Art Market Dynamics in Seicento Rome

Art Market Dynamics in Seicento Rome

Paul Albert
George Mason University
May 12, 2017

Section I: Overview

Challenge:

Art History scholarship has reached many rich and nuanced judgements about its subject by looking through a lens based on qualitative analysis. This is a very powerful lens whose interpretive power is well proven.

However, with a few notable exceptions, Art History scholarship has not studied its subject through the lens of quantitative analysis. This is a different type of lens that can help further our understanding of the subject.

Technology has provided art historians new and exciting ways to access materials to study. Equally important, technology has also provided art historians new and exciting ways to interpret data.

In speaking about the growing field of the Digital Humanities, Johanna Drucker observed that the goal for the Digital Humanities is not just making materials available electronically but also “the study of ways of thinking differently about how we know what we know and how the interpretive task of the humanist is redefined in these changed conditions.”[1]

The goal of this paper is to explore, for one specific topic, how both qualitative and quantitative analyses might act as foils to one another. My hope is to show how such a bi-modal approach can lead to a richer understanding of the topic and highlight promising avenues for new research.

Topic to Address:

The specific topic this paper seeks to examine is the dynamics of the Art Market for painters in Seicento Rome. As opposed to looking at a specific painter, a specific painting or a specific patron, I seek to see if we can gain a more generalized understanding of key factors driving overall earnings and overall sales for artist and patron activity reported in a specific dataset.

Dataset Foundation for Analysis:

Professor Richard Spear, as part of his seminal research on the Seicento Rome market for paintings, compiled a dataset of prices paid to painters for about 1,000 individual commissions.[2],[3]

The dataset created by Professor Spear contains information on the painter commissioned, the patron, the price, characteristics of the works such as subject matter, title and type of painting and information about the works destination for display. To advance scholarly knowledge, the Getty Institute has provided the Spear Payments to Artist dataset online to be queried and used for further research. [4]

The Spear Payments to Artists (P2A) Dataset was compiled from many reference sources (see Figure 7 in this paper) that listed commissions and then adjusted prices paid to reflect prices in Roman Silver Scudi (a currency that showed little inflation over the period studied). The dataset was pruned to include only sales in the primary market, by a painter directly to a patron, and only sales by artists who resided in Rome. To capture only the dynamics of the Rome art market, works by artists who normally resided in Rome but carried out the work in a different city were excluded and works by “foreign” artists done while in Rome were included.

Due to its very nature, the P2A Dataset offers an incomplete snapshot of a specific submarket, a market for high-end paintings. Considerable qualification needs to be given to be given as to whether findings based on it are truly representative of the overall market. In Section IV, I will examine this question further.

However, as an intellectual exercise, this paper proposes to posit the dynamics found in the P2A Dataset as true in order to question what the implications of the findings would be if they were truly representative of the topic.

At worst, this analysis seeks to develop “straw man hypotheses” to test current understanding of the topic. At best, this analysis hopes to help develop a more nuanced understanding of the topic and stimulate new lines of inquiry and discussion.

Figure 1. Prices to Artists Dataset Overview – Interactive Dashboard
(highlight elements for additional information)

Figure 1, above, is an interactive dashboard profiling the Prices to Artists Dataset.  Further information about each record can be displayed by highlighting the circles which each represent a separate commission.  Data can be filtered by using the controls on the right.

Summary of Findings:

Key Finding 1: Total painter earnings are best correlated to the size of their patron network (the distinct count of patrons).

Key Finding 2: Total patron spending is best correlated to the overall “aura” of the painters they collected and the breadth of painters they collected.

These findings illuminate the social underpinnings behind the specific market in Seicento Rome covered by the dataset. These key findings will be examined in Section III of this paper. Before addressing them, let us first look at the historical context of the market dynamics for painters and patrons in Seicento Rome.

Section II: Historical Context

What makes Seicento Rome unique on the Demand Side?

