P0 | Macro Paper Warehouse

"Compensate the Losers?" Economic Policy and the Origins of U.S. Partisan Realignment

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question. Why have less-educated voters in the United States abandoned the Democratic Party over recent decades? The paper argues that the Democratic Party’s evolution on economic policy — specifically its retreat from “predistribution” — is a central, previously understudied driver of partisan realignment by education.

Conceptual Framework. The authors distinguish between two categories of egalitarian economic policy: (1) predistribution — policies that alter the pre-tax-and-transfer earnings distribution, including job guarantees, minimum wage increases, union support, and protectionist trade policies (following Hacker 2011); and (2) redistribution — taxes and transfers. The paper’s central claim is that these two types of policy have sharply different educational gradients among voters, and that the Democratic Party moved away from predistribution beginning in the 1970s, triggering educational realignment.

Data and Methodology. The authors harmonize over 1,000 surveys (N ≈ 2.2 million observations) spanning 1942–2020, drawn from Gallup, ANES, GSS, CCES, and historical survey archives housed at iPoll/Cornell. Education is translated into a common metric (adjusted years of schooling) using Census data, controlling for sex, race, year, and birth cohort to address the changing selectivity of educational categories over time. Congressional roll-call data come from the Comparative Agendas Project (CAP). Campaign finance data come from FEC filings, Congressional hearing records, and watchdog sources. DLC membership data are compiled from official Democratic Leadership Council records (available for 1985, 1986, 1991, 1993, and 1997 onward) and DLC-aligned Congressional caucus lists. House election returns are taken from King and Palmquist (1997) at the minor-civil-division-group (MCDG) level (~60 units per Congressional district), matched to 1980 Census demographic data.

Main Findings.

Voter preferences (demand side): The educational gradient for predistribution is large and negative: averaged across the four predistribution questions (job guarantee, minimum wage, union support, trade protection), each additional year of education reduces support by 0.044 standard deviations (p < 0.001). A college graduate relative to a high school graduate supports predistribution 0.176 standard deviations less — equivalent to roughly half the average Democrat-Republican gap in predistribution support (which is 0.34 standard deviations). This gradient has been stable since at least the 1940s. By contrast, the educational gradient for redistribution (higher taxes on the rich, views on own taxes, welfare spending) is close to zero (summary β = 0.004, not distinguishable from zero in the full sample). The difference between the two gradients is statistically significant (p < 0.001). These results replicate in white-only samples. Notably, the educational gradient on social issues — measured across nine questions on racial attitudes, gender roles, sexual norms — is positive (more education predicts more liberal positions) but has been largely stable since the 1940s, not increasing, conditional on the long-run sample.

Party supply (supply side): Before 1976, predistribution topics accounted for roughly one-quarter of Democratic House roll-call votes when Democrats controlled the chamber. After 1976 (taking Jimmy Carter’s presidency as the start of the “New Democrat” era), this share falls by approximately nine to ten percentage points, while the redistribution share of votes holds steady. Between 1968 and 1980, the union share of total PAC donations to Democratic Congressional candidates falls from approximately 90 percent to 40 percent, coincident with 1970s campaign finance reforms that placed union and corporate PACs on equal legal footing and allowed corporations to exploit their naturally deeper pockets. Corporate PAC share of Democratic donations correspondingly rises from approximately 10 percent to 45 percent over the same period. In individual contributions to primary elections (data beginning in 1980), Democratic primaries rely on increasingly more-educated census tracts relative to Republican primaries; by 2018 Democratic primaries are financed from census tracts averaging 0.41 more years of education than Republican primaries (against a within-year standard deviation of 1.56 years).

The New Democrat/DLC faction: The authors identify the anti-predistribution faction through official DLC membership records and aligned caucus lists. DLC membership as a share of Democratic House seats grows from near zero in the mid-1970s to approximately half by the early 2000s. Roll-call voting analysis (N = 3,428,405 vote-observations) shows DLC members are more conservative than other Democrats overall, and especially so on predistribution: for a 10-percentage-point increase in the share of Republicans voting for a bill, the probability a DLC member votes in favor increases 36 percent more on predistribution bills than on other bills. DLC members show no differential conservatism on redistribution. They are also significantly more socially conservative — more likely than other Democrats to support the Defense of Marriage Act (by 16 pp), the Partial-Birth Abortion Ban (by 7 pp), and restrictive immigration bills (by 10 pp). DLC candidates receive significantly less from labor PACs and significantly more from corporate PACs, and draw their out-of-district individual donations from census tracts averaging more than 0.1 years more educated than non-DLC Democrats.

Voter reaction and the inflection point: Using the N ≈ 2.2 million partisan identification dataset, the authors estimate a structural break in the education-party identification gradient. From the 1940s through the mid-1970s, each additional year of education reduces the probability of identifying as a Democrat by approximately 3 percentage points. A Chow breakpoint test identifies 1976 as the inflection point. Since 1976, the gradient steadily rises; by 2000 it reaches zero; and today (as of the sample period end ~2020) each additional year of education increases Democratic identification by approximately 3 percentage points — an almost exact reversal. The breakpoint for Republican identification occurs later, in 1992, consistent with the Democratic agenda changing first. A Gallup prosperity question (“which party will better keep the country prosperous?”) shows a parallel pattern: controlling for views on parties’ economic performance explains approximately 44 percent of partisan realignment, interpreted as an upper bound on economic policy’s contribution.

Factional tests — hypothetical elections and actual results: In hypothetical general-election matchups from 1972–1992 Democratic primaries (in which most contests pitted a “New Democrat” against an “Old Democrat”), a voter with a college degree is roughly 3 percentage points more likely to vote Democratic when the candidate is a New Democrat rather than an Old Democrat. In 1980s actual House elections using MCDG-level data, DLC candidates out-perform other Democrats in more educated neighborhoods by a magnitude large enough to erase approximately 90 percent of the general Democratic underperformance in highly educated areas. Combining these estimates, the party’s shift toward the DLC accounts for a lower bound of approximately 20 percent, and an upper bound (from the prosperity question) of approximately 50 percent, of educational realignment.

Scope Conditions. The analysis focuses on the United States, 1942–2015 (with some post-2015 discussion in the conclusion). The faction analysis focuses on the Democratic side; Republican faction changes are discussed but not the primary focus. The paper is explicit that between 20–50 percent of realignment is explained, leaving room for other factors, including social issues. The analysis ends mostly before 2016 to avoid complications from the closure of the DLC in 2011 and shifting post-2010 party dynamics.

In depth

Q1. What is the paper’s central conceptual innovation, and how does it differ from prior realignment research?

The paper separates egalitarian economic policies into “predistribution” (pre-tax-and-transfer market interventions such as minimum wages, job guarantees, union support, and protectionism) and “redistribution” (taxes and transfers) and shows these two types have sharply different educational gradients. Prior work typically aggregated all economic policies into a single index, which the authors argue masks essential heterogeneity. By documenting that the educational gradient is large and negative for predistribution but close to zero for redistribution — a pattern stable since the 1940s — the paper reframes the “voting against economic interest” puzzle: less-educated voters leaving the Democratic Party may be responding rationally to changes in the supply of the type of economic policy they actually prefer.

The average coefficient on adjusted years of schooling across the four predistribution questions is -0.044 (p < 0.001), stable over eight decades. A four-year difference in education (high school vs. college) shifts an individual’s support for predistribution by 0.176 standard deviations in the conservative direction — about half the average Democrat-Republican gap in predistribution support (0.34 standard deviations). For social issues, the summary gradient is positive (+0.028, p < 0.001 for the full sample), but this gradient has been largely stable since the 1940s across nine social issue questions, not increasing over time. This stability undermines the interpretation that rising social liberalism among the educated is a new phenomenon driving realignment, at least through the supply of parties’ social positions.

Using the Comparative Agendas Project classification, predistribution topics (labor regulation, industrial policy, public works, trade) accounted for roughly one-quarter of all House roll-call votes during years Democrats controlled the Speakership before 1977. After 1977, this share falls by approximately 9–10 percentage points (a decline of nearly half from its pre-1977 share), and the decline is statistically significant (p < 0.001). The redistribution share of votes holds essentially constant. Party platform data from Hopkins et al. (2022) show a sharp decline in Democratic use of terms like “minimum wage,” “full employment,” and labor-relations language beginning in the 1970s and 1980s, while Republican platforms use these terms sparingly throughout.

Q4. How did 1970s campaign finance reforms change the financial composition of the Democratic Party?

Before the early 1970s, unions enjoyed substantially more freedom than corporations under separate legal regimes governing PAC donations; mid-1970s reforms placed them on equal legal footing, enabling corporations to exploit their deeper pockets. The union share of total PAC donations to Democrats fell from approximately 90 percent in 1968 to approximately 40 percent by 1980, while the corporate share rose from approximately 10 percent to 45 percent. For Republicans, both series barely changed: unions had never donated substantially to the GOP, and the corporate share rose only modestly (from approximately 70 to 80 percent). The authors note the rapid decline cannot be attributed to falling union density in the economy, since both union and corporate PAC donations grew in absolute terms during this period; the relative shift was the result of the regulatory change.

Q5. Who are the “New Democrats” / DLC, and when did they emerge?

The DLC officially operated from 1985 to 2011, but members who would join it began entering Congress in large numbers in the 1970s (“Watergate Babies” of 1974, “Atari Democrats”). The DLC grew to approximately half of all Democratic House seats by the early 2000s. Members were drawn from suburban, affluent districts; their founder Al From explicitly criticized all four predistribution policies the paper studies (minimum wage, job guarantees, unions, and protectionism). The breakpoint test on DLC share in Congress identifies 1975 as the pivotal year — one year before the 1976 inflection point in partisan identification.

Q6. How do DLC members vote differently from other Democrats, and how is this differential conservatism distributed across policy types?

In roll-call regressions (N = 3,428,405 observations, with roll-call fixed effects), a 10 pp increase in the Republican vote share for a bill increases the probability a DLC member votes in favor by 1.48 pp more than for other Democrats (baseline result for all bills). For predistribution-classified bills, this excess alignment with Republicans is 36 percent larger than for non-predistribution bills. Crucially, DLC members are no more conservative than other Democrats on redistribution-classified votes (the interaction with redistribution is near zero and insignificant). DLC members are also differentially more conservative on social issues, a result that proves useful in separating economic from social-issue explanations of realignment.

Q7. Do DLC members finance differently from other Democrats?

