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**Assessing the Economic Impact of**

**NRCS Programs on the Utah Economy**

## Abstract

Spending on non-transfer payment federal programs has an impact on the
economy that exceeds the value of the initial spending itself. Economic
multipliers provide a means of estimating the total economic impact
generated by government spending. This report provides estimates of the
total values of certain categories of NRCS spending to the economy of
the State of Utah as well as to the economy of the United States as a
whole. The analysis was conducted using IMPLAN within a social
accounting matrix (SAM) framework. The results indicate that in Utah,
each dollar of NRCS and private matching expenditures on NRCS
conservation programs generates an additional \$1.51 in output. Using
another measure of economic impacts, the same program spending generates
\$0.51 in value added income. At the national level, each dollar of NRCS
and private matching expenditures on NRCS conservation programs
generates an additional \$1.49 in national output. Using another measure
of economic impacts, the same program spending generates \$0.60 in value
added income. (As there is some degree of overlap between categories,
total economic impact can not be calculated by summing across multiplier
effects.)

## Introduction

While NRCS natural resource conservation programs create many beneficial
impacts on the nation's landscape, another important outcome is the
economic impact of conservation programs on the nation's economy. In
2004, NRCS analysts undertook the task of evaluating seven years of
financial and technical assistance expenditure outlays for eleven NRCS
programs in order to assess their impacts on the United States economy
at both national and state levels. This analysis was performed using
IMPLAN (Impact Analysis for Planning) software and databases within a
social accounting matrix (SAM) framework. IMPLAN was originally designed
by the USDA Forest Service, the Federal Emergency Management Agency, and
the USDI Bureau of Land Management in an effort to improve land and
resource management planning. SAM analysis, which is briefly explained
in the Appendix, gives a condensed picture of economic activity,
measured in terms of expenditures, income, and revenue, within a given
geographic region.

"Multipliers" indicate the total amount of economic activity that is
estimated to be generated by a specified amount of new spending within a
given economic sector and spending category. For example, for every
dollar of NRCS cost share money spent on the final products of a given
industry sector, the corresponding spending multiplier shows the total
amount of economic activity that will be generated by new spending
(including the original dollar spent) in the time period during which
spending continues. For every job created by NRCS cost-share spending in
a given industry sector, the corresponding employment multiplier shows
the total labor demand, given in the number of "jobs", that will be
created by that spending (including the original job created). Due to
variations in the ability of each state's economy to supply the inputs
necessary to producing the goods and services that are demanded within
that state, state-level output and value added income multipliers for a
particular industrial sector often differ from their corresponding
national measures. All estimates are based on IMPLAN-based SAM models
and the corresponding U.S. Census and Bureau of Economic Analysis data
used by the program and are subject to any errors and omissions that are
inherent to the data collection methodologies utilized by those
agencies.

If a spending multiplier is equal to 1.88, this means that if \$1,000
cost-share dollars are spent on the final goods produced by that
industry sector, an additional \$880 of economic activity will be
generated within the region of analysis through repeated circulation of
the money spent. For example, when a farm supply company is paid for
supplies used in implementing a conservation practice, that company pays
a percentage of the original cost-share money to its employees in the
form of wages. That money is then circulated again as those employees
spend their wages on goods and services. The degree to which each dollar
originally spent in a region re-circulates within that region is
reflected in the multiplier. When compared with other spending
multipliers, a lower multiplier generally indicates that goods or
services purchased within that industry sector or sectors within that
region tend to consist of relatively higher percentages of imported
inputs and/or imported finished goods. When any specific good or service
is simply unavailable for purchase within a given state, importation of
that good or service will result in a relatively lower multiplier effect
for the applicable industry in that state. The converse is true for
higher multipliers.

Employment multipliers are used to determine the increase in overall
labor demand that would be expected to occur as a result of purchases of
the output of a particular industry sector. For example, if the
employment multiplier for a given industry sector is equal to 1.5, then
for each "job's-worth" of demand created in that sector due to an
increase in spending on that sector's final products, the multiplier
effects associated with that industry sector would generate demand for
0.50 additional "jobs" within the geographic region analyzed.

## Methodology

The SAM multiplier analysis is an extension of input--output multiplier
analysis. It provides a framework for the modeling of revenue and income
flows among households and firms, given the introduction of a new
expenditure injection or "shock" into the circular flow of a state or
national economy. In this analysis, the expenditure shocks are the NRCS
expenditures introduced in the 3 scenarios described below. This
analysis is driven by new investment demands and/or income transfers to
households, which leads to new purchases of goods and services
reverberating through the economy. The analysis accounts not only for
the purchase of goods and services from final producers needed for the
implementation of conservation practices, but also the purchases of
inputs from intermediate suppliers, as well as household consumption
expenditures out of new wage and profit income generated from the new
expenditure shock. IMPLAN is a Windows based software and database that
creates the models, undertakes the simulations and produces easily
accessible results.

