AN ABSTRACT OF THE DISSERTATION OF
Smita Biswas for the degree of Doctor of Philosophy in Applied Economics, presented on
June 2, 2011
Title: The Economics of Forest-Dependent Regions
Abstract approved: ____________________________________________________
Andrew J. Plantinga
This dissertation explores two economic phenomena involving forest-dependent
areas: wage distribution and migration pattern of individuals. Are forest-dependent rural
areas less desirable for workers from the standpoint of labor market returns?
Are
different skills (e.g. education, experience) rewarded differently in these areas? If there
are interregional wage differences, would that influence a working-age individual‘s
migration decision and residential location choice? These questions are examined here
with data from Public Use Microdata Survey (PUMS) of the United States 2000
decennial census. I focus my inquiry on the socio-economic characteristics of forest and
non forest-dependent areas, the interregional difference in labor market outcomes, and
the influence of wage differential and other factors on the migration decisions and
residential location choice of working-age individuals.
Reduced form log wage equations are estimated for individuals, which
incorporate explanatory variables related to both skill factors (such as years of schooling
and potential experience) and to non-skill factors (such as minority status) potentially
influencing wage. Variations in the interregional wage distribution resulting from
differences in skill mix of workers are removed by using a standardized skill distribution.
The results indicate that average wages and variation in wage distribution are lower in the
forest-dependent areas than other areas.
For migration analysis, a Partially Degenerate Nested Logit (PDNL) model is
used to model migration decisions and location choice of individuals in the Northwest
between 1995 and 2000. Migration decision is modelled as a two stage process: first an
individual decides whether to move or stay in his/her original location. Conditional on
migration, the individual next chooses his/her destination location from a set of
alternative areas. Empirical results indicate that educational attainment and labor market
outcomes influence individual‘s migration decision. Conditional on moving, non-forest
dependent areas are more attractive to individuals as residential destinations.
©Copyright by Smita Biswas
June 2, 2011
All Rights Reserved
The Economics of Forest-Dependent Regions
by
Smita Biswas
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented June 2, 2011
Commencement June 2012
Doctor of Philosophy dissertation of Smita Biswas presented on June 2, 2011
APPROVED:
________________________________________________________________________
Major Professor, representing Applied Economics
________________________________________________________________________
Director of the Applied Economics Graduate Program
________________________________________________________________________
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of Oregon
State University Libraries. My signature below authorizes release of my dissertation to
any reader upon request.
________________________________________________________________________
Smita Biswas, Author
ACKNOWLEDGEMENTS
I would like to express my sincere appreciation to the many individuals who
contributed to my experience at Oregon State University. First and foremost, I would like
to thank my major professor, Dr. Andrew J. Plantinga, for his advice, patience, and
support throughout my graduate program. I am grateful for the opportunity to pursue my
doctoral degree under his guidance, and for his help in preparing this manuscript.
I would like to express gratitude to Dr. Munisamy Gopinath, Dr. Jeffery Reimer,
Dr. Bruce Weber, and Dr. Russell E. Ingham for serving in my graduate committee, and
kindly offering their valuable time and advice.
Gratitude is extended to the faculty, staff and students of the Applied Economics
Graduate Program for their support and collegial atmosphere. I am thankful to my fellow
classmates for their help and companionship during my years at Oregon State University.
Lastly, very special thanks to my family and friends, near and far, for being there
in good and bad times, and for their affection and understanding. To my brother, Bunty thank you for always encouraging me, and without your inspiration, I would have not
undertaken this endeavor. To my best friend, Pralay - thank you for always standing by
my side and for your unending love.
CONTRIBUTION OF AUTHORS
Dr. Andrew J. Plantinga contributed to the writing and preparation of this
dissertation. His direction was instrumental in developing the methodology, interpreting
results, and polishing the manuscript. In particular, Dr. Plantinga contributed to the
writing of Chapter 3, ―Wages and Returns to Skills in Forest-Dependent United States‖.
TABLE OF CONTENTS
Page
Chapter 1 - General Introduction ........................................................................................ 1
Chapter 2 - Forest-Dependent Areas in the United States .................................................. 6
2.1. Introduction .............................................................................................................. 6
2.2. Data .......................................................................................................................... 7
2.3. Identifying Forest-Dependent Areas ........................................................................ 7
2.4. Comparison of Forest and Non Forest-Dependent Areas ...................................... 11
2.5. Discussion .............................................................................................................. 15
Appendix 2A ................................................................................................................. 17
Chapter 3 - Wages and Returns to Skills in Forest-Dependent United States .................. 26
3.1. Introduction ............................................................................................................ 26
3.2. Conceptual Basis .................................................................................................... 27
3.3. Empirical Methodology.......................................................................................... 29
3.3.1. Estimating Area Mean Log Wage ................................................................... 32
3.3.2. Estimating Area Returns to Skills ................................................................... 33
3.4. Data ....................................................................................................................... 37
3.5. Results .................................................................................................................... 38
3.6. Discussion .............................................................................................................. 43
Appendix 3A ................................................................................................................. 46
TABLE OF CONTENTS (Continued)
Page
Chapter 4 - Migration Decision and Location Choice in the Northwest .......................... 59
4.1. Introduction ............................................................................................................ 59
4.2. Model Framework .................................................................................................. 61
4.3. Data ........................................................................................................................ 65
4.4. Results .................................................................................................................... 68
4.5. Discussion .............................................................................................................. 72
Appendix 4A ................................................................................................................. 74
Chapter 5 - Conclusion ..................................................................................................... 77
Bibliography ..................................................................................................................... 81
LIST OF FIGURES
Figure
Page
2.1. U.S. Areas with Five Percent or More Income from Forest Sectors .......................... 9
2.2. U.S. Areas with Five Percent or More Employment from Forest Sectors .................. 9
3.1. Forest-Dependency and Estimated Area Wage, Males............................................. 40
3.2. Forest-Dependency and Estimated Area Wage, Females ......................................... 40
4.1. Structure of Partially Degenerate Nested Logit (PDNL) Model .............................. 62
LIST OF TABLES
Table
Page
2.1. Socioeconomic Measures, 2000................................................................................ 12
2.A.1. NAICS Industries in Forestry and Wood Sector ................................................... 17
2.A.2. MIGPUMAs with 5% or More Income and Employment from Forestry .............. 18
2.A.3. MIGPUMAs and Counties of Northern California ................................................ 21
2.A.4. MIGPUMAs and Counties of Idaho ...................................................................... 22
2.A.5. MIGPUMAs and Counties of Oregon ................................................................... 23
2.A.6. MIGPUMAs and Counties of Washington ............................................................ 24
2.A.7. MIGPUMAs and Counties of Maine .................................................................... 25
2.A.8. MIGPUMAs and Counties of New Hampshire .................................................... 25
3.1. Mean Wages, and Index of Returns to Skills, Education and Experience ............... 41
3.A.1. Variable Definitions ............................................................................................... 46
3.A.2. Selected Summary Statistics, Males ...................................................................... 47
3.A.3. Selected Summary Statistics, Females ................................................................... 48
3.A.4. Estimates of Wage Equation, Males ...................................................................... 49
3.A.5. Estimates of Wage Equation, Females ................................................................... 50
3.A.6. Estimates of Area Mean Log Wage, Males ........................................................... 51
3.A.7. Estimates of Area Mean Log Wage, Females ........................................................ 53
3.A.8. Estimates of Returns to Skills, Education and Experience, Males ........................ 55
3.A.9. Estimates of Returns to Skills, Education and Experience, Females ..................... 57
LIST OF TABLES (Continued)
Table
Page
4.1.Maximum Likelihood Estimates of PDNL Model...................................................... 69
4.A.1. Variables Used in the Migration Model ................................................................. 74
4.A.2. Summary Statistics for the Migration Model, Males ............................................. 75
4.A.3. Summary Statistics for the Migration Model, Females ......................................... 76
THE ECONOMICS OF FOREST-DEPENDENT REGIONS
Chapter 1 – General Introduction
The United States has about 737 million acres of forest land, with approximately
two-thirds of the forest land used for the production of wood products. With a timberland
base of 490 million acres, the forest products industry harvested about 19 billion cubic
feet of softwood and hardwood timber in 1998 (Miller Freeman, 1998). The United States
is a world leader in producing lumber and wood products, and also a leader in the pulp
and paper industry, producing about 34 percent of the world's pulp and 29 percent of total
world output of paper and paperboard (Miller Freeman, 1998). The U.S. forest products
industry is a strong contributor to the nation's economy, producing 1.2 percent of the U.S.
GDP. The industry employed almost 1.3 million people in all regions of the country in
1997, and ranks among the top 10 manufacturing industries in 46 states (U.S. Department
of Commerce, 1997).
Yet, in recent decades, forest products industries have experienced challenges that
have had profound effects on regional employment and earnings. During the 1990s there
were significant structural changes in the Pacific Northwest timber industries. Previously,
the old-growth forests of the Northwest had served as the northern spotted owl‘s habitat.
Over time, these forests had also become primary sources of timber for forest based
industries. As a result of heavy logging, the old forests have dwindled and so has the
number of spotted owls. In June 1990, the northern spotted owl was declared a threatened
2
species, and the issue of habitat protection for the northern spotted owl came to the
forefront. Under the Northwest Forest Plan (NWFP) adopted in 1994, large areas of oldgrowth forest were set aside to protect the spotted owl and other species. This policy
affected federal lands on the west side of the Cascades in Oregon and Washington, and in
portions of Northern California. Less logging in the Northwest region affected
communities that were dependent on the resource for economic stability (Dumont, 1996).
A recent retrospective study (Eichman et al, 2010) found employment reductions from
the NWFP of about 80,000 jobs over the period of 1994 to 2003.
While the timber and timberland markets of the Southeast and Northwest
comprise a major portion of the forest products capacity in U.S., the Northeast continues
to be a region of importance. Despite a large forest resource base and extensive timber
cutting, wood markets in the Northeast, and especially in Northern Maine, are depressed.
Many mills have closed down or reduced production. These economic conditions have
affected employment across the entire industry. According to USDA Forest Service
research, the Northeast region – which includes Maine, Vermont, New Hampshire, and
New York – has experienced a 24 percent decline in softwood lumber capacity between
2000 and 2005. Timber industry employment accounts for 3.4 percent of Maine jobs, but
employment in the industry is shrinking.
Despite the challenges faced by the Northwest and the Northeast forest industry
over the last few years, forest products sector remains a major employer in these two
regions. Past studies (Kaufman and Kaufman, 1990; Machlis and Force, 1988; Machlis,
Force and Balice, 1990) have showed that some key issues encountered in forest-
3
dependent areas include poverty, lack of economic development and loss of population.
This dissertation explores the indicators that tell of the different economic struggles faced
by the forest-dependent areas of the Northwest and the Northeast. These two regions are
natural areas to study because of the different challenges faced by them. I focus my
inquiry on two economic phenomena involving forest-dependent areas: wage distribution
and migration pattern of individuals.
Chapter 2 uses United States census data for the year 2000 to identify areas which
can be considered as forest-dependent. I define forest-dependent areas as those which in
2000 had at least 5 percent of income from forest-based industries. Results of statistical
analysis reveal that forest-dependent areas are concentrated in the Pacific Northwest, the
Northeast, the upper Midwest, and parts of the Southeast United States. Not all forestdependent regions in United States have undergone similar changes over the years. There
is a difference in ownership pattern of forests in the Northwest and the Northeast. In the
Northwest, a large portion of the forest resources is under federal ownership and subject
to federal management, whereas forests in the Northeast are mostly under private
management. Because the experiences of the forest-dependent areas in the Northwest
may differ in interesting ways from experiences of their Northeast counterpart, this study
examines the socioeconomic characteristics of these two regions. A descriptive analysis
was conducted to provide for a regional comparison of the Northwest and the Northeast.
The general finding is that forest-dependent areas in the Northeast and the Northwest do
not vary significantly in socioeconomic characteristics, but the non forest-dependent
areas in both regions are overall better off than the forest-dependent areas.
4
Chapter 3 investigates the interregional differences in wage distribution in forest
and non forest-dependent areas. A Mincerian-style log wage equation is estimated at the
individual level, which incorporates explanatory variables related to both skill factors
(such as years of schooling and potential experience) and to non-skill factors (such as
minority status) potentially influencing wage. Separate wage equations are estimated for
forest and non forest-dependent areas, reflecting the idea that skills are rewarded
differently in different labor markets. Wages vary greatly by gender, and hence separate
wage equations are estimated for males and females. Variations in the interregional wage
distribution resulting from differences in skill mix of workers are removed by using a
standardized skill distribution. The standardized skills distribution controls for the skill
level of individuals across areas by normalizing each individual‘s skill to equal the mean
skill level of all other individuals in the sample. Wages earned by individuals depend on
their skills, and particular skills such as education and experience may be more highly
valued in some labor markets. This study investigates whether overall skills, as well as
particular skills such as education and experience, are rewarded differently in the forest
and non forest-dependent areas. The analysis produced area mean wages and returns to
skills indices1 for forest and non forest-dependent areas. The results indicate that average
wages and returns to skills indices are lower in the forest-dependent areas than other
areas.
1
An index that reflects the log wage variance in the jth area relative to the log wage variance in all areas.
5
Chapter 4 examines the factors that influence migration decisions and location
choice of individuals in the Northwest. There are 47 MIGPUMAs 2 in the Northwest
region, and between 1995 and 2000, an individual may choose to stay in his/her origin
location or move to any of the remaining 46 MIGPUMAs. Each area (MIGPUMA) is
characterized by a number of features that will contribute to an individual‘s well-being. A
migration model is constructed that hypothesizes that individuals seek to maximize their
utility and can do so by choosing to live in any of 47 MIGPUMAs in the Northwest. The
appropriate model for the empirical analysis of discrete choice among one origin area and
46 alternative non-origin destination areas is a Partially Degenerate Nested Logit (PDNL)
model (Hunt, 2000; Hunt and Mueller, 2004). The model encompasses both area and
individual characteristics that are found to be important in migration decision-making.
