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Preface

Technology Sector Employment Statistics for the 12 major U.S. Technology Sector geographic regions (CSAs) are presented below for the period from January 1992 to December 2011. The data presented is derived from the most recent U.S. Bureau of Labor Statistics’ Quarterly Census of Employment and Wages (BLS/QCEW) released on June 28 2012.

An earlier version of this analysis was presented in April 2010 for the period of January 1990 to September 2009. At that time, the Great Recession was causing a notable drop in employment across all sectors of the U.S. economy including the Technology Sector. As we see below, the actual bottom of U.S. Technology Sector employment occurred four months later in January 2010.

Since the January 2010 bottom, Technology Sector employment overall has been gradually improving. Nonetheless, the summary observations from the April 2010 analysis were still largely valid. With the recent BLS/QCEW release, some regions are recovering to pre-recession employment levels, and thus, this updated look at the numbers is now warranted.

Summary Observations

Note: These Summary Observations are more easily read and understood given the context of the charts and tables below. Thus, initially, you may want to skip this Summary, scan the data below, then return here.

• U.S. technology sector employment reached its low point in January 2010, down -7.8% from the pre-recession peak in July 2008. Since January 2010, U.S. technology sector employment has been recovering; by December 2011, it was down only -3.2%. If the 2011 employment trajectory continues, U.S. technology sector employment should recover to pre-recession levels by Q3 of 2012. Individual CSAs differ from the U.S. in their degree of losses during the recession and in their employment growth trajectories since January 2010.

• Technology sector employment in the San Jose/SF (encompassing Silicon Valley), Seattle, Atlanta, and Denver CSAs have recovered, and now exceed their pre-recession levels. The recovery in the Atlanta and Denver CSAs may be somewhat surprising given their relatively flat employment levels in the earlier 2000s.

• The San Jose/SF CSA showed the most rapid decrease in employment at the beginning of the recession across the CSAs; it was also among the deepest declines, -9.1% in January 2010 compared to July 2008, a loss of 31K jobs. Perhaps, this rapid decline was due to worries highlighted in Sequoia Capital’s presentation, titled “R.I.P. Good Times”, given to its portfolio companies at the beginning of October 2008, three weeks after the collapse of Lehman Brothers.

• Then, from January 2010 to December 2011, the San Jose/SF CSA showed the most rapid increase in employment, recovering to and exceeding pre-recession technology employment numbers, +2.5% compared to July 2008, adding 39K jobs since January 2010. Because this recovery began from a greater decrease in employment, there may appear to be more frenzy than would otherwise be apparent. Regardless, the sustained job growth and the large number of jobs created likely provides greater opportunity for those entering the technology sector workforce.

• The San Jose/SF CSA continues to have the highest concentration technology sector employment, in both absolute measure, 346K jobs, and relative measure, 3.1 LQ.

• As noted in the earlier analysis, for the full 20 year period, 1992-2011, the Washington DC and Seattle CSAs showed significantly greater technology sector job growth, +114K and +77K respectively. These are greater in both absolute and relative measures among the 12 CSAs. Both Washington DC and Seattle CSAs had the smallest declines in technology sector employment during the recession, -2.8% and -2.9% respectively. The Washington DC CSA has largely recovered to but not exceeded pre-recession employment levels. The Seattle CSA has exceeded its pre-recession employment levels, by +2.7%.

• Amazingly, the Seattle CSA has not only exceeded its pre-recession employment levels, it is the only CSA to exceed it’s peak dot com employment levels! Seattle had 10% greater technology sector employment in December 2011 than at its peak during the dot com bubble, December 2000. The Washington DC CSA is close to its dot com bubble technology sector employment, only -5% below, and is on trajectory to exceed those levels. In contrast, other CSAa are significantly below their dot com bubble levels; for example, the San Jose/SF, Boston, and New York CSAs are well below their dot com bubble peaks, by -29%, -28% and -26% respectively.

• The Minneapolis and Boston CSAs more closely tracked the U.S. technology sector trajectory of job losses and recovery. The Boston CSA lost fewer jobs relatively from the July 2008 to the January 2010 bottom, -6.3% for Boston compared to -7.8% for the U.S. The Boston CSA technology sector recovery lagged slightly behind the U.S. from July 2008 to December 2011, -3.9% vs -3.2%, and if the current job growth trajectory continues, it will be 2014 until the Boston CSA recovers to pre-recession July 2008 technology employment levels.

• The New York CSA also was among the CSAs with the deepest technology sector recession declines, down -9.5% on January 2010 compared to July 2008, losing 28K jobs, which is the second largest absolute loss among the 12 CSAs. The New York CSA technology sector recovery was on par relative to the U.S., down -3.2% in December 2011 compared to July 2008, but adding 19K jobs from January 2010 to December 2011, the second largest number of jobs added among the 12 CSAs. If this trajectory continues, the New York CSA technology sector will recover to pre-recession employment levels by the end of 2012. Thus, the New York CSA is similar to the San Jose/SF CSA in that the large number of recession job losses were followed by a large number of recovery job increases, by which the New York CSA technology recovery may similarly appear more frenzied.

• The Dallas and Chicago CSAs had the deepest relative technology sector recession declines; the bottom of their job losses came later than the U.S.; compared to July 2008 Dallas was down -10.5% in May 2010, and Chicago was down -9.8% in March 2010. Both have been recovering since their lows; in December 2011 compared to July 2008, Dallas is still down -4.7%, and Chicago is down -5.3%. If each maintains its 2011 job growth trajectory, each will recover to pre-recession technology employment levels during 2013.

• The Philadelphia and Los Angeles are the last 2 of the 12 CSAa. Both continued to lose technology sector jobs well beyond the U.S. bottom; their technology employment levels have stabilized somewhat in 2011, but recovery is not yet apparent. Philadelphia did have a noticeable increase in December 2011, but little can be extrapolated from the one data point. If recent trajectories continue, it seems that neither CSA will recover to pre-recession technology sector employment levels. If nothing changes, this may be a structural change to a lower concentration of technology sector employment in the Philadelphia and Los Angeles CSAs.

12 CSAs

Chart 1 shows technology sector employment for the 12 Combined Statistical Areas (CSAs) that have technology sector employment greater than 80,000 jobs; these are the same 12 CSAs analyzed in April 2010. Only private sector employment numbers are used.

Chart 1 covers the period from January 1992 to December 2011, which includes the most recent data available in Quarterly Census of Employment and Wages from the U.S. Bureau of Labor Statistics. The order of CSAs listed in the legend is the rank order of the CSAs’ technology sector employment in December 2011, which is the final data point in the chart.

Demographic data for the 12 CSAs is shown below.

Chart 1

Click on chart to see full size.

As before, the most notable feature in the chart is the burst in technology sector employment during the dot com bubble from 1996 to 2003; this bubble distorts employment and industry trends. Nonetheless, time has progressed far enough from the bubble that notable trends have emerged.

Also apparent is the drop in technology sector employment due to the current Great Recession. For the entire U.S., Technology Sector employment peaked in July 2008 prior to the recession, and bottomed in January 2010 during the recession. Since the bottom, technology sector employment has increased gradually. Some CSAs have seen better recovery than others; this is explored further below.

The San Jose/San Francisco CSA (dark blue) has the greatest number of technology sector employees. Followed by the New York CSA (pink) the Washington D.C. CSA (orange) the Los Angeles CSA (violet) the Boston CSA (turquoise) and so on.

A few notable trends or characteristics. The Washington D.C. CSA and Seattle CSA show significant sustained job growth over the 20 tear period. The Washington D.C. CSA went from 5th rank in 1992 to 3rd rank in 2011, adding ~114K technology sector jobs, overtaking the Boston CSA and Los Angeles CSA. The Seattle CSA went from 12th to 7th rank, adding ~77K technology sector jobs, and more than doubling the area’s number of technology sector employees.

With respect to total population, the San Jose/San Francisco CSA and Boston CSA are roughly comparable in size, and the Washington D.C. CSA is only slightly larger (+15%). The New York CSA and Los Angeles CSA have much larger populations (>2.5x) than the other CSAs. They do have substantial technology sector employment, but at a lower relative density to several of the other CSAs. Discussions of CSA demographics and of the relative density of technology sector employment is below.

Technology Sector employment is highly concentrated in these 12 CSAs. While the 12 CSAs represent ~34% of the U.S. population, they represent ~49% of all U.S. technology sector employment. This proportion has been fairly consistent during the 20 year period, varying between 48% and 51.5%.

After the top 12 CSAs, technology sector employment drops off very rapidly. As of December 2011, CSAs ranked 12-24 together add ~492K technology sector employees or an additional ~12%. And, the next 100 CSAs, ranked 25-124 add another ~512K technology sector employees or an additional 12%. Thus, the top 12 CSAs dominate technology sector employment.

Technology Sector Definition

In the charts and tables presented here, technology sector jobs are those classified by the following NAICS codes:

NAICS 334 Computer and electronic product manufacturing 25.5%
NAICS 517 Telecommunications 17.6%
NAICS 518 Data processing, hosting and related services 4.8%
NAICS 519 Other information services 5.0%
NAICS 5112 Software publishers 8.3%
NAICS 5415 Computer systems design and related services 38.9%

This list of 6 NAICS codes is purposely a more focused definition of technology sector than is often used, but it does broadly represent electronics, computers and computing, communications, and Internet technologies and business activities, and it does capture, as much as possible, employment related to the formulation and growth of these industries.

The percentage in the right column is the portion that the single NAICS industry represents of employment for the total of the 6 NAICS industries across all 12 major CSAs. Individual CSAs may vary from these aggregate proportions.

CSA Demographics

Before delving into more detail on the employment numbers, the next three tables (1a, 1b, 1c) provide more context for the 12 CSAs by listing most recently available selected U.S. Census Bureau statistics. For easier comparison, the last two rows of each table list the technology sector employment and the location quotient as of December 2011 derived from Chart 1 and Chart 10 data.

Location quotient (LQ) is discussed more fully below. Briefly, technology sector LQ is the concentration of technology sector employment in a CSA relative to the concentration of technology sector employment across the U.S. as a whole. An LQ greater than 1.0 means that the CSA has proportionally greater technology sector employment than the U.S. as a whole and that the technology sector is more important to the CSA’s economy; an LQ less than 1.0 means proportionally less employment concentration and less economic importance.

A few additional items to note. The San Jose/SF CSA is roughly comparable to the Boston CSA, roughly the same population, roughly the same land area, etc. The Washington DC CSA has a similar but slightly higher population. Seattle CSA population is one of the smaller of the 12 CSAs.

One notable difference is that San Jose/SF has the highest median house value, significantly higher than Boston, Washington DC, and Seattle, and higher than the 2nd and 3rd most costly CSAs, Los Angeles and New York. Though housing costs may become a consideration for later stage expansion of companies in the San Jose/SF area, housing costs have not been much of a deterrent to starting and growing major successful technology companies over the past 20 years, see S&P 1500 data.

Dallas, Washington DC, Seattle, and Denver CSAs have the highest relative number of new housing permits.

