1

Survey Methods and Reliability Statement for Occupational Employment and

Wages in Green Goods and Services

I. Introduction

The Occupational Employment and Wages in Green Goods and Services (GGS‐OCC) program provides

data on establishments that obtain some, none, or all of their revenue from the sale of green goods and

services. The estimates are based on data collected from two different Bureau of Labor Statistics (BLS)

surveys: the Green Goods and Services (GGS) survey and the Occupational Employment Statistics (OES)

survey, including supplemental units to the OES sample.   

The GGS survey asks selected establishments how much of their revenue comes from the sale of green

goods and services. The OES survey collects occupational employment and wage data for nonfarm

establishments. By coordinating the GGS and OES sample designs, occupational data are collected for

the majority of establishments selected into the GGS sample. A supplement to the OES survey collects

data on agricultural establishments, government establishments, and additional units necessary to fully

cover the scope of the GGS survey. By using data from both surveys, BLS produces occupational

employment and wage estimates for businesses with all green revenue, businesses with some green

revenue, and businesses with no green revenue.  

II. Green Jobs Definition

BLS uses two approaches to measuring green jobs. The output approach focuses on jobs associated with

producing green goods and providing green services, and the process approach focuses on jobs in which

workers use environmentally friendly production processes and practices. The GGS and GGS‐OCC data

are based on the output approach to measuring green jobs. Green jobs estimates from the GGS survey

are available at www.bls.gov/ggs/. A separate survey, the Green Technologies and Practices (GTP)

survey (www.bls.gov/gtp/), is based on the process approach to measuring green jobs.

Under the BLS output approach, green jobs are jobs in businesses that produce goods and/or provide

services that benefit the environment or conserve natural resources. These goods and services are sold

to customers, and include research and development, installation, and maintenance services. Green

goods and services fall into one or more of five groups:  

1. Energy from renewable sources. Electricity, heat, or fuel generated from renewable sources.

These energy sources include wind, biomass, geothermal, solar, ocean, hydropower, and landfill

gas and municipal solid waste.

  

2. Energy efficiency. Products and services that improve energy efficiency. Included in this group

are energy‐efficient equipment, appliances, buildings, and vehicles, as well as products and
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services that improve the energy efficiency of buildings and the efficiency of energy storage and

distribution, such as Smart Grid technologies.  

3. Pollution reduction and removal, greenhouse gas reduction, and recycling and reuse. These are

products and services that:  

 Reduce or eliminate the creation or release of pollutants or toxic compounds, or remove

pollutants or hazardous waste from the environment.  

 Reduce greenhouse gas emissions through methods other than renewable energy generation

and energy efficiency, such as electricity generated from nuclear sources.  

 Reduce or eliminate the creation of waste materials; collect, reuse, remanufacture, recycle,

or compost waste materials or wastewater.  

4. Natural resources conservation. Products and services that conserve natural resources. Included

in this group are products and services related to organic agriculture and sustainable forestry;

land management; soil, water, or wildlife conservation; and stormwater management.  

5. Environmental compliance, education and training, and public awareness. These are products

and services that:  

 Enforce environmental regulations.  

 Provide education and training related to green technologies and practices.  

 Increase public awareness of environmental issues.

For more information about the BLS green jobs definition, please visit www.bls.gov/green.  

III. GGS‐OCC Scope

The scope of the GGS‐OCC estimates is limited to a subset of industries in which business establishments

potentially produce green goods or provide green services as their primary activity. This subset consists

of 333 of the nearly 1,200 detailed industries defined by the 2007 North American Industry Classification

System (NAICS). In the second quarter of 2011, these industries covered about 26 million jobs, or

approximately 2.1 million establishments. These 333 industries were selected by BLS after consultations

with industry groups, government agencies, stakeholders, and the public, who helped identify industries

that may provide goods or services that benefit the environment or conserve natural resources. The list

of in‐scope industries can be found at www.bls.gov/ggs/ggsfaq.htm#3. The GGS‐OCC scope includes

private and government establishments in the 50 states and the District of Columbia.

  

Businesses and government establishments are assigned industry codes based on their primary activity.

Some establishments may produce green goods or provide green services as a secondary activity. BLS

recognizes that if these establishments are classified in industries outside the scope of the GGS‐OCC

data, these goods and services and jobs associated with them will not be captured by the output

approach.

   
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IV. Description of the GGS Sample Design

The GGS‐OCC sample is a subset of units in both the GGS sample and either the regular OES sample or

the OES supplement. There are four major steps in obtaining the sample for GGS‐OCC: (1) Use the OES

sample that has already been selected, (2) select the GGS sample, (3) maximize the overlap between the

OES and GGS samples, and (4) supplement the OES sample with units in GGS but not in OES. The GGS

sample is a sample of 120,000 establishments in the 333 in‐scope industries and was designed, in part,

to maximize the overlap between it and the regular OES sample. The OES sample is a sample of 1.2

million nonfarm establishments, collected over 3 years. The OES supplement is a subsample of the GGS

sample in strata where additional overlap was necessary. Below is a description of the GGS sample, the

OES sample design, the OES supplement, and efforts to coordinate both surveys. For more information

on GGS, please see www.bls.gov/ggs/ggs_technote_extended.pdf. For more information on OES, please

see www.bls.gov/oes/current/methods_statement.pdf.

