For more information concerning given names and popularity statistics,
see the Given Name Frequency Project



Comments and Suggestions Welcomed

Version 1.1[1]

 

 

 

A New Account of Personalization and Effective Communication

 

 

Douglas A. Galbi

Senior Economist

Federal Communications Commission[2]

 

September 16, 2001

 

 

 

 

 

 

Abstract

 

To contribute to understanding of information economies of daily life, this paper explores over the past millennium given names of a large number of persons.  Analysts have long both condemned and praised mass media as a source of common culture, national unity, or shared symbolic experiences.  Names, however, indicate a large decline in shared symbolic experience over the past two centuries, a decline that the growth of mass media does not appear to have affected significantly.  Study of names also shows that action and personal relationships, along with time horizon, are central aspects of effective communication across a large population.  The observed preference for personalization over the past two centuries and the importance of action and personal relationships to effective communication are aspects of information economies that are likely to have continuing significance for industry developments, economic statistics, and public policy.

 

 

 


 

 

 

 

 

 

Contents

 

 

 

I.   Analyzing Names

 

II.  A Statistical History of Personalization

 

III. Trends in Effective Communication

 

IV. Conclusions

 

References

 

Appendix A: Additional Data on Name Communication in England Before 1825

 

Appendix B: Evidence on Variations in Name Statistics

 

Appendix C: Mary, Group Polarization, and Symbolic Consensus

 

Appendix D: Analytical Details and Sources for Name Statistics


 

Broad, quantitative studies of information and knowledge economies have been primarily concerned with inputs, technology, and outputs.  A pioneering study pointed to the importance of knowledge growth by identifying growth in aggregate output that growth in aggregate capital and labor inputs cannot explain.[3]  Other studies, including an important US Office of Telecommunications report, have used national accounting data to estimate the value of knowledge production and the share of national output associated with information activities.[4]  Studies have also estimated the number of information workers and their share in the national workforce.[5]  More recently, measures of technology diffusion, such as the share of persons that have telephones, computers, and Internet connections, have played prominent roles in discussion and analysis.[6]

 

While measures of inputs, technology, and outputs associated with information have considerable value, they also have major weaknesses.  Classifying groups of workers, types of output, or output growth residuals as being associated with information involves a data naming exercise with considerable scope for discretion.[7]  The results may thus provide more evidence about the particular naming exercise than about the general nature of the economy.[8]  Moreover, consistent national level data on economic inputs and outputs are difficult to construct for a long period.  While a long-run historical perspective is important for understanding information economies, statistical agencies face significant challenges just in coping with the effects of recent information technology developments.[9]   Approaches that focus on inputs, technology, and outputs also can obscure that persons are the subjects of the information economy, and that persons thinking and communicating produce non-marketed human goods and create culture for common use.[10]

 

Creative empirical approaches are needed to complement widely recognized theoretical developments in the economics of information. The economics of information have shaped the way economists and others think.[11]  Information is in general imperfect and asymmetric, like a tomato selected at random from a backyard garden.  Areas in which the economics of information has thus far only made limited progress include:

how and how well organizations and societies absorb new information, learn, adapt their behavior, and even their structures; and how different economic and organizational designs affect the ability to create, transmit, absorb, and use knowledge and information.[12]

These questions require study that goes well beyond price systems.  The key questions relate to dynamics of the information economy not captured in traditional models of markets.

 

Personal given names offer several advantages for studying an information economy.[13] On a daily basis, for most types of information, and in much of human communications, “who” and “to whom” are key questions.  Personal names matter in normal human activity, they are a crucial aspect of personal identity and dignity, and they have deep cultural significance.  Moreover, from an operational perspective, personal names have been collected extensively and over a long period of time in the process of public administration.[14]  A given name, which forms part of a contemporary personal name, is generally given to a person shortly after birth, and given names are seldom changed.[15]  Given names thus provide a means for disciplined, quantitative study of information economies across major social, economic, and technological changes.

 

The work of influential analysts points to the importance of studying names.  Pierre Bourdieu has declared that the social sciences must focus on “the social operations of naming,” or using one of Bourdieu’s distinctive terms, naming habitus, meaning a social perspective on naming habits, an aspect of which will be measured in this paper in bits.[16]  Niklas Luhmann has elaborated upon the three-in-one unity (unitas multiplex) of information, message and understanding, and Luhmann has explored theoretically how communication constructs social systems and shifts them among different states.[17]  The distribution of name frequencies, which consistently produces a particular order as part of the communication that characterizes social life, is an important empirical example of Luhmann’s theory.[18]  Jürgen Habermas has discussed communicative rationality in relation to the historical emergence of the public sphere, its refeudalization, and the colonization of the lifeworld.[19]  Public discussion and public opinion concerning personal names affects practical private interests, such as the ability to attract attention, get respect, or communicate status.  Study of names can provide important historical evidence concerning Habermas’s distinction between communicative and instrumental rationality.  More generally, study of names can help one better understand the widely cited work of Habermas, as well as that of Luhmann and Bourdieu.

 

To contribute to understanding of information economies of daily life, this paper explores over the past millennium given names of a large number of persons.  Analysts have long both condemned and praised mass media as a source of common culture, national unity, or shared symbolic experiences.[20]  Names, however, indicate a large decline in shared symbolic experience over the past two centuries, a decline that the growth of mass media does not appear to have affected significantly.  Study of names also shows that action and personal relationships, along with time horizon, are central aspects of effective communication across a large population.  The observed preference for personalization over the past two centuries and the importance of action and personal relationships to effective communication are aspects of information economies that are likely to have continuing significance for industry developments, economic statistics, and public policy.

 

 

I. Analyzing Names

 

Choosing and communicating names have long been important actions in information economies.  In Hebrew scripture, the stewardship that human beings exercise over nature is expressed in God’s giving the first man power to name all living creatures, and the calling and giving of names played a key role in establishing God’s special relationship with Israel.[21]  The classical culture of learning recognized the importance of naming in the Latin saying “Nomen est numen”: to name is to know.  In Tudor and Stuart England (1485-1714):

Naming was a serious business, securing legal, social, religious, and semantic identity.  According to conventional commentators, the name given at baptism was indeed one’s Christian name, a sign of ‘our regeneration’ and ‘a badge that we belong to God’.  It also put one in fellowship with all others who had worn the name before, to be ‘recorded not only in the church’s register, but in the book of life, and stand there forever’.[22]

The importance attached to naming is not anachronistic today.  The popularity of name-your-baby books and websites emphasizes that fact.[23]  Consider as well the Society for Creative Anachronism (SCA), a worldwide group of person that study and re-create the European Middle Ages.  In its activities, the SCA puts considerable emphasizes on naming.  Each SCA member adopts a unique name appropriate to the Middle Ages through a formal SCA process of authentication and registration, and in all SCA activities and communications SCA members use these names.[24] 

 

Over the last several decades, choosing names for businesses and products has developed as a special line of commerce.  Firms such as Landor, Interbrand, Enterprise IG, Idiom, NameLab, TrueNames, and others provide commercial naming services: 

Each of the firms has its own jealously guarded methodology, a signature “naming module” that distinguishes it from its competitors.  Enterprise IG has its proprietary NameMaker program, good for generating thousands of names by computer.  Landor uses a double-barrelled approach; deploying both its “Brand Alignment Process” and a “BrandAsset Valuator.”  Others find that their module must be described in more than a few words.  “We have a wonderful approach,” says Rick Bragdon of Idiom.  “We use an imaginative series of turbo-charged naming exercises, including Blind Man’s Brilliance, Imagineering, Synonym Explosion and Leap of Faith…We find that when clients are playing, literally playing creative games, they create names that come from a place of joy, a place of fun. [25] 

The commercial goal is to find a “good name”: a name that sounds well, that is memorable, and that has appealing connotations with respect to the particular naming situation.

 

As for commercial names, the value of personal names depends on norms, memories, connotations, and other aspects of shared experiences.  Norms governing naming, such as naming after parents, grandparents, biblical figures, or deceased siblings, are common laws in the economy of names.  They evolve through common awareness of patterns of cases and possibilities for differences and exceptions.  Estimating the value of a particular name involves collecting and assessing information about other persons’ perceptions of the name within the information economy.   While norms and social values structure naming choices, the actual personal choice has largely been a domain of freedom, i.e. personal preference is the recognized ultimate authority.[26]  Thus chosen names provide evidence about the preferences that free persons express in a particular historical context.

 

Personal given names relate to a significant part of shared symbolic experience.  Persons who have the same given name literally share the experience of being called by that name; they share the experience of being associated with all the social meaning attached to the name.  Birth parents and chosen others, such as godparents, also share the experience of determining a good name for another person.  Through the course of their lives persons have a wide range of other symbolic experiences.  Naming, however, is probably unique in its combination of personal significance, universal prevalence, and consistency through time.

 

 

A. Charting Name Trends

 

How to analyze given names and their changes over time is not obvious.  One might ponder why particular names are chosen and think about factors that affect popularity trends.  A recent book, entitled A Matter of Taste, sought to develop theory to address such issues.[27]  A chapter entitled, “Broader Issues: The Cultural Surface and Cultural Change,” moves from subsections labeled “A Causal Hierarchy” and “Birth and Death Are Not the Same” to one labeled “Monica.”[28]  This subsection used the theory developed in the book to consider how the sexual liaison between President Bill Clinton and Monica Lewinsky would affect the popularity of the name Monica. 

 

The author’s analysis is interesting.  First, he notes that “the necessary basis for making a prediction is more complicated that it might appear.”[29]  He then commences by visualizing four possibilities: 1) the name was rarely used, 2) the name was gaining in popularity in the preceding years, 3) the name was failing in popularity in the preceding years, 4) the name was relatively stable in popularity.  Without the Lewinsky affair, “In each case, our best expectation would be more of the same,” although “in some small proportion of the time we would be wrong.”[30]   

 

The analysis of “what can cause something to happen that differs from these expectations” is essentially the same in all four cases.[31]  Here’s the analysis:

We could say that a modest proportion of parents, m, had been using the name Monica and that a far larger proportion of parents, o, had been using some other name.  If we can assume that the new set of parents in the year following the scandal had identical dispositions, then the net movement of Monica is the product of two transitions: what number of the m population are now turned away from the name and what number of the o population are now turned toward the name.  The difference between the two will mean that Monica gains or loses in popularity.  Again, because we start with so many more people initially disposed not to use Monica, it takes only a small proportion of the o parents to switch for the name to gain in popularity even if the vast number of m are no longer attracted to the name.[32]

The author does not provide any specific prediction about changes in the popularity of Monica.  He does, however, note “how easy it is to misinterpret the eventual answer – no matter what that answer is.”[33]

 

The above analysis, and much of the rest of the analysis in A Matter of Taste, is similar to what is known in the financial world as technical analysis.  Technical analysis concerns the study and interpretation of stock price trends separate from external factors or the fundamental value of a company.[34]  The focus is on “internal mechanisms” that drive price movements, such as momentum and symbolic enhancement or contamination from crossing levels of support or resistance (usually round numbers like multiples of ten or a hundred).[35]  Such analysis is commonplace in the financial world and a regular part of mainstream financial reporting.[36] 

 

While technical analysis provides a rich discourse for discussing observed trends and possible future developments, this paper seeks effective tools for uncovering hidden truths about information economies.  Three scientific virtues will guide the analysis: observability, simplicity, and consistency.  Important factors that affect the popularity of particular names may be difficult to observe, they may be many and complex, and they may vary significantly across names.  Thus the analysis will not address the popularity of particular names.  Instead, it will focus on characteristics of the over-all sample of names, characteristics that can be informatively measured in actual name samples of about 500 or more English-language names.

