
Endnotes

34. The 1993 peak was 15 percent when measured using the March CPS (U.S. Census Bureau 2000) rather than the PSID. It is well established that poverty rates in the PSID are lower than official poverty rates produced by the U.S. Census Bureau from the March Current Population Survey (CPS). Evidence suggests that the lower poverty rates are due to more complete income reporting at the lower end of the income distribution in the PSID than in the CPS (Citro and Michael 1995, p. 403). In addition, the PSID represents the nonimmigrant population and the CPS captures immigrants. While the poverty rates reported in Table 2 are lower, the trends over time are similar to the official rates produced by the CPS. See U.S. Census Bureau (2000) Historical Poverty Table 2 for comparable official poverty rates.
35. The PSID is not a strong data source for explaining population dynamics because it does not capture changes in immigration, a key component behind changes in the total U.S. population.
36. The 199396 data are from the early release PSID files and thus, are preliminary.
37. Due to the large size of the 1996 SIPP personmonth entry sample, we limit the sample to the 19972000 time period (dropping observations for 1996 and the first quarter of 1997).
38. As discussed in the methods section (Section VI.2), this model does not necessarily identify a causal relationship, but rather a conditional relationship (the relationship after controlling for other events and characteristics). This occurs because some of the events, such as employment status changes, are choice variables (i.e., potentially endogenous).
39. The value of the estimated coefficients from the discretetime multivariate hazard models do not have a straightforward interpretation. The coefficients can be used to determine whether an event increases or decreases an individuals' likelihood of experiencing a poverty transition, but alone, they do not provide information about the degree to which individuals are more or less likely to transition.
40. We calculate the likelihood of entering poverty (or exiting poverty) when an event occurs (or does not occur) using the estimated coefficients from the hazard model and individuals' characteristics. For details on how the probabilities are calculated, see Calculating the Likelihood an Event Occurs in Section IV.2.
41. Recall that trigger events are defined as a change in status between two periods. So, an event defined as occurring at time t occurred between the current period t and the pervious period t1, where t is measured in years for the PSID and months for the SIPP (with the exception of GDP, which is measured in quarters for the SIPP). And, an event defined as occurring at time t1 occurred between t2 and t1.
42. We examine whether the estimated relationship between poverty entries and changes in economic conditions are mitigated by the inclusion of employment changes in the model. Our analysis suggests this is not the case. We estimate a second set of models that exclude the employment change variables, and compare results across models that include and exclude the employment change variables. We find little difference in the relationship between poverty entries and the economic change variables across the two models.
43. Recall that the categories of headship capture all possible household structure combinations at time t: femaleheaded household at time t and became femaleheaded at t (i.e., between t1 and t), at t1, or prior to t1; single maleheaded household at time t; and twoadult household at time t.
44. In other words, the coefficient is picking up unobserved heterogeneity.
45. The likelihood of entering poverty when an event occurs is calculated using the estimated coefficients from the hazard model and individuals' characteristics. For more details on calculations, see Calculating the Likelihood an Event Occurs in Section IV.2.
46. Note that nonpoverty spells that do not result in a transition into poverty are not captured in either of these two models, since the individual neither entered a short or long poverty spell.
47. This percentage is calculated by summing the estimated effects (the third number presented in the tables) in the time periods where the coefficients are statistically significant at the five percent level. So, for this event we sum the time t, t1, t2, and t3 effects, where the effects at these time periods are 11.1 percentage points, 1.1 percentage points, 0.3 percentage points, and 0.2 percentage points. The effects for other variables are calculated in this same way.
48. Recall from the descriptive analysis section that the poverty entry models estimated with the 1996 SIPP panel do not use data from 1996 due to computer constraints encountered when estimating the models on nearly 3 million observations.
49. For details on how the probabilities are calculated, see Calculating the Likelihood an Event Occurs in Section IV.2.
50. The change in educational attainment is defined separately for households that experienced no change in household structure and those that experienced a change in household structure. This construct produces model results that provide information about whether education could increase individuals likelihood of exiting poverty, rather than mixing this with information about whether marrying or cohabiting with a more educated individual pulls a household out of poverty.
51. For details on how the probabilities are calculated, see Calculating the Likelihood an Event Occurs in Section IV.2.
52. The estimated likelihood of exiting poverty in a year using PSID data is higher by a total of 7.3 percentage points, 29.4 percentage points, and 15.0 percentage points, respectively.
53. These percentages are calculated by summing the estimated effects (the third number presented in the tables) in the time periods where the coefficients are statistically significant at the five percent level. So, for the event “other household member gains employment” we sum the time t and t1 effects. If another household member gains employment this month (t), the probability of exiting poverty this month (t) is higher by 24.0 percentage points (Table 9, column 2) and if another household member gained employment in the last quarter (t1) the probability of exiting poverty is higher by 5.6 percentage points (Table 9, column 2). These two pieces provide the total effect of 29.6 (24.0 plus 5.6) percentage points. The effects for other variables are calculated in this same way.
54. We examine whether the estimated relationship between poverty exits and changes in economic conditions are mitigated by the inclusion of employment changes in the model. Our analysis suggests this is not the case. We estimate a second set of models that exclude the employment change variables, and compare results across models that include and exclude the employment change variables. We find little difference in the relationship between poverty exits and the economic change variables across the two models.
55. The increase in the likelihood of exiting poverty by 24.9 percentage points is calculated by summing the effects in the three time periods that are statistically significant: t (35.1), t1 (4.6) and t2 ( 5.6).
56. For details on how the probabilities are calculated, see Calculating the Likelihood an Event Occurs in Section IV.2.
57. As discussed above, models that exclude employment changes find a similar relationship between poverty exits and household structure shifts in the 198892 and 199799 periods. This suggests that changes in household structure may be operating indirectly through employment to a greater extent in the 199799 period than in the 198892 period.

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