# Chapter 8 Limited Dependent Variables

## 8.1 Introduction

**Limited response variables:** Mixed discrete and continuous features of the dependent
variable.

**Typical sources:** Censoring and truncation.

**Economic explanations:**

- Corner solutions: Utility-maximizing choice of individuals is at the corner of the budget set, typically zero.
- Sample selection: Data deficiency causes response to be unknown (or different) for subsample.
- Treatment effects: Response for each individual is only observable for one level of a “treatment” variable.

**Examples:** Typical economic examples for limited responses.

Labor supply.

\(y_i\): Number of hours worked by person \(i\).

Potential covariates: Education, non-work income, …Expenditures for health services.

\(y_i\): Expenditures of person \(i\) for health services last month.

Potential covariates: Health status, income, gender, …Wage equations.

\(y_i\): Earnings of person \(i\) derived from tax returns.

Potential covariates: Education, previous experience, …Unemployment programs.

\(y_i\): Wage of person \(i\) after unemployment.

Treatment: Training program (potentially non-random assignment).

Potential covariates: Occupation, duration of unemployment, …

### 8.1.1 Example: PSID 1976 (Mroz data)

Cross-section d from the 1976 Panel Study of Income Dynamics (PSID), based on data for the previous year, 1975.

A data frame containing 753 observations on 21 variables, including:

Variable | Description |
---|---|

`participation` |
Did the individual participate in the labor force in 1975? (equivalent to `wage` \(> 0\) or `hours` \(> 0\)) |

`hours` |
Wife’s hours of work in 1975. |

`youngkids` |
Number of children less than 6 years old. |

`oldkids` |
Number of children between ages 6 and 18. |

`age` |
Wife’s age in years. |

`education` |
Wife’s education in years. |

`wage` |
Wife’s average hourly wage, in 1975 USD. |

`fincome` |
Family income, in 1975 USD. |

**Example: Corner solution**

**Example: Sample selection**

## 8.2 Tobin’s Corner Solution Model

**Problem:** As motivated above, many microeconomic variables have

- non-negative values,
- a cluster of observations at zero.

**Note:** OLS cannot be used sensibly due to

- possibly negative predictions,
- imposed constant marginal effects (which cannot be remedied by logs as \(\log(0)\) is not defined).

**Quantities of interest:**

- \(P(y = 0 ~|~ x) = 1 - P(y > 0 ~|~ x)\), probability of zero.
- \(E(y ~|~ y > 0,~ x)\) expectation conditional on positive \(y\).
- \(E(y ~|~ x) = P(y > 0 ~|~ x) \cdot E(y ~|~ y > 0,~ x)\) expectation.

**Ideas:**

- Two-part model with binary selection and truncated outcome.
- Latent variable driving both selection and outcome.

**Tobin’s solution:** Employ the latter approach with latent Gaussian variable \(y^*\)
and observed discrete-continuous response \(y\):

\[\begin{eqnarray*} y^* & = & x^\top \beta ~+~ \varepsilon, \qquad \varepsilon ~|~ x \sim \mathcal{N}(0, \sigma^2),\\ y\phantom{^*} & = & \max(0, y^*), \end{eqnarray*}\]

i.e., \(y\) is a censored version of \(y^*\).

**Likelihood:**

\[\begin{equation*} L(\beta, \sigma; y, x) ~=~ \prod_{i = 1}^n f(y_i ~|~ x_i, \beta, \sigma)^{I(y_i > 0)} \cdot P(y_i = 0 ~|~ x_i)^{I(y_i = 0)} \end{equation*}\]

where

\[\begin{eqnarray*} P(y_i = 0 ~|~ x_i) & = & P(y_i^* \le 0 ~|~ x_i) \\ & = & \Phi \left(\frac{0 - x_i^\top \beta}{\sigma} \right) ~=~ 1 - \Phi \left(\frac{x_i^\top \beta}{\sigma} \right) \\ f(y_i ~|~ x_i, \beta, \sigma) & = & \frac{1}{\sigma} \cdot \phi \left( \frac{y_i - x_i^\top \beta}{\sigma} \right) \end{eqnarray*}\]

**Remarks:**

- Model is known as
*tobit model*. - Despite the name it does not belong to the same model class as the binary logit and probit models.
- Log-likelihood can be shown to be globally concave.
- MLE is well-behaved (asymptotically normal etc.).