 

 

B. Statistically Measuring Names

 

Names present some subtle statistical challenges.  A sample of persons’ names may cover a significant share of the finite population under study.  Thus statistical issues associated with finite samples are relevant.  Moreover, the abstract sample space of names is of very high dimension, and all samples sparsely populate that space.  Thus the natural space of names as tokens is awkward to manipulate.  One way to simplify the sample space is to define the sample as a token frequency distribution.  A disadvantage is that the sample space then becomes a function of the sample size.  In such a context analysis of properties of estimators is complex. 

 

Rather than exploring such statistical issues abstractly, this paper takes an operational approach.  Conditional on interest in a particular name or set of names, the distribution of names is a binomial or multinomial distribution.  Based on available name sample sizes and associated sampling errors, the desirability of powerful statistics, and empirical evaluation of alternatives, this paper focuses on the ten most popular names in a sample.[37]  The values of the statistics in this paper depend on the rank cutoff used in the analysis.  However, the overall trends observed do not appear to depend on this choice.[38]

 

Measuring name frequencies in actual samples requires attention to name definition and standardization.  Given names can include multiple names and name variants, as well as abbreviations, non-standard spellings, and mistakes in recording.   Throughout the analysis in this paper, names have been truncated to the shorter of either the first eight letters of the given name or the letters preceding the first period, space, hyphen, or other non-alphabetic character.  These shortened names have then been standardized through a name coding available on the Internet for public inspection, use, and improvement on an open source basis.[39]  This procedure attempts to identify feasibly and consistently names with common communicative properties.[40]

 

For name samples comprising between 1,000 and 10,000 names, coding inconsistencies appear to be similar in magnitude to sampling variability.   Table 1 shows sampling variability for a single name, given different probabilities for the name in the population and different sample sizes.  Sampling variability is likely to be insignificant in modern name samples that can easily comprise over a million names.  Medieval name samples, however, are often limited to 1000 names or less.  For such sample sizes, sampling variability can easily account for a percentage point difference in a name frequency statistic.  The importance of coding depends on the particular name, time, place, and recording process.  Table 2 shows name variants coded to “Mary” for England/Wales and US name samples in different periods.   Clearly coding matters, but the nature of coding errors and inconsistencies is more speculative.  Experience with different name samples from the same population suggests that coding variability can be reduced to less that half a percentage point for the frequency of a single name and less than three percentage points for total frequency of the top ten names.[41]  

 

 

Table 1

Sampling Variability for Name Popularity

 

 

Name Probability

 

Sample Size

 

Expected Name Freq.

 

Standard Deviation

Std. Dev. (% of sample)

20.0%

100

20

4

4.0%

3.0%

100

3

2

1.7%

1.5%

100

2

1

1.2%

20.0%

1,000

200

13

1.3%

3.0%

1,000

30

5

0.5%

1.5%

1,000

15

4

0.4%

20.0%

10,000

2,000

40

0.4%

3.0%

10,000

300

17

0.2%

1.5%

10,000

150

12

0.1%

20.0%

100,000

20,000

126

0.1%

3.0%

100,000

3,000

54

0.1%

1.5%

100,000

1,500

38

0.0%

 

 

 

Table 2

Names Coded to Mary

 

 

 

US

 

 

England/Wales

 

Years

Name

Popularity

Year

Name

Popularity

1810-1819

Mary

7.6%

1820

Mary

18.1%

 

Mary A

1.8%

 

Maria

1.9%

 

Maria

1.1%

 

Maryann

0.1%

1900-1910

Mary

5.6%

1900

Mary

3.9%

 

Marie

1.3%

 

Marion

0.3%

 

Marion

0.6%

 

Maria

0.3%

1990-1999

Mary

0.5%

1975

Marie

0.6%

 

Maria

0.5%

 

Maria

0.2%

 

Marissa

0.3%

 

Mary

0.1%

Note: For sources for all the name statistics in this paper, see Appendix D and References.

 

 

 

C. An Important Empirical Regularity

 

For names occurring sufficiently frequently, name frequencies follow a power law.  This means that, to a good approximation, name frequency is log-linearly related to frequency rank.  Chart 1 shows on logarithmic scales the relationship between name frequency and frequency rank for females born in the US in 1831-40 and in 1990-99.  While some concavity is evident, in each case a line provides a high goodness of fit.[42] 

 

This empirical regularity is important for several reasons. First, it highlights an order associated with naming that is potentially amenable to explanation.[43]  Second, it provides a basis for describing changes in naming over time.  As Chart 1 shows, the slope and position of the line describing the relationship between log-rank and log-frequency has changed significantly from 1831-40 to 1990-99.  Changes in these parameters have taken a relatively smooth path that can be summarized simply.  Third, a variety of other phenomena, such as word frequencies, city sizes, income distribution, and the proportion of rock surface area that barnacles, mussels and other organisms occupy in an intertidal zone, follow power laws.[44]  Through this common regularity, evidence and insights regarding other phenomena can be related to naming, and insights from the study of naming gain more general significance.  

 

Power laws are in fact prevalent in the information economy.  Where persons and organizations are free to create and choose among many collections of symbols instantiated and used in a similar way, the relative popularity of the symbolic artifacts typically follows a power law.[45]  Thus the circulation of magazines of similar type have followed power laws throughout the twentieth century.[46]  The total box office receipts of movies follow a power law.[47]   The popularity of musical groups, as measured by “gold records,” follows a power law.[48]  The popularity of Internet web sites, measured in users or page views, also follows a power law.[49]  Insights into the evolution of such power laws over time can provide insights into personal preferences, media diversity, and industry structure in the information economy.

 

 

II. A Statistical History of Personalization

 

Mass media create shared symbolic experiences by producing and distributing common packages of symbols to large numbers of persons.  As little as one and half centuries ago, sharing symbols was largely a matter of decentralized, peer-to-peer diffusion, performances, public meetings, monuments, and other special-purpose artifacts.  In contrast, in many countries today, through mass media millions of persons regularly experience exactly the same presentations of sports, news, songs, and dramatic stories. 

 

Concern about the role of mass media in shaping shared experience has been commonplace.  As early as the mid-1940s observers warned that applying industrial technology and organization to symbol production and distribution was producing a “ruthless unity,” “the same stamp on everything,” a world in which “[t]the might of industrial society is lodged in men’s minds,” and “[r]eal life is becoming indistinguishable from the movies.”[50]  By the early 1990s, the same assumptions about the facts prevailed, but a sense of nostalgia had developed, at least among some:

For forty years we were one nation indivisible, under television.  That’s ending.  Television is turning into something else, and so are we.  We’re different.  We’re splintered.  We’re not as much ‘we’ as the ‘we’ we were.  We’re divisible.[51]

Many policy analysts and policy makers have considered mass media necessary both to promote diversity and to encourage national unity, and they have balanced according to current needs these important social and cultural values.[52] 

 

New computing and communications technologies may significantly affect the extent of shared experiences.  For most persons, purchasing goods and services is a significant shared experience; in the US, retail chains such as Wal-Mart, CVS, and 7-Eleven are real icons of consumer life.[53]  Some have argued that e-commerce and associated personalization technologies will radically reshape retailing.[54]  Particularly in societies in which a common experience of continually increasing material prosperity is an important political ideal, this change in shared experiences might present risks of political fragmentation and polarization.  New technologies are also expanding opportunities to personalize education, entertainment, news, and other forms of digital content.[55]  These new opportunities might lead to a reduction in shared symbolic experiences, less exposure to diverse views and topics, more social fragmentation, and more group polarization.[56]

 

Discriminating between the possible and the likely is worth attempting.  Given the vast opportunities for personalization in the information economy, a fundamental issue is whether opportunities for personal choices will lead to similar choices or diverse choices.  Similar choices might be produced from common, primal attractions such as sex, violence, and truth, from bandwagon, fashion, or tipping effects, or from social structures and institutions that homogenizes habits and preferences.  Diverse choices might express the uniqueness of each person, the requirements and processes of innovation and creativity, or social forces promoting differentiation and individualism.  Carefully interpreted facts with respect to aggregate symbolic choices could offer important insights into the potential social and economic significance of expanding technological possibilities for symbolic personalization.

 

 

A. Changes in Name Popularity

 

The popularity of the most popular given name provides an informative indicator of shared symbolic experiences.  In both England/Wales and the US early in the nineteenth century, the most popular names were highly popular.  Table 3 shows that in England/Wales in 1800, 23.9% of females were named Mary, the most common female name.  Since living siblings almost never bore the same given name and the average fecund marriage produced 3.28 recognized daughters, the share of married women who had a daughter named Mary was higher, probably about 30%.[57]   This represents a high degree of social consensus about an important symbol. 

 

That the name Mary would generate such consensus is particularly remarkable given the bitter split between the Church of England and the Roman Catholic Church.  Roman Catholicism highly venerates Mary, the mother of Jesus.  Anti-Catholicism in England since the mid-sixteenth century has included contempt for Catholic veneration of Mary.   Catholics were associated with irrational and idolatrous religious representation in which the name Mary figured highly:

A Papist is an Idolater, who worships Images, Pictures, Stocks and Stones, the Works of Men’s Hands; calls upon the Virgin Mary [distinctive typeface in original], Saints and Angels to pray for them…[58]

Yet, judging from names, there must have been something about Mary for ordinary English persons early in the nineteenth century.[59]


 

Table 3

Most Popular Names in England/Wales

 

 

Females

Males

Birth

Top

 

Top 10

Top 10

Top

 

Top 10

Top 10

Year

Name

Pop.

Pop.

Info Is

Name

Pop.

Pop.