Rome, compared to other cities of the same period, had a markedly different economic engine underlying its economy, the church. Unlike other cities where power concentrated based that city’s unique economic production (such as cloth manufacturing, cloth dying or banking), the amount of economic power in Rome and its distribution was largely driven by the fortunes of the church. [5]

Second, unlike other cities where economic power might be concentrated in one or a few noble families for a relatively lengthy period of time, wealth distribution and its accompanying ability and need to display status in Rome would change quickly and dramatically with the changing of the non-hereditary papal reign.[6]

All told, coinciding with the period that the bulk of the dataset covers, from 1585 to 1700, Rome saw 17 different popes for an average reign of about seven years. During this period, three popes had reigns that lasted less than a year. Pope Leo XI, a Medici pope, only managed to live 29 days into his reign in 1605.[7]

Figure 2. Patron Spending by Papal Reign – Interactive Dashboard
(highlight elements for additional information)

Patron spending, as shown in the dataset, is broken out by papal reign. What is most striking about this dashboard is how the spending by families changed based on the head that the papal tiara sat on.[8]

The fleeting nature of papal power encouraged a “carpe diem” attitude to art patronage.[9] This disposition is clearly seen in this dashboard. The impact of the expectation for the quick changes of papal power is noted by Francis Haskell quoting from a 1620 letter to Lord Arundel:

It [is] a strange and unnaturall thing that in that place [Rome], contrary to all others, the long life of the Prince is sayd to be the ruyne of the people; whose wealth consists in speedy revolutions, and the oft new preparations of new hopes in those that aspire to rise by new families, who… are loath to blast their future addresses by spending to court those that are despaired of.[10]

There are a few other factors affecting the demand for painting that bear briefly mentioning about Seicento Rome. The rebuilding of St. Peters, started by Pope Julius II in 1505, was still proceeding during the Seicento. In addition, during this period, the counter-reformation was in well underway. Not only were many new churches being built to house new orders, there was an overall increased demand for new types of art to support the Council of Trent’s dictums.[11]

Finally, compared with other periods, the bar to display patron magnificence was significantly higher than in the past. Painter’s brushes were needed not only for new chapels, but also for much larger and much more ornate palazzos.

Having briefly discussed the distinctive historical/economic factors on the demand side of the equation that characterized Rome in the Seicento, let us turn to distinguishing factors on the supply side of the equation.

What makes Seicento Rome unique on the supply side?

Certainly, Rome was very special for the educational opportunities it offered during the period. These opportunities include the many ancient roman sculptures available for study, the many public and private collections available to visit and the many opportunities to receive instruction and practice with fellow students.

Rome was also special, for the time, for the ease in which artists could come into the city and pursue the profession.[12] Though it did have a formal Academy for distinguished painters, Rome did not effectively regulate painters from other Italian cities or foreigners from other countries coming into Rome to compete for the patron’s wallet.[13]

Rome was indeed unique in the educational and work opportunities it offered the aspiring artitst. Indeed, for this period, Rome has been called the predominant artistic center in Europe. Rome was the city that painters in the Seicento were encouraged to go to if they wanted to get rich.[14]

Section III: Discussion of Key Findings

Key Finding 1: Total painter earnings are best explained by the size of their patron network.


Figure 3. Artist Total Earnings Factors – Interactive Dashboard
(highlight lines and circles for additional information)

Figure 3, above, offers an interactive dashboard showing the correlation between painter Total Earnings and factors found relevant in the dataset. The dropdown box controls which factor can be used for ranking painters.

For this Figure, and many other dashboards shown in this analysis, the dashboard provides the ability to filter the data to reflect sub-markets in the dataset. For example, setting Patron Type to “Institutional” would filter the data so that only records where the Patron Type is “Institutional” are analyzed.

Values for Work Display Type (Private/Public/Unspecified) was specified for each record by Richard Spear and included in the original dataset. The field Patron Type was created by this paper’s author based on the values given in the Patron field (e.g., St. Peters would be listed as “Institutional”). As has been noted, exploring this type of distinction between patrons can offer additional insights for scholarship.[15]

A visual analysis of Figure 3 shows that some factors can be seen to be more correlated with Total Earnings than other factors. While the eye can perceive trends between the different factors and Total Earnings, none of the factors shown offer a complete and unambiguous one-to-one correlation. To help supplement the visual correlation that the eye can find, we can statistically test for factor correlation using simple regression analysis techniques.

A regression analysis is a statistical test that examines how variations in one measure are affected by variations in another measure. Key results from the test are measures of how correlated a change in one variable is with the other variable (the R2) and how large of a change each change in one variable affects the other variable (the Slope).

Figure 4: Total Artist Earning Factors Correlation Analysis – Interactive Dashboard.
(highlight lines and circles for additional information)

Key quantitative measures found by the regression analyses can be displayed by highlighting the trend lines in Figure 4. To better aid a comparison of the factors, Table 1 lists the Correlation Strength (R2) and Slope for each factor.

