Aggregation and the estimated effects of economic conditions on health

Journal of Health Economics 40 (2015) 83–96

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Aggregation and the estimated effects of economic conditions on health Jason M. Lindo ∗ Texas A&M University, NBER, and IZA, Germany

a r t i c l e

i n f o

Article history: Received 6 January 2014 Received in revised form 6 November 2014 Accepted 26 November 2014 Available online 25 December 2014 JEL classification: I10 J20 E32

a b s t r a c t This paper considers the relationship between economic conditions and health with a focus on different approaches to geographic aggregation. After reviewing the tradeoffs associated with more- and less-disaggregated analyses, I update earlier state-level analyses of mortality and infant health and then consider how the estimated effects vary when the analysis is conducted at differing levels of geographic aggregation. This analysis reveals that the results are sensitive to the level of geographic aggregation with more-disaggregated analyses—particularly county-level analyses—routinely producing estimates that are smaller in magnitude. Further analyses suggest this is due to spillover effects of economic conditions on health outcomes across counties. © 2014 Elsevier B.V. All rights reserved.

Keywords: Health Recessions Mortality Infant health Aggregation

1. Introduction Although Harvey Brenner’s pioneering research suggested that health deteriorates during recessions (Brenner, 1973, 1975, 1979), follow-up work has revealed that estimates based on aggregate time-series data are quite fragile (Forbes and McGregor, 1984; McAvinchey, 1988; Joyce and Mocan, 1993; Laporte, 2004; Gerdtham and Johannesson, 2005). As Ruhm (2000) points out, this “fragility is not surprising since any lengthy time-series is likely to suffer from substantial omitted variables bias.”1 Out of concern for such biases, researchers have largely stopped considering nationwide changes in favor of an “area approach” that considers how the health of individuals living in an area changes over and above changes occurring across all areas when its economic conditions change over and above changes occurring across all areas. The intent, however, has remained the same: to estimate the degree to

∗ Correspondence to: Department of Economics, Texas A&M University, College Station, TX 77843, United States. E-mail address: [email protected] 1 For example, it is problematic for this approach that penicillin became increasingly available as the United States began to recover from the Great Depression. http://dx.doi.org/10.1016/j.jhealeco.2014.11.009 0167-6296/© 2014 Elsevier B.V. All rights reserved.

which health outcomes respond to changes in macroeconomic conditions. These studies have repeatedly concluded that “recessions are good for health” in developed countries, though this interpretation relies on the assumption that health is similarly influenced by macroeconomic conditions (broadly defined) and more local areaspecific economic conditions.2 This study is motivated by the idea that this assumption cannot be tested directly since credible estimates of the effects of macroeconomic conditions (broadly defined) would appear to be out of our reach, but that it can be tested indirectly by investigating the extent to which more local and less local economic conditions have different estimated effects on health. More generally, this paper is concerned with the way in which geographic aggregation (or disaggregation) influences the conclusions we draw from our analyses and what analyses we can perform.3

2 See Ruhm (2000, 2003, 2005, 2007), Ruhm (2013), Dehejia and Lleras-Muney (2004), Johansson (2004), Neumayer (2004), Tapia Granados (2005), Gerdtham and Ruhm (2006), Lin (2009), Miller et al. (2009) and Stevens et al. (2011). 3 With few exceptions nearly all area studies using U.S. data define “areas” as states. To my knowledge, the only exceptions are Currie and Tekin (2011) who consider foreclosures at the zip-code level in four states and Dehejia and Lleras-Muney (2004) who consider unemployment rates and supplement their state-level analysis with a county-level analysis of California.

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There are several issues to consider when choosing the level of geographic aggregation and interpreting the results of the analysis. From an economic perspective, we must recognize that there are many mechanisms through which economic conditions can affect health and, from a statistical perspective, we must recognize that the level of geographic aggregation influences the degree to which the estimates capture these different mechanisms. For example, we would expect the effects of individuals’ job losses to be captured by changes in economic conditions in the local area where individuals work. However, economic conditions both near and far may affect an individual’s health through impacts on re-employment, migration decisions, perceptions about economic conditions, traffic congestion, levels of pollution, the quality of medical care, government policies, and through effects on the members of one’s social network. In terms of identification, it is important to keep in mind that the estimated effects of an area’s economic conditions are fully inclusive of spillover effects across “subareas” within the area whereas the estimated effects of “subarea economic conditions” are not. For example, estimated effects of state economic conditions are fully inclusive of spillover effects across counties within a state whereas more-disaggregated analyses are not. At the same time, more-disaggregated analyses can offer more precise estimates because they use variation in economic conditions idiosyncratic to the area in addition to variation driven by broader changes. In particular, more-disaggregated analyses can improve power by leveraging variation in economic conditions that are masked in more-aggregate measures. For example, a contraction in one part of a state that is offset by an expansion in another part of the state would contribute to county-level estimates but not to state-level estimates. Similarly, a county-level analysis would exploit variation in the severity of contractions (and expansions) across different parts of the state whereas a statelevel analysis would not. Thus, more-disaggregated analyses may be able to detect statistically significant effects of economic conditions where less-aggregated conditions cannot, even if they do not capture some of the spillover effects captured in more-aggregated analyses.4 Another important consideration is that economic indicators are subject to measurement error, which is especially problematic for fixed-effects estimators (Griliches, 1977; Griliches and Hausman, 1986; Hausman, 2001). The most common measure of economic conditions used in this literature is the unemployment rate, produced by the Bureau of Labor Statistics (BLS). However, as one considers smaller areas, one needs to be more and more concerned about measurement error in unemployment rates since they are based in part on household surveys (Bartik, 1996; Hoynes, 2000).5 For this reason, employment-topopulation ratios would seem preferable because they are based solely on administrative data. Still, one may have concerns about measurement-error bias that may be influenced by the level of aggregation.

4 On a related note, more-disaggregated analyses can have improved power because they allow for a richer set of control variables in a manner that reduces the amount of unexplained variation in outcomes. This will not necessarily be the case, however, because smaller areas may have more variation in outcomes overall. The richer set of control variables, of course, may also help to mitigate concerns about omitted variable bias. 5 Angrist and Krueger (1999) provide intuition: “errors tend to average out in aggregate data.” Another problematic aspect of unemployment rate data is that the BLS’s substate estimates prior to 1990 are no longer considered “official BLS data” because they have not been revised to be consistent with the BLS’s current estimation procedure.

