• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Abstract
 Table of Contents
 List of Tables
 Introduction
 Economic theory
 Study area, and estimation...
 Selection of employment catego...
 Measurement of variables and empirical...
 Agricultural analysis
 Construction analysis
 Manufacturing analysis
 Summary
 Conclusions
 Limitations
 Appendix A. Definitions of...
 Bibliography
 Back Cover














Title: Effects of resource investment programs on labor employment in the Southeast
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Title: Effects of resource investment programs on labor employment in the Southeast
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Creator: Cato, James C.
Publisher: Agricultural Experiment Stations, Institute of Food and Agricultural Sciences, University of Florida
Publication Date: 1976
Copyright Date: 1976
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Table of Contents
    Front Cover
        Front Cover
    Title Page
        Page i
    Abstract
        Page ii
    Table of Contents
        Page iii
        Page iv
    List of Tables
        Page v
        Page vi
    Introduction
        Page 1
        Page 2
        Page 3
        Page 4
    Economic theory
        Page 5
        Page 6
    Study area, and estimation models
        Page 7
        Page 8
        Page 9
        Page 10
    Selection of employment categories
        Page 11
    Measurement of variables and empirical expectations
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
    Agricultural analysis
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
    Construction analysis
        Page 25
        Page 26
        Page 27
    Manufacturing analysis
        Page 28
        Page 29
        Page 30
        Page 31
    Summary
        Page 32
        Page 33
    Conclusions
        Page 34
    Limitations
        Page 35
    Appendix A. Definitions of variables
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
    Bibliography
        Page 41
        Page 42
        Page 43
        Page 44
        Page 45
    Back Cover
        Page 46
Full Text



)-"
Bulletin 785 1 December 1976












Effects of Resource Investment Programs

On Labor Employment in the Southeast







James C. Cato and B. R. Eddleman













Agricultural Experiment Stations
Institute of Food and Agricultural Sciences
University of Florida, Gainesville
in cooperation with
U. S. Department of Agriculture




















EFFECTS OF RESOURCE INVESTMENT PROGRAMS
ON LABOR EMPLOYMENT IN THE SOUTHEAST
James C. Cato and B. R. Eddleman

Food and Resource Economics Department
Florida Agricultural Experiment Stations
in Cooperation With
Natural Resource Economics Division
Economic Research Service
U.S. Department of Agriculture

Contributing project of Southern Regional Research Project S-71-
"Income and Employment Effects of Public Investment in
Natural Resources"







AUTHORS
James C. Cato is an Assistant Professor, Food and Resource
Economics Department, University of Florida. B. R. Eddleman was
Director of the Center for Rural Development at the University of
Florida.












ABSTRACT
Effects of Resource Investment Programs
on Labor Employment in the Southeast
by
James C. Cato and B. R. Eddleman

This bulletin examines the importance of investments in human
and natural resources along with several other variables in ex-
plaining employment changes among counties comprising the four-
state region of Mississippi, Alabama, Georgia, and Florida over the
time period 1960 to 1970. Counties were delineated into urban and
rural for analysis.
Industries studied for their employment effects were agriculture,
construction, textile mill products, food and kindred products,
transportation, furniture and fixtures, lumber and wood products,
electrical equipment, and durable and nondurable products manufac-
turing. Factors examined for their influence on employment were
per pupil education expenditures, Corps of Engineers investments,
Soil Conservation Service investments in the PL-566 Small Water-
shed Program, Agricultural Stabilization and Conservation Service
payments in the Agricultural Conservation Program, and loans and
grants for community water and sewer systems made by the Farm-
ers Home Administration. Changes in county product price indexes,
number of farms, and alternative wage and employment opportuni-
ties represented other factors studied.
Results indicated that employment changes because of these
factors differed substantially among industries and county groups
studied. Investments influenced employment in some industries.
Urban and rural counties also differed. The most satisfactory re-
sults were obtained for agriculture.

KEY WORDS
Natural resources, Employment, Small Watershed Program, Natural
resource investments, Income, Investment impact












ii









TABLE OF CONTENTS

Page
LIST OF TABLES ............................... ......................................................... v

LIST OF FIGURES ................................ ......................................... v

INTRODUCTION ............................................................................ ........................................ 1
O objectives ............................ .. ............................................................. ............ . 2
R review of L literature ............................................................................................... ............... 2

E C O N O M IC T H E O R Y ..................... ...... .................................... ............................................. .... 5

STUDY AREA .................. .. ............. .. ................... ............... 7

ESTIMATION MODELS ...................... ..................................................................... 7
A g ricu ltu re ...... ............. ...................................................... ..................................... 9
Construction ........... ....................................................................................... 10
M an u fa ctu rin g .................... .. ................................................................. ............ 10

SELECTION OF EMPLOYMENT CATEGORIES .................. ........ 11

MEASUREMENT OF VARIABLES AND
EMPIRICAL EXPECTATIONS ........ ........ .. .......................................................... 12
F actor S upp lies .................................................. ........................................................................... . .. 12
E du cation .................................................................................... ..................... .... ..... 12
Corps of E engineers .................................................................................. .................. 13
Small Watershed Program .................................................... ........... .......... 14
Agricultural Conservation Program .......... ........... ...... 15
Water and Sewer Program ............................................ .............. 15
A llotm ent ........................ .............. ................... 15
P rodu ct D em an d .. .............. ..................................................... ........... .............................. 16
Agricultural product price ............................. ............................................................. ..... 16
Manufacturing product price ........................................................................................ 16
F a ctor P rice .................................................. ............................... ............... 17
A agricultural w age rate .......................................................................... ....................... 17
Manufacturing wage rate ................................................... ..... .. 17
T technology ............................................... ............ .............................................. ....... ....... ..... 17
A agriculture ...................... ........................... ..................................... 17
M manufacturing ............................................................................................. ............................. 17
F arm O operator Supplies ....................................................... ... ......... ................. ................ 18
W age opportunity .............. ................... ............................. 18
Employment opportunity ....................................................... ............. 18
F arm operator ag e ......... ........................................................... ............................... 18
Manufacturing Labor Supplies ................................................................................................ 18
W age opportunity ...................................... ................................ .............. ....... 18
Employment opportunity ..................... ....... ............................................ 19
N um ber of firm s ........................... .............. ............................ ......... 19

iii









TABLE OF CONTENTS (Continued)

Page
AGRICULTURAL ANALYSIS ..................... 19
Employment and Farm Numbers .......................................................... ................. 19
Employment Shifters and Farm Numbers ................................ ...... 22
E du cation ........... .... .............. ........................ ....................... ................ 22
C orp s of E ngin eers ............................... .......... ......................................... 22
Sm all W atershed Program .................................................................. ........................ 23
Agricultural Conservation Program .......... ............... .. 23
Water and Sewer Programs ... ............... ................. ..... 23
A llotm en t .................................................................................. ............. ............ 2 4
Product price ................................ ....... . . . ......... ... 24
W age rate ....... .......................................... .............................. ..... ............ 24
Technology ................................................................................................. 25
Wage opportunity ............................ ........................... ...................... 25
Employment opportunity ........................................... 25
Farm operator age .............................................. ..................... .......... 25

CONSTRUCTION ANALYSIS ............................................... ......................................... 25
E m p loy m ent ................ ............................... ............................. ................. ........... .. ........ 26
Employment Shifters ......................................... 26
E du cation ......................... ....................... ............... ......... ..... ...... ..... 26
Corps of Engineers ........................ ....... ....................................................... .. 27
Small Watershed Program ........................... ................................................... 27
Agricultural Conservation Program .......................................... .. 27
Water and Sewer Programs ............................. ................................................ 27
W age rate .......................................................... .................... ............. .......... . ............... 27
W ag e op portu n ity ............................................................................. ........................... ... 27
Employment opportunity ............... .................................................. 28

MANUFACTURING ANALYSIS ........... ...................... ............... 28
E m ploym ent ........ .................................................... .................. 28
Em ploym ent Shifters .. .............. ................................... ................... .. .... ........ 28

S U M M A R Y .................. .................................. ............................................. ........................................ 3 2

CO N CLU SIO N S ........ ......... ......................................................... ........... .......... 34

L IM IT A T IO N S .......... .............. .......... ................ .................................................. 35

APPENDIX A: DEFINITIONS OF VARIABLES .............................. ........... 36

BIBLIOGRAPHY ................................................. .... 41







iv









LIST OF TABLES

Table Page
1 Industry identification and employment rankings for
the four-state area (1967) ........ ........................................................................................ .... 12
2 Structural form and reduced form coefficients for
change in agricultural employment and number of
farm firm s, urban counties, 1960 to 1970 ................................................................. 20
3 Structural form and reduced form coefficients for
change in agricultural employment and number of
farm firms, rural counties, 1960 to 1970. ........................................ ............21
4 Regression equations for construction employment change
for urban and rural counties, 1960 to 1970 ............................................ .................. 26
5 Regression equations for selected manufacturing
industry employment changes for urban counties,
1960 to 1970 ................................................................................................. ...... ............................ 30
6 Regression equations for selected manufacturing
industry employment changes for rural counties,
1960 to 197 0 ................................................................. .............................................. ....... 31











LIST OF FIGURES

Figure Page
1 Grouping of counties for the four-state study area ................................. 8





















