Economic Loss due to Health Funding Cuts as Distributed Across Geospatial Units

Economic Loss due to Health Funding Cuts as Distributed Across Geospatial Units | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Economic Loss due to Health Funding Cuts as Distributed Across Geospatial Units View ORCID Profile Mallory J. Harris , View ORCID Profile Alyssa H. Sinclair , View ORCID Profile Clio Andris , View ORCID Profile Joshua S. Weitz doi: https://doi.org/10.1101/2025.07.24.25332092 Mallory J. Harris 1 Department of Biology, University of Maryland , College Park, MD, USA 2 University of Maryland Institute for Health Computing , North Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mallory J. Harris Alyssa H. Sinclair 3 Penn Center for Science, Sustainability, and the Media, University of Pennsylvania , Philadelphia, PA, USA 4 Annenberg Public Policy Center, University of Pennsylvania , Philadelphia, PA, USA 5 Annenberg School for Communication, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alyssa H. Sinclair Clio Andris 6 School of City and Regional Planning, Georgia Institute of Technology , Atlanta, GA, USA 7 School of Interactive Computing, Georgia Institute of Technology , Atlanta, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Clio Andris For correspondence: clio{at}gatech.edu jsweitz{at}umd.edu Joshua S. Weitz 1 Department of Biology, University of Maryland , College Park, MD, USA 2 University of Maryland Institute for Health Computing , North Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joshua S. Weitz For correspondence: clio{at}gatech.edu jsweitz{at}umd.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract The National Institutes of Health (NIH) has proposed a 15% cap on indirect research costs and the Department of Health and Human Services (HHS) has terminated or frozen over 5,000 grants between February and August 2025. Nationally, we estimate $16.6 billion in annual future losses from the proposed indirect cost cap and $11.0 billion in current losses from terminated/frozen grants. However, assessing the local economic impacts of realized and proposed cuts to NIH research requires accounting for commuting flows between where individuals work and live. Accounting for commuting flows, we estimate that 2,136 (of 3,144) U.S. counties will experience over $100,000 in economic losses if indirect cost caps are implemented – far more widespread than the 376 counties where NIH-supported grantees are located. Likewise under a commuter model, 989 counties could lose more than $100,000 due to realized NIH grant terminations and freezes, again far more widespread than the 152 counties where affected institutions are located. By incorporating commuting flows, the present study reveals the extent to which biomedical research funding cuts will lead to substantial economic losses in local communities throughout the U.S. 1 Introduction On February 7, 2025, the National Institutes of Health (NIH) introduced a policy to substantially reduce funding for indirect costs (IDCs) of research [ 16 ]. IDCs represent the costs associated with grant administration, safety compliance, contract negotiation, equipment, cleaning and maintenance, utilities, and other operations and materials that support science. The proposed policy would cap indirect costs (IDCs) at 15% of the direct costs associated with each grant [ 16 ], a significant reduction from current levels — currently, realized indirect costs are estimated to be ≈ 42% of direct costs [ 1 ]. Beginning in February, the U.S. Department of Health and Human Services (HHS) also terminated or froze more than 5,000 research grants totaling nearly $7.3B in research support. According to White House statements and termination letters, grants have been terminated because they no longer align with revised agency priorities (e.g., many targeted grants included a focus on climate change, health equity, COVID, and vaccines) and/or grant recipients are affiliated with certain, targeted universities (e.g., Harvard University and Columbia University) [ 12 , 19 , 18 ]. However, the local economic impacts on communities arising from cuts to federal support of medical research remains underexplored. Efforts to measure and communicate the local economic impacts of cuts to biomedical research can build public awareness and motivate action-taking [ 21 , 22 , 3 ]. In doing so, analyses focused on the impacts of these cuts at an institutional level help articulate what is lost [ 14 , 15 , 2 , 1 ]). Many universities have released public communications detailing the