This document discusses discounting in cost-benefit analysis of investment projects and climate change adaptation investments. It covers the general methodology of discounting, including calculating the present value of costs and benefits that occur over different time periods. It addresses questions about setting the time horizon (T) of analysis, how to handle infinite time horizons, accounting for inflation, and theories on which discount rate to use. The document provides outlines, explanations, and examples related to discounting in economic analysis.
This document provides an overview of forecasting methods for operations management. It defines forecasting and identifies key principles. Quantitative and qualitative forecasting methods are described, including time series models, causal models, and techniques for addressing trends, seasonality, and error measurement. Guidelines for selecting the appropriate forecasting method and software are also provided.
This document describes the trend adjusted exponential smoothing forecasting method. It is a quantitative time series forecasting technique that calculates the weighted average of the current actual value and previous forecast, with an adjustment made for any trend present in the data. The trend adjustment is calculated using a smoothing constant and prior trend value. The method is preferred when a trend or seasonal pattern is evident in historical data. An example is provided to illustrate how to compute forecasts using this method by calculating the unadjusted forecast, trend, and adjusted forecast over multiple time periods.
This document outlines demand forecasting methods for supply chain planning. It discusses the role of forecasting in production scheduling, inventory management, sales planning and other supply chain functions. Several forecasting techniques are described, including qualitative, time series and causal methods. Time series methods use historical demand patterns to forecast future demand. The document explains how to decompose time series data, estimate trend and seasonal factors, and generate forecasts. Measures of forecast accuracy like mean squared error, mean absolute deviation and mean absolute percentage error are also defined. An example of demand forecasting for Tahoe Salt is presented to illustrate the techniques.
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
- Choosing the appropriate forecasting model based on data availability, time horizon, required accuracy, and resources
- Comparing forecast accuracy using metrics like forecast error which measure the difference between actual and forecasted values
This document discusses aggregate demand forecasting through a case study of HP's supply chain. It explains that aggregate forecasts at higher supply chain levels are more accurate than at lower levels. The case study shows how HP forecasted demand for desktop PCs with 110V and 220V power supplies separately and in total. Forecast accuracy improved when considering total demand rather than specific configurations. Key lessons are that delayed product differentiation through aggregate forecasting and demand management helps supply chain competitiveness and cost reduction.
This document discusses various forecasting methods including linear regression, exponential smoothing, and moving averages. Linear regression finds the trend line that best fits historical data points to forecast future values. Exponential smoothing gives more weight to recent observations and is useful for smoothing out trends and seasonality in time series data. Moving averages smooth random fluctuations by calculating the average of the previous n periods. Forecasting methods are chosen based on the characteristics of the time series data such as whether it is stationary, has trends or seasonality.
This document discusses quantitative forecasting methods, including time series and causal models. It covers key time series components like trend, seasonality, and cycles. Three main time series methods are described: smoothing, trend projection, and trend projection adjusted for seasonal influence. Moving averages and exponential smoothing are explained as common techniques for forecasting stationary time series. The document also covers decomposing a time series into trend, seasonal, and irregular components. Regression methods are mentioned as another approach when a trend is present in the data.
Building Institutional Capacity in Thailand to Design and Implement Climate P...UNDP Climate
23-25 November 2016, Thailand - A centerpiece of the Integrating Agriculture in National Adaptation Plans Programme (NAP-Ag) in Thailand is its support to develop a new five-year Strategy on Climate Change in Agriculture (2017-2021). This is spearheaded by the Ministry of Agriculture and Cooperatives (MOAC) and its Office of Agriculture Economics (OAE). The strategy was unveiled after a series of meetings by a Technical Working Group at a three-day workshop held on 23-25 November 2016 in Bangkok, organized by UNDP. Over 60 participants from each MOAC line department and 10 participants from academia and civil society were briefed by the Office of the Natural Resources and Environmental Policy and Planning (ONEP) and GIZ on the status of the National Adaption Plan (NAP) and learned how NAP-Ag programme efforts could support a broader NAP process and align with the Sector Plan. The new strategy focuses on improving evidence and data for informing policy choices, building the capacity of farmers and agri-businesses to adapt, promoting low-carbon development and productivity growth in the sector, and building institutional and managerial capacities to cope with climate change impacts.
This document provides an overview of forecasting methods for operations management. It defines forecasting and identifies key principles. Quantitative and qualitative forecasting methods are described, including time series models, causal models, and techniques for addressing trends, seasonality, and error measurement. Guidelines for selecting the appropriate forecasting method and software are also provided.