GGS Sample  

The GGS sample design ensures a minimum reliability for the two main estimation domains: state by

major industry sector and national by detailed industry. The GGS survey uses NAICS classifications for

industry definitions.

GGS Frame Creation

The GGS sampling frame is a subset of all business establishments in the 50 states and the District of

Columbia. Only establishments found in the 333 in‐scope GGS industries are included on the frame.

These industries were identified as the most likely to have establishments producing environmentally

friendly goods or services. Private and government (federal, state, and local) establishments are

included on the frame, excluding any establishment with zero employment for the past 12 months.

The GGS survey uses the BLS Quarterly Census of Employment and Wages (QCEW) as its sampling frame.

The data for the QCEW come from state unemployment insurance files that are collected by individual

state agencies. These files are made up of several descriptive variables such as business name, address,

monthly employment, and industry classification for nearly all establishments in the United States. The

2010 QCEW has over 8 million business establishments containing about 128 million employees. The

GGS sample frame, which is restricted to the 333 in‐scope industries, has approximately 1.8 million

establishments containing about 25.5 million employees.

About 13,000 in‐scope establishments comprising about one million employees were previously

identified as being involved with some kind of green activity. These units were identified internally by

BLS by use of the internet and an environmental database maintained by Environmental Business

International, an environmental publishing, research, and consulting company. In this statement, these

13,000 establishments will be referred to as the environmental establishment frame. These

establishments have special treatment during the GGS allocation and selection phases.
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The frame excludes U.S. Census Bureau establishments consisting of temporary workers hired for the

2010 decennial census.

GGS Sample Allocation

The GGS sample size was about 120,000 establishments, where 116,000 establishments were selected in

a second quarter initial sample and 4,000 were selected in a fourth quarter birth sample. The initial

sample was divided in the following way:  

Table 1: GGS Allocation Breakout

Type of Frame Unit Sample

Allocated

Private Establishments 94,500

Local Government Establishments 7,700

State Government Establishments 4,000

Federal Government Establishments 3,300

Environmental Establishments 6,500

   

Total: 116,000

Each type of frame unit has its own independent allocation, described below.

The GGS private establishment allocation

The GGS private establishment allocation can be thought of as two separate allocations, one that

stratifies the frame by state / 2‐digit NAICS industries and the other that stratifies by 4‐ or 6‐digit NAICS

industries. These 4‐ or 6‐digit industries will be called GGS allocation NAICS, or GGS ANAICS, for the

remainder of this statement. For the most part, the GGS ANAICS industries are at the 4‐digit NAICS level

of detail; however, some industries that seemed to be highly environmental—for example, 221119

Other Electric Power Generation—were allocated at the 6‐digit NAICS level.  

If the number of units in a state / 2‐digit NAICS industry does not meet some threshold, all the units

from the stratum are used in the GGS private sample. Otherwise, a minimum allocation is used.  

This allocated about 24,000 sample units for the initial 2011 sample. Next, 1,000 sample units are

allocated within each state using a power allocation:

݊௛ ൌ ݊௦

ඥ௑೓

∑೓אೞ ඥ௑೓

             (1)   

Where,

݊௛ ൌ the amount of sample allocated to stratum h (state by 2‐digit NAICS)

݊௦ ൌ the sample size for state s, which is 1,000  

ܺ௛ ൌ the number of employees in stratum ݄  
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After the minimum and power allocation, a total of about 60,000 sample units were allocated for private

establishments in 2011. Thus, a sample of 60,000 ensures a minimum sample of 1,000 establishments

per state and 40 establishments for each 2‐digit NAICS within a state.

Next, the sample is allocated nationally to GGS A_NAICS industry strata, using the following power

allocation:

݊௛ ൌ ݊ ඥ௑೓

∑ೌ೗೗ ೓ ඥ௑೓

               (2)

Where,

݊௛ ൌ the amount of sample allocated to stratum (GGS A_NAICS)

݊ ൌ the national sample size  

ܺ௛ ൌ the number of employees in stratum ݄  

The national sample size is iteratively increased until the total private allocation, after reconciling the

state by 2‐digit and national GGS A_NAICS allocations, is close to 94,500. The last step of the private

allocation is to set a minimum allocation to each 6‐digit NAICS stratum. However, if the number of units

in the 6‐digit national stratum does not meet some threshold, all of the establishments in the stratum

are used in the sample. In the initial 2011 sample, there were a total of about 94,800 sample units

allocated for the private sample.

The local, state, and federal government GGS allocations

The sample units for local, state, and federal government establishments are allocated the same way.

The frame is stratified into state and 2‐digit NAICS industry strata. If the number of establishments

within a stratum does not meet some threshold, all of the units from the stratum are allocated into the

sample. Otherwise, a minimum allocation is used. In the initial 2011 sample, the local allocation was

7,700 units, the state allocation was 3,950 units, and the federal allocation was 3,300 units.