V











EFFECTS OF RESOURCE INVESTMENT PROGRAMS
ON LABOR EMPLOYMENT IN THE SOUTHEAST
James C. Cato and B. R. Eddleman

INTRODUCTION
Investments in natural resources usually are undertaken for
the expressed purposes of conserving, developing, or managing
the nation's supply of soil, water, timber, mineral, and marine
resources. Many public investment programs in natural resources
also have contained explicit development objectives. These ob-
jectives were concerned with alleviating depressed regional eco-
nomic conditions or improving the incomes of specific groups of
people.
Senate Document 97 [52], issued in 1962, made explicit a
national policy of natural resource investments for purposes of
increasing income and employment in particular regions. The
Appalachian Regional Development Act of 1965 [3] provided
for the construction of water resource projects to stimulate eco-
nomic growth of the region. Guidelines concerning principles and
standards for the planning of water and related land resource
use, issued for review by the Water Resources Council [53] in
1971, stressed the role of water resource investments in the
development of a regional economy. This orientation in policy
has given added emphasis to natural resource development pro-
grams and projects as instruments for dealing with regional
economic problems. Many other programs have evolved that focus
on goals of community improvement by concentrating on such
areas as increasing local employment and income, increasing pub-
lic revenues, and improving the quality of the environment.
Local employment and income of an area depend on many
factors other than investments in natural resources. Any explana-
tion of employment and income changes occurring within a re-
gion requires analysis of the many variables which interact to
determine these changes. Identification and measurement of
these interdependencies are necessary in order to assess previous
or prospective effects of the various programs in influencing the
level of employment and income. Changes in investment levels
that shift the supplies of critical resources often occur concur-
rently with changes in the demands for products, supplies of
other resources, firm production possibilities, and the number of
firms. An important element is the consideration of how equili-
bration in product and factor markets is affected by programs

1








designed to change the supplies of resources and, in turn, how
changes in product and factor prices affect the level of output,
resource employment, and income within the recipient region.
Since similar investments in heterogeneous regions have different
effects on employment, dissimilarities among regions must be
considered. These regional differences exist in the form of a
differing resource base or differing industrial structures.
Planners and decision-makers need information on the effec-
tiveness of natural resource projects in fostering growth in em-
ployment and income. Back [4] has pointed out that the assess-
ment of the role of natural resource investments in stimulating
growth of a regional economy will be a difficult task without
knowledge of the relationships between natural resource invest-
ments and other important stimuli on changes in employment
and income of the region. Information on the effectiveness of
natural resource investment programs in meeting specified ob-
jectives is critical for future program planning. This research,
though only conducted for a sub-region, should provide informa-
tion applicable to the Southeastern region.


Objectives
The general objective of this study is to evaluate the effec-
tiveness of selected types of natural resource investments in ac-
celerating employment growth in local recipient areas. The study
includes the four-state region of Mississippi, Alabama, Georgia,
and Florida over the time period 1960 to 1970. The general ob-
jective will be accomplished by:
1. Developing an economic model to explain changes in indus-
try employment and the number of firms within the region.
2. Empirically applying the model to selected industries in
order to determine the importance of changes in natural resource
investments on changes in employment and the number of firms.

Review of Literature
Previous studies of the effects of investments in natural re-
sources on employment, income, and output are quite varied in
purpose, objective, and scope. All previous work can be grouped
into three basic categories consisting of (1) case studies of in-
dividual projects and their impacts on local areas, (2) studies
proposing various procedures that could be used in evaluating
project effects, and (3) studies that attempt to determine the

2








effect of water resource investments over large multi-county or
multi-state areas.1
The first group consists of case studies of various small water-
shed projects and the impact of the investment program on the
local economy and/or the sectors they were intended to benefit.
Jansma and Back [22] estimated the local secondary effects of
the construction of watershed structures for upstream flood pro-
tection in Roger Mills County, Oklahoma. Input-output analysis
was used to estimate income multipliers. The multipliers were
used to determine the effect on the county's economy of in-
creases in agricultural and recreational income as a result of the
watershed program. Gray and Trock [18] in an evaluation of the
Green Creek Watershed Project in Texas used traditional bene-
fit-cost analysis to compare the actual benefit-cost ratio derived
from post-project evaluation with the ratio estimated in the
watershed work plan.
Input-output analysis was used by Kasal [24] to estimate
the local economic impacts in a four-county area as a result of
five Colorado watershed projects. Several other studies not men-
tioned were concerned mainly with benefits to primary project
users, land use, and increases in farmland values.
The second category of studies has been concerned primarily
with the methodology for use in evaluation of water resource
investments. Eidman [12] presented a method to describe the
linkages or interdependence of the various sectors and subsec-
tors of Southwestern Oklahoma. He used a simple five-equation
economic model that employed economic base multipliers and
regression multipliers. The model was designed to explain em-
ployment and income changes as a result of resource investments.
Mazuera [25] used the model to determine the secondary impact
of using water in the Sugar Creek Watershed for irrigation de-
velopment.
The work of Bromley et al. [6] outlines the role of economic
logic and methods in analyzing the consequences of water re-
source investments. Their work does not offer a concrete method
of project evaluation but rather is concerned with the many
questions raised in such an undertaking. A later study by Gibbs
and Loehman [17] deals with the evaluation of resource invest-
ment projects in terms of multiple public goals.
The last category of studies also has been concerned with the
estimation of the impacts of water resource development at the

1 A more complete discussion of these studies can be found in Cato [8].

3








local level, but includes studies concerned with a much broader
geographical area than the local project area. Many of these
studies used county observations in various forms of econometric
models to examine resource investment effects. Others examined
regional differences in the effects of resource investments. Have-
man and Krutilla [19] used input-output analysis to look at both
the national and regional effects of twelve types of water re-
source projects with respect to their influence on various occu-
pational and industrial categories.
Water availability in relation to regional economic growth
was assessed by Howe [21]. He determined that water deficit
areas did not experience drawbacks to economic growth and that
water surplus areas were not guaranteed rapid growth. Howe's
study suggested that water resource developments are likely to
be poor tools for accelerating regional economic growth if mar-
kets, resources, and other factors vital to development are lack-
ing.
Wiebe [54] evaluated the effectiveness of water resource in-
vestment projects in alleviating regionally depressed economic
conditions in the Tennesee River Watershed. Another analysis
by Cox, et al. [9] was specifically designed to assess broad based
economic growth emanating from multipurpose projects by the
application of multiple regression analysis to many socioeconomic
indicators. Boxley and Harmon" worked on a study to determine
the relationship between Public Law 566 watershed investments
and economic growth in the Southeastern United States since
1959. Economic growth is measured as income changes. They at-
tempted to use a modified form of the shift-share analytical
technique for the period 1959-1968.
Eddleman and Cato [11] have examined the relation of
changes in income over the last two decades to the level of in-
vestment in natural resources for the same time period. Counties
of a nine-state region of the Southeastern United States have
been delineated into four groups on the basis of their natural
and human resource endowments and level of economic activity.
Multiple correlation analysis has been used to examine the as-
sociation between the level of natural resource investment and
changes in various income measures for the four groups.
Survey of the literature concerning the effect of water re-
source investments on economic growth leads to two observations.

2 Robert F. Boxley and Marie Harmon, USDA, ERS, NRED, personal com-
munication.

4









Either water resource investments are poor tools for stimulating
economic growth, or the methodology for measuring these effects
falls far short of accomplishing the purposes for which it was de-
signed. Many of the studies cited have failed to consider the
importance of interdependencies among other important factors
within a region which affect employment and income changes.
Changes in investment levels that shift the supplies of critical
factors, i.e., investments in water resources, often occur concur-
rently with changes in the demands for products, supplies of
other resources, firm production possibilities, and shifters of the
supply of firms. Equilibration in product and factor markets is
affected by programs designed to change the supplies and/or
productivity of resources, and this in turn causes changes in
product and factor markets which affect the level of employment
and income within the recipient region.
A general approach that can be used in considering these ad-
ditional changes has been presented by Tolley and Schrimper [27]
and Schrimper [31]. This approach simultaneously considers ag-
gregate and micro adjustments in product and factor markets. A
variant of the general model was used by Schrimper [32] to
determine the extent to which changes in various exogenous fac-
tors explained changes in the number of farms between 1954 and
1959 for six comparable groups of farms among states as well as
among counties within North Carolina and Illinois. Eddleman
[10] proposed another variant of the model to analyze the effects
of resource development on regional employment. The model used
in this study has adapted these approaches so that absolute
changes in number of employees and firms in an industry can
be examined.

ECONOMIC THEORY
Changes in the production of products and utilization of re-
sources in a region occur at several levels in the economic struc-
ture of the region. At the firm level, the demand for production
factors and supplies of products made available by the firm are
influenced by changes in the price of the factors and the tech-
nology available for production. The aggregate effects on all firms
are also important. Variation in the demand for the firms'
products and the supplies of factors available for use in produc-
tion are also important. However, since local employment and
firm number changes are under consideration for this report,
each region was considered to have only one critical factor.

5









The economic model used in this study consisted of three
basic types of components: (1) product supplies and factor de-
mands for all firms in individual industries, (2) aggregate
product demand and factor supply functions, and (3) the num-
ber of firms in each industry." Changes from outside the region
that affect any of these components can have substantial effects
on regional adjustments at all levels in the economic structure
of the region. Primary causes of this are changes in the prices
of products and factors which occur at the aggregate level but
still have an important impact on product output and resource
input decisions made by individual firms. Firm adjustments re-
sulting from changes at the aggregate level may result in
changes in the number of firms which lead to additional changes
at the aggregate level involving still further price adjustments.
An equilibrium would exist when all product and factor prices
and the number of firms in the industry are consistent with their
total demand and supplies.
Changes in the demand for labor were expressed as a function
of changes in shifters of factor supplies, product prices, factor
prices, firm production possibilities, and the number of firms.
Changes in the number of firms were then expressed as a func-
tion of the same shifters as well as shifters of firm entrepreneur
supplies. A discussion of the effect of an increase of some critical
factor in a region on employment and the number of firms in the
region will illustrate how the economy of the region operates.
If water is a critical factor in the production of products in
any industry in a particular region, an investment program to
make more water available would influence the economic struc-
ture of that industry and the region. First, a shift in the supply
of water would make more available at a lower price. More of the
critical factor available at a lower price would increase returns
to each firm in the industry and encourage the entrance of new
firms. A greater supply of products from that industry would
result. Increases in the demand and quantity of the other factors
that are used in the production of the industry product will also
occur. Finally, increases in the demand for labor (employment)
used in the production process will result.
Ultimately, the entrance of enough firms will result in demand
increases for the critical factor water and cause an increase in
the price of this factor. Some firms would then leave the industry

3 For a complete mathematical and economic discussion of the model used
for the analysis see Cato [8].

6









since returns were no longer as attractive. The supply of prod-
uct would fall, demand for other production factors would de-
cline, and less labor would be necessary.
The resultant equilibrium for the number of firms in the
industry and employment levels would then depend on the degree
of interaction of all the factors discussed. A similar discussion
would explain the effects of initial changes in product prices,
factor prices, firm production possibilities, number of firms, and
shifters of firm entrepreneur supplies.

