economics masters thesis
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Identification and Analysis of the effects from deregulation on Delaware, South Dakota and
the United States: 1970-1998
William ComptonFebruary 3, 2011
1. Introduction.
Throughout the 1900s, the United States economy has been placed on a roller coaster of
regulation and deregulation. Most of the decisions to impose further regulation or relax current
regulations have been made in response to economic distress. The goals of the authorities
making these decisions are often to spur short term growth, and are designed to pull us out of the
red and back into the black. The short run effects may have been favorable; however, these
decisions were made sporadically, with little thought focusing on the long term effects to the
economy. Economists generally favor less regulation as opposed to more regulation depending
on the individual circumstances. When deregulation does occur, the full impact of potential long
run growth isn’t always realized right away.
An example of such regulation and deregulation comes from the American airline
industry. In 1938, congress passed the Civil Aeronautics Act in an effort to save a threatened
and declining airline industry. The act created a control board with the authority to control entry
and exit, as well as market competition. The industry remained regulated until 1978 where slow
economic growth, high inflation, high interest rates, and severe fuel supply shocks of the late
1960s and early 1970s, coupled with rapidly advancing technological change, forced congresses
to respond with the Airline Deregulation Act. From then on, the airline industry was placed in
the hands of competitive market forces. Since this deregulation, the airlines industry has
boomed. Companies are now quick to adopt new technologies, to provide more efficient
services, and increased competition has kept prices for air travel low.
The financial industry is a very large piece of the economy but there is disagreement on
the level of impact it has in economic growth. Early studies by Robinson (1952) and Solow
(1956) argue that financial institutions play a minor role in economic growth. More recent
studies from McKinnon (1973), Shaw (1973), Levine and Zervos (1993), and Abrams, Clarke
and Settle (1999) suggest otherwise. Like the airline industry, the financial industry has
followed a similar pattern of regulation and deregulation. The Federal Reserve was created in
1913 in response to a series of financial panics, with the panic of 1907 being the climax. In
1907, the New York Stock Exchange fell 50% from the previous year and caused a tremendous
loss of confidence in the financial system. There were numerous bank runs which led to
widespread panic, and resulted in the bankruptcy of many local banks and businesses. Similarly,
the Federal Deposit Insurance Corporation followed was created in 1933 in response to
widespread bank failures, with the attempt to restore confidence in the banking industry. These
regulatory institutions are still in existence today despite constant criticisms of the Federal
Reserve.
State level financial regulatory change has been observed as well. In the United States,
each state had its own independent law on the level of interest that an individual could be
charged before it was determined unlawful, a usury law. This was a regulatory ceiling on the
amount of interest someone could be charged. In 1980, high inflation rates pushed up the
nominal interest rate required for credit card agencies to charge to earn a profit. These rates were
often above the regulatory ceiling placed by the state government. Due to the lack of
profitability available, credit card companies looked to the government to relax the regulations
on state usury laws.
In 1978, the credit card companies received some help from a Supreme Court ruling in
the case of “Maquette National Bank of Minneapolis v. First Omaha Service Corp.” The court
ruled that a financial institution could charge people in other states the highest interest rate
allowed in their home state. South Dakota in 1980 and Delaware in 1981 saw this as a huge
opportunity to gain attract new financial industry and employment to their states. They enacted
legislation to eliminate usury laws and targeted credit card companies to relocate within their
borders. By acting as first movers, these two states hoped to quickly turn around their economic
situations and significantly expand growth.
About thirty years have passed since the aggressive moves by Delaware and South
Dakota allowing for examination of each states long run growth from expansion of the financial
sector. This natural experiment allows me to examine the period before and after the relaxing of
financial regulation and observe the long run effects on growth for each state in the United
States.
Section 2 of this paper examines South Dakota and Delaware before the reform. Section
3 examines the expected impact to South Dakota, Delaware, and the rest of the United States
following the financial deregulation. Section 4 examines the data used in my analysis and
presents the empirical model. Section 5 contains the findings from the model and section 6
provides the conclusion to my study.
