Jeanes Fund on Black Schooling in the South, 1900–1930

The Next Needed Thing: The impact of the Jeanes Fund on Black schooling in the South, 1900–1930

Daniel Kreisman Georgia State University [email protected]

December, 2015

Abstract

At the outset of the 20th century, two large philanthropies targeted southern black schools to combat “separate but equal”. The first, The Rosenwald Fund, built nearly 5,000 school- houses. The second, The Jeanes Fund, built a corps of trained “Supervisors” who undertook tasks ranging from teacher training to fundraising – hence their motto, “the next needed thing.” I exploit variation in the timing and placement of Jeanes and Rosenwald to estimate the impact of the Jeanes Fund, to revise estimates of the effects of Rosenwald, and to compare per-dollar effects of investments in human resources (Jeanes) and physical capital (Rosenwald) respectively.

JEL No. I24, I25, N01, N32. Keywords: Education, Achievement Gap, Philanthropy, Jeanes, Rosenwald.

∗Dept. of Economics, Georgia State University, P.O. Box 3992, Atlanta, GA 30302-3992. Thanks to Jeff Grog- ger, Kerwin Charles, Bob Lalonde and two anonymous referees for two rounds of suggestions and insight, to Dan Aaronson and Bhash Mazumder for Rosenwald data and helpful suggestions, to Celeste Carruthers and Marianne Wanamaker for county level educational records, and to Josiah Pamoja, Kayin Shabazz, Katherine Hollis and Peyman Firouzi for archival work and research assistance. Thanks also to seminar participants at the University of Chicago, the University of Michigan, UPenn and UMass Amherst, and to Chicago’s Committee on Education and the Institute of Education Science for fellowship support. This research was directly supported by a grant from the American Educational Research Association which receives funds for its Grants Program from the National Science Foundation under Grant #DRL-0941014. Opinions reflect those of the author and do not necessarily reflect those of the granting agencies. Any and all errors are my own.

1 1 Introduction In the years between the end of Reconstruction in the late 1870’s and Brown v. Board of Edu- cation in 1954, educational resources for Southern blacks were a fraction of those for whites. By as late as 1940, black per-pupil expenditures were less than half, student-teacher ratios were 25 percent higher and the average term was 10 percent shorter (Margo, 1990; Card and Krueger, 1992). Yet, Southern blacks born between 1900 and 1930, during the height of “Jim Crow”, improved on their white counterparts and their Northern black peers in both schooling outcomes and even school inputs (see Figure 1). Margo (1990, 1991) attributes some of this to improved schooling conditions resulting from the threat of northern migration. He and others (Donohue, Heckman and Todd, 2002; Collins and Margo, 2006) also cite the existence of large Northern philanthropies, but stop short of estimating their impact. Recent empirical evidence has begun to emerge demonstrating that these philanthropies indeed played an instrumental role. Aaronson and Mazumder (2011) estimate that the Julius Rosenwald Fund, which built nearly 5,000 rural schools for Southern black students between 1914 and 1931, accounts for up to 40% of gains in enrollment and literacy made by blacks relative to whites during these years. Subsequent work by Carruthers and Wanamaker (2013) suggests, though, that these funds crowded out additional public expenditures for blacks, mitigating relative gains in school inputs. I add to this literature by estimating the impact of the Anna T. Jeanes Fund, which built a corps of trained and experienced black educators to serve as “Supervisors” in Southern black schools beginning in 1909. These “Jeanes Supervisors,” most of them women, were assigned to Southern counties and served a variety of tasks in each county’s black schools, including: teacher training, curriculum development, administrative work and fundraising, often for Rosenwald Schools, amongst myriad other tasks. The varied nature of their work inspired their motto, The next needed thing. By 1930 the Jeanes Fund had placed Supervisors in over 40% of Southern counties, ostensibly reaching nearly half of Southern black pupils. To my knowledge, no attempt has been made to estimate the program’s effects and little research, even of an historical nature, exists.1 In the following I use novel county-level data created from recently archived administrative records from the Jeanes Fund to estimate the program’s impact on enrollment and literacy measured in the Decennial Census from 1900-1930. I combine these data with records from the Rosenwald Fund to estimate the impact of each program through 1930, exploiting variation in timing, location and level of treatment across counties and birth cohorts. I take advantage of the fact that each program targeted only black students by differencing off the white population, effectively estimating impacts on black-white gaps, allowing me to control for county fixed effects and state-cohort time trends. Variation in both the timing and nature of these two efforts allows me to compare returns on investments in human resources (Jeanes Supervisors) and physical

1See Fultz (1995), Jones (1937), Liston (1928), McCluer (2009) and Pincham (2005) for historical studies of the Jeanes program.

2 infrastructure (Rosenwald Schools) as inputs in education when initial resource levels are low. I find that “full exposure” to a Jeanes Supervisor, ranging from 8 to 14 years of a Supervisor’s presence in the county by including lags, would have closed the black-white enrollment gap by approximately 3.5 percentage points and the literacy gap by between 3.5 and 4.3 percentage points. In relative terms, this is enough to close the baseline enrollment gap of 8.5 percentage point by roughly 40%, and the baseline literacy gap of 10 percentage points by almost the same margin. In reality, few if any students experienced this level of “full” exposure. Black children born between the turn of the century and 1915 in fact had a Jeanes Supervisor for about 3.2 years on average between the time they were born and when they exited middle school. Taking into account the actual level of exposure Southern black children experienced, I estimate that the Jeanes program decreased the black-white literacy gap by 1 percentage point, or 5% of the 20 percentage point decrease in the black-white gap between 1900 and 1930. Importantly, I estimate that each year a Supervisor was in the county increased the likelihood of getting a Rosenwald School by 5 percent, and that accounting for the presence of the Jeanes program reduces estimates of the effect of Rosenwald on enrollment and literacy by roughly one-third. Ultimately, I use these estimates to calculate per-dollar impacts of both Jeanes and Rosenwald using archived records of salary and construction costs respectively. I find that Rosenwald investments yielded higher returns per-dollar spent by between 17 and 42 percent, suggesting that when initial physical resource levels are very low or non-existent, returns to infrastructure investments can be high. The remainder of the paper is as follows. Section 2 discusses background on both programs, relevant literature and historical context; Section 3 describes the data used for analysis; Section 4 lays out the empirical strategy and estimates impacts on enrollment and literacy; Section 5 discusses the relationship between Jeanes Supervisors and the placement of Rosenwald Schools; Section 6 presents back of the envelope comparisons of dollar-for-dollar impacts; and Section 7 summarizes with suggestions for future research.

2 Background 2.1 Race, Education and Philanthropy in the South In 1896 the Supreme Court ruled in Plessy v. Ferguson that laws enacted with the explicit purpose of segregating blacks from whites, known as “separate but equal,” did not violate the 14th Amendment as long as the separate facilities were in fact equal. In reality, the resulting diﬀerences were quite large and it would be another 58 years until the Court would overturn this ruling in Brown v. Board of Education. The eﬀects of “separate but equal” were most obvious in education, evidenced in the sub- standard conditions chronicled by DuBois (1911), Myrdal (1944), Bond (1966) and others. In comprehensive work on the relationship between race, education and earnings, Margo (1986, 1990) argues that even had educational resources been equalized, black children still would have

3 lagged due to overwhelming differences in family background; what he calls “intergenerational drag”; Fishback and Baskin (1991) reach a similar conclusion using individual-level from Geor- gia. There is some evidence of convergence in school quality during this time though. Donohue et al. (2002) argue that from 1910 to the mid-1930’s black school quality in the South improved, but much less so relative to whites. Fewer explanations are offered for convergence in schooling outcomes during these years, when the effects of “Jim Crow” were most harshly felt. Donohue et al. conclude that schools built by Northern philanthropies, primarily Rosenwald, accounted for at most one-third of the decrease in the black-white gap between 1910 and 1930, and put no estimate on the effects of other funds such as Jeanes and Slater.2 The key questions are, what factors led to this early input convergence, and why did modest input convergence lead to relatively large gains in achievement? While the role of philanthropy in these matters has been documented in the historical literature, attempts at causal estimates of the impacts of these programs on schooling outcomes are scarce.3 Evidence presented here, in Aaronson and Mazumder (2011), and in Carruthers and Wanamaker (2013), indicates that Northern philanthropy played an instrumental role in black education before 1940 supporting both infrastructure and human resources, ultimately narrowing the black-white attainment gap, and less so the black-white resource gap. Of note, The George Peabody Fund, which supported Southern education (although primarily not for blacks) between 1867 and 1914, became a template for American philanthropy. The John F. Slater Fund similarly donated over 1 million dollars in 1882 to higher education and training schools for blacks, where much of this money was used for black normal schools and Historically Black Colleges. Between 1902 and 1964, John D. Rockefeller and Frederick T. Gates donated several million dollars to various causes in the South benefitting both blacks and whites, including agricultural development and state universities. Despite robust support for higher education and denominational schools for blacks, the Jeanes and Rosenwald Funds would be among the first to give to secular black primary schools.

2.2 The Jeanes Fund In 1907 Anna T. Jeanes, a wealthy widow from Pennsylvania with family money from coal investments, donated 1 million dollars to be used in her words, “solely to the assistance of Rural, Community, or Country Schools for Southern Negroes, and not for the beneﬁt or use of large institutions, but for the purpose of rudimentary education.”4 This money became the foundation of the Rural Negro School Fund, renamed the Anna T. Jeanes Fund a few years later, whose original board members included Booker T. Washington, William Taft, Hollis Frissell, George

2The authors mistakenly claim that the Jeanes Fund’s activites ended in 1928, which they did not. The Jeanes Fund was active through 1937 when it merged with remaining money left over from the Rosenwald and Slater Funds, amongst others, to form the Southern Education Foundation. Jeanes Supervisors were active in the South through the early 1960’s. 3See Eric Anderson and Moss (1999) for an overview of philanthropy, and James Anderson (1978, 1988) for a comprehensive review of black education in the South. 4From Anna Jeanes will (Wright, 1933). Italics are the original author’s (Jeanes’) emphasis.

4 Peabody and Andrew Carnegie. The first request for money came from Jackson Davis, superintendent of schools in Henrico County, Virginia. Davis requested funds, $40 per month, to pay Virginia Randolph, a highly regarded teacher in one of the county’s rural black schools, to help other teachers in the county improve their schools in a similar fashion. Mrs. Randolph combined an academic education with training in industrial skills and a strong belief in the importance of well maintained facilities, much along the lines of Booker T. Washington’s philosophy. This became known as the “Henrico Plan” and set a template for the Fund’s activities in the early years.5 Between 1909 and 1937, the Jeanes Fund placed trained educators in counties to serve as “supervising teachers.” Supervisors’ work varied widely from county to county, but a theme of industrial education and fundraising was prevalent in the early years. Among other tasks, Supervisors provided teaching support, trained local educators, gave lectures on health and sanitation, organized canning drives and book clubs, taught courses and raised funds for school improvements, often this money went to match Rosenwald grants. Supervisors were each assigned to a county in which they would live and work, and each of that county’s schools would ostensibly fall under the Supervisor’s purview. Administrative records from the Jeanes Fund in most cases do not list which schools within a county each Supervisor attended. Reports and travel expenses indicate that most if not all schools were visited regularly. The assignment process involved State Agents for Negro Education, Southern whites sympathetic to black education who served as liaisons between philanthropic organizations and state and local school districts. Agents worked with the directors of the Fund, local county superintendents and local schools to seek out and hear appeals for the placement of Jeanes Supervisors, in addition to Rosenwald Schools. Figure 2 shows the location of Jeanes Supervisors and Rosenwald Schools through 1930. Supervisors were predominantly female, most were either former or current teachers with six years of experience on average according to Fund records. Many of these women were educated at Historically Black Colleges or normal schools; their salaries varied primarily by location, number of months worked and the difficulty of travel in their county. By 1914, five years into the Fund’s activities, 10 percent of Southern counties had a Jeanes Supervisor working with local schools and just over 15 percent of counties had a Jeanes Super- visor at some point in the past five years. In the same year, the Jeanes Fund began receiving matching funds for Supervisors from local school districts. As the Fund progressed, investments from other philanthropic organizations, including the Rosenwald, Peabody and Slater funds, augmented the initial endowment. After a few years, the Jeanes Fund begin paying local teachers and Jeanes Supervisors a stipend to attend summer teaching institutes at normal schools and

5Mrs. Randolph would be remembered as the ﬁrst Jeanes Supervisor and became an icon within the Jeanes organization. In 1954 the Virginia Randolph Foundation was established to award scholarships to Henrico County high school students attending a four year college. This fund is still active today. Visit http://www.varfoundation.org/index.html

5 Historically Black Colleges. By 1920, spending had reached $100,000 annually and by 1930 the Jeanes Fund had sent trained teachers to over 600 individual Southern counties, nearly one-half of all Southern counties. Figure 3 shows coverage by county through 1930.

[Figures 2 and 3 about here.]

