An assessment of the economic impact of climate change on the Agriculture, Health and Tourism sectors in Jamaica

This report analyses the agriculture, health and tourism sectors in Jamaica to assess the potential economic impacts of climate change on the sectors. The fundamental aim of this report is to assist with the development of strategies to deal with the potential impact of climate change on Jamaica. It also has the potential to provide essential input for identifying and preparing policies and strategies to help move the Region closer to solving problems associated with climate change and attaining individual and regional sustainable development goals. Some of the key anticipated manifestations of climate change for the Caribbean include elevated air and sea-surface temperatures, sea-level rise, possible changes in extreme events and a reduction in freshwater resources. The economic impact of climate change on the three sectors was estimated for the A2 and B2 IPCC scenarios until 2050. An evaluation of various adaptation strategies was also undertaken for each sector using standard evaluation techniques. The outcomes from investigating the agriculture sector indicate that for the sugar-cane subsector the harvests under both the A2 and B2 scenarios decrease at first and then increase as the mid-century mark is approached. With respect to the yam subsector the results indicate that the yield of yam will increase from 17.4 to 23.1 tonnes per hectare (33%) under the A2 scenario, and 18.4 to 23.9 (30%) tonnes per hectare under the B2 scenario over the period 2011 to 2050. Similar to the forecasts for yam, the results for escallion suggest that yields will continue to increase to mid-century. Adaptation in the sugar cane sub-sector could involve replanting and irrigation that appear to generate net benefits at the three selected discount rates for the A2 scenario, but only at a discount rate of 1% for the B2 scenario. For yam and escallion, investment in irrigation will earn significant net benefits for both the A2 and B2 scenarios at the three selected rates of discount. It is recommended that if adaptation strategies are part of a package of strategies for improving efficiency and hence enhancing competitiveness, then the yields of each crop can be raised sufficiently to warrant investment in adaptation to climate change. The analysis of the health sector demonstrates the potential for climate change to add a substantial burden to the future health systems in Jamaica, something that that will only compound the country’s vulnerability to other anticipated impacts of climate change. The results clearly show that the incidence of dengue fever will increase if climate change continues unabated, with more cases projected for the A2 scenario than the B2. The models predicted a decrease in the incidence of gastroenteritis and leptospirosis with climate change, indicating that Jamaica will benefit from climate change with a reduction in the number of cases of gastroenteritis and leptospirosis. Due to the long time horizon anticipated for climate change, Jamaica should start implementing adaptation strategies focused on the health sector by promoting an enabling environment, strengthening communities, strengthening the monitoring, surveillance and response systems and integrating adaptation into development plans and actions. Small-island developing states like Jamaica must be proactive in implementing adaptation strategies, which will reduce the risk of climate change. On the global stage the country must continue to agitate for the implementation of the mitigation strategies for developed countries as outlined in the Kyoto protocol. The results regarding the tourism sector suggest that the sector is likely to incur losses due to climate change, the most significant of which is under the A2 scenario. Climatic features, such as temperature and precipitation, will affect the demand for tourism in Jamaica. By 2050 the industry is expected to lose US$ 132.2 million and 106.1 million under the A2 and B2 scenarios, respectively. In addition to changes in the climatic suitability for tourism, climate change is also likely to have important supply-side effects from extreme events and acidification of the ocean. The expected loss from extreme events is projected to be approximately US$ 5.48 billion (A2) and US$ 4.71 billion (B2). Even more devastating is the effect of ocean acidification on the tourism sector. The analysis shows that US$ 7.95 billion (A2) and US$ 7.04 billion is expected to be lost by mid-century. The benefit-cost analysis indicates that most of the adaptation strategies are expected to produce negative net benefits, and it is highly likely that the cost burden would have to be carried by the state. The options that generated positive ratios were: redesigning and retrofitting all relevant tourism facilities, restoring corals and educating the public and developing rescue and evacuation plans. Given the relative importance of tourism to the macroeconomy one possible option is to seek assistance from multilateral funding agencies. It is recommended that the government first undertake a detailed analysis of the vulnerability of each sector and, in particular tourism, to climate change. Further, more realistic socio-economic scenarios should be developed so as to inform future benefit-cost analysis.

