Results for Stratified Samples

Previous research has documented significant differences in birth outcomes for black and white infants, so we stratify our sample by race and present the results for the outcomes LBW and

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VLBW in the first four columns of Table 5.15 _{We do not find any significant effect of smoking} bans on infant health for either black or white infants. We also estimate the models for three vulnerable sub-groups: teenage mothers (14-19), black teenage mothers, and mothers with low levels of education. For the teenage mothers and black teenagers, the results (shown in Columns 5-8 of Table 5) indicate that both workplace bans and restaurants/bar bans are associated with significantly higher probability of VLBW, and the magnitude is larger than what we find in the baseline model. For the group of mothers with high school level of education or below, we fail to find any significant effects of smoking bans on the probability of having LBW or VLBW infants (Columns 9-10 of Table 5).

5.3 Specification Checks

We perform several specification checks to test the robustness of our baseline results.16 First, anti-smoking sentiments may bias our results. If the passage of the clean indoor air laws in certain areas directly reflect more concern for health and distaste for smoking, then failing to control for differences in anti-smoking sentiment within state and over time will over-estimate the beneficial effects of smoking policies (DeCicca et al. 2008). In our case, the potential detrimental effects of smoking bans on infant health will be under-estimated. Following DeCicca et al. (2008), using data from Tobacco Use Supplement to the Current Population Survey (TUS-CPS) during the period 1995-2011, we include a direct measure of state-level anti-smoking sentiment as

15 _{The results for the other birth outcomes are not shown in the paper, but available upon request. We do not find any} significant results for the other outcomes among white or black infants.

16 _{Because of the number of outcomes we have and models we estimate, we only present the results for the outcomes} of LBW and VLBW in a few specification checks of interest in Table 6. Other results are available upon request.

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additional explanatory variables in our baseline model.17 _{This measure is based on responses to} two questions on public attitudes towards smoking. In all TUS-CPS surveys during our study period, respondents were asked whether they think smoking should be allowed in bars and lounges, and whether they think smoking should be allowed at home. We create two variables – the percentage of people saying that smoking should not be allowed in bars and the percentage of people saying that smoking should not be allowed at home – to proxy for public attitudes towards smoking across states and over time. These variables are merged to the infant birth data based on state of residence and year of conception.

As shown in the first two columns of Table 6, the positive association between smoking bans and the incidence of LBW and VLBW infants born to mothers aged less than 24 still exists and is significant. When smoking bans are specified as Any Ban in the first row, the results indicate that a 0 percent to 100 percent change in coverage by any type of smoking ban would lead to an increase in the probability of having LBW and VLBW infants born to young mothers, and the size of the effects is similar to that we find in our baseline models. When each type of smoking bans enters into the equation separately, we continue to find a significant, positive effect for the outcome of VLBW and the magnitude doesn’t change.

Second, we estimate models in which we control for pre-natal care during pregnancy. We do not include this control in our baseline models because it is likely to be endogenous (Rosenzweig and Schultz 1983). However, it may be an omitted variable if it affects smoking behavior during pregnancy and access varies by location and time. When we add a pre-natal care

17 _{DeCicca et al. (2008) provide an in-depth discussion of the benefits of controlling for anti-smoking sentiment and} present a way to measure this sentiment. Since our study period is longer than DeCicca et al. (2008), and TUS-CPS asked different questions after 2003, we are only able to use the replies to two questions (instead of nine questions in their paper) to measure the anti-smoking sentiment.

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variable to our models, we still do not find any effect of smoking bans on mean birth weight or length of gestation, but the positive correlation between Any Ban and the rate of LBW and VLBW become insignificant (See Columns 3-4 of Table 6). However, pre-natal care is self-reported and incompletely reported in the birth certificate data, so this is not our preferred specification.

Third, the potential multi-collinearity between cigarette taxes and smoking bans may affect the significance of our results. To assess whether or not the time trend for cigarette tax increases is collinear with the trend in state adoption of smoking bans, we both informally observe whether there is a tax hike at the time of adoption of bans, and formally test the collinearity by calculating variance inflation factor (VIF). The VIF of workplace bans, bar/restaurant bans, and taxes are below 2.5, and the correlation between Any Ban and taxes is around 0.35, indicating no severe multi-collinearity. In addition, we replace the cigarette tax variable with a real cigarette price variable that includes taxes to test the robustness of results.18 The results, in Columns 5-6 of Table 6, are not sensitive to this change.

Finally, we address the fact that in our 2005-2009 data, counties of all sizes are identified, while in the pre-2005 data, counties with populations below 250,000 are not identified, and so those observations with unidentified counties before 2005 are dropped in our baseline specifications. We estimate a set of models in which counties with populations below 250,000 were dropped in all years in our sample and report our results in Columns 7-8 of Table 6. Although the precision of the estimates falls slightly (some estimates are only significant at 10 percent level), the pattern of results is qualitatively similar to the baseline estimates. In the last two columns of Table 6, we report the results when including the county-specific linear time trends as additional

18 _{Cigarette price data is from the Tax Burden on Tobacco (Orzechowski and Walker 2011). The price is a weighted} average after-tax price for a pack of 20 cigarettes, varying by state and by year.

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controls and using only observations in large counties (pop>250,000). As we can see, the smoking bans are still associated with higher rates of LBW and VLBW infants, although the significance level drops slightly.