- 2SLS Migrants Pr(in migrant sample) Migrated between 1935-40 preP neumonia ∗ post1937 -0.0219** -0.0386* (0.0102) (0.0211) preP neumonia ∗ sulf ayears 0.0020 -0.0045 (0.0031) (0.0072) N 6349665 6319430 1122827 56722 columns 1 and 2: See notes to Table 1 for a description of the baseline regression. The robustness checks in each column is described in detail in Section 3.4. columns 3 and 4: The dependent variable in column 3 is a dummy variable that equals one if a woman’s census state is different from her birth state, and zero otherwise.
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- AboveM edP neu is a dummy variable that equals one if a state had an above median average value of preP neumonia in 1930-1936 and zero otherwise. These are Logistic regressions with standard errors (in parentheses) clustered at the state of birth level.
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- Children >0 Childless preP neumonia ∗ sulf ayears -0.0209* -0.0187* 0.0021** (0.0118) (0.0106) (0.0009) N 518933 421983 518933 Mean 2.5750 3.1660 0.1866 These regressions are comparable to Panel B of Table 3, except that fertility outcomes are measured based on gross fertility (total number of live births). The sample contains women aged 40-50 at census. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01.
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- Children 30 40 50 60 70 80 Infant Mortality per 1000 Live Births (c) Labor force participation Botswana Ethiopia Ghana Kenya Liberia Malawi Mali Mozambique South Africa Sudan Burkina Faso Zambia .3 .4 .5 .6 .7 .8 In labor force (%) 30 40 50 60 70 80 Infant Mortality per 1000 Live Births (d) Marital status Botswana Ethiopia Ghana Kenya Liberia Malawi Mali Mozambique South Africa Sudan Burkina Faso Zambia .5 .6 .7 .8 .9 1
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- Children>0 3.1660 2.1428 423423 Childless (0-1) 0.1866 0.3896 520591 Sulf ayears 14.8679 8.6624 520591 Labor Market Working (0-1) 0.3510 0.4773 730498 In labor force (0-1) 0.3710 0.4831 730498 Hauser-Warren SEI 14.4093 17.181 519972 Personal income 1505.191 2817.12 307378 Hours worked 12.8097 19.1029 730498 Sulf ayears 18.2949 7.5187 730498 Marriage Market Currently married (0-1) 0.7258 0.4461 496783 Ever married (0-1) 0.8499 0.3572 926552 Age at 1st marriage 21.1798 3.4153 106814 Sulf ayears 17.5947 7.9902 926552 Age at birth Age at 1st birth 24.0750 4.9714 440156 Age at 2nd birth 26.7165 5.0326 316185 Age at 3rd birth 28.6299 5.0395 183840 Age at 4th birth 30.1623 4.9682 101896 Table A.3: Outcomes in Stock Model Dataset by Childlessness Status Outcome Childless women Not childless women Mean St.dev. N Mean St.dev. N
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- Children>0 Childless preP neumonia ∗ sulf ayears -0.2112 -0.3751 -0.0170 (0.2053) (0.2319) (0.0323) N 61918 42447 61918 B: Excl. 10 and older preP neumonia ∗ sulf ayears -0.0227 ∗∗ -0.0174 ∗∗ 0.0074 ∗∗ (0.0085) (0.0078) (0.0029) N 494437 236499 494437 C: Excl Mountain West preP neumonia ∗ sulf ayears -0.0.0417** -0.0329** 0.0072*** -0.0225 -0.0196 0.0016 (0.0155) (0.0138) (0.0026) (0.0156) (0.0142) (0.0011) N 483852 306588 483852 509656 413931 509656 D: Excl Deep South preP neumonia ∗ sulf ayears -0.0395** -0.0311** 0.0077** -0.0104 -0.0107 0.0016 (0.0157) (0.0134) (0.0030) (0.0130) (0.0121) (0.0011) N 435017 274490 435017 464042 376270 464042 The variables and specification are described in the notes to Table 3 and the robustness checks are described in Section 3.4. For Panel A, our dataset is a cross-section of fertility outcomes of women aged 6-44 in 1897, with outcomes drawn from the 1910-1930 censuses.
