8  Company Size, Hiring, and Firing

8.1 How many dancers are in the top American ballet companies?

See the key for company codes.

Company	2018	2019	2020	2021
ABT	94	91	85	84
BB	53	56	49	52
HB	57	61	55	59
JB	46	45	43	43
NYCB	89	94	96	98
PNB	46	48	46	41
SFB	81	78	80	78

Company	2019	2020	2021
ATL	37	38	39
AUS	26	20	21
BAZ	28	30	28
BW	40	37	37
CB	27	26	25
CHAR	20	17	18
COB	32	32	32
DB	19	18	18
KCB	30	29	30
MB		22	24
MCB	52	50	54
OBT	26	19	26
PAB	45	46	43
PBT	32	30	30
RICH	18	12	16
SARA	44	42	42
TB	26	22	22
TBT	38	36	27
WB	36	35	31
Company size of the largest 26 US ballet companies by domestic budget. We did not collect data for Milwaukee Ballet for the 2019 - 2020 season. Does not include second company programs, NYCB apprentices, or special ranks (like guest principal). These numbers may be off by a few dancers due to late joiners or people who are part of multiple companies.

8.1.0.1 Company Size Over Seasons

viewof compN = Inputs.checkbox(
  ["HB","SFB","NYCB","PNB","ABT","BB","JB","MCB","PAB","BW","KCB","ATL","PBT","WB","CB","DB","BAZ","TBT","COB","SARA","AUS","CHAR","TB","RICH","OBT","MB"], 
  { value: ["ABT"], 
    label: "Companies:"
  }
)

filteredData = transpose(dataCR).filter(function(dancer) {
  return compN.includes(dancer.Company);
})

Plot.plot({
y: {
    grid: true,
    domain: [0, 120]
  },
  marks: [
    Plot.line(filteredData, {
      x: "Season",
      y: "# of Dancers",
      stroke: "Company",
      strokeWidth: 2,
      curve: "linear"
    }),
    Plot.dot(filteredData, {
      x: "Season",      // feature for the x channel
      y: "# of Dancers",     // feature for the y channel
      fill: "Company",  // feature for the stroke
    }), 
  ],
  width: 554, 
  height: 280, 
  x: { ticks: 3 }, 
  marginLeft: 50, 
  color: {
    legend: true,
    width: 554,
    columns: "120px"
  }
})

Above is a table tracking the sizes of our companies across seasons.

Danger

Interpret with caution: This information may miss some dancers for a variety of reasons. We collect company roster data on January 1st of each season. Sometimes, companies will not update rosters by this date. Dancers may also move or leave companies during seasons. Finally, companies may not report certain ranks some seasons, creating inconsistencies. For example, apprentice bios were not available for Pacific Northwest Ballet apprentices on January 1st, 2022.

The range in company size is staggering. ABT is over twice the size of PNB. A cursory glance at financial reports from around 2017-2018 indicates ABT has nearly twice the operating expenses of PNB (approximately $45 million to $25 million).

We break down company size by gender and rank below.

8.1.0.2 Company Size By Gender

First, here are some tables with all dancers broken down by gender. For more information on gender, see our section on how we record gender and our section on representation.

Top 7 Companies
Gender	2018	2019	2020	2021
F	253	254	244	241
M	213	219	210	214

Next 19 Companies
Gender	2019	2020	2021
F	309	303	303
M	267	258	260
Number of dancers for each gender for the largest 26 US ballet companies by domestic budget. We did not collect data for Milwaukee Ballet for the 2019 - 2020 season. Does not include second company programs, NYCB apprentices, or special ranks (like guest principal). These numbers may be off by a few dancers due to late joiners or people who are part of multiple companies.

Hover over the bars to see group labels:

data = transpose(dataNDG)


viewof year = Inputs.select(["2018","2019","2020","2021"], {value: "2021", label: "Season"})
Plot.plot({
  x: {
    axis: null
  },
  y: {
    grid: true,
    tickFormat: "s"
  },
  color: {
    range: ["#FFCCCB", "#ADD8E6"],
    legend: true
  },
  facet: {
    data: data,
    x: "Company",
    marginBottom: 50
  },
  fx: {
    tickRotate: -45
  },
  marks: [
    Plot.barY(data, {x: "Gender", y: year, fill: "Gender", title: "Gender"}),
    Plot.ruleY([0])
  ]
})

viewof compNDG = Inputs.checkbox(
  ["HB","SFB","NYCB","PNB","ABT","BB","JB","MCB","PAB","BW","KCB","ATL","PBT","WB","CB","DB","BAZ","TBT","COB","SARA","AUS","CHAR","TB","RICH","OBT","MB"], 
  { value: ["ABT"], 
    label: "Companies:"
  }
)


filteredNDG = transpose(dataNDG).filter(function(dancer) {
  return compNDG.includes(dancer.Company);
})

Inputs.table(filteredNDG)

8.1.1 Top or Bottom?

Danger

There are currently some inconsistencies in some of the Houston Ballet soloist ranks between seasons. We used to combine multiple soloist ranks together to make them more comparable across companies. However, we changed this in more recent seasons to preserve ranking structure.

