Sunday, April 29, 2018


Overview of Silver Nanowires
Aaron Linder

1.      Introduction

Silver Nanowires are some of the most modern materials being available on the market.  There is many recent publications and academic research about Silver Nanowires and what is the methods of fabrication, synthesis, and what are the properties of these nanowires.  This paper will attempt to make a summary of Silver Nanowires various topics in a short summary.

2.      Properties of Silver Nanowires

a.      Mechanical Properties of Silver Nanowires

In recent years nanowires, have attracted significant interest because of their superior mechanical properties [5].  Silver Nanowires Grown in the á1 1 2ñ direction have been shown to have significantly increased yield strength compared to Ag bulk. Size dependent Young’s Moduli have been reported for FCC metals, both in simulation and experimental studies. [1-4] 
b.      Electrical Properties of Silver Nanowires
Silver is considered a 1-d nanomaterial so it experiences Ballistic Transport.  Ballistic transport means that the nanowire has negligible electrical resistivity caused by scattering.  Without scattering, electrons simply obey newtons second law of motion at non-relativistic speeds.  There is only a base resistance once the electrons reach their target of a larger substrate to connect to.  This is the basic resistivity of the quantum-mechanical contact resistance, there are other sources of contact resistance, such that as produced of poor coupling between the mesoscopic conductor and the leads.  Ballistic conduction occurs when the length of the conductor is smaller than the mean free path of the electron [6].  The lack of impurities that cause elastic scattering in the conduit material is imperative for this to occur; loss of quantization results if this happens. 
c.      Optical Properties of Silver Nanowires
Silver nanowires and silver-nanowire thin films have attracted much attention due to their extensive applications in Surface-Enhanced Raman Scattering (SERS) and Surface-Enhanced Fluorescence (SEF). [7] Silver nanowires are one of the most extensively studied metal nanostructures for applications in nanoelectronics and nanodevices, due to their novel optical and physical properties, such as dual plasmon resonance bands, strong SERS and SEF activity. [8-11] Plasmonics refers to the study of enhanced electromagnetic properties of metallic nanostructures. The term is derived from plasmons, the quanta associated with longitudinal waves propagating in matter through the collective motion of large numbers of electrons. [8]


Silver Nanowires can be used with other ions and nanoparticles attached to have enhanced fluorescence.  High enhancement of fluorescence emission, improved fluorophore photostability, and significant reduction of fluorescence lifetimes have been obtained from high aspect ratio (>100) silver (Ag) nanowires. These quantities are found to depend on the surface loading of Ag nanowires on glass slides, where the enhancement of fluorescence emission increases with the density of nanowires. The surface loading dependence was attributed to the creation of intense electric fields around the network of Ag nanowires and to the coupling of fluorophore excited states that takes place efficiently at 10 nm from the surface of nanowires, which was confirmed by theoretical calculations.  [12]

3.      Fabrication of Silver Nanowires

a.      Polyol Synthesis of Silver Nanowires

One of the methods used to synthesize Silver nanowires is the polyol process.  There are many different variables to take into consideration when doing polyol synthesis.  The variables are the temperature, injection ate, molar ratio of the precursor poly(vinylpyrrolidone) to silver nitrate, sodium chloride amount, and stirring rate. [14] The reagents to create Silver Nanowires using Polyol Synthesis is  Ethylene glycol (>99.0%, J. T. Baker), AgNO3 (99+%, Aldrich), polyvinylpyrrolidone (PVP, MW ≈ 55000, Aldrich), glycolaldehyde dimer (Aldrich), concentrated nitric acid (70% reagent grade, Aldrich),

concentrated hydrochloric acid (37% reagent grade, Aldrich), and acetone (reagent grade, Aldrich).[13] 
 The chemical kinetics can be mapped out and plotted to reach ideal diameter nanowires but that is not within the scope of this text.  This is a reliable method of making nanowires using self-assembly and heterogenous nucleation.

