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Loans and its Type

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Title Loan

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loans

Loans on Installment

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Credit Loans

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Secure your Information

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Your Comfort and Convenience

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Confidence

Talking about online lenders, then you don’t need to worry about security reasons. The website, who provide lenders have a license which issued by the government. So, there are fewer chances of mischief. The transaction is highly secured with encryption code. These codes are responsible for making your information confidentially on the network. The reputation of the lenders depends on upon the rating rate and you can choose your lender depending upon your requirement. Some of the websites makes the recommendation for the lender and to go with these lenders is the most secure option. The recommended lenders use to put their terms and condition clearly, which reduce chances of conflicts.

Applying for Loans

Now, the question arises how to access such a great facility. You can access these facilities in such easy manner. There is website, who offers such kind of service via online application and some of them make you access facility of speedy loan via phone. While some offer both options. The online applications are acceptable for twenty-four hours, while telephone service is not available for all hours. If the store is within your reach you can visit the store and make yourself more confident on the deal of loan. But, if you go for the online option then also you can remain confident for your deal.

Rating of Speedy Payday Loans

creditorData sources

The main data sources are the long-term and short-term borrowings data of the publicly-held companies contained in the database of the Taiwan Economic Journal together with the financial statements of these companies and financial data kept by relationship creditor banks. If part of the sample is missing any data, we will search for it from the Market Observation Post System (MOPS) or banking website. For materiality considerations, observations in this study possess two characteristics: the largest borrowing amount of the sample company in the current year; the relationship creditor bank must be on the list of the top 20 creditor banks. According to the statistics, the average firms’ borrowing amount from the largest creditor banks is NT$765 million; from the second largest creditor banks it is NT$389 million on average; the median from the 20th is NT$100 million. As mentioned before, we aim to understand after the first and second financial reforms the impact of the WOBD and HCCL events, and how the banks have set their loan spreads in response to the borrowers’ credit state and banking relationship. Therefore, our empirical periods purposely select the period 2006-200819, and include 513 listed companies, 404 OTC companies, 133 emerging market companies and 160 companies that are publicly-held but not belonging to the former three. Due to bank credit officers requiring the borrower to provide financial statements for the past three years as an important basis for credit ratings, therefore the corresponding empirical periods are 2003-2008 for borrowers.

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PRODUCTIVITY DIFFERENCES: The Basic Model 2

Therefore, increases in Nh (Nl) improve the productivity of skilled (unskilled) workers in all sectors. Nh and Nl are the only state variables of this economy.

R&D (in the North) leads to the discovery of new machine types (blueprints). We assume that technical change is directed, in the sense that the degree to which new technologies are skill-complementary is determined endogenously (see Acemoglu, 1998). Some firms improve technologies complementing unskilled workers, while others work to invent skill-complementary machines. The cost of discovering a new machine complementing workers of group z (z ~ L от H) is 1 /фг units of final output, so Nz = фг • Xz where Xz denotes total output devoted to improving the technology of group z. We assume that фг — ф(хг), фг w6879-4 Continue reading »

PRODUCTIVITY DIFFERENCES: The Basic Model

New technologies are developed using final output. As we will see shortly, due to a market size effect in the creation of new technologies, countries in the South will perform no R&D. All technological progress will therefore originate in the North. But the South can adopt these technologies. All consumers have linear preferences given by f Ce~ridt, where С is consumption and r is the discount rate, which will also be the interest rate. We suppress time indexes when this causes no confusion.

Technology

We first describe the production technology which is common across countries, and the R&D technology in the North. To simplify notation, we omit the country indexes for
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where kz(i, v) is the quantity of machines of type v used in sector i together with workers of skill level г (i.e. this is sector and skill-specific capital). There is a continuum of machines, denoted by j [0,A^], that can be used with unskilled workers, and a continuum of machines (different) j € [0, N# ] used with skilled workers. Technical progress in this economy will take the form of increases in NL and JV#, that is, technical change expands the range of machines that can be used with unskilled and skilled workers.
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PRODUCTIVITY DIFFERENCES: Introduction 4


Second, our results do not follow because productivity depends on the exact capital-labor or skilled-unskilled labor ratios in use, but because skilled workers use different technologies than unskilled workers, and in the North skilled workers perform some of the tasks performed by unskilled workers in the South. Third, and perhaps most important, technological change is not an unintentional by-product of production, but a purposeful activity. In particular, R&D firms in the North direct their innovations towards different technologies depending on relative profitability. All our results originate from the fact that the relative abundance of skills in the North induces “skill-biased” innovations. In this respect, our model is closely related to Acemoglu (1998), which models directed technical change, but primarily focuses on its implications for wage inequality.