An assessment of the economic impact of climate change on the water sector in the Turks and Caicos Islands

The best description of water resources for Grand Turk was offered by Pérez Monteagudo (2000) who suggested that rain water was insufficient to ensure a regular water supply although water catchment was being practised and water catchment possibilities had been analysed. Limestone islands, mostly flat and low lying, have few possibilities for large scale surface storage, and groundwater lenses exist in very delicate equilibrium with saline seawater, and are highly likely to collapse due to sea level rise, improper extraction, drought, tidal waves or other extreme event. A study on the impact of climate change on water resources in the Turks and Caicos Islands is a challenging task, due to the fact that the territory of the Islands covers different environmental resources and conditions, and accurate data are lacking. The present report is based on collected data wherever possible, including grey data from several sources such as the Intergovernmental Panel on Climate Change (IPCC) and Cuban meteorological service data sets. Other data were also used, including the author’s own estimates and modelling results. Although challenging, this was perhaps the best approach towards analysing the situation. Furthermore, IPCC A2 and B2 scenarios were used in the present study in an effort to reduce uncertainty. The main conclusion from the scenario approach is that the trend observed in precipitation during the period 1961 - 1990 is decreasing. Similar behaviour was observed in the Caribbean region. This trend is associated with meteorological causes, particularly with the influence of the North Atlantic Anticyclone. The annual decrease in precipitation is estimated to be between 30-40% with uncertain impacts on marine resources. After an assessment of fresh water resources in Turks and Caicos Islands, the next step was to estimate residential water demand based on a high fertility rate scenario for the Islands (one selected from four scenarios and compared to countries having similar characteristics). The selected scenario presents higher projections on consumption growth, enabling better preparation for growing water demand. Water demand by tourists (stopover and excursionists, mainly cruise passengers) was also obtained, based on international daily consumption estimates. Tourism demand forecasts for Turks and Caicos Islands encompass the forty years between 2011 and 2050 and were obtained by means of an Artificial Neural Networks approach. for the A2 and B2 scenarios, resulting in the relation BAU>B2>A2 in terms of tourist arrivals and water demand levels from tourism. Adaptation options and policies were analysed. Resolving the issue of the best technology to be used for Turks and Caicos Islands is not directly related to climate change. Total estimated water storage capacity is about 1, 270, 800 m3/ year with 80% capacity load for three plants. However, almost 11 desalination plants have been detected on Turks and Caicos Islands. Without more data, it is not possible to estimate long term investment to match possible water demand and more complex adaptation options. One climate change adaptation option would be the construction of elevated (30 metres or higher) storm resistant water reservoirs. The unit cost of the storage capacity is the sum of capital costs and operational and maintenance costs. Electricity costs to pump water are optional as water should, and could, be stored for several months. The costs arising for water storage are in the range of US$ 0.22 cents/m3 without electricity costs. Pérez Monteagudo (2000) estimated water prices at around US$ 2.64/m3 in stand points, US$ 7.92 /m3 for government offices, and US$ 13.2 /m3for cistern truck vehicles. These data need to be updated. As Turks and Caicos Islands continues to depend on tourism and Reverse Osmosis (RO) for obtaining fresh water, an unavoidable condition to maintaining and increasing gross domestic product(GDP) and population welfare, dependence on fossil fuels and vulnerability to increasingly volatile prices will constitute an important restriction. In this sense, mitigation supposes a synergy with adaptation. Energy demand and emissions of carbon dioxide (CO2) were also estimated using an emissions factor of 2. 6 tCO2/ tonne of oil equivalent (toe). Assuming a population of 33,000 inhabitants, primary energy demand was estimated for Turks and Caicos Islands at 110,000 toe with electricity demand of around 110 GWh. The business as usual (BAU), as well as the mitigation scenarios were estimated. The BAU scenario suggests that energy use should be supported by imported fossil fuels with important improvements in energy efficiency. The mitigation scenario explores the use of photovoltaic and concentrating solar power, and wind energy. As this is a preliminary study, the local potential and locations need to be identified to provide more relevant estimates. Macroeconomic assumptions are the same for both scenarios. By 2050, Turks and Caicos Islands could demand 60 m toe less than for the BAU scenario.

