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 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(Pop j,t+1 /Pop j,t )=γ log(Pop j,t )+βX jt +U There are 58 counties in California. Author calculates logged change in population over two time periods: 1969 to 1980 and 1980 to So each county is observed twice – the author stacks the data over the two times periods Pop is population in a given year and X it 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 period than in 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: – X 2 =1 if dependent variable is an observation indicating growth over the period =0 otherwise –X 4 =1 if county is San Bernardino or Riverside =0 otherwise Model –population growth = ….b 4 X 4 +b 6 (X 2 *X 4 ) = ….058X (X 2 *X 4 )

Model –population growth = ….b 4 X 4 +b 6 (X 2 *X 4 ) = ….058X (X 2 *X 4 ) Growth in San Bernardino/Riverside relative to the other California Counties in period: –X 2 =0 and X 4 =1 Over San Bernardino/Riverside grew roughly 5.8% faster than the remaining counties in California

Model –population growth = ….b 4 X 4 +b 6 (X 2 *X 4 ) = ….058X (X 2 *X 4 ) Growth in San Bernardino/Riverside relative to the other California Counties in period: –X 2 =1 and X 4 =1 Over 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 –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 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 R 2 ? 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.”