In this study, financial and technical assistance program expenditure
data for the Environmental Quality Incentives Program (EQIP), Watershed
Surveys and Planning, Watershed Rehabilitation, Farmland and Ranchland
Protection Program, Grassland Reserve Program, Wetlands Reserve Program,
Wildlife Habitat Incentives Program, Agricultural Management Assistance,
Conservation Operations, Watershed Operations, and Resource Conservation
and Development programs from 1997 through 2003 were used to construct
the scenarios. These data were disaggregated and sorted into five major
industry sectors, depending upon the type of expenditure. Financial
assistance expenditures were subdivided into expenditures on
conservation practices and easement payments. Conservation practice
expenditures were partitioned into agricultural services, construction,
and materials sectors, depending on the type of practice and type of
inputs necessary to install each practice. Producer cost share
expenditures were similarly accounted for. Easement expenditures were
treated as income transfers to households. Technical assistance
expenditures reflected salary payments to government employees and
therefore were recorded in the "government services" sector. Data on
expenditures that were actually obligated and disbursed were assembled
for each state by year for the years 1997-2003. These data do not
reflect annual financial obligations for contracts, but rather
expenditures on practices that were actually installed and certified
that year. Data for each year were then converted to 2004 dollars and
aggregated across years to get the total NRCS program expenditures for
each of the five sectors described above by state.

Next, SAM models were constructed from the IMPLAN database for the
nation and each of the fifty states. The program expenditure data were
developed in three scenarios: total program expenditures for 1997-2003,
total program expenditures for the year 2003, and EQIP expenditures
alone for the years 1997-2003. For each of these scenarios, the
economy-wide impacts on total output, value added income, and employment
were reported for the nation as a whole as well as for each state. Total
output is defined as total sales of goods and services produced in a
particular state or national economy. Value added income is defined as
the wages and profits and indirect business taxes generated from
producing these goods and services. In our results, net contributions to
value-added income exclude indirect business taxes since they are
exogenous to the SAM multiplier. Employment impacts are expressed as the
number of full-time and part-time jobs required to produce these goods
and services. It is not appropriate to add together changes in total
output and changes in value-added income since each measure captures the
effects of the new expenditure shock, albeit at different points in the
circular flow of economic activity. In other words, total output effects
measure the impact of NRCS expenditures at the firm level, while total
value-added income effects measure the same impact on labor and capital
income flowing to households. These represent different measures of the
same impact of NRCS expenditures, hence value-added income and total
output are complementary measures.

**Summary of Results**

Financial and technical assistance program expenditures for eleven
programs from 1997 through 2003 are examined in this analysis. Three
scenarios were developed: total program expenditures for 1997-2003,
total program expenditures for the year 2003, and EQIP expenditures
alone for the years 1997-2003. For these three scenarios, the resulting
impacts on total output and value added income are reported for the
nation as well as each state. These represent different measures of the
same impact of NRCS expenditures, hence value-added income and total
output are complementary measures. In order to avoid double counting,
employment effects of NRCS program expenditures are reported only for
the 2003 year. Total output is defined as total sales of goods and
services produced in a particular state or national economies. Net
contributions to value added income are defined as the wages and profits
generated from local production of goods and services. Employment
impacts are expressed as the number of full-time and part-time jobs
required to produce these goods and services. All of the multipliers and
corresponding dollar values or numbers of jobs in this report are
estimates that were generated by the models applied in the analysis, as
explained above.

The total output impact in Utah of NRCS program and private matching
expenditures from the years 1997-2003 of approximately \$103 million was
\$258 million in 2004 dollars. This reflects the output multiplier
showing that for every \$1 spent in Utah as a result of NRCS programs,
the multiplier effect generated \$2.51 in output, of which \$1.51
represents additional induced and indirect effects. Using the same
measures, the national output impact of NRCS program and private
matching expenditures of \$10.1 billion during the same time period was
\$25.2 billion in 2004 dollars. This shows that for every \$1 spent
nationwide as a result of NRCS programs, the multiplier effect generated
\$2.49 in output, of which \$1.49 represents additional induced and
indirect effects.

In Utah, the total impact of value added income of the eleven NRCS
program and private matching expenditures of \$103 million for the years
1997-2003, was nearly \$156 million. This reflects the value added
multiplier at the national level showing that for every \$1 spent on
NRCS programs, the multiplier effect generated \$1.51 in value-added
income, of which \$0.51 represents additional induced and indirect
effects. The total national impact of value added income of the eleven
NRCS program and private matching expenditures of \$10.1 billion for the
years 1997-2003, was \$16.2 billion. This shows that for every \$1 spent
nationwide on NRCS programs, the multiplier effect generated \$1.60 in
value-added income, of which \$0.60 represents additional induced and
indirect effects.