This document describes the trend adjusted exponential smoothing forecasting method. It is a quantitative time series forecasting technique that calculates the weighted average of the current actual value and previous forecast, with an adjustment made for any trend present in the data. The trend adjustment is calculated using a smoothing constant and prior trend value. The method is preferred when a trend or seasonal pattern is evident in historical data. An example is provided to illustrate how to compute forecasts using this method by calculating the unadjusted forecast, trend, and adjusted forecast over multiple time periods.
This document outlines demand forecasting methods for supply chain planning. It discusses the role of forecasting in production scheduling, inventory management, sales planning and other supply chain functions. Several forecasting techniques are described, including qualitative, time series and causal methods. Time series methods use historical demand patterns to forecast future demand. The document explains how to decompose time series data, estimate trend and seasonal factors, and generate forecasts. Measures of forecast accuracy like mean squared error, mean absolute deviation and mean absolute percentage error are also defined. An example of demand forecasting for Tahoe Salt is presented to illustrate the techniques.
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
- Choosing the appropriate forecasting model based on data availability, time horizon, required accuracy, and resources
- Comparing forecast accuracy using metrics like forecast error which measure the difference between actual and forecasted values
This document discusses aggregate demand forecasting through a case study of HP's supply chain. It explains that aggregate forecasts at higher supply chain levels are more accurate than at lower levels. The case study shows how HP forecasted demand for desktop PCs with 110V and 220V power supplies separately and in total. Forecast accuracy improved when considering total demand rather than specific configurations. Key lessons are that delayed product differentiation through aggregate forecasting and demand management helps supply chain competitiveness and cost reduction.
This document discusses various forecasting methods including linear regression, exponential smoothing, and moving averages. Linear regression finds the trend line that best fits historical data points to forecast future values. Exponential smoothing gives more weight to recent observations and is useful for smoothing out trends and seasonality in time series data. Moving averages smooth random fluctuations by calculating the average of the previous n periods. Forecasting methods are chosen based on the characteristics of the time series data such as whether it is stationary, has trends or seasonality.
This document discusses quantitative forecasting methods, including time series and causal models. It covers key time series components like trend, seasonality, and cycles. Three main time series methods are described: smoothing, trend projection, and trend projection adjusted for seasonal influence. Moving averages and exponential smoothing are explained as common techniques for forecasting stationary time series. The document also covers decomposing a time series into trend, seasonal, and irregular components. Regression methods are mentioned as another approach when a trend is present in the data.
Building Institutional Capacity in Thailand to Design and Implement Climate P...UNDP Climate
23-25 November 2016, Thailand - A centerpiece of the Integrating Agriculture in National Adaptation Plans Programme (NAP-Ag) in Thailand is its support to develop a new five-year Strategy on Climate Change in Agriculture (2017-2021). This is spearheaded by the Ministry of Agriculture and Cooperatives (MOAC) and its Office of Agriculture Economics (OAE). The strategy was unveiled after a series of meetings by a Technical Working Group at a three-day workshop held on 23-25 November 2016 in Bangkok, organized by UNDP. Over 60 participants from each MOAC line department and 10 participants from academia and civil society were briefed by the Office of the Natural Resources and Environmental Policy and Planning (ONEP) and GIZ on the status of the National Adaption Plan (NAP) and learned how NAP-Ag programme efforts could support a broader NAP process and align with the Sector Plan. The new strategy focuses on improving evidence and data for informing policy choices, building the capacity of farmers and agri-businesses to adapt, promoting low-carbon development and productivity growth in the sector, and building institutional and managerial capacities to cope with climate change impacts.
This document provides an overview of forecasting methods for operations management. It begins by listing the key learning objectives which are to identify principles of forecasting, explain the forecasting process, describe different forecasting methods and models, and compute forecast accuracy. The document then covers various qualitative and quantitative forecasting techniques including time series models, causal models, and methods for addressing trends, seasonality, and accuracy evaluation. Linear regression is presented as a tool for causal modeling.
The document discusses life-cycle costing techniques used in engineering economics and construction project design. Life-cycle costing considers all costs over the full life of a project, not just initial construction costs, to identify the design with the highest net benefits. It allows comparison of alternatives with different costs and benefits over time by using the time value of money. Examples are provided to illustrate compound interest calculations and the use of interest tables to evaluate alternatives based on their present worth.
NAP Training Viet Nam - Session 7 Appraising Adaptation OptionsUNDP Climate
This two-day workshop supported the Government of Viet Nam in building the necessary capacity to advance its National Adaptation Plan (NAP) process. The workshop closely focused on building National Adaptation Plans in the agricultural sector through multi-stakeholder collaboration, and increased knowledge and capacity on a number of topics including: prioritization of adaptation options, cost-benefit analysis, overview of the broad-based nature of climate change adaption impacts, analysis of challenges, and creation of an open discussion with key stakeholders on defining a road-map for the NAP process. The workshop was delivered using discussions and case studies to enhance interactive learning for participants, with supporting presentations by GiZ and SNV.
