## PSID Earnings Data 1982

### Description

Cross-section data originating from the Panel Study on Income Dynamics, 1982.

### Usage

`data("PSID1982")`

### Format

A data frame containing 595 observations on 12 variables.

- experience
Years of full-time work experience.

- weeks
Weeks worked.

- occupation
factor. Is the individual a white-collar (`"white"`

) or blue-collar (`"blue"`

) worker?

- industry
factor. Does the individual work in a manufacturing industry?

- south
factor. Does the individual reside in the South?

- smsa
factor. Does the individual reside in a SMSA (standard metropolitan statistical area)?

- married
factor. Is the individual married?

- gender
factor indicating gender.

- union
factor. Is the individual's wage set by a union contract?

- education
Years of education.

- ethnicity
factor indicating ethnicity.
Is the individual African-American (`"afam"`

) or not (`"other"`

)?

- wage
Wage.

### Details

`PSID1982`

is the cross-section for the year 1982 taken from a larger panel data set
`PSID7682`

for the years 1976–1982, originating from Cornwell and Rupert (1988).
Baltagi (2002) just uses the 1982 cross-section; hence `PSID1982`

is available as a
standalone data set because it was included in AER prior to the availability of the
full `PSID7682`

panel version.

### Source

The data is from Baltagi (2002).

### References

Baltagi, B.H. (2002). *Econometrics*, 3rd ed. Berlin, Springer.

Cornwell, C., and Rupert, P. (1988). Efficient Estimation with Panel Data:
An Empirical Comparison of Instrumental Variables Estimators.
*Journal of Applied Econometrics*, **3**, 149–155.

### See Also

`PSID7682`

, `Baltagi2002`

### Examples

```
data("PSID1982")
plot(density(PSID1982$wage, bw = "SJ"))
## Baltagi (2002), Table 4.1
earn_lm <- lm(log(wage) ~ . + I(experience^2), data = PSID1982)
summary(earn_lm)
## Baltagi (2002), Table 13.1
union_lpm <- lm(I(as.numeric(union) - 1) ~ . - wage, data = PSID1982)
union_probit <- glm(union ~ . - wage, data = PSID1982, family = binomial(link = "probit"))
union_logit <- glm(union ~ . - wage, data = PSID1982, family = binomial)
## probit OK, logit and LPM rather different.
```