Table 1a

Census Quickfacts CS488  CS500  CS548  CS148 
San Jose-SF  Seattle  Washington DC  Boston 
Tech Empl, Dec 2011 345,879  127,209  263,454  210,059 
Tech LQ, Dec 2011 3.11  2.07  2.03  1.70 
Population est July 1 2011 7,563,460  4,269,349  8,718,083  7,601,061 
Education BA+, 25+yr, %, 2006-10 41.3%  35.1%  42.3%  37.5% 
Housing units, 2010 2,908,294  1,803,069  3,461,848  3,170,897 
Owner-occupied housing % 2006-10 58.1%  63.8%  66.6%  64.2% 
Median house value 2006-2010 646,012  345,138  383,295  344,916 
New house building permits 2011 10,261  13,275  27,185  9,073 
Households, 2006-2010 2,674,138  1,635,748  3,133,313  2,889,441 
Median household income, 2006-10 76,600  64,035  80,795  66,236 
Private nonfarm estblmt 2009 194,655  115,257  212,389  198,561 
Private nonfarm employment 2009 3,047,403  1,663,283  3,557,236  3,390,892 
Land area, square miles, 2010 8,741  9,888  9,999  8,796 
Population per square mile, 2010 854.5  424.7  857.4  859.4 

Table 1b

Census Quickfacts CS216  CS206  CS122  CS378 
Denver  Dallas  Atlanta  Minneapolis 
Tech Empl, Dec 2011 84,595  151,715  105,542  84,649 
Tech LQ, Dec 2011 1.76  1.49  1.36  1.34 
Population est July 1 2011 3,157,520  6,887,383  5,712,148  3,655,558 
Education BA+, 25+yr, %, 2006-10 38.4%  30.4%  33.3%  36.1% 
Housing units, 2010 1,302,189  2,670,033  2,308,439  1,493,637 
Owner-occupied housing % 2006-10 66.6%  63.6%  67.7%  72.8% 
Median house value 2006-2010 253,733  142,627  193,020  235,440 
New house building permits 2011 8,223  25,106  8,999  5,630 
Households, 2006-2010 1,183,204  2,343,230  1,996,671  1,391,143 
Median household income, 2006-10 61,400  57,037  57,820  64,350 
Private nonfarm estblmt 2009 90,461  147,196  137,520  99,587 
Private nonfarm employment 2009 1,263,691  2,637,451  2,181,147  1,775,645 
Land area, square miles, 2010 13,060  14,059  10,376  9,523 
Population per square mile, 2010 236.7  478.8  541.5  379.7 

Table 1c

Census Quickfacts CS348  CS428  CS408  CS176 
Los Angeles  Philadelphia  New York  Chicago 
Tech Empl, Dec 2011 216,014  88,312  290,473  119,707 
Tech LQ, Dec 2011 0.95  0.93  0.92  0.83 
Population est July 1 2011 18,081,569  6,562,287  22,214,083  9,729,825 
Education BA+, 25+yr, %, 2006-10 28.0%  31.1%  35.2%  33.0% 
Housing units, 2010 6,276,022  2,654,272  8,820,997  3,890,941 
Owner-occupied housing % 2006-10 55.8%  69.6%  55.4%  67.8% 
Median house value 2006-2010 484,162  232,534  464,269  253,887 
New house building permits 2011 19,551  7,467  25,096  7,798 
Households, 2006-2010 5,729,728  2,420,895  7,979,972  3,507,131 
Median household income, 2006-10 59,891  61,317  66,752  60,979 
Private nonfarm estblmt 2009 418,604  157,105  614,903  242,156 
Private nonfarm employment 2009 6,285,022  2,671,180  8,594,442  3,988,657 
Land area, square miles, 2010 33,955  5,942  11,793  8,472 
Population per square mile, 2010 526.5  1,099.5  1,872.8  1,143.4 

Employment Numbers

Table 2 shows technology sector employment numbers from Chart 1 for 5 specific dates:

January 1992 begins the 20 year data set used here
February 2004 minimum U.S. tech sector employment after the dot com bubble
July 2008 peak employment prior to the decline due to the Great Recession
January 2010 minimum employment during the recession
December 2011 ends the data set showing a beginning of job recovery

The dates for the minimums and maximums are determined by using the total U.S. technology sector employment numbers. The dates also happen to coincide with the minimum and maximum of the 12 CSAs in aggregate. The actual minimums and maximums for an individual CSA may differ from the aggregate minimums and maximums; these differences generally are not notable; any notable differences are discussed when appropriate.

Table 2 also shows technology sector employment for the 12 CSAs combined and for the entire U.S.

Employment 1992.01  2004.02  2008.07  2010.01  2011.12 
12 CSAs 1,781,207  2,005,618  2,137,498  1,982,329  2,087,608 
US000 3,604,129  4,137,612  4,392,731  4,051,011  4,252,936 
CS488 San Jose-SF CSA 285,936  314,831  337,517  306,645  345,879 
CS408 New York CSA 291,901  283,860  299,956  271,518  290,473 
CS548 Washington DC CSA 149,662  237,619  265,068  257,552  263,454 
CS348 Los Angeles CSA 271,703  239,873  238,600  217,506  216,014 
CS148 Boston CSA 221,759  204,548  218,492  204,820  210,059 
CS206 Dallas CSA 120,241  154,336  159,222  143,768  151,715 
CS500 Seattle CSA 49,855  96,208  123,809  120,214  127,209 
CS176 Chicago CSA 120,823  121,147  126,420  114,688  119,707 
CS122 Atlanta CSA 62,094  101,300  102,369  98,260  105,542 
CS428 Philadelphia CSA 84,965  87,994  95,661  87,867  88,312 
CS378 Minneapolis CSA 68,192  82,801  87,021  80,566  84,649 
CS216 Denver CSA 54,076  81,101  83,363  78,925  84,595 

Trends and changes across CSAs might be a little more apparent in Table 3 which shows the difference in technology sector employment, jobs added in black and lost in red, between several pairs of the above dates:

January 1992 - December 2011 from start to end of the 20 year data set
January 1992 - July 2008 from start to the pre-recession employment peak
February 2004 - December 2011 from post bubble low to the end of the data set
July 2008 - January 2010 from pre-recession high to the recession low
January 2010 - December 2011 from recession low to the end of the data set
July 2008 - December 2011 from pre-recession high to the end of the data set

Jobs Added/Lost 1992.01  1992.01  2004.02  2008.07  2010.01  2008.07 
2011.12  2008.07  2011.12  2010.01  2011.12  2011.12 
12 CSAs 306,401   356,291   81,990   -155,169   105,279   -49,890  
US000 648,807   788,602   115,324   -341,720   201,925   -139,795  
CS488 San Jose-SF CSA 59,943   51,581   31,048   -30,872   39,234   8,362  
CS408 New York CSA -1,428   8,055   6,613   -28,438   18,955   -9,483  
CS548 Washington DC CSA 113,792   115,406   25,835   -7,516   5,902   -1,614  
CS348 Los Angeles CSA -55,689   -33,103   -23,859   -21,094   -1,492   -22,586  
CS148 Boston CSA -11,700   -3,267   5,511   -13,672   5,239   -8,433  
CS206 Dallas CSA 31,474   38,981   -2,621   -15,454   7,947   -7,507  
CS500 Seattle CSA 77,354   73,954   31,001   -3,595   6,995   3,400  
CS176 Chicago CSA -1,116   5,597   -1,440   -11,732   5,019   -6,713  
CS122 Atlanta CSA 43,448   40,275   4,242   -4,109   7,282   3,173  
CS428 Philadelphia CSA 3,347   10,696   318   -7,794   445   -7,349  
CS378 Minneapolis CSA 16,457   18,829   1,848   -6,455   4,083   -2,372  
CS216 Denver CSA 30,519   29,287   3,494   -4,438   5,670   1,232  

Very apparent are the notable and sustained job growths over 20 years in the Washington DC CSA (~114K jobs added) and in the Seattle CSA (~77K jobs added). The Seattle CSA has approximately half the population of the Washington DC CSA, making its job growth that much more significant. The data here only counts private sector jobs, not government jobs. Though many of the DC area jobs likely directly or indirectly support government activities and programs, they are private sector jobs nonetheless.

The Atlanta and Denver CSAs also show job growth over the 20 year period, but their growth mostly occurred in the 1990s, and has been relatively flat in the 2000s. However, Atlanta and Denver CSAs do show surprising recent job growth from the recession low at January 2010 to December 2011 and from pre-recession peak at July 2008 to December 2011.

All CSAs, except Los Angeles, added jobs from the current recession minimum in January 2010 to December 2011.

Only 4 CSAs, San Jose/SF, Seattle, Atlanta, and Denver have recovered to pre-recession employment levels, with more jobs in December 2011 than at the pre-recession peak employment in July 2008.

Only 4 CSAs, Los Angeles, Boston, New York, and Chicago have net job losses for the 20 year period from January 1992 to December 2011.

Only 3 CSAs, Los Angeles, Dallas, and Chicago have fewer technology sector jobs in December 2011 than at the post dot com bubble minimum of February 2004.

The Los Angeles CSA stands out as the only CSA of the 12 that is consistently showing job losses across all of the time periods and a downward trend in total technology sector employment.

Table 4 shows the annualized growth rate (CAGR) of technology sector jobs during the 6 time periods for each of the 12 CSAs, the 12 CSAs in aggregate, and the U.S. as a whole. Of course, a begin-to-end calculated growth rate averages out monthly and yearly fluctuations, but it does allow comparing growth between CSAs for these time periods.

CAGR 1992.01  1992.01  2004.02  2008.07  2010.01  2008.07 
2011.12  2008.07  2011.12  2010.01  2011.12  2011.12 
12 CSAs 0.80%   1.11%   0.51%   -4.90%   2.74%   -0.69%  
US000 0.83%   1.21%   0.35%   -5.26%   2.57%   -0.94%  
CS488 San Jose-SF CSA 0.96%   1.01%   1.21%   -6.19%   6.48%   0.72%  
CS408 New York CSA -0.02%   0.17%   0.29%   -6.42%   3.58%   -0.94%  
CS548 Washington DC CSA 2.88%   3.52%   1.33%   -1.90%   1.19%   -0.18%  
CS348 Los Angeles CSA -1.15%   -0.78%   -1.33%   -5.98%   -0.36%   -2.87%  
CS148 Boston CSA -0.27%   -0.09%   0.34%   -4.22%   1.33%   -1.15%  
CS206 Dallas CSA 1.17%   1.72%   -0.22%   -6.58%   2.85%   -1.40%  
CS500 Seattle CSA 4.82%   5.67%   3.63%   -1.95%   2.99%   0.80%  
CS176 Chicago CSA -0.05%   0.27%   -0.15%   -6.29%   2.26%   -1.58%  
CS122 Atlanta CSA 2.70%   3.08%   0.53%   -2.69%   3.80%   0.90%  
CS428 Philadelphia CSA 0.19%   0.72%   0.05%   -5.51%   0.26%   -2.31%  
CS378 Minneapolis CSA 1.09%   1.49%   0.28%   -5.01%   2.61%   -0.81%  
CS216 Denver CSA 2.27%   2.66%   0.54%   -3.58%   3.69%   0.43%  

The growth rate numbers in Table 4 reinforce the observations made for Table 3 jobs added/lost numbers.