It is also important to consider the fact that migration is influenced by economic conditions (Blanchard and Katz, 1992; Saks and Wozniak, 2011), particularly among highly educated (Bound and Holzer, 2000; Glaeser and Gyourko, 2005; Wozniak, 2010; Notowidigdo, 2011) and healthy individuals (Halliday, 2007). This heterogeneity implies that the estimated improvements in health associated with recessions will understate the true improvements in health if standard demographic controls do not fully capture these sorts of compositional changes. That said, it is unknown whether education and other characteristics associated with health are more- or less-strongly related to the economicconditions-migration relationship when one considers different types of moves and different measures of economic conditions. As such, it is unclear whether this sort of composition bias is likely to be of greater or lesser concern for more-disaggregated analyses.6 Given the complexity of these issues, I take as my starting point that it is not at all clear what level of geographic aggregation is preferred but that the tradeoffs deserve consideration and that much can be learned by comparing the results of alternative approaches. As such, after describing the different ways that I define areas throughout the subsequent sections in Section 2, I then replicate and update earlier state-level estimates of the relationship between economic conditions and mortality (Ruhm, 2000; Stevens et al., 2011) and the relationship between economic conditions at the time of conception and infant health (Dehejia and Lleras-Muney, 2004) in Section 3. I then consider how and why estimated effects vary when the analysis is conducted at differing levels of geographic aggregation for mortality and infant health in Sections 4 and 5, respectively, before concluding. My main findings are as follows: 1. The estimated links between economic conditions and health outcomes are sensitive to the level of geographic aggregation with more-disaggregated analyses—particularly county-level analyses—routinely producing estimates that are smaller in magnitude. 2. Analyses that simultaneously consider the economic conditions of a county and the economic conditions of surrounding areas reveal significant spillover effects on health outcomes. For example, the economic conditions outside a county in the same state have economically and statistically significant effects on mortality. These estimates offer an explanation for why the estimated effects of state (or economic area or region) economic conditions are larger than the estimated effects of county economic conditions when each is considered alone—the estimated effects of state economic conditions are inclusive of spillover effects across counties within a state, which are quite important. 3. Because they have more power, more-disaggregated analyses have the potential to reveal statistically significant links between economic conditions and health outcomes even when the estimated effects are smaller in magnitude. For example, while state-level estimates using recent years of data suggest that the link between mortality and economic conditions may no longer exist (Ruhm, 2013), more-disaggregated analyses indicate that the relationship remains highly significant at conventional levels.

6 The systematic outmigration that occurs when an area’s unemployment rises also highlights the importance of well measured population denominators in calculating mortality rates—population measures that do not account for the systematic outmigration caused by economic downturns will lead to mechanical reductions in mortality rates.

J.M. Lindo / Journal of Health Economics 40 (2015) 83–96

4. Two-way clustering (Cameron et al., 2011) on areas and years tends to yield larger standard-error estimates than the typical approach that clusters only on areas—particularly for countylevel analyses—suggesting that it is important to allow shocks to be correlated within areas over time and to be correlated across all areas within year in estimating the effects of economic conditions on health outcomes. 2. Defining areas Throughout my analyses, I separately identify the effects of economic conditions using data at five different levels of geographic aggregation, from counties to regions. Except where replicating earlier research, all of my analyses are based on data that are available at the county level to ensure that all estimates are based on the same underlying population. In particular, I do not use data from: Alaska or Hawaii where county population data (described in greater detail below) are unavailable prior to 1990; counties without at least one person of each age (0–85) in 1990 so that age-adjusted mortality rates can be calculated at the county level; and Virginia where there have been substantial changes to county definitions over time.7 Section 3 demonstrates the trivial impact these sample restrictions have on the estimated effects of economic conditions on health. My most-aggregated analyses use region-level data based on the eight regions defined by the Bureau of Economic Analysis (BEA), which were developed in the 1950s with the intention of grouping states with similar economic and social factors.8 My next mostaggregated analyses use state-level data, followed by analyses that use data at the BEA-Economic-Area level and analyses that use data at the BEA-Component-Economic-Areas level.9 Last, as mentioned above, I conduct analyses based on data at the county level. Naturally, the sample restrictions described above reduce the number of states, Economic Areas, Component Economic Areas, and counties below the number that exist. 3. Methodology and updates to earlier research Before presenting estimates of the link between economic conditions and health outcomes using different definitions of areas in the next section. In this section, I present the estimated effects from prior state-level studies, adapt the estimation strategies in a way that allows for more-disaggregated analyses, consider how these

7 In an additional effort to achieve consistency in the data over time, for all years I combine the counties of La Paz and Yuma (Arizona) which split in 1983; Washabaugh and Jackson (South Dakota) which merged in 1983; and Adams, Boulder, Jefferson, and Weld (Colorado) from which Broomfield split off in 2001. 8 In contrast, the five regions used in the United States census were developed in the 1880s with similar intentions. The BEA region groupings are as follows. New England: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont. Mideast: Delaware, District of Columbia, Maryland, New Jersey, New York, Pennsylvania. Great Lakes: Illinois, Indiana, Michigan, Ohio, Wisconsin. Plains: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota. Southeast: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia. Southwest: Arizona, New Mexico, Oklahoma, Texas. Rocky Mountain: Colorado, Idaho, Montana, Utah, Wyoming. Far West: Alaska, California, Hawaii, Nevada, Oregon, Washington. 9 BEA Economic Areas define the relevant regional markets surrounding metropolitan or micropolitan statistical areas and are constructed such that all counties are a part of some BEA Economic Area. Component Economic Areas are much the same as Economic Areas except they can be based on fewer counties and fewer residents. For example, a Component Economic Area would be considered too small to form an Economic Area if it consisted of fewer than three counties and fewer than 500,000 employed residents. Also note that 167 of 344 Component Economic Areas are sufficiently large that they also serve as independent Economic Areas; the remaining 177 combine to create the remaining 12 Economic Areas.

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changes alter the estimates, and then extend the analyses to include additional years of data. I begin by focusing on mortality outcomes and then turn to infant health outcomes. 3.1. Mortality In his highly influential study, Ruhm (2000) considers the effects of macroeconomic conditions on health using mortality rates from Vital Statistics of the United States publications and estimates of the unemployment rate from the BLS. In order to focus on withinarea variation, Ruhm’s estimates are based on the regression equation Hat = Eat ˇ + Xat ı + ˛t + a + a t + at ,

(1)

where Hat is a measure of health for individuals living in area a in year t, Eat is a measure of economic conditions, Xat is a vector of time-varying controls, ˛t are year fixed effects,  a are area fixed effects, and  a t are area-specific time trends. To operationalize this identification strategy, Ruhm defines a as a state, Hat as the natural log of the mortality rate (deaths per 100,000), Eat as the unemployment rate, and Xat includes the fraction of the state population that: is less than five years old, is 5–64 years old, is greater than 64 years old, is black, is hispanic, is a high school dropout, has some college, is a college graduate.10 In Column 1 of Table 1, I display the estimate based on these data and definitions while clustering the standard-error estimate on the state.11 The point estimate indicates that a one-percentage-point increase in the unemployment rate is associated with a 0.54-percent decrease in overall mortality. As the starting point for my analysis, I begin with the approach used in Stevens et al. (2011) who extend Ruhm (2000) in several ways. First, they create mortality rates using death counts from Vital Statistics’ micro-record multiple-cause-of-death files and population data from SEER (described above). Second, they pool monthly Current Population Survey data in order to construct overall unemployment rates and unemployment rates for separate demographic groups, which requires them to focus on data from 1978 to 2006. Third, they include a richer set of controls for the fraction of a state’s population in various age brackets (less than 5, 5–17, 18–30, greater than 64) using the aforementioned population data, construct the fraction black with the aforementioned population data, and use CPS data to measure the fraction Hispanic and the fraction in each education group.12 Last, they replace the natural log of the mortality rate with the natural log of the age-adjusted mortality rate to account for uneven changes in the age distribution across states. The age-adjusted mortality rate is calculated by taking the weighted average of the mortality rate for each age, using as weights the fraction of individuals in each age category in the US in 1990. After making these changes, their estimate (shown in Column 2 of Table 1) indicates that a one-percentage-point increase in the unemployment rate is associated with a 0.33-percent decrease in overall mortality. In columns 3 through 9 of Table 1, I progressively make changes to this estimation strategy, ultimately arriving at an approach that can also be used in more-disaggregated county-level analyses. These changes are as follows:

10 These control variables are calculated by interpolating between decennial censuses. Additionally, estimates are weighted by the population in each state. 11 As Ruhm (2000) did not present estimates that clustered standard errors, this estimate is taken from Stevens et al. (2011) who replicate and extend Ruhm’s analysis. 12 The fraction in each educational group is based solely off of individuals who are at least 25 years old.