Finally, there is now a large literature on innovation, imitation and technology transfer, for example, Vernon (1966), Krugman (1979), Grossman and Helpman (1991), River a-Batiz and Romer (1991), Eaton and Kortum (1997) and Barro and Sala-i-Martin (1997). Some of these models, as well as the more traditional models of trade and innovations, such as Krugman (1987), Feenstra (1991) and Young (1991), obtain the result that trade may reduce the growth rate of less developed countries, but the channel is very different.
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PRODUCTIVITY DIFFERENCES: Introduction 3

When property rights are enforced internationally, firms in the North have more incentive to develop technologies suited to the South, and output per worker differences decline. However, each less developed country individually benefits from not enforcing these rights, creating a potential for a classic Prisoner’s Dilemma.

Finally, our theory suggests a stylized pattern of cross-country convergence in productivity and GDP. A less developed country diverges from the technological leader when it chooses to use local technologies for which there is no (or little) R&D, but eventually cross-country productivity and income differences tend to become stable as the LDCs start importing the technologies developed in the North. On the other hand, productivity (and income) convergence occurs when a country improves its skill base relative to the North, which concurs with the experiences of Korea and Japan (see for example, Rhee, Ross-Larson and Pursell, 1984; Lockwood, 1968).

The two building blocks of our approach, that most technologies are developed in the North and that these technologies are designed for the needs of these richer economies (directed technical change), appear plausible. For example, over 90% of the R&D expenditure in the world is carried on in the OECD, and over 35% is in the U.S..

Moreover, many recent technologies developed in the North appear to be highly skill-complementary and substitute skilled workers for tasks previously performed by the unskilled (e.g. Katz and Murphy, 1992; Berman, Bound and Machin, 1998). So it should perhaps not be surprising that there are many examples of developing countries, abundant in unskilled workers, which adopt labor-saving technologies requiring specialized technical skills. This has led many development economists, like Frances Stewart (1977, p. xii), to conclude that “…the technology Third World countries get from rich countries is inappropriate”, which is consistent with the approach in this paper.
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A number of other papers have emphasized the difficulties in adapting advanced technologies to the needs of the LDCs. Evanson and Westphal (1995) suggest that new technologies require a large amount of tacit knowledge, which cannot be transferred, slowing down the process of technological convergence. The importance of “appropriateness” of technology has also received some attention, for example Salter (1966), Atkinson and Stiglitz (1969) and David (1974). Diwan and Rodrik (1991) use some of the insights of this literature to discuss the incentives of Southern countries to enforce intellectual property rights, as we do in Section V.

An important recent contribution to the appropriate technology literature is Basu and Weil (1998), who adopt the formulation of Atkinson and Stiglitz whereby technological change takes the form of learning-by-doing and influences productivity at the capital labor ratio currently in use (see also Temple, 1998). Basu and Weil characterize the equilibrium in a two-country world where the less advanced economy receives productivity gains from the improvements in the more advanced economy. Our paper differs from Basu and Weil, in particular, and the rest of the appropriate technology literature, in general, in a number of ways. First, what matters in our theory is not capital-labor ratios (as in Atkinson and Stiglitz and Basu and Weil) or size of plants (as in Stewart), but relative supplies of skills, which we believe to be more important in practice.

