G22 | Macro Paper Warehouse

Competing under Information Heterogeneity: Evidence from Auto Insurance

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

This paper studies imperfect competition in selection markets where competing firms have heterogeneous information about consumers — a layer of asymmetry distinct from the classic buyer-seller information gap. The central questions are: how do inter-firm information asymmetries shape equilibrium pricing, consumer sorting, and market efficiency; and whether a centralized bureau that aggregates and equalizes firms’ risk information can promote competition and improve welfare.

The empirical setting is the Italian mandatory motor vehicle liability insurance market (Responsabilità Civile Auto). The authors use the IPER dataset from IVASS, a nationally representative panel of matched insurer-insuree contracts covering 124,428 liability insurance contracts for new customers in the province of Rome from 2013 to 2021. The panel tracks consumers across insurer switches, enabling construction of individual-specific risk estimates from ex-post claim records using Poisson regressions for claim frequency and log-normal regressions for claim severity. The analysis focuses on the top 10 largest firms plus a composite fringe firm.

The paper’s empirical strategy proceeds in three stages. First, individual risk types are estimated from multi-year claim panels. Second, demand parameters — price sensitivity and firm-level unobserved product attributes — are recovered using a novel fixed-point algorithm (extending Berry et al. 1995) that infers the full offered-price distribution from observed transaction prices alone, without parametric restrictions on price distributions across firms. Third, supply-side parameters — pricing coefficients, signal variances, and cost parameters — are identified by exploiting the monotone mapping between offered prices and private signals, borrowing from the nonparametric auction literature.

The model features firms that each draw a private Gaussian signal about a consumer’s true risk type theta, with firm-specific signal standard deviation sigma_j. Lower sigma_j means higher information precision. Firms set prices as a linear function of their posterior risk rating: p_j = alpha_j + beta_j * E(theta | theta_j, D=j). Firms simultaneously choose pricing coefficients to maximize expected profits.

Key empirical findings: (1) Firms differ substantially in how sensitively their premiums respond to realized consumer risk — a reduced-form measure of information precision — with Figure 2 showing wide cross-firm variation in premium-to-risk coefficients. (2) Structural estimation confirms substantial heterogeneity in signal standard deviations sigma_j across all 11 firms. Firms with less accurate risk-rating algorithms (higher sigma_j) tend to have more efficient cost structures (lower claim-processing cost parameter k_j), generating distinct comparative advantages. (3) Baseline pricing coefficients alpha_j and risk-sensitivity coefficients beta_j vary dramatically across firms. (4) Senior drivers are less price sensitive; urban drivers are more price sensitive. Lower-risk consumers show stronger preferences for Firms 3 and 5, while higher-risk consumers disproportionately choose Firm 8.

Counterfactual simulations assess three information policies relative to the baseline. Under a centralized risk bureau — which collects each firm’s signal, aggregates them weighted by precision, and distributes the combined signal equally — average premiums fall by 21.6% and consumer surplus rises by 15.7%. The efficiency benchmark (firms observe true risk perfectly) yields a 25.7% premium reduction and a 16.9% consumer surplus gain, so the bureau recovers almost all the efficiency gap. The privacy benchmark (all firms restricted to the coarsest signal in the market) raises surplus for high-risk consumers by 6.9% but harms low-risk consumers.

The bureau’s price reduction operates through two channels: it eliminates the market power that accrues to firms with superior private information, and it aligns firms’ risk evaluations, enabling sharper undercutting. The bureau also reduces average costs by 12 euros per contract by enabling more efficient insurer-insuree matching — cost-efficient claim processors can better target the consumer types they have a comparative advantage in serving.

The analysis is confined to new customers in Rome’s provincial market to avoid complications from dynamic pricing and consumer-firm learning. The model abstracts away from optional contract clauses (treated as observable characteristics) and does not model the specific mechanisms generating information heterogeneity.

Q: What is the paper’s core research question? A: The paper asks how information asymmetries between competing firms (not just between buyers and sellers) shape equilibrium pricing strategies, consumer sorting, and market efficiency in a selection market, and whether a centralized bureau that equalizes firms’ access to aggregated risk information can improve competition and welfare. This extends the classic Akerlof-Rothschild-Stiglitz framework by introducing a second layer of asymmetry — across sellers themselves.

Q: Why is the Italian auto insurance market well suited for this study? A: Italy mandates liability insurance for all drivers and prohibits rejections, so the analysis focuses entirely on how consumers sort across insurers rather than on participation margins. The IPER dataset from IVASS is a nationally representative panel tracking policyholders even across insurer switches, providing both premium and ex-post claim records needed to construct individual risk types. The market has roughly 50 competing firms using demonstrably heterogeneous pricing algorithms, documented through a survey of major insurers and reduced-form regressions.

Q: How do the authors measure firm-level information precision in the reduced-form analysis? A: They estimate individual-specific risk types from a panel of claim records using Poisson regressions (claim frequency) and log-normal regressions (claim severity), then regress each firm’s premiums on those estimated risk measures. Firms whose premiums respond more sensitively to realized risk are inferred to have higher information precision. Figure 2 shows that these premium-to-risk coefficients vary significantly across firms — for example, Firm 7’s premiums are considerably more sensitive to risk than Firm 8’s — providing reduced-form evidence of heterogeneous information precision before any structural estimation.

Q: What is the structural model’s signal structure? A: Each firm j draws a private signal theta_j ~ N(theta, sigma_j^2) about a consumer’s true risk type theta, where sigma_j is the firm-specific signal standard deviation. A smaller sigma_j means higher precision. Signals are independent across firms conditional on theta, analogous to common-value auctions where firms receive noisy estimates of a shared unknown value (expected claim payouts). The parameter sigma_j is the key structural object the paper identifies and estimates.

Q: What is novel about the demand estimation strategy? A: Standard demand estimation assumes the same price is offered to all consumers or that the full price menu is observed. Here, only transaction prices are observed — the prices of unchosen insurers are not in the data. The authors apply the Wu and Xin (2024) fixed-point algorithm, which jointly estimates consumers’ sorting probabilities, offered price distributions, and demand parameters by adding an outer loop over sorting propensities to the Berry (1994) contraction mapping. No parametric restrictions are imposed on the offered price distributions, and they are allowed to vary fully across firms.

Q: How are firms’ signal variances identified separately from pricing coefficients? A: There is a one-to-one mapping between a firm’s offered price and its signal (prices increase monotonically in the signal, analogous to bids in auctions). After recovering the offered price distribution from the demand step, the authors observe price dispersion at a fixed risk level. By focusing on average prices conditional on each risk level, signal noise averages out, identifying the pricing coefficients beta_j. The residual price dispersion at fixed risk then identifies signal variance sigma_j^2.