consequences of cuts to research at their institutions [ 23 , 18 , 24 , 31 , 4 ] while others have remained largely silent. Although prior analyses and communications highlight harm to specific institutions, an institution-centered approach obscures the extent to which negative economic impacts extend beyond the immediate geographic vicinity of research institutions. This spatially-extended impact arises, in part, because institutions with NIH grants are often major employers and employees at affected institutions commute in from surrounding areas. Here, we estimate the economic impact of cuts to funding for IDCs and terminated grants and demonstrate that integrating quantitative data on employee movement geographies (i.e., commutes) provides a more comprehensive near-term assessment of the economic impacts of biomedical research cuts on local communities throughout the United States. Measuring the impact of losses beyond grantee institutions can explain loss of purchasing power and salaries in nearby regions beyond the immediate vicinity of each impacted institution. To quantify this impact, we model the county-to-county spatial distribution of economic losses due to IDC funding cuts and terminated/frozen grants across jurisdictions using commuter flows from the U.S. Census at the county level. We measure the difference between our method and prevailing methods of mapping and aggregating losses that use only the location of the institution, not of the workers’ home locales, to describe how communities are affected by losses. Focusing on connected geospatial units reveals the widespread negative impact of NIH funding cuts and we recommend that efforts to map and aggregate data illustrating current and future losses account for commuter flows. 2 Methods 2.1 Data and processing 2.1.1 NIH Grant Data for IDC Reduction NIH data were retrieved from NIH RePORTER ( https://reporter.nih.gov/ ) for all grants that were active in fiscal year 2024, totaling 78,069 entries. For each award, available data include direct costs, indirect costs, awardee organization name, and awardee coordinates (latitude and longitude). We excluded 12,324 entries without a Catalog of Federal Domestic Assistance (CFDA) code, as these entries indicate sub-awards (e.g., cores within a center grant) when total funds are already accounted for in the center grant. The final dataset included 65,745 grants. In two cases where a total grant value was listed without division into direct and indirect costs, we assumed no indirect costs. 2.1.2 Data on Terminated/Frozen Grants We obtained data on reported NIH grant terminations and freezes as of August 19, 2025 from Grant Witness, a publicly available dataset [ 20 ]. The Grant Witness database is updated daily based on integrated data from news reports, the HHS TAGGS system, DOGE.gov , USAspending.gov , NIH’s account on X, and NIH RePORTER, as well as self-reporting by scientists. Grants that were terminated or frozen but subsequently reinstated by the time of analysis were excluded from loss calculations. 2.1.3 Census Unit Assignment We mapped grant data at the coordinate (latitude, longitude) level based on coordinates provided in the NIH RePORTER data. We joined point-level records to congressional districts and to U.S. county TIGER/Line shape files using a spatial join. Block group centroids that did not overlap with the congressional district polygons (e.g., whose centroid is on the coast) were assigned to their nearest congressional district. One record did not have valid coordinates and was therefore excluded. 2.1.4 Classifying Regions Based on Urbanicity and Partisanship We classified counties as metropolitan (metro) or non-metro based on 2023 Rural-Urban Continuum Codes provided by the U.S. Department of Agriculture (USDA) ( https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation ) [ 9 ]. U.S. House of Representatives Congressional districts were classified as Democratic or Republican (or Other) based on the party affiliation of their representative elected to the 119th Congress. 2.1.5 Commuter Flows We used commuter flows for all job types (JT00) from the Origin-Destination Employment Statistics (LODES)-Longitudinal Employer Household Dynamics (LEHD) data set (version LODES8) provided by the U.S. Census [ 27 ]. These data are released at the census block level (JT00 2016). We used the 2016 dataset, as this is the most recent dataset that includes all states (Michigan, Alaska and Mississippi have not had full coverage since 2016). We summed flows from census blocks to produce county-level flows. For congressional district flows, we summed the commuter data at the block group level. We created centroids (center points) of the block groups and spatially joined the centroids with congressional district polygon files (500K scale) for the 119th Congress (Jan 2025–Jan 2027). We included all 435 districts with voting members, plus the district corresponding to Washington, D.C., which has a non-voting delegate. 