Yes. In primary elections, DLC candidates receive approximately 9.7 pp less of their PAC financing from labor unions and approximately 6.7 pp more from corporate PACs (with state fixed effects) relative to non-DLC Democrats. Out-of-district individual contributions to DLC primary candidates come from census tracts averaging more than 0.1 years more educated than those for non-DLC Democrats, while within-district contributions show no significant difference (0.060 years, insignificant). This pattern suggests educated out-of-district donors, rather than local constituency demands, drive DLC candidates’ anti-predistribution orientation.

Q8. When precisely did educational realignment in Democratic party identification begin, and what does the inflection-point analysis show?

Using N ≈ 2.2 million observations from 1,006 surveys, a Bai-Perron breakpoint test on the year-by-year education gradient in Democratic party identification identifies 1976 as the inflection point (with robustness to alternative specifications yielding breakpoints of 1978–1980 for white-only samples and unadjusted years of schooling). Before 1976, each additional year of education reduces the probability of Democratic identification by approximately 3 percentage points (a stable, significantly negative relationship since the 1940s). After 1976, the gradient steadily rises; it reaches zero around 2000 and today is approximately +3 percentage points per year of education — nearly an exact reversal of the baseline. The corresponding Republican inflection point occurs in 1992, about 16 years later, consistent with the Democratic Party’s agenda changing first.

Q9. How do hypothetical presidential matchup surveys test the DLC mechanism?

The authors identify six Democratic primaries from 1972–1992 where a “New Democrat” and an “Old Democrat” were the top two contenders (e.g., Hart vs. Mondale in 1984, Clinton vs. Brown in 1992). Gallup and other surveys asked all respondents — regardless of party — whom they would vote for if either the New or the Old Democrat faced the eventual Republican nominee. A voter with a college BA is approximately 3 percentage points more likely to vote for the Democrat when the candidate is a New Democrat versus an Old Democrat (the “difference in differences” of hypothetical vote shares). This holds after controlling for state × election fixed effects and in five of the six election cycles studied (the 1976 exception is attributed to Mo Udall’s low name recognition, with 28 percent of respondents unfamiliar with him in a May 1976 poll). The result is attenuated but remains marginally significant when excluding non-white respondents, consistent with New Democrats’ success with white voters due in part to their more conservative civil rights positioning.

Q10. What do actual House election results (MCDG-level data) show about DLC electoral performance by neighborhood education?

Using 1980s House returns at the MCDG level (~60 neighborhoods per Congressional district), the authors regress Democratic vote share on neighborhood years of education interacted with a DLC candidate indicator, with Congressional district fixed effects. More-educated neighborhoods generally depress Democratic vote share (reflecting the still-negative overall educational gradient in the 1980s), but DLC candidates dramatically out-perform other Democrats in educated areas: the interaction coefficient is positive and significant, and its magnitude is large enough to erase approximately 90 percent of the general Democratic underperformance in highly educated neighborhoods. This result is robust to including District × Year fixed effects (so the identification comes from within-election, cross-neighborhood variation) and to adding controls for share white and share under age 35.

Q11. How much of educational realignment can the paper’s mechanism account for, and how is this calculated?

Two bounding estimates are provided. Upper bound (~44–50%): controlling for a respondent’s view on which party is better for economic prosperity (from Gallup since 1950) explains approximately 44 percent of the change in the education-party identification gradient (specifically, the total difference in the unconditional gradient between the 1948–1967 baseline and 2001–2020 is 2.411 pp per year of schooling; after controlling for the prosperity question, the unexplained residual is 1.342 pp, leaving a share explained of 44.3 percent). Lower bound (~20%): the difference in the education gradient between matchups involving New versus Old Democrats in Table 4 (~0.75 pp) divided by the total realignment shift (~4 pp from pre-1976 to post-2008 for presidential voting) implies the faction shift accounts for at least approximately one-fifth of realignment. The authors interpret these as bounds because the prosperity question may partly capture party identification itself (upper bound concern), while the hypothetical matchup estimate misses the broader ideological shift not captured in a single election (lower bound).

Three alternative explanations are addressed. (1) Civil Rights: Regional analysis shows that educated white Southerners left the Democrats in the 1940s–1960s (not the 1970s), consistent with their realignment being driven by Democrats’ liberal turn on civil rights rather than economic policy. After the 1960s, the South follows all other regions in the pace of educational realignment. (2) Republican changes: The Republican party identification inflection point occurs in 1992, about 16 years after the Democratic inflection in 1976. Reagan elections in 1980 and 1984 do not appear to have differentially attracted less-educated voters (the “Reagan Democrats” were not differentially less educated). (3) Social issues: The New Democrats were actually more socially conservative than other Democrats (more likely to vote for DOMA, anti-abortion bills, restrictive immigration legislation), yet they disproportionately attracted educated voters. This internal inconsistency rules out a pure social-issues explanation for why educated voters preferred the DLC faction. (4) Religion: Flexibly controlling for religious affiliation explains essentially none of partisan realignment (Appendix Figure A.24).

Q13. What is the role of out-of-district individual donors in shifting Democratic Party positions?

Out-of-district primary donors are analytically important because they influence candidate supply without being able to vote in the election, isolating the “within-party” financial influence of educated supporters. By 1980, out-of-district primary donors to Democratic candidates already come from census tracts more educated than those for Republican candidates, even as local Democratic voters and within-district donors remain less educated than Republican counterparts. Democratic candidates also receive a substantially higher share of out-of-district contributions than Republican candidates — by almost 10 percentage points (Appendix Table A.7). Out-of-district donors thus represent a channel through which educated, anti-predistribution preferences are transmitted into the Democratic Party’s candidate supply before the electoral realignment is visible in vote totals.

Q14. Are predistribution policies becoming less popular overall, which might independently push Democrats away from them?

The paper tests this alternative in Appendix Table A.9 and finds no evidence that predistribution has become less popular relative to redistribution over time. Predistribution appears on average more popular than redistribution across the sample period. If anything, support for predistribution has held steady or slightly risen relative to redistribution over time, conditional on the paper’s survey harmonization. The stability of the educational gradient (shown in Appendix Table A.10 to be unchanged even using educational rank within cohort rather than raw years of schooling) further suggests the negative education-predistribution relationship is a relative, not absolute, phenomenon — consistent with rising average education and stable preferences by education rank.

Key Concepts

Predistribution: Policies that aim to change the distribution of earnings or income before taxes and transfers are applied. In this paper, this comprises government job guarantees, minimum wage increases, support for unions and collective bargaining, and protectionist trade policies. Distinguished from redistribution in that it operates on pre-tax market income rather than post-tax outcomes. The paper uses this term following Hacker (2011): “a focus on market reforms that encourage a more equal distribution of economic power and rewards even before government collects taxes or pays out benefits.”

Redistribution: Policies that change post-market income through the tax and transfer system, including higher taxes on the rich, views on own tax burden, prioritization of tax cuts, and transfers to the poor (welfare spending). In the paper’s usage, redistribution is analytically distinct from predistribution and has a near-zero educational gradient, in contrast to predistribution’s strongly negative gradient.

Educational Gradient: The coefficient on adjusted years of schooling in a regression of an outcome variable (policy preference or partisan identification) on education, estimated separately by time period. The paper’s core finding is that the educational gradient for predistribution is stably negative (approximately -0.044 per year of schooling over the full sample), while the gradient for redistribution is close to zero, and the gradient for Democratic party identification shifts from approximately -0.03 to +0.03 per year of schooling between the 1940s and 2020.

New Democrats / DLC (Democratic Leadership Council): An explicitly anti-predistribution faction within the Democratic Party, identified through official DLC membership records and affiliated Congressional caucus lists. Founded formally in 1985 (operating through 2011), the DLC arose in part from the “Watergate Babies” cohort of 1974. DLC members were more conservative than other Democrats especially on predistribution and social issues, relying differentially on corporate PACs and educated out-of-district donors. The paper treats DLC membership as a proxy for an anti-predistribution faction that gained bargaining power within the Democratic Party from the 1970s onward.

Adjusted Years of Schooling (AdjYearsEduc): The paper’s harmonized education variable across more than 1,000 surveys spanning eight decades. Because raw educational categories change over time and represent different selectivity (e.g., in 1940 only one-quarter of adults had completed twelfth grade, versus nearly 90 percent today), the authors use Census microdata to predict years of schooling as a function of self-reported educational category, sex, race, year, and birth cohort in ten-year bins. This provides a common unit of measurement across surveys with incompatible category systems.

Inflection Point (1976): The structural break in the trend of the education-Democratic identification gradient, estimated using Bai-Perron (1998) methods on N ≈ 2.2 million observations. The data select 1976 as the year at which the previously stable negative gradient begins its upward trajectory. The corresponding Republican inflection point occurs in 1992. The paper argues that identification of this inflection point — not previously documented in the realignment literature — is made possible only by the large historical dataset assembled.

Minor Civil Division Group (MCDG): The granular geographic unit used in the House election analysis for the 1980s, with approximately sixty MCDGs per Congressional district. Matched to 1980 Census demographic data to assign average years of education. Used to test whether DLC candidates out-perform other Democrats in more-educated neighborhoods, within the same Congressional district and election year, to address the concern that DLC candidates sort into more-educated districts.

Ideological Alignment and Evidence-Based Policy Adoption

Mon, 01 Jan 0001 00:00:00 +0000

This paper investigates how the ideological alignment between knowledge-disseminating institutions and policymakers affects the adoption of evidence-based policies. The core research question is whether, and through which mechanisms, the ideology of the messenger — rather than the content of the message — determines whether local policymakers act on rigorous research evidence.

The authors conduct a country-wide randomized controlled trial (RCT) across 5,678 touristic Spanish municipalities. The policy recommendation derives from Hinnosaar et al. (2021), an RCT demonstrating that minor improvements to municipalities’ Wikipedia pages (adding photographs, local festival information, touristic landmark details) increased overnight tourist stays by 9%. This policy was chosen because it is ideologically neutral, low cost, within local policymakers’ remit, and its implementation is directly traceable via Wikipedia edit histories.

Municipalities were randomized into five treatment arms and a control group (approximately 950 municipalities each), stratified by ruling party ideology, population, and touristic accommodation count. Three arms received the same policy brief endorsed by: (1) an ideologically aligned think tank (FAES for right-wing municipalities, Fundación Alternativas for left-wing), (2) the ideologically opposite think tank, or (3) an ideologically nonsalient researcher from the London School of Economics. Two further arms received links to newspaper articles covering the same research from either an ideologically aligned outlet (El Mundo for right, Eldiario.es for left) or an ideologically opposite outlet. The control group received no information. The experiment ran from May to December 2022, with multiple reminder emails sent across the period.

