}}{\sum_{i=1}^{n} D_{i}} - \frac{\sum_{i=1}^{n} (1 - D_{i}) Y_{i}}{\sum_{i=1}^{n} (1 - D_{i})}$, provides an unbiased estimate of which causal parameter under conditions of treatment effect homogeneity?
Given the linear regression model $Y_i = \beta_0 + \beta_1 D_i + U_i$ employed in the analysis of a randomized experiment, which statement most accurately delineates the role of randomization in ensuring the validity of causal inferences derived from this regression?
Given the linear regression model $Y_i = \beta_0 + \beta_1 D_i + U_i$ employed in the analysis of a randomized experiment, which statement most accurately delineates the role of randomization in ensuring the validity of causal inferences derived from this regression?
The provided text posits that randomization inherently fulfills the zero conditional mean assumption. Within the context of the regression model $Y_i = \beta_0 + \beta_1 D_i + U_i$, what is the most precise econometric interpretation of this fulfillment?
The provided text posits that randomization inherently fulfills the zero conditional mean assumption. Within the context of the regression model $Y_i = \beta_0 + \beta_1 D_i + U_i$, what is the most precise econometric interpretation of this fulfillment?
Consider a randomized controlled trial conducted within an elite, specialized institution to assess a novel pedagogical intervention. The study demonstrates a statistically robust and substantial enhancement in student performance metrics. Evaluating this scenario through the lens of internal versus external validity, which statement is most tenable?
Consider a randomized controlled trial conducted within an elite, specialized institution to assess a novel pedagogical intervention. The study demonstrates a statistically robust and substantial enhancement in student performance metrics. Evaluating this scenario through the lens of internal versus external validity, which statement is most tenable?
In a randomized controlled trial evaluating a vocational training program, significant partial compliance is observed: only 55% of those offered the training participate, and 15% of the control group independently access similar training resources. What is the most salient econometric implication of this partial compliance for the interpretation of the difference-in-means estimator?
In a randomized controlled trial evaluating a vocational training program, significant partial compliance is observed: only 55% of those offered the training participate, and 15% of the control group independently access similar training resources. What is the most salient econometric implication of this partial compliance for the interpretation of the difference-in-means estimator?
Within an encouragement design, individuals are randomly provided with a voucher intended to facilitate access to preventative healthcare services; however, the utilization of these services remains discretionary. In such a design framework, which causal parameter is most directly and consistently estimated by the difference-in-means estimator when comparing outcomes between the voucher-offered group and the voucher-not-offered group?
Within an encouragement design, individuals are randomly provided with a voucher intended to facilitate access to preventative healthcare services; however, the utilization of these services remains discretionary. In such a design framework, which causal parameter is most directly and consistently estimated by the difference-in-means estimator when comparing outcomes between the voucher-offered group and the voucher-not-offered group?
A researcher initiates an experiment to examine the efficacy of an advanced neurocognitive training regimen on fluid intelligence. Participants are randomized to either receive an invitation to enroll in the training or not. The training is delivered exclusively via a high-bandwidth online platform, and participant access to reliable internet infrastructure varies considerably. Furthermore, a subset of the control group independently adopts comparable cognitive training methodologies available through open-access online resources. Which of the following statements most accurately characterizes the predominant threats to achieving valid causal inference and the most appropriate analytical strategy in this complex scenario?
A researcher initiates an experiment to examine the efficacy of an advanced neurocognitive training regimen on fluid intelligence. Participants are randomized to either receive an invitation to enroll in the training or not. The training is delivered exclusively via a high-bandwidth online platform, and participant access to reliable internet infrastructure varies considerably. Furthermore, a subset of the control group independently adopts comparable cognitive training methodologies available through open-access online resources. Which of the following statements most accurately characterizes the predominant threats to achieving valid causal inference and the most appropriate analytical strategy in this complex scenario?
Flashcards
ATE = ATET (with randomization)
ATE = ATET (with randomization)
With randomization, the Average Treatment Effect (ATE) equals the Average Treatment Effect on the Treated (ATET).
