.
4.1 Trends in Entrepreneurship
"How has entrepreneurship fared over the same period of time in which unemployment
rates have increased rapidly and the housing market has dropped significantly? Figure 3
displays annual estimates of the monthly entrepreneurship rate from 1996 to 2009. As
noted above the entrepreneurship rate measures the percentage of the adult, nonbusiness
owner population that starts a business each month. It captures all new business owners,
including those who own incorporated or unincorporated business, and those who are
employers or non employers. In 2009, an average of 0.34% of the adult population, or
340 out of 100,000 adults created a new business each month. The business formation
rate increased from 2008 when it was 0.32%. It was the third straight year that the
index increased, resulting in an increase from 0.29% in 2006 to 0.34% in 2009.
The recent increase is the largest over the 14year sample period. In fact, over the period from 1996
to 2009, the business creation rate fluctuated within the range of 0.27–0.31%. It was not
until 2008 and 2009 that it rose above the high end of this range, which coincides with the
recent recession. In the late 1990s, the entrepreneurship rate decreased slightly, then rose
from 2001 to 2003. It remained relative constant over the next 3 years before increasing
in the recent recession."[7]
"Another interesting finding is that home owners are more likely to start businesses.
The coefficient is positive and statistically significant, although relatively small. Home
owners have a 0.012 percentage point higher rate of entrepreneurship than non home
owners, which is roughly a 4% higher rate relative to the mean. In the presence of
liquidity constraints, the ability of owners to borrow against the value of their homes,
such as home equity loans, may make it easier to finance new business ventures."[7]
"Figure 4 displays the entrepreneurship rate against the national median home price
in $2009. The negative relationship between the two trends in the recent recession is very
clear. Home prices have dropped sharply over the past few years as entrepreneurship
rates have increased. These patterns run counter to the decline in home equity decreasing
entrepreneurship and are likely due to stronger positive effects of rising unemployment
rates. Entrepreneurship rates also dropped in the late 1990s when home prices were
rising. Interestingly, however, both entrepreneurship and home prices rose steadily in
the early 2000s. In this period, rising home equity may have provided capital for would be
entrepreneurs to start businesses."[7]
"At the national level, trends in entrepreneurship appear to be primarily counter
cyclical—rising in economic downturns and declining in strong economic growth periods.
The national patterns for entrepreneurship, however, are weaker than unemployment
patterns over the business cycle. Trends in home prices and their effects on access
to capital may have offset some of the business cycle effects. But, these are only broad
strokes based on national trends. Instead, it is important to focus on variation in local
labor market and housing conditions. Unemployment rates and housing prices differ
substantially across metropolitan areas, and these differences can be used to more carefully
examine the relationship between entrepreneurship, and unemployment and home
prices."[7]
"Summary measures of goodness of ﬁt are useful for comparing the performance of different models ﬁt to the same data. Measures include information criteria or R2type measures. Calculation of these summary measures usually depends on the sample size (or range of data used) and model structure used (such as linear or logistic regression), and so comparisons should only be made between models of the same structure built using the same data. It should be noted that for nonlinear models there are multiple different ways to calculate R2type measures, with no consensus as to which should be used. For nonlinear models, goodnessofﬁt measures based on the residuals (such as the deviance) or information criteria are preferred. There are sometimes also measures speciﬁc to the model type, such as the Hosmer– Lemeshow test for logistic regression; for further details see Campbell. If Markov Chain Monte Carlo methods have been used, additional properties such as the convergence of the chains, autocorrelation plots, and deviance information criteria should be reported [16]."
"The central puzzle in economic development is to explain what accounts for diﬀerences in output per capita (inequality) across nations. This is what Lucas (1988) posited as the problem of economic development. Based on the neoclassical production function, diﬀerences in output per capita can be attributed to diﬀerences in physical capital, human capital and total factor productivity (TFP). Chari et al. (1997) argue that observed diﬀerences in output per capita can be explained by diﬀerences in factors of production (e.g., physical and human capital). However, Hall and Jones (hereinafter HJ 1999), and Parente and Prescott (1999, 2000) show that the diﬀerence in TFP is the key determinant of diﬀerences in international incomes. Thus, to be able to answer Lucas' question, we must ﬁrst answer the question: What explains international diﬀerences in TFP?[17]
"Recently, considerable attention has been given to the role of institutions in explaining not only diﬀerences in productivity across countries, but also why some countries invest more in physical and human capital (North 1990, Knack and Keefer 1995; Nugent and Robinson 1998; HJ 1999; Parente and Prescott 2000; Acemoglu et al. 2001, Easterly and Levine 2002, among others). By institutions, North (1990) means the formal (laws, constitutions) and informal (customs, traditions) constraints, and government policies (enforcement, punishment) that shape the interactions of economic actors1. For instance, countries with more secure property rights have, in general, higher productivity and therefore higher levels of income per capita. According to North (1990, p.107), institutions ''are the underlying determinant of the longrun performance of economies.''[17]
The output per worker gap among nations, would tend to widen. Quantile regression provides the appropriate tools to determine whether there are different marginal responses of output per worker to changes in institutions. In Fig. 2, we depict the IV estimates (horizontal dashed lines) along with the corresponding quantile regression estimates.
The shaded areas represent 90% confidence intervals; at all estimated quantiles, institutions are statistically and foremost economically significant. As expected, at higher quantiles ðsÞ the return for each additional ''unit'' of institutions decreases relative to lower conditional quantiles of output per worker. Returns vary from approximately 6.2 to 3.8 as s increases. This first difference relative to HJ, as a result of applying the more robust and descriptive methodology of quantile regression, reinforces the importance attributed to institutions in promoting not only development, but also in closing differences in output per worker across nations."
"The state housing collateral ratio is computed using the Lustig and van Nieuwerburgh (2005) method. The unemployment rates are from the BLS. The relative unemployment rate is the ratio of the current unemployment rate to the moving average of the unemployment rates from the previous 16 quarters. Labor income is from the BEA. U.S. cay and U.S. They are downloaded from Sydney Ludvigson's and Stijn van Nieuwerburgh's web sites, respectively.
The three spreads use quarterly data obtained from the Board of Governors of the Federal Reserve System web site. We use 13(f) institutional holdings data from Thomson Reuters to compute the institutional trading variables, while the retail trading variables are computed using retail holdings data from a large U.S. discount brokerage house. The retail data are available only for the 1991 to 1996 period.
The change in local institutional or retail holdings is the percentage change in the value of local holdings (i.e., the total value of shares of ﬁrms headquartered in a state held by investors in the same state). The change in nonlocal institutional or retail holdings is the percentage change in the value of nonlocal holdings (i.e., the total value of shares of ﬁrms headquartered in a state but held by outofstate investors). To compute the state economic activity index, we add the standardized values of state income growth and state, subtract the standardized value of relative unemployment, and divide this sum by three.
"Lowpaid, repetitive positions are most likely to go, with people earning less than £30,000 a year five times more likely to see their jobs taken over by machines than those paid £100,000, new research has warned. Huge advances in technology risks creating an underclass of lowskilled people whose jobs have been automated, according to a joint report from Deloitte, the Big Four accountancy firm, and the University of Oxford.[18]"