2.3 The Rosenwald Fund Julius Rosenwald, a wealthy Chicago businessman and philanthropist, funded the construction of six rural schools in Alabama in 1912 with the aid of Booker T. Washington. The success of these schools, the first of which opened in 1914, led to the founding of the Julius Rosenwald Fund in 1917, with the goal of building rural schools for black communities throughout the South. Aaronson and Mazumder (2011) undertake a detailed analysis of the program’s impact finding large and significant effects on several outcomes. The authors exploit the rural nature of Rosenwald Schools and estimate a triple-difference, using the interaction between black, rural and exposure to a Rosenwald School as their primary measure. They estimate that moving from no exposure to a Rosenwald School to full exposure between ages 7 and 13, where full exposure is defined as having enough Rosenwald Schools to house all rural black pupils in a county, close the enrollment gap by 6.5 percentage points and the literacy gap by 16.5 percentage points between 1900 and 1930. Using WWII enlistment records, the authors estimate that the effect of full exposure is a 1.2 to 1.4 year increase in completed schooling and a 0.20 to 0.45 standard deviation increase on a standardized ability test. It is unclear from the Rosenwald data whether and to what extent each school building either increased the schooling capacity of the county or simply provided improved facilities, possibly by consolidating several previously unsuitable schools. Moreover, it is unclear how teacher supply responded to these changes in both quantity and quality. The authors do find that the program’s effects are largest in counties with initially low enrollment rates in 1900 and 1910. Their analysis does not take into account the Jeanes Fund in any way. The establishment of a matching grant was a major factor in the longevity and success of the Rosenwald Fund. It not only required counties to increase funding levels for black education, an unlikely prospect in the program’s absence, but also guaranteed that the schools would be maintained by the community after the program’s initial funding ceased. Rosenwald Schools were required to meet minimum standards, including a required length of school term, satisfactory teaching resources and at least two sanitary facilities, all of which were significant improvements on existing conditions. By 1927, additional Rosenwald funds were used to build libraries, extend school terms, and in some cases provide transportation for students to consol- idated schools. Funds were raised by local communities, primarily through taxes and in large part from contributions made by the local black community, and less so from whites. In many cases, Jeanes Supervisors were instrumental in helping secure local funds to meet Rosenwald’s matching criteria, which typically required local counties to raise 85% of funds.

6 Carruthers and Wanamaker (2013) estimate the impact of Rosenwald Schools on public school resources during that time, concluding that although Rosenwald school construction led to short term improvements for black students, some of these funds were diverted to serve white students mitigating long-term relative impacts on black-white spending diﬀerentials. They estimate that each private dollar contributed led to an additional $2.12 of public spending on black and white schools, $1.34 of which, 63%, went to white schools. The authors ultimately conclude that although the black-white school spending gap decreased less than dollar for dollar from private funds, these expenditures had a relatively larger impact on black students, likely due to very low initial levels of public investment.

3 Data 3.1 Census data I estimate the impact of the Jeanes Fund on both enrollment and literacy in the Decennial Census, 1900-1930.6 The 1900 and 1920 samples are 1-in-100 nationally representative samples while the 1910 sample includes a 1.4% oversample of blacks and the 1930 is a 1-in-20 sample, thus sampling weights are applied. The Census in these years was taken in person and allowed for proxy responses. I use all black and white respondents not living in group quarters with literacy or enrollment data. I also omit Macon County, Georgia, where the original Rosenwald Schools were built around Tuskegee University as this was not a typical case. Individual and family level controls are included to account for heterogeneity across families. Table 1 shows demographic characteristics for each sample.

[Table 1 about here.]

3.1.1 Enrollment For respondents ages 6 and older, interviewers ask about enrollment in school between a fixed date and the interview date. In the 1900 Census, enrollment is defined as having been enrolled in school between June 1 and the Census date, 1910 refers to enrollment between April 15 and the Census date, January 1 in 1920, and between April 1 and the Census date in 1930. It is important to note that enrollment at a given age does not imply that the respondent has been enrolled in previous years; program effects should therefore be interpreted as each program’s impact on enrollment in that Census year. Panel A of Figure 4 shows rates by age, race and Census year. Evident from this figure and Table 1 is that while white enrollment increased substantially between 1900 and 1910 and then leveled off, black enrollment saw large increases between 1900 and 1910, and then again between 1910 and 1920. Carruthers and Wanamaker largely limit their analysis to the effects of Rosenwald Schools build after 1920 as the quality of these buildings was much higher than those built during the war years. The substantive increase in black enrollment and closing of the black-white gap between 1910 and 1920, coupled with

6IPUMS; Ruggles, et al. (2010).

7 the fact that the Rosenwald program was not in full eﬀect until after 1920, suggests that other factors were at play. Amongst candidates such as increasing school funding, northern migration and evolving social, political and economic conditions of the type mentioned by Margo (1991), I demonstrate that the Jeanes Fund also played an important role.

3.1.2 Literacy Literacy is asked of respondents ages 10 and older in each year and is defined by four categories: Can neither read nor write, Can read but cannot write, Can write but cannot read, Can both read and write. Few respondents were classified in the second category and very few in the third. To avoid ambiguity I classify only those who can both read and write as literate. To avoid confounding effects of enrollment on literacy, and to make results interpretable with respect to previous work, I estimate literacy for respondents ages 15 and older. This has the added benefit of abstracting the measure from age as there is little change in literacy for respondents older than 15, while literacy is increasing in these cohorts between ages 10-15. The sample is bounded at age 22 in order to avoid spurious correlation resulting from high literacy adults moving to Jeanes or Rosenwald counties. Aaronson and Mazumder in fact find that moving the upper bound to age 30 weakens the effect of literacy, suggesting that selective migration is not affecting results. Literacy should be interpreted here as a measure of basic literacy and, unlike enrollment, is subject to interpretation by Census respondents. By including county fixed effects and main effects and interactions for age, state and Census year in the primary econometric specification, I control for differential interpretation across counties and within states over time. As a validation check, I estimate enrollment rates for four occupations reported by both black and white respondents in each Census year, two of which should have literacy rates of 100 percent (teachers and clergy), and two that should vary over time (farm workers and farm laborers). Figure 5 shows mean literacy rates for respondents ages 15 to 45 in these occupations by race and year. The occupational definitions are taken from the harmonized 1950 occupational classification and do not vary over time. The consistency of trends both within and across race and occupation over time support the use of literacy as an outcome measure. Panel B of Figure 4 shows literacy rates by age, race and Census year.

[Figure 4 about here.]

3.2 The Jeanes Fund Data for the Jeanes Fund come from the Southern Education Foundation Archive housed in The Woodruﬀ Library in Atlanta, Georgia. The archive contains an individual record for each county that had a Jeanes Supervisor, indicating the years in which a Supervisor was present, the Supervisor’s name, salary paid by the Fund, salary paid by the county if any, and number of months worked. Data was collected for all Southern states: AL, MS, LA, VA, GA, SC, TN, TX, NC, MD, AR, KY, FL and OK. Missouri and Delaware were dropped from the ﬁnal analysis for inconsistent reporting and lack of treatment variation across counties respectively. Data on

8 salaries were only available for selected states: AL, KY, LA, MS, NC, SC and TN. In few cases more than one Supervisor was present in a county in a given year in larger counties. Records do not distinguish if the two or more Supervisors’ terms overlapped or were consecutive. I assume that Supervisors are perfect substitutes and define treatment as the total number of months in a given year, thus in some cases this number is greater than 1. Similarly, in approximately four percent of cases one Supervisor served in two or more small neighboring counties. I treat these as separate occasions as there is little distinction between two small neighboring counties and one larger county in terms of Jeanes Supervision. Results are robust to relaxing either or both of these assumptions. These records allow me to create a panel of treatment intensity by county from 1909 through 1930. I then match this county level panel to Census records from 1900-1930. From this combined data I can identify retrospectively at which ages each Census respondent would have been exposed to a Jeanes Supervisor and the number of treatment months at each age. Estimating exposure to a Jeanes Supervisor requires assumptions about the rate of depreciation of the impact Jeanes Supervisors had on the quality of education in each county. In the empirical exercises below I compare different parameterizations of Jeanes exposure that account for contemporaneous impacts (having a Supervisor in the year enrollment is measured), for lagged impacts (residual impacts that account for cases where a Supervisor was present in prior years but not in the current year), plus specifications that assume the impacts of a Supervisor’s presence decay at a various rates of depreciation. These specifications assume i) that impacts are increasing in the number of years a Supervisor was present in a given county, and ii) that once a Supervisor leaves, there are residual effects that linger for some number of years following. Details of each exposure measure are described in each empirical specification. Data for the Jeanes Fund was not present in the archive for 6 states in years 1928-1930. I use Jeanes presence in previous years and Rosenwald presence in previous and subsequent years along with several county specific measures to impute the likelihood that theses counties had a Supervisor and the number of months. Details of the imputation are provided in the Appendix. Robustness checks will show that results are not sensitive to the omission of these states in these years.

3.3 The Rosenwald Fund Data for the Rosenwald Fund were created by Aaronson and Mazumder (2011) from digitized archives at Fisk University containing detailed records of all Rosenwald Schools created through 1931, including location by county, date of completion and teacher and student capacity. The authors calculate the number of students that could be accommodated by each school by multiplying the number of classrooms by 45. They then calculate the number of black school-age children in the county using searches on www.ancestry.com, which contains full Census counts by race and year of birth from years 1900-1930. The authors calculate their exposure measure using the share of black, rural children who could be accommodated in each county. In the following

9 I use the share of all black students since the Jeanes fund targeted the entire county and I do not difference off of the rural population. Sensitivity checks show that this decision does not affect the relative contributions of Jeanes and Rosenwald on either outcome, but does make the estimate of each program larger than if I only use the share of rural black children. Records for the Rosenwald Fund specify where but not when Rosenwald Schools built between 1914 and 1919 were constructed. Most were likely built after 1916-17, when the Fund was officially incorporated, and likely in 1919-1920 after price declines following WWI. Carruthers and Wanamaker (2013) limit their analysis to schools completed on or after 1920 as historical documentation indicates that Rosenwald Schools built prior to 1920 were of inferior quality. Similar to Aaronson and Mazumder, I assign these Schools a completion date of 1919 to avoid assigning treatment to untreated individuals. This does not significantly impact the estimation of enrollment effects, as having a Rosenwald School in the Census year accounts for nearly the entire impact of the program and there are no Schools unaccounted for by 1920.

3.4 Exposure Table 2 shows levels of exposure to both Jeanes Supervisors and Rosenwald Schools for black school aged students (7-14) in each Census year. The measure Jeanes At Census shows the share of black children ages 7-14 living in a county with a Jeanes Supervisor in each Census year. Column 2 shows that as early as 1910, 16 percent of black southern children were living in a county with a Jeanes Supervisor. By 1920, 38 percent of children lived in a county with a Supervisor, and by 1930, 43.5 percent of Black children lived in a county currently served by Jeanes. Similarly the second measure, Jeanes Ever, shows the share of black children living in a county that had ever had a Jeanes Supervisor by each Census year. By 1930, two-thirds of black children between 7 and 14 lived in a county that had a Supervisor at some point since 1909. Since Jeanes and Rosenwald focused on counties with large black populations, the share of children treated by the Jeanes and Rosenwald philanthropies was substantially larger than the share of counties that saw these programs. Next, Months Ever / 9 in Table 2 shows the average cumulative number of months Jeanes Supervisors had been present since 1909 divided by 9 to make the measure such that a one unit change is equivalent to a school year’s worth of exposure. I estimate that in 1920, on average, black children lived in counties that had a Supervisor for 2.5 years since 1909, and that by 1930 this rose to an average of 6.8 years of cumulative treatment. Similarly, Years of Jeanes shows the average cumulative years in which a Supervisor was present in each respondent’s county regardless of the number of months. When present, Supervisors averaged 8-9 months of work in a county, which is reﬂected in similarity between Years of Jeanes and Months Ever / 9. These measures show that in 1910, on average 19% of black students lived in a county that had ever had a Supervisor, and that by 1930 fully 66% of black children lived in a county that was ever visited by a Supervisor. The discrepancy between Jeanes At Census and the cumulative measures below is informative insofar the impact of a Supervisor may last beyond her presence in the county. To

10 illustrate, although in 1920 on average black students lived in counties that had been visited by a Supervisor on 3 separate occasions (Years of Jeanes), only 38% of students currently lived in a county with a Supervisor present (Jeanes at Census). The econometric exercises below attempt to accurately capture this relationship. The last two elements of Table 2 show average exposure to Rosenwald Schools for black students. Any Rosenwald At Census shows the share of black students living in a county with a Rosenwald School by each census year, and Rosenwald Coverage At Census shows, on average, the share of black students that could be accommodated by Rosenwald Schools. For example by 1930, 89% of black students lived in a county with a Rosenwald School, and on average black students lived in a county where Rosenwald Schools could accommodate 24.4% of black students. [Table 2 about here.]