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Table 1 State-level estimates of the effects of economic conditions on overall mortality (per 100,000 residents). Prior estimates

New estimates

Ruhm (2000)

Stevens et al. (2011)

Adding later years

Using county matched data

1978–2006 (2)

Using controls available for counties 1978–2006 (4)

Adding earlier years

1972–1991 (1)

Using BLS unemployment data 1978–2006 (3)

1976–2006 (5)

1976–2010 (6)

1976–2010 (7)

Not taking log of mortality rate 1976–2010 (8)

Unemployment −0.0054*** rate (0.0010) Emp-to-Pop ratio

−0.0033*** (0.0010)

−0.0039*** (0.0010)

−0.0040*** (0.0010)

−0.0049*** (0.0010)

−0.0044*** (0.0013)

−0.0045*** (0.0013)

−3.6979*** (0.9378)

Years analyzed

Observations

930

Alt. measure of economic conditions 1976–2010 (9)

3.7142*** (0.8644) 1479

1479

1479

1581

1785

1680

1680

1680

Notes: See Section 3.1 for details on the data and measures used in each column. All regressions include year fixed effects, state fixed effects, and state-specific trends. In addition, all regressions are weighted by the population counts in each cell and cluster standard-error estimates on the state. * Significant at 10%; ** significant at 5%; *** significant at 1%.

• Use state-level unemployment rates published by the BLS for convenience. This change causes the estimated semi-elasticity to change from −0.0033 to −0.0039 (Column 3). • Omit controls for the fraction Hispanic and the fraction in each education category, which cannot be reliably measured for small counties. This change has a trivial impact on the estimated effect (Column 4). • Add data from 1976–1977 and 2007–2010 to the analysis. Incorporating the earlier years of data causes the semi-elasticity estimate to change from −0.0040 to −0.0049 (Column 5). Adding 2007–2010 data changes the estimate to −0.0044 (Column 6). • Restrict the sample to individuals living in counties with at least one person of each age (0–85) in 1990 so that age-adjusted mortality rates can be calculated at the county level. Also, omit data from Alaska and Hawaii where county codes are unavailable in the early years of the data and from Virginia where there have been substantial changes to county definitions over time. So that the unemployment rates correctly correspond to the counties contributing to the state-level estimates, use county-level BLS data to construct unemployment rates. These changes have a negligible impact on the estimated effect (Column 7). • Use the death rate as the outcome variable rather than the log of the death rate, which may be undefined in small counties. This changes the interpretation of the estimate, which indicates that a one-percentage-point increase in the unemployment rate is associated with 3.7 fewer deaths per 100,000 (Column 8). The semi-elasticity implied by this estimate, calculated by dividing coefficient estimate by the mean of the outcome, is nearly identical to the direct estimate provided in Column 7 (−0.0044 versus −0.0045). • Use the employment-to-population ratio as the measure of economic conditions. The estimated effect on mortality (Column 9) is nearly identical in magnitude to the estimate based on unemployment rates with the opposite sign.

3.2. Infant health Like Ruhm (2000), Dehejia and Lleras-Muney (2004) conduct a state-level analysis—also characterized by Eq. 1—but instead focus on various measures of infant health and the effects of economic conditions at the time of conception. As one of their primary outcome measures, they consider the fraction of children who have low birth weight (less than 2500 grams), calculated at the race-bystate-by-year level based on vital statistics birth records for white and black mothers at least 18 years old. As their primary measure of economic conditions, they use state unemployment rates in the year of conception, which they base on the timing of the

mother’s last menstrual period. Column 1 of Table 2 displays their estimates, which indicate that a one-percentage-point increase in the unemployment rate reduces the fraction of newborns with low birth weight by 0.018.13 In columns 2 through 9 of Table 2, I progressively make changes to the estimation strategy, ultimately arriving at an approach that can also be used in more-disaggregated county-level analyses. In Column 2, I instead define a child’s year of conception as nine months prior to birth and include in the analysis children born to mothers for whom information on the last menstrual cycle is missing. The subsequent columns progressively include children classified as “other race” to the analysis and an indicator variable for “other race” to the regression model (Column 3); include children born to mothers of all ages (Column 4); collapse the state-by-yearby-race level data to the state-by-year level while adding controls for the fraction in each race category (Column 5); control for the age distribution of mothers with variables corresponding to the fraction of mothers who are less than 18 years old, 18–22 years old, 23–28 years old, and 29–34 years old (Column 6); update the sample to include data from 2000–2010 (Column 7); and then construct the sample based on data available at the county level in the same manner described above (Column 8). These changes tend to have only minor impacts on the estimated effects with one exception. When the data is extended from 1976–1999 to 1976–2006 the estimated effect of unemployment increases by approximately 80 percent, suggesting that effect of economic conditions on newborn health has grown over time. Column 9 indicates that using the employment-to-population ratio as the measure of economic conditions produces similar results of the opposite sign. 4. Estimates of the using different definitions of area In this section, I begin by exploring how the estimated effects of economic conditions on mortality vary with different levels of geographic aggregation using the identification strategy described in the previous section. Though the convention in this literature is to cluster standard errors at the area level, it may instead be appropriate to cluster on broader areas where possible, e.g., clustering on regions for state-level analyses, because shocks to health outcomes (that are not captured by the variables in the model) may be correlated across adjacent areas.14 That said, the standard-error

13 Estimates are weighted by the number of births in each cell and standard-error estimates are clustered at the state level. 14 For example, a heat wave that strikes a particular region of the country would likely lead to a positive correlation in the errors for the areas in that region, all else equal.

J.M. Lindo / Journal of Health Economics 40 (2015) 83–96

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Table 2 State-level estimates of the effect of economic conditions at the time of conception on fraction with low birth weight.

Years analyzed

Dehejia and LlerasMuney (2004) 1976–1999 (1)

Unemployment −0.0180*** rate (0.0063) Emp-to-Pop ratio Observations

2447

Impute conception year

Including all races

Including all ages of mothers

Collapsing race-level data

Adding mother age controls

Adding recent years

Using county matched data

Alt. measure of economic conditions

1976–1999 (2)

1976–1999 (3)

1976–1999 (4)

1976–1999 (5)

1976–1999 (6)

1976–2010 (7)

1976–2010 (8)

1976–2010 (9)

−0.0171* (0.0088)

−0.0167** (0.0080)

−0.0157* (0.0079)

−0.0144* (0.0072)

−0.0177** (0.0072)

−0.0320*** (0.0089)

−0.0325*** (0.0093) 0.0324*** (0.0084)

2448

3672

3672

1224

1224

1785

1680

1680

Notes: See Section 3.2 for details on the data and measures used in each column. All regressions include year fixed effects, state fixed effects, and state-specific trends. In addition, all regressions are weighted by the number of births in each cell and cluster standard-error estimates on the state. * Significant at 10%; ** significant at 5%; *** significant at 1%.