STUDY AREA
The four-state region of Mississippi, Alabama, Georgia, and
Florida containing 375 counties was chosen as the study area.
The area was delineated into two groups of homogenous sub-
areas. Counties in the four states were classified into two groups
using discriminant analysis on the basis of a set of ten variables
depicting the county's human and natural resource endowments,
and its urban, industrial, and agricultural structure. Division of
the counties into subareas was done since regions differ in geo-
graphic, institutional, sociological, and economic characteristics.
Conclusions about the effect of natural resource investments on
employment must consider these regional differences.
Some judgment was warranted as to the meaningfulness of
the delineations by persons familiar with the four-state region.
Members of the Southern Land Economic Research Committee4
from each of the four states were asked to evaluate the initial
classifications for their state. These evaluations were used to
adjust the mathematical delineations and resulted in the final
grouping shown in Figure 1. About one-fourth of the counties
(91) represent the urban-oriented group, while the remaining
counties (284) exhibit a rural orientation. A complete discussion
of the technique used to group the counties and variables con-
sidered can be found in Cato [8] and Eddleman and Cato [11].

ESTIMATION MODELS
A two-equation system used to estimate the effect of invest-
ments on agricultural employment was specified according to the
theoretical framework outlined in Cato [8]. Single equation models

SThis committee consisted of representatives from eleven southern land-
grant universities and several federal agencies, and is supported in part
by the Farm Foundation, Chicago, Illinois.

7
































Urban oriented counties


SRural oriented counties




Figure 1. Grouping of counties for the four state study area.


were specified for the construction industry and the manufactur-
ing industries. These models are presented below in general form.
Changes in factor supplies are represented by X, changes in
shifters of the number of firm entrepreneurs by W, changes in
product demands by PP, changes in factor prices by FP, and
changes in firm production possibilities by Z. Each change vari-
able is measured from 1960 to 1970 unless otherwise specified.
Two stage least squares was used to estimate the structural para-
meters of equation (1.1) and ordinary least squares was used' to
estimate equation (1.2) and all single equation models.5

SA computer program written by Raduchel was used [30].

8








Agriculture

The system of equations representing the agricultural indus-
try is:
6
(1.1) E = /3,,+ i pX,+ p- PP+ P FP+ t,Z + N + ,
r=1
6
(1.2) N = po + 2 IfX,, + ( PP + p/ FP + -p,Z + f/,3WW + 3,,WE
r=l
P12 WA + ,t
where:

E = Change in agricultural employment.
X, = Change in federal and state expenditures per pupil for primary
and secondary education.
X, = Change in total construction expenditures in water develop-
ment projects by the Corps of Engineers.

X: = Change in total construction expenditures in the PL 566 Small
Watershed Program by the Soil Conservation Service.
X, = Change in total investment in the Agricultural Conservation
Program (renamed the Rural Environmental Assistance Pro-
gram in 1971) by the Agricultural Stabilization and Conserva-
tion Service.

X, = Change in total loans and grants for community water and
sewer systems and waste disposal systems made by the
Farmers Home Administration.
X,; = Weighted change in acreage of allotment crops due to reduc-
tion in allotments between 1959 and 1969.
PP = Weighted change in price index of agricultural commodity
groups.
FP = Change in the average annual wage per hired farm worker
from 1959 to 1969.
Z = Weighted change in the Southeast index of agricultural output
per man-hour for commodity groups.
N = Change in the number of farms from 1959 to 1969.
WW= Change in total annual nonagricultural wage payments per
agricultural employee in 1960.
WE= Change in total nonagricultural employment per agricultural
employee in 1960.

9








WA= Change in the number of farm operators who were 55 or more
years of age from 1959 to 1969.
t = Disturbance term.
All observations were on a county basis. Variables endogenous
to the system are E and N. Equation (1.1) is over-identified and
equation (1.2) is just-identified.

Construction
Data on the construction industry were not adequate to
permit calculation of changes in the product price and technology
variables. Data on firm number changes also were inadequate.
However, since the construction industry would be one of the
more important in evaluating the primary employment effects of
investments in natural resources, a single equation model was
formulated for this industry. The equation with counties as ob-
servational units for the construction industry is:
5
(2) E = p + /3,X, -t Io FP + pWW + PWE +
r=1
where:
E = Change in construction employment.
X, to X, = Same as in equation (1.1).
FP = Change in average annual wages per construction em-
ployee from 1959 to 1967.
WW= Change in total annual wage payments for all nonagricul-
tural and nonconstruction employees per construction
employee in 1960.
WE= Change in total nonagricultural and nonconstruction em-
ployment per construction employee in 1960.
11 = Disturbance term.

Manufacturing
Much of the impact of changes in the exogenous variables for
manufacturing industries is transmitted through employment
changes within existing firms rather than through changes in
the number of firms. Therefore, changes in the number of firms
in the manufacturing industries were treated as an exogenous
variable and a single-equation model was specified for each man-
ufacturing industry. As before, counties served as observational
units.

10








The model for each manufacturing industry is of the form:
5
(3) E = o + 2 B,X, + ,,PP + p, FP + PZ + WW + floWE 4-
r=l
p11N + p
where:
E = Change in industry employment.
X, to X, = Same as in equation (1.1).
PP = Weighted change in the derived price index for the in-
dustry.
FP = Weighted change in average annual wages per production
worker in the industry from 1959 to 1967.
Z = Weighted change in the index of output per man-hour in
the industry.
WW= Change in total annual nonagricultural and other indus-
try wage payments per industry employee in 1960.
WE = Change in total nonagricultural and other industry em-
ployment per industry employee in 1960.
N = Change in the number of firms in the industry from 1959
to 1969.
4, = Disturbance term.


SELECTION OF EMPLOYMENT CATEGORIES

Agriculture, construction, and five individual manufacturing
industries comprising both durable and nondurable manufacturing
were selected according to their importance in the study area as
determined by the total number of employees in each industry
(See Table 1.) These industries are likely candidates for growth
since they depend to a large degree on raw resources as would
be made available by investments of the type considered in the
study. Growth in the remaining industries would be dependent
on them. For this reason, and since data of the nature needed for
this study are not readily available for the more service-oriented
industries, the service industries such as wholesale and retail
trade, finances, and communications were not included in the
analysis. Also, the initial employment effects of investments in
natural resources would most likely be felt in the industries
selected.

11









Industries three through seven of Table 1 accounted for ap-
proximately 57 percent (762,098 employees) of all manufacturing
employment in the four-state region in 1967 [34]. Construction
and agriculture ranked first (460,771 employees) and third
(286,528 employees), respectively, when compared to employ-
ment in the individual manufacturing industries.

Table 1.-Industry identification and employment rankings for the four-
state area (1967).
Standard Total
Industry industrial four-
identification Industrya classifica- state
for this tion code employment
study (SIC) rank

1 Agriculture 15-17 3
2 Construction 01, 07-09 1
3 Textile mill products and
other fabricated textile
products manufacturing 22, 23 2
4 Food and kindred products
manufacturing 20 4
5 Transportation equipment
manufacturing 37 5
6 Furniture, lumber, and wood
products manufacturing 24, 25 6
7 Electrical equipment
manufacturing 36 7
a Several industries were grouped together to make data from the U.S. Census
of Population [35] and the U.S. Census of Manufacturers [36] comparable
for later uses in the study.

MEASUREMENT OF VARIABLES AND EMPIRICAL EXPECTATIONS
Each variable defined in the earlier equations, and the ex-
pected effects of changes in these variables on industry employ-
ment and the number of firms are discussed in the following
sections. A complete discussion of how each variable was meas-
ured is given in Appendix A.

Factor Supplies
Education.-Increases in federal and state education ex-
penditures represent an exogeneous investment for counties. In-
creased education expenditures reflecting among other things
more education facilities and more teachers should partially re-
flect an increase in the number of persons in the recipient county
attaining higher education levels as well as an increase in the

12








average productivity of the county's labor force. In urban areas
where job opportunities are more readily created, an upgraded
labor force would be expected to attract potential employers and
eventually result in increased employment within the recipient
counties.
Increases in education expenditures in rural areas may have
quite the opposite effect on employment and firm numbers, par-
ticularly for construction, agriculture, and the low wage manu-
facturing industries. Since these industries draw heavily from
the unskilled labor force, it seems likely that increased education
levels would reduce this labor force and lead to outmigration
from these industries and from rural areas to higher paying job
alternatives. This would lead to fewer agricultural, construction,
and manufacturing workers and perhaps smaller numbers of
farms as consolidation of existing farms to gain operational
efficiencies occurs.
Corps of Engineers.-For this variable as well as the other
types of natural resource projects, total project expenditures in
each county from 1960 to 1970 were used as the independent vari-
ables. These project expenditures in civil works and new work
construction were obtained for each county from records of vari-
ous district offices which administer portions of the four state
area. Examination of the relationships between these investment
variables and employment changes should provide insights into
two components of the analysis. First, an empirical estimate of
the effect of the various types of natural resource investments
on local employment and firm numbers can be developed. Second,
the relative importance of the various natural resource invest-
ments in influencing employment and firm numbers can be ap-
praised.
Investments by the Corps of Engineers should have differing
effects on agricultural, construction, and manufacturing employ-
ment. Increased levels of flood control would be beneficial to agri-
cultural areas by making more land available for use. This would
bring about a two-fold reaction. Initially, the lower price of land
due to increased supply through flood control measures would
cause expansion of existing farms and possible entrance of new
farms. Competition for the land would soon bid land prices up-
ward, with decreased residual returns to farm operators occur-
ring. It is assumed that the area would be small enough such that
the price of a product produced and prices of other factors used
would not be affected. The probable consequence would be further
expansion of the larger more established farms, with the overall