b.      Nano-Imprint and E-beam Evaporation

Nano-Imprinting via self-assembly is a popular method of fabricating silver nanowires, afterwards E-beam is used to evaporate the silicon.  This method of polymer stamping is very good because it can more accurately control the diameter of the Silver nanowires from a stamp.  The only limitation is the wavelength of UV-light which can get down to 15 nm.  Most nanowires desired length and diameter are much larger for their optical and mechanical properties to be tuned to the correct size.
This method is an efficient method of producing highly uniform nanowires that is relatively inexpensive compared to etching.  According to Feng “Over the past 60 years, numerous techniques have been developed to fabricate nanostructure



arrays, including electron-beam lithography (EBL), chemical vapor deposition (CVD), hydrothermal growth, reactive inkjet printing (RIJ), and
template-assisted synthesis. The EBL and CVD methods are limited by their expensive and time-consuming processes.”[18]  This method appears to be one of the most efficient methods overall of producing Silver Nanowires.

c.      Electrochemical deposition

Although CVD is classically thought to be one of the worst methods of creating silver nanowires, a variation method has been tested using electrochemical deposition.  In a Nature 2016 paper a method for digitally controlled, optically induced electrochemical deposition was journaled.  Projected light patterns were used to induce an electrochemical reaction in a specialized sandwich-like microfluidic device composed of one indium tin oxide (ITO) glass electrode and an optically sensitive-layer-covered ITO electrode. Silver polyhedral nanoparticles, triangular and hexagonal nanoplates, and nanobelts were controllably synthesized in specific positions at which projected light was illuminated. The silver nanobelts had rectangular cross-sections with an average width of 300 nm and an average thickness of 100 nm. By controlling the applied voltage, frequency, and time, different silver nanostructure morphologies were obtained.[18]

d.       Hydrothermal Growth

Much effort has been devoted to the syntheses of silver nanowires, such as electrochemical techniques, templates, and carbon nanotubes, directed synthesis, and polymer-directed synthesis. Recently, Murphy and co-workers have reported a seedless and surfactantless wet chemical approach to prepare silver nanowires. Uniform silver nanowires with diameters of about 100 nm and lengths up to 500 mm were successfully prepared on a large scale through a simple hydrothermal process without the use of any surfactants or polymers. [19]

e.      Reactive inkjet Printing

The production of low-cost electronic devices will be revolutionized by the ability to print functional materials. Printed electronics involves the additive fabrication of devices on substrates and over large areas at relatively low temperatures. One of the simplest methods is inkjet printing, a non-contact, additive process which can deposit droplets of ink on a substrate in predefined patterns.  Conversely, metallic nanowires, most commonly found as silver nanowires (AgNWs), have recently demonstrated huge potential in transparent electrode applications. Highly conductive silver nanowire electrodes can be fabricated at low temperature and over large areas and are extremely flexible. They have demonstrated sheet resistances as low as 5 Ω/□ for transparencies of 90%. However, while many methods for the deposition of metallic nanowires have been investigated, direct writing of patterned, high-quality silver nanowire networks has proven extremely difficult. To the best of the our knowledge, very few papers have described direct writing of metallic nanowire traces, in this case using inkjet printing.[20] Of the papers that do exist, only one describes the printing of AgNWs in any detail.[21]

4.      Uses of Silver Nanowires

a.      Thin Films

One use of Silver Nanowires is to create organic solar cells.  Using core-shell nanowires, silver electrodes can be created.  I am not sure about the details of the construction of solar panel parts.  Organic photovoltaics (OPVs) are considered as a future alternative for conventional silicon based solar cells, owing to their low cost, ease of production and high-throughput. The transparent conducting electrode (TCE) is a fundamental component of OPVs. Traditionally, indium tin oxide (ITO) has been mainly utilized as a TCE in OPV applications due to its relatively high transparency and low sheet resistance. However, increasing demand for the optoelectronic devices has led to large fluctuations in ITO prices in the past decade and ITO is known to account more than 50% of the total cost of OPV devices. Thus, it is believed that development of solution-processable alternative materials is of great importance in reducing the cost of OPVs.  Silver nanowires have been proposed as a replacement for ITO films in solar cells. [22]
b.      Sensors
Silver Nanowires can be used as biosensors, not only because of their surface plasmonics being sensitive to biomolecules, but also as glucose sensors. [23]  Integrated sensors for sensing bodily measurements can be used also. [24]  Silver nanowire (AgNW) films are particularly promising because they can be fabricated economically from a readily available abundant metal and are flexible enough to enable larger screens or even electronic newspapers in the future. Now researchers from the Universities of Surrey and Sussex have shown that the industrial techniques used to fabricate the sensors in smart phones and tablets could just as easily incorporate AgNWs instead of ITO. [25]