2.2 Methods of estimating losses Proposed indirect cost cap We calculated losses from the proposed indirect cost cap for each grant by taking the difference between the listed indirect costs and the maximum indirect costs under the proposed policy (15% of the listed direct costs). In cases where this value is zero or negative (i.e., current indirect costs (15%), we assume no losses would be incurred. In other words: Terminated/frozen grants All remaining funding from terminated/frozen grants (calculated by Grant Witness as total obligated funds minus outlays (i.e., disbursed funds) according to USASpending.gov ) is treated as a loss. Calculating economic loss We estimate economic loss by applying a multiplier of $2.56 to IDC and terminated/frozen grant losses based on a report by United For Medical Research [ 25 ], which found that every dollar spent by the NIH produces $2.56 in economic activity. The same report estimates that NIH funding supported approximately 407,782 jobs and $94.58 billion in economic activity in FY2024. Based on this ratio, we estimate one job is lost for every $232,938 in reduced economic activity arising from $90,601 in cuts in NIH support. In a supplemental analysis, we estimated losses using state-specific economic multipliers derived from the same report. 2.3 Distributing losses based on commuter flows We redistribute losses across spatial units based on commuter flows. For each area (i.e., county or congressional district), we estimate the total number of people who work in a given spatial unit A by summing total commutes with unit A as the destination. For each spatial unit B that houses residents who work in unit A, we then calculate the proportion p A,B of people working in unit A who live in B. Unit B will experience losses from unit A ( L A ) proportionally to its share of commuters, p A,B ∗ L A (this process is visualized across a set of example counties in Figure 1 ). Total loss to unit B is then the sum of its share of losses across all units to which its residents commute, including the contributions from losses in unit B (as applicable) that are not redistributed. In a sensitivity analysis, we defined a parameter ρ as the proportion of funding that is treated as static (i.e., remains in the area where an insitution is located); the remaining fraction of the funding (1 − ρ ) was redistributed under the commuter model. We tested the effects of varying ρ from one (static model) to zero (commuter model). Download figure Open in new tab Figure 1: Economic losses extend outward from the counties where they originate/ Highlighted counties include major cities: (A) Dekalb County, Georgia (Atlanta); (B) Allegheny County, Pennsylvania (Pittsburgh); and (C) Harris County, Texas (Houston). Counties are colored by future estimated annual economic loss due to the proposed cap on indirect costs based on commuter flows that connect to a focal county (indicated by light pink outline). Institutions within focal counties are indicated as black points. State outlines are indicated with darker lines. 3 Results We calculate anticipated annual losses associated with the IDC cap at the county level ( n =3,144 units) with a static (i.e., without accounting for commuter flows) versus commuter model (as illustrated in Figure 1 ). In a static model, the majority of counties do not register any loss (2,768 counties with no losses from the IDC cap) and losses are concentrated in major metropolitan areas ( Figure 2A ). The greatest economic losses are in Suffolk County, Massachusetts ($1.2 billion) and New York County, New York ($1.1 billion) (including Boston and Manhattan, respectively). Without applying commuter flows, we estimate a total of $16.6 billion in economic losses from the proposed indirect cost cap, applied to institutions located in 376 different counties. Download figure Open in new tab Figure 2: Economic losses from the proposed IDC cap are contained to a subset of counties when estimated using a static model (A), while losses are widespread when using the commuter model (B)/ Maps indicate economic losses (in USD) across the United States at the county level. In contrast, in the commuter