13








effect being a reduction in farm numbers through consolidation.
It follows that the larger farms would operate with a smaller
total labor force through more mechanized operations and, con-
sequently, total agricultural employment would decline. Previous
research documenting situations in which this has occurred
includes that of Farmer and Tolley [13], Farmer [14] Hedrick
[20], Johnston and Tolley [23], Moak [28], and Pasour, Tous-
saint and Tolley [29].
In contrast to the effect on agriculture, construction and
manufacturing employment would likely increase as the result
of Corps of Engineer's investments. Employment in the construc-
tion industry would increase in the recipient areas during the
initial construction phase of the project. Initial effects (direct
effects) might also be felt in manufacturing, provided that locally
produced materials were used. More importantly for manufactur-
ing, however, would be the effect occurring during the post-con-
struction phase (indirect effects) of any project. Improved trans-
portation facilities, protection from flood damage, and increased
available water supplies, etc., would encourage expansion of ex-
isting firms and the entrance of new firms with resultant employ-
ment increases.
Small Watershed P, I,, I,..-The Small Watershed Program
is designed to aid in the solution of several types of problems.
Reduction in floodwater damages to cropland, residences, and
businesses, and protection of the health and lives of people from
floods are of major importance. Other potential and existing
problems that this program attempts to alleviate include erosion
and sediment damage, improper drainage, and irrigation needs.
Recreation, fish and wildlife enhancement, and improvements in
the economic and social well-being of people have been given in-
creased emphasis in recent years. PL-566 investments by the
SCS should affect agricultural and manufacturing employment
and firm numbers within the local areas in a manner quite simi-
lar to that of Corps of Engineer's investments. Reduction of
floodwater damage to cropland should in total reduce the number
of farms and agricultural employment. Although some new farms
might become established, consolidation of existing farms into
larger units to take advantage of improvements made possible
by PL-566 projects should be dominant, particularly in the rural
areas where opportunities for part-time farming are limited.
Construction employment should increase in the recipient
area during both the initial and secondary project phases in a
manner similar to that discussed for Corps of Engineer's invest-

14








ments. Also to be expected is an increase in manufacturing em-
ployment.
Agricultural Conservation Program.-Investments by the
Agricultural Stabilization and Conservation Service constitute a
joint effort by the public sector, farmers, and ranchers to share
the cost of establishing needed conservation measures. These
conservation programs include practices to protect, improve, and
renew soil, water, woodland, and wildlife resources of private
landowners.
Conservation measures should influence the expansion of
existing farms. The trend historically has been toward larger
farms. Since this is a cost-sharing program, the larger farmers
may be expected to take advantage of this program and expand
operations even more. Consolidation of existing farms should
more than offset the effect of entrance of new farms and lead to
a decrease in total agricultural employment and farm numbers.
Increased output of food and fiber products would also be ex-
pected to result from the application of conservation measures.
This could lead to more processing and support facilities, which
in turn should have some positive effect on nondurable manufac-
turing employment and on construction employment.
Water and Sewer Program.-Loans and grants for com-
munity water, sanitary sewer, and solid waste disposal systems
were also considered to be investments that would influence em-
ployment and farm numbers in each county. This program pro-
vides financial assistance to communities in developing essential
new public service facilities and in expanding existing facilities.
Services and facilities provided by this type program are
sometimes necessary before a community can expand with re-
gard to attracting new industry and, in turn, services to support
these industries. Communities having adequate services often
attract new industry and thus expand employment in construc-
tion and manufacturing industries. Expansion of existing firms
in the community might also occur, leading to increased employ-
ment. This program does not specifically influence a production
input used in agriculture such as land or water in the same man-
ner as the other investment programs and would probably lead
to a negative effect on agricultural employment and farm num-
bers through job migration from agriculture.
Allotment.-Changes in crop allotments represent the effect
of shifts in a perfectly inelastic factor supply on the number of
farms and employment in agriculture. A decrease in allotment
acreage would be expected to increase the market price of allot-

15








ments, or of land. This assumes that farms selling allotments
would decrease their magnitudes of operation or cease completely,
and that the farms buying allotments would find them more ex-
pensive, with a concomitant reduction in residual returns to farm
operators occurring and ultimately leading to a reduction in the
number of farm firms. The magnitude of the decrease in the
number of farm firms and consequently in agricultural employ-
ment due to a decrease in allotments would depend on farm
operator's responsiveness to changes in their residual returns,
the amount of allotment used in the production process, and the
actual level of operator returns.

Product Demand
Use of product price as an indicator of product demand is
based on the assumption of perfectly elastic demand functions
at the county level. Producers in both agricultural and manufac-
turing industries at the county level are assumed to be price
takers and thus face perfectly elastic demand functions. A
product price variable was not included for the construction in-
dustry since output is not easily defined in terms of a product
with an established market price.
Agricultural Product Price.-In rural counties decreases in
agricultural product prices should lead to a reduction in farm
numbers due to out-migration fostered by lower agricultural in-
comes and subsequent farm consolidation. As a result agricul-
tural employment would be expected to decline in rural counties.
A similar effect on farm numbers and agricultural employment
would be expected in urban counties. However, the magnitude
of the changes may be less because of greater opportunities to
adjust to part-time farming operations in these areas.
Manufacturing Product Price.-Existing firms would be ex-
pected to expand output and new firms would be expected to
enter the industry as the result of a product price increase. This
assumes there are no major barriers to firm entry. Direct in-
creases in employment should occur. Output increases should
ultimately cause factor prices to increase and product prices to
decrease, with some of the earlier expansion being offset. With
manufactured products, unlike agricultural commodities, some
apparent downward inflexibility in prices would help support in
part a conclusion that the indirect effect of decreasing employ-
ment would not completely offset the direct effect leaving a posi-
tive overall effect on manufacturing employment. It does remain

16








possible that due to economies of size in manufacturing indus-
tries, output and, consequently, employment can increase as
product prices decrease. Hence, the expected relationships be-
tween product price changes and employment levels would be
negative.
Factor Price
Agricultural Wage Ratc.-Employment effects of increases
in hired farm labor wage rates should be negative, particularly
in rural areas. Wage increases would result in higher factor costs
to farm operators. This would encourage substitution of other
factors for labor. Smaller employers would not be able to make
sufficient substitutions and would not be able to compete with
larger and more efficient farm operators. Farm numbers would
then decline through consolidation and expansion of existing
firms.
Manufacturing Wage Rate.-Increases in construction and
manufacturing wages result from either reductions in the supplies
of labor to the industries or increases in the demand for labor as
the demand for the industrial output increases. In general,
growth in labor demand is expected to outweigh labor supply re-
duction to these industries. Thus, employment is expected to in-
crease as wage rates in the industry increase.

Technology
Changes in technological forces that affect agriculture and
manufacturing industries should have an effect on the amount
of labor employed. Similar to the other types of shifters discussed
previously, technology changes would also affect factor demand,
product supplies, and the number of firms.
Agriculture.-Trends in output per man-hour and advances
in agricultural mechanization suggest that technology increases
in agriculture are likely to be labor decreasing. A negative effect
on agricultural employment should result. Similar effects would
be expected on farm numbers. Technology advances should en-
able the operation of larger farms with resultant decreases in
farm numbers.
Manufacturing.-Technology changes in the manufacturing
industries have employment effects similar to those in agricul-
ture. Technology changes are generally labor decreasing. A tech-
nology variable for the construction industry was not included,
since output per man-hour indexes for construction were not
available.

17








Farm Operator Supplies
Changes in the numbers of farm operators affect agricultural
employment in various ways. Several variables used in this study
are quite unique with respect to the types of shifters discussed
earlier. These shifters are thought to affect farm operator sup-
plies which in turn affect agricultural employment.
Wage Opportunity.-Wages in industries other than agri-
culture represent opportunity costs to farm operators of remain-
ing in present employment. Initially, wage increases in alterna-
tive employment would be expected to decrease the number of
farm operators remaining in agriculture. As the larger farms
realize greater residual returns some increase in farm numbers
might occur. This effect should be minimal with an overall de-
cline in farm numbers expected. The movement to fewer, larger,
and more efficient farms should then cause a negative effect on
agricultural employment.
Employment Opportunity.-Increases in employment oppor-
tunity in alternative employment situations would be expected
to decrease the number of farm operators remaining in agricul-
ture in a manner similar to that of increases in wages in em-
ployment alternatives.
Farm Operator Age.-Farm operator age represents the
change in the number of farmers who were 55 or more years of
age during the period 1959 to 1969. This variable is intended to
reflect the relative effects of potential operator deaths and/or re-
tirements on the numbers of farms during 1959 to 1969. The
greater the number of farmers who are reaching an older age,
the greater should be the decline in farm numbers and employ-
ment during the entire period. Declines in the number of older
farm operators would be expected to cause declines in farm num-
bers through a greater impact of farm consolidation rather than
operator replacement.

Manufacturing Labor Supplies
Wage Opportunity.-Wages in manufacturing industries
other than the industry of present employment represent oppor-
tunity costs to employees of remaining in present employment as
a result of changes in other employment alternatives. Initially,
wage increases in other industries would entice employees to
leave their present industry if their skills were transferable.
Their present industry might bid wages upward and regain to
some extent, but an overall negative effect would be expected.

18








Employnmnt Oppoirtunity.-Increases in employment oppor-
tunity in alternative employment industries would be expected to
affect the number of employees in the given industry in a manner
similar to that of increases in wages in other industries. Increases
in employment opportunity in other manufacturing industries
should decrease employment in the industry of present employ-
ment. Growth in other industries that are complementary to a
particular industry would be expected to positively affect employ-
ment in the industry. In the construction industry, employment
increases are likely to occur as the number of jobs in other in-
dustries increases since this reflects overall expansion in the
area's economy.
Number of Firms.-Changes in the number of manufactur-
ing firms were used as predetermined variables in the manufac-
turing analysis. An increase in the number of firms in general
would be expected to bring about an increase in employment.

AGRICULTURAL ANALYSIS
Estimates of the effects of each independent variable on agri-
cultural employment follow. Equations (1.1) and (1.2) were esti-
mated separately for the urban and rural groups of counties.
Counties were excluded if no employ- .ent was reported in both
1960 and 1970. The estimates for the two-equation models used
for agriculture are given in Tables 2 and 3 with each table pre-
senting three equations for one of the two groups of counties.