1) The document contains examples and explanations of present worth factors, equivalent cash flows, and unconventional calculations involving compound interest from the 4th edition of Contemporary Engineering Economics.
2) Various techniques are presented for handling cash flows with missing payments, multiple interest rates, and irregular payment patterns.
3) Examples show how to establish economic equivalence between cash flows, find an unknown interest rate using linear interpolation, and use the goal seek function in spreadsheets.
Math 141 Exam Final Exam Name____________________.docxendawalling
The document is a math exam for Math 141. It contains 14 multi-part questions testing a variety of math concepts including factoring, functions, inequalities, sequences, series, logarithms, and optimization problems. Students must show their work, box their answers, and use scientific calculators. The exam covers domains, ranges, increasing/decreasing intervals, relative and absolute extrema of functions, zeros, asymptotes, graphing, rational zeros of polynomials, solving inequalities, volumes of boxes, sums of sequences and series, and solving word problems involving rectangles and rates of change.
Math 141 Exam Final Exam Name____________________.docxwkyra78
Math 141 Exam Final Exam Name:_____________________________
SCIENTIFIC CALCULATORS ONLY! YOU MUST SHOW WORK FOR CREDIT!!! BOX ANSWERS!
1. [6 ea] Factor completely; simplify.
a) 12𝑥
!
" − 5𝑥
#
" − 2 b)3(4𝑥 + 5)! − 2(4𝑥 + 5) − 1
2. [4 each (a-e)] Approximate if necessary. For the given function , find/determine:
a) Domain: Range:
b) On what interval(s) is 𝑓:
i) Increasing:
ii) Decreasing:
c) ______ when ______.
d) Find any relative extrema (tell me if you have a max or min, what it is, and where it is):
e) Find any absolute extrema (tell me if you have a max or min, what it is, and where it is):
f) [2] Is 𝑓 even, odd , or neither? g) [4] What is the end behavior?
h)[4] What are the x and y- intercepts?
( )f x
( )2f - = ( ) 4f x = x =
3. [8 pts] a) Graph the function 4. [1 ea] Given 𝑔(𝑥) = −"
!
|𝑥 + 3| − 1
𝑓(𝑥) = /
2 − 𝑥, −3 ≤ 𝑥 < 1
𝑥! − 2𝑥 + 2, 𝑥 > 1
. Label 3+ points. Explain how the graph of 𝑔(𝑥) is obtained from the graph
𝑓(𝑥) = |𝑥|. (Hint: four actions=transformations)
a)
b)
c)
d)
5. [8;6] Given that
𝑓(𝑥) = #
$
, ℎ(𝑥) = log(𝑥 + 1) , 𝑗(𝑥) = !
$%"
, 𝑔(𝑥) = √𝑥 − 10
find the following and their DOMAINS. Use Interval Notation! and simplify the function when possible.
a) b)
6. [14 pts] Given 𝑅(𝑥) = &$
$#%$%"!
find:
a) Zero(s):
b) Vertical Asymptote(s):
c) Horizontal OR Slant Asymptote: d) Sketch . You do not need
specific values except for zero(s) and
asymptote. The shape needs be accurate!!!
j! f( ) x( ) ( )
( )
h x
g x
( )R x
y-
( )R xx
7. [9] Let 𝑝(𝑥) = 2𝑥' − 9𝑥! + 7.
a) List all possible rational zeros.
b) Find the remaining zeros of if one zero is −1.
8) [8] Solve the inequality 𝑥& + 2𝑥! − 3𝑥 > 0. Graph the solution on a number line and state the solutions in interval
notation.
9. [8 pts] A box with an open top is to be constructed from a rectangular piece of cardboard with dimensions inches
by inches by cutting out equal squares of side at each corner and then folding up the sides as in the figure.
Express the volume of the box as a function of and simplify completely.
( )p x
8
20 x
V x
x
x
10. [10 pts] Find for 𝑓(𝑥) = !
"#$
. Simplify!
11. [9 pts] Solve - use “zones” or sign-chart. Graph solution and express in Interval Notation.
%
"&'
≥ (
"&)
12. [8 pts] A landscape engineer has 200 feet of border to enclose a rectangular pond. What dimensions will result in the
largest pond?
