Matthew effect

The Matthew effect, sometimes called the Matthew principle or cumulative advantage,^[1] is the tendency of individuals to accrue social or economic success in proportion to their initial level of popularity, friends, and wealth. It is sometimes summarized by the adage or platitude "the rich get richer and the poor get poorer".^[2]^[3] Also termed the "Matthew effect of accumulated advantage", taking its name from the Parable of the Talents in the biblical Gospel of Matthew, it was coined by sociologists Robert K. Merton and Harriet Zuckerman in 1968.^[4]^[5]

Early studies of Matthew effects were primarily concerned with the inequality in the way scientists were recognized for their work. However, Norman W. Storer, of Columbia University, led a new wave of research. He believed he discovered that the inequality that existed in the social sciences also existed in other institutions.^[6]

Later, in network science, a form of the Matthew effect was discovered in internet networks and called preferential attachment. The mathematics used for this network analysis of the internet was later reapplied to the Matthew effect in general, whereby wealth or credit is distributed among individuals according to how much they already have. This has the net effect of making it increasingly difficult for low ranked individuals to increase their totals because they have fewer resources to risk over time, and increasingly easy for high rank individuals to preserve a large total because they have a large amount to risk.^[7]

Etymology

The concept is named according to two of the parables of Jesus in the synoptic Gospels (Table 2, of the Eusebian Canons). The concept concludes both synoptic versions of the parable of the talents:

For to every one who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.
— Matthew 25:29, RSV.

I tell you, that to every one who has will more be given; but from him who has not, even what he has will be taken away.
— Luke 19:26, RSV.

The concept concludes two of the three synoptic versions of the parable of the lamp under a bushel (absent in the version of Matthew):

For to him who has will more be given; and from him who has not, even what he has will be taken away.
— Mark 4:25, RSV.

Take heed then how you hear; for to him who has will more be given, and from him who has not, even what he thinks that he has will be taken away.
— Luke 8:18, RSV.

The concept is presented again in Matthew outside of a parable during Christ's explanation to his disciples of the purpose of parables:

And he answered them, "To you it has been given to know the secrets of the kingdom of heaven, but to them it has not been given. For to him who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away."
— Matthew 13:11–12, RSV.

Yule process

Prior to being called "The Matthew effect", Udny Yule in 1925, noticed the effect in flower populations, which in population growth studies is called "Yule process" in his honor.

Sociology of science

Cumulative advantage

In the sociology of science, the first description of the Matthew effect was given by Price in 1976.^[8] (He referred to the process as a "cumulative advantage" process.) His was also the first application of the process to the growth of a network, producing what would now be called a scale-free network. It is in the context of network growth that the process is most frequently studied today. Price also promoted preferential attachment as a possible explanation for power laws in many other phenomena, including Lotka's law of scientific productivity and Bradford's law of journal use.

Coining the "Matthew effect"

"Matthew effect" was a term coined by Robert K. Merton and Harriet Anne Zuckerman to describe how, among other things, eminent scientists will often get more credit than a comparatively unknown researcher, even if their work is similar; it also means that credit will usually be given to researchers who are already famous.^[4]^[5] For example, a prize will almost always be awarded to the most senior researcher involved in a project, even if all the work was done by a graduate student. This was later formulated by Stephen Stigler as Stigler's law of eponymy – "No scientific discovery is named after its original discoverer" – with Stigler explicitly naming Merton as the true discoverer, making his "law" an example of itself. Merton and Zuckerman further argued that in the scientific community the Matthew effect reaches beyond simple reputation to influence the wider communication system, playing a part in social selection processes and resulting in a concentration of resources and talent. They gave as an example the disproportionate visibility given to articles from acknowledged authors, at the expense of equally valid or superior articles written by unknown authors. They also noted that the concentration of attention on eminent individuals can lead to an increase in their self-assurance, pushing them to perform research in important but risky problem areas.^[4]