PRODUCTIVITY DIFFERENCES: Introduction 2

Our model gives a simple expression for output per worker as a function of the ratio of capital per worker, ratio of skilled to unskilled workers, and the equilibrium skill-bias in the North’s technology. By considering the U.S. as the North, we perform some back-of-the-envelope calculations. These exercises suggest that the differences predicted by our model are sizeable, and significantly larger than those predicted by a simple “neoclassical” model. More concretely, for example, using cross-country variations in physical and human capital (secondary school attainment), we find that the neo-classical model predicts, on average, that output per worker in the LDCs should be approximately 40% of the U.S.

while our model predicts the same number to be 23%, much closer to the 21% number we observe in the data. Moreover, our calculations suggest that if technologies were not biased towards the needs of the U.S. economy, output per worker differences would be much smaller. For example, when technologies are appropriate to the needs of the “average” country in our sample, predicted differences in output per worker axe reduced by a factor of more than two.
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PRODUCTIVITY DIFFERENCES: Introduction


Most economists view technological differences as an important part of the large disparities in per capita income across countries. For example, Paul Romer (1993, p. 543) argues that many nations are poor, in large part, “…because their citizens do not have access to the ideas that are used in industrial nations to generate economic value.” (see also Prescott, 1998). This view receives support from a number of recent studies, such as Klenow and Rodriguez (1997), Caselli et al. (1997), and Hall and Jones (1998), which find significant “total factor productivity” (TFP) differences across countries. Large crosscountry differences in technology are difficult to understand, however.

Ideas, perhaps the most important ingredient of technologies, can flow freely across countries, and machines, which embed better technologies, can be imported by less developed countries. This compelling argument has motivated papers such as Mankiw, Romer and Weil (1992), Mankiw (1995), Chari, Kehoe and McGrattan (1997), Parente, Rogerson and Wright (1998) and Jovanovic and Rob (1998) to model cross-country income differences as purely driven by differences in factors rather than in technology.

In this paper, we argue that even when all countries have access to the same set of technologies, there will be large productivity differences among them.1 The center-piece of our approach is that many technologies used by less developed countries (LDCs/the South) are imported from more advanced countries (the North) and, as such, are designed to make optimal use of the prevailing factors and conditions in these richer countries.
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FINANCIAL MARKETS’ ASSESSMENT: Conclusion


Interest rate data were obtained from two sources. The Bank for International Settlements provided daily 3-, 6-, and 12-month Eurocurrency deposit rates for 21 countries, collected around 10 AM Swiss time. The data begin on January 3, 1977 for five currencies (German mark, Dutch guilder, Swiss franc, pound, and U.S. dollar), on September 1, 1977 for most other currencies, and run through August 31, 1998.

Swap rate data were downloaded from Datastream for nine European countries. Swap rates are the European coupon rates in a semiannual exchange of fixed European interest payments against floating dollar interest rate payments indexed to the Eurodollar interest rate; principal is also exchanged at maturity. Since the dollar floating-rate cash flows are at par at the swap’s inception, swap rates provide the European coupon rate such that a European bond with semiannual coupons would be at par — or, equivalently, the European semiannual yields to maturity, times two. Swap rate data are more readily available than country-specific bond yields. As with other Euromarket data, swap rates also alleviate concerns about country-specific default risk, capital controls, and tax issues, as well as eliminating heterogeneity across national data sources.
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FINANCIAL MARKETS’ ASSESSMENT: Prospects for the Future 2

Filtration-based assessments

As indicated above in Figure 3, forward rates for all countries participating in the currency union have currently essentially converged to a common “Euro” term structure. Some insights into financial markets’ assessment of the future interest rate policies of the ECB relative to earlier Bundesbank policies can potentially be gleaned by comparing this yield curve with historical German norms. To this end, a state space representation of the vector of Eurocurrency deposit rates and swap rates for German instruments maturing prior to January 1, 1999 was estimated via Kalman filtration on weekly data:
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where y t is a 9 x 1 vector of 3-, 6-, and 12-month Euro-DM deposit rates and 1-7 year swap rates (excluding 6-year rates), expressed as continuously compounded yields; and zt is a 4 x 1 vector of underlying state variables.

The Euro-DM data were available from January 5, 1997, while most of the swap rate data began in June 24, 1991.22 Data for post-1998 maturities were treated as missing data, with only 3-and 6-month Euro-DM rates available for inference on the final date of July 1, 1998. The estimated state space model summarizes the time-series based information contained in the level and shape of the German term structure with regard to future term structure evolution, as well as the current assessment of the state vector conditional on pre-1999 maturities. The model was used to address two questions:

1. How does the current term structure of German swap yields compare with the German pre-1999 norm represented by the final filtered value yT|T ?

2. How does the current term structure of German forward rates compare with the future 3-month spot rates that would be predicted based upon pre-1999 norms?
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