search fund case study pdf

2022 Search Fund Study: Selected Observations

via Stanford Graduate School of Business

By peter kelly , lecturer in management &, sara heston , assistant director, search fund project.

This 2022 study reports on the financial returns and key characteristics of 526 search funds formed in the U.S. and Canada since 1984. It presents data and analysis regarding search funds formed, operating companies acquired, and the resulting financial returns as of December 31, 2021.

Read the full study via Stanford Graduate School of Business here

2022 Stanford Search Fund Study released this afternoon - 7/15/22

By a searcher from texas a&m university - mays business school.

2022 Stanford Search Fund Study released this afternoon -- https://www.gsb.stanford.edu/faculty-research/case-studies/2022-search-fund-study-selected-observations

Overview - The 2022 Search Fund Study reports on the financial returns and key qualities of search funds formed in the United States and Canada since###-###-#### This report updates the previous 2020 study with data through December 31, 2021.

Learning Objective - This study provides data to support the search fund community including current searchers, CEOs, investors and entrepreneurs evaluating whether they want to pursue a search fund.

More on Searchfunder

Just closed on my first deal reflections and takeaways for other searchers, 45,152 transactions are available on searchfunder (now searchable by keyword), now everyone has unlimited ibisworld access, family office looking for traditional searchers, examples of self-funded searcher websites, investor seeking self-funded search deals, what are market terms for seller notes, 2 best ways to find motivated sellers, off-market deals.

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Excerpts from Stanford's 2022 Search Fund Study

Definition of a search fund from the study: “A search fund is an entrepreneurial path undertaken by one or two individuals (the “searchers”) who form an investment vehicle with a small group of aligned investors, some of whom become mentors, to search for, acquire, and lead a privately held company for the medium to long term, typically 6 to 10 years. When successful, this has resulted in a relatively fast path to becoming an owner-CEO, attractive financial returns for both investors and searchers, and growing, well-run enterprises.”

Number of Search Funds and Acquisitions:

In 2020 and 2021 there were 124 search funds launched and 66 acquisitions made. The median purchase price increased to $16.5 million. The total amount invested in traditional searchers and the acquired companies reached a record $776 million for this period.

Acquisition Metrics:

• EBITDA Multiple: 7.3x
• Revenue Multiple: 2.1x

For the 2020 and 2021 period, returns achieved on a pre-tax IRR basis reached 35.3% and 5.2x ROI. Four exits realized in this period achieved ROIs in excess of 10x, bringing the total number of search acquired companies to achieve north of a 10x ROI to 17.

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That's an extensive and valuable list of resources about Entrepreneurship through Acquisition and Search Funds . This list covers a range of media types, including social media accounts, videos, podcasts, courses, MBA programs, studies, news articles, and books. It's an excellent starting point for anyone interested in this area.

We selected the best information of each kind, listed from the fastest ones to read, where you can just click, to the the more developed ones like books, dissertations and academic articles.

Remember you can help us to improve this list by making your suggestions by email:  [email protected] .

Twitter accounts to follow

Alex Mears (Search Fund expert)

Jim Stein Sharpe (Search Fund expert)

Karen Spencer (founder of Searchfunder.com)

Searchfunder.com (Search Fund industry platform)

Steve Blank (Lean Startup conceiver)

Steve Ressler (Search Fund expert)

Walker Deibel (US SME M&A expert)

Columbia ETA Club (Search Fund Club) Suggest more...

LinkedIn accounts to connect

Blake Winchell (Partner Ventures)

Coley Andrews (Pacific Lake, USA)

George Jankovic (T3)

Ibrahim Rahim (Moonbase Capital, Spain)

Jake Nicholson (SMEVentures, Singapore)

Jan Simon (Vonzeo, Canada)

Karen Spencer (Searchfunder.com)

Lacey Wismer (Hunter, USA)

Luke Tatone (Searchfunder.com)

Miguel "Mick" Albero (JB46, Spain)

Newton Campos (Newton Equity Partners)

Peter Kelly (Stanford, USA)

Peter Schober (Milk Street, USA)

Rob LeBlanc (Ambit, South Africa)

Santiago Teuffer (Cerralvo, Mexico)

Sara Rosenthal (TTCER, USA)

Simon Webster (UK)

Will Thorndike (Housatonic, USA) Suggest more...

Youtube videos to watch

Crash Course on Entrepreneurship through Acquisition

Walker Deibel (2022): The Four Paths of Acquisition Entrepreneurship

Stanford Panel (2021): New Horizons

Bridger Pennington ( 2020): SFs and SPACs

IESE (2020): Search Fund Studies

Gino Wickman's Traction Video Summary (2019)

Stanford (2017): Conversation with Irv Grousbeck

Shamus Hines (2016): Search Fund Insights

Chicago Booth (2016): Legal aspects

Coley Andrews (2015): What it takes?

Stanford (2010): Investor's perspective

Suggest more...

Podcasts to listen

Search Funded: The Eta Podcast (2022)

The Search Fund Podcast (2021)

• Acquiring Minds (2021)

Owned & Operated (2021): Steve Ressler

Let's Buy a Business (2021): Steve Ressler

Think like owners (2021): Steve Ressler

The Next Step (2021)

Insead Emerging Markets (2021)

Wisdom.MBA (2021): SF/ETA

Bridger Pennington (2020): SFs and SPACs

Think like owners (2019): Searchfunder.com

Intentional Growth (2017)

Short courses to attend

Acquisition Lab

Youtube free Course on Entrepreneurship through Acquisition

Udemy Fundamentals of Entrepreneurship through Acquisition

MBAs to attend

Chicago Booth MBA Program

Harvard MBA Program

IE Business School MBA Programs

IESE MBA Program

INSEAD MBA Program

Kellogg MBA Program

LBS MBA Program

MIT Sloan MBA Program

Stanford MBA Program

Wharton MBA Program Suggest more...

Studies to read

Yale Prof. Wasserstein's Studies (up to 2023)

Prof. Campos Latin America Search Fund Study (2022)

Stanford Primer on Search Funds (2021)

CBS Search Funds in Denmark (2020)

Yale The Evolution of a SF CEO (2020)

Stanford Search Fund Study (2020)

IESE Search Fund Study (2020)

Stanford Primer on Search Funds (2020)

Stanford Search Fund Study (2018)

IESE Search Fund Study (2018)

Chicago Booth The Evolution of EtA (2016)

Search Funds: Death and the Afterlife (2012)

News articles to read

$10M to create ETA at the Wharton School (2022)

MBAs Latest pitch to investors (2022)

Stop looking for the exit (2021)

Colca Capital: Peru's First Search Fund (2019)

Is Acquisition Entrepreneurship you best bet? (2019)

How CEOs buy the companies they want to run? (2018)

Germany: Search Funds to the Rescue (2017)

Why entrepreneurs are turning to SF? (2017)

Searching for Impact (2017)

HBR: Buying your way into Entrepreneurship (2017)

The Economist: Private Equity for beginners (2016)

HBR: Why MBAs should buy SMEs? (2016)

The most exciting Asset Class in PE? (2015)

Forbes: How to become a 28-year-old CEO? (2014) Suggest more...