Q: What does structural estimation reveal about the relationship between information precision and cost efficiency? A: Firms with higher signal standard deviations (less precise risk evaluation) tend to have lower claim-processing cost parameters k_j — they are more efficient at handling claims. This creates distinct comparative advantages: some firms excel at risk identification but face higher processing costs, while others process claims cheaply but evaluate risk less precisely. This heterogeneity means information-equalizing policies have differentiated firm-level impacts.

Q: What are the quantitative effects of the centralized risk bureau on premiums and consumer surplus? A: The bureau reduces average premiums by 21.6% relative to baseline and increases consumer surplus by 15.7%. The efficiency benchmark — where firms observe consumers’ true risk perfectly — produces a 25.7% premium reduction and a 16.9% consumer surplus gain. The bureau therefore closes nearly all of the gap to the first-best allocation in surplus terms (15.7% vs. 16.9%).

Q: Through what mechanisms does the bureau reduce prices? A: Two distinct channels are identified. First, equalizing information precision eliminates the informational market power held by firms with superior signals, compelling them to compete more aggressively on price. Second, when all firms share the same risk evaluation of a consumer, they can undercut each other more precisely, which intensifies price competition further. Both channels operate simultaneously under the bureau.

Q: How does the bureau affect consumer surplus distribution across risk types? A: The bureau primarily benefits low-risk consumers because improved information allows firms to price discriminate more accurately on risk type, lowering prices for those who are low risk. High-risk consumers see smaller benefits and may face relatively higher premiums. This contrasts with the privacy benchmark, where restricting all firms to the coarsest signal in the market raises high-risk consumers’ surplus by 6.9% — because it becomes harder for firms to distinguish them from low-risk consumers.

Q: What is the cost efficiency effect of the bureau? A: Under the centralized risk bureau, average costs per contract fall by 12 euros. This reflects more efficient insurer-insuree matching: when firms have equal and better information, those with cost advantages in claims processing can better identify and attract the consumer types they are relatively best equipped to serve. The authors note that given the scale of the Italian auto insurance market (approximately 31 million contracts annually), this per-contract saving implies a substantial aggregate impact.

Q: What happens to firm profits under the bureau, and is the impact uniform? A: Average profits decline overall due to lower prices. However, the impact is heterogeneous across firms. Firms that rely most heavily on superior information precision — often smaller, more specialized firms — experience greater profit losses, since the bureau most directly erodes their competitive advantage.

Q: How does the privacy benchmark differ from the bureau scenario? A: The privacy benchmark simulates a regulation that restricts all firms to using only basic consumer information, setting signal variance to the highest level observed in the market. Unlike the bureau (which improves and equalizes information), this benchmark degrades information uniformly. It produces opposite distributional effects: high-risk consumers gain 6.9% in surplus as cross-subsidization from low-risk to high-risk consumers increases, while low-risk consumers are worse off.

Q: Why does the paper focus on new customers only? A: Focusing on new customers avoids complications from dynamic pricing, where insurers update premiums based on accumulated claim history with a specific consumer, and from consumer-firm learning dynamics. This follows standard practice in the empirical asymmetric information literature, as cited in Chiappori and Salanie (2000) and Crawford et al. (2018).

Q: How does this paper relate to and extend prior work on selection markets? A: Prior empirical work on imperfect competition in selection markets — including Einav et al. (2010), Crawford et al. (2018), and related studies — assumes that competing firms have symmetric information about consumers. This paper is described as introducing the first tractable empirical framework for analyzing selection markets where firms have heterogeneous information. It also incorporates multidimensional cost heterogeneity on the supply side, adding to work by Salanié (2017) and Nelson (2025).

Q: What do the reduced-form regressions reveal about pricing heterogeneity across insurers? A: Firm-level regressions of premiums on observable risk factors show R-squared values ranging from 0.39 to 0.59. Estimated coefficients on key risk factors vary dramatically: being one year older reduces premiums by 0.25 to 1.68 euros depending on the firm; a higher bonus-malus class increases premiums by 12 to 32 euros; one additional accident in the previous five years raises premiums by 74 to 181 euros. These ranges reflect genuine differences in actuarial algorithms, not just sampling variation.

Q: What is the bonus-malus system and why does its saturation matter for the paper’s setting? A: Italy’s bonus-malus (BM) system assigns drivers to one of 18 risk classes based on accident history. Because approximately 80% of policyholders are in the best class (BM class 1), the public BM system provides limited granularity for risk evaluation. This saturation creates strong incentives for firms to develop proprietary risk-rating algorithms, which is the institutional basis for the substantial information heterogeneity that the paper documents and models.

Information Precision (sigma_j): In the paper’s model, the firm-specific parameter measuring the dispersion of a firm’s private signal about a consumer’s true risk type. Firm j draws signal theta_j ~ N(theta, sigma_j^2); 1/sigma_j is information precision. A smaller sigma_j means the firm more accurately identifies consumer risk. This is not merely a theoretical construct — the paper identifies and estimates sigma_j structurally for each of the 11 firms.

Heterogeneous Information: The condition where competing firms hold signals of different precision about the same consumer’s unobserved risk type, introducing asymmetry not just between buyers and sellers (as in Akerlof 1970) but among sellers themselves. This is the paper’s central departure from prior literature on selection markets, which assumed symmetric information among firms.

Centralized Risk Bureau: A policy institution that collects each firm’s analyzed risk signal, aggregates them weighted by each firm’s information precision (producing a combined signal more precise than any individual firm’s signal), and makes the aggregated information equally accessible to all firms. The bureau is the paper’s primary policy counterfactual, and it is modeled as equalizing both the level and heterogeneity of information precision across competitors.

Offered vs. Accepted Price Distribution: A distinction central to the paper’s identification strategy. The accepted price distribution is what is observed in transaction data — prices conditional on the consumer having chosen that firm. The offered price distribution is the full set of prices the firm would charge across all consumers, including those who did not select it. The paper recovers the offered distribution from the accepted distribution using a fixed-point algorithm, without imposing parametric restrictions.

Selection Loop: The paper’s methodological extension of the Berry (1994) BLP contraction mapping for mean utilities. An outer loop iterates over consumers’ sorting propensities to jointly recover offered price distributions, sorting probabilities, and demand parameters when only transaction prices are observed. This technique handles the endogeneity of which prices are accepted.

