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TIME, in partnership with Statista, the leading global provider of market and consumer data and rankings, has published the second annual "Best Employers” ranking. Employers can help shape the workforce in a country. However, employee workplace satisfaction not only impacts company culture, but can influence productivity, innovation, profitability, and industry reputation. Here's how the winners were selected.

Methodology

The research project “Best Employers of 2026” was based on surveys conducted using several online access panels to guarantee a representative sample of employees across each country, starting with Brazil, India, and Australia. In Australia, 200,000 employer evaluations were conducted for companies from all sectors employing at least 200 people in the country. In India, 760,000 employer evaluations were conducted from companies employing at least 500 people in the country. In Brazil, more than 900,000 employer evaluations were conducted from companies employing at least 500 people in the country. Participants were asked, through an open-ended question with an auto-complete function, to name their current employer. This method ensures neutrality and prevents companies from influencing the selection of respondents.

Data collection 

The survey was conducted using several online access panels to guarantee a representative sample of employees across the country. Participants were asked, through an open-ended question with an auto-complete function, to name their current employer. This method ensures neutrality and prevents companies from influencing the selection of respondents. 

Scoring model 

The final score combines two types of evaluations: employees’ willingness to recommend their own employer (direct score) and their willingness to recommend other employers in the same industry (indirect perception score), using data from 2025 and 2026.

Direct score:  Respondents were asked to rate their willingness to recommend their employer to friends and family. The responses were graded on a scale from 0 to 10, where 0 means “I wouldn’t recommend my employer under any circumstances” and 10 means “I would definitely recommend my employer”. 

Indirect score: Employees were also asked about their willingness to recommend other employers within their industry. Respondents were shown an industry list of employers and asked to give an opinion on those that stood out, either positively or negatively (Response options: “would recommend”, “would not recommend”, “no opinion”). Additionally, an open-ended question allowed respondents to name other employers. Greater weight is given to direct recommendations, as they provide the strongest reflection of employee satisfaction. 

Ranking and recognition 

This ranking reflects not only how employees view their own workplace but also how companies are perceived across their sector, creating a balanced and independent view of the top employers.

As jobs have become more technological, job markets have become more competitive, and the costs of tuition have risen, college students are increasingly considering the ROI of their education, or how efficiently it can be used to acquire a high-paying job post-grad. “We need to be more outcome-oriented, more career-oriented when we think about what we're recommending for students in higher ed,” says John Friedman, a professor of economics and international and public affairs at Brown University. “There are a lot of people who really do want to get career advancement, to get economic value out of the time and money they're spending in college, and we should help them do that.”

To highlight U.S. institutions that provide the best value for their students, TIME partnered with data research firm Statista on the inaugural edition of America’s Best Colleges of 2026-2027, ranking the colleges that excel at student outcomes, learning environment, and attractiveness to students.

Methodology: How TIME and Statista Determined America’s Best Colleges of 2026-2027

The search for high ROI education has led many U.S. college students to pursue STEM careers. According to a 2026 report from the National Science Foundation, the STEM workforce grew by 26% between 2013 and 2023 compared to the non-STEM workforce, which grew 9%—and STEM jobs are projected to continue growing at a faster rate than non-STEM jobs for the next decade or so. From 2021 to 2023, higher-education degrees awarded particularly in science and engineering reached record highs, and the U.S. was the most popular post-secondary STEM education destination for international students.

Schools with historically strong STEM programs—and pipelines into high-paying tech jobs—rank high on the list, like research universities Stanford (no. 1), known for its startup culture and connection to Silicon Valley, and MIT (no. 2), a patent powerhouse.

At no. 3 is Harvey Mudd, a small liberal arts college with a student population of around 900. It offers only majors in STEM—appealing to Gen-Z students who are increasingly turning to trade schools to learn technical skills—but competes with the big schools by encouraging students to be engaged in humanities, social sciences, and arts so they can understand the ethical impact of their work on society.

“If anything, this is the time that the world needs more liberal arts colleges, because we need people to question what's happening,” says Thyra Briggs, Harvey Mudd’s VP of admission and financial aid. “We hear from the graduate programs that admit our students and from the companies that hire them that they are often the translators in their office because they can do the very high level technical conversations that you would expect, but they also know how to translate that to people who may not have that same background.”