The Matthew Effect also relates to broader patterns of scientific productivity, which can be explained by additional sociological concepts in science, such as the sacred spark, cumulative advantage, and search costs minimization by journal editors. The sacred spark paradigm suggests that scientists differ in their initial abilities, talent, skills, persistence, work habits, etc. that provide particular individuals with an early advantage. These factors have a multiplicative effect which helps these scholars succeed later. The cumulative advantage model argues that an initial success helps a researcher gain access to resources (e.g., teaching release, best graduate students, funding, facilities, etc.), which in turn results in further success. Search costs minimization by journal editors takes place when editors try to save time and effort by consciously or subconsciously selecting articles from well-known scholars. Whereas the exact mechanism underlying these phenomena is yet unknown, it is documented that a minority of all academics produce the most research output and attract the most citations.^[9]

In addition to its influence on recognition and productivity, the Matthew Effect can also be observed in the distribution of scientific resources, such as funding. A large Matthew effect was discovered in a study of science funding in the Netherlands, where winners just above the funding threshold were found to accumulate more than twice as much funding during the subsequent eight years as non-winners with near-identical review scores that fell just below the threshold.^[10]

Education

In education, the term "Matthew effect" has been adopted by psychologist Keith Stanovich^[11] and popularised by education theorist Anthony Kelly to describe a phenomenon observed in research on how new readers acquire the skills to read. Effectively, early success in acquiring reading skills usually leads to later successes in reading as the learner grows, while failing to learn to read before the third or fourth year of schooling may be indicative of lifelong problems in learning new skills.^[12]

This is because children who fall behind in reading would read less, increasing the gap between them and their peers. Later, when students need to "read to learn" (where before they were learning to read), their reading difficulty creates difficulty in most other subjects. In this way they fall further and further behind in school, dropping out at a much higher rate than their peers.^[13] This effect has been used in legal cases, such as Brody v. Dare County Board of Education.^[14] Such cases argue that early education intervention is essential for disabled children, and that failing to do so negatively impacts those children.^[15]

A 2014 review of Matthew effect in education found mixed empirical evidence, where Matthew effect tends to describe the development of primary school skills, while a compensatory pattern was found for skills with ceiling effects.^[16] A 2016 study on reading comprehension assessments for 99 thousand students found a pattern of stable differences, with some narrowing of the gap for students with learning disabilities.^[17]

Network science

In network science, the Matthew effect was noticed as preferential attachment of earlier nodes in a network, which explains that these nodes tend to attract more links early on.^[18]

The application of preferential attachment to the growth of the World Wide Web was proposed by Barabási and Albert in 1999.^[19] Barabási and Albert also coined the name "preferential attachment", and suggested that the process might apply to the growth of other networks as well. For growing networks, the precise functional form of preferential attachment can be estimated by maximum likelihood estimation.^[20]

Due to preferential attachment, Matjaž Perc writes "a node that acquires more connections than another one will increase its connectivity at a higher rate, and thus an initial difference in the connectivity between two nodes will increase further as the network grows, while the degree of individual nodes will grow proportional with the square root of time."^[7] The Matthew Effect therefore explains the growth of some nodes in vast networks such as the Internet.^[21]

Career progression

A model for career progress quantitatively incorporates the Matthew Effect in order to predict the distribution of individual career length in competitive professions. The model predictions are validated by analyzing the empirical distributions of career length for careers in science and professional sports (e.g. Major League Baseball).^[22] As a result, the disparity between the large number of short careers and the relatively small number of extremely long careers can be explained by the "rich-get-richer" mechanism, which in this framework, provides more experienced and more reputable individuals with a competitive advantage in obtaining new career opportunities.

Bask (2024) reviewed theoretical research on academic career progression and found that Feichtinger et al. developed a model where a researcher’s reputation grows through scientific effort but declines without continual activity^[23] Their model incorporates the Matthew effect, in that researchers with high initial reputations benefit more from their efforts, while those with low reputations may see theirs diminish even with similar effort. They showed that if a researcher starts with low reputation, their career is likely to decline and eventually end, whereas researchers starting with high reputation may either sustain a successful career or face early exit depending on their effort over time.^[23]

Markets with social influence

Experiments manipulating download counts or bestseller lists for books and music have shown consumer activity follows the apparent popularity.^[24]^[25]^[26]

