QUESTION
MICROECONOMICS 200 ASSIGNMENT SEMESTER 1 2012
Biofuels are increasingly being used as an alternative source of fuel. This is due both to constraints
on the supply of oil and concerns over climate change. However, whilst biofuels can lower fuel costs
and reduce CO2 emissions, they are associated with higher food prices. (Useful background
information on the economics of biofuels can be found at:
siteresources.worldbank.org/INTARD/Resources/zilber_worldBank.pdf. *A copy of this document is
also in the assignment folder.)
This assignment challenges you to apply microeconomic models and concepts to the issues raised by
biofuels. There are 3 parts to the challenge, each of equal weight in the final mark.
Part A: Your first task is to use models and concepts relating to producer behaviour to analyse the
effects of increased demand for biofuels on the inputs to its production (including crops,
such as corn).
i. Analyse the impacts of increased demand for biofuels on the demand for and price
of crops such as corn.
ii. Analyse how developments in agricultural and conversion technology might
influence the impacts identified in Part A (i).
iii. Analyse the effects of increased competition between participants in the oil market
on the impacts identified in Part A (i).
Part B: Your second task is to use models and concepts relating to consumer behaviour to analyse
the effects of changing fuel and crop prices on individuals.
i. Analyse the impact of an increase in the price of crops and a (proportionately
smaller) decrease in the price of fuel on a low income person who spends most of
her income on food (derived from crops).
ii. Repeat the Part B (i) analysis for a high income person who spends a relatively small
proportion of her income on food. Comment on the distributional consequences of
the changes in the price levels.
SOLUTION
Part A
(i). There was an increase in the demand of bio-fuel by 18 cars/1000 people in China vs. 800 in US. Two main factors responsible for agricultural growth were identified: area expansion and intensification of land. Indeed, these are the two ‘margins’ along which expansion or substitutions can take place – either through more extensive displacement of the agricultural landscape or more intensive use of inputs. There might be constraints to one of these factors (e.g. limited availability of good quality land), which means production growth has to rely more on the intensification. These issues are, of course, relevant to the discussion on the sustainability of biofuels. As a result, IFPRI has been looking at these issues with a view to assessing the role of biofuels among other major ‘drivers of change’ in global food systems and to highlight issues that countries should pay attention to. The model components which matter to the analysis are:
• Disaggregation between irrigated and rainfed area – one can increase yield by expanding more on irrigated versus rainfed
• Sub-national disaggregation of crop area — gives a better idea of where production changes occur (especially for big regions – US, China, India, Brazil). There are 281 spatial units, but more work needs to be done here.
• Price response for yield as well as for area – allows for yields to increase due to price effects, as well as due to irrigation and technological change (however technological change is not endogenized to price, at the moment) The increase in production needed to meet demand can come from either additional yield on existing land or achieving a sufficient level of production on new land. In terms of the marginal calculations made in order to determine where new production will come from.
(ii). Analysis
The report makes clear that, except for scenario 1, the increase in global biofuel production in the “1Mtoe” biofuels scenarios (compared to baseline) is reduced to about 0.9 Mtoe, by resulting reductions in biofuel production outside the target country. A production increase of 1 Mtoe of biodiesel in Germany (scenario 1) corresponds to 1.08 million tonnes (Mt) of biodiesel (N.B. These figures differ slightly from JEC-WTW figures possibly due to the difference between Lower Heating Values, LHV, and Higher Heating value, HHV, definitions). However, price changes mean that a little less biodiesel is produced in the rest of the world, so that the net world increase is only 1.06 Mt, whilst world ethanol production falls by 0.11 Mtoe. For some reason, extra ethanol production in France leads to extra ethanol production elsewhere. Counterintuitive results from increased ethanol demand in the US (scenario 3) have also been presented. Land demand and agricultural production slightly decrease in South America, despite increasing in the world by 817 km2. This may be an artefact caused by the lack of disaggregation in the model between vegetable oil and oilseed meal: DDGS production in US reduces not only soybean meal imports, but (automatically) soybean oil imports from South America. There is also a smaller loss of grazing land in S. America. There is also a tiny reduction in EU land use in the US corn scenario, which may be the result of lower biofuel production in EU. It was suggested that this could not happen if the biofuel use in EU is modelled as an obligation rather than a subsidy.