Table 1. Factor Correlation to Total Artist Earnings

FactorCorrelation Strength (R2)Slope
Patron Network Size63%1,376
Number of Commissions56%706
Patron Aura54%0.14
Years Worked38%305

Paton Network Size:

The analysis shows that variance in the Patron Network Size explained 63 % of the variance in Total Earnings among artists in the dataset. Further, for every new patron an artist sold to  career earnings are correlated to increase by 1,376 scudi.

As a correlation factor, Patron Network Size is more significant than any of the other factors examined. Indeed, while adding a new patron explained an overall career revenue increase of 1,376 scudi to a painter, the analysis shows that across the dataset, painters could only expect 706 scudi in career Total Earnings for capturing a new commission or 305 scudi for working an additional year.

The importance of the Patron Network Size as a predictor of Total Earnings highlights the social nature of the Seicento Rome market the dataset captures. The “social power” of the artist, as measured by the size of their network, explains a large amount of the artist’s economic success.

While the perceived skill of the artist was a necessary prerequisite for economic success, just as important to the artist’s wallet was the skill of the artist in building their network. It was not just what the artist was able to accomplish with their brush, it was also whom the artist knew (and the connections of the artist’s connections) that led to higher earnings.[16]

In being incented to grow their Patron Network, artists were not only incented to think about the wishes of their patrons, but just as importantly, the audience of their patrons. While this objective might have been implicitly incented by the Seicento Rome, it has been explicitly embraced by today’s social media marketers. In contemporary social media marketing, there is a clear and stated understanding that success is not gained so much by speaking to one’s primary audience as by reaching and speaking to the audience of one’s audience. [17]

This implicit incentive, found in the dataset, would likely have motivated the painter’s brush not just to consider the wishes of their patrons, but also what the patron’s audience would find valuable. The painter was incented to go “viral.”

The fact that many artists of the period employed a practice of “gifting” their work is often discussed when considering how different works could be valued against one another in determining market dynamics. In many cases, the practice of gifting a painting would be made with the expectation for payment. However, in some cases, there appears to be other motivations and pure outright gifting.[18]

In 1683, the painter Niccolò Cassana wanted to place a self-portrait in the Medici collection and sought the help of Matte del Teglio, a Florentine agent who agreed to write to Cosimo III. Teglio writes to Cosimo III “I want to quiet an ambitious painter here [in Venice] who cannot be calmed with reason…the painter… wants to send his own self-portrait because there is one in your collection by Liberi and Bombelli.” [19]

While Cassana might expressly be motivated in this to serve his pride, and might have indeed expected a gift in return for his painting in this case, the practice of outright gifting to build relationships was common in the period. Outright gifting of a painting was not only a way to potentially gain future sales from the recipient, but done sparingly and strategically, could have been a way to play to the audience of their patron. Being able to claim that one’s work was part of a prestigious collection would have helped build the artist’s brand. If displayed, the work would introduce the artist to new potential clients. This practice of outright gifting could be seen generally as a long game by the artist to build their social power and Total Earnings.

Primary sources of the Rome Seicento suggest a clear recognition of the artist’s need to build a brand and go “viral.” The painter Giovanni Battista Passari, when speaking about Lanfranco’s career and support from a patron in this endeavor, Passari noted at the time how necessary a “favorevole aperture” (favorable coming out) was to an artist and stated, “Truth be told, to launch one’s name it is critical to have the protection of a patron from the start.”[20]

The wish for a “favorevole aperture” in building an anchor point for artist valuation is exactly echoed today by companies seeking to build an anchor point for their stock valuation by having a successful initial public offering.

To grow their Patron Network, artists were told it was necessary to be perceived as inhabiting the same social field as the patron[22]. In the Giovanni Battista Armenini 1587 treatise on painting, De’ veri precetti della pittvra, Richard Goldwaithe notes that Armenini spends the bulk of the treatise focusing on how an artist should interact with his patron – “he should counduct himself in accordance with the norms of the client’s social status”[23]

The market incentive to grow Patron Network Size would likely have been greater in Rome as compared to other cities in the Seicento. As compared to Rome, other cities had more effective guild regulations barring market entry and the concentration of economic power led often to the practice of court painters (which was much rarer in Rome).