( ) ( )f x h f x
h
+ -
13. a) [4 pts] Express in terms o
Consequences of learning curves for energy policyLeonardo ENERGY
Learning curves show that government deployment programmes do not subsidise technologies but provide required learning investments to make energy efficiency and low-carbon supply technologies competitive in mass markets. The programmes spur investment and market experience for currently expensive technologies and the learning curve shows how these experiences continually reduce cost and improve performance for the deployed technologies. This technology learning pervades all levels of organisations that produce, install and operate technology. This demonstrates that from identical boundary conditions many energy futures can be obtained, demanding the same economic resources but having completely different technological structures and thus very different properties regarding environment, energy security and job creation. Learning curves challenge current dogma on the long-range efficiency of the market and point to the need to realize political choices in deployment programmes.
Cost-benefit analysis is a more practical way to evaluate public expenditures than using the social welfare function directly, which is difficult to calculate accurately. It involves calculating the present value of both the costs and benefits of a policy or project using discount rates. For public projects, the appropriate discount rate to use is debated, as both social and market rates have pros and cons depending on whether the project funding decreases private investment or consumption. Calculating the costs and benefits also requires determining appropriate shadow prices for goods and services where the market is imperfect.
Thailand UNDP-GIZ workshop on CBA - Enhancing resilience in Thailand through ...UNDP Climate
Thailand, 27-28 November 2017 - UNDP and GIZ partnered with the Thailand Office of Agriculture Economics (OAE) to launch a workshop designed to connect vital stakeholders to build an effective National Adaptation Plan.
The two-day workshop at the Rama Garden Hotel had 20 participants from each department under the Ministry of Agriculture and Cooperatives (MOAC). The workshop was designed to build capacity of planning officers to formulate better projects and budget submissions as well as potential climate finance proposal using cost-benefit analysis and ecosystem-based analysis appraisal tools.
This document contains technical information about interest formulas and calculations from Contemporary Engineering Economics, 4th edition. It includes examples of using present worth factors, uniform series factors, and gradient factors to calculate equivalent present values for various cash flow patterns including linear, declining, and geometric gradients. It also discusses unconventional calculations for cash flows with missing payments, varying interest rates over time, and payments occurring at irregular intervals.
This document provides an overview of economic feasibility assessment methods for evaluating energy projects. It discusses using net present value analysis to calculate the levelized cost of energy in cents per kilowatt-hour for different options, accounting for factors like capital costs, operations and maintenance costs, fuel costs, inflation, interest rates, and revenues over the project lifetime. The document also addresses dealing with uncertainties in input parameters and their impact on the overall cost estimate using statistical analysis methods.
This document discusses several methods for evaluating and selecting between mutually exclusive project alternatives when capital is limited. It provides an example of using net present value (NPV) and internal rate of return (IRR) to compare two investment projects. When capital is limited, the optimal selection is the set of projects that maximizes total NPV without exceeding the budget. Linear programming techniques can be used to model and solve the capital rationing problem.
The document discusses cost estimation and depletion. It begins by outlining a case study where student teams will evaluate an after-tax business expansion problem from a textbook. The document then defines relevant terms for projects using both debt and equity financing. It explains two methods for calculating depletion: cost depletion based on usage and percentage depletion which applies a constant percentage of gross income. It provides examples of applying both cost and percentage depletion calculations. The document concludes by noting current tax law allows taking the larger of cost or percentage depletion deductions each year.
The dangers of policy experiments Initial beliefs under adaptive learningGRAPE
The paper studies the implication of initial beliefs and associated confidence on the system’s
dynamics under adaptive learning. We first illustrate how prior beliefs determine learning dynamics
and the evolution of endogenous variables in a small DSGE model with credit-constrained agents,
in which rational expectations are replaced by constant-gain adaptive learning. We then examine
how discretionary experimenting with new macroeconomic policies is affected by expectations that
agents have in relation to these policies. More specifically, we show that a newly introduced macroprudential policy that aims at making leverage counter-cyclical can lead to substantial increase in
fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect
information about the policy experiment. This is in the stark contrast to the effects of such policy
under rational expectations.
- Regression models were estimated to examine the determinants of CEO salaries, wages, and exchange rates.
- Explanatory variables like firm performance, inflation rates, and unemployment were found to significantly impact the dependent variables in expected directions based on economic theory.
- While most coefficients were statistically significant, one variable in the wage model was found to be insignificant and could potentially be omitted to improve model specification.
- The interpretations focused on the estimated partial effects of the regressors, both in levels and rates of change, depending on the variable transformations used. Adjusted R-squared values indicated the models explained a substantial portion of the variation in the dependent variables.