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- Children>0 Childless preP neumonia ∗ treated -0.8565*** -0.7925** 0.1386** -0.4930 -0.5562 0.0325 (0.3830) (0.3472) (0.0676) (0.3667) (0.3502) (0.0253) N 279899 182808 279899 163036 137303 163036 B: Labor market (1) (2) (3) (4) (5) Working In labor force H-W SEI Personal inc.
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- Children>0 Childless preP neumoniaU 5 ∗ sulf ayears -0.0021* -0.0004 0.0006*** -0.0005 -0.0006 0.0001 (0.0012) (0.0009) (0.0002) (0.0011) (0.0010) (0.0001) preP neumonia25to34 ∗ sulf ayears -0.0272 -0.0226 0.0026 -0.0264 -0.0140 0.0045* (0.0402) (0.0323) (0.0060) (0.0334) (0.0314) (0.0022) N 494437 313981 494437 518933 421983 518933 preP neumoniaU 5 ∗ sulf ayears and preP neumonia25to34 ∗ sulf ayears are the average state-level pneumonia mortality rates between 1930-36 among under 5s and among 25-34 year olds respectively, interacted with the number of fertile years that a woman was exposed to sulfa drugs. See notes to Table 3 for definitions of outcomes. The robustness checks are described in Section 3.4. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01.
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- Ever married (%) 30 40 50 60 70 80 Infant Mortality per 1000 Live Births These figures show the relationship between the average country-level outcomes of women in different African countries and the infant mortality rate in these countries. The source of the fertility, labor market and marriage market data is the IPUMS International Database: all countries for which IPUMS data was available in 2000 or later are included, and all women aged 18-50 at the time of the census are included. We chose the census year closest to 2015 for each country. The mortality data are for 2015 and these data are sourced from UNESCO. Fertility is measured using the gross fertility measure (total births); childlessness is zero births.
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- Figure 2: Pneumonia Mortality, United States (a) All age groups -2.8 -2.6 -2.4 -2.2 -2 Log Pneumonia Mortality per 1000 persons 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 Year (b) By age group 0 2 4 6 8 10 Pneumonia Mortality Rate 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 Year Under 1 Age 1 to 5 Age 25 to 64 Age over 65 These figures show the log average pneumonia mortality rate for all ages (left) and by age group (right) in the United States over time. Source: Vital Statistics.
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- Figure 9: Relationship Between Infant Mortality and Fertility, Labor Supply and Marriage in 2015 across African Countries (a) Childlessness Botswana Ethiopia Ghana Kenya Liberia Malawi Mali Mozambique South Africa Sudan Burkina Faso Zambia .15 .2 .25 .3 .35 Childlessness (gross, %) 30 40 50 60 70 80 Infant Mortality per 1000 Live Births (b) Number of children Botswana Ethiopia Ghana Kenya Liberia Malawi Mali Mozambique South Africa Sudan Burkina Faso Zambia 2 2.5 3 3.5 #
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- Figure A.5: Nonparametric Patterns of Outcomes by Above/Below Median Pneumonia Mortality (a) Childlessness 0 .1 .2 .3 Proportion Childless 1920 1930 1940 1950 1960 1970 Year Above median mortality Below median mortality (b) Number of children 1 1.5 2 2.5 3 3.5 Number of Children 1920 1930 1940 1950 1960 1970 Year Above median mortality Below median mortality (c) Labor force participation 0 .1 .2 .3 .4 .5 .6 Proportion In Labor Force 1920 1930 1940 1950 1960 1970 Year Above median mortality Below median mortality (d) Marital status .6 .7 .8 .9 1
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- For other panels, our dataset is a cross-section of outcomes of women aged 6-44 in 1937 and 18-40 at census (columns 1-3) or at least 40 (columns 4-6), born in the U.S. and resident in their birth state at census.
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- For other panels, our dataset is a cross-section of outcomes of women aged 6-44 in 1937 and 18-40 at census (columns 1-3) or at least 40 (columns 4-6), born in the U.S. and resident in their birth state at census.