c.      Transistors

Thin-film field-effect transistor is a fundamental component behind various modern electronics. The development of stretchable electronics poses fundamental challenges in developing new electronic materials for stretchable thin-film transistors that are mechanically compliant and solution processable.  Wearable and biomedical electronic applications demand advanced materials and electronic devices to simultaneously possess deformability, solid state, light weight, visual transparency and low processing cost. Stretchable thin-film field-effect transistor (TFT) would become a fundamental building component enabling a variety of stretchable electronic devices including displays, sensor arrays, thin-film circuits and identification tags for control, data-processing and communication. It has been recently reported intrinsically stretchable solid-state organic light-emitting devices (OLED) and simple pixelated displays. [26,27,28]


5.      Bibliography

[1]        F. Ma, K.W. Xu
Mater. Res., 21 (2006), p. 2810
[2]
M.T. McDowell, A.M. Leach, K. Gall
Nano. Lett., 8 (2008), p. 3613
[3]
J.R. Greer, J.T.M. De Hosson
Prog. Mater. Sci., 56 (2011), p. 654
[4]
Y. Zhu, Q. Qin, F. Xu, F. Fan, Y. Ding, T. Zhang, B.J. Wiley, Z.L. Wang
Phys. Rev. B, 85 (2012), p. 045443

[5]        Kobler, A., Beuth, T., Klöffel, T., Prang, R., Moosmann, M., Scherer, T., … Bitzek, E. (2015). Nanotwinned silver nanowires: Structure and mechanical properties. Acta Materialia, 92, 299–308. https://doi.org/10.1016/j.actamat.2015.02.041
[6] G. Cao, Nanostructures & nanomaterials: Synthesis, properties & applications, Imperial college Press, London (2004)
[7] Luu, Q., Doorn, J., Berry, M., Jiang, C., Lin, C., & May, P. (2011). Preparation and optical properties of silver nanowires and silver-nanowire thin films. Journal of Colloid and Interface Science, 356(1), 151-158. doi:10.1016/j.jcis.2010.12.077
[8] T. Vo-Dinh, A. Dhawan, S.J. Norton, C.G. Khoury, H.N. Wang, V. Misra, M.D. Gerhold
J. Phys. Chem. C, 114 (16) (2010), pp. 7480-7488
[9]
E. Galopin, J. Barbillat, Y. Coffinier, S. Szunerits, G. Patriarche, R. Boukherroub
ACS Appl. Mater. Interface, 1 (7) (2009), pp. 1396-1403
[10]
S.J. Lee, Z.Q. Guan, H.X. Xu, M. Moskovits
J. Phys. Chem. C, 111 (49) (2007), pp. 17985-17988
[11]
S.J. Zhuo, M.W. Shao, L.A. Cheng, R.H. Que, D.D.D. Ma, S.T. Lee
J. Appl. Phys., 108 (3) (2010), p. 034305