This document contains 50 multiple choice questions about management science concepts from Chapter 1 of the textbook "Introduction to Management Science, 10e". The questions cover topics like the management science approach, modeling techniques, variables, parameters, data, models, break-even analysis, decision support systems, and linear programming. For each question, the answer, difficulty level, page reference, and keywords are provided.
Building Institutional Capacity in Thailand to Design and Implement Climate P...UNDP Climate
23-25 November 2016, Thailand - A centerpiece of the Integrating Agriculture in National Adaptation Plans Programme (NAP-Ag) in Thailand is its support to develop a new five-year Strategy on Climate Change in Agriculture (2017-2021). This is spearheaded by the Ministry of Agriculture and Cooperatives (MOAC) and its Office of Agriculture Economics (OAE). The strategy was unveiled after a series of meetings by a Technical Working Group at a three-day workshop held on 23-25 November 2016 in Bangkok, organized by UNDP.
Over 60 participants from each MOAC line department and 10 participants from academia and civil society were briefed by the Office of the Natural Resources and Environmental Policy and Planning (ONEP) and GIZ on the status of the National Adaption Plan (NAP) and learned how NAP-Ag programme efforts could support a broader NAP process and align with the Sector Plan. The new strategy focuses on improving evidence and data for informing policy choices, building the capacity of farmers and agri-businesses to adapt, promoting low-carbon development and productivity growth in the sector, and building institutional and managerial capacities to cope with climate change impacts.
This document discusses cost-benefit analysis for forestry projects. It begins by defining appraisal and outlining the nature of forestry projects, which have long production periods as trees are both the production unit and product. Common objectives of forestry projects include improving economic efficiency, social conditions, stability, and the environment. The stages of cost-benefit analysis are then outlined, including defining the issue, identifying options and costs/benefits, adjusting for future values, assessing risks, distributional impacts, and using techniques like net present value to evaluate projects. Environmental impacts are an important consideration in cost-benefit analysis to account for externalities. Sensitivity analysis is also recommended to assess how sensitive results are to changes in key parameters.
Partnering with ICCCAD, LUCCC, GRP, Climate-KIC, WRI
with support from Adaptation Fund, EU Commission and The GEF, @UNDP launched the #AdaptationInnovationMarketplace at the #Gobeshona conference to catalyze innovative climate solutions. Learn more about the platform.
The document summarizes integrated climate change strategies implemented by UNDP since 2008. It discusses several main areas of work, including mainstreaming climate change adaptation, developing national adaptation plans, building capacity on climate resilience, and establishing financing mechanisms. Over $2 billion has been invested across 64 countries through projects supporting livelihoods, food security, ecosystem protection, water resources, urban resilience, and early warning systems. Millions of people and large areas of land have benefitted from these initiatives.
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This document provides an overview of forecasting methods for operations management. It begins by listing the key learning objectives which are to identify principles of forecasting, explain the forecasting process, describe different forecasting methods and models, and compute forecast accuracy. The document then covers various qualitative and quantitative forecasting techniques including time series models, causal models, and methods for addressing trends, seasonality, and accuracy evaluation. Linear regression is presented as a tool for causal modeling.
The document discusses life-cycle costing techniques used in engineering economics and construction project design. Life-cycle costing considers all costs over the full life of a project, not just initial construction costs, to identify the design with the highest net benefits. It allows comparison of alternatives with different costs and benefits over time by using the time value of money. Examples are provided to illustrate compound interest calculations and the use of interest tables to evaluate alternatives based on their present worth.
NAP Training Viet Nam - Session 7 Appraising Adaptation OptionsUNDP Climate
This two-day workshop supported the Government of Viet Nam in building the necessary capacity to advance its National Adaptation Plan (NAP) process. The workshop closely focused on building National Adaptation Plans in the agricultural sector through multi-stakeholder collaboration, and increased knowledge and capacity on a number of topics including: prioritization of adaptation options, cost-benefit analysis, overview of the broad-based nature of climate change adaption impacts, analysis of challenges, and creation of an open discussion with key stakeholders on defining a road-map for the NAP process. The workshop was delivered using discussions and case studies to enhance interactive learning for participants, with supporting presentations by GiZ and SNV.
1) The document contains examples and explanations of present worth factors, equivalent cash flows, and unconventional calculations involving compound interest from the 4th edition of Contemporary Engineering Economics.
2) Various techniques are presented for handling cash flows with missing payments, multiple interest rates, and irregular payment patterns.
3) Examples show how to establish economic equivalence between cash flows, find an unknown interest rate using linear interpolation, and use the goal seek function in spreadsheets.