Difference-in-means estimator
Difference-in-means estimator
Estimating ATE by comparing the average outcomes of the treatment group to the average outcomes of the control group.
Regression with Randomization
Regression with Randomization
A regression model where the treatment status is included as a predictor, and randomization ensures the treatment is independent of other unobserved factors.
Randomization and Unobserved Factors
Randomization and Unobserved Factors
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Internal Validity
Internal Validity
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External Validity
External Validity
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Partial Compliance
Partial Compliance
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Intention-to-Treat (ITT) Estimates
Intention-to-Treat (ITT) Estimates
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Z vs. D in ITT
Z vs. D in ITT
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ITT Relevance
ITT Relevance
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Attrition
Attrition
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Non-random Attrition
Non-random Attrition
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Attrition and Optimizing Behavior
Attrition and Optimizing Behavior
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Monitoring Dropouts
Monitoring Dropouts
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Nonrepresentative Treatments
Nonrepresentative Treatments
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Leap of Faith
Leap of Faith
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Hawthorne and John Henry effects
Hawthorne and John Henry effects
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Hawthorne effect
Hawthorne effect
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John Henry effect
John Henry effect
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General equilibrium effects
General equilibrium effects
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Non-generalizable sample
Non-generalizable sample
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Being experimentally measured
Being experimentally measured
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Placebo effect (in experiments)
Placebo effect (in experiments)
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Awareness of observation
Awareness of observation
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Volunteer bias
Volunteer bias
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Field Experiment
Field Experiment
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General Equilibrium Effects (Vouchers)
General Equilibrium Effects (Vouchers)
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Lab Experiments in Economics
Lab Experiments in Economics
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Manipulating Perceptions of Race
Manipulating Perceptions of Race
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Discrimination Measurement
Discrimination Measurement
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Field Experiments
Field Experiments
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Incidental Experiments
Incidental Experiments
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Bias Term (in Discrimination)
Bias Term (in Discrimination)
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Resume Equivalence
Resume Equivalence
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Bertrand & Mullainathan (2004) Method
Bertrand & Mullainathan (2004) Method
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Treatment Effect (Discrimination)
Treatment Effect (Discrimination)
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Early Regression Approach
Early Regression Approach
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Audit Studies
Audit Studies
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Audit Studies: Limitations
Audit Studies: Limitations
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Bertrand & Mullainathan Solution
Bertrand & Mullainathan Solution
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Study Notes
Analyzing Data from Experiments
- Randomization implies ATE (Average Treatment Effect) = ATET (Average Treatment Effect on the Treated)
- ATET is calculated as the expected difference in outcomes between the treated and control groups
Regression and Randomization
- Regression model can be specified as Yi = Bo + B₁Di + Ui
- Randomization ensures that Di is independently distributed of the unobserved factors in Ui
- Treatment status is not systematically related to any unobserved factors, fulfilling the zero conditional mean assumption
Internal and External Validity of Experiments
- Internal validity assesses if the experiment provides an accurate estimate of the causal effect in the population
- External validity is the extent to which the causal effect can be generalized to other populations, economic settings, or related treatments
- A trade-off exists between internal and external validity
Threats to Internal Validity: Partial Compliance
- Partial compliance occurs when only a fraction of those offered the treatment take it up
- It also occurs when the comparison group receives the treatment
- Experiments that don't intend to treat everyone are called encouragement designs
- Randomization affects the probability of exposure to treatment
- With partial compliance, fine resulting estimates are intention-to-treat (ITT) estimates
- Z is variable randomly assigned while D is the actual treatment when ITT-estimates work