### The impact of a given \$1 of program expenditure varies widely between states. There are three reasons for this: First, the types of programs used most frequently and the rates of cost share for conservation practices vary within the states. Programs supporting more construction-intensive activities induce more direct and indirect purchases. When coupled with lower cost share rates, this encourages more private spending and, therefore, greater total spending given a fixed government program budget. Second, the amount of funding available to the state varies greatly. Therefore, the more spent in a state the larger the economic impact. Third, the ability of a given state's economy to meet the new demand for goods and services also varies by state. Some states are able to produce more of the required intermediate goods internally, which preserves more valued-added income within the state. Other states must import more intermediate goods, allowing these income generating opportunities to leak out to other states. Generally, the more services and industries residing within a state's borders to meet the demand generated from conservation program expenditures, the larger the impact realized within that state.

The total employment impact for Utah for fiscal year 2003 NRCS programs
was 773 jobs created and/or maintained. The total employment impact for
the nation for the same fiscal year for all NRCS programs was 50,227
jobs created and/or maintained. The total national employment impact for
the fiscal year 2003 EQIP program was 12,251 jobs created and/or
maintained. The aggregate multipliers for all NRCS program expenditures
for value added income and total output that resulted in these figures
for the period 1997-2003 are listed for the Unites States as a whole and
for each state in Table 1 near the end of this document.

## Results

### NRCS Program Impacts on Total Output

The change in total output reflects the total impact on final and
intermediate demand induced by NRCS program expenditures. This impact
consists of a direct effect, indirect effects, and induced effects. The
direct effect is the initial NRCS expenditure on a particular set of
sectors. The indirect effects represent firms' purchases of
intermediated goods by the particular sector. The induced effects occur
when these new firm activities generate new income for households who,
in turn, spend this additional income on goods and services. In other
words, as NRCS payments are disbursed to farm and non-farm enterprises,
the multiplier models account for the changes in income and expenditures
of direct and supporting industries and services as well as the new jobs
needed to implement the NRCS programs.

### Figure 1.

![](media/image2.jpeg){width="6.498611111111111in"
height="4.972222222222222in"}

### Figure 2.

![](media/image3.jpeg){width="6.495833333333334in" height="4.9625in"}

### 

NRCS program and matching private expenditures in Utah of \$103 million
for the 1997-2003 period are estimated as having generated approximately
\$258 million in new production of goods and services. This reflects the
output multiplier showing that for every \$1 spent in Utah as a result
of NRCS programs, the multiplier effect generated \$2.51 in output, of
which \$1.51 represents additional induced and indirect effects.
Nationwide, NRCS program and matching private expenditures of \$10.1
billion for the 1997-2003 period are estimated as having generated
\$25.2 billion in new production of goods and services. This shows that
for every \$1 spent nationwide as a result of NRCS programs, the
multiplier effect generates \$2.49 in output, of which \$1.49 represents
additional induced and indirect effects.

Figure 3 illustrates the impact of NRCS programs on the economy.

**Figure 3.**

![](media/image4.jpeg){width="6.495138888888889in"
height="5.0055555555555555in"}

**NRCS Program Impacts on Value Added Income**

Value added income refers to the additional labor income (wages) and
property income (rents) that are realized as a result of the increase in
new economic activity spurred by the conservation program expenditures.
Value-added income is sometimes call "factor income" because it
represents payments for the use of the factors of production: labor, and
capital. The \$103 million in government and private matching outlays
for the eleven NRCS programs for the years 1997-2003 generated nearly
\$156 million in wages and capital income. This reflects the value added
multiplier for Utah showing that for every \$1 spent on NRCS programs,
the multiplier effect generated \$1.51 in value-added income, of which
\$0.51 represents additional induced and indirect effects. Nationally,
the \$10.1 billion government and private matching outlays for the
eleven NRCS programs for the years 1997-2003 generated \$16.2 billion in
wages and capital income. This reflects the value added multiplier at
the national level showing that for every \$1 spent on NRCS programs,
the multiplier effect generated \$1.60 in value-added income, of which
\$0.60 represents additional induced and indirect effects.

Figure 4 illustrates the effect of all NRCS programs on value added
income for the cumulative 1997-2003 expenditures on a state by state
basis. Figure 5 displays the effect of EQIP expenditures only over the
same time period. Finally, Figure 6 illustrates the effect of all 2003
program expenditures.

### Figure 4.

![](media/image5.jpeg){width="6.5in" height="4.9743055555555555in"}

Although the effects of value added income are smaller in magnitude than
total output, they are nonetheless significant because they are a
measure of regional well-being, or gross regional income.

EQIP impacts are more sensitive to cost share rate within the states.
The lower the cost share rates for conservation practices, the more
total expenditures within the state there are due to increased producer
and/or state expenditures given the same government expenditures. This
is, furthermore, a function of the amount of EQIP funds initially
allocated to each of the states as well as the ability of the
infrastructure within each state to handle the increased demand for
goods and services.

