# Gender gaps in reading and math: report Chapter 3.2 of ZIP Code Destiny, by Vishal Singh (NYU Stern). Generated from the chapter's data files; the reading version is at https://vishalsingh.org/zipcode/gender-gaps-in-reading-and-math. Table n of this report is figure n of the chapter. ## Identification - Title: Gender gaps in reading and math - Label: Two gaps, one district - Chapter: 3.2 (Part III, School; An American life) - Question: Where do girls lead boys in reading and math, and does the math gap change sign with district income? - Dek: Girls outscored boys in reading in 99.9% of school districts over 2009 to 2019, and the math gap, boys minus girls, rose from −0.20 grade levels in the lowest-income tenth of districts to +0.08 in the highest. - Grain: institutions - Status: published - Published: 2026-10-09 - Updated: not revised - Data sources: - Stanford Education Data Archive. Male-female test score gaps and all-student scores in reading and math for school districts and counties, third through eighth grade pooled over 2009 to 2019 (version 6.0); years: 2009-2019 (version 6.0); weights: none in the estimates (empirical-Bayes); districts weighted by their number of test scores or counted once, as each figure says - American Community Survey. Tract median household income and households, 2019 to 2023, apportioned to school districts with the NCES tract to school district crosswalk (2021 to 2022 school year); years: 2019-2023 (5-year); weights: households, with each tract split across districts by land area - Canonical URL: https://vishalsingh.org/zipcode/gender-gaps-in-reading-and-math - Report: https://vishalsingh.org/zipcode/gender-gaps-in-reading-and-math/report - Markdown: https://vishalsingh.org/zipcode/gender-gaps-in-reading-and-math/report.md - BibTeX key: singh2026gendergapsinreadingandmath ```bibtex @misc{singh2026gendergapsinreadingandmath, author = {Singh, Vishal}, title = {Gender gaps in reading and math}, year = {2026}, url = {https://vishalsingh.org/zipcode/gender-gaps-in-reading-and-math}, note = {In ZIP Code Destiny. Published 9 October 2026.} } ``` ## Principal estimates The facts the dek and the opening prose cite, in order of appearance. | Estimand | Population | Period | Value | Interval | n | Weights | Source | Fact id | | --- | --- | --- | ---: | --- | ---: | --- | --- | --- | | share of districts (each counted once) where girls' mean reading score exceeds boys', bootstrap interval over states | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 99.9% | 95% CI 99.8% to 100.0% | 12,327 districts | none (each district counts once) | Stanford Education Data Archive | pct_girls_rla | | test-weighted mean of the male minus female math gap within a tenth of districts (equal numbers of districts), bootstrap interval over states | school districts in the lowest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | −0.20 | 95% CI −0.26 to −0.15 | 1,213 districts | number of math and reading test scores in the district | Stanford Education Data Archive | mgap_inc_d1 | | test-weighted mean of the male minus female math gap within a tenth of districts (equal numbers of districts), bootstrap interval over states | school districts in the highest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | +0.08 | 95% CI 0.04 to 0.12 | 1,214 districts | number of math and reading test scores in the district | Stanford Education Data Archive | mgap_inc_d10 | | count of districts | school districts with a male-female reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 12,327 | | | none (full count) | Stanford Education Data Archive | n_rla | | mean over districts of the male minus female reading gap, bootstrap interval over states | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | −0.76 | 95% CI −0.82 to −0.70 | 12,327 districts | none (each district counts once) | Stanford Education Data Archive | rla_mean | | count of districts | school districts with a male-female math gap estimate (Virginia has none, DC is absent and Hawaii has no districts) | test scores of third through eighth graders, pooled over 2009 to 2019 | 12,207 | | | none (full count) | Stanford Education Data Archive | n_mth | | mean over districts of the male minus female math gap, bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | −0.031 | 95% CI −0.060 to −0.004 | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | mth_mean | | share of districts (each counted once) where boys' mean math score exceeds girls', bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 43.8% | 95% CI 38.1% to 49.4% | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | pct_boys_mth | ## Findings Girls scored higher than boys in reading in 99.9% (95% CI 99.8% to 100.0%) of the 12,327 school districts with a reading estimate, and the average district's reading gap was −0.76 (95% CI −0.82 to −0.70) grade levels. Each gap in this chapter is the mean score of boys minus the mean score of girls in the same district, so a negative gap means girls scored higher. A grade level is the scale of the Stanford Education Data Archive (SEDA), on which one unit is the learning of an average school year, and the scores pool third through eighth grade over the 2009 to 2019 school years. The math gap has no common sign. Across the 12,207 districts with a math estimate its mean is −0.031 (95% CI −0.060 to −0.004) grade levels, and boys lead in 43.8% (95% CI 38.1% to 49.4%) of districts. The gap moves with district income. It averages −0.20 (95% CI −0.26 to −0.15) grade levels in the tenth of districts with the lowest median household income and +0.08 (95% CI 0.04 to 0.12) in the tenth with the highest. District income is measured for 2019 to 2023, after the 2009 to 2019 test scores. Girls and boys attend school in the same district, so a gap between them is measured inside one set of schools and neighborhoods. It does not come from setting a rich district beside a poor one, as a comparison of average scores across districts does. If a gap has one sign in almost every district, the sign does not depend on the district. If the sign changes with a district's income, the gap varies with place. This chapter applies both readings to reading and to math. The gaps between income groups and between racial groups are the subject of Chapter 3.1, Test scores, class and race (https://vishalsingh.org/zipcode/test-scores-class-and-race). ### How the gaps are measured SEDA builds district estimates from state accountability tests in reading and math for third through eighth graders and places the tests from every state on one scale through the National Assessment of Educational Progress. For each district it reports the mean score of all students, of girls and of boys, and the difference between the last two. This chapter reads the gap estimates for 12,327 districts in reading and 12,207 in math, and the county estimates that SEDA builds the same way. Weights depend on the question. A statement about districts, such as the share where girls lead, counts each district once. A statement about the average student weights each district by its number of test scores, where a test score is one student's result in one grade and one year. The text names the weight wherever it is not each district once. Intervals come from a bootstrap that resamples whole states, because the districts of one state share tests and policies and are not independent draws. The intervals for state means in the state figure are the exception, and the state section says so. ### The two gaps across school districts Table 1 plots the district gaps. The reading distribution lies almost wholly on the girls' side of zero. The central 90% of district reading gaps, from the 5th to the 95th percentile, run from −1.18 to −0.36 grade levels. Girls scored higher in 12,313 districts and boys in 14. Weighting each district by its number of reading test scores gives a mean of −0.79 (95% CI −0.85 to −0.73) grade levels. SEDA also reports gaps in standard deviations of the national cohort, and in those units the district means are −0.228 (95% CI −0.246 to −0.211) for reading and −0.010 (95% CI −0.019 to −0.002) for math. A district's gap is an estimate with its own error, so a negative estimate does not always mean that girls scored higher. Reading passes this test. The 95% interval, built from the estimate and the empirical-Bayes standard error that SEDA reports, lies below zero in 98.9% (95% CI 98.3% to 99.4%) of the districts that report a standard error. Math does not. The interval lies below zero in 31.3% (95% CI 25.7% to 37.8%) of districts, above zero in 21.5% (95% CI 17.3% to 26.3%) and on both sides of zero in 47.2% (95% CI 42.2% to 51.8%). The median standard error of a district's math gap is 0.06 grade levels, which exceeds the size of the mean math gap. The math distribution is centered close to zero, with 43.8% of districts on the boys' side. The central 90% of math gaps run from −0.35 to +0.29. Weighting by test scores moves the mean toward girls, to −0.06 (95% CI −0.09 to −0.02) grade levels, and districts where boys lead hold 36.5% (95% CI 29.8% to 44.1%) of math test scores. The difference comes from the largest districts and not from a steady pattern with size. The 100 districts with the most math test scores average −0.09 (95% CI −0.16 to −0.05) grade levels, and boys lead in 28% (95% CI 14% to 39%) of them. Chicago Public Schools (−0.34 (95% CI −0.35 to −0.33)) and the Philadelphia City School District (−0.33 (95% CI −0.35 to −0.32)) are among the ten largest and have gaps well toward girls. The