2. South Dakota and Delaware before 1980
During the 1970s, South Dakota was the most agriculturally dependent state in the whole
United States, with about 20% of its GDP attributed to agriculture. From 1970-1979 South
Dakota’s economy grew on average only 1.7 %. South Dakota needed a major change because
they were going nowhere fast. They suffer geographically from their location as well as their
environment as a whole. Table 1 shows agriculture output compared to financial output for the
United States and the six most agriculturally dependent states in the nation for selected years
between 1970 and 1997. From the table you can see the United States percentage of GDP
Table 1
Comparison of Agriculture and finance as a Percentage of Gross Domestic Product
State/Year Sector 1970 1979 1980 1981 1990 1997
United StatesAgriculture 0.026966 0.028108 0.023054 0.025129 0.018508 0.015286
Finance 0.027035 0.031858 0.031671 0.03382 0.041181 0.050037
IdahoAgriculture 0.127695 0.085182 0.097519 0.104524 0.093979 0.053345
Finance 0.023881 0.025199 0.025615 0.028379 0.026161 0.02405
IowaAgriculture 0.135216 0.119268 0.098664 0.122701 0.076014 0.065994
Finance 0.025006 0.026093 0.027384 0.030701 0.032751 0.03387
NebraskaAgriculture 0.111192 0.122941 0.090194 0.128888 0.104494 0.06973
Finance 0.026929 0.03128 0.031048 0.030053 0.031541 0.035243
North DakotaAgriculture 0.150828 0.166667 0.069457 0.129697 0.106091 0.066602
finance 0.027899 0.027986 0.027861 0.023006 0.03797 0.033331
South DakotaAgriculture 0.199836 0.19193 0.13432 0.168646 0.126077 0.093347
Finance 0.029955 0.035736 0.037426 0.044903 0.08285 0.081508
MontanaAgriculture 0.127783 0.075737 0.071261 0.081855 0.063586 0.043471
Finance 0.027064 0.028264 0.027899 0.028036 0.031717 0.034756
Figure1
Agriculture vs. Finance % GDP
00.050.10.150.20.250.30.35
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
Years
Per
cen
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es
US-AG US-FIN SD-AG SD-FIN
attributed to agriculture steadily declined from 1970 to 1997. Although South Dakota’s
dependence also declined, it remained the highest out of all five states 27 years later.
Figure 1 shows the changes of agriculture compared to finance as a percentage of GDP
for South Dakota and the United States from 1967 through 1997. You can see that South
Dakota’s dependence on agriculture has been relatively high compared to the United States
dependence throughout the thirty year period. During 1980, figure 1 shows a large decrease in
agriculture as a percentage of GDP. Table 1 shows that from 1979-1980 agriculture output as a
percentage of GDP fell from 19% to 13.5%. This was due to a major drought in 1980 that placed
economic pressure for change in South Dakota.
Delaware, like South Dakota, had major economic dependence on one sector. From the
1950s through the 1970s, Delaware concentrated most of its output in the manufacturing
industry, focusing mainly on automobile assembly and chemicals. E.I. du Pont de Nemours and
Company, or commonly known as DuPont, was Delaware’s one headed monster and was a major
producer of war supplies. After the end of World War II, output demand could not continue at
its current rate and Delaware’s dependence on the manufacturing sector started to hurt the state.