### Figure 5.

![](media/image6.jpeg){width="6.499305555555556in" height="4.98125in"}

### Figure 6.

![](media/image7.jpeg){width="6.495833333333334in"
height="5.022916666666666in"}

### NRCS Program Impacts on Employment

Employment reflects the impact on jobs due to the increased economic
activity and by increased goods and services demanded. The employment
figure does not necessarily reflect the number of new jobs created due
to the new economic activity, but rather the combination of new jobs
created and current jobs supported via the new activity. The employment
impacts are reported as the number of full and part-time jobs created or
retained. Reporting the cumulative employment figure for all NRCS
programs over the entire study period would be double-counting jobs,
since the same set of jobs could complete a set of projects from year to
year. For example, NRCS outlays for technical services in a particular
state probably pays in part the annual salaries of the same local
experts from year to year. Or, as an induced effect on employment, NRCS
expenditures may support the same jobs in retail services in rural areas
from year to year. The problem faced here is that sufficient data do not
exist to distinguish between those jobs supported or maintained by these
expenditures versus the new jobs created for each year. Therefore,
simulating the impacts of NRCS program expenditures for the 2003 year
best captures the employment impacts.

The total employment impact for Utah for fiscal year 2003 NRCS programs
was 773 jobs created or supported. Nationally, the total employment
impact for fiscal year 2003 NRCS programs was 50,230 jobs created or
supported. The total national employment impact for the fiscal year 2003
EQIP program was 12,250 jobs created or supported. Therefore, based on
the multiplier effect at the national level, EQIP alone accounts for
roughly 25% of the total jobs created or supported through NRCS program
funding. Figure 7 displays the employment impact for all programs in
fiscal year 2003 by state and Figure 8 illustrates the employment impact
of the EQIP program in fiscal year 2003 by state.

### Figure 7.

![](media/image8.jpeg){width="6.495833333333334in"
height="5.018055555555556in"}

### Figure 8.

![](media/image9.jpeg){width="6.495833333333334in"
height="5.018055555555556in"}

The number of jobs that NRCS program expenditures create or support
represents the direct, indirect, and induced changes in labor demand and
reflects the magnitudes of the potential increase in jobs. Although our
simulations project increases in labor demand, changes in actual
employment levels cannot be assessed by this framework since it assumes
that excess supply of capital and labor are readily available to support
the new labor demands. The dynamics of a local labor supply response
depend on the actual level of current unemployment as well as depending
on the expected duration of the jobs created. For example, if a
particular region is experiencing tight labor market conditions and the
new construction jobs created by a particular NRCS project are temporary
in nature, then a relatively higher wage may be required in order to
entice labor away from other, longer-term employment in order to get the
NRCS project completed. Conversely, if the new project is perceived as
part of a stream of annual projects funded by NRCS state-level outlays
occurring over a period of years, then suppliers will be more likely to
hire permanent new workers in order to meet this demand. In rural areas,
to the extent that there exists open unemployment, NRCS projects will
tend to induce the creation of new jobs. On the other hand, to the
extent to which there is disguised underemployment in the form of rural
workers who are visibly active but underutilized, NRCS projects will
tend to contribute to job retention.

SAM modeling is not designed to provide complex analyses of labor
markets. The model's assumption that labor supply is perfectly
responsive to demand shocks is the opposite of the standard assumption
of full employment in local labor markets. For this reason, the
employment impacts predicted by the SAM model represent, at best, the
*upper limit* of the number of jobs created or supported by NRCS
programs. Taking into account actual adjustment dynamics and the
structures of local labor markets would probably mean that fewer actual
new jobs would be created than predicted by the SAM model. It is not
expected that the number of jobs that the model predicts will be
annually maintained would be affected by local labor market conditions.

**Economic Multipliers**

Table 1 lists the NRCS program multiplier effects for value added income
and total output over the years 1997-2003 as well as listing the
aggregate multipliers both for the nation as a whole and state-by-state.
The first column displays the total NRCS financial and technical
assistance expenditures by state in 2004 dollars for the eleven
programs. The second column in Table 1 shows total expenditures when
producer cost-share contributions are included in total spending. This
column represents the new expenditure shocks used in our simulations.
Columns three and four show the total value added and total output
impacts of total NRCS program and producer expenditures. Columns five
and six show the NRCS multipliers for value added and total output.
These multipliers reflect the total output and total value-added income
generated from \$1 of NRCS program outlays. This can be interpreted as
follows, using the United States as an example: Each dollar spent on
NRCS programs generates \$2.49 in total output and generates \$1.60 in
total value-added income.