The school’s famous Clinic Program is a year-long capstone project developed in the early 1960s that has a team of students work directly with companies like Blue Origin, Sokil, and the Federal Aviation Administration to solve real-life research problems complete with a budget, a deliverable, a due date, and a corporate liaison. Very often, these projects can lead directly to jobs, Briggs says.

Of course, studying science and engineering is not the only option for students to get the most out of their post-secondary education. For example, Claremont McKenna (no. 9), part of the same liberal arts college consortium as Harvey Mudd, offers a wider range of non-STEM majors. Its career center provides career resources for “interest clusters” to help students think about potential post-grad pathways and give them relevant organizations to explore. In a post last year, Claremont reported that over 96% of the recent graduating class had defined plans and a median salary of $80,000. “There are going to be a lot of different fields that provide ROI,” says John Friedman, a professor of economics and international and public affairs at Brown University. “If you look within particular fields, there's often a ton of variation between what people get paid.”

While data shows that STEM jobs tend to pay off, there are also benefits to studying in fields that are undersupplied and growing; but research into more niche high-earning career paths can be more sparse, and sometimes students don’t realize which fields are high-earning or ways in which they can apply their degree creatively.

For students who want certainty of outcome, sector-specific workforce training programs have been found to be most successful in increasing people’s earnings because they train people for a particular job at a company. Some colleges are catching on to the need to link the skills learned during class to what jobs require. For example, Fashion Institute of Technology (no. 51) is partnering with Lightcast to display data about career outlooks, job availability, salary potential, and hard and soft skills required for the niche majors they offer like technical design, spatial experience design, packaging design and toy design.

See the full list of America’s Best Colleges of 2026-2027 below:

TIME, in partnership with Statista, the leading global provider of market and consumer data and rankings, has published the inaugural edition of the “America’s Best Colleges 2026-2027” ranking. The underlying quantitative study highlights institutions that excel at student outcomes, learning environment, and attractiveness in the United States.

Methodology

This research project conducted a comprehensive analysis to identify top-performing colleges nationwide. Eligibility criteria required institutions to be: 
(a) Currently active and financially solvent—the institution is confirmed as currently operating and fully open
(b) Federally recognized and eligible—The institution holds active Title IV federal financial aid eligibility status
(c) Public or private not-for-profit—For-profit institutions are excluded
(d) Primarily four-year, degree-granting—The institution's primary focus is on bachelor's degrees or higher; exclusively two-year or certificate-focused institutions are excluded
(e) Located in a U.S. state or the District of Columbia—Institutions in U.S. territories (e.g. Puerto Rico, Guam, the U.S. Virgin Islands) are excluded
(f) Minimum undergraduate enrollment—The institution must have enrolled an average of at least 750 full-time equivalent undergraduate students across the past four years

The analysis is structured around three key pillars: Student Outcome, Learning Environment, and Attractiveness. Institutions receive scores on each pillar, which are then aggregated into a final score used to produce the ranking.

Restrictions

This analysis is subject to several data-related limitations. First, all indicators are based on the most recent data releases from IPEDS and the College Scorecard available as of the beginning of April 2026; subsequent updates or revisions to these datasets are not reflected in the results. Second, earnings data are derived only from graduates who received Pell Grants (Title IV aid), as reported in the College Scorecard. As a result, these figures may not fully represent the outcomes of the entire student population at an institution.

Study design

With this ranking, TIME and Statista evaluate U.S. colleges with a focus on three pillars: student outcomes, learning environment, and attractiveness. This framework retains classical components used in higher education assessments, such as the instructional environment and institutional resources, while placing particular emphasis on what students gain from attending an institution relative to its cost.

In addition, Statista emphasizes indicators that measure institutional performance net of student intake, isolating the value an institution itself contributes from the characteristics of the students it enrolls. These pillars are operationalized through a set of quantitative indicators derived from federal datasets, which are normalized and aggregated according to a transparent weighting scheme. The three pillars are weighted as follows in the overall scoring model: student outcomes – 75%, learning environment – 15%, and attractiveness – 10%.