The Seattle CSA (4.82%) and the Washington DC CSA (2.88%) show high sustained growth across the 20 year period.

Not surprisingly, all CSAs show negative growth (loss) from the July 2008 pre-recession peak to the January 2010 recession low. However, the Washington CSA and the Seattle CSA show relatively lower losses, indicating relative job resilience during the recession. Though not as dramatic, Atlanta, Denver, and Boston CSAs also show lower relative losses compared to the U.S. as a whole and the 12 CSAs in aggregate. Dallas, New York, Chicago, and San Jose-SF CSAs show more severe relative losses compared to the U.S. as a whole.

Of the 4 CSAs that show positive growth from the July 2008 pre-recession high to December 2011, the Atlanta, Seattle, and Denver CSAs are in the group showing relatively lower recession losses. Whereas the San Jose CSA moved from severe relative recession losses to positive growth.

Job Loss and Recovery of the Great Recession

Chart 2a shows technology sector employment for the 12 CSAs from January 2010 to December 2011, from the low of technology sector employment during the current recession to the end of the available data. The chart’s data is indexed to January 2010, so that the relative growths can be compared. Also, shown is a white line for the U.S. technology sector as a whole, so that CSAs can be compared to the overall U.S. technology sector.

Chart 2a

Click on chart to see full size.

A very notable one month dip in employment is apparent in August 2011 across the New York (pink), Boston (turquoise), Washington DC (orange), and Philadelphia (lime) CSAs. This decrease is in the Telecom Sector (NAICS 517), and is most likely entirely due to a temporary work stoppage at Verizon. Most of the same workers returned to work in September 2011. Seemingly all of the August 2011 dip in technology employment in the U.S. as a whole is also due to this work stoppage.

In order to more easily see data trends, Chart 2b shows the same data as Chart 2a, but averages out the August 2011 dip in the effected CSAs and in the total U.S. technology sector employment. The same August 2011 adjustment is made in subsequent charts.

Chart 2b

Click on chart to see full size.

Now, it is more apparent that the 12 CSAs fall into roughly 4 clusters in their rate of recovery from the bottom of he recession. The San Jose CSA (dark blue) stands out from the other CSAs, growing more than 12.8% during the 2 year period. Seven CSAs, Atlanta (plum), Denver (light yellow), New York (pink), Seattle (blue), Dallas (tan), Minneapolis (light orange), and Chicago (teal), ranging 4.4% to 7.4%, show better than or comparable growth to the U.S. technology sector as a whole. The Boston (turquoise) and Washington DC (orange) CSAs, ~2.4%, are lagging the U.S. as a whole. And the Philadelphia CSA (lime), slight above 0%, and the Los Angeles CSA (violet), slightly below 0%, lagging further still.

Chart 3 shows the same data as Chart 2b, but in addition to being indexed to January 2010, it is normalized to the U.S. technology sector as a whole. In this form, it might be easier to see the differences among the CSAs for the two year period.

Chart 3

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As noted earlier, the 12 CSAs had different relative losses from the pre-recession maximum in July 2008 to the recession minimum in January 2010. Thus, a portion of the San Jose-SF CSA gain from January 2010 to December 2011 is because it started from a relatively lower base. Charts 4 and 5 are similar to Charts 2b and 3, but start at the pre-recession high at July 2008.

Chart 4 shows technology sector employment for the 12 CSAs from the pre-recession peak at July 2008 to December 2011. The chart’s data is indexed to July 2008, and as in Chart 2b above, the data at August 2011 is averaged to remove the temporary work stopage.

Chart 4

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The San Jose-SF CSA (dark blue) is the lowest line on the left half of the timeline, most apparent from April to October 2009. After hitting its January 2010 low, San Jose-SF CSA employment increases relatively rapidly to end as the third highest data point at December 2011, 2.5% above its pre-recession high at July 2008.

The relatively rapid fall in technology sector employment in the San Jose-SF CSA compared to the other CSAs from July 2008 to January 2010 might be related to the general psychological mood at the time, which was perhaps most typified in a widely circulated presentation titled “R.I.P. Good Times” prepared by Sequoia Capital on October 6 2008 for its portfolio companies. Global financial markets had been gradually spiraling down since late 2007, and were scrambling during the summer of 2008; then, Lehman Brothers filed bankruptcy on September 15 2008; and global financial systems were getting slammed. Three weeks after Lehman’s collapse, the Sequoia presentation painted a grim financial picture for venture funds and venture backed companies. There was a lot of talk about panic, if not actual panic across the technology community in Silicon Valley and beyond.

Three CSAs, Atlanta (plum), Seattle (blue), Denver (light yellow), and Washington DC (orange), did consistently better overall than the U.S. as a whole. All four had smaller relative losses to the U.S. for the 3.5 year period. Atlanta, Seattle, and Denver finished the period higher than their July 2008 employment levels. Washington DC lost the fewest jobs relatively, but hasn’t yet reached its pre-recession peak. The Washington DC technology sector may have been protected from early losses by government spending, and then, may have slowed in recovery as more policy pressure has been placed on reducing government spending.

Chart 5 shows the same technology sector employment data as Chart 4 indexed to the pre-recession peak at July 2008, but in addition, it normalizes CSA data to the U.S. technology sector as a whole. Thus, Chart 5 shows CSA employment relative to the U.S.

Chart 5

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The Atlanta (plum), Seattle (blue), Denver (light yellow), and Washington DC (orange) CSAs consistently show relatively higher technology sector employment relative to the U.S. as a whole. Though they had job losses, their losses were relatively less than the U.S. as a whole.

As mentioned before, the San Jose/Sf CSA (dark blue) shows the widest swing in relative employment, falling more rapidly after July 2008 and then rising more rapidly after late 2009.

The Minneapolis (light orange) and Boston (turquoise) CSAs roughly track the U.S. as a whole.

The New York CSA (pink) is consistently below the U.S. during most of the period, but recovers to match the U.S. toward the end of 2011.

Dallas (tan), Chicago (teal), Philadelphia (lime), and Los Angeles (violet) CSAs consistently show relatively lower technology sector employment relative to the U.S. as a whole. The 4 CSAs lost jobs at a greater rate relative to the U.S., and have not yet recovered to match the U.S. The Philadelphia and Los Angeles CSAs show the lowest relative employment, and they continue to further lag the U.S. The Chicago CSA, after the relative losses at the beginning of the period, appears to stabilize. The Dallas CSA shows significant relative job losses into 2010, then gradual relative improvement, but not enough to match the U.S. as a whole.

Note again that Chart 5 shows the CSA technology sector employment data that is normalized to the U.S. as a whole. The U.S. has 3.2% less technology sector employment in December 2011 than in the pre-recession peak in July 2008. Only 4 CSAs have recovered to pre-recession employment levels.

Longer Term Trends of Technology Sector Employment

The next 4 charts are similar to the previous charts, but for completeness, show longer periods. These charts further reinforce many of the observations above. Only a few notable observations will be repeated.

Charts 6 and 7 show the CSAs from the post dot com bubble minimum at February 2004 to December 2011. Chart 6 is indexed to February 2004. Chart 7 is indexed to February 2004 and normalized to the U.S. technology sector.

The Seattle CSA (blue) shows the most dramatic relative increase in technology sector employment since February 2004. The Washington DC CSA (orange) also shows a significant relative increase. The San Jose/SF CSA (dark blue) roughly tracks the U.S. through 2009, then breaks away from other CSAs. The other CSAs cluster around or below the U.S. as a whole.

Chart 6

Click on chart to see full size.

Chart 7

Click on chart to see full size.

Charts 8 and 9 show the CSAs for the entire 20 year period from January 1992 to December 2011. Chart 8 is indexed to January 1992. Chart 9 is indexed to January 1992 and normalized to the U.S. technology sector.

Again, the Seattle CSA (blue) shows the most dramatic relative increase (77K jobs added) across the 20 tear period, with the Washington DC CSA (orange) also showing a significant relative increase (114K jobs added).

Strikingly apparent in Chart 8, Seattle is the only CSA to have exceeded its peak technology employment levels during the dot com bubble, c.2000, and has done so by 10%. The Washington DC CSA is within 5% of its dot com bubble peak employment levels, and could exceed those as well. Other CSAs are typically 25-30% below their dot com bubble levels.

The relative gains in the Atlanta (plum) and Denver (light yellow) CSAs in the 1990s and early 2000s and the leveling off in the 2000s is more clearly apparent here, especially in Chart 9.

Chart 8

Click on chart to see full size.

Chart 9

Click on chart to see full size.

Location Quotient

Location quotient (LQ) here is the ratio of the concentration of technology sector employment in a CSA compared to the concentration of technology sector employment across the U.S. as a whole:

    lq = ( csa.tech_empl / csa.all_empl ) / ( US.tech_empl / US.all_empl )

Thus, if a CSA’s LQ is greater than 1.0, then the CSA’s ratio of technology sector employment to its employment across all sectors is greater than the ratio for the U.S. as a whole of technology sector employment to employment in all sectors. Conversely, iF a CSA’s LQ is less than 1.0, then the CSA’s ratio of technology sector employment to its employment across all sectors is less than the ratio for the U.S. of technology sector employment to employment in all sectors.

Location quotient is an indication of an industry sector’s relative importance to a region’s overall economy.

Chart 10 shows the technology sector location quotient for the 12 CSAs from January 1992 to December 2011.

Chart 10

Click on chart to see full size.

Immediately apparent is the San Jose/SF CSA’s significantly higher technology sector LQ, starting at 2.77 in 1992 and rising to 3.10 at the end of 2011. The San Jose/SF region has substantially higher LQ than other regions, and has continued to increase its LQ over the past 20 years.

Most of the 12 major CSAs maintained location quotients that were greater than 1.0.

The location quotient curves do not show a marked effect from the dot com bubble. This indicates that the dot com bubble was not localized to particular regions but was more pervasive across all regions and across the U.S. as a whole.

The Seattle (blue) and the Washington DC (orange) CSAs show significant increases. Seattle roughly doubles its location quotient, rising from 1.02 to 2.07, and rises from 9th ranked to 2nd ranked. Washington DC increases its location quotient from 1.47 to 2.03, and rises from 5th ranked to 3rd ranked. Washington DC was 2nd ranked for much of the 2000s, but was gradually superseded by Seattle in 2010.

The Boston CSA (turquoise) started as the 2nd ranked location quotient at 2.04, but gradually decreased to 5th rank at 1.70.

The Los Angeles CSA (violet) also shows a notable decrease in LQ ranking from 7th at 1.29 to 9th at 0.95.

Table 5 shows the location quotient for the 5 transition dates used above for each of the 12 CSAs and for the aggregate of the 12 CSAs. Also, shown is the LQ for the U.S. as a whole, which is of course always 1.00. A location quotient that is less than 1.0 is shown in a red font color.