**Conclusion**

This analysis reveals how important NRCS program expenditures are to the
national and individual states' economies. Depending on the state, for
every dollar expended by NRCS and private matching funds, a range of
\$1.95 to \$2.96 of total output is generated, with an average of \$2.49
nationally. The total employment impact for the nation for fiscal year
2003 NRCS programs was 50,230 jobs created or maintained. The total
national employment impact for the fiscal year 2003 EQIP program was
12,250 jobs created or supported. Programs that provide a perceived
steady stream of income and which encourage the construction of
structural practices have the greatest impact on long-term employment.

Table 1. All NRCS Program Multiplier Effects for U.S. and Individual
States

+-------------+---------+----------+-------+-------+-------+-------+
|             | **      |          | **Eco |       | *     |       |
|             | Program |          | nomic |       | *Aggr |       |
|             | Expen   |          | Im    |       | egate |       |
|             | ditures |          | pacts |       | Mul   |       |
|             | (Th     |          | (Thou |       | tipli |       |
|             | ousands |          | sands |       | ers** |       |
|             | of 2004 |          | of    |       |       |       |
|             | \$)**   |          | 2004  |       | **for |       |
|             |         |          | \$)** |       | All   |       |
|             |         |          |       |       | Pro   |       |
|             |         |          |       |       | grams |       |
|             |         |          |       |       | (Tot  |       |
|             |         |          |       |       | al)** |       |
+-------------+---------+----------+-------+-------+-------+-------+
|             | **NRCS  | **NRCS   | **    | **    | **    | **    |
|             | Program | Program  | Total | Total | Total | Total |
|             | Expendi | and      | Value | Out   | Value | Out   |
|             | tures** | Matching | Ad    | put** | Ad    | put** |
|             |         | Private  | ded** |       | ded** |       |
|             |         | or State |       |       |       |       |
|             |         | Expend   |       |       |       |       |
|             |         | itures** |       |       |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **UNITED    | \$9,    | \$10     | \$    | \$    | 1.60  | 2.49  |
| STATES**    | 027,041 | ,109,535 | 16,20 | 25,20 |       |       |
|             |         |          | 3,873 | 1,540 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **ALABAMA** | 126,266 | 145,930  | 21    | 33    | 1.46  | 2.32  |
|             |         |          | 2,624 | 7,861 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **ALASKA**  | 43,929  | 46,296   | 6     | 9     | 1.41  | 2.09  |
|             |         |          | 5,365 | 6,584 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **ARIZONA** | 102,677 | 120,565  | 16    | 26    | 1.39  | 2.19  |
|             |         |          | 7,505 | 3,473 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 291,586 | 329,464  | 40    | 75    | 1.24  | 2.28  |
| *ARKANSAS** |         |          | 8,743 | 1,848 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **C         | 352,461 | 420,241  | 60    | 96    | 1.45  | 2.30  |
| ALIFORNIA** |         |          | 9,603 | 5,893 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 206,125 | 231,273  | 33    | 50    | 1.45  | 2.18  |
| *COLORADO** |         |          | 6,000 | 4,319 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **CO        | 37,773  | 39,464   | 6     | 8     | 1.55  | 2.24  |
| NNECTICUT** |         |          | 1,085 | 8,330 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 35,840  | 38,354   | 5     | 9     | 1.52  | 2.46  |
| *DELAWARE** |         |          | 8,483 | 4,406 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **FLORIDA** | 268,349 | 286,562  | 52    | 84    | 1.84  | 2.96  |
|             |         |          | 6,059 | 7,822 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **GEORGIA** | 173,953 | 202,223  | 32    | 50    | 1.61  | 2.51  |
|             |         |          | 6,058 | 7,281 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **HAWAII**  | 59,016  | 64,580   | 9     | 14    | 1.44  | 2.23  |
|             |         |          | 3,032 | 3,947 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **IDAHO**   | 115,234 | 144,801  | 18    | 29    | 1.25  | 2.05  |
|             |         |          | 1,595 | 6,903 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 265,824 | 285,065  | 46    | 73    | 1.63  | 2.58  |
| *ILLINOIS** |         |          | 4,367 | 5,417 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **INDIANA** | 193,527 | 201,425  | 27    | 46    | 1.38  | 2.30  |
|             |         |          | 7,770 | 2,613 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **IOWA**    | 344,793 | 385,114  | 49    | 85    | 1.29  | 2.23  |
|             |         |          | 7,639 | 9,050 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **KANSAS**  | 233,804 | 258,377  | 36    | 57    | 1.41  | 2.22  |
|             |         |          | 3,266 | 3,881 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 161,172 | 173,033  | 22    | 35    | 1.31  | 2.07  |
| *KENTUCKY** |         |          | 6,253 | 7,747 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **          | 315,929 | 381,307  | 50    | 94    | 1.31  | 2.49  |
| LOUISIANA** |         |          | 0,708 | 7,569 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **MAINE**   | 52,287  | 58,862   | 8     | 14    | 1.52  | 2.46  |
|             |         |          | 9,435 | 5,039 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 72,220  | 76,166   | 10    | 15    | 1.41  | 2.07  |