The main outcome is a binary indicator for whether a municipality’s Wikipedia page was changed in line with the recommended guidelines during the study period, coded blind to treatment status by two independent coders.

Key findings: Pooled across all treatment arms, information provision increased the probability of policy adoption by approximately 0.98 percentage points (a 38% relative increase over the control group baseline), but this effect is only marginally above conventional significance thresholds (p-value = 0.13). The aggregate effect masks sharp heterogeneity by ideological alignment. When the informing institution’s ideology aligns with the policymaker’s, policy adoption increases by 1.68 percentage points (think tank) and 1.67 percentage points (newspaper) relative to the control group — equivalent to a 66% and 65% relative increase, respectively, both statistically significant at the 5% level. By contrast, information from an ideologically opposite institution produces a coefficient that is negligible and statistically indistinguishable from zero, indicating that misaligned information is no more effective than receiving no information at all. The ideologically nonsalient LSE researcher arm produced an intermediate effect (0.94 percentage points, 37% relative increase), but the p-value (0.27) exceeds conventional thresholds, and the effect is not statistically distinguishable from either the aligned or the control condition. Policy briefs and newspaper articles are equally effective when ideologically aligned (difference of 0.1 percentage points, p-value = 0.82).

To decompose mechanisms, the authors propose a three-stage framework: (1) selective exposure to information, (2) belief updating, and (3) policy implementation. Email click-through rates (access to the full policy brief or article once the informing institution is revealed) do not differ significantly across treatment arms, ruling out selective exposure as the operative mechanism. A post-intervention online survey experiment with 1,600 policymakers from 1,196 municipalities shows that those receiving information from an aligned or nonsalient institution updated their beliefs about policy effectiveness significantly more than those receiving information from an opposite institution, implicating belief updating as one operative channel. However, comparing the survey experiment (where nonsalient and aligned treatments produce similar belief updating) with the main experiment (where the aligned arm adopts at nearly twice the rate of the nonsalient arm, though not statistically distinguishable) suggests that ideological alignment also affects the third stage — policy implementation — beyond mere belief updating.

The estimated monetary cost of ideological misalignment is 2,192 euros per municipality per year, calculated using the impact of Wikipedia changes on touristic revenues from Hinnosaar et al. (2021).

Scope conditions: The context is Spanish local government, a policy that is explicitly non-ideological, low-cost, and easily implemented. Generalizability to ideologically charged or costly policies is not established. Left-wing municipalities show larger responses to aligned information, though this heterogeneity is not statistically significant at conventional levels.

Q: What is the baseline rate of policy adoption in the control group, and what does the aligned-institution treatment achieve in absolute terms?

A: The paper reports that ideologically aligned institutions increase the share of municipalities implementing recommended Wikipedia changes by 1.68 percentage points (think tank) and 1.67 percentage points (newspaper) relative to the control group. Working backward from the stated 66% and 65% relative increases, this implies a control group baseline of approximately 2.5 percentage points. The aligned effects are statistically significant at the 5% level.

Q: Does information from an ideologically opposite institution have any effect on policy adoption?

A: No. The coefficient for opposite-ideology treatment arms is negligible in magnitude, closely resembling the near-zero coefficients from the placebo analysis conducted for the same months in 2019 (pre-intervention). The authors conclude that receiving information from an ideologically opposite institution is statistically indistinguishable from receiving no information at all. This null result is consistent across heterogeneity analyses by mayor ideology, municipality population, Wikipedia page length, and party type.

Q: How does the ideologically nonsalient (LSE researcher) treatment compare to aligned and opposite arms?

A: The nonsalient arm increases policy adoption by 0.94 percentage points (a 37% relative increase), approximately half the effect of the aligned arm (1.68 percentage points). However, the p-value is 0.27, and the effect is not statistically different from either the aligned arm (p-value = 0.34) or the control group at conventional confidence levels. The result should therefore be interpreted with caution.

Q: Are policy briefs or newspaper articles more effective in promoting policy adoption?

A: Neither format is significantly more effective than the other. Conditional on ideological alignment, the difference between policy brief and newspaper article effects is 0.1 percentage points with a p-value of 0.82. Both are equally effective when ideologically aligned with the receiving policymaker, a finding the authors describe as a novel contribution to the policy communication literature.

Q: Does ideological alignment affect whether policymakers choose to access the full information (selective exposure)?

A: No. Click-through rates on the links to policy briefs or newspaper articles — measured after policymakers have seen the informing institution’s identity — do not differ significantly across treatment arms. The observed average click-through rate is 6.42%. This null result is consistent with the hypothesis that policymakers do not strategically filter information acquisition based on the messenger’s ideology, at least for non-ideological policies.

Q: What does the survey experiment reveal about belief updating?

A: In the post-intervention survey experiment with 1,600 policymakers, participants first reported beliefs about a purportedly beneficial (but actually harmful) policy, then were randomly assigned to receive information about its negative effects from an aligned, opposite, or nonsalient think tank. Those receiving information from an aligned or nonsalient institution updated their beliefs significantly more than those receiving information from an ideologically opposite institution. This implicates belief updating — not just selective exposure — as a channel through which ideological alignment affects policy adoption.

Q: Why do the authors conclude that ideological alignment also affects the third stage (policy implementation) beyond belief updating?

A: In the survey experiment, aligned and nonsalient institutions produce statistically similar belief updating. Yet in the main field experiment, the aligned arm adopts policy at nearly twice the rate of the nonsalient arm (1.68 vs. 0.94 percentage points), although this difference is not statistically significant. The authors interpret this gap as suggestive evidence that ideological alignment affects policy implementation through channels beyond belief updating — such as career concerns, party cues, or the political economy of implementation — though they acknowledge the evidence is indirect and the treatment difference is not statistically distinguishable.

Q: What is the estimated economic cost of ideological misalignment?

A: The authors estimate a cost of 2,192 euros per municipality per year attributable to ideological misalignment between the informing institution and the receiving policymaker. This calculation uses the estimated impact of Wikipedia changes on touristic revenues from Hinnosaar et al. (2021) and reflects not the cost of not implementing the policy, but the marginal cost of using an ideologically opposite rather than aligned institution to disseminate the research evidence.

Q: How did outside researchers’ predictions compare to actual results?

A: Researchers surveyed on the Social Science Prediction Platform correctly anticipated the rank ordering of treatment effectiveness (aligned > nonsalient > opposite > control) but substantially overestimated adoption rates in every arm. They predicted relative increases of 144%, 103%, and 48% for aligned, nonsalient, and opposite conditions respectively, compared to actual relative increases of roughly 65%, 37%, and ~0%. Email opening rates were the most accurately predicted (49% predicted vs. 38% actual). The results highlight the difficulty of translating evidence into policy even for simple, low-cost interventions.

Q: What are the main threats to validity and how are they addressed?

A: Three main threats are considered. First, differential email opening rates across treatment arms: addressed by showing the informing institution was revealed only after email opening, and confirmed by finding no significant differences in opening rates across groups. Second, spillovers between municipalities: the endline survey shows only 5 of 236 control-group respondents reported receiving any information from external sources; spillover distance analyses in Table D.II find no significant effect on control municipalities’ adoption rates. Third, contamination bias in multi-arm RCTs with strata fixed effects: addressed by replicating main results using the Goldsmith-Pinkham et al. (2022) method, yielding nearly identical estimates.

Q: What heterogeneity is observed across left- and right-wing municipalities?

A: The positive effect of receiving information from an ideologically aligned institution appears larger for left-wing municipalities, with coefficients approximately three times larger than for right-wing municipalities, but this difference is not statistically significant at conventional confidence levels. The authors caution that the strength of ideological alignment may differ systematically between the partner think tanks on the left and right, making direct comparisons between left- and right-wing effects difficult to interpret cleanly.

Q: How does the paper relate to prior work on evidence-based policymaking?

A: The closest prior work is Hjort et al. (2021) and Mehmood et al. (2024), which examine the impact of scientific evidence access on actual policy adoption, and DellaVigna and Kim (2022), which identifies ideology as a factor in the diffusion of innovative policies across governments. The present paper’s main contribution is being the first to isolate the causal effect of ideological alignment on policy adoption using a large-scale field experiment with real, authoritative ideological institutions — rather than surveys or hypothetical scenarios — while using a non-ideological policy recommendation to avoid confounding messenger ideology with policy ideology.

Ideological alignment: In this paper’s usage, the congruence between the political ideology of the institution disseminating research evidence (think tank or newspaper) and the political ideology of the local government receiving that information. Alignment is operationalized by matching right-wing municipalities with right-leaning institutions (FAES, El Mundo) and left-wing municipalities with left-leaning institutions (Fundación Alternativas, Eldiario.es).

Evidence-based policy adoption: The actual implementation by local policymakers of a policy recommendation derived from published peer-reviewed research — measured here as whether a municipality’s Wikipedia page was edited in line with specific recommended guidelines during the study period, not merely expressed intention or stated support.

Knowledge brokers: Institutions, such as think tanks, that serve as intermediaries between academic researchers and policymakers, translating and disseminating research findings in accessible formats (policy briefs) to bridge the gap between evidence and policy.

Nonsalient ideology: A condition in which the informing institution carries no salient or recognizable partisan affiliation, operationalized here by a foreign research university professor (LSE) whose institutional identity does not carry a clear left-right signal in the Spanish political context.

Three-stage policy adoption framework: The authors’ conceptual structure positing that ideology can interfere at three sequential stages: (1) selective exposure — whether policymakers choose to access information once the messenger’s ideology is revealed; (2) belief updating — whether policymakers revise their assessment of a policy’s effectiveness upon receiving evidence; and (3) policy implementation — whether policymakers act on updated beliefs to adopt the policy.

Selective exposure: The tendency of individuals to avoid information from sources whose ideology conflicts with their own prior beliefs; in this paper, operationalized as differential click-through rates on links to policy briefs or news articles after the informing institution’s identity is revealed.

Motivated reasoning: A documented tendency, also observed in policymakers, to reject or discount evidence that contradicts ideologically held prior beliefs — the mechanism proposed to explain why opposite-ideology information fails to update beliefs as effectively as aligned-ideology information.

Investing in Influence: Investors, Portfolio Firms, and Political Giving

Mon, 01 Jan 0001 00:00:00 +0000

This paper investigates whether institutional investors influence the political activities of their portfolio firms, using political action committee (PAC) giving as a window into the broader question of whether institutional investors can leverage their concentrated ownership to extract benefits from portfolio firms for their own interests rather than those of their clients.