Delaware suffered a mild economic recession during the 1970s with an average employment
growth of only 1.3% for the period 1970-1980. Table 2 lists manufacturing output vs. financial
output as a percentage of GDP for the United States and the six most dependent states on the
manufacturing sector. In 1970, Delaware was the 4th most dependent state in the Country on the
manufacturing sector at 36% of GDP. By 1997, Delaware’s dependence on manufacturing fell to
15.8% and last out of the top six states. Figure 2 shows Delaware’s manufacturing and finance
sector versus the United States manufacturing and finance sector from 1969-1997. Figure 2
Table 2
Comparison of Manufacturing and finance as a percentage of Gross Domestic Product
State/Year Sector 1970 1980 1981 1982 1990 1997
United StatesManufacturing 0.24425 0.212811 0.210144 0.199383 0.180764 0.166719
Finance 0.027035 0.031671 0.03382 0.033557 0.041181 0.050037
ConnecticutManufacturing 0.335169 0.283321 0.274572 0.268986 0.192565 0.162824
Finance 0.020738 0.025264 0.030761 0.027452 0.039563 0.052887
DelawareManufacturing 0.361023 0.336161 0.346872 0.324489 0.23058 0.158792
Finance 0.026216 0.03254 0.038013 0.043588 0.174528 0.217424
IndianaManufacturing 0.371751 0.325191 0.327931 0.300945 0.297294 0.293351
Finance 0.022774 0.022184 0.023299 0.022606 0.02983 0.029695
MichiganManufacturing 0.37193 0.301054 0.321192 0.305047 0.269965 0.260097
finance 0.022587 0.022212 0.02337 0.022741 0.028451 0.032667
North Carolina
Manufacturing 0.374737 0.327244 0.325525 0.310754 0.301164 0.260012
Finance 0.018739 0.023541 0.026811 0.025213 0.03084 0.046168
OhioManufacturing 0.354433 0.32636 0.331543 0.306656 0.282939 0.254864
Finance 0.021224 0.022495 0.022962 0.022802 0.03433 0.038505
Figure 2
Manufacturing vs. Finance % GDP
0
0.1
0.2
0.3
0.4
0.5
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
Years
Per
cen
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US-MAN US-FIN DE-MAN DE-FIN
shows a decline in manufacturing for both Delaware and the United States, with Delaware
experiencing a larger rate of decline.
Due to the changes in the United States economy, Delaware and South Dakota could not
keep the current economic structure. They needed to take on new industry and congress gave
them the perfect opportunity. South Dakota acted first in 1980 with Governor William Janklow
pushing the legislation through the States legislature. In response, the large credit card company
Citibank, quickly packed their bags and moved within the borders of South Dakota. One year
later, Delaware followed South Dakota’s lead passing their own legislation to eliminate usury
laws with the help of Governor Pierre S. du Pont IV.
3. Expected Effects to SD, DE, and the United States from Deregulation.
The ceilings imposed on the financial industries through usury laws reduced the amount
of people that could obtain credit. Since the interest rate tops out at an artificial level, for
example 6%, people with greater default risk requiring a higher interest rate could not obtain
credit. This reduces output from the financial industries who realize a loss in potential profits.
There was an additional cost imposed by the usury laws a well. High nominal borrowing costs
caused financial agencies to have to lend at high rates of interest, even to people will low risk of
default. The usury laws reduced or eliminated all profitability of lending to anyone. When
South Dakota and Delaware eliminated their usury laws, they created a huge incentive for
financial agencies to relocate within their borders to dramatically increase output and profits.
People who could not obtain credit before could now obtain credit if they wanted, but at an
interest suited for the risk they brought to the table. Because Delaware and South Dakota were
the first movers in eliminating usury laws, they gained a large portion of the national financial
market share. From all the new corporations that relocated to these two states, within a few
years both states financial sectors began to grow rapidly. Table 1 and 2 show the shares of the
newly defined financial sector as a percentage of GDP for South Dakota and Delaware. In 1970,
South Dakota’s financial sector comprised 3% of its GDP, while Delaware’s represented 2.6% of
its GDP. In 1990 both states financial sector shares grew to 8.3% and 17.4% respectively.
Looking at figures 1 and 2, they show financial growth of South Dakota and Delaware from
1970 through 1997. Looking at each figure, one can observe clear growth in each states financial
sector. Though each state realized rapid growth, South Dakota’s growth reached its peak at
about 1987 then declined and plateaued at about 9%. Delaware’s growth continued through
1993-1994 at the level of 23%. Delaware gained its advantage from its central location in the
Northeast corridor and its location near another financial hub, New York.
Almost every state had eliminated their usury laws by 1982 (Ellis), and this certainly
would have played a role in the expansion of the entire financial industry in the United States.
The impact of the other states did not compare to the magnitude of first movers South Dakota
and Delaware, but other states may have benefited from the deregulation. Although Delaware
and South Dakota attracted a large portion of the market not all of the financial firms moved and
remained in their current states. The financial firms would have increased output and profit the
same as the firms that relocated, just a couple of years later. The long run effects can be seen in
figure 1, looking at the United States financial sector growth. There is a slight, steady growth
starting about 1981-1983 and stretching through 1997 that undoubtedly can be attributed to the
deregulation. Deregulation not only increased output in existing financial firms, other firms
came into existence because of the increased profits that could then be realized. New financial
products would be introduced and the financial boom had officially begun.
