Two approaches to estimation and testing of fixed effects models are
commonly found in the econometrics literature. The first involves variations on
instrumental variables. The second, a Minimum Chi-Square (MCS) procedure
introduced by Chamberlain, minimizes a quadratic form in the difference between
unrestricted regression coefficients and the restrictions implied by the fixed
effects model. This paper is concerned with the relationship between Three-Stage
Least Squares (3SLS) and MCS. A 3SLS equivalent of the MCS estimator is
presented and, in the usual case wherein the time varying error component has a
scalar covariance matrix, 3SLS is shown to simplify to the conventional
deviations from means estimator. Furthermore, the corresponding over-
identification test statistic is the degrees of freedom times the R2 from a
regression of residuals on all leads and lags of right hand side variables. The
relationship between MCS and some recently introduced efficient instrumental
variables procedures is also considered.
An empirical example from the literature on life-cycle labor supply is used
to illustrate properties of 3SLS procedures for panel data under alternative
assumptions regarding residual covariance. Estimated labor supply elasticities
and standard errors appear to be insensitive to these assumptions. In contrast,
the over-identification test statistics are found to be substantially smaller
when residuals are allowed to be intertemporally correlated and heteroscedastic.
At conventional levels of significance, however, even the smallest of the test
statistics leads to rejection of the over-identifying restrictions implicit in
the labor supply models.

This paper proposes a method for estimating a censored panel data model with a lagged
latent dependent variable and individual-specific fixed effects. The main insight is to trim
observations in such a way that a certain symmetry, which was destroyed by censoring, is
restored. Based on the restored symmetry, orthogonality conditions are constructed and GMM
estimation is implemented. The estimation method is used to study earnings dynamics, using
matched data from the Current Population Survey and Social Security Administration (CPS-
SSA) Earnings Record for a sample of men who were born in 1930-39 and living in the South
dining the period of 1957-73. The SSA earnings are top-coded at the maximum social security
taxable level. Although linear GMM estimation yields no difference in earnings dynamics by
race, the earnings process for white men appears to be more persistent than that for black men
(conditional on individual heterogeneity) after censoring is taken into account.

We re-examine the evidence presented by Neumark and Wascher (1992) on
the employment effect of the minimum wage. We find three critical flaws in
their analysis. First, the school enrollment variable that plays a pivotal
role in their specifications is derived on the false assumption that
teenagers either work or attend school. Measurement error biases
contaminate all the empirical estimates that use this enrollment variable.
Second, Neumark and Wascher measure the effect of the minimum wage by a
coverage-weighted relative minimum wage index. This variable is negatively
correlated with average teenage wages. Taken literally, their results show
that a rise in the coverage-weighted relative minimum wage lowers teenage
wages. Examining the direct effects of state-specific minimum wages, we
find that increases in state minimum wages raise average teenage wages but
have essentially no employment effects.
Finally, a careful analysis of Neumark and Wascher's data shows that
subminimum wage provisions are rarely used. This casts doubt on their
claim that subminimum provisions blunt any disemployment effect of the
minimum wage.
Neumark and Wascher contend that other minimum wage studies are biased
by failing to control for school enrollment, and by failing to consider the
lagged effects of minimum wages. We re-analyze the experiences of
individual states following the April 1990 increase in the Federal minimum
wage, allowing for a full year lag in the effect of the law and controlling
for changes in (properly measured) enrollment rates. Contrary to their
claims, allowing for lagged effects and controlling for enrollment status
actually strengthens the conclusion that the 1990 increase in the Federal
minimum had no adverse employment effect.

The lifecycle labor supply model has been proposed as an
explanation for various dimensions of labor supply, including
movements over the business cycle, changes with age, and within-
person variation over time. According to the model, all of these
elements are tied together by a combination of intertemporal
substitution effects and wealth effects. This paper offers an
assessment of the model's ability to explain the main components of
labor supply, focusing on microeconometric evidence for men.

This paper considers estimation and testing of vector autoregression coefficients in panel data, and uses the techniques to analyze the dynamic
properties of wages and hours among American males. The model allows for non-
stationary individual effects, and is estimated by applying instrumental
variables to the quasi—differenced autoregressive equations. Particular
attention is paid to specifying lag lengths and forming convenient test
statistics. The empirical results suggest that the wage equation contains at
most a single lag of hours and wages, and that one cannot reject the hypothesis that lagged hours may be excluded from the wage equation. Our results
also show that lagged hours is important in the hours equation, which is
consistent with alternatives to the simple labor supply model that allow for
costly hours adjustment or preferences that are not time separable.

Econometrics played a major role in the investigation and litigation of the Federal Trade Commission’s (FTC) successful challenge to the proposed merger between two office superstore chains, Staples and Office Depot. Our goal in writing this essay is to describe the econometric issues at stake in evaluating the FTC’s central claim that the price charged by office supply superstores was related to the number and identity of superstore firms participating in the market. Similar statistical models were relied upon by the FTC and the merging firms to analyze pricing. Our discussion of these models highlights the advantages and disadvantages of alternative approaches to analyzing a panel data set: cross-sectional estimates versus fixed effects estimates. We also describe and evaluate modeling choices that appeared to have substantial influence on the empirical results.