The sample covers 574 institutional investors (those with at least $100 million in assets under management, i.e., 13-F filers) matched to 2,456 portfolio firms that had PACs, over the period 1980–2018. The primary source of variation is the first acquisition by an institutional investor of at least one percent of a portfolio firm’s outstanding shares, yielding 68,387 large acquisition events. PAC giving data come from FEC records matched by name to investor and firm entities. The main regression specification examines how the relationship between investor and firm PAC contributions to the same congressional district changes after such an acquisition, using a saturated set of fixed effects including firm × investor, firm × congressional district, firm × election cycle, investor × congressional district, investor × election cycle, and district × election cycle.

The central finding is that, following a large block purchase, a firm’s PAC giving mirrors more closely that of the acquiring investment management company. In the preferred specification (column 8 of Table 2), the probability that a portfolio firm gives to a politician supported by its investor’s PAC increases by 31 percent after an acquisition. Using a cosine similarity measure of investor-firm PAC giving, the mean similarity of 0.10 at the acquisition cycle rises by 0.02–0.03 (a 20–30 percent increase) by the fourth post-acquisition election cycle.

A key identification concern is that acquisitions may be driven by shared political preferences rather than representing a causal effect. To address this, the authors exploit stock index inclusions as exogenous shifters of institutional investor block purchases: when a firm is added to an index for the first time, passive indexers are compelled to rebalance toward that firm regardless of political alignment. Restricting to 5,601 index-inclusion acquisitions by passive investors, the authors find near-identical effect sizes (beta1 = 0.0132 in column 8 versus 0.0135 in the full sample), and an event study shows no pre-trend in giving convergence for the index subsample, in contrast to a slight pre-trend in the full sample. Divestment events exhibit the symmetric negative pattern: the interaction of post-divestment and investor PAC giving falls by between -0.074 and -0.058 across specifications.

The authors argue that investors drive the convergence rather than portfolio firms adjusting investor preferences. Around acquisition dates, firms exhibit a larger drop in between-election-cycle cosine similarity than investors do. In a difference-in-differences comparison of the acquisition period relative to the preceding period, the difference in stability between investors and firms is 0.075 (significant at the 1 percent level), indicating that firms shift their giving more than investors. Investors obtaining a board seat at the portfolio firm amplifies the effect: in the preferred specification, the board-seat interaction is more than twice as large as the acquisition-alone interaction.

Heterogeneity analysis provides evidence that the convergence reflects investors’ partisan tastes rather than coordinated profit-maximizing political strategy. Acquisitions by more partisan investors (those whose giving is more skewed toward one party) produce a convergence coefficient roughly twice as large (0.020) as less partisan investors (0.010). Private fund families show more than twice the convergence effect of publicly owned fund families. The partisan composition of firm giving also shifts: a firm acquired by an investor giving exclusively to Republicans sees its Republican share increase by 2.8 percentage points relative to a baseline of 47.4 percent (a 5.9 percent increase).

Finally, higher overall institutional ownership is associated with an increase in total PAC giving at the firm level, and this expanded giving does not go disproportionately to politicians on committees overseeing issues the firm actively lobbies — suggesting the ownership-driven increment in political spending is non-strategic from the firm’s profit standpoint and likely serves investors’ own interests.

Q: What is the central research question and why does it matter? The paper asks whether institutional investors influence the political giving of portfolio firms, motivated by the broader concern that the rise of institutional ownership — from 6 percent of U.S. public equities in 1950 to 65 percent in 2017 — concentrates not only economic but also political power in the hands of a small number of asset managers. This matters because if investors shape firms’ PAC giving to serve investors’ own preferences rather than firms’ profit interests, it represents a misuse of corporate resources and a potential amplification of a small group’s political voice.

Q: What data are used and how is the sample constructed? The analysis draws on 13-F filings (investors with at least $100M AUM) from Thomson-Reuters, matched to FEC PAC records via fuzzy and manual name matching. The resulting sample contains 574 investors with PACs and 2,456 portfolio firms with PACs, spanning 1980–2018. The Cartesian product of investor-firm pairs is restricted to those connected by at least one large acquisition event (defined as first acquisition of at least 1 percent of outstanding shares), yielding 68,387 such events. PAC contributions are measured at the investor- and firm-congressional-district-election-cycle level, linked to House of Representatives winners using MIT Election Data files.

Q: What is the baseline regression and what does it find? The baseline regression (equation 1) interacts Log Investor PAC with a Post indicator (equal to 1 after the first large acquisition and while the stake is maintained) at the investor-firm-congressional-district-election-cycle level, with a saturated set of fixed effects. The coefficient on the interaction (beta1) is positive and highly significant (p < 0.001) across all eight specifications, ranging from 0.013 to 0.032. In the preferred specification, the increase in giving similarity is 31 percent relative to the pre-acquisition baseline.

Q: How do the authors establish causality and rule out endogenous acquisitions? The primary identification strategy uses first-time inclusions of firms in stock indices (approximately 1,000 indices tracked in the sample) as exogenous shifters: passive indexers must rebalance toward the included firm regardless of political alignment. This subsample of 5,601 index-inclusion acquisitions produces near-identical coefficient estimates (0.0132 versus 0.0135 in the full sample), and the event study for this subsample shows no pre-trend in giving convergence, unlike the slight pre-trend in the full sample. Equality of the two coefficients cannot be rejected at standard significance levels.

Q: What evidence shows it is firms adjusting to investors rather than the reverse? The authors compute between-election-cycle cosine similarity separately for investors and firms around acquisitions. On average, investors exhibit more stable giving than firms at acquisition dates (Cos(xi,t, xi,t+1) > Cos(xf,t, xf,t+1)). The difference-in-differences estimate — comparing the acquisition period to the preceding period — is 0.075 (significant at 1 percent), indicating a relatively larger break in firm giving. Over a two-cycle window, the difference-in-differences estimate is 0.083, again indicating convergence is driven by firms shifting toward investors rather than the reverse.

Q: What role does board representation play? In approximately 5 percent of acquisitions in the sample, the investor obtains a board seat. In specifications that include both the acquisition effect (Post × Log Investor PAC) and a board-membership interaction (Board × Log Investor PAC), both terms are positive and significant at the 1 percent level. In the preferred specification, the board-seat interaction is more than twice as large as the acquisition-alone interaction, indicating that a direct governance channel — board representation — substantially amplifies the convergence in political giving.

Q: What does the divestment analysis show? Symmetric to the acquisition results, divestment events (where an investor exits a stake of at least 1 percent held for at least one election cycle) are associated with a decline in investor-firm PAC giving correlation. Post-divestment interaction coefficients range from -0.074 to -0.058 across specifications, and an event study confirms the correlation falls sharply after the divestment cycle.

Q: Does investor partisanship affect the magnitude of influence? Yes. Classifying investors as “More Partisan” (above-mean absolute deviation from 50/50 party split) versus “Less Partisan,” the interaction coefficient for More Partisan investors (0.020) is roughly twice that of Less Partisan investors (0.010). After a large acquisition by a fully Republican-giving investor, the acquired firm’s giving to that politician increases by 23.5 percent; the comparable figure for a Less Partisan investor is 7.6 percent. This pattern holds in both the full sample and the index-inclusion subsample.

Q: How do private versus public fund families differ in their influence? Private fund families (e.g., Vanguard, Fidelity) show more than twice the convergence coefficient of publicly owned fund families (e.g., BlackRock, State Street, Invesco). The authors attribute this to private fund managers facing less outside scrutiny, allowing their giving to more readily reflect the preferences of owners and managers. Private investors also show greater partisan polarization: the 10th–90th percentile Republican-giving range for private investors is 6.3–100 percent, versus 21.7–88.3 percent for public investors.

Q: Does increased institutional ownership expand overall firm PAC spending? Yes. In firm-year level regressions, institutional ownership is a positive and significant predictor of total firm PAC giving (significant at at least the 5 percent level in both cross-sectional and firm-fixed-effects specifications). Total corporate political expenditure by sample firms increased by nearly a factor of six over 1980–2018. The authors note that while many factors contribute, increased institutional ownership may be at least partly responsible for this expansion.

Q: Does the additional giving driven by institutional ownership go to strategically important politicians for the firm? No. Regressions relating institutional ownership to giving to politicians on congressional committees overseeing issues the firm actively lobbies (a standard measure of politicians’ strategic importance to firms) yield near-zero and statistically weak point estimates. In the preferred firm-fixed-effects specification, the share of total PAC giving devoted to such strategically relevant politicians is negatively associated with institutional ownership at marginal significance (p < 0.10), consistent with the interpretation that ownership-driven incremental political spending is non-strategic from the firm’s own profit perspective and expands total giving rather than displacing strategic giving.

Q: What are the policy and legal implications? The authors flag three concerns: (i) the ownership-driven increment in political spending may represent a misuse of corporate resources that does not serve portfolio firm shareholders; (ii) it may constitute an illegal activity, since using a firm’s PAC to reimburse or proxy for an investor’s own political preferences can run afoul of campaign finance law; and (iii) it is a channel through which unequal resources amplify the political voice of a small number of fund managers at the expense of dispersed ultimate investors who are likely unaware of and do not sanction these contributions. The findings challenge the Supreme Court’s premise in Citizens United that corporate political speech reflects shareholder profit maximization.

PAC comovement (investor-firm giving similarity): The increase in the probability that a portfolio firm’s PAC donates to a politician also supported by an acquiring investor’s PAC, measured as the interaction coefficient between Log Investor PAC and a Post-acquisition indicator in the baseline regression. In the preferred specification this represents a 31 percent increase relative to the pre-acquisition baseline.

Cosine similarity (cross-time and cross-entity): A measure defined as the Euclidean dot product between two vectors of PAC giving (either the same entity across adjacent election cycles, or investor versus firm in the same cycle), taking values between 0 and 1, where 1 indicates identical giving patterns. Used both to confirm convergence post-acquisition and to attribute that convergence to firm rather than investor adjustment.

Index-inclusion acquisition: A large block purchase that results from a firm being added for the first time to a stock index tracked by a passive institutional investor, used as an exogenous shifter of investor stakes that is orthogonal to investor-firm political alignment. There are 5,601 such events in the sample.

Partisanship (investor): Classified as “More Partisan” if an investor’s absolute deviation from a 50/50 party split in PAC donations is above the sample mean. More partisan investors produce roughly twice the convergence effect on portfolio firm giving compared to less partisan investors, used as evidence that personal political preferences rather than profit-maximizing business strategy drive the convergence.

Post indicator (Postift): A binary variable equal to 1 for all election cycles following an investor’s first acquisition of at least 1 percent of a portfolio firm’s outstanding shares, and remaining 1 as long as the investor holds any stake in the firm. The key source of temporal variation in the baseline regression.

