A Difference-in-Difference Model Econometric Model

weight rates, respectively. This result is driven by the low fraction of minorities Blacks or Hispanics in Massachusetts and the high fraction in Arizona. These two groups tend to have low smoking rates but poor birth outcomes. Pretreatment smok- ing and low birth-weight rates were 15 and 7.7 percent in Illinois, respectively, and 20 and 8 percent respectively in Michigan. How these four states spent the revenues generated from the tax hikes differed. Arizona, Illinois and Michigan raised cigarette taxes primarily to generate state tax revenues to pay for nontobacco-related programs. More than 60 percent of the rev- enue collected from Arizona’s 1994 40-cent tax hike went toward medical care for the uninsured and poor. The remainder of the revenue went toward antitobacco pro- grams and health research on prevention and treatment of tobacco-related illnesses The Arizona Republican, February 16, 1995, p. A5. Illinois’ 1993 14-cent cigarette tax hike was passed to ease budgetary woes over funding for health services for the poor St. Louis Post Dispatch, July 13, 1993, p. 1A. Michigan’s 1994 50-cent tax increase was passed to raise revenues to replace a portion of those lost when resi- dents voted to eliminate the property tax New York Times, March 14, 1994, p. A15. Massachusetts’ 1992 25-cent state cigarette excise tax increase funded the Massachusetts Tobacco Control Program that included activities such as antismok- ing media campaigns, enforcement of local antismoking laws, and educational programs targeted primarily at teenagers, and pregnant women The Boston Globe, December 5, 1993, p. 4, and December 19, 1993, p. 50. Revenue from Massa- chusetts’ tax hike also went to health programs unrelated to smoking The Boston Globe , March 5, 1993, p. 10. In the next section, we describe a model that estimates the “treatment effect” asso- ciated with each state’s major tax hike. In the case of Arizona, Illinois, and Michigan, the text above suggests that we can reasonably attribute all of the change in con- sumption to the higher cigarette tax. In the case of Massachusetts, however, the treat- ment included a tax hike followed by a large antismoking media campaign. In a companion paper, Lien 2001 examines in detail the impact of the Massachusetts antismoking campaign on tobacco use among pregnant women and finds no addi- tional change in smoking after the start of the media campaign. This suggests that the drop in smoking we show for Massachusetts is driven by the tax change and not the media campaign.

III. Econometric Model

A. A Difference-in-Difference Model

We are interested in whether higher cigarette excise taxes altered pregnant women’s decision to smoke and smoking’s impact on birth outcomes. In each treatment state, there are two groups of pregnant women affected by the tax hike. The first group is women who conceived in the months before the tax hike and were exposed to the treatment effect for only a portion of their pregnancy. The second group is pregnant women who conceived after the tax hike and were exposed to the treatment effect for their entire pregnancy. We distinguish the impact on these groups by estimating two treatment effects: a partial tax effect for women who conceived during the eight Lien and Evans 377 months prior to the tax hike, and a full tax effect for women who conceived after the tax hikes were implemented. 3 For each treatment state, we include data for 56 monthly periods: 24 months prior to any exposure to the tax hike, the eight months of a partial tax effect, and 24 months after the tax hike. For these periods, we also include data for the corresponding set of control states. Outcomes are measured at the individual level i and data varies across states s and months m. The primary outcome of interest is a binary indicator for maternal smoking, denoted as S. The basic research question is whether the excise tax hike in a particular state decreased maternal smoking. Births that occurred in a treat- ment state are indicated by the dummy variable D. The equation estimated is of the form 1 FULL TAX + + + a n y PART TAX c D + S X D s sm s sm ism ism s m ism 1 1 1 1 1 1 = + b where X is a vector of characteristics that describe the mother and the pregnancy. We include variables on maternal age, race, ethnicity, marital status, and education. 4 We also include variables for the infant’s sex, parity of birth, plurality of birth, and Kessner adequacy index of prenatal care. 5 State and month of conception effects are represented by µ and υ respectively and is a random error. The variable PART TAX equals one for women who conceived during the eight months before the tax hike, and FULL TAX equals one for women who conceived during the 24 months following the tax hike. Since DFULL TAX equals one only for the treatment state in the post-tax- hike period, the full tax-hike treatment effect is measured by α 1 . The econometric model described in Equation 1 is a standard difference-in-difference model where states that did not raise their excise tax form a comparison group.

B. Choosing Control Groups