Hawai i's Green Workforce A Baseline Assessment December 2010

After critical review of remaining NAICS by both
the Hawai‘i Labor Market Research Section and the
Hawai‘i Green Jobs Initiative Team, several additional
industries were designated as green. All told, there
were 113 NAICS 4-digit level industry codes that
Hawai‘i classified as green. However, it should be
noted that ALL the remaining non-green industries at
the 2-digit NAICS level were sampled, though at a
much lower rate than those in the 4-digit NAICS green
industries.
For purposes of stratification, “green” means that at
least a small number of codes at the 6-digit level in
that particular 2-digit NAICS were likely green. The
entire 4-digit NAICS was categorized green, even
though most of the jobs within those codes are likely
to be non-green.
Industry (NAICS )
Not including Public Administration, there are 23
2-digit NAICS codes that cover 19 industrial sectors.
Of these 2-digit NAICS, 16 contained the presence of
at least some green 4-digit NAICS codes. Because the
remainder of these 16 NAICS are not classified green,
it is required that there be two separate sampling cells
for each individual 2-digit NAICS, green and nongreen.
In addition, there were seven 2-digit NAICS
that had no “green” 4-digit NAICS. Thus, these
individual 2-digit NAICS strata only require one cell,
non-green, for purposes of sample selection. For the
sample size of each NAICS strata, see Table 21.
Estimation
After random sampling and data collection, the
following estimation procedure was followed.
1) Sum across Green job descriptions in the sample
data and remove non-unique survey IDs (multiple
job descriptions for one employer)
a. Out of business worksites (OOBs) are
counted as 0 jobs for purposes of summing
and weighting of green jobs for all
categories. For example, if ½ of a sample
cell is composed of OOBs, then infer ½
OOBs in the universe cell (0 jobs for half
of the cell population).
2) Divide data into Wave 1 (data received prior to
June 14) and Wave 2 (data received on or after
June 14). The cut-off date, June 14, was chosen
because it provided respondents with a ten-day
grace period, and coincided with the start of an
intensive campaign to improve response using
phone calls, emails, postcard reminders, and
additional survey mailings to nonrespondents.
3) Make histograms and determine summaries
comparing Wave 1 covariates (NAICS, Size,
Green, etc) to Wave 2 covariates, and do a Z-test to
determine whether systematic bias in Wave 1 and
Wave 2 data is likely. The Z-test showed with 90
percent certainty that bias existed between Wave
1 and Wave 2 in terms of size category. This nonresponse
bias will be corrected in estimation of
green jobs in the universe below.
4) Load data from QCEW universe
a. Use file “UNIVERSEEQUI093.csv”
5) Estimate logit model from the sample data
stratified between Wave 1 and Wave 2 data to use
for the propensity to respond variable (propensity
score) in the universe of data. Wave 1 sample data
will then be used to infer green jobs in the universe
of likely responders, and Wave 2 sample data will
be used to infer green jobs in the universe of likely
non-responders. This procedure removes any nonresponse
bias that may exist.
a. Linear model: logit(y=BX+e)
Logit(Responder = B(Green+C2.NAICS+
County+Size)+e), where B is a vector of
Four coefficients estimated by logistic
regression.
b. The model resulting from the Wave 1 and
Wave 2 sample data is used to predict
which unsampled observations would
have been likely to respond or not respond
(given their covariates – Green, NAICS,
County, and Size). This is the unsampled
worksite’s “propensity” to respond. Those
with the highest propensity to respond
(with cutoff propensity = x) are coded
as Responders. The cutoff propensity is
determined such that the proportion of
responders in the universe of data equals
the proportion of responders in the sample,
Hawaiÿi’s Green Workforce: A Baseline Assessment 53