Environmental GGS allocation

The environmental allocation includes establishments in the private and government sectors. The frame

is stratified by 6‐digit NAICS industry and size class. Size classes are seven categories that put

establishments of similar employment size together. For example, if an establishment has 1 to 9

employees, it would be in size class 1 for GGS. The environmental sample is allocated using the following

rules:

݊௛ ൌ

ە

ۖ

۔

ۖ

௛ܰ ۓ

5

1 ݏݏ݈ܿܽ ݁ݖ݅ݏ ݎ݋݂ ൗ

ܰ௛

3

2 ݏݏ݈ܿܽ ݁ݖ݅ݏ ݎ݋݂ ൗ

ܰ௛

2

3 ݏݏ݈ܿܽ ݁ݖ݅ݏ ݎ݋݂ ൗ

൅ 4 ݏݏ݈ܿܽ ݁ݖ݅ݏ ݎ݋݂ ௛ܰ 

            (3)
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Where,

݊௛ ൌ the amount of sample allocated to stratum (6‐digit NAICS by size class)

ܰ௛ ൌ the number of frame units in stratum

In the initial 2011 sample, there were about 6,550 sample units allocated for the environment sample.  

GGS Sample Selection

The private and government samples are selected using a probability proportionate to size approach

where the size for an establishment is defined below:

10 ൑ ௜ܺ ݂݅ 10൜ ൌ௜ ݁ݖ݅ݏ

ܺ௜ ݂݅ ܺ௜ ൐ 10            (4)

Where,

ݏ݅ݖ݁ ௜ൌ unit ݅’s measure of size

ܺ௜ ൌ unit ݅’s maximum employment over previous 12 months

The smallest establishments are treated differently because of the assumption that they have the

potential for very large relative employment shifts between the time period of the QCEW data on the

frame and when the establishment is sampled.  

The environmental sample is selected using simple random sampling within each national 6‐digit NAICS

by size class stratum. The sample is allocated at higher rates as the size class increases, resulting in an

implicit probability proportionate to size selection scheme.  

GGS Birth Sample

A fourth quarter birth sample of about 4,000 establishments is selected to represent the newly formed

establishments that become in business or in‐scope between the second quarter and the fourth quarter

of  2010. Any establishment in the fourth quarter 2010 sample frame that is not in the second quarter

2010 sample frame is considered a birth. The birth sample is allocated at the same rate as the initial

sample for each of the five different allocations.   

Sampling Weights

Each sampled establishment has a known probability of selection. The inverse of the probability of

selection is called the sampling weight.

V. Description of the OES Sample Design

The OES survey is designed to collect occupational employment and wage data on employees working in

the 50 states, the District of Columbia, the Virgin Islands, Puerto Rico, and Guam. The main estimation

domain is at the level of detailed Metropolitan Statistical Areas (MSA) and residual areas within each
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state called balance of state (BOS) areas. To produce estimates in such detail, a sample of 1.2 million

business establishments is selected over 3 years in semiannual samples. A sample of 200,000

establishments is selected in the second and fourth quarter of each year.

OES Frame Creation

The OES survey also uses the QCEW as its sampling frame. The majority of NAICS industries are in scope

for OES, except for NAICS 814 Private households, and most of the agriculture sector with the exception

of 113310 Logging, 1151 Support activities for crop production, and 1152 Support activities for animal

production. The second quarter 2011 OES frame had about 6.8 million in‐scope business establishments

that account for about 121 million employees.  

OES Sample Allocation

The OES frame stratification is by state, MSA or BOS area, and 4‐, 5‐, or 6‐digit NAICS industries. The

majority of the strata use 4‐digit NAICS detail, but some industries are stratified in more detail because

they have unique occupational distributions at the 5‐ and 6‐digit level. These 4‐, 5‐, or 6‐digit NAICS

industries will be referred to as OES allocation NAICS, or OES A_NAICS.  

For each semiannual sample, a full 1.2 million establishment sample is allocated, and then the allocation

is divided by six at the stratum level. First, a minimum sample is allocated using the following rules:

݊௛ ൌ ቐ

ܰ௛ ݂݅ ܰ௛ ൑ 3 

3 ݂݅ 4൑ܰ௛ ൑ 11

6 ݂݅ ܰ௛ ൒ 12 

           (5)

Where,

݊௛ ൌ the amount of sample allocated to stratum (State by MSA/BOS by OES A_NAICS)

ܰ௛ ൌ the number of frame units in stratum   

Next, the sample is allocated using a power Neyman allocation, using the following formula:

݊௛ ൌ ݊ ඥ௑೓ ௌ೓

∑ೌ೗೗ ೓൫ඥ௑೓ ௌ೓൯

              (6)

Where,

݊௛ ൌ the amount of sample allocated to stratum (State by MSA/BOS by OES A_NAICS)

݊ ൌ the national sample size   

ܺ௛ ൌ the number of employees in stratum ݄  

ܵ௛ ൌ the measure of occupational employment variability within stratum

The final amount of sample allocated for each stratum is the maximum of the minimum and power

Neyman allocations. The national sample size used in formula (6) is iteratively changed until the final

amount of sample allocated, after reconciling the two different allocations, is about 1.2 million. The last
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step of the OES allocation is to divide each stratum allocation amount by six, to get the final allocation

for the semiannual sample.  

OES Sample Selection

After the sample is allocated, the semiannual sample is selected using a probability proportionate to size

approach. Every establishment within an OES‐defined size class is given the average employment value

for that size class. This is referred to as a step‐wise probability proportionate to size scheme.