Social influence often induces a rich-get-richer phenomenon where popular products tend to become even more popular.^[27] An example of the Matthew Effect's role on social influence is an experiment by Salganik, Dodds, and Watts in which they created an experimental virtual market named MUSICLAB. In MUSICLAB, people could listen to music and choose to download the songs they enjoyed the most. The song choices were unknown songs produced by unknown bands. There were two groups tested; one group was given zero additional information on the songs and one group was told the popularity of each song and the number of times it had previously been downloaded.^[28] As a result, the group that saw which songs were the most popular and were downloaded the most were then biased to choose those songs as well. The songs that were most popular and downloaded the most stayed at the top of the list and consistently received the most plays. To summarize the experiment's findings, the performance rankings had the largest effect boosting expected downloads the most. Download rankings had a decent effect; however, not as impactful as the performance rankings.^[29] Abeliuk et al. (2016) also proved that when utilizing "performance rankings", a monopoly will be created for the most popular songs.^[30]

Cumulative inequality theory

The ideas of this theory were developed by Kenneth Ferraro and colleagues as an integrative or middle-range theory. Originally specified in five axioms and nineteen propositions, cumulative inequality theory incorporates elements from the following theories and perspectives, several of which are related to the study of society:

Robert Merton articulated the Matthew effect to explain accumulating advantage
Glen Elder's life course perspective
Stress process theory
Age stratification theory.

In recent years, Ferraro and several other researchers have been testing and elaborating elements of the theory on a variety of topics to provide evidence for the theoretical framework. In the following information you will find some of the uses of this theory in sociological studies. '"social systems generate inequality, which is manifested over the life course via demographic and developmental processes."^[31]

McDonough, Worts, Booker, et al. (2015) for example studied cumulative disadvantage in the generations of health inequality among mothers in Britain and the United States. The study examined "if adverse circumstances early in the life course cumulate as health harming biographical patterns across working and family caregiving years."^[32] Also, it was examined if institutional context moderated cumulative effects of micro level processes. The results showed that existing health disparities of women in midlife, during work and family rearing time, were intensified by cumulative disadvantages caused by adversities in early life. Thus, the accumulation of disadvantage had negative connotations for the well-being of women's occupational experiences and family life.

McLean (2010), on the other hand, studied U.S. combat and non combat veterans through cumulative disadvantage. He discovered that cumulated negative disadvantages caused by disability and unemployment were more likely to influence the lives of combat veterans versus non combat veterans. Combat veterans suffered physical and emotional trauma that had a disabling effect which impeded their ability to successfully obtain employment. . The research is crucial for social policy implementation that assist United States Veterans to find and retain employment that is suitable to their personal conditions.^{[citation needed]}

In continuation, Woolredge, Frank, Coulette, et al. (2016) studied the prison sentencing of racial groups. specifically of African American males with prior felony convictions. They examined how pre-trial processes affect trial outcomes. It was determined that cumulative disadvantage was existent for African American males and young men; the results were measured by: set bail amounts, pre-trial detention, prison sentencing, and no reduction in sentencing length. The research are striving to create changes in the justice system that reduce incarceration rates of African American Males by reducing bail amounts, and pre trial imprisonment. Further studies are important to decrease the incarceration of minority groups in society, and to create a non biased justice system.^{[citation needed]}

Additionally, Ferraro & Moore (2003) have applied the theory to the study of long-term consequences of early obesity for midlife health and socioeconomic attainment. The study shows that obesity experienced in early life leads to lower-body disability, but higher risk factors to health.^[33] Moreover. The research mentions a risk that has been brought to attention in the past years; it ties being over weight to negative stigma (DeJong 1980),and has influenced fair labor market positioning^[34] and wages.^[35]

Lastly, Crystal, Shea, & Reyes (2016) studied the effects of cumulative advantage in increasing within age cohort economic inequalities in diverse periods of time. The study utilized economic patterns such as annual wealth value and household size. The inequalities of age were analyzed by using the gini coefficient. The study took place between 1980 and 2010. The results showed that at age 65 plus individuals had higher rates of inequality and it increased significantly for baby boomers or during economic recession and times of war. The research is written to estimate the possible impacts of social security changes on older adults in American Society.

In conclusion, Cumulative Inequality or Cumulative Disadvantage Theory, is broadly examining various topics that impact public policy, and the view of our role within society. Further benefits of the theory are still to be seen in the next coming years.