rank correlation between a district's size and its math gap is −0.02 (95% CI −0.09 to 0.05). Table 1. Girls scored higher than boys in reading in 99.9% of districts, while math gaps fall on both sides of zero ### The reading gap by state The reading gap has one sign in every state and a size that differs by state (Table 2). The test-weighted mean reading gap is below zero in each of the 48 states that have both gaps and a district income. It runs from −1.28 (95% CI −1.32 to −1.25) grade levels in Arkansas to −0.46 (95% CI −0.50 to −0.42) in Iowa. Differences between states account for 70% (95% CI 68% to 72%) of the test-weighted variance of district reading gaps and 36% (95% CI 34% to 40%) of the test-weighted variance of district math gaps. Counting each district once, the shares are 47% (95% CI 45% to 49%) for reading and 24% (95% CI 23% to 26%) for math. Reading is the larger share by either count. The data cannot say why the size differs. States give different tests, SEDA puts them on one scale, and a difference between states in a gap can come from schools, from students or from what a test asks. What the reading comparison establishes is the sign, and that holds in every state. The intervals for the state means treat a state's districts as independent, which the bootstrap used elsewhere in this chapter does not, so they may be too narrow. Table 2. The reading gap is below zero in all 48 states, while the math gap is above zero in 20 of them ### The math gap by district income District income here is the mean of tract median household incomes in the district, weighted by households, from the American Community Survey for 2019 to 2023. That period is after the 2009 to 2019 test scores. Districts are the boundaries of the 2021 to 2022 school year, matched to SEDA by district code. The income sample holds the 12,136 districts with both gaps and an income, which cover 193.8 million math test scores. Tenths of districts, or deciles, hold equal numbers of districts. Incomes in the lowest tenth are at most $51,268, and incomes in the highest tenth are at least $115,294. The math gap rises with income (Table 3). Weighted by test scores, it averages −0.20 (95% CI −0.26 to −0.15) grade levels in the lowest tenth and +0.08 (95% CI 0.04 to 0.12) in the highest, and the two intervals do not overlap. The ten decile means span 0.28 (95% CI 0.22 to 0.35) grade levels from the smallest to the largest. Two other rankings of districts check the income ranking. Ranking districts by their average score in math and reading instead of by income gives −0.21 (95% CI −0.29 to −0.18) in the lowest tenth and +0.13 (95% CI 0.10 to 0.17) in the highest. Ranking by the poverty rate from the same survey gives +0.12 (95% CI 0.09 to 0.16) in the tenth of districts with the lowest poverty and −0.19 (95% CI −0.24 to −0.14) in the tenth with the highest. Income is the ranking used here because an average score is itself a test outcome. Counting each district once gives −0.16 (95% CI −0.22 to −0.10) and +0.07 (95% CI 0.05 to 0.11), the same change of sign. Assigning each tract to a single district, as the book's tract file does, leaves 9,993 districts. The lowest and highest tenths then give −0.21 (95% CI −0.27 to −0.16) and +0.09 (95% CI 0.04 to 0.13), so the change of sign does not depend on how tracts are split between districts. The mix of districts shifts with income as well. Boys lead in math in 23.7% (95% CI 15.3% to 33.1%) of the districts in the lowest tenth, and girls lead in 33.4% (95% CI 25.2% to 40.5%) of the districts in the highest. Districts of both signs appear at every income (Table 4), so income moves the balance between the two signs and does not sort districts into two kinds. Across the income sample, the test-weighted correlation between a district's math gap and its percentile rank in income is +0.40 (95% CI 0.31 to 0.48). The reading gap follows income much less. Its decile means run from −0.79 (95% CI −0.89 to −0.71) in the lowest tenth to −0.87 (95% CI −0.97 to −0.78) in the highest, and each of the ten lies below zero. They span 0.13 (95% CI 0.06 to 0.24) grade levels, against 0.28 for the math gap. The correlation of the reading gap with income rank is −0.09 (95% CI −0.21 to 0.04), and its interval includes zero. Table 3. The math gap rises from −0.20 grade levels in the lowest-income tenth of districts to +0.08 in the highest, while the reading gap stays below zero Table 4. Districts at one income level differ widely in their math gap, and the decile means rise with income, a correlation of +0.40 with income rank #### Within states Income differs across states, and so do tests and policies, so the national pattern could come from differences between states. A regression of the math gap on a district's income rank with a separate intercept for each state gives a slope of +0.027 (95% CI 0.022 to 0.032) grade levels for each 10 points of rank, and the slope without the state intercepts is +0.025 (95% CI 0.019 to 0.031). Of the 45 states with at least 25 districts in the sample, the slope is positive in 45 and its 95% interval lies above zero in 38. The pattern appears within states and does not come from comparing states. ### Where the math gap favors boys Table 5 maps the math gap for 2,996 counties. Boys scored higher in 1,043 of them (34.8% (95% CI 25.9% to 44.7%)) and girls in 1,953. The reading gap is below zero in each of the 3,125 counties with a reading estimate, so a map of it would carry one color. The tooltip on each county gives both gaps. Among the 2,938 counties that report a standard error, the 95% interval for the math gap lies above zero in 599 counties, below zero in 1,400 counties and on both sides of zero in 939. The map shows differences between states. The test-weighted mean math gap is above zero in 20 of the 48 states. The 13 states whose intervals lie above zero are in the Plains, the Great Lakes states, the Mountain West and Oregon. The count depends on the interval, and a linearized standard error gives 14, adding Montana. The largest state means favor boys in Iowa (+0.26 (95% CI 0.23 to 0.30)) and Utah, and the largest favor girls in Mississippi (−0.34 (95% CI −0.38 to −0.30)) and Alabama. Counties where boys scored higher cluster in the states with intervals above zero, and every county in Alabama, Arkansas, Louisiana, Mississippi and South Carolina is on the girls' side. SEDA has no math estimates for Virginia and no rows for the District of Columbia. It has no Hawaii districts, but its county file covers Hawaii, and Hawaii's counties are drawn with data except Kalawao. In all, 146 map units are drawn as no data, and the tooltip on each gives its reason. Table 5. Boys scored higher in math than girls in 34.8% of counties, and those counties cluster in the Plains, the Great Lakes states and the Mountain West ### What other studies find Reardon and colleagues estimated the same gaps with SEDA data for the school years ending in 2009 through 2016.[1] The average district had no math gap and a reading gap favoring girls, and math gaps favored boys more in districts of higher socioeconomic status and in districts with larger gender disparities in adult income, education and occupation. For English language arts they did not find strong associations with those measures. With the 2009 to 2019 data and district income in place of their socioeconomic composite, this chapter finds a math gap near zero on average, a reading gap favoring girls and a math gap that rises with income. It finds no detectable association of the reading gap with income rank, and it adds that the reading gap differs by state. Hyde and colleagues analyzed state mathematics assessments for grades two through eleven and found average differences between girls and boys close to zero.[2] Fryer and Levitt followed a national panel of children and found no mean difference in math at school entry, with girls losing ground relative to boys over the first years of school.[3] A gap pooled over third through eighth grade, as here, mixes grades at which the gap may differ. #### Description, not a causal test Nothing in this chapter tests why the math gap moves with district income. A district's income goes together with its schools, families, teachers and peers, and a comparison of girls and boys inside the district does not separate those from one another. The sign of the reading gap and the slope of the math gap are descriptions of districts. An explanation would need a design that changes one of these conditions for some children and not for others, and none is used here. ### What these data cannot show The patterns describe school districts and counties. A district's income is not a family's income, and the data do not say that a girl or a boy from a poorer family scores differently from one in a richer family in the same district (ecological inference). A district gap is an average over every student the state tested in third through eighth grade, so it cannot show when in school a gap opens. Each district's gap carries error, and the standard error of a district's math gap is typically larger than the mean math gap. The decile means, with intervals, carry the income finding, and single districts do not. SEDA's estimates are empirical-Bayes estimates, which pull the noisiest districts toward the average and narrow the spread of district gaps. If the shrinkage pulls toward a state or national mean, it could change the shares of variance that lie between states. The 95% intervals built from the empirical-Bayes standard errors