8.1.1.1 Company Size By Rank

viewof compNDR = Inputs.checkbox(
  ["HB","SFB","NYCB","PNB","ABT","BB","JB","MCB","PAB","BW","KCB","ATL","PBT","WB","CB","DB","BAZ","TBT","COB","SARA","AUS","CHAR","TB","RICH","OBT","MB"], 
  { value: ["ABT"], 
    label: "Companies:"
  }
)


filteredNDR = transpose(dataNDR).filter(function(dancer) {
  return compNDR.includes(dancer.Company);
})

Inputs.table(filteredNDR)

Danger

Interpret with caution: Some companies have changed ranking systems over time. We initially tried to standardize ranks for analysis purposes (such as labeling the “demi-soloist” rank from some companies as “second soloist”). More recently, we have decided to report actual rank names. There may be inconsistencies due to this.

The larger companies are noticeably “bottom heavy,” with more corps dancers than smaller companies with similar amounts of soloists and principals. In bottom heavy companies, more people compete for each individual soloist and principal spot. Even after staying in the company for many years, the artistic staff may hire someone new as a soloist or principal for their first season with the company. Theoretically, you have a better chance of climbing the ranks in a “top heavy” company. However, this is better addressed by looking at promotion rates between different companies.

Additionally, the corps dancers in larger companies are mostly women. This supports the idea that ballet companies do not need as many male corps members for their repertoire. Male ensemble dances are generally less common in the classical ballet canon. For example, Houston Ballet has several these male ensemble dances in director Stanton Welch’s choreography, but this fact is not necessarily reflected in its number of male corps members.

8.1.2 On Ranking

Houston Ballet, Ballet West, and Sarasota Ballet are some of the most tiered companies included in our analysis. Joffrey Ballet and Ballet Austin, at the other extreme, have no formal ranks. The system most beneficial for dancers is not readily apparent.

More ranks likely mean more opportunities for small bumps in pay. While companies are not transparent when it comes to pay, salary is generally linked to dancer rank and seniority (there are, of course, exceptions). Ranked systems also create expectations in terms of roles. A principal dancer will generally dance principal roles, with a few soloist parts thrown into the mix. For many companies, this expectation is written into dancer contracts.

Joffrey claims this last point as an advantage of their unranked system. Theoretically, anyone in the company, regardless of experience, could be cast in lead roles. Nonetheless, certain people are still favored for lead roles, just as in a ranked company. We currently do not have data showing if the time to first performance of a principal role is shorter for dancers at unranked companies. It is also unclear how unranked companies differ from ranked companies in terms of pay scaling.

8.2 How long do dancers stay at a company?

Below is an interactive histogram that shows how long dancers stayed at a particular company. This is related to time to promotion and company turnover rate each season.

Danger

Interpret with caution: Some companies do not have complete bios for all dancers on their websites. For some companies (namely Sarasota Ballet), there may be missing information on how long dancers have been with the company if they are not certain ranks.

8.2.0.1 Time Spent at a Company

viewof companies = Inputs.checkbox(
  ["HB","SFB","NYCB","PNB","ABT","BB","JB","MCB","PAB","BW","KCB","ATL","PBT","WB","CB","DB","BAZ","TBT","COB","SARA","AUS","CHAR","TB","RICH","OBT","MB"], 
  { value: ["ABT"], 
    label: "Companies:"
  }
)


filtered = transpose(dataNDY).filter(function(dancer) {
  return companies.includes(dancer.company);
})


Plot.plot({
  y: {
    grid: true
  },
  color: {
    legend: true
  },
  marks: [
    Plot.rectY(filtered, Plot.binX({y: "count"}, {x: "Years in the Company", fill: "rank.x", thresholds: 8})),
    Plot.ruleY([0])
  ]
})

8.3 How many new dancers spots are filled internally versus externally?

When trying to join companies, dancers generally have no idea how many spots the artistic director is looking to fill each year. There are two main pools of talent: internal hires from the school or second company and external hires from auditions. External hiring can be further broken down between invitation-only company class auditions and that notorious free-for-all, “cattle call” auditions.