Risk Rating: The firm’s posterior assessment of a consumer’s expected cost, computed as the posterior mean E(theta | theta_j, D=j) — the expected true risk type conditional on the firm’s private signal and the consumer selecting that firm. Firms set prices as a linear function of their risk rating: p_j = alpha_j + beta_j * E(theta | theta_j, D=j).

Comparative Advantage (information vs. cost): The paper’s finding that firms with lower information precision (higher sigma_j) tend to have more efficient cost structures (lower k_j), and vice versa. This cross-sectional negative correlation between information advantage and cost advantage means that policy interventions that equalize information precision shift the basis of competition from information asymmetry to cost specialization.

Firm Accommodation After Workplace Disability: Labor Market Impacts and Implications for Subsidy Design

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

Layer 1 — Overview

Research Question

This paper studies (1) how firm accommodation decisions respond to financial incentives in the context of workplace disability under workers’ compensation, (2) what the causal effect of accommodation is on workers’ subsequent labor market outcomes, and (3) whether the equilibrium level of accommodation is socially efficient, and what the welfare implications of wage subsidies for accommodation are.

Empirical Context and Data

The analysis uses the universe of Oregon workers’ compensation claims from 2005 through 2017 — over 131,000 disabling claims — linked to longitudinal quarterly earnings records from the Oregon Employment Department. The setting exploits Oregon’s Employer at Injury Program (EAIP), which subsidizes employers who provide “transitional work” accommodations (primarily through wage subsidies) to workers with temporary workplace disabilities. EAIP accounts for roughly 25 percent of claims on average, with the wage subsidy component representing over 96 percent of EAIP expenses.

Identification Strategy

The authors exploit a policy change in July 2013 that reduced the EAIP wage subsidy rate from 50 percent to 45 percent. They construct a firm-level “exposure” measure — the fraction of a firm’s claims that used EAIP in a baseline period (2005–2009) — and estimate a continuous difference-in-differences specification in which the interaction of exposure and a post-2013 indicator instruments for accommodation. The identifying assumption is strong parallel trends: firms with low baseline exposure are unlikely to respond to the subsidy reduction, while high-exposure firms respond more, generating cross-firm variation in accommodation rates after 2013. An MTE framework (Heckman and Vytlacil 2005) is then used to explore heterogeneous treatment effects along an unobserved resistance-to-treatment dimension.

Main Empirical Findings

The subsidy reduction from 50% to 45% decreased accommodation rates by 2.9 percentage points (9.3 percent) for claims in firms with average exposure, implying a subsidy elasticity of accommodation of 0.9.
The policy change led to a 0.95 percentage point decrease in employment and a $120 decrease in quarterly earnings four quarters after disability for claims in average-exposure firms (roughly 1.3–1.5 percent declines relative to means), with no significant effect on worker turnover to other firms.
IV estimates of the effect of accommodation itself (using predicted EAIP as instrument) show accommodation increases the probability of employment four quarters after disability by 33 percentage points and increases quarterly earnings by approximately $4,100.
The MTE analysis reveals negative selection on gains: workers with workplace disabilities who are least likely to receive accommodation have the highest potential gains from it, driven largely by severe disabilities with high accommodation costs.
Descriptive and IV evidence is consistent with accommodation operating primarily as general human capital investment: accommodation has no statistically significant effect on the probability of moving to a new firm, and earnings gains are not systematically lower for workers who change employers after accommodation.

Structural Model and Counterfactual Findings

A two-period frictional labor market model with risk-averse workers, risk-neutral firms, Nash bargaining, imperfect experience rating in workers’ compensation, and firm accommodation as human capital investment is developed and estimated. Two inefficiency sources are identified: (1) a human capital externality — because accommodation builds general human capital, firms cannot capture the full surplus when workers separate, reducing accommodation incentives; and (2) a fiscal externality — imperfectly experience-rated firms do not fully internalize the workers’ compensation cost savings from accommodation, further depressing it below the efficient level. Counterfactual simulations show:

Eliminating wage subsidies (from 50% to 0%) reduces accommodation rates from 33% to 11%, leading to a 7% decline in post-disability employment and a 15% decline in post-disability quarterly wages (roughly $1,358).
A revenue-neutral reform eliminating wage subsidies reduces average welfare and the welfare of more than 90% of workers.
Welfare gains from the subsidy are larger for low-skilled workers than high-skilled workers.
Conditional on experiencing disability, eliminating wage subsidies decreases welfare by about 10%, while increasing the subsidy to 100% raises welfare for disabled workers by around 30%.
Firm profit is maximized at a subsidy rate around 80%, after which higher taxes offset accommodation gains.

In depth

Q1. What is the Employer at Injury Program (EAIP), and how does it differ from standard workers’ compensation?

A1: EAIP is an optional component of Oregon’s workers’ compensation system that subsidizes employers for the costs of accommodating workers with temporary disabilities during a transitional return-to-work period. Unlike standard workers’ compensation premiums (which are experience-rated at the firm level), EAIP is funded through a flat payroll tax on all firms that is not experience-rated — meaning firms that use EAIP do not pay higher premiums. The wage subsidy component accounts for over 96 percent of EAIP expenses; other reimbursable costs (worksite modifications up to $5,000, retraining up to $1,000, clothing up to $400) are rarely used. Eligible employers must be the employer at which the disability occurred, and accommodation is limited to a transitional period during which workers cannot simultaneously receive time-loss benefits.

Q2. How is firm-level “exposure” constructed, and what is the rationale for using it as an instrument?

A2: Exposure is the fraction of a firm’s workers’ compensation claims that used EAIP during a five-year baseline period from 2005 to 2009 — a separate historical period chosen to reduce volatility and avoid mean-reversion. The rationale draws on prior work (Aizawa et al., 2022) showing that firm fixed effects account for nearly 25 percent of variation in accommodation, far more than worker or disability characteristics (1 and 3 percent, respectively), suggesting permanent firm-level heterogeneity in the relative benefits and costs of accommodation. Firms with zero historical exposure are unlikely to change accommodation behavior in response to a subsidy reduction, while high-exposure firms respond more, creating differential quasi-experimental variation in accommodation rates after July 2013.

Q3. What are the first-stage and reduced-form results from the DID specification?