[12]  Abel, B., Coskun, S., Mohammed, M., Williams, R., Unalan, H. E., & Aslan, K. (2015). Metal-Enhanced Fluorescence from Silver Nanowires with High Aspect Ratio on Glass Slides for Biosensing Applications. The Journal of Physical Chemistry. C, Nanomaterials and Interfaces119(1), 675–684. http://doi.org.ncat.idm.oclc.org/10.1021/jp509040f
[13] Schuette, W. M., & Buhro, W. E. (2014). Polyol Synthesis of Silver Nanowires by Heterogeneous Nucleation; Mechanistic Aspects Influencing Nanowire Diameter and Length. Chemistry of Materials, 26(22), 6410–6417. https://doi.org/10.1021/cm502827b
[14] Coskun, S., Aksoy, B., & Unalan, H. E. (2011). Polyol Synthesis of Silver Nanowires: An Extensive Parametric Study. Crystal Growth & Design, 11(11), 4963–4969. https://doi.org/10.1021/cg200874g
            [15]
N.C. Bigall, B. Nandan, E.B. Gowd, A. Horechyy, A. EychmüllerHigh-resolution metal nanopatterning by means of switchable block copolymer templates
ACS Appl. Mater. Interfaces, 7 (2015), pp. 12559-12569
[16]
T. Jang, S. Kim, H. Jung, J. Chung, H. Kim, Y. Koh, J. SongLarge-scale nanopatterning of metal surfaces by target-ion induced plasma sputtering (TIPS)
RSC Adv., 6 (2016), pp. 23702-23708
            [17]
Erb, Denise. Nanopatterning via self-assembly: Highly ordered metal nanostructures on diblock copolymer templates. IKS institute seminar. No. DESY-2013-00812. DOOR-User, (2013).
[18] Feng, Yuyi. “Silver Nanowire Arrays: Fabrication and Applications.” University Konstanz, 2016, webcache.googleusercontent.com/search?q=cache:HCYsZ7GmVLUJ:https://kops.uni-konstanz.de/bitstream/handle/123456789/38932/Feng_0-406729.pdf%3Fsequence%3D3+&cd=4&hl=en&ct=clnk&gl=us.
[18] Li, P., Liu, N., Yu, H., Wang, F., Liu, L., Lee, G.-B., … Li, W. J. (2016). Silver nanostructures synthesis via optically induced electrochemical deposition. Scientific Reports, 6, 28035.
[19] K. K. Caswell, C. M. Bender, C. J. Murphy, Nano Lett. 2003, 3, 6 6 7– 669.
[20] Finn, D. J., Lotya, M., & Coleman, J. N. (2015). Inkjet Printing of Silver Nanowire Networks. ACS Applied Materials & Interfaces, 7(17), 9254–9261. https://doi.org/10.1021/acsami.5b01875
[21] WuJ.-T.HsuS. L.-C.TsaiM.-H.LiuY.-F.HwangW.-S. Direct Ink-Jet Printing of Silver Nitrate-Silver Nanowire Hybrid Inks To Fabricate Silver Conductive Lines J. Mater. Chem. 2012221559915605
[22] Basarir, F., Irani, F. S., Kosemen, A., Camic, B. T., Oytun, F., Tunaboylu, B., … Choi, H. (2017). Recent progresses on solution-processed silver nanowire based transparent conducting electrodes for organic solar cells. Materials Today Chemistry, 3, 60–72. https://doi.org/10.1016/j.mtchem.2017.02.001
[23] Wang, L., Gao, X., Jin, L., Wu, Q., Chen, Z., & Lin, X. (2013). Amperometric glucose biosensor based on silver nanowires and glucose oxidase. Sensors and Actuators B: Chemical, 176, 9–14. https://doi.org/10.1016/j.snb.2012.08.077
[24] Boland, C. S., Khan, U., Benameur, H., & Coleman, J. N. (2017). Surface coatings of silver nanowires lead to effective, high conductivity, high-strain, ultrathin sensors. Nanoscale, 9(46), 18507–18515. https://doi.org/10.1039/C7NR06685F
[25] Cann, M., Large, M. J., Henley, S. J., Milne, D., Sato, T., Chan, H., … Dalton, A. B. (2016). High performance transparent multi-touch sensors based on silver nanowires. Materials Today Communications, 7, 42–50. https://doi.org/10.1016/j.mtcomm.2016.03.005
[26]
Yu, Z., Niu, X., Liu, Z. & Pei, Q. Intrinsically stretchable polymer light-emitting devices using carbon nanotube-polymer composite electrodesAdv. Mater. 23, 3989–3994 (2011).
[27] Liang, J. et al. Silver nanowire percolation network soldered with graphene oxide at room temperature and its application for fully stretchable polymer light-emitting diodesACS Nano 8, 1590–1600 (2014).
[28] Liang, J., Li, L., Niu, X., Yu, Z. & Pei, Q. Elastomeric polymer light-emitting devices and displaysNat. Photonics 7, 817–824 (2013).





 

Saturday, April 21, 2018

A quick description of New Cancer detection, tumor fighting, and metastatic fighting cancer cures.