3.5 Placement of Jeanes Supervisors and Rosenwald Schools A central concern to the identification strategy is the selective placement of Supervisors and Schools across counties. To address this I test for pre-existing trends in black enrollment, and in the black-white enrollment gap, in counties that received Jeanes and/or Rosenwald by 1920 compared with those that did not. Table 3 shows results from a pooled regression of enrollment on treatment indicators, state level time trends and a full set of individual level controls for respondents ages 7-14 in Census years 1900 and 1910. In column 1 I begin with enrollment trends for black children only. Jeanes 1920 is the number of years a Supervisor was in the county by 1920, and Rose 1920 is a dummy indicating if a Rosenwald School was built in that county by 1920; Year 1910 is an indicator for Census year 1910. Results in column 1 suggest that conditional on a full set of individual level covariates and state and birth year dummies, there were no enrollment trend differences for black children in counties that did and did not receive Jeanes Supervisors or Rosenwald Schools. In column 2, I test for trends in the black-white gap by interacting dummies for black and Census year 1910 with the pre-treatment indicators. The coefficient on Rose 1920*Year 1910, 0.096, indicates that enrollment for white students was increasing in counties receiving a Rosenwald School by 1920. The negative coefficient on Black*Rose2 190* Year 1910, -0.113, suggests that the black-white gap in enrollment in counties that received a Rosenwald School by 1920 was widening compared to those that did not receive a school. There is no evidence of a pre- treatment effect for the number of years a Jeanes Supervisor would be present by 1920. Taken together, there is some evidence confirming results in Aaronson and Mazumder that Rosenwald Schools were targeting counties with initially low enrollment, or widening enrollment gaps, while there is little evidence in trend differences for Jeanes. A separate issue concerns the relationship between having a Jeanes Supervisor and a Rosen- wald School. The few existing historical studies on Jeanes Supervisors suggest that a large part of what Jeanes Supervisors did was raise money for Rosenwald Schools. This raises concerns that estimated impacts from exposure to Jeanes Supervisors reflect both the direct impact of

11 Jeanes Supervisors on black-white schooling gaps, and the impact Jeanes Supervisors had on increasing the likelihood of raising suﬃcient funds for a Rosenwald School. I address the exact nature of this relationship in Section 5.

[Table 3 about here.]

4 Main estimation To estimate effects of each program, I define “treatment” in terms of exposure to either Jeanes Supervisors or Rosenwald Schools. This exposure measure is defined differently for each program and for each outcome, enrollment and literacy. This is necessary for several reasons. First, I assume enrollment is only affected by the level of treatment in the current Census year. In the case of Rosenwald Schools, this means having a school in one’s county last year has no effect on enrollment this year, once I take into account the fact that the presence of a Rosenwald school in year t-1 naturally implies presence in year t (i.e. schools are fixed objects). Yet, this same reasoning cannot not apply to exposure to Jeanes Supervisors. Since, unlike schools, Supervisors can come and go in a county, the presence of a Supervisor in year t-1 does not necessarily imply that the same Supervisor was there in year t. Thus, when defining exposure to Jeanes, I take into account lagged treatment effects. For example, a Supervisor may have been in a county in year t-2, but not in year t-1 or in year t. This does not mean that there is no impact of the Jeanes program in year t on enrollment, it simply implies that impacts will be the result of residual effects from Jeanes presence two years prior. In empirical exercises, this requires taking into account that the impacts in year t from the presence of a Supervisor in some year t-τ will depreciate over time. Estimating impacts on literacy requires a slightly different parameterization, though the same principles apply. Literacy, which is measured post-schooling, is the result of cumulative exposure to each program. Therefore, to measure exposure over the lifetime, I quantify exposure at each age and average over the schooling years. For Rosenwald, this means measuring Rosenwald coverage at each age and averaging over ages 7-14 (as in Aaronson and Mazumder). For Jeanes, this means taking exposure at each age, including depreciated exposure as described above, and averaging over the schooling years. Details of these measures are described below. The empirical strategy relies on variation in the placement of Supervisors and Schools across counties over time. To identify program effects, I exploit variation in exposure to each “treatment” as a function of what year Census respondents were born and which county they lived in. A main concern is that the placement of Supervisors or Schools is not random. To address this, I difference effects off of the white population, who were not beneficiaries of these programs, allowing me to include county fixed effects in regression models. The result is a difference-in- differences model where I estimate effects on the black-white gap in enrollment and literacy across children who were exposed to more or fewer years of Jeanes and Rosenwald.

12 At its most basic, I estimate versions of the following:

yibct = α + β1Blacki + γ1f(Rosebct) + γ2f(Jeanesbct) (1)

+ δ1Blacki ∗f(Rosebct) + δ2Blacki ∗f(Jeanesbct) + XiΩ + ϕt + θc + ψbst + εibct

Where Rose is a measure of exposure to Rosenwald Schools and Jeanes is a measure of exposure to Jeanes Supervisors. Xi is a vector of individual level controls, including: female, a quadratic in age, farm status, home ownership, dummies for metropolitan status, dummies for mother’s and father’s literacy and a quadratic in mother’s age at first birth, which are not reported for parsimony. ϕt are indicators for Census year, θc is a set of county fixed effects, and ψbst includes a full set of interactions between age (b), state (s) and year (t). Taken together, these control for state by birth-cohort trends in enrollment, and difference out unobserved time-invariant county level effects. εibct is an idiosyncratic error term, and standard errors are clustered on counties in all specifications. As described below, I test several definitions of both Rose and Jeanes to avoid arbitrary assumptions about the relationship between exposure to the treatment and outcomes.

4.1 Estimating impacts on enrollment I begin by estimating impacts of Jeanes and Rosenwald presence on the black-white enrollment gap between 1900-1930. Aaronson and Mazumder have the benefit of exploiting the rural nature of the Rosenwald program, as it specifically targeted rural schools. Thus they estimate a triple difference, including interactions between Black∗Rural∗Rosenwald, allowing them to include county-by-year fixed effects. Since Jeanes Supervisors ostensibly were responsible for entire counties, and since their work included raising funds, assuming that non-rural areas were untreated is inappropriate here; thus, I restrict my analysis to a difference-in-differences estimate using the black-white gap. I use data provided by Aaronson and Mazumder to measure Rosenwald exposure, which they define as the share of rural black students ages 7-17 in a given county, c, that could have been housed by all existing Rosenwald Schools in each Census year, t. Since I am not differencing off of the rural population, I define exposure simply as the share of all black students that could have been accommodated by existing Rosenwald Schools.

# Rosenwald classroomsct ∗ 45 Rosect = (2) # black studentsct Moving from 0 to 1 on this measure then shows the effect of going from a county with no Rosenwald Schools to one with enough Rosenwald Schools to house all black students. Dividing by all black students, rather than only rural black students, increases the estimated impact of Rosenwald Schools since more students are assigned treatment, fewer of whom likely received it. Yet, Aaronson and Mazumder do find that non-rural black students also benefited from exposure to Rosenwald Schools, and that the non-rural black-white gap declined with exposure as well. Given that schools could not restrict non-rural students from attending, that most students lived

13 in rural areas, and that Jeanes affected the entire county, this specification seems appropriate. Sensitivity checks show that reestimating impacts using the number of rural black students have no effect on the relative effects of Jeanes and Rosenwald on either enrollment or literacy. Because Supervisors can come and go in a county over time, simply measuring the impact of the existence of a Supervisor in a given census year will not accurately capture the degree to which that Supervisor affected enrollment. For example, if a Supervisor was present in a county in 1919, and during that year she conducted teacher training and secured resources for local black schools, even if she subsequently left before the 1920 school year, her presence may have had lasting impacts. To capture this relationship I begin by creating two measures of exposure to Jeanes. The first, is an indicator equal to 1 if there is a Jeanes Supervisor in county c in census year t, 1[Jeanes Census]ct in Equation 3. I then create a continuous measure of the cumulative number of school years (equal to total number of months/9) a Supervisor has been present in the county up until census year t, termed Jeanes Y earsct below. Taken together these measures account for contemporaneous and lagged impacts of Jeanes Supervisors on student enrollment. In the full specification, which also includes measures of exposure to Rosenwald Schools and a full set of interactions between both exposure measures and an indicator for black, treatment impacts are interpreted as effects of exposure to Rosenwald and/or Jeanes on the black-white gap in schooling with county and state-cohort fixed effects differenced out. The full specification in terms is as follows:

Enrolledibct = α + βBlacki + γ11[Jeanes Census]ct + δ1Black∗1[Jeanes Census]ct

+ γ2Jeanes Y earsct + δ2Blacki ∗Jeanes Y earsct (3)

+ γ3Rosebct + δ3Blacki ∗Rosebct + XiΩ + ϕt + θc + ψbst + εibct.

Results from Equation 3, estimated using a linear probability model, are shown in Table 4 below; the sample is composed of census respondents ages 7-14 in census years 1900-1930. For reference, column 1 shows that the black-white gap in enrollment during this time, conditional only on county, age and cohort-by-state effects, was 13.8 percentage points. Adding controls for individual characteristics in column 2 reduces this gap by more than half, to 7.1 percentage points. The coefficient on Black∗Rose in column 3 indicates that without accounting for the presence of Jeanes Supervisors, having enough Rosenwald Schools to house all black students in a county would have increased enrollment for blacks relative to whites by 11.8 percentage points, similar to diff-in-diff estimates found in Aaronson & Mazumder (2011). Adding only a binary indicator for the presence of a Jeanes Supervisor in the census year in column 4, 1[Jeanes Census], suggests that the presence of a Supervisor decreased the black-white enrollment gap by 2.2 percentage points. Moreover, simply accounting for Jeanes presence in each Census year lowers the estimated impact of Rosenwald by 1.7 percentage points, or about 14 percent. In column 5 I drop the binary indicator for Jeanes in the Census year and add a measure

14 of the cumulative school years of Jeanes exposure up until, but not including, Census year t. This specification is meant to set up the full model in column 6. If it is the case that the Jeanes Supervisors only affect enrollment in the contemporaneous year, then accounting for treatment in years prior to Census year t will have no impact on the coefficient on Black∗1[Jeanes Census] as we move from column 4 to column 6. If, on the other hand, it is the case that Jeanes presence in prior years has staying power, then we should expect the coefficient on Black∗1[Jeanes Census] to move toward 0 moving from columns 4 to 6, and the coefficient on Black∗Jeanes Y ears to be relatively unchanged as we move from column 5 to column 6. The realization of this exercise suggests that the impact of Jeanes Supervisors in prior years has staying power and retains a measurable effect on enrollment even when Jeanes presence in the current year is accounted for. Moreover, I find that once this residual treatment is accounted for, the impact of a Supervisor in the current year is positive, but not statistically distinguishable from zero. That is, accounting for Jeanes presence in years prior to Census year t reduces the estimated impact of having a Supervisor in year t from 2.2 percentage points to 0.7 percentage points and is no longer statistically significant. On the other hand, accounting for presence in the current year has no impact on the estimated effect of each additional year of presence prior to year t. This exercise suggests that accounting for lagged treatment is important in accurately estimating the effects of the Jeanes program on both enrollment and likely literacy as well. Importantly, this set of regressions also shows the degree to which impacts of Rosenwald Schools on enrollment are overestimated by not accounting for the Jeanes program. Differences in point estimates across specifications show that accounting for Jeanes reduces the estimate effect of full exposure to Rosenwald from 11.8 percentage points to 7.7, or by roughly one-third. These estimates suggest that the impact of Jeanes Supervisors is increasing in cumulative years of presence, on the order of about a 1 percentage point decrease in the black-white gap for every 3 to 4 years a supervisor was in the county. This interpretation makes sense if we think that there are lasting impacts of the work that Supervisors undertook. Yet, this basic specification treats each year a Supervisor was in the county equally, regardless of how long in the past the treatment occurred. That is, as measured in 1920 for example, this simple linear specification applies the same level of exposure to a Census respondent living in a county where a Supervisor was present only in 1919 the same as a respondent in a county where a Supervisor was present only in 1909. This would only be correct in the case where there is no depreciation of the services that Jeanes Supervisors undertook – an unlikely scenario. Thus, a more detailed specification should fully take into account three elements: first, that the work of Jeanes Supervisors, such as training, materials or support, likely has lasting effects even after a Supervisor is no longer present; second, that impacts increase with each additional year a Jeanes Supervisor was in a county; and third, that these impacts may depreciate (or accumulate) over time. Thus, estimating impacts from exposure to Jeanes Supervisors requires taking into account both the rate of depreciation and cumulative years treatment. Whether and to what rate these