Books to read

Glossary of Search Fund Terms (2022)

Search Funds & Entrepreneurial Acquisitions (2021)

The Myth of The Idea (2020)

Acquiring Entrepreneurship (2019)

Buy then Build (2018)

HBR Guide to Buying a Small Business (2017)

Traction: Get a Grip on Your Business (2012) Suggest more...

• Meet our Deal Team
• Transaction Process: What to Expect
• Am I Ready to Search?
• Why Should I Partner with an Accelerator?
• Intermediaries

⟵ Back to Media

Entrepreneurship Through Acquisition Resource Library

For entrepreneurs, for business owners, getting started - introductory materials.

Stanford search fund primer ⟶

Best practices for the search phase ⟶

Recent trends ⟶

Stanford study on failure factors ⟶

How to prepare for a search fund during your MBA ⟶

An overview of ETA ⟶

Do you have what it takes to be an entrepreneur?  ⟶

Coursework for aspiring searchers⟶

Understanding EIR Economics ⟶

Finance Training Courses ⟶

Finance Self-Study Guides ⟶

Am I Ready To Search? ⟶

The Psychology and Pitfalls of Search ⟶

Don’t Start Your Own Business — Take One Over ⟶

Why More Entrepreneurs are Choosing To Build Search Funds ⟶

Why More MBAs Should Buy Small Businesses ⟶

How CEOs-In-Waiting Buy the Companies They Want to Run ⟶

MBAs’ Latest Pitch to Investors: Skip the Startup, Invest in Me ⟶

Commonly Asked Questions

Is search the right career path for me? ⟶

Why should I partner with an accelerator? ⟶

How do I fund the search? ⟶

Finding the right capital partner ⟶

Should I limit my search to a specific geography? ⟶

Should I conduct an industry based search? ⟶

Should I become an entrepreneur? ⟶

Exploring Search Fund Entrepreneur Economics ⟶

What are the financial implications behind choosing the career path? ⟶

When Search Funds Don’t Go As Planned (Part 1): 3 Searcher Case Studies ⟶

When Search Funds Don’t Go As Planned (Part 2): Trends & Observations With Jim Sharpe ⟶

Searching Best Practices

Establishing a process ⟶

Establishing seller trust ⟶

Defining selection criteria ⟶

On the nature of revenue ⟶

How to maximize the chance of a 10x outcome ⟶

Why things don’t always work out as planned ⟶

Busting the Biggest Myth About Purchasing and Operating Small Companies ⟶

On the Nature of Modeling and Valuation in a Search Fund Acquisition ⟶

Modeling for Traditional Search Deals – Intro to Building Financial Models ⟶

Sample LBO Model used for ETA Transactions ⟶

How Resilience Works ⟶

Making an Offer

Overview of an LOI ⟶

Debt considerations ⟶

Why valuation is so important ⟶

Establishing seller trust ⟶

If your LOI falls apart ⟶

Due Diligence

What to expect during due diligence ⟶

On the nature of due diligence in a search fund acquisition ⟶

20 key due diligence activities ⟶

A sample of questions to use during due diligence ⟶

Sample due diligence checklist ⟶

Why deals fall apart ⟶

Quality of earnings ⟶

The First Year As CEO

Transitioning the seller after closing ⟶

Taking over the business ⟶

Assessing talent ⟶

Selecting your direct reports ⟶

Communicating with the employees ⟶

Learning the business ⟶

Becoming a salesperson ⟶

Growing revenue ⟶

Pricing for value ⟶

Developing an accountability culture ⟶

Improving communication protocols ⟶

Work-life integration ⟶

Constructing, Managing, and Working with a Board of Directors ⟶

The Entrepreneurship Through Acquisition ⟶

Think Like an Owner ⟶

Messy Marketplace ⟶

Private Equity Funcast ⟶

Owned and Operated ⟶

Buy and Build ⟶

Let’s Buy a Business ⟶

Getting the Deal Done Podcast ⟶

The Search Fund Podcast ⟶

Acquiring Minds ⟶

Sweaty Startup ⟶

Beyond 8 Figures ⟶

‍ The Michael Girdley Show ⟶

‍ ‍ In the Trenches ⟶

‍ Small Business and Deal Making Podcast ⟶

Search Funds & Entrepreneurial Acquisitions: The Roadmap for Buying a Business and Leading it to the Next Level ⟶

HBR Guide to Buying a Small Business: Think Big, Buy Small, Own Your Own Company ⟶

Buy Then Build: How Acquisition Entrepreneurs Outsmart the Startup Game ⟶

Recommended Videos

Booth Alumni Series on ETA ⟶

Perspective on Search Funds ⟶

Booth/Kellogg ETA Conference ⟶

7th Annual Booth/Kellogg ETA Conference – NGP Programming ⟶

Brent Beshore, Trish Higgins, and Will Thorndike at Capital Camp ⟶

Irv Grousbeck (Stanford professor) discussion on search funds ⟶

An Introduction to NGP

What is a Search Fund?     ⟶

5 Reasons to Sell Business to Search Fund    ⟶

Private Equity, Search Funds, and Strategic Buyers: who is the right fit for your business? ⟶

What Does NGP Look For in Prospective Partnerships?

Our Approach ⟶

Our Investment Criteria ⟶

Out Portfolio ⟶

Valuation Resources ⟶

Yale Entry Multiples Valuation ⟶

Business Value Does Not Equal Selling Price ⟶  

Business Valuation: Busting Common Myths⟶

Why partner with NGP?

Our Values ⟶

Our Team and Advisors ⟶

Our Investment Professionals ⟶

Our Success Stories: MHW ⟶

Our Success Stories: DAWGS ⟶

Hear from a previous seller ⟶

What's involved in getting a deal done?