A3: The first-stage DID coefficient shows that a ten-percentage-point increase in exposure is associated with a one-percentage-point decrease in EAIP take-up after 2013, implying a 2.9 percentage point decrease for claims in firms with average exposure (mean 0.27). The corresponding reduced-form results show a 0.35 percentage point decrease in employment four quarters post-disability and a $45 decrease in quarterly earnings for every ten-percentage-point increase in exposure, scaling to 0.95 percentage points and $120 at average exposure. There is no statistically significant effect on the probability of moving to a new firm. Pre-trend tests show parallel accommodation trends across exposure terciles prior to 2013, supporting the identifying assumption.

Q4. What do the IV estimates imply about the causal effect of accommodation on labor market outcomes?

A4: Under the exclusion restriction that the subsidy change affects labor market outcomes only through accommodation, the IV estimates imply that receipt of accommodation increases the probability of employment four quarters after disability by 33 percentage points (against a mean of 72 percent) and increases quarterly earnings by approximately $4,100 (against a mean of $7,807). There is no significant effect on the probability of working at a new firm four quarters later. The authors note these large estimates reflect local average treatment effects for compliers — workers whose accommodation status was changed by the instrument — who disproportionately have high unobserved resistance to treatment and high accommodation returns, explaining the magnitude.

Q5. What does the MTE framework reveal about the distribution of accommodation effects and selection?

A5: The MTE curves show that workers with the highest unobserved resistance to treatment (least likely to receive accommodation) have the highest potential employment and earnings gains from accommodation. This negative selection on gains arises because these workers tend to have worse employment outcomes in the untreated state, consistent with more severe disabilities commanding higher accommodation costs. IV weights are concentrated at high-resistance values, explaining the large IV estimates. Negative selection on gains is also found along observable dimensions: workers in self-insured firms, healthcare support occupations, women, and those with wounds/cuts/burns show larger gains but lower likelihood of receiving accommodation.

Q6. What evidence supports characterizing firm accommodation as general rather than firm-specific human capital investment?

A6: Three pieces of evidence point toward general human capital. First, the IV estimate shows accommodation has no statistically significant effect on the probability of working at a new firm four quarters after disability. Second, a triple-interaction specification (DID interacted with new-firm indicator) yields suggestive evidence of even larger earnings gains for workers who move to a new firm post-accommodation, though this is not statistically significant — a pattern inconsistent with firm-specific human capital. Third, the subset of claims that receive non-wage EAIP benefits (worksite modifications, retraining) do show lower mobility, but this comprises fewer than 5 percent of the sample, meaning the predominant form of investment in the context is general in nature.

Q7. What are the two sources of market inefficiency in accommodation identified in the model?

A7: The first is a human capital externality operating through worker turnover. Because accommodation builds general human capital that workers carry to new employers, a firm accommodating a worker does not capture the portion of future surplus that accrues to future employers upon separation. In a Nash bargaining framework with lack of commitment, this dynamic inefficiency is larger when industry-wide turnover rates are higher — consistent with the descriptive finding that accommodation rates are strongly negatively associated with industry separation rates. The second is a fiscal externality from imperfect experience rating: firms whose workers’ compensation premiums are not fully linked to their own claim costs do not fully internalize the cost-savings from accommodation (i.e., reduced time-loss benefit payments), leading them to accommodate at inefficiently low rates.

Q8. How is heterogeneity incorporated in the structural estimation, and what do the estimated parameters show?

A8: The model incorporates observed heterogeneity (firm insurance status, worker skill type — measured by pre-disability wages — firm baseline exposure, and pre/post policy change) and unobserved heterogeneity mapped to the MTE framework’s unobserved resistance to treatment. Indirect inference matches cross-sectional accommodation rates, earnings by subgroup, and the DID coefficients. Key findings: net output during the disability period is negative (accommodation is a costly short-run investment), while post-disability output is higher for accommodated workers. Low-skilled workers experience larger productivity gains from accommodation than high-skilled workers. Accommodation cost shock variance is lower for higher unobserved types, meaning high-gain workers are also more sensitive to subsidy changes, consistent with the large IV estimates. The model fits the DID coefficients for accommodation, employment, and wages well.

Q9. What do the counterfactual simulations show about the welfare effects of varying the subsidy rate?

A9: Eliminating wage subsidies from the current 50% rate reduces the accommodation rate from 33% to 11% and lowers post-disability employment by 7 percentage points and post-disability quarterly wages by 15% ($1,358). From a welfare perspective, eliminating subsidies in a revenue-neutral reform reduces average ex-ante worker welfare and lowers welfare for more than 90% of workers. Conditional on experiencing disability, eliminating subsidies reduces welfare by about 10% while raising the subsidy to 100% increases welfare of disabled workers by around 30%. Firm profit is increasing in the subsidy rate up to about 80%, then decreases. Ex-ante worker welfare gains from the current 50% subsidy relative to no subsidy are modest in consumption-equivalent terms (at most 0.6% increase in consumption), partly because the disability probability is low (2.2%) and because unaccommodated workers still receive two-thirds wage replacement through time-loss benefits.

Q10. What distributional implications do wage subsidies have across worker and firm types?

A10: Welfare gains from higher wage subsidies are larger for low-skilled workers than high-skilled workers, so the subsidy has a redistributive dimension beyond efficiency correction. Welfare gains are also larger for workers in imperfectly experience-rated firms, where the fiscal externality creates the greater wedge from the efficient level. Self-insured firms, which already internalize workers’ compensation cost savings and thus accommodate closer to the optimal rate, benefit less from the subsidy and can even be made worse off if subsidies are set very high (since they bear higher flat payroll taxes with smaller marginal accommodation gains). The fraction of worker-firm matches experiencing welfare gains exceeds 90% under the benchmark subsidy level, indicating broad rather than narrowly concentrated gains.

Q11. How do the experience-rating channel and the worker-turnover channel interact in comparative statics?

A11: Model comparative statics show that reducing the job-to-job transition rate of workers with disabilities to one-quarter of its estimated value substantially raises accommodation rates, and this effect is more pronounced for imperfectly experience-rated firms than for self-insured firms. This occurs because self-insured firms already have a strong incentive to accommodate (to reduce workers’ compensation premiums), so turnover is less marginal for them. Forcing all firms to be self-insured (perfect experience rating) would substantially increase accommodation rates in currently imperfectly rated firms. Lowering the accommodation cost during the disability period (increasing net output during the disability period) also raises accommodation rates for both firm types.

Key Concepts

Firm Accommodation (EAIP): In this paper’s specific sense, accommodation refers to a firm’s decision to offer a worker with a temporary workplace disability “transitional work” — alternative tasks, modified duties, or flexible arrangements — during their recovery period, funded in part through Oregon’s Employer at Injury Program wage subsidy. Accommodation is distinct from simple early return to work; it functions as a form of human capital investment by potentially providing skill development opportunities and preventing human capital depreciation.

