nlsw88

National Longitudinal Surveys, Women sample

datasets

Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

Ivan FernandezVal

Counterfactual

Estimation and Inference Methods for Counterfactual Analysis

nlsw88 function

A data frame with 2246 observations on the following 17 variables:<dl class="dl-horizontal">
 <dt><code>idcode</code></dt><dd>a numeric vector, NLS id</dd>
 <dt><code>age</code></dt><dd>a numeric vector, age at current year</dd>
 <dt><code>race</code></dt><dd>a numeric vector, race</dd>
 <dt><code>married</code></dt><dd>a numeric vector</dd>
 <dt><code>never_married</code></dt><dd>a numeric vector</dd>
 <dt><code>grade</code></dt><dd>a numeric vector, current grade completed</dd>
 <dt><code>collgrad</code></dt><dd>a numeric vector, college graduate</dd>
 <dt><code>south</code></dt><dd>a numeric vector, lives in south</dd>
 <dt><code>smsa</code></dt><dd>a numeric vector, lives in SMSA</dd>
 <dt><code>c_city</code></dt><dd>a numeric vector, lives in central city</dd>
 <dt><code>industry</code></dt><dd>a numeric vector</dd>
 <dt><code>occupation</code></dt><dd>a numeric vector</dd>
 <dt><code>union</code></dt><dd>a numeric vector, union worker</dd>
 <dt><code>wage</code></dt><dd>a numeric vector, hourly wage</dd>
 <dt><code>hours</code></dt><dd>a numeric vector, usual hours worked</dd>
 <dt><code>ttl_exp</code></dt><dd>a numeric vector, total work experience</dd>
 <dt><code>tenure</code></dt><dd>a numeric vector, job tenure(years)</dd>
 </dl>

Format

NLSW, 1988 extract — nlsw88

A data frame with 2246 observations on the following 17 variables:<dl class='dl-horizontal'>
 <dt><code>idcode</code></dt><dd>a numeric vector, NLS id</dd>
 <dt><code>age</code></dt><dd>a numeric vector, age at current year</dd>
 <dt><code>race</code></dt><dd>a numeric vector, race</dd>
 <dt><code>married</code></dt><dd>a numeric vector</dd>
 <dt><code>never_married</code></dt><dd>a numeric vector</dd>
 <dt><code>grade</code></dt><dd>a numeric vector, current grade completed</dd>
 <dt><code>collgrad</code></dt><dd>a numeric vector, college graduate</dd>
 <dt><code>south</code></dt><dd>a numeric vector, lives in south</dd>
 <dt><code>smsa</code></dt><dd>a numeric vector, lives in SMSA</dd>
 <dt><code>c_city</code></dt><dd>a numeric vector, lives in central city</dd>
 <dt><code>industry</code></dt><dd>a numeric vector</dd>
 <dt><code>occupation</code></dt><dd>a numeric vector</dd>
 <dt><code>union</code></dt><dd>a numeric vector, union worker</dd>
 <dt><code>wage</code></dt><dd>a numeric vector, hourly wage</dd>
 <dt><code>hours</code></dt><dd>a numeric vector, usual hours worked</dd>
 <dt><code>ttl_exp</code></dt><dd>a numeric vector, total work experience</dd>
 <dt><code>tenure</code></dt><dd>a numeric vector, job tenure(years)</dd>
 </dl>

nlsw88: NLSW, 1988 extract

Description

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Examples