An Overview of the Partnership⟶  

Our Transaction Timeline ⟶

Mergers And Acquisitions: What Are The Steps In The Sale Of A Small Or Medium-Sized Business? ⟶ 

Breaking Down the NDA ⟶  

Art of the LOI ⟶  

Sealing the Deal: Breaking Down the LOI⟶   

Anatomy of an Earnout ⟶  

How does NGP add value to businesses

Our Portfolio Operations Team ⟶  

Why Value Creation is a Top Priority ⟶  

Identify What Holds Back Your Value Creation ⟶   

How Does Value Creation Lead to Business Success ⟶    

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IESE Insight

Search funds continue to go global

IESE's biennial study of international search funds tracks this asset class's progress on five continents.

• IESE's 2022 study of international search funds — covering those outside the United States and Canada — observed 211 first-time funds from 34 countries on five continents.
• International search-fund activity reached new peaks last year, with 44 new funds formed and 23 acquisitions made.
• For the searchers, who tend to recent MBA graduates, the four most popular sectors for acquisition-hunting were technology, healthcare, transportation and logistics, and manufacturing.

What is going on in the world of search funds lately? Attractive financial returns and record numbers of new searchers and deals, according to the latest studies out this year.

Working in close partnership with the Stanford Graduate School of Business, IESE Business School has just released its 2022 biennial report on international search funds , covering funds outside the United States and Canada. The sixth edition of IESE's international report shows that the search-fund model is maturing in Europe and Latin America while forging new paths in Africa and Asia. International results are similar to what's been happening on their original home turf over the years – as reported by Stanford's study tracking 526 funds formed within the U.S. and Canada since 1984.

IESE's study examines 211 first-time international search funds in four stages of their life cycles: (1) raising a search fund (i.e., a pool of capital from aligned investors) that is dedicated to finding a company to acquire; (2) searching and acquiring; (3) operating the acquired company in search of greater success and growth (typically the longest and most rewarding stage for searchers); and (4) exiting to achieve liquidity. The report provides an informative snapshot of the people and their work in this new asset class as it is taking root.

International search fund activity in 4 lifecycle stages

Source: Prepared by the authors based on IESE search fund surveys. Data as of December 31, 2021.

Moving on up

As also seen in the U.S. market, international search funds have been gaining momentum and hitting new peaks. In 2021, a record 44 new international search funds were formed. Among them were the first documented presence of the model in nine countries: in Belgium, the Czech Republic, Egypt, the Ivory Coast, Latvia, Paraguay, Russia, South Korea and Sweden.

Another peak in the past year was the 23 new acquisitions of companies by searchers – a sign of growing maturity for the model. Meanwhile, there were two new exits. (See graph below.)

International search fund activity by year

Early results

As seen in the graphs above, there have only been 16 exits outside of the U.S. and Canada so far: 12 with positive returns and 4 with negative. Meanwhile, 78 search-fund-acquired companies are currently being operated. The scarcity of exits is at least partly due to the newness of the model abroad; most funds are exiting after a period of 5 to 10 years.

Early returns are to be taken with a grain of salt, but the report authors note that they are generally in line with the trends observed over time in the Stanford study. Returns are calculated from the perspective of a fund's initial investors who participated in both the search and acquisition phases of the fund. For international funds, the latest returns on investment (ROI) clock in at 1.9x and the internal rate of return (IRR) comes to 19.4%. That's substantially below what the Stanford study observed in the U.S. and Canada (7.9x ROI and 36.8% IRR), but, again, consider international funds' much shorter holding periods. The strongest periods of growth usually occur after three years, and 37% of the international funds tracked were launched within the past two years.

Where in the world?

Where are international searches happening now? Five continents have search funds of their own. Over the past decade, the international model has been launched most often in Spain (with 42 first-time funds launched over the years tracked), Mexico (with 37) and Brazil (with 24) and the United Kingdom (also with 24 funds). See figure below.

International search funds by country

Advice gleaned from the report: ideal search fund targets

In any market, companies with high-quality (i.e., recurring) revenue and robust earnings before interest, taxes, depreciation and amortization (EBITDA), operating in a niche area with solid industry growth are top targets for the model. These were the investment criteria mentioned in most offering memoranda reviewed by the report's research team.

Technology, healthcare, transportation and logistics, and manufacturing are the four most popular areas for searches. What industry trends are notable over time? There has been increasing interest in technology, healthcare and manufacturing lately, and declining interest in finance and business services.

Anatomy of a searcher

It seems to take a certain type of person to pursue the search fund model internationally. The study points to relative youth, a recent MBA and often a connection to the United States (where the search is still better known).

In this sixth edition of IESE's international report, searchers ranged in age from 24-47. A resounding majority of 86% were MBA graduates, and over three quarters raised their search fund within two years of their graduation. While 52% of searchers hail from U.S. business schools, this percentage has fallen significantly over the years. Notably, for all the search funds in Europe (in 14 European nations now), 80% of searchers graduated from a European business school.

Is it better to join forces or go it alone? The jury is still out; 58% of international search funds were formed by solo searchers in 2020-1. Meanwhile, approximately half of acquisitions (46%, or 43 of 94) are made by searcher partnerships -- collaborations that may boast complementary skill sets and the capacity to search more efficiently.

Understanding search funds

From the field reports, it's clear this relatively new asset class still needs frequent introductions. Professor Jan Simon 's 2021 book Search funds and entrepreneurial acquisitions: The roadmap for buying a business and leading it to the next level is a useful guide for searchers, investors and others involved based on the best practices he has gleaned as a professional investor and as Academic Director of IESE's International Search Fund Center .

Also, two recent IESE case studies document different areas of the search fund model in first person. In “ Scribendi ,” written by professor Sampsa Samila and Michael Wurth, the operators of an editing and proofreading business in Canada must decide whether they should embrace AI development and convince their investors to adopt a radical shift in business model -- something search-fund investors may be loath to do. Professor Rob Johnson ’s case “ Marc Bartomeus ,” meanwhile, documents the challenges of searching and operating the first such fund in Spain, including the eventual sale and exit of the company, and the compromises required along the way.

Methodology, very briefly

This biennial study, undertaken in close partnership with the Stanford Graduate School of Business, targets all known first-time search funds outside the United States and Canada. It uses a quantitative, survey-based research method to look at the financial returns and other important characteristics of first-time funds identified through December 31, 2021.

An infographic of this article is published in IESE Business School Insight 163 (Jan.-April 2023).

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Abstract: Since the 1980s, international organizations such as the World Bank and International Monetary Fund (IMF) issued loans tied to structural adjustment program (SAPs), which required developing countries adopt economic policies aimed at reducing trade barriers and opening labor markets. These policies often included reduced public spending, privatization of services, dismantling state enterprises, and lowering import tariffs. While industrialized countries benefited greatly from open markets, developing nations became overrun by large corporations and small farmers were unable to compete in global markets. Today, many developing nations have been transformed into major exporters of agricultural products, largely to the benefit of developed nations. Under this façade of “development” and “efficiency,” the very nature of agriculture has transformed from an industry that serves communities to one that provides profits to corporations and foreign entities. Today, many of the global poor are left unable to sustain themselves at the most basic level: farming for food.

