Estimate Supply Elasticities Research Articles

Estimating timber supply elasticity of private forest landowners in the US South

In the US South, private forest landowners own the majority of timberland; therefore, understanding the timber supply behavior of these landowners is a fundamental forest economic question. This study estimates the pine stumpage supply equations and elasticities of private landowners in TimberMart-South (TMS) regions. It uses individual landowner pine stumpage (sawtimber, pulpwood, and Chip-N-Saw [CNS]) sales data from 2010 to 2019 and employs the seemingly unrelated regression (SUR) to estimate supply elasticities. Over the study period, nominal sawtimber stumpage price has decreased, while CNS stumpage and pulpwood stumpage prices have not effectively changed. The SUR estimate results revealed that pine sawtimber (2.452) and CNS (1.675) stumpage supply were price elastic, while pine pulpwood stumpage supply was price inelastic (0.352). Furthermore, there was a significant and negative effect of sawtimber price on pulpwood and CNS volume supplied and vice-versa, however, the effect of the former was larger in magnitude. This study provides not only a nuanced understanding of landowners' supply elasticities of three principal stumpage products but also a valuable empirical basis to better understand the market outcomes and welfare changes that market participants might experience due to new policies and market shocks.

Energy Transition Metals: Bottleneck for Net-Zero Emissions?

Abstract The energy transition requires substantial amounts of metals, including copper, nickel, cobalt, and lithium. Are these metals a bottleneck? We identify metal-specific demand shocks, estimate supply elasticities, and study the price impact of the transition in a structural scenario analysis. Prices of these four metals would reach previous historical peaks but for an unprecedented, sustained period in a net-zero emissions scenario, potentially derailing the energy transition. Their production value would rise nearly four-fold to USD 11 trillion for the period 2021–2040. These four metals markets alone could become as important to the global economy as the oil market.

Energy Transition Metals

The energy transition requires substantial amounts of metals such as copper, nickel, cobalt and lithium. Are these metals a key bottleneck? We identify metal-specific demand shocks, estimate supply elasticities and pin down the price impact of the energy transition in a structural scenario analysis. Metal prices would reach historical peaks for an unprecedented, sustained period in a net-zero emissions scenario. The total value of metals production would rise more than four-fold for the period 2021 to 2040, rivaling the total value of crude oil production. Metals are a potentially important input into integrated assessments models of climate change.

Energy Transition Metals

The energy transition requires substantial amounts of metals, including copper, nickel, cobalt, and lithium. Are these metals a key bottleneck? We identify metal-specific demand shocks with an ``anchor'' variable, estimate supply elasticities, and pin down the price impact of the energy transition in a structural scenario analysis. Metal prices would reach historical peaks for an unprecedented, sustained period in a net-zero emissions scenario. The total production value of these four metals alone would rise more than four-fold to USD 13 trillion for the period 2021 to 2040, rivaling the estimated total value of crude oil production. These metals could potentially become as important to the global economy as crude oil.

The Great Bee Migration: Supply Analysis of Honey Bee Colony Shipments into California for Almond Pollination Services

AbstractOver the last two decades, the number of honey bee colonies performing pollination services for the California almond industry has grown steadily and now equals a substantial share of all colonies in the United States. Most US beekeeping operations have not expanded their colony numbers at the current levels of almond pollination fees. Thus, as almond acreage has increased, the marginal supplier of colonies has moved further away from California, increasing interstate shipments. We provide a conceptual representation of the supply and demand of U.S. colonies for almond pollination, and utilize the relatively inelastic demand for colonies to explore spatial elasticities of supply. We combine colony shipment data from 2007 to 2018 provided by the California Department of Food and Agriculture with projected prices from the California State Beekeeper's Association pollination fee survey. We use a geographically weighted regression to estimate supply elasticities for each state, and provide supporting regional estimates. The eastern United States, where beekeepers have hesitated to participate in almond pollination due to relatively high transportation costs and the potential for local honey production at the time of almond bloom, have some of the highest price elasticities of supply. This suggests that beekeepers in areas with low transportation and/or opportunity costs have supplied all available colonies, and increases in almond pollination fees have had little effect. We estimate that Florida, Georgia, and Texas had the largest number of colonies that did not participate in almond pollination in 2017, so further increases in supply are likely to come from those states.

Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads

Equilibrium electricity spot prices and loads are often determined simultaneously in a day-ahead auction market for each hour of the subsequent day. Hence daily observations of hourly prices take the form of a periodic panel rather than a time series of hourly observations. We consider novel panel data approaches to analyse the time series and the cross-sectional dependence of hourly Nord Pool electricity spot prices and loads for the period 2000–2013. Hourly electricity prices and load data are characterized by strong serial long-range dependence in the time series dimension in addition to strong seasonal periodicity, and along the cross-sectional dimension, i.e. the hours of the day, there is a strong dependence which necessarily has to be accounted for in order to avoid spurious inference when focusing on the time series dependence alone. The long-range dependence is modelled in terms of a fractionally integrated panel data model and it is shown that both prices and loads consist of common factors with long memory and with loadings that vary considerably during the day. Due to the competitiveness of the Nordic power market the aggregate supply curve approximates well the marginal costs of the underlying production technology and because the demand is more volatile than the supply, equilibrium prices and loads are argued to identify the periodic power supply curve. The estimated supply elasticities are estimated from fractionally co-integrated relations and range between 0.5 and 1.17 with the largest elasticities being estimated during morning and evening peak hours.

