G0 | Macro Paper Warehouse

Identifying Preference for Early Resolution from Asset Prices

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

Layer 1: Overview

This paper develops a revealed-preference theory that uses asset-market data to identify whether investors have a preference for early resolution of uncertainty (PER), a property of non-expected utility preferences that is distinct from risk aversion. The central theorem shows that, under a condition called generalized risk sensitivity (GRS), the representative agent prefers early resolution if and only if claims to future stock market volatility earn a positive premium during the period in which the informativeness of upcoming macroeconomic announcements is resolved — a window the authors call the Resolution of Information Quality (ROIQ) period. Using S&P 500 index option data from 1996 to 2019, the paper identifies the ROIQ period as the five weekdays before FOMC announcements, demonstrates that the inverse slope of the implied-volatility term structure (9-day/90-day VIX ratio) significantly predicts the informativeness of upcoming announcements, and finds a statistically significant positive ROIQ premium on synthetic variance claims (beta = 1.085, t = 2.44) and on at-the-money straddles (beta = 0.428, t = 2.25). The evidence supports Epstein-Zin recursive utility with the intertemporal elasticity of substitution exceeding the reciprocal of risk aversion, and hence is consistent with the Bansal-Yaron long-run risk framework. Crucially, this identification requires no parametric calibration of the full asset pricing model.

Summary of a published paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is preference for early resolution (PER) and why is it hard to identify?

PER means that an agent with a given distribution over future outcomes strictly prefers to learn the outcome sooner rather than later, as formalized by Kreps and Porteus (1978); under Epstein-Zin recursive utility, PER is equivalent to risk aversion exceeding the reciprocal of the IES (or IES > 1/risk aversion). In standard applied asset pricing models with constant-elasticity recursive utility, PER is intertwined with risk aversion and the IES, so that the separate role of the timing of resolution is obscured. Existing papers either test joint implications of the full calibrated model (conflating PER with other preference properties) or use thought-experiment willingness-to-pay calculations without market-data grounding. The authors’ goal is to provide a necessary and sufficient condition for PER directly from asset prices, independent of a fully specified model.

Q2. What is the role of Generalized Risk Sensitivity (GRS) in the identification theorem?

GRS — the condition that the certainty-equivalent functional I is increasing in second-order stochastic dominance — provides the bridge between the unobservable ranking of utility levels across states and the observable ranking of marginal utilities (stochastic discount factors) across those states. The authors prove that under GRS (Theorem 1), the vector of partial derivatives of I with respect to continuation utility is strictly negatively comonotone with the level of continuation utility: higher utility states have lower marginal utility. This inversion is what allows asset prices to reveal the ordering of utility levels. GRS itself is empirically supported by the well-documented fact that assets earn positive announcement premia around scheduled macroeconomic releases (Savor and Wilson, 2013).

Q3. How does the main theorem (Theorem 2) identify PER from a single asset class?

Theorem 2 establishes that, under strict GRS, the premium earned by any asset comonotone with the informativeness of upcoming macroeconomic announcements during the ROIQ period is strictly positive if and only if the agent has PER; a negative ROIQ premium would indicate preference for late resolution. The intuition is that if the agent prefers early resolution, she assigns higher continuation utility to the early-resolution state (0E) than to the late-resolution state (0L); under strict GRS, higher continuation utility maps to lower marginal utility, meaning assets paying off more in the early-resolution state are negatively correlated with the SDF and therefore carry a positive risk premium. Claims to stock market return variance serve as the test asset because expected variance is high before informative announcements (early resolution) and low before uninformative ones (late resolution).

Q4. How do the authors operationalize the ROIQ period empirically?

The ROIQ period is identified as the five weekdays before FOMC announcements, during which market attention to the Fed (measured by RavenPack Fed-related news intensity) is significantly positively correlated with the change in the inverse slope of the implied-volatility term structure (coefficient = 1.076, t = 4.09), while no such correlation exists in the ten days 6–10 before or after the announcement. This correlation arises because, during those five days, investors regularly update their expectations about whether the upcoming FOMC statement will be informative; more expected informativeness raises the demand for short-dated options (driving up the 9-day VIX relative to the 90-day VIX) and simultaneously raises Fed-related news coverage. Outside the ROIQ window, the two series are uncorrelated (coefficient = −0.242, t = −1.13 unconditionally), confirming that the window is the correct testing period.

Q5. What is the empirical evidence for a positive ROIQ premium, and how is it constructed?

Synthetic variance claims constructed as option portfolios following Bakshi, Kapadia, and Madan (2003) earn a ROIQ premium (coefficient beta in the panel regression) of 1.085 percentage points per day (t = 2.44) above their average daily return; at-the-money straddles earn 0.428 pp/day (t = 2.25), both significantly positive. The panel regression controls for maturity fixed effects (11 dummies for weeks to expiration), FOMC-day effects, and day-of-week effects. Crucially, the market itself earns approximately 8 basis points lower than average during the ROIQ period, and the market loading on variance claims does not increase during the ROIQ window (Table 5), ruling out an interpretation in which the premium simply reflects a higher market beta at announcement times.