Math 141 Exam Final Exam Name____________________.docxendawalling
The document is a math exam for Math 141. It contains 14 multi-part questions testing a variety of math concepts including factoring, functions, inequalities, sequences, series, logarithms, and optimization problems. Students must show their work, box their answers, and use scientific calculators. The exam covers domains, ranges, increasing/decreasing intervals, relative and absolute extrema of functions, zeros, asymptotes, graphing, rational zeros of polynomials, solving inequalities, volumes of boxes, sums of sequences and series, and solving word problems involving rectangles and rates of change.
Math 141 Exam Final Exam Name____________________.docxwkyra78
Math 141 Exam Final Exam Name:_____________________________
SCIENTIFIC CALCULATORS ONLY! YOU MUST SHOW WORK FOR CREDIT!!! BOX ANSWERS!
1. [6 ea] Factor completely; simplify.
a) 12𝑥
!
" − 5𝑥
#
" − 2 b)3(4𝑥 + 5)! − 2(4𝑥 + 5) − 1
2. [4 each (a-e)] Approximate if necessary. For the given function , find/determine:
a) Domain: Range:
b) On what interval(s) is 𝑓:
i) Increasing:
ii) Decreasing:
c) ______ when ______.
d) Find any relative extrema (tell me if you have a max or min, what it is, and where it is):
e) Find any absolute extrema (tell me if you have a max or min, what it is, and where it is):
f) [2] Is 𝑓 even, odd , or neither? g) [4] What is the end behavior?
h)[4] What are the x and y- intercepts?
( )f x
( )2f - = ( ) 4f x = x =
3. [8 pts] a) Graph the function 4. [1 ea] Given 𝑔(𝑥) = −"
!
|𝑥 + 3| − 1
𝑓(𝑥) = /
2 − 𝑥, −3 ≤ 𝑥 < 1
𝑥! − 2𝑥 + 2, 𝑥 > 1
. Label 3+ points. Explain how the graph of 𝑔(𝑥) is obtained from the graph
𝑓(𝑥) = |𝑥|. (Hint: four actions=transformations)
a)
b)
c)
d)
5. [8;6] Given that
𝑓(𝑥) = #
$
, ℎ(𝑥) = log(𝑥 + 1) , 𝑗(𝑥) = !
$%"
, 𝑔(𝑥) = √𝑥 − 10
find the following and their DOMAINS. Use Interval Notation! and simplify the function when possible.
a) b)
6. [14 pts] Given 𝑅(𝑥) = &$
$#%$%"!
find:
a) Zero(s):
b) Vertical Asymptote(s):
c) Horizontal OR Slant Asymptote: d) Sketch . You do not need
specific values except for zero(s) and
asymptote. The shape needs be accurate!!!
j! f( ) x( ) ( )
( )
h x
g x
( )R x
y-
( )R xx
7. [9] Let 𝑝(𝑥) = 2𝑥' − 9𝑥! + 7.
a) List all possible rational zeros.
b) Find the remaining zeros of if one zero is −1.
8) [8] Solve the inequality 𝑥& + 2𝑥! − 3𝑥 > 0. Graph the solution on a number line and state the solutions in interval
notation.
9. [8 pts] A box with an open top is to be constructed from a rectangular piece of cardboard with dimensions inches
by inches by cutting out equal squares of side at each corner and then folding up the sides as in the figure.
Express the volume of the box as a function of and simplify completely.
( )p x
8
20 x
V x
x
x
10. [10 pts] Find for 𝑓(𝑥) = !
"#$
. Simplify!
11. [9 pts] Solve - use “zones” or sign-chart. Graph solution and express in Interval Notation.
%
"&'
≥ (
"&)
12. [8 pts] A landscape engineer has 200 feet of border to enclose a rectangular pond. What dimensions will result in the
largest pond?
( ) ( )f x h f x
h
+ -
13. a) [4 pts] Express in terms o
Consequences of learning curves for energy policyLeonardo ENERGY
Learning curves show that government deployment programmes do not subsidise technologies but provide required learning investments to make energy efficiency and low-carbon supply technologies competitive in mass markets. The programmes spur investment and market experience for currently expensive technologies and the learning curve shows how these experiences continually reduce cost and improve performance for the deployed technologies. This technology learning pervades all levels of organisations that produce, install and operate technology. This demonstrates that from identical boundary conditions many energy futures can be obtained, demanding the same economic resources but having completely different technological structures and thus very different properties regarding environment, energy security and job creation. Learning curves challenge current dogma on the long-range efficiency of the market and point to the need to realize political choices in deployment programmes.