| *MARYLAND** |         |          | 7,560 | 7,464 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **MASS      | 46,912  | 49,399   | 7     | 11    | 1.56  | 2.27  |
| ACHUSETTS** |         |          | 7,215 | 2,359 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 179,431 | 193,861  | 26    | 43    | 1.38  | 2.24  |
| *MICHIGAN** |         |          | 7,661 | 4,694 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **          | 219,389 | 232,423  | 36    | 57    | 1.55  | 2.47  |
| MINNESOTA** |         |          | 0,065 | 3,336 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **MI        | 307,020 | 339,917  | 40    | 72    | 1.19  | 2.14  |
| SSISSIPPI** |         |          | 5,213 | 6,872 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 325,825 | 348,827  | 51    | 84    | 1.48  | 2.41  |
| *MISSOURI** |         |          | 6,159 | 1,954 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **MONTANA** | 202,510 | 225,169  | 29    | 48    | 1.30  | 2.14  |
|             |         |          | 3,791 | 2,292 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 223,115 | 377,781  | 46    | 88    | 1.22  | 2.34  |
| *NEBRASKA** |         |          | 1,359 | 4,955 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **NEVADA**  | 49,732  | 55,149   | 8     | 11    | 1.46  | 2.16  |
|             |         |          | 0,273 | 8,969 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **NEW       | 46,088  | 52,636   | 8     | 12    | 1.55  | 2.43  |
| HAMPSHIRE** |         |          | 1,365 | 8,155 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **NEW       | 45,196  | 51,424   | 8     | 11    | 1.62  | 2.33  |
| JERSEY**    |         |          | 3,440 | 9,830 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **NEW       | 115,603 | 153,951  | 18    | 30    | 1.18  | 1.95  |
| MEXICO**    |         |          | 1,544 | 0,391 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **NEW       | 154,021 | 165,673  | 23    | 34    | 1.45  | 2.11  |
| YORK**      |         |          | 9,907 | 9,969 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **NORTH     | 147,088 | 166,729  | 24    | 38    | 1.45  | 2.33  |
| CAROLINA**  |         |          | 1,332 | 8,652 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **NORTH     | 166,047 | 177,072  | 22    | 35    | 1.26  | 1.98  |
| DAKOTA**    |         |          | 3,524 | 0,347 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **OHIO**    | 162,915 | 181,860  | 26    | 42    | 1.48  | 2.34  |
|             |         |          | 9,728 | 5,883 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 231,828 | 256,196  | 36    | 62    | 1.42  | 2.44  |
| *OKLAHOMA** |         |          | 4,840 | 4,549 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **OREGON**  | 136,678 | 150,111  | 22    | 34    | 1.49  | 2.32  |
|             |         |          | 3,194 | 8,123 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **PEN       | 140,204 | 164,358  | 25    | 42    | 1.58  | 2.58  |
| NSYLVANIA** |         |          | 9,134 | 3,268 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **RHODE     | 17,592  | 18,468   | 2     | 4     | 1.53  | 2.35  |
| ISLAND**    |         |          | 8,167 | 3,434 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **SOUTH     | 125,949 | 131,954  | 18    | 28    | 1.38  | 2.17  |
| CAROLINA**  |         |          | 1,556 | 6,288 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **SOUTH     | 149,340 | 160,808  | 21    | 33    | 1.34  | 2.10  |
| DAKOTA**    |         |          | 5,053 | 8,034 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **          | 153,999 | 164,018  | 26    | 41    | 1.63  | 2.56  |
| TENNESSEE** |         |          | 6,850 | 9,736 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **TEXAS**   | 472,493 | 498,837  | 82    | 1,26  | 1.66  | 2.54  |
|             |         |          | 5,890 | 5,870 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **UTAH**    | 91,359  | 103,012  | 15    | 25    | 1.51  | 2.51  |
|             |         |          | 5,899 | 8,378 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **VERMONT** | 51,284  | 57,767   | 8     | 13    | 1.46  | 2.38  |
|             |         |          | 4,414 | 7,327 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| *           | 111,891 | 126,017  | 17    | 25    | 1.36  | 2.03  |
| *VIRGINIA** |         |          | 1,442 | 5,559 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **W         | 188,795 | 208,689  | 27    | 43    | 1.30  | 2.07  |
| ASHINGTON** |         |          | 0,652 | 2,359 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **WEST      | 120,636 | 134,172  | 17    | 29    | 1.28  | 2.22  |
| VIRGINIA**  |         |          | 2,018 | 8,501 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **          | 198,019 | 247,692  | 34    | 58    | 1.40  | 2.38  |
| WISCONSIN** |         |          | 7,050 | 8,313 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+
| **WYOMING** | 104,903 | 125,010  | 16    | 28    | 1.30  | 2.26  |
|             |         |          | 3,009 | 2,704 |       |       |
+-------------+---------+----------+-------+-------+-------+-------+

**APPENDIX**

**(i) The Social Accounting Matrix Multiplier Model**

The social accounting matrix (SAM) provides a snapshot view of the
circular flow of economic activity in the form of double-entry
accounting. In this framework, national income and product accounts and
input-output production accounts are represented as expenditures
(debits) and income and revenue flows (credits) in balance sheets of
activities and institutions. Activities are industries and services, and
institutions are households, firms, government, and the rest of the
world. The SAM also serves as the basis for the SAM multiplier model.