model the $16.6 billion in economic losses impacts all 3,144 counties and 2,136 counties incur more than $100,000 in economic losses annually (1,764 of which do not experience any losses in the static model) ( Figure 2B ). All but two congressional districts are expected to incur more than $100,000 in economic losses ( Figure S1 ). In total, $8.0 billion (constituting 48% of all losses) would be reapportioned to counties other than that associated with grantee institutions. Counties with no estimated losses under the static model incur considerable economic losses under the commuter model (mean: $932,118 and median: $179,194) ( Figure 3A ). In counties with non-zero estimated losses under the static model, estimate losses under the static and commuter model are significantly correlated ( ρ = 0.74, p < 0.001, Figure 3B ). Download figure Open in new tab Figure S1: Economic losses for IDC cap by congressional district (119th Congress)/ Panel A shows estimates when using a static method, and Panel B shows estimates when using commuter flows (in USD). Download figure Open in new tab Figure 3: Across most counties, accounting for commuter flows increases the estimated economic losses from NIH funding cuts/ Faceted plots compare the impacts of economic losses from the proposed cap on indirect costs (top, A and B) and terminated grants (bottom, C and D). Histograms (left, A and C) indicate losses under the commuter model across counties with no local affected organizations (i.e., no losses under the static model). Scatterplots (right, B and D) compare losses under the commuter versus static model in counties with local affected organizations (e.g., non-zero losses under the static model). The y = x line is shown in red, with points above the line corresponding to counties with greater losses under the commuter model compared to the static model. In all panels, annotations indicate the number of counties meeting the corresponding criteria. Economic losses are displayed in log-scaled USD. Some counties’ estimated losses change by orders of magnitude when accounting for commuter flows ( Figure 3 ). The following areas have a relatively high share of outgoing economic impacts of funding cuts when accounting for commuter flows: New York City; Charlottesville and Richmond, VA; DeKalb Co., GA (home to Emory University); and Montour Co., PA (home to the large Geisinger Medical Center). Discounting commuter flows will overestimate the relative impact of NIH funding cuts in those focal counties. The following areas have a relatively high relative share of incoming economic impacts of funding cuts when accounting for commuter flows ( Table 1 ): areas outside Seattle (Pierce Co.); St. Louis (St. Charles Co.); Annapolis, Maryland and DC & Baltimore suburbs (Anne Arundel Co.); suburbs of Atlanta (Gwinnett Co., Figure 1A ); and suburbs of Pittsburgh (Westmoreland Co., Figure 1B ). These locales would not be recognized as losing notable (or any) funding without the commuter model. View this table: View inline View popup Download powerpoint Table 1: Several countieswith no losses under the static model experience the substantial losses under the commuter model. Five counties with the greatest economic losses from the proposed IDC cap under the commuter model and no losses under the static model are listed. The columns indicate economic losses under the commuter model (in millions of dollars), number of jobs lost under the commuter model, and the top three (proximal) organizations contributing to economic losses (with associated economic losses in millions of dollars in parentheses). MSA stands for metropolitan statistical area, CSA stands for combined statistical area. For both the county and district level estimates, the impact of research funding cuts on non-metropolitan areas and congressional districts represented by Republicans are more evident when using the commuter model ( Figure S2 ). There are 1,903 non-metropolitan counties and 54 Republican congressional districts (compared to 865 metropolitan counties and 23 Democratic congressional districts) that only experience economic losses when accounting for commuters. For counties and districts that have at least one organization funded by NIH and impacted by the IDC cuts, the ratio of losses under the static versus commuter model is approximately equivalent for non-metropolitan and metropolitan counties (non-metro median: 1.8, IQR: 0.8–9.7; metro median: 1.8, IQR: 0.7–11.9) but generally greater in Republican versus Democratic congressional districts (Republican median: 4.3, IQR: 