15 effects accrue and/or depreciate is unknown. For example, teacher training might have lingering effects for several years, or might even proliferate from teacher to teacher, but might also be mooted by high teacher turnover. If Supervisors only visited each county once, one could then estimate the rate of depreciation using variation in the timing of the Supervisor’s presence relative to the census year in question. But, since Supervisors were present over a number of years, their impact in any one year might be a product of their presence in that year plus the cumulative impact of all of the years a Supervisor was there before. One could also try to estimate the impact of lagged exposure by estimating a regression with a series of lagged inputs on the right hand side. But, since the likelihood of having a Supervisor in any one year is highly correlated with the likelihood of having on in the prior or subsequent year, estimates are not well identified in empirical exercises. In fact, only 6 percent of districts with no Jeanes presence in year t have a Supervisor in year t + 1, and only 13 percent of districts with a Supervisor in year t do not have one the following year. In lieu of a series of lags, I create two composite measures of exposure to Jeanes Supervisors in a manner more refined than those in Equation 3. In this case, rather than including a simple L indicator for Jeanes presence in the current Census year, I define Jeanes Lagc,t in Equation 4 below as the total number of school years a Supervisor was present in county c in census year t over the past L years. For example, a lag of L=1 would count the number of years in the current Census year plus one lag, and a lag of L=0 would just include the number of years in the current Census year. Then, I create an analogous measure of cumulative exposure in the L years between 1909 and year t−L, termed Jeanes P re Lagc,t. This is equal to the number of years a Supervisor was present in the years between the beginning of the program (1909) L up until the last year of the lag included in Jeanes Lagc,t. The purpose of this exercise is to empirically determine how many lags are relevant without assuming, a priori, any particular rate of depreciation. That is, δ1 in regression Equation 5 below tells us the decrease in the black-white enrollment gap for each year a Jeanes Supervisor was in the county over the past L years, conditional on the number of years a Supervisor was present in the county between 1909 and the beginning of the lag. In a series of regression equations, I will sequentially increase the L number of lags included in Jeanes Lagc,t, which mechanically decreases the number of years L L included in Jeanes P re Lagc,t, until the coefficient on Jeanes P re Lagc,t is no longer relevant. L L Jeanes Lagc,t and Jeanes P re Lagc,t are defined as follows:

L t−1909 X monthst−l X monthst−l Jeanes LagL = ; Jeanes P re LagL = (4) c,t 9 c,t 9 l=0 l=L+1

16 over lag lengths L = 1 ... 6. These measures are then included in the full speciﬁcation below:

Enrolledibct = α + βBlacki (5) L L + γ1Jeanes Lagct + δ1Black∗Jeanes Lagct L L + γ2Jeanes P re Lagct + δ2Blacki ∗Jeanes P re Lagct

+ γ3Rosect + δ3Blacki ∗Rosect

+ XiΩ + ϕt + θc + ψbst + εibct iterated over lag lengths L = 1 ... 6

Estimates from Equation 5 are presented in Table 5 below. Moving across rows shows estimates from separate regressions of different lag and pre-lag lengths; means of the two Jeanes exposure measures for black children are shown at the bottom of each panel for reference. For example L=1 column 1 shows results for a lag length of 1, meaning that Jeanes Lagc,t includes the number of school years a Supervisor was in a county in the current Census year plus a lag of 1 (i.e. L=1 1930 and 1929 for Census year 1930), and Jeanes P re Lagc,t then includes the total number of school years a Supervisor was present beginning in 1909 up until the current Census year plus a lag of 1 (i.e. all years between 1909 through 1928 in Census year 1930). The coefficient on Black∗Jeanes P re Lag, 0.002, suggests a 0.2 percentage point decrease in the black-white enrollment gap for every year a Supervisor was present between 1909 and two years before the current Census. The coefficient on Black ∗ Jeanes Lag in column 1 is not statistically distinguishable from zero, suggesting that once a long history of Jeanes presence is accounted for, contemporaneous effects are difficult to distinguish. The purpose of Table 5 is to determine at what point prior presence of Jeanes Supervisors no longer impacts contemporaneous enrollment. Empirically, this is when Black∗Jeanes P re Lag no longer predicts enrollment, conditional on Black∗Jeanes Lag. Economically, this is important for calculating back of the envelope gains per dollar spent. Moving across columns and comparing coefficients suggests that once 6 lags are included, meaning the current year plus 6 prior lags, the impact of Jeanes presence in prior years does not predict enrollment in the current Census year. Taking column 6 as the preferred specification suggests that each school year a Jeanes Supervisor was present in a county closed the black-white enrollment gap by 0.5 percentage points. If we define “full exposure” as having a Supervisor for an average of 9 months over each of the past 7 years (the current Census year plus 6 lags), we can then say that full exposure would have decreased the enrollment gap by 3.5 percentage points. In comparative terms, this suggests that full exposure to Jeanes, or 7 consecutive years of Jeanes presence, would have closed the enrollment gap by roughly one-half as much as enough Rosenwald Schools to house all black rural students would have done. In other words, it does not appear that full exposure to Jeanes could have achieved the same increase in enrollment as new or improved schools would have.

17 While this tells us what the effect of full exposure would have been, multiplying the treatment effect by the average amount of treatment black Census respondents experienced will give us the impact of the program on the actual enrollment gap. Using the means provided at the bottom of the table, on average black respondents lived in counties where Jeanes Supervisors were present for 2.15 of the past 7 school years. Multiplying this by the treatment effect suggests that on average the program closed the black-white enrollment gap by approximately 1 percentage point, or approximately 6.7 percent of the unconditional (15 percentage point) black-white enrollment gap in 1910.

[Table 5 about here.]

The specification above does not apply a rate of depreciation but rather infers a rate by empirically estimating how long treatment lingers. Another way to do this would be to assume a rate of depreciation and apply this to the treatment measure. While we might have prior beliefs about the rate of depreciation of physical capital, for example, there is little benchmark for the rate of depreciation of teacher support. In fact one could argue in this case for an accumulative effect; for example, if a Supervisor’s tasks include teaching how to design a curriculum, the regular teachers in the county might become more proficient in this skill every year, even in the Supervisor’s subsequence absence. While in my preferred specification above I am agnostic on the rate of depreciation, as a specification check I re-estimate these models assuming various different rates of depreciation. In this case, I re-define Jeanes Lag and Jeanes P re Lag as follows:

L X monthst−l Jeanes LagL,ρ = ∗(1 − ρ)l (6) c,t 9 l=0 t−1909 X monthst−l Jeanes P re LagL,ρ = ∗(1 − ρ)l c,t 9 l=L+1 For L = 1 ... 6 and ρ = 0.1, 0.3, 0.5

L,ρ Thus, when ρ = 0, Jeanes Lagc,t would be the same as in the prior speciﬁcation. When 0<ρ<1, L,ρ Jeanes Lagc,t measures the cumulative depreciated number of months a Supervisor was present, assuming that treatment depreciates at rate equal to ρ. Assuming ρ = 1 implies that there is no residual impact of Jeanes presence and that only the contemporaneous year matters, as was the case in Table 4 when only an indicator for presence in the current Census year was included. I then re-estimate the Equation 5 including these new terms. Results are shown in Table 6 below. Since estimates for main eﬀects and the impact of Rosenwald are substantively unchanged by this re-parameterization, I omit them for brevity.7

7I do not apply depreciation to Rosenwald Schools for two reasons, ﬁrst to be consistent with previous work as depreciation rates are not applied in Aaronson and Mazumder, and second because construction of Rosenwald Schools were predicated on counties maintaining the schools over time.

18 Using the same method of identifying the appropriate lag length applied above to Table 6 suggests that at a depreciation rate of 10%, 5 lags are relevant; at a depreciation rate of 30%, 5 lags are relevant; and at a depreciation rate of 50%, 6 lags are relevant. These indicate treatment eﬀects from ranging between 1 percentage points at a 10% depreciation rate and 0.9 percentage points at a 50% rate of depreciation, suggesting that estimates not explicitly applying a presupposed rate of depreciation in Table 5 are accurate.

[Table 6 about here.]

4.2 Estimating impacts on literacy Estimating and comparing impacts of Jeanes Supervisors and Rosenwald Schools on literacy is more straightforward than for enrollment as literacy is a cumulative process with treatment applied retrospectively. Moreover, while enrollment is not an end in itself, literacy is a better metric for educational gains. To estimate impacts I follow speciﬁcations by Aaronson and Mazumder and deﬁne Rosenwald exposure as the average exposure to Rosenwald Schools a student experienced over ages 7-14.8 That is, for each respondent age 15-22 in Census year t in county c,I calculate exposure at each age a and average over ages 7-14 as described in Equation 7 below. Moving from 0 to 1 on this measure is equivalent to going from no Rosenwald Schools to having enough Rosenwald classrooms to accommodate all black children in a respondent’s county for every year between ages 7 and age 14.

14 1 X # Rosenwald classrooms ∗45 Rose = ca (7) 7−14 8 # black students a=7 ca

Similarly, I begin with a measure of Jeanes exposure that approximates the Rosenwald exposure variable. This measures the total number of school years (months/9) a Supervisor was present in the respondent’s county between ages 7-14. Dividing this by 8, for ages 7-14, measures the share of schooling years a supervisor was present. Thus, moving from 0 to 1 on this measure is equivalent to having a Jeanes Supervisor for an average of 9 months for all schooling years.9

14 1 X Monthsca Jeanes = (8) 7−14 8 9 a=7

8Aaronson and Mazumder use 7-13 because they don’t know which schools built after 1926 were high schools. Their results are not changed by using 7-17. I add age 14 to be consistent with the ages over which outcomes are measured. Results are not substantively diﬀerent here either. 9For fewer than 1 percent of the sample, Jeanes exposure is greater than 1. This results from the case where on average a Supervisor was in a county for more than 9 months per year. In some cases, Supervisors did work during summer months as well, including their own inservice programs at Historically Black Colleges.

19 I then estimate the following equation:

Yibct = α + Blackiβ1 + γ1Jeanesbct + γ2Rosebct (9)

+ δ1Blacki ∗Jeanesbct + δ2Blacki ∗Rosebct + XiΩ + ϕt + θc + ψbst + εibct.

Notice that in Equation 9 above both treatment measures are indexed by county c, Census year, t, and birth cohort, b, unlike the enrollment measures which were only indexed by c and t. Unlike the case for enrollment where treatment was measured in the current Census year, treatment for literacy is measured retrospectively in terms of cumulative exposure. Thus children in the same county born in different years may have experienced different levels of exposure, allowing treatment effects to be estimated off of both across and within county differences. Results from Equation 9 are shown in Table 7. I begin by showing the black-white gap in literacy for 15-22 year olds in the 1910-1930 Censuses in column 1,10 which includes only age-by-year-by-state and county fixed effects (ϕt + θc + ψbst), and in column 2, which adds controls for individual demographics (Xi). The inclusion of these individual-level covariates reduces the black-white literacy gap from 14.2 to 9.3 percentage points. In column 3 I include Rosenwald exposure and an interaction with black. Estimates in column 3 suggest that, without accounting for Jeanes, full exposure to Rosenwald would have closed the black-white literacy gap by about 18.7 percentage points. Repeating this exercise for Jeanes in column 4 suggests that, without accounting for Rosenwald Schools in the regression model, having a Jeanes Supervisor for an average of 9 months per year from ages 7-14 would have closed the black-white literacy gap by 4.9 percentage points. Including both measures in the full model in column 5 suggests that full exposure to Rosenwald would have closed the black-white gap by 13.6 percentage points, and full exposure to Jeanes would have closed the gap by 3.5 percentage points. This final specification highlights two key results. First, that full exposure to Jeanes Su- pervisors would have decreased the black-white literacy gap by 3.5 percentage points even after accounting for the presence of Rosenwald Schools, under the identifying assumption that conditional on county fixed effects and age-by-year-by-state trends, the placement of Jeanes Supervisors and Rosenwald Schools are independent of the black-white gap. Second, that the initial estimated treatment effect of exposure to Rosenwald Schools, 18.7 percentage points in the difference-in-difference specification shown here, is reduced by roughly one-third to 13.6 percentage points by accounting for exposure to Jeanes.

[Table 7 about here.]

While this simple parameterization of Jeanes exposure is useful in estimating impacts of contemporaneous exposure to Jeanes Supervisors, i.e. while students are school-age, and for

10Since literacy is measures beginning at age 15, no respondents in the 1910 Census were exposed to either treatment. Thus I only use one pre-treatment Census wave, 1910, and omit 1900. Results are similar with their inclusion.