describe the shrunken estimates. The scores come from state tests that differ in content and format, and a gap can reflect what a test asks as well as how girls and boys learned. District income is built for this chapter and is not part of SEDA. It is a household-weighted mean of tract medians, split across overlapping districts by land area, so it is not the median income of a district's households, and the survey caps the highest incomes. It is measured for 2019 to 2023, after the 2009 to 2019 scores, on the district boundaries of the 2021 to 2022 school year. A district whose boundary or code changed may be matched to SEDA imperfectly or not at all. In all, 38 districts with both gaps have no income and are left out of the income figures. Districts that differ in income also differ in other ways, such as the racial composition of their students, and the chapter does not separate those. SEDA has no math estimates for Virginia, no rows for the District of Columbia and no districts in Hawaii (Hawaii's counties are in its county file and are drawn), and Puerto Rico is not covered. The estimates describe public school students tested in 2009 to 2019 and say nothing about later years. SEDA reports two groups, male and female. Whether gaps in school scores carry into adult outcomes is the subject of Chapter 4.4, Boys and mobility (https://vishalsingh.org/zipcode/boys-and-mobility; planned), which compares boys and girls raised in the same neighborhoods. ## Tables ### Table 1. Girls scored higher than boys in reading in 99.9% of districts, while math gaps fall on both sides of zero Distribution across school districts of the male minus female mean test score gap in grade levels, reading and math, third through eighth grade pooled over 2009 to 2019 | density | gap | subject | | ---: | ---: | --- | | 0 | −1.90 | Reading | | 1 | −1.85 | Reading | | 1 | −1.80 | Reading | | 3 | −1.75 | Reading | | 4 | −1.70 | Reading | | 9 | −1.65 | Reading | | 16 | −1.60 | Reading | | 24 | −1.55 | Reading | | 32 | −1.50 | Reading | | 49 | −1.45 | Reading | | 89 | −1.40 | Reading | | 117 | −1.35 | Reading | | 153 | −1.30 | Reading | | 226 | −1.25 | Reading | | 329 | −1.20 | Reading | | 446 | −1.15 | Reading | | 593 | −1.10 | Reading | | 778 | −1.05 | Reading | | 1035 | −1 | Reading | | 1247 | −0.950 | Reading | | 1375 | −0.900 | Reading | | 1510 | −0.850 | Reading | | 1588 | −0.800 | Reading | | 1578 | −0.750 | Reading | | 1525 | −0.700 | Reading | | 1406 | −0.650 | Reading | | 1290 | −0.600 | Reading | | 1139 | −0.550 | Reading | | 928 | −0.500 | Reading | | 727 | −0.450 | Reading | | 567 | −0.400 | Reading | | 421 | −0.350 | Reading | | 297 | −0.300 | Reading | | 195 | −0.250 | Reading | | 129 | −0.200 | Reading | | 71 | −0.150 | Reading | | 44 | −0.100 | Reading | | 29 | −0.050 | Reading | | 10 | 0 | Reading | | 6 | 0.050 | Reading | | 5 | 0.100 | Reading | | 2 | 0.150 | Reading | | 2 | 0.200 | Reading | | 1 | 0.250 | Reading | | 0 | 0.300 | Reading | | 0 | 0.350 | Reading | | 0 | 0.400 | Reading | | 0 | 0.450 | Reading | | 0 | 0.500 | Reading | | 0 | 0.550 | Reading | | 0 | 0.600 | Reading | | 0 | 0.650 | Reading | | 0 | 0.700 | Reading | | 0 | 0.750 | Reading | | 0 | 0.800 | Reading | | 0 | 0.850 | Reading | | 0 | 0.900 | Reading | | 0 | 0.950 | Reading | | 0 | 1 | Reading | | 0 | 1.05 | Reading | | 0 | 1.10 | Reading | | 0 | 1.15 | Reading | | 1 | 1.20 | Reading | | 1 | 1.25 | Reading | | 0 | 1.30 | Reading | | 0 | −1.90 | Math | | 0 | −1.85 | Math | | 0 | −1.80 | Math | | 0 | −1.75 | Math | | 0 | −1.70 | Math | | 0 | −1.65 | Math | | 0 | −1.60 | Math | | 0 | −1.55 | Math | | 0 | −1.50 | Math | | 0 | −1.45 | Math | | 0 | −1.40 | Math | | 0 | −1.35 | Math | | 0 | −1.30 | Math | | 0 | −1.25 | Math | | 1 | −1.20 | Math | | 0 | −1.15 | Math | | 0 | −1.10 | Math | | 0 | −1.05 | Math | | 0 | −1 | Math | | 0 | −0.950 | Math | | 0 | −0.900 | Math | | 1 | −0.850 | Math | | 7 | −0.800 | Math | | 13 | −0.750 | Math | | 15 | −0.700 | Math | | 24 | −0.650 | Math | | 55 | −0.600 | Math | | 94 | −0.550 | Math | | 119 | −0.500 | Math | | 185 | −0.450 | Math | | 313 | −0.400 | Math | | 507 | −0.350 | Math | | 748 | −0.300 | Math | | 1021 | −0.250 | Math | | 1354 | −0.200 | Math | | 1700 | −0.150 | Math | | 1931 | −0.100 | Math | | 2095 | −0.050 | Math | | 2058 | 0 | Math | | 1955 | 0.050 | Math | | 1662 | 0.100 | Math | | 1278 | 0.150 | Math | | 1007 | 0.200 | Math | | 708 | 0.250 | Math | | 485 | 0.300 | Math | | 274 | 0.350 | Math | | 182 | 0.400 | Math | | 100 | 0.450 | Math | | 52 | 0.500 | Math | | 21 | 0.550 | Math | | 19 | 0.600 | Math | | 6 | 0.650 | Math | | 4 | 0.700 | Math | | 1 | 0.750 | Math | | 1 | 0.800 | Math | | 0 | 0.850 | Math | | 0 | 0.900 | Math | | 0 | 0.950 | Math | | 0 | 1 | Math | | 1 | 1.05 | Math | | 0 | 1.10 | Math | | 0 | 1.15 | Math | | 0 | 1.20 | Math | | 0 | 1.25 | Math | | 0 | 1.30 | Math | Note: Each gap is the mean score of boys minus the mean score of girls in the district, in grade levels, where one grade level is the learning of an average school year. A negative gap means girls scored higher. Scores pool third through eighth grade over 2009 to 2019, and the estimates are empirical-Bayes estimates, which pull the noisiest districts toward the average. Note: The curves are Gaussian kernel densities with the bandwidth that Silverman's rule gives for each subject. Reading includes Virginia, which has no math estimates, so the two curves rest on different sets of districts. Source: Stanford Education Data Archive 6.0, 2009-2019; unweighted district estimates (each district counted once), modeled estimates Data: https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/gap_distributions.json ### Table 2. The reading gap is below zero in all 48 states, while the math gap is above zero in 20 of them Test-weighted mean male minus female gap in grade levels by state, reading and math, districts with both gaps and an income, third through eighth grade pooled over 2009 to 2019 | conf_high | conf_low | estimate | n | state | subject | | ---: | ---: | ---: | ---: | --- | --- | | 0.299 | 0.228 | 0.263 | 326 | Iowa | Math | | −0.424 | −0.499 | −0.461 | 326 | Iowa | Reading | | 0.219 | 0.095 | 0.157 | 41 | Utah | Math | | −0.722 | −0.799 | −0.760 | 41 | Utah | Reading | | 0.164 | 0.055 | 0.109 | 110 | Idaho | Math | | −0.618 | −0.727 | −0.672 | 110 | Idaho | Reading | | 0.144 | 0.068 | 0.106 | 323 | Minnesota | Math | | −0.573 | −0.641 | −0.607 | 323 | Minnesota | Reading | | 0.135 | 0.073 | 0.104 | 509 | Oklahoma | Math | | −0.495 | −0.563 | −0.529 | 509 | Oklahoma | Reading | | 0.132 | 0.070 | 0.101 | 513 | Michigan | Math | | −0.692 | −0.742 | −0.717 | 513 | Michigan | Reading | | 0.134 | 0.060 | 0.097 | 172 | Oregon | Math | | −0.660 | −0.737 | −0.698 | 172 | Oregon | Reading | | 0.151 | 0.020 | 0.085 | 150 | North Dakota | Math | | −0.685 | −0.834 | −0.760 | 150 | North Dakota | Reading | | 0.156 | 0.011 | 0.084 | 240 | Nebraska | Math | | −0.438 | −0.572 | −0.505 | 240 | Nebraska | Reading | | 0.108 | 0.054 | 0.081 | 288 | Indiana | Math | | −0.901 | −0.947 | −0.924 | 288 | Indiana | Reading | | 0.127 | 0.028 | 0.077 | 284 | Kansas | Math | | −0.479 | −0.566 | −0.523 | 284 | Kansas | Reading | | 0.130 | 0.006 | 0.068 | 48 | Wyoming | Math | | −0.531 | −0.651 | −0.591 | 48 | Wyoming | Reading | | 0.100 | 0.023 | 0.061 | 410 | Wisconsin | Math | | −0.676 | −0.749 | −0.712 | 410 | Wisconsin | Reading | | 0.089 | −0.017 | 0.036 | 210 | Montana | Math | | −0.747 | −0.866 | −0.806 | 210 | Montana | Reading | | 0.064 | −0.009 | 0.027 | 141 | New Hampshire | Math | | −0.937 | −1.01 | −0.973 | 141 | New Hampshire | Reading | | 0.055 | −0.008 | 0.024 | 174 | Maine | Math | | −0.968 | −1.05 | −1.01 | 174 | Maine | Reading | | 0.095 | −0.063 | 0.016 | 147 | South Dakota | Math | | −0.621 | −0.774 | −0.698 | 147 | South Dakota | Reading | | 0.057 | −0.028 | 0.014 | 158 | Connecticut | Math | | −0.595 | −0.654 | −0.625 | 158 | Connecticut | Reading | | 0.067 | −0.048 | 0.009 | 16 | Delaware | Math | | −0.705 | −0.872 | −0.788 | 16 | Delaware | Reading | | 0.069 | −0.059 | 0.005 | 166 | Colorado | Math | | −0.899 | −0.992 | −0.945 | 166 | Colorado | Reading | | 0.020 | −0.047 | −0.014 | 67 | Florida | Math | | −0.558 | −0.607 | −0.583 | 67 | Florida | Reading | | 0.004 | −0.041 | −0.019 | 607 | Ohio | Math | | −0.696 | −0.734 | −0.715 | 607 | Ohio | Reading | | −0.018 | −0.055 | −0.037 | 1,004 | Texas | Math | | −0.556 | −0.590 | −0.573 | 1,004 | Texas | Reading | | 0.006 | −0.089 | −0.041 | 499 | Pennsylvania | Math | | −0.897 | −0.961 | −0.929 | 499 | Pennsylvania | Reading | | 0.059 | −0.144 | −0.043 | 16 | Nevada | Math | | −0.776 | −1.06 | −0.920 | 16 | Nevada | Reading | | −0.015 | −0.077 | −0.046 | 492 | New Jersey | Math | | −0.971 | −1.01 | −0.992 | 492 | New Jersey | Reading | | −0.003 | −0.089 | −0.046 | 51 | Vermont | Math | | −1.02 | −1.13 | −1.07 | 51 | Vermont | Reading | | 0.056 | −0.150 | −0.047 | 652 | New York | Math | | −0.699 | −0.899 | −0.799 | 652 | New York | Reading | | −0.021 | −0.075 | −0.048 | 276 | Washington | Math | | −0.916 | −0.971 | −0.943 | 276 | Washington | Reading | | −0.016 | −0.084 | −0.050 | 512 | Missouri | Math | | −0.776 | −0.836 | −0.806 | 512 | Missouri | Reading | | −0.025 | −0.096 | −0.060 | 826 | California | Math | | −0.810 | −0.858 | −0.834 | 826 | California | Reading | | 0.014 | −0.193 | −0.090 | 47 | Alaska | Math | | −0.739 | −0.964 | −0.851 | 47 | Alaska | Reading | | −0.056 | −0.150 | −0.103 | 55 | West Virginia | Math | | −1.16 | −1.26 | −1.21 | 55 | West Virginia | Reading | | −0.046 | −0.179 | −0.113 | 35 | Rhode Island | Math | | −1.08 | −1.24 | −1.16 | 35 | Rhode Island | Reading | | −0.081 | −0.151 | −0.116 | 268 | Massachusetts | Math | | −1.12 | −1.17 | −1.14 | 268 | Massachusetts | Reading | | −0.074 | −0.167 | −0.120 | 182 | Arizona | Math | | −0.802 | −0.867 | −0.834 | 182 | Arizona | Reading | | −0.091 | −0.165 | −0.128 | 115 | North Carolina | Math | | −0.480 | −0.542 | −0.511 | 115 | North Carolina | Reading | | −0.062 | −0.197 | −0.129 | 754 | Illinois | Math | | −0.901 | −0.994 | −0.948 | 754 | Illinois | Reading | | −0.125 | −0.215 | −0.170 | 180 | Georgia | Math | | −0.868 | −0.941 | −0.904 | 180 | Georgia | Reading | | −0.131 | −0.231 | −0.181 | 140 | Tennessee | Math | | −0.806 | −0.879 | −0.843 | 140 | Tennessee | Reading | | −0.141 | −0.238 | −0.189 | 172 | Kentucky | Math | | −0.737 | −0.853 | −0.795 | 172 | Kentucky | Reading | | −0.127 | −0.265 | −0.196 | 83 | New Mexico | Math | | −0.957 | −1.13 | −1.04 | 83 | New Mexico | Reading | | −0.156 | −0.247 | −0.202 | 78 | South Carolina | Math | | −0.805 | −0.878 | −0.841 | 78 | South Carolina | Reading | | −0.173 | −0.258 | −0.215 | 69 | Louisiana | Math | | −0.986 | −1.05 | −1.02 | 69 | Louisiana | Reading | | −0.198 | −0.287 | −0.243 | 234 | Arkansas | Math | | −1.25 | −1.32 | −1.28 | 234 | Arkansas | Reading | | −0.153 | −0.350 | −0.252 | 24 | Maryland | Math | | −1.11 | −1.21 | −1.16 | 24 | Maryland | Reading | | −0.244 | −0.351 | −0.298 | 137 | Alabama | Math | | −0.764 | −0.854 | −0.809 | 137 | Alabama | Reading | | −0.301 | −0.376 | −0.339 | 135 | Mississippi | Math | | −0.819 | −0.884 | −0.852 | 135 | Mississippi | Reading | Note: Each gap is the mean score of boys minus the mean score of girls in the district, in grade levels, where one grade level is the learning of an average school year. A negative gap means girls scored higher. Scores pool third through eighth grade over 2009 to 2019, and the estimates are empirical-Bayes estimates, which pull the noisiest districts toward the average. Means weight a district by its number of math and reading test scores. Note: The intervals treat the districts of a state as independent draws, so they are narrower than the intervals in the income figures, which resample whole states. Virginia (no math estimates), Hawaii (no districts) and the District of Columbia (no rows) are not shown. Source: Stanford Education Data Archive 6.0, 2009-2019; test-weighted district estimates, modeled estimates Data: https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/state_gap_means.json ### Table 3. The math gap rises from −0.20 grade levels in the lowest-income tenth of districts to +0.08 in the highest, while the reading gap stays below zero Test-weighted mean male minus female gap in grade levels by tenth of district median household income, reading and math, third through eighth grade pooled over 2009 to 2019 | Subject | Income tenth | Median district income (dollars) | Mean gap (grade levels) | Lower limit of the 95% interval | Upper limit of the 95% interval | Districts | | --- | ---: | ---: | ---: | ---: | ---: | ---: | | Reading | 1 | 46,464 | −0.79 | −0.89 | −0.71 | 1,213 | | Reading | 2 | 54,714 | −0.80 | −0.88 | −0.72 | 1,214 | | Reading | 3 | 60,094 | −0.78 | −0.86 | −0.70 | 1,213 | | Reading | 4 | 64,792 | −0.78 | −0.85 | −0.70 | 1,214 | | Reading | 5 | 69,421 | −0.74 | −0.81 | −0.68 | 1,214 | | Reading | 6 | 74,142 | −0.76 | −0.84 | −0.69 | 1,213 | | Reading | 7 | 80,405 | −0.77 | −0.86 | −0.69 | 1,214 | | Reading | 8 | 88,948 | −0.80 | −0.85 | −0.72 | 1,213 | | Reading | 9 | 102,837 | −0.81 | −0.88 | −0.73 | 1,214 | | Reading | 10 | 136,556 | −0.87 | −0.97 | −0.78 | 1,214 | | Math | 1 | 46,464 | −0.20 | −0.26 | −0.15 | 1,213 | | Math | 2 | 54,714 | −0.14 | −0.19 | −0.10 | 1,214 | | Math | 3 | 60,094 | −0.14 | −0.20 | −0.09 | 1,213 | | Math | 4 | 64,792 | −0.10 | −0.15 | −0.05 | 1,214 | | Math | 5 | 69,421 | −0.05 | −0.09 | −0.01 | 1,214 | | Math | 6 | 74,142 | −0.07 | −0.11 | −0.03 | 1,213 | | Math | 7 | 80,405 | −0.06 | −0.11 | −0.02 | 1,214 | | Math | 8 | 88,948 | −0.06 | −0.10 | 0.00 | 1,213 | | Math | 9 | 102,837 | +0.01 | −0.03 | +0.06 | 1,214 | | Math | 10 | 136,556 | +0.08 | +0.04 | +0.12 | 1,214 | Note: Each gap is the mean score of boys minus the mean score of girls in the district, in grade levels, where one grade level is the learning of an average school year. A negative gap means girls scored higher. Scores pool third through eighth grade over 2009 to 2019, and the estimates are empirical-Bayes estimates, which pull the noisiest districts toward the average. Note: District income is the mean of tract median household incomes in the district, weighted by households, from the American Community Survey for 2019 to 2023. The test scores pool 2009 to 2019, so income is measured after the scores, and districts are the boundaries of the 2021 to 2022 school year matched to SEDA by district code. It is not the median income of the district's households, and it is capped where the survey caps the highest incomes. Note: Deciles, the tenths of districts ranked by income, hold equal numbers of districts. Each mean weights a district by its number of test scores, and the 95% intervals come from a bootstrap that resamples whole states. Source: Stanford Education Data Archive 6.0, 2009-2019 and American Community Survey 2019-2023; test-weighted district estimates, modeled estimates Data: https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/gap_by_income_decile.json ### Table 4. Districts at one income level differ widely in their math gap, and the decile means rise with income, a correlation of +0.40 with income rank Male minus female math gap in grade levels against district median household income in dollars (log scale), one dot per district, with test-weighted decile means No inline rows: the values of Table 4 are in https://vishalsingh.org/zipcode/data/gender-gaps-in-reading-and-math/math_gap_by_income.json. Note: The dots are a random sample of 5,000 districts (seed 12345) from the 12,136 in the income sample; the means, intervals, correlation and slope use all of them. Note: Each gap is the mean score of boys minus the mean score of girls in the district, in grade levels, where one grade level is the learning of an average school year. A negative gap means girls scored higher. Scores pool third through eighth grade over 2009 to 2019, and the estimates are empirical-Bayes estimates, which pull the noisiest districts toward the average. Note: District income is the mean of tract median household incomes in the district, weighted by households, from the American Community Survey for 2019 to 2023. The test scores pool 2009 to 2019, so income is measured after the scores, and districts are the boundaries of the 2021 to 2022 school year matched to SEDA by district code. It is not the median income of the district's households, and it is capped where the survey caps the highest incomes. Note: Deciles, the tenths of districts ranked by income, hold equal numbers of districts. Each mean weights a district by its number of test scores, and the 95% intervals come from a bootstrap that resamples whole states. Marker positions are the test-weighted mean income of each decile. Note: The income axis is logarithmic: equal distances are equal ratios of income. Source: Stanford Education Data Archive 6.0, 2009-2019 and American Community Survey 2019-2023; test-weighted decile means, modeled estimates Data: https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/math_gap_by_income.json ### Table 5. Boys scored higher in math than girls in 34.8% of counties, and those counties cluster in the Plains, the Great Lakes states and the Mountain West Male minus female mean math score gap in grade levels, county, third through eighth grade pooled over 2009 to 2019 No inline rows: the values of Table 5 are in https://vishalsingh.org/zipcode/data/gender-gaps-in-reading-and-math/math_gap_map.json. Note: Each gray county has the reason for its missing value in its tooltip. SEDA has no math estimates for Virginia and no rows for the District of Columbia, and a few small counties have no male-female estimate. SEDA has no Hawaii districts, but its county file covers Hawaii, and Hawaii's counties are drawn with data except Kalawao. Puerto Rico is not in SEDA and is not drawn. Note: The tooltip also gives the 95% interval for the math gap and for the reading gap of the county, built from SEDA's empirical-Bayes standard errors. Each gap is the mean score of boys minus the mean score of girls in the district, in grade levels, where one grade level is the learning of an average school year. A negative gap means girls scored higher. Scores pool third through eighth grade over 2009 to 2019, and the estimates are empirical-Bayes estimates, which pull the noisiest districts toward the average. Note: Valdez-Cordova Census Area (split in 2019) is drawn as its two successors, Chugach and Copper River, with the same value. Connecticut is drawn as its eight legacy counties, which is how SEDA codes it. Alaska is drawn. Source: Stanford Education Data Archive 6.0, 2009-2019; county estimates, modeled estimates, each county counted once Data: https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/math_gap_map.json ## All estimates Every fact in facts.json (83), sorted by id. | Fact id | Value | Unit | Estimand | Population | Period | Interval | n | Weights | Source | Note | | --- | ---: | --- | --- | --- | --- | --- | ---: | --- | --- | --- | | big_mean | −0.09 | math gap in grade