1.1–60.3; Democratic median: 2.0, IQR: 0.7– 22.1). Download figure Open in new tab Figure S2: Comparison of economic losses from IDC cap across static versus commuter models depending on urbanicity (top) or partisanship (bottom)/ Urbanicity is split into non-metro (green) or metro (blue). Partisanship is split into Democratic (blue) or Republican (red). Comparisons based on partisanship are estimated at the congressional district level. Regions are split into distinct panels depending on whether an individual region lacks (left, A and C) or contains (right, B and D) local affected organizations, as in Figure 3 . We find similar results when examining the economic impact of grant terminations and freezes , i.e., losses incurred from canceled and frozen grants. We find that the total losses are widespread, significant in magnitude, and impact far more counties in a commuter model relative to estimates derived from a static model ( Figure 3C, D ). Specifically, as of August 19, 2025, we estimate $11.0 billion in economic losses are associated with grant terminations across institutions in 152 counties. The greatest economic losses are located in Suffolk County, Massachusetts ($2.7B), New York County, New York ($2.7B), and Cook County, Illinois ($1.6B). When commuter flows are applied, 989 counties face losses of over $100,000 in NIH funding due to grant terminations ( Figure 4 , Figure S3 ). Moreover in the commuter model, a total of $5.9 billion in economic losses (54% of losses) impact counties outside the county where an organization’s grants were terminated. Figure S3 shows comparisons of economic losses from terminated grants at the level of congressional districts. Download figure Open in new tab Figure S3: Economic losses from terminated grants by congressional district (119th Congress)/ Panel A shows estimates when using a static method, and Panel B shows estimates when using commuter flows (in USD). Download figure Open in new tab Figure 4: Economic losses from terminated grants are contained to a subset of counties when estimated using a static model (A), while losses are widespread when using the commuter model (B)/ Maps indicate economic losses (in USD) across the United States at the county level. Economic losses are robust to varying the share of funding that remains static ( ρ ) versus flowing in the based on commuting ( Figure S4 ); over 1,000 counties are projected to lose more than $100,000 due to the IDC cap as long as at least 15% of funding follows commuter flows (ρ ≥0.85) ( Figure S5 ). The economic multiplier varies across states from 1.94 in Maryland to 6.10 in Nevada ( Figure S6 ), but estimates are also robust when a state-specific economic multiplier is applied ( Figure S7 ). Total estimated losses nationally from the IDC cap is consistent with the main analysis ($16.6B), while losses from terminated/frozen grants were slightly less ($10.6B) because the states with the greatest losses (e.g., Massachusetts and New York) have economic multipliers below the national average. Download figure Open in new tab Figure S4: The estimated economic loss from the IDC cap under the commuter model (x-axis, ρ = 0.00) is well-aligned with estimates where a quarter of funding is static and the rest follows commuter flows (y-axis, ρ = 0.25)/ The red line indicates perfect alignment (y=x). Values are log-scaled. Download figure Open in new tab Figure S5: The number of counties with greater than $100,000 in economic losses from the IDC cap (y-axis) when the share of funding that remains static ( ρ ) (x-axis) varies/ Values of ρ vary from 0 (commuter model) to 1 (static model). Download figure Open in new tab Figure S6: The economic multiplier for NIH investment varies across states/ Variation is displayed as: (A) a histogram where the mean value (2.56) is indicated as a dashed maroon line and (B) a map where states are shaded according to their economic multipliers, which is also displayed as an annotation. Download figure Open in new tab Figure S7: Results are robust to using state-specific economic multipliers/ Economic losses are displayed at the county level. Panel A shows losses from IDC cap, and Panel B shows losses from frozen/terminated grants. 