Number of Commissions:

Interestingly, while the average price for all commissions across the dataset is 453 scudi, the regression analysis suggests that for every new commission gained, regression analysis of this factor finds that the artist could expect 706 scudi in career Total Earnings for each new commissions.

This dynamic suggests that, for the market reflected in the dataset, there was a multiplier effect to each commission as it related to career Total Earnings.  For every 1 scudo earned in a single commission, the artist would see 1.6 scudi in career earnings.

Patron Aura:

It was not just painters that sought to promote and increase a brand identity for themselves. The drive to claim distinction permeated the social fabric in Seicento Rome and was as prevalent as the air the artist breathed. Patrons also sought to establish and burnish their own brands.

While the P2A Dataset cannot capture all of the dynamics of the social game inherent in this drive for distinction, we can try to measure the Patron Aura quantitatively. For the purposes of this analysis, the Patron Aura factor is calculated as the measure of the patron’s total expenditures across the dataset. This measure is then summed for all the patrons that collected an artist and compared with the artist’s Total Earnings across the dataset.

Not surprisingly, among institutional patrons, the Vatican shows the highest Patron Aura. For private patrons, the three highest Patron Auras were the Borghese Family, the Barberini Family and the Aldobrandini Family.

In deciding the best descriptive term to use for the factor “Patron Aura,” I was influenced by the work of Walter Benjamin who speaks about the aura of an image being driven by the uniqueness of that image.[24]In much the same way, each patron represents a unique contribution to the artist’s brand.

In examining the factors correlating with a painter’s Total Earnings, this analysis found that it is not just the size of the artist’s Patron Network that explains earnings; earnings are also highly explained by the patrons’ characteristics that the artist sold to. It is not just how many people an artist sold to, it is also whom the artist sold to.

Indeed, the analysis of the dataset shows that artist Total Earnings are more explained by the collective Patron Aura of the clients they sold to than the number of sales they made or the years that they worked. Variance in the collective Patron Aura of all the patrons an artist gained is able to explain 54% of the variance in artist Total Earnings in the dataset.

While this paper seeks to examine the relationship behind artist Total Earnings and Patron Aura, Frederico Etro used the P2A Dataset to examine, in part, the relationship between factors in determining the market valuation of specific paintings. One of Etro’s findings indicates that overall, a patron who spent relatively more on paintings would pay relatively less for a given painting, all else being equal.[25]

This finding tantalizingly indicates that the market dynamics as shown in the dataset incented artists to provide a discount based on Patron Aura. Like the practice of gifting, this can be seen as a long game strategy to maximize Total Earnings.

Years Worked:

Years Worked is measured by looking at the years between the date of the first sale by an artist and the last sale by that artist across the dataset.

Given the specific boundaries of the dataset, work by artists done while not in Rome are excluded.  Career Years is a measure that is somewhat problematic. Notably, an artist who had a good name could come to Rome and see more earnings in a set number of years than an artist who was struggling to make their name for the same number of years and left Rome to work elsewhere.

Nonetheless, since this analysis compares, for many artists overall, Years Worked while in Rome with Total Earnings while in Rome, Years Worked is a factor worth considering and correlative. 38% of the variance in Total Earnings can be explained by the variance in Years Worked.

If we look at this factor, what is striking is how relatively less the number of years worked explains earnings than the other factors considered above for the dataset. Overall, more years of effort did not always equal more earnings.

Figure 5. Artist Years Worked and Earnings – Interactive Dashboard
(highlight elements for additional information)

Seicento Rome was host to a number of notable painters; however, it is particularly striking to note how unique Caravagio was compared to his peers. This is not only demonstrated in Figure 5 above, but also in Figure 4.

Wallet Share:

Modern business theory urges firms to focus on wallet share over market share. Selling more things to an existing network of clients is said to be more profitable than trying to build a bigger network of clients for one particular thing.

For the P2A Dataset, Wallet Share can be measured by looking at the percentage of the total Patron Purchases an artist is able to capture. Analysis of the data does show a 38% correlation between Total Earnings and Wallet Share, the same degree of correlation found for Years Worked.

However, because the dataset only contains information on the purchases of paintings, it, by definition, does not capture any other type of goods for sale by the painter other than paintings.

Artists in the dataset might have sought to capture patron Wallet Share by providing more than the painting to their patrons. A portion of the artists in the P2A Dataset were not only were painters, but also earned income from their patrons as architects, “decorators,” and “building subcontractors.”