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For those contemplating either pursuing a search fund or investing in one, CES has created a practical guide to answer the most frequently asked questions.

The primer aims to provide an unbiased view of the benefits and challenges, explains the model from both the entrepreneur’s and investor’s perspectives, and gives many operational and execution tips from previous search fund entrepreneurs.

We collect basic information from those requesting access to our research on search funds. Complete the form below, and upon its submission, you will be taken to a webpage where you can download the materials.

Your information will be kept confidential by CES and will not be used for any other purposes.

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Enhancing pooling levels strengthens the risk resilience of healthcare insurance: a case study of basic medical insurance fund operations data in Gansu, China

• Feng Hu 1 ,
• Liu Heming 1 ,
• Cao Wenxuan 1 ,
• Wang Xuemei 1 ,
• Liang Qijun 2 &
• Hu Xiaobin 1  

BMC Public Health volume  24 , Article number:  1129 ( 2024 ) Cite this article

21 Accesses

Metrics details

In China, enhancing the pooling levels of basic health insurance has consistently been regarded as a pivotal measure to promote the refinement of the healthcare insurance system. From 2020 to 2022, the widespread outbreak of COVID-19 posed new challenges to China’s basic health insurance.

The research utilizes Data Envelopment Analysis (DEA), Malmquist index assessment, and fixed-effects panel Tobit models to analyze panel data from 2020 to 2022, assessing the efficiency of basic health insurance in Gansu Province.

From 2020 to 2022, the average overall efficiency of the municipal pooling of Basic Medical Insurance for Urban and Rural Residents was 0.941, demonstrating a stable trend with a modest increase. The efficiency frontier regions have expanded from 5 (35.71%) to 7 (50%). Operational efficiency exhibited a negative correlation with per capita hospitalization expenses and per capita fund balance but a positive correlation with per capita accumulated fund balance and reimbursement rates for hospitalized patients. In 2021, compared to 2020, the county-pooling Basic Medical Insurance for Urban Employees saw a decline of 0.126 in overall efficiency, reducing the efficiency frontier regions from 8 to 3. However, from 2021 to 2022, the municipal-coordinated Basic Medical Insurance for Urban Employees experienced a 0.069 increase in overall efficiency, with the efficiency frontier regions expanding from 3 to 5. Throughout 2020 to 2022, the operational efficiency of the Urban Employee Basic Medical Insurance showed a consistent negative correlation with per capita fund balance.

From 2020 to 2022, the overall operational performance of basic health insurance in Gansu Province was satisfactory, and enhancing the pooling level is beneficial in addressing the impact of unforeseen events on the health insurance system.

Peer Review reports

Introduction

Health holds paramount importance on a global scale [ 1 ]. The inception of a robust medical insurance system holds immense significance in preserving public health, promoting health awareness, optimizing the allocation of medical resources, and sustaining social progress [ 2 ]. The basic medical insurance system reflects the crucial emphasis of the state and society on the health and welfare of the populace. It is essential for promoting the overall health of the population and public health [ 3 , 4 ]. China’s social medical insurance system has undergone significant evolution since the establishment of urban employees’ basic medical insurance (UEBMI) in 1998 and the subsequent introduction of the new rural cooperative medical care system in 2003 [ 5 ]. The completion of nationwide coverage occurred in 2009 with the establishment of urban resident basic medical insurance [ 6 ]. In 2016, China’s State Council issued opinions on integrating urban and rural basic medical insurance systems, merging urban residents’ basic medical insurance and new rural cooperative medical care into a unified system to enhance operational efficiency.

However, in the ongoing process of enhancing basic medical insurance, the challenge of the pooling level, commonly referred to as “vertical fragmentation,” persists and requires further attention [ 7 , 8 ]. The term “vertical fragmentation” within the healthcare insurance system denotes the presence of independent policies and operational regulations across various administrative levels, including provinces, cities, and counties. This results in a fragmented state within the entire healthcare insurance system. In China’s basic medical insurance system, this phenomenon is notably conspicuous, primarily manifesting through institutional fragmentation, unequal benefits, and a lack of coordination in reform initiatives. Consequently, basic medical insurance at lower pooling levels is suggested to have a constrained ability to manage risks during unforeseen public events [ 6 , 9 ]. Upgrading the level of pooling is considered to be the main means of increasing the operational efficiency of health insurance and improving its risk resistance.

Municipal pooling of basic medical insurance has been extensively implemented across China [ 10 ]. In response to the challenge of vertical fragmentation, Gansu Province, in 2019, elevating the pooling of URRBMI to the municipal level, guided by the Gansu Province Municipal Pooling of Urban and Rural Residents’ Basic Health Insurance Implementation Opinions policy. And, in 2021, the Gansu Provincial Health Insurance Bureau and the Department of Finance issued a Circular on Further Implementing Municipal Pooling of Urban Employees’ Basic Medical Insurance. This circular stipulated that all municipalities in Gansu Province were to achieve municipal pooling of UEBMI by January 1, 2022.

Literature review

Elevating the standard of fundamental health insurance pooling is widely recognized as pivotal for addressing “vertical fragmentation,” enhancing operational efficiency within the health insurance system, and bolstering its risk tolerance. Smith P. C.‘s examination of diverse risk pooling models highlights the detrimental effects of coordination deficiencies on the health system, advocating for heightened pooling levels [ 11 ]. Similarly, Ali Ahangar argues for increased pooling levels in health insurance, citing the scale and uncertainty of individual medical expenditures, which, when addressed, can reduce uncertainty and facilitate effective risk-sharing [ 12 ]. Naoki Ikegami and peers qualitatively analyze Japan’s universal health insurance evolution, underscoring the necessity of elevated pooling levels for long-term sustainability and equity [ 13 ]. Shibuya et al. identify threats to Japan’s universal health insurance sustainability and propose elevating pooling levels as a safeguard [ 14 ]. Additionally, McIntyre et al.‘s retrospective study on South Africa’s health insurance development emphasizes the importance of establishing an integrated fund pool to rectify resource disparities between public and private sectors [ 15 ].