4 Discussion and Conclusion In this work, we evaluated the importance of accounting for commuter flows when assessing the economic impact of health research funding cuts across different geographic locations. Although a static model suggests that cuts will only affect specific institutions within a small subset of counties, a commuter-based model suggests that economic losses would be widespread throughout the country. An interactive map of our results under the commuter model is available at https://scienceimpacts.org/ . There are caveats associated with this approach. For example, the geographic level (e.g., county or congressional district level) to which data is aggregated will impact the specific quantitative degree of impact. This issue is known as the Modifiable Areal Unit Problem (MAUP) [ 7 ] and can be exacerbated when using flow data, as the aggregation affects both origin and destination [ 13 , 30 ]. Other potential limitations of the model are related to the types of workers reflected in commuting flows. We used U.S. Census data that represents commuting flows across professions and workplaces, without accounting for commuting patterns that might be specific to research institutions or the impact of student workers. Some proportion of economic losses may remain local to a given institution rather than following commuter flows. As a relevant estimate, two large academic research organizations report that 50%-80% of NIH grant spending goes to personnel [ 26 , 29 ], and therefore is likely to reflect commuter patterns. We project widespread economic losses in most counties in the United States throughout this range ( Figure S5 ). Furthermore, losses to distant firms and employees who sell medical materials and equipment are not captured in this model, as trade (and lack thereof) is not considered. The model also does not account for collaborative grants that may span institutions and geographic spillover of economic benefits beyond commuter patterns. In future work, the commuter model can be refined by examining flows based on job types (e.g., using the North American Industry Classification System (NAICS) code system). For example, commuter flow data for workers classified as Professional, Scientific, and Technical Services (in NAICS sector 54) could be emphasized. The model could also capture flows whose destination is a small set of Census blocks where the institution is located, which would more reliably show that the commuter’s target was the funded institution. GPS trace data may be helpful for validating origin-destination flows or capturing trips at a finer geographic and temporal scale. Future work could also aim to account for long-term economic impacts associated with cutting biomedical research, e.g., health-associated losses could cost Americans more than $8 trillion over a 25year period [ 5 ]. Each of these future potential refinements, however, bring a new set of assumptions and challenges. Tracking research funding cuts is particularly difficult in light of rapid changes that are not clearly or reliably documented by federal sources. Our evaluation of terminated and frozen grants therefore relies on a crowd-sourced database that integrates reports of terminations/freezes from multiple sources including the NIH, HHS, and self-reports; these records may not consistently align with federal reports [ 20 ]. Scientific funding cuts have been the subject of multiple lawsuits, which may lead to complete or partial restoration of funding. Notably, on June 16, 2025, a federal judge ruled that terminated grants should be restored in a case brought by the Democratic attorneys general of sixteen states. However, only terminated grants listed by the plaintiffs were addressed, suggesting that impacts may become increasingly geographically heterogeneous, potentially along partisan lines [ 17 ] – even as grant terminations have continued. Although we focus on economic losses, research funding cuts would likely lead to long-term health consequences, delays in drug discovery and scientific breakthroughs, disruptions to medical care, lost training opportunities for scientists and physicians, and other downstream consequences that are difficult to quantify and predict, especially in terms of geographic regions [ 1 , 8 , 5 ]. Here, we elected to focus on cuts to biomedical research in the form of the proposed cap on indirect costs and realized grant terminations and freezes; additional analyses could examine impacts of delays of NIH outlays for grants that have already been awarded [ 10 ], the potential impacts of further cuts in the White House’s proposed FY2026 budget [ 28 ], and funding cuts for other agencies (e.g., National Science Foundation, Department of Energy, the Environmental Protection Agency or others). We also excluded territories and international institutions that may also receive NIH funding. Although the proposed IDC cap could potentially enable