OCCUPATION | SIERRAS

Conservancy workers

The growth of conservancies in the Sierras is indicative of the region’s shift away from logging and mining and toward natural resource protection. State and federal governments, private foundations, and philanthropic organizations provide funding to conservancies that employ a wide range of workers. For example, natural scientists, administrative workers, outreach workers, and financial workers all benefit from the state’s renewed interest in protecting the environment. These workers will be critical to the sustainability and health of the state, with the region providing water to millions of Californians and serving as home to 66 percent of bird and mammal species and over 50 percent of reptiles and amphibians.

 
 

Data dashboard

Demographic and occupational data for conservancy workers in the Sierras region.

View demographic and employment data for this region

 

Average age

41

  • We use the Standard Occupation Classification 5 or 6 digit demographic information from the 2020 CPS, available here and here.

    If there is no demographic information (omitted because of data limitations/privacy information), we use the demographic characteristics from the Minor Group (SOC3) in place of the SOC5 demographic characteristics. To calculate the weighted average, we take the employment available from the CPS and aggregate to the job classification level. For Sales Engineers, we use Sales and related workers, all other. For makeup artists, we use Personal care and service occupations.

Gender

  • We use the Standard Occupation Classification 5 or 6 digit demographic information from the 2020 CPS, available from https://www.bls.gov/cps/cpsaat11.htm and https://www.bls.gov/cps/cpsaat11b.htm. If there is no demographic information (omitted because of data limitations/privacy information), we use the demographic characteristics from the Minor Group (SOC3) in place of the SOC5 demographic characteristics. To calculate the weighted average, we take the employment available from the CPS and aggregate to the job classification level. For Sales Engineers, we use Sales and related workers, all other. For makeup artists, we use Personal care and service occupations.

Race | Ethnicity

  • We use the Standard Occupation Classification 5 or 6 digit demographic information from the 2020 CPS, available from https://www.bls.gov/cps/cpsaat11.htm and https://www.bls.gov/cps/cpsaat11b.htm. If there is no demographic information (omitted because of data limitations/privacy information), we use the demographic characteristics from the Minor Group (SOC3) in place of the SOC5 demographic characteristics. To calculate the weighted average, we take the employment available from the CPS and aggregate to the job classification level. For Sales Engineers, we use Sales and related workers, all other. For makeup artists, we use Personal care and service occupations.

    Source: US Department of Labor

 

Number employed in California

2,000

  • We use the Standard Occupation Classification 5 or 6 digit demographic information from the 2020 Current Population Survey. If there is no demographic information (omitted because of data limitations/privacy information), we use the demographic characteristics from the Minor Group (SOC3) in place of the SOC5 demographic characteristics. To calculate the weighted average, we take the employment available from the CPS and aggregate to the job classification level. For Sales Engineers, we use Sales and related workers, all other. For makeup artists, we use Personal care and service occupations.

    Source: US Department of Labor Statistics

Projected job openings (2028)

240

  • TK

Projected employment (2028)

2,300

  • State projections are developed in the labor market information sections of each State Employment Security Agency (SESA). The projection period is 2018-2028, which includes the long-term period calculated up until 2028.

    Source: US Department of Labor

 

Average salary

$81,190

  • We use the Standard Occupation Classification 5 or 6 digit demographic information from the 2020 Current Population Survey. If there is no demographic information (omitted because of data limitations/privacy information), we use the demographic characteristics from the Minor Group (SOC3) in place of the SOC5 demographic characteristics. To calculate the weighted average, we take the employment available from the CPS and aggregate to the job classification level. For Sales Engineers, we use Sales and related workers, all other. For makeup artists, we use Personal care and service occupations.

    Source: US Department of Labor Statistics

SML rating

2.71

  • A score of 5 represents the highest possible exposure to machine learning (ML), while a score of 1 represents little exposure to ML at all.

    This is the Suitability for Machine Learning (SML) score available in Brynjolfsson, Mitchell, and Rock (2018) and Brynjolfsson, Frank, Mitchell, Rahwan, and Rock (2022). The aggregate score reflects a 21 question rubric designed to measure the overall exposure of a given Detailed Work Activity (DWA) available from the O*NET database. Activities are aggregated with equal weight within task, and tasks are aggregated using importance weights into occupations following the O*NET crosswalks between DWAs, tasks, and occupations. This is not an automation measure, but rather represents the relative extent to which an occupation will be impacted in some manner by machine learning and artificial intelligence.

    Source: Brynjolfsson, Mitchell, and Rock (2018) & Brynjolfsson & Frank, Mitchell, Rahwan, and Rock (2022)

Data intensity rating

3.82

  • A score of 5 indicates high data intensity in the job, and a score of 1 indicates low data intensity for that job category.

    This is an average of a subset of SML rubric items designed to represent the quantity of available data or exposure to data in this particular occupation.

    Source: Brynjolfsson, Mitchell, and Rock (2018) & Brynjolfsson & Frank, Mitchell, Rahwan, and Rock (2022)

 

Routine cognitive score

-0.65

  • A score of 5 represents the highest possible exposure to machine learning (ML), while a score of 1 represents little exposure to ML at all.

    This is the Suitability for Machine Learning (SML) score available in Brynjolfsson, Mitchell, and Rock (2018) and Brynjolfsson, Frank, Mitchell, Rahwan, and Rock (2022). The aggregate score reflects a 21 question rubric designed to measure the overall exposure of a given Detailed Work Activity (DWA) available from the O*NET database. Activities are aggregated with equal weight within task, and tasks are aggregated using importance weights into occupations following the O*NET crosswalks between DWAs, tasks, and occupations. This is not an automation measure, but rather represents the relative extent to which an occupation will be impacted in some manner by machine learning and artificial intelligence.

    Source: Brynjolfsson, Mitchell, and Rock (2018) & Brynjolfsson & Frank, Mitchell, Rahwan, and Rock (2022)

 

Remote work potential

0.33

  • The Remote Work Potential Score that represents whether a job can be performed remotely from Dingel and Neiman (2020).

    A score of 1 indicates a likely remotable occupation, and a score of 0 indicates an occupation with no remote work potential. Scores between 0 and 1 represent the aggregate of different constituent jobs that belong to the broader category indicated in the table.

    Source: Dingel and Neiman (2020)

 

Sierras

Sierras