estimates based on the conventional approach tend to be the same as—or more conservative than—estimates that cluster on broader areas, except when the analysis is conducted at the county level. For county-level estimates, clustering the standard errors at the state level yields the largest estimates (though quite similar to the estimates that cluster on BEA economic areas), which indicates that it is important to allow the errors to be correlated across counties within states when conducting inference. As such, I take this approach where applicable. Further recognizing that year-specific shocks may be correlated across areas, I have also investigated the additional importance of allowing the errors to be correlated across areas within a given year by estimating two-way standard estimates following Cameron et al. (2011). I demonstrate that this can be quite important—by way of a comparison with standard-error estimates that do not allow for two-way clustering—in the first table that follows before taking this approach in all subsequent tables. Table 3 focuses on how the estimated effect of economic conditions on overall mortality varies with different levels of geographic aggregation across columns 1 through 5. Using the broadest definition of area, region, the estimate indicates that a one-percentage-point increase in the employment-to-population ratio is associated with an additional 3.44 deaths per 100,000. The estimate is a bit larger when the analysis is instead conducted at the state level (3.71 per 100,000) and then the estimates become smaller and smaller as we progressively consider more narrowly defined areas. That said, these changes are relatively modest when we move from the state level to the BEA Economic Area to the BEA Component Economic Area (3.71 to 3.03 to 2.82). In contrast, the magnitude of the estimate falls substantially to 1.75 when the analysis is conducted at the county level. Table A1 in the Appendix takes the same approach to separately estimate the effects on youth mortality (age 0–17), working-age mortality (age 18–64), and elderly mortality (age 65+). This exercise is motivated by Miller et al. (2009) who explain that, even though unemployment has significant effects on younger and older individuals, the additional deaths that tend to be observed during recessions largely consist of the elderly. Again, the magnitudes of the estimates are smaller in more-disaggregated analyses, except for working-age mortality for which there is less robust evidence of a link between economic conditions and mortality. Table A1 also shows estimated effects on mortality due to cardiovascular problems, motor-vehicle accidents, and suicides.15

15 This is a subset of the categories considered in Stevens et al. (2011). See their appendix for cause-of-death codes.

Echoing earlier work, these estimates reveal that economic downturns reduce deaths due to cardiovascular causes and motor-vehicle accidents while increasing deaths due to suicides. Moreover, these estimates exhibit a pattern consistent with those for overall mortality—regional and state-level analyses yield estimates largest in magnitude while county-level analyses yield estimates smallest in magnitude, with economic-area-level and component-economic-area-level analyses yielding estimates inbetween. As a whole, the results shown in Tables 3 and A1 demonstrate that the estimated effects of economic conditions on mortality depend on the level of geographic aggregation. This raises the question: do the results vary because of the level of aggregation of the measure of economic conditions, because of the aggregation of other variables, or something else? In addition, are the differences in the estimates statistically significant? Table 4 sheds light on these questions by considering the effects of the economic conditions of regions, states, economic areas, component economic areas, and counties in separate regressions using county-level data and the set of associated county-level control variables. While this alternative approach does bring the estimated effects of economic conditions of regions, states, economic areas, and component economic areas closer to the estimated effects of county economic conditions, the latter remain considerably smaller in magnitude.16 In particular, the estimated effect of a onepercentage-point increase in county employment-to-population ratios is 1.75 additional deaths per 100,000 while the estimated effect of a one-percentage-point increase in state employmentto-population ratios is 3.25 additional deaths per 100,000. This difference is statistically significant at the one-percent level. We can similarly reject hypotheses that the effect of county economic conditions is the same as the effects of economic conditions of regions, economic areas, and component economic areas. We generally cannot, however, reject hypotheses that the effects of these broader measures are same.17 Table A2 in the Appendix

16 In ancillary analyses (not shown), I have taken the intermediate step between Tables 3 and 4 by estimating models using county-level data to examine the effects of the more-aggregated measures of economic conditions while controlling for other factors in a similarly aggregated manner (e.g., estimating the effects of state economic conditions controlling for state fixed effects, state-specific trends, and state demographics as opposed to estimating the effects of state economic conditions controlling for county fixed effects, county-specific trends, and county demographics). These estimates were nearly identical to those in Table 3. This indicates that the reduced variability in the estimates in Table 4 is due to the richer set of controls and not because of the level of aggregation of the outcome variable. 17 In order to estimate the standard error for the difference in parameter estimates across the different models in a way that allows for two-way clustering on states

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Table 3 Estimated effects of economic conditions on mortality (per 100,000 residents) using different definitions of “areas” to aggregate data. Level of aggregation

Region (1)

State (2)

BEA econ area (3)

BEA comp. econ area (4)

County (5)

Emp-to-Pop ratio

3.4422 (1.2055)** [1.3826]** 280

3.7142 (0.8644)*** [1.0089]*** 1680

3.0278 (0.6057)*** [0.8357]*** 6160

2.8246 (0.5222)*** [0.7343]*** 11760

1.7495 (0.2267)*** [0.4984]*** 102865

Observations

Notes: Data aggregated to the area level, spanning 1976–2010, are based on employment data produced by the BEA, population data produced by SEER, and death counts from restricted-use Vital Statistics’ micro-record multiple-cause-of-death files provided by the NCHS. All estimates control for year fixed effects, area fixed effects, areaspecific trends, and demographics, and are weighted by the population in each cell. Standard-error estimates shown in parentheses are clustered on the area defined by the level of geographic aggregation, except for the county-level analyses where standard-error estimates are more conservative when clustered at the state level. Two-way standard-error estimates that additionally allow errors to be correlated across areas within a given year are shown in brackets. * Significant at 10%; ** significant at 5%; *** significant at 1%.

Table 4 Estimated effects of economic conditions on mortality (per 100,000 residents) using county-level data and aggregate measures of economic conditions. Level of aggregation of Emp-to-Pop ratio

Region (1)

State (2)

BEA econ area (3)

BEA comp. econ area (4)

County (5)

Emp-to-Pop ratio

3.2707*** (1.1607)

3.2508*** (0.8484)

3.0333*** (0.7381)

2.9374*** (0.6893)

1.7495*** (0.4984)

Notes: Data spanning 1976–2010 are at the county level and are based on employment data produced by the BEA, population data produced by SEER, and death counts from restricted-use Vital Statistics’ micro-record multiple-cause-of-death files provided by the NCHS. All estimates control for year fixed effects, county fixed effects, countyspecific trends, and county demographics, and are weighted by the population in each cell. Standard-error estimates shown in parentheses allow for two-way clustering (state and year). * Significant at 10%; ** significant at 5%; *** significant at 1%.

shows similar results for age-specific and cause-specific mortality rates. Table 5 takes another step towards understanding why the estimated effects of county economic conditions are smaller than the estimated effects of broader measures by simultaneously considering the effects of economic conditions in counties and in their surrounding areas. To begin, Column 1 shows the results from a regression that estimates the effects of county and state economic conditions, where statewide economic conditions are calculated for each county using information from other counties in the state. This estimate indicates that the health of individuals living in a county is significantly related to economic conditions in the county itself and economic conditions in the rest of the state. In other words, there appear to be spillover effects across counties in the effects of economic conditions on mortality.18 This estimate is notably consistent with the estimates from regressions separately considering the effects of state and county economic conditions. In particular, it indicates that a one-percentage-point increase in the employmentto-population ratio across all counties in a state is expected to

and years, I follow Cameron et al. (2011) and obtain (1) an estimate of the variance for the difference in the parameter estimates that allows for clustering on states, (2) an estimate of the variance for the difference in the parameter estimates that allows for clustering on years, and (3) an estimate of the variance for the difference in the parameter estimates that allows for clustering on state-years. I obtain these estimates by (cluster) bootstrapping and then combine them in order to obtain an estimate of the standard error for the difference in parameter estimates that allows for two-way clustering on states and years. In particular, this standard-error estimate is calculated as the square root of (1) plus (2) minus (3). 18 Though the point estimate suggests that the effect of county economic conditions is smaller than the effect of economic conditions in the rest of the state, the difference is not statistically significant. That said, the difference is statistically significant when focusing on youth mortality rates and motor-vehicle-accident mortality rates, shown in Table A3 in the Appendix. This offers an interesting angle for future research as motor vehicle accidents are the leading cause of death for youths and one might suspect that this finding is related to the fact that over a quarter of workers commute across counties for work (McKenzie, 2013) but very few of these commuters are youths.

