FARM INVESTMENT AND OFFFARM INCOME
A STUDY OF FARMS IN ALABAMA
Except where reference is made to the work of others, the work described in this thesis is
my own or was done in collaboration with my advisory committee. This thesis does not
include proprietary or classified information.
____________________________________________
Mai Le Phuong Chi
Certificate of Approval:
_____________________________
Greg Traxler
Professor
Agricultural Economics and
Rural Sociology
_____________________________
Valentina M. Hartarska, Chair
Assistant Professor
Agricultural Economics and
Rural Sociology
_____________________________
Patricia A. Duffy
Professor
Agricultural Economics and
Rural Sociology
_____________________________
Joe F. Pittman
Interim Dean
Graduate School
FARM INVESTMENT AND OFFFARM INCOME
A STUDY OF FARMS IN ALABAMA
Mai Le Phuong Chi
A Thesis
Submitted to
the Graduate Faculty of
Auburn University
in Partial Fulfillment of the
Requirements for the
Degree of
Master of Science
Auburn, Alabama
December 15, 2006
iii
FARM INVESTMENT AND OFFFARM INCOME
A STUDY OF FARMS IN ALABAMA
Mai Le Phuong Chi
Permission is granted to Auburn University to make copies of this thesis at its discretion,
upon the request of individuals or institutions and at their expense. The author reserves
all publication rights.
______________________________
Signature of Author
______________________________
Date of Graduation
iv
VITA
Mai Le Phuong Chi graduated from Le Hong Phong High School in 1995. She
attended The University of Economics in Ho Chi Minh City the same year, and graduated
with a Bachelor of Art degree in Foreign Trade in 2000. She worked for Fresco
Cooperative as a Marketing manager and Lakewood Group Ltd. as a Country
representative from 1999 to 2004. She entered Graduate School, Auburn University, in
January 2005 where she held a graduate research assistantship.
v
THESIS ABSTRACT
FARM INVESTMENT AND OFFFARM INCOME
A STUDY OF FARMS IN ALABAMA
Mai Le Phuong Chi
Master of Science, December 15, 2006
(B.A., University of Economics, Vietnam, 2000)
63 Typed Pages
Directed by Valentina Hartarska
This thesis examines the effect of availability of internal finance, net farm
income, and net offfarm household income on farm investment. It presents a detailed
review of previous studies, develops and estimates an empirical model of farm
investment using annual data from the Alabama Farm Analysis Database. The results
show that the effect of internal finance on farm investment is positive and statistically
significant for the whole sample. Net farm income also has a positive and significant
effect on farm investment. Moreover, the results indicate that the more income farm
households earned from offfarm business, the more likely they were to invest it in the
farm business. Finally, farm investment of financially constrained farms was more
vi
sensitive to the availability of internal finance than that of financially unconstrained
farms. Internal finance has a significantly stronger effect on investment among smaller
farms than among larger farms.
vii
ACKNOWLEDGEMENTS
I wish to express my sincerest appreciation to my major professor, Dr. Valentina
Hartarska for her guidance, counsel, and patience during my graduate study. My
appreciation is extended to Dr. Greg Traxler and Dr. Patricia Duffy for serving on my
advisory committee. My acknowledgement is also expressed to the Department of
Agricultural Economics and Rural Sociology for the opportunity to pursue a graduate
study and to receive a graduate research assistantship.
viii
Style manual or journal used: The American Economic Review
Computer Software used: STATA 9.1
Microsoft Excel 2003
Microsoft Word 2003
ix
TABLE OF CONTENTS
I. INTRODUCTION........................................................................................................... 1
II. MOTIVATION AND RELATED LITERATURE.........................................................3
III. EMPERICAL MODEL AND THE FINAL SPECIFICATION..................................16
IV. DATA DESCRIPTION AND SUMMARY STATISTICS ?????.????...20
V. RESULTS ....................................................................................................................29
VI.CONCLUSIONS AND SUGGESTIONS FOR FUTURE ????????..?... 41
REFERENCES ????????????????????????..???...43
APPENDIX?????????????????????..????????. 47
x
LIST OF TABLES AND FIGURES
Table 1: Summary statistics of some financial indicators??????????..?...21
Table 2: Key Statistics of Variables used in Regressions Analysis????????...25
Table 3: OLS Regression analysis of fixed investment?????????????.30
Table 4: Results of regression analysis of the two subsamples??????????36
Figure 1: Graph of average net farm income and net nonfarm income of the sample?.22
Figure 2: Graph of average net nonfarm income and wage of the sample?????...23
Figure 3: Graph of average farm?s total assets, net worth and farm investment???...24
1
CHAPTER I: INTRODUCTION
The future of an industry is determined by the level of investment of individual firms. In
particular, fixed investment is important and has attracted much attention. A common
methodology in the research on fixed investment is to analyze firm investment behavior
focusing on the availability of investment capital and by incorporating financial
constraints considerations (e.g. Fazzari et al., 1988). Some empirical studies on fixed
investment focus on the impact of capital market imperfections and document
heterogeneity in the investment behavior between firms that financial constrained and
firms that are financially less constrained (e.g. Fazzari et al., 1988; Whited, 1992). The
others emphasize the impact of uncertainty on investment and analyze different channels
through which uncertainty may affect investment (e.g. Leahy and Whited, 1996; Guiso
and Parigi, 1999). The main conclusion is that investment is sensitive to availability of
internal finance, and that it is affected by imperfections in the capital market.
Many studies have discussed macro and microeconomic views of investment in
different countries and firms but there is a lack of analysis focused on farms. Using
annual data from 1997 to 2004 of about 150 farms in Alabama, this paper aims to
examine the effect of internal finance availability, net farm income, and net non farm
income of a farm household on farm investment.
2
The rest of the paper is organized as follows. The next section of the paper briefly
surveys the empirical issues raised in the investment literature, the main factors affecting
farm investment, and the linkages between internal finance and investment. Chapter III
presents the empirical specification of the investment equations and discusses
methodological issues. Chapter IV describes the data used. Chapter V is the core of the
paper, where empirical results are presented both for the full sample and for groups of
farms partitioned according to farm size. The conclusions of the paper with remarks on
possible future research are summarized in the final chapter VI. Some additional
empirical tests are presented in data appendix.
3
CHAPTER II: MOTIVATION AND RELATED LITERATURE
Since farms are very different from firms, reviewing the factors that affect farm
investment is a key step towards understanding and explaining the model of farm
investment specified and estimated in this thesis. This section reviews the research on
issues that may affect farm investment and, thus, were used in the development of the
empirical model.
A. FARM HOUSEHOLD INCOME
According to the report of the U.S. Department of Agriculture (USDA), most farms in the
United States (98 percent in 2003) are family farms. They are organized as
proprietorships, partnerships, or family corporations. Early in the 20th century, farmers
and their families did little offfarm work because the cost of such participation was
prohibitive (Mishra et al., 2002). Most farm families relied on farming as their primary
and usually sole source of income.
Agriculture in the United States changed dramatically during the 20th century.
Today, it is rare for any household to receive all of its income from a single source, hold
all its wealth in the form of a single asset, or use all its assets in just one activity. Multiple
motives prompt households and individuals to diversify assets, incomes and activities. It
is necessary to understand the components of income and distinguish between alternative
4
income sources in order to appreciate farm household differences, monitor the sensitivity
of farm household income to economic events and evaluate the effectiveness of farm
policy in supporting farm investment.
1. Sources of Farm Household Income
Farm household income originates from both farm and offfarm sources and includes
farm and offfarm income. Net farm income includes farm rental income, net income
from cash sales (livestock, crops, machinery, building, and equipment), inventory change,
home consumption (livestock, crops), government payment, and returns from farm
machinery custom work. Offfarm income includes income from offfarm businesses
(such as a machinery repair shop, seed agency, or insurance agency), labor earnings from
farm custom work, wages and salaries (farm operator, spouse, and other family member),
pensions, social security, nonfarm business income, royalties, interest (income from
interest includes the interest income from savings and investment accounts, bonds,
treasury bills), dividends (dividends earned by the household are from investment in
equities, such as stocks or mutual funds), and rental income from nonfarm properties.
Offfarm income and nonfarm business opportunities have become increasingly
important in many agricultural areas in recent years. In fact, nonfarm income sources
have dominated net farm income in the USA for many years (income from farming in the
USA, measured by net farm cash income, was $ 55.7 billion in 1999, as compared to 124
billion (USDA, 2001). Mishra et al. (2000) find that when all farms are considered, 92%
of total household income came from nonfarm sources. However, these figures depend
on the definition of a farm. For example, in large and very large commercial farms, the
5
share of farm income in total household income ranges from 50 to 75%. In their survey,
these authors find that more than half of all U.S. farm operators work offfarm, with 80
percent of these working fulltime jobs. Nearly half of all spouses were also employed off
the farm. Offfarm work is no longer viewed as a transitional position between the
agricultural and the industrial economy, but as a lifestyle choice, with farming as a
second job or investment. Their results also show that farm household income is
relatively stable. Fluctuation in farm output, commodity prices, and business cycles,