In a limited number of cases, university systems report key indicators (such as graduate income) only at an aggregated level across multiple campuses. Given the importance of these indicators, institutions sharing the same OPEID6 identifier in IPEDS were combined and evaluated as a single entity, with all relevant metrics aggregated accordingly. These cases are identified in the results by the use of the institution’s brand name without a specific campus designation. While relatively few, this approach ensures consistent and comprehensive inclusion of available data in the analysis.

Student outcomes

The student outcomes pillar assesses what students gain from attending an institution, measured after they leave it. It is operationalized through three components. The first is graduates' earnings, which evaluates whether an institution's graduates earn more than their intake would predict. The second is the graduation rate, which captures how effectively an institution carries its students through to degree completion. The third is return on education, which weighs the earnings students achieve against the cost of obtaining their degree. The first two components are constructed on a value-added basis, isolating the institution's own contribution from the characteristics of the students it enrolls, while the third reflects the financial payoff of attendance in absolute terms. Together, these components capture both what students achieve after graduating and what they paid to get there.

Student outcomes contribute 75% to the final score.

Graduates’ earnings (value-added income outcome)

The value-added income outcomes metric assesses whether an institution's graduates earn more than would be expected given the characteristics of the students it enrolls. Raw earnings figures alone are a poor basis for comparison: institutions that disproportionately enroll students from high-income backgrounds, or that concentrate in high-earning fields, will show strong earnings outcomes without necessarily adding value through their programs. The metric isolates the portion of graduate earnings attributable to the institution itself, net of student intake.

This is achieved through a linear regression of median graduate earnings on a set of student-body and program characteristics. The predictor set controls for the socioeconomic composition of the student body, the share of students in STEM fields, and the demographic composition of the student body. Earnings are log-transformed prior to estimation, in line with standard practice for wage models. The regression is estimated separately at three earnings horizons—six, eight, and ten years after enrollment—to capture both early-career and medium-term labor market outcomes.

For each horizon, the residual—the difference between an institution's actual log earnings and the level predicted by the model—represents its value-added contribution. These residuals are standardized and, alongside the standardized raw earnings level, combined into a per-horizon score expressed as percentile ranks. The final value-added score averages across the three horizons.

Graduation rate

The graduation outcomes metric assesses how effectively an institution supports its students through to degree completion, independent of the type of students it admits. Graduation rates are strongly shaped by student intake: an institution enrolling well-prepared, well-resourced students will graduate more of them than one serving a higher-need population, regardless of the quality of instruction or support it provides. The metric isolates the portion of an institution's graduation rate attributable to the institution itself, net of the characteristics of its incoming students.

This is achieved through a regression of the four-year graduation rate on a set of student-body and program characteristics. Because graduation rates are proportions bounded between zero and one, the model is estimated using beta regression. The predictor set controls for the socioeconomic composition of the student body, the share of students in STEM fields, and the demographic composition of the student body.

The residual—the difference between an institution's actual graduation rate and the rate predicted by the model—represents its value-added contribution to completion. This residual is standardized and, alongside the standardized raw graduation rate, combined into a single score expressed as percentile ranks across all ranked institutions.

Return on education

The return on education metric captures the financial payoff of attending an institution relative to its cost, expressed as the number of years required for graduate earnings gains to offset the total cost of a degree.

The cost side blends two cost of attendance figures—the average net price paid after financial aid and the total sticker-price cost of attendance—weighted by the share of Pell grant recipients at the institution. This weighting reflects the fact that the financially relevant cost differs systematically across the student population.

The earnings benchmark against which graduate earnings are compared is tailored to each institution's student population. Rather than applying a single national baseline, the benchmark is constructed as a weighted mix of state-level median high school earnings and a national figure, weighted by the proportion of in-state versus out-of-state students enrolled.

The metric is expressed as payback period: the blended four-year cost divided by the annual earnings premium over this baseline. Final scores are expressed percentile ranks, with shorter payback periods receiving higher ranks.

Learning environment

The learning environment pillar assesses the quality of the instructional setting and community that an institution provides for its undergraduate students. It is operationalized through three components. The first is the student-to-faculty ratio, measuring the degree to which students have direct access to teaching staff. The second is expenditure per student, capturing the financial resources an institution directs toward its students across instruction, academic support, and related activities. The third is a diversity index, assessing the demographic breadth of both the student body and the faculty. This index incorporates measures of representation across key demographic dimensions and is further combined with the share of Pell Grant recipients, reflecting socioeconomic diversity, and the share of students with disabilities, capturing inclusivity in access to higher education. Together, these three components reflect the conditions under which students learn, rather than the outcomes they ultimately achieve.