Strategically important politicians: Members of Congress sitting on committees that oversee issues on which a firm actively lobbies, identified by crosswalking lobbying reports from the Senate Office of Public Records to relevant committee jurisdictions. Used to test whether ownership-driven political giving displaces or supplements firm-profit-motivated giving.

Board seat channel: The mechanism through which investor influence on firm political giving is amplified when the investor obtains representation on the portfolio firm’s board of directors (present in approximately 5 percent of acquisitions). The board interaction coefficient is more than twice the acquisition-alone coefficient in the preferred specification.

Search Frictions and Product Design in the Municipal Bond Market

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question

This paper investigates whether intermediaries in the U.S. municipal bond market strategically exploit product design to increase search frictions and, through that channel, capture rents. Specifically, it asks: do underwriters who negotiate bond design with local governments have an incentive to add nonstandard provisions that raise their own competitive advantage in subsequent secondary-market intermediation, even at the expense of issuing governments and their taxpayers?

Setting and Data

The study focuses on tax-exempt general obligation and revenue bonds issued via negotiated sales by local governments (counties, cities, school districts, and other special-purpose governments) from 2010 to 2013, tracking all secondary-market transactions through 2014. The final sample comprises 13,118 bond issues with a total face value of $266.9 billion. Bond attribute data come from Mergent; transaction data come from the Municipal Securities Rulemaking Board (MSRB). Issuer financial health, demographics, and economic conditions are drawn from the Census and American Community Survey; state revolving-door regulations are compiled from the National Conference of State Legislatures database. Structural estimation uses a subsample of 927 bonds concentrated in the five states that enacted revolving-door regulations during the study period and neighboring border counties.

Identification Strategy

A core empirical challenge is that unobserved factors may jointly determine bond complexity and market outcomes. The authors exploit panel variation in state-level revolving-door regulations — laws that restrict former public officials from taking employment at firms regulated by their former agencies for a “cool-off” period — as an instrument for bond complexity. Between 2010 and 2013, three states (Arkansas 2011, Indiana 2010, Maine 2013) enacted new legislation covering state officials, and two states (New Mexico 2011, Virginia 2011) extended existing regulations to cover local officials. A difference-in-differences regression, with county and year-month fixed effects, shows that adopting revolving-door regulations covering local officials reduces bond complexity by 6% on average (coefficient −0.064, p < 0.01). Regulations targeting only state officials, who are not directly involved in bond negotiations, yield smaller and statistically fragile effects. Placebo checks on auctioned bonds, where underwriters cannot influence design, show no effect, and there is no evidence of pre-existing trends in complexity.

Main Findings

Flexibility vs. liquidity trade-off: A 1% increase in the bond complexity index lowers the number of negative credit-watch events (a proxy for default risk) by 0.002, a 3% decrease relative to the mean of 0.074, confirming that nonstandard provisions provide genuine financial flexibility. However, increasing the complexity index from its mean (1.46) to the 75th percentile (1.69) raises the intermediation spread — the cost for an investor to buy and immediately sell a bond — by 17 basis points (a 14% increase over the average of 120 basis points), confirming that complexity raises trading frictions. For context, the average intermediation spread of 120 basis points is large relative to the 30–60 basis point bid-ask spread of corporate bonds in 2010–2013.
Underwriter incentive to complicate: Increasing complexity from the mean to the 75th percentile raises the underwriter’s market share in secondary-market intermediation by 1.4 percentage points, an 11% increase over the average underwriter share of 12.2%. The underwriter’s gross profits from intermediation also increase with complexity.
Structural estimates — search costs: For a median bond, average dealer search costs amount to 10% of monthly gross profits ($2,625 per month). The underwriter’s exclusive initial sales generate a client network that lowers its effective search costs by 21% relative to an average dealer, more than offsetting its initial geographical disadvantage (for 72% of bonds, the underwriter’s baseline search cost exceeds the median dealer’s). Nonstandard provisions increase both the initial search cost parameter (φ₀) and the network-effect parameter (φ₁): a 1% increase in the complexity index increases φ₀ by 3.79% and φ₁ by 1.66%, implying complex bonds raise search costs broadly but amplify the advantage of a large client network — a position the underwriter occupies via exclusive primary-market sales.
Investor demand: Nonstandard provisions do not substantially change the average investor valuation but substantially increase the dispersion: the standard deviation of investor valuations is 0.003 for simple bonds and 0.013 for complex bonds, consistent with complex bonds being niche products that investors “either love or loathe.”
Government cost: The marginal cost of paying debt obligations is convex in complexity, reaching a minimum at an interior level of provisions; the government’s marginal financial cost increases by 42% when a median bond is stripped of all nonstandard provisions, reflecting the value of payment flexibility.
Conflict of interest: The estimated weight that government officials place on underwriter payoffs in the absence of revolving-door regulations (ψ₀) is 0.34, implying the underwriter’s value accounts for 6.7% of the government official’s payoff under the median unregulated issuer. With revolving-door regulations in place, ψ₁ is essentially zero.

Counterfactual Policies (on representative bond: face value $6.45 million, maturity 7.7 years)

Standardization mandate (ban on all nonstandard provisions): The coupon rate falls from 2.81% to 2.16% (−23%), average dealer search costs fall 47%, and investor surplus rises 13.3%. However, the marginal financial cost (c₀) rises by 41% (from 0.615 to 0.871), so the issuer’s total debt payment cost — principal plus interest, weighted by c₀ — rises by 35%, from $5.13 million to $6.96 million. The standardization policy harms issuers even while saving 7.8% of raw principal-and-interest payments ($8,349K to $7,997K), because the loss of flexibility more than offsets the liquidity gain.
Issuer-driven design (issuer sets complexity to minimize its own debt payment cost, then negotiates the coupon): Complexity falls 19% to 1.14, the interest rate falls to 2.37%, total issuer cost falls 1.5%, investor surplus rises 6%, and the underwriter’s secondary-market payoff falls 19.9%.
Underwriter intermediation ban (underwriter excluded from trading after six months): Complexity falls 5.7% to 1.33, the coupon falls to 2.59%, issuer cost falls 1.5%, but investor surplus falls 1.84% and even other dealers are worse off by 3.97%, because the underwriter’s information on primary-market buyers is lost, offsetting the liquidity gains from lower complexity.

In depth

Q1. What are the five nonstandard bond features tracked as proxies for complexity, and how are they combined into a single index?

Following Harris and Piwowar (2006), the paper focuses on five features that are particularly difficult for investors to price: (i) multiple or serial bonds per issue (as opposed to a single bond), (ii) call provisions allowing early redemption, (iii) sinking fund provisions requiring periodic debt retirement, (iv) nonstandard interest payment frequencies (other than semiannual), and (v) variable or floating interest rates. The complexity index is constructed as the simple average of the latter four provisions across bonds within an issue, plus a dummy for whether the issue contains multiple bonds.

Q2. Why do revolving-door regulations that target local officials reduce complexity more than those targeting state officials?

State officials are not directly involved in bond origination negotiations — they can only indirectly influence local governments through budget allocations. Local officials negotiate directly with underwriters and are thus the proximate counterparties whose incentives the regulations alter. Accordingly, revolving-door regulations covering local officials reduce complexity by 6% (coefficient −0.064, p < 0.01 with full controls), whereas regulations targeting only state officials produce a smaller effect (approximately 2%) that loses statistical significance once issuer financial health controls are added.

Q3. How does the paper validate that revolving-door regulations are a valid instrument for bond complexity?

The paper provides three pieces of evidence. First, the regulations have no effect on the credit ratings of bonds issued prior to their enactment, on the annual amount of bond issuance, or on the maturity length and sale method conditional on issuance — confirming the regulations do not alter governments’ risk management or underlying financing needs. Second, the regulations have no effect on complexity for competitively auctioned bonds, where underwriters cannot influence design — a direct placebo test. Third, a pre-trend analysis (Figure A1) finds no differential trend in complexity in states that subsequently adopted regulations.

Q4. What is the mechanism by which underwriters benefit from adding nonstandard provisions, and why does this advantage not diminish over time?

Underwriters purchase and distribute the entire bond issue at origination, giving them an exclusive network of investors who initially purchased the bonds. In the secondary market, knowing who owns a bond allows the underwriter to locate buyers and sellers with lower search effort. For complex bonds, this advantage is amplified: nonstandard provisions make investor education and persuasion more costly, increasing the value of pre-existing client relationships. The network-effect parameter φ₁ — which governs how rapidly search costs fall as a dealer’s cumulative trades grow — itself rises with complexity (by 1.66% per 1% increase in the complexity index), so the underwriter’s head start in client network accumulation translates into a persistently larger cost advantage precisely for the most complex bonds.

Q5. How large is the underwriter’s search cost advantage in equilibrium, and what drives it?

At the equilibrium meeting rate, the underwriter’s effective search cost of maintaining a given meeting rate is 21% lower than that of an average dealer. This advantage arises despite the underwriter having a higher initial search cost type (φ₀ of $3,609 vs. $3,216 for the average dealer at λ = 1), because for 72% of bonds the underwriter has less local trading experience than the median dealer. The advantage is entirely driven by the underwriter’s network: its exp(−φ₁ log(b)) cost discount factor averages 0.34, 32% lower than the average dealer’s 0.50. The underwriter meets investors 20% more frequently than the average dealer (0.23 vs. 0.19 per month), despite higher absolute search expenditures ($3,045 vs. $2,625 per month).

Q6. How does bond complexity affect investor demand — mean or dispersion of valuations?

Structural estimates show that increasing the complexity index by 1% increases the standard deviation of investor valuations (γ₂) by 4.60% but has no statistically significant effect on the mean valuation (coefficient −0.085, standard error 0.561). This pattern is consistent with complex bonds being niche products — they attract a subset of investors with specific preferences for the embedded features (e.g., certain tax or cash-flow attributes), while being unappealing to most investors. The standard deviation of valuations is 0.003 for a low-complexity bond (25th percentile) and 0.013 for a high-complexity bond (75th percentile).

Q7. What does the structural estimate of ψ₀ imply about the degree of collusion between government officials and underwriters?

The estimated collusion parameter without revolving-door regulations (ψ₀ = 0.34) implies that, for the median unregulated issuing government, the underwriter’s value from secondary-market trading accounts for 6.7% of the government official’s objective function. This is a substantial weight: it means officials act partly as agents for the underwriter rather than purely for taxpayers. With revolving-door regulations (ψ₁ ≈ 0), this collusive weight is essentially eliminated, explaining the empirical reduction in complexity found in Table 2.