increase the mortality rate (per 100,000) by 3.69 which is quite similar to the estimate when state employment-to-population ratios are considered on their own (3.71 and 3.25 in Tables 3 and 4, respectively). More broadly, these results suggest an explanation for why the estimated effects of state (or economic area or region) economic

Table 5 Estimated effects of economic conditions on mortality (per 100,000 residents) using county-level data to simultaneously consider the effects of local and broader measures of economic conditions.

County Emp-to-Pop ratio State Emp-to-Pop ratio

(1)

(2)

(3)

1.3054*** (0.3104) 2.3897*** (0.8452)

1.2860*** (0.3018) 2.1966*** (0.7921) 0.5988 (0.6886)

1.2552*** (0.3125)

Region Emp-to-Pop ratio In state in BEA econ area Emp-to-Pop ratio

1.1505** (0.4516)

In state out of BEA econ area Emp-to-Pop ratio

0.6498 (0.6779)

In BEA econ area out of state Emp-to-Pop ratio

1.2600** (0.5341)

In region out of state and BEA econ area Emp-to-Pop

0.0428 (0.6677)

P-value from test of equality

0.224

0.272

Notes: See Table 4. * Significant at 10%; ** significant at 5%; *** significant at 1%.

0.470

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Table 6 Estimated effects of economic conditions on mortality (per 100,000 residents) using county-level data post-1990. (1) Region Emp-to-Pop ratio State Emp-to-Pop ratio

(2)

(3)

(4)

(5)

0.6381 (1.0891) 1.4470*** (0.3567)

0.5006 (1.3607) 0.4383 (1.1175) 1.4347*** (0.3671)

0.416

0.350

2.3955 (1.6984) 2.4651* (1.3907)

County Emp-to-Pop ratio

1.5549*** (0.4886)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop P-value from test of equality

(6)

1.4689*** (0.3777) 0.9046 (0.6166) −0.6560 (0.7356) 0.0581 (0.4679) 0.1959 (1.2727) 0.012

Notes: See Table 4. * Significant at 10%; ** significant at 5%; *** significant at 1%.

conditions are larger than the estimated effects of county economic conditions when each is considered alone—the estimated effects of state economic conditions are fully inclusive of spillover effects across counties within a state, which this analysis has demonstrated to be quite important. This is also evident for age- and cause-specific mortality rates (Table A3). Column 2 of Table 5 extends the analysis from Column 1 by also considering the effects of the economic conditions of areas in different states of the same region. While the estimated effect of regional economic conditions has the same sign as the estimated effects of county and state economic conditions, it is small in magnitude and not statistically significant. This sheds light on why the estimated effects of region and state economic conditions tend to be similar when each is considered alone—while the effects of regional economic conditions can fully capture spillover effects across states within a region, these effects are not large enough to have a meaningful influence on the estimates. The estimates for suicides, shown in Table A3 in the Appendix along with other mortality outcomes, are something of an outlier but also serve to demonstrate this general line of reasoning. In particular, the estimated effect of regional economic conditions on suicides is statistically and economically significant, which is consistent with the rather large difference in the estimated effects of regional and state economic conditions when each is considered alone (Table A2). Column 3 further exploits of the county-level data to consider spillover effects by considering the effects of the economic conditions of the following areas which are mutually exclusive by construction: (1) the county itself, (2) counties in the same state and economic area, (3) counties in the same state but different economic areas, (4) counties in the same economic area but different states, and (5) counties in the same region but different states and economic areas. The results from this analysis indicate that economic conditions outside of the state but within the same economic area have independent effects on mortality while there is weaker evidence that economic conditions within the state but outside of the economic area have significant effects on mortality. While this finding could be taken as evidence regarding the mechanisms through which economic conditions affect health, as it suggests that the effects of economic conditions on health are unlikely to be strongly related to any state-level policy responses to changing economic conditions, it is important to note that the estimates are not precise enough for us to be able to reject the hypothesis that the effects of economic conditions are the same for all areas considered.

Table 6 explores these issues using data from 1990 to 2010. This analysis is motivated by Ruhm (2013), which consists of state-level analyses that suggest that the link between economic conditions and total mortality has weakened over time—so much so that estimates using recent 15- and 20-year windows are not statistically significant, raising a question about whether there remains a link at all. In particular, columns 1 through 3 of Table 6 provide estimates of the effects of the economic conditions of regions, states, and counties, respectively, on county mortality rates as in Table 4. Consistent with Ruhm (2013), the estimated effect of state economic conditions (Column 2) is smaller when using data beginning in 1990 than when using data beginning in 1976 (2.46 versus 3.25). Moreover, neither the estimated effect of region nor the estimated effect of state economic conditions is statistically significant at the five-percent level.19 While the estimated effect of county economic conditions (Column 3) is smaller in magnitude than the estimated effects of state economic conditions, it is sufficiently precise to leave little doubt that there remains a strong link between county economic conditions and mortality. Columns 4 through 6 of Table 6 simultaneously consider the effects of economic conditions in a county and the economic conditions in surrounding areas, as in Table 5. Column 4, which simultaneously considers the effects of county and state economic conditions, shows that county economic conditions have a significant effect on overall mortality that is independent of the effect of statewide economic conditions. While the corresponding point estimate for statewide economic conditions is smaller in magnitude and not statistically significant, suggesting a more limited role for spillover effects on overall mortality in recent years, we cannot reject the hypotheses that the independent effects of state and county economic conditions are the same. Moreover, for the specific causes of death considered in Table A4 (cardiovascular problems, motor vehicle, and suicide), the estimated effect of state economic conditions is statistically significant. In particular, these estimates indicate that state employment-to-population ratios have positive and significant (independent) effects on mortality due to cardiovascular causes and motor vehicle accidents while they have negative and significant effects on mortality due

19 Also consistent with Ruhm (2013), the estimated effects on mortality due to cardiovascular causes, motor-vehicle accidents, and suicides continue to be statistically significant, as shown in Table A4.