along with macroeconomic policies all contribute to the variability in farm income. Since
these factors are beyond any farmer?s control, many farm households have relied
successfully on offfarm income to stabilize their total household income.
In another study, Mishra et al. (2002) confirmed their previous findings that the
farm business as a source of income is playing an increasingly smaller role in
determining the wellbeing of farm households with nearly 90 percent of total farm
household income in the U.S in 1999 originated from offfarm sources. This study finds
that the contribution of earned income (offfarm) alone amounted to 53 percent of total
farm household income. The study also concluded that even for farms located in rural
areas, offfarm income is still the dominant source of household earnings. Income and
wealth of farm households based on the location of the farm follow a similar pattern:
those households in or near a metro area tend to be significantly better off than nonmetro
households. Farm households in metro areas depend heavily on offfarm income (95
percent of total income). Through their offfarm work, these households can invest in
both farm and nonfarm assets. These facts are evidence of how important offfarm
income has become to the majority of farm households in this country.
6
Studies carried out in many other countries also confirm that a fairly large share
of household income was earned offfarm (Bryceson and Jamal, 1997), recognize the
importance of offfarm income to the welfare of rural households (Rosenzweig 1988),
and find a strong positive relation between nonfarm income share and total household
income and, therefore, an even more pronounced relationship between the level of non
farm income and total income (Reardon, 1997). A study by Castagnini, Menon, and
Perali (2004) suggests that the economic situation and standard of living of farm
households cannot be adequately described by onfarm income alone. Ahearn and Lee
(1991) suggest that to reduce income risks and raise total income, farm families have
turned to offfarm work to supplement farm household income. Hazell et al. (1991), find
that offfarm income was somehow positively correlated with farm income. Field surveys
across many developing countries performed by Jacoby (1993), Newman and Gertler
(1994) show that between one third and one half of farm households derive income from
offfarm sources.
It has become widely accepted in both academic and policy researches that rural
offfarm activities make up a significant component of rural livelihoods. The hypothesis
explored here is that rural offfarm income is important for agricultural development as it
may help households to overcome cash constraints when making farm investments. This
view, if accurate, would be very important for the future of the agricultural sector and
especially for small producers.
Farm and offfarm employment and their contribution to farm household income
have also attracted attention. Many farm households are dualcareer, holding offfarm
jobs as well as farming (Hoppe, 2001). This is most obvious on residential/lifestyle
7
farms, but is also true to a lesser extent on large and very large farms. According to the
USDA, about 44 percent of all farm households were dualcareer in 2003, with a spouse
working off the farm and the principal operator engaged in farming (with or without off
farm work). Mishra and Goodwin (1997) find a positive correlation between offfarm
employment and farm income variability, as farm income variability increases, farm
families seek offfarm employment (as a source of income) to reduce the variance in their
household income. ElOsta et al. (1995) find that the distribution of income among farm
households with no offfarm employment to be more concentrated than the distribution of
income among farm households with offfarm employment. Schultz (1999) notes that off
farm employment was an important means by which farm households can manage risk
through diversification of income sources. Mishra and Goodwin (2002) confirm the
important role of offfarm employment as an avenue for managing the financial risks
faced by farmers.
A number of studies have also considered various demographic factors relevant to
participation in offfarm labor markets, including age, household size, experience, and
the presence of small children in the household.
2. Uses of Farm Household Income
Mishra et al. (2002) find that even though the living standards of farm families have
become comparable to those of nonfarm families, farm households appear to manage
expenditures differently from nonfarm households in several ways. Farm households
spend the majority of their income on food and household supplies, followed by
household rent/mortgage and other household expenditures such as clothing, education,
8
recreation, hobbies, and charitable contributions. The study also concluded that
consumption expenditures of farm households are lower than for all U.S. households.
Farm household expenditures appear to be lower than nonfarm household expenditures,
even when the analysis controlled for differences in income, age, location, and size of
farm. According to the USDA, mean (or average) farmoperator household income in
2003 was $68,500 or 16 percent greater than the mean for all U.S. households.
Considering that the mean income may not be the best choice for comparison because a
few veryhighincome households can raise the mean well above the income earned by
most households, authors also used the medians rather than means, and reported that
median farmoperator household income in 2003 was $47,620 or 10 percent greater than
the median for all U.S. households. Since almost half of farm households have both
higher incomes and greater wealth than U.S. households as a whole but spend less on
household consumption, it is reasonable to suppose that perhaps part of the income goes
to support the farming business.
Only two types of households, those operating limitedresource or retirement
farms, received median household income below the U.S. median. Associated with the
considerable rise in total farm household income in recent years have been a rise in
expenditures (on goods and services) and a rise in savings and/or investments. Income
not used for consumption is available for savings and other investment opportunities both
on and off the farm. Savings can be used to finance unexpected future needs in
agriculture, retirement income, or unexpected health expenditures. Mishra and Morehart
(1998) investigate factors affecting farm household savings, especially the important role
of farm income uncertainty. They find that the marginal propensity to save (MPS) for
9
farm households was 0.81, while average propensity to save (APS) for their sample of
U.S. farm households was 0.45. An MPS higher than an APS ensures a high degree of
responsiveness of savings to disposable income changes.
B. FARM INVESTMENT
Farm investment is financed with profits, household savings as well as reinvestment of
capital gains and dividend from capital market investments. According to Mishra et al.
(2002), investment by individual farmers/farm households will have important
implications for their financial wellbeing, the availability of venture capital for economic
development of rural areas, and the competitiveness of financial institutions in rural
areas. The authors suggest farmers/managers need to carefully consider their investment
(both farm and offfarm) portfolios because many of their financial decisions have
ramifications for liquidity, retirement, solvency, taxation, and profitability management.
There has been limited research focusing on factors affecting investment in farm assets or
type of farm investments. LaDue, Miller, and Kwiatkowski (1991), in a survey of New
York producers, find that gross income and age had a positive and negative significant
effect, respectively, on farm reinvestments. Mishra and Morehart (2000) compare the
savings and offfarm investment behavior of farm households with the behavior of non
farm households; the result is that farm households have a higher savings rate. They
maintain a diverse offfarm investment portfolio, and contribute to various retirement and
taxdeferred plans.
Nonfarm assets and investment also affect the farm business. Crisostomo and
Featherstone (1990) suggest that adding highrisk financial assets with expected higher
10
returns can reduce the overall risk associated with farm investment. Schnitkey and Lee
(1996) contend that stocks and bonds reduce the variability in farmland returns more
effectively than lower return Treasury bills, and that a riskefficient portfolio should not
have more than 50 percent of its value invested in farmland. In another study, Gustafson
and Chama identified the types of financial assets held by North Dakota farmers. They
found that most respondents invested in liquid, lowrisk financial assets such as savings
and checking accounts and certificates of deposit. In addition, approximately 31% of
producers held investments in mutual funds, common stocks and bonds. Lanjouw (2001)
argue that rural offfarm income may have the potential to assist in raising households?
farm investment. This suggestion will be further explored in the thesis.
C. INVESTMENT AND FINANCIAL CONSTRAINTS
The pioneering work of Fazzari, Hubbard, and Petersen (1988) examines the importance
of financing constraints. The authors separate a sample of US firms into subsamples
based on the dividend payout behavior. Dividends are assumed to relate to financial
constraints. The hypothesis is that lower dividends indicate higher financing constraints.
The results show larger impact of cash flow on investment for firms with low dividends,
which confirms the hypothesis. Other studies have replicated and extended this approach.
However, the success of this approach depends critically on the interpretation of cash
flow coefficients, which have been the main focus of many studies. One of the most well
known problems is that cash flow may imply investment opportunity, so the estimated
effects may arise from expectation factors, rather than reflecting liquidity effects. To
mitigate the problem it is necessary to use forwardlooking variables in the closed form
11
investment equations, but since variables (for example the expected value of future cash
flows) are not available in practice, they have been approximated by changes in sales,
stock prices, and Tobin's Q (for capital investment).
Devereux and Schiantarelli (1990) examine a set of UK firms to see whether
different cash flow investment sensitivities are found in subsamples based on proxies for
agency costs of external capital. The proxies are firm size (capital stock and employees),
the number of years since initial quotation, and the industry (growing or declining). The