Info Is

1800

Mary

23.9%

82.0%

0.511

John

21.5%

84.7%

0.356

1810

Mary

22.2%

79.4%

0.465

John

19.0%

81.4%

0.299

1820

Mary

20.4%

76.5%

0.433

John

17.8%

80.4%

0.274

1830

Mary

19.6%

75.8%

0.372

John

16.4%

78.2%

0.244

1840

Mary

18.7%

75.0%

0.333

William

15.4%

76.0%

0.231

1850

Mary

18.0%

72.1%

0.315

William

15.2%

73.8%

0.220

1860

Mary

16.3%

68.3%

0.265

William

14.5%

69.8%

0.209

1870

Mary

13.3%

61.1%

0.193

William

13.1%

63.5%

0.173

1880

Mary

10.6%

53.8%

0.116

William

11.7%

58.9%

0.144

 

 

 

 

 

 

 

 

 

1900

Elizabet

7.2%

38.5%

0.079

William

9.0%

50.9%

0.086

1925

Mary

6.7%

38.7%

0.070

John

7.3%

38.0%

0.100

 

 

 

 

 

 

 

 

 

1944

Margaret

4.5%

31.7%

0.050

John

8.3%

39.9%

0.181

1954

Susan

6.1%

32.5%

0.078

David

6.3%

37.8%

0.112

1964

Susan

3.6%

28.6%

0.022

Paul

5.6%

39.4%

0.073

1974

Sarah

4.9%

28.0%

0.089

Mark

4.6%

33.1%

0.033

1984

Sarah

4.1%

27.3%

0.049

James

4.3%

32.3%

0.021

1994

Emily

3.4%

23.8%

0.023

James

4.2%

28.4%

0.035

Note: See Appendix D and References for sources.

 

The position of Mary in England/Wales exemplifies the high popularity of the most popular names in other English-language populations early in the nineteenth century.  In England/Wales, 21.5% of males born in 1800 were named John, the most popular male name.  In the US, 15.0% and 12.7% of females and males born in 1800-1809 were named Mary and John, respectively, which were the most popular female and male names in those years (see Table 4).  The differences between England/Wales and the US, while deserving further study, might plausibly be related to their much different patterns of settlement and group formation.

 

Over the past two centuries, the most popular names in both England/Wales and the US have become much less popular.   In England/Wales from 1800 to 1994, the popularity of the most popular female and male names fell from 23.9% and 21.5% to 3.4% and 4.2%, respectively.  In the US, the popularity of the most popular female and male names declined from 15.0% and 12.7% to 2.2% and 2.7%.  Given the sharp reduction in average family size over the past two centuries, the extent of consensus in feasible naming choices fell even more than these simple statistics indicate.[60] 

 

Moreover, the total popularity of the ten most popular names, a figure much greater than the popularity of the most popular name, also fell sharply over the past two hundred years.  As Tables 3 and 4 show, in England/Wales and in the US the share of persons bearing the ten most popular names fell by roughly three times or more from about 1805 to about 1995.  Based on the evidence of name popularities, the extent of shared symbolic experiences has decreased significantly over the past two centuries. 

 

Table 4

Most Popular Names in the US

 

 

Females

Males

 

Top

 

Top 10

Top 10

Top

 

Top 10

Top 10

Year

Name

Pop.

Pop.

Info Is

Name

Pop.

Pop.

Info Is

1805

Mary

15.0%

53.7%

0.333

John

12.7%

46.8%

0.262

1815

Mary

14.9%

54.5%

0.320

John

12.3%

48.7%

0.259

1825

Mary

15.8%

55.3%

0.334

John

12.1%

48.5%

0.257

1835

Mary

15.7%

53.4%

0.342

John

11.6%

49.5%

0.242

1845

Mary

16.1%

50.8%

0.346

John

11.5%

50.5%

0.232

1855

Mary

14.6%

47.2%

0.277

John

11.0%

50.4%

0.202

1865

Mary

12.3%

43.2%

0.230

John

10.0%

50.3%

0.195

1875

Mary

10.1%

37.6%

0.209

William

9.1%

46.3%

0.182

1885

Mary

7.6%

32.6%

0.171

William

7.3%

40.1%

0.136

1895

Mary

7.1%

29.6%

0.172

William

6.0%

33.6%

0.111

1905

Mary

6.8%

28.4%

0.164

John

5.0%

29.0%

0.083

1915

Mary

7.9%

30.4%

0.179

John

5.3%

31.0%

0.089

1925

Mary

8.6%

30.0%

0.211

William

5.7%

34.4%

0.130

1935

Mary

7.4%

29.4%

0.144

Robert

6.3%

37.3%

0.122

1945

Mary

5.9%

30.6%

0.081

James

5.8%

37.2%

0.093

1955

Mary

4.5%

28.6%

0.080

Michael

4.5%

35.3%

0.047

1965

Elizabet

4.0%

22.4%

0.070

Michael

4.7%

32.3%

0.055

1975

Christin

3.5%

21.3%

0.079

Michael

4.3%

28.0%

0.035

1985

Christin

3.2%

20.9%

0.042

Michael

3.6%

24.6%

0.037

1995

Christin

2.2%

15.9%

0.018

John

2.7%

18.3%

0.036

Note: The data refer to persons named (born) in the ten years around the year given.  For details and sources, see Appendix D.

 

 

B. An Information-Theoretic Statistic

 

The change in name popularities reflects a change in the shape of the name popularity distribution.  As noted in Section I C above, the name popularity distribution follows a power law.   The reduction in popularity of the most popular name implies a reduction in the intercept of a line approximating the log-rank log-frequency relation.  The popularity of the ten most popular names relates to both the intercept and slope of the approximating line.  As Chart 1 shows, the slope has become flatter over time.  The effect of the change in intercept and slope is such that the ten most popular names cover a smaller share of the population, and changes in name popularities across the ten most popular names are relatively smaller.

 

Information theory provides an insightful alternative to power law approximations in describing the name distribution and changes in it over time.   To understand the relevance of information-theoretic measures, consider the following scenario.  Suppose that all females are named Mary.  Then being told a female’s name communicates no information.  There is complete social consensus about the value of the name Mary, as revealed in actual naming choices, and all females share the experience of being called Mary.  More generally, the social pattern of naming indicates a relatively high amount of common information and shared experience.

 

Now consider a different scenario.  Suppose that all female names are equally likely.  Each name may itself carry significant social meaning, perhaps such as Hilda, a traditional English name; Chastity, a virtue name associated with Puritans; or Brittany, a name with little history but recent prominence.  However, one can do no better than to guess randomly about who is Chastity and who is Brittany.  The popularity distribution of names, an important aspect of the social structure of naming, provides no additional information.   In this sense the information associated with naming is wholly personal.

 

Information-theoretic statistics capturing the above considerations are well known.[61]    Equation 1 defines information statistic Is in terms of name popularities pi.   Is represents the amount of social information associated with the popularity distribution of the most popular ten names.  Is is related to the slope of the popularity distribution; more social information is associated with a steeper slope.

 

(1)    ,  where

 

As a statistic, Is has the advantage of being measured in bits.  Bits have a specific meaning in terms of coding information, and changes in Is represent changes in bits of social information.  In contrast, the popularity of the top name and top ten names are percentages.  They are dimensionless numbers, and an absolute or relative change in percentages is difficult to interpret quantitatively.

 

The trend in social information is similar to the trends in the popularity of the most popular name and the total popularity of the top ten names.  This is not merely an arithmetic tautology.  Since Is depends only on relative name popularities, the popularity of the top name and total popularity of the top ten names could fall sharply while Is remained constant.  In fact, as Tables 3 and 4 show, all three have fallen dramatically.  Over the past two hundred years, Is has fallen by about a factor of ten, with the reduction for females being roughly three times as great as that for males. 

 

Changes in Is over the past two hundred years track the gross shape of changes in the popularity of the top name and the top ten names.  For both England/Wales and the US, all three series have trendless periods of 25-50 years at some point late in the nineteenth to early in twentieth centuries, with the trendless period for Is coming slightly (10-20 years) earlier than the trendless period for the popularity statistics (see Tables 3 and 4).  For the US, all three series show change concentrated in the mid-nineteenth century and in the second half of the twentieth century.  The England/Wales has a roughly similar pattern in the twentieth century, but shows a downward trend throughout the nineteenth century.  The similarities between changes in Is and changes in the name popularity statistics suggests that all these statistics are measuring the same underlying change in the information economy, a change which this paper will call an increase in personalization.

 

 

C. More than a Thousand Years of Information Economy History

 

Additional historical evidence helps to provide insight into name personalization and the information economy.  In early medieval England, personal names consisted of a single word, typically formed from a combination of two elements associated with name-words.  A large number of different personal names could thus be constructed.[62]  The repetition of names among persons related through blood, time, or space could hinder identification or violate the order of the spiritual world.  Repetition of names was not a general practice.[63]   The extensive name personalization that characterizes the late twentieth century US and England/Wales appears to have been also a feature of early medieval England before the tenth century.

 

The disproportionate favoring of a few names seems to have emerged in England during the tenth and eleventh centuries.[64]  Edmund, King of the East Angles late in the ninth century, was widely admired.  Moreover, he was martyred by the Danes.  He may have played an important part in personalizing the position of king and inspiring widespread repetition of his particular name.[65]  The change in the social pattern of names suggests the development of a public sphere in which the value of particular names, and the merits of the king as a person like other persons, were subject to discussion.[66]  Significant economic development also probably occurred in the tenth and eleventh centuries.  By 1066 more than 30% of economic output was marketed, and three-quarters of that entered international trade.[67]  The rise in social information and shared experience in naming thus occurred along with personalized celebrities and a significant volume of commercial transactions.

 

The Norman Conquest of England in 1066 produced a dramatic change in given names.  Within a few generations, most persons used given names brought by the invaders.  By about 1250 pre-Conquest names had essentially died out.[68]  The influx of new names and the shift to them must have decreased the popularity of the most popular names until the new naming practices were well established throughout society.  Thus an increased social flow of information, specifically, the social transmission of a new set of names, can coincide with a reduction in the level of social information and shared experience in naming. 

 

The available evidence indicates that more popular names increased in popularity from about the beginning of the twelfth century through the beginning of the fifteenth century.  Table 5 provides statistics for different areas and from different sources.[69] As the shift away from pre-Conquest names progressed, Norman names became the most popular names, and the popularity of the most popular names increased.[70]  The Black Death in 1347-49 and associated economic hardships appear to have prompted a sharp, further increase in the popularity of the most popular names.[71]  Such a change might indicate social solidarity in reaction to the catastrophe, at least with respect to social information and shared experience in naming.


Table 5

Name Popularity in England before c. 1825

 

 

Females

Males

 

Year, Location

Top Name

 

Pop.

Top 10 Pop.

Top 10 Info Is

Sample

Size

Top Name

 

Pop.

Top 10 Pop.