In Appendix Figure 1, we just consider the circular flow of economic
activity just among households and firms. Industries purchase
intermediate goods from other industries, pay out wages and remit
profits for the different labor and capital services, and take in
revenue for the sale of output. In turn, households allocate a portion
of their income on an array of consumption goods and savings and pay
taxes. The input-output multiplier (represented by the dotted green box
in Appendix Figure 1) just captures the indirect effects of firm
purchases of intermediate goods and services. In the input-output model,
the point of impact of the direct effects can only occur at the firm
level.

**Appendix Figure 1.**

The SAM multiplier model (represented by the dashed blue box)
incorporates not only the direct and indirect input-output effects but
also the induced effects of household expenditures. It endogenizes the
linkage effects in the circular flow of economic activity between
households and firms. The SAM multiplier model also opens up two
additional points of initial impact of an exogenous expenditure: (i)
income transfers to households impact households first and then
reverberate through the economy; and (ii) changes in factor income first
impacts the factor markets for labor and capital and then reverberates
through the economy.

The SAM multiplier model represents the most general case of
fixed-coefficient, linear multiplier models. It is considered the
benchmark multiplier model. Extended input-output models (such as Type
II, Type III, and Miyazawa multiplier models) represent partial closures
of multisectoral equilibrium. These latter models either do not capture
the full impacts, or, depending on the parameters used, produce
approximations that may understate or overstate the true impacts. This
analysis properly transfers NRCS payments directly to producer
households and to the household account in the SAM models.

Two assumptions require caution in interpreting policy results using the
SAM multiplier model. First, as is the case for the other
fixed-coefficient, linear multiplier models, prices are assumed to be
fixed in the SAM multiplier models. For the state economic models, this
assumption is quite appropriate since prices are determined nationally.
For the national model, this assumption causes little harm since the
size of the initial expenditure shock---that is, the total of all NRCS
expenditures---is very small relative to the size of the U.S. economy.
Second, these models assume that supply is perfectly elastic. The
assumption of perfect elasticity means that there will always be
unemployed resources waiting to meet increases in demand. To put this in
non-technical terms, whenever someone needs more workers, more workers
will be ready and waiting. The assumptions of fixed prices and perfectly
elastic supply together define a world without scarcity. Therefore,
results reported here represent at best the upper bounds of the response
to a positive stimulus and lower bounds to a negative stimulus.

The SAM multiplier framework is a major tool available in the regional
economics toolkit. It is designed to assess the output, value-added
income, and employment impacts of an exogenous stimulus. The strength of
this framework is that it can do so at many different levels of sectoral
detail. As noted above, this framework\'s weakness lies in the
assumptions underlying the model. These assumptions provide the policy
analyst with an imperfect, rigid framework possessing complex
multisectoral and institutional detail about a particular regional
economy.

By itself, what does the SAM framework leave out? Such policy issues as
changes in consumer or producer surplus, benefit-cost analysis for an
individual project, and imputing dead-weight losses associated with or
the values of non-market natural resource goods supported by an NRCS
project cannot be addressed by this framework. These policy issues exist
in a microeconomic theoretic framework and are best answered using
welfare economics modeling and analysis. A carefully-designed computable
social welfare model utilizing the SAM as its data platform can be used
to undertake aspects of welfare analysis in a multisector setting.
However, the technical requirements and the levels of staff expertise
required to complete such an analysis increase in orders of magnitude.

**(ii) Converting Investment Demand by Sector of Destination into
Investment Demand by Sector of Origin**

Many of the NRCS program outlays represent expenditures in completing a
project supporting an agricultural producer's conservation plan. The
task is to translate the NRCS program expenditures, expressed in terms
of project demands, into expenditures on capital goods and services
supplied by the industrial sectors in a state economy. This is a two
step process.