The individual characteristics include data on age, race, marital status, and educational
attainment. The area attributes include data on population density, employment growth,
amenity features, wages, and migration cost factors. Results of the empirical model
indicate that higher educational attainment and higher expected wages in non-origin areas
increase an individual‘s probability to migrate. Conditional on migration, individuals
prefer non forest-dependent areas as destination.
This dissertation contributes to the understanding of forest and non forestdependent areas by investigating regional wage distributions, and work-age individuals‘
migration and residential location choice.
2
MIGPUMA is the geographic unit of analysis, and is discussed in section 2.2 (Data) on page 7
6
Chapter 2 – Forest-Dependent Areas in the United States
2.1. Introduction
About 33 percent of U.S. land area, or 737 million acres, is forest land. Of this
forest land, about 490 million acres (67 percent of all forest land) is classified as
timberland—forest land capable of producing in excess of 20 cubic feet per acre per year
and not legally withdrawn from timber utilization. Within the U.S., there are regional
differences in the proportion of land in forest and the ownership patterns of forestlands.
About 94 percent of forests in the East, 80 percent of forests in the Pacific Northwest sub
region, 50 percent of the forests in Rocky Mountain region, and 10 percent of forests in
Alaska are classified as timberland (Smith, 2000). While most of the forests in the
Northwest are public lands, forests in the Northeast are largely owned by the private
sector.
In this chapter, I perform a descriptive analysis to identify regions in the United
State that can be considered forest-dependent. Then, a comparison is made between the
socioeconomic characteristics of forest-dependent areas in the Northwest (northern
California, Idaho, Oregon and Washington) and the Northeast (Maine and parts of New
Hampshire). The purpose of this research is to (1) identify forest-dependent areas of the
United States, and (2) examine whether or not the Northeast and the Northwest exhibit
interregional differences.
7
2.2. Data
The data for this study were obtained from the Public Use Microdata Survey
(PUMS) of the United States 2000 decennial census. The 2000 PUMS data were accessed
via the Integrated Public Use Microdata Series (IPUMS) website 3 . PUMS provide
detailed demographic and socioeconomic information on individuals and households. To
preserve confidentiality, the Census Bureau reports the residence of respondents at the
level of Public Use Microdata Area (PUMA). PUMAs are geographic areas with a
population of at least 100,000 people. MIGPUMAs are agglomerations of one or more
PUMAs. This research defines forest-dependent areas as MIGPUMAs with 5 percent or
more of the total income from forest based industries.
The forest industry is represented in the following four categories of the North
American Industry Classification System (NAICS): 113 forestry and logging, 1153
forestry support activities, 321 wood product manufacturing and 322 paper and allied
products. The specific industries encompassing the forestry sector in this paper are
consistent with those outlined by previous research and are delineated in appendix table
2.A.1 along with their NAICS codes.
2.3. Identifying Forest-Dependent Areas
Machlis and Force (1988) note that while forest dependency has been measured in
a number of different ways, economic measures dominate the literature. Following this
trend, forest dependency in this study may be defined as percent of income or
3
IPUMS website: http://usa.ipums.org/usa/
8
employment in forest-based industries. Both income and employment definition have
been used in previous studies to identify forest-dependent areas (Elo and Beale, 1985;
Fortmann et al., 1991; Weber, 1995). Here, a preliminary analysis is conducted taking
account of both income and employment in forest sectors. The income definition gives a
larger number of areas as forest-dependent than the employment definition. With 5
percent cut off level, the U.S. has 104 forest-dependent MIGPUMAs using the income
definition and 62 forest-dependent MIGPUMAs using the employment definition.
Figures 2.1 and 2.2 indicate the forest-dependent areas in United States, based on income
and employment definitions respectively. Considering 5 percent income cut off, there are
22 MIGPUMAs in the Northwest and 5 MIGPUMAs in the Northeast which are forestdependent. With 5 percent employment cut off, there are 18 MIGPUMAs in the
Northwest and 3 MIGPUMAs in the Northeast which are forest-dependent (appendix
table 2.A.2).
The location of the forest-dependent areas reflects the geographical distribution of
forest resource in the United States. The forest-dependent areas are concentrated in the
Pacific Northwest, the Northeast, the upper Midwest, and parts of the Southeast. Figures
2.1 and 2.2 graphically represent the areas in United States which can be considered as
forest-dependent, based on a 5 percent cut off level of income and employment
respectively.
9
Figure 2.1. U.S. Areas with Five Percent or More Income from Forest Sectors
Figure 2.2. U.S. Areas with Five Percent or More Employment from Forest Sectors
10
Previous studies have used different cut offs to define forest-dependency. Elo and
Beale (1985) used 20 percent employment as the cut off for high levels of forest
dependence. Other studies (Weber, 1995; Fortmann et al., 1991) have used less restrictive
cut off criteria, because the 20 percent cut off criterion results in fewer cases. Fortmann et
al. (1991) defined forest-dependent counties as counties with 3 percent or greater wages
in forest-related industries. I define timber dependent MIGPUMAs as those with 5
percent or more of the total income from forest-based industries. The whole United States
sample has 1.11 percent income and 1.08 percent employment from forestry sector, and a
choice of 5 percent cut off level for forest dependence seems very reasonable. Also
previous studies considered counties as the geographic unit of analysis, whereas here
MIGPUMAs (which in most cases are agglomerations of counties) are the geographic
unit of analysis. Considering a larger geographic unit such as MIGPUMA, a 5 percent cut
off would still indicate a high level of forestry activity. If the cut-off is set higher, say at
10 or 15 percent, the total number of MIGPUMAs identifiable as forest-dependent is
reduced considerably. I chose a 5 percent cut-off level as a compromise that indicates
dependence on the forest sector and at the same time ensures enough MIGPUMAs for
analysis.
There are 47 MIGPUMAs in the Northwest, out of which 22 are forest-dependent.
In the Northeast, there are 11 MIGPUMAs out of which 5 are forest-dependent.
Appendix table 2.A.3 - 2.A.8 lists the MIGPUMAs in the Northwest and the Northeast,
along with the names of counties that comprise the MIGPUMAs.
11
After identifying the forest-dependent regions, the Northwest and the Northeast
regions were selected for further study. Forest-based industries are important to both
regions, but each has faced different challenges in recent times. Both regions constitute
an important part of the local economy, and because of the recent challenges faced by
them, these two regions make compelling cases for further study.
2.4. Comparisons of Forest and Non Forest-Dependent Areas
An interregional descriptive analysis is conducted to determine whether forestdependent areas in the Northwest and the Northeast are similar in terms of socioeconomic
characteristics. The measures of socioeconomic characteristics have been grouped into
five sub-categories: demographic, economic, educational, health, and housing measures.
The findings are reported in Table 2.1.
12
Table 2.1. Socioeconomic Measures, 2000
Selected Variables
Northwest
Forest
Nonforest
Northeast
Forest
Nonforest
A. Demographic measures
Population density (person per sq. mile)
Percent change in population, 1990-2000
White population (percentage)
Black population (percentage)
Median age (years)
Sex ratio (males per 100 females)
22.6
17.4
93.8
0.8
39.3
102.2
135.6
20.7
93.2
1.2
34.8
100.3
32.9
-1.4
97.2
0.4
39.5
95.8
141.6
8.3
97.4
0.7
39.4
96.1
B. Economic measures
Per capita income (dollar)
Median household income (dollar)
Person in poverty (percentage)
Unemployment rate
21785
34005
13.9
6.9
24368
38402
11.8
5.03
23187
32639
11.9
4.2
26549
38830
9.3
3.0
High school diploma or more (percent)
Bachelors degree or more (percent)
81.4
16.5
83.3
21.4
82.1
17.7
86.5
24.8
D. Health measures
Births per 1000 population
Deaths per 1000 population
Infant mortality rate
11.6
9.9
6.7
14.3
7.9
6.1
10.1
10.5
4.4
10.5
9.8
3.6
109970
124586
77200
110588
C. Educational attainment
E. Housing measures
Median value of owner occupied
housing units (dollar)
A. Demographic Measures
Table 2.1 contains the findings for demographic measures. The population density
in the forest-dependent Northeast region is higher than in the Northwest, with 33 persons
per square mile compared to 23, respectively. In additional to differences in settlement
patterns, this difference may be due to the forest ownership patterns seen in the two
13
regions. Since the Northwest region has more public forestland, fewer people inhabit the
forest-dependent areas. The Northeast region is largely private owned, providing more
opportunities for people to live in the forest-dependent regions. Also for both regions,
population density is much higher in non forest-dependent areas as compared to the
forest-dependent areas.
The percent change in population between 1990 and 2000 in the forest-dependent
areas in the Northwest was higher (17.4 percent) than in the Northeast. In fact, the
Northeast forest-dependent areas experienced a decrease in population (1.4 percent) from
1990 to 2000. Although the non forest-dependent areas in both regions had increases in
population, the increase was higher in the Northwest (20.7 percent) than in the Northeast
(8.3 percent).
With regard to race, there is little difference in the composition of whites and
blacks in both regions. The Northwest has slightly fewer whites and more blacks than in
the Northeast.
There is also not much regional difference in the male to female sex ratio. The
average sex ratio for forest-dependent areas is higher in the Northwest (102.2) than in the
Northeast (95.8). While the mean sex ratio for the Northwest exceeds 100 males per 100
females, there are about 96 males for every 100 females in the Northeast. Generally, a
low sex ratio may indicate a poor economy. Women are less likely to work due to their
responsibilities of raising children and caring for other members of the family, while men
are more likely than women to find employment.
14
There is no difference in median age in the Northwest and the Northeast forestdependent areas. On average, the median age in the forest-dependent areas of the two
regions is about 39.5 years. The non forest-dependent areas in the Northwest have a
younger population (34.8 years) than in the Northeast (39.4 years).
B. Economic Measures
Table 2.1 reports the regional differences in economic measures. Non forestdependent areas in both the Northwest and the Northeast are economically better off than
those in the forest-dependent areas. The forest-dependent areas in the Northwest and the
Northeast have lower per capita income and median household income than their non
forest-dependent counterparts. Also the forest-dependent areas have higher percentage of
individuals in poverty, as well as higher unemployment rates.
C. Educational Attainment
Table 2.1 shows that on average, forest-dependent areas in both the Northeast and
Northwest have lower educational attainment at high school and college levels. The
average percentage of people who completed high school and college degree is slightly
higher in the Northeast than in the Northwest.
D. Health Measures
The data on health measures are presented in Table 2.1. There is not much
regional difference in the Northeast and the Northwest. The indicators of health
measures, in general, are slightly better in the non forest-dependent areas than in the
forest-dependent areas in both regions.
15
E. Housing Measures
Housing indicators are also reported on Table 2.1. For both forest-dependent and
non forest-dependent areas, the median value of owner occupied housing units in the
Northwest is higher than that in the Northeast. In both regions, the median owner
occupied house value is lower in the forest-dependent areas.
2.5. Discussion
The descriptive analysis shows that there is some variation in the socioeconomic
characteristics associated with forest-dependent areas in the Northeast and in the
Northwest, but the differences are not very large, at least when one examines average
characteristics. But, in both regions, the forest-dependent areas display socioeconomic
characteristics more indicative of poor economic conditions compared to the non forestdependent areas. For example in both the Northwest and the Northeast, population
density and population growth is lower in the forest-dependent areas. Per capita income
and median household income are also lower in the forest-dependent areas. More people
are unemployed, are in poverty, and have lower educational attainment in the forestdependent areas than their non-forest counterparts.
In the Northwest, a major problem for forest-dependent areas has been the
reduction in public timber harvests that once supported wood products industries. With
loss in employment in timber dependent jobs, many families had to suffer economic
distress. The challenge is to devise alternative economic development strategies to
minimize the negative consequences of challenges undergone in this region.
16
Forestlands of the Northeast are primarily privately owned lands, and forest
management practices for public lands cannot be upheld on private owned lands
(Drielsma, Miller and Burch, 1990). Here, the supply of timber is not the real issue, and
yet jobs have disappeared from timber industry in recent years. The forest-dependent
areas in the Northeast are poorer, less educated and have undergone worse
socioeconomic changes than the non forest-dependent areas. Forest-based industries are
important to the region and future economic development efforts have to be made to
improve the level of rewards for employment in this sector.
