Suppose I have two variables: (1) heat index for each county in a state, $h_{it}$, and (2) acres in each county, $acres_{it}$. The data has 10 years and also includes a variable for the amount of ice cream melted, $y_{it}$ for each county and year in the sample.

I'm told that a strong predictor of ice cream melt can be found by weighting the heat index by the size of the county and then aggregate to state-level data, such that:

$$\frac{\sum_{i} h_{it} \cdot acres_{it}}{\sum_{i} acres_{it}} = h_{st}$$

A simple linear regression can then predict the state-level ice cream melt by:

$$y_{st} = \beta_{1}h_{st} + \epsilon_{it}$$


My question is related to the weighting. Is there a specific name for this type of weight and how is the weighting performed?

To me, I'm thinking about it as subsetting each year, applying the weight for all counties in that year, and then aggregate down to state level data. This would provide a heat index for each state in each year.

Is this the appropriate way to do this type of weighting?

R Code:

In R I would do something like this:


# Sample Data
dat <- structure(list(year = c(2000L, 2001L, 2002L, 2000L, 2001L, 2002L, 
2000L, 2001L, 2002L, 2000L, 2001L, 2002L), county = c(1L, 1L, 
1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L), state = c("CA", "CA", 
"CA", "CA", "CA", "CA", "CO", "CO", "CO", "CO", "CO", "CO"), 
    y = c(5L, 10L, 7L, 4L, 2L, 8L, 9L, 11L, 2L, 5L, 6L, 8L), 
    h = c(5L, 7L, 1L, 9L, 6L, 4L, 8L, 2L, 5L, 8L, 7L, 1L), acres = c(10L, 
    25L, 40L, 8L, 13L, 42L, 50L, 24L, 57L, 24L, 35L, 15L)), .Names = c("year", 
"county", "state", "y", "h", "acres"), class = "data.frame", row.names = c(NA, 

# Build Weighted Variable
dat <- dat %>% 
  group_by(year) %>% 
  mutate(w = acres/sum(acres, na.rm = TRUE))

# Apply Weight
dat$h <- dat$h * dat$w

# Aggregate to State-level
dat <- dat %>% 
 group_by(year, state) %>% 
  summarise(h = sum(h, na.rm = TRUE))
  • $\begingroup$ Wouldn't total ice cream melt in a county be more closely related to population of the county than to its area? $\endgroup$ May 9, 2016 at 23:15
  • $\begingroup$ @MarkL.Stone Yes, you are correct. You could weight using population as well. I was trying to create an example close to my problem, so using population or heat index is fine. I'm more after the weighting procedure and interpretation. $\endgroup$
    – Amstell
    May 9, 2016 at 23:19

1 Answer 1


Your $h_{st}$ is just a weighted arithmetic mean for the heat index across the counties in each state. It makes sense because the more acres there are in a county, the more ice cream will melt in that county. By using $h_{st}$, you are controlling for the size of counties when averaging $h_{it}$ across counties.


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