Learning environment contributes 15% to the final score.

Student-to-faculty ratio

The student-to-faculty ratio measures how many undergraduate students are served, on average, by each instructional staff member at an institution. A lower ratio indicates that each faculty member is responsible for fewer students, which is generally associated with greater opportunity for direct interaction, individualized instruction, and academic mentorship. Final scores are expressed as percentile ranks, with lower ratios receiving higher ranks.

Core expenditure per student

This metric captures the financial resources an institution directs toward its students, expressed on a per-head basis. Institutions that spend more per student are generally better positioned to provide a high-quality learning environment, regardless of their overall size.

The expenditure figure is constructed by averaging across several spending categories that reflect direct and indirect investment in the student experience: instructional expenditure, academic support, student services, institutional support, and scholarships and fellowship expenses. This average is then divided by average full-time equivalent undergraduate enrollment to produce a per-student figure. To account for differing reporting forms across institution types in IPEDS, expenditure data is drawn from separate sources for public and private non-profit institutions respectively, and subsequently combined into a single figure per institution.

Both the expenditure components and the enrollment figure are averaged across four annual survey vintages before the per-student ratio is computed. Final scores are expressed as percentile ranks across all ranked institutions, with higher expenditure per student receiving a higher rank.

Diversity

This metric assesses the demographic diversity of an institution's community, capturing both its student body and its faculty. The underlying premise is that a more diverse learning environment—one in which students and staff come from a broad range of demographic and socioeconomic backgrounds—enriches the educational experience for all members of the institution.

Ethnic diversity is measured separately for students and faculty using the Simpson Diversity Index, a standard measure from ecology adapted here to the higher education context. The index captures the probability that any two individuals drawn at random from a group belong to different categories. It takes a value of zero when the entire population belongs to a single group, and approaches one as the population is spread more evenly across groups. Both student and faculty diversity are computed across the same set of ethnic categories reported in IPEDS.

In addition, the share of students with disabilities (as reported in IPEDS) is included as a measure of accessibility and inclusion. The share of Pell Grant recipients is incorporated to capture socioeconomic diversity.

All components—the ethnic diversity scores, disability inclusion measure, and socioeconomic indicator—are averaged across four annual survey vintages to reduce year-to-year volatility. The final diversity score is constructed from these averaged values and expressed as percentile ranks across all ranked institutions.

Attractiveness

Attractiveness assesses the degree to which an institution is genuinely sought-after by prospective students. Unlike measures of academic output or graduate outcomes, attractiveness reflects the demand side of higher education: how strongly students want to attend a given institution, and how that desire manifests in their decisions throughout the application and enrollment process. The pillar is operationalized through the selectivity gap metric.

The metric is constructed from two sequential components drawn from institutional admissions data: the admission rate and the enrollment yield rate. The admission rate captures how freely an institution grants access—what share of applicants receive an offer. The yield rate captures student preference after that offer is made—what share of admitted students ultimately choose to enroll. Where the admission rate reflects the institution's selectiveness, the yield rate reflects the student's revealed preference at the moment of decision.

The selectivity gap is defined as the yield rate minus the admission rate.

Raw values are averaged across four annual survey vintages prior to computing the gap, to reduce year-to-year volatility. Final scores are expressed as percentile ranks across all ranked institutions.

Attractiveness contributes 10% to the final score.

Scoring model

Once the data are collected and evaluated, they are consolidated and weighted within a three-dimension scoring model. Each college's overall score is calculated as a weighted sum of normalized indicator scores, with dimension-level weights reflecting their relative importance in the framework.

Student outcomes – 75% of the overall score

Learning environment – 15% of the overall score

Attractiveness – 10% of the overall score

Within each dimension, multiple indicators and sub-indicators are used (e.g., graduate earnings and graduation outcomes, return on education, resource and staffing ratios, and selectivity measures). Unless stated otherwise, each indicator is constructed by drawing on the four most recent years of available data and averaging across them, in order to reduce the influence of year-to-year fluctuations and reporting noise. Indicators are then, unless otherwise noted, converted into percentile ranks across all eligible institutions, and these ranks form the basis of the scores that are combined according to the detailed weighting scheme defined in the KPI overview and scoring model.