17
Appendix 2A
Table 2.A.1. NAICS Industries in Forestry and Wood Sector
Industry Number
Industry Name
113
11311
11321
11331
Forestry and logging
Timber tract operations
Forest nurseries and gathering of forest products
Logging
1153
Support activities for forestry
3211
Sawmills and wood preservation
3212
Veneer, plywood, and engineered wood product manufacturing
3219
Other wood product manufacturing
3221
Pulp, paper, and paperboard mills
3222
Converted paper product manufacturing
18
Table 2.A.2. MIGPUMAs with 5% or More Income and Employment from Forestry
State
Total
Number of
MIGPUMAs
Alabama
23
Arkansas
18
California
41
Florida
Georgia
39
42
Idaho
8
Indiana
34
Kentucky
Louisiana
25
23
Income from Forestry
Employment from Forestry
MIGPUMA Percentage MIGPUMA
Percentage
Number
Number
21
15
7
11
18
17
18
19
16
14
15
12
2
1
5
4
3
14
38
41
37
39
40
33
22
36
25
2
1
4
8
7
6
3
5
6
4
8
21.44
12.77
9.9
6.11
5.94
20.37
12.17
10.38
7.26
6.66
5.94
5.25
11.79
9.26
8.44
5.95
5.3
7.08
8.84
6.91
6.25
6.18
5.85
5.66
5.51
5.32
5.14
16.62
8.16
7.13
5.39
5.14
5.12
12.4
10.61
8.1
6.31
5.35
21
15
7
12.05
8.3
8.28
17
18
19
14
10.35
8.89
6.77
5.2
2
1
5
7.4
6.78
6.18
--38
41
39
---6.15
5.68
5.05
2
1
4
---
8.7
6.47
5.32
---
--3
5
6
--7.3
6.67
5.23
19
State
Total
Number of
MIGPUMAs
Maine
10
Maryland
Michigan
16
32
Minnesota
23
Mississippi
22
Missouri
Montana
23
7
New
Hampshire
New York
North
Carolina
11
Ohio
44
Oregon
13
39
42
Income from Forestry
Employment from Forestry
MIGPUMA Percentage MIGPUMA
Percentage
Number
Number
8
10
9
6
1
3
1
2
3
5
20
18
12
8
17
10
--1
7
1
17.81
17.63
9.95
5.77
6.05
9.31
8.87
5.9
13.24
5.77
8.6
8.24
7.99
7.27
5.81
5.15
--11.28
8.6
5.47
8
10
9
12.88
9.19
6.41
--3
1
--5.53
5.31
3
7.71
18
20
12
8
6.14
5.96
5.31
5.18
23
1
7
---
5.15
7.76
5.23
---
3
35
47
1
41
25
37
34
26
10
3
5
2
8
6
1
9
4
7
5.45
9.23
6.78
5.42
5.32
5.15
9.47
7.49
6.49
22.62
11.84
10.81
10.34
8.8
8.43
8.31
6.39
6.15
6.10
--35
--5.36
34
37
26
10
3
2
5
8
1
6
9
4
7.07
6.1
5.74
14.14
8.09
7.6
6.5
6.19
6.18
6.03
5.36
5.24
20
State
Total
Number of
MIGPUMAs
Pennsylvania
40
South
Carolina
Tennessee
21
28
Texas
63
Virginia
35
Washington
17
West
Virginia
Wisconsin
12
20
Total number of
MIGPUMAs
Income from Forestry
Employment from Forestry
MIGPUMA Percentage MIGPUMA
Percentage
Number
Number
12
4
5
26
12
23
28
27
11
17
7
16
17
32
33
11
11
15
16
4
5
9
15
6
16
2
1
13
7.8
6.36
6.20
5.29
9.31
5.9
6.38
5.88
5.42
5.39
5.1
13.6
7.6
7.94
7.25
5.29
20.61
12.81
9.39
5.65
7.98
5.78
14.17
13.21
9.97
8.93
7.65
6.29
104
12
4
5
5.53
5.06
5.01
14
5.81
---
---
16
17
33
32
8.9
5.15
5.7
5.35
11
15
16
11.53
9.23
6.49
5
6.39
15
6
16
1
2
9.11
8.78
7.19
6.66
6.62
62
21
Table 2.A.3. MIGPUMAs and Counties of Northern California
MIGPUMA
Number
County Names
Percentage of
Forest Income
1
Del Norte
Lassen
Modoc
Siskiyou
Humboldt
Shasta
Lake
Mendocino
Colusa
Glenn
Tehama
Trinity
Butte
Nevada
Plumas
Sierra
Sutter
Yuba
Yolo
9.3
2
3
4
5
6
7
8
9
11.8
5.3
5.9
8.4
non-forest
non-forest
non-forest
non-forest
22
Table 2.A.4. MIGPUMAs and Counties of Idaho
MIGPUMA
Number
1
2
3
4
5
6
8
9
County Names
Benewah
Bonner
Boundary
Kootenai
Shoshone
Clearwater
Idaho
Latah
Lewis
Nez Perce
Bonneville
Butte
Clark
Custer
Fremont
Jefferson
Lemhi
Madison
Teton
Adams
Boise
Elmore
Gem
Owyhee
Payette
Valley
Washington
Canyon
Ada
Blaine
Camas
Cassia
Gooding
Jerome
Lincoln
Minidoka
Twin Falls
Bannock
Bear Lake
Bingham
Caribou
Franklin
Oneida
Power
Percentage of
Forest Income
8.2
16.6
non-forest
7.1
non-forest
non-forest
non-forest
non-forest
23
Table 2.A.5. MIGPUMAs and Counties of Oregon
MIGPUMA
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
County Names
Baker
Umatilla
Union
Wallowa
Crook
Gilliam
Grant
Hood River
Jefferson
Morrow
Sherman
Wasco
Wheeler
Harney
Klamath
Lake
Malheur
Deschutes
Clatsop
Columbia
Lincoln
Tillamook
Benton
Linn
Lane
Coos
Curry
Josephine
Jackson
Douglas
Marion
Polk
Yamhill
Multnomah
Clackamas
Washington
Percentage of
Forest Income
8.3
10.3
11.8
6.2
10.8
8.4
6.1
8.8
6.4
22.6
non-forest
non-forest
non-forest
24
Table 2.A.6. MIGPUMAs and Counties of Washington
MIGPUMA
Number
1
2
3
4
5
7
8
9
10
11
12
13
15
16
17
18
21
County Names
Whatcom
Island
San Juan
Skagit
Chelan
Douglas
Kittitas
Okanogan
Adams
Ferry
Grant
Lincoln
Pend Oreille
Stevens
Spokane
Asotin
Columbia
Garfield
Walla Walla
Whitman
Benton
Franklin
Yakima
Snohomish
Cowlitz
Klickitat
Skamania
Wahkiakum
Thurston
Pierce
Grays Harbor
Lewis
Pacific
Clallam
Jefferson
Mason
Kitsap
King
Clark
Percentage of
Forest Income
non-forest
non-forest
non-forest
5.6
non-forest
non-forest
non-forest
non-forest
non-forest
20.6
non-forest
non-forest
12.8
9.4
non-forest
non-forest
non-forest
25
Table 2.A.7. MIGPUMAs and Counties of Maine
MIGPUMA
Number
1, 2, 3 & 4
5
6
7
8
9
10
County Names
York County
Cumberland
County
Lincoln
Sagadahoc
Hancock County
Knox County
Waldo County
Kennebec
County
Androscoggin
County
Franklin County
Oxford County
Somerset
County
Penobscot
County
Piscataquis
County
Aroostook
County
Washington
County
Percentage of
Forest Income
non-forest
non-forest
5.8
non-forest
17.8
9.9
17.6
Table 2.A.8. MIGPUMA and Counties of New Hampshire (included in analysis)
MIGPUMA
Number
1
County Names
Coos
Grafton
Percentage of
Forest Income
5.5
26
Chapter 3 – Wages and Returns to Skills in Forest-Dependent United States
3.1. Introduction
Forest-dependent areas in both the Northwest and the Northeast display
socioeconomic characteristics more indicative of poor economic conditions compared to
the non forest-dependent areas. Wages earned by individuals in an area may be a good
indicator of community well-being in that place, and hence wage analysis of the forest
and non forest-dependent areas may indicate how these regions compare relative to each
other and to the rest of the United States.
Past studies have found that wages vary across regions in the United States
because of differences in local amenities as well differences in the skill composition of
people residing there (Roback, 1982; Dickie and Gerking, 1987; Blomquist et al, 1988).
Most of these studies estimate log wage equations on individual data and investigate
regional wage distributions. I use this basic approach to estimate log wage equations
using individual data, and produce area mean wages and returns to skills indices for
forest and non forest-dependent areas.
If the skills of workers in non forest-dependent areas are in general higher than
workers in forest-dependent areas, then this may result in higher wages in the non forestdependent areas. If one can control for differences in the skill composition of workers in
forest and non forest-dependent areas, would there still be difference in wages between
these areas? Also, are different types of skills (e.g. education and experience) rewarded
differently in forest-dependent areas? This study addresses the question: are forest-
27
dependent rural areas less desirable for workers from the standpoint of labor market
returns? I focus my analysis on the Northwest and the Northeast United States because
of the recent upheavals in forest-based industries there.
This study allows me to
determine whether the regional labor markets have adjusted, or failed to adjust, in the
wake of such changes.
Although the present study relies on methods developed in Hunt and Mueller
(2002), it contributes to the literature in two ways. First, this study examines the
interregional differences in wages between forest and non forest-dependent areas, and
indicates that forest-dependent areas offer lower labor market returns. The study also
reports returns to particular skills, such as education and experience in the different areas.
Second, this study estimates area mean wages and returns to skills for smaller geographic
areas than previous studies, most of which considered states as the geographic unit of
study. This analysis facilitates a closer inspection of labor markets in forest and non
forest-dependent areas.
3.2. Conceptual Basis
The theoretical basis for this study is Hunt and Mueller (2002) and Borjas et al‘s
(1992) similar work. Consider that there are j distinct geographic regions. The natural
logarithm of individual i‘s wage in region j is written as:
(3.1)
ln(wij) = μj + ɳij
where μj is the mean income that would be observed in region j and ɳij is a random
component that measures person-specific deviations from mean income. The mean
28
income μj vary across regions, and the random component ɳij depends on factors related
to areas, as well as individual skill levels of workers.
The goal of this chapter is to compare wage distributions in forest-dependent and
non forest-dependent regions. To remove interregional differences in the composition of
skills, a standardized skill distribution is developed. The global mean skill level (i.e.,
mean skill level for all individuals in all regions) is denoted by υ and the variance of
skills is denoted by σ2. Thus, in expectation individual i‘s skill υi is equal to the global
mean skill level, i.e. E(υi) =υ, and the variance of individual i‘s skill in this distribution
has a constant value of σ2, i.e. Var (υi) = σ2.
The standardized skill distribution implies that individual earnings are perfectly
correlated across regions, so that Corr (υij, υik) = 1, j≠k, where j and k index regions. In
other words, individual skills are independent of area, and hence skills need not be
indexed by area. Then, from equation (3.1) individual i‘s log wage in region j can be
written as:
(3.2)
ln(wij) = μj + φj(υi - υ)
where μj is the mean log wage in area j, φj is the index of return to skills in area j, υi is
individual‘s skill level, and υ is the global mean skill level.
To investigate interregional variations, I estimated the values of μj and φj for forest and
non forest-dependent areas. Assuming a standardized skills distribution, let the log wage
of individual i in region j be denoted by ln(wij)*. From equation (3.2), the expected value
and variance of the standardized log wage distribution for area j are:
(3.3)
E[ln(wij)*]
= μj + φj [E(υi)- υ]
29
= μj + φj [υ- υ]
= μj
(3.4)
Var[ln(wij)*] = φ2j Var(υi)
= φ2j σ2
Rearranging (3.4) yields: 𝜑𝑗 =
𝑉𝑎𝑟 ln 𝑤𝑖𝑗 ∗
𝜎2
,
where φj is an index that reflects the returns to skills variance in the jth area relative to the
returns to skills variance for the standardized skill distribution over all regions. If φj is >1
(<1), then the jth area returns to skills variance is greater than (less than) the global
returns to skills variance (σ2).
3.3. Empirical Methodology
Past studies have applied reduced form hedonic
wage equations to explain
transactions in labor markets (Rosen, 1974; Smith, 1983). Hedonic wage functions are
considered to be equilibrium relationships, representing a double envelope – the lower
boundary of the individual worker‘s wage acceptance functions and the upper frontier of
firms‘ wage offer function. Consequently, the specification of a hedonic wage function
reflects both the demand and supply determinants of transactions in labor markets. As
hedonic functions describe the market equilibrium, it can be used to estimate individual‘s
equilibrium wage in the labor market.
A reduced form hedonic wage equation is used to estimate the area mean log
wage (μj) and area returns to skills index (φj) for the forest and non forest-dependent
areas. The reduced form wage equation is specified in terms of hypothesized correlates of
30
wages following Mincer (1974). A Mincerian-type log wage equation4 for individuals is
specified in terms of skill variables (e.g. educational attainment and potential experience)
and non-skill variables (e.g. minority status and part-time work status). For individual i,
the equation is specified:
(3.5)
ln(wi) = α + β1PXi + β2 (PX)i 2 + β3HHi + β4ENGi + ∑mβmEAmi + ∑nβnMSni
+ γ1 PTi + ∑r γr RCri + ∑s γs INDsi + εi
where:
ln(w) ≡ natural logarithm of average weekly wage
PX ≡ potential experience5 (i.e., age in years – years of schooling – 5)
HH ≡ household head (two categories: household head, non-household head)
ENG ≡ English language ability (two categories: speak English, do not speak English)
EA ≡ educational attainment (four categories: less than high school, high school, some
college, more than college)
MS ≡ marital status (three categories: single, married, separated)
PT ≡ part-time work status (two categories: works part-time, full-time)
RC ≡ race (three categories: white, black, other)
4
The human capital model of income determination, as developed by Becker (1975) and Mincer (1974) has
gained theoretical and empirical prominence within labor economics. Mincer's model of earnings (1974) is
a cornerstone of labor economics, and the Mincerian wage equation plays a central part in the literature
devoted to the returns to education as well as in the literature on wage inequality. In a standard form of the
Mincer earnings model, log earnings are regressed on a constant term, a linear term for years of schooling,
and linear and quadratic terms for years of labor market experience.
5
Potential experience is calculated using the Mincerian proxy. Potential experience is defined as the
difference between a worker‘s age and years of schooling completed, less the age when the worker began
school, usually assumed to be 5 years old (Mincer, 1974).
31
IND ≡ industry of employment (eleven categories: agriculture, mining, utility,
construction,
manufacturing,
trade,
transport,
information,
finance,
service,
administration)
ε ≡ classical stochastic error term
The independent variables of the log wage function are classified as either skill
variables or non-skill variables. The skill variables, associated with β- parameters, are
potential experience, household head, English language ability, educational attainment
and marital status. These variables indicate the level of skill individuals possess, and an
individual can influence or change these variables to affect his/her productivity or
contribution to employment. The non-skill variables, associated with γ- parameters, are
part-time work status, race and industry of employment. These variables relate to
characteristics that may affect an individual‘s earning capacity, but do not reflect the skill
possessed by a worker. Unlike the skill variables, an individual may not be able to
influence these variables, although these factors affect their wages.
