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Mathematica Inc.

Mathematica Inc. is an employee-owned research and consulting firm headquartered in Princeton, New Jersey, that specializes in rigorous evidence-based analysis to inform and enhance public policies and programs.^[1]^[2] Founded in 1968 as Mathematica Policy Research, the organization combines expertise in data science, social science, policy research, and technology to design, evaluate, and scale interventions aimed at improving outcomes in areas such as health, education, early childhood development, and labor markets.^[3]^[4] With more than 1,600 employees, Mathematica serves federal and state governments, foundations, and international clients, emphasizing objective, high-quality research to support decision-making and program accountability.^[5] The firm pioneered the use of randomized controlled trials in social policy, conducting the first such experiment in the United States—the New Jersey Negative Income Tax Experiment in the late 1960s—which tested the effects of guaranteed income on work incentives and family behavior.^[3]^[6] This work established Mathematica's reputation for methodological innovation and empirical rigor in addressing complex societal challenges.^[7]

History

Founding and Early Development

Mathematica Inc. was founded in 1958 by Oskar Morgenstern, a Princeton University professor of economics and co-developer of game theory, with the aim of applying operations research techniques—mathematical modeling of complex systems—to solve business and management problems.^[8]^[9] Morgenstern, who served as chairman of the board, drew on his expertise in economic forecasting and decision theory to position the firm as a pioneer in quantitative analysis for private and public sector clients.^[10] Initially operating as a division of the Market Research Corporation of America, Mathematica focused on operations research applications, including economic analyses for government and industry, such as urban transportation planning and systems optimization.^[11] In 1969, it was spun off as an independent entity to accelerate growth and specialization in analytical consulting.^[11] By 1968, the company established its policy research arm, Mathematica Policy Research (later rebranded as Mathematica Inc.), to undertake rigorous empirical studies, beginning with the first large-scale experimental social research project in the United States, funded by the Office of Economic Opportunity to evaluate income maintenance programs.^[12] This marked an early pivot toward evidence-based social policy evaluation, leveraging randomized controlled trials to assess interventions amid the War on Poverty initiatives.^[4] Early efforts emphasized data-driven methodologies over traditional observational studies, setting the stage for the firm's expansion into federal contracts.^[12]

Key Experiments in the 1970s

In the early 1970s, Mathematica Policy Research continued its pioneering role in social experimentation through the New Jersey Income Maintenance Experiment (NIMEX), which operated from August 1968 to September 1972 and enrolled approximately 1,300 low-income families across urban and suburban sites in New Jersey and Scranton, Pennsylvania.^[13]^[14] This randomized controlled trial tested a negative income tax (NIT) model, providing cash supplements to eligible families based on income guarantees ranging from $1,700 to $3,300 annually (adjusted for family size) and tax rates of 30% to 70%, with the aim of assessing labor supply responses, family stability, and consumption patterns among welfare-eligible populations.^[15] Results indicated modest work reductions, particularly among secondary earners like wives (averaging 10-20% fewer hours), but minimal effects on primary male earners, challenging assumptions of widespread work disincentives while highlighting potential marital dissolution risks in treatment groups.^[16] Building on NIMEX, Mathematica contributed to the Gary Income Maintenance Experiment (1971-1974), targeting 1,000 predominantly African American families in Gary, Indiana, to evaluate NIT effects in an urban, minority-heavy context with income guarantees up to $4,200 and tax rates similar to prior trials.^[17] The study, which included extended three-year exposure periods, found labor supply reductions comparable to New Jersey's, with wives cutting hours by about 15% and youth participation declining, though overall family income rose due to transfers; it also documented increased school enrollment among youth, suggesting compensatory behavioral shifts.^[18] Mathematica's involvement extended to the Rural Income Maintenance Experiment (1969-1973) in Iowa and North Carolina, involving over 4,900 households to test NIT variants in non-urban settings, where guarantees reached $3,400 for larger families and tax rates varied from 40% to 70%.^[17] Findings revealed smaller work reductions than urban trials (e.g., 5-10% for wives), attributed to higher baseline employment and agricultural work norms, but confirmed persistent effects on female labor participation and some evidence of asset accumulation in treatment groups.^[19] These experiments collectively advanced rigorous empirical methods in policy evaluation, influencing debates on welfare reform by providing causal evidence from randomized designs, though critics later noted selection biases in participant retention and underestimation of long-term dynamics.^[20] By mid-decade, Mathematica supported the Seattle-Denver Income Maintenance Experiment (SIME/DIME, initiated 1970 and running through 1982), a larger-scale trial with 4,800 families focused on the working poor, incorporating job search requirements and guarantees up to $11,000 for larger households at lower tax rates (50% average).^[17] Early 1970s phases emphasized marital stability and child outcomes, yielding data on reduced hours (7-10% overall) but improved health insurance coverage and youth achievement, with extended follow-ups revealing sustained positive effects on divorce rates and earnings trajectories.^[18] These efforts solidified Mathematica's expertise in field experiments, emphasizing baseline surveys, attrition tracking, and econometric analysis to isolate treatment effects amid real-world implementation challenges like participant mobility.^[13]

Growth and Expansion (1980s–2000s)

In the early 1980s, Mathematica experienced a significant decline in contracts amid broader economic shifts affecting social policy research funding, prompting its acquisition by Martin Marietta Data Systems, an aerospace firm, in 1983 for financial stabilization.^[8]^[11] This period saw the separation of its software products group, which was retained by Martin Marietta, while the core policy research operations adapted to corporate oversight, maintaining focus on data-driven evaluations for government clients despite integration challenges.^[21] By 1986, a management-led employee buyout repurchased the policy research division from Martin Marietta, restoring independence and enabling sharper alignment with social science methodologies like randomized controlled trials.^[22] This restructuring facilitated renewed growth, with expanded contracts from the U.S. Department of Labor on labor market interventions dating back to the decade's start and intensifying in the 1990s.^[23] The firm broadened its geographic footprint by establishing offices in Washington, D.C., and Cambridge, Massachusetts, alongside its Princeton headquarters, to enhance proximity to federal agencies and academic collaborators.^[23] The 1990s marked accelerated expansion through high-profile evaluations amid U.S. welfare reforms, including a 1994 longitudinal study of Iowa's incentive and sanction programs and a 1998 five-year assessment of New Jersey's welfare-to-work initiatives.^[23] In 1995, Mathematica launched the Center for Studying Health System Change, funded by the Robert Wood Johnson Foundation, to track national health policy dynamics via ongoing surveys.^[23] By the early 2000s, this momentum continued with contracts like the 2002 evaluation of Teach For America across five cities under the No Child Left Behind Act and an annual census of 20,000 U.S. substance abuse treatment facilities, achieving a 96% response rate by 2002, underscoring the firm's scaling of large-scale data collection capabilities.^[23] These efforts solidified Mathematica's role in evidence-based policy, with staff growth supporting multidisciplinary teams in health, education, and workforce domains.^[23]

Rebranding and Modern Era (2010s–Present)

In the 2010s, Mathematica broadened its offerings to integrate data science, methodological innovation, and technological tools with its core policy research capabilities, enabling comprehensive solutions from program design through evaluation and scaling. This evolution positioned the company to address complex challenges in public programs by leveraging large-scale data analysis and evidence-based advisory services for government, foundations, and private sector clients.^[1] A key milestone was the 2018 acquisition of EDI Group of Companies, an international firm specializing in rigorous data collection and research in developing regions. Announced on July 19, 2018, and completed later that year pending regulatory approvals, the merger established EDI as a wholly owned subsidiary, expanding Mathematica's footprint in East Africa with operations in Tanzania, Uganda, and Mozambique. This move enhanced the company's capacity for global surveys, fieldwork, and evaluation in international development, allowing for improved service delivery to clients seeking evidence on poverty alleviation, health, and education initiatives abroad.^[24]^[25] The same year, Mathematica marked its 50th anniversary since its 1968 founding, reflecting on decades of pioneering randomized controlled trials and data-driven insights that have shaped U.S. social policies, including early evaluations of income support programs and workforce interventions. The rebranding from Mathematica Policy Research to Mathematica—evident in updated branding and domain shifts—underscored this maturation, signaling a shift toward a unified identity that encompasses not only policy analysis but also advanced analytics, operations, and technology deployment to drive program effectiveness and efficiency.^[12]^[26]^[27] Into the 2020s, the company has sustained growth as a 100% employee-owned entity, prioritizing empirical rigor in areas like health care delivery, education outcomes, and human services amid evolving demands for causal evidence and scalable interventions. Its employee ownership model, rooted in an ESOP structure, continues to foster long-term alignment with mission-driven work, free from external shareholder pressures that might prioritize short-term gains over substantive research impact.^[1]