investments of large firms, newly listed firms and firms in growth sectors exhibit higher
cash flow sensitivities.
Oliner and Rudebusch (1992) interact the cash flow coefficient in an investment
regression model with proxies for information asymmetry (firm age, listing at exchange,
and stock trades by insiders), agency costs (insider shareholdings and ownership
concentration) and transaction costs (firm size). They also include the dividend yield for
comparison with Fazzari, Hubbard and Petersen (1988). Although the individual
interaction terms are insignificant for the set of US firms, a compound measure of
information asymmetry is significant and yields the predicted positive effect. The authors
conclude that information problems worsen financial constraints. Chirinko and Schaller
(1995) examine Canadian firms, and define subsamples based on age (years of inclusion
in a financial database), concentration of ownership, industry (manufacturing and others),
and group or independent. The results show that the cash flow constraints are most
relevant for young firms, firms with dispersed ownership, independent firms and
manufacturers.
12
Kadapakkam, Kumar and Riddick (1998) study six OECD (Organization for
Economic Cooperation and Development, an organization of industrial countries that
encourages trade and economic growth) countries including France, Germany, US, UK,
Canada and Japan. They define subsamples based on firm size. The results show that the
cash flow investment sensitivity is highest in the sample of large firms. This difference is
most obvious and strongly expressed in the US and UK. For France, Germany and
Canada, the results also show significant differences between the subsamples in most
analyses. For Japan, the difference is insignificant in several analyses. However, in their
study, firm size is only one criterion that may be important in explaining cash flow
investment sensitivity. Gugler (1998) analyzes Austrian investment spending and
corporate governance. He empirically investigates whether the validity of the asymmetric
information problem and managerial discretion problem depends on the ownership
structure of the firms. His findings suggest that investment of bankcontrolled firms is not
positively related to cash flow. Asymmetric information problems prevail in family
owned firms, while overinvestment is more prominent in statecontrolled firms and
pyramidal groups. Haid and Weigand (1998) focus on investment spending and corporate
governance in Germany. Using sample splits, they report that liquidity positively affects
investments in ownercontrolled firms, while management controlled firms show no cash
flow investment dependency.
Van Ees and Garretsen (1994) study a sample of Dutch firms over the period
19841990. The authors define subsamples based on the dividend payout ratio, the year
of the initial public listing, size (fixed assets) and interlocking directorates with banks.
They find that the cash flow investment sensitivity is significantly positive in Dutch
13
firms. Interlocks with banks are found to reduce the cash flow constraints. Firms with ties
to banks have a significantly lower impact of cash flow on investment. Van Ees and
Garretsen (1994) conclude that bank relations reduce the asymmetric information
problem in Dutch firms.
Carpenter, Fazzari and Peterson (1995) estimate withinfirm regressions for a
standard inventory stock adjustment model augmented with financial variables on
quarterly firmlevel panel data. They find strong support for the existence of financing
constraints due to adverse selection and moral hazard problems in debt and equity
markets generated as a result of asymmetric information between firms and potential
suppliers of external finance. They predict that investment depends primarily of internal
funds because of limited availability of debt.
D. CAPITAL MARKET IMPERFECTION
In the absence of capital market imperfections, finance and investment decisions can be
separated completely. This implies among other things that external and internal funds
are interchangeable for all purposes or that any particular type of investment can be
financed by every financial source. Since Fazzari, Hubbard and Petersen (1988), there
has been a substantial empirical literature showing a significantly positive influence of
cashflow on firms? investment spending. This so called ?investment cash flow
sensitivity? has been explained by financial constraints. Firms simply cannot invest
whenever profitable opportunity arise. When markets are imperfect some firms do not
have access to external funds. Types and levels of investment spending can only be
realized by internally generated cash flows. Hence there is a wedge between the price of
internal and external finance. The literature has explained these financial constraints by
14
pointing to capital market imperfections. In capital markets without imperfections, no
systematic relationship is predicted between cash flow availability and investment
expenditures. Investments should take place whenever they are expected to realize a
positive net present value and should not necessarily be linked to cash flow.
Financing constraints due to asymmetric information problems in the issuance of
equity cause the cash flow investment dependence. Stiglitz and Weiss (1981), and
Greenwald, Stiglitz and Weiss (1984) obtain similar results for debt. Myers and Majluf
(1984) argue that asymmetric information can cause firms being rationed in the issuance
of equity. A number of empirical studies test for asymmetric information problems.
Building on Fazzari, Hubbard and Petersen (1988), these studies apply a sample split
based on a priori criterion of asymmetric information. The results show that the impact of
cash flow on investment is larger for firms with higher information asymmetries (Oliner
and Rudebusch (1992), Schaller (1993), Gilchrist and Himmelberg (1995) and
Kadapakkam, Kumar and Riddick (1998)). Asymmetric information in debt financing
may increase the cost of new debt or restrict firms from borrowing due to credit rationing
(Stiglitz and Weiss, 1984). The reason is that lenders do not know how the money they
lend is being invested. For instance, increasing the interest rate may induce firms with
valuable projects to drop out (adverse selection). Thus, asymmetric information may
hinder firms with growth opportunities. Firms then only invest when internally generated
funds are available stemming from equilibrium credit rationing by providers of external
funds. This results in a positive dependence between cash flow and investment. There is a
large theoretical literature on capital market imperfections which argues that external
15
funds (debt and new equity finance) are a more costly substitute for internally generated
funds (cash flow), and hence firms face a ?hierarchy? of finance (Myers (1984)).
CHAPTER III: EMPERICAL MODEL AND THE FINAL SPECIFICATION
1. Conceptual model:
The general statement of the reducedform investment equations that have been applied
in previous studies is:
titititi
uKCFgKXfKI
,,,,
)/()/()/( ++=
where I is the investment in fixed assets for firm i at time t ; X represents a vector of
variables that have been identified as determinant of investment from a variety of
theoretical perspectives; u is the error term and u is assumed to be normally distributed.
The function g(.) depends on the firm?s internal funds or cash flow; it represents the
?sensitivity? of investment to available internal finance, after investment opportunities
are controlled for through the variables in X. All variables are divided by the beginning
ofperiod capital stock K.
Cash flow is defined in the literature as current revenues minus expenses and
taxes, and is used as the proxy of changes in net worth. The most appropriate measure for
investment opportunity (IO) is the expectation of the present value of future profits from
additional capital investment. In the neoclassical theory of the choice of capital stock, this
expectation is measured by marginal q, the shadow value to the firm of an additional unit
of physical capital (Hubbard, 1998).
16
The farm investment conceptual model follows the existing literature is:
Farm Investment= F (Change in Sales, Net farm income, Net nonfarm income, ) (1)
1
Z
where change in sales proxies for investment opportunity. Z
1
is the vector of variables
that may influence farm investment. Unlike in previous studies, farm income is divided
into net farm income and net non farm income to account for the fact that family farms
receive income form sources other than the farms. This model allows testing the main
hypothesis that farms may use offfarm sources to fund their farm investment. Given the
literature suggest that farming families spend less on consumption but are richer than the
average household, it is important to find out if the offfarm income is being used for
farm investment.
2. Empirical model:
The empirical model is constructed as follows: Total farm investment in period t ( ) is
modeled as a function of the change in sales (
ti
I
,
ti
Sales
,
? ), current net farm income
( ), lagged net farm income ( ), current net nonfarm income ( ),
lagged net nonfarm income ( ), return on farm assets (AVGROA, STDROA),
farm size (TA, TA2), solvency measure (SOLVENCY), dummy year (D97D04), dummy
industry (D30D100) and
ti
NFI
, 1, ?ti
NFI
ti
NNFI
,
1, ?ti
NNFI
ti,
? is random error term,
ti,
? is normally distribution with zero
mean and a constant variance.
17
For farm i at time t (measured in years): (2)
ti
titititi
titititititiiti
DINDUSTRY
DYEARSOLVENCYTATASTDROA
AVGROANNFINNFINFINFISalesI
,2719
1811,10
2
,9,8,7
,61,5,41,3,2,1,
)(
??
?????
???????
++
+++++
+++++?+=
?
?
??
(2)
where i indicates farm i and t indicates time.
Because sales and internal finance (net farm income, net nonfarm income) may be highly
collinear, the variable change in sales (
ti
Sales
,
? ) is used as the proxy of investment
opportunity. The net farm income and net nonfarm income terms in equation (2) are the
main focus of this study. The first variable (
ti
Sales
,
? ) and the rest of the variables are
selected based on what the literature suggest may also influence farm investment.
Equation (2) allows testing the importance of internal finance after controlling for the
accelerator (sales) and other possibly important controls. Given that this equation is
specified in levels and there are large differences between the farms in terms of size, all
the main variables used are scaled by the farm total assets to control for
heteroskedasticity.
The aim of estimating this model is to see whether the internal finance of a farm
has an effect on farm investment in general and the particular interest is the role of off