Wages vary by gender6, hence log wage distributions for males and females are
estimated separately. Below is a description of the estimation procedure applied to the
male sample. Estimation for the female sample was done using a similar approach. In this
study, I analyze 59 regions (or areas), including 58 MIGPUMAs in the Northwest and the
Northeast region, as well as a single region representing the rest of the United States.
6
The human capital approach recognized that the incentive to invest in human capital specific to a
particular activity is positively related to the time spent at that activity (Becker 1964). This recognition was
used to explain empirically why the average wage rate of men is higher than that of women, since women
have participated in the labor force much less than married men (Mincer and Polachek, 1974).
32
3.3.1. Estimating Area Mean Log Wage
Males in two areas can have different mean log wages due to different skill levels
as well as differences in returns to skills. Here the mean skills characteristics for all males
in the sample along with the area-specific skill parameter estimates are used to arrive at
the predicted mean log wage for each area (μj). In deriving this estimate, the skill mix is
held constant, thus allowing only the returns to skills to vary between the areas.
To explain the methodology, first assume that there are N individuals in the male
sample residing in j areas (j=1,…,59). A sub-sample of n individuals (n<N) resides in the
jth area. Equation (3.5) is separately estimated with sub-samples of males for each of the
59 areas using ordinary least squares (OLS). The parameter estimates of equation (3.6)
below, capture area specific effects for area j. That is, the parameter estimates α , βs and
γs capture both demand and supply side effects on log wage, specific to area j. In total,
59 sets of parameters are estimated, one for each area.
(3.6)
ln(wij) = α𝑗 + β1𝑗 PXi + β2𝑗 (PX)i 2 + β3𝑗 HHi + β4𝑗 ENGi + ∑mβ𝑚𝑗 EAmi
+ ∑nβ𝑛𝑗 MSni + γ1𝑗 PTi + ∑r γ𝑟𝑗 RCri + ∑s γ𝑠𝑗 INDsi
Next, the mean of each of the right-hand side variables specified in equation (3.5) is
calculated using data from the entire male sample (N observations). For jth area, the
independent variables in equation (3.6) are replaced with these sample means, as follows:
(3.7)
ln(𝑤𝑖𝑗 ) = α𝑗 + β1𝑗 𝑃𝑋 + β2𝑗 (𝑃𝑋)2 + β3𝑗 𝐻𝐻 + β4𝑗 𝐸𝑁𝐺 + ∑mβ𝑚𝑗 𝐸𝐴m
+ ∑nβ𝑛𝑗 𝑀𝑆n + γ1𝑗 𝑃𝑇 + ∑r γ𝑟𝑗 𝑅𝐶 r + ∑s γ𝑠𝑗 𝐼𝑁𝐷s
From (3.7), I obtain the predicted mean log wage for the for jth area (μj*).
33
In total, 59 values of μj* are estimated, one for each study area. By using the
mean of the entire sample of males versus the parameters obtained using only individuals
within jth area, the interregional differences in skills-mix was controlled. If predicted
mean log wages are higher in area j than another area, it is not because of higher level of
skills (such as education or experience) of males in area j. Differences may be due to
higher returns to skills in area j or to differences in area characteristics. Such areaspecific factors are measures by the constant terms in the wage model.
3.3.2. Estimating Area Returns to Skills
Here the motivation is to estimate the regional variance in log wages which
occurs due the differences among areas, and not because of any difference in skill
composition of workers in those areas.
(a) Area-specific variance of the standardized log wage distribution, Var[ln(wij)*]
As the first step in estimating the variance of the standardized wage distribution, I
control for the effects of non-skill variables on wages. Specifically, I substitute the
sample means of non-skill variables (i.e., variables with a corresponding γ- parameter)
into equation (3.6) as follows:
(3.9)
ln(wij) = α𝑗 + β1𝑗 PXi + β2𝑗 (PX)i 2 + β3𝑗 HHi + β4𝑗 ENGi + ∑mβ𝑚𝑗 EAmi
+ ∑nβ𝑛𝑗 MSni + γ1𝑗 𝑃𝑇 + ∑r γ𝑟𝑗 𝑅𝐶 r + ∑s γ𝑠𝑗 𝐼𝑁𝐷s
= α𝑗 + β1𝑗 PXi + β2𝑗 (PX)i 2 + β3𝑗 HHi + β4𝑗 ENGi + ∑mβ𝑚𝑗 EAmi + ∑nβ𝑛𝑗 MSni
where
α𝑗 = α𝑗 + γ1𝑗 𝑃𝑇 + ∑r γ𝑟𝑗 𝑅𝐶 r + ∑s γ𝑠𝑗 𝐼𝑁𝐷s
34
α𝑗 is defined as a constant effect on male log wages for the jth area, and captures
interregional variation in wages due to area-specific amenities and average non-skill
factors. For area j, α𝑗 has the same value for every individual and only the differences in
skill levels of individuals‘ results in wage differences. Thus, the constant effect α𝑗 will
not play a role in wage variation, Var[ln(wij)*].
The second step is to use the entire male sample (N individuals) along with the
area-specific estimated parameters to compute the effect of skill variables on individual‘s
area log wages. For the jth area, I substitute the N values of the skill variables into
equation (3.9), resulting in N log wage values, one for each individual. For example, the
value for the first individual in the sample is computed as:
(3.10) ln(𝑤1𝑗 ) = α1𝑗 + β1𝑗 PX1 + β2𝑗 (PX)1 2 + β3𝑗 HH1 + β4𝑗 ENG1 + ∑mβ𝑚𝑗 EAm1
+ ∑nβ𝑛𝑗 MSn1
The resulting log wages are the area-specific returns to skill effect for each individual;
i.e., how much would each individual receive in log wages as a return to his skills in area
j. By using the area-specific estimated parameters and skill related variables for the entire
sample of N individuals, I control for the skill mix and obtain a prediction for each male‘s
log wage due to only skill-related terms in area j.
For each area j, I then calculate the variance of log wages using the N values of
log wage obtained above. This estimated variance is the area-specific variance of the
standardized log wage distribution, Var[ln(wij)*]. There are in total 59 variance estimates,
one for each area of study.
35
(b) Variance of the standardized skill distribution, σ2
The variance of the standardized skill distribution, σ2, indicates how returns to
skills vary across all 59 areas. To estimate this variance, I begin by introducing a dummy
variable for areas (AREA) in equation (3.5):
(3.11) ln(wij) = α + ∑j γj AREAij + β1PXij + β2(PX)ij 2 + β3HHij + β4 ENGij
+ ∑mβmEAmij + ∑nβnMSnij + γ1 PTij + ∑r γr RCrij + ∑s γs INDsij + ηij
where ηij is a classical stochastic error term. Equation (3.11) is estimated with OLS using
entire male sample (N observations). The estimation produces a single set of parameters
that capture both demand and supply side effects on log wages across all areas.
As above, the sample means of non-skill related variables (i.e., variables with a
corresponding γ- parameter) are introduced in equation (3.11) to obtain:
(3.12)
ln(wij) = 𝛼 + ∑j γ𝑗 𝐴𝑅𝐸𝐴j + β1 PXij + β2 (PX)ij 2 + β3 HHij + β4 ENGij
+ ∑mβ𝑚 EAmij + ∑nβ𝑛 MSnij + γ1 𝑃𝑇 + ∑r γ𝑟 𝑅𝐶 r + ∑s γ𝑠 𝐼𝑁𝐷
= 𝛼 + β1 PXij + β2 (PX)ij 2 + β3 HHij + β4 ENGij + ∑mβ𝑚 EAmij + ∑nβ𝑛 MSnij
where
𝛼 = 𝛼 +∑j γ𝑗 𝐴𝑅𝐸𝐴j + γ1 𝑃𝑇 + ∑r γ𝑟 𝑅𝐶 r + ∑s γ𝑠 𝐼𝑁𝐷
𝛼 is a constant effect on male log wages across all areas, capturing the average effect of
non-skill factors on wages. Because 𝛼 takes the same value for every individual in the
sample, it does not influence the variance, σ2.
As in equation (3.10), each individual‘s skill-related variables are substituted into
equation (3.12). The resulting prediction of log wage varies only because of differences
in the skill related variables of workers. I obtain N individual specific results. An
estimate of σ2 is obtained by computing the variance of these N log wages.
36
An estimate of the returns to skills index for the jth area can then be computed as:
φj = {Var[ln(wij)*] / σ2 }1/2
Here φj denotes the variance in returns to skills in area j relative to all areas (i.e., the
entire U.S.). If φj is >1 (<1), then jth area returns to skills variance is greater than (less
than) the U.S. returns to skills variance (σ2). In other words, this indicates that jth area
has a return to skills distribution more (less) dispersed than that in the entire county.
Since both Var[ln(wij)*] and σ2 are computed with the entire sample of individuals (N
observations), the skill mix is controlled in each term. Therefore the ratio of the terms
reflects differences solely in returns to skill in the different areas, and not interregional
differences in skill mix.
Estimating Area Returns to Education and Experience
A similar methodology is used to estimate returns to specific skills, such as
education and experience. This analysis helps us to understand if different skills are
rewarded differently across areas.
For the returns to education calculation, the
explanatory variables in the wage equation are classified into two groups: an education
related variable (denoted by educational attainment) and non-education related variables.
For the returns to experience calculation the explanatory variables are also classified into
two groups: work experience related variables (denoted by potential experience) and nonexperience related variables. The parameter estimates of returns to education and returns
to experience have a similar interpretation as that of the returns to skills parameters.
37
3.4. Data
The data for this study is obtained from the Public Use Microdata Survey (PUMS)
of the United States 2000 decennial census. I restrict the sample to non-institutionalized
individuals between the age of 25 and 64 years, and who earned at least $1000 in wage
and salary income in 2000. Appendix table 3.A.1 provides precise definitions of the
variables used in this study. The wage variable is the natural logarithm of average weekly
salary wage, ln(w). The weekly average salary and wage (w) is calculated by dividing the
individual‘s yearly wage and salary income by the number of weeks worked. Potential
experience PX is calculated using the Mincerian proxy (i.e., age in years – years of
schooling – 5). The variables MS, HH, ENG and RC are dummy variables that are set to
equal unity if the individual is married, is the household head, speaks English, and is not
white/black respectively, and zero otherwise. PT is a dummy variable set to equal unity if
the respondent worked part time (i.e., less than 30 hours per week), and zero otherwise.
The variable IND is the industry variable indicating the individual‘s sector of
employment.
The male sample contains 2,874,576 observations and the female sample contains
2,588,733 observations. Appendix tables 3.A.2 and 3.A.3 present summary statistics for
the male and female samples, respectively. Mean weekly wage for males ($697) is higher
than those for females ($455). Males have slightly higher mean years of experience.
Approximately 99 percent of both males and females speak English. For the male sample,
80 percent are white, 69 percent are married, 78 percent are household heads and 4
percent work part-time. For the female sample, 78 percent are white, 63 percent are
38
married, 34 percent are household heads and 14 percent work part time. Most males
work in the service sector (27 percent), followed by manufacturing (21 percent), trade (14
percent) and construction (11 percent). For females, the highest employment is in service
sector (53 percent), followed by trade (13 percent) and manufacturing (11 percent).
3.5. Results
Although separate wage equations are estimated for each of the 59 study areas,
estimates produced with the entire male and female samples are presented in appendix
table 3.A.4 and 3.A.5 to indicate the general nature of the result. The estimates are
consistent with expectations. Wages increase with experience, but at a decreasing rate.
Wages also increase with educational attainment, which indicates that higher levels of
education are positively rewarded in the labor market. Wages earned by married people
are higher than unmarried people. Part-time work is found to impact an individual‘s wage
negatively, lowering per hour earnings from a job. Language difficulties can affect an
individual‘s ability to participate in the workforce, and English speaking ability affects
wage positively.
Area Mean Log Wage
The estimates of the mean log wage (μj) for the 59 study areas are reported in
appendix tables 3.A.6 and 3.A.7. The results are divided into two categories: forestdependent and non forest-dependent areas. The results suggest that, even after controlling
for composition of skills across areas, forest-dependent areas tend to generate lower
wages than their non forest-dependent counterparts. This is illustrated graphically in
39
figures 3.1 and 3.2 for the male and female samples respectively. The figures plot area
mean weekly wages (in dollars) against the percentage of forest income in those areas.
For both male and female samples, generally areas with higher percentage of income
from forestry tend to have lower expected wages.
40
Figure 3.1. Forest-Dependency and Estimated Area Wage, Males
800
750
Wage (dollars)
700
650
600
550
500
0
5
10
15
20
25
Forest Income (percentage)
Figure 3.2. Forest-Dependency and Estimated Area Wage, Females
550
Wage (dollars)
500
450
400
350
300
0
5
10
15
Forest Income (percentage)
20
25
41
Area Returns to Skills
The estimates of area returns to skills indices φj(S), returns to education indices
φj(E) and returns to experience indices φj(X) are reported in appendix tables 3.A.8 and
3.A.9. In general, values of φj are lower in the forest-dependent areas than in the non
forest-dependent areas. This indicates that variations in wage distribution are less
dispersed in the forest-dependent areas relative to other areas.
The μj and φj values for the 47 MIGPUMAs in the Northwest and 11 MIGPUMAs
in the Northeast (appendix tables 3.A.6 - 3.A.9) are averaged to produce table 3.1. Table
3.1 reports the regional mean weekly wages and index of returns to skills, education and
experience for the male and female samples.
Table 3.1 Mean Wages and Index of Returns to Skills, Education and Experience
Male Sample
Mean log wage
(dollar value)
Index of :
Returns to Skills
Returns to Education
Returns to Experience
Female sample
Mean log wage
(dollar value)
Index of :
Returns to Skills
Returns to Education
Returns to Experience
Northwest
Forest
Non forest
Northeast
Forest
Non forest
Rest of
U.S.