My goal of this paper is to quantify the effects of the 1980 deregulation for South Dakota,
the 1981 deregulation for Delaware, and the 1982 deregulation for the rest of the United States. I
will be running a linear ordinary least squares block diagonal regression on a balanced panel
sample from 1970-1998. The regression will quantify the individual effects for Delaware and
South Dakota. I will run another model will be run on the same balance panel identifying the
effects on the United States as a whole.
4. Data and Model.
The data used for the empirical model consists of a sample of all 50 U.S. states plus
Washington DC for the period 1970-1998. The chosen period is sufficiently long enough to
capture any long run effects of the deregulation for South Dakota, Delaware, and the rest of the
United States. From the sample I have 29 sets of observations for each of 51 cross-sections,
giving me a total of 1508 observations. To assess the effects due to the elimination of usury
laws, I estimated an empirical model examining per capita real total state income growth and
annual employment growth. The data used was gathered from the Bureau of Economic Analysis
under regional statistics. All income data was converted to real terms using the Consumer Price
Index with 1982-1984 being the base years.
According to the Neo-Classical growth (exogenous growth) model, growth of individual
states can only be affected in the short run as they converge to a new steady state equilibrium.
The growth rate of convergence to the steady state is determined by capital accumulation which
is determined by the savings rate. As people invest in financial products, they are in turn saving
their money instead of consuming it. As the financial industry expanded from the deregulation
in 1980-1982, saving became easier and convergence should have sped up. The only thing that
can affect the long run growth rate is a change in resources and technology which are
exogenously determined. There is no doubt that our country expanded its technology greatly
throughout the 1980s and 1990s taking us to greater steady state equilibriums. According to
Robert Barro and Xavier Sala-i-Martin, states should converge, at least conditionally, to the same
equilibrium level of output per capita (Barro and Sala-i-Martin, 1991). Once each state reaches
the new steady state equilibrium, each states growth should be driven by the growth of the
United States economy as a whole, allowing for any change in a states sectoral composition.
To measure the affect on South Dakota from their deregulation, two models were
estimated measuring their real per capita total state income growth and employment growth. To
capture Delaware’s effect, two more models were estimated using the same dependent variables.
To capture the affect of total elimination of all usury laws on the United States as a whole, the
same two dependent variables were used in two more models. In South Dakota’s models, a
dummy was inserted measuring the effect of the deregulation from 1980 on. The same dummy
was included in Delaware’s models, only it measured the effect from 1981 on. To measure the
effect of the deregulation on the United States, a dummy was included to measure the affect from
1982 on.
To capture the affect of the neoclassical growth model, I compared each states yearly
growth with the national yearly growth rate. The expected coefficient for national growth rate
will have a positive sign indicating that when the national economy grows, each state economy
will also grow. To account for change in a states sectoral composition, I included a weighted
average variable WA_SUM, defined as:
WA_Sumit = ΣWijt-1(Aijt)
Where the subscripts i and t denote state i at time t, j is one of the nine included sectors:
Agricultural; Mining; Construction; Manufacturing; Transportation and Public Utilities;
Wholesale and Retail Trade; Finance, Insurance, and Real Estate; Services; and Government. W
is the weight of each sector in a state’s personal income and A is the national average of per
capita personal income that originates in sector j at time t. This variable controls for national
influences and will be the main variable indentifying growth in the change of the sectoral
compositions from deregulation. I expect that this coefficient will be positive.
The next independent variable included was a variable that measured the financial depth
of each state. This variable was the sum of all depository institutions, non-depository
institutions, security brokers, commodity brokers, holding offices, and other investment offices
as a percentage of total state income. It measures the level of financial assets held by each state
and is entered in log form into the model. I expect this coefficient to be positive because of the
major increase in the financial industry expected after the deregulation.