(iii). The effect of the prices generally increase under all scenarios, but the effect is ten times greater in the EU, where each Mtoe increase-demand-for-biodiesel-in-Germany increases arable land price by 1.4%. (n.b. EU 10% biofuel target is ~30 Mtoe). Feed prices are not significantly affected in any scenario. Contrary to FAPRI-CARD model results, livestock production decreases in both EU and USA in the German biodiesel scenario (slide 27). This is because LEITAP takes into account the effect of land price on livestock production. However the value of net livestock exports increases slightly due to higher prices, especially in EU. In the US corn-ethanol scenario, the effect of DDGS on decreasing feed price dominates over increased land price, and that increases US livestock production and decreases US livestock price. All biofuels scenarios slightly reduce food consumption in EU and US compared to baseline, (except for the US maize ethanol scenario in US).
Conclusions:
The qualitative results can be explained with some plausible mechanisms
Especially the uncertainties with respect to land use decrease in South America and the EU27 have been solved.
General equilibrium effects seem to be important through Land supply curves Feed-land and feed substitution Crude oil prices
Size of the effects depends on data.
We are working to get a better controlled database of land use Nevertheless, for specific products like palm oil, a correction factor must be included
Part B
(i). The marginal calculations were made with a modified version of the GTAP general equilibrium model (Hertel, T. W., W. E. Tyner and D. K. Birur (forthcoming). “Global Impacts of Biofuels.” Energy Journal 31(1): 75-100). Some elements of the model and the data base used in the analysis reported here differ from the data and model used for the California Air Resources Board. With respect to data base, the differences include representation of oilseeds biodiesel production in all regions and EU wheat ethanol sector. Important model structure modifications include more nested structure of animal feed producing sectors. The baseline represents the world economy in 2001, with 87 GTAP regions aggregated into 19. Within each region, land endowment is divided into Agro-Ecological Zones (AEZ)13. There may be as many as 18 AEZs in a region. The reason why GTAP v6 was used instead of GTAPv7 (representing world economy in 2004) is because land use data consistent with global economic data are only available for 2001.
(ii). Additional model specifications, such as land use change mechanism, production structure, substitution among livestock feeds etc., may be found in the slides. In the GTAP model, crop replacement depends on trading patterns. The Armington approach is used here, instead of an integrated-world-market assumption. With this approach, the composition of trade (that determines land use change patterns) is not fixed, but it tends to be concentrated on the country where the demand occurs and its major trade partners. The stickiness of (1) the composition of trade and (2) the mix of imported and domestic goods depends on the elasticities of substitution among imports from different sources (regions in the model), and elasticities between imported and domestic goods, respectively. The equilibrium of market is a established concept of equilibrium (Callan, Thomas, 2007) of the economy, which is accurate and appropriate for commodity market analysis with lithe prices and several traders. It is also helping as the standard for the effectiveness in the study of the economy. This equilibrium relies basically on the expectation of an aggressive environment, wherein every single trader chooses regarding the quantity that is little enough in comparison with the trading of the total quantity. Here is the practical representation that why and how the company can earn profit in zero equilibrium.
Part C
The expectation is dependent on specific conditions or expectations. It is important to measure and understand that why this model makes the result that it does. 14 Whether the Armington structure increases or decreases net global land requirement relative to the integrated world market assumption depends on relative yields. As an example, the case of US coarse grains can be considered: US coarse grains yields are the highest in the world. When one hectare of corn grown for food is displaced by one hectare of corn for fuel in US, more than one hectare in the rest of the world will be needed to cover the shortage of corn for food. In the integrated world market assumption, the shock originated in US is more easily transmitted through the global economy than it is using the Armington approach. Because US corn yields are higher than corn yields in other regions of the world, the net global land requirement under integrated world market will be higher than under Armington assumption. The situation is opposite with EU biodiesel.
REFERENCES
Majone, G. 1996. Regulating Europe, London, Routledge.
Dorfman, S. Mark. 2007. Introduction to Risk Management and Insurance. Englewood Cliffs. N.J: Prentice Hall. ISBN 0-13-224227-3.
Peltzman, S. 1979. Toward a More General Theory of Regulation. Journal of Law and Economics. V 19. pp 211-240.
Blanchard, Olivier. 2000. Macroeconomics. Prentice Hall. ISBN 013013306X.
Keynes, John Maynard. 1919. The Economic Consequences of the Peace. New Brunswick: Transaction Publishers. ISBN0765805294.
Stein, L. Jerome. 1982. Monetarist, Keynesian & New classical economics. Oxford: Blackwell. ISBN0631129081.
KF51
But you can order it from our service and receive complete high-quality custom paper. Our service offers Economics essay sample that was written by professional writer. If you like one, you have an opportunity to buy a similar paper. Any of the academic papers will be written from scratch, according to all customers’ specifications, expectations and highest standards.”