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- For other panels, our dataset is a cross-section of outcomes of women aged 6-44 in 1937 and 18-40 at census (columns 1-3) or at least 40 (columns 4-6), born in the U.S. and resident in their birth state at census.
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- Gross total fertility is the total number of live births the woman ever had. Gross childlessness is a variable equal to one when this is zero and equal to zero otherwise. The number of live births was a question asked to ever-married women in the 1940 and 1950 censuses and to all women in subsequent censuses. The intensive margin of fertility for both of these measures is defined as total fertility conditional on not being childless; hence, this variable takes a missing value for childless women. The variable Working takes a value of one if the woman reports working at the time of the census and zero otherwise. The variable In Labor Force takes a value of one if the woman reports she is in the labor force at the time of the census.
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- Hypothesis preP neumonia ∗ sulf ayears 0.0058 ∗∗∗ 0.0055 ∗∗∗ 0.1991 ∗∗ 7.7366 0.2421*** (0.0018) (0.0019) (0.0857) (54.9474) (0.0675) N 727398 727398 517857 306451 727398 The dependent variables are: (1) a dummy variable equal to one if the woman reports working at the time of the census and zero otherwise; (2) a dummy variable equal to one if the woman is in the labor force and zero otherwise; (3) the Hauser-Warren Socioeconomic Index, based on occupation; (4) the US Dollar amount of personal earnings in the past year; (5) hours worked in the past week, where intervalled data is converted to a continuous measure using the midpoint of each interval.
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- Morgan, S. P. (1991). Late nineteenth-and early twentieth-century childlessnes. American Journal of Sociology 97(3), 779–807.
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- New Deal, WW2, Dust Bowl and Mean reversion checks (1) (2) (3) (4) (5) A: New Deal Working In labor force H-W SEI Personal income Hours worked preP neumonia ∗ sulf ayears 0.0055*** 0.0051*** 0.3308** 29.8135*** 0.2295*** (0.0016) (0.0017) (0.1472) (10.2308) (0.0637) N 727398 727398 247015 306280 727398 B: WW2 preP neumonia ∗ sulf ayears 0.0070*** 0.0066*** 0.3695*** 14.5503 0.2931*** (0.0017) (0.0017) (0.1293) (14.5801) (0.0533) N 517746 517746 246952 306209 517746 C: Dust Bowl preP neumonia ∗ sulf ayears 0.0057*** 0.0053*** 0.3128* 1.7650 0.2395*** (0.0017) (0.0018) (0.1627) (14.9465) (0.0639) N 654187 654187 223428 274954 654187 D: Mean reversion preP neumonia ∗ sulf ayears 0.0059*** 0.0053*** (0.0017) (0.0017) N 727398 727398 See notes to Table 5 for definitions of outcomes. The robustness checks are described in Section 3.4. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. Table A.11: Marriage market outcomes -
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- New Mexico Working In labor force H-W SEI Personal income Hours worked preP neumonia ∗ sulf ayears 0.0058 ∗∗∗ 0.0055 ∗∗∗ 0.1970 ∗∗ 7.9247 0.2419*** (0.0017) (0.0018) (0.0750) (15.3758) (0.0634) N 725118 725118 516184 305575 725118 B: Pneu only preP neumonia ∗ sulf ayears 0.0040 ∗∗ 0.0036 ∗ 0.2000 ∗∗ 7.0433 0.1642* (0.0017) (0.0018) (0.0753) (13.2783) (0.0662) N 727398 727398 517857 306280 727398 C: Mult.
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Olivetti, C. and B. Petrongelo (2017). The economic consequences of family policies: Lessons from a of legislation in high-income countries. Journal of Economic Perspectives.
- Our dataset is a cross-section of fertility outcomes of women aged 15-25 in 1937 and 21-40 at the time of the census for columns 1-3 and at least 40 for columns 4-6, born in the United States and resident in their birth state at the time of the census.
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- Our dataset is a cross-section of fertility outcomes of women aged 6-44 in 1937 and 18-40 at the time of the census for columns 1-3 and at least 40 for columns 4-6, born in the United States and resident in their birth state at the time of the census.