Employment and Farm Numbers
Trends in agricultural employment during the decade of 1960
to 1970 were generally downward. Urban counties experienced
about one-half the average decline per county in agricultural
employment as rural counties. However, urban counties had con-
siderably fewer agricultural employees. The number of farm
firms per county declined for each group although relative dif-
ferences were not as large as experienced in employment.
A decline in farm numbers would be expected to cause a de-
crease in the number of hired agricultural workers. Fewer, but
larger, more efficient, and more mechanized farms would also
reflect a decline in the number of farm operators and therefore
agricultural employment. The effect of a decrease of one farm
firm on agricultural employment was important in the rural
counties.

19








Table 2.-Structural form and reduced form coefficients for change in agri-
cultural employment and number of farm firms, urban counties,
1960 to 1970.
Endogenous variables'
Structural form Reduced form
coefficients coefficients
Agricultural Number of Agricultural
Predetermined employment farm firms employment
variables (E)b (N)e (E)
Constant 268.80 -78.21 263.30
Education (XI) .8879 .3991** .9159
(.8316) (.1619)
CE (X2) -.0058 .0018 -.0057
(.0104) (.0021)
PL-566 (X3) .3388 .0804* .3444
(.2186) (.0433)
ACP (Xi) -.8410 -.2482*** -.8584
(.2107) (.0427)
FHA (XB) -.0682 -.0311"* -.0703
(.0595) (.0121)
Allotment (X6) .1843 .0580 .1884
(.3383) (.0688)
Product Price (PP) -14.4830 4.9352** -14.1364
(9.5980) (1.9490)
Wages (FP) .0343 .0131 .0352
(.0427) (.0088)
Technology (Z) -1.1216 -.1356 -1.2014
(3.2520) (.6920)
Wage Opportunity (WW) -.3720 -.0261
(2.5290)
Employment opportunity (WE) .1350 .0095
(.7820)
Farm operator age (WA) 1.9111* .1342
(.0965)
Number of farms (N) .0702 -
(.2503)
R2 -.93

R- .92

"a Complete variable definitions can be found in earlier sections.
b Change in agricultural employment was estimated with two-stage least
squares. Figures in parenthesis for this equation are asymptotic standard
errors. Levels of significance are not indicated since they are approximations.
c Change in number of farm firms was estimated by ordinary least squares
with figures in parentheses indicating standard errors. Since this equation is
just-identified and contains all predetermined variables the structural coef-
ficients are identical to the derived reduced form coefficients.
Significant at 10 percent level.
"** Significant at 5 percent level.
*"* Significant at 1 percent level.







20









Table 3.-Structural form and reduced form coefficients for change in agri-
cultural employment and number of farm firms, rural counties,
1960 to 1970.
Endogenous variables
Structural form Reduced form
coefficients coefficients
Agricultural Number of Agricultural
Predetermined employment farm firms employment
variables (E)b (N)e (E)
Constant -470.60 58.59 -390.70
Education (Xi) -.3154 .1246 -.1454
(.2515) (.1312)
CE (X2) .0007 -.0021 -.0022
(.0034) (.0017)
PL-566 (X3) -.1474 -.0109 -.1623
(.0959) (.0481)
ACP (X4) -.2659 -4153*** -.8324
(.0940) (.0352)
FHA (X5) .0456 -.0796*** -.0630
(.0334) (.0143)
Allotment (Xe) .0821 .0779""* -.1884
(.0595) (.0290)
Product Price (PP) 4.2840 8.5897*"* 16.0029
(3.5910) (1.7430)
Wages (FP) -.0058 .0020 -.0030
(.0073) (.0037)
Technology (Z) 7.0522 -1.4802* 5.0328
(1.4980) (.7969)
Wage Opportunity (WW) .1759 .2400
(4.1390)
Employment opportunity (WE) 9.0983* 12.4128
(4.7030)
Farm operator age (WA) 1.4027*"" 1.9137
(.0947)
Number of farms (N) 1.3643 -
(.1322)
R2 -.80

R2 -.80

Complete variable definitions can be found in earlier sections.
"bChange in agricultural employment was estimated with two-stage least
squares. Figures in parenthesis for this equation are asymptotic standard
errors. Levels of significance are not indicated since they are approximations.
c Change in number of farm firms was estimated by ordinary least squares
with figures in parentheses indicating standard errors. Since this equation is
just-identified and contains all predetermined variables the structural coef-
ficients are identical to the derived reduced form coefficients.
Significant at 10 percent level.
"** Significant at 5 percent level.
"*** Significant at 1 percent level.








21








Employment Shifters and Farm Numbers
Education.-Changes in per capital education expenditures
were not very important in effecting agricultural employment
changes. A more skilled work force resulting from higher educa-
tion levels would be expected to migrate to nonfarm jobs to
realize their employment potential. This migration effect is sup-
ported by the negative coefficients for the rural county group.
Somewhat different results were obtained for changes in the
number of farm firms. Increases in per capital education expendi-
tures were associated with increases in farm numbers in the ur-
ban county group. Higher education attainments of potential
farm operators enabled them to take advantage of alternative
employment. The strong positive relationship for the urban coun-
ties indicates that higher education expenditures in these counties
coupled with greater nonfarm employment opportunities sug-
gested an increase in the number of rural residences classified
as part-time farms. An examination of the changes in the num-
ber of part-time farms in the urban groups of counties indicated
substantial growth in this group of farms over the 1960-1970
period.
Corps of Engineers.-A major portion of investments by
the Corps of Engineers in the four-state study area was for flood
control. Effective flood control should make more land available
for agricultural use. Expansion of farm size should encourage
more efficient labor saving operations with resultant declines in
agricultural employment. Displacement of existing farms by ex-
pansion also contributed to employment declines. For agricultural
employment in the urban counties the coefficient was negative
but considerably smaller than its standard error estimate. Any
employment increase due to new farms in the recipient areas was
offset by reduction in employment as the result of farm ex-
pansion.
A large proportion of Corps of Engineers' investments oc-
curred in the delta area of Mi;- ;I-''I,. and in an area in east-cen-
tral Alabama, predominantly rural areas. These areas have tra-
ditionally had large numbers of small f., u -. Flood protection
provided by Corps of Engineers' projects made new land avail-
able that was suitable for mechanized agriculture. This created a
movement to large farms through both the effect of entry of new
farms and farm consolidation. Increased agricultural output has
required employing more agricultural workers in these two areas.
This may give reason to the positive coefficient for employment
in the rural group.

22








Small Watershed Program.-Investments by the Soil Con-
servation Service in the Public Law 566 Small Watershed Pro-
gram yielded average investment levels per county that were
fairly uniform in both groups. This program also showed the
lowest average level of investment per county of any of the land
or water related investments. The rural counties experienced
negative effects on employment and farm numbers. In contrast,
the urban group showed positive coefficients. Some investments,
particularly in Florida, occurred near urban areas. Since one of
the major stated purposes of this investment program is to pre-
vent floodwater damage, it appears possible that previously
flood-prone land became available for new farms in these groups.
Small Watershed Program investments are similar to Corps
of Engineers' investments in that they are long term in nature.
Some effects of flood control structures and channelization proj-
ects may not yet have been measured. Some of the investments
considered during the latter part of the study period represented
the beginning of certain projects, and only the effects of the con-
struction phase have been measured.
Agricultural Conservation Program.-Program investments
in the Agricultural Conservation Program provided the most
uniform coverage over the four-state area of any of the invest-
ment programs analyzed. Only one county did not receive an in-
vestment, and the average investment per county did not vary
much among the two groups.
Both demonstrated the expected negative effect on changes
in employment and farm numbers. Since this is a cost sharing
program with farmers and is intended to introduce various con-
servation practices, the negative effect on employment indicates
that measures taken with regard to land stabilization, resource
improvement, and land retirement enable the use of labor saving
production practices. The effect on farm numbers was also nega-
tive in each group. Larger farms with the more responsive oper-
ators have been able to take advantage of the cost sharing pro-
gram to improve their production practices.
Water and Sewer Programs.-Investments by the Farmers
Home Administration for community water and sewer programs
facilities represented loans and grants made during the time
period under study. Additional grants for the projects made by
other federal agencies were included.
Loans and grants made during this time period resulted in
negative employment effects for the urban group. Investments in
this program were fairly widespread over the four-state region.

23








Most program investments were made in rural counties and
showed a positive relationship with employment.
A iamil .ni negative relationship was indicated in changes in
the number of farms for both areas. Improvement of water and
sewer facilities in local communities often result in an expansion
of nonfarm employment alternatives for farm operators and a
movement away from the farm.
Allotment.-Allotment reductions and agricultural employ-
ment moved the same direction in each of the two groups of
counties. Positive ...-1. ii .. ,l- for this variable indicate movement
in a general downward direction. Small standard errors were as-
sociated with the coefficients for the rural group. Allotment re-
ductions had the effect of reducing agricultural employment.
Positive coefficients also were obtained for the allotment
variable in the equations for changes in farm numbers. The
largest impact was observed for the rural group, where the major
proportion of allotment reductions occurred.
Product Pricc.-Changes in product demand as measured
by changes in the index of product prices did appear to be im-
portant in *:: 1.,l;ii ;n.-' employment changes. Decreases in product
prices were effective in reducing agricultural employment in the
rural county group. The mean of the product price variable ob-
servations was negative as were those for the agricultural em-
ployment variable observations. Product price declines were also
effective in reducing farm numbers for both groups of counties.
The effect on farm numbers was very important due to
the high level of statistical significance of the coefficients. Product
price declines tend to enhance the rate of farm firm disappear-
ance and foster consolidation of farm units, since prices are very
important to small marginal farmers in maintaining their net
income levels. The importance of product demand points out the
responsiveness of agriculture to produce price changes.
Wage Ratc.-Changes in factor prices as indicated by in-
creases in the hired wage rate did not appear important in in-
Jl, ,i. ing. employment changes. Mechanization is the normal
substitute for labor employment in agriculture. Increased mecha-
nization is also normally associated with increases in farm size
and reductions in farm numbers. These expected results were not
substantiated in the analysis of changes in farm numbers or
changes in agricultural employment. In most cases the standard
errors of the estimates were considerably larger than the partial
regression coefficients.

