As shown in column 1 of Table 7, we find, as predicted, a positive coefficient on Cash: Farm revenues are higher in villages where households received cash transfers (and hence where food prices rose) relative to control villages by 490 pesos. Similarly, we find that farm revenues are lower in in-kind villages relative to cash villages by 290 pesos. The effect of the transfers on revenues appears to be due partly to the direct price change, holding quantities fixed, and also to adjustments in production. We do not have data on quantity produced, only the monetary value of production, but the fact that profits change by a smaller amount than revenues (column 2) suggests that farmers expanded or contracted the quantity they produced in response to the price changes. In other words, in cash villages, a farmer receives higher revenues both because she earns more per unit sold and because she sells more units. Of course, price effects are not the only reason that transfers might affect farm produc- tion. If farmers are credit constrained, then the income effect of the program might lead to more investment and increased production. Through this channel, for both the cash and in- kind treatment, one expects an increase in farm revenues (and either an increase or decrease in profits depending on whether long-run investments were also made), though there is no obvious reason that the credit-constraint channel would cause differential effects for cash versus in-kind villages.
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We analyze the factors that affect security issuance and its impacts on the volatility of stock returns before and after the issuance under heterogeneous beliefs and short sale constraints using the issuing sample of convertible bonds, corporate bonds and stocks in China’s security market during 2007 to 2012. The empirical tests identify that abnormal turnover can portray heterogeneous beliefs much better than dispersion in analysts’ earnings forecasts. Firms’ pecking order of external financing is convertible bonds, stocks and corporate bonds for the bigger firm size and higher market to book ratio. However, for those firms with higher leverage, issue amount and market volume, their optimal options are stocks, convertible bonds and then corporate bonds. The empirical results also reveal that convertible bonds issuance has a negative impact on price- earnings, but this effect diminishes in the long run. Besides, the influence of stock issuance on its return is negative only on the seasoned equity offering day. However, there is no significant effect on the yield rate in a short-term if corporate bonds are issued. Heterogeneous beliefs have a significant impact on the stock return, while negative for convertible bonds and positive for stocks and corporate bonds.
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Estimates experience little change when labor earnings are included among the regressors (see columns (3) and (4) of Table 2). Again, the main exception to this is the positive effect of education on internet usage, which becomes larger and statistically significant once earnings are controlled for. As to the household income indicators, their estimated coefficients decrease only slightly in both the adoption and usage equations. Hence, and given that we are now holding fixed the respondent’s labor earnings category, which can be considered a proxy for the respondent’s opportunity price of internet usage, the inverse relationship between household income and usage seems the result of time spent online being an inferior leisure activity. The estimated effect of labor earnings on time spent online presents, in general, negative sign, although usage does not decrease uniformly as labor earnings increase. Indeed, the reduction in usage of just 2 minutes estimated for a person earning at least €3000 per month in comparison with a similar person earning less than €500 does not attain statistical significance. Interestingly, keeping constant household income the probability of internet adoption tends to increase with the respondent’s labor earnings.
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A decrease in tariff rates reduces the prices of all selected traded goods significantly. Thus trade induced fall in the domestic prices would induce household demand of the goods. The reasonable magnitude of coefficients of all independent variables/tariff given international prices further confirms that the change in domestic prices due to tariff change is quiet sizeable. This change in the price of some traded goods is as high as 1.15% (Rice), 1.09% (Tea) and 1.01% (Other Vegetables) with one percent change in the tariff rates in the same direction. The signs of coefficients of independent variables in Household demand functions for all selected traded and nontraded goods are in line with the theory of demand except Sugar price. The link between prices of traded and nontraded goods is also proved significantly in the stepwise regressions of Firewood and Electricity prices on prices of all selected traded goods. The magnitudes of constants in both cases are relatively large due to other factors influencing the prices of traded goods such as technology and factor costs along with prices of traded goods.
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Within IntERACT, we rely on the CGE model to determine the activity and price effects for the ten final energy using industrial sectors. Armington elasticities are used to model trade, i.e., foreign goods are imperfect substitutes for domestically produced (Armington, 1969). The model consists of the following factor markets: labor, machinery capital, and building capital. Labor and capital markets are homogeneous and mobile (aside from the capital tied to energy service capacity). These properties, combined with the absence of price rigidity, make the model relevant as a long-term perspective. Two components determine the activity effect within the CGE model: i) the baseline GDP calibration, and; ii) the relative change in sectoral activity. For the baseline scenario, we calibrate the GDP growth rate in the CGE model to match the exogenous baseline path by adjusting a Hicks-neutral technology innovation index (Barro and Sala-I-Martin, 1995). The level of investments, foreign trade balance, as well as public sector activity, are fixed for each modeling year based on the same exogenous macroeconomic baseline. Hence, the baseline reflects a consistent long term projection for the Danish Economy. When using IntERACT for a policy scenario, we fix the Hicks-neutral technology innovation index at its baseline scenario level, which means that GDP becomes endogenous in the policy scenario.