Research on enhancing the pooling level of basic medical insurance in China encompasses empirical, theoretical, necessity, and factor analysis studies. Luo Jiaying construct a provincial-level model for UEBMI in Fujian Province, utilizing hierarchical analysis with data on insurance participation across coordination areas, advocating for its feasibility [ 16 ]. Fu Mingwei. address the transition from municipal to provincial-level pooling, employing a Probit model for empirical analysis, marking a significant empirical inquiry into provincial pooling [ 17 ]. Some scholars offer qualitative insights on elevating China’s basic medical insurance pooling level [ 18 , 19 ]. Regarding influencing factors, Li Yaqing identifies moral hazard escalation as a risk factor post-coordination level increase [ 20 ]. Fu Mingwei, utilizing a Probit model, identify urban workers’ insurance participation and the proportion of financially governed counties and districts as pivotal factors influencing provincial-level coordination advancement [ 17 ]. Qingyue Meng, synthesizing domestic and international studies on insurance integration, assert its significance in reducing inter-scheme disparities, enhancing risk-sharing, service coverage, financial protection, operational efficiency, and health system integration [ 6 ]. They stress the broader service and protection for rural residents, the elderly, and the chronically ill, along with addressing challenges posed by population mobility [ 6 ].

In the realm of health insurance pooling elevation, research has predominantly focused on delineating its benefits, imperative, strategies, and influencing factors. However, extant studies regarding how augmenting basic health insurance levels can bolster risk resilience primarily lean on the “law of large numbers” as a theoretical foundation. Hence, this study delves into pertinent research on advancing health insurance coordination through the following avenues: (i) Utilizing operational data from Gansu Province’s basic health insurance fund spanning 2020–2022, we employ the DEA model to assess the efficiency of UEBMI alongside URRBMI. (ii) Considering the disparate municipal pooling attainment timelines for the two basic health insurance types in Gansu Province, China, we analyze the COVID-19 pandemic’s impact on system operational efficiency across varied pooling levels, comparing the URRBMI with the UEBMI and assessing enhancement pooling efficiency scores. (iii) Employing the Malmquist index and panel data Tobit model, we comprehensively scrutinize basic health insurance operational efficiency across different periods, probing its influential factors. Ultimately, our study furnishes empirical evidence supporting the notion that elevating basic medical insurance pooling levels can enhance fund operational efficiency and fortify its resilience against risks, thereby furnishing a groundwork for policy formulation geared toward pooling enhancement in medical insurance.

To facilitate reader comprehension, a graphical representation illustrating the research framework has been devised (Fig.  1 ).

Research framework

Methodology

Data sources.

The data employed in this study comprises 14 variables, covering 14 cities in Gansu Province from 2020 to 2022. These variables constitute panel data. Specifically, the Fund Operation Report of the Gansu Provincial Bureau of Medical Security provided data on fund income (in RMB 100 million), fund expenditure (in RMB 100 million), the number of insured individuals, fund balance (in RMB 100 million), cumulative fund balance (in RMB 100 million), hospitalization rate (%), and the actual reimbursement ratio of hospitalized patients (%) for both URRBMI and UEBMI. Furthermore, data on the number of health technicians per 10,000 people for the years 2020–2021, the average salary of the urban population, GDP per capita, and year-end population figures were obtained from the Gansu Provincial Statistical Yearbook (2021–2022) [ 21 ]. For 2022, data from the Gansu Provincial Bureau of Statistics Statistical Bulletin of National Economic and Social Development of Cities and Prefectures (2022) [ 22 ]. The Gansu Provincial Health and Wellness Statistical Yearbook (2021–2023) provided data on per capita outpatient expenses (in RMB), per capita inpatient expenses (in RMB), the number of medical institutions, and the number of tertiary hospitals.

Selection of indicators

Selection of appropriate input and output indicators is a crucial prerequisite for using the DEA model to assess the operational efficiency of URRBMI and UEBMI. In establishing the evaluation framework, this study has summarized existing research on the use of the DEA model to assess social insurance efficiency. Notably, in the context of China, studies related to the operational efficiency of social insurance commonly employ fund input, the number of healthcare personnel, and the number of insured individuals as input indicators, while fund expenditure and indicators reflecting patient hospitalization characteristics are used as output indicators (Table  1 ).

Input variables refer to the resources or factors utilized in the production process, representing the critical resources consumed by decision units in the production. On the other hand, output variables signify the outcomes or products generated by decision units through the utilization of these input variables. In developing the indicator framework, this study considered the principles of representativeness, objectivity, and feasibility. Combining these principles with expert opinions, the study ultimately determined the input indicators for URRBMI to be: fund income, the number of healthcare personnel per 10,000 people, and the number of insured individuals. For UEBMI, the input indicators include: fund income, the number of healthcare personnel per 10,000 people, the number of insured individuals, and the average wage of urban employees. The output indicators for both types of basic medical insurance were determined to be fund expenditure and hospitalization rate. Additionally, factors such as per capita inpatient costs, per capita outpatient costs, per capita fund balance, per capita cumulative fund balance, the actual reimbursement ratio for inpatients, per capita GDP, the number of medical institutions per 10,000 people, and the proportion of tertiary-level medical institutions were considered as variables influencing the comprehensive technical efficiency of the fund. Table  2 provides a detailed explanation of the indicators.

Statistical test

Data envelopment analysis.

Data Envelopment Analysis (DEA) was a widely used non-parametric technique for determining each decision-making units (DMUs) relative efficiency score and evaluating the linkages between inputs and outputs within different DMUs [ 27 ]. A linear programming model was employed to determine the weights assigned to each DMU, either minimizing inputs or maximizing yield, to gauge the relative efficiency levels concerning resource utilization and output. An advantage of DEA was its ability to manage the complexities associated with multiple inputs and outputs. By comparing the relative efficiency scores of DMUs, one could identify the most efficient DMU, with a score of 1 denoting optimal efficiency. The input-oriented BCC model (Variable Returns to Scale) was used in this study to evaluate efficiency. The linear form of the BCC model is as follows

Malmquist index

The Malmquist index was a significant indicator used to measure changes in technical efficiency between two periods (usually involving two points in time) [ 28 ]. Its calculation relies on the DEA principle, where the Total Factor Productivity Efficiency (TFPCH) index was determined using a distance function. Efficiency Change (EFFCH) and Technical Change (TECHCH) are the two components of the Malmquist index, respectively, and Pure Efficiency Change (PHCH) and Scale Efficiency Change (SECH) are the two further components of EFFCH. The formula appears as follows:

M 0 represented the Total Factor Productivity Efficiency (TFPCH) index for the period (m + 1) relative to the period (m). Assuming the variable returns to scale, if M 0 was more than 1, it indicates an improvement in production efficiency for that period; if M 0 was less than 1, it signified a decrease in production efficiency.