Q6. How does the paper rule out alternative explanations for the ROIQ premium?

A placebo test using VIX futures — which pay the forward-looking VIX level (expected volatility over the next 30 days after expiry) rather than realized variance over the announcement — shows no significant ROIQ premium, confirming that the effect operates specifically through exposure to volatility during the announcement itself rather than through general volatility-level exposure. The paper also shows that controlling for the Fama-French three factors does not appreciably change the ROIQ coefficient. An additional test using individual stock options (5 weekdays before earnings announcements) also yields positive ROIQ premiums, extending the result beyond FOMC to firm-level announcements.

Q7. What does the finding imply for macroeconomic preference modeling and policy?

The empirical finding that investors have a positive ROIQ premium — i.e., PER — without assuming any particular utility functional form confirms the central calibration assumption of Bansal-Yaron long-run risk models (risk aversion > 1/IES) and provides the market-based evidence that Epstein, Farhi, and Strzalecki (2014) stated was unavailable. The paper’s approach is significant for macro modeling because it establishes PER from minimal assumptions (GRS and monotonicity of preferences), meaning that the result holds across expected utility deviations including robust control, smooth ambiguity, and disappointment aversion preferences — as long as they satisfy GRS — making it a broadly applicable empirical anchor for calibrating non-expected utility models.

Q8. What are the identification limitations and scope conditions?

The identification relies on three maintained conditions: (i) GRS holds for the representative agent, (ii) FOMC announcements genuinely resolve macro uncertainty (so that the ROIQ window is correctly specified), and (iii) the pre-announcement period does not contain price-relevant news (so that market return premia during the ROIQ are not confounded with the news content of the announcement itself). The empirical support for condition (iii) comes from the fact that the market does not earn abnormal returns during the ROIQ (negative, not positive, as expected from the announcement drift literature), and from the lack of a ROIQ premium for VIX futures that expire after but not over the announcement. The framework abstracts from heterogeneous agents and assumes a representative-agent economy, which is standard but may not fully capture distributional effects.

Key Concepts

preference for early resolution of uncertainty (PER) : the property of a dynamic preference that the agent strictly prefers to learn the realization of a future uncertain outcome earlier rather than later, holding the distribution unchanged; equivalent in Epstein-Zin recursive utility to risk aversion exceeding the reciprocal of the IES.

generalized risk sensitivity (GRS) : the condition that the certainty-equivalent functional I is strictly increasing in second-order stochastic dominance; equivalent to the existence of strictly positive announcement premia for all assets comonotone with continuation utility; the paper’s key maintained assumption connecting utility levels to asset prices.

resolution of information quality (ROIQ) period : the period during which investors learn whether the upcoming macroeconomic announcement will be informative; empirically identified as the five weekdays before FOMC meetings, during which Fed-related news intensity co-moves with the inverse slope of the VIX term structure.

ROIQ premium : the excess return earned by a claim to market volatility (synthetic variance claim or straddle) during the ROIQ period over its average daily return on non-ROIQ days; the paper’s operational test for PER; estimated at 1.085 percentage points per day for variance claims.

inverse slope of the implied-volatility term structure : the ratio IV9/IV90 (9-day CBOE VIX divided by 90-day CBOE VIX); the paper’s market-based predictor of FOMC announcement informativeness; a higher ratio reflects investor anticipation of large announcement-day volatility relative to long-run baseline uncertainty.

Politics at Work

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

Layer 1 — Overview

Research Question

Do individual political views shape firm behavior and labor market outcomes in the private sector? Specifically, do business owners sort copartisan workers into their firms, and does employers’ political discrimination drive this sorting?

Data and Setting

The paper studies the complete Brazilian formal labor market over 2002–2019, assembling a novel longitudinal worker-firm-owner-party matched dataset from three administrative sources: (1) RAIS (Relação Anual de Informações Sociais), the universe of formal-sector workers (87 million unique workers, 7.6 million unique firms); (2) the Receita Federal do Brazil (RFB) and Cadastro Nacional de Empresas (CNE), containing business ownership structures for all registered firms; and (3) the Tribunal Superior Eleitoral (TSE) registry of all party members (19.3 million individuals) over 2002–2019. Matching these sources yields political affiliation for 11.4% of all private-sector owners and 7.8% of all private-sector workers in the sample. Party affiliation in Brazil requires an active registration step and is interpreted as a signal of strong and visible political views, distinguishing affiliated from unaffiliated individuals who likely hold milder views. The 35 parties in the sample are highly fragmented; the top 7 account for nearly 70% of all party members.