Organizational Structure

Headquarters and Global Presence

Mathematica Inc. is headquartered in Princeton, New Jersey, at 600 Alexander Park, Suite 100, Princeton, NJ 08540.^[28] This location serves as the primary hub for the company's operations, research, and administrative functions, with a mailing address of P.O. Box 2393, Princeton, NJ 08543-2393.^[28] The firm maintains a network of seven additional offices across the United States to support its domestic research and client engagements: Washington, D.C. (1100 1st Street NE, 12th Floor); Ann Arbor, Michigan; Cambridge, Massachusetts; Chicago, Illinois; Oakland, California; Seattle, Washington; and Tucson, Arizona.^[29] ^[30] These facilities enable proximity to key federal agencies, academic institutions, and policy stakeholders, facilitating data collection, surveys, and collaborative projects.^[31] Mathematica Inc. lacks physical offices outside the United States, reflecting its origins as a U.S.-focused policy research organization founded in 1986. However, the company conducts international evaluations and partnerships, particularly in global health and development, through remote collaborations rather than overseas branches.^[32]

Leadership and Governance

Paul Decker serves as President and Chief Executive Officer of Mathematica, bringing expertise in policy research, data analytics, education, and labor policy; he holds a Ph.D. in economics from Johns Hopkins University and has previously served as president of the Association for Public Policy Analysis and Management.^[33] Under his leadership, the organization has expanded to over 1,500 employees across 12 locations, achieved annual revenues exceeding $300 million, and diversified its research into areas such as child welfare, housing, veterans' affairs, health care, and international development through acquisitions like EDI Global.^[33] The executive leadership team comprises senior vice presidents in key functional areas, including Lisa Schwartz as Chief Operating Officer, Alison Barger as Chief Financial Officer, Jill Albanese as Chief Human Resources Officer, Akira Bell as Chief Information Officer, Adam Coyne as Chief Growth Officer, and Christopher Trenholm as Executive Vice President and Chief Business Officer.^[34] These leaders oversee operations, finance, human resources, technology, business development, and strategic growth, supporting Mathematica's mission-driven work in evidence-based policy analysis.^[34] As an employee-owned company structured as an Employee Stock Ownership Plan (ESOP), Mathematica's governance is directed by a Board of Directors established in 1986, which provides strategic oversight and informs the organization's vision amid its for-profit operations.^[34]^[35] The board includes experts such as Jaideep Bajaj, who joined in 2020 and serves as chairman of ZS Associates, a global professional services firm; Matthew Salganik, appointed in 2018 and a sociologist at Princeton University; and Maria Cancian, who joined in 2021.^[36]^[37]^[38] Former presidents like Chuck Metcalf also contribute to the board, ensuring continuity in the firm's research-oriented culture.^[35]

Internal Divisions and Research Centers

Mathematica houses four specialized research centers that concentrate on targeted policy research areas, fostering expertise in evaluation, evidence synthesis, and program improvement. These centers support the organization's broader mission by conducting rigorous studies, disseminating findings, and advising policymakers.^[39] The Center for Studying Disability Policy (CSDP), established in 2007, directs Mathematica's disability-focused research initiatives, including data collection, policy analysis, and evaluation of programs affecting individuals with disabilities. It emphasizes advancing evidence on employment, health access, and social safety net interventions for this population.^[40]^[41] The Center for Improving Research Evidence (CIRE), created in early 2008, specializes in assessing and promoting high-quality evaluation methods, such as rapid-cycle assessments and implementation science, to enhance the credibility and usability of policy research. It produces resources like video series and forums to guide practitioners in applying advanced analytics and evidence-building strategies.^[42]^[43] The Center on Health Care Effectiveness (CHCE), founded in 2010, functions as a hub for analyzing health care delivery systems, comparative effectiveness research, and policy implications for improving outcomes and efficiency. It hosts forums and studies on topics including physician payment reforms and primary care comprehensiveness, aiding stakeholders in evidence-based decision-making.^[44]^[45] The Center for International Policy Research and Evaluation (CIPRE), launched in 2013, focuses on global development challenges, conducting impact evaluations of interventions in areas like early childhood, water management, and literacy in low-resource settings across more than 40 countries. It integrates health, education, and economic perspectives to inform international aid and policy.^[46]^[47] In addition to these centers, Mathematica structures its operations through business divisions aligned with core practice areas—health, human services, and international research—which coordinate multidisciplinary teams for client projects in domestic and global contexts.^[48]

Research Focus Areas

Mathematica's domestic social policy research emphasizes empirical evaluations of income support mechanisms, welfare-to-work transitions, and programs targeting poverty and family stability, often employing randomized controlled trials to isolate causal effects.^[3] Pioneering large-scale social experiments in the United States, the organization tested guaranteed income proposals during the late 1960s and early 1970s, including the New Jersey Graduated Work Incentive Experiment from 1968 to 1972, which involved over 1,300 low-income families and assessed labor supply responses to varying benefit levels and tax-back rates.^[13] Results demonstrated modest reductions in work hours—approximately 7% for wives and 4% for female heads of household—but no significant effects on primary male earners, informing debates on work disincentives in cash transfer programs.^[15] Following the 1996 Personal Responsibility and Work Opportunity Reconciliation Act, which imposed time limits and work requirements on Aid to Families with Dependent Children, Mathematica led the National Evaluation of the Welfare-to-Work Grants Program from 1998 to 2004, analyzing federal grants totaling $3 billion aimed at the hardest-to-employ populations, such as long-term welfare recipients and noncustodial parents.^[49] The evaluation, covering 16 grantees and tracking over 10,000 participants, found that intensive case management and job placement services increased employment rates by 5-10 percentage points in the short term but yielded limited sustained earnings gains, with average quarterly earnings rising by $200-$300 for participants after two years, highlighting barriers like childcare and transportation.^[50] State-specific assessments, such as the Iowa welfare reform evaluation completed in 2002, similarly showed heightened training and job entry—boosting employment by 15%—yet inconsistent family income improvements due to low-wage jobs and benefit cliffs.^[51] In disability policy, Mathematica's Center for Studying Disability Policy, established in collaboration with the Social Security Administration, has analyzed Supplemental Security Income and Social Security Disability Insurance outcomes since the early 2000s, revealing that only 1% of beneficiaries exit programs annually via employment while 20% enter due to health declines, and estimating that targeted interventions could reduce rolls by 10-20% over a decade without increasing poverty.^[52] The organization's Pathways to Work Evidence Clearinghouse, launched in 2019 under contract with the Social Security Administration, rates employment strategies for disability recipients, finding moderate evidence for benefits counseling and strong support for individual placement models that connect participants to subsidized jobs, based on syntheses of over 100 studies.^[53] These efforts underscore Mathematica's role in quantifying trade-offs, such as employment gains versus fiscal costs, in social safety net designs.^[54]

Health and Human Services

Mathematica conducts extensive research and evaluation on health and human services programs, focusing on improving outcomes for vulnerable populations through rigorous evidence-based methods. The organization has secured multiple contracts with the U.S. Department of Health and Human Services (HHS), including the Administration for Children and Families (ACF), to assess program effectiveness, such as the Home Visiting Evidence of Effectiveness (HomVEE) review, which evaluates early childhood home visiting models for their impact on family well-being and child development.^[55]^[56] This work includes systematic reviews of interventions like behavioral strategies to boost participation in job training and employment services, as well as analyses of centralized service models to enhance coordination across health, education, and welfare systems.^[57]^[58] In health policy, Mathematica evaluates access to care for individuals with disabilities, documenting functional status, health disparities, and barriers across subgroups, including children and adults.^[59] The firm has supported HHS initiatives like the Patient-Centered Medical Home (PCMH) model by developing conceptual frameworks and communication tools to facilitate its implementation in primary care settings.^[60] Recent contracts include a $10 million HHS ACF Project Support Phase 2 award in 2025 for technical assistance and evaluations, alongside participation in the $350 million Health Resources and Services Administration (HRSA) Evidence Building and Evaluation IDIQ contract vehicle, which funds assessments of maternal, child, and rural health programs.^[61]^[62] Human services research at Mathematica addresses employment barriers, child welfare, and family support, often integrating data analytics to inform policy. For instance, studies examine coordinated approaches linking early care, education, and health services to better serve families, while exploring AI applications to streamline public health and service delivery.^[63]^[64] Evaluations of state Medicaid programs highlight unique challenges and solutions, such as improving data systems for better program administration.^[65] Mathematica's contributions earned recognition, including the 2010 HHS Outstanding Contractor Award for work on pregnancy prevention and home visiting programs.^[39]