Life course inequality

The concept of cumulative advantage, based on Merton and Zuckerman's Matthew Effect, has been widely applied to the study of life course inequality.^[36]^[37] Dannefer (2003) argued that inequalities in resources, health, and social status systematically widen over time, shaped by social institutions, economic structures, and psychosocial factors like perceived agency and self-efficacy. Early advantages or disadvantages become amplified, producing growing disparities as individuals age. Pallas (2009) further highlighted how cumulative advantage involves shifts between different types of capital, such as human, economic, and symbolic, complicating efforts to measure inequality over time.^[38]

Research has expanded cumulative advantage beyond aging to domains such as education, work, health, and wealth.^[37] In education, early academic differences lead to greater access to opportunities and resources, compounding over time. In the workforce, initial job placements and early career achievements create divergent paths in earnings and occupational mobility. Family background and neighborhood contexts also play a role, reinforcing early disparities across the life course^[37]

Mitigation

Open Science is "the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of society, amateur or professional". One of its key motivations is increasing equity in scientific endeavors. However, Ross-Hellauer, T. et. al. (2022) argue that Open Science's ambition to reduce inequalities in academia may inadvertently perpetuate or exacerbate existing disparities caused by cumulative advantage.^[39] As Open Science progresses, it faces the challenge of balancing its goals of openness and accessibility with the risk that its practices could reinforce the privileges of the more advantaged, particularly in terms of access to knowledge, technology, and funding. The authors make this critique to urge professionals to reflect "upon the ways in which implementation may run counter to ideals".^[39]