Main Findings

Political assortative matching. Using a likelihood ratio index (Eika et al., 2019; Chiappori et al., 2020), the paper finds that workers and owners belonging to the same party are on average about twice as likely to match in the labor market relative to random matching. Once within-municipality geographical sorting is accounted for, this figure falls to approximately 55% excess probability of copartisan matching, and increases over time: from 1.41 in 2002–2006 to 1.67 in 2016–2019. A dyadic regression approach — constructing all worker-firm dyads within industry-municipality labor markets and controlling for shared gender, race, age, and education — confirms the result: across all years, a politically affiliated worker is between 41% and 75% more likely to be employed by a copartisan owner than by an owner affiliated with a different party. Political assortative matching is driven both by higher hiring probabilities (range: 32%–59% more likely for copartisans, hiring margin only) and by longer tenure: copartisan workers stay in the firm roughly 5.5% longer than otherwise comparable workers of a different party, even within the same firm and hire-year (column 3 of Table 2). In every year and by every method, the degree of political assortative matching exceeds that of gender (15%–31% excess probability under dyadic approach) and race (approximately 3.4%), which are themselves both positive and significant.

Mechanisms: political discrimination. Three sets of evidence point to employer political discrimination as a relevant driver. First, in the administrative micro-data: assortative matching decreases strongly with firm size — it is more than twice as large in firms with up to 10 employees than in medium firms and more than six times as large as in firms with more than 50 employees — and is stronger for higher occupational layers and for jobs requiring above-median social skills or interpersonal relationships. Political assortative matching is, if anything, larger for parties not in power locally, inconsistent with a patronage mechanism. An event study of 5,262 owners who switched party finds a sharp increase of about 0.2 standard deviations in hires from the new party and a corresponding drop in hires from the old party at the time of the switch, with the share of workers from the new party rising by roughly 5 percentage points persistently. Second, an incentivized resume rating (IRR) field experiment (150 business owners; nondeceptive design) shows that owners rate copartisan resumes 0.213 points higher on a 1–7 Likert scale (a 7.4% increase relative to the mean rating for different-party resumes, statistically significant at p < 0.05), with no significant effect on perceived candidate acceptance probability. Third, a representative survey of 891 owners and 1,003 workers finds that belief-based and taste-based discrimination are ranked as the leading explanations by both groups; 47% of owners and 58% of workers agree with the belief-based discrimination statement. Additionally, 29% of surveyed owners (22% say “Yes” and 7% “In some cases”) explicitly reveal that political views affect their hiring decisions.

Real consequences. Conditional on employment, copartisan workers are promoted faster: they are 0.448 percentage points more likely to be promoted from white-collar to managerial positions (against a base rate of 2.58%) and 0.44 percentage points more likely to be promoted from blue-collar to white-collar positions (base rate 2.98%). Workers from a different party than the owner face a promotion penalty of 0.104–0.180 percentage points for white-collar-to-manager promotions. On wages, copartisan workers earn 3.9% more than unaffiliated coworkers within the same firm and year (firm-year FE specification); the effect is 2.8% when restricting to the same occupation within the firm. Workers from a different party earn 1.6% less. Decomposing by tier: managers (copartisan premium 1.6%), white-collar workers (3.4%), blue-collar workers (1.5%). Despite better outcomes, copartisan workers are 2.1 percentage points (2.3% relative to the mean) less likely to be educationally qualified for their occupation, conditional on firm-year and controlling for a full set of demographics. Finally, a higher share of copartisan workers in the prior year is associated with lower firm employment growth (estimated β = −0.071), corresponding to approximately a 1 percentage point gap in annual growth rate for a one-standard-deviation difference in copartisan share — substantial relative to an average annual growth rate of 10%.

Scope Conditions

All findings pertain to the formal private sector in Brazil over 2002–2019. Political affiliation in the Brazilian system requires an active step and signals strong views; results apply to the approximately 7.8%–11.4% of workers and owners who are party-registered. The field experiment sample is limited to 150 business owners affiliated with major Brazilian parties who were actively seeking to hire. The firm growth result is explicitly characterized as suggestive, without a source of exogenous variation.

In depth

Q1. What is the likelihood ratio index and what does it show for political matching in Brazil?

The likelihood ratio index measures how many times more likely a match between a worker and owner of the same party is, relative to the expected frequency under random matching (conditional on the population shares of each party). Across 2002–2019, the unconditional index ranges from 1.56 to 1.85, implying workers and employers of the same party are on average about twice as likely to match as under random matching. After accounting for geographic sorting within municipalities, the index ranges from approximately 1.41 (2002–2006 average) to 1.67 (2016–2019 average), showing a clear increasing trend. The corresponding gender and race indexes average about 1.2 and 1.35, respectively, in the basic specification, both significantly lower than the party index in every year of the sample.

Q2. How do the dyadic regression estimates control for omitted characteristics, and what do they find?

The dyadic regression constructs all possible worker-firm pairs within each municipality-industry labor market in a given year. The dependent variable is an indicator for whether worker i is employed by firm f. The key coefficient of interest is the differential probability of employment for a copartisan pair relative to a different-party pair, controlling for indicators for shared gender, race, age bracket, and education level, as well as worker occupation fixed effects and experience. This controls for the concern that politically affiliated individuals share non-political traits that correlate with employment choices. After these controls, a politically affiliated worker is 41%–75% more likely (depending on year) to be employed by a copartisan owner than by a different-party owner. The effect stems primarily from copartisan workers being preferentially hired (not just from unaffiliated owners preferring any affiliated worker indiscriminately). The analogous dyadic estimate for shared gender is 15%–31% and for shared race is approximately 3.4%, both lower than the party estimate in all years.