Education and Workforce Development

Mathematica conducts research on education policy to evaluate interventions aimed at enhancing student outcomes, teacher effectiveness, and system-level improvements in K-12 and postsecondary settings. For instance, the organization has analyzed the impacts of teacher induction programs through multi-year, multi-state randomized controlled trials, finding mixed effects on teacher retention and student achievement depending on program design.^[66] In early childhood education, Mathematica's studies, including evaluations of video-based coaching, have demonstrated potential benefits for teacher practices but limited direct gains in student learning without sustained implementation.^[67] A core contribution is the Education-to-Workforce (E-W) Indicator Framework, released in 2022, which provides a standardized approach for linking data across pre-K-12, postsecondary, and labor market systems to track progress toward equitable outcomes and economic mobility.^[68] The framework emphasizes 25 recommended disaggregates—such as race, ethnicity, income, and disability status—to identify inequities and inform evidence-based practices, drawing from peer-reviewed methodologies and stakeholder input to prioritize actionable metrics over descriptive statistics alone.^[69]^[70] In workforce development, Mathematica evaluates federal programs like the Workforce Innovation and Opportunity Act (WIOA), conducting evidence scans and portfolio assessments from 2020 to 2024 to assess training efficacy, participant employment rates, and cost-effectiveness for underserved populations.^[71] The organization also leads the Partners for Reentry Opportunities in Workforce Development (PROWD) evaluation for the U.S. Department of Labor, analyzing employment-focused reentry grants since 2018 to measure recidivism reductions and job placement success among formerly incarcerated individuals.^[72] Youth-focused initiatives include examinations of apprenticeship programs, with a multi-site study of eight U.S. models revealing improved earnings and skill acquisition for participants aged 16-24, alongside employer benefits like reduced turnover, based on longitudinal tracking of over 1,000 apprentices.^[73] Internationally, Mathematica supports workforce strategies in regions like Central America, producing annual reports—such as the third edition covering 2018-2023 data—that assess program scalability, skill-matching interventions, and labor market integration for youth and migrants.^[74] These efforts integrate randomized evaluations and administrative data linkages to prioritize causal evidence over correlational analyses.^[75]

International Development and Evaluation

Mathematica's international development and evaluation efforts center on providing measurement, evaluation, research, and learning (MERL) services to support evidence-based decision-making in global programs. Through its subsidiary EDI Global and partnerships in Africa and Asia, the organization addresses challenges in sustainable development, with operations spanning more than 90 countries across Africa, Asia, Europe, and Latin America.^[76]^[77] These activities expanded significantly under leadership that grew the global portfolio from a limited set of projects to a dedicated international unit based in Seattle around 2015.^[78]^[79] The firm's evaluation approaches emphasize rigorous methods, including impact assessments, formative studies, evidence reviews, and adaptive program management, often incorporating data science and collaboration with local partners to adapt to complex contexts.^[76] Where feasible, evaluations employ experimental designs such as randomized controlled trials or quasi-experimental methods, alongside retrospective long-term analyses using programmatic and geocoded data, as seen in collaborations with USAID's Expanding the Reach of Impact Evaluation (ERIE) initiative.^[80] These efforts prioritize generating actionable insights for program improvement, with services extending across the project lifecycle from strategy design to outcome measurement.^[81] Key sectors include education and workforce development, global health and nutrition (encompassing maternal and child health, reproductive health, and disease prevention), agriculture (focusing on crop resilience and market access), and economic opportunities.^[76] Partners comprise bilateral donors like USAID and the World Bank, alongside foundations such as the Bill & Melinda Gates Foundation, Children's Investment Fund Foundation, and Rockefeller Foundation.^[76] Notable projects demonstrate this scope, such as the Rapid Feedback MERL initiative for USAID, which tested components of early grade reading programs and health interventions (including family care and mobile health) in Senegal, India, Guinea, Cambodia, and Tanzania, in partnership with entities like Results for Development and Abt Associates.^[82] In Latin America, Mathematica conducted eight evaluations of reading interventions across Guatemala, El Salvador, Honduras, Nicaragua, and Peru under USAID-funded efforts.^[83] Other evaluations include the long-term impact assessment of the Amazonía Lee reading program in Peru, with a report issued on January 4, 2021, and the Early Grade Reading and Math Project (RAMP) in Jordan.^[84]^[85] These works have informed adaptive management and policy adjustments in education and health sectors.^[86]

Methodology and Technical Approach

Randomized Controlled Trials and Experimental Design

Mathematica prioritizes randomized controlled trials (RCTs) as the gold standard for establishing causal relationships in policy evaluations, leveraging random assignment to treatment and control groups to isolate intervention effects from selection bias and confounding variables.^[66] This approach, rooted in their foundational work on large-scale social experiments since the late 1960s, ensures that observed outcome differences reflect the intervention's impact rather than baseline group disparities.^[87] In practice, Mathematica designs RCTs with sufficient sample sizes to detect meaningful effects, incorporating baseline data collection to adjust for pretest imbalances and enhance precision in intent-to-treat analyses.^[88] Experimental designs at Mathematica often account for clustered randomization, common in education and community-based interventions, using design-based or model-based estimators to handle intra-cluster correlations and produce unbiased variance estimates.^[89] For instance, in multi-site trials, they stratify randomization by key covariates like site characteristics or participant demographics to improve balance and generalizability.^[90] To mitigate risks of false discoveries in analyses with multiple outcomes or subgroups, Mathematica applies conservative adjustments such as Bonferroni corrections or false discovery rate controls, prioritizing confirmatory hypotheses over exploratory ones.^[91] When ethical, logistical, or cost constraints preclude RCTs, Mathematica employs quasi-experimental designs like regression discontinuity or propensity score matching as complements, but consistently validates these against RCT benchmarks where possible to assess bias.^[90] Their methodology emphasizes transparency in power calculations, attrition monitoring, and compliance tracking to maintain internal validity, with protocols for handling noncompliance through instrumental variable approaches when randomization adherence falters.^[92] This rigorous framework has underpinned evaluations across domains, including employment programs and early childhood interventions, where random assignment protocols are embedded in study protocols from inception.^[54]^[93]

Data Analytics and Computational Methods

Mathematica Inc. employs advanced data analytics and computational methods to support rigorous policy evaluations, integrating statistical modeling, machine learning, and large-scale data processing to derive evidence-based insights. These approaches complement experimental designs by enabling predictive simulations, pattern detection in complex datasets, and synthesis of heterogeneous data sources, particularly in domains such as health, education, and human services.^[94] The firm emphasizes scalable computational techniques that leverage big data, including administrative records, surveys, and novel inputs like remote sensor and satellite data, to uncover causal relationships and forecast outcomes beyond traditional randomized trials.^[94] A cornerstone of Mathematica's computational toolkit is Bayesian methods, which update prior knowledge with new evidence to produce probabilistic estimates of program effectiveness, offering advantages over frequentist approaches in handling uncertainty and incorporating external data. For instance, these methods facilitate decision-making by quantifying the likelihood that a policy intervention yields positive results for specific subgroups, as demonstrated in applications to social welfare evaluations where sample sizes are limited or data is noisy.^[95] ^[96] Mathematica researchers advocate for a paradigm shift toward Bayesian frameworks in evidence-based policymaking, arguing that they provide interpretable outputs for real-world implementation, such as assessing scalability of interventions across diverse populations.^[97] ^[98] In addition to Bayesian inference, the firm utilizes machine learning techniques, including supervised and unsupervised models, to automate classification tasks and identify latent structures in high-dimensional data. Natural language processing is applied to analyze textual data from program documents or participant feedback, extracting themes and sentiment to inform qualitative-quantitative hybrids in evaluations. Predictive analytics models simulate policy impacts prior to rollout, while factor analysis reveals underlying patterns in multivariate datasets, aiding in cost-effectiveness studies. Spatial analysis tools generate geospatial visualizations to map disparities, such as access to services, and network analysis traces interconnections in social systems to model ripple effects of interventions.^[94] Microsimulation modeling represents another key computational method, particularly for projecting the distributional effects of policy changes on large populations. In evaluations of programs like the Supplemental Nutrition Assistance Program (SNAP), Mathematica constructs agent-based simulations that replicate individual-level behaviors and interactions, enabling policymakers to estimate fiscal impacts and eligibility trends under hypothetical reforms. These models incorporate stochastic elements and iterative computations to handle variability, providing granular forecasts that inform legislative debates.^[99] Such techniques underscore Mathematica's commitment to computational realism, where simulations are validated against empirical benchmarks to ensure reliability in causal inference.^[39] Overall, these methods are deployed within an integrated data science pipeline that emphasizes reproducibility and computational efficiency, often processing terabyte-scale datasets through cloud-based infrastructures. By fusing domain expertise with algorithmic innovation, Mathematica addresses limitations in observational data, such as confounding variables, through techniques like propensity score matching augmented by machine learning. This approach has been pivotal in generating actionable evidence for federal agencies, though it requires careful validation to mitigate risks of overfitting in predictive models.^[94] ^[100]

