Description Usage Arguments Value Examples

calculates the 'outside option index' (defined as
*-ā P(Z|X) * log(P(Z|X) / P(Z))*)
for workers, using employer-employee data.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

`formula` |
a formula describing the model to be fitted in order to estimate P(Z|X) / P(Z). This formula uses a syntax similar to STATA, and so "x_" refers to all variables with the prefix "x", while "z_" refers to all variables with the prefix "z". Similarly, "d" refers to the distance polynomial (see the example below). |

`X` |
matrix or data frame with workers characteristics. Note that all column names should start with "x" (necessary for the inner function 'coef_reshape'). |

`Z` |
an optional matrix or data frame with jobs characteristics. Note that all column names should start with "z" (necessary for the inner function 'coef_reshape'). |

`X.location` |
an optional matrix or data frame with location for workers. Could be geographical location (i.e., geo-coordinates) or any other feature that can be used in order to measure distance between worker and job using 'dist.fun'. Currently the package supports only numeric inputs. |

`Z.location` |
same as 'X.location' but for jobs. |

`wgt` |
an optional numeric vector of weights. |

`pred` |
logical. If TRUE (default), predicts the ooi for the provided data. |

`method` |
a method for estimating P(Z|X) / P(Z). Currently not in use. |

`sim.factor` |
a variable that determines how much fake data to simulate (relative to real data). |

`dist.fun` |
a distance function to calculate the distance between X.location and
Z.location. Users interested in using more than one distance metric
should provide a function that returns for each row of X.location and
Z.location a vector with all the necessary metrics. Also - the function
should use columns by their index and not by their names.
The default function is |

`dist.order` |
a numeric vector specifying for each distance metric an order of the distance polynomial. |

`seed` |
the seed of the random number generator. |

An "ooi" object. This object is a list containing the following components:

`coeffs` |
coefficients from the estimated logit. |

`coeffs_sd` |
coefficients SE. |

`pseudo_r2` |
McFadden's pseudo-R squared for the estimated logit. |

`standardized_coeffs` |
standardized coefficients. |

`ooi` |
the Outside Option Index. |

`hhi` |
the Herfindahl-Hirschman Index, an alternative measure for outside options. |

`job_worker_prob` |
the log probability of each worker to work at his *specific* job (rahter than to work at a job with his specific z) |

`orig_arg` |
a list containing the original arguments (necessary
for |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
#generate data
#worker and job characteristics:
n <- 100
men <- rbinom(n, 1, 0.5)
size <- 1 + rgeom(n, 0.1)
size[men == 0] <- size[men == 0] + 2
worker_resid <- data.frame(r = round(runif(n, 0, 20), 1))
job_location <- data.frame(l = round(runif(n, 20, 40), 1))
#prepare data
#define distance function:
dist_metric <- function(x, y){abs(y - x)}
X <- data.frame(men = men)
Z <- data.frame(size = size)
#add "x" / "z" to column names:
X <- add_prefix(X, "x.")
Z <- add_prefix(Z, "z.")
#estimate P(Z|X) / P(Z) and calculate the ooi:
ooi_object <- OOI(formula = ~ x_*z_ + x_*d + z_*d, X = X, Z = Z,
X.location = worker_resid, Z.location = job_location,
sim.factor = 3, dist.fun = dist_metric, dist.order = 3)
#we can extract the ooi using predict():
ooi <- predict(ooi_object)
summary(ooi)
``` |

OOI documentation built on Jan. 13, 2021, 6:07 a.m.

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