Q8. What are the effects of a full standardization mandate on each class of market participant, and why does the issuer lose overall despite paying a lower coupon?

Under standardization, the coupon falls 23% (from 2.81% to 2.16%) and the raw principal-plus-interest payment falls 7.8% (from $8,349K to $7,997K). However, the marginal financial cost c₀ rises 41% (from 0.615 to 0.871), reflecting the loss of payment flexibility previously provided by call provisions and other features; the total issuer cost — c₀A(1 + rT) — rises by 35% (from $5.13 million to $6.96 million). Investors gain 13.3% in surplus because they value liquidity and, on average, do not value nonstandard features. The underwriter loses 36.6% of its secondary-market value while other dealers gain 36.1%, as standardization erodes the underwriter’s network advantage.

Q9. Why does the issuer-driven design scenario outperform standardization in terms of total issuer cost, even though complexity does not fall to zero?

Under issuer-driven design, the government minimizes its total cost of debt payment c₀A(1 + rT), accounting for both the flexibility value of provisions and their effect on the negotiated coupon. The optimal complexity index is 1.14 — positive, but 19% below the current baseline of 1.41 — because some provisions genuinely lower c₀ by allowing flexible debt service. The cost of search frictions (and hence the liquidity premium embedded in the coupon) falls 32% and the negotiated coupon falls to 2.37%, sufficient to reduce total issuer cost by 1.5%. By contrast, full standardization imposes a complexity of zero, which overshoots: c₀ rises more than the coupon savings compensate, increasing total costs by 35%.

Q10. What are the net welfare effects of the underwriter intermediation ban, and why is investor surplus negative despite lower complexity?

The ban reduces complexity by 5.7%, lowering the coupon to 2.59% and reducing issuer costs by 1.5%. However, the underwriter’s client network — built during exclusive initial sales — is a productive resource that improves match quality in the secondary market; banning the underwriter from trading after six months wastes this information. Average dealer search costs rise 1.2% and the meeting rate falls 1.7%, net of the complexity reduction. Investors face bonds with lower coupons and higher effective search frictions, so their surplus falls 1.84%. Non-underwriter dealers also lose 3.97% because lower coupons reduce the rents extractable from intermediation.

Q11. How is the structural model estimated, and what role do revolving-door regulations play in the estimation?

Estimation proceeds in three steps. In Step 1, bond-specific trading market parameters (investor demand, dealer search costs, meeting rates, bargaining parameters) are recovered separately for each bond by minimizing squared differences between observed and simulated trading prices, quantities, and transaction timing. In Step 2, IV regressions using revolving-door regulations and their interactions with county/state attributes as instruments for endogenous complexity map Step 1 parameters to bond attributes, addressing the endogeneity of complexity in determining search costs and investor demand. In Step 3, GMM moment conditions derived from Nash bargaining first-order conditions for the equilibrium complexity and coupon rate identify government preference parameters (θ_c, ψ₀, ψ₁), using the orthogonality condition that unobserved financing cost shocks are mean-zero conditional on observed attributes, regulations, and bond supply from neighboring counties.

Q12. Does the underwriting market show signs of concentration that might amplify the conflict-of-interest problem?

Yes. The mean state-level Herfindahl-Hirschman Index (HHI) for underwriting is 0.12, with the top three firms covering 45% of the market on average. For smaller deals (under $10 million), concentration is markedly higher: mean HHI of 0.24 and top three firms covering 64% of the market. Repeat relationships are common — 41% of bonds issued in 2011–2017 were underwritten by a firm that had underwritten a prior bond for the same issuer within five years — reflecting both informational advantages of local presence and potentially entrenched relationships that may increase government officials’ susceptibility to underwriter influence.

Key Concepts

Complexity index (nonstandard provisions): A bond-level measure computed as the simple average, across bonds within an issue, of four nonstandard features — call provisions, sinking fund provisions, nonstandard interest payment frequency, and variable/floating interest rates — plus a dummy for whether the issue contains multiple bonds. Used as the primary measure of bond complexity in all regressions and the structural model.

Revolving-door regulation: A state-level law restricting former public officials or employees from engaging in lobbying or taking employment at regulated firms for a specified “cool-off” period (typically one to two years) after leaving office. The paper uses the presence and scope of such regulations (whether they cover state officials, local officials, or both) as a source of exogenous variation in government officials’ incentives to align with underwriter interests.

Intermediation spread: The logarithm of the average dealer-to-investor sale price minus the logarithm of the average dealer-from-investor purchase price for a given bond. Used as the empirical measure of trading frictions; the sample average is 120 basis points.

Network effect in search (φ₁): The parameter governing how a dealer’s cumulative prior trades with investors in a given bond reduce its cost of meeting new investors for that bond. A higher φ₁ means a larger client network translates into steeper cost savings. The paper estimates that φ₁ itself increases with bond complexity, so complex bonds amplify the advantage of dealers (especially the underwriter) who accumulate large client networks.

Marginal cost of debt payment (c₀): A bond- and issuer-specific parameter capturing the effective cost to the government of repaying each dollar of principal and interest, net of the flexibility benefits provided by nonstandard provisions. Normalized to one for a bond with zero nonstandard provisions at average issuer characteristics; estimated to be convex in complexity with an interior minimum, implying some nonstandard provisions are beneficial from the government’s perspective.

Collusion weight (ψ): The weight a government official places on the underwriter’s secondary-market value from trading when negotiating bond design. Estimated at ψ₀ = 0.34 in the absence of revolving-door regulations (implying the underwriter’s interest accounts for 6.7% of the official’s objective) and at ψ₁ ≈ 0 when such regulations are present.

Underwriter dual role: The institutional arrangement in which the same investment bank (i) negotiates and purchases the entire bond from the issuing government at origination, and (ii) subsequently acts as a dealer in the bond’s secondary market. This dual role creates an incentive to design complex bonds that strengthen the underwriter’s competitive advantage in secondary intermediation via network effects in search.

Issuer-driven design: A counterfactual policy scenario in which the government sets the complexity level to minimize its total cost of debt payment — accounting for both the flexibility value of provisions and the anticipated effect on the negotiated coupon rate — before bargaining with the underwriter only over the coupon. This policy allows some nonstandard provisions (complexity index 1.14 vs. baseline 1.41) and reduces total issuer cost by 1.5% relative to the baseline.

State Capacity as an Organizational Problem

Mon, 01 Jan 0001 00:00:00 +0000

Mastrorocco and Teso study how the internal organization of a state evolves during national development, framing state capacity as an organizational — specifically a principal-agent — problem. Using a new micro-database covering the U.S. federal bureaucracy from 1817 to 1905, they ask: once rulers have incentives to build a state apparatus, how do they organize it to perform its functions across a vast territory, and what drives transitions between organizational forms?

The dataset is constructed from every issue of the Official Register of the United States published between 1817 and 1905 (44 biennial volumes, 15,801 pages digitized). It records full name, state of birth, state of appointment, occupation, salary, department, office, and location for 304,410 unique federal employees across 810,942 employee-year observations. The authors reconstruct the bureaucracy’s four-layer hierarchy (department → office/bureau → division → local office), link employees over time to track careers, categorize all 11,930 occupation codes into five tiers, and geo-code 9,651 places of employment to 1890 county boundaries.

The paper first documents three sets of descriptive facts. On growth: the federal workforce expanded very slowly before the 1860s and then rapidly, with geographic expansion accounting for none of state growth before 1859 but roughly 29% after. On location: state presence responded positively to local manufacturing activity (a one standard deviation increase in manufacturing employment share raises presence probability by 1.3 percentage points), but distance from Washington DC significantly attenuated this relationship in 1817–1859 and not in 1861–1905. On organization: before the 1860s, employee turnover was high and spiked sharply at presidential transitions (reaching 72% of employees departing in 1861), supervisors’ departures strongly predicted subordinates’ departures (a one-for-one supervisor exit raised subordinate turnover probability by 37% pre-1841), and managerial delegation outside DC was stagnant or declining. After the 1860s, turnover trended down (35% at the 1897 transition), the supervisor-subordinate career link weakened materially, and field managers tripled relative to the 1850s.

The authors argue that high monitoring costs in the early century made trust-based, personalistic organization the second-best solution to principal-agent problems. The limited supply of sufficiently trusted individuals constrained geographic expansion, delegation, and total size. As railroad and telegraph networks lowered communication and transportation costs, monitoring capacity increased, enabling a transition to a Weberian bureaucracy no longer constrained by trust supply.

The causal identification strategy uses the staggered expansion of the railroad network. For each county and decade (1820–1900), the authors compute the minimum-travel-time route from the county centroid to DC using Donaldson and Hornbeck (2016) data on railroads, steamboat waterways, coastal routes, and land routes. The specification includes county fixed effects, state-by-decade fixed effects, and controls for local railroad presence in the county and for the county’s market access, so the identifying variation comes from distant changes in the network that altered travel time to DC without directly affecting the county’s local economy or trade access.

Results: a one standard deviation decrease in travel time to DC raises the probability of federal state presence by approximately 3 percentage points (about 8% of the mean), raises log employment similarly, raises the probability of observing a local managerial layer by approximately 3 percentage points (about 8% of the mean), and reduces employee turnover by approximately 2 percentage points (about 4% of the mean turnover rate). Placebo tests confirm that travel time to other major economic centers does not predict state presence. Telegraph network data (1845–1852, Wang 2020) yield consistent results. An additional test using the post-Civil War decline in Southern-born employee shares shows that better railroad connection to DC narrowed the North-South employment gap, consistent with monitoring substituting for trust-based selection.

Scope conditions: the paper covers the civilian executive branch of the federal government, excluding the Postal Office, navy yards, and the engineer department; results are robust to restricting to states already in the union at the start of the sample, ruling out frontier-specific dynamics.

Q: What is the central theoretical claim of the paper? A: The paper argues that state capacity is fundamentally an organizational problem shaped by principal-agent constraints. When communication and transportation costs are high, the government cannot effectively monitor distant agents, so the second-best solution is to staff the bureaucracy with trusted individuals connected through personal networks. This personalistic form limits size and delegation because the supply of sufficiently trusted individuals is inherently scarce. Technological reductions in monitoring costs allow a transition to a Weberian bureaucracy based on procedural oversight rather than trust, removing the supply constraint on organizational growth.

Q: What data source does the study rely on, and what time period does it cover? A: The study draws on the Official Register of the United States, a biennial government publication listing all federal employees, digitized for every issue from 1817 to 1905. The resulting dataset includes 304,410 unique employees and 810,942 employee-year observations, with each record carrying name, state of birth, state of appointment, occupation, salary, department, office, location, and — through hierarchical reconstruction — position in a four-layer chain of command.