Q3. How is political assortative matching decomposed into hiring versus retention margins?

To isolate the hiring margin, the authors estimate the dyadic regression restricting to newly hired workers (not present in the firm in year t-1). They find that the probability of being hired by a copartisan owner is 32%–59% higher than by a different-party owner across years. The retention (tenure) margin is estimated by regressing the share of subsequent years a worker remains at the firm on partisan alignment at the time of hire. In the most stringent specification (year-of-hire × firm fixed effects), copartisan hires stay 5.5 percentage points longer (as a share of post-hire years) than different-party hires from the same firm and hire-year cohort. Both margins are significant, and both exhibit stronger political sorting than equivalent estimates for gender or race.

Q4. What is the evidence against political patronage as the primary driver of political assortative matching?

If political patronage (parties pressuring owners to hire copartisans) were the main driver, we would expect political assortative matching to be stronger when the owner’s party is in power locally, as those parties have greater leverage over business owners. The authors estimate a modified dyadic regression distinguishing between cases where the owner’s party is in the ruling coalition of the municipal mayor or state governor versus not in power. The results show that political assortative matching is, if anything, larger for parties not in power. This is inconsistent with patronage being the dominant mechanism and consistent with the discrimination channel being driven by owner preferences rather than external political pressure.

Q5. What does the event study of owner party changes show?

The event study tracks 5,262 owners who switch party affiliation during 2002–2019, comparing their firms to control firms in the same market whose owners remain affiliated to the original party. At the time of the switch, there is a sharp increase of approximately 0.2 standard deviations in hires from the owner’s new party and a corresponding sharp decrease in hires from the old party. Hires from other parties and unaffiliated hires also decline modestly. The share of the workforce affiliated with the new party increases by roughly 5 percentage points and remains elevated in subsequent years. Because nonpolitical network ties (shared school, neighborhood, sports team) are unlikely to dissolve abruptly when an owner changes party, this design provides additional evidence that the change in hiring is driven by a direct change in the owner’s political preferences rather than by network overlap.

Q6. What was the design of the incentivized resume rating experiment and why does it identify political discrimination?

The experiment was conducted with 150 Brazilian business owners recruited from the administrative data (who are already known to be affiliated with one of six major parties), targeting owners with active hiring interest through a leading job platform. Owners rated 20 synthetic resumes with fully randomized features (education, experience, training, skills, formatting). Sixteen resumes had no partisan cues; two contained cues signaling copartisanship with the rating owner; two signaled a party from the opposite side of the political spectrum. Incentives were provided by committing to send respondents real job-seeker profiles from the platform chosen by machine learning based on revealed preferences. Because all resume features other than the partisan cue were randomized, the experiment shuts down shared nonpolitical networks and patronage as explanations; the only channel is the employer’s direct preference for the candidate’s partisan affiliation. The response rate was 11% and the survey was conducted March–May 2022.

Q7. What is the quantitative magnitude of the field experiment result?

Owners rate copartisan resumes 0.213 points higher on the 1–7 Likert scale relative to resumes from the opposite side of the political spectrum (statistically significant at p < 0.05), representing a 7.4% increase relative to the mean rating of different-party resumes (2.950). When resume-level controls (gender, high-skill experience flag, years of experience, programming skills, training) are added, the estimate is 0.254. There is no statistically significant effect on owners’ perceived likelihood that a candidate would accept a job offer (coefficient 0.150–0.158, not significant), suggesting that the observed difference in interest ratings reflects a genuine direct preference for copartisans, not an expectation that copartisans are more likely to accept.

Q8. What do the survey findings add about mechanisms and the prevalence of political discrimination?

The survey of 891 owners and 1,003 workers (response rate 26.84%) presents five candidate mechanisms and asks respondents to evaluate each. Both groups rank belief-based discrimination (owners believe copartisans would be more productive) as the most likely explanation: 47% of owners and 58% of workers partially or strongly agree. Taste-based discrimination is second (36% owners, 52% workers agree), followed by networks (39% owners, 49% workers). Patronage and workers’ preferences attract little agreement from either group. Among owners ranked by single strongest agreement, 29.7% most strongly agree with belief-based discrimination and 22.0% with taste-based, while 29% of all surveyed owners explicitly stated that political views do affect their hiring decisions. These patterns are broadly similar regardless of the respondent’s own political affiliation status.

Q9. How large are the political promotion and wage premia, and how do they compare to gender and race effects?

