EssayproSolutions

The unit of observation is the county and there are 462 observations of 19 variables. Each observation records the change between 1972 and 1971 (i.e., the 1972 minus the 1971 value) for each of the variables. The lone exception is the tbirth variable that equals the sum of the 1972 and 1972 number of births. This variable should be used as a weight (in STATA language this means w=tbirth) in ALL regressions in this exercise.

The relevant variables (with descriptions in quotations) are:

dimr7271 “# inf death per 1,000 births 72-71”

tbirth “total births 71 & 72”

dwhite “% births, white mom 72-71”

dothr “% births, nonwhite/nonblack mom 72-71”

dfemale “% female births 72-71”

dedudad “father yrs of ed 72-71”

dedumom “mother yrs of ed 72-71”

dlwght “% births with weight<2,500 g 72-71”

dmaried “% mother married 72-71”

dunmard “% mother unmarried 72-71”

dagemom “mother age 72-71”

dpcare1 “% mom began month 1 or 2 72-71”

dpcare2 “% mom began 3rd month 72-71”

dpcare3 “% mom began 4-6th month 72-71”

dpcare4 “% mom began 7-9th month 72-71”

dpcinc “county-level per cap income 72-71”

dmtspgm “county-level tsps concen 72-71” (units are in µg/m³ )

fstate “fips state code”

reg_tsp “=1 if county regulated for tsps”

Suppose God has told us (or that we suspect) that there are many unmeasured/unobservable confounding factors that determining both dimr7271 and dmtspgm. Examples of these types of variables include: health insurance status, rates of smoking across mothers, and parents’ income. Explain how this could lead to “omitted variables” bias in the LS equation. Show this in a derivation of the LS parameter estimate of the influence of TSPs on IMR.