levels, male minus female (negative means girls scored higher) | mean of the male minus female math gap, each district counted once, bootstrap interval over states | the 100 districts with the most math test scores | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.16 to −0.05 | 100 districts | none (each district counts once) | Stanford Education Data Archive | | | big_n | 100 | districts | definition: the districts with the most math test scores | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (definition) | Stanford Education Data Archive | | | big_pct_boys | 28% | percent of districts where the math gap is above zero | share of districts where boys' mean math score exceeds girls', bootstrap interval over states | the 100 districts with the most math test scores | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 14% to 39% | 100 districts | none (each district counts once) | Stanford Education Data Archive | | | central_share | 90% | percent of districts between the 5th and 95th percentiles of the gap | definition: the central range of district gaps, from the 5th to the 95th percentile | school districts with a gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (definition) | Stanford Education Data Archive | | | chicago_gap | −0.34 | math gap in grade levels, male minus female (negative means girls scored higher) | SEDA's male minus female math gap for the district; the interval uses SEDA's empirical-Bayes standard error | students in the Chicago Public Schools District 299 | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.35 to −0.33 | 1,628,988 math test scores | none (one district) | Stanford Education Data Archive | | | cty_boys | 1,043 | counties | count of counties where boys' mean math score exceeds girls' | counties with a male-female math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | cty_boys_clear | 599 | counties | count of counties whose 95% interval (estimate and standard error) for the math gap lies above zero | counties with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | cty_girls | 1,953 | counties | count of counties where girls' mean math score exceeds boys' | counties with a male-female math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | cty_girls_clear | 1,400 | counties | count of counties whose 95% interval for the math gap lies below zero | counties with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | cty_unclear | 939 | counties | count of counties whose 95% interval for the math gap includes zero | counties with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | inc_d10_min | 115,294 | dollars of district median household income | lowest income in the highest tenth (equal numbers of districts in each tenth) | the highest tenth of districts by income | household income in 2023 dollars, ACS 2019 to 2023 | | 1,214 districts | none (a boundary) | American Community Survey | A household-weighted mean of tract medians, top-coded at 250,001 dollars. | | inc_d1_max | 51,268 | dollars of district median household income | highest income in the lowest tenth (equal numbers of districts in each tenth) | the lowest tenth of districts by income | household income in 2023 dollars, ACS 2019 to 2023 | | 1,213 districts | none (a boundary) | American Community Survey | A household-weighted mean of tract medians, top-coded at 250,001 dollars. | | mgap_ach_d1 | −0.21 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts ranked by average score (equal numbers of districts), bootstrap interval over states | school districts in the lowest tenth of districts by average math and reading score of all students | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.29 to −0.18 | 1,213 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_ach_d10 | +0.13 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts ranked by average score (equal numbers of districts), bootstrap interval over states | school districts in the highest tenth of districts by average math and reading score of all students | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 0.10 to 0.17 | 1,214 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_inc1_d1 | −0.21 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts ranked by an income built from each tract's single assigned district (equal numbers of districts), bootstrap interval over states | school districts in the lowest tenth of districts by income when each tract belongs to one district only | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.27 to −0.16 | 999 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_inc1_d10 | +0.09 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts ranked by an income built from each tract's single assigned district (equal numbers of districts), bootstrap interval over states | school districts in the highest tenth of districts by income when each tract belongs to one district only | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.04 to 0.13 | 1,000 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_inc_d1 | −0.20 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts (equal numbers of districts), bootstrap interval over states | school districts in the lowest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.26 to −0.15 | 1,213 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_inc_d10 | +0.08 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts (equal numbers of districts), bootstrap interval over states | school districts in the highest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.04 to 0.12 | 1,214 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_inc_d10_unw | +0.07 | math gap in grade levels, male minus female (negative means girls scored higher) | mean of the male minus female math gap within a tenth of districts, each district counted once, bootstrap interval over states | school districts in the highest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.05 to 0.11 | 1,214 districts | none (each district counts once) | Stanford Education Data Archive | | | mgap_inc_d1_unw | −0.16 | math gap in grade levels, male minus female (negative means girls scored higher) | mean of the male minus female math gap within a tenth of districts, each district counted once, bootstrap interval over states | school districts in the lowest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.22 to −0.10 | 1,213 districts | none (each district counts once) | Stanford Education Data Archive | | | mgap_inc_range | 0.28 | grade levels (largest minus smallest of the ten decile means of the math gap) | largest minus smallest test-weighted mean math gap across the ten income tenths, bootstrap interval over states | school districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.22 to 0.35 | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_pov_d1 | +0.12 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts ranked by the share of residents below the poverty line (ACS 2019 to 2023; equal numbers of districts), bootstrap interval over states | school districts in the lowest tenth of districts by poverty rate | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.09 to 0.16 | 1,213 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mgap_pov_d10 | −0.19 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female math gap within a tenth of districts ranked by the share of residents below the poverty line (ACS 2019 to 2023; equal numbers of districts), bootstrap interval over states | school districts in the highest tenth of districts by poverty rate | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.24 to −0.14 | 1,214 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | min_state_districts | 25 | districts | inclusion rule: a state needs at least this many districts in the income sample | states given a slope of their own | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (definition) | Stanford Education Data Archive | | | mth_inc_r | +0.40 | correlation coefficient | test-weighted correlation of the district math gap with the district's percentile rank of income, bootstrap interval over states | school districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.31 to 0.48 | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mth_mean | −0.031 | math gap in grade levels, male minus female (negative means girls scored higher) | mean over districts of the male minus female math gap, bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.060 to −0.004 | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | | | mth_mean_sd | −0.010 | math gap in standard deviations of the national cohort, male minus female (negative means girls scored higher) | mean over districts of the male minus female math gap, bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.019 to −0.002 | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | | | mth_mean_w | −0.06 | math gap in grade levels, male minus female (negative means girls scored higher) | math-test-weighted mean over districts of the male minus female math gap, bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.09 to −0.02 | 12,207 districts | number of math test scores in the district | Stanford Education Data Archive | | | mth_p5 | −0.35 | math gap in grade levels, male minus female (negative means girls scored higher) | 5th percentile of district gaps, each district counted once | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | | | mth_p95 | +0.29 | math gap in grade levels, male minus female (negative means girls scored higher) | 95th percentile of district gaps, each district counted once | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | | | mth_slope | +0.025 | grade levels of the math gap per ten points of district income rank | slope of the math gap on the district's percentile rank of income (0 to 100), test-weighted regression without state indicators, errors clustered on state | school districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.019 to 0.031 | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mth_slope_fe | +0.027 | grade levels of the math gap per ten points of district income rank | slope of the math gap on the district's percentile rank of income (0 to 100), test-weighted regression with a separate intercept for each state and state indicators, errors clustered on state | school districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.022 to 0.032 | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mth_state_high | +0.26 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of district gaps in the state (the interval treats districts as independent) | school districts in the state with the largest math lead for boys | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.23 to 0.30 | 326 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mth_state_low | −0.34 | math gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of district gaps in the state (the interval treats districts as independent) | school districts in the state with the largest math lead for girls | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.38 to −0.30 | 135 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | mth_state_share | 36% | percent of the variance of district math gaps that lies between states | R squared of district math gaps on state, weighted by test scores; interval from resampling districts | school districts with a reading gap, a math gap and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 34% to 40% | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | Resampling districts adds a small upward bias to the share, so the interval sits slightly high. | | mth_state_share_unw | 24% | percent of the variance of district math gaps that lies between states | R squared of district math gaps on state, weighted by districts, each counted once; interval from resampling districts | school districts with a reading gap, a math gap and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 23% to 26% | 12,136 districts | none (each district counts once) | Stanford Education Data Archive | Resampling districts adds a small upward bias to the share, so the interval sits slightly high. | | n_boys_rla | 14 | districts | count of districts where boys' mean reading score exceeds girls' | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_county | 2,996 | counties | count of counties | counties with a male-female math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_county_rla | 3,125 | counties | count of counties | counties with a male-female reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_county_se | 2,938 | counties | count of counties | counties with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_girls_rla | 12,313 | districts | count of districts where girls' mean reading score exceeds boys' | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_inc | 12,136 | districts | count of districts | school districts with a reading gap, a math gap, an all-student score and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_inc1 | 9,993 | districts | count of districts | school districts in the income sample that also have an income when each tract belongs to one district only | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_map_nodata | 146 | counties | count of drawn counties with no math gap estimate | counties and county equivalents on the map (Puerto Rico is not drawn) | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_mth | 12,207 | districts | count of districts | school districts with a male-female math gap estimate (Virginia has none, DC is absent and Hawaii has no districts) | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_no_income | 38 | districts | count of districts left out of the income figures | school districts with both gaps but no district income or no all-student score | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_rla | 12,327 | districts | count of districts | school districts with a male-female reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | | none (full count) | Stanford Education Data Archive | | | n_sample | 5,000 | districts | size of a random sample drawn with a fixed seed | districts drawn at random for the scatter | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | 12,136 districts | none (sample size) | Stanford Education Data Archive | | | n_state_slopes | 45 | states | count of states given a slope | states with at least the minimum number of districts in the income sample | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_state_slopes_clear | 38 | states | count of states where the 95% interval (heteroskedasticity-robust) for that slope lies above zero | states with at least the minimum number of districts in the income sample | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_state_slopes_pos | 45 | states | count of states where the test-weighted slope of the math gap on income rank is positive | states with at least the minimum number of districts in the income sample | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_states | 48 | states | count of states | states with districts in the income sample (no Virginia, Hawaii or DC) | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_states_boys | 20 | states | count of states whose test-weighted mean math gap is above zero | states in the income sample | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_states_boys_clear | 13 | states | count of states whose 95% interval for the test-weighted mean math gap lies above zero | states in the income sample | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | n_states_boys_clear_lin | 14 | states | count of states whose 95% interval for the test-weighted mean math gap lies above zero when the standard error is the Taylor-linearized one for a weighted mean of independent districts | states in the income sample | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (full count) | Stanford Education Data Archive | | | pct_boys_inc_d1 | 23.7% | percent of districts where boys' mean math score exceeds girls' | share of districts (each counted once) where boys' mean math score exceeds girls', bootstrap interval over states | school districts in the lowest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 15.3% to 33.1% | 1,213 districts | none (each district counts once) | Stanford Education Data Archive | | | pct_boys_mth | 43.8% | percent of districts where the math gap is above zero | share of districts (each counted once) where boys' mean math score exceeds girls', bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 38.1% to 49.4% | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | | | pct_boys_mth_w | 36.5% | percent of math test scores in districts where the math gap is above zero | share of math test scores that are in districts where boys' mean exceeds girls', bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 29.8% to 44.1% | 12,207 districts | number of math test scores in the district | Stanford Education Data Archive | | | pct_cty_boys | 34.8% | percent of counties where the math gap is above zero | share of counties (each counted once) where boys' mean math score exceeds girls', bootstrap interval over states | counties with a male-female math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 25.9% to 44.7% | 2,996 counties | none (each county counts once) | Stanford Education Data Archive | | | pct_girls_inc_d10 | 33.4% | percent of districts where girls' mean math score exceeds boys' | share of districts (each counted once) where girls' mean math score exceeds boys', bootstrap interval over states | school districts in the highest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 25.2% to 40.5% | 1,214 districts | none (each district counts once) | Stanford Education Data Archive | | | pct_girls_rla | 99.9% | percent of districts where the reading gap is below zero | share of districts (each counted once) where girls' mean reading score exceeds boys', bootstrap interval over states | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 99.8% to 100.0% | 12,327 districts | none (each district counts once) | Stanford Education Data Archive | | | pct_mth_clear_boys | 21.5% | percent of districts whose 95% interval for the math gap lies above zero | share of districts (each counted once) whose 95% interval, built from the estimate and its standard error, lies above zero; bootstrap interval over states | school districts with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 17.3% to 26.3% | 12,140 districts | none (each district counts once) | Stanford Education Data Archive | | | pct_mth_clear_girls | 31.3% | percent of districts whose 95% interval for the math gap lies below zero | share of districts (each counted once) whose 95% interval, built from the estimate and its standard error, lies below zero; bootstrap interval over states | school districts with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 25.7% to 37.8% | 12,140 districts | none (each district counts once) | Stanford Education Data Archive | | | pct_mth_unclear | 47.2% | percent of districts whose 95% interval for the math gap includes zero | share of districts (each counted once) whose 