20 assessing how accounting for Jeanes affects estimates of the impact of the Rosenwald program, it fails to account for residual effects of Jeanes Supervisors in the years prior to ages 7-14 of the type described for enrollment. In other words, if a Census respondent had a Supervisor in her county at ages 5 and 6 but never again, the measure Jeanes included in Equation 9 above would be 0. Yet, models specifying residual impacts on enrollment suggest that lags up to 6 years are relevant. Similarly, this parameterization fails to account for the fact that if hysteresis exists, exposure only at age 7, e.g., should have larger effects than exposure only at age 14, all else equal. Accordingly, I respecify the model above to account for residual effects in a manner similar to that for enrollment, defined as follows:

14 L 1 1 X X Monthsc,(a−l) JeanesL = (10) L+1 8 9 a=7 l=0 for lag lengeths L = 1. . . 6

To create a full exposure measure, I first divide by what full exposure would be at each age (L+1), and then divide by 8 to average over schooling ages as above. Full exposure should now be interpreted as having a Supervisor on average for 9 months at each age and for an average of 9 months in each of the L years prior. Thus, moving from 0 to 1 when L=1 is equivalent to having no Jeans Supervisors to having one for an average of 9 months at each age 6-14. For L=2, an average of 9 months at each age 5-14, and for L=6, an average of 9 months at each age 1-14. The purpose of this exercise is to test how sensitive estimates are to different assumptions about how long effects of Supervisors linger. Using this definition of Jeanes exposure I reestimate Equation 9 above iterating over lag lengths L = 1 ... 6 for person i in birth cohort b in county c in census year t:

L Yibct = α + Blackiβ1 + γ1Jeanesbct + γ2Rosebct (11) L + δ1Blacki ∗Jeanesbct + δ2Blacki ∗Rosebct + XiΩ + ϕt + θc + ψbst + εibct over lags L = 1 ... 6

Comparing differences in δ1 across different lag lengths shows how effects vary with the inclusion of each additional lag in assigning exposure. Results from Equation 11 are shown in Table 8 below. Each column replicates the full model with different numbers of lags included in JeanesL. For example, the coefficient on Black∗JeanesL in column 1 indicates that having a Supervisor for an average of 9 months over ages 6-14 would have closed the black-white literacy gap by 3.6 percentage points, nearly identical to the 3.5 percentage point decrease in Table 7. Moving across rows shows that in fact estimates without including lags were biased upwards. That is, comparing column 5 of Table 7 with column 6 of Table 8 shows that when exposure is measured only over ages 7-14, having a Supervisor for all 8 years would have closed the literacy gap by 3.5 percentage points, or 0.44 percentage points per year. Yet, when measuring exposure over ages

21 1-14, having a Supervisor over all 14 years would have closed the gap by 4.3 percentage points, or 0.3 percentage points per year. While there is no way to determine, statistically, which of these parameterizations is “correct”, we can use the range to determine upper and lower bounds on the eﬀect (per-dollar) of Jeanes Supervisors. I take the conservative estimate in column 6 of Table 8 as my preferred speciﬁcation.

[Table 8 about here.]

4.3 Specification checks To ensure that these results are not sensitive to decision rules or sample definitions, I estimate a series of robustness checks, shown in Table 9. The first column replicates column 6 of Table 8, indicating a 3.6 percentage point decrease in the black-white literacy gap from full exposure with full treatment including 6 lags. In column 2 I then reestimate the same specification where Rose, defined in Equation 7 as the share of all black children in the county who could be accommodated by Rosenwald Schools, is now share of all rural black children who could be accommodated as in Aaronson and Mazumder (2011). While this decreases the estimated impact of both Rosenwald and Jeanes on literacy by 1.8 and 0.7 percentage points, or 14 and 16 percent, respectively, the relative contribution of Jeanes compared to Rosenwald is essentially unchanged. In column 3 I drop states for which results Jeanes presence was imputed in years 1928-1930, the result of which increases the estimated impact of Jeanes treatment to a 5.7 percentage point decrease in the black-white literacy gap. In column 4 I drop respondents who were school-age in 1917 or 1918 and who lived in counties where Rosenwald Schools and Jeanes Supervisors were present by 1919. These children lived in counties where the timing of Rosenwald School construction was unknown (though most likely in 1919), meaning I might have applied treatment only to Jeanes when in fact they were also treated by Rosenwald as well. The result of omitting these respondents again slightly increases the estimated impact of Jeanes, suggesting that this was not the case. In column 5 I restrict the sample only to rural respondents as the Rosenwald focused on rural school construction, while the Jeanes program was not, though it favored rural counties. The result of omitting non-rural respondents is an increase in the estimated impact of Rosenwald, as expected, but no change in the estimated impact of Jeanes. In column 6 I limit the regression sample to Census years 1910 and 1920 and keep only counties where Rosenwald was not present by 1920, meaning I am estimating impacts of Jeanes such that the impact of Rosenwald could not have played a role as no children were yet exposed. In addition, I add a indicator to the regression equal to 1 if respondents are black and live in a county where a Rosenwald School would be present by 1930 (the main effect is differenced out). The result is a doubling of the estimated impact of full exposure to Jeanes, from 4.3 to 7.2 percentage points. Yet, the estimate is not statistically different from zero likely due to the decrease in sample size, from nearly 400,000 to just under 81,000. Still, that the coefficient moves away from zero provides some evidence that effects from Jeanes could not have been entirely

22 explained by the presence of Rosenwald. Finally, in columns 7 and 8 I reestimate the preferred specification in column 1 separately for men and women finding no difference in the effect of either Jeanes or Rosenwald by gender.

[Table 9 about here.]

As a ﬁnal speciﬁcation check, I apply a rate of depreciation to the exposure measure in a manner similar to enrollment:

14 L−1 1 1 X X Months(a−l) JeanesL = ∗ (1 − ρ)l (12) ρ PL l 8 9 l=0(1 − ρ) a=7 l=0

L L The differences between Jeanesρ and Jeanes above are that I now apply a rate of depreciation 1 to Jeanes treatment and, rather than dividing by full exposure, (L+1) , I divide by full exposure 1 under each rate of depreciation, PL l . The econometric specification is then the same as l=0(1−ρ) Equation 11 above, iterated over various values of L and ρ. Results are shown in Table 10 below with three panels corresponding to depreciation rates ρ = 0.1, 0.3, 0.5 and six columns showing lags 1...6. The purpose is to avoid arbitrarily assuming a single rate of depreciation, or that at a given rate of depreciation impacts can be detected a certain number of years prior. I choose relatively low rates of depreciation to estimate lower bounds on treatment impacts. Included in the bottom of each panel is the mean of Jeanes exposure, indicating the average depreciated exposure black black Southern children experienced at each age. The array of parameter estimates in Table 10 indicate that including lags beyond 5 has little effect on estimated impacts, implying that at any rate of depreciation no more than roughly 5 lags matters. Moreover, as the imposed rate of depreciation increases, the importance of each additional lag included is smaller, as expected. As a whole, the panel suggests treatment effects ranging from a 3.6 to a 4.2 percentage point decrease in the literacy gap from full exposure. The key difference between these estimates is that when more lags are included, the cost of full exposure increases, as it takes more years to achieve the full impact. Thus, while estimates in Tables 7- 10 are consistent in suggesting that the upper bound of full exposure to Jeanes is roughly a 4.3 percentage point decrease in the black-white literacy gap, and the lower bound is a 3.5 percentage point decrease, the depreciation rate and number of included lags will affect the cost estimate of full exposure described in Section 6.

[Table 10 about here.]

5 The relationship between Jeanes and Rosenwald placement Since the work of Jeanes Supervisors included raising funds for Rosenwald Schools, it is fair to assume that part of the impact of Jeanes presence on enrollment and literacy was indirect, resulting from the increased likelihood of exposure to Rosenwald. To explore this, I estimate

23 the impact of Jeanes on the likelihood of receiving a Rosenwald School. To do so, I create a county-by-year panel of Jeanes and Rosenwald presence, and a set of county-by-race level measures of enrollment, literacy and demographics in 1900 and 1910. I add to this measures of student-teacher ratios and student-school ratios by race from 1910 using data from Carruthers and Wanamaker (2013). I use only measures from 1910 or earlier assuming that there was little if any impact of Jeanes by 1910 and to avoid endogenously modeling the relationship between Jeanes and public expenditures on schooling thereafter. That is, these measures are meant to account for pre-program county-by-race level characteristics. Student-teacher and student-school ratios were only available for AL, GA, NC, SC and TN, thus I limit this analysis to these states. To estimate the impact of Jeanes Supervisors on Rosenwald placement, I assume that each county has some probability of receiving a Rosenwald School beginning in 1919, and that this probability is a function of baseline county-level characteristics, cumulative Jeanes presence, and time. Figure 6, a chart of the relative timing of Jeanes and Rosenwald, shows that of counties ever receiving either Jeanes or Rosenwald, 38% received Jeanes first, 14% received Rosenwald first, and that while 32% received Jeanes but no Rosenwald, only 5% received only a Jeanes Supervisor. Assuming Rosenwald Schools built between 1916-19 were build in 1919 would increase the share of counties that had Jeanes first.

[Figure 6 about here.]

To model the relationship I estimate a hazard function where I define each year as a spell and characterize failure as the first year in which a county receives a Rosenwald School. I begin the panel in 1918, assuming that the first Rosenwald Schools were built in 1919 or later. In Equation 13 below, the hazard is a function of time, t, a dummy indicating whether a jeanes supervisor was present by 1918, county demographic characteristics for black and white students in 1910, county level changes in these characteristics between 1900 and 1910, and student-teacher and school-teacher ratios in 1910. Lastly, I include a time-varying measure of the cumulative years of Jeanes presence in the county, defined by the total number of months/9 up to and including year t. In terms, the model is:

λ(t, Jeanes(t),X) = λ0(t) exp(γ1Jeanes1918 + γ2Jeanes(t) + β1X1910 + β2X∆1910−1900) (13)

Figure 7 shows the baseline survival probabilities for counties with and without a Jeanes Su- pervisor by 1918, where analysis time=0 in 1918. The intercept shift indicates that over half of counties with a Supervisor by 1918 received a Rosenwald School in the ﬁrst years of construction while fewer than 20% of those without Jeanes received a school. Figure 7 also suggests that the hazard rate was steeper for these counties with a Supervisor by by 1918 in the ﬁrst 5 years of post-war Rosenwald construction. Table 11 shows results from Equation 13, which conditions on covariate measures from 1900 and 1910, and includes a time varying measure of cumulative Jeanes presence up to year t.

24 Results suggest that each additional school year of Jeanes presence increased the likelihood of receiving a Rosenwald School by 5.6 percentage points. While not necessarily causal, this result suggests that Jeanes impacts estimated above are not pure Jeanes eﬀects, but also partially explained by Jeanes work to raise money for Rosenwald Schools.

[Figure 7, and Table 11 about here.]

6 Marginal dollar impacts Estimating marginal impacts will have to take into account the scalability of each program. While schools have fixed capacity constraints, Supervisors were authorized to maximize their impact on students through the most effective means at their disposal - hence “the next needed thing.” Still, there likely exists a tradeoff between how many schools or towns a Supervisor visited in her county each school year and her impact. Yet, community organizing, in the form of fundraising for school resources, lobbying for improved conditions, canning drives and lessons in health and sanitation, or teacher training, might exhibit larger economies of scale than working directly with pupils. To address the relationship between county size and Supervisors’ effectiveness, I reestimate my preferred specification for literacy by black county-level population.11 Results shown in Table 12 suggest that Supervisors were far more effective in smaller counties. Column 1 of Table 12 shows results from column 6 of Table 8 for respondents in the smallest 2 quintiles of black population, with roughly 210 black school-age children per county. Results suggest that full exposure, including 6 lags, or 14 years in total, would have closed the black-white gap by 7.3 percentage points, equal to the effect of full exposure to Rosenwald for counties that size and larger than the roughly 4 percentage point effect estimated on the full sample. Yet, for districts in the 3rd and 4th quintles of black population, with 1,277 black children on average, the impact of full exposure to Jeanes shrinks considerably, to a 2.4 percentage point decrease in the black-white gap, while the impact of full exposure to Rosenwald increases to 11.5 percentage points. This trend persists for the largest 20 percent of counties by black population. Column 3 shows that for the largest counties, with 4,183 black children on average, there is small, positive but not statistically significant impact of Jeanes, and a robust 10 percentage point impact of Rosenwald. Taken together these results suggest that the impact of Jeanes was clearly decreasing in county size, but not so for Rosenwald.

[Table 12 about here.]

I use actual cost estimates from Rosenwald School construction records and salary records from Jeanes Supervisors to back out comparative per-dollar impacts. Figure 8 shows total Jeanes expenditures between 1909 and 1930 and the share that was paid by the Fund and the share paid

11I use the 1930 census to determine county size since it is a 5% sample whereas the other waves are 1 or 1.2 percent samples and thereby far less accurate for small counties. Aaronson and Mazumder (2011) use a web scrape of Ancestry.com to get more precise counts, but their data only contains this for Rosenwald counties.

25 through public funds. After 1920, the share and amount of public funds increased dramatically, coinciding with the implementation of Rosenwald Schools. From 1909-1920 the Jeanes fund spent approximately $80,000 per year, after 1920 the Fund spent over $200,000 per year, reaching nearly $300,000 in 1930 (all in real 1925 dollars). Figure 9 shows average monthly salaries for Supervisors in selected states. Average monthly salaries remained around $80 per month until WWI when they dipped to around $60 and then recovered and increased steadily through 1930, averaging about $95 per month during the years both Jeanes and Rosenwald were active, or about $855 per school year of treatment. Monthly salary does not appear to vary systematically across states.

[Figures 8 and 9 about here.]