Q: How did the size of the U.S. federal bureaucracy evolve over the nineteenth century? A: Growth was slow before the 1860s. The first Register for 1817 listed 1,056 employees across 33 pages; the 1905 volume listed over 120,000 employees across 1,254 pages. Geographic expansion contributed zero to state growth before 1859 — the share of counties with any federal employee hovered around 15% from 1817 to 1859 — but contributed approximately 29% of growth after 1859, when county presence rose to 24% by 1871, 38% by 1881, and 61% by 1905.

Q: What were the three sources of state growth, and how did their relative importance change? A: The authors decompose growth into: (1) functions (new offices/bureaus), (2) geographic expansion (new counties), and (3) intensity (more employees per county-office pair). Before 1859, growth was entirely driven by functions (~40%) and intensity (~60%), with zero contribution from geographic expansion. After 1859, geographic expansion accounted for ~29%, intensity for ~32%, and functions for ~39% of growth.

Q: How did employee turnover behave across the century, and what pattern emerges at presidential transitions? A: Turnover trended upward through the late 1850s and then declined. During presidential transitions, the rate rose from 52–53% in 1841 and 1845 to 60–63% in 1849 and 1853 and peaked at 72% in 1861; it then fell to 55% in 1869, 44–48% in 1885/1889/1893, and 35% in 1897. Turnover was consistently lower in DC than in the field: controlling for year-bureau-position fixed effects, being employed in DC was associated with a 40% reduction in turnover probability.

Q: How tight was the link between supervisors’ and subordinates’ careers, and how did it change? A: Before 1841, moving from none to all supervisors leaving an organizational unit increased subordinate turnover probability by 37 percentage points. The effect was similar between 1841 and 1859, then dropped substantially to 22 percentage points in the following twenty-year period, and remained roughly constant after 1881. This pattern is consistent with the early bureaucracy relying on chains of personal trust that broke when a supervisor departed.

Q: What evidence describes the evolution of delegation outside DC? A: The number of field managers did not grow between 1817 and 1859 — it actually declined in the 1820s and was flat through the mid-1850s — and then tripled by 1905 relative to the 1850s level. The probability that workers in a local office had an additional managerial layer between them and DC was unchanged between pre-1841 and 1841–1859, increased by 5 percentage points between 1861 and 1881, and by 6 percentage points post-1881.

Q: How does the paper measure monitoring capacity for the causal analysis? A: The primary measure is travel time in hours from each county centroid to Washington DC, computed decade by decade (1820–1900) as the minimum-cost route across the available railroad network, steamboat waterways, coastal routes, and land routes, using data from Donaldson and Hornbeck (2016). A second, complementary measure is the number of telegraph connections between a county and DC using data from Wang (2020) for 1845–1852.

Q: What is the identification strategy for the railroad analysis, and why are controls for local railroads and market access important? A: The specification includes county fixed effects, state-by-decade fixed effects, an indicator for whether the county itself has railroad (LocalRailroad), and the county’s market access. County fixed effects mean beta is identified within-county from changes over time. Controlling for local railroad removes the direct correlation between local construction and local economic growth. Controlling for market access removes the effect of distant rail expansion on trade flows that raised agricultural land values and manufacturing activity. The remaining variation in travel time to DC — coming from distant network changes that altered the DC-county connection without affecting local conditions or broader trade access — is the identifying source.

Q: What are the main quantitative effects of reduced travel time to DC? A: A one standard deviation decrease in travel time to DC is associated with: (1) approximately 3 percentage point increase in the probability of federal state presence (~8% of the mean); (2) a similar magnitude increase in log employment conditional on presence; (3) approximately 3 percentage point higher probability of an additional managerial layer (~8% of the mean); and (4) approximately 2 percentage point reduction in employee turnover (~4% of the mean turnover rate).

Q: How do placebo tests support the monitoring interpretation? A: The authors show that, conditional on the same controls, travel times from a county to a set of other major economic centers are not associated with larger federal state presence. Since these other cities had no role as monitoring headquarters, the absence of an effect for them and the presence of an effect specifically for DC is consistent with the channel operating through the government’s ability to supervise agents from the capital, rather than through generic economic connectivity.

Q: What does the telegraph evidence add, and what is its limitation? A: Telegraph data (1845–1852, Wang 2020) show that counties with more telegraph connections to DC have larger state presence, more managerial delegation, and lower turnover, consistent with the monitoring mechanism. The limitation is that the authors have limited ability to address the endogeneity of telegraph network timing — the telegraph analysis is treated as corroborating evidence rather than the primary causal identification.

Q: How do the Southern-born employee results illuminate the trust mechanism? A: After the Civil War, the share of Southern-born federal bureaucrats fell sharply, consistent with reduced trust toward individuals from former Confederate states. However, counties that became better connected to DC via railroad expansion experienced a relative increase in the share of Southern-born employees. This shows that when monitoring costs fell, the government was willing to hire individuals from groups with lower baseline trust — monitoring substituted for trust as the mechanism ensuring agent performance.

Q: Does federal state presence crowd out state and local government? A: No. The presence of federal bureaucrats is positively correlated with the presence of state and local government employees at the county level, suggesting complementarity rather than substitution across levels of government.

Q: What alternative mechanisms do the authors consider and how do they address them? A: Three alternatives are discussed. First, demand shocks (Civil War debt repayment, industrialization) could explain the post-1860s expansion; the empirical specifications control for year fixed effects to absorb aggregate time-varying incentives, and the identification relies on differential cross-county variation in DC connectivity. Second, patronage as an electoral tool is consistent with spoils-driven turnover spikes but cannot explain why better-connected counties show lower turnover before civil service reform. Third, cognitive models of the firm (lower communication costs complement managerial problem-solving even without agency problems) could also predict the positive delegation result; the authors note they cannot empirically distinguish the monitoring and cognitive channels, and both may contribute.

Q: What are the implications for developing countries today? A: The authors suggest that their findings from nineteenth-century U.S. history may apply to understanding why modern Weberian bureaucracies remain elusive in many developing countries. Where communication infrastructure is limited and monitoring costs remain high, personalistic organizational forms based on trust networks may persist as constrained optima — not failures of will or design, but rational responses to structural conditions. Infrastructure investment that lowers monitoring costs could be a precondition for bureaucratic modernization.

Personalistic state organization: The paper’s term for the organizational form that prevails when monitoring costs are high. It is characterized by staffing decisions based on personal character, moral reputation, and relationships of trust between principals and agents — and between supervisors and subordinates — rather than on formal procedural monitoring of performance. Frequent turnover at leadership transitions and constrained delegation are defining features, because the supply of trusted individuals is limited.

Weberian bureaucracy: In the paper’s usage (following Weber 1978), a modern state organization defined by a fixed hierarchy of officials monitored through procedural rules rather than personal trust, lower turnover, and effective delegation of managerial power to geographically dispersed units. The paper treats this as the organizational form enabled by low monitoring costs.

Monitoring capacity: The principal’s (politicians in DC and their cabinets) ability to observe and evaluate the behavior of agents (federal employees) throughout the territory. In the paper’s operationalization, monitoring capacity is proxied inversely by travel time and communication cost between DC and the county: lower travel time and more telegraph connections mean higher monitoring capacity.

Geographic expansion component: One of three decomposed sources of state growth. Defined as the increase in state size attributable to the state becoming present in more county locations. This component contributed zero to federal growth before 1859 and approximately 29% of growth after 1859.

Employee turnover: In the paper’s measurement, the share of employees who leave the federal bureaucracy in a given year. The paper distinguishes politically-driven spikes at presidential transitions — reaching 72% of employees in 1861 — from the secular trend, which rose through the late 1850s and then declined, reaching 35% by the 1897 transition.

Delegation of managerial power: The probability that a local county office has an additional managerial layer between its workers and DC, rather than reporting directly to the bureau-level supervisor in Washington. The paper uses this as its measure of whether decision authority has been decentralized to the field.

Trust substitution: The paper’s mechanism linking monitoring capacity to organizational form. In the absence of effective monitoring, principals substitute trust for oversight — selecting agents whose personal loyalty, moral character, or political alignment gives the principal confidence they will not shirk or defect. As monitoring costs fall, trust becomes less necessary as a screening device, and the trust-constrained supply limit on organizational growth is relaxed.

Vanguard: Black Veterans and Civil Rights After World War I

Mon, 01 Jan 0001 00:00:00 +0000

This paper provides the first causal evidence on how military service shaped Black civil rights activism in the aftermath of World War I. The research question is whether random induction into the segregated National Army caused Black men to join the nascent NAACP and become prominent community leaders during the New Negro era. The authors leverage the WWI draft lottery — in which each registrant’s unique serial number was drawn from a bowl to determine induction order — as an instrument for military service, a source of exogenous variation not previously exploited in the literature.

To support this analysis, Ang and Chinoy construct an unusually rich dataset by digitizing nearly one million Black draft registration cards from the first registration (June 17, 1917), linking them through the 1930 full-count census to 233,517 NAACP member observations across 227 branches from 1912 to 1940, and supplementing with Veterans Administration records, Army Transport Service passenger lists, and biographical dictionaries of prominent African Americans. The instrument — serial number percentile within draft board and race (SNP%) — is validated against all observed pre-draft registrant characteristics and yields a first-stage F-statistic of 1,051 in the preferred specification.

The main finding is that Black men randomly induced to serve in the military were nearly three times more likely to join the NAACP than observably similar registrants from the same draft board (TSLS coefficient 0.0219, se = 0.0049, against a sample mean NAACP participation rate of 0.8%). The authors estimate that the draft induced more than 10,000 Black men to join the NAACP in total. Military service also raised the probability of appearing in biographical dictionaries of historically prominent African Americans by a factor of roughly 1.6 (TSLS coefficient 0.0027, se = 0.0012, sample mean 0.17%). These results are robust to alternative instruments, flexible polynomial specifications of SNP%, state-year fixed effects, and alternative veteran-status measures from VAMI and ATS records. They are also not explained by differential residential mobility: adding controls for interstate and North-South migration leaves the main coefficient essentially unchanged (0.0217-0.0218).

In contrast, TSLS estimates for all socioeconomic outcomes — literacy, home ownership, employment, census-predicted income, actual 1940 income, and educational attainment — are small and insignificant, ruling out human capital acquisition as a mechanism. Club involvement measured in the census is likewise unaffected, indicating that NAACP membership reflects specifically civil rights activism rather than generically greater social participation.