Location Quotient 1992.01  2004.02  2008.07  2010.01  2011.12 
12 CSAs 1.366   1.381   1.391   1.388   1.392  
US000 1.000   1.000   1.000   1.000   1.000  
CS488 San Jose-SF CSA 2.771   2.881   2.913   2.903   3.106  
CS408 New York CSA 1.011   0.943   0.948   0.908   0.919  
CS548 Washington DC CSA 1.474   1.927   2.018   2.076   2.032  
CS348 Los Angeles CSA 1.287   1.036   1.001   0.995   0.951  
CS148 Boston CSA 2.039   1.714   1.738   1.727   1.702  
CS206 Dallas CSA 1.654   1.689   1.559   1.502   1.487  
CS500 Seattle CSA 1.017   1.709   1.929   2.052   2.068  
CS176 Chicago CSA 0.879   0.844   0.840   0.838   0.831  
CS122 Atlanta CSA 1.119   1.322   1.280   1.333   1.358  
CS428 Philadelphia CSA 0.959   0.931   0.972   0.948   0.925  
CS378 Minneapolis CSA 1.303   1.365   1.347   1.356   1.340  
CS216 Denver CSA 1.544   1.805   1.685   1.739   1.762  

Table 6 shows the percentage change in location quotient for the 6 time periods used above. This is the total change for the time period; the annualized change is shown in Table 7. A decrease in location quotient across a time period is shown in red.

Percent Change LQ 1992.01  1992.01  2004.02  2008.07  2010.01  2008.07 
2011.12  2008.07  2011.12  2010.01  2011.12  2011.12 
12 CSAs 1.92%   1.80%   0.84%   -0.20%   0.32%   0.12%  
US000 0.00%   0.00%   0.00%   0.00%   0.00%   0.00%  
CS488 San Jose-SF CSA 12.09%   5.14%   7.82%   -0.35%   6.99%   6.61%  
CS408 New York CSA -9.10%   -6.26%   -2.55%   -4.22%   1.25%   -3.03%  
CS548 Washington DC CSA 37.84%   36.92%   5.44%   2.84%   -2.11%   0.67%  
CS348 Los Angeles CSA -26.07%   -22.22%   -8.13%   -0.63%   -4.35%   -4.95%  
CS148 Boston CSA -16.53%   -14.75%   -0.73%   -0.64%   -1.45%   -2.09%  
CS206 Dallas CSA -10.05%   -5.75%   -11.93%   -3.63%   -0.98%   -4.57%  
CS500 Seattle CSA 103.31%   89.60%   21.01%   6.41%   0.77%   7.23%  
CS176 Chicago CSA -5.47%   -4.41%   -1.47%   -0.25%   -0.87%   -1.11%  
CS122 Atlanta CSA 21.31%   14.31%   2.74%   4.15%   1.89%   6.12%  
CS428 Philadelphia CSA -3.50%   1.35%   -0.55%   -2.43%   -2.41%   -4.78%  
CS378 Minneapolis CSA 2.80%   3.35%   -1.81%   0.70%   -1.23%   -0.54%  
CS216 Denver CSA 14.10%   9.07%   -2.39%   3.21%   1.36%   4.61%  

Table 7 shows the annualized percentage change in location quotient for the 6 time periods used above. A negative change is shown in red.

Annualized % Change LQ 1992.01  1992.01  2004.02  2008.07  2010.01  2008.07 
2011.12  2008.07  2011.12  2010.01  2011.12  2011.12 
12 CSAs 0.10%   0.11%   0.11%   -0.14%   0.17%   0.03%  
US000 0.00%   0.00%   0.00%   0.00%   0.00%   0.00%  
CS488 San Jose-SF CSA 0.57%   0.30%   0.97%   -0.23%   3.59%   1.89%  
CS408 New York CSA -0.48%   -0.39%   -0.33%   -2.83%   0.65%   -0.90%  
CS548 Washington DC CSA 1.62%   1.92%   0.68%   1.88%   -1.10%   0.20%  
CS348 Los Angeles CSA -1.50%   -1.51%   -1.08%   -0.42%   -2.29%   -1.47%  
CS148 Boston CSA -0.90%   -0.96%   -0.09%   -0.43%   -0.76%   -0.62%  
CS206 Dallas CSA -0.53%   -0.36%   -1.61%   -2.43%   -0.51%   -1.36%  
CS500 Seattle CSA 3.63%   3.95%   2.46%   4.23%   0.40%   2.06%  
CS176 Chicago CSA -0.28%   -0.27%   -0.19%   -0.16%   -0.45%   -0.33%  
CS122 Atlanta CSA 0.97%   0.81%   0.35%   2.75%   0.98%   1.75%  
CS428 Philadelphia CSA -0.18%   0.08%   -0.07%   -1.63%   -1.26%   -1.42%  
CS378 Minneapolis CSA 0.14%   0.20%   -0.23%   0.47%   -0.64%   -0.16%  
CS216 Denver CSA 0.66%   0.53%   -0.31%   2.13%   0.71%   1.33%  
* * * * * * *

References and Notes

1. U.S. Bureau of Labor Statistics.
Quarterly Census of Employment and Wages.
http://www.bls.gov/cew/cewover.htm

The BLS/QCEW data set provides detailed monthly employment data for every county across the U.S. classified by industry segments using NAICS industry codes. The BLS/QCEW is reported quarterly, and is published approximately 6 months after the close of the reported quarter. This 6 month lag is the tradeoff for gaining access to this granular level of detail. The BLS provides various interactive tools for perusing the data.

BLS QCEW Databases.
http://www.bls.gov/cew/data.htm

BLS Location Quotient Calculator.
http://data.bls.gov/location_quotient/ControllerServlet

2. Office of Management and Budget, Bulletin No. 10-02
Update of Statistical Area Definitions and Guidance on Their Uses
http://www.whitehouse.gov/sites/default/files/omb/assets/bulletins/b10-02.pdf

U.S. Office of Management and Budget maintains the definitions of regional statistical areas. Roughly speaking, a Combined Statistical Area (CSA) contains 2 or more Core Based Statistical Areas (CBSA). A CBSA is designated as either a Metropolitan Statistical Area (MSA) or Mircopolitan Statistical Area (μSA). A CBSA is made up of a collection of counties. The most recent area definitions are listed in the above Bulletin.

“Combined Statistical Areas can be characterized as representing larger regions that reflect broader social and economic interactions, such as wholesaling, commodity distribution, and weekend recreation activities, and are likely to be of considerable interest to regional authorities and the private sector.”

There are 128 CSAs, which represent ~2/3 of the U.S. population. Not all counties in the U.S. are included in a CSA. For example, San Diego is not part of a CSA. Austin County TX was not part of a CSA until a new CSA was designated a couple of years ago in OMB 10-2 for Austin and the greater Austin area.

A CSA can extend beyond state boundaries. For example, the Boston CSA includes most of eastern Massachusetts plus southern New Hampshire and Rhode Island. The New York CSA includes New York City, Long Island, Southern Connecticut, Northern New Jersey, and nearby portions of Pennsylvania.

The San Jose/San Francisco CSA includes the counties north and south of San Francisco including Santa Clara County and Silicon Valley.

Wikipedia provides an easily accessible list of U.S. CSAs, though the OMB Bulletin is the definitive reference.
http://en.wikipedia.org/wiki/Table_of_United_States_Combined_Statistical_Areas

3. North American Industry Classification System (NAICS)
http://www.bls.gov/bls/naics.htm
http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007

NAICS classifies all business establishments in a hierarchical 2 to 6 digit code. NAICS has replaced Standard Industrial Classification (SIC) codes in most uses; though, SIC codes continue to be used by the U.S. Securities and Exchange Commission (SEC). There are mappings between NAICS and SIC.

4. U.S. Census Bureau American Fact Finder and Quick Facts
http://factfinder2.census.gov
http://quickfacts.census.gov/qfd/download_data.html

The U.S. Census Bureau’s American Fact Finder and QuickFacts provide access to all of the underlying demographic data presented above and more.

5. Verizon strike to hit monthly jobs reading. Chris Isidore. @CNNMoney. August 22, 2011.
http://money.cnn.com/2011/08/22/news/companies/verizon_strike_jobs_report/index.htm

Approximately 45,000 Verizon employees were on strike during the period when the Department of Labor collected August jobs data. The employees returned to work at the end of August.

6. ”R.I.P. Good Times”. Sequoia Capital. 2008.10.06.
http://www.scribd.com/doc/73886447/R-I-P-Good-Times-10-7-08-Final

From the Valley Comes a Warning. The New York Times. 2008.11.16.
http://www.nytimes.com/2008/11/17/business/17views_ready.html

“Maybe it was the tombstone that did it. Even as Wall Street burned, Silicon Valley seemed strangely sanguine. Then a PowerPoint presentation from Sequoia Capital prophesying Armageddon — featuring an “R.I.P. Good Times” headstone — made the rounds. A month later, venture capital firms are slashing investments and counseling portfolio companies to cut jobs.” – NYT

* * * * * * *

When visiting Facebook and Google headquarters, I noticed a lot of women employees, even though Silicon Valley has traditionally been male dominated. At Google and Facebook, you see women in technical roles and management roles, not just clerical roles. There is nothing wrong with clerical roles, but it is good to see women contributing more broadly throughout their organizations.

From my personal experience, the work environments where women were broadly represented in the organization were more productive, more collaborative, more civil, and more enjoyable to work in than corresponding male dominated environments. There is a long history of research and analysis that supports these observations.

For example, recent research on collective intelligence supports the benefits of women in groups.[1] The researchers brought together groups of 2 to 5 people, and gave them various tasks to perform. The groups that performed better at the tasks were not correlated with highest average group IQ or highest individual IQ, but were correlated with groups with higher social sensitivity and in turn with a higher proportion of women. (In general, women measure better in social sensitivity than do men; a socially sensitive man, given the chance, would improve a group’s performance just as well.) The research further showed that the measure of social sensitivity and collective intelligence was predictive of how a group would perform on future tasks. In other words, the inclusion of women in a group makes the group smarter and more productive.

The following videos show Sheryl Sandberg, Facebook, Marissa Mayer, Google, and Madeleine Albright, former Secretary of State, describing their views of the role of women.

As noted earlier, Sheryl Sandberg was an important addition as COO to Facebook. She brought not only considerable corporate and growth experience to Facebook, but also importantly a considerable degree of social intelligence. For further background on and material from Sheryl Sandberg, see [2].

The second video is Marissa Mayer, Google’s VP of Local, Maps & Location Services; Meyer was one of Google’s earliest employees. She describes herself not as a women at Google, but as a geek at Google (apparently one with fashion sense). For more material, see [3].

Since a global view is increasing important, the third video is from Madeleine Albright, who “broke the glass ceiling” to become the first woman U.S. Secretary of State, 1997-2001. Interestingly, her granddaughter now says, “So what’s the big deal about Grandma Maddie being Secretary of State? Only girls are Secretary of State.” For more material, see [4].

Sheryl Sandberg: Why we have too few women leaders
TEDWomen, 2011 December 7

Google VP Marissa Mayer
Women In the Economy Conference, The Wall Street Journal, 2011 April 4

Madeleine Albright: On being a woman and a diplomat
TEDWomen, 2011 December 8

Boston does have women in some leadership roles, but they are largely absent from national lists of influential women in technology.[5] Truly more substantive opportunities for women in Boston companies would help to foster development of the next major technology companies.