To measure individual state effects that took place outside of the model in the collective
U.S. model, fixed effects were included giving each state an individual constant. Also random
effects will be run for this model to conduct a Hausman test comparing random effects and fixed
effects, and determining the best estimated coefficients for the model. An AR(1) term was
entered into the models that showed evidence of autocorrelation.
5. Results.
Table 3 reports the results of the block diagonal regressions on both of Delaware’s
growth equations. For the annual employment growth model, the Delaware specific constant
term gives a t-stat with an absolute value of .8711 which suggests that it is not significant at the
90% level. A Wald test of the coefficient confirms this. This means that no other effects not
included in the model played a significant role in employment growth during the period 1971-
1998. The coefficient for the national growth rate is 1.044 and has the expected sign. The
coefficient states that Delaware and the United states employment grows at about the same rate.
This gives strong evidence for the Neo-Classical growth model and that Delaware over the 29
year period has reached the steady state equilibrium for employment growth. The t-stat
accompanied with the coefficient is 7.27 suggesting its significance at the 99% level, and a Wald
test confirms its significance. The variables weighted average and financial depth report t-stats
of 1.41 and .177 respectively. This would signify that the weighted average is significant and
financial depth is not significant, but Wald tests of both coefficients conclude that both variables
do not significantly affect annual employment growth for Delaware in the sample period. The
dummy variable for 1981 proved significant at the 95% level with a t-stat of 2.229 and a Wald
test to confirm its significance. The coefficient for the dummy is .023, signifying that after 1981;
Delaware’s employment grew at an additional 2.3% per year after the deregulation.
Delaware’s per capita total state income growth model reports that the variables: national
growth rate, financial depth, 1981 dummy, and the constant all significantly affected Delaware’s
growth during the sample period. The constant and financial depth are both significant at the
95% level, and weighted average and 1981 dummy are significant at the 99% level. Weighted
average and the 1981 dummy have the expected positive coefficient. Financial depth has an
unexpected negative coefficient, although it is very small. The negative financial depth
coefficient signifies that state growth is hindered with more financial assets in holding by the
financial sector. The significant constant signifies that other forces not included in the model
played a significant role in Delaware’s per capita income growth. All variables significance, or
lack there of, were confirm through Wald tests as well as t-statistics. Both models for Delaware
lost one observation correcting for autocorrelation.
Table 4 reports the results for both of South Dakota’s growth equations. Like Delaware,
the variables weighted average, financial depth and the constant all proved insignificant for
employment growth. Also like Delaware, the national growth rate significantly affected the
growth rate of South Dakota 1 to 1. Unlike Delaware, the 1980 dummy did not play a significant
role in the annual employment growth throughout the sample period. Growth was affected by
looking at the data in the short run, but throughout the whole sample period the deregulation did
not come through as a significant effect. All significance levels were double checked through t-
statistics and Wald tests.
For South Dakota’s second model on real per capita total state income growth, the
variables weighted average, financial depth, and the constant were all significant at the 90%
level. The variable weighted average is significant at the 99% level. Like the previous model on
employment growth, the 1980 dummy did not play a significant role in South Dakota’s growth
throughout the sample period. The large negative, significant coefficient for the weighted
average variable signifies that even with the change in sectoral composition towards the finance
sector, South Dakota is still very sensitive to shocks in other possible sectors. South Dakota was
still very heavily dependent on agriculture despite the increased financial sector. The United
States was turning away from agriculture and this may have had a significant affect on the
weighted average for South Dakota.
Tables 5 and 6 report the results of grand regressions estimated to identify the effect of
the 1982 deregulation on the United States as a whole. Table 5 has the estimates for random
effects and table 6 has the estimates for the fixed effects. All grand regression models were
estimated using the 1970-1998 sample. Both models for annual employment growth and annual
real per capita total state income growth were presented. A Hausman test statistic of .001 for
real per capita growth is compared to a critical value of 9.49. Comparing the two statistics, I
conclude that the fixed effects are consistent but inefficient because of the loss of data. The
Random effects prove to be consistent and efficient. Since the random effects are superior to the
fixed effects I focus on table 5 for employment growth. Weighted average, financial depth,
National Growth rate, and the collective constant all prove significant at the 99% level.