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Table 7 Estimated effects of economic conditions on at the time of conception on fraction of newborns with low birthweight. Level of aggregation

Region (1)

Panel A: All data aggregated to “area” level 0.0353* Emp-to-Pop ratio (0.0193)

State (2)

BEA econ area (3)

BEA comp. econ area (4)

County (5)

0.0324*** (0.0102)

0.0277*** (0.0069)

0.0240*** (0.0064)

0.0073* (0.0042)

0.0247*** (0.0085)

0.0222** (0.0086)

0.0073* (0.0042)

Panel B: County level data, only Emp-to-Pop ratio aggregated further Emp-to-Pop ratio 0.0267* 0.0254** (0.0145) (0.0105)

Notes: Data spanning 1976–2010 are based on employment data produced by the BEA, population data produced by SEER, and birth information from restricted-use natality files provided by the NCHS. The estimates in Panel A are based on data aggregated to the area level indicated by the column heading and control for year fixed effects, area fixed effects, area-specific trends, and demographics at that level. The estimates in Panel B use county-level data and consider the effects of aggregate measures of economic conditions (at the level indicated by the column heading) while controlling for year fixed effects, county fixed effects, county-specific trends, and county demographics. All estimates and are weighted by the population in each cell. In Panel A the standard-error estimates shown in parentheses allow for two-way clustering (area and year), except for the county-level analyses where standard-error estimates are more conservative when clustered at the state level. In Panel B the standard-error estimates shown in parentheses also allow for two-way clustering (state and year). * Significant at 10%; ** significant at 5%; *** significant at 1%.

to suicides, which explains why the estimated effects on overall mortality are somewhat muted. The estimate in Column 5, which considers spillover effects across areas in different states in the same region, is too imprecise to reject that these effects are zero but also too imprecise to reject that they are the same as the effects of state and county economic conditions. The one exception is for the estimates focused on suicides (in Table A4), which indicates that regional economic conditions are more strongly related to suicides than are county or state economic conditions. This evidence is further supported by Column 6, which considers the effects of economic conditions of the county itself, counties in the same state and economic area, counties in the same state but different economic areas, counties in the same economic area but different states, and counties in the same region but different states and economic areas. As a whole, the estimates across Tables 3 through 6 (and the associated tables in the Appendix) demonstrate that the estimated effects of economic conditions on mortality are sensitive to the level of aggregation of the data, that this sensitivity results primarily from the use of aggregated measures of economic conditions, and that this sensitivity results from spillover effects across areas in the effects of economic conditions on health outcomes. In the next section, I consider these issues in the context of the estimated effects of economic conditions on infant health. 5. Estimates of the unemployment–infant-health relationship using different definitions of area Panel A of Table 7 follows the identification strategy described in Section 3.2 to estimate the effects of economic conditions at the time of conception on the fraction of children born with low birth weight. In particular, the estimates are based on data aggregated to the “area” level and models that control for area fixed effects, area-specific trends, year fixed effects, and demographics, with different definitions of area across columns 1 through 5. Though the estimates are always positive, indicating that downturns are associated with improvements in infant health, the magnitude of the estimated effects decline as areas are defined more narrowly in a manner similar to the estimated effects on mortality. Panel B of Table 7 begins to shed light on why the estimated effects of economic conditions on infant health are sensitive to the level of geographic aggregation by considering the effects of the economic conditions of regions, states, economic areas, component economic areas, and counties in separate regressions using countylevel data and the set of associated county-level control variables (as in Table 4). As with the analysis of mortality outcomes, this approach brings the estimated effects of economic conditions of

regions, states, economic areas, and component economic areas closer to the estimated effects of county economic conditions and thus closer to one another, but the estimated effects of county economic conditions remain significantly smaller in magnitude. Table 8 further explores why the estimated effects of county economic conditions are smaller than the estimated effects of broader measures of economic conditions by simultaneously considering the effects of the economic conditions in counties and in their surrounding areas (as in Table 5). Like the results for mortality, the results of this analysis indicate that health outcomes in a county are significantly related to economic conditions in surrounding areas in a manner that can explain why the estimated effects tend to be larger in analyses that focus on more-aggregate measures of economic conditions. In particular, the results in all of these panels indicate that infant health is significantly related Table 8 Estimated effects of economic conditions on at the time of conception on fraction of newborns with low birthweight using data at county level and simultaneously considering the effects of local and broader measures of economic conditions.

County Emp-to-Pop ratio State Emp-to-Pop ratio

(1)

(2)

(3)

0.0011 (0.0040) 0.0273** (0.0111)

0.0014 (0.0039) 0.0303*** (0.0101) −0.0103 (0.0137)

0.0003 (0.0039)

Region Emp-to-Pop ratio In state in BEA econ area Emp-to-Pop ratio

0.0247*** (0.0063)

In state out of BEA econ area Emp-to-Pop ratio

0.0078 (0.0094)

In BEA econ area out of state Emp-to-Pop ratio

−0.0026 (0.0082)

In region out of state and BEA econ area Emp-to-Pop

−0.0095 (0.0144)

P-value from test of equality

0.038

0.035

0.004

Notes: Data spanning 1976–2010 are at the county level and are based on employment data produced by the BEA, population data produced by SEER, and birth information from restricted-use natality files provided by the NCHS. All estimates control for year fixed effects, county fixed effects, and county-specific trends, and demographics of mothers. All estimates are weighted by the population in each cell. Standard-error estimates shown in parentheses allow for two-way clustering (state and year). * Significant at 10%; ** significant at 5%; *** significant at 1%.

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Table A1 Estimated effects of economic conditions on mortality (per 100,000 residents) using different definitions of “areas” to aggregate data. Level of aggregation

Region (1)

State (2)

BEA econ area (3)

BEA comp. econ area (4)

County (5)

Outcome: Youth (0–17) mortality 1.5358 Emp-to-Pop ratio (0.2398)*** [0.2618]***

1.4232 (0.1969)*** [0.2255]***

1.2067 (0.1129)*** [0.1456]***

1.0915 (0.1067)*** [0.1428]***

0.5296 (0.0610)*** [0.1035]***

Outcome: Working-age (18–64) mortality Emp-to-Pop ratio 0.8080 (1.2671) [1.3754]

1.5571 (0.9290) [1.0211]

0.9774 (0.5767)* [0.7671]

0.9348 (0.4794)* [0.6583]

0.9908 (0.1724)*** [0.4128]**

Outcome: Elderly (65+) mortality 18.5204 Emp-to-Pop ratio (3.9578)*** [5.6205]***

17.0419 (4.1183)*** [4.7881]***

15.2447 (2.8501)*** [3.8160]***

14.4166 (2.4747)*** [3.4055]***

8.1578 (1.1032)*** [1.9851]***

Outcome: Mortality due to cardiovascular causes Emp-to-Pop ratio 1.3715 (0.7584) [0.8789]

1.5043 (0.4965)*** [0.5591]***

1.4673 (0.3163)*** [0.4539]***

1.3337 (0.2700)*** [0.3822]***

0.6987 (0.1494)*** [0.2242]***

Outcome: Mortality due to motor vehicle accidents 0.4660 Emp-to-Pop ratio (0.1604)** [0.1698]***

0.4148 (0.0880)*** [0.1004]***

0.3980 (0.0691)*** [0.1080]***

0.3575 (0.0573)*** [0.0940]***

0.1564 (0.0319)*** [0.0444]***

Outcome: Mortality due to suicides Emp-to-Pop ratio −0.2197 (0.0547)*** [0.0594]***

−0.1675 (0.0408)*** [0.0426]***

−0.1333 (0.0429)*** [0.0587]**

−0.1019 (0.0408)** [0.0509]**

−0.0331 (0.0212) [0.0237]

Note: This table corresponds to Table 3 for additional outcomes.