Exposure (Instrument): A firm-level continuous measure defined as the fraction of a firm’s workers’ compensation claims that used EAIP during a five-year baseline period (2005–2009). Exposure captures permanent, time-invariant firm-level propensity to accommodate, and is used to construct a difference-in-differences instrument for the causal effect of accommodation by interacting exposure with a post-2013 indicator (when the subsidy rate was cut from 50% to 45%).

Imperfect Experience Rating: The degree to which a firm’s workers’ compensation insurance premium adjusts to reflect that firm’s own claims costs, rather than being set at an industry average. Fully experience-rated (self-insured) firms internalize 100% of claim costs and thus have strong incentives to accommodate. Partially experience-rated firms face a fiscal externality: because their premiums do not fully reflect their own time-loss benefit expenditures, they do not capture all the cost savings from accommodating workers, leading to under-accommodation relative to the social optimum.

Human Capital Externality (Dynamic Inefficiency in Accommodation): The mechanism — analogous to Acemoglu and Pischke (1999) and Fang and Gavazza (2011) — by which worker turnover reduces firms’ incentives to invest in general human capital (here, accommodation). When accommodation raises workers’ general productivity, part of the future surplus from this investment accrues to future employers upon job-to-job separation. With Nash bargaining and lack of commitment (re-bargaining in the second period), the accommodating firm cannot capture this surplus, creating a dynamic inefficiency that is more severe in high-turnover industries.

Negative Selection on Gains: The empirical finding, established via the MTE framework, that workers with workplace disabilities who are least likely to receive accommodation (highest unobserved resistance to treatment) have the largest potential employment and earnings gains from accommodation. This pattern arises because workers with more severe disabilities have high accommodation costs (making firms unwilling to accommodate them) but also face far worse counterfactual labor market outcomes without accommodation, creating large potential gains.

Marginal Treatment Effect (MTE): Following Heckman and Vytlacil (2005), the treatment effect of accommodation evaluated at a specific quantile of unobserved resistance to treatment — defined here as the propensity score value at which a worker is indifferent between treatment and non-treatment. The MTE curve maps out the full distribution of treatment effects and reveals who benefits (and by how much), how IV estimates are weighted averages over this distribution, and which compliers drive the large IV estimates.

General vs. Firm-Specific Human Capital (in Accommodation Context): Accommodation is characterized as general human capital investment if the productivity and earnings gains it produces are transferable across employers — i.e., if accommodated workers who move to new firms retain their wage gains. It is firm-specific if gains are tied to the current match. In this paper, general human capital is supported by the null effect of accommodation on new-firm employment probability, suggestive evidence of non-lower (possibly larger) earnings gains for new-firm movers, and the observation that fewer than 5% of claims use non-wage EAIP benefits associated with firm-specific investment.

Revenue-Neutral Counterfactual: A counterfactual policy experiment in which the wage subsidy rate for accommodation is varied while imposing that both the time-loss benefit program and the EAIP wage subsidy program remain budget-balanced. Higher subsidy rates raise firm accommodation, reduce time-loss benefit payouts (lowering base premiums for imperfectly experience-rated firms), but require a higher flat EAIP payroll tax on all firms, some of which is passed through to workers via lower first-period wages.

Insuring Peace: Index-Based Livestock Insurance, Droughts, and Conflict

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

This paper provides quasi-experimental evidence that Index-Based Livestock Insurance (IBLI) — a remote-sensing-triggered, automated payout scheme for pastoralists — substantially reduces drought-induced conflict in Kenya over the 2001–2020 period.

The research question is whether a market-based financial instrument can mitigate the causal chain running from drought shocks to violent conflict between nomadic pastoralists and sedentary farmers and other land users. The authors motivate the study by documenting that droughts force pastoralists out of their traditional grazing grounds and into mixed-land-use areas (farms, ranches, urban settlements, nature reserves), where miscoordination with other land users escalates into violence. A case study of the Samburu-Laikipia-Isiolo-Meru region in central Kenya — drawing on georeferenced survey data from Lengoiboni et al. (2010) and ACLED conflict events — validates this spatial mechanism: during droughts, roughly 60–90% of non-pastoral land users report encounters with pastoralists, and conflicts accumulate precisely where drought migration routes cross into non-pastoral land.

The empirical design combines two sources of variation: (1) plausibly exogenous changes in rainfall deficits at the 0.1 × 0.1-degree grid-cell level (roughly 10 × 10 km), derived from NASA GPM satellite data; and (2) the staggered, five-wave rollout of IBLI across 146 insurance districts in Kenya from 2010 onward, which the authors argue was driven primarily by technical challenges rather than pre-existing conflict or drought patterns. The unit of observation is 94,300 cell-periods. Because conflicts due to pastoralist drought migration occur in the neighborhood of affected areas rather than within them, both drought and IBLI coverage are measured as inverse-distance-weighted averages over surrounding cells. The estimating equation is a linear probability model with cell and period fixed effects, interacting neighborhood rainfall deficit with neighborhood IBLI coverage; the coefficient on this interaction term (delta3) is the parameter of interest.

The main finding is that a one-standard-deviation increase in neighborhood IBLI coverage reduces the semi-elasticity of neighborhood rainfall deficit on conflict probability by approximately 23%. In absolute terms, a one-percentage-point increase in the rainfall deficit raises the probability of conflict by 6.92 percentage points at average IBLI coverage; with one additional standard deviation of neighborhood IBLI, that same deficit raises conflict probability by only 5.34 percentage points — a reduction of 1.58 percentage points against a baseline conflict probability of roughly 2.5%.

Scope conditions: the effect is estimated for Kenya specifically, over a pastoralist-heavy population of approximately 8.8 million out of 53 million Kenyans, during 2001–2020. The conflict-mitigating effect is approximately four times larger in mixed-land-use areas (nine times when rollout-cluster-times-period fixed effects are included), consistent with the theoretical expectation that IBLI matters most where pastoralists are most likely to encounter other land users during drought migration.

Two mechanisms are identified. First, IBLI reduces migratory pressure: when pastoral homelands have IBLI coverage, the distance between the ethnic homeland centroid and conflict events involving that group decreases, indicating reduced drought migration. Second, IBLI smooths incomes — corroborated with Afrobarometer geo-coded data — raising the opportunity cost of fighting. An instrumental-variable specification finds that actual IBLI payouts in the neighborhood reduce conflict probability by approximately 150% relative to the baseline risk.