Ethical Considerations in Research

Mathematica conducts research involving human subjects in compliance with U.S. federal regulations, including 45 CFR 46 for the protection of human subjects, as well as 21 CFR 50 and 56 for informed consent and institutional review boards (IRBs).^[101] These standards require minimizing risks to participants, ensuring voluntary informed consent, and maintaining confidentiality, particularly in studies with vulnerable populations such as low-income families or welfare recipients. For instance, in surveys and evaluations, Mathematica obtains IRB approvals to verify that protocols safeguard participant privacy and address potential harms from data collection or intervention effects.^[102] ^[103] The organization frequently partners with external IRBs, such as Health Media Lab IRB (HML IRB), which has reviewed and approved Mathematica projects since 2000, including international and domestic studies for clients like UNICEF and the World Bank.^[102] This external oversight helps mitigate biases in self-review and ensures adherence to ethical principles like beneficence and justice, especially in randomized controlled trials (RCTs) where control groups may be denied benefits available to treatment groups. In data-sharing initiatives, Mathematica emphasizes securing IRB exemptions or approvals for low-risk analyses while protecting sensitive information, such as in child maltreatment surveillance or administrative data linkages.^[104] ^[105] Historical projects, like the New Jersey Negative Income Tax Experiment (1968–1972), operated under pre-Belmont Report standards, with limited formalized ethical review compared to contemporary practices; ethical concerns included the randomization of financial supports to families, potentially exacerbating short-term inequities without modern consent protocols.^[106] Today, Mathematica's approaches incorporate data equity principles, such as addressing racial and socioeconomic biases in research design and IRB processes, to enhance fairness in evidence generation for policy.^[107] No major ethical violations have been documented in recent evaluations, reflecting a focus on rigorous, low-harm methodologies.^[108]

Notable Studies and Projects

New Jersey Negative Income Tax Experiment (1968–1972)

The New Jersey Graduated Work Incentive Experiment, also known as the New Jersey Negative Income Tax Experiment, was a pioneering randomized controlled trial conducted to assess the labor supply responses of low-income families to a negative income tax (NIT) scheme, which supplements earnings below a guaranteed threshold while imposing a tax on additional income.^[14] The study, funded by the U.S. Office of Economic Opportunity and initiated in 1967, aimed to inform welfare policy by testing whether NIT could replace traditional categorical aid without significantly discouraging work.^[15] Mathematica, Inc., headquartered in Princeton, New Jersey, partnered with the Institute for Research on Poverty at the University of Wisconsin–Madison to design, implement, and analyze the experiment, handling participant enrollment, benefit payments, data collection through quarterly interviews and audits, and statistical evaluation.^[14]^[15] Eligible families were two-parent households with an able-bodied male head aged 18–58, annual income below 150% of the poverty line, and including Black, white, and Latino participants across urban and rural sites in Trenton, Paterson-Passaic, Jersey City (New Jersey), and Scranton (Pennsylvania).^[14] From August 1968 to September 1972, 1,216 families were enrolled, with 725 randomly assigned to experimental groups and 491 to a control group receiving no payments; an additional 141 families were later added to the control.^[14] Experimental families were further randomized into one of eight NIT treatments, varying guarantee levels at 50%, 75%, 100%, or 125% of the poverty line (adjusted for family size, ranging roughly $1,000–$3,300 annually) and implicit marginal tax rates of 30%, 50%, or 70%.^[14]^[15] Benefits were calculated quarterly as the difference between the guarantee and taxable earnings, with verification via pay stubs and employer checks to minimize underreporting.^[15] The design addressed potential selection bias through pre-experimental income truncation and used maximum likelihood estimation to model labor supply elasticities for hours worked and wages.^[15] Key findings revealed modest overall reductions in labor supply, with effects concentrated among secondary earners. Husbands in treatment groups showed negligible responses, with employment rates 1.5% lower and hours worked 2% fewer than controls.^[14] Wives, who averaged 4.4 hours per week pre-experiment, reduced hours by 23% and employment by 24%, reflecting greater sensitivity to the effective wage reduction from the tax-back mechanism.^[14] Family-level effects included 8.7% fewer total hours worked and 9.5% lower employment rates, with earnings declining by approximately 6%; responses were stronger among white families than Black or Spanish-speaking ones, and urban participants exhibited slightly larger disincentives than rural.^[14]^[15] Secondary analyses indicated hours elasticity of about 14% with respect to wages and -2% to non-wage income, with no evidence of mass labor force withdrawal but some substitution toward leisure or home production.^[15] Other outcomes included improved school performance among teenagers, though initial reports found limited impacts on health or family stability.^[109] The experiment's results fueled policy debates, highlighting NIT's potential to simplify aid but raising concerns over work disincentives, particularly for women, which contributed to the failure of President Nixon's Family Assistance Plan and informed subsequent reforms like the 1996 welfare overhaul.^[109] Mathematica's rigorous implementation established precedents for large-scale social experiments, influencing federal evaluation standards despite critiques of attrition (reducing analyzable sample to 983 families) and short-term focus potentially underestimating long-run behavioral adjustments.^[109]^[14]

Housing and Welfare Evaluations (1970s–1990s)

During the 1970s, Mathematica Policy Research served as a primary contractor for the Housing Assistance Supply Experiment (HASE), a component of the U.S. Department of Housing and Urban Development's Experimental Housing Allowance Program. Conducted in Green Bay, Wisconsin, and South Bend, Indiana, from 1972 to 1982, HASE tested the supply-side impacts of providing housing certificates to approximately 5,000 low-income households, aiming to assess whether demand subsidies would stimulate new rental housing construction or rehabilitation by landlords. Mathematica managed site operations, including participant enrollment, payment administration, and data collection in Green Bay, where it established a local office in 1974.^[110] HASE findings revealed limited supply elasticity; landlord participation rates averaged 40-50 percent, with modest improvements in housing quality but negligible increases in overall rental stock or new units, as subsidies primarily raised rents rather than expanding supply. By 1979, experimental payments totaled over $10 million, yet vacancy rates remained stable, and administrative costs exceeded 20 percent of outlays due to certification and compliance verification challenges. These results, contrasting with demand-focused experiments by other firms, underscored potential inflationary pressures from vouchers without concurrent supply incentives, influencing subsequent policy shifts toward project-based assistance.^[66] In parallel welfare evaluations, Mathematica contributed to the Rural Income Maintenance Experiment (RIME) from 1970 to 1972, testing negative income tax guarantees on 4,900 households in Iowa and North Carolina small towns. Building on urban pilots, RIME measured labor supply responses among rural families, finding smaller work reductions—about 5 percent overall—than in metropolitan settings, attributed to higher baseline employment and fewer childcare options. Guarantee levels ranged from $1,200 to $5,400 annually (adjusted for family size), with tax-back rates of 50-70 percent; secondary effects included slight increases in school enrollment but no significant marital stability changes.^[6]^[18]^[111] Through the 1980s and 1990s, Mathematica evaluated state-level welfare-to-work demonstrations under the Family Support Act of 1988, including job search and education mandates for Aid to Families with Dependent Children (AFDC) recipients. In programs like California's Greater Avenues for Independence (GAIN), Mathematica's analyses of over 20,000 participants showed rapid employment gains—up to 10 percentage points in quarter 1 post-enrollment—but fading long-term earnings impacts without ongoing support, with human capital-focused variants yielding higher sustained income (e.g., $1,000+ annual increases after 2 years) over immediate job placement. These randomized trials, involving sites in Riverside and San Diego, highlighted the role of earnings supplements in reducing welfare dependency by 15-20 percent, informing the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA). Evaluations emphasized causal mechanisms like reduced barriers via childcare subsidies, though critics noted selection biases in voluntary participation.^[112]^[113]^[114]