- E[Yį |Zi = 1] - E[Yį |Z₁ = 0] equals the causal effect of Z, but not the same as the effect of the treatment, D
- ITT estimates may be of interest when policies won't reach everyone
- Partial compliance can compromise external validity
- Randomization can be used as an instrumental variable to recover the Local Average Treatment Effect (LATE)
Internal Validity: Attrition
- Attrition, where individuals drop out, threatens internal validity
- Random drop-out is not a problem, but reduces statistical power
- Non-random drop-out related to treatment is a problem
- Optimizing behavior can cause dropout, with those gaining little dropping out more
- Attrition rates may be equal between treatment and control, but produced differently
External Validity
- "Treatments" and policies will not always have the same impact in other places and times
- Individuals may not be a random sample of the population
- Volunteers in medical studies exemplify non-generalizable samples
- Volunteers may be more motivated, leading to a greater treatment effect
External Validity: Hawthorne and John Henry Effects
- Hawthorne and John Henry effects happen when being in an experiment changes behavior
- Hawthorne effects refer to changes in behavior among the treatment group
- John Henry effects refer to changes in behavior among the comparison group
- The study of light manipulation in the Hawthorne factories in the 1920s, found increase in productivity no matter the type of light
- A legendary American steel worker in the 1870s, upon learning his output was being compared with that of a steam drill, pushed extremely hard to outperform the machine that caused his eventual death
- This raises concern as Hawthorne and Henry effects might not be the same in other settings
External Validity: General Equilibrium Effects
- Experiments often fail to reflect general equilibrium effects that occur when scaled up
- A small-scale experiment may not reflect the effects of a large--scale programs
- Vouchers may increase pressure on public schools, improving performance
- Vouchers may pull motivated children/parents out of public schools, potentially reducing pressure
Examples of Experiments in Economics
- Experiments are increasingly common in economics
- Lab experiments randomly assign the characteristics of the game, e.g., trust games
- Field experiments take experiments to the field
- Incidental experiments, e.g., draft lotteries, random assignment of judges
Example of Field Experiments: Discrimination
- Measuring discrimination on the labor market involves estimating the "effect" of race
- Early evidence used earnings regressions with a dummy variable for race
- Bertrand and Mullainathan (2004) examined if Emily and Greg were more employable than Lakisha and Jamal
- They manipulated perceptions of race by assigning ethnic names to job applications
- This study measured discrimination by comparing callback rates based on name sound
- The probability of a callback can be written as E [Yoi] = ai if the name is white-sounding(Yoi)
- The probability of a callback can be written as E[Y1i] = a¡ + γN₁ if the name is black-sounding(Y₁₁)
- For resume i, if N₁ = 1, the treatment effect becomes E[Y1i - Yoi Ni = 1] = a¡ + γΝ¡ - α¡ = γ
- The difference in potential call-backs for resumes with a black and white-sounding names equates to the Bias term
- Since resumes are identical except for names, E [Yoi Ni = 1] = E[Yoi Ni = 0] where the bias term cancels out
- Differences in callback probability are due to random name assignment
Field Experiments on Anonymity
- Anonymity in Giving in a Natural Context, written by Soetevent, A. (2005), was measured using the collection bag and baskets
- The causal question asked how anonymity affects giving contributions to good causes
- Anonymity of giving to offerings were randomized in 30 Baptist churches in the Netherlands
- In this study, using baskets initially increased contributions by 10% but this effect decreased over the experimental period
- People tended to switch to giving larger coins when baskets were used, even when the total amount was the same
- Receiving approval from others play an important role in giving, and the anonymity removal triggers this
Incidental Experiments: Policymakers
- Chattopadhyay and Duflo (2004) created an incidental experiment to study women as policy makers
- The causal question asked if female politicians implement different policies than male ones
- Finding the answer includes men and women having different preferences
- Also finding the answer means the identity of policymaker must affect outcomes
- In these councils only women were elected to the position of head
- A sample of villages were surveyed in two districts: Birbum in West Bengal and Udaipur in Rajasthan
- Villages were selected to be reserved for women at random
- Differences in investment can be contributed to villages reserved for women when chosen at random
- Women invest more in infrastructure that is directly relevant to the needs of their own genders
Summary of Social Experiments
- Randomized trial is the "gold standard" to establish causality
- A "true" experiment can evaluate non-experimental experiments
- Starting point may not be random, a good starting point is to think of the perfect experiment to run
- Then one must come is close as possible to what would have been the perfect experiment, using non-experimental data
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Description
In discrimination studies using field experiments, a key assumption is that names are randomly assigned. Random assignment ensures that any observed differences in outcomes are due to name and not other factors. This allows for a valid estimate of discrimination.