For promotions, copartisan white-collar workers are 0.448 percentage points more likely to be promoted to manager (relative to unaffiliated co-workers hired in the same firm-year), against a base promotion rate of 2.58% — an effect of approximately 17% of the mean. For blue-collar-to-white-collar promotion, the copartisan premium is 0.44 percentage points against a base rate of 2.98%. For wages, copartisans earn 3.9% more than unaffiliated co-workers within the same firm and year; restricting to the same occupation within the firm, the premium is 2.8%. The political wage premium (3.9%) exceeds the gender wage premium (1.5%) and the race wage premium (1.0%) in the same specification. Workers from a different party than the owner earn 1.6% less than unaffiliated co-workers within the same firm-year.

Q10. Are copartisan workers better qualified than those they displace, and what does this imply for firm performance?

Copartisan workers are significantly less qualified in terms of education relative to their occupation: they are 2.1 percentage points less likely to be educationally qualified for their position than their unaffiliated co-workers within the same firm-year (2.3% relative to the mean qualification rate of 93.2%), with the largest effects for managers. Workers of a different party show only a small and economically negligible qualification gap. The fact that copartisans are paid more, promoted faster, and yet are less qualified is consistent with political discrimination substituting for competence in personnel decisions. The qualification shortfall is specifically attributed to copartisanship and not to shared gender, race, age, or education between owner and worker, as those coefficients are economically small.

Q11. What is the evidence on firm growth and what are the limitations of that evidence?

Firms with a higher share of copartisan workers in the prior year grow less. The estimated coefficient β = −0.071, and a one-standard-deviation difference in the copartisan share is associated with approximately a 1 percentage point gap in annual employment growth, relative to a mean growth rate of 10%. The specification compares firms of the same size and with the same number of affiliated workers in the same year. The result is robust to adding municipality and municipality-industry fixed effects. The authors explicitly characterize this evidence as suggestive, noting the absence of an exogenous source of variation in political discrimination. The negative association is more consistent with taste-based discrimination (Becker, 1957) — in which politically homogeneous firms sacrifice productivity for the owners’ amenity of employing copartisans — than with accurate belief-based discrimination.

Q12. How is political assortative matching distributed across parties and does it depend on party ideology?

The likelihood ratio index shows large assortative matching across the entire political spectrum. For most years, relatively more ideologically extreme parties — on the left (PT, PDT) and on the right (PP, DEM) — display higher assortative matching than more centrist parties (PMDB, PSDB). This pattern is consistent with stronger partisan identity at the extremes leading to stronger preferences for copartisan workers, but the paper does not formally model the mechanism behind this heterogeneity.

Q13. What is the role of workers’ preferences as opposed to employers’ discrimination, and how can wages distinguish them?

If workers have a preference for working with copartisan owners (treating this as a job amenity), compensating differentials theory would predict a negative wage premium for copartisan workers — they would accept lower wages in exchange for working with like-minded owners. The data show the opposite: copartisan workers earn significantly more, not less, than their unaffiliated co-workers. This evidence is inconsistent with workers’ preferences being the primary driver of political assortative matching, and is instead consistent with employers’ discrimination. The survey evidence corroborates this: both owners and workers assign low priority to the “workers’ preferences” mechanism.

Key Concepts

Political assortative matching: The phenomenon by which workers and business owners belonging to the same political party are matched in the labor market at rates significantly exceeding what would occur under random matching within the local labor market. Measured via the likelihood ratio index and dyadic regressions that control for shared demographic characteristics. In this paper, political assortative matching is larger in magnitude than assortative matching along gender or racial lines.

Likelihood ratio index (S): A measure of assortative matching defined as the weighted sum of the ratios of observed same-party co-occurrence probabilities to their expected probabilities under random matching. S > 1 indicates positive assortative matching. The paper uses both a basic version and a geography-adjusted version that computes the index within municipalities to control for geographic concentration of party membership.

Dyadic regression: A regression approach that constructs all possible worker-firm pairs within a defined labor market (municipality × 2-digit industry) to estimate the differential probability that a worker is employed by a copartisan firm relative to a different-party firm. The key advantage is the ability to control simultaneously for multiple shared demographic characteristics between worker and owner, accounting for the correlation of assortative criteria.

Incentivized resume rating (IRR) experiment: A nondeceptive field experiment design (following Kessler et al., 2019) in which business owners rate synthetic resumes with fully randomized characteristics. Truthful rating is incentivized because respondents are told that their revealed preferences will be used to select real job-seeker profiles sent to them by a partner platform via machine learning. This design allows direct identification of employer preference for copartisan candidates while ruling out alternative channels such as shared nonpolitical networks or patronage.

Political wage premium: The percentage wage difference earned by copartisan workers relative to unaffiliated co-workers within the same firm-year (and occupation), after controlling for a full set of socio-demographic characteristics. A positive political wage premium is the paper’s primary piece of evidence that workers’ compensating differentials cannot explain political assortative matching, since amenity-based sorting would predict a negative premium.

Political promotion premium: The differential probability that a copartisan worker is promoted to a higher organizational layer (blue-collar to white-collar, or white-collar to manager) relative to an unaffiliated co-worker hired in the same firm and year, net of demographic controls.