Contemporary Policy Evaluations (2000s–Present)

In the 2000s, Mathematica evaluated the impacts of welfare reform under the 1996 Personal Responsibility and Work Opportunity Reconciliation Act, analyzing state-level implementations such as Iowa's Family Independence Program, which combined administrative data with surveys to assess employment gains and poverty reductions, revealing sustained increases in work participation but persistent challenges in deep poverty alleviation.^[115] Similarly, follow-up studies on the Job Corps program, a federal vocational training initiative, used randomized assignment data to demonstrate modest long-term earnings improvements for participants, though benefits diminished over time and varied by subgroup, informing adjustments to youth employment policies.^[116] Education evaluations during this period included a randomized study of Teach For America from 2001 to 2003, which found that TFA corps members produced student achievement gains comparable to or exceeding those of experienced teachers in high-poverty schools, particularly in math, supporting expansions of alternative certification pathways despite debates over teacher preparation.^[117] In early childhood, the Early Head Start Research and Evaluation Project, spanning 1996 to 2010 with Mathematica as the data coordination center, tracked randomized cohorts through elementary school, showing program participation linked to improved cognitive and social-emotional outcomes at age 5, though effects faded by third grade without sustained supports, influencing federal investments in comprehensive family services.^[118] Health policy assessments expanded in the 2010s, with Mathematica leading the evaluation of the Health Care Innovation Awards under the Centers for Medicare & Medicaid Services from 2012 to 2017, employing mixed-methods including claims data analysis across 57 initiatives, which demonstrated reductions in hospital readmissions and improved care coordination in select models but inconsistent cost savings and scalability challenges.^[119] Disability-focused work included the Promoting Readiness of Minors in Supplemental Security Income (PROMISE) demonstration evaluation, completed in 2022, analyzing randomized trials across six states from 2016 onward, where interventions boosted competitive employment rates by 5-7 percentage points for youth with disabilities but yielded limited reductions in Supplemental Security Income dependency, prompting refinements in transition services.^[120] More recent efforts address workforce and justice reintegration, such as the 2021 evaluation design for the Retaining Employment and Talent after Injury/Illness Network (RETAIN), a randomized multi-state study aimed at preventing long-term disability through early intervention, with interim findings indicating potential delays in benefit claims via coordinated health and employment supports.^[121] In human services, assessments of healthy marriage and relationship education programs since 2000 reviewed 32 evaluations, finding short-term improvements in couple communication but negligible effects on child well-being or economic stability, underscoring the need for targeted adaptations in federal funding priorities.^[122] These evaluations, often leveraging administrative data and RCTs, have shaped evidence-based adjustments to programs like Medicaid managed care and recidivism reduction initiatives, though critics note variability in generalizability across diverse populations.^[123]^[124]

Impact and Achievements

Influences on U.S. Policy and Legislation

Mathematica's New Jersey Negative Income Tax Experiment (1968–1972), the first large-scale randomized controlled trial in social policy, provided empirical data on labor supply responses to cash transfers, revealing modest reductions in work hours among secondary earners, particularly wives, averaging 5–10% under tested guarantee levels and tax rates.^[15] These findings informed congressional debates on President Nixon's proposed Family Assistance Plan in 1969–1970, which incorporated a negative income tax element but ultimately failed amid concerns over work disincentives highlighted in preliminary experiment results; the data underscored the need for work incentives in income support, contributing to the evolution toward hybrid programs like the Earned Income Tax Credit enacted in 1975.^[109]^[125] In the lead-up to the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), Mathematica's evaluations of state welfare-to-work demonstrations in the 1980s and early 1990s, including programs mandating employment services, demonstrated that such interventions increased employment by 5–15% and reduced welfare caseloads without significant harm to participants' children, providing evidence that bolstered arguments for time limits and work requirements over unconditional aid.^[126]^[127] Post-enactment, Mathematica's analyses of Temporary Assistance for Needy Families (TANF) implementation across states, such as profiling efforts in Florida, Oregon, Tennessee, and Utah, helped policymakers assess compliance with work participation rates and refine block grant allocations, influencing subsequent reauthorizations and state-level adjustments.^[128]^[129] More recently, Mathematica has supported the Foundations for Evidence-Based Policymaking Act of 2018, which requires federal agencies to develop evidence-building plans and prioritize rigorous evaluations; the firm's expertise in randomized trials and data analytics informed implementation guidance, including capacity-building for agencies under the Evidence Act, fostering a shift toward data-driven legislation in areas like health and education policy.^[130]^[131] This methodological legacy has embedded experimental approaches in legislative oversight, as seen in evaluations shaping disability policy under the Social Security Administration and health reforms, though direct causal attribution to specific bills remains mediated by broader political processes.^[52]^[132]

Contributions to Evidence-Based Decision-Making

Mathematica has advanced evidence-based decision-making by conducting rigorous program evaluations and providing technical assistance to government agencies, enabling the use of empirical data to assess policy effectiveness and guide resource allocation. Their work includes designing large-scale studies in areas such as health, education, and disability policy, often employing randomized controlled trials and microsimulation models to forecast policy outcomes and inform adjustments.^[39] ^[66] The organization has actively supported the implementation of the Foundations for Evidence-Based Policymaking Act of 2018 by hosting discussions and offering recommendations to enhance federal evidence-building practices. In May 2023, Mathematica coordinated a Capitol Hill event with Congressman Derek Kilmer's office, featuring panelists from the Department of Health and Human Services and the Data Foundation, to evaluate the Act's impact and propose improvements like better data aggregation and agency capacity-building.^[131] This included advocacy for a bipartisan evidence-based policymaking commission, as outlined in House Concurrent Resolution 49 introduced that month.^[131] ^[133] Further contributions involve developing standards for assessing research quality and providing tools for data collection and analysis, such as their Survey Operations Center, which supports secure, large-scale data gathering for program monitoring.^[39] In November 2024, Mathematica's CEO Paul Decker moderated a roundtable at the Association for Public Policy Analysis and Management (APPAM) conference, discussing strategies to integrate evidence into agency workflows, including fostering cultures that prioritize data-driven decisions and embedding evidence teams in leadership structures.^[130] These efforts have helped agencies like the National Science Foundation and the State Department refine evidence practices, emphasizing structural reforms for sustained policymaking improvements.^[130]

Awards, Partnerships, and Scalable Solutions

Mathematica has sponsored the David N. Kershaw Award and Prize since 1992, recognizing exceptional early-career contributions to public policy research and social science; the award, named after the organization's founding president, includes a $20,000 prize and has been given annually, with recipients such as Zachary Parolin in 2025 for work on child poverty metrics.^[134]^[135] Its researchers have received external honors, including the Association for Public Policy Analysis and Management's (APPAM) Peter H. Rossi Award to Randall S. Brown in 2020 for lifetime methodological contributions, and the American Statistical Association's Founders Award to John L. Czajka in 2016 for service in survey statistics.^[136]^[137] The organization itself earned the Agency for Healthcare Research and Quality's grand prize in 2020 for visualizing community-level social determinants of health data.^[138] Mathematica maintains extensive partnerships with U.S. federal agencies such as the Centers for Medicare & Medicaid Services and the Agency for Healthcare Research and Quality, conducting evaluations and providing data analytics under multi-year contracts totaling billions in federal funding since its founding.^[139]^[140] It collaborates with foundations including the William T. Grant Foundation on youth inequality research and the Atlantic Philanthropies on health access evaluations, embedding rigorous evidence into grantmaking strategies.^[141]^[142] Recent alliances include a 2024 partnership with Socially Determined to integrate social determinants of health data for government and payer applications, and membership in the Modernizing Foreign Assistance Network since August 2025 to advance data-driven global aid reforms.^[143]^[144] State and local governments, universities, and nonprofits like AmeriCorps also engage Mathematica for program assessments and technical assistance.^[145]^[39] In scalable solutions, Mathematica develops and supports evidence-based interventions adaptable to broader implementation, such as rural health redesign initiatives that deliver analytics and technical assistance for sustainable models addressing access disparities.^[146] Its data solutions platform enables large-scale processing of administrative and survey data for policy optimization, including ETL pipelines using cloud services like AWS for efficient, expandable analytics in education and workforce programs.^[32]^[147] Through partnerships, it facilitates scaling of innovations, as in evaluations for the Learning Accelerator's networks promoting evidence-backed educational tools, and advisory services for foundations to prototype and expand workforce policies.^[148]^[149] These efforts emphasize modular, data-driven frameworks that transition pilot findings into national or global applications, prioritizing cost-effective replication over bespoke designs.^[150]