SUMMARY OUTPUT ENTREPENEURSHIP AND UNEMPLOYMENT  
Regression Statistics  
Multiple R  0.804484  
R Square  0.647195  
Adjusted R Square  0.620056  
Standard Error  0.018609  
Observations  15  
ANOVA  
 df  SS  MS  F  Significance F  
Regression  1  0.008258  0.008258  23.84756  0.000299  
Residual  13  0.004502  0.000346  
Total  14  0.01276 


 
 Coefficients  Standard Error  t Stat  Pvalue  Lower 95%  Upper 95%  Lower 95.0%  Upper 95.0% 
Intercept  0.165243  0.026801  6.165666  3.4E05  0.107344  0.223142  0.107344  0.223142 
X Variable 1  0.475703  0.097412  4.883397  0.000299  0.265257  0.68615  0.265257  0.68615 
 
SUMMARY OUTPUT MEDIUM HOME PRICE AND ENTRPENEURSHIP  
Regression Statistics  
Multiple R  0.086584  
R Square  0.007497  
Adjusted R Square  0.06885  
Standard Error  0.031212  
Observations  15  
ANOVA  
 df  SS  MS  F  Significance F  
Regression  1  9.57E05  9.57E05  0.098195  0.758977  
Residual  13  0.012664  0.000974  
Total  14  0.01276 


 
 Coefficients  Standard Error  t Stat  Pvalue  Lower 95%  Upper 95%  Lower 95.0%  Upper 95.0% 
Intercept  0.314501  0.065916  4.771234  0.000365  0.172098  0.456903  0.172098  0.456903 
X Variable 1  1.1E07  3.38E07  0.31336  0.758977  8.4E07  6.24E07  8.4E07  6.24E07 
CONCLUSION:
Through many tests of Data analysis, regression, studies of quotations, that has shown through multiple TTests, that
Entrepreneurship and unemployment and home prices are correlated through regression, I have not been able to prove that Institutions are increasing Entrepeneurship rates because of lack of Data available, except through graphs available but not data.
ENTREPENEURSHIP_i = .16*UNEMPLOYMENT_i + .32*HOMEPRICE_i +Log(C*INSTITUTIONS(0))+ e_i
(6.16) (4.77)
N=15 R^2=.64
Bibliography:
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