A recent report from the Anita Borg Institute for Women and Technology suggests that companies apply a variation of the “Rooney Rule”, requiring that at least one qualified female candidate be included in the pool of candidates to be considered for an open technical position.[6]

Maybe the next major technology company will be founded by a woman…

Notes, References, and Additional Materials.

1. Anita Williams Woolley; Christopher F. Chabris; Alexander Pentland; Nada Hashmi; Thomas W. Malone. Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science, v330, p686-688. 2010.10.29.
http://www.sciencemag.org/content/330/6004/686.full
http://www.sciencemag.org/cgi/content/full/science.1193147/DC1
http://www.sciencemag.org/content/suppl/2010/09/30/science.1193147.DC2.html (Podcast)

Defend Your Research: What Makes a Team Smarter? More Women. Anita Woolley; Thomas Malone. Harvard Business Review. 2011.06.
http://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women/ar/1

A step in the wrong direction. Steven Grossman; Gloria Cordes Larson. The Boston Globe. 2012.02.22.
http://www.boston.com/bostonglobe/editorial_opinion/blogs/the_podium/2012/02/a_step_in_the_wrong_direction.html

2. Why we have too few women leaders. Sheryl Sandberg. TEDWomen. 2011.12.07.
http://www.ted.com/talks/sheryl_sandberg_why_we_have_too_few_women_leaders.html

For additional videos from TEDWomen:
http://conferences.ted.com/TEDWomen/

Sheryl Sandberg On Why We Have Too Few Women Leaders. The Huffington Post. 2011.12.13.
http://www.huffingtonpost.com/2011/12/13/sheryl-sandberg-ted-talk_n_1145415.html

A Woman’s Place – Can Sheryl Sandberg upend Silicon Valley’s male-dominated culture? Ken Auletta. The New Yorker. 2011.07.11.
http://www.newyorker.com/reporting/2011/07/11/110711fa_fact_auletta

Why Facebook Needs Sheryl Sandberg. Brad Stone. Business Week. 2011.05.11.
http://www.businessweek.com/magazine/content/11_21/b4229050473695.htm

3. Marissa Mayer on Being a Google ‘Geek’. WSJ. 2011.04.05.
Google’s VP of new products Marissa Mayer tells WSJ’s Julia Angwin that even in the male-dominated field of technology, she’s less aware of her status as a woman, than her status as a geek.
http://online.wsj.com/video/marissa-mayer-on-being-a-google-geek/65DF05B0-1A28-41E8-8B0F-43E2F1196A04.html

Women in the Economy, April 2011. WSJ.
http://online.wsj.com/public/page/women-04112011.html

A Blueprint for Change. Women in the Economy. WSJ. 2011.04.10.
http://online.wsj.com/article/SB10001424052748704415104576250900113069980.html

4. Madeleine Albright: On being a woman and a diplomat. TEDWomen. 2011.12.08.
http://www.ted.com/talks/madeleine_albright_on_being_a_woman_and_a_diplomat.html

Madeleine Korbel Albright, U.S. Secretary of State, Biography.
http://secretary.state.gov/www/albright/albright.html

The Madeleine Korbel Albright Institute For Global Affairs, Wellesley College.
http://www.wellesley.edu/albright/index2.html

Wintersession Program, Albright Institute, Wellesley College.
http://www.wellesley.edu/albright/program.html

5. The 10 most powerful women in Boston tech (plus 5 up-and-comers). Scott Kirsner. Innovation Economy. The Boston Globe. 2012.02.08.
http://www.boston.com/business/technology/innoeco/2012/02/the_10_most_influential_women.html

Most Influential Women In Technology, 2011. FastCompany.
http://www.fastcompany.com/women-in-tech/2011

A step in the wrong direction. Steven Grossman; Gloria Cordes Larson. The Boston Globe. 2012.02.22.
http://www.boston.com/bostonglobe/editorial_opinion/blogs/the_podium/2012/02/a_step_in_the_wrong_direction.html

The 2011 Census of Women Directors and Executive Officers of Massachusetts Public Companies. The Boston Club. 2011.11.11.
http://www.thebostonclub.com/index.php/download_file/view/164/99/

The Eighth Annual Status Report of Women Directors and Executive Officers of Public Companies in 14 Regions of the United States. InterOrganization Network (ION). 2011.12.22.
http://www.ionwomen.org/wp-content/uploads/2011/12/ION_StatusReport_2011.pdf

Most Influential Women In Technology, 2011. FastCompany.
http://www.fastcompany.com/women-in-tech/2011

Silicon Valley women are on the rise, but have far to go. Vivek Wadhwa. The Washington Post. 2011.11.09.
http://www.washingtonpost.com/national/on-innovations/silicon-valley-women-are-on-the-rise-but-have-far-to-go/2011/09/14/gIQAP5b84M_story.html

J. McGrath Cohoon; Vivek Wadhwa; Lesa Mitchell. The Anatomy of an Entrepreneur: Are Successful Women Entrepreneurs Different from Men? Kauffman Foundation. 2010.05.11.
http://www.kauffman.org/uploadedFiles/successful_women_entrepreneurs_5-10.pdf

6. How to Get More Women Hired for Technical Roles. Joseph Walker. FINS/WSJ. 2012.02.28.
http://it-jobs.fins.com/Articles/SBB0001424052970204520204577249441813206790/How-to-Get-More-Women-Hired-for-Technical-Roles

Solutions to Recruit Technical Women. Caroline Simard; Denise Gammal. Anita Borg Institute. 2012.02.28.
http://anitaborg.org/news/archive/top-ten-solutions-to-recruit-technical-women-identified-in-new-research-report/
http://anitaborg.org/files/Anita-Borg-Inst-Solutions-To-Recruit-Technical-Women.pdf

Anita Borg Institute for Women and Technology.
http://anitaborg.org/

Rooney Rule. Wikipedia.
http://en.wikipedia.org/wiki/Rooney_Rule

Alternate Title: What will it take for the next major technology company to develop in Boston? Or, anywhere else for that matter?

Facebook recently released their much anticipated initial S-1 IPO filing.[1] It’s been all over the trade and national news along with other recent and forthcoming IPOs.

The general expectation is that Facebook will be valued at $80-100 billion. At that valuation, Facebook will create 10s of billionaires and approaching a 1000 millionaires.[2,3] The created wealth will have a lasting effect on the technology sector and future landscape as Facebook millionaires begin investing in new technologies and entrepreneurs.[4] Also, the resulting capital gains and income from stock and options will generate a lot of tax revenue for California and the U.S.[5]

Boston has not built any major technology companies, on the scale of Google or Facebook, in past 20 years.[6]

The Facebook IPO again raises the question: What would it take, what needs to change in order to build the next major technology company in the Boston area? [7] And the corollary questions: Are stakeholders actually willing to do what it takes? Or, will inertia and myopic irrationality continue to erode Boston’s potential regional advantage in the technology sector.

Here, we’ll look at some of the specific conditions that were primary enablers for Facebook and other major technology companies, and how they relate to Boston. The listed order of the conditions tends to be consistent with Facebook’s chronology, but otherwise, the order or length of discussion is not meant to indicate any particular meaning.

1. The next Facebook won’t be like Facebook.

Searching the dorms of Harvard (or another university) for the next Mark Zuckerberg completely misses the point. The next Facebook won’t look like Facebook, it will go against whatever is current conventional wisdom.

At the time (c. 2004), conventional wisdom doubted that social networking could expand beyond a fad on a few university campuses, and doubted whether it could be monetized beyond being a hobby.

Facebook was actually pitched first in Boston. For an investigative look at how Boston VCs passed on Facebook, see [8]. Not much has changed since then, so that it is hard to see why Boston wouldn’t pass on the next Facebook.

Also at the time, the investment industry was still trying to recover from the dotcom bust, and many were reluctant to invest in ventures (a) that were based on a “get big fast, figure out the revenue later” business model, or (b) whose business models were based primarily on advertising.[9]

The key observation here is that “conventional wisdom” was wrong.[10] Facebook, Google, Apple, etc., all went against their era of conventional wisdom. The next major technology company will go against its context of conventional wisdom as well.

“Doing the same thing over and over again and expecting different results is stupid.” ~ Aphorism [11]

2. Serendipity matters a lot.[12]

Facebook was not the first social network. At the time (c. 2004), many thought it wasn’t particularly better than or distinctly different from other social networks like Friendster (started c. 2002 [13]) or MySpace (started c. 2003 [14]) or even earlier social platforms. For a snapshot of the history of social networks c. 2007, see [15].

The various social networking efforts were known to each other. MySpace was billed as a next-generation Friendster. As MySpace became edgier, perhaps too racy, and plagued with spam, Facebook was thought to be a “safer” alternative.

In retrospect, MySpace’s biggest obstacle is typically attributed to News Corp.’s attempts to prematurely make MySpace a profit center. Friendster’s biggest obstacle is typically attributed its unstable platform or uncertain strategy. Certainly, those are valid criticisms. However, events could have just as easily gone the other way. What in retrospect is an obstacle, at the time, might have been turned into opportunity. News Corp. provided MySpace with very strong media industry access. Friendster had many notable Silicon Valley advisers, investors, and board members.

Facebook was able to learn a lot from looking at the missteps of others, but serendipity appears to have been as important as hard work.

“Chance favors the prepared mind.” ~ Louis Pasteur [16]

3. Technological and market inevitability.

Along with serendipity, a high degree of inevitability is necessary.

As the Internet began to scale, and more and more people began spending more and more time on the Internet, it was inevitable that one or more entities would provide one or more social networking platforms. The precise form of the platforms would remain uncertain until it became obvious.

Social networking, as currently defined, would not have made much sense during the early days of the Internet. There was not enough technology, not enough scale, and not enough diversity. However, in the context of the early Internet, there was a lot of discussion, research, and small scale developments of the kinds of social environments that eventually would be enabled by the Internet (for a very early view, see Licklider 1968 [17]). It was inevitable that social networking would exist, but a few other technologies needed to be invented and built, and access to the Internet needed to become more pervasive and ubiquitous.

PCs, the Internet, graphical user interfaces, digital media, smart phones, etc. — each were seen as inevitable by some people long before a market was defined and created. Each of these developments also had many detractors, some of whom were hostile or obstructive.

“We stand on the shoulders of giants.” ~ Isaac Newton

4. Founder control matters a lot too.

Zuckerberg has maintained dominant control over Facebook’s direction since its beginnings. He had important advisers, but the final say was his. Whether or not you agree with the details of the path that Zuckerberg took Facebook, and apparently, many insiders did disagree with the details, it is hard to dispute Facebook’s current success.

The Facebook S-1 outlines the degree of control that Mark Zuckerberg has and will continue to have over the governance of Facebook.[1] News reports discuss this in more detail.[18] Zuckerberg directly holds 28.2% of the shares, and has voting proxy for another 30.6% of shares; thus, he controls 56.9% of the vote. Almost all of these are Class B shares which yield 10 votes per share compared to 1 vote per share for the Class A shares that will issue with the IPO. Further, when current holders sell their Class B shares, the sold shares convert to Class A, which would further consolidate Zuckerberg’s control.