Rosenwald Schools varied in cost depending on several factors, including the number of schools built in the same county in the same year and school size, measured by number of classrooms. Figure 10 shows school construction costs (in real 1925 dollars) by the number of classrooms (or teachers) and by the number of schools built in that county-year.12 The data suggest that there is no savings to building larger schools with more teachers. Schools with one classroom per building cost as much per classroom as schools with 5, each at about $1,600 per room. Similarly, cost is declining only marginally with each additional school built in that county-year. When only one school is built at a time, which was the modal case, for 32% of schools, the cost was nearly $6,000 per school. When 2 schools were built in a year, the case for 24% of schools, the cost per building fell to around $5,000 per school. For 3 schools in a year, 15% of cases, the cost is only $4,500 per school. These patterns hold in conditional means as well. Regressing cost per school on the number of schools built in that county-year, the number of classrooms, and state and year ﬁxed eﬀects shows that the cost of each additional classroom is $1,700 and the cost declines only slightly with each additional school built.

[Figure 10 about here.]

I combine these ﬁgures with impact estimates from Table 12 by county size to compare marginal dollar impacts of Jeanes and Rosenwald to compare returns per dollar spent on human resources and physical infrastructure respectively. Column 1 of Table 12 suggests that for districts with an average of 210 black school-age children the eﬀect of 14 consecutive years of Jeanes is roughly equivalent to building enough Rosenwald Schools to house all 210 students. At a salary of approximately $855 per school year, the total cost of Jeanes would be $11,970 over 14 years. At a discount rate of 3% the present value of this stream of payments is $9,947. To calculate the cost of housing all 210 students, I assume that schools will each have 2 classrooms at 45 students each, as is the modal case. Thus, 2.33 2-classroom buildings are

12I omit 43 schools (1.3%) costing over $40,000. These were most likely county training, or high schools, and which were disproportionately expensive.

26 required on average. The average cost per school of constructing 2 schools with 2 teachers was $3,109 (sd=932) on average for the 381 cases matching this description in the Rosenwald data. Then, 2.33 schools would cost approximately $7,243. Using point estimates from column 1 of Table 12 showing a 7.5 percentage point decrease in the black-white gap from full exposure to Rosenwald suggests that the per-dollar impact for these smaller counties is an approximately 0.1 percentage point decrease in the black-white gap per $100 spent on Rosenwald Schools. Conducting the same calculation for Jeanes using a 7.3 percentage point effect size and total discounted cost of $9,947 suggests a 0.07 percentage point decrease in the black-white gap per $100 spent on Jeanes. Taken together these estimates suggest that, in smaller counties, the impact of Jeanes was 30% smaller than Rosenwald per dollar spent. Conducting the same calculation for larger counties yields different results. Column 2 of Table 12 shows effect sizes for the 3rd and 4th quintiles of county size with an average of 1,277 black children per county. The cost of the 28 classrooms with 45 students each necessary to house all pupils, assuming 4 rooms per school, would be $44,106 in total, or $6,288 per school. The effect of full coverage for Rosenwald for these larger districts was 11.5 percentage points, giving a per-dollar impact of 0.028 percentage points per $100 dollars spent. Using the $9,947 salary cost for a Jeanes Supervisor and an effect of 2.4 percentage points yields a 0.024 percentage point decrease in the black-white gap per $100 spent on Jeanes. A rate of return roughly 85% of that realized by Rosenwald. These results suggest that to some degree Jeanes Supervisors were able to economize on scale in a manner that Rosenwald Schools could not. Column 3 of Table 12 suggests that effects of Jeanes are not distinguishable from zero in the largest counties, but given their size nearly any effect would have had a higher rate of return per dollar than Rosenwald. Because in nearly all cases only one Supervisor was sent to a county, I cannot estimate how returns to multiple Supervisors would have been different, I can only estimate the effect of those who were there longer. Taken together these results results suggest that per-dollar Rosenwald was more effective, with the caveat that we do not know how effective multiple Supervisors in each county would have been. Likely constraints on the supply of trained educators was a limiting factor. It is also important to note that these estimates are based on what the impact of full exposure would have been. In reality, gross impacts of the Jeanes Fund were very small compared with those of the Rosenwald Fund. Taking a conservative estimate of a rate of depreciation of 0.1 and including 6 lags from the literacy estimate in Table 10, where the average black student experienced 3.22 years of depreciated exposure on average (or 23% of full exposure) implies that the Jeanes Fund closed the black-white literacy gap by 1 percentage point in total. If the literacy gap closed by 20 percentage points between 1900 and 1930, one could argue that roughly 5 percent of this decline can be attributed to Jeanes. Moreover, if each year of Jeanes increased the likelihood of Rosenwald by 5%, then 14 years of exposure would have virtually guaranteed a Rosenwald school. In this sense, completely disentangling the impacts of these two programs

27 is difficult if not impossible. Historical evidence makes clear that the two programs were indeed intertwined both financially and organizationally, including cooperation with State Agents for Negro Education and shared board members. Taking these facts into account suggests that recent studies estimating the impact of the Rosenwald Fund on convergence in the fates of black and white students in the first part of the 20th century are missing an important contributing factor in the Jeanes Fund. For an initial investment of one million dollars, it seems like a nice payoff.

7 Conclusion Despite dramatic inequalities in educational expenditures, rural Southern blacks born between 1900 and 1920 made significant educational gains on their white counterparts. Several factors contributed to this, among them Northern philanthropies played a substantive role. I estimate the impact of two large scale interventions aimed at improving the quality of black schooling in the South between 1900 and 1930, one that sent trained teachers to support black schools through teacher training, administrative work and fundraising, while the other focused largely on improving and expanding physical infrastructure. By estimating the impact of the Jeanes Fund, and the combined effects of the Jeanes and Rosenwald Funds, I add to a robust literature chronicling factors that contributed to convergence in enrollment and literacy rates between black and white students in the early 20th century. I find that full exposure to Jeanes Supervisors over a 14 year period would have decreased the black-white literacy gap by between 3.5 and 4.3 percentage points. On average during this time black children had a Supervisor in their county for an average of 3.2 years between birth and age 14, suggesting that in total the Jeanes program closed the black-white literacy gap by approximately 1 percentage point in total. That is, of the 20 percentage point decrease in the gap between 1900 and 1930, the Jeanes Fund may explain up to 5 percent. This impact is attributable to both the direct effect of Jeanes on teacher quality and student learning, and through a secondary impact whereby each year of Jeanes presence increased the likelihood of securing a Rosenwald School by roughly 5%. Back of the envelope cost estimates suggest that Jeanes Supervisors were able to economize on scale. In smaller counties, each dollar spent by Rosenwald closed the black-white gap by 42% more than each dollar spent by Jeanes. Yet, in larger counties this advantage narrowed to 17%. Thus, while these results suggest that at the time Rosenwald was more effective dollar-for-dollar, and certainly more impactful in total as the program was considerably larger, concluding that all funding should have gone to physical infrastructure would be mistaken. In fact, estimates presented here suggest that had the supply of trained educators and program funding allowed for multiple Supervisors per county, investments in skilled labor may well have been more effective. That said, teachers and pupils need schools, and the evidence here suggests that when initial school facilities are inadequate or non-existent, building schools is at least if not more important than additional labor. While these conclusions may not have direct bearing on education in

28 the US today, they certainly speak to developing nations where infrastructure is often low and inequality often runs along ethnic, racial or gender lines. Despite the remarkable efforts of Jeanes Supervisors over several decades, this study is the first to estimate the Fund’s impact on black education in the 100 years since its inception. The results presented here are significant not only in an historical sense. The influence of philanthropic spending on educational resources and education policy is increasing, both at home and abroad. Moreover, such programs often target disadvantaged groups, in particular minorities. These findings demonstrate that such programs can affect educational outcomes, even under the most arduous of circumstances.

29 References Aaronson, D. and Mazumder, B. (2011). The impact of rosenwald schools on black achievement. Journal of Political Economy, 119(5):821 – 888.

Anderson, E. and Moss, A. A. (1999). Dangerous donations: northern philanthropy and southern Black education, 1902 – 1930. University of Missouri Press.

Anderson, J. D. (1978). Northern foundations and the shaping of southern black rural education, 1902–1935. History of Education Quarterly, 18(4):371–396.

Anderson, J. D. (1988). The education of Blacks in the South, 1860–1935. Chapel Hill: University of North Carolina Press.

Bond, H. M. (1966). The Education of the Negro in the American Social Order. New York, NY: Octagon Books.

Card, D. and Krueger, A. (1996). School resources and student outcomes: an overview of the literature and new evidence from north and south carolina. The Journal of Economic Per- spectives, 10(4):31–50.

Carruthers, C. and Wanamaker, M. (2013). Closing the gap? the eﬀect of private philanthropy on the provision of african-american schooling in the u.s. south. Journal of Public Economics, (101):53–67.

Collins, W. J. and Margo, R. A. (2006). Historical perspectives on racial diﬀerences in schooling in the united states. Handbook of the Economics of Education, 1:107–154.

Donohue III, J., Heckman, J., and Todd, P. (2002). The schooling of southern blacks: The roles of legal activism and private philanthropy, 1910-1960. Quarterly Journal of Economics, 117(1):225–268.

DuBois, W. and (Eds.), A. D. (1911). The Common School and the Negro American. New York: Arno Press.

Fishback, P. and Baskin, J. (1991). Narrowing the black–white gap in child literacy in 1910: The roles of school inputs and family inputs. Review of Economics and Statistics, 64:725–728.

Fultz, M. (1995). Teacher training and african american education in the south, 1900-1940. The Journal of Negro Education, 64(2):196–210.

Georgia Association of Jeanes Curriculum Directors and Southern Education Foundation (1975). Jeanes Supervision in Georgia Schools: A Guiding Light in Education: a History of the Pro- gram from 1908-1975. The Georgia Association of Jeanes Curriculum Directors in Cooperation with The Southern Education Foundation.

Jones, L. G. E. (1937). The Jeanes Teacher in the United States, 1908—1933: An Account of Twenty-ﬁve Years’ Experience in the Supervision of Negro Rural Schools. Chapel Hill: University of North Carolina Press.

Liston, H. (1928). A study of the work of the jeanes supervising teachers for negro rural schools. Unpublished Master’s Thesis, University of Chicago: Chicago, IL.

30 Loeb, S. and Bound, J. (1996). The eﬀect of measured school inputs on academic achievement: Evidence from the 1920s, 1930s and 1940s birth cohorts. The Review of Economics and Statistics, 78(4):653–664.

Margo, R. A. (1986). Educational achievement in segregated school systems: The eﬀects of “separate-but-equal”. The American Economic Review, 76(4):794–801.

Margo, R. A. (1990). Race and schooling in the South, 1880-1950: an economic history. Uni- versity of Chicago Press.

Margo, R. A. (1991). Segregated schools and the mobility hypothesis: A model of local govern- ment discrimination. Quarterly Journal of Economics, 106:301–310.

McClure, P. (2009). Jeanes Teachers: a View Into Black Education in the Jim Crow South. BookSurge Publishing.

McCuistion, F. (1931). The South’s Negro Teaching Force. Nashville, TN. Phamplet.

Myrdal, G. (1944). An American Dilemma. Harper and Brothers, NY.

Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. J. Wiley & Sons, New York.

Ruggles, S., Alexander, J. T., Genadek, K., Goeken, R., Schroeder, M. B., and Sobek, M. (2010). Integrated public use microdata series: Version 5.0 [machine-readable database].

Williams, M. et al. (1979). The jeanes story: A chapter in the history of american education, 1908-1968. Southern Education Foundation.

Wright, A. D., Jeanes, A. T., and Redcay, E. E. (1933). The Negro Rural School Fund, Inc. (Anna T. Jeanes Foundation) 1907-1933: A Record of the Establishment of the Fund, a Sketch of Its Donor, the Minutes of the Proceedings of the Board of Trustees from 1908 to 1932, and the Policies Developed Under the Directions of the Board of Trustees. Negro Rural School Fund.

31 Tables

Table 1: Summary statistics.

A: Enrollment sample, ages 7-14. 1900 1910 1920 1930 black white black white black white black white Female 0.49 0.50 0.50 0.49 0.50 0.49 0.50 0.49 Rural 0.87 0.85 0.87 0.82 0.83 0.78 0.77 0.73 Farm 0.61 0.66 0.63 0.61 0.68 0.58 0.60 0.50 MSA-Metro 0.02 0.02 0.03 0.03 0.03 0.04 0.06 0.07 MSA-City 0.04 0.06 0.04 0.08 0.06 0.09 0.12 0.13 Mom Literate 0.28 0.75 0.44 0.81 0.58 0.86 0.69 0.90 Dad Literate 0.30 0.74 0.41 0.78 0.49 0.81 0.54 0.84 OwnHome 0.25 0.56 0.26 0.54 0.25 0.51 0.26 0.46 Enrolled 0.42 0.63 0.65 0.85 0.80 0.91 0.83 0.91 n 16,054 30,138 23,762 58,360 14,769 41,989 84,056 223,387 N 492,515 Counties 1,361

B: Literacy sample, ages 15-22. 1910 1920 1930 black white black white black white Female 0.52 0.50 0.53 0.50 0.53 0.50 Rural 0.79 0.72 0.77 0.72 0.72 0.68 Farm 0.52 0.50 0.59 0.52 0.54 0.46 MSA-Metro 0.03 0.04 0.04 0.05 0.05 0.06 MSA-City 0.08 0.10 0.10 0.13 0.14 0.16 Mom Literate 0.23 0.53 0.33 0.60 0.43 0.64 Dad Literate 0.22 0.50 0.27 0.55 0.33 0.58 OwnHome 0.25 0.49 0.23 0.49 0.25 0.45 Literate 0.72 0.91 0.80 0.96 0.87 0.98 n 20,171 52,429 12,343 34,121 79,613 201,237 N 399,914 Counties 1,353 Table shows means for black and white respondents ages 7-14 in panel A and ages 15-22 in panel B in decennial Census years 1900-1930 in Southern states (AL, MS, LA, FL, GA, TX, NC, SC, TN, KY, AR, OK, MD and VA). IPUMS sample weights are applied.