Weighted average and national growth rate report positive signs consistent with expectations.
Financial depth continues to report a negative coefficient. The 1982 dummy proves to not
significantly affect the United States as a whole. I performed a Chow test on the fixed effect
model to see if each state would report a consistent specific constant term. The null hypothesis
was rejected and I concluded that the fixed effects were consistent if used.
For the second model testing the real per capita total state income growth, a Hausman test
was perform testing the random effects against the fixed effects. The test presented the same
results as the employment growth model, concluding in favor of the random effects. Focusing
on table 5, the variables weighted average, financial depth, and the constant all were significant
at the 95% level. National growth continued to be highly significant at the 99% level. The 1982
dummy proved insignificant in the random effects model, but if we looked at the fixed effects
model it proved significant at the 90% level. A Chow test was also constructed for the fixed
effects model and concluded that the individual state constants were significant and consistent.
Table 7 reports the means and standard deviations for South Dakota, Delaware and the
United States for the period 1970-1980 before the deregulation and the period 1981-1998 after
the deregulation. For employment growth, South Dakota, Delaware and the United States all
experienced a slight decline in their standard deviation from the period before the deregulation
compared to the period after. Average employment growth also increased for South Dakota and
Delaware even though it declined in the United Stats as a whole. Looking at per capita income
growth Delaware and the United States experience a decline in standard deviation; however
South Dakota experiences a much larger decline from .13 to .03. Despite the United States slight
increase in average per capita growth between the two periods, Delaware and South Dakota both
experienced a large growth in their average growth rate from the period before the deregulation
to the period after the deregulation.
6. Conclusion
In an attempt to escape their current economic hardship, South Dakota and Delaware
attempted to change their sectoral composition. In 1980 and 1981, they passed legislation that
eliminated usury laws and created huge incentives for financial firms to relocate within their
borders. Because they were first movers, they gained much of the market share in the United
States, particularly in the credit card sector. With Delaware being located on the east coast, its
prime location allowed them to attract a larger share of the market than South Dakota. Looking
at the raw data, it is clear that Delaware fared much better than South Dakota. South Dakota
experienced short run growth but remained heavily dependent on agriculture. Delaware
experienced long term growth and moved away from a dominant manufacturing sector towards a
strong financial sector.
Examining the regressions for employment growth and real per capita total state income
growth, South Dakota did not show any significant long term effects in either category.
Delaware showed strong effects in both categories, but was stronger in the per capita income
growth. Despite South Dakota’s small responses to the financial data, they did manage to move
away from an extremely heavy reliance on the agriculture sector and lessened their growth
volatility.
Delaware was small in size, and both Delaware and South Dakota were small in their
relative economy size. They had hoped to significantly improve their economies by acting as
first movers in the elimination of their usury laws, but Delaware enjoyed most of the spoils due
to its excellent geographic location and its close proximity to large cities such as Washington
D.C., Baltimore, Philadelphia, and New York City.
References.
Abrams, Burton A, Margaret Z. Clarke, and Russell F. Settle (1999). "The Impact of Banking and Fiscal Policies on Economic Growth." Southern Economic Journal 66, 367-378.
Barro, Robert J, and Sala-i-Marin, Xavier (1991). “Convergence across Regions and States.” Brookings Papers on Economic Activity. No. 1, pp. 107-182.
Ellis, Diane (1998). “The Effect of Consumer Interest Rate Deregulation on Credit Card Volumes, Charge-Offs, and the Personal Bankruptcy Rate.” Federal Deposit Insurance Company, Bank Trends, 98-05.
Levine, Ross, and Sara J. Zervos (1993). "What have We Learned about Policy and Growth from Cross-country Regressions?" American Economic Review 83, 426-430.
McKinnon, Ronald I. (1973). "Money and Capital in Economic Development." Washington, DC: Brookings Institution.
Robinson, J. (1952). “The Rate of Interest and Other Essays.” London: Macmillan
Shaw, E.S. (1973). "Financial Deepening in Economic Development." New York: Oxford University Press.
Solow, Robert (1956). "A Contribution to the Theory of Economic Growth." Quarterly Journal of Economics 70, 65-94.