There are several techniques recently developed that allow for the detection, selective cell-destroying, and eventually, cure for Metastasis in early trials in the last 5 years.  These are breakthrough technologies, if released to the public, could greatly lengthen human lifespans. 
The first technology that will be discussed is the ability to screen for cancer.  Imaging techniques of in-vivo tumors have several techniques.  This fluophore in the first paper discussed is a targetable “activatable” fluophore that can be controlled to only fluoresce when a current target molecule: “fluorophores could be controlled and predicted precisely by using the concept of intramolecular photoinduced electron transfer”[1].  Several fluophores were created DPAX and DMAX for singlet oxygen, DAF, DAMBO and DACals for nitric oxide. HPF, APF and APC for highly reactive oxygen, NiSPYs for peroxynitrite, DNAT-Me for glutathione S-transferase, TG-BetalGal for Beta-galactosidase and some more.

These fluophores are specifically targeted for specific ions and compounds known in cancer cells.  The reactions of each to determine which fluophore for which molecule is beyond my skill in metallo-organic chemistry.  Here is the table.



In-vivo cancer visualization is created by making targeted activatable fluorescent imagine probes.  Novel acidic pH-activatable probes based on the BODIPY fluophore were developed using the PeT – based rational design strategies, and conjugated them to a cancer-targeting monoclonal antibody.  This agent is activated after endocytotic internalization by sensing the pH change in the lysosome.  Here is a chart of the functioning of the fluorescence dye.



This reaction occurs within 1 minute after being applied.  Tiny tumors less than 1 mm size were successfully detected based on the concept of signal activation by using cancer-specific antibodies labeled with acidic-pH activatable fluorescence probes.

After the cancer is detected there is a lot of research into which drugs to use to kill the cancer.  A programmed drug-delivery system that can transport different anticancer therapeutics to their distinct targets holds vast promise for cancer treatment.  Herein, a core–shell-based “nanodepot” consisting of a liposomal core and a crosslinked-gel shell (designated Gelipo) is developed for the sequential and site-specific delivery (SSSD) of tumor necrosis factor-related apoptosis inducing ligand (TRAIL) and doxorubicin (Dox). As a small-molecule drug intercalating the nuclear DNA, Dox is loaded in the aqueous core of the liposome, while TRAIL, acting on the death receptor (DR) on the plasma membrane, is encapsulated in the outer shell made of crosslinked hyaluronic acid (HA). The degradation of the HA shell by HAase that is concentrated in the tumor environment results in the rapid extracellular release of TRAIL and subsequent internalization of the liposomes. The parallel activity of TRAIL and Dox show synergistic anticancer efficacy. The half-maximal inhibitory concentration (IC 50 ) of TRAIL and Dox co-loaded Gelipo (TRAIL/Dox-Gelipo) toward human breast cancer (MDA-MB-231) cells is 83 ng mL –1 (Dox concentration), which presents a 5.9-fold increase in the cytotoxicity compared to 569 ng mL –1 of Dox-loaded Gelipo (Dox-Gelipo). Moreover, with the programmed choreography, Gelipo significantly improves the inhibition of the tumor growth in the MDA-MB-231 xenograft tumor animal model.[2]



The conventional chemotherapeutic drugs attack the tumors by interrupting processes or inhibiting substances essential for the replication and proliferation of the tumor cells. For example, co-delivery of doxorubicin (Dox) and paclitaxel (Ptx) by a polymeric nanoparticle could release both drugs simultaneously and efficiently within the cells. The released Ptx inhibits the intracytoplasmic microtubules disassembly that is required for cell proliferation, while Dox intercalates into the nuclear DNA and induced cell apoptosis. For the cancer gene therapy, siRNA for silencing the target genes in cancer cells and pDNA for implanting corrective genetic material into the cells, have been applied to coordinate with small-molecule drugs. A typical example involves a micellar nanocarrier for co-delivery of MDR-1 siRNA and Dox, the released siRNA in the cells downregulates the P-glycoprotein expression to improve the efficacy of Dox in the multi-drug resistant
cancer cells. [2]


Here is the results of the drugs administered to the tumor and the testing of various drugs. [C]  In vitro cytotoxicity of TRAIL-Gelipo, Dox-Gelipo and TRAIL/Dox-Gelipo after 30 min
of HAase pre-treatment toward MDA-MB-231 cells for 24 h.