Criticisms and Controversies

Methodological Limitations and Replication Issues

Mathematica's randomized controlled trials (RCTs), while valued for establishing causal impacts, have been critiqued for methodological limitations including small sample sizes that reduce statistical power and increase the risk of Type II errors, particularly in early experiments like the New Jersey Negative Income Tax (NIT) study involving 1,357 families. Attrition rates, often exceeding 20% in social policy RCTs, can bias estimates if dropouts differ systematically between treatment and control groups, though Mathematica employed reweighting techniques to mitigate this. Additionally, purposive site selection—common in Mathematica's evaluations of programs like charter schools—limits external validity, as only about 15% of national charter middle schools met criteria for lottery-based analysis, excluding many due to oversubscription or data issues, thereby hindering broader policy inferences.^[151] Replication efforts highlight further challenges, with initial Mathematica findings on programs such as the Center for Employment Training (CET) showing significant earnings gains fading in a 12-site national replication where only four sites achieved high implementation fidelity, yielding no overall effects at 54 months. Similarly, the Quantum Opportunity Program (QOP), evaluated positively by Mathematica in single-site trials, exhibited poor fidelity in multi-site expansions, underscoring how unaddressed "black box" elements of program success—such as local economic conditions or operator expertise—undermine scalability. In the NIT experiments, while labor supply reductions (e.g., 7% for husbands, 17% for wives) replicated consistently across sites like Seattle-Denver, critics note the short three-year duration failed to capture long-term behavioral adaptations or macroeconomic interactions, and the focus on urban poor families precluded testing in diverse populations.^[152]^[152]^[153] These issues reflect broader tensions in social experiments: strong internal validity from randomization contrasts with external validity gaps when effects do not generalize beyond controlled settings, as evidenced by diminished impacts in unemployment insurance bonus replications (e.g., 1.15 weeks in Illinois versus 0.5–0.9 weeks elsewhere due to design variations). Mathematica researchers have acknowledged the need for better fidelity monitoring and generalizability assessments, yet systematic reviews of their work, such as on teacher certification, caution against overreliance due to unrepresentative samples excluding most programs nationwide.^[152]^[154]^[155]

Alleged Biases Toward Expansive Government Interventions

Critics from limited-government perspectives have alleged that Mathematica Policy Research's extensive reliance on contracts from federal agencies, such as the U.S. Department of Health and Human Services and the Department of Labor, fosters a structural incentive to favor research outcomes supporting expansive social interventions, as unfavorable findings could risk future funding. This concern arises from the organization's role in evaluating programs like welfare reforms, Medicaid expansions, and income support experiments, where selection of projects and emphasis on certain metrics might align with agency priorities aimed at justifying program continuation or growth. For instance, in the New Jersey Negative Income Tax Experiment (1968–1972), Mathematica's implementation and analysis documented modest work disincentives (approximately 5–13% reduction in hours worked among recipients), yet critics contended that the experiment's short duration (three years) and guarantee levels understated long-term behavioral responses to expansive cash transfers, potentially biasing toward viewing such policies as viable despite evidence of labor supply effects.^[106] Despite these allegations, Mathematica's findings have often been leveraged by conservative analysts to advocate against government expansion. The Heritage Foundation, for example, cited a 2013 Mathematica evaluation of Trade Adjustment Assistance, which found no significant earnings improvements for participants, to argue for program cuts or elimination, highlighting inefficacy in a key federal intervention.^[156] Similarly, Heritage referenced Mathematica's assessments of federal job training initiatives, such as the Workforce Investment Act, revealing limited long-term employment gains (e.g., only 1–2% earnings increase fading after two years), to critique the fiscal inefficiency of expansive workforce programs.^[157] The Cato Institute has drawn on Mathematica data, including studies showing minimal benefits from minimum wage hikes on firm survival and employment, to oppose regulatory expansions.^[158] These uses indicate that while funding sources raise questions about potential selection bias in project choice, Mathematica's rigorous randomized designs have produced results challenging interventionist narratives, countering claims of systematic pro-expansion tilt. Further scrutiny of methodological choices in welfare-related studies has fueled debate. In evaluations of post-1996 welfare reforms, Mathematica documented caseload declines and employment rises but also persistent poverty rates and subgroup challenges, which some interpret as downplaying the reforms' success in favor of advocating hybrid expansions (e.g., combining work requirements with supplemental supports).^[128] Critics argue this framing sustains a bias toward maintaining government involvement rather than market-oriented alternatives, though empirical transparency in reporting null or negative effects—such as in abstinence education trials showing no behavioral impacts—undermines assertions of deliberate favoritism.^[159] Overall, allegations remain unsubstantiated by patterns of distorted findings, with Mathematica's cross-ideological citations reflecting causal evidence over preconceived policy advocacy.

Fiscal and Efficiency Critiques of Recommended Policies

Critics have argued that policies akin to the negative income tax (NIT), evaluated positively in early experiments by Mathematica for their poverty-reducing potential, entail substantial fiscal inefficiencies due to induced reductions in labor supply. The New Jersey and other NIT pilots, conducted by Mathematica from 1968 to 1972, found that families reduced work hours by an average of 5-9%, with secondary earners (primarily wives) showing declines up to 17%, leading to lower household earnings and heightened program dependency.^[109] This behavioral response increases net fiscal costs, as static benefit calculations fail to account for diminished tax revenues and the need for larger guarantees or higher implicit tax rates to sustain income floors, effectively raising the effective cost per dollar of poverty reduction by 10-20%.^[109] Such efficiency concerns extend to Mathematica's involvement in contemporary guaranteed income pilots, where short-term evaluations emphasize consumption boosts but underplay long-term fiscal scalability amid similar disincentive risks. For instance, Mathematica's oversight of cash transfer projects for youth and families, including the Cash Transfer for Youth Empowerment initiative, has drawn implicit critique in broader economic reviews for replicating NIT-era patterns without fully modeling sustained labor distortions, potentially inflating taxpayer burdens through reduced workforce participation and forgone productivity gains.^[160] Analyses of analogous programs estimate that even modest work reductions could double fiscal outlays over time if scaled nationally, prioritizing transfers over incentives for self-reliance.^[161] In health policy domains, Mathematica's evaluations of Medicaid-related interventions have been faulted for insufficient emphasis on cost-efficiency trade-offs, with findings often highlighting access gains while sidelining evidence of high marginal spending per health outcome. Literature reviews commissioned by federal agencies, incorporating Mathematica's methodologies, reveal persistent inefficiencies in state Medicaid programs, such as administrative overheads exceeding 10-15% of budgets and limited long-term morbidity reductions despite per-enrollee costs averaging $7,000 annually as of 2009 data.^[162] Conservative policy analysts contend that recommendations to expand such programs, informed by Mathematica's rigorous but intervention-favoring analyses, overlook opportunity costs—like diverted funds from preventive or market-based alternatives—exacerbating federal deficits without commensurate efficiency improvements.^[162]