External dancers trying to break into a new organization have to spend a considerable amount of time and money traveling to auditions with no clear idea of their true chances.

8.3.1 What are my real chances?

We use data from past seasons as well as an interactive table to illustrate the hiring practices of different companies.

In the seven largest US ballet companies (American Ballet Theatre, Boston Ballet, Houston Ballet, Joffrey Ballet, New York City Ballet, Pacific Northwest Ballet, San Francisco Ballet) for the Season 2018 - 2019, there were 467 dancers employed.

417 returned (89.3%) and 50 left (10.7%; moved out of the top seven, retired, or were fired).

473 were employed by those same seven companies in Season 2019 - 2020.

59 (12.5%) hiring events happened between those seasons:

• 4 people moved between companies within the top seven

• 55 dancers from outside the top seven companies were hired (1 of these was a reinstatement)

• 39 hired had previous ties to the company (or 38, not counting the reinstatement)

• 32 received training from the affiliate school (or 31, not counting the reinstatement)

• 7 did not attend the affiliate school but were part of the affiliated second company

• 17 were completely unaffiliated with the top seven companies before joining

Therefore, less than a third of open spots were filled by dancers breaking into the top seven companies.

8.3.2 But what does this all mean?

Other analyses show that training at the affiliated school offers a huge advantage for being hired by particular companies. We did not know how affiliate school training impacted a dancer’s chances of being hired season to season. The data makes it abundantly clear that a dancer’s chances of breaking into the top seven companies from an external path are very slim. Considering the number of dancers seen over the course of the audition season, you must be exceptional to be considered.

The take-home message is to be realistic about your chances. You probably should not limit your auditions to only these companies if you know you’ll need a job next season. Going to all of the top seven’s open auditions is likely not a good idea, financially.

Season	Leavers	Joiners
2019	50	57
2020	135	79
2021	118	121
Number of dancers leaving and joining recorded companies since the preceding season. The 2019 season only includes the top 7 US companies by domestic budget. Milwaukee Ballet was not tracked the 2020 season. Does not include second company programs, NYCB apprentices, or special ranks (like guest principal).

Season	No Affiliation	Not Enough Data	Went to School	Went to School & Second Company	Went to Second Company
2019	17	1	14	15	10
2020	29	15	13	7	15
2021	60	8	13	10	30
Number of dancers joining recorded companies since the preceding season. The 2019 season only includes the top 7 US companies by domestic budget. Does not include second company programs, NYCB apprentices, or special ranks (like guest principal).

8.3.2.1 Number of Dancers Joining All Companies Recorded Broken Down By Season, Company, Rank, Group, & Gender

Hover over the bars to see group labels:

tdataNCG = transpose(dataNCG)
viewof yearNCG = Inputs.select(["2019","2020","2021"], {value: "2021", label: "Season"})
Plot.plot({
  x: {
    axis: null
  },
  y: {
    grid: true,
    tickFormat: "s",
    label: "# of Dancers"
  },
  color: {
    scheme: "category10",
    legend: true
  },
  facet: {
    data: tdataNCG,
    x: "Company",
    marginBottom: 50
  },
  fx: {
    tickRotate: -45
  },
  marks: [
    Plot.barY(tdataNCG, {x: "Group", y: yearNCG, fill: "Group", title: "Group"}),
    Plot.ruleY([0])
  ]
})

viewof compNCRS = Inputs.checkbox(
  ["HB","SFB","NYCB","PNB","ABT","BB","JB","MCB","PAB","BW","KCB","ATL","PBT","WB","CB","DB","BAZ","TBT","COB","SARA","AUS","CHAR","TB","RICH","OBT","MB"], 
  { value: ["ABT"], 
    label: "Companies:"
  }
)

viewof compS = Inputs.checkbox(
  ["2019","2020","2021"], 
  { value: ["2021"], 
    label: "Season:"
  }
)

filteredNCRS = transpose(dataNCRS).filter(function(dancer) {
  return compNCRS.includes(dancer.Company) && compS.includes(dancer.Season);
})

Inputs.table(filteredNCRS)

8.4 Which companies consider each other to be equals?

As we’ve already seen, dancers often move between companies. By gathering data over the seasons, we can look at movers among the top 26 companies and see if certain companies prefer movers from certain other companies. Currently, we are limited to movers within the top 26 (a dancer moving from Royal Ballet to San Francisco Ballet would not be counted), but we are looking to collect such data in the future.