Educational mismatch (Qualified): An indicator variable equal to one if a worker’s educational level meets or exceeds the educational level required by their specific occupation in the CBO (Classificação Brasileira de Ocupações) classification. Used to assess whether politically favored (copartisan) workers are less competent along this observable dimension.

Belief-based discrimination vs. taste-based discrimination: Two distinct theoretical channels for employer political discrimination. Belief-based discrimination (Phelps, 1972; Arrow, 1973) occurs when employers perceive copartisans to be more productive — e.g., because shared political views reduce intra-firm conflict. Taste-based discrimination (Becker, 1971) occurs when employers have a direct utility-affecting preference for copartisan workers, independent of productivity beliefs. The paper treats these as observationally distinct from patronage and network overlap, and uses the negative correlation between political homogeneity and firm growth as suggestive evidence favoring the taste-based channel.

Why Is Intermediating Houses So Difficult? Evidence from iBuyers

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

This paper examines frictions in dealer intermediation in durable consumer goods markets, using iBuyers — technology-driven real estate companies such as Opendoor and Offerpad — as a lens. The central research question is why dealer intermediation, which provides immediate liquidity by purchasing assets onto a balance sheet and reselling, is so limited in the U.S. housing market (valued at $50 trillion and representing roughly 70% of the median household’s net worth) relative to other durable goods markets such as automobiles.

The authors use CoreLogic deed transaction data and MLS listing data from five markets with substantial iBuyer presence (Phoenix, Las Vegas, Dallas, Orlando, and Gwinnett County, Georgia) over 2013–2018, covering arm’s-length, non-foreclosure single-family home and condominium transactions. They supplement this with Redfin ZIP-level data on listing speed and American Community Survey demographics. iBuyers are identified as Opendoor, Offerpad, Knock, Zillow, and Redfin.

The empirical analysis documents that iBuyers grew from roughly 1% market share in Phoenix in 2015 to about 6% by 2018, acting as balance-sheet intermediaries who hold properties for a median of 105 days. iBuyers purchase homes at a 3.1 percentage point (pp) discount relative to comparable homes sold in the same ZIP-quarter, and sell at a 2.2 pp premium relative to other institutional sellers, for a combined gross spread of approximately 5.3 pp (reported in the abstract and body as ~5%). Sellers to iBuyers show a 6.8 pp higher rate of market exit post-sale and a 4.0 pp higher probability of purchasing before selling, consistent with demand for immediacy from impatient, relocating households.

Two key frictions constrain intermediation. First, adverse selection: iBuyers rely on algorithmic valuation models (AVMs) that explain over 80% of price variation in iBuyer transactions versus only 68% in non-iBuyer transactions, leaving a residual of soft information (odor, neighbor quality) that sellers know but algorithms cannot capture. iBuyer presence is over three times greater in the lowest pricing-uncertainty tercile versus the highest, and a one standard deviation increase in pricing uncertainty reduces iBuyer presence by 1.23 pp within a ZIP and reduces gross spread per transaction by 1.5 pp. Second, underlying illiquidity: iBuyers are almost entirely absent in market segments where the probability of sale within three months (PSALE) falls below 50%, despite strong seller demand.

To quantify these frictions, the authors build and calibrate a continuous-time directed search equilibrium model with a dealer intermediary subject to adverse selection. Six parameters are calibrated to match empirical moments: iBuyer market share (5%), purchase discount (3.1 pp), sale premium (2.2 pp), iBuyer concentration in the most versus least liquid PSALE quartiles, impatient seller fraction, and median iBuyer holding time. The calibrated adverse selection parameter (α = 0.35) means the intermediary correctly identifies 35% of low-quality homes as such; the impatient seller share (μ = 0.18) means 18% of unmatched sellers are highly impatient; and the vacancy depreciation rate (d = 0.02) means 2% per period for unoccupied homes. External validation via a difference-in-differences comparison of Phoenix against other markets yields model-consistent predictions of a 0.5 pp reduction in time on market and a 0.8 pp increase in house prices.

Counterfactual experiments reveal that introducing a 30-day acquisition delay (rather than near-instantaneous) reduces iBuyer market share from 5% to below 2%; eliminating the signal entirely (α = 0) drops market share to just above 1%; and enabling iBuyers to rent vacant properties during the holding period could raise market share above 7.5 pp. A 50% reduction in PSALE reduces iBuyer market share roughly proportionally.

The calibrated model is then applied to other durable goods markets by varying informational asymmetry, liquidity, and depreciation parameters. Cars — more homogeneous (year/make/model/mileage fully characterizes value), mobile (transportable across markets), and depreciating primarily through use — are predicted to support dealer intermediary market shares of 40–55%, consistent with observed U.S. car dealer market share of ~50%. Reducing the depreciation rate from the housing level (d = 0.02) to a car-like level (d = 0.005) alone increases intermediary market share by about 5 pp. Houses — heterogeneous, immobile, and depreciating through time rather than use — are predicted to support near-zero intermediation under pre-iBuyer technology. The authors also explain COVID-19 iBuyer suspensions (reduced market liquidity made resale untenable) and Zillow’s November 2021 exit (very liquid markets eroded the iBuyer speed premium, worsening adverse selection while rapid price appreciation degraded AVM accuracy).