An assessment of the economic impact of climate change on the Agriculture Sector in Jamaica

The agricultural sector‟s contribution to GDP and to exports in Jamaica has been declining with the post-war development process that has led to the differentiation of the economy. In 2010, the sector contributed 5.8% of GDP, and 3% to the exports (of goods), but with 36% of employment, it continues to be a major employer. With a little less than half of the population living in rural communities, agricultural activities, and their linkages with other economic activities, continue to play an important role as a source of livelihoods, and by extension, the economic development of the country. Sugar cane cultivation has, with the exception of a couple of decades in the twentieth century when it was superseded by bananas, dominated the agricultural export sector for centuries as the source of the raw materials for the manufacture of sugar for export. In 2005, sugar cane itself accounted for 6.4% of the sector‟s contribution to GDP, and 52% of the contribution of agricultural exports to GDP. Production for the domestic market has long been the larger subsector, organized around the production of root crops, especially yams, vegetables and condiments. To analyse the potential impact of climate change on the agricultural sector, this study selected three important crops for detailed examination. In particular, the study selected sugar cane because of its overwhelming importance to the export subsector of agriculture, and yam and escallion for both their contribution to the domestic subsector as well as the preeminent role yams and escallion play in the economic activities of the communities in the hills of central Jamaica, and the plains of the southwest respectively. As with other studies in this project, the methodology adopted was to compare the estimated values of output on the SRES A2 and B2 Scenarios with the value of output on a “baseline” Business As Usual (BAU), and then estimate the net benefits of investment in the relevant to climate change for the selected crops. The A2 and B2 Scenarios were constructed by applying forecasts of changes in temperature and precipitation generated by INSMET from ECHAM inspired climate models. The BAU “baseline” was a linear projection of the historical trends of yields for each crop. Linear models of yields were estimated for each crop with particular attention to the influence of the two climate variables – temperature and precipitation. These models were then used to forecast yields up to 2050 (table1). These yields were then used to estimate the value of output of the selected crop, as well as the contribution to overall GDP, on each Scenario. The analysis suggested replanting sugar cane with heat resistant varieties, rehabilitating irrigation systems where they existed, and establishing technologically appropriate irrigation systems where they were not for the three selected crops.

Car ownership modeling and forecasts for China

A great increase of private car ownership took place in China from 1980 to 2009 with the development of the economy. To explain the relationship between car ownership and economic and social changes, an ordinary least squares linear regression model is developed using car ownership per capita as the dependent variable with GDP, savings deposits and highway mileages per capita as the independent variables. The model is tested and corrected for econometric problems such as spurious correlation and cointegration. Finally, the regression model is used to project oil consumption by the Chinese transportation sector through 2015. The result shows that about 2.0 million barrels of oil will be consumed by private cars in conservative scenario, and about 2.6 million barrels of oil per day in high case scenario in 2015. Both of them are much higher than the consumption level of 2009, which is 1.9 million barrels per day. It also shows that the annual growth rate of oil demand by transportation is 2.7% - 3.1% per year in the conservative scenario, and 6.9% - 7.3% per year in the high case forecast scenario from 2010 to 2015. As a result, actions like increasing oil efficiency need to be taken to deal with challenges of the increasing demand for oil.

Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk.

ELECTRICITY GENERATION MODELING AND PHOTOVOLTAIC FORECASTS IN CHINA

With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979-2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.

Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, nonfarm employment, and the unemployment rate.

A methodology proposal to obtain operational parameter forecasts and operational risk assessment in container terminals