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- Our dataset is a cross-section of labor and marriage outcomes of women aged 6-44 in 1937 and 18-40 at the time of the census for columns 1 and 3, 18-50 for column 2, born in the United States and resident in their birth state at the time of the census.
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- Our dataset is a cross-section of labor and marriage outcomes of women aged 6-44 in 1937, born in the United States and resident in their birth state at the time of the census, with age at census restrictions shown above the relevant columns in the table.
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- Our dataset is a cross-section of labor outcomes of women aged 6-44 in 1937 and 18-50 at the time of the census, born in the United States and resident in their birth state at the time of the census.
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- Our dataset is a cross-section of marriage outcomes of women aged 6-44 in 1937, born in the United States and resident in their birth state at the time of the census, with age at census restrictions shown above the relevant columns in the table.
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- Our dataset is a panel of woman-year birth outcomes for women aged 15 to 40 in the period 1930-1936, born in the United States, resident in their birth state at the time of the census.
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- Panel A: The dataset is a cross-section of fertility outcomes of women aged 6-15 or 40-44 in 1937 and 18-50 at the time of the census for columns 1-3 and at least 40 for columns 4-6, born in the United States and resident in their birth state at the time of the census.
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- Panel A: The dependent variables are: (1) a dummy variable equal to one if the woman reports working at the time of the census and zero otherwise; (2) a dummy variable equal to one if the woman is in the labor force and zero otherwise; (3) the Hauser-Warren Socioeconomic Index, based on occupation, available for the 1950+ censuses; (4) the US Dollar amount of personal earnings in the past year, available for the 1950+ censuses; (5) hours worked in the past week, converted from intervalled data to a continuous measure using the midpoint of each interval.
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- Panel B: The dataset is a cross-section of labor outcomes of women aged 6-15 or 40-44 in 1937 and 18-50 at the time of the census, born in the United States and resident in their birth state at the time of the census.
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- Panel C: The dataset is a cross-section of marriage outcomes of women aged 6-15 or 40-44 in 1937 and 18-50 at the time of the census, born in the United States and resident in their birth state at the time of the census. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. C.3 Additional Robustness Checks C.3.1 Alternative sample definitions First, we show that our stock model results are not sensitive to sample definitions. We reestimate the net fertility results for 18-36 year olds at the time of the census (a child born to a woman aged 18 would leave home at 36, hence this measure minimises underreporting of children who have left home). These results are in Panel A of Table A.29. All the results are statistically significant and the magnitudes are comparable to those in the main text. Panel B complements this analysis by presenting results for gross uncompleted fertility; that is, gross fertility for 18-40 year olds.
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Pei, Z., J.-S. Pischke, and H. Schwandt (2018). Poorly measured confounders are more useful on the left than on the right. Journal of Business Economics and Statistics.
- Personal income is the reported own income from all sources in the last year. It is available for the 1950 census and onwards.
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- Placebo test, Age of conception and Mountain/South states checks (1) (2) (3) (4) (5) A: Placebo Working In labor force H-W SEI Personal income Hours worked preP neumonia ∗ sulf ayears 0.0102 0.0211 (0.0388) (0.0435) N 54852 54842 B: Excl Mountain West preP neumonia ∗ sulf ayears 0.0065*** 0.0062*** 0.2163*** 35.0206*** 0.2744*** (0.0014) (0.0014) (0.0538) (12.7874) (0.0460) N 712693 712693 507062 300013 712693 C: Excl Deep South preP neumonia ∗ sulf ayears 0.0056*** 0.0052*** 0.1668 -15.0912 0.2047*** (0.0018) (0.0018) (0.1032) (16.5964) (0.0683) N 642475 642475 459065 274429 642475 The variables and specification are described in the notes to Table 5 and the robustness checks are described in Section 3.4. For Panel A, our dataset is a cross-section of fertility outcomes of women aged 6-44 in 1897, with outcomes drawn from the 1910-1930 censuses.