Q: What discount do iBuyers pay when purchasing homes, and what premium do they earn when selling? A: iBuyers purchase homes at a 3.1 pp discount relative to comparable homes sold in the same ZIP code and quarter, with a t-statistic of 8.55. They sell at a 2.2 pp premium relative to other institutional sellers. The combined gross spread is approximately 5.3 pp (referred to throughout the paper as roughly 5%).

Q: How large is the iBuyer market share, and in which markets did they operate? A: iBuyer market share grew from approximately 1% in Phoenix in 2015 to roughly 6% by 2018. In Gwinnett County, Las Vegas, and Dallas/Orlando, shares reached approximately 4%, 4%, and 2% respectively by 2018. The analysis covers five markets: Phoenix, Las Vegas, Dallas, Orlando, and Gwinnett County (suburban Atlanta).

Q: What is the evidence that iBuyer sellers are impatient rather than simply lower-quality-house owners? A: Sellers to iBuyers exhibit a 6.8 pp higher rate of market exit (defined as purchasing a home outside the county or making no subsequent real estate purchase within 12 months), consistent with relocation-driven impatience. They also have a 4.0 pp higher probability of purchasing a new home before completing the sale of their current home, which is enabled by the iBuyer transaction’s speed facilitating mortgage approval conditional on the existing property’s sale.

Q: How do the authors measure adverse selection risk and what is its relationship to iBuyer presence? A: Adverse selection is proxied by the squared residual from a hedonic pricing regression — the variation in transaction prices unexplained by observable characteristics — computed at the ZIP-year level for non-iBuyer transactions. iBuyer presence is over three times greater in the lowest pricing-uncertainty tercile than in the highest. A one standard deviation increase in pricing uncertainty reduces iBuyer presence by 1.23 pp within a ZIP (controlling for ZIP fixed effects, local prices, house age, and square footage), and reduces gross spread per transaction by 1.5 pp.

Q: What role does underlying asset liquidity play in constraining iBuyer intermediation? A: iBuyers concentrate almost entirely in market segments where the ex ante probability of selling within three months (PSALE) exceeds 50%, and are essentially absent where PSALE falls below 50%. This holds even though sellers in low-PSALE segments have strong demand for immediacy, implying that illiquidity raises intermediation costs above the demand-side willingness to pay a discount.

Q: What does the model’s calibration reveal about the share of impatient sellers and the accuracy of iBuyer signals? A: The calibrated adverse selection parameter α = 0.35 means the intermediary correctly identifies 35% of low-quality homes as low quality (the signal is moderately but imperfectly informative). The calibrated impatient seller share μ = 0.18 means approximately 18% of unmatched sellers are highly impatient and willing to accept a significant price discount for immediacy. The vacancy depreciation rate d = 0.02 implies a 2% per period cost for unoccupied properties.

Q: How important is transaction speed to the iBuyer model? A: Introducing a 30-day acquisition delay (rather than near-instantaneous purchase) reduces iBuyer market share from 5% to below 2% — a reduction of more than 60%. The model mechanism is that the primary iBuyer customers are highly impatient sellers who place extreme value on immediate transactions; even a moderate delay substantially reduces their willingness to accept a price discount.

Q: What happens if iBuyers lose their ability to distinguish between high- and low-quality homes? A: Setting the signal accuracy to zero (α = 0, the “naive intermediary” case) causes iBuyer market share to fall from 5% to just above 1%. Without any quality signal, severe adverse selection forces the intermediary to offer substantially lower prices to break even, which in turn reduces the number of sellers willing to transact.

Q: How much would enabling iBuyers to rent vacant properties during the holding period affect market share? A: The rental-enabled iBuyer counterfactual shows that market share could increase above 7.5 pp from the baseline 5%, because rental income would allow iBuyers to offer higher purchase prices while offsetting carrying costs. This suggests that rental infrastructure or policy changes permitting temporary rentals would substantially expand the scope of dealer intermediation in housing.

Q: How does the model validate itself externally? A: The authors use a difference-in-differences design comparing Phoenix (earlier and larger iBuyer entry) to the other four markets. The model predicts iBuyer entry should reduce average time on market and increase house prices; the DiD results show a 0.5 pp reduction in time on market and a 0.8 pp increase in house prices in Phoenix relative to comparison markets post-entry, consistent with model predictions.

Q: Why did iBuyers suspend operations during the COVID-19 pandemic despite having a contactless technological advantage? A: The model explains the suspension through the liquidity channel: iBuyers’ value proposition depends on quickly reselling acquired properties, not merely on contactless buying. When market liquidity collapsed during lockdowns (transaction volumes fell sharply), iBuyers could not resell properties quickly, making intermediation unprofitable regardless of their purchasing-side technological advantage. As liquidity recovered, iBuyers resumed operations.