32 Table 2: Treatment summary statisitcs, black respondents ages 7-14.

1900 1910 1920 1930 Jeans at Census 0.000 0.162 0.383 0.435 (0.000) (0.369) (0.486) (0.496) Jeanes ever 0.000 0.189 0.590 0.660 (0.000) (0.391) (0.492) (0.474) Months ever/9 0.000 0.274 2.511 6.851 (0.000) (0.641) (3.284) (7.173) Years of Jeanes 0.000 0.309 3.013 7.271 (0.000) (0.674) (3.643) (6.616) Any Rosenwald at Census 0.000 0.000 0.491 0.891 (0.000) (0.000) (0.500) (0.312) Rosenwald Coverage at Census 0.000 0.000 0.049 0.244 (0.000) (0.000) (0.089) (0.222) n 16,054 23,762 14,769 84,056 Table shows means and (sd) for black respondents ages 7-14 in decennial Census years 1900- 1930 in Southern states (AL, MS, LA, FL, GA, TX, NC, SC, TN, KY, AR, OK, MD and VA). Jeanes at census=1 if the respondent lived in a county with a Supervisor in the Census year. Jeanes ever=1 if the respondent’s county ever had a supervisor by the Census year. Months ever/9 is total months Jeanes were ever in the county by the Census year / 9. Years of Jeanes is the number of years, regardless of intensity, Jeanes were in the county by the Census year. Any Rosenwald at Census=1 if there was a Rosenwald School by each Census year. Rosenwald Coverage is the share of black students that could be housed by Rosenwald Schools.

33 Table 3: Dependent variable is enrollment in 1900 and 1910.

Black only Black & White Jeanes 1920 0.003 0.002 (0.004) (0.002) Rose 1920 -0.017 -0.031∗ (0.018) (0.014) Jeanes 1920*Rose 1920 -0.004 -0.002 (0.005) (0.003) Year 1910 0.201∗∗∗ 0.129∗∗∗ (0.015) (0.024) Jeanes 1920*Year 1910 -0.003 -0.002 (0.005) (0.010) Rose 1920*Year 1910 -0.015 0.096∗∗∗ (0.023) (0.028) Jeanes 1920*Rose 1920*Year 1910 0.005 0.003 (0.005) (0.011) Black -0.085∗∗∗ (0.012) Black*Jeanes 1920 0.000 (0.004) Black*Rose 1920 0.002 (0.019) Black*Jeanes 1920*Rose 1920 -0.001 (0.005) Black*Year 1910 0.072∗∗ (0.024) Black*Jeanes 1920*Year1910 -0.001 (0.011) Black*Rose 1920*Year1910 -0.113∗∗∗ (0.031) Black*Jeanes 1920*Rose 1920*Year 1910 0.003 (0.012) Controls X X State & Birth Year Dummies X X N 39,816 128,314 R2 0.086 0.099 Dependent variable is a binary indicator of enrollment. Results are OLS regression coeﬃcients from a linear probability model. Sample is pooled 1900-1910 decennial Census. Jeanes 1920 is the number of years a Jeanes Supervisor was in the county by 1920. Rose 1920 is a dummy indicating if a Rosenwald School was in the county by 1920. Year 1910 is a dummy for census year 1910. Controls include: female, farm status, MSA status, mother’s and father’s literacy, if family owns the home, father’s occupation score, quadratic in mother’s age at birth and dummies for population size categories. IPUMS sample weights are applied. Standard errors clustered on counties in parentheses. ∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.

34 Table 4: Impacts of Jeanes and Rosenwald exposure on Enrollment.

(1) (2) (3) (4) (5) (6) Black -0.138*** -0.071*** -0.082*** -0.085*** -0.086*** -0.086*** (0.004) (0.003) (0.004) (0.004) (0.004) (0.004) Rose -0.005 -0.003 0.001 0.000 (0.008) (0.008) (0.008) (0.008) Black*Rose 0.118*** 0.101*** 0.078*** 0.077*** (0.012) (0.013) (0.013) (0.013) 1[Jeanes Census] -0.002 0.002 (0.007) (0.008) Black*1[Jeanes Census] 0.022*** 0.007 (0.007) (0.009) Jeanes Years -0.000 -0.000 (0.000) (0.001) Black*Jeanes Years 0.003*** 0.003*** (0.000) (0.001) Individual controls X X X X X Age*State*Year X X X X X X County FE X X X X X X R2 0.196 0.220 0.221 0.221 0.221 0.221 Counties 1,361 1,361 1,361 1,361 1,361 1,361 N 492,515 492,515 492,515 492,515 492,515 492,515 Notes: Results are from Equation 3. Dependent variable=1 if enrolled for respondents ages 7-14 in Census years 1900- 1930. Coeﬃcients are estimated using a linear probability model. Rose is the share of black school age children that could be housed by all Rosenwald schools in the county in Census year t. 1[JeanesCensus] is in indicator equal to 1 if a Supervisor was present in the county during the Census year. Jeanes Years is the number of school years (months/9) a Supervisor was in the county up until the Census year t. Standard errors clustered on counties are in parentheses. ∗p < 0.10, ∗∗ p < 0.05, ∗∗∗p < 0.01

35 Table 5: Impacts of Rosenwald and depreciated lagged Jeanes exposure on Enrollment

Number of lags included, not including current census year 1 2 3 4 5 6 Black -0.086*** -0.086*** -0.086*** -0.086*** -0.086*** -0.086*** (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Rose -0.000 -0.000 -0.000 -0.001 -0.001 -0.001 (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) Black*Rose 0.077*** 0.076*** 0.076*** 0.076*** 0.076*** 0.076*** (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) Jeanes LagL 0.002 0.002 0.001 0.001 0.001 0.001 (0.004) (0.003) (0.002) (0.002) (0.001) (0.001) Jeanes Pre LagL -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Black*Jeanes LagL 0.007 0.006** 0.006** 0.005*** 0.005*** 0.005*** (0.004) (0.003) (0.002) (0.002) (0.001) (0.001) Black*Jeanes Pre LagL 0.002*** 0.002*** 0.002*** 0.002** 0.002** 0.002 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Mean Lag (black) 0.70 1.01 1.30 1.58 1.88 2.15 Mean Pre Lag (black) 3.77 3.45 3.16 2.88 2.59 2.32 Individual controls X X X X X X Age*State*Year X X X X X X County FE X X X X X X R2 0.221 0.221 0.221 0.221 0.221 0.221 Counties 1,361 1,361 1,361 1,361 1,361 1,361 N 492,515 492,515 492,515 492,515 492,515 492,515 Dependent variable=1 if enrolled for respondents ages 7-14 in Census years 1900-1930. Rose is the share of black school age children that could be accommodated by Rosenwald Schools. JeanesLag is total months/9 that supervisors were in the county in the Census year plus the past L years, where L is indicated in the column header. JeanesPreLag is the number of months/9 that supervisors were in the county for all years between 1909 up until the year measured in JeanesLag. Lagged exposure to Jeanes is depreciated at the rate indicated in the panel header. Standard errors clustered on counties. ∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.

36 Table 6: Impacts of Rosenwald and depreciated lagged Jeanes exposure on Enrollment

Number of lags included, not including current census year 1 2 3 4 5 6

(A) 10% depreciation Black*LagL 0.005 0.006* 0.006** 0.006** 0.006*** 0.006*** (0.005) (0.004) (0.003) (0.002) (0.002) (0.002) Black*Pre LagL 0.006*** 0.006*** 0.006** 0.006** 0.006* 0.005 (0.002) (0.002) (0.002) (0.003) (0.003) (0.004) Mean Lag (black) 0.66 0.92 1.13 1.32 1.49 1.63 Mean Pre Lag (black) 1.67 1.41 1.20 1.01 0.84 0.70

(B) 30% depreciation Black*LagL 0.004 0.006 0.007* 0.007** 0.008*** 0.010*** (0.006) (0.005) (0.004) (0.004) (0.003) (0.003) Black*Pre LagL 0.021*** 0.025*** 0.031** 0.042** 0.052** 0.050 (0.007) (0.009) (0.013) (0.018) (0.025) (0.035) Mean Lag (black) 0.60 0.75 0.85 0.92 0.96 1.00 Mean Pre Lag (black) 0.47 0.31 0.21 0.14 0.10 0.06

(C) 50% depreciation Black*LagL 0.005 0.007 0.008 0.008* 0.010** 0.013*** (0.007) (0.006) (0.005) (0.005) (0.004) (0.004) Black*Pre LagL 0.065*** 0.109*** 0.190** 0.390*** 0.719*** 0.923* (0.023) (0.041) (0.074) (0.138) (0.268) (0.494) Mean Lag (black) 0.52 0.60 0.64 0.66 0.67 0.67 Mean Pre Lag (black) 0.15 0.07 0.03 0.02 0.01 0.004

Controls Individual controls X X X X X X Age*State*Year X X X X X X County FE X X X X X X R2 0.221 0.221 0.221 0.221 0.221 0.221 Counties 1,361 1,361 1,361 1,361 1,361 1,361 N 492,515 492,515 492,515 492,515 492,515 492,515 Dependent variable=1 if enrolled for respondents ages 7-14 in Census years 1900-1930. RoseCensus is the share of rural black school age children that could be accommodated by Rosenwald Schools. JeanesLag is total months/9 that supervisors were in the county in the Census year plus the past L years, where L is indicated in the column header. JeanesPreLag is the number of months/9 that supervisors were in the county for all years between 1909 up until the year measured in JeanesLag. Lagged exposure to Jeanes is depreciated at the rate indicated in the panel header. Standard errors clustered on counties. ∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.

37 Table 7: Eﬀects of cumulative exposure on Literacy.

(1) (2) (3) (4) (5) Black -0.142*** -0.093*** -0.100*** -0.102*** -0.104*** (0.004) (0.003) (0.004) (0.004) (0.004) Rose7−14 -0.030*** -0.022** (0.011) (0.010) Black*Rose7−14 0.187*** 0.136*** (0.020) (0.019) Jeanes -0.010** -0.008* (0.005) (0.005) Black*Jeanes 0.049*** 0.035*** (0.006) (0.006) Individual controls X X X X Age*State*Year X X X X X County FE X X X X X R2 0.121 0.173 0.174 0.174 0.174 Counties 1,353 1,353 1,353 1,353 1,353 N 399,914 399,914 399,914 399,914 399,914

Notes: Dependent variable=1 if literate for respondents ages 15-22 in Census years 1910-1930. Rose7−14 is the average share of black school age children that could be housed by Rosenwald schools in the respondent’s county. Jeanes is exposure to Jeanes averaged over ages 7-14.