6.418
(613)
6.555
(703)
6.377
(588)
6.452
(634)
6.548
(698)
0.7438
0.6321
0.7023
0.8664
0.7409
0.8549
0.7537
0.6618
0.7134
0.8558
0.7547
0.8300
1.0073
1.0107
1.0042
Northwest
Forest
Non forest
Northeast
Forest
Non forest
Rest of
U.S.
6.008
(407)
6.145
(466)
5.901
(366)
6.027
(414)
6.123
(456)
0.7334
0.7068
0.6944
0.8264
0.7693
0.8247
0.7122
0.6992
0.7072
0.8442
0.7380
0.8242
1.0088
1.0098
1.003
42
There is statistically significant difference in the expected mean wages of forest
and non-forest areas for male and female samples7. For the male sample, the Northwest
and the Northeast forest-dependent areas have lower average wages ($613 and $588
respectively) than the non forest-dependent areas ($703 and $634 respectively) and rest
of the United States ($698). Similar results are obtained for the female sample: average
wages in the Northwest and Northeast forest-dependent areas are $407 and $366
respectively, compared to $466 and $414 in the non forest-dependent areas, and $456 in
the rest of the United States. For both males and females, the average wages are lower in
the Northeast than in the Northwest.
For male sample, the Northwest and Northeast returns to skills index in the forestdependent areas are lower (0.7438 and 0.7537 respectively) than in the non forestdependent areas (0.8664 and 0.8558 respectively), and the rest of the United States
(1.0073). The returns to education and returns to experience are also lower in the forestdependent areas, than other areas. Similar results hold for the female subsample.
The result of this paper suggests that the expected mean wage is lower in the
forest-dependent areas. Also, variations in wage distribution are less in these areas. The
following example will illustrate this point. Let us consider two areas from Oregon:
MIGPUMA 10 (comprising Douglas county) and MIGPUMA 13 (comprising of
Multinomah, Clackamas and Washington counties). MIGPUMA 10 is highly forest-
7
The t-test assesses whether the area mean wages are statistically different from each other. I performed a
two-tail t-test to find whether the difference between the area mean wages is not likely to have been a
chance finding. To test the significance, I set the null hypothesis that there is no significant difference in the
area mean wages. Given a 5% significance level, the null hypothesis was rejected which indicates that
there is significant difference between the forest and non-forest area mean wages.
43
dependent with about 22.6 percent of income from forestry, whereas MIGPUMA 13 is
non forest-dependent with only about 1.4 percent income from forestry. For males, the
estimated average weekly wages in MIGPUMA 10 and MIGPUMA 13 are $640 and
$709 respectively. For females, the estimated average weekly wages in MIGPUMA 10
and MIGPUMA 13 are $399 and $482 respectively. Also for both male and female
samples, the returns to skills, education and experience is higher in MIGPUMA 13 than
in MIGPUMA 10 (appendix table 3.A.8 and 3.A.9). The methodology used in this
analysis standardizes the skill distribution across areas, yet interregional differences in
wage distribution are observed. That is, this analysis controls for any differences in the
skill composition of people residing in MIGPUMA 10 and MIGPUMA 13, yet the
average expected wage and variation in wage distribution are higher in MIGPUMA 13
than in MIGPUMA10.
3.6. Discussion
The empirical approach used in this study controls the skill composition of people
across areas, and therefore differences in interregional wage distributions are not due to
differences in the skill composition of people residing in these areas. What, then, can
explain differences in mean wages and returns to skill between forest and non forestdependent areas?
One explanation for this can be the reduced economic opportunities in the forestdependent areas. Less economic activity in forest-dependent areas may cause these areas
to lack the agglomeration economies of larger areas. Economics literature on spatial
44
location or economic geography (Krugman, 1991) points out that economic activity
might exhibit increasing returns to scale and increased welfare due to agglomeration
effect. The mechanism underlying agglomeration economies is that by locating close to
one another, firms can produce at a lower cost (O‘Sullivan, 2000). Locating a firm in an
area where other similar types of firms (or suppliers/demanders) are in close proximity
may lead to enhanced productivity and economic growth of an area. The forest-dependent
areas may be lacking in spatial and industrial agglomeration, as compared to other areas,
and therefore do not have the advantage of agglomeration efficiency.
Another explanation for lower mean wages in forest-dependent areas might be the
nature of jobs in these areas. While manufacturing jobs are more prevalent in forestdependent areas, jobs in manufacturing have been declining over time. Between 1969 and
1992, rural manufacturing employment fell from 20 percent to 17 percent of total
employment (Parker, 1995). In addition, literature on forest sector employment suggests
that jobs in logging and forestry face the challenges of low durability (Freudenburg and
Gramling, 1994; Mann, 2001; Moseley, 2006). The forest-based jobs may be seasonal or
part-time, involving work in only certain times of the year. Some jobs could be full time,
but the industry may be structured in such a way that people tend to leave the industry
after short periods. A competitive equilibrium is assumed to prevail in the labor markets
in forest-dependent areas, but the nature of jobs in these areas may set the equilibrium
value of wages at a lower level, compared to the equilibrium wages in other areas.
The difference in cost of living between forest and non forest-dependent areas
may have some influence on the area mean wage. Lower cost of living in forest-
45
dependent areas could account for lower mean wages in these areas (but do not account
for lower returns to skills). If the cost of living is significantly lower in rural forestdependent areas, then even with lower wages, a family's purchasing power may nearly be
the same. This perception is both supported and refuted by studies. A paper estimates that
the cost of living in non-metro counties across United States was about 16 percent less
than in metro counties - although in the west, the non-metro advantage was only 9
percent (Nord, 2000). Another paper analyzed different cost factors associated with
residence in different counties in Kentucky, including housing cost, driving costs and
food cost, and found that rural areas were not less expensive than urban areas, and that
the costs were highly variable by the type of non-urban county (Zimmerman, 2008).
46
Appendix 3A
Table 3.A.1. Variable Definitions
Variable Name
Wage
White
Black
Other
Married
Separated/divorced
Single
English
No English
Household
Less than high school
High school
Some college
College or more
Part-time work
Full-time work
Household head
Agriculture & Forestry
Mining
Utilities
Construction
Manufacturing
Trade
Transportation
Communications
Finance
Services
Administration
Description
Log of annual salary divided by number of weeks worked
Indicator variable for white race
Indicator variable for black race
Indicator variable for other race
Indicator variable for married
Indicator variable for separated or divorced
Indicator variable for single
Indicator variable for English fluency
Indicator variable for lack of English fluency
Indicator variable for head of household
Educational attainment is less than high school
Educational attainment is high school
Educational attainment is 1-3 years of college
Educational attainment is 4 years college or more
Worked part time (less than 30 hours per week)
Worked full time (at least 30 hours per week)
Indicator variable for head of household
Indicator variable for employment in agriculture and forestry
Indicator variable for employment in mining sector
Indicator variable for employment in utilities sector
Indicator variable for employment in construction sector
Indicator variable for employment in manufacturing sector
Indicator variable for employment in trade sector
Indicator variable for employment in transportation sector
Indicator variable for employment in communication sector
Indicator variable for employment in finance sector
Indicator variable for employment in services sector
Indicator variable for employment in administration sector
47
Table 3.A.2. Selected Summary Statistics, Males (n = 2,874,576)
Variable name
Natural log of weekly wage
[ln(w)]
Potential experience
White
Black
Other
Married
Separated/divorced
Single
English
No English
Less than high school
High school
Some College
College or more
Part-time work
Full-time work
Household head
Non-household head
Agriculture and Forestry
Mining
Utilities
Construction
Manufacturing
Trade
Transportation
Communications
Finance/Insurance
Services
Administration
Mean
6.54651
($696.81)
23.51654
0.79622
0.08791
0.11587
0.68775
0.12698
0.18527
0.98904
0.01096
0.11218
0.31772
0.28976
0.28034
0.03626
0.96374
0.77508
0.22492
0.02028
0.00863
0.01647
0.11336
0.21145
0.14166
0.06551
0.03046
0.04989
0.27413
0.06817
Standard
Deviation
0.75431
10.52099
0.40281
0.28317
0.32007
0.46341
0.33295
0.38851
0.10412
0.10412
0.31559
0.46559
0.45365
0.44917
0.18694
0.18694
0.41753
0.41753
0.14096
0.09248
0.12729
0.31703
0.40834
0.34870
0.24742
0.17184
0.21771
0.44607
0.25205
Min
Max
2.95651
($19.23)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12.77705
($354000)
59
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
48
Table 3.A.3. Selected Summary Statistics, Females (n = 2,588,733)
Variable name
Natural log of weekly wage
[ln(w)]
Potential experience
White
Black
Other
Married
Separated/divorced
Single
English
No English
Less than high school
High school
Some College
College or more
Part-time work
Full-time work
Household head
Non-household head
Agriculture and Forestry
Mining
Utilities
Construction
Manufacturing
Trade
Transportation
Communications
Finance/Insurance
Services
Administration
Mean
6.12083
($455.24)
23.42662
0.78193
0.11486
0.10321
0.62548
0.19014
0.18438
0.99156
0.00844
0.07743
0.30512
0.33508
0.28237
0.13546
0.86454
0.34305
0.65696
0.00617
0.00136
0.00523
0.01522
0.11467
0.13322
0.02688
0.02964
0.08492
0.52777
0.05492
Standard
Deviation
0.75099
10.5896
0.41294
0.31886
0.30423
0.48399
0.39241
0.38779
0.09147
0.09147
0.26728
0.46046
0.47202
0.45015
0.34221
0.34221
0.47473
0.47473
0.07828
0.03681
0.07215
0.12245
0.31862
0.33982
0.16173
0.16958
0.27876
0.49923
0.22783
Min
Max
2.95651
($19.23)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12.68231
($322001)
59
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
49
Table 3.A.4. Estimates of Wage Equation, Males
Variable
Coefficient
Standard error
t- statistic
Intercept
5.64410
0.00440
1281.53
Potential experience
0.02872
0.00016389
175.25
Potential experience squared
-0.00043
0.00000316
-136.57
White
0.10565
0.00124
85.52
Black
-0.02643
0.00173
-15.26
Married
0.18010
0.00110
164.21
Separated/divorced
0.04407
0.00145
30.44
English
0.24109
0.00379
63.67
Less than high school
-0.19510
0.00139
-140.67
Some College
0.17345
0.00099232
174.79
College or more
0.60691
0.00106
571.45
Part-time work
-0.86324
0.00206
-418.78
Non-household head
-0.15447
0.00098883
-156.21
Agriculture & Forestry
-0.34597
0.00282
-122.62
Mining
0.11844
0.00419
28.30
Utilities
0.08784
0.00309
28.47
Construction
-0.03371
0.00141
-23.89
Trade
-0.11853
0.00131
-90.39
Transportation
-0.00367