The first step is to cost out these projects according to the industrial
sector supplying the good or service. For example, as illustrated in
Appendix Table 1, we identify 5 conservation practices funded by the
EQIP program in South Dakota for the year 2000. Row 1 represents the
EQIP outlays, while row 2 represents the producers' cost shares.
Different projects have different cost shares. The larger the cost
share, the more EQIP pays out---summed together in row 3, the EQIP and
producer cost share outlays represent the project's total expenditures.
The total expenditures represent the costs of building a pipeline or a
pond or of undertaking pasture and hayland planting. For each project
(lines 4a-4e), NRCS technical staff apportioned these outlays into how
much was spent on the inputs: construction, manufactured goods, and/or
business services. Technical assistance provided by NRCS staff is
recorded as an expenditure to the government services sector. In line 5,
the values of the goods or services supplied by the industrial sector
are summed across all projects. Hence, in this example, NRCS and NRCS
cooperators and partners spent \$1,251,829 on construction, \$312,957 on
manufactured goods, and \$251,773 on business services in order to
implement these 5 projects. This is what is meant by converting
investment demand by sector of destination---which sector demands the
goods (in this case agriculture) into investment demand by sector of
origin---which sector supplies the goods. NRCS analysts separated out
the costs of materials, construction services, and business services for
all of the EQIP projects that were undertaken in each state within the
U.S. for each year from 1997 through 2003. Next, they added together
these expenditures by sector for each state, calculating state-wide
direct impacts on construction, manufactured goods, and service sectors.
Together with household transfers and outlays for technical services,
the values of the inputs constitute the direct effect of NRCS outlays on
a state economy.

**Appendix Table 1.**

  ---------------- ------------------------ ---------------------------- ------------------------- -------------------- ------------------------ ----------------------- -------------------- ---------------------- --------------------
  *Investment by                                                                                                                                                                                                     
  sector of                                                                                                                                                                                                          
  origin*                                                                                                                                                                                                            

                   [Project:]{.underline}                                                                                                                                                                            

  Expenditures:    [Pasture & Hayland                                    [Pest                                          [Pipeline]{.underline}                           [Pond]{.underline}   [Prescribed            
                   Planting]{.underline}                                 Management]{.underline}                                                                                              Grazing]{.underline}   

  1\. EQIP         \$116,836                                             \$1,725                                        \$344,792                                        \$397,913            \$1,900                

  2\. Producer     130,631                                               0                                              266,764                                          555,316              681                    
  cost share                                                                                                                                                                                                         

  3\. Total        247,467                                               1,725                                          611,556                                          953,230              2,581                  
  project                                                                                                                                                                                                            

                                                                                                                                                                                                                     

  *Investment by                                                                                                                                                                                                     
  sector of                                                                                                                                                                                                          
  destination                                                                                                                                                                                                        
  (suppliers of                                                                                                                                                                                                      
  goods &                                                                                                                                                                                                            
  services)*                                                                                                                                                                                                         

                                            [Sector:]{.underline}                                                                                                                                                    

  Project:                                  [Construction]{.underline}                             [Manufactured                                 [Business                                                           [Project
                                                                                                   Goods]{.underline}                            Services]{.underline}                                               Total]{.underline}

  4a. Pasture &                             \$0                                                    \$0                                           \$247,467                                                           \$247,467
  Hayland Planting                                                                                                                                                                                                   

  4b. Pest                                  0                                                      0                                             1,725                                                               1,725
  Management                                                                                                                                                                                                         

  4c. Pipeline                              489,245                                                122,311                                       0                                                                   611,556

  4d. Pond                                  762,584                                                190,646                                       0                                                                   953,230

  4e. Prescribed                            0                                                      0                                             2,581                                                               2,581
  Grazing                                                                                                                                                                                                            

  5\. Total                                 1,251,829                                              312,957                                       251,773                                                             
  ---------------- ------------------------ ---------------------------- ------------------------- -------------------- ------------------------ ----------------------- -------------------- ---------------------- --------------------

The second step in transforming the data is to recognize that certain
state economies do not possess a sufficiently varied industrial
structure capable of supplying specialized capital goods. These state
economies must import the capital goods. The IMPLAN software allows us
to use regional purchasing coefficients to adjust how much of the
demands reported in row 5 of Appendix Table 1 can be supplied by a
particular state economy. If the regional purchasing coefficients
approach 1.00, then most of the demands can be supplied by the state
economy. As a general rural of thumb, most, if not all of construction
services are supplied by firms residing in a state. If the regional
purchasing coefficients approach 0.2, for example, then only 20 percent
of the demand in a state economy could be supplied by local industries.
For each aggregated industry sector, the aggregated regional purchasing
coefficient is a weighted average of the regional purchasing
coefficients of the 364 manufacturing sectors in the national and state
models.

**Regional Impacts of NRCS Programs Analysis Team Members:**

David Buland, Economist, NRCS National Technology Support Center --
Central Region

Lynn Knight, Former Senior Economist, NRCS NHQ, Washington, DC

Ted Kuntz, State Economist, NRCS Oklahoma

Gerald Schaefer, State Economist, NRCS Montana

Julie Suhr Nelson, State Economist, NRCS Utah

Charles Zelek, Economist, NRCS NHQ, Washington DC