The 500 colleges with the highest final scores are featured in the “America’s Best Colleges 2026-2027” ranking by TIME and Statista.

Sources

The quantitative analysis underlying the ranking draws on a small number of authoritative federal data sources. Institutional characteristics, enrollment figures, admissions data, faculty information, graduation rates, and financial variables are sourced from the Integrated Postsecondary Education Data System (IPEDS), maintained by the National Center for Education Statistics. Graduate earnings data are drawn from the College Scorecard, published by the U.S. Department of Education. State-level earnings benchmarks used in the return on education calculations are derived from the American Community Survey (ACS), published by the U.S. Census Bureau.

Disclaimer:

The ranking is comprised exclusively of colleges that are eligible regarding the scope described in this document. A mention in the ranking is a positive recognition based on available data sources at the time. The ranking is the result of an elaborate process which, due to the interval of data-collection and analysis, is a reflection of the last calendar years. Furthermore, events following June 30, 2026, and/or pertaining to individual persons affiliated/associated with the institutions were not included in the metrics. As such, the results of this ranking should not be used as the sole source of information for future deliberations. The information provided in this ranking should be considered in conjunction with other available information about colleges or, if possible, accompanied by a visit to an institution. The quality of colleges that are not included in the ranking is not disputed.

See the full list here.

The Supreme Court has handed President Donald Trump a victory in his efforts to restrict mail-in voting ahead of November’s crucial midterm elections. 

In an unsigned 6-3 ruling, with the court’s liberal justices dissenting, the Supreme Court lifted a lower court’s June block on the President’s Executive Order targeting mail-in voting after 23 Democratic states and Washington D.C. sued the Administration for the order’s alleged unconstitutionality and presidential overreach.

Democrat lawmakers have responded furiously to Monday’s decision. “Another day, another Trump attempt to rig elections and destroy democracy,” said California Governor Gavin Newsom on social media. “We can stop this. Vote this November.”

Trump, in his push to remake American elections, has repeatedly blamed mail-in voting for widespread electoral fraud. He claimed that mail-in ballots helped him lose in the 2020 election despite audits already refuting those claims, and his own voting by mail in recent elections.

The ruling does not settle whether the Executive Order will proceed ahead of the November midterms, where the Republican Party needs to defend its narrow majority in Congress. Part of the order covering the U.S. Postal Service remains blocked under a separate injunction in August, which still needs to be resolved.

The Supreme Court also did not outright rule on the legality of Trump’s order—only on the timing of the challenges to it—and left it open to future lawsuits, which critics have signaled they will file.

Here’s how other Democrats, as well as some Republicans, have responded to the decision, and what it means for November’s pivotal midterms. 

Democrats vow legal challenges against “terrible” Supreme Court decision 

Gov. Josh Shapiro of Pennsylvania said Monday that the legal battle against Trump’s Executive Order would continue, after a previous challenge put a temporary halt on the directive. 

“Not so fast. Today’s SCOTUS decision does not deal with the substance of Trump’s unconstitutional Executive Order and does not mean his illegal attempt to restrict mail-in voting will go forward,” argued Shapiro. “We’ll see the Trump Administration in court.”

Gov. Mikie Sherrill of New Jersey also vowed a response to the decision. “This is a terrible decision from Trump’s Supreme Court,” she said. “States run elections, not Donald Trump. I will do everything I can to protect New Jerseyans’ right to vote – by mail and in person.”

Newsom built upon his condemnation on Monday. “California will be suing again to block these Orwellian rules from being implemented,” he said. 

“This decision is a painful setback,” Attorney General Letitia James of New York, one of the plaintiff states from June’s challenge against the Order, said in a statement. “But it will not be the final word.”

More widely, other Democrat lawmakers criticized the Supreme Court’s decision and the impact it could have on the upcoming midterms. 

Senate Minority Leader Chuck Schumer said: “It’s a disgrace that the highest court in the nation is allowing Trump to put a dagger into the heart of our democracy,” following the decision. 