Sampling Weights

Each sampled establishment has a known probability of selection. The inverse of the probability of

selection is called the sampling weight.

VI. Coordination between the GGS and OES Surveys

A 100‐percent sample overlap between the GGS and OES would be the ideal situation for producing the

GGS‐OCC estimates because it would allow OES to survey every establishment in the GGS sample. To

achieve a 100‐percent sample overlap, the GGS sample would need to be selected as a subsample of the

OES sample. For several reasons, including coverage bias and budget constraints, this was not a viable

option. Instead, special methods are used to ensure a maximum sample overlap between the GGS and

OES samples. The GGS‐OCC estimates use the GGS initial and birth samples collected in the second and

fourth quarters of 2011, respectively, and the OES samples collected in the second and fourth quarters

of 2009, 2010, and 2011. Three years of OES sample are used based on the assumption that

occupational employment distributions stay consistent over 3‐year time periods. Special methods are

used to age OES wage data that were collected during a time period outside of the reference period.

Natural Sample Overlap between GGS and OES

In both the GGS and OES sample designs, a greater probability of selection is given to establishments

with more employees. This causes a substantial amount of overlap between the two samples, even if

they have independent sample designs. In 2011, approximately 41 percent (about 41,300 sampled

establishments) of the GGS initial and birth sample overlaps naturally with the OES sample collected

between 2009 and 2011. The overlap is higher for large establishments, decreasing as establishments

get smaller. This causes the sample employment overlap to be significantly larger than the unit overlap,

at 80 percent of in‐scope employment (about 8.3 million employees).  

Most of the state and local government units had to be excluded from these sample overlap counts

because OES and GGS define their public Primary Sampling Units (PSUs) differently. In the OES sample,

state and local government PSUs are aggregated to specific geographic areas to make data collection

easier for the state data collectors. In the GGS sample, state and local government PSUs are single

business establishments. Only OES state and local aggregate PSUs that contain only one establishment

are used when identifying the natural overlap between the two surveys and in the replacement

algorithm described later.  
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Increasing Overlap through Replacement  

To increase the overlap for the smaller establishments, an algorithm is used that replaces

nonoverlapping GGS sampled units with nonoverlapping OES sampled units. All establishments from the

environmental frame were excluded from this process because they were predetermined to have green

activity and therefore important to keep in the GGS sample. The replacement algorithm uses strict

criteria to minimize any bias this process could introduce. In order for an establishment sampled for GGS

to be replaced by one from OES, it must meet the following criteria:

 Industrial criterion: The GGS and OES sampled establishments must have the same 6‐digit NAICS

industry classification

 Geographic criterion: The GGS and OES sampled establishments must be within the same state,

first giving preference to establishments in the same state and MSA/BOS area, then relaxing the

search to just state

 Age criterion: The GGS and OES sampled establishments must have begun their business in the

same year and quarter if they have been in business less than 3 years

 Employment criterion: The GGS and OES sampled establishments must meet the following

employment tolerances:

GGS Employment OES Employment

1 to 4 1 to 4

 ݌݉ܧ_ܵܩܩ כ 1.1 to ݌݉ܧ_ܵܩܩ כ 0.9 ൅ 5

 Multi‐establishment criterion: The GGS and OES sampled establishments must both be from a

company that has multiple establishments, or both from a company that has only one

establishment

In 2011, after using this replacement algorithm, the amount of sample overlap between the GGS and

OES surveys increased to 64 percent (about 64,700 sampled establishments). The amount of sample

employment overlap increased slightly to 83 percent (about 8.6 million employees).  

Subsample of Nonoverlapping GGS Sample

To collect occupational employment and wage data for the piece of the GGS sample that does not

overlap with OES, a subsample of 25,000 establishments was selected. These establishments are asked

occupational information by being sent an OES survey form in addition to a GGS survey form.  

The nonoverlapping GGS sample is stratified by 6‐digit NAICS industries and the subsample is allocated

using the following formula:

݊௛ ൌ ݊ ·

ே೓ඥௌ೓ ௉೓

∑ೌ೗೗ ೓൫ே೓ඥௌ೓ ௉೓൯

            (7)
10

Where,

݊௛ ൌ   the number of subsample units allocated to industry ݄ (6‐digit NAICS)

݊ ൌ the total subsample size  

ܵ௛ ൌ   the measure of occupational employment variability within stratum ݄ (6‐digit NAICS)

ܰ௛ ൌ    the number of nonoverlapping GGS sample units within stratum ݄

ܲ௛ ൌ   the nonoverlap employment percentage for stratum ݄

This allocation method allows the sample size for a particular 6‐digit NAICS industry to increase as the

amount of nonoverlapping GGS sample and the occupational employment variability of that industry

increase.  