**In R:** `tobit()`

from package *AER*. Convenience interface to `survreg()`

from package *survival* which provides much more general regression tools for censored
responses.

**Alternatively:** `crch(..., left = 0)`

from package *crch* for censored regression with conditional heteroscedasticity. Dedicated interface for tobit-type models with either censoring or truncation, different response distributions (beyond normal).

**Example:** Regression for annual hours of work in PSID 1976 data, using
reduced-form specification. Wage is missing as a regressor (as unobserved for
non-working subsample).

Model equation of interest:

```
hours ~ nwincome + education +
hours_f <- experience + I(experience^2) + age + youngkids + oldkids
```

Fitting tobit model and naive OLS models:

```
tobit(hours_f, data = PSID1976)
hours_tobit <- lm(hours_f, data = PSID1976)
hours_ols1 <- lm(hours_f, data = PSID1976,
hours_ols2 <-subset = participation == "yes")
```

Compare coefficients:

```
sapply(list("Tobit" = hours_tobit, "OLS (all)" = hours_ols1,
"OLS (positive)" = hours_ols2), coef)
```

```
## Tobit OLS (all) OLS (positive)
## (Intercept) 965.305 1330.4824 2056.6428
## nwincome -8.814 -3.4466 0.4439
## education 80.646 28.7611 -22.7884
## experience 131.564 65.6725 47.0051
## I(experience^2) -1.864 -0.7005 -0.5136
## age -54.405 -30.5116 -19.6635
## youngkids -894.022 -442.0899 -305.7209
## oldkids -16.218 -32.7792 -72.3667
```

### 8.2.1 Truncated normal distribution

**Question:** How can these models be interpreted?

**Needed:** Better understanding of truncated (normal) distributions.

**Probability density function:** Random variable \(y\) with density function \(f(y)\),
truncated from below at \(c\). General form and \(\mathcal{N}(\mu, \sigma^2)\) distribution.

\[\begin{eqnarray*} f(y ~|~ y > c) & = & \frac{f(y)}{P(y > c)} ~=~ \frac{f(y)}{1 - F(c)} \\ & = & \frac{1}{\sigma} \cdot \phi\left( \frac{y - \mu}{\sigma} \right) \left/ \left\{ 1 - \Phi \left(\frac{c - \mu}{\sigma} \right) \right\} \right. \end{eqnarray*}\]

**Example:** Standard normal distribution truncated at \(c = -1\) and \(c = 0\).

**Expectation:** For \(\varepsilon \sim \mathcal{N}(0, 1)\).

\[\begin{eqnarray*} E(\varepsilon ~|~ \varepsilon > c) & = & \int_c^\infty \varepsilon \cdot f(\varepsilon ~|~ \varepsilon > c) ~d \varepsilon \\ & = & \frac{1}{1 - \Phi(c)} ~ \int_c^\infty \varepsilon \cdot \phi(\varepsilon) ~d \varepsilon \\ & = & \frac{1}{1 - \Phi(c)} ~ \left\{ \left. - \phi(\varepsilon) \phantom{\frac{.}{.}} \! \right|_c^\infty \right\} \\ & = & \frac{\phi(c)}{1 - \Phi(c)} \end{eqnarray*}\]

Because \(\phi'(x) = -x \cdot \phi(x)\).