The mechanism the paper identifies is experienced discrimination. Effects on NAACP participation increase monotonically with the racial gap in induction rates across draft boards (significant at p = 0.01). Effects are large and significant for men assigned to camps that restricted Black soldiers’ access to military training (coefficient 0.0351, se = 0.0104) and to officer promotion (coefficient 0.0360, se = 0.0111), and are large for men in both restriction types simultaneously (coefficient 0.0367, se = 0.0114). In contrast, men attending less discriminatory camps show small and insignificant effects. Among the two all-Black combat divisions, NAACP participation is highest for veterans of the 92nd Division — subjected to constant racial abuse under U.S. command — and lower for the 93rd Division, which served under more hospitable French command. Previously unstudied veteran surveys from Virginia and Connecticut corroborate this narrative: respondents from camps with training and promotion restrictions were more than twice as likely to mention racial injustice, and mentions of injustice were more predictive of postwar civic engagement than any other survey theme.

The scope of the paper is Black male registrants in the first WWI draft registration (men aged 21-30 as of June 17, 1917), linked to a sample of approximately 300,000 in the 1930 census. Effects are attenuated for men from counties with greater racial hostility — proxied by Confederate state status, Confederate monument density, and county lynching rates — consistent with the interpretation that activism was more feasible in less repressive environments.

Q: What is the core identification strategy and why was it not feasible to use it before this paper? A: The paper uses each Black registrant’s serial number percentile within his draft board and racial group (SNP%) as an instrument for WWI military service. Unlike the WWII and Vietnam drafts, which used birthday-based lotteries, the WWI lottery assigned induction order by drawing unique serial numbers from a bowl, making serial number rank the source of quasi-random variation. This source had never been exploited in the literature, partly because the serial numbers had to be hand-captured from digitized draft card images.

Q: How strong is the first stage, and was the lottery truly random? A: The first-stage F-statistic is 1,051, and a ten-percentile decrease in SNP% is associated with a 34.5 percentage point increase in the probability of serving. Bivariate serial numbers show some non-random patterns — nine of 13 pre-draft characteristics correlate with raw SN% — likely because some Southern boards inflated numbers for white registrants. Conditioning on board fixed effects and using SNP% within board-race cells eliminates these correlations; Panel B of Appendix Table A1 shows the largest standardized coefficient falls to 0.006.

Q: What is the magnitude of the effect on NAACP membership and how does the causal estimate compare to a naive OLS? A: The TSLS coefficient is 0.0219 (se = 0.0049) against a sample mean of 0.8%, implying roughly a threefold increase in NAACP membership. The OLS estimate of 0.0116 understates the causal effect, consistent with the marginal man induced by the lottery being observationally weaker than infra-marginal volunteers.

Q: Does the effect reflect simply that veterans moved to Northern cities where NAACP branches were more accessible? A: No. Adding indicators for interstate migration and North-South migration leaves the TSLS coefficient essentially unchanged at 0.0218 and 0.0217, respectively. The Great Migration channel is thus not the operative mechanism.

Q: Did military service improve Black veterans’ economic outcomes? A: TSLS estimates for literacy, home ownership, employment, census-predicted income, actual 1940 income, and educational attainment are all small and statistically insignificant. This contrasts sharply with evidence on Black veterans of WWII and Korea (Greenberg et al., 2022) and is consistent with the documented absence of meaningful postwar benefits or training for Black WWI soldiers.

Q: If it was not human capital or migration, what mechanism does the paper establish? A: The primary mechanism is exposure to institutional discrimination during military service. Three distinct empirical patterns converge: (1) effects increase monotonically with draft board racial disparities in induction rates; (2) effects are large and significant for men at camps that denied training and promotion, and near zero for men at less discriminatory camps; (3) veteran survey mentions of racial injustice are more common among men from discriminatory camps and are more predictive of postwar NAACP membership than any other survey theme.

Q: How do the two all-Black combat divisions differ in their postwar NAACP participation, and what does this reveal? A: Veterans of the 92nd Division, who fought under U.S. command amid constant racial abuse, show the highest NAACP participation rates. Veterans of the 93rd Division, who fought under French command and were received with relative hospitality, show lower (though not statistically significantly lower) participation. Since both divisions received similar formal training and neither group shows socioeconomic gains, the differential reflects discrimination exposure rather than skill acquisition.

Q: What is the quantitative scale of the effect for the most discriminatory camps? A: For men assigned to camps with restrictions on both training and promotion, the TSLS coefficient on NAACP membership is 0.0367 (se = 0.0114) — more than 1.5 times the average estimate of 0.0219. Men at camps without restrictions show coefficients that are small and statistically insignificant.

Q: How does county-level racial hostility moderate the effect? A: The effects of military service on NAACP membership are larger — more positive — for men from counties with fewer Confederate monuments, lower lynching rates, and non-Confederate state status. This is interpreted as evidence that activism in response to discriminatory military experiences was more feasible in less racially hostile local environments, rather than as evidence that discrimination exposure was lower.

Q: What is the paper’s aggregate policy implication regarding the scale of the draft’s effect on the civil rights movement? A: The authors estimate that the WWI draft induced more than 10,000 Black men to join the NAACP. Veterans accounted for nearly 15% of all male NAACP members, against roughly 8% of Black male adults in the population, and were significantly more likely to appear in biographical dictionaries of prominent African Americans. The draft thus constituted a sizable and measurable contribution to the organizational vanguard of the early civil rights movement.

Q: How does the paper contribute to the economics of discrimination beyond documenting discriminatory behavior by majority actors? A: Most economics research on discrimination studies the conduct of white decision-makers (e.g., racial bias in hiring, lending, or bail). This paper examines how experiences of discrimination reshape the political behavior and aspirations of the minority group itself. The results show that institutional betrayal — systematic exclusion, degradation, and denial of training — generated deep discontent that translated into aggressive political mobilization, a dynamic the authors trace through subsequent episodes including the WWII Double V campaign and responses to police killings.

Serial number percentile within draft board and race (SNP%): The instrument constructed by the authors. Each WWI registrant received a serial number from 1 to the size of his draft board; those numbers were drawn in random order to determine induction priority. SNP% measures where a registrant fell in that draw relative to others in his board and racial group, and serves as the source of quasi-random variation in veteran status.

New Negro era: The period of invigorated Black political and cultural assertiveness following WWI, characterized by renewed racial pride, economic independence, and progressive politics. The movement spanned the Harlem Renaissance, the Universal Negro Improvement Association, the American Negro Press, and the Brotherhood of Sleeping Car Porters, and represented a rejection of the “conservatism, parochialism, and political accommodationism” of older Black leaders.

Draft board racial gap: The authors’ measure of draft board discrimination, defined as the difference in induction rates between Black and white registrants within a given draft board. The interquartile range spans roughly 0 to 20 percentage points, with a notable fraction of boards exhibiting gaps exceeding 30 percentage points.

Camp discrimination: The denial of military training and officer promotion opportunities to Black soldiers, documented in War Department reports by military intelligence officers tasked with monitoring the treatment of Black soldiers. The paper classifies each camp as restricted or unrestricted on each dimension and uses this classification to estimate heterogeneous treatment effects.

Institutional betrayal: The paper’s characterization of the U.S. government’s treatment of Black WWI soldiers — drafting them at higher rates than whites, denying them training and promotion, and assigning them to menial labor — as generating a profound sense of injustice that motivated postwar political activism rather than loyalty or accommodation.

NAACP membership as civil rights activism proxy: The paper uses dues-paying membership in local NAACP branches as its primary quantitative measure of civil rights participation. Membership involved active financial cost (annual fees of $1 to $10 at a time when median Black family income was below $500), exposure to harassment and violence in the South, and participation in local protest and legal advocacy, distinguishing it from passive civic engagement.

P0 | Macro Paper Warehouse

"Compensate the Losers?" Economic Policy and the Origins of U.S. Partisan Realignment

Layer 1 — Overview

In depth

Q1. What is the paper’s central conceptual innovation, and how does it differ from prior realignment research?

Q2. How large and stable is the educational gradient on predistribution, and how does it compare to social issues?

Q3. What happened to predistribution as a share of the Democratic House agenda after the 1970s?

Q4. How did 1970s campaign finance reforms change the financial composition of the Democratic Party?

Q5. Who are the “New Democrats” / DLC, and when did they emerge?

Q6. How do DLC members vote differently from other Democrats, and how is this differential conservatism distributed across policy types?

Q7. Do DLC members finance differently from other Democrats?

Q8. When precisely did educational realignment in Democratic party identification begin, and what does the inflection-point analysis show?

Q9. How do hypothetical presidential matchup surveys test the DLC mechanism?

Q10. What do actual House election results (MCDG-level data) show about DLC electoral performance by neighborhood education?

Q11. How much of educational realignment can the paper’s mechanism account for, and how is this calculated?

Q12. Can social issues, Civil Rights realignment, or Republican changes better explain the 1970s inflection point?

Q13. What is the role of out-of-district individual donors in shifting Democratic Party positions?

Q14. Are predistribution policies becoming less popular overall, which might independently push Democrats away from them?

Key Concepts

Ideological Alignment and Evidence-Based Policy Adoption

Investing in Influence: Investors, Portfolio Firms, and Political Giving

Search Frictions and Product Design in the Municipal Bond Market

Layer 1 — Overview

In depth

Q1. What are the five nonstandard bond features tracked as proxies for complexity, and how are they combined into a single index?

Q2. Why do revolving-door regulations that target local officials reduce complexity more than those targeting state officials?

Q3. How does the paper validate that revolving-door regulations are a valid instrument for bond complexity?

Q4. What is the mechanism by which underwriters benefit from adding nonstandard provisions, and why does this advantage not diminish over time?

Q5. How large is the underwriter’s search cost advantage in equilibrium, and what drives it?

Q6. How does bond complexity affect investor demand — mean or dispersion of valuations?

Q7. What does the structural estimate of ψ₀ imply about the degree of collusion between government officials and underwriters?

Q8. What are the effects of a full standardization mandate on each class of market participant, and why does the issuer lose overall despite paying a lower coupon?

Q9. Why does the issuer-driven design scenario outperform standardization in terms of total issuer cost, even though complexity does not fall to zero?

Q10. What are the net welfare effects of the underwriter intermediation ban, and why is investor surplus negative despite lower complexity?

Q11. How is the structural model estimated, and what role do revolving-door regulations play in the estimation?

Q12. Does the underwriting market show signs of concentration that might amplify the conflict-of-interest problem?

Key Concepts

State Capacity as an Organizational Problem

Vanguard: Black Veterans and Civil Rights After World War I