A note on the role of price risk in soya bean supply functions

This article examines whether the inclusion of the price risk variable as an explanatory variable in a Gardner-type acreage response model is statistically significant. By estimating six separate soya bean acreage response equations, including two linear equations with and without risk, two double logarithmic equations with and without risk and two semi-logarithmic equations with and without risk, we find that adding a risk variable to the futures price model yields a better statistical result in every case. Furthermore, the models that have incorporated the risk variable have higher own and cross-price elasticities. This is a desirable result, since in the literature, many previous estimated supply elasticities were considered lower than plausible.

A Review of International Agricultural Supply Response

Existine literature has orovided a wide ranee of estimates for supply elasticities. This study reviews agricultural supply response in developed and developing countries. The main obiective is to provide an understanding of the causal factors accounthg for the diversity of estimates. It is discussed that the estimated elasticities vary depending on the methodology used, the time period the cover, the definition of price variable, and the type of data utilized: The various methodologies employed to estimate supply elasticities are discussed. Finally, some conclusions are derived which are supported by the theory and the weight of the evidence.

The Demand for Housing in Developing Countries: Empirical Estimates from Household Data

Cities in developing countries are growing at extraordinary rates, often compressing into decades the urbanization process that has taken centuries in developed countries. In coping with this growth, public authorities have devised a wide range of policy instruments to influence the rate and character of city expansion, to meet the needs of people for shelter and urban services, and to allocate resources in ways that redistribute both the costs and benefits of urban growth. Ideally, such policy formulation should be informed by a careful understanding of the behavior of urban markets; in fact, little information on market behavior is available to the policymakers of developing countries. Such basic information is needed for improved project design and, even more important, for improved sector-wide policies. This paper reports on research conducted at the World Bank to increase understanding of developing country housing markets; in particular, of housing-demand behavior. While the overall project examines both the demand for housing characteristics' and the demand for housing as a composite good, this paper addresses only the latter. The objectives of this paper are (1) to review briefly previous evidence on housing-demand parameters in developing countries; (2) to present new evidence on housing-demand parameters based on application of standardized models and comparable variable definitions in 16 cities in eight developing countries (Colombia, Egypt, El Salvador, Ghana, India, Jamaica, Korea, and the Philippines); and (3) to examine similarities and differences among cities in housing demand and, in a preliminary way, offer explanations for place-to-place differences. Limited comparisons are also made to two U.S. cities in order to begin comparison of developing and developed country market behavior.

The Determinants of Teacher Supply

In the economic literature, occupational choice has been interpreted as a decision which will be determined by the expected lifetime earnings associated with each of the available alternatives. Despite its apparent simplicity, it has been notoriously difficult to incorporate this idea into the analysis of occupational choice, the main reason being the inadequacy of empirical data on earnings. Given this difficulty, some models of occupational choice have from the outset formulated the problem as a one-period decision. All the work carried out in the US to investigate the factors determining the choice of a military career belongs to this class of models (cf. Fisher (1969), Altman (1969) and Altman and Barro (1971)). Other researchers have taken into account the effect of lifetime earnings in their theoretical analysis, but have focused their empirical work more on evaluating rates of return than on estimating supply elasticities (e.g. Weiss (1972)). Probably the most complete attempt to deal with this problem has been that of Freeman (1971). The bulk of his empirical analysis considers either starting or average salaries as the relevant variables of the occupational supply functions, and in this sense it is not substantially different from the studies on military manpower reported above. In his analysis of career decisions of doctorate manpower, however, he explicitly takes into account lifetime earnings as the explanatory variable of occupational choices. For each of the specialities considered, Freeman estimates discounted lifetime earnings on the basis of geometric mean incomes for graduates in several previous classes. However, he is faced with serious problems to arrive at a similar measure for alternative occupations. Although still consistent with theoretical expectations, his results are not as neat and convincing as those obtained with only starting or average salaries, and in subsequent work (Freeman (1975a) and (1975b)) he has abandoned this line of analysis, to come back to the simpler specifications. In our view, the analysis of occupational choice developed in this paper goes some way towards the solution of these persistent difficulties. The effect of lifetime earnings on occupational decisions is explicitly considered in a theoretical model from which we derive empirically testable hypotheses. The basic idea is to reduce the earnings profile to two dimensions: its level and the rate at which earnings progress over time. In this manner we achieve two results. First, by measuring the profile with two variables rather than one (as is in fact done when only average, or starting salaries, or even present values, are used), we are able to disentangle two different supply responses: the response to starting earnings, and the response to future earnings prospects. This procedure gives considerable insight into the behaviour of groups of individuals with different characteristics (e.g. men and women). Secondly, our model suggests an empirically convenient way of taking into account lifetime earnings which relies only on starting and average salaries. Given the inadequate data with which most occupational studies have to work, this procedure may prove a useful methodological advance in this area. The rest of the paper is as follows. In Section 2 we develop a simple model of occupational choice, which will help in organizing the discussion of the factors that determine the supply of teachers. Then, in Section 3, the model is applied to data on entry and wastage of teachers. Finally, Section 4 summarizes the main results obtained.

Reliability of Supply Equations Derived from Production Functions

AbstractThis paper examines the reliability of supply functions derived from empirically estimated production functions. Firm‐level supply equations are derived from production functions for selected agricultural commodities, and these are used to predict firm output and to estimate supply elasticities. The results are then compared with actual or expected output and with supply elasticities estimated directly by regression analysis of time series data. Output predictions ranged from slight under‐estimates to extreme over‐estimates of actual output, with the latter being most prevalent. The derived supply elasticities were generally higher than those obtained by direct regression analysis.