Though the dataset only allows us to compare artist Total Earnings based on the sale of painting goods, it is important to note that many of the highest Total Earnings painters enjoyed considerable success in complementary endeavors. Indeed, it is extremely possible that achieving success as an architect and success as a painter might have played off one another as artists sought to grow the value of their network to maximize Total Earnings.

Key Finding 2: Total patron spending is best explained by the “aura” of the painters they collected and the breadth of the painters they collected.

Just as Finding 1 highlight the social factors underlying Seicento Rome art markets so to do social factors come to the foreground when looking at patron Total Spending.

Figure 6. Patron Spending Factors – Interactive Dashboard
(highlight lines and circles for additional information)

Figure 6, above, allows a visual inspection of the correlation between patron Total Spending and various factors found in the P2A Dataset. To help supplement the visual correlation that the eye can find in Figure 6, as we did above for Figure 4, we can statistically test for factor correlation using simple regression analysis techniques.

Figure 7: Total Patron Spending Factors Correlation Analysis – Interactive Dashboard.
(highlight lines and circles for additional information)

Table 2. Factor Correlation with Patron Spending

FactorCorrelation Strength (R2)Slope
Artist aura55%0.11
years collecting45%689
Distinct artists40%526
Number of Purchases32%245

Artist Aura:

In determining the market value of any given work of art, the artist’s name has often been mentioned as a key factor driving value. Artist Aura, for this analysis, is the measure of the total earnings for an artist across the dataset. 61% of total Patron Spending variation can be explained by the variation in Artist Aura.

In other words, if we sum up all the artist Total Earnings for the artists that were collected by a patron and then compare that with the total spending for the patron, patrons who spent more tended to collect artists that earned more.

Certainly, there is a bit of a recursive dynamic in this correlation. Artists in the dataset who sold the most to patrons who spent the most would have tended to earn more than their peers. However, there are other alternative market scenarios that could have been possible. For example, if patrons were looking merely to maximize the the number of artists they collected or the number of works collected, this correlation between artist Total Earnings and patron Total Spending would not be as strong in the dataset.

As with the Patron Aura factor examined in Finding 1, the importance of the Artist Aura as an explanatory factor highlights the social nature underlying the dynamics of the Seicento Rome art market. In the case, variance in Artist Aura, as a measure for how valued an artist is by the patron’s peers explains 55% of the variances in patron overall Patron Spending.

Years of Collecting:

Patron Years of Collecting was calculated by looking at the time span between the year of the first purchase and the last purchase in the dataset. Across the dataset, 45% of the variation in overall Patron Spending is explained by variations in Years of Collecting.

Because the overall analysis looks at both Institutional Patrons (such as the Vatican with 112 Years of Collecting) and Private Patrons (such as the Fabrizio Valguarnera with 18 Years of Collecting), grouping these patrons together might be problematic. However, in fact, there remains a striking consistency when these sub-markets are compared. For Institutional Patrons, 42% of the variation in Total Spending is explained by variations in Years of Collecting, while for Private Patrons, 46% is explained.

Count of Distinct Artists:

The analysis of the dataset shows that 40% of the variation in patron Total Spending is explained by the Count of Distinct Artists.

Works of painting in the Seicento period were characterized by a play of the overall parts to the whole. Focus was not on a single figure in the painting, but by the interplay of all the figures together. As opposed to a single musical note, paintings of the Seicento strove to create a chord.

Picture 1. Pietro da Cortona, Palazzo Barberini, Rome, 1633.

In a meaningful way, it seems that this visual aesthetic embodied in singular works was echoed on a larger scale by patrons themselves as they sought distinction in the collection and display of multiple works and multiple artists.[26] To the patron, the whole ensemble was likely to seen to be greater than the sum of the parts.[27]

In a sense, to differentiate themselves in the market, Seicento painters might have sought to build for themselves by striking a distinguishable note. In contrast, to distinguish themselves socially, patrons seem to have sought to differentiate themselves by displaying a chord comprising a harmonious collection of these notes. This dynamic suggests patrons would not only seek to display a wide variety of painting styles and types, but, inasmuch as the artists’ names were salient characteristics of paintings, collect different artists.

Indeed, the value of any particular work purchased by any particular patron was undoubtably influenced by the other works/painters the patron had already collected.