Tobit model

Since the operational efficiency of URRBMI and URBMI fell within the range of 0 to 1, categorizing them as bounded dependent variables, this study employed the Tobit model to investigate the factors that influenced the efficiency of basic medical insurance operations in Gansu Province. The Tobit model, initially proposed by James Tobin, was widely utilized for handling truncated data [ 29 ]. Both the fixed effects panel data Tobit model and the random effects panel data Tobit model were simultaneously utilized in this study. The Stata program developed by Honor, Bo E. (1992) was employed for fixed effects panel data Tobit model fitting [ 30 ]. The Hausman test was used to choose between the panel data Tobit fixed effects model or the random effects model. Model fitting was conducted using Stata 15.0 software, with a significance level set at α = 0.05.

Current status of the operation of the basic medical insurance

This study analyzed the operation of URRBMI and UEBMI in Gansu Province through non-parametric tests. Table  3 revealed significant differences between these two types in several aspects. Specifically, these differences were observed in fund income ( Z = -2.89, P  = 0.004), the number of insured individuals ( Z = -6.566, P  = 0.000), hospitalization rate ( Z = -3.525, P  = 0.000), fund expenditure ( Z = -3.306, P  = 0.001), cumulative fund balance ( Z = -2.774, P  = 0.006), and the actual reimbursement ratio for hospitalized patients ( Z = -7.694, P  = 0.000).

DEA modeling results

As indicated in Table  4 , from 2020 to 2022, the average TE of URRBMI operation in Gansu Province was 0.941, with an average PTE of 0.955 and SE of 0.985. In 2022, the TE was 0.957, showing a generally stable and slightly increasing trend, with 7 out of 14 (50%) DMUs operating on the efficiency frontier. Meanwhile, for UEBMI operation during the same period, the average TE was 0.900, with an average PTE of 0.923 and an average SE of 0.974. The overall trend exhibited an initial decrease followed by an increase. In 2022, the TE was 0.904, with 35.71% of the regions operating on the efficiency frontier.

Results of the Malmquist index model

Malmquist index-urrbmi.

Figure  2 illustrates the regional distribution of the TFPCH index for URRBMI between 2020 and 2022. Over the study period, TFPCH indexes greater than 1.000 were observed in 6 cities (42.86%), indicating an increasing trend in the operational efficiency of URRBMI in these areas. Conversely, eight municipalities and prefectures (57.14%) had TFPCH indexes of less than 1.000, suggesting a declining trend in URRBMI’s operational efficiency in those regions. It is noteworthy that Jiuquan City (8), Zhangye City (6), and Pingliang City (7) had relatively higher TFPCH values, while Linxia Hui Autonomous Prefecture (12), Tianshui City (4), and Longnan City (11) exhibited relatively lower TFPCH values.

The variation in the total factor productivity changes (TFPCH) of the basic medical insurance for urban-rural residents in 14 municipalities during the period from 2020 to 2022

Table  5 presents the Malmquist indices for the operation of URRBMI in each city and prefecture. The TFPCH index for URRBMI from 2020 to 2021 was 1.036, with an EFFCH index of 0.998 and a TECHCH index of 1.039. From 2021 to 2022, the TFPCH index was 0.884, with an EFFCH index of 1.010 and a TECHCH index of 0.875. Over the period from 2020 to 2022, the average TFPCH was 0.957, indicating an overall declining trend in production efficiency. During the study period, the TECHCH was 0.953, contributing to the TFPCH being less than 1.000.

Malmquist index-UEBMI

Figure  3 shows the regional distribution of TFPCH of UEBMI operation from 2020 to 2022. During this period, seven municipalities and prefectures (50.00%) had TFPCH indices of more than 1.000, indicating an increasing trend in the operational efficiency of UEBMI in these areas. Conversely, seven other municipalities and prefectures (50.00% of the total) had TFPCH indices of less than 1.000, suggesting a declining trend in UEBMI’s operational efficiency in those regions. It is noteworthy that Jiuquan City (8) and Pingliang City (7) had relatively higher TFPCH indices, while Jinchang City (2) had the relatively lowest TFPCH index.

The variation in the total factor productivity changes of the urban employees’ basic medical insurance funds for in 14 municipalities during the period from 2020 to 2022

Table  6 displays the Malmquist indices for UEBMI operation in various cities and prefectures. The TFPCH index from 2020 to 2021 was 1.035, exceeding 1.000, indicating an increase in the production efficiency of UEBMI operations in Gansu Province during this period. However, from 2021 to 2022, the TFPCH index was 0.957, below 1.000, suggesting a decrease in the production efficiency of UEBMI operations in Gansu Province during this period. When considering the data from 2020 to 2022, the TFPCH index was 0.995, also below 1.000, indicating a declining trend in the overall production efficiency of UEBMI operation in Gansu Province. The decrease in UEBMI’s TFPCH index owed to a reduction in the EFFCH index.

Panel Tobit model regression results

This study utilized the TE scores from the DEA models for the operation of basic medical insurance in various cities and prefectures from 2020 to 2022 as the dependent variable. Two separate models, the random effects panel data Tobit model and the fixed effects panel data Tobit model, were fitted, and the model selection using the Hausman Test. Table  7 indicated that the results of the random effects panel data Tobit model and the fixed effects panel data Tobit model were relatively consistent for the operational efficiency of the URRBMI. The Hausman Test results demonstrated that the fixed effects panel data Tobit model outperformed the random effects panel data Tobit model ( χ 2 = -2.32). However, for the operational efficiency of the UEBMI, there were significant differences between the results of the random effects panel data Tobit model and the fixed effects panel data Tobit model. The Hausman Test results revealed that the fixed effects panel data Tobit model was superior to the random effects panel data Tobit model ( χ 2 = -10.02).

Table  8 presents the results of the fixed effects Tobit regression model for the efficiency of URRBMI operation. The results indicate that the lower the per capita hospitalization cost, the higher the operational efficiency ( β = -0.000052, 95% CI : -0.000099 ∼ -0.000004, P  = 0.033); the lower the per capita fund balance, the higher the operational efficiency ( β = -0.000116, 95% CI : -0.000180 ∼ -0.000051, P  = 0.000); the more per capita cumulative fund balance, the higher the operational efficiency ( β  = 0.000036, 95% CI : 0.000002  ∼  0.000071, P  = 0.041); and the higher the actual reimbursement ratio for hospitalized patients, the higher the operational efficiency ( β  = 0.005960, 95% CI : 0.000494  ∼  0.011427, P  = 0.033).