A cost-effectiveness analysis finds that even using conservative World Health Organization or World Bank estimates of the value of statistical life, IBLI delivers fatality savings of between 10 and 22 cents per dollar spent on government subsidies for the program, making it a cost-effective complement to political and institutional conflict-mitigation approaches.

Q: What is the core causal mechanism linking droughts to conflict that IBLI interrupts?

A: Droughts deplete forage in pastoralists’ traditional grazing grounds, forcing them to migrate into mixed-land-use areas — farms, ranches, urban settlements, and nature reserves — where encounters with other land users are more likely to escalate into violence. Without insurance, pastoralists hold excess livestock as precautionary savings, amplifying the extent of necessary migration during dry periods. IBLI payouts allow pastoralists to purchase forage locally, reducing migration distance and intensity, and also smooth income, raising the opportunity cost of engaging in violence.

Q: How does IBLI work technically, and why does it overcome problems of traditional livestock insurance?

A: IBLI uses satellite remote sensing to calculate whether a district-specific drought threshold has been crossed; if so, automated payments are triggered immediately without requiring direct loss assessment or field inspections. This design eliminates moral hazard and adverse selection problems inherent in traditional indemnity insurance, reduces monitoring costs, and enables fast delivery via mobile payment platforms such as MPESA even to remote households. The Kenyan government rebranded the program as the Kenyan Livestock Insurance Program (KLIP) in 2015 and fully subsidizes coverage for up to five tropical livestock units per household.

Q: What is the magnitude of the main conflict-mitigation result?

A: A one-standard-deviation increase in neighborhood IBLI coverage reduces the semi-elasticity of the neighborhood rainfall deficit on conflict probability by approximately 23% (delta3/delta1 = -0.0158/0.0692). In absolute terms, this translates to a reduction from a 6.92 percentage-point increase in conflict probability per one-percentage-point rainfall deficit to a 5.34 percentage-point increase — a decline of 1.58 percentage points against a mean conflict probability of roughly 2.5%.

Q: Why do the authors use a neighborhood rather than cell-level treatment measure?

A: Drought-induced pastoralist conflicts occur primarily not in the pastoral home areas themselves but in neighboring regions where drought migration routes cross into non-pastoral land. The case study documents this pattern directly: ACLED conflict events accumulate where migration routes from Namelok, Lodungokwe, and Ngaremara communities intersect urban or agricultural areas, not within the pastoral zones. The neighborhood approach, using inverse-distance-weighted averages, captures both the probability of migration from surrounding cells and the declining probability of migration with distance.

Q: What is the main identification concern and how do the authors address it?

A: The main concern is that the timing of the IBLI rollout is endogenously determined — areas with a higher latent drought-conflict elasticity might receive coverage earlier or later, biasing the interaction coefficient. The authors show that the pre-treatment drought-conflict elasticity has no systematic correlation with either IBLI eligibility or the timing of coverage receipt. Placebo tests interacting the neighborhood rainfall deficit with pre-treatment eligibility or eventual coverage indicators yield positive, statistically insignificant coefficients, suggesting any bias would run in the direction of underestimating the mitigation effect. A permutation test randomly reassigning IBLI coverage across the six rollout clusters finds the actual point estimate is in the bottom 2.2% of the simulated distribution, indicating it is unlikely to arise from cluster-level confounders.

Q: How do the authors rule out that other programs — cash transfers or development aid — explain the result?

A: The authors control for cell-level and neighborhood-level coverage of Kenya’s Hunger Safety Net Programme (HSNP), which provides unconditional cash transfers to vulnerable households and covers most IBLI-eligible areas, as well as for World Bank agricultural aid projects. Across these specifications, the estimated conflict mitigation ranges from -19.16% to -42.24%, with the baseline estimate of -22.79% remaining robust, indicating neither HSNP nor development aid is a plausible alternative explanation.

Q: What is the alternative identification strategy using within-rollout-cluster variation?

A: The authors exploit pre-determined (1984 government land-use map) variation in mixed-land-use status across cells within the same IBLI rollout cluster-period, including rollout-cluster-times-period fixed effects that absorb any omitted variable related to the potentially endogenous rollout steps. The conflict-mitigating effect of IBLI is approximately four times larger in mixed-land-use cells, and approximately nine times larger in the most restrictive specification with rollout-cluster-times-period fixed effects, consistent with the prediction that IBLI matters most where pastoralists encounter other land users.

Q: How do the authors establish the migratory pressure mechanism?

A: Following Eberle et al. (2023), the authors match conflict actors to ethnic homelands using Murdock (1967) boundaries and test whether IBLI coverage in a homeland reduces the distance between the homeland centroid and conflict events involving that group. They find that it does, indicating that IBLI coverage reduces the spatial range of pastoralist drought migration and thus the probability of conflict-generating encounters with other land users.

Q: How do the authors establish the income-smoothing mechanism?

A: Using geo-coded Afrobarometer survey data, the authors show that IBLI coverage is associated with higher reported incomes among pastoralist households, consistent with Jensen et al. (2017). Higher incomes raise the opportunity cost of fighting (following Grossman, 1991), contributing to the overall conflict-mitigating effect alongside reduced migratory pressure.

Q: What does the instrumental variable specification find?

A: The authors instrument inverse-distance-weighted IBLI payouts in the neighborhood with the interaction of neighborhood rainfall deficit and neighborhood IBLI coverage. The first stage confirms that rainfall deficits trigger payouts conditional on coverage. The second stage finds that the occurrence of payouts in the neighborhood reduces the probability of conflict by approximately 150% relative to the baseline risk, corroborating the reduced-form results.

Q: How do the authors assess cost-effectiveness?

A: The authors predict plausible drought-induced conflict fatalities in Kenya over the pre-treatment period and calculate yearly lives saved from the main estimates, then compare the monetary value of saved lives to government subsidy expenditures on IBLI. Using conservative VSL estimates from the WHO and World Bank, IBLI delivers between 10 and 22 cents of pure fatality savings per dollar of public subsidy expenditure.

Q: How robust are the results to alternative drought and conflict measures?

A: Results are qualitatively similar using an Aridity Index or Dry Matter Productivity (DMP) as drought proxies instead of rainfall deficit. The estimated interaction effect maintains a t-statistic above two for spatial decay functions ranging from distance^-0.5 to distance^-1.5 and for Conley standard error cutoffs from 200 km up to 400 km. Results also hold when restricting to conflict events not involving the government, or to battles, riots, and violence against civilians only, and when excluding the pre-IBLI period (2000–2009) entirely.