**Note:** This simple solution does not hold in general.

**Inverse Mills ratio:** Defined as

\[\begin{equation*} \lambda(x) ~=~ \frac{\phi(x)}{\Phi(x)}. \end{equation*}\]

Hence, due to symmetry of the normal distribution, the following holds for \(\varepsilon \sim \mathcal{N}(0, 1)\) and \(y \sim \mathcal{N}(\mu, \sigma^2)\), respectively:

\[\begin{eqnarray*} E(\varepsilon ~|~ \varepsilon > c) & = & \lambda(-c), \\ E(y ~|~ y > c) & = & \mu ~+~ \sigma \cdot \lambda\left(\frac{\mu - c}{\sigma}\right). \end{eqnarray*}\]

**Example:** \(y \sim \mathcal{N}(\mu, 1)\) with truncation at \(c = 0\).

### 8.2.2 Interpretation of the tobit model

**Uninteresting:** Expectation of latent variable is straightforward but lacks substantive interpretation.

\[\begin{equation*} E(y^* ~|~ x) ~=~ x^\top \beta \end{equation*}\]

**Of interest:** Depending on substantive question, consider

\[\begin{eqnarray*} P(y > 0 ~|~ x) & = & \Phi(x^\top \beta / \sigma),\\ E(y ~|~ y > 0,~ x) & = & x^\top \beta ~+~ \sigma \cdot \lambda(x^\top \beta / \sigma),\\ E(y ~|~ x) & = & P(y > 0) \cdot E(y ~|~ y > 0,~ x) \\ & = & \Phi(x^\top \beta / \sigma) \cdot \left\{ x^\top \beta ~+~ \sigma \cdot \lambda(x^\top \beta / \sigma) \right\}. \end{eqnarray*}\]

**Example:** For \(\sigma = 1\).

**Marginal effects:**

\[\begin{eqnarray*} \frac{\partial P(y > 0 ~|~ x)}{\partial x_l} & = & \phi(x^\top \beta / \sigma) \cdot \beta_l / \sigma\\[0.5cm] \frac{\partial E(y ~|~ y > 0,~ x)}{\partial x_l} & = & \beta_l \cdot \left[ 1 - \lambda(x^\top \beta / \sigma) ~ \{ x^\top \beta / \sigma ~+~ \lambda(x^\top \beta / \sigma) \}\right] \\[0.5cm] \frac{\partial E(y ~|~ x)}{\partial x_l} & = & \frac{\partial P(y > 0 ~|~ x)}{\partial x_l} \cdot E(y ~|~ y > 0,~ x) ~+~ \\ & & P(y > 0 ~|~ x) \cdot \frac{\partial E(y ~|~ y > 0,~ x)}{\partial x_l} \\ & = & \beta_l \cdot \Phi(x^\top \beta / \sigma) \\ \end{eqnarray*}\]

**Remarks:**

- Overall effect on \(E(y ~|~ x)\) is sum of an effect at the
*extensive margin*(e.g., how much more likely a person is to join the labor force as education increases times the expected hours) and an effect at the*intensive margin*(e.g., how much the expected hours of work increase for workers as education increases times the probability of participation).

- OLS estimation (both for full and positive sample) is biased towards zero due to inability to capture non-constant marginal effects.
- Effects (e.g., at mean regressors) can again be considered instead of marginal effects.

**Example:** Effects in tobit model for PSID 1976 data (hand-crafted).

**Specification issues:**

- The classical (uncensored) OLS regression model is rather robust to misspecifications of distributional form and even non-constant variances. Estimates remain consistent.
- In the tobit model, this is
*not*the case. Misspecification of the likelihood (including constant variances) will lead to misspecification of the score. Hence, \(E(y ~ |~ y > 0,~ x)\) and \(E(y ~|~ x)\) are misspecified and estimates inconsistent. - Crucial assumption: The same latent process drives the probability of a corner solution and the expectation of positive outcomes.
- Example: A single factor, such as number of old kids, can
*not*increase the probability to work but decrease the expected number of hours conditional on participation in the labor force. - Check for the latter: Cragg two-part model.