The first step of selecting the subsample is to identify the units within each 6‐digit NAICS industry that

will make the largest contribution to the variance estimate, and select them with certainty. The amount

of the GGS universe each nonoverlapping GGS sample unit represents is calculated as:

(8)                ௜ݔ௜ݓ ൌ௜ ܧ

Where,

ܧ ௜ൌ   establishment ݅’s weighted employment

ݓ ௜ൌ establishment ݅’s GGS sampling weight  

ݔ ௜ൌ establishment ݅’s QCEW employment

Next, the average weighted employment is calculated for each 6‐digit NAICS industry by:

ܧതூ ൌ 

1

݊ூ

௜ܧ ෍

ூא௜

 ሺ9ሻ

Where,

ܧതூ ൌ    the amount of weighted employment each subsample unit will represent on average  

If any unit’s ܧ ௜is greater than or equal to ܧത

ூ, then it is selected into the subsample with certainty. This is

an iterative process in which each time establishments are selected with certainty, ܧതூ is recalculated and

compared to the remaining unit’s ܧ௜. Once there are no more units to select with certainty, the

remaining units are selected within each industry using simple random sampling (SRS). The final weight

that will be used for the occupational estimates for the GGS sampled units selected into the subsample

is the product of their original GGS weight and the inverse of their subsampling selection probability.  

   
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VII. Data Collection

OES Data Collection

The OES survey collects data on occupational employment and wage rates for wage and salary workers.

Respondents report their number of employees by occupation across twelve nonoverlapping wage

intervals:  

Table 2: OES Wage Intervals

Interval Wages

Hourly Annual

A < $9.25 < $19,240

B $9.25 to $11.49 $19,240 to $23,919

C $11.50 to $14.49 $23,920 to $30,159

D $14.50 to $18.24 $30,160 to $37,959

E $18.25 to $22.74 $37,960 to $47,319

F $22.75 to $28.74 $47,320 to $59,799

G $28.75 to $35.99 $59,800 to $74,879

H $36.00 to $45.24 $74,880 to $94,119

I $45.25 to $56.99 $94,120 to $118,559

J $57.00 to $71.49 $118,560 to $148,719

K $71.50 to $89.99 $148,720 to $187,199

L ൒ $90.00 ൒ 187,200

The OES survey form asks the respondent to report how many employees fall within each occupation by

wage interval category. The respondent reports this information into a reporting matrix, where the

occupations run down the rows, and the wage intervals span across the columns. The Standard

Occupational Classification (SOC) system is used to define occupations. Below is an example of an OES

reporting matrix for a restaurant establishment:

Wage Intervals   

Occupations A B C D E F G H I J K L Total

Cooks, short order                   

Food preparation workers                   

Waiters and waitresses                   

  

GGS Data Collection

The GGS survey asks establishments to report the percent of revenue received from green goods and

services as defined by BLS. The percentage is multiplied by the employment level to derive the number

of GGS jobs for that establishment. Units that do not generate revenue, such as nonprofits, government

units, and business start ups without positive revenue, are asked to supply a percent of employment

associated with green goods and services. Thus, the employment figure includes workers of all
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occupations, as long as they worked in the establishment with GGS employment or revenue. For

example, a solar panel installation business might report that all of its revenue is included in the

definition. In this case, all workers are counted, including installers, managers, secretaries, etc. Similarly,

mass transit businesses reporting GGS revenue would include workers such as bus and subway drivers,

maintenance and repair workers, managers, and administrative personnel.  

Response Rates

Among GGS‐OCC sample units, the overall national response rates for the GGS survey were 66.4 percent

based on establishments and 59.9 percent based on weighted employment. Response rates for the OES

survey were 66.7 percent based on establishments and 65.3 percent based on weighted employment.

About 48.6 percent of sampled establishments, representing 42.6 percent of weighted sample

employment, responded to both surveys.

VIII. Estimation Methods

GGS‐OCC Micro Data

The data that are used for the GGS‐OCC estimates come from establishments that were sent both OES

and GGS survey forms. These establishments fall into two groups: (1) the establishments that were

selected into both the GGS and OES samples, and (2) the establishments that were selected into the GGS

sample but not the OES sample, and then were subsampled from the GGS to receive an OES survey

form. In 2011, the sample consisted of approximately 93,200 establishments (including federal units).

The sampling weight used for group (1) is the GGS sampling weight, and for group (2) is the product of

the subsampling weight and the GGS sampling weight. The sampling weight for establishment ݅ from the

GGS‐OCC data is:

ሻ1ሺ ݌ݑ݋ݎ݃ ݅݊ ݂݅ ௜ݓ ൜ ൌ௜ ݓݏ

௜ݓ

(10)            ሻ2ሺ ݌ݑ݋ݎ݃ ݅݊ ݂݅ ௜ݓ כ ஻௎ௌ

Where,

ݓ ௜ൌ    establishment ݅’s GGS sampling weight  

௜ݓ

ௌ௎஻ ൌ establishment ݅’s subsampling weight  

When using these sampling weights, the 93,200 GGS‐OCC establishments correctly represent all in‐

scope establishments for the GGS universe.

Nonresponse Adjustments

Survey nonresponse occurs when units that were selected into the sample fail to provide all the

information that was solicited. Sometimes the failure is partial, meaning the unit provides information

for only some items in the survey. Other times the failure is complete, meaning the unit provided no

information for the survey. The partial failure is referred to as item nonresponse, and the total failure as

unit nonresponse. If the information collected from the respondents differs from the information that
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would have been collected for the nonrespondents, then there is potential for bias in the survey

estimates. To reduce this nonresponse bias, imputation is often used to handle item nonresponse, and

weight adjustments for unit nonresponse.   