Table  9 presents the results of the fixed effects Tobit regression model for the efficiency of UEBMI operation. The research findings indicate that the lower the per capita fund balance, the higher the operational efficiency ( β = -0.0000787, 95% CI : -0.0001181 ∼ -0.0000392, P  = 0.000); and the lower the medical institution density, the higher the operational efficiency ( β = -0.0031376, 95% CI : -0.0049025 ∼ -0.0013726, P  = 0.000).

This study employed the DEA-Malmquist-Tobit model to assess the operational efficiency of basic medical insurance in the Gansu region of China and conducted an in-depth exploration of its influencing factors. The results revealed that the average TE score for URRBMI was 0.941, showing a generally stable trend with slight improvement, while UEBMI had an average TE score of 0.900 with noticeable fluctuations. From 2020 to 2022, the TFPCH for URRBMI was 0.957. The decrease in TECHCH explained the decline in overall production efficiency during 2021–2022. For UEBMI, the TFPCH was 0.995, with EFFCH being less than 1.000, serving as a primary reason for TFPCH being below 1.000. The study found that a rise in per capita hospitalization expenses and per capita fund balance negatively affected the operational efficiency of URRBMI. Conversely, an increase in per capita cumulative fund balance and the actual reimbursement ratio had a positive impact. For UEBMI, its operational efficiency decreased with an increase in per capita fund balance and medical institution density.

From 2020 to 2022, the average TE of URRBMI in Gansu Province was 0.941, while that of UEBMI was 0.900. China’s URRBMI from 2017 to 2020 was estimated to have a TE of 0.921 compared to other research using comparable methodologies [ 23 ]. Some research has characterized Gansu Province’s URRBMI as a “low-input, high-efficiency” fund operation model [ 32 ]. The average TE of China’s UEBMI from 2017 to 2019 was 0.817 [ 33 ]. Additionally, studies have affirmed that regions with lower economic levels can more effectively utilize healthcare resources [ 34 , 35 , 36 ]. Throughout the study period, URRBMI outperformed UEBMI in terms of both average TE and stability. The outbreak of COVID-19 at the end of 2019 posed significant threats to global economic development, social stability, and human health, particularly challenging healthcare systems. Gansu Province experienced two waves of pandemic peaks in 2021 and 2022. In Gansu Province, URRBMI had implemented city-level pooling earlier than UEBMI. Relevant studies have reported that enhancing the pooling level of healthcare insurance can improve its operational efficiency and risk resilience [ 11 , 37 ]. That could be one of the reasons why the average level and stability of URRBMI’s operational efficiency during the pandemic period were superior to those of UEBMI.

The results of the Malmquist index analysis indicate that the average TFPCH index for both URRBMI and UEBMI from 2021 to 2022 was below 1.000, suggesting an overall decline in the efficiency of healthcare resource utilization within the basic medical insurance systems during this period. In the case of URRBMI, the TFPCH below 1.000 was driven by TECHCH. On one hand, due to the COVID-19 pandemic, a significant portion of healthcare resources was allocated to managing the outbreak and researching COVID-19 treatments [ 38 ], resulting in a temporary reduction in the supply of certain healthcare services. On the other hand, to mitigate the risk of infection, some patients postponed or avoided non-urgent medical assistance, leading to a temporary decrease in the demand for healthcare services [ 39 , 40 ]. While this enhanced financial stability for the fund management agencies of URRBMI, it may have translated to reduced accessibility to healthcare services for insured individuals. However, for UEBMI, the TFPCH below 1.000 was attributed to EFFCH. Suggests that UEBMI may face multifaceted challenges. Firstly, the healthcare system experienced significant stress due to the impact of COVID-19 [ 38 ]. Secondly, in 2022, Gansu Province’s UEBMI had just achieved municipal-level pooling, and the department responsible for the fund might not yet be familiar with the policies and management methods. Indicates that the fund management agencies for UEBMI need to address issues related to inadequate resource utilization, improvements in fund operation strategies, and sustainability. For insured individuals, these challenges may impact the quality of services and hinder the satisfaction of healthcare needs.

Per capita hospitalization expenses show a negative correlation with the operational efficiency of URRBMI. First off, as per capita hospitalization costs increase, the health insurance fund is put under more financial strain, which could result in diminished solvency or even financial difficulties. Second, the irrational distribution of healthcare resources among districts may be exacerbated as costs increase, thus reducing the efficiency of the fund and affecting the provision of other healthcare services [ 41 ].

Per capita fund surplus exhibits a negative correlation with the operational efficiency of URRBMI and UEBMI, whereas per capita accumulated fund surplus shows a positive correlation with URRBMI. The outbreak and widespread prevalence of COVID-19 at the end of 2019 led to a decrease in the accessibility of healthcare services [ 42 , 43 ], resulting in the underutilization of essential medical insurance funds, leading to resource wastage and diminished efficiency. Maintaining a moderate level of accumulated fund surplus contributes to enhancing the financial robustness of the fund, ensuring its stability when faced with sudden increases in healthcare expenses or other emergencies. In turn, this supports the sustainability of the fund. Furthermore, the rise in accumulated fund surplus also provides greater flexibility for healthcare insurance, such as expanding coverage or improving service quality, thereby enhancing the fund’s efficiency and service levels and benefiting a larger population.

Per capita fund balance is negatively correlated with the operational efficiency of URRBMI and UEBMI. The outbreak and widespread prevalence of COVID-19 at the end of 2019 led to a decrease in the accessibility of healthcare services [ 42 , 43 ], resulting in the underutilization of basic medical insurance funds, leading to resource wastage and diminished efficiency. However, per capita cumulative fund balance exhibits a positive correlation with URRBMI. Maintaining a moderate level of accumulated fund surplus contributes to enhancing the financial robustness of the fund, ensuring its stability when faced with sudden increases in healthcare expenses or other emergencies. In turn, supports the sustainability of the fund. Furthermore, the increase in accumulated fund surplus also provides greater flexibility for healthcare insurance, such as expanding coverage or improving service quality, thereby enhancing the fund’s efficiency and service levels, benefiting a larger population.

The UEBMI fund’s operational efficiency decreases as healthcare facility density rises. This tendency may be explained by the fact that beneficiaries of the UEBMI receive higher actual reimbursement rates for inpatient care than those whose insurance is provided by the URRBMI. As a result, participants in the UEBMI program may experience problems with excessive use of medical services, which wastes medical resources and makes insurance administration more difficult. In turn, the UEBMI’s operational effectiveness suffers as a result.