farm income as a source of funds used for onfarm investment. Another goal of this
analysis is to see whether there is difference in the investment of small and large farms.
In particular, it is important to find out if only small farms (with less than $250,000 in
sales as defined by USDA) use their offfarm income to invest in farming or if this is also
true for large commercial farms. For that purpose, equation (2) is estimated for two sub
18
19
samples ? small farms (farms with sales less than $250,000) and large farms with annual
sales more than $250,000.
Detailed description of the data and definitions of the variables used in the
analysis are presented in the following section, while the section on empirical results
describes estimation procedures and the tests performed to identify the best empirical
model.
20
CHAPTER IV: DATA
Data come from Alabama Farm Analysis Database. The database contains 8 consecutive
years of data. The observations which have missing values on the key variables used in
the regressions were deleted. The panel is unbalanced, consists of 1060 observations and
covers the period 19972004. The CPI (consumer price index) is used to convert the data
into constant 2004 dollars. Since farms in the sample of Alabama Farm Analysis
Database are likely to be different than the average farm in Alabama, this section begins
with a comparison of the characteristics of the sample with the characteristics of the
average farm in Alabama and proceeds to describe the variables used in the empirical
model.
A. A COMPARISON OF FARMS FROM THE ALABAMA FARM ANALYSIS
DATABASE AND THE AVERGAE ALABAMA FARM
Table 1: Summary statistics of some financial indicators for farms in the sample and
Alabama?s farms:
21
Year
Number
of farms
in sample
Number
of farms
in
Alabama
Net farm
income
(sample)
Net farm
income
(Alabama)
Sales
(sample)
Sales
(Alabama)
1997 118 49,000 64,022 22,052 309,117 65,671
1998 113 49,000 26,501 24,064 303,772 67,229
1999 121 48,000 63,689 29,449 262,418 70,875
2000 127 47,000 33,952 24,740 245,038 67,752
2001 135 46,000 39,399 36,061 234,461 75,175
2002 148 45,000 5,081 26,086 215,861 64,892
2003 148 45,000 84,093 35,748 230,250 78,766
2004 158 44,000 36,127 46,794 237,686 92,591
Year Total
assets
(sample)
Total
assets
(Alabama)
Debt/Assets
(sample)
percent
Debt/assets
(Alabama)
percent
ROA
(sample)
percent
ROA
(Alabama)
percent
1997 1,241,859 294,200 31 11.5 8.77 7.31
1998 1,253,471 303,224 28.1 12.1 7.53 9.40
1999 1,034,383 328,613 29.8 12.1 8.82 12.02
2000 1,197,407 351,516 29.3 12.4 7.92 7.40
2001 1,145,739 373,926 31.6 12.6 9.77 7.91
2002 991,610 398,206 37.9 12.8 7.18 5.42
2003 1,129,495 420,388 32.3 12.5 9.49 8.95
2004 1,245,321 N/A 35.1 N/A 10.34 N/A
Source: National Agricultural Statistics Service (NASS); Economic Research Service/USDA;
Alabama Farm Analysis Database.
The number of farms in the sample is small compared to the large number of
farms in the state of Alabama. With about 130 observations for each year during the
period 19972004, the farms analysis account for only 0.3% of the total number of farms
in state of Alabama. Compared to the average total assets of about $300400,000 for the
average farm in Alabama, the average farm in the sample is larger, with average total
assets of $1.1 million. The sales volume of farms in the sample is about 45 times bigger
than the average volume of sales of farms in Alabama, suggesting that the farms in the
sample depend more on agricultural activity than do farms not included in the analysis.
Net farm income for Alabama?s farms has increased gradually during the period without
big fluctuation compared to a lot of fluctuations in this variable in the Alabama Farm
Analysis Database. Farms in the sample are also much more leveraged than the average
farm in the state  the ratio of farm?s total debt to total assets from farm analysis is much
higher than Alabama?s farms as a whole. The proportions of total farm liabilities to total
farm assets of farms from the sample is more than 30% compared to 12% for Alabama?s
farms. This means farms in the sample use greater external finance source to invest in
farms and for those farms which do not have access to external funds, then their
investment may be dependent on internally available cash flows. The rate of return on
asset is almost the same for farm analysis and for Alabama?s farms as a whole.
B. OVERVIEW OF THE FARMS IN THE SAMPLE:
Figure 1: Graph of average net farm income and net nonfarm income of the sample
during the examined period:
10
0
10
20
30
40
50
60
70
80
90
1997 1998 1999 2000 2001 2002 2003 2004
Tho
us
a
nds
Net farm income
Net nonfarm income
Source: Alabama Farm Analysis Database
22
The average net farm income of a farm household shows significant fluctuation during
the study period from 1997 to 2004. The average net farm income was at a very high
level in 1997 with the value of $64,022. In 1998, it fell to the average value of $26,501.
In 2002, the average net farm income even has negative value of $ 5,081. The average net
farm income of a farm in the sample peaked at $84,093 in 2003.
Offfarm income plays an important role in the total income of farm households
in the sample. It accounts for a large percentage in total household income. During this
time, the average offfarm income of a farm household has fluctuated around $29,000. In
2002, average offfarm income in the sample has increased to the peak of $ 33,735. In the
components of offfarm income of farm in the analysis, wages account for the largest
percentage and the average wage has fluctuated around $12,000. Other nonfarm income
also accounts for a significant portion of total offfarm income; it has increased over time
from 1997 to 2004 and has reached the highest point of $15,236.
Figure 2: Graph of average net non farm income and wage of the sample during the
examined period:
0
5
10
15
20
25
30
35
40
1997 1998 1999 2000 2001 2002 2003 2004
Th
ou
s
a
nd
s
Wage
Net nonfarm income
23
Source: Alabama Farm Analysis Database.
Figure 3: Graph of average farm household?s total assets, net worth and farm investment
of the sample during the examined period:
200
0
200
400
600
800
1000
1200
1400
1997 1998 1999 2000 2001 2002 2003 2004
Th
ou
s
a
n
ds
Total Assets
Net worth
Farm Investment
Source: Alabama Farm Analysis Database.
For farms in the sample, liquid assets of the average farm households consist
primarily of checking accounts and savings deposits. Data show that total assets of an
average farm household in the sample have fluctuated around $ 1.1 million in the period
of 1997 2004. The average level of investment is rather small compared to the level of
farm?s total assets.
C. SUMMARY STATISTICS AND DEFINITION OF THE VARAIBALE USED IN
THE REGRESSION ANALYSIS
24
Table 2: Key Statistics of Variables used in Regressions Analysis
Variable Mean Standard
Deviation
Minimum Maximum
Total Assets 1,164,783 1,170,645 1,204.5 8,638,882
Sales 251,263.5 385,208.9 0 4,201,605
Net farm income 42,791.37 132,823.8 893,662 894,094.4
Net nonfarm
income
28,127.91 56,544.73 470,249 470,000
Investment 42,698.9 398,383.1 4,370,748 4,151,636
Investment(t)/ Total
Assets(t1)
.067 .415 .976 6.347
? Sales(t)/ Total
Assets(t1)
.018 1.402 14.037 11.336
Net Farm Income(t)/
Total Assets(t1)
.167 1.504 1.037 19.781
Net nonfarm
Income(t)/ Total
Assets(t1)
.058 .642 .477 18.272
solvency .321 .365 .0006 7.022
Definition of variables used in the regressions:
Dependent Variable:
Investment (I): investment on farm?s fixed asset and intermediate asset,
including investment on farm real estate bare land and building, machinery and
equipment, and breeding livestock. It is defined as the change in farm?s
investment capital in fixed assets and intermediate assets: ; with K
is investment capital on farm?s fixed asset and intermediate asset. The change is
calculated by subtracting last period capital from capital in the current period.
1?
?=
ttt
KKI
25
Independent Variables:
Change in Sales ( ): Total sales is defined as the sum of total crop sales,
market livestock sales, breeding livestock sales and the value of consumed
livestock products, i.e., milk and eggs. The change is calculated by subtracting
last period sales from sales in the current period.
Sales?
Net Farm Income (NFI): comes directly off of the accrual income statement and
is calculated by matching farm revenues with the expenses incurred to create
those revenues, plus the gain or loss on the sale of farm capital assets.
Lagged Net Farm Income ( ): Net farm income of the previous year.
1, ?ti
NFI
Net Nonfarm income ( ): comes directly off of the income statement and
is defined as sum of net sources of income from all nonfarm businesses.
ti
NNFI
,
Lagged Net Nonfarm income ( ): net nonfarm income of the previous
year.
1, ?ti
NNFI
Solvency measures variable: measures the amount of borrowed capital used by
the farm relative the amount of farm?s equity capital invested in farming business.
Solvency used in this paper to see whether farm?s ability to withstand risks will
affect investment in farm. Solvency is concerned with longterm as well as short
term assets and liabilities. It is defined by the ratio of farm?s total debt to total
assets. This ratio expresses total farm liabilities as a proportion of total farm
assets, the higher the ratio, the greater the risk exposure of the farm.
26
27
Rate of return on farm assets (ROA): A measure of farm profitability. The ROA
measures the return to all farm assets and is used as a proxy of farm profitability,
the higher the value of ROA, the more profitable the farm business.
Dummy Industry (DINDUSTRY): Data used the SPR (Soil Productivity Rating)
to separate farms. Dummy variables for farm sectors are set up based on the code
used in the data.
Variables Definition of Variables
D30 Whether farm produces Cotton, 1 yes, 0 no
D35 Whether farm produces Cotton and Peanuts, 1 yes, 0 no
D40 Whether farm produces Peanuts, 1 yes, 0 no
D50 Whether farm is Contract Broilers, 1 yes, 0 no
D60 Whether farm produces CowCalf, 1 yes, 0 no
D70 Whether farm produces Catfish, 1 yes, 0 no
D90 Whether farm produces Dairy, 1 yes, 0 no
D99 Whether farm produces Feeding Livestock, 1 yes, 0 no
D100 Whether farm produces Corn and Soybeans, 1 yes, 0 no
Dummy Year (DYEAR): Dummy variables for year are set up for every year;
cover the examined period 19972004.
Variables Definition of Variables
D97 Whether year is 1997, 1 yes, 0 no
D98 Whether year is 1998, 1 yes, 0 no
D99 Whether year is 1999, 1 yes, 0 no
D00 Whether year is 2000, 1 yes, 0 no
D01 Whether year is 2001, 1 yes, 0 no
D02 Whether year is 2002, 1 yes, 0 no
D03 Whether year is 2003, 1 yes, 0 no
D04 Whether year is 2004, 1 yes, 0 no
Farm size variables: (based on farm?s total assets)
TA (Total Assets): farm?s total assets.
TA2 (Total Assets squared): quadratic farm?s total assets.