Las terminales de contenedores son sistemas complejos en los que un elevado número de actores económicos interactúan para ofrecer servicios de alta calidad bajo una estricta planificación y objetivos económicos. Las conocidas como "terminales de nueva generación" están diseñadas para prestar servicio a los mega-buques, que requieren tasas de productividad que alcanzan los 300 movimientos/ hora. Estas terminales han de satisfacer altos estándares dado que la competitividad entre terminales es elevada. Asegurar la fiabilidad de las planificaciones del atraque es clave para atraer clientes, así como reducir al mínimo el tiempo que el buque permanece en el puerto. La planificación de las operaciones es más compleja que antaño, y las tolerancias para posibles errores, menores. En este contexto, las interrupciones operativas deben reducirse al mínimo. Las principales causas de dichas perturbaciones operacionales, y por lo tanto de incertidumbre, se identifican y caracterizan en esta investigación. Existen una serie de factores que al interactuar con la infraestructura y/o las operaciones desencadenan modos de fallo o parada operativa. Los primeros pueden derivar no solo en retrasos en el servicio sino que además puede tener efectos colaterales sobre la reputación de la terminal, o incluso gasto de tiempo de gestión, todo lo cual supone un impacto para la terminal. En el futuro inmediato, la monitorización de las variables operativas presenta gran potencial de cara a mejorar cualitativamente la gestión de las operaciones y los modelos de planificación de las terminales, cuyo nivel de automatización va en aumento. La combinación del criterio experto con instrumentos que proporcionen datos a corto y largo plazo es fundamental para el desarrollo de herramientas que ayuden en la toma de decisiones, ya que de este modo estarán adaptadas a las auténticas condiciones climáticas y operativas que existen en cada emplazamiento. Para el corto plazo se propone una metodología con la que obtener predicciones de parámetros operativos en terminales de contenedores. Adicionalmente se ha desarrollado un caso de estudio en el que se aplica el modelo propuesto para obtener predicciones de la productividad del buque. Este trabajo se ha basado íntegramente en datos proporcionados por una terminal semi-automatizada española. Por otro lado, se analiza cómo gestionar, evaluar y mitigar el efecto de las interrupciones operativas a largo plazo a través de la evaluación del riesgo, una forma interesante de evaluar el effecto que eventos inciertos pero probables pueden generar sobre la productividad a largo plazo de la terminal. Además se propone una definición de riesgo operativo junto con una discusión de los términos que representan con mayor fidelidad la naturaleza de las actividades y finalmente, se proporcionan directrices para gestionar los resultados obtenidos. Container terminals are complex systems where a large number of factors and stakeholders interact to provide high-quality services under rigid planning schedules and economic objectives. The socalled next generation terminals are conceived to serve the new mega-vessels, which are demanding productivity rates up to 300 moves/hour. These terminals need to satisfy high standards because competition among terminals is fierce. Ensuring reliability in berth scheduling is key to attract clients, as well as to reduce at a minimum the time that vessels stay the port. Because of the aforementioned, operations planning is becoming more complex, and the tolerances for errors are smaller. In this context, operational disturbances must be reduced at a minimum. The main sources of operational disruptions and thus, of uncertainty, are identified and characterized in this study. External drivers interact with the infrastructure and/or the activities resulting in failure or stoppage modes. The later may derive not only in operational delays but in collateral and reputation damage or loss of time (especially management times), all what implies an impact for the terminal. In the near future, the monitoring of operational variables has great potential to make a qualitative improvement in the operations management and planning models of terminals that use increasing levels of automation. The combination of expert criteria with instruments that provide short- and long-run data is fundamental for the development of tools to guide decision-making, since they will be adapted to the real climatic and operational conditions that exist on site. For the short-term a method to obtain operational parameter forecasts in container terminals. To this end, a case study is presented, in which forecasts of vessel performance are obtained. This research has been entirely been based on data gathered from a semi-automated container terminal from Spain. In the other hand it is analyzed how to manage, evaluate and mitigate disruptions in the long-term by means of the risk assessment, an interesting approach to evaluate the effect of uncertain but likely events on the long-term throughput of the terminal. In addition, a definition for operational risk evaluation in port facilities is proposed along with a discussion of the terms that better represent the nature of the activities involved and finally, guidelines to manage the results obtained are provided.

The economic and financial crisis in Russia – background, symptoms and prospects for the future. OSW Report 02/2015

The crisis in Russia’s financial market, which started in mid-December 2014, has exposed the real scale of the economic problems that have been growing in Russia for several years. Over the course of the last year, Russia’s basic macroeconomic indicators deteriorated considerably, the confidence of its citizens in the state and in institutions in charge of economic stability declined, the government and business elites became increasingly dissatisfied with the policy direction adopted by the Kremlin, and fighting started over the shrinking resources. According to forecasts obtained from both governmental and expert communities, Russia will fall into recession in 2015. The present situation is the result of the simultaneous occurrence of three unfavourable trends: the fact that the Russian economy’s resource-based development model has reached the limits of its potential due to structural weaknesses, the dramatic decline in oil prices in the second half of 2014, and the impact of Western economic sanctions. Given the inefficiency of existing systemic mechanisms, in the coming years the Russian leadership will likely resort to ad hoc solutions such as switching to a more interventionist “manual override” mode in governing the state. In the short term, this will allow them to neutralise the most urgent problems, although an effective development policy will be impossible without a fundamental change of the political and economic system in Russia.