Table 3Delaware Growth Equations
Dep. Variable Annual Employment
Annual Growth in Real
GrowthPer Capita Total State
IncomeSample 1970-1998 1970-1998Variable Coefficients
Constant -0.034089 -0.032262 (0.871197) (2.23533)National Growth Rate 1.044609 1.033192 (7.274253) (7.307788)Weighted Average 0.173151 -0.076197 (1.412111) (0.716882)Financial Depth -0.001542 -0.007694 (0.177245) (2.321601)1981-Dummy 0.023829 0.01211 (2.229644) 3.661067AR(1) 0.473527 -0.472906 (2.738672) (2.492848)Durbin-Watson 1.976895 2.066984
Adj R2 0.705594 0.601287Absolute values of t-stats in parenthesesBoth models lost one observation to Autocorrelation
Both models were estimated Using a block-diagonal regression to identify Delaware individual effects on the entire panel sample.
Table 4South Dakota Growth Equations
Dep. Variable Annual Employment
Growth
Annual Growth in Real
Per Capita Total State
IncomeSample 1970-1998 1970-1998Variable Coefficients
Constant 0.130132 -0.470492 (0.868599) (1.245833)National Growth Rate 0.619977 2.222813 (3.354365) (4.157519)Weighted Average -0.044834 -1.188179 (0.180503) (2.028404)Financial Depth 0.028289 -0.13147 (0.816871) (1.430817)1980-Dummy -0.012775 0.019465 (0.691934) (0.465404)AR(1) 0.259945 -0.194623 (1.167869) (1.325171)Durbin-Watson 1.959104 2.11512
Adj R2 0.713152 0.604036Absolute values of t-stats in parenthesesBoth models lost one observation to Autocorrelation
Both models were estimated Using a block-diagonal regression to identify Delaware individual effects on the entire panel sample.
Table 5United States Growth Equations-Random Effects
Dep. VariableAnnual
Employment Growth
Annual Growth in Real
Per Capita Total State
IncomeSample 1970-1998 1970-1998
Variable Coefficients
Constant -0.031401 -0.016977 (2.450976) (1.317531)National Growth Rate 0.875731 1.01248 (29.65151) (20.39103)Weighted Average 0.047694 -0.047317 (1.965523) (1.116386)Financial Depth -0.007858 -0.005493 (2.623557) (1.659874)1981-Dummy -3.53E-05 -0.001389 (0.027833) (0.67613)Durbin-Watson 0.941471 1.761695
Adj R2 0.395943 0.415432Absolute values of t-stats in parenthesesBoth models were estimated with a grand regression on the balanced panel data set
Table 6United States Growth Equations-Fixed Effects
Dep. VariableAnnual
Employment Growth
Annual Growth in Real
Per Capita Total State
IncomeSample 1970-1998 1970-1998Variable Coefficients
Constant -0.03446 -0.086935 (1.648464) (2.114836)National Growth Rate 0.876588 1.010815 (32.23076) (20.97751)Weighted Average 0.044913 -0.057603 (1.476188) (1.327172)Financial Depth -0.008635 -0.02223 (1.754996) (2.167606)1981-Dummy 6.03E-05 0.002124 (0.034281) (1.064537)AR(1) 0.523375 0.068309 (5.338028) (0.656738)Durbin-Watson 0.977917 1.784289
Adj R2 0.501853 0.411402Absolute values of t-stats in parenthesesBoth models lost one observation to AutocorrelationBoth models were estimated with a grand regression on the balanced panel data set
Table 7Means and Standard Deviations-Employment Growth
1970-1980 1981-1998 Mean Std. Dev Mean Std. Dev
Delaware 0.013166 0.0225 0.024586 0.018179South Dakota 0.013999 0.01794 0.018985 0.015983United States 0.020781 0.018434 0.018549 0.012277
Table 8Means and Standard Deviations-Per Capita Income Growth
1970-1980 1981-1998 Mean Std. Dev Mean Std. DevDelaware 0.008024 0.025872 0.017769 0.021437South Dakota 0.010047 0.132542 0.024012 0.0314United States 0.014835 0.026672 0.01759 0.016772