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Such projects include workforce development (WFD) programs, behavior-change ... Washington, DC: Mathematica Policy Research, July 2018. Available at: https ...
[76]
International Education and Workforce Development - Mathematica
We offer flexible solutions that help international organizations develop strategies, design interventions, build capacity, generate evidence, and improve ...
[77]
Global Focus Area - Mathematica
We work in more than 90 countries across the world to offer tailored measurement, evaluation, research, and data science services.
[78]
Introducing Mathematica Global
Dec 13, 2023 · Mathematica Global cultivates the evidence and insights needed for sustainable progress on the most urgent and universal issues of our time.Missing: Inc. | Show results with:Inc.<|separator|>
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Research in Health, Education and Development: Building an ...
May 3, 2024 · At Mathematica, Dr. Murray has led the growth of the company's global research and evaluation work from a handful of projects and staff to a ...
[80]
Quantitative Work with a Direct Social Impact: Harris Alumni Discuss ...
May 1, 2023 · The organization that conducted that sexual abstinence evaluation was Mathematica Policy Research. Twenty years later, Stagner was heading up ...
[81]
Expanding the Reach of Impact Evaluation (ERIE)
This evaluation will assess the long-term impact of receiving a prize for innovators on follow-on funding, jobs, revenue or profit, as compared with other ...
[82]
Mathematica Global Services
Mathematica Global offers end-to-end measurement, evaluation, research, and learning (MERL) services to accelerate sustainable development.
[83]
Rapid Feedback Monitoring, Evaluation, Research, and Learning
Mathematica was engaged in five RF MERL projects or pilots, including two on early grade reading and three on family care and mobile health. Four of these ...
[84]
Latin American and the Caribbean (LAC) Reading and Education ...
Under this project, Mathematica conducted eight evaluations across five countries: Guatemala, El Salvador, Honduras, Nicaragua, and Peru. Project Motivation.
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[PDF] Amazonía Lee Long-Term Impact Evaluation Report
Jan 4, 2021 · This report evaluates the long-term impact of the Amazonía Lee reading intervention in Peru, commissioned by USAID and prepared by Mathematica ...
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Jordan Monitoring and Evaluation Support - Mathematica
Mathematica conducted an evaluation of the Early Grade Reading and Math Project (RAMP) funded by the U.S. Agency for International Development (USAID) in ...
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Global Health and Development - Mathematica
We help our clients drive impact through monitoring, evaluation, research, and learning to advance global development in sectors such as agriculture and food ...
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No Randomized Trial? No Problem! - Mathematica
Sep 10, 2024 · Mathematica's methods experts have mastered a range of propensity score techniques and developed new ones to meet clients' needs. Published: Sep ...
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[PDF] The Late Pretest Problem in Randomized Control Trials of Education ...
Mathematica Policy Research, Inc. Abstract. Pretest-posttest experimental designs are often used in randomized control trials (RCTs) in the education field to.
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[PDF] Findings from Design-Based and Model-Based Methods - GovInfo
Design-based and model-based methods for clustered RCTs ................................................................... 6. II.C. Finite-population and ...
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Comparative Regression Discontinuity and ... - Mathematica
Aug 31, 2022 · In this paper we use data from an evaluation of the Benefit Offset National Demonstration to evaluate the efficacy of using comparative ...
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[PDF] Guidelines for Multiple Testing in Impact Evaluations of Educational ...
Multiple testing in education can lead to false results. Guidelines address this by adjusting individual test alpha and limiting outcomes/subgroups.
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[PDF] Random Controlled Trials (RCTs)
Well-designed RCTs are superior to other study designs ... “Nonexperimental Replications of Social Experiments: A Systematic Review,” Mathematica Policy Research.
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[PDF] Evaluation of Demonstration Projects to End Childhood Hunger
The evaluation contractor, Mathematica Policy Research, developed the study design and data collection protocols. The study design relies on random assignment― ...
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Advanced Analytics - Mathematica
We use a variety of technologies to collect and analyze data, including remote sensor and satellite data collection, to help you understand the bigger picture.
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Bayesian Methods: Making Research, Data, and Evidence More ...
Mathematica's approach reimagines Bayesian methods for real-world impact, making complex estimates easy to interpret and ready to act on.
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What's Different About Bayesian Methods? - Mathematica
Nov 7, 2022 · Mathematica's experts explain how Bayesian Methods are different from traditional statistical methods and the value this provides in policy research.Missing: Inc. | Show results with:Inc.
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A Paradigm Shift to Bayesian Methods Could Improve Evidence ...
Feb 16, 2022 · A modernized, evidence-informed update to Bayes's Rule can help decision makers assess whether a policy or program works and whether it would work for certain ...
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[PDF] Bayesian Methods for Social Policy Research and Evaluation
The Bayesian approach is best represented by a “bet.” Probability is not based on an infinite number of events (as it is in a frequentist approach), but rather ...
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The Use of Microsimulation in Supplemental Nutrition Assistance ...
Mathematica uses microsimulation modeling to produce simulations and tabulations that enable FNS and other policymakers to better understand the populations ...Missing: Inc. computational
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It Takes a Village: Integrating Data Science into Policy Research ...
Feb 28, 2017 · New methodologies. New methods can harness Big Data and use the available computing power to enable better analysis and prediction of outcomes.Missing: techniques | Show results with:techniques
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[PDF] liberia - Mathematica
Nov 8, 2020 · This institution is in compliance with requirements for protection of human subjects, including 45 CFR. 46, 21 CFR 50, and 56, and 38 CFR16.
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About HML IRB | HML IRB Research & Ethics | DC
Since 2000, HML IRB has provided research ethics approval for the international and domestic projects of UNICEF, The World Bank, Mathematica Policy Research ...
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Promising Practices on Sharing and Linking Data to Enhance ...
Jun 29, 2022 · Protecting the data's security, confidentiality, and privacy; Securing institutional review board (IRB) and other approvals; Accessing the data.
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[PDF] Sharing and Accessing Administrative Data - Mathematica
Apr 1, 2022 · An IRB that allows organization to use the data for numerous projects and analyses of linked admin- istrative data might also support these ...
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Sharing and Accessing Administrative Data: Promising Practices ...
Short issue brief on sharing and accessing administrative data to better understand child maltreatment incidence.<|separator|>
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A Critique of the Negative Income Tax Experiment on JSTOR
Shows what happens when a specific social policy is tried out on an experimental basis prior to being enacted into law. By providing a trial of a variety of ...
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[PDF] Chapter V. Data equity principles
Jul 28, 2022 · ... human subjects' protections in research (but does not have a racial ... This Child Trends blog post describes why racial equity matters in IRB ...
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[DOC] Appendix E. IRB approval letter_Jan2025.docx - Reginfo.gov
This study's human subjects' protection protocols, as stated in the materials submitted, received research ethics review approval on 11/25/2024 in accordance ...
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[PDF] The Labor Market Findings of the Negative Income Tax Experiments ...
First, the fear that a negative income tax would cause some segment of the population to completely withdraw from the labour force was not confirmed by the ...<|control11|><|separator|>
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[PDF] Second Annual Report of the Housing Assistance Supply Experiment
HOUSING ASSISTANCE SUPPLY EXPERIMENT PUBLICATIONS. 153. B. CHRONOLOGY OF ... a division of Mathematica, Inc. For Wave 2, a different organization, the ...
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The Effects of the Rural Income Maintenance Experiment on ... - jstor
Empirical Investigation of the Effects of an. Income Maintenance Program on School. Performance," Mathematica Policy Research. Working Paper, No. C-8, 1976.
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[PDF] HEHS-99-179 Welfare Reform - GAO
Sep 9, 1999 · Five evaluations begun in the 1980s with 5-year results indicated that programs focusing on rapid employment and job search activities combined ...
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[PDF] Comparing welfare-to-work programs
1The Welfare to Work grants program has been evaluated for the U.S.. Department of Health and Human Services by Mathematica Policy. Research, Inc. An overview ...
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Welfare-to-Work Programs in America, 1980 to 2005: Meta-Analytic ...
This meta-analysis extended a Campbell Collaboration review of welfare-to-work programs. Its synthesis of 65 randomized trials in America over the past ...
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Status of Research on the Outcomes of Welfare Reform, 2000
Nov 30, 2000 · Iowa builds on an existing study of its Family Independence Program (FIP), conducted by Mathematica Policy Research (MPR) and partially funded ...
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David C Stapleton Fellow at Mathematica Policy Research
We use data from a randomized evaluation of the Job Corps program to ... Washington, DC: Center for Studying Disability Policy and Mathematica Policy Research.
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Teach for America is a deeply divisive program. It also works.
Sep 10, 2013 · The best evidence we had before today was a randomized evaluation conducted by Mathematica Policy Research between 2001 and 2003, which ...
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Early Head Start Research and Evaluation Project (EHSRE)
Mathematica Policy Research, Inc. served as the Data Coordination and Analysis Center for this phase of the study. Elementary School Follow-up (2005-2010). In ...Missing: notable | Show results with:notable