Q: What does the model say about Zillow’s exit from iBuying in November 2021? A: In very liquid markets, the iBuyer speed advantage shrinks because homeowners can sell quickly in the traditional market anyway, reducing the discount sellers accept when selling to an iBuyer. With a smaller discount, adverse selection worsens because only sellers with unfavorable private information (knowing their house has problems the algorithm overvalued) choose the iBuyer route. The pandemic-era housing market also featured rapid price appreciation that degraded AVM accuracy trained on historical data, compounding adverse selection. Zillow reported having significantly overpaid for homes, consistent with this mechanism.

Q: Why is dealer intermediation approximately 50% in car markets but near-zero historically in housing? A: The model, applied to car-market parameters, predicts 40–55% dealer intermediation, consistent with observed U.S. car market shares. Three structural differences explain the gap: (i) cars are more homogeneous (year/make/model/mileage sufficiently characterizes value), reducing adverse selection; (ii) cars are mobile and can be transported across markets, increasing effective liquidity; and (iii) cars depreciate primarily through use, so holding a car on a dealer lot incurs lower value loss than leaving a house vacant. Reducing the depreciation rate from the housing calibration (d = 0.02) to a car-like level (d = 0.005) alone raises predicted intermediary market share by about 5 pp.

Q: Does subjective value dispersion (heterogeneity in buyer preferences) play a large role in limiting intermediation? A: While subjective value dispersion plays a significant role in shaping search market equilibrium (affecting match quality and the gains from household-to-household search), the model finds its effect on the overall level of intermediation is comparatively less pronounced than informational asymmetry, market liquidity, or the opportunity cost of vacancy.

Q: What evidence supports the claim that iBuyers use algorithmic pricing? A: Observable property characteristics and ZIP-quarter fixed effects explain over 80% of price variation in iBuyer transactions, compared to only 68% in non-iBuyer transactions. The higher R-squared for iBuyer transactions is consistent with iBuyers relying on measurable, formalizable characteristics rather than soft information (such as odors or neighbor property conditions) that traditional buyers gather through physical visits.

Q: What are the structural limits on iBuyer expansion even with improved technology? A: Even with enhanced pricing technology (lower α), the scope for dealer intermediation remains narrow because strong incentives persist for iBuyers to avoid markets where algorithmic valuation is difficult, such as older and less homogeneous housing stock. The fundamental barriers — heterogeneity, immobility, and high vacancy opportunity cost — cannot be overcome by technology alone, meaning iBuyers are unlikely to reach the ~50% market share seen in automobile dealer markets.

iBuyers: Technology-driven real estate companies (principally Opendoor and Offerpad) that use automated valuation models and online platforms to make near-instantaneous cash offers on homes, functioning as dealer intermediaries who purchase properties onto their balance sheet and resell after a short holding period, thereby providing immediate liquidity to sellers who would otherwise wait 90+ days in the traditional listing process.

Dealer (Balance Sheet) Intermediation: A form of market-making in which an intermediary purchases an asset outright and holds it on its own balance sheet while finding a subsequent buyer, as distinct from matchmaking intermediaries (brokers) who connect buyers and sellers without taking ownership. The intermediary earns a gross spread between purchase and sale prices.

Adverse Selection (in iBuyer context): The problem arising because sellers possess soft private information about their property (odors, hidden defects, neighbor quality) that algorithmic valuation models cannot capture, while traditional buyers can acquire this information through physical visits. Because iBuyers price quickly without visits, they disproportionately attract sellers of unobservably lower-quality homes, as measured in the paper by the calibrated parameter α = 0.35 (the fraction of low-quality homes the intermediary correctly identifies).

Algorithmic Valuation Model (AVM): The pricing technology used by iBuyers to value homes near-instantaneously using observable property characteristics. The paper measures AVM performance by the R-squared of a hedonic regression: over 80% for iBuyer transactions versus 68% for non-iBuyer transactions, with the residual representing information the algorithm misses and traditional buyers discover through visits.

PSALE (Probability of Sale within 3 Months): An ex ante measure of a property’s underlying liquidity, estimated from a probit model on non-iBuyer listings, capturing the probability that a given home sells within three months of listing. The paper uses PSALE as the key liquidity variable; iBuyers are almost entirely absent where PSALE falls below 50%.

Occupancy Cost: The value loss incurred when a house is held vacant on an intermediary’s balance sheet — encompassing both foregone housing service flows (which continue to benefit occupants under traditional listing but are lost under iBuyer ownership) and ongoing maintenance and depreciation costs (calibrated at d = 0.02 per period). This cost distinguishes housing from goods like cars that depreciate primarily through use rather than time.

Gross Spread: The difference between the price at which an iBuyer sells a property and the price at which it purchased that property, expressed as a percentage of the acquisition price. The paper documents a gross spread of approximately 5% (combining the 3.1 pp purchase discount and the 2.2 pp sale premium), which is persistently positive over the sample period.