Quote:
Originally Posted by VivaLFuego
At least as far as CTA rail is concerned, ridership peaked around 1984 and lost 20% before bottoming out in 1992, and didn't start recovering until 1998 even with the opening of the Orange Line in 1993. And it wasn't just the flood in 1992, it was an 8-year downward trend, through both good and bad economic times, following a period of relative stability from the mid-70s to mid-80s when there was no downtown garage construction. Causational proof? Of course not. But to say it's a total coincidence would be....dubious.
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Association is not causation, or a more statistical term
Correlation is not Causation. In any introductory statistics or econometric analysis course you would learn that there are a number of dependent variables that impact the independent variable.
To say that their is a relationship between amount of parking downtown and public ridership you would have to do a bit of cross-sectional time series data analysis (also known as panel data or longitudal data analysis) which also looks at other contributing factors such as price of oil (we saw a big decline in price of oil in mid 1980s through late 1990s), amount of jobs downtown vs. suburbs, amount of people living downtown vs. suburbs, amount of crime recorded in the city vs. suburbs, amount of crime on CTA trains, cost of driving one mile in a car vs. cost of taking CTA train one mile, cost of parking, etc. Maybe a bigger contributing factor to a
decline in ridership after 1984 was decline in price of oil and thus cost of drving and not building of parking in the loop. I might poke around some databases to see if such a study had ever been done for any major American city.
We also have to worry about omitted variable bias (or confounding) since we aren't talking about a controlled experiment but an observational study (looking at historical data). For example we can't measure perception of how safe people feel taking CTA trains as opposed to a car.
Also social attitudes (towards driving, commuting downtown, living in the city) in mid-70s might have been similar to those in early 80s since social attitudes are fairly similar from one year to the next, but they may vary considerably over longer period of time. So if this is true that social attitudes in late 90s are different than in 70s and assumption of independent error terms across observations in a time series is violated. The reason why this is important is because under the classical econometric model error terms for each observation need to be independent of one another. Otherwise error terms reflect omitted variables that influence the demand for parking or public transit ridership. This could also lead to autocorrelation and other problems.
Hope this helps you understand the sheer complexity of analyzing such complex problems as this one, those results are scientific and unbiased.