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We consider the classical micro-economic foundation of discrete choice, additive random utility models, with conditional utilities depending on expenditure on the numéraire. We show that signs of own- and cross-price effects are identified on the basis of the primal problem only, and Giffen behaviour is ruled out. For the translog specification, we prove that the alternative with highest price behaves as normal good, and the alternative with lowest price behaves as inferior good. We establish conditions for equivalence between the primal and the dual problem. We provide a dis- crete choice version of the Slutsky equation which, similarly to divisible goods, decomposes the own-price effect into a substitution and an income effect.
Recently, it appears that economic growth plays a key role in industrial innovation (Malatyali, 2016). Since the first fluctuations in crude oil prices in 1973s, macroeconomists have viewed sharp movements in the oil price are generally as an important source of economic fluctuations, for example, Hamilton (2005) suggests that In the last few decades, nine of the ten recessions in the USA were preceded by large positive increases in crude oil price. Moreover, the very recent highs registered in the crude oil market are causing concern about slowing in the economies of many developed countries. After nearly four years of stability, the crude oil price more than halved in a period of fewer than five months from September 2014. The price of a barrel of Brent crude oil in European countries fell from than $100 p/b in Sept 2014 to less than $46 p/b in January 2015. The oil price has more than halved in less than eleven months since Sept 2014. Besides, the decline was the third largest over the past 30 years, has particularly
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source of the shock. The paper sampling analysis coverage for the period from 2010 to in this connection, and besides contribution of the current research paper is that the estimate our statistical tool with monthly data. Indeed, as the study will explain below, our empirical set up profoundly relies on delay constraints that are economically credible only at monthly frequency. More precisely, the paper finds that important huge and persistent fluctuations in the real price of oil of market from 2010 to 2018 have been related with the cumulative effects of crude oil demand rather than supply of oil shocks. The circumstance that flow supply quantity disruptions have had minor effects on the real oil price on the stock market does not mean that political dealings in the nation do not matter. On the conflicting, these kind of situation have exaggerated the real oil price by fluctuating expectations about forthcoming shortages of oil supply comparative to oil demand. In our data analysis these anticipations are captured by shocks on defensive demand for oil.
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Presently the research paper has drawn the conclusion based on the data analysis and findings, based on the outcome of the table no 1 the study found that the huge variations is there crude oil prices fluctuation and great draw back and insignificant impact on the economic development activity (2010 - 2018). In this concern, policymakers, financial analyst and economists have paid close thoughtfulness to alterations in internationally traded crude oil prices and apprehensive about the prospective impact of oil price shocks on Indian economic development activity. The study has examined the how the effect of crude oil price changes on the Indian economy fluctuates, the paper finds the fluctuation of economy indicators has dependent on the fundamental cause of the oil price shock, that is, whether oil price variations are motivated by shifts in oil demand or supply. Moreover, the paper find that the explicit cause of the oil price shock distresses the size and nature of the responses of foremost Indian macroeconomic aggregates. In specific, when an increase in oil prices is instigated by an oil supply disorder in India with this an insistent to deterioration in its GDP and a rise in the CPI inflation. The outcome of the table no -4 has revealed that the Indian Government debt volatility is mostly elucidated by foreign oil intensity shocks as these shocks cause main lead to create the fluctuations in Indian Government revenue. And the study found that the overseas oil shocks are the highest responsible of Indian crude oil price fluctuation. Further This research paper examined the impact of crude oil price shockwaves on the eight macroeconomic variables i.e. GDP, IIP, WPI, real output, real exchange rate interest rate and inflation using Structural Vector Autoregressive (SVAR) model during period from 2010 to 2018.