0.00171
-2.15
Communications
0.07643
0.00235
32.50
Finance/Insurance
0.06551
0.00193
34.02
Services
-0.15770
0.00113
-139.11
Administration
-0.09101
0.00170
-53.63
Dependent variable: Log of weekly wage
Omitted categories: other race, single, no English, high school, full-time work,
household head, manufacturing
Number of observations: 2,874,576
Adj. R-squared: 0.2671
50
Table 3.A.5. Estimates of Wage Equation, Females
Variable
Coefficient
Standard error
t- statistic
Intercept
5.66029
0.00491
1152.98
Potential experience
0.02078
0.00016271
127.71
Potential experience squared
-0.00030596
0.00000319
-95.88
White
0.01278
0.00131
9.78
Black
-0.00711
0.00169
-4.21
Married
0.03456
0.00118
29.17
Separated/divorced
-0.01027
0.00131
-7.85
English
0.17377
0.00437
39.73
Less than high school
-0.18023
0.00162
-111.32
Some College
0.21421
0.00097904
218.79
College or more
0.65917
0.00107
615.22
Part-time work
-0.85031
0.00114
-742.88
Non-household head
-0.09942
0.00102
-97.44
Agriculture & Forestry
-0.27502
0.00506
-54.36
Mining
0.09607
0.01052
9.13
Utilities
0.14545
0.00545
26.68
Construction
-0.01186
0.00333
-3.56
Trade
-0.18096
0.00156
-115.87
Transportation
0.06636
0.00262
25.33
Communications
0.07553
0.00253
29.83
Finance/Insurance
0.04820
0.00176
27.31
Services
-0.13687
0.00129
-106.34
Administration
0.01034
0.00202
5.11
Dependent variable: Log of weekly wage
Omitted categories: other race, single, no English, high school, full-time work,
household head, manufacturing
Number of observations: 2,588,733
Adj. R-squared: 0.319
51
Table 3.A.6. Estimates of Area Mean Log Wage, Males
MIGPUMAs
Area mean wage
Number of
Percent of
observations
forest income
Log value (μj)
Dollar value
Forest Dependent Areas
CA1
CA2
CA3
CA4
CA5
ID1
ID2
ID4
OR1
OR2
OR3
OR4
OR5
OR6
OR7
OR8
OR9
OR10
WA4
WA11
WA15
WA16
ME6
ME8
ME9
ME10
NH1
1620
1605
1616
1292
1431
1917
1243
1305
1597
1651
1305
939
1918
1996
2930
1272
1535
1124
2472
1654
2075
1538
1013
1820
2007
1533
1829
CA6
CA7
CA8
CA9
ID3
ID5
ID6
ID8
ID9
1783
1112
1483
1582
1603
1109
2455
1692
1552
9.3
11.8
5.3
5.9
8.4
8.2
16.6
7.1
8.3
10.3
11.8
6.2
10.8
8.4
6.1
8.8
6.4
22.6
5.6
20.6
12.8
9.4
5.8
17.8
9.9
17.6
5.5
6.349
6.386
6.449
6.477
6.481
6.434
6.348
6.342
6.381
6.417
6.284
6.349
6.442
6.440
6.470
6.437
6.395
6.461
6.400
6.532
6.394
6.522
6.384
6.380
6.330
6.323
6.466
572
594
632
650
653
623
571
568
591
612
536
572
628
627
646
625
599
640
602
687
598
680
592
590
561
557
643
Non-forest Dependent Areas
1.6
6.465
4.8
6.499
2.2
6.436
0.81
6.554
1.7
6.346
2.9
6.319
2.1
6.515
2.04
6.390
0.69
6.880
642
664
624
702
570
555
675
596
973
52
MIGPUMAs
OR11
OR12
OR13
WA1
WA2
WA3
WA5
WA7
WA8
WA9
WA10
WA12
WA13
WA17
WA18
WA21
ME1
ME2
ME3
ME4
ME5
ME7
Rest US
Number of
observations
3006
1652
15125
1584
1936
2202
4063
1500
1968
2029
6346
2046
7933
2673
18813
3586
1400
731
906
979
1689
858
2732943
Percent of
forest income
3.5
4.8
1.4
4.1
2.3
3.3
1.4
3.8
1.2
3.3
1.4
1.9
2.4
0.25
0.7
3.8
1.14
2.6
2.4
0.79
4.2
3.4
Area mean wage
Log value (μj)
Dollar value
6.458
638
6.417
612
6.936
1028
6.523
680
6.508
671
6.434
622
6.892
984
6.434
623
6.533
688
6.459
639
6.892
984
6.551
700
6.580
720
6.563
709
6.682
798
6.613
745
6.480
652
6.489
658
6.500
665
6.423
616
6.351
573
6.471
646
Rest of United States
1.8
6.5483
698
53
Table 3.A.7. Estimates of Area Mean Log Wage, Females
MIGPUMAs
CA1
CA2
CA3
CA4
CA5
ID1
ID2
ID4
OR1
OR2
OR3
OR4
OR5
OR6
OR7
OR8
OR9
OR10
WA4
WA11
WA15
WA16
ME6
ME8
ME9
ME10
NH1
Area mean wage
Number of
Percent of
observations
forest income
Log value (μj)
Dollar value
Forest Dependent Areas
1403
1574
1511
1222
1214
1621
1041
1033
1422
1437
1127
844
1697
1721
2590
1209
1426
987
2059
1283
1833
1334
993
1694
1815
1410
1794
9.3
11.8
5.3
5.9
8.4
8.2
16.6
7.1
8.3
10.3
11.8
6.2
10.8
8.4
6.1
8.8
6.4
22.6
5.6
20.6
12.8
9.4
5.8
17.8
9.9
17.6
5.5
5.965
5.992
6.079
6.032
6.040
5.924
5.982
5.911
5.974
5.980
5.994
6.050
6.036
5.985
6.001
5.944
6.068
5.989
5.975
6.067
6.106
6.080
5.957
5.739
5.973
5.854
5.985
389
400
437
416
420
374
396
369
393
395
401
424
418
397
404
381
432
399
394
431
449
437
386
311
393
349
397
Non-forest Dependent Areas
CA6
CA7
CA8
CA9
ID3
ID5
ID6
ID8
ID9
1614
1089
1213
1447
1341
945
2010
1453
1275
1.6
4.8
2.2
0.81
1.7
2.9
2.1
2.04
0.69
6.050
6.233
6.120
6.172
6.169
6.167
6.081
6.039
6.298
424
509
455
479
478
477
438
419
543
54
MIGPUMAs
Number of
observations
Percent of
forest income
OR11
OR12
OR13
WA1
WA2
WA3
WA5
WA7
WA8
WA9
WA10
WA12
WA13
WA17
WA18
WA21
ME1
ME2
ME3
ME4
ME5
ME7
2546
1371
12894
1376
1584
2008
3605
1305
1557
1684
5346
1924
6617
2059
16269
2999
1400
764
864
989
1747
835
3.5
4.8
1.4
4.1
2.3
3.3
1.4
3.8
1.2
3.3
1.4
1.9
2.4
0.25
0.7
3.8
1.14
2.6
2.4
0.79
4.2
3.4
Rest US
2465309
Area mean wage
Log value (μj)
Dollar value
Rest of United States
1.8
6.181
6.258
6.278
6.032
6.071
6.017
6.052
6.179
6.110
6.045
6.228
6.125
6.165
6.125
6.251
6.181
6.071
6.062
6.045
5.997
5.957
6.028
484
522
533
417
433
410
425
483
450
422
507
457
476
457
519
484
433
429
422
402
386
415
6.1227
456
55
Table 3.A.8. Estimates of Returns to Skills, Education and Experience, Males
Area
MIGPUMAs
Number of
observations
Percent
Returns to
of forest
Skills
income
φj(S)
Forest Dependent Areas
CA1
CA2
CA3
CA4
CA5
ID1
ID2
ID4
OR1
OR2
OR3
OR4
OR5
OR6
OR7
OR8
OR9
OR10
WA4
WA11
WA15
WA16
ME6
ME8
ME9
ME10
NH1
1620
1605
1616
1292
1431
1917
1243
1305
1597
1651
1305
939
1918
1996
2930
1272
1535
1124
2472
1654
2075
1538
1013
1820
2007
1533
1829
9.3
11.8
5.3
5.9
8.4
8.2
16.6
7.1
8.3
10.3
11.8
6.2
10.8
8.4
6.1
8.8
6.4
22.6
5.6
20.6
12.8
9.4
5.8
17.8
9.9
17.6
5.5
0.6530
0.7717
0.7645
0.7417
0.6482
0.6709
0.6129
0.7844
0.6826
0.6358
0.7274
0.8940
0.7654
0.8409
0.8208
0.8267
0.8298
0.7289
0.7134
0.8219
0.7010
0.7282
0.8013
0.6933
0.8214
0.6392
0.8134
Returns to
Education
φj(E)
Returns to
Experience
φj(X)
0.6063
0.6270
0.6990
0.6627
0.4874
0.5825
0.3987
0.6276
0.6612
0.6298
0.7006
0.6849
0.6276
0.8309
0.5898
0.7781
0.7420
0.6282
0.6172
0.6712
0.4856
0.5653
0.7347
0.6702
0.6371
0.5751
0.6920
0.5085
0.7336
0.7341
0.5437
0.5313
0.7678
0.7548
0.6505
0.5716
0.5904
0.8646
0.7285
0.6950
0.6805
0.7239
0.7453
0.8030
0.8948
0.8052
0.7197
0.7354
0.6690
0.5296
0.8610
0.8365
0.4909
0.8492
0.7283
0.7342
0.7711
0.9500
0.7869
0.5877
0.8170
0.8567
0.8018
0.8234
0.8123
0.7893
0.8493
0.7994
Non-forest Dependent Areas
CA6
CA7
CA8
CA9
ID3
ID5
ID6
1783
1112
1483
1582
1603
1109
2455
1.6
4.8
2.2
0.81
1.7
2.9
2.1
0.8878
0.8818
0.8687
0.9042
0.9187
0.7719
0.9328
56
Area
MIGPUMAs
Number of
observations
Percent
of forest
income
Returns to
Skills
φj(S)
Returns to
Education
φj(E)
Returns to
Experience
φj(X)
ID8
ID9
OR11
OR12
OR13
WA1
WA2
WA3
WA5
WA7
WA8
WA9
WA10
WA12
WA13
WA17
WA18
WA21
ME1
ME2
ME3
ME4
ME5
ME7
1692
1552
3006
1652
15125
1584
1936
2202
4063
1500
1968
2029
6346
2046
7933
2673
18813
3586
1400
731
906
979
1689
858
2.04
0.69
3.5
4.8
1.4
4.1
2.3
3.3
1.4
3.8
1.2
3.3
1.4
1.9
2.4
0.25
0.7
3.8
1.14
2.6
2.4
0.79
4.2
3.4
0.8214
0.8166
1.0142
0.8523
1.2999
0.7396
0.8554
0.8677
0.9192
0.8033
0.7239
0.7743
0.8992
0.7998
0.7843
0.8056
0.8237
0.8719
0.8219
0.8195
1.0078
0.8101
0.8548
0.8206
0.6839
0.7244
0.8627
0.5482
0.9001
0.5301
0.6746
0.7316
0.7640
0.7295
0.9415
0.9603
0.5085
0.6383
0.6417
0.6677
0.8699
0.7696
0.7255
0.7352
0.8093
0.7802
0.6983
0.7795
0.7945
0.8093
0.7892
0.7447
1.1473
0.8446
0.7593
0.8348
0.8187
0.7728
0.7564
0.8976
0.9192
0.8629
0.8345
1.0569
0.9345
1.0622
0.8623
0.7863
0.8932
0.7693
0.8006
0.8684
Rest of United States
1.8
1.0073
1.0107
1.0042
Rest US
2732943
57
Table 3.A.9. Estimates of Returns to Skills, Education and Experience, Females
Area
MIGPUMAs
Number of
observations
Percent
Returns to
of forest
Skills
income
φj(S)
Forest Dependent Areas
CA1
CA2
CA3
CA4
CA5
ID1
ID2
ID4
OR1
OR2
OR3
OR4
OR5
OR6
OR7
OR8
OR9
OR10
WA4
WA11
WA15
WA16
ME6
ME8
ME9
ME10
NH1
1403
1574
1511
1222
1214
1621
1041
1033
1422
1437
1127
844
1697
1721
2590
1209
1426
987
2059
1283
1833
1334
993
1694
1815
1410
1794
9.3
11.8
5.3
5.9
8.4
8.2
16.6
7.1
8.3
10.3
11.8
6.2
10.8
8.4
6.1
8.8
6.4
22.6
5.6
20.6
12.8
9.4
5.8
17.8
9.9
17.6
5.5
0.7818
0.7977
0.6751
0.6980
0.7283
0.6574
0.7641
0.6911
0.7388
0.8237
0.7796
0.6924
0.6765
0.8379
0.7015
0.6732
0.8294
0.7963
0.6777
0.7341
0.6978
0.6835
0.6871
0.7210
0.7136
0.6814
0.7581
Returns to
Education
φj(E)
Returns to
Experience
φj(X)
0.7085
0.7097
0.6962
0.7533
0.6693
0.7308
0.7624
0.6886
0.7071
0.7328
0.7853
0.6727
0.6691
0.7261
0.6788
0.6510
0.8530
0.6574
0.6857
0.6907
0.6760
0.6456
0.6744
0.6900
0.6919
0.7117
0.7281
0.7085
0.6859
0.7314
0.7474
0.7207
0.6313
0.6067
0.6419
0.6663
0.7381
0.6945
0.6173
0.6987
0.7839
0.7853
0.6593
0.7856
0.6488
0.6738
0.7292
0.6419
0.6793
0.7124
0.7049
0.6933
0.7068
0.7186
0.8266
0.8091
0.8753
0.8394
0.8793
0.5683
0.8390
0.7603
0.7593
0.8371
0.7785
0.6557
0.6260
0.7721
Non-forest Dependent Areas
CA6
CA7
CA8
CA9
ID3
ID5
ID6
1614
1089
1213
1447
1341
945
2010
1.6
4.8
2.2
0.81
1.7
2.9
2.1
1.0354
0.9558
0.9223
0.9873
0.9125
0.6033
0.8611
58
Area
MIGPUMAs
Number of
observations
Percent
of forest
income
Returns to
Skills
φj(S)
Returns to
Education
φj(E)
Returns to
Experience
φj(X)
ID8
ID9
OR11
OR12
OR13
WA1
WA2
WA3
WA5
WA7
WA8
WA9
WA10
WA12
WA13
WA17
WA18
WA21
ME1
ME2
ME3
ME4
ME5
ME7
1453
1275
2546
1371
12894
1376
1584
2008
3605
1305
1557
1684
5346
1924
6617
2059
16269
2999
1400
764
864
989
1747
835
2.04
0.69
3.5
4.8
1.4
4.1
2.3
3.3
1.4
3.8
1.2
3.3
1.4
1.9
2.4
0.25
0.7
3.8
1.14
2.6
2.4
0.79
4.2
3.4
0.7607
0.9116
0.8760
0.8108
0.8363
0.6657
0.7753
0.7354
0.8201
0.7715
0.8232
0.8388
0.7312
0.8680
0.8005
0.8156
0.7887
0.7533
0.8716
0.8396
0.8788
0.8565
0.7677
0.8509
0.7310
0.8751
0.8074
0.7070
0.7950
0.6225
0.7261
0.6689
0.7767
0.7246
0.7899
0.8315
0.7103
0.7925
0.7738
0.7731
0.7567
0.7337
0.6894
0.7240
0.7160
0.8057
0.7517
0.7409
0.6255
0.7306
0.9643
0.8374
1.0425
0.9358
0.8738
0.8661
0.9101
0.7918
0.8721
0.6223
0.8758
0.8263
0.8614
0.9631
0.9279
0.9025
0.8637
0.8005
0.8205
0.7919
0.8482
0.8202
Rest of United States
1.8
1.0088
1.0098
1.003
Rest US
2465309
59
Chapter 4 – Migration Decision and Location Choice in the Northwest
4.1. Introduction
Past studies have documented that economic incentives play a significant role in
migration decisions (Greenwood, 1975). While income differentials are important for
individuals‘ migration decision, there are other factors such as employment opportunities,
regional amenities and socioeconomic characteristics of individuals that can influence
migration decisions (Roback, 1982; Mueser and Graves, 1995; McLeman and Smit,
2006). Greenwood (1997) summarizes the theoretical approaches to migration in the
economics literature and categorizes them into two theories: the disequilibrium theory
and the equilibrium theory. Both theories assume that workers respond to regional wage
differentials, but give different explanations for interregional differences in wages. The
disequilibrium perspective of migration assumes that migration decisions are made in
response to differences in wages. If a worker expects a higher wage in a different area, he
or she will move to that area in order to obtain higher wage, and the higher utility this
implies. Spatial heterogeneity arising from amenities can also influence individual‘s
migration decision. The equilibrium perspective assumes that the spatial location of
individuals is in equilibrium, and that any differences in wages are compensated by
amenities. So although there may be differences in wages across regions, such differences
are compensated by amenities. Any uncompensated differences influence the migration
decision, which again sorts individuals back to equilibrium.