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- Placebo test, Age of conception and Mountain/South states checks (1) (2) (3) A: Placebo Currently married Ever married Age at 1st marriage preP neumonia ∗ sulf ayears 0.0003 -0.0018 -0.2592*** (0.0326) (0.0024) (0.0112) N 61918 135524 7625 B: Excl Mountain West preP neumonia ∗ sulf ayears -0.0022 -0.0028** -0.0274 (0.0014) (0.0011) (0.0177) N 483852 904574 302364 C: Excl Deep South preP neumonia ∗ sulf ayears -0.0023 -0.0022 -0.0187 (0.0014) (0.0013) (0.0152) N 435017 817174 274187 The variables and specification are described in the notes to Table 5 and the robustness checks are described in Section 3.4. For Panel A, our dataset is a cross-section of fertility outcomes of women aged 6-44 in 1897, with outcomes drawn from the 1910-1930 censuses.
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- Popenoe, P. (1936). Motivation of childless marriages. Journal of Heredity 27(12), 469–472.
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- preP neumonia ∗ post1937 is the average state-level pneumonia mortality rate between 1930-36, interacted with a dummy variable for the years 1937 and later. These are marginal effects from logistic (2SLS in Panel B, column (3)) regressions with standard errors (in parentheses) clustered at the woman’s birth state level and the table shows marginal effects at the means of all covariates in the estimating sample. In Panel B, column (1) removes state-year varying controls, column (2) adds allows the state-year controls to have flexible coefficients by year, column (3) instruments the under 5 pneumonia mortality rate with the adult rate, column (4) replaces the census region-year fixed effects with state linear trends, and column (5) replaces them with census division*year fixed effects. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01.
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- preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level (and adjusted for multiple hypothesis testing in Panel C).
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- preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level (and adjusted for multiple hypothesis testing in Panel C).
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- preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level.
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- preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level.
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- preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level.
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- preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. Table 4: Total gross fertility (number of children) as a function of sulfa exposure (1) (2) (3) # Children # Children |
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- preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level. Regressions include individual birth state, birth year, race and education fixed effects, as well as state level mortality rates for maternal mortality, malaria, heart disease, cancer, tuberculosis and diarrhea among the under 2s, income and public services, literacy, female labor force participation, and the year of state birth and death registration, all interacted with sulf ayears. Panel A: Definitions based on net fertility.
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- preP neumonia is the average state-level pneumonia mortality rate between 1930-36. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level.
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- preP neumoniaU 5 ∗ sulf ayears and preP neumonia25to34 ∗ sulf ayears are the average state-level pneumonia mortality rates between 1930-36 among under 5s and among 25-34 year olds respectively, interacted with the number of fertile years that a woman was exposed to sulfa drugs. See notes to Table 5 for definitions of outcomes. The robustness checks are described in Section 3.4. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01.
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- R in 1930-1936, interacted with trend. Both regressions include individual birth state, birth year, race, and education, child birth order, time since last birth and year and census region*year fixed effects, as well as state level mortality rates for malaria, heart disease, cancer, tuberculosis and diarrhea among the under 2s, income and public services, literacy, female labor force participation, and the year of state birth and death registration, all interacted with post1937. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. Table A.28: Binary DiD estimates of the effect of sulfa exposure on fertility, labor and marriage market outcomes A: Fertility (1) (2) (3) (4) (5) (6) Net Fertility Gross Fertility # Children # Children | Children>0 Childless # Children # Children |
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- Ruggles, S., J. T. Alexander, K. Genadek, R. Goeken, M. B. Schroeder, and M. Sobek (2010). Intergrated public use microdata series: Version 5.0 [machine-readable database]. Minneapolis: University of Minnesota.
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Soares, R. (2005). Mortality reductions, educational attainment, and fertility choice. American Economic Review 95(3), 580–601.
- Table 1: Probability of birth as a function of sulfa exposure A: Main estimates (1) (2) (3) Birth Extensive Margin Intensive Margin preP neumonia ∗ post1937 -0.0233** -0.0121** -0.0095* (0.0100) (0.0060) (0.0051) N 4499588 2894976 1604613 Mean 0.0865 0.0513 0.1491 B: Specification checks (1) w/out state controls (2) w/yr interact (3) 2SLS Under5s (4) State trend (5) Division*year FE Birth preP neumonia ∗ post1937 -0.0143*** -0.0100*** -0.0218 ∗ -0.0288 ∗∗∗ (0.0033) (0.0034) (0.0127) (0.0092) preP neumoniaU 5 ∗ post1937 -0.0039* (0.0023) N 4053834 4499588 4499792 4499588 4499588 The dependent variable is a dummy variable that equals one if the woman gave birth in that year, and zero otherwise.