Top 10 Info Is

Sample

Size

1080, Winchester 1)

 

 

 

 

 

Robert

6.6%

35%

0.16

228

1120, Winchester 1)

 

 

 

 

 

William

6.6%

30%

0.24

912

1180, Winchester 1)

 

 

 

 

 

William

10.2%

57%

0.15

383

 

 

 

 

 

 

 

 

 

 

 

1200, Essex 2)

Alice

11.3%

56%

0.17

c. 1400

William

12.4%

61%

0.18

c. 4000

1210, South 3)

Matilda

16.2%

70%

0.27

173

William

14.4%

65%

0.16

877

1270, Rutland 4)

Alice

19.4%

84%

0.32

206

William

15.2%

76%

0.16

1627

1300, Lincoln 5)

Alice

17.1%

75%

0.32

1213

John

22.7%

79%

0.47

9390

 

 

 

 

 

 

 

 

 

 

 

1260, London 6)

 

 

 

 

 

John

17.6%

69%

0.32

814

1290, London 6)

 

 

 

 

 

John

23.3%

73%

0.42

1852

1510, London 7)

 

 

 

 

 

John

24.4%

74%

0.53

427

1610, London 7)

 

 

 

 

 

John

21.0%

72%

0.36

463

1825, London 8)

Mary

19.2%

73%

0.38

63809

William

16.3%

80%

0.19

48275

 

 

 

 

 

 

 

 

 

 

 

1350, Manchester 9c)

 

 

 

 

 

John

22.7%

92%

0.32

717

1610, Manchester 10)

 

 

 

 

 

John

18.6%

77%

0.21

1298

1805, Manchester 11)

Mary

25.8%

84%

0.34

1866

John

21.7%

81%

0.39

1935

 

 

 

 

 

 

 

 

 

 

 

1350, Yorkshire 9d)

Alice

22.4%

86%

0.30

1794

John

33.5%

93%

0.72

1665

1620, Yorkshire 12)

Ann

24.0%

88%

0.47

342

John

16.2%

86%

0.21

427

1670, Yorkshire 12)

Ann

21.5%

79%

0.69

228

William

18.7%

78%

0.34

283

1720, Yorkshire 12)

Mary

25.7%

87%

0.57

413

John

25.5%

86%

0.52

377

1770, Yorkshire 12)

Mary

22.8%

84%

0.34

381

John

25.6%

86%

0.47

433

1825, Yorkshire 8)

Mary

20.1%

81%

0.35

99299

John

18.8%

79%

0.30

91111

Table 5 (cont’d)

 

 

Females

Males

 

Year, Location

Top Name

 

Pop.

Top 10 Pop.

Top 10 Info Is

Sample

Size

Top Name

 

Pop.

Top 10 Pop.

Top 10 Info Is

Sample

Size

1350, North/Cumbria 9a)

 

 

 

 

 

John

34.5%

89%

0.74

328

1530, North/Cumbria 13)

Jane

16.0%

84%

0.24

852

John

23.1%

74%

0.47

870

1550, North/Cumbria 13)

Margaret

15.6%

86%

0.23

1491

John

21.7%

75%

0.40

1515

1580, North/Cumbria 13)

Margaret

16.8%

84%

0.24

3750

John

18.0%

71%

0.32

3765

1610, North/Cumbria 13)

Elizabet

15.8%

84%

0.26

4000

John

18.2%

74%

0.32

4044

1640, North/Cumbria 13)

Elizabet

16.6%

87%

0.30

2888

John

19.7%

75%

0.40

2914

1670, North/Cumbria 13)

Elizabet

16.5%

86%

0.31

3813

John

19.6%

75%

0.40

3834

1700, North/Cumbria 13)

Ann

16.4%

86%

0.35

3064

John

21.1%

77%

0.44

3070

1730, North/Cumbria 13)

Ann

18.1%

87%

0.36

2038

John

21.6%

80%

0.41

2038

1760, North/Cumbria 13)

Ann

18.8%

89%

0.35

2830

John

23.2%

81%

0.44

2830

1790, North/Cumbria 13)

Mary

19.4%

89%

0.37

2139

John

23.4%

83%

0.48

2141

1825, North/Cumbria 8)

Mary

20.3%

88%

0.29

24857

John

21.8%

85%

0.37

21966

 

 

 

 

 

 

 

 

 

 

 

1350, Hereford 9b)

Alice

21.9%

84%

0.34

576

John

34.8%

89%

0.57

2066

1700, Hereford 14)

 

 

 

 

 

John

20.3%

78%

0.44

931

1825, Hereford 8)

Mary

21.7%

85%

0.47

6832

John

18.9%

90%

0.31

6350

 

 

 

 

 

 

 

 

 

 

 

1280, East Anglia 15)

 

 

 

 

 

John

22.3%

74%

0.45

391

1400, East Anglia 15)

 

 

 

 

 

John

36.1%

90%

0.80

590

 

 

 

 

 

 

 

 

 

 

 

1385, soldiers 19)

 

 

 

 

 

John

28.1%

84%

0.60

829

1550, sailors 16)

 

 

 

 

 

John

21.4%

70%

0.35

583

1560, Canterbury 17)

Elizabet

13.6%

74%

0.18

661

John

20.3%

75%

0.42

5986

1560, Gloucester 18)

Joan

18.7%

88%

0.25

c. 4000

John

21.4%

80%

0.53

c. 4000

Note: The data refer to persons named (born) in a generation about the year given.  For details and sources, see Appendix D.


About the year 1300, a high point of medieval economic development, the popularity of the most popular names was similar to that about the year 1800.[72]  Since the popularity of the most popular names about 1300 is similar to that early in the sixteenth century and the Black Death decreased name personalization, there must have been some increase in personalization across the fifteenth century.[73]  The extent of name personalization shows some fluctuations from early in the sixteenth century to the end of the eighteenth century, but there is no overall trend.  Name personalization for males decreased from the early sixteenth to early seventeenth centuries, while little change occurred for females.[74]  Name personalization subsequently rose for both males and females.  One period of relatively rapid change appears to have been the early to mid seventeenth century.[75]

 

While more research might be able to explicate the social and economic forces shaping these changes, a broad contrast is clear.  Long-term secular changes in agricultural productivity, urbanization, and commercialization before 1800 laid the foundation for the industrial revolution in England.  In contrast, the information economy, at least with respect to naming, changed in a less directed way before the nineteenth century.  Figures of 20%, 80%, and 0.4 are representative for top name popularity, top ten name popularity, and social information Is, respectively, from 1500 to 1800, as well as circa 1300.  As the changes in the most popular female name suggest, this constancy in the extent of name personalization did not reflect stable preferences over a fixed set of names (Mary did not become the most popular female name until the eighteenth century).   The trend toward name personalization over the past two hundred years is a significant change relative to the trend over the previous five hundred years.  Setting aside the name dynamics that the Norman Conquest created, the reduction in popularity of the most popular names over the past two hundred years appears distinctive relative to the previous thousand years of naming history.

 

 

D.   Further Insights from Disciplined Description

 

Important changes in communications technology in England before 1800 did not generate lasting changes in naming.   The spread of printing presses has been described as a key agent of a “massive and decisive cultural ‘change of phase’ that occurred five centuries ago.”[76]  The growth of broadsides from early in the sixteenth century and the growth of large public meetings from the eighteenth century were also important communications developments in England.[77]  Yet statistics on name popularities suggest that these developments were not sufficient to change the amount of information in the over-all distribution of names, i.e. the extent of shared symbolic experience in naming. 

 

In both England/Wales and the US, the growth of mass media also did not drive changes in the extent of shared symbolic experiences in naming.  Dramatic increases in name personalization were occurring by the mid-nineteenth century, before radio, and television, and large newspaper companies.  In contrast, the popularity of the top name, the popularity of the top ten names, and social information Is all changed little in the first half of the twentieth century, a period in which the newspaper and magazine business grew significantly in scale and scope.  Overall, the end of the twentieth century features the production and distribution of common packages of symbols on a scale scarcely imaginable as little as a century ago.  It also features name personalization to an extent unprecedented in at least a millennium. There does not, however, appear to be a strong connection between these two contemporary features of the information economy.  At least with respect to names, mass media appear to be relatively unimportant in shaping the extent of shared symbolic experience.

 

The similarity of developments in naming in the US and England/Wales over the past two hundred years should be appreciated in light of important differences between the countries.  First, consider geography.  The England/Wales consists mainly of a small island with many settlements that have a long and relatively continuous cultural history.  The US spans a large continent in which long-distance immigrants established many new settlements.  Second, consider population. The population of the US was about 70% of the population of England and Wales in the early nineteenth century, but it grew to five times the population of England and Wales by the end of the twentieth century.  Third, consider economic growth.  The England/Wales was the first industrial country, and it had acquired a global empire by the end of the nineteenth century.  At that point, the US, relative to England/Wales, was a less developed economy.  In the twentieth century the US grew much faster than England/Wales, and the US become the world’s leading economy. 

 

Given these differences, the similarity of personalization trends in the US and England/Wales is significant.  Personalization appears to be an aspect of personal preferences relatively invariant to population size, geography, and income.  Further research might explore what caused the trend of increasing personalization.  This paper merely provides a historical description[78]: a new trend toward personalization appears as a major change about the early nineteenth century, at least among persons sharing the English language and much European culture.

 

 

III. Trends in Effective Communication

 

For the past three decades or longer, industrial-scale symbolic production and distribution has been thought to be reshaping information economies, along with all of society.  In 1969 the dean of a leading US school of communication declared:

In only two decades of massive national existence television has transformed the political life of the nation, has changed the daily habits of our people, has molded  the style of the generation, made overnight global phenomena out of local happenings, redirected the flow of information and values from traditional channels into centralized networks reaching into every home.  In other words, it has profoundly affected what we call the process of socialization, the process by which members of our species become human.[79]

Names such as the “age of information” (1971), “post-industrial society” (1973), “information revolution” (1974) and “communications age” (1975) show recognition of the importance of symbol production.[80]  The novelty of these phenomena, however, remains contested through the contest over naming.  As one analyst noted in the mid 1990s:

Contemporary culture is manifestly more heavily information laden than any of its predecessors.  We exist in a media saturated environment….Experientially this idea of an “information society’ is easily enough recognised, but as a definition of a new society it is considerably more wayward  than any of the notions we have considered.[81]

From this perspective, a lack of attention to the moral dimension or “master idea” of daily life deprives information of meaning and purpose.[82]  This lacuna “must make one deeply sceptical of the ‘information society’ scenario (while not for a moment doubting that there has been an extensive ‘informatisation’ of life)….”[83]  

 

Suppose, however, that one suffers from doubts that direct personal experience does not assuage, and one seeks systematic quantitative evidence concerning the extent to which communication has changed.  Unfortunately there is no standard approach to quantifying communication from an aggregate, socio-economic perspective.  About forty years ago a scholar put forward an extensive theory for analyzing urban growth in terms of information flows.  He defined the term “hubits,” a bit of information received by a single human being, to enable aggregation of information flows across persons.[84]  Information flows were predominately associated with media, and reading was estimated to communicate more than five times as much information as talking and observing the environment.  A study fifteen years ago constructed for Japan and the US a census of communication and attempted to measure the supply and consumption of media in word equivalents.[85]  Total words consumed were estimated to be only a few percent of total words supplied.  A recent study estimated US and world production of information in bytes, and estimated as well as the total amount of information stored in media such as paper, film, and optical and magnetic disks.[86]  This study did not explore the significance of the measured information or how it relates to actions.