## 8.3 Cragg Two-Part Model

**Idea:** Similar to hurdle model for count data, employ two parts.

- Is \(y\) equal to zero or positive?
- If \(y > 0\), how large is \(y\)?

**Formally:** Likelihood has two separate parts.

- \(P(y > 0 ~|~ x)\): Binomial model (typically probit) for \(I(y > 0)\).
- \(E(y ~|~ y > 0,~ x)\): Truncated normal model for \(y\) given \(y > 0\).

**Remarks:**

- The tobit model is nested in the two-part model with probit link. Thus, LR tests etc. can be easily performed.
- If the tobit model is correctly specified, estimates from the probit model \(\hat \gamma\) would be consistent (but less efficient) for the scaled coefficients from the tobit model. Thus, \(\hat \beta / \hat \sigma\) should be similar to \(\hat \gamma\) in empirical samples.

**In R:** Employ separate modeling function. `glm()`

with `"probit"`

link can be used for selection part.

```
update(hours_f, participation ~ .)
part_f <- glm(part_f, data = PSID1976,
part_probit <-family = binomial(link = "probit"))
coeftest(part_probit)
```

```
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.27007 0.50808 0.53 0.5950
## nwincome -0.01202 0.00494 -2.43 0.0149
## education 0.13090 0.02540 5.15 2.6e-07
## experience 0.12335 0.01876 6.58 4.8e-11
## I(experience^2) -0.00189 0.00060 -3.15 0.0017
## age -0.05285 0.00846 -6.25 4.2e-10
## youngkids -0.86833 0.11838 -7.34 2.2e-13
## oldkids 0.03601 0.04403 0.82 0.4135
```

`truncreg()`

from package *truncreg* (or `trch()`

from package *crch*) can be employed for estimating the truncated normal regression model.

```
library("truncreg")
truncreg(hours_f, data = PSID1976,
hours_trunc <-subset = participation == "yes")
coeftest(hours_trunc)
```

```
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2055.713 461.265 4.46 8.3e-06
## nwincome -0.501 4.935 -0.10 0.91912
## education -31.270 21.796 -1.43 0.15139
## experience 73.007 20.229 3.61 0.00031
## I(experience^2) -0.970 0.581 -1.67 0.09521
## age -25.336 7.885 -3.21 0.00131
## youngkids -318.852 138.073 -2.31 0.02093
## oldkids -91.620 41.202 -2.22 0.02617
## sigma 822.479 39.320 20.92 < 2e-16
```

Comparison of (scaled) parameter estimates from tobit, probit, and truncated model.

```
cbind("Censored" = coef(hours_tobit) / hours_tobit$scale,
"Threshold" = coef(part_probit),
"Truncated" = coef(hours_trunc)[1:8] / coef(hours_trunc)[9])
```

```
## Censored Threshold Truncated
## (Intercept) 0.860327 0.270074 2.4994098
## nwincome -0.007856 -0.012024 -0.0006093
## education 0.071875 0.130904 -0.0380188
## experience 0.117256 0.123347 0.0887641
## I(experience^2) -0.001661 -0.001887 -0.0011788
## age -0.048488 -0.052852 -0.0308044
## youngkids -0.796795 -0.868325 -0.3876719
## oldkids -0.014454 0.036006 -0.1113943
```

LR test and information criteria:

```
c("Tobit" = logLik(hours_tobit),
loglik <-"Two-Part" = logLik(hours_trunc) + logLik(part_probit))
c(9, 9 + 8)
df <--2 * loglik + 2 * df
```

```
## Tobit Two-Part
## 7656 7620
```

`-2 * loglik + log(nrow(PSID1976)) * df`

```
## Tobit Two-Part
## 7698 7698
```

`pchisq(2 * diff(loglik), diff(df), lower.tail = FALSE)`

```
## Two-Part
## 1.273e-08
```

## 8.4 Sample Selection Models

**Idea:** Employ censored regression model for observations \(y ~|~ y > c\)
but model censoring threshold \(c\) as random variable.