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An array of 25 different consumer durable items was selected on a judgment basis to represent a variety of products used in various consumer activities and pursuits. For each product line or class, a specific model was selected and specified by number and description; to the extent that identical models were available the same model numbers were used in 2011 as 2007. However, when model numbers were no longer available new model numbers were selected based on similarity in product description. Thus price comparisons within years are of precisely identical goods, and across years either identical or the most similar models available are compared. As we are not concerned with absolute price difference across time periods but the change in price dispersion, changes in model numbers between 2007 and 2011 is not problematic. These items were than submitted to price comparisons using both Yahoo! Shopping and Google Product Search sites. Only prices for new goods available directly from the online marketers' websites are shown, together with the ratio of high to low price in for each product example. Prices listed by auction sites (e.g., eBay) were ignored. Similarly, prices listed by sited specifying "new and used," (e.g., Amazon) as well as prices for "open box" or refurbished goods were excluded. The highest and lowest prices for exactly the same items were recorded from each site during July 2007 and then again in July 2011.
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We use a simple two-period model (now and the future) to show that if an amount of oil that is discovered is significant enough to reduce prices in the future, that drop in future prices reduces the future profitability of oil, reducing the marginal user costs or the scarcity rents of oil now. That reduction in the marginal user costs reduces the current price of oil just as if there were a reduction in the marginal costs of extracting oil now. We explore the effects of the reduction in marginal user costs because of a discovery that will not contribute to production for some years to come in three scenarios:1) a monopoly scenario, 2) a scenario with a monopolist or dominant supplier who responds to a substantial discovery by another seller, and 3) a scenario with price taking or competitive producers. We find that oil that is expected to reach the market some years hence has an immediate impact on oil prices.
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The effect of a decline in oil prices on the global economy has been highlighted in the media  . The oil price decline was supposed to catalyze a transfer of wealth from oil-producing countries to oil-consuming coun- tries. It was estimated that a $10-a-barrel fall in the oil price transfers around 0.5 percent of world GDP from oil exporters to oil importers. Increased spending by consumers in importing (mostly developed) countries will boost global output. It was estimated that a drop of $30 in the oil price (prolonged for a year) would transfer more than $400 billion from oil exporters to importers. China and Japan have large economies that spend more than other economies on net oil imports relative to GDP. Along with China and Japan, the Eurozone countries will also benefit a lot from the oil price drop. Countries like Nigeria and Venezuela that depend on crude oil for exports and government revenues were expected to be the most vulnerable.
positive impact on profits due to the tightening of capacity margin, i.e. more high-cost marginal generation needs to run to cover the higher demand; 2. Solar generation (GWh), which we generally expect to have a negative impact on profits because the typical mid-day peak price coincides with the period when solar generation produces the most, which causes a downward pressure on the peak/off-peak spread; 3. Wind generation (GWh), which we hypothesize to have mostly positive effect on profits due to the lower predictability of wind production causing greater spot price volatility; 4. Gas-coal price spread, defined as the difference between NBP gas price (p/therm) and API2 coal price (USD/t) presented in MWh thermal, which is a unit omitting assumptions about power plants’ efficiency. We expect this effect to be positive, i.e. higher spread implies higher gas prices (peak prices) relative to coal (baseload), but this will depend on the generation mix of a given power system; 5. EUA carbon price (EUR/t), which we generally expect to have negative effect on profits because carbon price affects coal (baseload) more than gas (peak-load), reducing the peak/off-peak spread. However, the effect of carbon price will highly depend on the generation mix of a power system in question; 6. Daily spot price volatility, which we measure as daily standard deviation based on hourly electricity spot prices and hypothesize to have a positive impact on profits; 7. Autoregressive (AR) terms, which we use to control for the high persistency of the time-series; and 8. Seasonal
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Findings in Table 6 show that nearly 60 percent of the sample craftsmen do not have any formal education. To see the effect of formal education of the craftsmen on sales revenue, raw bamboo price, final product price and proportion of sells to traders, Figure 3 has been developed. In Figure 3, sample craftsmen are divided into two groups: not formally educated and formally educated and then sells revenue, raw bamboo price, final product price and proportion of sells to traders are compared. The figure shows that formally educated craftsmen receive higher product price, buy high priced raw bamboo, though they receive less revenue and sell less to the traders compared to uneducated craftsmen. Although I do not have exact information why formally educated craftsmen earn less sells revenue, however, my assumption is probably educated craftsmen are the part timer in the business. They might also engage in other activities, such as agriculture and operates small shops. As a result, although formally educated craftsmen receive higher product price and use high priced raw bamboo, they tend to keep their operation size smaller (low sells revenue).