24








Technology.-Output per man-hour increases for agriculture
appeared to increase total agricultural employment in rural coun-
ties. Technological changes apparently caused large output in-
creases which in turn increased the total demand for agricultural
labor in these areas, particularly in the production of labor in-
tensive commodities such as vegetables.
The expected negative effect occurred with respect to farm
number changes. Technological advancements which increased
output per man-hour contributed to significant declines in farm
numbers in the rural counties. Since larger farms are able to take
advantage of improved technology and increase output and farm
size, many smaller farms were forced out of business and farm
consolidation occurred.
Wage Opportunity.-Increases in the opportunity cost of
remaining a farm operator should influence operators making a
low return on their farming investment to seek a higher income-
earning alternative. Changes in nonagricultural wages were nega-
tively related to farm number changes for the urban group. The
positive relationship for the rural groups may be explained by
the relative lack of employment alternatives in rural areas.
Neither coefficient was significant.
Employment Opportunity.-Changes in off-farm employment
opportunities were significant and positively related to changes
in farm numbers in the rural county group. This indicates that as
off-farm employment opportunities have expanded the number
of farm firms has also increased through part-time farming
coupled with off-farm work.
Farm Operator Age.-Changes in the ages of farm opera-
tors were importantly associated with changes in the number of
farms. As older farm operators disappeared through deaths
and/or retirements, the number of farm operators declined and
fewer farm numbers resulted. Farm consolidation was occurring
rather than older operators being replaced by younger farm op-
erators.

CONSTRUCTION ANALYSIS
Estimates of the effects of natural resource investments and
the other variables hypothesized as affecting employment in the
construction industry in each of the two county groups are given
in Table 4. The estimated equations differ slightly from those
used for agriculture and manufacturing since the variables repre-
senting product price, technology, and the number of firms were
not included.

25









Table 4.-Regression equations for construction employment change for
urban and rural counties, 1960 to 1970.a
Urban Rural
Independent variables counties counties
Constant -240.70 217.10
Education (Xi) 1.1809 -.3252***
(1.6510) (.1102)
CE (X2) .1426*** .0002
(.0251) (.0015)
PL-566 (X3) -.4362 -.0271
(.5501) (.0419)
ACP (Xi) -.9751** .0420
(.4399) (.0270)
FHA (X5) -.0328 -.0297""
(.1427) (.0119)
Wages (FP) .2725"* .2756***
(.1198) (.0066)
Wage opportunity (WW) -67.7790** -2.4612***
(31.8000) (.7480)
Employment opportunity (WE) 63.9780**" 4.2331*
(5.7870) (2.4340)
R2 .45 .13
R2 .39 .10
Number of observations 90 283
"a Standard errors are in parentheses.
Complete variable definition can be found in earlier sections.
C Counties were excluded if no employment was reported.
"* Significant at 10 percent level.
"** Significant at 5 percent level.
"*** Significant at 1 percent level.


Employment

Explanation of the variation in changes in the number of
construction employees was not as large as that found in agricul-
ture, as indicated by the R"'s. The urban counties where increases
in construction employment were the largest exhibited the great-
est amount of explained variation. Since the product price, tech-
nology, and number of firms variables were not included in the
construction equations, their omission may reflect the low per-
centages of total variation explained.

Employment Shifters
Education.-Changes in per pupil education expenditures
were important for the rural group of counties. Education invest-
ments which improved the ability of workers to compete in other
labor markets stimulated outmigration from the rural counties.
The negative effect on construction employment in rural areas
reflects this relationship.

26








Corps of Engineers.-Corps of Engineers' investments
seemed quite important in influencing construction employment
in urban counties. Much of this construction activity results in
reported employment in urban areas although the actual work
may be done in a rural county.
Small Watershed Program.-Flood and water control in-
vestments were expected to result in increased construction em-
ployment. Negative effects were obtained for both county groups,
although the estimated regression coefficients were considerably
smaller than the estimate of the standard errors of PL-566
investments. Large numbers of construction workers are not
required, since many of these projects are not large scale.
Agricultural Conservation Program.-Since this is a cost-
share program, participating landowners and farm operators
probably implement some practices using their own equipment.
This may be reflected in the fact that employment effects were
negative and significant for the urban county group. Contract
work is often done by contractors from outside the project
county. Larger farm operators with special equipment also do
custom construction work with no apparent construction employ-
ment increase.
Water and Sewer Programs.-Since most projects are lo-
cated in rural communities, the negative employment effects ob-
tained for rural areas may reflect the use of contractors located
outside the recipient county. The initial construction employment
impact is not felt in the county where sewer or water systems
are installed. Also, these investments are often made in areas
where overall economic activity and employment has been de-
clining. In such cases a negative relationship would be expected.
Wage Rate.-Factor price increases in the form of wage
increases would be expected to decrease employment if other
factors could be substituted for labor. Growth in the demand
for labor used in construction activity resulting from overall
increases in economic activity appears to have outweighed any
substitutions of other factors for labor as indicated by the posi-
tive relationships obtained for both county groups. As wages
increased through an expansion in demand for construction ac-
tivities, the level of employment also increased.
Wage Opportunity.-Opportunity costs of construction em-
ployment are apparently quite high. Many workers transferred
to higher paying industries, as evidenced by the negative coeffi-
cients obtained for both county groups.

27








Employment Opportunity.-The positive effect on construc-
tion employment from increases in other employment opportuni-
ties points out the complementary nature of this industry. As the
economy of the area expands through the influence of other sec-
tors, accompanying expansion is felt in the construction sector.

MANUFACTURING ANALYSIS
Five manufacturing industries were examined to determine
the effect of each employment shifter on employment in each of
the five industries. Manufacturing industries producing products
normally classified as nondurables were the textile mill and other
fabricated textile mill products and the food and kindred products
industries. Transportation equipment, lumber and wood products
(including furniture and fixtures), and electrical equipment man-
ufacturing were the individual durable manufacturing industries
analyzed.
Employment
Average employment changes varied quite substantially
among the industries. Employment in the textile products indus-
try was generally declining for both county groups, although
there were large isolated county employment differences. Average
employment changes for food and kindred products were nega-
tive for the urban county group and positive for rural counties.
Transportation equipment average employment change was
largest in urban counties with an increase of slightly over 600
employees per county. Average employment change for lumber
and wood products and furniture and fixtures was negative for
both county groups with very little variation in magnitude of
change among counties. Electrical equipment average employ-
ment change also varied substantially with the urban counties
showing the largest average increase of 320 employees.

Employment Shifters
The effect of the various employment shifters on employment
changes in the individual I]i,;,jii.. I Iii;i' industries is shown in
Table 5 for urban counties and Table 6 for rural counties.
Per capital education expenditures were not important in ex-
plaining employment changes in the industries for both groups
of counties. The relationships were negative for the two non-
durable industries. These industries were experiencing employ-
ment declines with the exception of food processing in rural
counties. Generally, positive relationships between education ex-

28








penditures and employment changes in the durable manufactur-
ing industries were obtained. However, in most cases estimated
coefficients were smaller than the estimates of the standard
errors. Thus, very little importance can be placed on the role of
increased education expenditures from federal and state sources
in fostering increased manufacturing employment in recipient
areas.
Investments in water resource projects were not importantly
associated with changes in manufacturing employment. Corps of
Engineers' investments seemed to influence employment in food
manufacturing industries in urban counties. However, the rela-
tionship was not as expected since employment declines in food
processing were occurring in those counties receiving the invest-
ments. Neither investments in PL-566 watershed development
nor ACP program investments appeared to be importantly in-
fluencing employment changes in the manufacturing industries.
Investments in water and sewer facilities by the Farmers
Home Administration had the greatest impact in rural counties
where most of the investments were made. Increased employment
in furniture, lumber and wood products, and electrical machinery
and equipment was importantly related to FHA investments in
rural counties with each $100,000 of investment resulting in 4.1
and 3.1 new jobs, respectively. A greater impact was felt in urban
counties for furniture and lumber manufacturing, where each
$100,000 of investment resulted in 5.9 new jobs over the 1960-
1970 decade. In general, the other industries experienced em-
ployment declines in counties where FHA investments were being
made. However, in all of these industries the estimates of the
standard errors of the regression coefficients were generally
much larger than the estimate of the regression coefficient.
I 'l.,' in product prices were negatively related to employ-
ment changes in the manufacturing industries. Variations in
product prices were important in effecting employment changes
in the urban counties in the food processing and furniture and
lumber industries. In both these industries employment was de-
clining during the 1960 to 1970 period while product prices were
generally increasing resulting in the negative relationships ob-
tained. The important effects of these movements were to slow
down the rate of employment decline. That is, had product prices
been decreasing, the employment declines in the manufacturing
industries could have been even greater than those experienced.
Increases in wage payments importantly influenced employ-
ment in transportation equipment manufacturing in both groups

29










Table 5.-Regression equations for selected manufacturing industry employment changes for urban counties, 1960 to 1970a,
Food and Transportation Furniture, lumber, Electrical
Textile mill kindred equipment and wood equipment
Independent variables" products products products products products
Constant 446.10 227.60 134.80 478.40 -298.90
Education (X1) -1.2722 -.1050 1.7013 .2705 1.3037
(.7672) (.4485) (1.4710) (.3285) (.8308)
CE (X2) .0024 -.0173** .0013 .0028 -.0048
(.0125) (.0066) (.0254 (.0049) (.0130)
PL-566 (X3) .0065 .2070 -.1240 .0027 -.1476
(.2329) (.1395) (.5270) (.1008) (.2569
ACP (X4) .1953 .1834 -.0055 -.0362 .2375
(.1896) (.1127) (.3950) (.0836) (.2016)
FHA (X5) .0190 -.0139 -.0791 .0591** -.0220
(.0600) (.0360) (.1268) (.0260) (.0651)
c Product price (PP) -16.6440 -51.1450** -19.6250**"
0 (14.3500) (12.2100) (7.2510)
Wages (FP) -.0323 .0499 .2625** .0057 .0088
(.0541) (.0353) (.1276) (.0217) (.0107)
Technology (Z) -4.5918 17.0690* -24.8780 -6.8045** -2.1746
(4.7150) (9.2860) (28.8300) (2.7710) (9.1240)
Wage opportunity (WW) 34.3810 17.0650 -127.0800"* 5.5614 -27.4060
(25.4300) (15.9300) (63.6500) (11.3600) (28.7400)
Employment opportunity (WE) .2603 .9148 -.7591 .0502 -.1918
(.1811) (1.2560) (.5372) (.2065) (.2135)
Number of firms (N) 105.5400"** 10.3570*** 30.5550 -1.6980 106.0400"*
(6.4310) (2.9550) (19.0700) (1.9960) (15.7700)
R2 .83 .40 .22 .22 .54
R2 .80 .31 .12 .11 .48
Number of observations 90 89 86 90 86
"a Standard errors are in parentheses. Significant at 10 percent level.
b Complete variable definition given earlier. ** Significant at 5 percent level.
c Counties excluded if no employment was reported. *** Significant at 1 percent level.