Though this degree of founder control may seem off scale, it is not when compared to other major successful technology companies. Bill Gates held 49% of Microsoft at its IPO in 1986, and Sergey Brin and Larry Page together held 32% of Google at its IPO in 2004.[18]

This then brings to question whether Facebook could have achieved even a fraction of its current success if investors had taken their usual early control of the company. You can just imagine how Zuckerberg would have been repeatedly chided and overridden by those in “control” who were convinced they “knew better” (c.f., Steve Jobs ouster from Apple, c. 1985). Reportedly, Zuckerberg always had a definite idea of how Facebook should evolve, though he may not have always been very good at communicating a strategy to the satisfaction of others. Nonetheless, his control of the company likely prevented Facebook from suffering a fate similar to Friendster or MySpace.

“If one does not know to which port one is sailing, no wind is favorable.” – Seneca

5. Founder as CEO.

Founder as CEO is another type of control that is more operational than the control provided by the above voting rights. It directly effects company strategy and direction, decision making, as well as governance.

Because Zuckerberg always maintained the CEO position, he was able to drive company strategy and decision making directly without having to explain to someone else what decisions to make and why. The founder as CEO is much more effective operationally than an outsider CEO. To augment any skills or experience that the founder CEO may lack, someone with that experience can always be hired to support the founder CEO.

Quoting a recent article on this topic [19]:

“This year, dozens of startup company founders will be forced out and replaced by experienced outsider CEOs, often from public companies, brought in by venture capital investors to provide “adult supervision.” You can bet that none of these companies will become the next Hewlett-Packard, the original Silicon Valley technology company – or the next Intel, Microsoft, Oracle, or Apple.* These technology juggernauts span software, hardware, services, and media, but they all have something in common. Their founders served as transformative chief executives.

“Still, the conventional wisdom among investors, if not the media, is that founders need to move out of the way for an experienced CEO to take a company ‘to the next level’. …

“Outsider, non-founder CEOs are often overvalued because many corporate boards think the answer to their problems is a superstar CEO with an outsized reputation. This leads them to overpay for people who are good at creating outsized reputations through networking, interviewing, and taking credit for other peoples’ achievements–all bad indicators of future success. …

“Founders tend to know their companies better than outsiders. They know their industries better than most outsiders. They are often more motivated than the average hired-gun CEO to improve a company’s long-term prospects. They are more likely to be innovators, having taken the step of founding a company. In tech firms, they usually know technology better than a typical outsider CEO, whose specialty skews toward marketing and networking.”

And with respect to Google, the article notes:

“Google, arguably, did succeed with adult supervision in the person of Eric Schmidt, CEO from 2001 to 2011. But note that the two founders remained in top positions (outsiders referred to the three as “co-CEOs”) and Schmidt was succeeded by one of the founders.”

Further note that Eric Schmidt was a technologist and worked well with Sergey Brin and Larry Page. Schmidt was not as much of an outsider.

Another key question: Is an outsider CEO’s interests aligned with the founders or with the investors? Founder alignment (c.f., Schmidt) portends greater success than investor alignment. Unfortunately, outsider CEOs tend more naturally to align themselves with investors to the detriment of founders and notably to the detriment of the success of the company.

“The chief strategist of an organization has to be the leader – the CEO.” Michael Porter

6. A trusted adviser who gets “it” and amplifies “it”.

Sometimes the early trusted adviser is also the first investor. Sometimes he or she is a mentor. Sometimes, a co-founder or business partner or perhaps a relative. There needs to be someone with whom the founder can openly, speculatively, safely, and efficiently bounce around ideas.

From accounts, it is clear that Zuckerberg getting together with Sean Parker was very important, and came at a critical time for the company. It was serendipitous. Parker provided a significant amplification for Facebook’s strategy. “Back then Parker apparently believed even more passionately in the company’s potential than did Zuckerberg himself.” Parker became Facebook’s president. However, Parker came with notable liabilities. See the references [20] for more of the history.

In some respects, Parker was like a more experienced Zuckerberg with some successes and some failures.

Would Facebook have been as successful if Zuckerberg had not met Parker? Some adviser was probably needed at that time. Would a less controversial person have been able to provide the needed amplification for Zuckerberg and Facebook?

(For a few related interesting startup stories of other major technology companies, see here.)

“Getting it is much more than just saying the words ‘I get it’.” – Anonymous

7. Access to the first (angel) investor.

The first investor of an ultimately successful major technology company, like Facebook, is often important for reasons that go beyond just the money brought to the venture. The investment needs to be substantial enough to achieve a major milestone for the company, then it is helpful if the investor brings more to the effort than just money.

As has been noted in recent years, the initial critical investment often comes not from venture capital but from an angel investor, investing his/her own money. The angel investor typically made money as a recently successful entrepreneur within the prior 10 year innovation cycle. Because of his/her recent success, the angel investor is motivated to support the next generation of innovation and entrepreneurs.[21]

In 1977, Mike Markkula, having participated in Intel’s success, invested $250K with Steve Jobs and Steve Wozniak to fund development of the Apple II. Markkula then became Apple’s third founder.[22]

In 1999, Andy Bechtolsheim, one of Sun Microsystems’ founders, invested $100K with Sergey Brin and Larry Page to form Google, even though at the time, there already were many search engines, and many people questioned whether another search engine was really needed.[23]

Everyone that’s been paying attention (or saw the movie) knows that Peter Thiel, a founder of PayPal, was first to invest in Facebook, investing $500K. Thiel invested even though Facebook was being threatened with litigation, and even though Thiel had invested in other social networks.

Angel investors often have their own reasons for investing that typically is more than just making money.

(a) Thiel said, “You always hope for strong returns, but the companies that make you proud as an investor are the ones that produce that return by transforming the world for the better—technologically, socially, and economically.”[24]

(b) Guy Kawasaki, an early employee of Apple, said, “Here’s how angel investors differ from venture capitalists. Typically, angel investors have a triple bottom line. First, they’ve “made it,” so now they want to “pay back” society by helping the next generation of entrepreneurs. Second, they’d like to stay current with technology and tinker with interesting products and technologies. Finally, they want to make money. Thus, they are often willing to invest in less proven, more risky deals to provide entrepreneurs with the ability to get to the next stage. I know many nice venture capitalists, but I cannot tell you that many of them are motivated by the desire to pay back society or seek intellectual stimulation. :-)” [25]

In the past 20 years, Boston has not built any major technology companies on the scale of Google or Facebook, which in turn has not produced a large number financially successful employees of these companies looking to be angel investors. Thus, though there are angel investors in Boston, they are fewer in number, they invest with a much smaller pool of personal funds, and many built their companies and successes more than 20 years ago or in businesses which aren’t the basis of current innovations (c.f., Web 2.0 Internet).[26]

8. A broad pool of potential employees with relevant skills.

To build beyond the initial team of founders and employees and scale the company, there needs to be a broad pool of available potential employees who have the skills and experience that are relevant to growing the company’s products and services. Importantly, it needs to be straightforward to hire these new employees with few obstructions in the hiring process.

Perhaps the greatest obstruction to the hiring process is the use of non-compete agreements. Non-competes are especially problematic when they overreach or simply are used to provide an overall chilling effect to employee mobility.

Facebook hired many of its employees from Google and other area technology companies. Because non-compete agreements are not valid in California, Google employees were free to move to Facebook without untoward obstacles. Google could make counteroffers to convince employees not to go to Facebook, but it could not pretend that there may be some illusory competition between search and social networks, and it could not threaten employees and their potential employers with costly litigation.

Google similarly benefited during its early growth period when it was able to hire unimpeded from other companies. Google continued to thrive even though several of its employees moved to Facebook.

The ability to hire to scale each company during its respective critical growth period was extremely important to each company’s success. That ability to hire was a result of the region’s employee mobility due to the lack of employee non-compete agreements.[27]

Because of the Boston area’s use of non-compete agreements and the resulting limits to employee mobility, Google and Facebook could not have been built in the Boston area.

Non-competes hinder innovation in two ways. (1) The non-compete is a direct obstacle for an employee moving from one employer to a different employer. This is true even if there is no actual competition between the former and future employer. (2) Perhaps more importantly, an employee who is constrained from moving to a new position often tends to work within his/her current narrow job description. The employee often does not experience new or alternate work environments; thus, may never gain the experiences that would be valuable to a new growing company.

“Practically every enterprise [is] threatened and put on the defensive as soon as it comes into existence.” – Schrumpeter [28]

9. Ability to attract a few experienced executive employees.

Even though founder control is important, a growing company still needs to hire experienced senior management to support and augment the founder.

Sheryl Sandberg was an important addition to Facebook as COO in 2008. Her skills and experience complemented Zuckerberg without replacing him or lessening his role. Importantly, Zuckerberg as CEO still gets to make the final call. Zuckerberg and Sandberg formed a partnership for joint decision making and running the company, somewhat similar to Google’s earlier triumvirate of Sergey Brin, Larry Page, and Eric Schmidt. For more background on Sheryl Sandberg, see [29]

Sandberg joined Facebook from Google in 2008 after spending 7 years at Google as Vice President of Global Online Sales & Operations. During those 7 years, Google grew its annual revenue geometrically from ~$86M to ~$20B. When she joined Facebook, it had ~$150M annual revenue; it now has $3.7B annual revenue and growing.

Many other executives and senior managers came to Facebook with experience from other major technology companies in addition to Google, including, LinkedIn, Yahoo, Mozilla, Friendfeed, Ebay, MySpace, Amazon, etc. Note that most of the companies were founded within the past 20 years. Also note that most of the companies are local to Facebook.

The absence of non-compete agreements also figures prominently in hiring key executives as well.

“Hire people who are better than you are, then leave them to get on with it. Look for people who will aim for the remarkable, who will not settle for the routine.” – David Ogilvy

“If each of us hires people who are smaller than we are, we shall become a company of dwarfs. But if each of us hires people who are bigger than we are, we shall become a company of giants.” – David Ogilvy [30]

Next Steps.

The primary enablers discussed above are not necessarily the only conditions that factor into developing a major technology company. For example, sometimes access to capital or co-location with customers or partners can be important as well; but now, capital is fairly mobile, and location is more globally oriented. Thus, the above enablers can carry more weight in determining the ability to develop the next major technology company.

Of the above enablers:

    1. The next Facebook won’t be like Facebook.
    2. Serendipity matters a lot.
    3. Technological and market inevitability.
    4. Founder control matters a lot too.
    5. Founder as CEO.
    6. A trusted adviser who gets “it” and amplifies “it”.
    7. Access to the first (angel) investor.
    8. A broad pool of potential employees with relevant skills.
    9. Ability to attract a few experienced executive employees.

Items 1,4,5 simply run counter to “the way things are done” traditionally in the Boston technology culture. The ideas and companies that get funded are not like the next Facebook; control is generally taken away from founders; and, an outsider CEO often installed. Since these traditional behaviors obviously have not been working, you’d think that the behavior would change.