Q: What are the policy implications regarding scalability?

A: Pastoralism covers 43% of the African landmass across 36 countries, supporting approximately 268 million people (FAO, 2018). The World Bank and private equity were planning to invest close to 900 million dollars in East African pastoralist programs over 2023–2027. The authors argue that IBLI’s cost structure — high fixed costs of technology and setup but low marginal costs of expansion — gives it a scalability advantage over cash transfer programs or public works schemes that require sustained state capacity. Market-based IBLI complements rather than substitutes for political and institutional reforms.

Index-Based Livestock Insurance (IBLI): A financial instrument that uses satellite remote sensing to automatically trigger preemptive cash payouts to pastoralists when a pre-determined district-specific drought threshold is crossed, bypassing direct loss assessment and thereby eliminating moral hazard and adverse selection problems inherent in traditional indemnity insurance.

Drought-conflict semi-elasticity: The percentage-point change in the probability of conflict associated with a one-percentage-point increase in the rainfall deficit; the paper’s main outcome quantity, estimated at 6.92 percentage points at mean IBLI coverage, reduced by 23% for a one-standard-deviation increase in neighborhood IBLI coverage.

Neighborhood approach: An empirical strategy that measures both drought severity and IBLI coverage as inverse-distance-weighted averages over all surrounding grid cells, reflecting the authors’ finding that pastoralist drought-migration generates conflicts not in the pastoral home area but in neighboring mixed-land-use zones where migration routes intersect other land users.

Migratory pressure: The mechanism by which drought forces pastoralists — who hold excess livestock as precautionary savings in the absence of insurance — to migrate farther from traditional grazing grounds into mixed-land-use areas, increasing the probability of encounters and violent miscoordination with farmers, urban dwellers, and protected-area managers.

Mixed land use: Areas, designated using a 1984 Kenyan government land-use map, where pastoral grazing zones are proximate to farms, ranches, urban settlements, or nature reserves; the paper identifies these as the locations with the highest expected treatment intensity, where IBLI coverage reduces drought-induced conflict approximately four to nine times more than elsewhere.

Tropical Livestock Unit (TLU): The standard unit of account for IBLI contracts in Kenya; one TLU corresponds to one head of cattle or ten goats or sheep; the Kenyan government fully subsidizes IBLI for up to five TLUs per household.

Rollout-cluster-times-period fixed effects: A restrictive set of fixed effects included in the alternative identification strategy that absorbs all omitted variables varying at the level of the six IBLI spatial rollout clusters over time, allowing the authors to identify the conflict-mitigating effect purely from within-cluster variation in mixed-land-use exposure.

Rent Guarantee Insurance

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

Abramson and Van Nieuwerburgh study Rent Guarantee Insurance (RGI), a product in which an insurer pays the landlord on behalf of a tenant who defaults on rent due to a negative income or health expenditure shock, in exchange for a monthly premium proportional to rent. The central question is whether RGI can be designed to be both welfare-improving and financially viable, given the frictions of moral hazard and adverse selection.

The authors develop a dynamic overlapping-generations equilibrium model of the rental market that features endogenous rent default, security deposits, evictions, and homelessness. Households face idiosyncratic persistent and transitory income risk, idiosyncratic medical expenditure risk, and aggregate (cyclical) income risk. Rental contracts are non-contingent, households face borrowing constraints, and housing is indivisible with a minimum quality floor. Landlords set deposits to break even in expectation given observed tenant characteristics. An insurance agency can offer RGI and must also break even in the long run. The model is calibrated to the United States at monthly frequency. Income dynamics are estimated from CPS data (1994–2023) and incorporate transitions among employment, unemployment, out-of-labor-force, and retirement states along with transfer income (unemployment insurance, disability, food stamps) and a progressive tax system. Key moments targeted by Simulated Method of Moments include a delinquency rate of 12.15% (model: 12.69%), average security deposit of $984 (model: $992, from approximately 500,000 Craigslist listings across the 100 largest MSAs), homelessness rate of 1.43% (model: 1.42%), and home-ownership rate of 63.6% (model: 63.2%).

The model’s pre-RGI analysis establishes that persistent income shocks — not transitory shocks or medical shocks — are the primary driver of rent defaults. Default risk remains elevated for 3–6 months following a persistent shock, implying that short-duration RGI coverage is insufficient to prevent eviction; coverage must span multiple months.

The paper’s main policy experiments introduce RGI under different access rules and provider types. Unrestricted RGI (available to all renters) generates large welfare gains through improved risk-sharing and lower security deposits — because insured tenants pose less default risk, landlords lower deposit requirements — but is not financially viable for either a public or private insurer due to moral hazard and adverse selection. Even a public insurer that internalizes the fiscal savings from reduced homelessness cannot break even under unrestricted access.

Restricting access changes the viability calculus sharply. A publicly provided RGI targeted to households at the bottom of the wealth distribution can achieve financial viability: these households are precisely those most prone to homelessness, so the reduction in homelessness expenses — which the public insurer internalizes — offsets the insurance deficit. This restricted public RGI generates substantial welfare gains for the most vulnerable households.

A privately provided RGI must instead target higher-wealth renters to break even, because these households have low default risk (limiting claim payouts) while remaining sufficiently risk averse to pay the premium. The intersection of financial viability and take-up is small, yielding a limited target audience. The private program has minimal impact on housing insecurity, and the most vulnerable households derive little benefit. This pattern matches observed private RGI markets, where providers restrict access to renters in good financial condition.

An RGI mandate — requiring all renters to purchase coverage — mitigates adverse selection by improving the pool of insured tenants, dramatically increasing financial viability and allowing the insurer to reduce the premium substantially while still breaking even. Mandated RGI is highly effective at preventing housing insecurity and generates welfare gains concentrated among the most financially vulnerable households.

Scope conditions: results are calibrated to U.S. income, medical, and housing market parameters as of 2019. The insurer’s borrowing cost matters: the public insurer faces lower, counter-cyclical municipal bond spreads, whereas private insurers face higher, pro-cyclical corporate spreads, which constrains the generosity of private contracts in recessions.

Q: What is Rent Guarantee Insurance and how does it work mechanically in the model? A: RGI is a contract under which a tenant pays a flat monthly premium equal to a fraction kappa of rent. When the insured tenant defaults, the insurer pays the landlord directly and deducts one period from the tenant’s stock of “insurance credit.” The tenant remains housed. Once insurance credit is exhausted, the insurer no longer covers defaults. The insurer sets the premium and the maximum coverage duration to break even in the long run.