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- Table 5: Labor market and marriage market outcomes as a function of sulfa exposure A: Labor market outcomes (1) (2) (3) (4) (5) Working In labor force H-W SEI Personal Income Hours worked preP neumonia ∗ sulf ayears 0.0058*** 0.0055*** 0.1991** 7.3736 0.2421*** (0.0017) (0.0018) (0.0771) (15.3400) (0.0652) N 727398 727398 517857 306280 727398 Mean 0.3510 0.3710 14.4093 1505.191 12.8097 B: Marriage market outcomes (1) (2) (3) Currently married Ever married Age at 1st marriage preP neumonia ∗ sulf ayears -0.0023* -0.0032** 0.0021 (0.0012) (0.0012) (0.0243) N 494437 727398 116632 Mean 0.7258 0.8499 21.1798 preP neumonia ∗ sulf ayears is the average state-level pneumonia mortality rate between 1930-36, interacted with the number of fertile years (aged 15-40) that a woman was exposed to sulfa drugs. These are OLS regressions with standard errors (in parentheses) clustered at the state of birth level.
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- Table A.14: Marriage outcomescomparing child and adult pneumonia mortality (1) (2) (3) Currently married Ever married Age at 1st marriage preP neumoniaU 5 ∗ sulf ayears -0.0002 -0.0002** 0.0014 (0.0001) (0.0001) (0.0021) preP neumonia25to34 ∗ sulf ayears 0.0001 -0.0016 -0.0315 (0.0031) (0.0021) (0.0551) N 494437 727398 116632 preP neumoniaU 5 ∗ sulf ayears and preP neumonia25to34 ∗ sulf ayears are the average state-level pneumonia mortality rates between 1930-36 among under 5s and among 25-34 year olds respectively, interacted with the number of fertile years that a woman was exposed to sulfa drugs. See notes to Table 5 for definitions of outcomes. The robustness checks are described in Section 3.4. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01.
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- Tables and Figures Figure 1: Pneumonia Incidence by Age, United States, 1935 0 2 4 6 8 10 Deaths per 1000 Under 1 1-4 5-14 15-24 25-34 35-44 45-54 55-64 65-74 75-84 Age group This figure shows the average pneumonia mortality rate by age group in 1935 in the United States. Source: Britten (1942).
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- The cohorts in this sample were born in the years 1893-1931 and are drawn from the 1940, 1950, 1960 and 1970 US decennial population censuses (column 3) and the 1940 US decennial population census (column 4). Regressions include individual birth state, birth year, race and education fixed effects, as well as state level mortality rates for maternal mortality, malaria, heart disease, cancer, tuberculosis and diarrhea among the under 2s, income and public services, literacy, female labor force participation, and the year of state birth and death registration, all interacted with sulf ayears. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01.
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- The cohorts in this table were born in 1900-1931 (columns 1-3) and 1893-1931 (columns 4-6) and are drawn from the 1940-1970 US decennial population censuses. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. Table A.21: Labor market outcomes -
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- The cohorts in this table were born in 1900-1931 (columns 1-3) and 1893-1931 (columns 4-6) and are drawn from the 1940-1970 US decennial population censuses. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. Table A.22: Marriage market outcomes -
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- The cohorts in this table were born in the years 1893-1928 and are drawn from the 1940 and 1950 US decennial population censuses. Regression (1) includes while Regression (2) omits a dummy variable for above median average preM M
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- The cohorts in this table were born in the years 1900-1931 (columns 1-3) and 1893-1931 (columns 4-6) and are drawn from the 1940, 1950, 1960 and 1970 US decennial population censuses. Regressions include individual birth state, birth year, race and education fixed effects, as well as state level mortality rates for maternal mortality (not in Panel B), malaria, heart disease, cancer, tuberculosis and diarrhea among the under 2s, income and public services, literacy, female labor force participation, and the year of state birth and death registration, all interacted with sulf ayears. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. Table A.34: Labor market outcomes as a function of sulfa exposure: Additional robustness checks (1) (2) (3) (4) (5) A: Excl.