References

^ Merton, Robert K. (1 December 1988). "The Matthew Effect in Science, II: Cumulative Advantage and the Symbolism of Intellectual Property". Isis. 79 (4): 606–623. doi:10.1086/354848. S2CID 17167736.
^ Gladwell, Malcolm (2008-11-18). Outliers: The Story of Success (1 ed.). Little, Brown and Company. ISBN 978-0-316-01792-3.
^ Shaywitz, David A. (2008-11-15). "The Elements of Success". The Wall Street Journal. Retrieved 2009-01-12.
^ ^a ^b ^c Merton, Robert K. (1968). "The Matthew Effect in Science" (PDF). Science. 159 (3810): 56–63. Bibcode:1968Sci...159...56M. doi:10.1126/science.159.3810.56. PMID 17737466. S2CID 3526819.
^ ^a ^b Merton, Robert K (1988). "The Matthew Effect in Science, II: Cumulative advantage and the symbolism of intellectual property" (PDF). Isis. 79 (4): 606–623. doi:10.1086/354848. S2CID 17167736.
^ Rigney, Daniel (2010). "Matthew Effects in the Economy.” The Matthew Effect: How Advantage Begets Further Advantage. Columbia University Press. pp. pp. 35–52.
^ ^a ^b Perc, Matjaž (2014). "The Matthew effect in empirical data". Journal of the Royal Society Interface. 12 (104): 20140378. arXiv:1408.5124. Bibcode:2014arXiv1408.5124P. doi:10.1098/rsif.2014.0378. PMC 4233686. PMID 24990288.
^ Price, D. J. de S. (1976). "A general theory of bibliometric and other cumulative advantage processes" (PDF). J. Amer. Soc. Inform. Sci. 27 (5): 292–306. doi:10.1002/asi.4630270505. Archived (PDF) from the original on 2020-12-01. Retrieved 2008-07-19.
^ Serenko, A.; Cox, R.; Bontis, N.; Booker, L. (2011). "The Superstar Phenomenon in the Knowledge Management and Intellectual Capital Academic Discipline" (PDF). Journal of Informetrics. 5: 333–345.
^ Bol, T.; de Vaan, M.; van de Rijt, A. (2018). "The Matthew Effect in Science Funding" (PDF). PNAS. 115 (19): 4887–4890. Bibcode:2018PNAS..115.4887B. doi:10.1073/pnas.1719557115. PMC 5948972. PMID 29686094.
^ Stanovich, Keith E. (1986). "Matthew Effects in Reading: Some Consequences of Individual Differences in the Acquisition of Literacy". Reading Research Quarterly. 21: 360–407. doi:10.1177/0022057409189001-204.
^ Kempe, C., Eriksson-Gustavsson, A. L., & Samuelsson, S (2011). "Are There any Matthew Effects in Literacy and Cognitive Development?". Scandinavian Journal of Educational Research. 55 (2): 181–196. doi:10.1080/00313831.2011.554699. S2CID 145163197.{{cite journal}}: CS1 maint: multiple names: authors list (link)
^ Adams, Marilyn J. (1990). Beginning to Read: Thinking and Learning about Print. Cambridge, MA: MIT Press. pp. 59–60.
^ "Wrightslaw - North Carolina, Review Officer Special Education Decision". www.wrightslaw.com. Retrieved 2022-12-22.
^ "Assessment & Testing - The Matthew Effect - Wrightslaw.com". www.wrightslaw.com. Retrieved 2022-12-22.
^ Pfost, Maximilian; Hattie, John; Dörfler, Tobias; Artelt, Cordula (2014). "Individual Differences in Reading Development: A Review of 25 Years of Empirical Research on Matthew Effects in Reading". Review of Educational Research. 84 (2): 203–244. doi:10.3102/0034654313509492. ISSN 0034-6543.
^ Schulte, Ann C.; Stevens, Joseph J.; Elliott, Stephen N.; Tindal, Gerald; Nese, Joseph F. T. (2016). "Achievement gaps for students with disabilities: Stable, widening, or narrowing on a state-wide reading comprehension test?". Journal of Educational Psychology. 108 (7): 925–942. doi:10.1037/edu0000107. ISSN 1939-2176.
^ Barabási, A-L; Albert, R (1999). "Emergence of scaling in random networks". Science. 286 (5439): 509–512. arXiv:cond-mat/9910332. Bibcode:1999Sci...286..509B. doi:10.1126/science.286.5439.509. PMID 10521342. S2CID 524106.
^ Barabási, A.-L.; R. Albert (1999). "Emergence of scaling in random networks". Science. 286 (5439): 509–512. arXiv:cond-mat/9910332. Bibcode:1999Sci...286..509B. doi:10.1126/science.286.5439.509. PMID 10521342. S2CID 524106.
^ Pham, Thong; Sheridan, Paul; Shimodaira, Hidetoshi (September 17, 2015). "PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks". PLOS ONE. 10 (9): e0137796. Bibcode:2015PLoSO..1037796P. doi:10.1371/journal.pone.0137796. PMC 4574777. PMID 26378457.
^ Guadamuz, Andres (2011). Networks, Complexity And Internet Regulation – Scale-Free Law. Edward Elgar. ISBN 9781848443105.