Q: Why do most rent defaults arise from persistent rather than transitory shocks? A: The model shows that the renter population is disproportionately exposed to persistent unemployment and labor-force-exit spells, and that negative persistent income shocks are harder to smooth through savings than transitory ones. Default risk remains elevated for 3–6 months after a persistent shock but dissipates quickly after a transitory shock. This implies that RGI coverage periods of only a few months would fail to prevent eviction for the majority of defaulting tenants.

Q: How does RGI affect security deposits in equilibrium? A: Because landlords observe the tenant’s insurance status at lease signing and deposits are set to make landlords break even in expectation, insured tenants pose lower default risk and thus face lower upfront deposit requirements. This deposit reduction is a key welfare channel of RGI, as large deposits tie up a disproportionate share of poor households’ wealth and price the most vulnerable out of housing entirely.

Q: Why is unrestricted RGI financially non-viable even for the public insurer? A: Unrestricted access induces both adverse selection — riskier households self-select into coverage — and moral hazard — insured households alter their default and savings behavior. These effects cause the insurer to run a persistent deficit. Even a public insurer that internalizes the fiscal cost savings from reduced homelessness cannot recoup enough to break even, implying that an unrestricted program would require an ongoing subsidy.

Q: How does publicly provided restricted RGI achieve financial viability? A: By targeting households at the bottom of the wealth distribution — precisely those most prone to homelessness — the public RGI program produces large reductions in homelessness. Because the public insurer internalizes the fiscal expenses associated with shelters, health services, and policing that accompany homelessness, these savings are passed through to the insurer and are sufficient to offset the insurance deficit. No such mechanism is available to a private insurer.

Q: Why must private RGI target higher-wealth renters, and what are the consequences? A: Private insurers must break even using only premium revenue, without access to homelessness cost savings. Higher-wealth renters have lower default probabilities, which limits claim payouts, while remaining sufficiently risk averse to demand coverage and pay the premium. The viable target audience is small given these competing requirements. As a result, private RGI covers few households, has minimal effect on housing insecurity, and provides essentially no benefit to the most vulnerable renters. This pattern is consistent with observed private RGI markets.

Q: What are the two differences between public and private insurers in the model? A: First, the public insurer internalizes the fiscal costs of homelessness (shelters, health services, policing), raising its net benefit from offering coverage. Second, the public insurer borrows at municipal bond spreads — which are lower than corporate spreads and counter-cyclical — whereas the private insurer faces higher, pro-cyclical corporate spreads. Counter-cyclical borrowing costs allow the public insurer to extend more generous coverage precisely when aggregate conditions deteriorate and claims rise.

Q: How does an RGI mandate improve financial viability? A: Mandatory enrollment forces all renters, including low-risk ones, into the insurance pool, which counteracts adverse selection. The expanded and higher-quality pool dramatically reduces per-insured expected claim costs, allowing the insurer to lower the premium substantially while still breaking even. The low-premium mandated policy is then both affordable and effective at preventing housing insecurity, with welfare gains concentrated among the most financially vulnerable renters.

Q: What novel data does the paper use for calibration of security deposits? A: The authors construct a dataset of approximately 500,000 Craigslist rental listings scraped across the 100 largest U.S. metropolitan statistical areas between November 2022 and March 2024 to measure the cross-sectional distribution of security deposits. The average deposit in this dataset is $984, which the model matches closely at $992. The data also reveal that the deposit-to-rent ratio is decreasing in house quality, reflecting the higher default risk of low-income renters in lower-quality units.

Q: What is the paper’s definition of homelessness and what rate does the model match? A: Homelessness is defined broadly to include sheltered homeless, unsheltered homeless (0.6% of households), and doubled-up families (0.83% of households), for a total of 1.43% of U.S. households. The model matches this rate closely at 1.42%.

Q: What is the paper’s key implication for the design of housing policy? A: The central implication is that financial viability and impact on housing insecurity are in tension for private insurers, and cannot both be achieved simultaneously. Only a publicly provided program that internalizes homelessness fiscal costs and faces counter-cyclical borrowing spreads can target the most vulnerable renters, break even, and materially reduce housing insecurity. Private RGI, while viable for a narrow segment, cannot substitute for public provision as a tool against homelessness.

Q: How does RGI relate conceptually to rental assistance programs? A: The paper distinguishes RGI from rental assistance on a structural basis: insurance contracts require tenants to pay premiums, making them potentially self-financing for private providers, whereas rental assistance is a net transfer that can never be self-financing. This conceptual distinction motivates studying whether RGI can be designed to eliminate the need for ongoing fiscal transfers, though the analysis ultimately shows that a public subsidy or mandate is required to serve the most vulnerable renters.

Rent Guarantee Insurance (RGI): A contract under which an insured tenant pays a monthly premium equal to a flat percentage of rent; when the tenant defaults, the insurer pays the landlord directly, preserving tenancy, for a limited number of periods governed by the tenant’s stock of insurance credit.

Insurance Credit: An endowment of periods of RGI coverage that households receive upon entry into the model; each time the insurer pays on behalf of a defaulting tenant, one unit of credit is consumed, and no further coverage is available once credit is exhausted.

Housing Insecurity: In the paper’s framework, the set of outcomes — rent delinquency, eviction, and homelessness — arising from the combination of non-contingent rental contracts, borrowing constraints, and idiosyncratic or aggregate income and medical shocks.

Security Deposit: An upfront payment from tenant to landlord, set by the competitive landlord to break even in expectation given the tenant’s characteristics and insurance status; a key channel through which RGI affects welfare by reducing the upfront cost barrier to obtaining housing.

Moral Hazard (in RGI context): The change in a tenant’s default, savings, and housing choices induced by the presence of insurance coverage, which increases expected claim costs for the insurer relative to a world where behavior is held fixed.

Adverse Selection (in RGI context): The tendency of renters with higher default risk to self-select into RGI when access is unrestricted, worsening the insurer’s risk pool and driving up expected payouts relative to premiums.

Homelessness Externality: The fiscal costs borne by government — for shelters, health services, and policing — that accompany homelessness; the public insurer internalizes these costs, creating a net benefit from RGI that private insurers cannot capture.

Counter-cyclical Borrowing Spread: The feature of public (municipal bond) financing whereby borrowing costs fall during recessions, allowing the public insurer to expand coverage when claims are highest; contrasted with private insurers’ pro-cyclical corporate bond spreads that tighten precisely when aggregate conditions worsen.