Number of Purchases:

While the Number of Purchases does correlate with the Total Spending by a patron, explaining 32% of the variation between factors, it is notable that a stronger correlation was not found such as with other factors examined. The relative weakness of this correlation highlights the distinct nature of the Art Market as found in the dataset.

While one record might be for decorating an entire gallery or chapel and another purchase might be for a single easel painting, if the Art Market shown in the dataset acted like a pure commodity market (for example, grain), overall the number of purchases would likely have a much stronger correlation with total spending in the dataset.

Section IV: Dataset Sources and Representativeness

Dataset Sources

The Spear/Getty Prices to Artist Dataset presents data that was preserved in by history, in sources such as exhibition catalogs and books about painters and patrons. All told, for the 818 records in the dataset that were analyzed, data from over 149 different historical records were recorded for 129 artists.

Figure 8. Dataset Reference Sources – Interactive Dashboard

Representativeness of the Dataset

As remarkable as the effort to collect the data in the dataset was, a dataset built on reference sources such as books focused on particular artists and patrons or catalogs from museum exhibitions is inherently bounded. The sources from which the dataset is drawn means that the dataset only contains works and artists that history has deemed worth noting. This results in a dataset intrinsically skewed to reflect the high end of the market for paintings in Seicento Rome, a specific sub-market of a larger market.

Many of the findings in this analysis of the dataset would likely be different for other sub-markets in the period. For example, artists on the low end who sold in the open market would likely have seen their Total Earnings much more correlated with the Number of Sales or Years of Work than those contained in the dataset.

Much of Art History is built on the qualitative judgement of scholars piecing together isolated historical anecdotes and building on the work of one another. The dataset examined in this analysis is at worst, just an incomplete set of individual historical anecdotes. At best, however, by collecting a large number of historical anecdotes and allowing for a quantitative analysis lens to be used, the dataset provides a qualitatively different type of research resource whose sum offers more insight than its individual parts.

As a quick test for how complete the data set records are for the artists that it tracks, we can do a bit of back of the envelope estimation. In 1624, Guilio Mancini estimated that a good painter in Rome could earn between 3 to 6 scudi per day. [28] This would equate to between 750-1,500 scudi a year.

Looking at the Spear/Getty data set and taking the number of years between each artist’s first commission and last commission recorded, we find the 129 artists in the dataset show a combined work span of 1,633 years and average annual earnings of 226 scudi a year.

If Mancini’s range is accurate, this would suggest that the payments to artists recorded in the dataset would have accounted for 15% to 30% of the total earnings for the work years covered and the artists listed. While not the desired 100%, this suggests the dataset can be seen as fairly representative of the bigger picture.

In the final analysis, we must take as a given that the dataset offers just a fragmentary view of a particular sub-market.  Ultimately, the most important test of the dataset is how well it can be used to inform our understanding of “how we know what we know” [29] and point to new avenues of inquiry.

Section V: Conclusion

The findings of this analyses of the high-end market dynamics in Seicento Rome between overall painter earnings and overall patron spending illustrate the importance of social networks to the Art Market. Painters maximized their earnings the most by building a larger network of patrons. Patron who spent the most focused on artists that their peers found the most economically worthy.

Qualitative analysis should never be given a second place to quantitative analysis in the practice of Art History. However, by using the new tools for quantitative analysis available to the scholar, we can gain a richer understanding of the subject and possibly reach new understandings and discover new directions for research.

Appendix: Methodological Considerations

Dataset Modifications:

  • Excluded records where the patron or the date of work was not specified. Since many correlations were based on these variables, I considered it the most conservative way to get the cleanest data possible. The reduced the total number of records available for analysis from 954 to 818.
  • Specified the date of a work to be the average of when the work was begun and when the date of the work was ended.
  • Converted all text notes on payments to an actual figure. In a few cases, personal judgement was exercised when payment was given as an approximate range to come up with a figure.
  • Created fields to reflect Patron Type (Institutional/Private), Number of Works in Commission (One/Several), and calculated fields for Papal Reign
  • Grouped Patron values to reflect patron families (e.g., consolidated all Barbarini patrons into one value, “Barbarini Family.”)

Figure 9. Dataset Modifications – Interactive Dashboard
All changes made to the original Spear/Getty Prices to Artists Dataset for key factors used in analysis

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Spear, Richard E. “A Database of Prices Paid to Painters in Seventeenth-Century Rome.” Getty Research Journal, no. 2 (2010): 147–50.