to the economic conditions of other counties in the same state. In contrast, these results also indicate that a county’s own economic conditions are not significantly related to infant health. In terms of the bigger picture question regarding how and why economic conditions are related to infant health, we must consider the degree to which economic conditions affect the number and composition of newborns. Towards this end, Tables A5 and A6 in the Appendix present analyses corresponding to Tables 7 and 8 but instead focus on birth rates (defined as the number of births per 10,000 women between the ages of 15 and 44), the fraction of newborns with a white mother, and the fraction of newborns with a mother younger than 18 years old. The estimates for birth rates in Table A5 are positive and usually significant, indicating that fertility is procyclical, while the estimates in Table A6 suggest that we cannot rule out the possibility that fertility is affected equally by local economic conditions and broader economic conditions.20 As such, the estimated effects on infant health are difficult to interpret because they may be influenced by non-random selection. Indeed, the estimated effects on the fraction of children born to white mothers and to young mothers are sometimes statistically significant and are often too imprecise to rule out economically significant effects in one direction or the other. 6. Conclusion Because time-series analyses of health outcomes are subject to such a large suite of potential biases, approaches that make use geographically disaggregated data are a natural alternative to understanding the effects of macroeconomic conditions. This paper shows that the level of (dis)aggregation has important consequences for the estimated effects of economic conditions in panel

20

Schaller (2012) also finds that fertility is procyclical.

data models that exploit variation in the timing and severity of contractions and expansions across areas using the now-common “area approach.” Most significantly, the estimated effects based on county-level variation in economic conditions are significantly smaller in magnitude than those based more-aggregate variation. Analyses using county-level data to simultaneously consider the effects of the economic conditions in a county and the effects of the economic conditions in surrounding areas indicate that this occurs because there are significant spillover effects of economic conditions on health outcomes across counties. As such, estimates based on more aggregate analyses may be more reliable in the sense that they can fully capture such effects. That said, analyses using moredisaggregated data can offer increased precision and the ability to consider such spillover effects directly. Having demonstrated that such spillover effects are empirically relevant, understanding how and why they occur would appear to be a critical next step to be taken in future work.

Acknowledgements I am extremely grateful to Maria Padilla-Romo and Eric Steinmann for research assistance. I also thank the Editor, David Cutler, and anonymous referees for their valuable comments, in addition to Alan Barreca, Ben Hansen, Mark Hoekstra, Doug Miller, Adriana Lleras-Muney, Marianne Page, Chris Ruhm, Jessamyn Schaller, Ann Huff Stevens, Glen Waddell, Wes Wilson, and Xiaohan Zhang and seminar participants at the University of Maryland, the University of Oregon, and the 2012 Meetings of the Society of Labor Economists.

Appendix A. Additional tables

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Table A2 Estimated effects of economic conditions on mortality (per 100,000 residents) using county-level data and aggregate measures of economic conditions. Level of aggregation of Emp-to-Pop ratio

Region (1)

State (2)

BEA econ area (3)

BEA comp. econ area (4)

County (5)

1.1050*** (0.2837)

1.2549*** (0.1689)

1.1699*** (0.1584)

1.0575*** (0.1574)

0.5296*** (0.1035)

1.1550 (1.1188)

1.1408 (0.8173)

1.3134* (0.7310)

1.3698** (0.6765)

0.9908** (0.4128)

16.8945*** (4.9889)

16.4891*** (3.8895)

14.0185*** (3.2345)

13.3693*** (2.9448)

8.1578*** (1.9851)

Outcome: Mortality due to cardiovascular causes Emp-to-Pop ratio 1.2127* (0.7130)

1.2142** (0.5040)

1.2602*** (0.4678)

1.2252*** (0.4207)

0.6987*** (0.2242)

Outcome: Mortality due to motor vehicle accidents 0.4345*** Emp-to-Pop ratio (0.1069)

0.4263*** (0.0849)

0.4037*** (0.0771)

0.3675*** (0.0736)

0.1564*** (0.0444)

−0.1022*** (0.0334)

−0.0980** (0.0431)

−0.0759* (0.0430)

−0.0331 (0.0237)

Outcome: Youth (0–17) mortality Emp-to-Pop ratio Outcome: Working-age (18–64) mortality Emp-to-Pop ratio Outcome: Elderly (65+) mortality Emp-to-Pop ratio

Outcome: Mortality due to suicides Emp-to-Pop ratio

−0.1627*** (0.0432)

Note: This table corresponds to Table 4 for additional outcomes.

Table A3 Estimated effects of economic conditions on mortality (per 100,000 residents) using county-level data to simultaneously consider the effects of local and broader measures of economic conditions.

Outcome: Youth (0–17) mortality County Emp-to-Pop ratio State Emp-to-Pop ratio

(1)

(2)

(3)

0.3478*** (0.0909) 0.9947*** (0.1698)

0.3449*** (0.0938) 0.9666*** (0.2044) 0.0892 (0.2790)

0.3357*** (0.0835)

Region Emp-to-Pop ratio In state in BEA econ area Emp-to-Pop ratio

0.5786*** (0.1211) 0.2606 (0.1875) 0.2075** (0.0847) 0.0794 (0.2739)

In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Working-age (18–64) mortality County Emp-to-Pop ratio State Emp-to-Pop ratio

0.8377*** (0.2708) 0.8212 (0.9085)

Region Emp-to-Pop ratio

0.8325*** (0.2647) 0.7703 (0.8578) 0.1567 (0.7033)

In state in BEA econ area Emp-to-Pop ratio

0.5394 (0.5039) −0.1337 (0.6878) 1.1955** (0.5718) −0.4151 (0.6509)

In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Elderly (65+) mortality County Emp-to-Pop ratio State Emp-to-Pop ratio Region Emp-to-Pop ratio In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio

0.8047*** (0.2840)

6.0401*** (1.2783) 11.1979*** (3.4992)

5.9017*** (1.1664) 9.6288*** (3.2606) 4.7894* (2.6656)

5.9026*** (1.1140)

3.6381** (1.8029) 4.4421 (3.2410)

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Table A3 (Continued) (1)

(2)

(3)

In BEA econ area out of state Emp-to-Pop ratio

3.8752* (2.1989) 3.2569 (2.8606)

In region out of state and BEA econ area Emp-to-Pop Outcome: Mortality due to cardiovascular causes County Emp-to-Pop ratio

0.5512*** (0.1755) 0.7940* (0.4397)

State Emp-to-Pop ratio

0.5356*** (0.1565) 0.6393 (0.3963) 0.4795 (0.5983)

Region Emp-to-Pop ratio

0.5372*** (0.1522)

In state in BEA econ area Emp-to-Pop ratio

0.3553 (0.2656) 0.2309 (0.3861) 0.3282 (0.2316) 0.1389 (0.6003)

In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Mortality due to motor vehicle accidents County Emp-to-Pop ratio

0.0896** (0.0377) 0.3591*** (0.0691)

State Emp-to-Pop ratio

0.0882** (0.0371) 0.3445*** (0.0695) 0.0453 (0.0775)

Region Emp-to-Pop ratio

0.0809** (0.0374)

In state in BEA econ area Emp-to-Pop ratio

0.1330*** (0.0394) 0.2306*** (0.0560) 0.0384 (0.0364) 0.0488 (0.0795)

In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Mortality due to suicides County Emp-to-Pop ratio

−0.0170 (0.0224) −0.0867*** (0.0255)

State Emp-to-Pop ratio

−0.0138 (0.0217) −0.0546* (0.0281) −0.0994** (0.0433)

Region Emp-to-Pop ratio

−0.0114 (0.0204)

−0.0440 (0.0362) −0.0322 (0.0415) −0.0060 (0.0381) −0.0758* (0.0441)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Note: This table corresponds to Table 5 for additional outcomes. Table A4 Estimated effects of economic conditions on mortality (per 100,000 residents) using county-level data post-1990. (1) Outcome: Youth (0–17) mortality Region Emp-to-Pop ratio