Every establishment within the GGS‐OCC data can be grouped into one of four different response

categories: (1) responds to both the GGS and OES survey forms, 48.6 percent of sampled units; (2)

responds to the GGS survey form but does not respond to the OES survey form, 17.9 percent of sampled

units; (3) does not respond to the GGS form but responds to the OES form, 15.8 percent of sampled

units; and (4) does not respond to either the GGS or OES forms, 17.8 percent of sampled units. For the

GGS‐OCC estimates, response category (2) is considered item nonresponse and (3) and (4) are

considered unit nonresponse. To reduce nonresponse bias, imputation is used to handle the GGS‐OCC

item nonresponse, and weighting class adjustments are used to handle the unit nonresponse.  

Imputation  

A nearest neighbor hot deck imputation is used to impute staffing patterns—the share of an

establishment’s employment that falls into each occupation—for establishments found in response

category (2). State, industry, employment, and green revenue percent are used to determine a donor for

the nonrespondents. The donor will provide occupational employment distributions for the

establishment with no OES data. For units that report having 0 or 100 percent green revenue, the

imputation cells are defined by state/6‐digit NAICS industry/green revenue percentage and the nearest

neighbor donor is found based on employment (the donor will use the reported total OES employment,

and the nonrespondent will use QCEW employment). For units that have green percentages greater

than 0 and less than 100, the imputation cells are defined by state/6‐digit NAICS and the nearest

neighbor donor is found based on both green revenue percentage and employment. A hierarchical

structure is used to determine how to collapse the imputation cells in case there are no or too few

available donors. In this hierarchy, geographical level is the first criterion to be relaxed, because industry

is the most important determinant of staffing patterns.

A mean imputation procedure is used to impute average wage distributions for establishments in

response category (2). Geography, industry, employment, and green revenue percent are used to create

imputation cells for imputing the missing wage distributions. Using all responding units, average wage

distributions are calculated within each imputation cell. Units with missing wage data receive the

average wage distribution of their imputation cell. A hierarchical structure is used for collapsing

imputation cells that do not have enough respondents to make an average wage distribution. Wages are

more homogeneous within geography, so in this hierarchy industry is the first characteristic to be

relaxed.  

Weighting class adjustments  

For establishments in response categories (3) and (4), an iterative weighting class adjustment process is

used to adjust the sample weights so that the respondents are representative of the entire GGS‐OCC

universe. This process has 3 stages: the first adjusts weights at the ownership by size class level, the

second adjusts weights at the 6‐digit NAICS by collapsed size class level, and the third adjusts weights at
14

the state by 2‐digit NAICS level. The weights are modified at each stage by multiplying a nonresponse

adjustment factor (NRAF) by all weights within weight classes so that weighted respondent counts are

calibrated to equal weighted sample counts. The raking procedure is repeated until the NRAFs for all

weight classes converge to 1. A final NRAF value is calculated for each establishment by multiplying the

NRAFs calculated at each stage and iteration. The final NRAF for establishment i is denoted ܨܴܰܨܣ௜.    

Benchmarking

Due to differences between the date of sample selection and the reference period of the estimates, the

GGS‐OCC data are benchmarked to QCEW average employment levels for May and November 2011. Like

the weighting class adjustments, the benchmarking process is an iterative process. It has two stages: the

first adjusts weights at the allocation NAICS (or GGS A_NAICS) level, and the second adjusts weight at

the state by 2‐digit NAICS level. The weights are modified at each stage by multiplying all weights within

the benchmarking classes by a benchmarking factor (BMF) so that weighted respondent counts are

calibrated to equal average QCEW counts. The raking procedure is repeated until the BMFs for all

benchmarking classes converge to 1. A final BMF value is calculated for each establishment by

multiplying the BMFs calculated at each stage and iteration. The final BMF for establishment i is denoted

.    ௜ܨܯܤܨ

Point Estimates

The GGS‐OCC data consist of employment, mean wage, and median wage estimates by occupation,

presented for three groups of establishments: those with all, none, or some, but not all, of their revenue

from green goods and services. Estimates are available at the national level for 2‐digit NAICS industries

and for all in‐scope industries combined. There are three different occupational levels for the estimates:

all‐occupations totals, major occupational groups, and detailed occupations. These levels are defined by

the Standard Occupational Classification (SOC) system. The reference period for these estimates is

November of 2011.   

Employment estimates  

A Horvitz‐Thompson (HT) estimator is used for the GGS‐OCC employment estimates. The GGS‐OCC

employment estimate for estimation domain k is calculated using the formula:

෠ܧ

௞,௢ ൌ ෍൫݂ݓ ௜כ ݁௜,௢൯

࢑א࢏

 ሺ11ሻ

Where,

෠ܧ

௞,௢ ൌ the total employment estimate for occupation ݋, in estimation domain ݇

݂ݓ ௜ൌ the final estimation weight for establishment ݅, which is the product of the sampling

weight, the final nonresponse adjustment factor, and the final benchmark factor:

  ௜ܨܯܤܨ כ ௜ܨܣܴܰܨ כ ௜ݓݏ ൌ௜ ݓ݂

݁௜,௢ ൌ the OES reported employment for occupation ݋, within establishment ݅  
15

Mean wage estimates  

Because wages are collected in intervals rather than point values, an external file from the National

Compensation Survey (NCS) is used to calculate interval means that are assigned to all employees falling

within each of the 12 wage intervals. Also, because the wage data are collected over 3 years, the older

data are aged by the Employment Cost Index (ECI) for different occupational groups. The ECI is produced

by NCS and measures the rate of change in employee compensation over time.   

