Smog Reduction’s Impact on California County Growth

• Study looks at the relationship between changes in environmental quality and population change.

• San Bernardino and Riverside counties suffer from highest ozone levels in the country.

• Ozone: “a strong irritant that can cause constriction of the airways, forcing the respiratory system to work harder to provide oxygen. For healthy people it makes breathing more difficult….but may pose a worse threat to those who are already suffering from respiratory diseases such as asthma…..”

• Due to vehicle and manufacturing regulations in California the number of high ozone days has drastically decreased in the counties: San Bernardino had 40 fewer high ozone days in 1996 compared to 1980.

• The paper’s thesis: “…county quality of life increased in areas where ozone fell sharply and this has encouraged in-migration.”

• The population of many California counties increased over time.

• The authors task is the link the timing of this growth to changes in environmental quality.

• This link would imply a relationship between environmental quality and migration.

• Typically a study wants to take local evidence (the sample) to say something generally about a population.

• Author first estimates equation to establish that San Bernardino and Riverside Counties growth has accelerated over period that pollution declined.

• Runs the regression model using data for all counties in California:

• log(Popj,t+1/Popj,t)=γ log(Popj,t)+βXjt+U• There are 58 counties in California.

• Author calculates logged change in population over two time periods: 1969 to 1980 and 1980 to 1994.

• So each county is observed twice – the author stacks the data over the two times periods

• Pop is population in a given year and Xit is a series of dummy variables accounting for such factors as which time period the observation is in and whether the observation represents San Bernardino/Riverside County

• Why does author use only California counties?

Regression Model Results

• The coefficient for the log 1969 county population implies the inverse relationship between the population size of county in 1969 and subsequent growth over the two time periods.

• Larger counties in 1969 did not grow as fast in percentage terms as smaller.

• Significance?

• Coefficient for 1994 Calendar-Year dummy implies counties on average grew in population more slowly over the 1980-1994 period than in 1969-1980 period.

• Los Angeles Region grew at faster pace than the remaining counties in the state.

Growth in San Bernardino/Riverside relative to rest of the state

• Define:– X2=1 if dependent variable is an observation

indicating growth over the 1980-1994 period =0 otherwise

– X4=1 if county is San Bernardino or Riverside =0 otherwise

• Model– population growth = ….b4X4+b6(X2*X4)

= … .058X4+.421(X2*X4)

• Model– population growth = ….b4X4+b6(X2*X4)

= … .058X4+.421(X2*X4)

• Growth in San Bernardino/Riverside relative to the other California Counties in 1969-1980 period:– X2=0 and X4=1

• Over 1969-1980 San Bernardino/Riverside grew roughly 5.8% faster than the remaining counties in California

• Model– population growth = ….b4X4+b6(X2*X4)

= … .058X4+.421(X2*X4)

• Growth in San Bernardino/Riverside relative to the other California Counties in 1980-1994 period:– X2=1 and X4=1

• Over 1980-1994 San Bernardino/Riverside grew roughly 47.9% faster than the remaining counties in California.

• The period of accelerated growth coincides with period of decreased pollution

• Regression model is more descriptive than one suggesting causation.

• Does the finding on the increased population growth “prove” the author’s thesis?

• Could other factors have accounted for the growth?

• What role does the concept of statistical significance play in the author’s thesis?

• Author more closely investigates relationship between environmental quality and population growth:– Runs regression estimating the determinants of

California county growth only over 1980-1994– Includes continuous variables that can impact county

growth in regression, such as 1980 home price, 1980 percent hispanic…

– One of the variables in the regression is the difference in the number of high ozone days in 1980 and 1994.

• For example if a county had 40 high ozone days in 1980 and 32 high ozone days in 1994 then the value of the variable for that county is -8 indicating pollution fell in the county by that magnitude

Log/linear Model• Interpret coefficients in the two models• Analyze significance of estimated effects• R2?• Why should author include other factors in

regression model?

Author’s finding:

“A county that experienced a 10-day reduction in high ozone days between 1980 and 1994 grew by 7.8 percent more than a county whose ozone level remained unchanged.”