As you can see, the chemical cocktail has a dramatic effect on the tumor after 14 days.  A further problem with tumors is the metastatic ability to grow in regions far away from the original tumor.  A recent technology was developed at Duke University to combat this.  Metastatic spread is the mechanism in more than 90% of cancer deaths, and current chemical options, such as systemic chemotherapy, are often ineffective.  This paper is about Synergistic Immuno Photothermal Therapy (SYMPHONY).    Immune checkpoint inhibition is a promising immunotherapy that aims to reverse signals from immunosuppressive tumor microenvironment. Programmed death-ligand 1 (PD-L1), a
protein overexpressed by many cancers, contributes to the suppression of the immune system and cancer immune evasion. PD-L1 binds to its receptor, PD-1 found on activated T cells, and inhibits
cytotoxic T-cell function, thus escaping the immune response. To reverse tumor-mediated immunosuppression, therapeutic anti-PD-1/PD-L1 antibodies have been designed to block the PD-L1/PD-1 interaction.  nanoparticle (NP)-mediated thermal therapy has recently demonstrated the potential to combine the advantages of precise cancer cell ablation with benefits of mild HT in tumor microenvironments. NPs have a natural propensity to extravasate from the tumor vascular network and accumulate in and around cancer cells due to the enhanced permeability and retention (EPR) effect. Among various types of nanoparticles, gold nanostars (GNS), whose sharp branches create a “lightning rod” effect that dramatically enhances the local electromagnetic (EM) field, are the most effective in converting light into heat for photothermal therapy (PTT).  The unique tip-enhanced plasmonics property of GNS can be optimally tuned in the near infrared (NIR) tissue optical window, where photons can travel further in healthy tissue to be ‘captured’ and converted into heat by GNS taken up preferentially in cancer cells. We have investigated the PEGylated GNS bio-distribution in mice as well as GNS uptake at both macroscopic and microscopic scales by using radiolabeling, CT and optical imaging methods. In addition, a recent toxicity study of aptamer-loaded GNS found no signs of acute toxicity.



In this experiment, only 1 tumor was treated, and the other tumor was not.  It was found that using the GNS+Laser+Anti-PD-L1 had the only group of mice that survived the cancer at all.  It is shown that this cocktail has the only chance of survival in bladder cancer tumors for any mice at all.

Hopefully, these cancer treatments will assist in the future for detecting, treating, and then treating metastatic cancer in the future and can be applied to human use.  Unfortunately, even in experiments, the survival rate is not very high at this point for cancer cures, but some chance of survival is better than no chance of survival.  It appears that Phototherapy, in addition with chemical cocktails gives the best chance of survival.

[1] Urano, Yasuteru. “Novel Live Imaging Techniques of Cellular Functions and in Vivo Tumors Based on Precise Design of Small Molecule-Based ‘Activatable’ Fluorescence Probes.” Current Opinion in Chemical Biology, vol. 16, no. 5-6, 2012, pp. 602–608., doi:10.1016/j.cbpa.2012.10.023.
[2] Jiang, Tianyue, et al. “Gel-Liposome-Mediated Co-Delivery of Anticancer Membrane-Associated Proteins and Small-Molecule Drugs for Enhanced Therapeutic Efficacy.” Advanced Functional Materials, vol. 24, no. 16, 2014, pp. 2295–2304., doi:10.1002/adfm.201303222.

Liu, Yang, et al. “Synergistic Immuno Photothermal Nanotherapy (SYMPHONY) for the Treatment of Unresectable and Metastatic Cancers.” Scientific Reports, vol. 7, no. 1, 2017, doi:10.1038/s41598-017-09116-1.

Sunday, November 16, 2014

Econometrics paper (Difficult Subject)

.


 

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 14-year 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 R2-type 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 non-linear models there are multiple different ways to calculate R2-type measures, with no consensus as to which should be used. For non-linear models, goodness-of-fit 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 neo-classical production function, differences in output per capita can be attrib-uted 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 long-run 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 out-of-state 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.