Using the data collected by OES plus the information obtained from NCS, the GGS‐OCC wage estimates

are calculated using the following formula:    

ܹഥ௞,௢ ൌ 1

෠ܧ

௞,௢

௢,௜ොݕ כ ௜ݓ݂ ෍ ቌ෍ 

ሻ௞תሺ௭א௜

ቍ

௧

௭ୀ௧ିହ

 ሺ12ሻ

   

ݕො௜,௢ ൌ ݁ܿଓ ෞ ௭,௢ ෍ ݁௜,௢,௥ כ ݕത ௭,௥

 ே஼ௌ

௅

௥ୀ஺

 ሺ13ሻ

Where,

ܹഥ௞,௢ ൌ the hourly wage rate estimate for occupation ݋, in estimation domain ݇  

ݐ ൌ   the current panel (2011Q4), ݐെ1 equals the previous panel, etc.

ݕො௜,௢ ൌ the estimated wage for occupation ݋, within establishment ݅

݁ܿଓ ෞ ௭,௢ ൌ the ECI aging factor for panel ݖ and occupation ݋  

݁௜,௢,௥ ൌ the reported employment for occupation ݋, within establishment ݅, within wage interval

ݎ

ݕത ௭,௥

 ே஼ௌ ൌ the NCS hourly wage interval mean for wage interval ݎ, in panel ݖ  

To calculate GGS‐OCC annual wage rate estimates, the hourly estimates are multiplied by 2,080, based

on the assumption that most full‐time employees are paid for 2,080 hours a year (52 weeks x 40 hours

per week).

Median hourly wage rate estimates  

The median or 50th percentile hourly wage rate for an occupation is the wage where 50 percent of all

workers earn that amount or less and where 50 percent of all workers earn that amount or more. The

wage interval containing the median hourly wage rate is located using a cumulative frequency count of

estimated employment across all wage intervals. After the targeted wage interval is identified, the

median wage rate is then estimated using a linear interpolation procedure. This statistic is calculated by

first uniformly distributing federal, state, local government, and private workers inside each wage

interval. Next, workers are ranked from lowest paid to highest paid. The product of the total

employment for the occupation and the 50th percentile is then calculated to determine the worker that

earns the median wage rate. Finally, the following formula is then used to calculate estimates of the 50th

percentile wage rate for each occupation in the predetermined estimation cell.    
16

ݔ෤Ω,௢,ହ଴ ൌ ܮ൅ ௥

݁̂Ω,௥,௢,ହ଴

݁̂Ω,௥,௢

ሺܷ௥ െ ܮ௥ሻ ሺ14ሻ

Where,

ݎ ൌ   the universal wage interval that encompasses the 50th percentile wage rate estimate

Ω ൌ a predetermined estimation cell

ܮ ௥ൌ the lower bound of universal interval ݎ expressed in terms of hourly wage rate

ܷ௥ ൌ the upper bound of universal interval ݎ expressed in terms of hourly wage rate

݁̂Ω,௥,௢ ൌ the OES reported employment in estimation cell Ω and universal interval ݎ for

݋ occupation

݁̂Ω,௥,௢,ହ଴ ൌ the OES reported employment in estimation cell Ω and universal interval ݎ for

occupation ݋ needed to reach the 50th percentile

ݔ௢෤,ହ଴ ൌ the estimated 50th percentile wage rate estimate for occupation ݋ in cell Ω 

Variance Estimates

When a sample, rather than an entire population, is surveyed, estimates differ from the true population

values that they represent. This difference, or sampling error, occurs by chance and its variability is

measured by the variance of the estimate or the standard error of the estimate (square root of the

variance). The relative standard error is the ratio of the standard error to the estimate itself.

             

Estimates of the sampling error for the GGS‐OCC employment and wage estimates allow data users to

determine if those statistics are reliable enough for their needs. Only a probability‐based sample can be

used to calculate estimates of sampling error. The formulas used to estimate GGS‐OCC variances are

adaptations of formulas appropriate for the survey design used.  

The particular sample used in this survey is one of a large number of many possible samples of the same

size that could have been selected using the same sample design. Sample estimates from a given design

are said to be unbiased when an average of the estimates from all possible samples yields the true

population value. In this case, the sample estimate and its standard error can be used to construct

confidence intervals, or ranges of values that include the true population value with known

probabilities. To illustrate, if the process of selecting a sample from the population were repeated many

times, if each sample were surveyed under essentially the same unbiased conditions, and if an estimate

and a suitable estimate of its standard error were made from each sample, then:

1. Approximately 68 percent of the intervals from one standard error below to one standard error

above the estimate would include the true population value. This interval is called a 68‐percent

confidence interval.
17

2. Approximately 90 percent of the intervals from 1.645 standard errors below to 1.645 standard

errors above the estimate would include the true population value. This interval is called a 90‐

percent confidence interval.

3. Approximately 95 percent of the intervals from 1.96 standard errors below to 1.96 standard

errors above the estimate would include the true population value. This interval is called the 95‐

percent confidence interval.