greater spending on direct costs of research, the White House’s proposed FY2026 budget includes cuts that are far greater than potential reductions from the IDC cap, suggesting that funds will not simply be redistributed from indirect to direct costs. Federal funding cuts to research are likely to have far-reaching and substantial impacts; concerted effort is necessary to understand and quantify their national and international extent [ 11 ]. In sum, we demonstrated that accounting for commuter flows reveals the magnitude and geographic extent of economic losses associated with cuts to NIH funding. Despite claims that biomedical research cuts are targeted and/or limited in scope, we find that total economic losses are substantial, represent losses of approximately $27.5B, and are geographically widespread impacting urban and rural communities nationwide. Data Availability Code and data to conduct analyses are publicly available on Github at https://github.com/WeitzGroup/SciMap-Methods https://github.com/WeitzGroup/SciMap-Methods Data availability Code and data to conduct analyses are available on Github at doi.org/10.5281/zenodo.16930109. Map visualizations were generated using Esri ArcPro version 3.0.3 [ 6 ]. Acknowledgments J.S.W. is supported, in part, by the Simons Foundation award 930382. M.J.H. and J.S.W. are investigators at the University of MarylandInstitute for Health Computing, which is supported by funding from Montgomery County, Maryland and The University of Maryland Strategic Partnership: MPowering the State, a formal collaboration between the University of Maryland, College Park and the University of Maryland, Baltimore. A.H.S. is supported by the Annenberg School for Communication, the Annenberg Public Policy Center, and the Penn Center for Science, Sustainability, and the Media via the Joan Bossert Postdoctoral Research Fellowship. Footnotes This version of the manuscript has been revised to: *Include terminated and frozen grants reported to Grant Witness as of August 19, 2025 (adding an additional ∼1.5 months of data) *Include to sensitivity analyses (using state-specific economic multipliers and setting some proportion of funding to remain in the region where an affected organization is located) *Updating Congressional District analyses to use maps for 119th Congress (previously was 118th) References [1]. ↵ P. Azoulay , D. P. Gross , and B. N. Sampat . Indirect cost recovery in U.S. innovation policy: History, evidence, and avenues for reform . Unpublished report , March 2025 . URL https://mitsloan.mit.edu/shared/ods/documents?PublicationDocumentID=10587 . [2]. ↵ E. Badger , A. Bhatia , I. Cabreros , E. Murray , F. Paris , M. Sanger-Katz , and E. Singer . NIH funding cuts under Trump administration . The New York Times , February 2025 . URL https://www.nytimes.com/interactive/2025/02/13/upshot/nih-trump-funding-cuts.html . [3]. ↵ J. T. Cacioppo , S. Cacioppo , and R. E. Petty . The neuroscience of persuasion: A review with an emphasis on issues and opportunities . Soc. Neurosci ., 13 ( 2 ): 129 – 172 , Mar . 2018 . OpenUrl PubMed [4]. ↵ Columbia Mailman School of Public Health . Grant terminations undermine relationships between researchers and the communities they serve , May 2025 . URL https://www.publichealth.columbia.edu/news/grant-terminations-undermine-relationships-between-researchers-communities-they-serve . [5]. ↵ D. M. Cutler and E. Glaeser . Cutting the NIH—the $8 trillion health care catastrophe . JAMA Health Forum , 6 ( 5 ): e252791 , 2025 . doi: 10.1001/jamahealthforum.2025.2791 . OpenUrl CrossRef [6]. ↵ Esri. ArcGIS Pro , 2022 . [7]. ↵ A. S. Fotheringham and D. S. Wong . The modifiable areal unit problem in multivariate statistical analysis . Environment and Planning A , 23 ( 7 ): 1025 – 1044 , 1991 . OpenUrl CrossRef [8]. ↵ S. Galea and K. Bibbins-Domingo . The value of academic health research . JAMA , 333 ( 12 ): 1039 – 1040 , Mar . 2025 . OpenUrl PubMed [9]. ↵ F. K. Hines , D. L. Brown , J. M. Zimmer , F. K. Hines , D. L. Brown , and J. M. Zimmer . Social and economic characteristics of the population in metro and nonmetro counties, 1970 . Economic Research Service, USDA, Agricultural Economic Report No. 272 , 1975 . [10]. ↵ M. Jarlenski and E. Mairson . Trends in NIH outlays , June 2025 . URL https://grant-watch.us/posts/trends-in-nih-outlays/ . xAccessed: 2025-06-09 . [11]. ↵ M. Kozlov . NIH grant cuts will axe clinical trials abroad — and could leave thousands without care . Nature , 642 : 279 – 280 , 2025 . doi: 10.1038/d41586-025-01721-9 . OpenUrl CrossRef PubMed [12]. ↵ M. Kozlov and S. Mallapaty . Exclusive: NIH to terminate hundreds of active research grants . Nature , 639 ( 8054 ): 281 – 282 , Mar . 