Against the backdrop of the COVID-19 outbreak and the proactive efforts of the Gansu Provincial Medical Insurance Bureau to promote the level of pooling of basic medical insurance, this study used the DEA-Malmquist-Tobit model to objectively and scientifically evaluate the operational efficiency. The findings of this study hold value for policymakers and healthcare insurance management agencies in formulating and improving policies. However, this study does have certain limitations. Firstly, the Gansu Provincial Medical Insurance Bureau was established in November 2018. Consequently, this study analyzed data on the operation of the basic medical insurance fund only for the years 2020 to 2022. The relatively short period may impact the long-term trends and stability of the research findings. Future studies may benefit from considering a broader time range. Secondly, this research predominantly focused on the Gansu region of China, potentially limiting the generalizability of the results. This limitation restricts the ability to make broad inferences to other areas. Therefore, future research could explore comparisons between multiple regions to gain a more comprehensive understanding of the variations and commonalities in the operational efficiency of healthcare insurance funds. In summary, despite these limitations, this study provides valuable insights into the operational efficiency of the medical insurance fund in Gansu Province. It serves as a beneficial reference for future research and policy formulation.

Conclusions and policy recommendations

Conclusions.

In this study, we conducted a comprehensive analysis of the operational efficiency of the basic medical insurance system in Gansu Province over the past three years using the DEA model, Malmquist total factor productivity index, and Tobit model. Fund revenue, the number of healthcare professionals per 10,000 population, and the number of insured individuals were considered as input indicators. Fund expenditure and hospitalization rate were taken as output indicators. The main findings of the study are summarized below:

By comparing the operational efficiency of URRBMI and UEBMI, as well as the efficiency of UEBMI before and after the enhancement of the pooling level, this study revealed that URRBMI outperforms UEBMI in terms of average efficiency and stability in fund operation. It is noteworthy that the operational efficiency of UEBMI experienced a significant decline from 2020 to 2021. However, after the implementation of municipal-level pooling at the end of 2021, its efficiency showed improvement. Therefore, the study concludes that enhancing the pooling level can effectively improve the operational efficiency and risk resistance of the basic medical insurance system.

From the perspective of financial management of medical insurance funds, an analysis of the influencing factors of URRBMI and UEBMI revealed a negative correlation between per capita fund balance and the operational efficiency of both systems. However, the per capita cumulative fund balance is positively correlated with the operational efficiency of URRBMI. Consequently, the study concludes that financial management of medical insurance funds is a crucial factor influencing the operational efficiency of basic medical insurance.

Regarding the incentive mechanism of basic medical insurance, the analysis in this study indicates a positive correlation between the operational efficiency of URRBMI and the actual reimbursement ratio for hospitalized patients. Thus, the study suggests that the operational efficiency of URRBMI is significantly influenced by the incentive mechanism of basic medical insurance.

From the perspective of healthcare service costs, the study found a negative correlation between per capita hospitalization costs and the operational efficiency of URRBMI. Consequently, the study concludes that high healthcare service costs will lead to a decrease in the efficiency of URRBMI.

Policy recommendations

Based on the conclusions drawn from our study, the following recommendations are proposed:

(1) Control the Growth of Per Capita Hospitalization Costs :

The increase in per capita hospitalization costs has adversely impacted the operational efficiency of the fund. Therefore, it is recommended that healthcare insurance management authorities strengthen their monitoring of healthcare service prices and quality, while actively promoting the implementation of tiered diagnosis and treatment policies. This will contribute to curtailing the unnecessary escalation of per capita hospitalization costs, thereby enhancing the efficiency of the fund.

(2) Strengthen Financial Management of the Fund :

The rise in per capita fund surplus has had unfavorable effects on fund efficiency. Hence, close attention to the financial status of the fund is warranted. Healthcare insurance management authorities may consider formulating rational fund utilization policies to ensure funds are allocated to meet the medical needs of the insured while maintaining an appropriate fund surplus for unforeseen circumstances. This approach will ensure the judicious use of funds and long-term sustainability.

(3) Optimize the Basic Medical Insurance Incentive Mechanism :

The positive impact of an increased actual reimbursement ratio on fund operational efficiency is noted. Therefore, it is suggested to implement tiered diagnosis and treatment policies and judiciously raise the actual reimbursement ratio for hospitalized patients within reasonable limits. This measure will incentivize healthcare service providers to deliver more efficient and economical medical services, contributing to the optimization of the basic medical insurance operational framework.

(4) Elevate Coordination Levels Further Upon Improving Municipal-Level Integration of Basic Medical Insurance :

Our study reveals that elevating coordination levels significantly benefits the operational efficiency and risk resilience of the basic medical insurance fund. Consequently, it is recommended that Gansu Province, building upon improvements in municipal-level integration, actively advances provincial-level integration of basic medical insurance to further enhance the overall efficiency of the insurance system. This proactive step will better equip the system to cope with risks and elevate the overall effectiveness of the medical insurance system.

Data availability

The dataset used during the current study is available from the corresponding author on reasonable request.

Abbreviations

Data Envelopment Analysis

Urban and rural residents’ basic medical insurance

Urban employees’ basic medical insurance

Decision-making units

total efficiency

pure technical efficiency

scale efficiency

Efficiency change

Technical change

Pure efficiency change

Scale efficiency change

Total factor productive efficiency change

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Acknowledgements

We are greatly grateful to the participants and staffs involved in building the resource. Data used for this study can be accessed by contacting the Gansu Provincial Medical Insurance Service Centre.

This research received financial support from the Gansu Provincial Medical Insurance Service Centre (Study on provincial pooling of medical insurance, Grant Number: 20230113).

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F.H.: Conceived and designed the study, collected and analyzed data, and wrote the initial draft of the manuscript.L.H.: Contributed to data collection, conducted statistical analysis, and critically revised the manuscript for important intellectual content.C.W.: Participated in the study’s design, provided expertise in data interpretation, and contributed to manuscript writing and revision.W.X.: Contributed to the study design, and helped finalize the manuscript for submission.L.Q.: Assisted with data collection and analysis, reviewed the manuscript, and provided valuable input for intellectual content.H.X.: Conceived and designed the study, and oversight of the study, and critically reviewed and edited the manuscript.All authors have read and approved the final version of the manuscript. They have agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Hu, F., Heming, L., Wenxuan, C. et al. Enhancing pooling levels strengthens the risk resilience of healthcare insurance: a case study of basic medical insurance fund operations data in Gansu, China. BMC Public Health 24 , 1129 (2024). https://doi.org/10.1186/s12889-024-18558-y

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Received : 30 October 2023

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DOI : https://doi.org/10.1186/s12889-024-18558-y

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