28
The definition of ?small farm? developed by the National Commission on Small Farms is
used to separate farms (based on farm?s gross sales). The gross sales of $250,000 is the
cutoff between small and large farms. Farms with less than $250,000 of gross sales (in
2004 dollars) are placed into the small farm size class.
Correlations matrix of explanatory variables: (see Appendix)
Correlation is a measure of the relation between two or more variables. It is one of the
most common and most useful statistics. The correlation matrix of explanatory variables
shows that there are some positive relationships between the main variables used in the
model but these relationships are not very strong. For net farm income and net nonfarm
income variables, the correlation is 0.50; for lagged net farm income and lagged net non
farm income variables, the correlation is 0.51.
29
CHAPTER V: RESULTS
The results are set out in Tables 3 and 4. Table 3 reports the results of estimating several
specifications for farm investment using the whole sample. Ftest was performed to test
several joint exclusion restrictions and identify the best model among these
specifications. All the specifications were tested for heteroskedasticity and
multicollinearity. Firstly, the BreuschPagan/CookWeisberg test was used to test for
heteroskedsticity. The null hypothesis states that the regression analysis is
homoskedasticity and the alternative is that there is heteroskedasticity. All the
specifications show very high Chisquare. The probabilities of all the functions being
greater than the Chisquare are nearly zero. This implies that the models suffer from
heteroskedasticity. Therefore, the null hypothesis is rejected. Heteroskedasticty is
corrected for and all specifications are reestimated with robust (HuberWhite) standard
errors.
These heteroskedasticityrobust results are set out in Table 3.
30
Table 3: OLS Regression analysis of fixed investment, using alternative
specifications:
(1) (2) (3) (4) (5) (6)
?Sales 0.571 0.554 0.551 0.548 0.548 0.542
(2.07)** (1.99)** (1.98)** (1.96)* (1.97)** (2.14)**
NFI 0.381 0.356 0.359 0.341 0.350 0.178
(2.08)** (1.99)** (1.99)** (1.88)* (1.92)* (0.94)
Lagged NFI 0.046 0.012 0.013 0.012 0.004
(0.26) (0.07) (0.08) (0.07) (0.03)
NNFI 0.618 0.671 0.635 0.459 0.662 0.782
(2.58)** (2.78)*** (2.56)** (1.93)* (2.65)*** (1.32)
Lagged NNFI 0.005 0.067 0.067 0.035 0.057
(0.02) (0.34) (0.34) (0.18) (0.29)
AVGROA 0.006 0.002 0.001 0.001
(1.16) (1.16) (0.88) (0.52)
STDROA 0.001 0.000 0.000 0.000
(1.49) (0.17) (0.05) (0.06)
TA 8.02e08 7.12e08 6.68e08 6.56e08 6.29e08
(2.37)** (2.31)** (2.16)** (2.16)** (1.98)**
TA2 7.49e15 5.89e15 5.46e15 5.57e15 3.06e15
(1.41) (1.23) (1.15) (1.19) (0.61)
solvency 0.040 0.054
(0.61) (0.84)
D1997 0.000
(.)
D1998 0.000
(.)
D1999 0.031
(0.30)
D2000 0.061
(0.55)
D2001 0.035
(0.33)
D2002 0.092
(0.82)
D2003 0.017
(0.17)
D30(Cotton) 0.000
(.)
D35(C&P) 0.211
(1.51)
D40(Peanut) 0.158
(1.34)
D50(CBroi) 0.181
(1.56)
D60(Cow) 0.113
(1.43)
D70(Catfish 0.304
(2.59)***
D90(Dairy) 0.000
(.)
D99(FLive) 0.151
(1.42)
31
(1) (2) (3) (4) (5) (6)
D2004 0.137
(1.22)
D100(C&S) 0.155
(1.57)
Constant 0.081 0.060 0.023 0.070 0.057
(2.10)** (1.69)* (1.51) (1.94)* (1.18)
Observations 623 623 623 623 623 793
Rsquared 0.36 0.32 0.32 0.30 0.32 0.23
F statistic F= 14.50 F= 28.82 F= 31.83 F= 37.86 F= 40.67 F= 47.57
Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
The investment model in the first specification contains the industry dummy and year
dummy variables to see the effect of individual farm?s sector and individual year on
investment. The first column reports the regression analysis with all dummies included
but without intercept. This specification can also be estimated with a regression analysis
in which the dummies for year 2004 (D2004) and for corn and soybeans sector (D100)
serve as a base and to be excluded. In the second specification, all industry dummy and
year dummy variables were dropped. The results in column (2) report the regression
analysis without the dummies. An Ftest was performed to determine whether or not it is
important to include the industry and year dummy variables. The null hypothesis states
that the sector characteristics and year characteristics are jointly equal to zero and the
alternative states that individual industry dummy and year dummy variables are not
jointly equal to zero. The results show that individual industry and year are jointly equal
to zero and thus should not be included in the farm investment equation.
1
1
The Ftest statistic is calculated as follows:
Ftest = [R? (full model)  R? (reduced model)]/ (1R?) * (nk1)/q , where n is the number of observations,
nk1 is the degrees of freedom, q is the number of restrictions, the fcalculation then is:
Ftest = (0.1156 ? 0.1024)/ (10.1156) * (623221)/17 or Ftest = 0.0149 * 35.294 = 0.53.
The critical F (17, 600) is 1.60. Since F < critical F, we fail to reject the null hypothesis.
32
The difference between the second and the third specification is that the later one
does not contain the solvency variable. Ftest is performed to study whether the solvency
variable should be included. Fvalue is 0.71, compared to the critical F value of 6.63, thus
the solvency variable should not be included in the specification.
Ftest is also performed to determine whether or not it is important to include the
size and the size of the farm squared. The results of the two specifications are set out in
column (3) and (4). The null hypothesis states that farm size and quadratic farm size
variables are jointly equal to zero and the alternative states that farm size and quadratic
farm size variables are not jointly equal to zero. The unrestricted model is estimated and
test command is used to do the Ftest. The calculated Fvalue is 4.66, while the critical F
value is 4.61. Since the estimated Fvalue is larger than the critical F, the null hypothesis
is rejected. These two variables should be included in the model, they are important
factors in the farm investment equation.
The difference between the two specifications in column (3) and (5) is that the
later one does not contain the average mean of return on farm assets for sector and
average standard deviation return on farm assets for sector variables. Ftest is performed
to determine whether the average mean of return on farm assets for sector and average
standard deviation return on farm assets for sector variables have a jointly significant
effect on farm investment. The null hypothesis states that these two variables are jointly
equal to zero and the alternative states that they are not jointly equal to zero. Results
indicate that the null hypothesis cannot be rejected.
2
This implies that the return on farms
2
The calculated Fvalue is 0.91 and the critical F value is 4.61. Thus Fvalue < critical F.
assets for farm sector variables are jointly equal to zero, and that these two indicators of
return on farm assets for sector should not be included in the model.
The difference between the fifth and the sixth specification is that the later one
does not contain the lagged net farm income and lagged net non farm income variables.
That means the later model assume that internal finance of the previous year does not
affect farm investment of the current year. An Ftest is performed to determine whether
or not it is important to include the lagged internal finance variables. The null hypothesis
states that the lagged net farm income and lagged net non farm income are jointly equal
to zero and the alternative states that these two variables are not jointly equal to zero.
Results permit rejecting the null in favor of including the lagged values of net incomes.
3
The best model identified after testing is:
titi
titititititiiti
TA
TANNFINNFINFINFISalesI
,
2
,7
,61,5,41,3,2,1,
)( ??
???????
++
+++++?+=
??
where fixed farm investment in period t is modeled as a function of the current change in
sales, current net farm income, lagged net farm income, current net nonfarm income,
lagged net nonfarm income, farm size and quadratic size variables. The fifth column of
Table 3 reports the results of this model. The results indicate that the estimated
coefficient for the current sales growth is highly positively significant, indicating an
accelerator effect. The effect of change in sales on farm investment is 0.548 points for
one point increase in the change of sales variable.
33
3
The Fvalue is calculated to be 16.96 and the Critical F (2, 615) is 4.61. Fvalue > critical F, therefore the
null hypothesis is rejected.
34
Internal finance is found to be important in the investment equation. Current net
farm income has a positive and significant effect on farm investment. Internal finance and
fixed investment are annual data and are scaled by farm?s total asset, on average, a single
annual increase of one unit in the ratio of net farm income to farm?s total assets will lead
to an increase of 0.35 units in the ratio of fixed investment to total farm?s assets. The
effect of lagged net farm income on farm investment is 0.004 points, but this effect is not
significant. The estimated coefficient for the net nonfarm income is also positive and
significant. The effect of current net nonfarm income on farm investment is very strong,
with the value of 0.662 points. On average, an annual increase of one unit in the ratio of
net nonfarm income to farm?s total assets will lead to an increase of 0.662 unit in the
ratio of fixed investment to total farm?s assets. The result shows the important role of off
farm income in farm business. Farm households in the sample use a large percentage of
their income from offfarm business to invest on farms; it seems the more they earn from
offfarm business the more likely they are to invest in the farm business. The finding is
inconsistent with the idea that farm households reduce their investment on farm when
they earn more from the offfarm business. Lagged net nonfarm income is not
statistically significant in the on farm investment equation.
Farm size (farm?s total assets) has a positive sign and TA2 (quadratic size) has a
negative sign in the estimated equation. This variable seems to have concave functional
form  after a point it has a diminishing impact effect on farm investment. This implies
that farm households will decide to stop investing in the farm at some specified level. The
estimated Rsquared is 0.32, this suggest that 32% of the variation in the sample is
explained by the model.
35
The sample is small compared to the more than 45,000 farms in state of Alabama.
Nevertheless, the findings help explain how so many small farms in Alabama continue to
exist although the average operating profit margins and average rates of return on assets
and equity are negative. Smallfarm households and even largefarm households receive