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- The cohorts were born in the years 1904-1931 and are drawn from the 1940, 1950, 1960 and 1970 US decennial population censuses. Panel B: Definitions based on gross fertility.
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- The dataset is a cross-section of fertility outcomes of women aged 6-44 in 1937 and 18-36 at census enumeration, born in the United States and resident in their birth state at the time of the census.
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- The dataset is a cross-section of fertility outcomes of women aged 6-44 in 1937 and 18-40 at the time of the census, born in the United States and resident in their birth state at the time of the census.
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- The dependent variable in column 4 equals one if a woman reported in the 1940 census that she has migrated in the last 5 years, and equals zero if she reported that she did not migrate.
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- The Hauser and Warren Socioeconomic Index (H-W SEI) is a measure of occupational status based on earnings and education. It assigns a measure of prestige to each occupation. See ipums.org for a detailed explanation of its construction. It is available for the 1950 census and onwards. We also considered occscore from the IPUMS data and the Duncan socioeconomic score as outcomes, with similar results. Hours worked is the reported number of hours worked in the past week. The original data is an intervalled variable and it is converted to a continuous variable using the midpoint of each interval. The variable Currently married takes the value one if a woman is married at the time of the census and zero otherwise. Ever married is a dummy variable equal to one if a woman has been married at some point in her life and zero otherwise.
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- The mortality rates from diseases are the average between 1930-1936, per 1000 population (or 1000 live births in the case of MMR), and all other state characteristics are measured in 1930, except for the year of entering the birth and death registration systems, which is simply the year when that occurred. Table A.2: Descriptive statistics: Individual characteristics in the stock model dataset Variable Mean Standard deviation N Net Fertility (childbearing sample) # Children 1.6590 1.8316 496783 # Children | Children>0 2.6118 1.6712 315548 Childless (0-1) 0.3648 0.4814 496783 Sulf ayears 20.0 6.0626 496783 Net Fertility (completed fertility sample) # Children 1.9282 1.9473 239432 # Children |
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- The robustness checks in each column is described in detail in Section 3.4. For column (1), only potential births between 1940-43 are included from the 1950 census. * denotes p-value<0.1, ** denotes p-value<0.05 and *** denotes p-value<0.01. Table A.20: Fertility outcomes -Placebo test, Age of conception and Mountain/South states checks (1) (2) (3) (4) (5) (6) Net Fertility Gross Fertility A: Placebo # Children # Children | Children>0 Childless # Children # Children |
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Thomasson, M. E. and J. Treber (2008). From home to hospital: The evolution of childbirth in the United States, 1928-1940. Explorations in Economic History 45(1), 76–99.
- Wilkie, J. R. (1981). The trend toward delayed parenthood. Journal of Marriage and Family 43(3), 583–591.
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- Working 0.56 0.50 251652 0.24 0.43 478846 In labor force 0.59 0.49 251652 0.25 0.44 478846 H-W SEI 21.10 17.46 147691 11.76 16.33 372281 Personal income 2295.83 3328.75 88539 1185.31 2511.73 218839 Hours worked 21.06 21.05 251652 8.37 16.33 478846 Currently married 0.43 0.50 251652 0.92 0.27 478846 Ever married 0.51 0.50 251652 0.99 0.07 478846 Age at 1st marriage 21.01 5.38 50079 20.92 3.92 150534 Graduated from HS 0.25 0.44 251652 0.21 0.41 478846 Attended some college 0.12 0.32 251652 0.08 0.27 478846 This table shows the mean and standard deviation of outcomes by (net) childlessness status in the stock model dataset. All differences in means between childless and not childless women are statistically significant at the 1% level.
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- Young, L. J. and S. Gary (2011, August). The Fragility of Estimated Effects of Unilateral Divorce Laws on Divorce Rates. The B.E. Journal of Economic Analysis & Policy 11(1), 1–11.
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