**Name:** *Incidental censoring* or *self selection*.

**Example:** Distribution of wages.

- Wages are only observed for workers.
- Thus, the wages that non-workers would receive (if they decided to work) are unknown/latent.
- Individuals decide to work if their (possibly latent) offered wage \(w_0\) exceeds their individual reservation wage \(w_r\).
- The distribution of wages for workers are thus \(f(w_o ~|~ w_o > w_r)\) with random \(w_r\).

**Formally:** With \(y\) and \(c\) stochastic rewrite

\[\begin{equation*} f(y ~|~ y > c) ~=~ f(y ~|~ y - c > 0) ~=~ f(y_1 ~|~ y_2 > 0). \end{equation*}\]

**Special cases:**

- \(y_1 = y_2\): Standard censoring regression (i.e., tobit).
- \(y_1\) and \(y_2\) independent: \(f(y_1 ~|~ y_2 > 0) = f(y_1)\) (i.e., no selection).

**Simple approach:** Bivariate normal distribution for \((y_1, y_2)\) with correlation \(\varrho\), including special cases \(|\varrho| = 1\) (identity) and \(\varrho = 0\) (independence).

**Alternatively:** Employ different bivariate distribution or semiparametric approach.

**Regression model:** Employ (potentially overlapping) regressors for mean of both
components.

\[\begin{eqnarray*} \mbox{Outcome equation:} & y_1 ~= & x^\top \beta ~+~ \varepsilon_1, \\ \mbox{Selection equation:} & y_2 ~= & z^\top \gamma ~+~ \varepsilon_2, \end{eqnarray*}\]

where

\[\begin{equation*} \left( \begin{array}{cc} \varepsilon_1 \\ \varepsilon_2 \end{array} \right) ~\sim~ \mathcal{N}\left( \left( \begin{array}{cc} 0 \\ 0 \end{array} \right), \left( \begin{array}{cc} \sigma^2 & \varrho \\ \varrho & 1 \end{array} \right) \right). \end{equation*}\]

**Remarks:**

- Observation rule: \(y_1\) is non-censored, observed for \(y_2 > 0\).
- In selection equation, only \(y_2 > 0\) vs. \(y_2 \le 0\) is observed. Hence, standardize \(Var(y_2) = 1\) for identifiability (as in probit model).
- Terminology:
*Heckman model*,*heckit model*,*sample selection model*,*self-selection model*,*tobit-2 model*.

**Conditional expectation:**

\[\begin{equation*} E(y_1 ~|~ y_2 > 0,~ x) ~=~ x^\top \beta ~+~ \sigma \cdot \varrho \cdot \lambda(z^\top \gamma). \end{equation*}\]

**Selection effect:** Sample is

*positively selected*for \(\varrho > 0\): \(E(y_1 ~|~ y_2 > 0,~ x) > E(y_1 ~|~ x)\),*negatively selected*for \(\varrho < 0\),- and no selection occurs for \(\varrho = 0\).

**Estimation:**

- Efficient: Full maximum likelihood.
- Biased: OLS for non-censored observations.
- Consistent (and employed only for historical reasons): Two-step estimator. Probit model yielding \(\hat \gamma\) followed by OLS regressing \(y\) on \(x\) and \(\lambda(z^\top \hat \gamma)\).

**Classical example:** Estimation of mean wage offers (or worker’s productivity) from
a sample of workers and wages.

**In this context:** Sample selection model called Gronau-Heckman-Roy model (by Winkelmann & Boes 2009) due to the authors that first recognized this problem and established its inference, respectively.

**Identification:** Economic considerations guide selection of variables \(x\) that
affect the outcome and variables \(z\) that affect the selection.

- Typically, all variables \(x\) for the outcome also affect selection, especially in the case of “self selection”.
- Additional variables might affect the selection but not the outcome. Example: Presence of small children will affect selection but probably not the reservation wage.
- Such variables play a similar role as “instruments”.