Schumer argued the Order was “blatantly unconstitutional,” and that “his MAGA Supreme Court is refusing to stop it from going forward.”

Hitting out at the President more broadly, the lawmaker continued: “[Trump] wants to make it harder for Americans to vote so they don’t hold him accountable for the skyrocketing costs, illegal war, and rampant corruption that are a hallmark of his administration.”

Sen. Catherine Cortez Masto of Nevada issued another direct response at the President himself. “Trump voted by mail eleven days ago. Apparently it’s good enough for him, but he wants to take away your right to vote in the same way,” she said. 

“Trump’s voter suppression order is about making it harder for Americans to hold him accountable - it is shameful that the Supreme Court is enabling it,” the lawmaker continued. 

Sen. Maria Cantwell also expressed concerns over the privacy of voters. “Today’s Supreme Court decision gets the Trump Administration one step closer to implementing its voter suppression order and forcing states to hand over personal voter data to the federal government,” she said. 

Republicans celebrate Supreme Court decision

“With today’s SCOTUS ruling, President Trump has made a giant leap forward in securing our 2026 elections,” said Utah Sen. Mike Lee, a long-standing supporter of Trump’s efforts to restrict mail-in voting. 

“Congress must still pass the SAVE America Act to secure future elections. We should’ve passed it months ago,” he continued in reference to the Act which is still awaiting approval in the Senate. 

Lee also responded to Gov. Shapiro’s commitment to continue legal challenges against the President’s Executive Order. “Why are you so determined to let non-citizens vote?” he said. 

“Bravo! Glad to see the Supreme Court get this one right. Election integrity is nonnegotiable,” said Rep. Keith Self of Texas, also pushing the Senate to approve the SAVE Act. 

“This is a major win for the security of American elections,” said White House spokesperson Lauren Bis in a statement to TIME. “These are commonsense measures that protect the security of mail-in ballots and ensure only Americans are electing American leaders.

What does the ruling mean for federal elections, and how did we get here?

On June 25, U.S. District Judge Indira Talwani in Boston ordered an injunction on provisions of Trump’s March Executive Order. Those provisions direct the Department of Homeland Security and Social Security Administration to create “state citizenship lists” that cover eligible voters, and the Postal Service to create rules that would end sending absentee ballots to individuals not on a state’s mail-in or absentee participation list.

Talwani’s block covered the 23 plaintiff states and Washington, D.C. On July 25, the First Circuit rejected the Trump Administration’s request to block Talwani’s order pending appeal.

The Trump Administration sought the Supreme Court’s intervention in July, arguing that the lower court “lacked jurisdiction” to resolve the dispute, as implementing rules based on the President’s Order haven’t been finalized.

In the Monday ruling, the Supreme Court’s conservative majority agreed with the Trump Administration and paused Talwani’s injunction.

The top court said that Trump’s order regarding state citizenship lists was “an internal directive from the President to a subordinate” and that it “imposes no obligations on the States,” which in turn “suffer no concrete harm.”

It added that, in the case of Trump directing the Postal Service to propose rules on mail-in ballots, the legal challenge was premature and lacked standing. When states filed the suit, the Postal Service had not issued a final rule.

The Supreme Court’s majority said the district court had to engage in a “string of speculations” to arrive at its June injunction: “Federal courts review final rules, not proposed rules—and certainly not antecedent internal directives to propose a rule.”

What the ruling did not cover

While the Supreme Court’s majority paused Talwani’s June injunction for being premature, it does not cover a separate injunction—also from Talwani—on Aug. 11. Ruling on another suit, Talwani barred the Postal Service from implementing the same Executive Order for the November midterms nationwide.

The Supreme Court ruling also simply granted the Trump Administration’s emergency request to pause Talwani’s June injunction. The case on Trump’s Executive Order is still pending before the First Circuit.

It also explicitly left room for challenges on the legality of the rules stemming from Trump’s order, especially with the Postal Service’s final rule.

Norm Eisen, a voting-rights advocate leading one of several legal challenges to Trump’s order, downplayed the Supreme Court’s decision. “Please don’t overreact to Roberts Court decision on the contemplated [Postal Service] ballot moves,” Eisen posted on microblogging platform Bluesky Monday. “They simply held that the injunction came too early in the process, before there was a final rule.”