 

For insights into communication that has been very socio-economically significant, consider how Islam, Judaism, and Christianity express God’s communication with humanity.  In Islam, God’s message refers to itself as the Qur’an and the Book, the former term associated with recited words and the latter with written words.  Communication is a call for response and change:  “O you, who believe, respond to Allah and His messenger as they call you to that which gives you life.”[87]  The response concerns not merely the mind and consciousness, but also a physical change:

Allah has revealed the most beautiful message in the form of a book…The skins of those who fear their Lord tremble because of it; then their skins and their hearts do soften to the remembrance of Allah…[88] 

Hebrew scriptures also emphasize that the word of God effects intentional change in the world.  According to Isaiah, the Lord declares:

As the rain and the snow come down from heaven, and do not return to it without watering the earth and making it bud and flourish, so that it yields seed for the sower and bread for the eater, so is my word that goes out from my mouth: It will not return to me empty, but will accomplish what I desire and achieve the purpose for which I sent it.[89]

For Christians, sacred communication has been actualized in a person:

In the beginning was the Word, and the Word was with God, and the Word was God….The Word became flesh and made his dwelling among us. We have seen his glory, the glory of the One and Only…[90]

Islam, Judaism, and Christianity are all known as religions of “people of the book,” the book that is revered as a key source of knowledge about the same, single God.  In these religions, however, communication means much more than reading.  Communication is broadband multimedia of the most fantastic sort, and God’s word is understood to be intimately related to changes in persons and the world.[91]

 

Changes in the popularity of specific personal names indicate effective communication in this broad sense: changes in symbols personally enacted, incarnated, and embedded in ongoing personal interactions.  Physical travel of persons, interpersonal oral networks, performers and performances, and media such as books, newspapers, and television all potentially influence the diffusion of names and knowledge about their values.  Changes in occupational structures, urban structures, religious beliefs, and family patterns also affect naming norms, and hence can be consider part of communication in a broad sense.  Quantifying communication as messages or media growth points to further difficult research into the effects of messages or media.  Quantifying communication as changes in important symbolic choices provides a more direct description of the information economy.

 

This quantification of communication also identifies communication as change in relation to a specific, historical order defining a related type of information.  The previous section explored the patterns of name popularity that exist independent of the specific names that constitute the pattern.  These patterns were interpreted as expressing social information associated with naming, and changes in social information in this sense have occurred over time.  Changes have also occurred in the particular names that configure these patterns.  This section explores those changes.[92]

 

 

A. Measuring Effective Name Communication

 

One measure of effective name communication is the number of new names that appear on successive lists of most popular names.  For example, consider lists of the ten most popular names in a population in 1950 and 1960.  One could count how many names appear on the 1960 list but not on the 1950 list.  An important advantage of this statistic is that it can be calculated using only short name popularity rankings.  Studies of names thus need only communicate a small amount of information in order to make such calculations feasible for scholars in different times and places.

 

The number of new names can be divided by the number of years separating the compared lists to compute name turnover per annum.  A recent study uses this statistic.  It computes turnover per annum based on name lists ranging from the top 10 to the top 50 most popular names, and on intervals between lists that appear to range from 2 years to 100 years.[93]  Turnover per annum is a better statistic than the number of new names if turnover per annum reflects some interpretable model of continual change that encompasses the number of new names measured over different time intervals.  This does not appear to be the case.

 

The number of new names has major weaknesses as a statistic, and turnover per annum exacerbates these weaknesses.  The number of new names is a rank-based measure that does not consider the magnitude of changes in name popularities.   Limiting in this way the information considered makes sense only given a clear understanding of error processes that justify such a limitation.  Moreover, the number of new names is not independent of the size of the list, the time between lists, or, given any additive error in popularities, the popularity of names on the list.  In addition, the probability of an additional new name appearing on a list is directly related to the number of new names that already appear on the list.  The significance of all these issues is obscured in the turnover per annum statistic, which does not fix list size and averages the number of new names across a varying number of years.[94]

 

Information theory points to better statistics for measuring changes in name popularity.  Consider lists of name popularities in year 1 and in subsequent year 2.  Let  represent the popularities of the most popular names in year 2 for lists from years i=1,2 and name ranks j=1,…10.  Let  and  be the total popularity in year 1 and year 2, respectively, of the ten most popular names in year 2:

(2)     

Define communication statistic  as the amount of information associated with the change to the popularity of the ten most popular names and “other names”:

(3)   

, which has units of bits, is always non-negative and is zero if and only if .

 

As a communication statistic,  has some general limitations.  It measures differences between name probabilities at two points in time, not the flow of information that changes name probability.  Thus if from year 1 to year 2 the name Mary gains popularity and then loses exactly the same amount of popularity,  will measure no communication.  Moreover, in some cases .  For a third year with name probabilities , it may also be the case that .  These properties suggest that it is not useful to compute an annual rate of change based on .  Instead,  should be calculated with respect to clearly specified years and interpreted in conjunction with other evidence regarding changes in name popularities.

 

 also has some limitations related specifically to name popularity.  As Table 1 shows, the total popularity of the ten most popular names has changed significantly over the past two hundred years.  measured across a decade in the beginning of the nineteenth century will differ from  measured across a decade at the end of the twentieth century partly because much different weights are attached to the “other names” category. 

 

To avoid the effects of such changes in the “other names” category, probabilities can be normalized across the top ten names.  Define communications statistic :

(4)   

 has properties like that of , but  measures the amount of information associated with changes to the relative popularities of the ten most popular names in year 2.  does not depend on the popularity of the “other names” category.

 

While  and  are forms of a well-recognized information-theoretic statistic,[95] one of a slightly different type offers an insightful alternative.  Note that

(5)   

because the ten most popular names in year 1 may be different from the ten most popular names in year 2.  Define communication statistic  by replacing with  in equation (4) above.  Thus

(6)  

 

 has some appealing properties.  From equations (4) and (5), , and if the top ten names are the same in year 1 and year 2,  = rises above as the popularity share of new names in the top ten names increases.  Thus  measures increased popularity associated with new names as increased communication relative to measure .  Now consider the total popularity of the top ten names.  If it doesn’t change from year 1 to year 2, .  Then , for small changes in name popularities, approximates a weighted average of the proportional changes in the top ten name popularities.   indicates more communication relative to that weighted average if probability flows out of the top ten names from year 1 to year 2, and less if the reverse occurs.  Thus associates increased name personalization with increased communication. This association is reasonable in light of the aggregate trends described in Section II. 

 

To supplement the above information-theoretic statistics, this paper will also consider an average deviation statistic.  Define communication statistic :

(7)  

is the weighted average absolute percentage change in name popularity for the ten most popular names in year 2.   

 

While the number of new names and communication measure are useful supplements, this paper will focus primarily on information-theoretic measures of communication.  The nature of these statistics, which provide measurements in bits, can be understood as follows.  Suppose that a person in year 2 knows which names are the ten most popular names, but she knows the popularity of these names only as of year 1.  , , and  measure, with some variations in scope, the amount of information communicated to her by informing her of the popularities of these names in year 2.  This relatively general interpretation of  , , and , and their meaningful scale, may help them to contribute to more general understanding of the development of the information economy.

 

To allow useful compilation and analysis of different studies, the communication statistics , , , and require more extensive reporting from name studies than short name popularity rankings.  Calculating these statistics requires information on the popularities on the ten most popular names taken from a different sample.  Communicating widely large lists of name popularities is not practical using print, but this can be done easily using the Internet.  For many of the name samples analyzed here, sufficient name information has been made available on the Internet for additional studies to calculate the statistics , , , and with respect to new name samples.[96]  Other scholars working with name statistics should recognize the importance of this reporting practice for promoting the development of knowledge.

 

 

B. The Shape of Change

 

Over ten year periods covering the past two centuries, effective communication of new names has changed most dramatically roughly from the beginning of World War I to the end of World War II.   Tables 6 and 7 present name communication statistics covering England/Wales and the US over the past two centuries.  These statistics show considerable decade-to-decade variation, as well as differences among the different statistics.[97]  For both males and females in the US, a general trend increase in effective communication appears to have occurred from 1915 to 1945.  The data for England/Wales is more fragmentary.  For England/Wales females and males, name communication across decades increased significantly sometime between 1880 and 1944.  Evidence from twenty-five year intervals suggests that the increase happened between 1900 and 1950, with the increase coming later and more sharply for males than for females.  These measures suggest that information economies were transformed in the first half of the twentieth century, rather than with the growth of mass media in the second half of the twentieth century.


 

Table 6: Name Communication Statistics for England/Wales

 

Females

Males

 

Ending

Year

 

C1

 

C2

 

C3

 

C4

New

Top 10

 

C1

 

C2

 

C3

 

C4

New

Top 10

Ending

Year

 

comparisons across 10-year intervals

 

1810

0.0063

0.0044

0.0065

6%

1

0.0110

0.0087

0.0176

10%

1

1810

1820

0.0055

0.0039

0.0130

7%

1

0.0037

0.0039

0.0039

6%

0

1820

1830

0.0111

0.0147

0.0249

9%

1

0.0042

0.0026

0.0026

6%

0

1830

1840

0.0075

0.0097

0.0097

8%

0

0.0021

0.0010

0.0074

4%

1

1840

1850

0.0080

0.0065

0.0065

9%

0

0.0030

0.0013

0.0013

4%

0

1850

1860

0.0156

0.0200

0.0475

13%

2

0.0111

0.0103

0.0243

10%

1

1860

1870

0.0250

0.0137

0.0137

15%

0

0.0196

0.0159

0.0330

14%

1

1870

1880

0.0380

0.0558

0.1063

27%

2

0.0096

0.0050

0.0050

10%

0

1880

 

comparisons across 25-yer intervals

 

1875

0.3558

0.6486

0.8787

464%

4

0.0856

0.1179

0.1655

45%

2

1875

1900

0.4002

1.0401

1.4859

4237%

4

0.0342

0.0236

0.0654

20%

2

1900

1925

0.5482

1.3097

1.7903

2509%

5

0.1772

0.4475

0.6923

217%

3

1925

1950

1.0529

2.3962

3.5065

3709%

7

0.6538

0.8054

2.0464

723%

8

1950

1975

0.7826

1.2914

3.7448

2753%

9

0.4838

1.2094

1.9572

989%

6

1975

 

comparisons across 10-year intervals

 

1954

0.0769

0.2322

0.2822

69%

1

0.1361

0.3573

0.5100

151%

3

1954

1964

0.1887

0.5729

1.2198

234%

6

0.1115

0.2582

0.4197

103%

5

1964

1974

0.2196

0.2984

1.5670

282%

9

0.0287

0.0756

0.1853

31%

3

1974

1984

0.1769

0.5852

1.0152

291%

4

0.0348

0.1042

0.2252

39%

3

1984

1994

0.0903

0.3096

0.9310

120%

6

0.2014

0.5368

1.3947

326%

7

1994

 

 

 

 

 

 

 

 

 

 

 

 

1800-1880 ave.