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[PDF] Evaluation of the Round Two Health Care Innovation Awards (HCIA ...
... EVALUATION REPORT. MATHEMATICA POLICY RESEARCH. Implementation effectiveness. • ... notable progress, they also increased the challenge of assessing what the ...
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[PDF] Executive Summary - Promoting Readiness of Minors in ...
Dec 29, 2022 · Implications for policy, practice, and research. The findings from the PROMISE evaluation offer insights for policy, practice, and research.
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[PDF] Evaluation Design for the Retaining Employment and Talent after ...
Dec 8, 2021 · Monitoring treatment enrollee progress. Tracking and monitoring the treatment enrollee's medical and employment progress. Page 47. Chapter IV.
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[PDF] Measuring Child Well-Being in Evaluations of Healthy Marriage and ...
In the third section of the paper, we describe findings from our review of 32 HMRE evaluations and 23. RF evaluations conducted since 2000. ... for Policy ...
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[PDF] Evaluation of the Medicaid Managed Care Program of the Center for ...
We conducted surveys of three core audiences—all states, Medicaid health plans, and diverse consumer groups to learn how well the audiences know the program and ...
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[PDF] Common Indicators of Recidivism Used in Program and Policy ...
Developing American Job Centers in Jails: Implementation of the Linking to Employment Activities Pre-. Release (LEAP) Grants: Mathematica Policy Research.
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[PDF] A Political Scientist's View of the Income Maintenance Experiments
The negative income tax experiments were designed to influence political debate on income support policy in at least two ways. One was.
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Effects of welfare reform on teenage parents and their children
The evaluation results suggest that supportive, mandatory welfare-to-work interventions need not harm parents or their children in the short term.
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Status Report on Research on the Outcomes of Welfare Reform, 2002
Nov 13, 2002 · Under a task order, Mathematica Policy Research, Inc. profiled the efforts of four states (Florida, Oregon, Tennessee, and Utah) to address the ...
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[PDF] Consequences of Welfare Reform: A Research Synthesis
To this end, this document—the final report for the synthesis project—considers a range of outcomes, including the welfare caseload, employment and earnings, ...
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Advancing and Enhancing Evidence-Based Policymaking
Dec 17, 2024 · The Evidence Act aims to advance evidence-building in the federal government. It mandates that federal agencies enhance their capacity for evidence-building.
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Building on the Evidence Act to Increase the Positive Impact of ...
Jun 21, 2023 · This episode features a discussion among experts in government, think tanks, research organizations, and academia on the impact of the Evidence ...Missing: influence | Show results with:influence
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[PDF] THE PROMISE OF EVIDENCE-BASED POLICYMAKING
Beginning in January 2017, the Commission on Evidence-Based Policymaking (CEP) ex- tended invitations to over 200 Federal offices to.
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https://www.mathematica.org/news/zachary-parolin-wins-the-25th-david-kershaw-award-and-prize
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Zachary Parolin Wins the 25th David Kershaw Award and Prize
Oct 1, 2025 · Zachary Parolin of Oxford University has received the David N. Kershaw Award for his research. Zachary Parolin, Professor of Social Policy, ...
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Northwestern's Kirabo Jackson Earns Prestigious David N. Kershaw ...
Aug 6, 2020 · The David N. Kershaw Award and Prize is among the largest awards offered in recognition of outstanding work in public policy research and social ...
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Mathematica's Randall Brown Honored with Association for Public ...
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Czajka Earns ASA Founders Award - Mathematica
Aug 16, 2016 · Established in 1988, the Founders Award recognizes members who have rendered distinguished service to the association.Missing: awards | Show results with:awards
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Mathematica Named Grand Prizewinner in the Visualization ...
Feb 11, 2020 · AHRQ announced today that Mathematica is the grand prizewinner of the Agency's Visualization Resources of Community-Level Social Determinants of Health ...Missing: awards | Show results with:awards
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MATHEMATICA INC. | Federal Award Recipient Profile | USAspending
541720 - Research and Development in the Social Sciences and Humanities. -$17,594,067. -286.8%. Product Service Codes. Name. Obligations. % of Total. 1. R410 ...
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Federal | Mathematica
We combine data science, social science, policy research, and technologists' expertise to help you drive smarter decisions and better outcomes. Explore our ...Missing: company | Show results with:company
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Mathematica Policy Research Archives - William T. Grant Foundation
The William T. Grant Foundation invests in high-quality research focused on reducing inequality in youth outcomes and improving the use of research evidence in ...Missing: awards | Show results with:awards
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Mathematica Policy Research, Inc. - The Atlantic Philanthropies
Grants Awarded · Matched Grantmaking Preliminary Evaluation · Evaluation of National League of Cities Competitive Funding Model to Improve Access to Health ...
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Mathematica and Socially Determined Partner to Illuminate the ...
Jun 26, 2024 · By leveraging Socially Determined's data, Mathematica will offer its clients unparalleled insights into community-level SDoH risk exposure and ...
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The Modernizing Foreign Assistance Network Welcomes ... - MFAN
Aug 27, 2025 · “Mathematica is proud to join MFAN in advancing evidence-based decision making that enhances the impact and accountability of U.S. foreign ...
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AmeriCorps Shared Impact in Communities Across the Country
Jun 11, 2024 · Foundations · Global Health and Development · Healthcare and Life ... Mathematica | Progress Together. Keep in touch. Interested in the ...<|separator|>
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Rural Health Analytics and Technical Assistance - Mathematica
Mathematica and our strategic partner, the Rural Health Redesign ... Our services support the design and implementation of sustainable, scalable solutions ...
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Mathematica Policy Research Data Engineer Interview Guide
I prioritize using scalable solutions like AWS Glue for ETL and ensure that ... Mathematica Policy Research Business Analyst Interview Questions + Guide in 2025 ...
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Meet TLA's Accelerating Adoption Network Cohort
or “networks” — focused on scaling these innovations, and partnering with Mathematica to rigorously evaluate their ...
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[PDF] Policy Prototyping for the Future of Work - Ash Center
. There is an urgent need for the development of scalable solutions to equip Americans with ... Annalisa Mastri, Mathematica Policy Research. Bill Eggers, ...
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Transforming Rural Health Through Innovation and Evidence
... scalable solutions ... By submitting my contact information, I confirm that I have read and agree to Mathematica's Privacy Policy, which explains how Mathematica ...
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Charter School Research Has a Long Way to Go to Inform Policy ...
Sep 26, 2018 · One national study by my colleagues at Mathematica found that less than 15 percent of charter middle schools could be included in a lottery- ...Missing: criticisms | Show results with:criticisms
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[PDF] Replication issues in social experiments: lessons from US labor ...
Jul 5, 2013 · 4 These experiments were conducted to test an approach to welfare known as a “negative income tax,” that was then popular with both liberal ...
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[PDF] What (If Anything) Can we Learn from the Negative Income Tax ...
The effect of these two nonlinear functions was to impose higher marginal tax rates on lower levels of income and lower marginal tax rates on higher levels of ...
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Mathematica Study of Alternative Teacher Certification Does Not ...
They Note Methodological Limitations and Caution against Relying on the Report to Inform Teacher Certification Policy. FOR IMMEDIATE RELEASEMissing: criticisms | Show results with:criticisms
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External Validity in Policy Evaluations that Choose Sites Purposively
Much less attention has been paid to external validity—i.e., whether the evaluation yields an unbiased estimate of the impact of the program on the population ...
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Trade Adjustment Assistance: Another Evaluation Finds the Program ...
Jul 29, 2013 · [15]Another TAA impact evaluation by Mathematica Policy Research was originally set to be published in 2010. However, the U.S. Department of ...
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Federal Job Training Fails Again | The Heritage Foundation
Mar 10, 2017 · ... Mathematica Policy Research, May 2016). The peculiar timing and months-long delay of releasing a report that was finalized in May 2016 ...
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With Minimum Wage, Diners Lose | Cato Institute
In “The Impact of the Minimum Wage on Firm Exit: Evidence From Yelp Data” (Research Briefs in Economic Policy no. 81), Dara Lee Luca of Mathematica Policy ...
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Evidence on the Effectiveness of Abstinence Education: An Update
... Mathematica Policy Research, August 2007, p. 1, at www.mathematica-mpr.com/publications/PDFs/heritagekeepers08-07.pdf (February 27, 2008). Elaine Borawski ...
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Jennifer Herard-Tsiagbey - Mathematica
Jennifer Herard-Tsiagbey's work focuses on programs that support families, youth, and young adults, including universal income, coaching, case management, and
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[PDF] Universal Basic Income (UBI): A Cure-All or Band-Aid?
welfare state, Universal Basic Income (UBI) has returned to popular attention. ... In addition to criticism of presentation, researchers for the Finnish UBI.
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Defining and Measuring State Medicaid Spending Efficiency
Mar 11, 2009 · The approach that Mathematica Policy Research, Inc. (MPR) will actually use to define, measure, and compare state Medicaid spending efficiency ...