8.4.0.1 Hiring Between the Top 26 Companies for All Seasons

The plot below shows dancers moving between companies. Hover over connections for detailed information. All numbers are numbers of dancers.

d3 = require("d3@6")

chart = {
  const svg = d3.create("svg")
      .attr("viewBox", [-width / 2, -height / 2, width, height]);

  const chords = chord(data1);

  const group = svg.append("g")
      .attr("font-size", 10)
      .attr("font-family", "sans-serif")
    .selectAll("g")
    .data(chords.groups)
    .join("g");

  group.append("path")
      .attr("fill", d => color(names[d.index]))
      .attr("d", arc);

  group.append("title")
      .text(d => `${names[d.index]}
${d.value}`);

  const groupTick = group.append("g")
    .selectAll("g")
    .data(ticks)
    .join("g")
      .attr("transform", d => `rotate(${d.angle * 180 / Math.PI - 90}) translate(${outerRadius},0)`);

  groupTick.append("line")
      .attr("stroke", "currentColor")
      .attr("x2", 6);

  groupTick.append("text")
      .attr("x", 8)
      .attr("dy", "0.35em")
      .attr("transform", d => d.angle > Math.PI ? "rotate(180) translate(-16)" : null)
      .attr("text-anchor", d => d.angle > Math.PI ? "end" : null)
      .text(d => d.value);

  group.select("text")
      .attr("font-weight", "bold")
      .text(function(d) {
        return this.getAttribute("text-anchor") === "end"
            ? `↑ ${names[d.index]}`
            : `${names[d.index]} ↓`;
      });

  svg.append("g")
      .attr("fill-opacity", 0.8)
    .selectAll("path")
    .data(chords)
    .join("path")
      .style("mix-blend-mode", "multiply")
      .attr("fill", d => color(names[d.source.index]))
      .attr("d", ribbon)
    .append("title")
      .text(d => `${d.source.value} ${names[d.target.index]} → ${names[d.source.index]}${d.source.index === d.target.index ? "" : `\n${d.target.value} ${names[d.source.index]} → ${names[d.target.index]}`}`);

  return svg.node();
}

data1 = Object.assign(dataCD, {
  names: dataCDnames,
  colors: dataCDcolors
})

names = data1.names === undefined ? d3.range(data1.length) : data1.names
colors = data1.colors === undefined ? d3.quantize(d3.interpolateRainbow, names.length) : data1.colors

function ticks({startAngle, endAngle, value}) {
  const k = (endAngle - startAngle) / value;
  return d3.range(0, value, tickStep).map(value => {
    return {value, angle: value * k + startAngle};
  });
}

tickStep = d3.tickStep(0, d3.sum(data1.flat()), 100)

chord = d3.chord()
    .padAngle(10 / innerRadius)
    .sortSubgroups(d3.descending)
    .sortChords(d3.descending)
    
arc = d3.arc()
    .innerRadius(innerRadius)
    .outerRadius(outerRadius)
    
ribbon = d3.ribbon()
    .radius(innerRadius - 1)
    .padAngle(1 / innerRadius)
    
color = d3.scaleOrdinal(names, colors)

outerRadius = Math.min(width, height) * 0.5 - 60

innerRadius = outerRadius - 10



height = width


Inputs.table(transpose(dataCDexplore))

8.5 What are the characteristics of dancers who leave these companies?

Season	No Affiliation	Not Enough Data	Went to School	Went to School & Second Company	Went to Second Company
2018	35		4	6	4
2019	41	77	6	3	7
2020	57	14	16	8	21
Number of dancers leaving recorded companies since the preceding season. The 2018-2019 season only includes the top 7 US companies by domestic budget. Does not include second company programs, NYCB apprentices, or special ranks (like guest principal).

8.5.0.1 Number of Dancers Leaving All Companies Recorded With Years At Company Broken Down By Company, Rank, & Group

viewof compLCR = Inputs.checkbox(
  ["HB","SFB","NYCB","PNB","ABT","BB","JB","MCB","PAB","BW","KCB","ATL","PBT","WB","CB","DB","BAZ","TBT","COB","SARA","AUS","CHAR","TB","RICH","OBT","MB"], 
  { value: ["ABT"], 
    label: "Companies:"
  }
)


filteredLCR = transpose(dataLCR).filter(function(dancer) {
  return compLCR.includes(dancer.Company);
})

Inputs.table(filteredLCR)