df_opt_caschool_prep_i: Test Dataset California Test Score Data: *PREP I Frame*,...

In FanWangEcon/PrjOptiAlloc: The Optimal Allocation of Resources Among Heterogeneous Individuals

Description

\text{ID}_i: individual id D^{\text{max}}_{i}: maximimum discrete allocation each person, in addition to existing levels D^{\text{o}}_{i}: what I call observed allocation, just any comparison allocation, in addition to existing levels, this is the additional allocation (ignoring existing levels), this is the not the total allocation. If fully redistributing, this could be the observed level of allocation. Ω_{i}: This is the expected outcome when the input of interest is completely zero, Ω_{i}\neq A_{i,l=0}, because l=0 does not mean input is zero, using Ω_i rather than A_i to avoid confusion because A_i sounds like A_i = A_{i, l=0}, but again, the problem does not start with allocation at zero, but each district already has some existing number of teachers. θ_{i}: coefficient in front of student teacher ratio β_{i}: i specific preference \text{enrltot}_{i}: enrollment per district/school, assume no within district variations \text{teachers}_{i}: teacher per distrct/school \text{stravg}_{i}: average of student teacher ratio district/school

Usage

data(df_opt_caschool_prep_i)

Format

csv

Source

https://fmwww.bc.edu/ec-p/data/stockwatson/caschool.des https://fanwangecon.github.io/PrjOptiAlloc/articles/ffv_opt_sodis_rkone_casch_allrw.html

References

Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers, chapter 4–7.

Examples

data(df_opt_caschool_prep_i)
head(df_opt_caschool_prep_i, 30)

FanWangEcon/PrjOptiAlloc documentation built on Jan. 25, 2022, 6:55 a.m.