4. Almost all (99.7 percent) of the intervals from 3 standard errors below to 3 standard errors above

the estimate would include the true population value.  

Employment variance  

To measure the variability of the GGS‐OCC employment estimate, a random group jackknife replicate

variance estimator is used. In this technique, each sampled establishment is assigned to one of

ܩ random groups. All establishments in each group are considered one of ܩ different subsamples. Each

subsample is reweighted to represent the universe.  

ܩ estimates of total occupational employment, ܧ෠

௞,௢,௚ (one estimate per subsample) are calculated. The

variability among the ܩ employment estimates is a good variance estimate for occupational

employment. The two formulas below are used to estimate the variance of occupational employment

for an estimation domain defined by ݇:

෠ܧ൫ݒ

௦,௞,௢൯ ൌ ∑ ൫ா෠ೞ,ೖ,೚,೒ିாത෠

ೞ,ೖ,೚൯

మ ಸ

೒సభ

ீሺீିଵሻ            (15)

  

Where,  

෠ܧ

௦,௞,௢ ൌ the total employment estimate for occupation ݋, in estimation domain ݇, for size class ݏ

෠ܧ

௦,௞,௢,௚ ൌ the total employment estimate for occupation ݋, in estimation domain ݇, for size class

ݏ, using subsample ݃

෠തܧ

௦,௞,௢ ൌ the average total employment  for occupation ݋, in estimation domain ݇, for size class ݏ,

across all ܩ subsamples. Where,

෠തܧ

௦,௞,௢ ൌ ∑ ܧ෠

௦,௞,௢,௚

ீ

௚ୀଵ

ܩ

The variance of the GGS‐OCC employment estimate is the sum of all the variances for each size class

estimate calculated in formula (16):

෠ܧ൫ݒ

௞,௢൯ ൌ ෍ ݒ൫ܧ෠௦,௞,௢൯

௦

 ሺ16ሻ
18

The standard error and percent relative standard error are calculated using the formulas below,

respectively:

෠ܧ൫݁ݏ

௞,௢൯ ൌ ටݒ൫ܧ෠௞,௢൯                (17)

෠ܧ൫݁ݏݎ݌

௞,௢൯ ൌ ௦௘ሺா෠ೖ,೚ሻ

ா෠ೖ,೚

(18)         100   כ

Wage variance  

To measure the variability of the GGS‐OCC wage estimates, Taylor Series Linearization techniques were

used to develop an estimator. Some components of the wage variance are to capture the added

variability to the wage estimates caused by collecting interval data and using NCS data to give each

employee a wage value. The first component of the variance formula accounts for the design‐based

variance. The other three components account for using intervals to collect the data. The formula below

is used to estimate the variance of occupational hourly wage rate estimates for an estimation domain

defined by ݄:

ݒ൫ܹഥ௞,௢൯ ൌ 1

෠ܧ൫

௞,௢൯

ଶ ቐ෍݊௛,௞,௢ሺ1 െ ݂௛,௞,௢ሻ

݊௛,௞,௢ െ 1 ቌ ෍ ݂ݓ௜

ଶ

ሻ௞ת௛ሺא௜

כ ൫ݍ௜, ௢െ ݍത௛,௢൯

ଶ

ቍ

௛

ቑ ൅ ෍θ௥,௞,௢

௥,௖ߪ ଶ

ଶ

௥

൅

1

෠ܧ൫

௞,௢൯

ଶ෍ቌ ෍ ൫݂ݓ ௜כ ݁௜,௢,௥൯

ଶ

௡೓,ೖ,೚

௜ୀଵ ቍ௥

௥,௘ߪ

ଶ ൅

1

෠ܧ

௞,௢

௢,௞,௥ߠ ෍

௥

௥,௪ߪ

ଶ ሺ19ሻ

Where,

݊௛,௞,௢ ൌ the number of sampled establishments in stratum ݄, and estimation domain ݇, that

have occupation ݋  

݂௛,௞,௢ ൌ the sampling fraction in stratum ݄, from establishments in estimation domain ݇, that

have occupation ݋  

ݍ௜, ௢ൌ ሺݕො௜,௢ െ ܹഥ௞,௢ כ ݁௜,௢ሻ for occupation ݋ in establishment ݅

ݍത௛,௢ ൌ the average  ݍ௜, ௢value in stratum ݄

ߠ௥,௞, ௢ൌ the proportion of employment in wage interval ݎ, for occupation ݋, within

establishments in estimation domain ݇

௥,௖ߪ൫௥,௘ߪ , ଶ

ଶ , ߪ௪,௥ଶ ൯ ൌ within wage interval r, these are estimated using the NCS and, respectively,

represent the variability of the wage value imputed to each worker, the variability of

wages across establishments, and the variability of wages within establishments
19

Confidentiality

BLS has a strict confidentiality policy that ensures that the survey sample composition, lists of reporters,

and names of respondents will be kept confidential. Additionally, the policy assures respondents that

published figures will not reveal the identity of any specific respondent and will not allow the data of

any specific respondent to be inferred. Each published estimate is screened to  make certain that it

meets these confidentiality requirements. To further protect the confidentiality of the data, the specific

screening criteria are not listed in this publication.