substantial offfarm income and do not rely primarily on farm income for their livelihood
or as the only source of investment in the farm.
The result is consistent with the report of the U.S. Department of Agriculture that
over the past fifty years, the nonfarm rural economy has grown in importance as more
and more farmers have become increasingly dependent on offfarm income. For the
majority of U.S. farm households, the availability of offfarm income is a more
significant factor for the financial wellbeing of the farm. Usually, the increases in off
farm income were more than sufficient to compensate for declines in farm income. Off
farm income from the spouse and/or the farm operator supports the farm. With the
existence of financial constraints, market imperfection, limited availability of debt, farm
operator uses offfarm income to invest on farm instead of looking for external finance
from banks.
In many empirical studies, firm size has been used as an indicator of whether or
not a firm is more likely to be financially constrained. For example, Carpenter et al. use
firm size in their work using US firm data, and Devereux and Schiantarelli (1990) use it
in their work on financial effects and fixed investment using data on UK firms. The basic
idea is that, in general, larger firms have access to a wider range of suppliers of finance
than smaller firms, and as a consequence larger firms are less likely to be financially
constrained than smaller firms.
36
To see whether it is only the investment of financially constrained farms which is
affected by the availability of internal finance and offfarm income, the final specification
is applied to two types of farms. USDA classifies farm size based on the volume of
sales?more than $250,000 and less than $250,000. This classification is used to separate
the farms. The results are set out in Table 4. The results in the first column relate to the
subsample which is defined as large farm (farms with more than $250,000 of gross sales,
in 2004 dollars).
Table 4: Results of regression analysis of the two subsamples:
Large Farms Small Farms
?Sales 0.080 0.601
(1.73)* (16.84)***
Net farm income 0.134 0.205
(1.84)* (1.70)*
Lagged net farm income 0.211 0.631
(4.00)*** (5.37)***
Net nonfarm income 0.384 0.741
(2.77)*** (4.33)***
Lagged net nonfarm income 0.214 0.109
(2.96)*** (5.76)***
Total assets 5.79e08 1.13e07
(2.42)** (4.36)***
Total assets squared 3.91e15 1.63e14
(1.12) (3.57)***
Constant 0.079 0.182
(1.79)* (4.28)***
Observations 354 269
Rsquared 0.15 0.31
Fvalue F (7, 346)= 7.40 F(7, 261)= 25.07
Absolute value of t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
37
Tests for Models:
To see whether the investment equation for these two groups should be estimated jointly
or together a Chow test is performed. The null hypothesis is that the two groups (small
farms and large farms) follow the same regression function and there are no differences at
all between the groups. The alternative states that there is one or more of the slopes differ
across the groups. Results indicate that there is a difference between the groups.
4
The
model should be applied separately for the small farms and large farms.
There are indeed big differences between the estimates of two subsamples. The
effect of change in sales is much stronger for the farms classified as small farms, 0.601
percent points compares to 0.08 points effect for large farms. This implies that the
accelerator effect is very important for investment of small farms. The main differences
between the analyses for two groups, which are also the main focus of this paper, are the
coefficients on net farm income and on net nonfarm income. Among the farms defined
as unconstrained, the coefficient on current net farm income variable is positive and
significant, and the magnitude of net farm income on farm investment for large farms is
0.134 points, compared to a significantly larger magnitude of for small farms of 0.205
points. The effect of lagged net farm income on investment of small farms is also
stronger with the level of 0.631 points compares to the level of 0.211 points for large
farms. The coefficients on lagged net farm income variable are significant and positive
4
The Ftest statistic is calculated as follows:
Ftest= [SSR (full model)  (SSR1+SSR2)]/ (SSR1+SSR2) * [n2(k+1)]/ (k+1);
SSR1: the sum of squared residuals obtained from estimating for the large farms; this involves 354
observations. SSR2: the sum of squared residuals obtained from estimating the model using the small farms
(269 observations); n= number of observations.
Then Ftest = [49.84 ? (14.26+18.27)/ (14.26+18.27) * [623 2(7+1)]/ (7+1)= 39.68
and the critical F (7, 623) = 2.64; Fvalue > critical F, therefore the null hypothesis is rejected.
38
for the two subsamples. Net nonfarm income also has strong effect on investment of
both large farms and small farms. The effect of current net nonfarm income on farm
investment of the large farms accounts for only 0.384 points, smaller than that of small
farms which accounts for 0.741 points. The effect of lagged net nonfarm income is
0.214 points for large farms and 0.109 points for small farms.
These findings are consistent with many empirical works on firms? financial
constraint. They found that although the effect of internal finance on fixed investment
was concentrated among firms defined as financially constrained by their financial
policy, internal finance still had a positive effect on the fixed investment of unconstrained
firms. The results suggest that the investment of financially constrained farms is more
sensitive to the availability of internal finance than that of financially unconstrained
farms. Net farm income and net nonfarm income both have significantly larger effect on
farm investment among smaller farms than among larger farms. This is consistent with
what Carpenter et al. and Gertler and Gilchrist (1994) report for the United States. They
both found that the investment in smaller firms was more sensitive to current cash flow
than investment in larger firms. The conclusion is that large farms have easier access to
external finance than small farms.
The F statistic for the model with large farms is 7.40, with small farms is 25.07,
which are higher than the critical values, suggesting all the variables are jointly different
from zero in both cases.
These models were tested for the presence of potential problems such as
multicollinearity, heteroskedasticity, and specification or omitted variables error. The
BreuschPagan/CookWeisberg test was used to test for heteroskedsticity in the analysis
39
of small farms and large farms. The null hypothesis states that the variance of the
residuals is homogenous and the alternative is that it has problem of heteroskedasticity.
The Chisquared statistic for the BreuschPagan test for the model is 0.17 for large farms
group, which is well below the critical value. Therefore, it can be concluded that
heteroskedasticity is not a problem in this model. For small farms the The Chisquare is
equal to 0.62 and the pvalue of 0.4306. Therefore, the null hypothesis cannot be rejected
and the regression analysis for small farms does not have a problem of heteroskedasticity.
Multicollinearity
was assessed in the specifications using the variance inflation
factor
(VIF). The mean VIFs confirms that multicollinearity is not a problem of all the
regressions. (see Appendix). Lastly, specification error and omitted variables bias was
tested for using the Ramsey RESET test in both subsamples. The null hypothesis states
that the model does not have specification error and the alternative is that the model does
have specification error. The Ftest for the Ramsey RESET in group of large farms was
calculated to be 3.27, pvalue is 0.0214, and thus, the null hypothesis cannot be rejected.
Therefore, it can be concluded that for the large farms, the model does not have
specification error. In group of small farms, Fvalue is rather high, to be 7.43, pvalue is
0.0005, which may be acceptable. For the RESET test, the lower the Fstatistic is, the
more certain it can be concluded that specification error or omitted variables test is not a
problem. For the group of small farms, it is not highly certain that these problems cannot
be proven to exist in the model. Maybe there are still some other factors which have
effect on investment of the small farms in this sample. Since the quality of demographic
variables available in the dataset is very poor, these factors cannot be included in the
analysis. The limitations of this paper come from poor data quality since this is a small
40
sample, unbalanced panel data, a lot of missing values in the sample, thus further
improvement cannot be performed. Future work can refine the model with better data.
41
CHAPTER VI: CONCLUSIONS
This paper tries to give a general view of finance and investment of the sample of about
150 farms in Alabama during the period of 19972004. Using annual farm data, the paper
has examined the relationship between farm investment and internal finance, the effect of
net farm income and net nonfarm income on farm investment, and in particular whether
the effect of cash flow on farm investment is concentrated among farms that are more
likely to be financially constrained.
Firstly, the finding shows that the effect of internal finance on farm investment is
positive and significant for the whole sample; net farm income has a positive and
significant effect on farm investment. Secondly, in contrast to studies of other businesses,
farm households used a large percentage of their income from offfarm business to invest
in the farming business. The finding shows that the more income a farm household earns
from offfarm business the more likely it is to invest in the farm business. Thirdly, the
results suggest that farm investment of financially constrained farms is more sensitive to
the availability of internal finance than that of financially unconstrained farms. Internal
finance has a significantly stronger effect on investment among smaller farms than
among larger farms. This is consistent with what Carpenter et al. and Gertler and
Gilchrist (1994) report for the United States. They both found that the investment of
smaller firms was more sensitive to current cash flow than the investment of larger firms.
42
However, the results obtained need to be viewed with the limitation of non
availability of more frequently data for farms, the small sample compared to the large
number of 45,000 farms in state of Alabama and the unbalanced nature of the panel data.
43
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APPENDIX
Correlations matrix of explanatory variables:
 ChangeSales NFI laggedNFI NNFI laggedNNFI AVGROA STDROA
+
changeSales  1.0000