0.0146

0.0161

0.0285

12%

0.9

0.0080

0.0061

0.0119

8%

0.5

 

1944-1994 ave.

0.1505

0.3997

1.0030

199%

5.2

0.1025

0.2664

0.5470

130%

4.2

 

Ratio

10

25

35

17

6

13

44

46

16

8

 

 

Table 7: Name Communication Statistics for the US

(on a decade-to-decade basis)

 

Females

Males

 

Ending Decade Midpoint

 

C1

 

C2

 

C3

 

C4

New

Top 10

 

C1

 

C2

 

C3

 

C4

New

Top 10

Ending

Dec.

1815

0.0031

0.0052

0.0107

6%

1

0.0061

0.0088

0.0280

11%

1

1815

1825

0.0054

0.0094

0.0094

9%

0

0.0037

0.0076

0.0076

6%

0

1825

1835

0.0026

0.0028

0.0028

5%

0

0.0018

0.0030

0.0030

6%

0

1835

1845

0.0064

0.0124

0.0365

10%

1.5

0.0039

0.0048

0.0174

7%

1

1845

1855

0.0164

0.0319

0.0353

18%

1

0.0075

0.0140

0.0304

10%

1

1855

1865

0.0136

0.0217

0.0278

16%

1

0.0050

0.0099

0.0099

11%

0

1865

1875

0.0118

0.0113

0.0475

15%

1.5

0.0074

0.0027

0.0027

10%

0

1875

1885

0.0098

0.0114

0.0357

14%

1

0.0154

0.0124

0.0361

16%

1

1885

1895

0.0058

0.0135

0.0450

12%

1

0.0151

0.0084

0.0145

17%

1

1895

1905

0.0038

0.0127

0.0345

12%

2

0.0089

0.0070

0.0070

14%

0

1905

1915

0.0063

0.0171

0.0286

12%

1

0.0051

0.0157

0.0157

11%

0

1915

1925

0.0326

0.1072

0.1940

42%

2

0.0161

0.0374

0.0752

23%

1

1925

1935

0.0818

0.2482

0.5404

93%

4

0.0135

0.0277

0.0416

20%

2

1935

1945

0.1505

0.4070

0.8016

213%

5

0.0354

0.0902

0.1929

38%

2

1945

1955

0.1342

0.4662

0.6515

208%

2

0.0279

0.0787

0.1267

34%

1

1955

1965

0.0574

0.2513

0.4955

72%

5

0.0111

0.0320

0.0885

17%

2

1965

1975

0.0719

0.3149

0.6161

107%

5

0.0803

0.2866

0.4712

134%

4

1975

1985

0.0909

0.3945

0.7494

205%

5

0.0182

0.0737

0.2297

29%

3

1985

1995

0.0195

0.1200

0.4298

43%

4

0.0193

0.0626

0.1888

32%

2

1995

 

 

 

 

 

 

 

 

 

 

 

 

1805-1915 ave.

0.0077

0.0136

0.0285

12%

1.0

0.0073

0.0086

0.0157

11%

0.5

 

1945-1995  ave.

0.0748

0.3094

0.5885

127%

4.2

0.0313

0.1067

0.2210

49%

2.4

 

Ratio

10

23

21

11

4

4

12

14

5

5

 

 


The amount of effective communication has risen significantly relative to the amount of information in the naming distribution. As noted above,  and  provide measures in bits, and these statistics are not affected by the secular fall in the popularity of the ten most popular names.  Hence these statistics probably provide the most useful long-term comparisons.  They show that effective communication increased by about factors of 22 and 13 for US females and males, respectively, and by factors of 30 and 45 for England/Wales females and males, respectively.[98]  In contrast, the amount of social information in the naming distribution decreased by factors of  10 and 15 for US females and males, respectively, and factors of 5 and 10 for England/Wales females and males, respectively.[99]  In absolute terms, in the beginning of the nineteenth century the amount of information in the naming distribution was large relative to changes in names on a decade-to-decade basis.  At the end of the twentieth century the opposite is true.

 

The magnitude of effective name communication differs significantly between the US and England/Wales, and between females and males.  At the end of the twentieth century effective name communication in the US averages about one-half as much as that in England/Wales.[100]  This difference might be attributable to factors traditionally considered significant  to communication:  the US has a larger, more geographically dispersed and more culturally heterogeneous population than England/Wales.  Effective communication of new names is also about two to three times higher for females than for males.  This might reflect more familial inertia in men’s names, perhaps resulting from more importance being attached to naming males after older male kin.  The difference might also indicate more extensive social networks among females and their value in effective communication.  

 

Effective communication differs significantly over different time horizons.  Table 8 shows name communication statistics over periods of a hundred years or longer.  In the US name communication in the twentieth century was roughly twice that in the nineteenth.  For England/Wales, female name communication seems to have fallen from the nineteenth to the twentieth century, while male name communication shows no clear trend.  These figures contrast sharply with increases by factors of 13 to 45 for name communication on a decade-to-decade basis.  Effective name communication does not seem to aggregate simply; more change in the short term does not necessarily imply more long-term change.[101]  From a long-term perspective, slow and steady change can be equivalent to rapid short-term change that dies out or cycles over time.[102] 


 

Table 8

Name Communication Over a Century or Longer

 

 

Females

Males

 

Time Span, Place

 

C1

 

C2

 

C3

 

C4

New Top 10

 

C1

 

C2

 

C3

 

C4

New Top 10

1066-1166, Conquest Model

 

 

 

 

 

1.083

 

4.117

982%

10

 

 

 

 

 

 

 

 

 

 

 

1080-1180 Winchester

 

 

 

 

 

0.508

0.377

2.303

367%

5

 

 

 

 

 

 

 

 

 

 

 

1290-1510, London

 

 

 

 

 

0.446

0.510

0.804

658%

5

1510-1610, London

 

 

 

 

 

0.294

0.406

0.518

236%

3

1610-1825, London

 

 

 

 

 

0.312

0.292

0.433

112%

3

 

 

 

 

 

 

 

 

 

 

 

1350-1610, Manchester

 

 

 

 

 

1.058

1.379

1.647

2025%

4

1610-1805, Manchester

 

 

 

 

 

0.235

0.263

0.335

92%

2

 

 

 

 

 

 

 

 

 

 

 

1350-1620, Yorkshire

4.120

3.943

4.959

12660%

6

1.413

1.640

1.799

3819%

5

1620-1720, Yorkshire

0.353

0.374

0.507

320%

3

0.107

0.084

0.233

36%

2

1720-1825, Yorkshire

0.364

0.445

0.504

192%

3

0.415

0.525

0.665

202%

3

 

 

 

 

 

 

 

 

 

 

 

1350-1525, North/Cumbria

 

 

 

 

 

0.341

0.438

0.586

255%

5

1525-1625, North/Cumbria

0.216

0.200

0.330

72%

1

0.030

0.035

0.047

21%

3

1625-1725, North/Cumbria

0.260

0.271

0.373

347%

2

0.058

0.055

0.099

26%

2

1725-1825, North/Cumbria

0.068

0.075

0.088

31%

1

0.081

0.071

0.098

28%

1

 

 

 

 

 

 

 

 

 

 

 

1350-1700, Hereford

 

 

 

 

 

0.939

1.180

1.500

3182%

4

1700-1825, Hereford

 

 

 

 

 

0.189

0.123

0.145

52%

3


 

Table 8 (continued)

 

 

Females

Males

 

Time Span, Place

 

C1

 

C2

 

C3

 

C4

New Top 10

 

C1

 

C2

 

C3

 

C4

New Top 10

 

 

 

 

 

 

 

 

 

 

 

1280-1400, East Anglia

 

 

 

 

 

0.198

0.038

0.110

38%

3

 

 

 

 

 

 

 

 

 

 

 

1800-1900, England/Wales

1.577

3.915

4.512

21603%

7

1.280

2.324

2.629

12548%

5

1850-1954, England/Wales

0.976

2.965

3.876

135285%

6

0.838

1.899

3.781

1808%

8

1900-1994, England/Wales

0.463

1.132

3.556

5587%

10

0.837

2.295

4.612

11068%

8

 

 

 

 

 

 

 

 

 

 

 

1805-1905, US

0.448

1.513

2.062

1753%

5

0.223

0.693

1.031

297%

5

1855-1955, US

0.752

2.735

3.845

8952%

8

0.268

0.755

1.224

207%

5

1895-1995, US

0.467

3.276

4.054

43436%

8

0.320

1.674

3.065

1386%

7

 


Across a century simple forms of communication can be astonishingly powerful.  When the Normans conquered England in 1066, the inhabitants of England rapidly adopted Norman names.  Table 8 shows that effective communication of new names following the Norman Conquest was about the same magnitude as that over the twentieth century in England and Wales.   The Conquest probably was a powerful impetus to listen, observe, discuss, and travel, particularly given that the conquerors were well-received.[103]  The figures for London suggest that subsequent rates of name communications in the Middle Ages and early modern period were significantly lower than in the nineteenth and twentieth centuries (for additional data, see Appendix A, Table A1).   The Norman Conquest appears to have spurred an exceptional amount of name communication for its time.  Yet there is nothing exceptional about the normal, personal means of communication that brought about the dramatic symbolic change.