Cost-benefit analysis is a more practical way to evaluate public expenditures than using the social welfare function directly, which is difficult to calculate accurately. It involves calculating the present value of both the costs and benefits of a policy or project using discount rates. For public projects, the appropriate discount rate to use is debated, as both social and market rates have pros and cons depending on whether the project funding decreases private investment or consumption. Calculating the costs and benefits also requires determining appropriate shadow prices for goods and services where the market is imperfect.
Thailand UNDP-GIZ workshop on CBA - Enhancing resilience in Thailand through ...UNDP Climate
Thailand, 27-28 November 2017 - UNDP and GIZ partnered with the Thailand Office of Agriculture Economics (OAE) to launch a workshop designed to connect vital stakeholders to build an effective National Adaptation Plan.
The two-day workshop at the Rama Garden Hotel had 20 participants from each department under the Ministry of Agriculture and Cooperatives (MOAC). The workshop was designed to build capacity of planning officers to formulate better projects and budget submissions as well as potential climate finance proposal using cost-benefit analysis and ecosystem-based analysis appraisal tools.
This document contains technical information about interest formulas and calculations from Contemporary Engineering Economics, 4th edition. It includes examples of using present worth factors, uniform series factors, and gradient factors to calculate equivalent present values for various cash flow patterns including linear, declining, and geometric gradients. It also discusses unconventional calculations for cash flows with missing payments, varying interest rates over time, and payments occurring at irregular intervals.
This document provides an overview of economic feasibility assessment methods for evaluating energy projects. It discusses using net present value analysis to calculate the levelized cost of energy in cents per kilowatt-hour for different options, accounting for factors like capital costs, operations and maintenance costs, fuel costs, inflation, interest rates, and revenues over the project lifetime. The document also addresses dealing with uncertainties in input parameters and their impact on the overall cost estimate using statistical analysis methods.
This document discusses several methods for evaluating and selecting between mutually exclusive project alternatives when capital is limited. It provides an example of using net present value (NPV) and internal rate of return (IRR) to compare two investment projects. When capital is limited, the optimal selection is the set of projects that maximizes total NPV without exceeding the budget. Linear programming techniques can be used to model and solve the capital rationing problem.
The document discusses cost estimation and depletion. It begins by outlining a case study where student teams will evaluate an after-tax business expansion problem from a textbook. The document then defines relevant terms for projects using both debt and equity financing. It explains two methods for calculating depletion: cost depletion based on usage and percentage depletion which applies a constant percentage of gross income. It provides examples of applying both cost and percentage depletion calculations. The document concludes by noting current tax law allows taking the larger of cost or percentage depletion deductions each year.
The dangers of policy experiments Initial beliefs under adaptive learningGRAPE
The paper studies the implication of initial beliefs and associated confidence on the system’s
dynamics under adaptive learning. We first illustrate how prior beliefs determine learning dynamics
and the evolution of endogenous variables in a small DSGE model with credit-constrained agents,
in which rational expectations are replaced by constant-gain adaptive learning. We then examine
how discretionary experimenting with new macroeconomic policies is affected by expectations that
agents have in relation to these policies. More specifically, we show that a newly introduced macroprudential policy that aims at making leverage counter-cyclical can lead to substantial increase in
fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect
information about the policy experiment. This is in the stark contrast to the effects of such policy
under rational expectations.
- Regression models were estimated to examine the determinants of CEO salaries, wages, and exchange rates.
- Explanatory variables like firm performance, inflation rates, and unemployment were found to significantly impact the dependent variables in expected directions based on economic theory.
- While most coefficients were statistically significant, one variable in the wage model was found to be insignificant and could potentially be omitted to improve model specification.
- The interpretations focused on the estimated partial effects of the regressors, both in levels and rates of change, depending on the variable transformations used. Adjusted R-squared values indicated the models explained a substantial portion of the variation in the dependent variables.
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Identifying what linkages are already being fostered between NAPs, NDCs and LTS and the key considerations in advancing climate change adaptation in agriculture.
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UNDP-FAO Integrating Agriculture in National Adaptation Plans HighlightsUNDP Climate
The joint United Nations Development Programme (UNDP) and Food and Agriculture Organization of the United Nations (FAO) Integrating Agriculture in National Adaptation Plans – Programme (NAP– Ag) is a multi–year initiative (2015–2018) funded by the International Climate Initiative of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB). The NAP–Ag Programme is supporting countries in Africa, Asia and Latin America to identify and integrate climate change adaptation measures into relevant national planning and budgeting processes. This integration will help enhance institutional capacities and processes for operationalization of climate response strategies in the agriculture sectors as well as the facilitation of stronger partnerships between ministries of agriculture, environment, planning and finance, and other national partners. NAP–Ag provides support to countries for accessing climate finance through international mechanisms, such as the Green Climate Fund (GCF), bilateral and multilateral funding mechanisms, as well as national financing. The Programme contributes to NAPs and the achievement of targets laid out in partner countries’ Nationally Determined Contributions (NDC) and the Sustainable Development Goals (SDG), in particular SDG–2 “Zero Hunger” and SDG –13 “Climate Action” , by strengthening resilience and adaptive capacity to climate–related hazards and natural disasters.