 



 


 

"Low-paid, 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 under-class of low-skilled people whose jobs have been automated, according to a joint report from Deloitte, the Big Four accountancy firm, and the University of Oxford.[18]"


 


 


 


 

Year

Entrepeneurship Rate

Unemployment Rate

Median home price

Entrepeneurship

1996

0.33

0.28

160000

210000

1997

0.27

0.26

165000

185000

1998

0.28

0.24

167000

180000

1999

0.26

0.23

180000

182000

2000

0.27

0.24

182000

183000

2001

0.26

0.23

185000

185000

2002

0.28

0.25

187000

190000

2003

0.29

0.27

205000

210000

2004

0.29

0.26

215000

215000

2005

0.28

0.27

220000

225000

2006

0.27

0.24

240000

185000

2007

0.31

0.24

230000

192000

2008

0.33

0.27

210000

210000

2009

0.34

0.38

185000

225000

2010

0.35

0.4

175000

235000


 


 


 


 

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

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.165243

0.026801

6.165666

3.4E-05

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

9.57E-05

9.57E-05

0.098195

0.758977

   

Residual

13

0.012664

0.000974

     

Total

14

0.01276

  

  

  

   
         

  

Coefficients

Standard Error

t Stat

P-value

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

-1.1E-07

3.38E-07

-0.31336

0.758977

-8.4E-07

6.24E-07

-8.4E-07

6.24E-07

         
         
         


 

CONCLUSION:


 

Through many tests of Data analysis, regression, studies of quotations, that has shown through multiple T-Tests, 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:


 

References

  1. Baharum, A., & Ataharul, M. The analysis of transitions in economic performance using co variate dependent statistical models.Journal of Developing Areas, 43(2), 289-298.
  2. Chien-Chung Nieh, Russel, P. S., Hung, K., & Ya-Kai Chang. The asymmetric impact of financial intermediaries development on economic growth. International Journal of Finance, 21(2), 6035-6069.
  3. Chris Forman, Avi Goldfarb, & Shane Greenstein. The internet and local wages: A puzzle. The American Economic Review, 102(1), 556-575.
  4. De V. Cavalcanti, T. V., & Novo, Á. A. Institutions and economic development: How strong is the relation? Empirical Economics, 30(2), 263-276.
  5. Edelman, B. Using internet data for economic research. Journal of Economic Perspectives, 26(2), 189-206.
  6. Einav, L., & Levin, J. The data revolution and economic analysis. NBER Innovation Policy & the Economy (University of Chicago Press), 14(1), 1-24.
  7. Fairlie, R. W. Entrepreneurship, economic conditions, and the great recession. Journal of Economics & Management Strategy, 22(2), 207-231.
  8. Florens, J., Johannes, J., & Van Bellegem, S. Instrumental regression in partially linear models. Econometrics Journal, 15(2), 304-324.
  9. Kearns, B., Ara, R., Wailoo, A., Manca, A., Alava, M., Abrams, K., et al. Good practice guidelines for the use of statistical regression models in economic evaluations. Pharmacoeconomics, 31(8), 643-652.
  10. KORNIOTIS, G. M., & KUMAR, A. State-level business cycles and local return predictability. Journal of Finance, 68(3), 1037-1096.
  11. Naiana, Ţ., Teodora, V., Cristian, C., & Ioan, Ţ. Study regarding the use of spreadsheet applications in the economic field. Annals of the University of Oradea, Economic Science Series, 19(1), 834-837.
  12. Nelson, N. E., & Germani, P. J. The use of economic data in collective bargaining. Labor Law Journal, 38(11), 715-719.
  13. Rodrigues, J. F. D. A bayesian approach to the balancing of statistical economic data. Entropy, 16(3), 1243-1271.
  14. Rodríguez, F. What can we really learn from growth regressions? Challenge (05775132), 51(4), 55-69.
  15. Webber, D. J., & Mearman, A. Students' perceptions of economics: Identifying demand for further study. Applied Economics, 44(9), 1121-1132.
  16. Cooper NJ, Sutton AJ, Mugford M, Abrams KR. Use of Bayesian Markov Chain Monte Carlo methods to model cost-of-illness data. Med Decision Mak. 2003;23(1):38–53.
  17. North D (1990) Institutions, institutional change, and economic performance. Cambridge University Press, Cambridge.

18. Ten Million Jobs at Risk from Advancing Technology." The Telegraph. Telegraph Media Group, 28 Apr. 0010. Web. 10 Nov. 2014.