Silicon Valley’s culture has its own conventions, and in some respects, may not be that different from Boston’s, but there are plenty of examples of companies not getting funding in Boston that do get funding when they move to Silicon Valley.

Items 7,8,9 expose the significant regional disadvantage for Boston that derives from not having any major technology companies on the scale of Google or Facebook developed in Boston in the past 20 years. There are no angel investors on the scale of employees from PayPal, Google, or Facebook to fund the ideas that are too speculative for traditional investors. There aren’t the Googles from which to hire key employees, and you couldn’t hire them anyway because of non-compete agreements. Not much can be done about missing out on the developments and successes of the past 20 years. However, eliminating non-compete agreements is likely the most obvious and effective corrective action that could be taken to set the stage for the next 20 years.

Nonetheless, all is not lost. Items 2 and 3 offer hope. The Boston area is still a home of prepared minds ready to pursue the next inevitable convergence of new technologies and market opportunities. The question is whether the Boston culture has learned enough from past failure to welcome the opportunity, or whether the Boston culture will continue its failed past myopic irrationality, and thereby, cede the development of the next major technology company to a more fertile environment. Many regions around the world are looking to provide such fertile environments for innovation…

- – - – -

Postscript.

While I was finishing this writeup, Scott Kirsner published an excellent article briefly comparing Facebook and Brightcove, their histories and IPOs:

“[...]Facebook and Brightcove, evolved very differently, and they’ll have very different IPOs. In 2004, Facebook left Boston for California; Brightcove remained in Cambridge. The growth of the firms says a great deal about the kind of companies the soil supports in Boston, versus what germinates out West…”

“The numerical differences are stark. Brightcove’s 2011 revenue was $63 million, but the company had a net loss of $17 million. Brightcove has 300 employees, and is expected to have a stock market value of about $300 million when it goes public… Allaire held on to 7 percent of the company after numerous funding rounds.

Zuckerberg owns 28 percent of Facebook, which made a $1 billion profit last year on $3.7 billion of revenue. The company has 3,000 employees and could be worth as much as $100 billion.”[31]

Sad, but True. Now, let’s fix it…

- – - – -

Notes and References

1. Facebook 2012-02-01 S-1 General form for registration of securities under the Securities Act of 1933.
http://www.sec.gov/Archives/edgar/data/1326801/000119312512034517/d287954ds1.htm

2. Facebook IPO will create billionaires. Benny Evangelista. San Francisco Chronicle. 2012.02.05.
http://www.sfgate.com/cgi-bin/article.cgi?f=/c/a/2012/02/04/BUME1N2RTT.DTL
3. From Founders to Decorators, Facebook Riches. Nick Bilton; Evelyn M. Rusli. The New York Times. 2012.02.01.
http://www.nytimes.com/2012/02/02/technology/for-founders-to-decorators-facebook-riches.html
4. What will the Facebook millionaires do? Lise Buyer. CNN. 2012.02.02.
http://www.cnn.com/2012/02/02/opinion/buyer-facebook-ipo/index.html
5. Facebook’s Zuckerberg may face $2 billion tax bill. Stacy Cowley. CNN Money. 2012.02.07.
http://money.cnn.com/2012/02/07/technology/zuckerberg_tax_bill/

‘Facebook Effect’ Shows California’s Reliance on Capital Gains. Michael B. Marois; James Nash; Bloomberg News. San Francisco Chronicle. 2012.01.12.
http://www.sfgate.com/cgi-bin/article.cgi?f=/g/a/2012/01/12/bloomberg_articlesLXNWD46TTDS6.DTL

6. “The San Jose/San Francisco CSA continues to expand its lead in the creation of S&P Technology Sector companies, and has 28% of the Technology Sector companies founded since 1991. These companies have 57% of the market capitalization and 45% of the revenue of all Technology Sector companies founded since 1991.

“Compared to the Boston CSA, since 1991, and normalizing for population size, the New York and Los Angeles CSAs, each created Technology Sector companies at a slightly greater rate; the Washington DC CSA, at more than twice the rate; the Seattle CSA, at more than 5 times the rate; and the San Jose/San Francisco CSA, at 10 times the rate.”

S&P 1500 Company Founding Dates. Empirical Reality. 2011.05.26.
http://www.empiricalreality.com/2011/05/26/sp-1500-company-founding-dates/

7. Can Massachusetts produce the next Google? Scott Kirsner. The Boston Globe. 2011.12.18.
http://www.bostonglobe.com/business/2011/12/18/can-massachusetts-produce-next-google/O90EMAerdirqGTVTQQOifL/story.html

Building more ‘pillar’ companies in Boston: Bonus material. Scott Kirsner. The Boston Globe. 2011.12.19.
http://www.boston.com/business/technology/innoeco/2011/12/building_more_pillar_companies.html

Facebook “Shock” Has Boston Firms Searching for Next Zuckerberg. Laura Keeley. Bloomberg. 2011.07.07.
http://www.bloomberg.com/news/2011-07-07/facebook-departure-prompts-boston-venture-capital-firms-to-return-to-city.html

8. Why Facebook went west. Turned down by a local venture capitalist, two Harvard students look to Silicon Valley for funding instead. The result: Boston misses out on an online phenomenon worth up to $6 billion. Scott Kirsner. 2007.09.09.
http://www.boston.com/business/technology/articles/2007/09/09/why_facebook_went_west/
9. Lessons of the Last Bubble. Tim Laseter, David Kirsch, and Brent Goldfarb. Strategy+Business. Issue 46, Spring 2007. 2007.02.28
http://www.strategy-business.com/article/07102

Goldfarb, Brent D., Kirsch, David and Miller, David A. Was There Too Little Entry During the Dot Com Era? Robert H. Smith School Research Paper No. RHS 06-029. 2006.04.24. Available at SSRN: http://ssrn.com/abstract=899100 or doi:10.2139/ssrn.899100

10. Conventional wisdom. Wikipedia.
http://en.wikipedia.org/wiki/Conventional_wisdom

Herd behavior. Wikipedia.
http://en.wikipedia.org/wiki/Herd_behavior

11. The quote is paraphrased in various ways, and attributed to various people. The earliest version in printed form appears to be from a 1981 book on a 12 step program from Narcotics Anonymous World Services, Inc. The direct quote from the book is: “Insanity is repeating the same mistakes and expecting different results.” Stated in this way, it has a basis in pathological human behavior. However, sentiment sounds more familiar and older than the 1981 date suggests. The quote also seems to echo, though in counterpoint, the earliest concepts in scientific method, reproducibility of experimental results, and the philosophy of empiricism.
http://en.wikiquote.org/wiki/Narcotics_Anonymous
12. The word “serendipity” was first used by Horace Walpole in a letter written in 1754 to Horace Mann. Walpole coins the word from a fairy tale called “The Travels and Adventures of Three Princes of Serendip”. Serendip is Persian for what is now Sri Lanka. See the following reference for a summary of the story.

The Three Prines of Serendip. Wikipedia.
http://en.wikipedia.org/wiki/The_Three_Princes_of_Serendip

13. Wallflower at the Web Party. Gary Rivlin. The New York Times. 2006.10.15.
http://www.nytimes.com/2006/10/15/business/yourmoney/15friend.html

The Friendster Tell-All Story. Michael Arrington. TechCrunch. 2006.10.15.
http://techcrunch.com/2006/10/15/the-friendster-tell-all-story/

14. MySpace, History. Wikipedia.
http://en.wikipedia.org/wiki/Myspace#History

The Rise and Inglorious Fall of Myspace. Felix Gillette. BusinessWeek. 2011.06.22.
http://www.businessweek.com/magazine/content/11_27/b4235053917570.htm

15. Danah Boyd and Nicole Ellison. Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication, 13 (1), article 11. 2007.10.00.
http://www.danah.org/papers/JCMCIntro.pdf
16. The paraphrased quote is from Louis Pasteur (1822-1895). He made the statement during his inaugural lecture as Dean of the Faculty of Science at the University of Lille, France (7 Dec 1854).

The original french is: “Dans les champs de l’observation le hasard ne favorise que les esprits préparés”, which is directly translated as: “In the fields of observation, chance favors only the prepared mind”.

Prior to coming to Lille, Pasteur worked in chemistry and crystallography, which he planned to continue. Reportedly, by chance in 1855, Pasteur was asked by a local beet alcohol producer to look at why they were having inconsistent results producing alcohol. Pasteur experimented with converting sugar to alcohol, and in 1857, published a paper that first presented his hypothesis of the role of yeast in fermentation. This then led to Pasteur’s life work on fermentation, brewing, microorganisms, sterilization and pasteurization, etc.

What makes the quote so impactful is that Pasteur stated that chance favors the prepared mind before he was by chance so favored.

http://en.wikipedia.org/wiki/Louis_Pasteur
http://books.google.com/books?id=RzOcl-FLw30C&pg=PA82#v=onepage&q&f=false

17. J.C.R. Licklider. The Computer as a Communication Device. Science and Technology. 1968.04.00.
http://memex.org/licklider.pdf

Though not a direct description of Facebook, Licklider describes an environment that in some ways goes beyond the current concept of social network: “An OLIVER is, or will be when there is one, an ‘on-line interactive vicarious expediter and responder,’ a complex of computer programs and data that resides within the network and acts on behalf of its principal, taking care of many minor matters that do not require his personal attention and buffering him from the demanding world. … It will know your value structure, who is prestigious in your eyes, for whom you will do what with what priority, and who can have access to which of your personal files.

18. Zuckerberg Remains the Undisputed Boss at Facebook. Somini Sengupta. The New York Times. 2012.02.03.
http://www.nytimes.com/2012/02/03/technology/from-earliest-days-zuckerberg-focused-on-controlling-facebook.html

Power play: How Zuckerberg wrested control of Facebook from his shareholders. Jolie O’Dell. VentureBeat. 2012.02.01.
http://venturebeat.com/2012/02/01/zuck-power-play/

19. Steve Jobs’s Law: Why Founders Make the Best Leaders. James Kwak. The Atlantic. 2011.09.01.
http://www.theatlantic.com/business/archive/2011/09/steve-jobss-law-why-founders-make-the-best-leaders/244439/
20. With a Little Help From His Friends. David Kirkpatrick. Vanity Fair. 2010.10.01
http://www.vanityfair.com/culture/features/2010/10/sean-parker-201010?currentPage=all

Business, Casual. Kevin J. Feeney. The Harvard Crimson. 2005.02.24.
http://www.thecrimson.com/article/2005/2/24/business-casual-a-year-ago-mark/

21. The Power of Angels. Empirical Reality. 2010.02.27.
http://www.empiricalreality.com/2010/02/27/the-power-of-angels/
22. Early Apple Business Documents. Computer History Museum.
http://www.computerhistory.org/highlights/earlyapple/

The Apple World According to Markkula. John Markoff. The New York Times. 1997.09.01.
http://www.nytimes.com/1997/09/01/business/an-unknown-co-founder-leaves-after-20-years-of-glory-and-turmoil.html

You’ve got to find what you love. Steve Jobs. Stanford University Commencement Address. 2005.06.12.
http://news.stanford.edu/news/2005/june15/jobs-061505.html