95% interval, built from the estimate and its standard error, includes zero; bootstrap interval over states | school districts with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 42.2% to 51.8% | 12,140 districts | none (each district counts once) | Stanford Education Data Archive | | | pct_rla_clear_girls | 98.9% | percent of districts whose 95% interval for the reading gap lies below zero | share of districts (each counted once) whose 95% interval, built from the estimate and its standard error, lies below zero; bootstrap interval over states | school districts with a reading gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI 98.3% to 99.4% | 12,254 districts | none (each district counts once) | Stanford Education Data Archive | | | philly_gap | −0.33 | math gap in grade levels, male minus female (negative means girls scored higher) | SEDA's male minus female math gap for the district; the interval uses SEDA's empirical-Bayes standard error | students in the Philadelphia City SD | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.35 to −0.32 | 960,980 math test scores | none (one district) | Stanford Education Data Archive | | | rank_step | 10 | points of district income rank (0 to 100) | step of the income rank in which slopes are reported | school districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | | none (definition) | Stanford Education Data Archive | | | rgap_inc_d1 | −0.79 | reading gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female reading gap within a tenth of districts (equal numbers of districts), bootstrap interval over states | school districts in the lowest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.89 to −0.71 | 1,213 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | rgap_inc_d10 | −0.87 | reading gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of the male minus female reading gap within a tenth of districts (equal numbers of districts), bootstrap interval over states | school districts in the highest tenth of districts by district median household income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.97 to −0.78 | 1,214 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | rgap_inc_range | 0.13 | grade levels (largest minus smallest of the ten decile means of the reading gap) | largest minus smallest test-weighted mean reading gap across the ten income tenths, bootstrap interval over states | school districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 0.06 to 0.24 | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | The largest minus the smallest of ten noisy means is biased upward. | | rla_inc_r | −0.09 | correlation coefficient | test-weighted correlation of the district reading gap with the district's percentile rank of income, bootstrap interval over states | school districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.21 to 0.04 | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | rla_mean | −0.76 | reading gap in grade levels, male minus female (negative means girls scored higher) | mean over districts of the male minus female reading gap, bootstrap interval over states | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.82 to −0.70 | 12,327 districts | none (each district counts once) | Stanford Education Data Archive | | | rla_mean_sd | −0.228 | reading gap in standard deviations of the national cohort, male minus female (negative means girls scored higher) | mean over districts of the male minus female reading gap, bootstrap interval over states | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.246 to −0.211 | 12,327 districts | none (each district counts once) | Stanford Education Data Archive | | | rla_mean_w | −0.79 | reading gap in grade levels, male minus female (negative means girls scored higher) | reading-test-weighted mean over districts of the male minus female reading gap, bootstrap interval over states | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.85 to −0.73 | 12,327 districts | number of reading test scores in the district | Stanford Education Data Archive | | | rla_p5 | −1.18 | reading gap in grade levels, male minus female (negative means girls scored higher) | 5th percentile of district gaps, each district counted once | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | 12,327 districts | none (each district counts once) | Stanford Education Data Archive | | | rla_p95 | −0.36 | reading gap in grade levels, male minus female (negative means girls scored higher) | 95th percentile of district gaps, each district counted once | school districts with a reading gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | | 12,327 districts | none (each district counts once) | Stanford Education Data Archive | | | rla_state_high | −0.46 | reading gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of district gaps in the state (the interval treats districts as independent) | school districts in the state with the smallest reading lead for girls | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −0.50 to −0.42 | 326 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | rla_state_low | −1.28 | reading gap in grade levels, male minus female (negative means girls scored higher) | test-weighted mean of district gaps in the state (the interval treats districts as independent) | school districts in the state with the largest reading lead for girls | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI −1.32 to −1.25 | 234 districts | number of math and reading test scores in the district | Stanford Education Data Archive | | | rla_state_share | 70% | percent of the variance of district reading gaps that lies between states | R squared of district reading gaps on state, weighted by test scores; interval from resampling districts | school districts with a reading gap, a math gap and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 68% to 72% | 12,136 districts | number of math and reading test scores in the district | Stanford Education Data Archive | Resampling districts adds a small upward bias to the share, so the interval sits slightly high. | | rla_state_share_unw | 47% | percent of the variance of district reading gaps that lies between states | R squared of district reading gaps on state, weighted by districts, each counted once; interval from resampling districts | school districts with a reading gap, a math gap and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | 95% CI 45% to 49% | 12,136 districts | none (each district counts once) | Stanford Education Data Archive | Resampling districts adds a small upward bias to the share, so the interval sits slightly high. | | se_med_mth | 0.06 | standard error of a district's math gap, grade levels | median over districts of the standard error SEDA reports for the male minus female math gap | school districts with a math gap estimate and a standard error | test scores of third through eighth graders, pooled over 2009 to 2019 | | 12,140 districts | none (each district counts once) | Stanford Education Data Archive | | | size_r | −0.02 | correlation coefficient | correlation of the percentile rank of a district's math test scores with the percentile rank of its math gap (a rank correlation), each district counted once, bootstrap interval over states | school districts with a math gap estimate | test scores of third through eighth graders, pooled over 2009 to 2019 | 95% CI −0.09 to 0.05 | 12,207 districts | none (each district counts once) | Stanford Education Data Archive | | | tests_inc | 193.8 million | math test scores (one per student, grade and year tested) | sum of the math test scores behind the district estimates | students in the districts with both gaps and a district income | test scores of third through eighth graders, pooled over 2009 to 2019; district income from the American Community Survey, 2019 to 2023 | | 12,136 districts | none (full count) | Stanford Education Data Archive | | ## Definitions - Bootstrap interval: A range for an estimate found by recomputing it on many random resamples of the data and reading the range from the results. It shows how much the estimate would vary from sample to sample. - SD units (SEDA): Test scores stated in units of the typical spread of scores around the national average, so that a score reads as the number of such spreads above or below the national average. SEDA also reports scores in grade levels, which are a separate scale. - Ecological inference: Drawing conclusions about individuals from data about places or groups. A pattern across counties need not hold for the people inside them. ## Methods This chapter compares male-female test score gaps in reading and math across school districts and counties, from the Stanford Education Data Archive 6.0, pooled over third through eighth grade and the 2009 to 2019 school years. A negative gap means girls scored higher. District income is not in the archive: it is built from tract incomes of the American Community Survey for 2019 to 2023, split across districts by land area, and it is measured after the scores (2009 to 2019). Means weight a district by its number of math and reading test scores, or count each district once where the text says so. ### Which grades, years and districts the gaps cover Gaps come from the Stanford Education Data Archive 6.0, pooled over third through eighth grade and the 2009 to 2019 school years, through the book's SEDA loader. The build loads every SEDA row and filters to the gender gap rows, because the loader's default filter returns only headline rows and its grade-unit check rejects