60
Results from chapter 3 indicate that the expected wage and variation in wage
distribution are lower in the forest-dependent areas than other areas. This interregional
wage differential may push individuals away from existing forest-dependent locations,
and may pull migrants towards non forest-dependent areas. This study draws on the
disequilibrium theory of migration (Becker, 1962; Sjaastad, 1962), which assumes that
potential migrants seek to maximize their utility resulting from location change
(Nakosteen and Zimmer, 1980; Polachek and Horvath, 1977), and investigates the
determinants of migration and location choice in the Northwest.
The Northwest sample for 2000 has 235,987 working-age residents (considering
both males and females), representing approximately 4.7 million individuals. Of these,
205,696 individuals were living in the Northwest in 1995 as well. That is, between 1995
and 2000, about 87 percent of 2000‘s Northwest residents remained within the region.
Modeling the behavior of individuals who moved into the Northwest from elsewhere, and
individuals who moved out of the Northwest to other areas would require modeling a
large number of potential locations and destinations, and that is beyond the scope of this
study. Since 87 percent is a large share of individuals, I model the migration and location
choice decisions of only those who remained within the Northwest region between 1995
and 2000.
This chapter is organized as follows: section 4.2 discusses the conceptual and
empirical framework used in this analysis. Section 4.3 presents the set of independent
variables used to model migration and discusses the dataset. Section 4.4 presents the
results and a final section summarizes the findings.
61
4.2. Model Framework
This section formulates the migration decision-making process in which an
individual chooses a destination from a set of available locations to maximize his utility.
Consistent with the disequilibrium theory of migration, a discrete choice model based on
random utility maximization is used, which assumes that individuals migrate in response
to utility differentials between locations. The utility function is specified following
McFadden (1974), and the migration model is empirically estimated using a nested-logit
formulation (Train, 2003).
The Northwest region is comprised of 47 MIGPUMAs8, and between 1995 and
2000, an individual is faced with a choice of whether to stay in his/her original area (the
1995 MIGPUMA), or to migrate to a non-origin area (one of the 46 non-origin
MIGPUMAs). The migration decision can be thought of as a two-stage process: the
decision of moving versus staying in the origin location, followed by the decision of
which of the 46 areas to move to, conditional on the decision to migrate having been
made. The structure of this migration decision problem gives rise to a Partially
Degenerate Nested Logit (PDNL) model, where the origin and destination choices form
two separate nests. The model is partially degenerate because the nest associated with the
stay decision contains only one location choice: the origin. However, if an individual
chooses to move, the area choice set is not degenerate; the individual has a set of 46
location choices. The following diagram illustrates the structure of the PDNL model. The
8
Locations of individuals are reported at the level of MIGPUMAs. There are in total 47 MIGPUMAs in the
Northwest states of northern California, Idaho, Oregon and Washington. I have discussed MIGPUMAs in
details in chapter 2.
62
two upper-level branches represent the stay and move choices. The lower-level nests
contain the origin and non-origin areas, respectively.
Figure 4.1 Structure of Partially Degenerate Nested Logit (PDNL) Model
Stay
Move
Origin area
Non-origin areas
Formally, I consider an individual i who is faced with the choice in 1995 of
staying in his/her origin area or moving to a non-origin area. The choice is observed in
2000 and I assume that no intermediate location decisions are made. The origin is
denoted j=0 and the 46 alternative area choices are denoted j = 1, 2,…, 46. Each area
choice provides utility to individual i, expressed as:
(4.1)
Uij =
𝛼0 𝑌𝑖 + 𝛽𝑋𝑗 + 𝛾𝑍𝑖𝑗 + 𝜀𝑖𝑗
𝛼1 𝑌𝑖 + 𝛽𝑋𝑗 + 𝛾𝑍𝑖𝑗 + 𝜀𝑖𝑗
𝑗=0
𝑗 = 1, 2, . . . , 46
where α0, α1, β, and γ are parameter vectors, and εij is a random disturbance with a
Generalized Extreme Value (GEV) distribution, following Train (2003). The Yi‘s are
individual attributes that do not vary with areas and are used to explain the decision to
stay or move. The parameters on the Yi variables (  0 , 1 ) differ to capture utility
differences associated with staying or moving. The Xj‘s measure area attributes that do
not vary across individuals and the 𝑍𝑖𝑗 ‘s are variables that vary both by individuals and
areas.
63
Denoting the two nests by m=0 (stay) and m=1 (move), the probability that individual i
chooses area j is:
(4.2)
𝑃𝑖𝑗 = 𝑃𝑖𝑚 . 𝑃𝑖𝑗 ⃓ 𝑚
where 𝑃𝑖𝑚
is the marginal probability of choosing stay or move, and 𝑃𝑖𝑗 ⃓ 𝑚 is the
conditional probability of choosing area j given that the nest m is chosen.
The marginal probability (stay or move decision) corresponds to the upper model
in Figure 4.1 and the conditional probability (choice of area j within the nest m)
corresponds to the lower model. The lower level utility depends on characteristics that
vary across areas, and the upper level utility depends on individual characteristics that
vary with the choice of staying or moving. The maximum utility attainable in non-origin
areas as well as the utility attained in the origin also influences the upper level choice of
staying or moving. These utilities are captured in nested logit models by the nest-specific
inclusive value variables. The inclusive value Iim enters as an explanatory variable in the
upper model, and links the upper and lower models by bringing information about
utilities from lower-level choices into the upper model.
The marginal probability that individual i chooses stay (m=0) or move (m=1) is
denoted:
(4.3)
𝑃𝑖𝑚 =
𝑒 𝛼 𝑚 𝑌 𝑖 + 𝜆 𝑚 𝐼 𝑖𝑚
𝛼 𝑚 𝑌 𝑖 + 𝜆 𝑚 𝐼 𝑖𝑚
1
𝑚 =0 𝑒
where λm is a parameter measuring the degree of substitutability among the alternatives
in nest m. For stay (m=0), the origin (j=0) is the only area choice and, thus:
𝐼𝑖𝑜 = ln
j=0 𝑒
𝛽 𝑋0 +𝛾𝑤 𝑖0
𝜆0
64
=
𝛽𝑋0 + 𝛾𝑤𝑖0
𝜆0
For move (m=1), the area choices are the non-origin destinations (j = 1, 2,…, 46),
yielding:
𝐽
𝛽𝑋 𝑗 +𝛾𝑤 𝑖𝑗
𝑗 =1 𝑒
𝐼𝑖1 = ln
𝜆1
The probability that individual i chooses area j, conditional on the stay (m=0) or
move (m=1) decision is:
(4.4)
𝑃𝑖𝑗 ⃓𝑚 =
𝑒
𝛽 𝑋 𝑗 + 𝛾 𝑤 𝑖𝑗
𝑗
𝑒
𝛽 𝑋 𝑗 +𝛾 𝑤 𝑖𝑗
𝜆𝑚
𝜆𝑚
Conditional on staying (m=0), the probability that individual i selects the origin (j=0) is:
𝑒 𝛽 𝑋 0 +𝛾 𝑤 𝑖0 𝜆 0
𝑃𝑖0 ⃓𝑚 =0 =
𝑗 =0
𝑒 𝛽 𝑋 0 +𝛾 𝑤 𝑖0 𝜆 0
𝑒 𝛽 𝑋 0 +𝛾 𝑤 𝑖0 𝜆 0
=
𝑒 𝛽 𝑋 0 +𝛾 𝑤 𝑖0 𝜆 0
= 1
Conditional on moving (m=1), the probability that individual i selects area j (j=1,2,..,46)
is:
𝑃𝑖𝑗 ⃓𝑚 =1 =
𝑒
46
𝑗 =1
𝛽 𝑋 𝑗 + 𝛾 𝑤 𝑖𝑗
𝑒
𝜆1
𝛽 𝑋 𝑗 +𝛾 𝑤 𝑖𝑗
𝜆1
There are two alternative forms that can be specified for a nested logit model: (1) the
non-normalized form developed by Ben-Akiva (1973), and (2) the utility maximizing
form developed by McFadden (1978, 1981). This study follows McFadden‘s form
because of its consistency with the utility maximization principle. McFadden (1978,
1981) showed that the estimates of the nested logit model will be consistent with utility
65
maximization behavior if the value of λk is within the interval (0, 1). The PDNL model is
analyzed in detail by Hunt (2000).
To estimate the model parameters, I apply the
normalization λ0 = 1, and check whether λ1 lies in the interval (0, 1) for the model to be
consistent with utility maximization.
4.3. Data
The migration model is estimated with a sample of working-age (between 25 and
64 years), non-institutionalized individuals who earned at least $1000 in wage and salary
income in 2000. I focus on this sample given my interest in the role that wages and other
labor market outcomes play in influencing migration decisions. Because labor market
outcomes differ for males and females (Hunt and Mueller, 2002), the migration model is
estimated separately for males and females. The sample includes 109474 and 96026
working age males and females, respectively, who lived in the 47 Northwest
MIGPUMAs during 1995 and 2000.
The empirical model encompasses a number of individual and area characteristics
that are found to be important in migration research (Hunt and Mueller, 2004). As noted
above, the three types of variables used are individual variables, area variables, and
individual-area variables. Individual variables include measures of age (Age), race
(White), marital status (Married) and educational attainment (Education). Definitions for
all variables are given in Appendix Table 4.A.1
Area variables capture interregional variations in amenities and other area
attributes. The area variables used in this study include measures of population
66
(Population density), economic opportunity (Employment growth), crime rate (Crime),
climate (January temperature, July temperature, Rainfall), forest dependency (Nonforest) and housing price (Housing). The Non-forest variable indicates whether a location
is forest dependent or not, and this variable is expected to indicate whether forest
dependency increases or decreases the attractiveness of an area as destination. Population
density indicates the number of individuals present per square mile, and the Employment
growth variable is lagged for the period 1990-1995. The housing price variable used is
the median value of owner-occupied housing units in 1990 (used in previous migration
studies such as Clark and Hunter, 1992).
The individual-area variables differ across individuals and areas, and include
expected wage and distance between an individual‘s origin and possible destinations.
Distance is calculated as the radial distance between the origin and destination
MIGPUMAs, and is used as a proxy for the economic and psychological costs of moving.
Although most of the data in this study are obtained from secondary sources, the Wage
variable is calculated. This variable had to be estimated since we do not observe an
individual‘s wage in the non-selected areas. Here an individual‘s expected wages in
different areas are generated according to the methodology laid out in the previous
chapter9. For every male and female in the sample, I estimate the log wages he or she can
expect to receive in each of the 47 possible destination areas.
9
The reduced form log wage equation (3.5) is estimated for individual i in each of the j areas (j= 1,…, 47).
Using area specific parameters and individual variable values, I estimate the log wage individual i can
expect to receive if he or she was living in the jth area.
ln(𝑤𝑖𝑗 ) = αj + β1j PXij + β2j (PX)ij 2 + β3j HHij + β4j ENGij + ∑mβmj EAmij + ∑nβnjMSnij
+ γ1j PTij + ∑r γrj RCrij + ∑s γsj INDsij + εij
67
Area wage variance may be another factor influencing individual‘s utility and
location choice. Individual characteristics as well as area attributes will interact to
determine how area wage variation may influence utility. If individual skill is higher than
the average skill level, then he or she may prefer an area with higher mean wage and
wider wage distribution. If individual skill is lower than the average, he or she may still
prefer an area with higher mean wage, but a tighter wage distribution. Thus, how area
wage variance influences individuals location choice depends on individuals skill level
and the area mean wage. Introducing a variable for area wage variance in the model
leads to non-convergence of maximum likelihood estimates. Hence in this study, I limit
my analysis to only area mean wage as an explanatory variable for location choice.
Future research may investigate how area wage variance influences individual‘s location
choice.
Individual data are obtained from the Public Use Microdata Survey (PUMS) of
the United States 2000 decennial census. PUMS is not a longitudinal data set, however, it
can be used to model point-to-point migration decisions between 1995 and 2000, because
respondents in 2000 are asked where they lived five years ago (i.e., in 1995). Thus, the
data reveal whether individuals stayed in the origin area or moved to another area. Data
sources for the area variables are State and Metropolitan Area Data Book 1997-98 (U.S.
Bureau of the Census), County and City Data Book 1994 (U.S. Bureau of the Census)
and McGranahan (1999). Because MIGPUMAs are agglomerations of counties, I
computed MIGPUMA-level area variables as averages of corresponding county-level
observations.
68
Summary statistics for the male and female samples are reported in appendix
tables A.4.2 and A.4.3. The average age of males and females, residing in the Northwest
in 1995 and 2000, is 42.68 and 42.81 years respectively. For both males and females,
about 87 percent are white. 69 percent of males are married, while 64 percent of females
are married. 23 percent of males and 27 percent of females have four or more years of
college education. In the Northwest, the average population density is about 60 persons
per square miles and annual employment growth between 1990 and 1995 was about 2.6
percent. On average, there were approximately 4,000 serious crimes per 100,000 persons
in 1991. About 53 percent of Northwest MIGPUMAs are non forest-dependent.
Averaged over areas and individuals, the average weekly wage is $660 for males and
$440 for females. The average distance between origin and all potential destinations for
both males and females is approximately 290 miles.
4.4. Results
The maximum likelihood estimates of the PDNL model for males and females are
presented in Table 4.1. Overall, the estimated parameters presented in Table 4.1 have
signs that are consistent with expectations for whether a variable should increase or
decrease the likelihood of migration and location choice. In addition, most of the
estimated parameters are statistically significant. The results have same signs for the male
and female samples, and therefore a general discussion for the migration decision and
location choice is presented below.