Video of Steve Jobs’ Commencement Address.
http://www.youtube.com/watch?v=UF8uR6Z6KLc

23. If the Check Says ‘Google Inc.,’ We’re ‘Google Inc.’ Wired. 1998.09.07.
http://www.wired.com/science/discoveries/news/2007/09/dayintech_0907

Uniquely Google. The Newsletter of Stanford’s Office of Technology Licensing (OTL). 2000.03.01.
http://infolab.stanford.edu/pub/voy/museum/google.htm

24. Peter Thiel: An Angel on a Hot Streak. Business Week. 2010.02.25.
http://www.businessweek.com/magazine/content/10_10/b4169039646363.htm
25. The Art of Raising Angel Capital. Guy Kawasaki. 2006.03.02.
http://blog.guykawasaki.com/2006/03/the_art_of_rais.html
26. Outing Boston’s top angel investors. Scott Kirsner. The Boston Globe. 2010.04.14.
http://www.boston.com/business/technology/innoeco/2010/04/outing_bostons_top_angel_inves.html

Boston’s Best Angel Investors. Jon Pierce Blog.
http://blog.jonpierce.com/post/520863618/bostons-best-angel-investors

27. The Absence of Non-Competes Fosters Innovation and Growth. Empirical Reality. 2011.03.16.
http://www.empiricalreality.com/2011/03/16/the-absence-of-non-competes-fosters-innovation-and-growth-addendum/
28. Joseph A. Schumpeter. Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. McGraw-Hill. New York. 1939.
29. A Woman’s Place – Can Sheryl Sandberg upend Silicon Valley’s male-dominated culture? Ken Auletta. The New Yorker. 2011.07.11.
http://www.newyorker.com/reporting/2011/07/11/110711fa_fact_auletta

Why Facebook Needs Sheryl Sandberg. Brad Stone. Business Week. 2011.05.11.
http://www.businessweek.com/magazine/content/11_21/b4229050473695.htm

30. David Mackenzie Ogilvy, 1911-1999.
http://www.qotd.org/search/search.html?aid=1474

The House That Ogilvy Built. Kenneth Roman. strategy + business. 2009.02.24.
http://www.strategy-business.com/article/09103

31. What Brightcove, Facebook tell us about Mass. start-up climate. Scott Kirsner. The Boston Globe. 2012.02.12.
http://www.bostonglobe.com/business/2012/02/12/facebook-and-brightcove-tale-two-companies-and-two-cities/tONaU5UzCBButExNgyysLJ/story.html

Response to: Facebook ‘Shock’ Has Boston Firms Searching for Next Zuckerberg.[1]

Anyone, who is truly serious about “searching” for the next Facebook in the greater Boston area, first has to be serious about getting rid of non-compete agreements in Massachusetts. Not doing so, is to ignore the data and simply to repeat the same mistakes.

The data shows that no companies of any scale have been built in the past 20 years in the restrictive immobile jobs environment that is the result of how non-competes are written, misused, and adjudicated in Massachusetts. Under those conditions, it is not surprising that the pie is getting smaller. Even if the next Facebook was found, the company still couldn’t be built in the environment of non-competes.

During Facebook’s critical growth period, it was able to hire experienced key employees from Google and other Silicon Valley technology companies, without having to consider whether there was a non-compete issue. Though Google lost some employees to Facebook, Google also thrived during the same period. And, it can be argued that Google’s efforts in social media are brought more into focus with a strong Facebook as a competitor.[2]

There are other issues in the Boston innovation culture, but eliminating non-competes would produce a major positive shift in the culture, and would set the stage for addressing the other issues as well. The innovation culture that will result from eliminating non-compete agreements will produce promising scalable companies.

Notes and References

1. Facebook ‘Shock’ Has Boston Firms Searching for Next Zuckerberg. Laura Keeley. Bloomberg. 2011.07.07.
http://www.bloomberg.com/news/2011-07-07/facebook-departure-prompts-boston-venture-capital-firms-to-return-to-city.html

2. For more information on Facebook’s growth, see:
http://www.empiricalreality.com/2011/03/16/the-absence-of-non-competes-fosters-innovation-and-growth-addendum/

3. In the making: A revolution to rid Massachusetts of noncompete agreements. Scott Kirsner. The Boston Globe. 2011.07.07, 2011.07.03.
http://www.boston.com/business/technology/innoeco/2011/07/needed_a_revolution_to_rid_mas.html
http://www.boston.com/business/technology/articles/2011/07/03/noncompete_clauses_stifling_to_creativity_in_mass/

The charts and data below update the previous analysis of S&P-1500 Technology Company Founding Dates. Data is shown for all 7 S&P Industry Sectors across the S&P-1500 Companies located in the 12 major CSAs. This uses the same data set as used in the previous several posts, based on the S&P-1500 as of close of business on Friday May 6 2011. See the Overview for further background.

Summary Observations:

One third of all S&P-1500 companies founded since 1991 are in the Technology Sector. This rate is almost twice the rate of any other Sector. These Technology Sector companies represent 55% of the market capitalization and 35% of the revenue of all S&P-1500 companies founded since 1991.

The San Jose/San Francisco CSA continues to expand its lead in the creation of S&P Technology Sector companies, and has 28% of the Technology Sector companies founded since 1991. These companies have 57% of the market capitalization and 45% of the revenue of all Technology Sector companies founded since 1991.

Compared to the Boston CSA, since 1991, and normalizing for population size, the New York and Los Angeles CSAs, each created Technology Sector companies at a slightly greater rate; the Washington DC CSA, at more than twice the rate; the Seattle CSA, at more than 5 times the rate; and the San Jose/San Francisco CSA, at 10 times the rate.

The S&P Financial Sector has 17% of all S&P-1500 founded since 1991. The New York CSA clearly leads, with 20% of the Sector’s companies founded since 1991. The Chicago CSA also shows strong relative growth.

In the S&P Health Care Sector, no CSA particularly stands out; although the San Jose/San Francisco CSA since 1991 has created notably more companies than other CSAs.

In the S&P Consumer Sector, the Los Angeles CSA leads in the creation of companies founded since 1991.

In the S&P Natural Resources Sector, the Houston CSA clearly dominates.

Technology Sector Company Founding Dates

Chart 1a plots the founding dates for the S&P-1500 Technology Sector Companies located in the 12 major CSAs.

Each line represents a CSA. A square marker on the line represents the founding of a company. The x axis is the year the company was founded. The y axis is the cumulative number of companies founded in the corresponding CSA. The legend lists the CSAs with abbreviated names, their local airport or train station code; the CSAs are listed in decreasing order of cumulative number of companies founded, corresponding to the end point of each line. The color assigned to each CSA is consistent across all charts below to more easily enable comparisons. Selecting the chart will provide a full size view of the chart. A line at 1991 delineates companies founded before and since 1991.

As before, the San Jose CSA dominates the Technology Sector by a wide margin; the San Jose CSA includes the greater San Francisco metropolitan area.

Chart 1a.
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Chart 1b also plots the founding dates for the S&P-1500 Technology Sector Companies as above, but changes the scale of the y-axis to better show the relationship of CSAs other than the San Jose CSA.

Since 1991, the New York CSA created 10 companies in the S&P technology sector; the Boston CSA created 2 companies; the Los Angeles CSA created 7; the Washington DC CSA and the Seattle CSA each created 6 companies.

Compared to the Boston and San Jose CSAs, which have roughly equal total population, the New York CSA has roughly 3 times the population; the Los Angeles CSA has ~2.4 times the population; Washington DC DCA has ~1.1 times the population; and the Seattle CSA ~0.55 times the population.

Thus, compared to the Boston CSA, since 1991, and normalizing for population size, the New York and Los Angeles CSAs, each created S&P Technology Sector companies at a slightly greater rate; the Washington DC CSA, at more than twice the rate; and the Seattle CSA, at more than 5 times the rate.

Chart 1b.
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Health Care Sector Company Founding Dates

Chart 2 plots the founding dates for the S&P-1500 Health Care Sector Companies located in the 9 major CSAs having companies founded since 1991. The remaining 3 CSAs had no Health Care companies founded since 1991. Here and in following charts for the respective S&P Sectors, only CSAs having companies founded since 1991 are shown in the charts.

The New York CSA has the largest number of S&P-1500 Health Care Sector companies overall at 22, but only 1 was founded since 1991. The Boston CSA and the San Jose CSA each have 15 companies overall. The Boston CSA has 2 companies founded since 1991, whereas the San Jose CSA has 4, and the Los Angeles and the Washington DC CSAs each have 3.

Chart 2.
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Consumer Sector Company Founding Dates

Chart 3 plots the founding dates for the S&P-1500 Consumer Sector Companies located in the 7 major CSAs having companies founded since 1991. A chart line for the Boston CSA is also shown, though the most recently founded company was in 1985 (Staples).

The New York CSA comparatively has significantly more S&P-1500 Consumer Sector companies overall at 22, but only 1 was founded since 1991. The Los Angeles CSA has 7 companies founded since 1991, more than any other CSA. The Chicago CSA has 3 companies founded since 1991.

Chart 3.
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Cyclical Sector Company Founding Dates

Chart 4 plots the founding dates for the S&P-1500 Cyclical Sector Companies located in the 8 of 12 CSAs having companies founded since 1991. In addition, the Houston CSA is also shown.

The New York CSA has both the largest number overall of companies in the Cyclical Sector at 30 and the largest number of companies founded since 1991 in the Sector at 7. Relative to the size of the population, the Minneapolis, Atlanta and Chicago CSAs also are well represented in the S&P-1500 Cyclical Sector and in companies founded since 1991.

The Cyclical Sector is less concentrated in the 12 CSAs relative to the concentration of companies in the Technology, Health Care, and Consumer Sectors. Half of Cyclical Sector companies are located outside of the 12 CSAs. The Cleveland and Milwaukee CSAs each have a significant concentration of Cyclical Sector companies, roughly on par with Minneapolis, and probably should be shown in the chart.

Chart 4.

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Financial Sector Company Founding Dates

Chart 5 plots the founding dates for the S&P-1500 Financial Sector Companies located in the 9 major CSAs having companies founded since 1991.

Unsurprisingly, the New York CSA has the largest concentration of Financial Sector companies. The Los Angeles, Chicago, Boston, and Washington DC CSAs are also well represented in both total number of companies and companies founded since 1991.

Chart 5.
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Natural Resources Sector Company Founding Dates

Chart 6 plots the founding dates for the S&P-1500 Natural Resources Sector Companies located in the 5 major CSAs having companies founded since 1991. The Houston CSA is also shown.

Unsurprisingly, the Houston CSA with its concentration of oil industry companies has the largest number of Natural Resources Sector companies.

Chart 6.

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Utilities Sector Company Founding Dates

Chart 7 plots the founding dates for the S&P-1500 Utilities Sector Companies located in the 5 major CSAs having companies founded since 1991. Each CSA has 1 company founded since 1991

The Utilities Sector is the smallest of the 7 Sectors by market capitalization, and even relative to its size has smallest number of companies founded since 1991.

Chart 7.
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Notes and References

See the overview of this series for Notes and References.

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