2025 . OpenUrl PubMed [13]. ↵ C. Luo , Y. Hu , and F. Wang . A big data approach to mitigating the MAUP in measuring excess commuting . Computational Urban Science , 5 ( 1 ): 14 , 2025 . OpenUrl [14]. ↵ M. Molteni , J. E. Parker , and J. Wosen . Trump’s first 100 days: NIH new grants cut . STAT News , April 2025 . URL https://www.statnews.com/2025/04/24/trump-100-days-nih-new-grants-cut/ . Accessed: 2025-05-05 . [15]. ↵ J. S. Murphy . Estimated single year loss of NIH funding if 15% indirect cost rate is imposed . Technical report , February 2025 . URL https://datawrapper.dwcdn.net/l0ZqA/9/ . [16]. ↵ National Institutes of Health . Supplemental guidance to the 2024 NIH grants policy statement: Indirect cost rates , February 2025 . URL https://grants.nih.gov/grants/guide/notice-files/NOT-OD-25-068.html . [17]. ↵ A. Oza . NIH cuts: Red states losing out as canceled grants are reinstated . STAT News , July 3 2025 . URL https://www.statnews.com/2025/07/03/nih-cuts-grant-restoration-complicated-by-limits-to-court-order-trump-dei-restrictions/ . xAccessed 2025-07-07 . [18]. ↵ A. Powell . Trump administration freezes $2.2 billion in grants to Harvard . Harvard T.H. Chan School of Public Health News , April 2025 . URL https://hsph.harvard.edu/news/trump-administration-freezes-2-2-billion-in-grants-to-harvard/ . [19]. ↵ S. Reardon . NIH freezes all research grants to Columbia University, Apr . 2025 . URL https://www.science.org/content/article/nih-freezes-all-research-grants-columbia-university . [20]. ↵ N. Ross and S. Delaney . Grant Witness , April 2025 . URL https://grant-witness.us/nih-data.html . NIH Data Section. [21]. ↵ A. H. Sinclair , S. Hakimi , M. L. Stanley , R. A. Adcock , and G. R. Samanez-Larkin . Pairing facts with imagined consequences improves pandemic-related risk perception . Proc. Natl. Acad. Sci. U. S. A ., 118 ( 32 ): e2100970118. Aug . 2021 . OpenUrl Abstract / FREE Full Text [22]. ↵ H. Sinclair , M. J. Harris , C. Andris , D. Cosme , E. Peters , A. Fagerlin , E. B. Falk , and J. S. Weitz . NIH indirect cost cuts will affect the economy and employment . Nat. Hum. Behav ., 9 : 1301 – 1302 , June 2025 . OpenUrl PubMed [23]. ↵ Stanford Report . Indirect costs and their impact on our research mission , February 2025 . URL https://news.stanford.edu/stories/2025/02/nih-s-cap-on-indirect-costs-could-threaten-stanford-university-research . [24]. ↵ The University of Kansas Life Span Institute . Explainer: What are indirect costs, and how do they support university research? , 2025 . URL https://lifespan.ku.edu/explainer-what-are-indirect-costs . [25]. ↵ United for Medical Research . NIH’s Role in Sustaining the U.S. Economy. Technical report, United for Medical Research , 2025 . URL https://www.unitedformedicalresearch.org/wp-content/uploads/2025/03/UMR_NIH-Role-in-Sustaining-US-Economy-FY2024-2025-Update.pdf . [26]. ↵ University of Florida Department of Psychology . Grant budget calculation information , 2025 . URL https://psych.ufl.edu/info-forms/grants/psychology-grant-budget-calculation-information/ . Accessed August 21, 2025 . [27]. ↵ U.S. Census Bureau . LODES-LEHD origin-destination employment statistics , 2016 . URL https://lehd.ces.census.gov/data/lodes/LODES7/LODESTechDoc7.2.pdf . JT00 Commuter Flows. [28]. ↵ U.S. Department of Health and Human Services . FY 2026 budget in brief. Technical report , 2025 . URL https://www.hhs.gov/sites/default/files/fy-2026-budget-in-brief.pdf . [29]. ↵ UTHealth Houston . Nih budgets -sponsored projects administration , 2025 . URL https://www.uth.edu/sponsored-projects-administration/plan-propose/budgets/nih-budgets . xAccessed August 21, 2025 . [30]. ↵ J. M. Viegas , L. M. Martinez , and E. A. Silva . Effects of the modifiable areal unit problem on the delineation of traffic analysis zones . Environment and Planning B: Planning and Design , 36 ( 4 ): 625 – 643 , 2009 . OpenUrl [31]. ↵ P. Webb and D. Hagen . Memo: NIH 15% indirect cost rate cap . Sponsored Projects Administration, The University of Minnesota , February 2025 . URL https://research.umn.edu/units/spa/news/memo-nih-15-indirect-cost-rate-cap-pamela-webb-and-david-hagen . View the discussion thread. Back to top Previous Next Posted August 25, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Economic Loss due to Health Funding Cuts as Distributed Across Geospatial Units Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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