Cost and economic impact of tire reserve load requirements. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Implications of seasonal climate forecasts on world wheat trade: a stochastic, dynamic analysis

Improvements in seasonal climate forecasts have potential economic implications for international agriculture. A stochastic, dynamic simulation model of the international wheat economy is developed to estimate the potential effects of seasonal climate forecasts for various countries' wheat production, exports and world trade. Previous studies have generally ignored the stochastic and dynamic aspects of the effects associated with the use of climate forecasts. This study shows the importance of these aspects. In particular with free trade, the use of seasonal forecasts results in increased producer surplus across all exporting countries. In fact, producers appear to capture a large share of the economic surplus created by using the forecasts. Further, the stochastic dimensions suggest that while the expected long-run benefits of seasonal forecasts are positive, considerable year-to-year variation in the distribution of benefits between producers and consumers should be expected. The possibility exists for an economic measure to increase or decrease over a 20-year horizon, depending on the particular sequence of years.

Density forecasts of crude-oil prices using option-implied and ARCH-type models

The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994–2006 period. Moving beyond standard ARCH type models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities obtained by option prices and non-parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:727–754, 2011

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

A vállalati informatika szerepe a versenyképesség alakításában a pénzügyi és gazdasági válság időszakában (Corporate IT in shaping the competitiveness of the Hungarian enterprises in the financial and economic crisis)

A cikk az informatika és a versenyképesség kapcsolatát vizsgálja. A Budapesti Corvinus Egyetem Versenyképesség Kutatási Programjának korábbi felmérései óta számos új technológia bukkant fel, illetve hazánkat is elérte a világméretű pénzügyi és gazdasági válság hatása. E kihívások tükrében érdemesnek tűnt újra megvizsgálni az információtechnológia (IT) szerepét a versenyképesség alakításában. / === / In this paper the relationship between information technology (IT) and competitiveness is tackled. Since the authors’ previous surveys within their Competitiveness Research Program several new technologies have emerged, and the influence of the word wide financial and economic crisis has reached Hungary as well. In the face of these challenges it is worth reexamining the role of IT in shaping the competitive position of companies. The structure of the paper is as follows. A brief theoretical introduction is provided before their research questionsare presented. After that, the paper contains an analysis on selected fields of the corporate IT function, namely IT infrastructure, IT applications, IT management and IT strategy. Based on this, conclusions are made both at the end of the main parts, and in the final section of the paper. As far as the final conclusions are concerned, the majority of respondents do not regard IT today as a source of sustainable or contestable competitive advantage, though the dominant opinion underlines that IT is a strategic necessity. Besides this, their research results suggest a kind of association between corporate performance and the maturity level of the IT function. However, even the best performing companies are not prepared yet to effectively respond to their own prediction that forecasts the strengthening role of IT as a competitive factor.

Eliciting and Aggregating Forecasts When Information is Shared

Using the wisdom of crowds---combining many individual forecasts to obtain an aggregate estimate---can be an effective technique for improving forecast accuracy. When individual forecasts are drawn from independent and identical information sources, a simple average provides the optimal crowd forecast. However, correlated forecast errors greatly limit the ability of the wisdom of crowds to recover the truth. In practice, this dependence often emerges because information is shared: forecasters may to a large extent draw on the same data when formulating their responses.

To address this problem, I propose an elicitation procedure in which each respondent is asked to provide both their own best forecast and a guess of the average forecast that will be given by all other respondents. I study optimal responses in a stylized information setting and develop an aggregation method, called pivoting, which separates individual forecasts into shared and private information and then recombines these results in the optimal manner. I develop a tailored pivoting procedure for each of three information models, and introduce a simple and robust variant that outperforms the simple average across a variety of settings.

In three experiments, I investigate the method and the accuracy of the crowd forecasts. In the first study, I vary the shared and private information in a controlled environment, while the latter two studies examine forecasts in real-world contexts. Overall, the data suggest that a simple minimal pivoting procedure provides an effective aggregation technique that can significantly outperform the crowd average.