 

 

C. Talking about Communication

 

Effective communication concerns not only media and messages but also personal activity and personal relationships.[104]  The most dramatic increase in effective communication of names on a decade-to-decade basis occurred between 1915 and 1945, when mobilization for world wars stirred persons into many new types of activities and reshaped a wide range of organizations.[105]   Effective communication of female names has differed significantly from effective communication of male names, a fact consistent with the importance of sex to personal relationships and lived experiences.  Over a century, even dramatic differences in social organization and technology may not matter.  Human nature, including the capacities of the human body, is such as to enable actions and create personal relationships that can support in widely differing circumstances similar levels of effective communication.[106]

 

An important aspect of actions and personal relationships is the scope of communicative peers.  By the beginning of the sixteenth century, dominant political, commercial, and religious elites had established among themselves a communications system such that, in two weeks, a message could cross the length and breadth of Europe.[107]   But in the Middle Ages communication also occurred in less contrived ways among a much wider set of communicative peers:

…gentry and higher clergy traveled constantly about, and with considerable trains, bringing glimpses and whispers of new fashions to the peasantry; pilgrims set off, and sometimes returned, while others passed through on their way to or from distant shrines; townspeople saw and heard foreign traders of all sorts, and many of them went on trading voyages of their own; peasants often had carrying-services to perform, …no doubt returning full of the novelities seen and heard on the way.  Thus, townspeople would have been well up-to-date with fashions, and not even the humblest villagers need have been quite unaware of the great world and of its ways of thinking and behaving.[108]

This much wider set of peers allowed communication to scale better to cover large areas.   In the words of a contemporary observer, “Tongues carry tales to every place/Much faster than a coach could race.”[109]

 

While accounts of the spread of Luther’s Ninety-Five Theses emphasize the impact of printing technology, that historical incident also shows the power of peer-to-peer networking.  When Luther issued his theses in 1517, they were said to have been known throughout Europe in a month.[110]   In that amount of time an official communication, under good circumstances, might have traveled from Leipzig to Rome to Paris and on to London, leaving of course many other European towns without the information.[111]  Peer-to-peer networking among a new group of peers played a key role in quickly spreading Luther’s theses:

The educated élite who could understand Latin and theological debate was no longer composed only of churchmen and professors.  [Luther’s theses] were initially read by a small group of learned laymen who were less likely to gather on the church steps than in urban workshops where town and gown met to exchange gossip and news, peer over editors’ shoulders, check copy and read proof.  There, also, new schemes for promoting bestsellers were being tried out.[112]

Without any central direction and using only existing social networks and organizations, Luther’s theses were translated from Latin into multiple vernaculars, printed in multiple towns, promoted at newsstands, and sold by peddlers.[113]   If only the Roman Catholic Church ordered the printing and distribution of Luther’s theses, Luther’s arguments probably would have been no more widely communicated than the decrees and canons of the Council of Trent.[114]

 

 

IV. Conclusions

 

Scientific study of personal names provides important insights into personalization and effective communication.  A strong trend toward name personalization developed at the beginning of the nineteenth century, before the rise of mass media.  Moreover, effective communication of names has always been strongly related to actions and personal relationships.  The Norman Conquest, sex differences, and world wars have significantly shaped effective communication of names.  Increased personal exposure to television and the growth of large media companies in the second half of the twentieth century have not dramatically changed trends in personalization and effective communication.[115]  

 

Other evidence in addition to personal names also points to the importance of personalization and its likely growth in the future.   Spending on person-to-person communication is much greater than spending for content not produced for a specific person.[116]  E-mail and instant messaging, which primarily involve personalized content produced on a non-commercial basis, are widely considered to be the Internet services that persons value most highly.[117]  Brand proliferation is a rudimentary form of personalization associated with fee-simple transactions for goods and low bandwidth feedback from consumption to production.   While there is evidence of increasing brand proliferation, the growth of more complex service transactions and the development of Internet technologies point toward much more extensive personalization.[118]  In fact, firms are investing significantly in technologies to personalize interactions with their customers, and much of the marketing literature now focuses on ways to build personal relationships with customers.[119]

 

The trend toward personalization has important implications for communications policy.  Given that new, diverse, personalized media are likely to create new personal activities and drain personal attention and spending from mass media, the balance of traditional anxieties over diversity versus national unity is likely to shift toward national unity.[120]   Moreover, network and local spot television, the traditional primary channels of political competition, will present a shrinking share of relatively inert media consumers while confronting increasingly acute strains related to access and campaign financing.[121]  Since attempts to regulate shared symbolic experiences through mass media are impractical and ineffective, and these facts are becoming more apparent, the net effect is likely to be an increase in fear, uncertainty and doubt about the future of communications.[122]  Communications policy needs to confront directly such anxieties and to ensure that the value of peer-to-peer information creation and communication is widely experienced.[123] 

 

The evidence in this paper suggests that economic theory needs to focus more attention on relationships among persons.  Information flows depend significantly on personal contacts, even in situations in which advanced information and communications technology is available.    The discipline of psychology has helped to enrich economic models of individual decision-making.  Social psychology and sociology might similarly provide insights into information flows and the network economics of communicative ties among persons.  Study of personal networks in an important sense provides the micro-foundations for study of organizations that are increasingly understood as networks of capabilities, rather than as units engaging in anonymous market transactions of a well-specified and narrow sort.

 

The growth of personalization also has important implications for statistical agencies and for the measurement and monitoring of macro-economies.  Changes in the quality and range of goods and services present major challenges to conventional national accounting.  The growth of personalized services, customized pricing, highly malleable information goods, and relationship-oriented transactions greatly magnifies these challenges.[124]  To help address these developments, statistical agencies need to develop new statistics different from those traditional associated with production and distribution of goods.  Personal names offer the advantage of already being collected through major government statistical programs.   Names, however, are not currently being compiled and analyzed systematically to measure and monitor developments in the information economy.   Such analysis potentially could contribute at low cost to understanding better the impact of information technology and the development of a “new economy.”


References

 

Primary Data Sources

 

Barlow, Frank, Martin Biddle, Olof von Feilitzen, and D.J. Keene, with contributions from T.J. Brown, H.M. Nixon, and Francis Wormald, Winchester in the Early Middle Ages: An Edition and Discussion of the Winton Doomesday (Oxford: Clarendon Press, 1976) Table 8, p. 187 (source 1).

 

Benicoeur, Arval (Josh Mittleman) and Talan Gwynek (Brian M. Scott), “A List of 15th Century English Men’s Names,” compiled from “The Agincourt Honor Roll,” Family Chronicle, March/April 1997 (online at http://www.familychronicle.com/agincort.htm), [compilation online at http://www.s-gabriel.org/names/arval/agincourt/ ] (source 19).

 

de Bracton, Nicolaa (Susan Carroll-Clark), “Anglo-Norman Names,” compiled from the Essex Feet of Fines, 1182-1272 [compilation online at http://www.panix.com/~mittle/names/nicolaa/anglonorman.html ] (source 2).

 

Crompton, Frank, Director of Research, History Department, University College Worcester,  transcription of given names in eighteenth century Herefordshire poll books, electronic dataset made available to the author via personal communication (source 14).

 

Dunkling, Leslie Alan, First Names First (New York: Universe Books, 1977) pp. 186-95.

 

Dunkling, Leslie Alan, The Guinness Book of Names, 7’th ed (New York: Facts on File, 1995) pp. 57-77.

 

Ekwall, Eilert, ed., Two early London subsidy rolls, with introd., commentaries, and indices (Lund: C.W.K. Gleerup, 1951) pp. 34-37 (source 6).

 

Elspeth, Mari nic Bryan (Kathleen M. O’Brien), “An Index to the 1332 Lay Subsidy Rolls for Lincolnshire, England,” compiled from the transcription of Postles, Dr. D.A., Lincolnshire Lay Subsidy Rolls (online at http://www.le.ac.uk/elh/pot/lincscon.html); Elspeth compilation version 1.2 (updated 10 April 2000) [online at

http://www.s-gabriel.org/names/mari/LincLSR/ (source 5).

 

Elspeth, Mari nic Bryan (Kathleen M. O’Brien), “Names of Members of the Frobisher Voyages, 1576-1578,” compiled from Fitzhugh, William W. and Jacqueline S. Olin, Archeology of the Frobisher voyages (Washington: Smithsonian Institution Press, 1993) Appendix 1 [compilation online at http://www.s-gabriel.org/names/mari/frobisher/ ] (source 16).

 

Elspeth, Mari nic Bryan (Kathleen M. O’Brien), “Naming Practices in 16th Century Gloucestershire,” compilation from parish registers of marriage, birth and death (compilation online at http://www.s-gabriel.org/names/mari/GlocNamesPractices/ ) (source 18).

 

Fenwick, Carolyn C., The Poll Taxes of 1377, 1379 and 1381, Records of Social and Economic History, New Series 27 (Oxford: Oxford University Press, 1998).

 

Galbi, Douglas, “Given Names in Carlisle, Cumberland, 1377,” compiled from Fenwick (1998) (online at http://www.galbithink.org/names/engb1800.htm) (source 9a).

 

Galbi, Douglas, “Given Names in Herefordshire, 1379,” compiled from Fenwick (1998) (online at http://www.galbithink.org/names/engb1800.htm) (source 9b).

 

Galbi, Douglas, “Given Names in Salford Hundred, Lancashire, 1381,” compiled from Fenwick (1998) (online at http://www.galbithink.org/names/engb1800.htm) (source 9c).

 

Galbi, Douglas, “Persons Working in Cotton Factories about Manchester 1818-19,” compiled from House of Lords Papers, vol. 96 (1818) and vol. 110 (1819) (online at http://www.galbithink.org/names/manfacdd.htm) (source 11).

 

Galbi, Douglas, “Names in the Rutland Lay Subsidy 1296/7,” compiled from the transcription of Postles, D.A., Rutland Lay Subsidy 1296/7 (online at http://www.le.ac.uk/elh/pot/ruthome.html) (online at http://www.galbithink.org/names/engb1800.htm) (source 4).

 

Galbi, Douglas, “Names in the Protestation of 1642 in Manchester,” compiled from The Palatine Note-book (1881) (online at http://www.galbithink.org/names/engb1800.htm) (source 10).

 

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Appendix A

 Additional Data on Name Communication in England Before 1825

 

Table A1

 

 

Females

Males

 

Time Span, Place

 

C1

 

C2

 

C3

 

C4

New

Top 10

 

C1

 

C2

 

C3

 

C4

New

Top 10

1080-1120, Winchester

 

 

 

 

 

0.027

0.091

0.297

36%

3

1120-1180, Winchester

 

 

 

 

 

0.463

0.248

0.506

317%

5

1200,1210; Essex, South

0.340

0.224

0.535

136%

4

0.036

0.041

0.060

25%

1

1210,1270; South, Rutland

0.258

0.075

0.280

57%

4

0.127

0.094

0.143

47%

2

1270,1300; Rutland, Lincoln

0.139

0.184

0.324

67%

4

0.179

0.178

0.313

52%

2

1260-1290, London

 

 

 

 

 

0.028

0.026

0.044

20%

1

 

 

 

 

 

 

 

 

 

 

 

1620-1670, Yorkshire

0.112

0.141

0.279

53%

4

0.065

0.039

0.039

20%

0

1670-1720, Yorkshire

0.258

0.225

0.282

119%

3

0.102

0.050

0.110

33%

2

1720-1770, Yorkshire

0.096

0.110

0.126

42%

2

0.103

0.120

0.124

32%

1

1770-1825, Yorkshire

0.192

0.237

0.323

103%

2

0.222

0.281

0.390

127%

2

 

 

 

 

 

 

 

 

 

 

 