(2)

(3)

(4)

(5)

−0.0695 (0.2984) 0.3951*** (0.1248)

0.3735 (0.3555) −0.2167 (0.3430) 0.3865*** (0.1230)

0.4469 (0.3679)

State Emp-to-Pop ratio

0.4625 (0.3205)

County Emp-to-Pop ratio

0.3837*** (0.1291)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Working-age (18–64) mortality Region Emp-to-Pop ratio

2.0516 (1.6222)

0.4238 (0.8304)

(6)

0.3837*** (0.1178) 0.2489* (0.1423) −0.4351 (0.2799) −0.0245 (0.1090) 0.3158 (0.3258)

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Table A4 (Continued) (1) State Emp-to-Pop ratio

(2)

(3)

(4)

(5)

1.0413** (0.4434)

0.7182 (1.0544) 0.9185*** (0.3107)

0.5491 (0.9150) 0.9077*** (0.3050)

0.2639 (5.0315) 7.3827*** (1.3689)

4.3082 (5.4216) −1.4922 (5.2421) 7.2833*** (1.4272)

0.9337** (0.4228) 0.3529* (0.1830)

−0.0908 (0.6370) 0.9699** (0.4811) 0.3551** (0.1802)

0.2365*** (0.0857) 0.0668* (0.0355)

0.0446 (0.1096) 0.2187** (0.0994) 0.0657* (0.0364)

−0.1076** (0.0422) −0.0110 (0.0193)

−0.1043** (0.0462) −0.0659 (0.0510) −0.0085 (0.0177)

1.9518 (1.3620)

County Emp-to-Pop ratio In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Elderly (65+) mortality Region Emp-to-Pop ratio

9.4872 (6.2207)

State Emp-to-Pop ratio

8.8148 (5.6797)

County Emp-to-Pop ratio

7.4274*** (2.0399)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Mortality due to cardiovascular causes Region Emp-to-Pop ratio

1.0599* (0.6038)

State Emp-to-Pop ratio

1.2295*** (0.4255)

County Emp-to-Pop ratio

0.5108** (0.2240)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Mortality due to motor vehicle accidents Region Emp-to-Pop ratio

0.3237*** (0.0812)

State Emp-to-Pop ratio

0.3194*** (0.0794)

County Emp-to-Pop ratio

0.1068*** (0.0341)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Outcome: Mortality due to suicides Region Emp-to-Pop ratio

−0.1677*** (0.0479)

State Emp-to-Pop ratio County Emp-to-Pop ratio In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop Note: This table corresponds to Table 6 for additional outcomes.

−0.1175*** (0.0381) −0.0292* (0.0175)

(6)

0.9280*** (0.3148) 0.5303 (0.4743) −0.0315 (0.5322) −0.0241 (0.3402) 0.2472 (0.7998)

7.5996*** (1.5323) 0.7919 (2.5386) −4.4903 (4.3451) 1.4530 (2.5935) 2.9396 (5.1082)

0.3902** (0.1687) 0.4430** (0.1802) 0.2858 (0.4688) −0.0674 (0.2781) −0.0114 (0.6071)

0.0651* (0.0351) 0.1169** (0.0462) 0.0947 (0.0890) −0.0075 (0.0529) 0.0642 (0.1078)

−0.0093 (0.0179) −0.0076 (0.0305) −0.0560** (0.0240) −0.0030 (0.0257) −0.1083** (0.0475)

J.M. Lindo / Journal of Health Economics 40 (2015) 83–96

95

Table A5 Estimated effects of economic conditions on at the time of conception on birth rates and mothers’ characteristics. Level of aggregation

Region (1)

State (2)

BEA econ area (3)

BEA comp. econ area (4)

County (5)

Panel A: All data aggregated to “area” level Outcome: Birth rate 0.4210 Emp-to-Pop ratio (0.3831)

0.5286** (0.2691)

0.4679* (0.2766)

0.4877** (0.2426)

0.3797** (0.1611)

Outcome: Fraction with white mother Emp-to-Pop ratio −0.0654 (0.0880)

0.0155 (0.0692)

0.0311 (0.0434)

0.0407 (0.0365)

0.0432 (0.0337)

Outcome: Fraction with mother less than 18 years old Emp-to-Pop ratio 0.0334 (0.0336)

0.0065 (0.0144)

−0.0019 (0.0177)

−0.0028 (0.0155)

−0.0074 (0.0075)

Panel B: County level data, only Emp-to-Pop ratio aggregated further Outcome: Birth rate 0.4114 0.5278* Emp-to-Pop ratio (0.3067) (0.3591)

0.4820* (0.2925)

0.4987* (0.2756)

0.3797** (0.1611)

Outcome: Fraction with white mother −0.0095 Emp-to-Pop ratio (0.0714)

0.0162 (0.0627)

0.0255 (0.0559)

0.0374 (0.0499)

0.0432 (0.0337)

Outcome: Fraction with mother less than 18 years old 0.0256 Emp-to-Pop ratio (0.0191)

0.0041 (0.0157)

−0.0032 (0.0146)

−0.0037 (0.0131)

−0.0074 (0.0075)

Note: This table corresponds to Table 7 for additional outcomes, omitting demographic control variables.

Table A6 Estimated effects of economic conditions on at the time of conception on infant outcomes using data at county level and simultaneously considering the effects of local and broader measures of economic conditions.

Outcome: Birth rate County Emp-to-Pop ratio State Emp-to-Pop ratio

(1)

(2)

(3)

0.3110*** (0.0894) 0.3029 (0.2483)

0.3027*** (0.0911) 0.1805 (0.2958) 0.4066 (0.4645)

0.2830*** (0.0983)

Region Emp-to-Pop ratio

−0.0404 (0.1879) 0.2503 (0.2299) 0.1833 (0.1491) 0.4014 (0.4182)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop P-value from test of equality Outcome: Fraction with white mother County Emp-to-Pop ratio State Emp-to-Pop ratio

0.970

0.891

0.221

0.0563** (0.0242) −0.0578 (0.0615)

0.0541** (0.0243) −0.0907 (0.0684) 0.1092 (0.1093)

0.0549** (0.0239)

Region Emp-to-Pop ratio

−0.0656 (0.0461) −0.0492 (0.0484) 0.0598 (0.0460) 0.0953 (0.0998)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop P-value from test of equality

0.097

0.093

0.014

96

J.M. Lindo / Journal of Health Economics 40 (2015) 83–96

Table A6 (Continued)

Outcome: Fraction with mother less than 18 years old County Emp-to-Pop ratio State Emp-to-Pop ratio

(1)

(2)

(3)

−0.0104* (0.0061) 0.0130 (0.0151)

−0.0113** (0.0054) 0.0003 (0.0135) 0.0424** (0.0192)

−0.0074 (0.0051)

Region Emp-to-Pop ratio

−0.0047 (0.0128) −0.0069 (0.0162) −0.0054 (0.0094) 0.0419** (0.0174)

In state in BEA econ area Emp-to-Pop ratio In state out of BEA econ area Emp-to-Pop ratio In BEA econ area out of state Emp-to-Pop ratio In region out of state and BEA econ area Emp-to-Pop P-value from test of equality

0.157

0.007

0.075

Note: This table corresponds to Table 8 for additional outcomes, omitting demographic control variables.

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