**Example:** Female wages in PSID 1976 data. Employ semi-logarithmic
wage equation with education and quadratic polynomial in experience.

` log(wage) ~ education + experience + I(experience^2) wage_f <-`

**OLS estimation:** For selected subsample.

` lm(wage_f, data = PSID1976, subset = participation == "yes") wage_ols <-`

**Sample selection model:** Additionally employ age, number of old and young children,
and other income as regressors in selection equation (corresponding to previously used `part_f`

).

In R available in `selection()`

from package *sampleSelection*:

```
library("sampleSelection")
selection(part_f, wage_f, data = PSID1976) wage_ghr <-
```

Sample selection models

`coeftest(wage_ghr)`

```
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.266449 0.508958 0.52 0.6006
## nwincome -0.012132 0.004877 -2.49 0.0129
## education 0.131341 0.025382 5.17 2.3e-07
## experience 0.123282 0.018724 6.58 4.6e-11
## I(experience^2) -0.001886 0.000600 -3.14 0.0017
## age -0.052829 0.008479 -6.23 4.7e-10
## youngkids -0.867399 0.118651 -7.31 2.7e-13
## oldkids 0.035872 0.043475 0.83 0.4093
## (Intercept) -0.552696 0.260379 -2.12 0.0338
## education 0.108350 0.014861 7.29 3.1e-13
## experience 0.042837 0.014879 2.88 0.0040
## I(experience^2) -0.000837 0.000417 -2.01 0.0449
## sigma 0.663398 0.022707 29.21 < 2e-16
## rho 0.026607 0.147078 0.18 0.8564
```

**Interpretation:** \(\hat \varrho\) is essentially zero signaling that there are no significant selection effects. Outcome part of Gronau-Heckman-Roy model and OLS for selected subsample are thus virtually identical.

```
cbind("GHR (outcome)" = coef(wage_ghr, part = "outcome"),
"OLS (positive)" = coef(wage_ols))
```

```
## GHR (outcome) OLS (positive)
## (Intercept) -0.5526963 -0.5220406
## education 0.1083502 0.1074896
## experience 0.0428368 0.0415665
## I(experience^2) -0.0008374 -0.0008112
```

**Similarly:** Selection part of Gronau-Heckman-Roy model and
probit for selection are thus virtually identical.

```
cbind("GHR (selection)" = coef(wage_ghr)[1:8],
"Probit (0 vs. positive)" = coef(part_probit))
```

```
## GHR (selection) Probit (0 vs. positive)
## (Intercept) 0.266449 0.270074
## nwincome -0.012132 -0.012024
## education 0.131341 0.130904
## experience 0.123282 0.123347
## I(experience^2) -0.001886 -0.001887
## age -0.052829 -0.052852
## youngkids -0.867399 -0.868325
## oldkids 0.035872 0.036006
```

**Effects:** Compare education effects graphically.

Set up auxiliary data with average regressor and varying `education`

.

```
model.matrix(hours_tobit)[, -c(1, 5)]
X <- lapply(colnames(X), function(i)
psid <-if(i != "education") mean(X[,i]) else
seq(from = min(X[,i]), to = max(X[,i]), length = 100))
names(psid) <- colnames(X)
do.call("data.frame", psid) psid <-
```

Predictions for OLS:

` predict(wage_ols, newdata = psid) wage_ols_out <-`

Compute predictions for sample selection model by hand:

```
model.matrix(delete.response(terms(wage_f)),
xb <-data = psid) %*% coef(wage_ghr, part = "outcome")
model.matrix(delete.response(terms(part_f)), data = psid) %*%
zg <- head(coef(wage_ghr), 8)
xb + coef(wage_ghr)["sigma"] *
wage_ghr_out <- coef(wage_ghr)["rho"] * dnorm(zg)/pnorm(zg)
```