1550-1580, North/Cumbria

0.014

0.013

0.013

9%

0

0.014

0.013

0.013

12%

0

1580-1610, North/Cumbria

0.056

0.047

0.154

26%

1

0.011

0.012

0.012

11%

0

1610-1640, North/Cumbria

0.039

0.034

0.057

20%

1

0.007

0.008

0.008

10%

0

1640-1670, North/Cumbria

0.015

0.018

0.019

12%

1

0.013

0.016

0.050

9%

2

1670-1700, North/Cumbria

0.012

0.013

0.026

12%

1

0.006

0.006

0.018

8%

2

1700-1730, North/Cumbria

0.013

0.013

0.026

10%

1

0.009

0.006

0.012

5%

2

1730-1760, North/Cumbria

0.014

0.015

0.015

9%

1

0.006

0.006

0.009

6%

1

1760-1790, North/Cumbria

0.016

0.014

0.040

12%

1

0.012

0.008

0.046

9%

2

1790-1825, North/Cumbria

0.042

0.048

0.065

22%

2

0.033

0.035

0.039

18%

1

 

 

 

 

 

 

 

 

 

 

 

1385,1550; Soldiers, Sailors

 

 

 

 

 

0.161

0.151

0.191

53%

3

1550,1560; Sailors, Cantbry

 

 

 

 

 

0.040

0.042

0.042

24%

0

1560, 1560; Cantbry, Glcstr

0.204

0.122

0.157

55%

2

0.065

0.053

0.112

27%

2


Appendix B

 Evidence on Variations in Name Statistics

 

Analysis of independent samples from closely related populations helps indicate the nature and magnitude of variations in name statistics.  Table B1 concerns names of white females born in the US in the 1870s.  The first part of the table presents data from the Census of 1880 (females ages 0-9 years), and the second part presents data from the Census of 1920 (females ages 40-49 years).  Considering figures for individual years helps to show measurement variance about the long-term trends discussed in Section II A (text and Table 4) and Section III B (text and Table 7).  Comparing averages of individual years to figures based on a decade grouping indicates effects of aggregation across years.  Comparing samples from the different censuses indicates the importance of changes in recording over historical time and changes in recording in relation to a person’s age.[125]

 

Variations in the name personalization statistics are modest relative to secular trends.  Based on a binomial model, sampling variation (standard deviation) for tokens with 10% and 40% probability in a sample size of 4000 are 0.47%  and 0.77% of the sample size, respectively.  Year-to-year variations in the top name and top ten name probabilities are roughly this size.  Female names for females ages 40-49 recorded in the Census of 1920 appear to more personalized, with a lower probability for the top name and less social information in naming.  Nonetheless, the long-term trend toward more personalization is discernable in the year-to-year figures across the decade, and the differences between samples are generally less than the effects of the trend over the decade.

 

The name communication statistics show much more variability.  , , and  vary year-to-year by up to a factor of two, and  varies up to a factor of five.  For , , , and new names, the decade-to-decade figures are no more than twice as large as the average year-to-year figures.  These facts suggest that, as least for the US in the 1870s, year-to-year variations in name communication statistics, whether created by coding errors or different aggregate choices, dominate ten-year trends.  Across the nineteenth century, however, the communication statistics show much greater change than the year-to-year variability in the 1870s.  As these figures indicate, interpreting name communication statistics requires description of the time horizon and discrimination between long-term trends and measurement variability.

 

More research is needed to describe name communication in a way that more simply and consistently encompasses different time horizons and closely related samples.  One would like to better understand what is driving year-to-year variations, and, to the extent that such variation does not reflect interesting aspects of communication, one would like to know how to control for it.  One would also like statistics that can scale change over a measured time period to change over other time periods given the same communication process. 

 


Table B1: Variations in Name Statistics

Personalization Statistics

Communication Statistics

 

 

Birth Year

Top

Name

 

Pop.

Top 10

Pop.

Top 10

Info Is

 

Year Span

 

C1

 

C2

 

C3

 

C4

New

Top 10

Sample

Size

Samples from Census of 1880, Ages 0-9

1871

Mary

11.7%

41.0%

0.226

1870-1871

0.0016

0.0037

0.0037

5%

0

4637

1872

Mary

11.6%

40.2%

0.239

1871-1872

0.0024

0.0060

0.0235

7%

1

5043

1873

Mary

10.9%

39.1%

0.219

1872-1873

0.0014

0.0030

0.0095

7%

1

5170

1874

Mary

10.5%

38.8%

0.207

1873-1874

0.0016

0.0040

0.0040

6%

0

5415

1875

Mary

10.7%

39.4%

0.202

1874-1875

0.0012

0.0026

0.0081

4%

1

5339

1876

Mary

10.2%

37.3%

0.217

1875-1876

0.0030

0.0041

0.0041

7%

0

5510

1877

Mary

10.4%

36.7%

0.231

1876-1877

0.0019

0.0048

0.0048

7%

0

5568

1878

Mary

10.6%

37.5%

0.232

1877-1878

0.0020

0.0047

0.0068

7%

1

5748

1879

Mary

9.7%

35.4%

0.206

1878-1879

0.0031

0.0057

0.0158

8%

1

5099

1880

Mary

9.5%

34.8%

0.224

1879-1880

0.0012

0.0032

0.0048

6%

1

5737

ave. years 1870s

Mary

10.6%

38.0%

0.220

ave. y-to-y 1870s

0.0019

0.0042

0.0085

6%

0.6

5327

grp 1870s

Mary

10.2%

37.3%

0.206

grp 1860s to 70s

0.0130

0.0054

0.0082

14%

1

53258

Samples from Census of 1920, Ages 40-49

1871

Mary

10.9%

41.6%

0.211

1870-1871

0.0030

0.0068

0.0221

8%

2

3304

1872

Mary

10.1%

40.9%

0.174

1871-1872

0.0036

0.0085

0.0085

9%

0

3673

1873

Mary

10.4%

40.2%

0.198

1872-1873

0.0041

0.0102

0.0266

9%

1

3401

1874

Mary

9.8%

39.2%

0.180

1873-1874

0.0034

0.0086

0.0493

9%

2

3496

1875

Mary

10.2%

38.4%

0.191

1874-1875

0.0035

0.0087

0.0118

9%

1

4373

1876

Mary

9.2%

37.1%

0.166

1875-1876

0.0029

0.0064

0.0064

8%

0

3672

1877

Mary

8.6%

37.4%

0.150

1876-1877

0.0039

0.0102

0.0102

10%

0

3917

1878

Mary

9.3%

36.4%

0.196

1877-1878

0.0055

0.0141

0.0141

10%

0

4580

1879

Mary

8.4%

34.7%

0.144

1878-1879

0.0070

0.0175

0.0175

13%

0

3480

1880

Mary

8.8%

36.0%

0.175

1879-1880

0.0052

0.0129

0.0129

11%

0

5320

ave. years 1870s

Mary

9.6%

38.2%

0.178

ave. y-to-y 1870s

0.0042

0.0104

0.0180

10%

0.6

3922

grp 1870s

Mary

9.1%

37.1%

0.165

grp 1860s to 70s

0.0219

0.0164

0.0174

18%

1

38988

Name Communication Across the Nineteenth Century

1800-9 to 1900-9

 

 

 

 

 

0.4479

1.5132

2.0621

1753%

5

82319


Appendix C

 Mary, Group Polarization, and Symbolic Consensus

 

 

An extensive consensus about the value of Mary, as revealed in female names, emerged in England in the late sixteenth and early seventeenth centuries.  In the early middle ages, Mary was considered a name too sacred to be applied to new uses.  Few churches were dedicated to Mary in England through the early eighth century, and the first documented use of Mary as a personal name in England was in the eleventh century.[126]  Table C1 shows the popularity of Mary in various name compilations over time.   The popularity of the name Mary rose from less than 1% of females in the fourteenth century to about 20% of females from the late seventeenth century through the early nineteenth century (see also Table 3 in main text).  The change appears to have been rather sudden and concentrated in the late sixteenth and early seventeenth centuries.  The extent of this new consensus is astonishing: over the eighteenth and early nineteenth centuries, perhaps 30% of women who had at least one daughter had a daughter named Mary.[127]

 

The rise of this consensus contrasts sharply with contemporaneous political and religious polarization in England.  In 1534 King Henry VIII broke from Catholic authority, established a new Church of England with himself as head, and in defiance of Roman judgement had his marriage to Anne Boleyn declared legal.  All ecclesiastical and government officials were required to sign an oath of loyalty to the Church of England.  The conflict between the Church of England and the Roman Catholic Church was largely political, not religious.  But it occurred in the context of the Protestant Reformation, which provided detailed and passionate criticism of Roman Catholic doctrine and practices.  Thus there was an environment with rich resources for creating enduring polarization between Protestants and Catholics.


 


Table C1

Percent of Females Named Mary

 

 

Year, Location

 

Percent

Sample Size

1210, South 3)

0.0%

173

1270, Rutland 4)

0.0%

206

1300, Lincoln 5)

0.6%

1213

1350, Hereford 9)

0.7%

576

1350, Yorkshire 9)

0.2%

1794

1560, Canterbury 17)

7.3%

661

1560 Gloucester 18)

3.2%

3745

 

 

 

1530, North/Cumbria 13)

1.9%

852

1550, North/Cumbria 13)

1.9%

1491

1580, North/Cumbria 13)

3.1%

3750

1610, North/Cumbria 13)

6.4%

4000

1640, North/Cumbria 13)

9.9%

2888

1670, North/Cumbria 13)

14.1%

3813

1700, North/Cumbria 13)

16.2%

3064

1730, North/Cumbria 13)

16.7%

2038

1760, North/Cumbria 13)

18.1%

2830

1790, North/Cumbria 13)

19.4%

2139

 

 

 

1620, Yorkshire 12)

16.7%

342

1670, Yorkshire 12)

20.6%

228

1720, Yorkshire 12)

25.7%

413

1770, Yorkshire 12)

22.8%

381

 

 

 

1625, England 20)

17.0%

n.a.

1675, England 20)

20.5%

n.a.

1725, England 20)

20.0%

n.a.

1775, England 20)

24.0%

n.a.

Note: The number following the location indicates the data source.  See References.

 

Protestants and Catholics historically have clashed sharply over Mary, the mother of Jesus.  Catholics highly venerate Mary as the Mother of God, a model disciple of Christ, and Queen of Heaven.  Repeatedly invoking Mary’s name with the prayer Ave Maria (“Hail Mary”) was a standard Catholic devotional practice from the fourteenth century.  In 1572 Pope Gregory XIII proclaimed the Feast of the Most Holy Rosary, an official celebration of a prayer that repeatedly calls to Mary for her spiritual help.[128]  The significance of Mary in Roman Catholicism is underscored in doctrines concerning Mary’s immaculate conception and bodily assumption into heaven. 

 

Protestants have tended to consider Catholic Marian practices and beliefs to be unbiblical, irrational, or idolatrous.[129]  Here is a typical view from The Weekly Observer in 1716:

A Papist is an Idolater, who worships Images, Pictures, Stocks and Stones, the Works of Men’s Hands; calls upon the Virgin Mary [distinctive typeface in original], Saints and Angels to pray for them…[130]

The Puritan Book