Table 6.-Regression equations for selected manufacturing industry employment changes for rural counties, 1960 to 1970a.
Food and Transportation Furniture, lumber, Electrical
Textile mill kindred equipment and wood equipment
Independent variables products products products products products
Constant 241.70 82.52 234.10 45.06 32.42
Education (X1) -.2270 -.0204 -.1461 .1550 .0146
(.2323) (.0933) (.1408) (.1043) (.1118)
CE (X2) .0044 -.0009 -.0013 .0002 .0005
(.0030) (.0012) (.0020) (.0015) (.0014)
PL-566 (Xa) .0792 -.0310 .0101 -.0275 .0230
(.0821) (.0332) (.0472) (.0398) (.0355)
ACP (X4) .0112 -.0207 -.0129 -.0083 .0082
(.0529) (.0208) (.0312) (.0258) (.0248)
FHA (Xa) -.0143 .0182 -.0261* .0419*"* .0307***
(.0232) (.0091) (.0135) (.0112) (.0101)
C Produce price (PP) -.1181 -.9850 -.4624 -
"" (2.1130) (2.1560) (2.2320)
Wages (FP) -.0138 -.0050 .0269"* -.0171* .0013
(.0209) (.0074) (.0122) (.0091) (.0100)
Technology (Z) -.0269 -.6951 -5.6737 -2.0076** -1.8103
(.8504) (1.0430) (3.8760) (.8818) (1.6340)
Wage opportunity (WW) -11.9390** .6182 -15.1340*** .1800 -4.8866
(5.1000) (1.4890) (4.2770) (1.7170) (3.7650)
Employment opportunity (WE) -.0575 .0603 .0045 .9489** .0051
(.1346) (.1253) (.0406) (.3697) (.0281)
Number of firms (N) 62.8000* 15.6860*** 46.3260*** -.7391 99.9960***
(6.5150) (3.7200) (10.9400) (.8872) (14.0100)
R2 .32 .10 .17 .11 .30
R2 .29 .06 .14 .08 .26
Number of observations 227 274 258 283 216
"; Standard errors are in parentheses. Significant at 10 percent level.
"b Complete variable definition given earlier. ** Significant at 5 percent level.
c Counties excluded if no employment was reported. *** Significant at 1 percent level.








of counties and furniture and lumber manufacturing in rural
counties. In the case of transportation equipment, increases in
the demand for the industry's output stimulated increases in
the demand for labor within the industry. As a result, employ-
ment increases occurred along with rising wage payments in the
industry. As wages increased in the furniture and lumber indus-
tries, employment declined in the rural areas where much of this
industry is located.
In general, increases in output per man-hour were negatively
associated with employment changes. As new technology was
adopted, labor requirements declined, resulting in decreased em-
ployment. Employment in food processing in urban counties ex-
hibited the only situation where employment and labor produc-
tivity changes moved in the same direction.
Wage changes in other types of employment appeared to be
more important in influencing employment changes within the
manufacturing industries than changes in the number of jobs
per employee. Particularly in the transportation equipment in-
dustry, increased wages in other industries resulted in employ-
ment declines for both urban and rural areas. In rural counties,
rising wage rates in other industries resulted in decreased em-
ployment within the textile m.i !,u1,.l. i'ng industry. Employ-
ment opportunities were important in influencing changes in em-
ployment in furniture and lumber manufacturing in rural coun-
ties. Declines in employment in this industry were similar to
overall industry employment declines in many of these areas.
Cih..,.-i -. in the number of firms within the industry were a
major factor influencing the change in employment level, par-
ticularly in rural areas. The relationships were positive as ex-
pected, with the exception of the furniture and lumber industry,
where employment declines occurred with only slight changes in
the number of firms and the coefficient was not significant. The
impact of new firms on employment changes during 1960 to 1970
were greatest for the textile mill, transportation equipment, and
electrical machinery manufacturing industries as compared to
food processing and furniture and lumber manufacturing.



SUMMARY

The general objective of this study was to examine the im-
portance of investments in human and natural resources along
with other shifters of employment in explaining employment

32









changes within county groups for the four-state region of Mis-
sissippi, Alabama, Georgia, and Florida from 1960 to 1970.
Changes in employment and firm numbers brought about by
changes in the supplies of resources, demand for products, sup-
plies of other factors, firm production possibilities, and shifters
of the number of firms in the agriculture, construction, and five
durable and nondurable manufacturing industries were examined.
Previous analyses indicated that either water resource in-
vestments were poor tools for stimulating economic growth or
attempts to measure the effect of resource investments have been
inadequate.
Counties within the four states were grouped into urban and
rural counties according to their human and natural resource
endowments, and their urban, industrial, and agricultural orien-
tations. Two groups of counties were selected for empirical analy-
sis. One group consisted of 91 urban-oriented counties. A second
group consisted of 2Li rural-oriented counties.
Industries were chosen for analysis primarily on the basis
of total employment with some consideration given to water use.
Those chosen were agriculture, construction, textile mill products
and other fabricated textile products, food and kindred products,
transportation, the combination of furniture and fixtures and
lumber and wood products, and electrical equipment.
Shifters used to explain employment and firm number changes
were of five types. Changes in factor supply included changes in
state and federal per pupil education expenditures, Corps of En-
gineers investments, investments by the Soil Conservation Serv-
ice in the Public Law-566 Small Watershed Program, Agricul-
tural Stabilization and Conservation Service payments in the
Agricultural Conservation Program, and loans and grants for
community water and sewer systems made by the Farmers Home
Administration. Reduction in crop allotments were also included
for agriculture. Changes in product demand were indicated by
changes in derived county product price indexes. Changes in
factor prices were indicated by changes in the annual wage rate
for .-in|l.-e, -, in each particular industry in a county. Changes
in firm production possibilities were computed using changes in
derived county technology indexes for each individual industry.
Shifters of entrepreneur supply and labor supply for the con-
struction and manufacturing industries were considered to be
variables that indicated alternative wage and employment op-
portunities. Farm operator age was also included in the agricul-
tural equations.

33








CONCLUSIONS
Effects on employment and firm number changes as the result
of the various exogenous shifters differed quite substantially
among industries and among the individual groups of counties
considered. It was concluded from these variations that the ex-
ogenous shifters do have specified effects on employment and
firm number changes depending on the type of shifter, the area
of employment location, and the type of industry under consider-
ation. For example, certain factor supply changes were very con-
sistent in their effect on employment and firm number changes
as well as consistently influencing employment changes in a given
county group.
Increases in ACP payments, FHA loans and grants for water
and sewer systems, and output per man-hour seemed to be im-
portant in influencing farm consolidation, which resulted in re-
duced farm numbers and agricultural employment. Increases in
education expenditures by state and federal governments seemed
important in influencing positive changes in farm numbers and
employment only in urban oriented counties. Decreases in crop
allotment and changes in the number of older farm operators
seemed important in reducing both the number of farms and
agricultural employment in the rural oriented counties, and in
reducing the number of farms in urban counties. None of the
other shifters of operator supplies seemed important in influenc-
ing employment and farm number changes, except for changes
in nonagricultural employment opportunities in rural counties.
Decreases in agricultural product prices were found to be
consistently influencing the decline in farm numbers in both
urban and rural counties. Water resource investments during the
construction phase of projects were not influential on agricultural
employment or changes in farm numbers. It is possible that suf-
ficient time had not elapsed to measure adequately the employ-
ment and farm number response in agriculture resulting from
water resource investments.
Increases in Corps of Engineers' investments were of major
importance in influencing construction employment in urban
counties. Other investment programs did not demonstrate this
pattern, since investment level increases generally reflected em-
ployment declines.
Wage increases in the construction industry led to higher
employment levels as expected. The availability of high wages
and more jobs in other industries caused a decrease in construc-
tion employment, indicating that construction employment can

34









easily be influenced not only by wages in the construction indus-
try but also in other industries.
Employment effects in the manufacturing industries were not
as definite or consistent as in agriculture and construction. Only
general statements could be made about the effects of the vari-
ous types of shifters on manufacturing employment.
In general, changes in product prices, technology levels, and
wages in other industries led to decreased labor employment
within a given industry. Water and sewer investments by the
FHA resulted in employment increases, particularly in rural
areas. Investments in water resource projects appeared unim-
portant in influencing employment changes within the manufac-
turing industries analyzed. Changes in firm numbers were con-
sistently a major factor influencing employment changes in the
manufacturing industries.


LIMITATIONS
Problems in obtaining and transforming data into forms suit-
able for empirical application of the theoretical model represent
a major limitation of the empirical results of this study. The
data used in this study included both investment information
provided by the administering agency and published data of the
type found in census publications. The time period under study
was 1960 to 1970. Data sometimes were reported for periods that
did not coincide. For example, data from some census sources
were from 1959 to 1969. There also occurs some deviation from
the theoretical definition of variables. Private investments and
their employment effects in the same areas were not measured.
Regardless of these shortcomings, the best possible data sources
were utilized.
Another major limitation concerns the application of the re-
search results. Employment of firm number effects that occur
as the result of a resource investment may occur for each indi-
vidual project. This analysis suggests that in general the results
as reported will occur for the given type of area. No definite
statement can be made that the same effect will absolutely
occur for an individual project.
Finally, the long term effects of water resource investments
may be of quite a different magnitude and distribution than those
resulting during and as a direct result of the construction phase
of projects. Many of the long term effects would have escaped
measurement in the study.