NFI  0.0007 1.0000
Pvalue  0.9851

laggedNFI  0.0827 0.1302 1.0000
Pvalue  0.0364 0.0010

NNFI  0.2829 0.5064 0.0761 1.0000
Pvalue  0.0000 0.0000 0.0543

laggedNNFI  0.0307 0.0322 0.5112 0.2766 1.0000
Pvalue  0.4379 0.4157 0.0000 0.0000

AVGROA  0.0163 0.0942 0.0959 0.0378 0.0508 1.0000
Pvalue  0.6385 0.0067 0.0149 0.2774 0.1974

STDROA  0.0526 0.0373 0.0438 0.0614 0.0751 0.8115 1.0000
Pvalue  0.1299 0.2831 0.2663 0.0774 0.0564 0.0000

TA  0.0137 0.1015 0.1085 0.0598 0.0525 0.0712 0.0752
Pvalue  0.6947 0.0035 0.0058 0.0852 0.1824 0.0206 0.0145

TA2  0.0052 0.0477 0.0523 0.0300 0.0217 0.0780 0.0594
Pvalue  0.8811 0.1701 0.1846 0.3879 0.5820 0.0112 0.0535

solvency  0.0754 0.0484 0.0158 0.0739 0.0631 0.2082 0.1226
Pvalue  0.0300 0.1641 0.6883 0.0335 0.1093 0.0000 0.0001

D1997  . . . . . 0.0006 0.0123
Pvalue 1.0000 1.0000 1.0000 1.0000 1.0000 0.9850 0.6904

D1998  0.0072 0.0396 . 0.0148 . 0.0359 0.0124
Pvalue  0.8349 0.2547 1.0000 0.6715 1.0000 0.2431 0.6861

D1999  0.0850 0.0004 0.0453 0.0144 0.0170 0.0008 0.0055
Pvalue  0.0143 0.9902 0.2503 0.6785 0.6663 0.9788 0.8594

D2000  0.0092 0.0346 0.0030 0.0122 0.0184 0.0265 0.0272
Pvalue  0.7915 0.3199 0.9391 0.7254 0.6411 0.3893 0.3778

D2001  0.0125 0.0003 0.0445 0.0179 0.0157 0.0307 0.0235
Pvalue  0.7199 0.9928 0.2588 0.6058 0.6908 0.3193 0.4459

D2002  0.0066 0.0232 0.0046 0.0889 0.0221 0.0519 0.0053
Pvalue  0.8505 0.5042 0.9069 0.0105 0.5748 0.0918 0.8632
47

D2003  0.0725 0.0746 0.0175 0.0168 0.0900 0.0231 0.0042
Pvalue  0.0369 0.0317 0.6569 0.6298 0.0222 0.4535 0.8927

D2004  0.0062 0.0296 0.0712 0.0149 0.0211 0.0535 0.0145
Pvalue  0.8575 0.3955 0.0707 0.6682 0.5922 0.0821 0.6382

D30  0.0596 0.0162 0.0249 0.0694 0.0829 0.7710 0.9739
Pvalue 0.0863 0.6423 0.5286 0.0456 0.0351 0.0000 0.0000

D35  0.0573 0.1298 0.1169 0.0367 0.0337 0.4099 0.0280
Pvalue 0.0990 0.0002 0.0029 0.2919 0.3927 0.0000 0.3631

D40  0.0035 0.0190 0.0157 0.0132 0.0110 0.0617 0.1229
Pvalue 0.9207 0.5856 0.6909 0.7051 0.7809 0.0448 0.0001

D50  0.0069 0.0354 0.0405 0.0034 0.0046 0.1260 0.2413
Pvalue 0.8428 0.3093 0.3048 0.9229 0.9068 0.0000 0.0000

D60  0.0029 0.0408 0.0469 0.0085 0.0062 0.3758 0.2411
Pvalue 0.9337 0.2406 0.2344 0.8072 0.8746 0.0000 0.0000

D70  0.0031 0.0133 0.0150 0.0118 0.0121 0.0393 0.1019
Pvalue  0.9293 0.7024 0.7029 0.7337 0.7580 0.2020 0.0009

D90  0.0098 0.0344 0.0357 0.0181 0.0221 0.4197 0.0215
Pvalue 0.7786 0.3235 0.3651 0.6035 0.5752 0.0000 0.4847

D99  0.0061 0.0388 0.0427 0.0156 0.0193 0.2300 0.2830
Pvalue 0.8612 0.2644 0.2790 0.6537 0.6247 0.0000 0.0000

D100  0.0041 0.0159 0.0186 0.0142 0.0040 0.1058 0.0967
Pvalue  0.9052 0.6469 0.6374 0.6824 0.9200 0.0006 0.0016

 TA TA2 solvency D1997 D1998 D1999 D2000
+
TA  1.0000
Pvalue

TA2  0.9168 1.0000
Pvalue 0.0000

solvency  0.1066 0.0627 1.0000
Pvalue  0.0005 0.0415

D1997  0.0259 0.0341 0.0110 1.0000
Pvalue 0.4002 0.2678 0.7203

D1998  0.0088 0.0055 0.0347 0.1226 1.0000
Pvalue 0.7759 0.8580 0.2598 0.0001

D1999  0.0019 0.0115 0.0231 0.1275 0.1244 1.0000
Pvalue 0.9517 0.7086 0.4529 0.0000 0.0001