———. “Scrambling for Scudi: Notes on Painters’ Earnings in Early Baroque Rome.” The Art Bulletin 85, no. 2 (June 1, 2003): 310–20. doi:10.1080/00043079.2003.10787074.

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  1. Johanna Drucker, SpecLab: Digital Aesthetics and Projects in Speculative Computing(University of Chicago Press, 2009), xii. ↑
  1. Richard E. Spear, “A Database of Prices Paid to Painters in Seventeenth-Century Rome,” Getty Research Journal, no. 2 (2010): 147–50. ↑
  1. Richard E. Spear, “Scrambling for Scudi: Notes on Painters’ Earnings in Early Baroque Rome,” The Art Bulletin 85, no. 2 (June 1, 2003): 310–20, doi:10.1080/00043079.2003.10787074. ↑
  1. Getty Institute, “Payments to Artists Database,” n.d., http://www.getty.edu/research/tools/provenance/payments_to_artists/. ↑
  1. Spear, “Scrambling for Scudi,” 310. ↑
  1. Richard E. Spear et al., Painting for Profit: The Economic Lives of Seventeenth-Century Italian Painters (New Haven, Conn: Yale University Press, 2010), 34. ↑
  1. “Catholic Encyclopedia,” n.d., http://www.newadvent.org/cathen/12272b.htm. ↑
  1. Jill Burke and Michael Bury, Art and Identity in Early Modern Rome (Aldershot, England ; Burlington, VT: Ashgate, 2008), 4. ↑
  1. Gail Feigenbaum editor and Francesco Freddolini editor, Display of Art in the Roman Palace, 1550-1750, 2014, 7. ↑
  1. Francis Haskell, Patrons and Painters: A Study in the Relations Between Italian Art and Society in the Age of the Baroque (Yale University Press, 1980), 3. ↑
  1. Spear et al., Painting for Profit, 34. ↑
  1. Patrizia. Cavazzini, Painting as Business in Early Seventeenth-Century Rome (University Park, Pa.: Pennsylvania State University Press, 2008), 19. ↑
  1. Ibid. ↑
  1. Spear et al., Painting for Profit, 8. ↑
  1. Michael. Baxandall, Painting and Experience in Fifteenth Century Italy: A Primer in the Social History of Pictorial Style. (Oxford: Clarendon Press, 1972), 5. ↑
  1. Patrizia. Cavazzini, Painting as Business in Early Seventeenth-Century Rome, 130. ↑
  1. Derek Thompson author, Hit Makers: The Science of Popularity in an Age of Distraction, 2017, 215. ↑
  1. Genevieve Warwick, “Gift Exchange and Art Collecting: Padre Sebastiano Resta’s Drawing Albums,” The Art Bulletin 79, no. 4 (1997): 630–46, doi:10.2307/3046279. ↑
  1. Spear et al., Painting for Profit, 19. ↑
  1. Ibid., 77. ↑
  1. Eric C. Chang, Yan Luo, and Jinjuan Ren, “Cross-Listing and Pricing Efficiency: The Informational and Anchoring Role Played by the Reference Price,” Journal of Banking & Finance 37, no. 11 (November 2013): 4449–64, doi:10.1016/j.jbankfin.2012.12.018. ↑
  1. Haskell, Patrons and Painters, 20. ↑
  1. Richard A. Goldwaithe, “The Painting Industry in Early Modern Italy,” in Painting for Profit(Yale University Press, 2010), 288. ↑
  1. Miriam Bratu Hansen, “Benjamin’s Aura,” Critical Inquiry 34, no. 2 (January 1, 2008): 340, doi:10.1086/529060. ↑
  1. Federico Etro, Silvia Marchesi, and Laura Pagani, “The Labor Market in the Art Sector of Baroque Rome,” Economic Inquiry 53, no. 1 (January 2015): 380, doi:10.1111/%28ISSN%291465-7295/issues. ↑
  1. Renata Ago, “Splendor and Magnificence,” in Display of Art in the Roman Palace 1550-1750, 2014, 67. ↑
  1. D. V. Kent, Cosimo De’ Medici and the Florentine Renaissance: The Patron’s Oeuvre (New Haven: Yale University Press, 2000), 367. ↑
  1. Spear et al., Painting for Profit, 22. ↑
  2. Drucker, SpecLab, xii. ↑
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