35












APPENDIX A
DEFINITIONS OF VARIABLES

EMPLOYMENT.-Changes in employment for each county were
computed from 1960 to 1970 as reported in the 1960 and
1970 Censuses of Population [37, 35]. Employment in some
industries was combined to conform to the Standard Indus-
trial Classification (SIC) industry classification.

FIRM NUMBERS.-Changes in the number of manufacturing firms
from 1958 to 1967 were obtained from the 1958 and 1967
Census of Manufacturers [38, 7]. Changes in the number of
farms from 1959 to 1969 were obtained from the 1959 and
1969 Censuses of Agriculture [39, 40].

EDUCATION INVESTMENTS. (X3). Federal and state education
expenditures over the period 1959-1960 to 1969-1970 were
included as a measure of exogenous shifts of the investment
in a county's human resources. Expenditure estimates were
obtained from state education agencies in Alabama [1, 2],
Mississippi [26, 27], and Georgia [15, 16] and the Florida
Statistical Abstract [7].

CORPS OF ENGINEERS' NATURAL RESOURCE INVESTMENTS (X2).-
Investments by the Corps of Engineers in civil works and
new work construction were obtained for each county from
the various district offices which administer portions of the
four-state area. Investments were measured in thousands of
dollars. These investments covered projects categorized into
multipurpose, navigation, flood control, beach control, and
recreation projects. The major portion of expenditures was
for flood control and navigation with a very small portion
allocated to beach control and recreation. Due to the large
amount of the investments going into flood control and navi-
gation projects, no distinction among the above investment
categories was made. Some expenditures by the Corps for
construction projects along the Mississippi River could not
be allocated to counties. Therefore, these investments were
not included in the analysis. Investments occurred in 148 of
375 counties comprising the four-state area.

36








SOIL CONSERVATION SERVICE PL-566 INVESTMENTS (Xs).-Con-
struction expenditures by county from 1960 to 1970 for the
Small Watershed Program were obtained from personnel in
each of the four state offices of the Soil Conservation Serv-
ice. Data were tabulated from 239-B forms which gave the
actual dates of construction expenditures for each project.
Investments were then allocated to each county based on
project location as identified on maps prepared by the Soil
Conservation Service. A total of 111 counties received these
types of investments during the study period. Investments
were measured in thousands of dollars.

AGRICULTURAL STABILIZATION AND CONSERVATION SERVICE ACP
INVESTMENTS (X,).-Data on ACP investments were ob-
tained from annual state ASCS reports during 1960 to 1970
for Alabama [48], Mississippi [49], and Florida [50]. Ex-
penditure information included cash payments to farmers
and allowances paid to vendors for conservation materials
furnished farmers. Data for Georgia were taken directly
from computer printouts obtained from the Data Division,
Agricultural Stabilization and Conservation Service, U.S.
Department of Agriculture, Washington, D. C. A total of
374 counties had participants in the ACP program during
the study period. Investments were measured in thousands
of dollars.

FARMERS HOME ADMINISTRATION INVESTMENTS (X5,).-Data for
these investment loans in thousands of dollars were obtained
from various state directors of the Farmers Home Adminis-
tration. A total of 278 counties received financial assistance
from FHA during the study period.

CROP ALLOTMENT (X,,).-Reductions in acreage allotments be-
tween 1959 and 1969 for all allotted crops were computed
for all counties having acreages of these crops. Annual re-
ports for 1959 and 1969 from the Agricultural Stabilization
and Conservation Service in Alabama [48], Mississippi
[49], Georgia [45], and Florida [50] provide data on al-
lotted crop acreages. County reductions were weighted ac-
cording to total value of sales of each crop as a proportion
of total value of crop and livestock sales in the county in
1959 as derived from data available in the 1959 Census of
Agriculture [39]. Declines in harvested acres in each county

37








were also computed, using the same data sources that pro-
vided information on allotment reductions. The smaller of
these two changes was then selected as the effective cut in
allotments. Allotment reductions were not considered rele-
vant for a county if its harvested acreage in 1959 was less
than the county's acreage allotment for the selected crop in
1969.

AGRICULTURAL PRODUCT PRICE (PP).-Changes in price indexes
were computed for each of the seven major agricultural
commodity groups produced in the study area using three
year averages centered in 1959 price indexes [46] and 1969
price indexes [47]. These changes were weighted by the
1959 value of each commodity group as a proportion of the
total value of crops and livestock produced in each county.
The resulting measure was a weighted change in product
prices faced by farm producers at the county level. Value
of products sold was obtained from the 1959 Census of Agri-
culture [39].

MANUFACTURING PRODUCT PRICE (PP).-Changes in national
wholesale price indexes as reported by the Bureau of Labor
Statistics [44] for three-digit level (SIC) industries be-
tween 1959-61 and 1969-71 were used in computing a county
product price for each two-digit level industry. For each
industry, county price changes were obtained by weighting
the change in the three-digit national wholesale price in-
dexes by the 1958 value added by manufacturing for each
three-digit level industry as a proportion of the total value
added for the two-digit industry in the county. Data used
to calculate value added for each industry were obtained
from published data made available by the U. S. Bureau of
the Census [38, 41].

AGRICULTURAL WAGE RATE (FP).-A proxy variable was used as
the annual wage rate for agricultural employees. Total ex-
penditures for hired farm labor in 1959 and 1969 for all
farms in each county were divided by the total number of
hired farm laborers working 150 days or more each year
in that county to obtain an annual wage per worker. The
change in this wage was then computed. These data were
obtained from the 1959 and 1969 Census of Agriculture [39,
40].

38








MANUFACTURING WAGE RATE (FP).-Changes in average annual
production worker wage rates between 1959 and 1970 for
each two-digit level manufacturing industry in each county
were used for manufacturing wage rate changes. Data were
obtained from the 1959 and 1970 County Business Patterns
for each industry [42, 43]. If the two-digit level industry
wage was not reported for a county due to disclosure prob-
lems, the change in average annual production worker wage
for all manufacturing industries in the county was used.

AGRICULTURAL TECHNOLOGY (Z).-Changes in output per man-
hour in agriculture were used as indicators of changes in
agricultural technology. Changes in the index of output per
man-hour for six major commodity groups in the South-
east were computed using three-year averages centered on
1959 and 1969 [51]. These changes were then weighted by
the 1959 value of each commodity group produced as a pro-
portion of the total value of crops and livestock produced in
each county obtained from the 1959 Census of Agriculture
[39]. The resulting measure was a weighted change in labor
productivity for each county.

MANUFACTURING TECHNOLOGY (Z).-Changes in technology for
each manufacturing industry were computed in a manner
similar to that for agriculture. Changes in national output
per man-hour indexes between 1960 and 1970 for three-digit
level industries were used in computing a county technology
change variable for each two-digit level industry. These in-
dexes are published by the Federal Reserve System [5].
Changes in the national output per man-hour indexes for the
three-digit level industry were weighted by the 1959 value
added for each three-digit level industry as a proportion
of the total value added by the two-digit level industry in
the county. The same value added data used in calculating
industry product price was used in the weighting procedure.

AGRICULTURAL WAGE OPPORTUNITY (WW).-Change in agricul-
tural opportunity wages between 1960 and 1970 in each
county was determined using data on employee wages ob-
tained from County Business Patterns [42, 43] and the
Census of Population [37]. Changes in annual nonagricul-
tural wages between 1959 and 1970 per agricultural em-
ployee in 1960 was used as the indicator of wage opportunity

39








for agricultural employees. Changes in county unemploy-
ment levels would have provided an alternative measure for
this variable.

AGRICULTURAL EMPLOYMENT OPPORTUNITY (WE).-Changes in
employment alternatives were calculated using employment
data obtained from the 1960 and 1970 Censuses of Popula-
tion [35, 37]. Changes in nonagricultural employment be-
tween 1960 and 1970 per agricultural employee in 1960 in-
dicate employment opportunities for agricultural workers
and farm operators.

FARM OPERATOR AGE (WA).-This variable was calculated for
each county from the 1959 Census of Agriculture [39] and
the 1969 Census of Agriculture [40].

MANUFACTURING WAGE OPPORTUNITY.-Changes in opportunity
wages between 1960 and 1970 in each county was deter-
mined for construction and manufacturing using data on
employee wages obtained from County Business Patterns
[42, 43] and the Census of Population [37]. It was hy-
pothesized that employees would not be moving into the
agricultural industry because of its low average wage level.
The change in annual nonagricultural wages between 1959
and 1970 per construction worker in 1960 in each county
was used to indicate the wage opportunity for construction
industry employees. A similar measure was calculated per
manufacturing employee in each county.

MANUFACTURING EMPLOYMENT OPPORTUNITY (WE).-Changes
in alternative manufacturing employment were calculated
for each county using employment data obtained from the
1960 and 1970 Census of Population [35, 37] by computing
the change in all employment other than agriculture and in-
dustry of present employment per worker in industry of
present employment.








40









BIBLIOGRAPHY

1. Alabama State Board of Education. Annual Report: 1960. Montgomery,
Alabama: Division of Administration and Finance, 1960.

2. Alabama State Board of Education. Annual Report: 1970. Montgomery,
Alabama: Division of Administration and Finance, 1970.

3. Appalachian Regional Development Act. 89th Congress. 1st Session,
Public Law 89-4, Statute 79.5. Washington, D. C.: Government
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Institute ot Food ndm Agricultural Sciencs



TEACHING IFA S
RESEARCH
EXTENSION
















This public document was promulgated at an annual cost
of $2,065.21 or a cost of 69t per copy to provide community
governments, regional planning boards, state and federal
agencies, investing agencies, and the general public with
information on the effect of resource investment programs
on labor employment in the Southeast.





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