69
Table 4.1. Maximum Likelihood Estimates of PDNL Model
Males
Coefficient
Standard
Error
1
Upper Level: Stay – Move Decision
Age
0.0415**
0.0058
White
0.1784
0.076
Married
0.1656**
0.0732
Education
-0.3453**
0.0702
Lower Level: Area (MIGPUMA) Choice
Population density
0.00086**
0.00000077
Employment growth
0.091*
0.041
Crime
-0.00007** 0.0000048
Housing cost
-0.000073* 0.000031
January temperature
0.0006
0.000058
July temperature
-0.0000672* 0.0000292
Rainfall
-0.00020
0.000190
Non-forest dependent area 0.00041*
0.00017
Wage
0.0303**
0.0015364
Distance
-0.0003**
0.0001
Inclusive Value
λ1
0.016
0.0005
Variables
Females
Coefficient Standard
Error
0.0726**
0.1307
0.2068**
-0.2551**
0.0072
0.0566
0.1104
0.0626
0.00106**
0.0057*
-0.00015**
-0.0002*
0.0004
-0.00019*
-0.0001
0.0006*
0.01859**
-0.0004**
0.0000042
0.0029
0.000001
0.00009
0.00041
0.0000803
0.00012
0.0012
0.0069
0.0001
0.022
0.0006
a. 1Normalized on the decision to stay in origin (λ0 = 1)
b. **: Significant at 1%; * : Significant at 5%
First, let us consider the coefficient estimates for the individual variables. These
coefficients are normalized with respect to the decision to stay in the origin area, and
hence a positive (negative) estimate indicates an increased (decreased) probability of
staying. The positive and statistically significant coefficient estimate on age implies that
as age increases the probability also increases that a worker stays in his/her origin area.
This negative effect of aging on migration propensity is consistent with other studies
(Rogers and Castro, 1984; Lucas, 1997; de Haan, 1999). The likelihood of migration
among workers has been found to decrease with age, because the expected future benefits
70
of migration decreases as a worker ages. Individuals who are married have higher
probabilities of staying in the origin (i.e., they have lower probabilities of migrating),
ceteris paribus. The probability of remaining in the origin declines as education
increases, implying that mobility are positively related to educational attainment. This is
consistent with the literature on geographic mobility and educational attainment.
Economic theory predicts that education is an investment that makes a worker more
productive and thereby able to earn more (Becker, 1975), and there is empirical evidence
in support of this theory (Buchmann and Hannum, 2001; Psacharopoulos, 1981; Lau,
Jamison and Loua, 1991). Education increases the probability of migration because it
induces workers to migrate in search of jobs that match their skill levels (de Haan, 1999;
Narman, 1995).
The area and individual-area interaction variables influence the choice of a nonorigin area conditional on the decision to move. . The signs of coefficient estimates on
these variables indicate whether a change in the corresponding variable increases or
decreases the likelihood that a location will be chosen. Higher population density and
employment growth positively affect the probability that an area is selected as a
destination by migrants. The negative estimate for crime rate indicates that crime is a
repulsive factor in the area choice decision. The negative coefficient estimate on house
value indicates that migrants are more likely to select areas with lower housing cost, all
else equal. July temperature, which is used as a proxy for hot summers, has a negative
coefficient estimate, indicating that areas with cooler summers are more attractive to
migrants.
January temperature has a positive estimated coefficient, implying that
71
individuals prefer warmer winters.
Areas with less annual precipitation are more
desirable to migrants.
Lower forest dependency increases the attractiveness of an area, and migrants are
more likely to choose non forest-dependent areas as destinations. Forest-dependent areas
tend to be relatively remote, and these areas may attract fewer working-age individuals as
they lack economic opportunities. Working-age individuals moving to forest-dependent
areas may have to sacrifice income and access to services. For some, moving to forestdependent areas may mean returning to family and friends. But without a hometown or
family connection, working-age individuals generally are not drawn to rural forestdependent areas. Non forest-dependent areas might be more attractive to working-age
individuals, and location choice of forest-dependent areas as destination might be more of
psychology driven than jobs-driven.
Utility-maximizing behavior of individuals encourages them to choose locations
which offer higher wages. The positive and significant coefficient on wages indicates that
higher wages increase the likelihood that an area is chosen, all else equal. Distance has a
negative sign which suggests that higher moving costs tend to discourage migration.
Areas that are at a greater distance from the origin are less likely to be chosen.
The inclusive value (λ) variable represents an aggregate index of the utility
obtained from residing in non-origin areas. As utility in the non-origin areas (relative to
origin area) increases, the non-origin areas become more attractive, and this increases the
probability of moving as opposed to staying. Therefore, the correct a priori sign on the
inclusive value is positive. In addition to being positive, the inclusive value parameter
72
must lie in the interval (0, 1) for the model to be consistent with utility maximization for
all possible values of the explanatory variables (McFadden, 1978, 1981; Train, 2003).
The PDNL model is normalized on the decision to stay in origin (i.e., λ0 = 1), and the
estimated values of λ1 for both the male and female models are positive and lie in the
interval (0, 1).
4.5. Discussion
The human capital theory of migration posits that working-age individuals
contemplating a move consider the present value of benefits minus costs of moving
(Sjaastad, 1962). Benefits of migration include, for example, higher expected wages or a
more favorable social, cultural, or physical environment at the destination location.
Moving costs may include both economic costs such as information gathering and
relocation, and psychic costs associated with moving away from family, friends, and
familiar surroundings.
This study examined the factors that influence the migration decisions of
working-age individuals in the Northwest. Migrants appear to value amenities, as results
show that they are less likely to move into areas that have extreme climate (hot and
humid summers, and cold winters) and high annual precipitation. The results indicate that
the migrants are less likely to move into areas which are further away from their origins,
which I interpret as evidence that migration costs reduce mobility. Two important results
of this study are the effects of educational attainment and expected wages on migration
and location choice.
73
The result suggests a positive association between education attainment and
migration. Higher educated workers have greater incentives to move because the size of a
worker‘s relevant labor market increases with education level, and this makes it easier to
find better employment suited to their skills in a new location. However, low educational
attainment may deter the ability of lower educated people to find new jobs, and people
with lower educational attainment may be unable to finance moving costs.
The human capital theory views migration as an investment in human capital that
yields future monetary returns (Sjaastad, 1962). The result that working-age Northwest
migrants are more likely to choose non forest-dependent areas as destinations (as these
areas offer greater economic opportunities) conforms to the theory.
In summary, migrants in the Northwest tend to be young, educated and
unmarried. Conditional on migration, individuals prefer areas with high population
density and employment growth rates. High crime rate and housing cost are expected to
decrease the attractiveness of an area. Finally, areas with moderate climates, higher mean
wages, and geographic proximity to the origin are more attractive to migrants.
74
Appendix 4A
Table 4.A.1. Variables Used in the Migration Model
Variable
Individual-variables
Description
Age
White
Married
Education
Age in years
Indicator for white: =1 if white and =0 otherwise
Indicator for married: =1 if married and =0 otherwise
Indicator for education: =1 if four years of college or more and=0 otherwise
Area-variables
Population density
Employment growth
Crime
Housing
January temperature
July temperature
Rainfall
Non-forest
MIGPUMA area population in 1995 divided by land area
Average annual growth in employment, 1990-1995
Serious crimes per 100,000 population in 1995
Median value of owner-occupied housing units, 1990
Mean temperature for January, 1941-1970
Mean temperature for July, 1941-1970
Annual precipitation in inches
Indicator variable for non-forest areas: =1 if non-forest and =0 otherwise
Individual-area variables
Wage
Distance
Individual‘s expected weekly dollar wage in different areas
Distance in miles from individual‘s origin to destination MIGPUMAs
75
Table 4.A.2. Summary Statistics for Migration Model, Males
Variables
Mean
Standard Deviation
Individual variables: 109518 observations
Age
42.683
10.095
White
0.868
0.338
Married
0.692
0.461
Education
0.239
0.443
Area variables: 47 observations
Population density
61.804
166.2
Employment growth
0.026
0.013
Crime
3964
1657
Housing
82942
31767
January temperature
31.436
8.063
July temperature
67.943
5.090
Rainfall
25.022
18.433
Non-forest dependent area
0.532
0.498
Individual-area variables: 5147346 observations
Wage
663.23
228.71
Distance
289.05
169.3
Each of the 109,474 males has 47 area choices, so that the
number of observations for the individual/area variables is
109,474 × 47 = 5,145,278.
76
Table 4.A.3. Summary Statistics for Migration Model, Females
Variables
Mean
Standard Deviation
Individual variables: 96178 observations
Age
42.807
9.904
White
0.873
0.333
Married
0.636
0.478
Education
0.271
0.444
Area variables: 47 observations
Population density
61.804
166.2
Employment growth
0.026
0.013
Crime
3964
1657
Housing
82942
31767
January temperature
31.436
8.063
July temperature
67.943
5.090
Rainfall
25.022
18.433
Non-forest dependent area
0.532
0.498
Individual-area variables: 4520366 observations
Wage
448.6
170.88
Distance
289.5
169.42
Each of the 96,026 females has 47 area choices, so that the
number of observations for the individual/area variables is
96,026 × 47 = 4,513,222.
77
Chapter 5 – Conclusion
This dissertation contributes to the understanding of forest and non forestdependent areas by investigating regional wage distributions, and working-age
individuals‘ migration and residential location choices.
Forest-dependent areas in the Northwest and the Northeast displayed
socioeconomic characteristics more indicative of poor economic conditions compared to
other areas. Results from chapter 2 indicate that population density, per capita income,
median household income, and educational attainment are lower in the forest-dependent
areas. Also, poverty and unemployment rates are higher in these areas. This conforms to
previous studies (Drielsma, 1984; Fortmann et al., 1991; Overdevest, 1992) in that areas
with more timberland characteristically have lower well-being. One important aspect for
future research might be to consider non-economic indicators of well-being associated
with forest-dependence. Past studies on quality of life indicate that compensation for
interregional differences in amenities yield differences in both labor and housing markets
(Roback, 1982; Gyourko and Tracy, 1991; Blomquist et al., 1988). What non-economic
values do forest-dependent areas provide? Does recreational and other environmental
quality values associated with forest-dependent areas outweigh the lack of relatively low
economic returns? Further advances in the conceptual modeling of well-being and forest
dependence will assist in this line of inquiry.
One of the main findings of this dissertation is that expected mean wages are
typically lower in the forest-dependent areas. Also, the wage distribution is less dispersed
78
in the forest-dependent areas than in other areas. The empirical approach in this study
controls the skill composition of people across areas, and therefore differences in
interregional wage distributions are not due to differences in the skill composition of
people residing in these areas. Other factors10 prevailing in the forest-dependent areas,
such as lack the agglomeration economies and nature of jobs may explain this
interregional wage difference.
The interregional wage differences in forest and non forest-dependent areas may
influence individuals‘ migration decision and residential location choice. Neoclassical
economic theory predicts that workers are responsive to spatial wage differences and they
move to areas where wage levels are relatively high (Smith, 1975). In chapter 4,
educational attainment was found to be a significant determinant of the migration
decision, and higher educational attainment predicted a higher likelihood of outmigration
to areas with higher monetary returns. Well-educated workers may have improved access
to information about employment opportunities outside their local labor market, and may
be more efficient at researching new job opportunities (Borjas, 2000). Highly educated
workers may also have greater incentives to move because the size of a worker‘s relevant
labor market increases with education level, and chances of finding a job and improved
matches are higher compared to lower educated workers (Costa and Kahn, 2000).
It is unlikely that the population in any area is completely mobile, because of the
costs associated with migration. In the economics literature on migration (Sjaastad,
1962), a large component of migration costs is psychological costs - the personal costs of
10
Discussed in details in section 3.6, page 43
79
leaving family and friends and familiar surroundings. Such personal connections increase
the mobility cost, and may explain why some people choose to accept lower wages and
reside in rural, forest-dependent areas. The Northwest sample for 2000 has 235987
working-age residents, of which 67623 individuals (that is, about 29%) live in the forestdependent areas. This indicates that a fairly large share of individuals chose to live in
forest-dependent areas, even though these areas may not be the most rewarding in terms
of labor market returns. Hummon (1992) reported that irrespective of the social and
economic characteristics of the community, residents of smaller, more rural, places
express greater satisfaction with their communities than residents of more densely
populated areas. Factors other than economic incentives, such as how deeply a person is
rooted to his/her community, may influence migration decisions (Loveridge et al., 2009).
Other studies have also found attachment to place to be associated with migration
decisions (Elder, 1996; Herting, 1997; Goudy, 1990). People may build up social and
place based ties over time, and become psychologically rooted to a community. Social
ties such as presence of friends and family in their neighborhood is often highly valued
by individuals (Brehm et al., 2004).
A limitation of this study is that it is based on cross-sectional data (U.S. 2000
census data). Determinants and effects of migration may differ in important ways with
time, and a time-series analysis11 would be ideal for studying such relationships. Future
research using a data set that includes information on the characteristics of explanatory
variables across years can better assess the patterns observed in the current study.
11
A recent study (Bishop, 2008) of dynamic location decisions of individuals used panel data from the
National Longitudinal Survey of Youth (NLSY79).
80
There are two additional issues that could also be investigated in future research.
The first concerns the relationship between migration and poverty. Higher expected mean
wages in non forest-dependent areas might attract workers to move out of forestdependent rural areas, but this does not mean that the migration decision will make the
workers better-off. In fact, Fitchen's (1995) work on short-term rural movers shows that
migration from rural to urban areas substantially increased the probability of being poor.
Fitchen finds that many rural poor people move to urban areas not because of better job
opportunities, but rather due to the loss of a job or other major changes in their lives.
Many rural-to-urban migrants struggle to find employment, housing and stability in new
locations, and experience losses in the process. Additional research on what happens
after the migration decision has been made will bring new perspective to the migration
problem. Also, research on long-term effects of migration may help to investigate
whether migration decision is beneficial or not over a broad time horizon. Analysis of
long-term effects of migration would provide a better understanding of the lifetime
tradeoffs between residing in forest and non forest-dependent areas.
81
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