D2000  0.0040 0.0161 0.0286 0.1310 0.1279 0.1329 1.0000
Pvalue  0.8978 0.6010 0.3523 0.0000 0.0000 0.0000
48

D2001  0.0261 0.0216 0.0053 0.1345 0.1313 0.1364 0.1402
Pvalue 0.3968 0.4835 0.8628 0.0000 0.0000 0.0000 0.0000

D2002  0.0057 0.0005 0.0587 0.1385 0.1352 0.1405 0.1444
Pvalue 0.8539 0.9872 0.0564 0.0000 0.0000 0.0000 0.0000

D2003  0.0239 0.0421 0.0014 0.1425 0.1391 0.1445 0.1485
Pvalue 0.4375 0.1717 0.9634 0.0000 0.0000 0.0000 0.0000

D2004  0.0237 0.0386 0.0343 0.1486 0.1450 0.1507 0.1549
Pvalue  0.4418 0.2094 0.2650 0.0000 0.0000 0.0000 0.0000

D30  0.0503 0.0398 0.1059 0.0542 0.0215 0.0057 0.0289
Pvalue 0.1020 0.1958 0.0006 0.0783 0.4843 0.8537 0.3472

D35  0.0620 0.0799 0.1518 0.1530 0.1073 0.0245 0.0076
Pvalue  0.0438 0.0094 0.0000 0.0000 0.0005 0.4266 0.8061

D40  0.0679 0.0561 0.0543 0.0387 0.1533 0.0040 0.0080
Pvalue 0.0273 0.0681 0.0775 0.2089 0.0000 0.8976 0.7958

D50  0.0309 0.0473 0.0627 0.0237 0.0475 0.0079 0.0141
Pvalue 0.3160 0.1245 0.0416 0.4421 0.1230 0.7964 0.6480

D60  0.1269 0.0872 0.1627 0.0200 0.0560 0.0482 0.0066
Pvalue 0.0000 0.0045 0.0000 0.5153 0.0687 0.1173 0.8299

D70  0.1846 0.1320 0.0001 0.0024 0.0001 0.0038 0.0296
Pvalue  0.0000 0.0000 0.9983 0.9391 0.9967 0.9018 0.3357

D90  0.0950 0.0343 0.0758 0.1114 0.0519 0.0532 0.0161
Pvalue 0.0020 0.2650 0.0137 0.0003 0.0920 0.0836 0.6004

D99  0.0681 0.0475 0.0491 0.2328 0.0634 0.0033 0.0782
Pvalue 0.0268 0.1231 0.1105 0.0000 0.0393 0.9136 0.0109

D100  0.0297 0.0348 0.0303 0.0540 0.0527 0.0548 0.0563
Pvalue 0.3348 0.2578 0.3253 0.0791 0.0866 0.0749 0.0672

 D2001 D2002 D2003 D2004 D30 D35 D40
+
D2001  1.0000
Pvalue

D2002  0.1482 1.0000
Pvalue  0.0000

D2003  0.1525 0.1570 1.0000
Pvalue 0.0000 0.0000

D2004  0.1591 0.1638 0.1685 1.0000
Pvalue 0.0000 0.0000 0.0000

D30  0.0168 0.0150 0.0230 0.0427 1.0000
Pvalue 0.5842 0.6259 0.4546 0.1658
49

D35  0.0322 0.0843 0.0970 0.1691 0.1962 1.0000
Pvalue 0.2950 0.0061 0.0016 0.0000 0.0000

D40  0.0247 0.0287 0.0572 0.0504 0.1065 0.1011 1.0000
Pvalue  0.4231 0.3511 0.0629 0.1013 0.0005 0.0010

D50  0.0020 0.0348 0.0187 0.0326 0.1634 0.1552 0.0842
Pvalue  0.9478 0.2579 0.5447 0.2895 0.0000 0.0000 0.0061

D60  0.0333 0.0319 0.0230 0.0317 0.1955 0.1856 0.1008
Pvalue 0.2792 0.3005 0.4551 0.3032 0.0000 0.0000 0.0010

D70  0.0085 0.0123 0.0152 0.0031 0.0750 0.0712 0.0386
Pvalue 0.7833 0.6892 0.6211 0.9207 0.0148 0.0206 0.2093

D90  0.0095 0.0138 0.0075 0.0204 0.1428 0.1356 0.0736
Pvalue 0.7574 0.6533 0.8067 0.5073 0.0000 0.0000 0.0167

D99  0.0195 0.1146 0.0862 0.1181 0.2169 0.2059 0.1118
Pvalue  0.5257 0.0002 0.0050 0.0001 0.0000 0.0000 0.0003

D100  0.0578 0.1091 0.1039 0.0430 0.0693 0.0658 0.0357
Pvalue 0.0602 0.0004 0.0007 0.1627 0.0243 0.0325 0.2460

 D50 D60 D70 D90 D99 D100
+
D50  1.0000


D60  0.1546 1.0000
Pvalue  0.0000

D70  0.0593 0.0709 1.0000
Pvalue 0.0539 0.0211

D90  0.1130 0.1352 0.0518 1.0000
Pvalue  0.0002 0.0000 0.0921

D99  0.1715 0.2052 0.0787 0.1499 1.0000
Pvalue 0.0000 0.0000 0.0105 0.0000

D100  0.0548 0.0656 0.0251 0.0479 0.0727 1.0000
Pvalue 0.0749 0.0331 0.4141 0.1196 0.0180

50
APPENDIX 2:
Results of the VIFs (variance inflation factor) for the test of
multicollinearity:
As a rule of thumb, the variables whose VIF values are greater than 10
may merit futher investigation. Some variables in the specifications
show rather high VIF but the means VIFs are less than 10 for all the
specifications,indicating that multicollinearity is not a problem of the
models.
. vif (specification 1)
Variable  VIF 1/VIF
+
STDROA  44.26 0.022596
AVGROA  30.22 0.033087
laggedNFI  20.17 0.049575
NNFI  19.67 0.050834
D35  18.62 0.053694
D50  8.86 0.112859
TA  8.67 0.115345
TA2  7.92 0.126296
D99  7.70 0.129813
D60  4.88 0.205095
D70  3.30 0.303309
D40  3.04 0.329478
D100  2.45 0.407495
D2004  2.07 0.483149
D2003  2.02 0.493882
D2002  1.95 0.513341
D2001  1.83 0.547384
D2000  1.76 0.568613
NNFITA1  1.37 0.730445
NFITA1  1.33 0.749259
solvency  1.23 0.810281
changeSales  1.11 0.904819
+
Mean VIF  8.84
. vif (specification 2)
Variable  VIF 1/VIF
+
laggedNFI  18.30 0.054643
51
NNFI  17.90 0.055857
TA  7.69 0.130045
TA2  7.39 0.135365
AVGROA  3.13 0.319163
STDROA  2.84 0.352129
NNFI  1.30 0.769329
NFI  1.27 0.788124
solvency  1.17 0.852858
changeSales  1.06 0.939935
+
Mean VIF  6.21
. vif (specification 3)
Variable  VIF 1/VIF
+
laggedNFI  18.30 0.054643
NNFI  17.90 0.055857
TA  7.69 0.130045
TA2  7.39 0.135365
AVGROA  3.13 0.319163
STDROA  2.84 0.352129
NNFI  1.30 0.769329
NFI  1.27 0.788124
solvency  1.17 0.852858
changeSales  1.06 0.939935
+
Mean VIF  6.21
. vif (specification 4)
Variable  VIF 1/VIF
+
laggedNFI  18.30 0.054657
NNFI  17.90 0.055860
TA  7.54 0.132712
TA2  7.33 0.136409
AVGROA  2.88 0.347036
STDROA  2.75 0.363348
NNFI  1.27 0.787387
NFI  1.27 0.788619
changeSales  1.06 0.944608
+
Mean VIF  6.70
52
. vif (specification 5)
Variable  VIF 1/VIF
+
laggedNFI  18.02 0.055479
NNFI  17.63 0.056729
AVGROA  2.83 0.352834
STDROA  2.74 0.365026
NFI  1.26 0.792472
NNFI  1.19 0.836931
changeSales  1.06 0.945110
+
Mean VIF  6.39
. vif (specification 6)
Variable  VIF 1/VIF
+
laggedNFI  17.81 0.056143
NNFI  17.45 0.057314
NFI  1.24 0.806631
NNFI  1.17 0.852491
changeSales  1.05 0.949334
+
Mean VIF  7.75
53