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Price promotions, especially the price bundling strategies, may create decoupling cost of purchase and benefits of consumption (Prelec & Loewenstein, 1998; Soman & Gourville, 2001). Soman & Gourville (2001) reveal in their investigations that the effects of sunk cost will be influenced by the decoupling the cost of product purchase and the benefits of product consumption. When the both are decoupled closely, the effects of sunk cost will increase the possibility of consumers’ consumptions. However, when the cost of product purchase and the benefits of product consumption are decoupled, the possibility of consumers’ consumption could be reduced, the requirement of compensation if there is no consumption and regret caused from product will be reduced. For consumers will face on cognitive difficulties about how much the cost should be shared to the delayed consumptions with the increasing consumptions and lapse of time. Price bundling strategy will increase the possibility of decoupling transaction cost and product benefits for consumers.
Researchers have applied linear and nonlinear methodologies to measure the effects of oil price shocks on macroeconomic variables. Most of the empirical studies have focused on analyzing the impact of oil price shocks on advanced economies. Starting from this fact, this paper contributes oil price shocks literature by analyzing the impact of oil price shocks on net oil importing developing Turkish economy. This paper departs from previous studies relating Turkish economy and oil price shocks by considering the effects of positive and negative oil price shocks on the Turkish economy. Turkey's economy expanded over the past two decades, and hence its oil consumption has increased. With scarce domestic oil reserves, more than 90% of its crude oil consumption came from imports. As a net oil importing economy, Turkey’s economy is exposed to oil price shocks. Therefore, it is important to analyze the impact of oil price shocks on Turkish economy. Furthermore, understanding the effects of oil price shocks on the Turkish economy might help to define appropriate policies to diminish the adverse effects of these shocks.
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Traditional models of economics presume that consumer utility for goods/services is determined by how much utility they will receive from a product, and then con- sumer accepts the transaction if the selling price is below his/her baseline price. The demand for a product is in- dependent of the actual utility given by the product. Past research has shown that consumers sometimes use price information as a proxy for quality. In other words, price information may be used as a heuristic by which people infer the relative merit of a product or service . Rele- vant to the current research, if price can increase ex- pected value, then in the case of products it may be able to modify not merely perception but the actual product performance via the placebo effect.
The oil price shock is considered as a major contributor to economic fluctuation. In this paper, we investigate whether the impulse responses of different macroeconomic variables and financial variables to the oil price shock and the effect of interest rates change. And we also use Granger Causality Test to evaluate the correlation between oil prices, stock markets and gold prices. Estimation results based on the U.S. data suggest that: (i) The oil price shock has a significant impact on inflation, stock markets and gold prices and it also has a short-term impact on interest rates. (ii) Co-movement of oil prices, stock markets and gold prices exist. (iii) Changing interest rates as monetary policy can induce price puzzle in order to reduce the inflation caused by the oil price shock.
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As Ghana continues to grow into high middle income status with her recent discovery and production of crude oil, the stakes are quite high that in the near future the country could wean herself oﬀ the high crude product importation. Nonetheless, Ghana is still a developing net-oil exporting economy with heavy dependence on energy. The veriﬁ cation of the relationship between oil price shocks and macroeconomic variables seems very essential for most policy implications and investment decisions. There is a large amount of literature on the eﬀ ects of oil shocks on developed economies which have largely driven theoretical suggestions about the oil price-macroeconomy relationship. Formal study on the impact of oil price ﬂ uctuations on Ghana as well as most developing oil importing economies seems to be lacking especially from Africa. This study departs from other papers in focusing on the impacts of oil shocks on the macroeconomy of a developing net oil exporter in the case of Ghana, thereby providing a fresh perspective into the oil-macroeconomy relationship. Applying the Johansen cointegration methodology as an empirical modeling, we examine the long-term relationship between the oil price shocks and the macroeconomy of Ghana.
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The aim of the Consumer Price Index is to measure the changes in the general price levels of the goods and services that are purchased by household in order to cover their needs. However, since it is not possible to monitor the prices of all of the goods and services in the market are impossible, the services and goods that have the biggest share in consumption expenses of the household are taken into consideration. The consumption expenses are classified according to their purposes (for example, food expenses, clothing expenses, health expenses, transport expenses, etc.), and it is ensured that none of the goods and service groups are excluded from the evaluations. Then, the goods and services that represent each group are put in order from the biggest to the smallest, and the goods and services that have higher values than a certain value (for example 1/1000) are included in the index. (TUIK, 2008; 3-4)
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