^ Petersen, Alexander M.; Jung, Woo-Sung; Yang, Jae-Suk; Stanley, H. Eugene (2011). "Quantitative and Empirical demonstration of the Matthew Effect in a study of Career Longevity". PNAS. 108 (1): 18–23. arXiv:0806.1224. Bibcode:2011PNAS..108...18P. doi:10.1073/pnas.1016733108. PMC 3017158. PMID 21173276.
^ ^a ^b Bask, Mikael (2024-12-01). "Skill, status and the Matthew effect: a theoretical framework". Journal of Computational Social Science. 7 (3): 2221–2253. doi:10.1007/s42001-024-00298-z. ISSN 2432-2725..
^ Salganik, Matthew J.; Dodds, Peter S.; Watts, Duncan J. (2006). "Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market" (PDF). Science. 311 (5762): 854–856. Bibcode:2006Sci...311..854S. doi:10.1126/science.1121066. PMID 16469928. S2CID 7310490.
^ Sorenson, Alan T (2007). "Bestseller Lists and Product Variety" (PDF). Journal of Industrial Economics. 55 (4): 715–738. doi:10.1111/j.1467-6451.2007.00327.x. S2CID 49028945.
^ van de Rijt, A.; Kang, S.; Restivo, M.; Patil, A. (2014). "Field Experiments of Success-Breeds-Success Dynamics" (PDF). PNAS. 111 (19): 6934–6939. Bibcode:2014PNAS..111.6934V. doi:10.1073/pnas.1316836111. PMC 4024896. PMID 24778230.
^ Altszyler, E; Berbeglia, F.; Berbeglia, G.; Van Hentenryck, P. (2017). "Transient dynamics in trial-offer markets with social influence: Trade-offs between appeal and quality". PLOS ONE. 12 (7): e0180040. Bibcode:2017PLoSO..1280040A. doi:10.1371/journal.pone.0180040. PMC 5528888. PMID 28746334.
^ Berbeglia, F.; Van Hentenryck, P. (2017-02-10). Taming the Matthew Effect in Online Markets with Social Influence (PDF). Thirty-First AAAI Conference on Artificial Intelligence. Vol. 31. San Francisco. doi:10.1609/aaai.v31i1.10511. Archived from the original on 2022-12-30. Retrieved 2022-12-30.
^ Salganik, Matthew J.; Dodds, Peter S.; Watts, Duncan J. (2006-02-10). "Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market". Science. 311 (5762): 854–856. Bibcode:2006Sci...311..854S. doi:10.1126/science.1121066. PMID 16469928. S2CID 7310490.
^ Abeliuk, Andrés; Berbeglia, Gerardo; Cebrian, Manuel; Van Hentenryck, Pascal (2015-04-01). Huerta-Quintanilla, Rodrigo (ed.). "The Benefits of Social Influence in Optimized Cultural Markets". PLOS ONE. 10 (4): e0121934. Bibcode:2015PLoSO..1021934A. doi:10.1371/journal.pone.0121934. PMC 4382093. PMID 25831093.
^ Ferraro, K. F.; Shippee, T. P. (17 April 2009). "Aging and Cumulative Inequality: How Does Inequality Get Under the Skin?". The Gerontologist. 49 (3): 333–343. doi:10.1093/geront/gnp034. PMC 2721665. PMID 19377044.
^ McDonough, Peggy; Worts, Diana; Booker, Cara; McMunn, Anne; Sacker, Amanda (September 2015). "Cumulative disadvantage, employment–marriage, and health inequalities among American and British mothers". Advances in Life Course Research. 25: 49–66. doi:10.1016/j.alcr.2015.05.004.
^ Ferraro, Kenneth F.; Kelley-Moore, Jessica A. (October 2003). "Cumulative Disadvantage and Health: Long-Term Consequences of Obesity?". American Sociological Review. 68 (5): 707–729. doi:10.2307/1519759. JSTOR 1519759. PMC 3348542. PMID 22581979.
^ Averett, S.; Korenman, S. (February 1999). "Black-white differences in social and economic consequences of obesity". International Journal of Obesity. 23 (2): 166–173. doi:10.1038/sj.ijo.0800805. PMID 10078852.
^ Register, Charles A; Williams, Donald R (March 1, 1990). "Wage Effects of Obesity among Young Workers". Social Science Quarterly. 71 (1): 130–141.
^ Dannefer, Dale (2003-11-01). "Cumulative Advantage/Disadvantage and the Life Course: Cross-Fertilizing Age and Social Science Theory". The Journals of Gerontology: Series B. 58 (6): S327 – S337. doi:10.1093/geronb/58.6.S327. ISSN 1079-5014.
^ ^a ^b ^c DiPrete, Thomas A.; Eirich, Gregory M. (2006). "Cumulative Advantage as a Mechanism for Inequality: A Review of Theoretical and Empirical Developments". Annual Review of Sociology. 32: 271–297. ISSN 0360-0572.
^ Pallas, Aaron M.; Jennings, Jennifer L. (2009). "Cumulative knowledge about cumulative advantage". Schweizerische Zeitschrift für Soziologie = Revue suisse de sociologie = Swiss journal of sociology. 35 (2): 211. doi:10.5169/seals-815047. ISSN 0379-3664.
^ ^a ^b Ross-Hellauer, Tony; Reichmann, Stefan; Cole, Nicki Lisa; Fessl, Angela; Klebel, Thomas; Pontika, Nancy (2022-01-19). "Dynamics of cumulative advantage and threats to equity in open science: a scoping review". Royal Society Open Science. 9 (1): 211032. doi:10.1098/rsos.211032. PMC 8767192. PMID 35116143.