38 Table 8: Eﬀects of cumulative exposure on Literacy, including lags.

Number of lags included L=1 L=2 L=3 L=4 L=5 L=6 Black -0.104*** -0.104*** -0.104*** -0.104*** -0.104*** -0.104*** (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Rose7−14 -0.022** -0.022** -0.022** -0.021** -0.021** -0.021** (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) Black*Rose7−14 0.134*** 0.133*** 0.133*** 0.132*** 0.131*** 0.131*** (0.019) (0.019) (0.019) (0.019) (0.020) (0.020) JeanesLag=L -0.008* -0.008 -0.008 -0.008 -0.008 -0.008 (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Black*JeanesLag=L 0.036*** 0.037*** 0.038*** 0.040*** 0.041*** 0.043*** (0.006) (0.006) (0.007) (0.007) (0.007) (0.007) Mean JeanesLag=L, black 0.278 0.268 0.259 0.249 0.240 0.230 Individual controls X X X X X X Age*State*Year X X X X X X County FE X X X X X X R2 0.174 0.174 0.174 0.174 0.174 0.174 Counties 1,353 1,353 1,353 1,353 1,353 1,353 N 399,914 399,914 399,914 399,914 399,914 399,914

39 Table 9: Literacy robustness checks.

(1) (2) (3) (4) (5) (6) (7) (8) Black -0.104*** -0.104*** -0.123*** -0.103*** -0.122*** -0.088*** -0.121*** -0.088*** (0.004) (0.004) (0.005) (0.004) (0.004) (0.014) (0.005) (0.005) Rose7−14 -0.021** -0.047*** -0.024* -0.018* -0.046*** -0.019 -0.026* (0.010) (0.013) (0.012) (0.010) (0.009) (0.012) (0.012) Black*Rose7−14 0.131*** 0.113*** 0.126*** 0.141*** 0.176*** 0.132*** 0.134*** (0.020) (0.015) (0.023) (0.021) (0.020) (0.025) (0.021) L=6 Jeanesρ=0 -0.008 -0.004 -0.016*** -0.013** -0.005 -0.014 -0.006 -0.010 (0.005) (0.006) (0.006) (0.006) (0.006) (0.020) (0.007) (0.006) L=6 Black*Jeanesρ=0 0.043*** 0.036*** 0.057*** 0.049*** 0.041*** 0.072 0.042*** 0.044*** (0.007) (0.008) (0.009) (0.010) (0.008) (0.044) (0.011) (0.009) Black*1[Rose by 1930] -0.024 (0.016) Main speciﬁcation X Rose div. by black, rural X Non-imputed states X

40 Only known Jeanes timing X Rural counties only X No Rosenwald by 1920 X Males only X Females only X R2 0.174 0.174 0.184 0.177 0.184 0.188 0.196 0.149 n 1,353 1,353 652 1,353 1,329 949 1,353 1,352 N 399,914 399,914 219,460 364,186 282,713 81,300 196,622 203,292 Notes: Column 1 is the same speciﬁcation as column 6 of Table 8. Rose div. by black, rural means shows results when Rosenwald capacity is divided by the rural black students, rather than all black students in the county. The non-imputed states sample omits six states in three years where Jeanes presence was imputed due to missing data. Only known jeanes timing omits counties during which the exact timing of Rosenwald Schools is not known between 1916 and 1919. Rural counties only restricts to entirely rural counties. No Rosenwald by 1920 restricts the sample to year 1910 and 1920 in counties that did not have a Rosenwald School by 1920. Table 10: Eﬀects of cumulative depreciated exposure on Literacy, including lags.

Number of lags included L=1 L= 2 L= 3 L=4 L=5 L=6

At 10% depreciation (ρ = 0.1) JeanesLag=L -0.008* -0.008 -0.008 -0.008 -0.008 -0.008 (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Black*JeanesLag=L 0.036*** 0.037*** 0.038*** 0.039*** 0.041*** 0.042*** (0.006) (0.006) (0.006) (0.007) (0.007) (0.007) Mean JeanesLag=L, black 0.278 0.269 0.260 0.249 0.240 0.230

At 30% depreciation (ρ = 0.3) JeanesLag=L -0.008* -0.008 -0.008 -0.008 -0.008 -0.008 (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Black*JeanesLag=L 0.036*** 0.037*** 0.037*** 0.038*** 0.039*** 0.040*** (0.006) (0.006) (0.006) (0.006) (0.007) (0.007) Mean JeanesLag=L, black 0.279 0.273 0.267 0.262 0.258 0.255

At 50% depreciation (ρ = 0.5) JeanesLag=L -0.008* -0.008 -0.008 -0.008 -0.008 -0.008 (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Black*JeanesLag=L 0.036*** 0.036*** 0.037*** 0.037*** 0.037*** 0.037*** (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) Mean JeanesLag=L, black 0.280 0.276 0.273 0.271 0.270 0.269

Controls Individual controls X X X X X X Age*State*Year X X X X X X County FE X X X X X X R2 0.174 0.174 0.174 0.174 0.174 0.174 Counties 1,353 1,353 1,353 1,353 1,353 1,353 N 399,914 399,914 399,914 399,914 399,914 399,914

Notes: Dependent variable=1 if literate for respondents ages 15-22 in Census years 1910-1930. JeanesL is average exposure to Jeanes including L lags at each age assuming treatment depreciates at rate ρ.

41 Table 11: Impact of Jeanes Supervisors on likelihood of receiving a Rosenwald School.

(1) (2) (3) coeff. (s.e.) coeff. (s.e.) coeff. (s.e.) Jeanes(t) 1.137*** (0.014) 1.066*** (0.019) 1.056*** (0.019) Jeanes by 1918 1.760*** (0.215) 1.245* (0.151) Controls X N 4,179 4,179 4,179 Counties 702 702 702 Notes: Model estimates time-varying hazard from Equation 13 above. County-by-race characteristics are taken from weighted decennial Census. Student- and school-teacher ratios are taken from data provided by Carruthers and Wanamaker (2013). Jeanes by 1918 is an indicator=1 if any Jeanes were in the county by 1918. Jeanes(t) is a time-varying measure of the cumulative number of months/9 a Supervisor was present in the county. Observations are counties from AL, GA, NC, SC, TN. Analysis time=0 in 1918. Standard errors are clustered on counties in parentheses. ∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.

42 Table 12: Eﬀects of Jeanes and Rosenwald on Literacy by county-level black population.

(1) (2) (3) Black -0.060*** -0.088*** -0.092*** (0.006) (0.005) (0.007) Lag=6 Jeanesρ=0 0.001 0.006 0.011 (0.016) (0.011) (0.010) Lag=6 Black*Jeanesρ=0 0.073** 0.024* 0.015 (0.036) (0.014) (0.011) Rose7−14 0.007 -0.065*** -0.101*** (0.007) (0.018) (0.027) Black*Rose7−14 0.075*** 0.115*** 0.100*** (0.022) (0.018) (0.027) Quintiles of black pop. 1-2 3-4 5 Average # of black children 210 1,277 4,183 Individual controls X X X Age*State*Year X X X County FE X X X R2 0.148 0.169 0.162 n 468 468 233 N 94,204 123,987 159,596 Notes: Model repeats the speciﬁcation in column 6 of Table 8 by quintiles of county-level black population in 1930. Dependent variable=1 if literate for respondents ages 15-22 in Census years 1910-1930. Rose7−14 is the average share of black school age children that could be housed by Rosenwald schools in the respondent’s county. JeanesL is average exposure to Jeanes including L lags at each age. ∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01.

43 Figures

Figure 1: Black-white gaps in Literacy and Enrollment, 1900- 1930. .3 .25 .2

Gap, White-Black .15 .1

1900 1910 1920 1930 Census year

Enrollment gap, ages 7-14 Literacy gap, ages 15-22

Notes: Sample is from decennial Census 1900-1930 for black and white respondents. Enrollment and Literacy are binary indicators. IPUMS sample weights are applied.

44 Figure 2: Jeanes and Rosenwald placement by county, 1920 and 1930

Jeanes and Rosenwald placement by 1920 Jeanes and Rosenwald placement by 1930

Legend Legend Treatment by County Treatment by County 1920 1930

45 Neither Neither Jeanes only Jeanes only Rosenwald only Rosenwald only Both Both

Figures above show whether a county had a Rosenwald School, a Jeanes Supervisor or both by 1920 (left) and 1930. The existence of a Jeanes Supervisor indicates a Supervisor had been in the county for any number of months, although not necessarily in the Census year. Figure 3: Share of Southern counties (ever) with a Jeanes Su- pervisor. .7 .6 .5 .4 .3 .2 .1 Share of counties with Jeanes with of counties Share 0

1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 Year Share of counties with Jeanes Share of counties ever with Jeanes

Data are from administrative records of the Jeanes Fund from the Southern Edu- cation Foundation archive in Atlanta. Solid line shows share of all Southern counties in each year with a Jeanes Supervisor. Dashed line shows share of Southern counties that had ever had a Jeanes Supervisor by each year.

46 Figure 4: Enrollment by age, decade and race.

Enrollment Literacy White White 1 1 .9 .8 .8 .6 .7 .6 .4 .5 .2 .4 0 .3

Black Black 1 1 Share literate Share Share enrolled Share .9 .8 .8 .6 .7 .6 .4 .5 .2 .4 0 .3

6 7 8 9 10 11 12 13 14 15 16 17 18 10 12 14 16 18 20 22 Age Age

1900 1910 1920 1930

IPUMS decennial census, 1900 1920 and 1930 1% samples and 1910 1.8% oversample, weighted. Sample is black and white respondents, not living in group quarters. Enrollment is deﬁned as enrolled between June 1 and the Census date in 1900, Apr. 15 in 1910, Jan. 1 in 1920 and between Apr. 1 and the Census date in 1930. Literacy is deﬁned as the ability to both read and write in any language.

47 Figure 5: Black-white literacy by occupation and decade.

White Black 1 .8 Literacy .6 .4

1900 1910 1920 1930 1900 1910 1920 1930 Census year Teacher Clergy Farm Farm, wage laborer

IPUMS decennial census, 1900 1920 and 1930 1% samples and 1910 1.8% oversample, weighted. Sample is black and white respondents ages 18 to 45. Literacy is a binary measure indicating respondent can read and write. Occupations taken from IPUMS Occ1950 variable.

48 Figure 6: Jeanes and Rosenwald timing.

Sample is all Southern counties that ever received a Jeanes Supervisor or a Rosen- wald School by 1930. Both 1916-19 accounts for unknown timing of Rosenwald Schools built during that time.

49 Figure 7: Survivor function. Failure = receiving a Rosenwald School. 0.80 0.60 0.40 Survival Probability Survival 0.20 0.00 1918 1920 1922 1924 1926 1928 1930 Year (analysis time) No Jeans by 1918 Had Jeanes by 1918

Notes: Figure plots survival probabilities where failure=1 if the county received a Rosenwald School. Jeanes by 1918 is an indicator=1 if any Jeanes were in the county by 1918. Observations are counties from AL, GA, NC, SC and TN. Analysis time=0 in 1918.

50 Figure 8: Jeanes expenditures, by year. 300 200 100 Expenditures (in $000's) (in Expenditures 0

1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930

Jeanes Public

Data are from Jones (1937). Dark bars show Jeanes Fund expenditures in each ﬁscal year, light bars show public contributions in each year. Expenditures are in real (1925) terms.

Figure 9: Average monthly salary for selected states.

Notes: Figure plots monthly salaries in real ($1925) terms for Jeanes Supervisors in selected states. Author’s calculations from archival records, Woodruﬀ Library, Atlanta.

51 Figure 10: Rosenwald cost per-teacher and per-school.

.32 6,000 6,000 .24 .04 .04 .15 .08 .06 .03 .03 4,500 4,500

3,000 3,000 .13 Cost per School Cost per Cost per Cost per Teacher .18 .12 .41 .16 1,500 1,500 0 0 1 2 3 4 5 1 2 3 4 5 6 7 8 9+ Number of Teachers Number of Schools

(a) Cost per teacher (b) Cost per school

Notes: From data made available by Aaronson and Mazumder (2011). Bars show cost of each school in real 1925 dollars by (a.) the number of teachers the school could house and (b.) the number of schools built in that county in that year. Numbers above bars show share of all schools in each category.

52 Appendix Imputed treatment Jeanes Fund data for 6 states (AR, FL, GA, MD, TX and VA) was not available at the time of data collection. It is not clear if these items are permanently missing or if they have yet to be archived. These missing data affect respondents who were ages 7-14 between 1928 and 1930 in these 6 states in the 1930 Census, accounting for 7% of the total sample. To resolve this, I impute Jeanes presence and a Jeanes intensity (months) variable at the county level for these three years via multiple imputation (see Rubin, 1987). First, I predict a binary treatment likelihood from a logistic regression. To ensure that all individuals in the same birth-cohort and county receive the same imputed treatment, imputation is at the county-year level. I condition on state, Jeanes months in 1910, 1920 and 1923-1927, Rosenwald presence in 1920, 1927 and 1930, a quadratic in the black population, the black rural share of the population, and the black literacy and enrollment rates in 1920. I run 10 iterations of each equation and take the mean of the resulting values. Thus the binary predictor results in a value ranging from 0 to 1 for each county with missing values in each year. I then repeat this procedure for months of Jeanes presence using predictive mean matching. In both cases I exploit the monotone nature of the missing variables in imputation. Tables A1 and A2 below show results of the imputation for each variable. The first two columns compare means for imputed counties with non-imputed counties for counties that had a Jeanes Supervisor present in 1927. The last two columns repeat this for counties without a Jeanes Supervisor in 1927. The predictive power of the regression appears to decline slightly over time possibly under-predicting in the final two years. This will assign treated individuals to the control group, weakening the estimated effects of the Jeanes program and strengthening the estimated effects of the two have complementary effects.

53 Table A1: Imputed Jeanes presence.

Jeanes in 1927=True Jeanes in 1927=False Non-missing Imputed Non-missing Imputed 1928 0.85 0.82 0.07 0.06 1929 0.79 0.74 0.10 0.07 1930 0.74 0.67 0.08 0.05 N 174 137 475 556 Unit of observation is the county. Cells show mean values for existence of a Jeanes Supervisor. Imputed counties are in AR, FL, GA, MD, TX and VA.

Table A2: Imputed Months presence.

Jeanes in 1927=True Jeanes in 1927=False Non-missing Imputed Non-missing Imputed 1928 7.85 7.37 0.57 0.43 1929 7.47 6.63 0.84 0.63 1930 7.02 6.24 0.69 0.45 N 174 137 475 557 Unit of observation is the county. Cells show mean values months of supervision. Imputed counties are in AR, FL, GA, MD, TX and VA.