gap rows. ### How the gaps are signed and scaled The gap rows are SEDA's male-female differences, which equal the male mean minus the female mean (the build checks this on both scales), so a negative gap means girls scored higher. Figures and facts use grade levels, in which one unit is the national average learning gain over a school year; two facts use standard-deviation units and say so. Explains Table 1 and Table 2. ### How district income is built and matched to the scores District income is not in SEDA. It is the household-weighted mean of tract median household income (ACS 2019 to 2023, dollars, capped at 250,001), with each tract split across the districts it overlaps in proportion to its land area in each; districts are the 2021 to 2022 school-year districts of the NCES crosswalk, matched to SEDA by district code, and the income (2019 to 2023) is measured after the scores (2009 to 2019). A check assigns each tract to one district only and recomputes the income deciles. The income sample holds the districts with both gaps, an income and an all-student score, and ranks and tenths hold equal numbers of districts. Explains Table 3 and Table 4. ### How districts and counties are weighted and drawn Means weight a district by its number of math and reading test scores, or count each district once where the text says so. County estimates are SEDA's county rows, each county counted once; the Alaska code 02261 is drawn on its two successors. Missing values are never set to zero: a county without a math estimate is drawn as no data with its reason. Explains Table 5. ### How uncertainty is measured Intervals for decile means, shares, correlations and differences resample whole states (2,000 draws, seed 12345); intervals for state means use a normal approximation on the effective number of districts and treat a state's districts as independent (a linearized standard error is reported as a check); slopes come from weighted regressions with errors clustered on state. The state share of variance is the R squared of a regression on state indicators, test-weighted and with each district counted once. A district or county is counted as clearly signed when its 95% interval, built from the estimate and its standard error with the normal multiplier, lies on one side of zero. ### How the scatter and density figures are drawn Scatter dots are a random sample drawn with the same seed. Densities use Gaussian kernels on a fixed grid. Explains Table 1 and Table 4. ## What these data cannot show The patterns describe school districts and counties. A district's income is not a family's income, and the data do not say that a girl or a boy from a poorer family scores differently from one in a richer family in the same district (ecological inference). A district gap is an average over every student the state tested in third through eighth grade, so it cannot show when in school a gap opens. Each district's gap carries error, and the standard error of a district's math gap is typically larger than the mean math gap. The decile means, with intervals, carry the income finding, and single districts do not. SEDA's estimates are empirical-Bayes estimates, which pull the noisiest districts toward the average and narrow the spread of district gaps. If the shrinkage pulls toward a state or national mean, it could change the shares of variance that lie between states. The 95% intervals built from the empirical-Bayes standard errors describe the shrunken estimates. The scores come from state tests that differ in content and format, and a gap can reflect what a test asks as well as how girls and boys learned. District income is built for this chapter and is not part of SEDA. It is a household-weighted mean of tract medians, split across overlapping districts by land area, so it is not the median income of a district's households, and the survey caps the highest incomes. It is measured for 2019 to 2023, after the 2009 to 2019 scores, on the district boundaries of the 2021 to 2022 school year. A district whose boundary or code changed may be matched to SEDA imperfectly or not at all. In all, 38 districts with both gaps have no income and are left out of the income figures. Districts that differ in income also differ in other ways, such as the racial composition of their students, and the chapter does not separate those. SEDA has no math estimates for Virginia, no rows for the District of Columbia and no districts in Hawaii (Hawaii's counties are in its county file and are drawn), and Puerto Rico is not covered. The estimates describe public school students tested in 2009 to 2019 and say nothing about later years. SEDA reports two groups, male and female. Whether gaps in school scores carry into adult outcomes is the subject of Chapter 4.4, Boys and mobility (https://vishalsingh.org/zipcode/boys-and-mobility; planned), which compares boys and girls raised in the same neighborhoods. ## References 1. Reardon, Sean F.; Fahle, Erin M.; Kalogrides, Demetra; Podolsky, Anne; Zárate, Rosalía C. (2019). Gender Achievement Gaps in U.S. School Districts. American Educational Research Journal 56(6): 2474-2508. https://doi.org/10.3102/0002831219843824 2. Hyde, Janet S.; Lindberg, Sara M.; Linn, Marcia C.; Ellis, Amy B.; Williams, Caroline C. (2008). Gender Similarities Characterize Math Performance. Science 321(5888): 494-495. https://doi.org/10.1126/science.1160364 3. Fryer, Roland G., Jr.; Levitt, Steven D. (2010). An Empirical Analysis of the Gender Gap in Mathematics. American Economic Journal: Applied Economics 2(2): 210-240. https://doi.org/10.1257/app.2.2.210 ## Provenance - Pipeline version: 0.1.0 - Build script: book2/chapters/gender-gaps-in-reading-and-math/build.py (sha256 f03bc4b1a85f5545112e2e7510329aa697d066922bec68920f5a20ac1b989663) - Built on: 2026-10-07 - Site commit: c2b1ff3309cb4a10833f884da91ac0057a66ad0a Inputs: | File | Size (bytes) | SHA-256, first 1 MB | SHA-256, last 1 MB | | --- | ---: | --- | --- | | book2/chapters/gender-gaps-in-reading-and-math/gaps.py | 18,838 | bb96ad502a57c7b7093967c7beb2111e2f0a951e674ff8e0c8b52dfebb0c9b7b | bb96ad502a57c7b7093967c7beb2111e2f0a951e674ff8e0c8b52dfebb0c9b7b | | data/geo/dim_geo.parquet | 1,180,921 | 29d5e4d943552d54aeb6ba92aadc939d9bd15bdea553c78357dbd516757be627 | e58c8c488df54ab3187f5461f179322afdc49c9bd2315404003fe1272eea32c3 | | data/staged/crosswalks/tract_to_district_2022.parquet | 3,162,231 | 06a12eab3f61d43d70bbc874beea499f78d4b3fb7c833839909a853479f3753e | 13ffd00dbaec66f9662d8268232313e57938326840cb9303dcc9e3da6eab0a12 | | data/staged/education/seda_county_poolsub_6.0.parquet | 3,604,804 | 730134713ed3cae8983a255caa2a1be786a2d50688a949b11372f86e90065976 | 356fb555b2de0cc487cb4c37c95f29cee0497a96b4f7385fa6f10b9975bee58d | | data/staged/education/seda_geodist_poolsub_6.0.parquet | 12,344,123 | 48de785e0dc62aacfc4608757670e6912b318c6c02baa3a716ef4c131c0eb6b5 | 5a22f706cfd0295923dd169d985d44d0da9076236e3afe38d59c5f6dd3e518a6 | | data/staged/ses/acs5_tract2020_2019_2023.parquet | 7,232,424 | 5fe6f730a35225f14d5d8abfc6b9bce58b52e545f309a7eac74f1dae74d7a31c | 7b4a2475b3852d3f91804716a04566ef8ceadacec2be9fb85f5eb642078efac6 | Outputs: | File | SHA-256 | | --- | --- | | facts.json | fbcfab3b9afd6a3d57b4f038fcd8374da3bfa9f119d19bc43352f8ac1a612edc | | figures/gap_by_income_decile.json | a03a2c5902a4a1d61ad9cede1dec83114f09eeed7325a98fa43fdd93d3af6087 | | figures/gap_distributions.json | cafb0c0869e8fed221b3780aca0106b85088eb00a60abb410981f9715a838493 | | figures/math_gap_by_income.json | fa6db9b26a4406bbe2bb944728ef380c7980df933e46c0f646a91b6e90bb7ff4 | | figures/math_gap_map.json | cc7576e47363af473d7b8d9401654da7487158ea7a7e43dc3aa6b222a289cb29 | | figures/state_gap_means.json | a5c88632fd30625f6b573e64160e4f59d39a3fd8aa80dd215fb7ec338087e831 | | meta.json | ba82023f1da7e23d9d49372cda72d90e54c92432f60ee44a06feb0a637e19010 | | public/data/gender-gaps-in-reading-and-math/math_gap_by_income.json | a5ae9c5156b17219a1a803d34ca8e18301fb80af0ed3d8bc01bf954b5a4eab93 | | public/data/gender-gaps-in-reading-and-math/math_gap_map.json | 769ff45da54cace745a8ec788e610b37fba44ced7514b2f3e4711b6731103557 | | refs.json | 7c37e6d73029fbec5885e382c2491bf425c7cc156bc02a9a492b1ba2d7c675b4 | ## Machine formats - Report (HTML): https://vishalsingh.org/zipcode/gender-gaps-in-reading-and-math/report - Report (Markdown): https://vishalsingh.org/zipcode/gender-gaps-in-reading-and-math/report.md - Every estimate (JSON): https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/facts.json - Table 1, figure "gap_distributions" (JSON): https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/gap_distributions.json - Table 2, figure "state_gap_means" (JSON): https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/state_gap_means.json - Table 3, figure "gap_by_income_decile" (JSON): https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/gap_by_income_decile.json - Table 4, figure "math_gap_by_income" (JSON): https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/math_gap_by_income.json - Table 5, figure "math_gap_map" (JSON): https://vishalsingh.org/zipcode/report/gender-gaps-in-reading-and-math/figures/math_gap_map.json - Payload of "math_gap_by_income" (JSON): https://vishalsingh.org/zipcode/data/gender-gaps-in-reading-and-math/math_gap_by_income.json - Payload of "math_gap_map" (JSON): https://vishalsingh.org/zipcode/data/gender-gaps-in-reading-and-math/math_gap_map.json - Index of every report: https://vishalsingh.org/zipcode/llms.txt; all reports in one file: https://vishalsingh.org/zipcode/llms-full.txt