Five key achievements at a glance
1. Advanced the development of climate change adaptation planning strategies and frameworks in the agriculture sectors in Kenya, Philippines, Thailand and Uganda
2. Enhanced capacities of agriculture sector's decision-makers to appraise adaptation options, using cost-benefit analysis and impact evaluation, as a means to advance the NAP processes in Uganda, Uruguay and Zambia
3. Initiated the leveraging of climate finance for the implementation of climate change adaptation strategies and frameworks in the agriculture sectors in Nepal, Thailand and Viet Nam
4. Boosted capacity for gender mainstreaming through: technical training; incorporation of sex-disaggregated data and gender analysis into cost-benefit analyses and impact evaluations; and development of a tool to increase women’s incomes in the agriculture sector's value chains
5. Catalysed global attention to the integration of agriculture into NAPs through engagement of NAP-Ag country representatives in: Least Developed Country Expert Group (LEG) training workshops and meetings; side events at COP 21 and COP 22; Adaptation Committee meetings; and NAP Expos
- Uruguay has developed an advanced suite of climate change policies and plans including its National Climate Change Policy approved in 2017.
- The agricultural sector contributes significantly to Uruguay's economy but is vulnerable to climate change impacts. Uruguay is developing a National Adaptation Plan specifically for the agricultural sector (Agriculture NAP) to clarify adaptation needs and strategies.
- The UNDP-FAO NAP-Ag programme is supporting the formulation of the Agriculture NAP through assessments, stakeholder mapping, and analysis to identify adaptation options and policies for the agricultural sector.
FAO-UNDP Integrating Agriculture in National Adaptation Plans programme (NAP-...UNDP Climate
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The document provides details about Kenya's efforts to integrate agriculture into its national adaptation planning process. Some key points:
- Kenya has developed advanced national climate policies, strategies, and plans, including its National Adaptation Plan (NAP) from 2015, which supports the country's constitution and development goals.
- The NAP process in Kenya aligns with UNFCCC guidelines and focuses on reducing vulnerability and integrating adaptation into all sectors. Significant efforts have been made to implement agricultural sector interventions through frameworks like the Climate-Smart Agriculture Strategy.
- The UNDP-FAO NAP-Ag programme is working closely with six Kenyan ministries to ensure successful integration of agriculture into adaptation planning and implementation. Since 2016
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Economics of Climate Change Adaptation Training - Session 9
1. Photo credit: UNDP Climate Change Adaptation
ECONOMICS OF CLIMATE CHANGE
ADAPTATION
BLOCK 2 TRACK 2
Cost-Benefit Analysis of Investment
Projects and of Climate Change
Adaptation Investments
SESSION 9
Discounting
2. 8/21/2017 2
Session 9
Outline of presentation
2.1 What is T?
1 General methodology
2.2 What if T is infinite?
2.3 What about inflation? (nominal vs. real?)
2.4 Which discount rate to use: theory and
practice
2 Four questions
3. 8/21/2017 3
Session 9
Outline of presentation
2.1 What is T?
1 General methodology
2.2 What if T is infinite?
2.3 What about inflation? (nominal vs. real?)
2.4 Which discount rate to use: theory and
practice
2 Four questions
4. 8/21/2017 4
Session 9
General methodology
Principle of discounting
Discounting is simply a technique that allows us to compare costs and
benefits that happens at different point in time into a common unit of
measurement.
Assume 10% return on investment
Year
0
Year
1
Year
2
110 121100
Compounding
Discounting
Calculate Present Value
5. 8/21/2017 5
Session 9
General methodology
Typically, the flows of costs and benefits of a project look like this:
C0
1nvestment
costs
Benefits
C1 C2 C3 C4 C5 C6Costs C7
Operational
costs
0 0 0 B3 B4 B5 B6 B7
7. 8/21/2017 7
Session 9
General methodology
C0
Benefits
C1 C2 C3 C4 C5 C6Costs C7
0 0 0 B3 B4 B5 B6 B7
PV of
Costs
PV of
Benefits
8. 8/21/2017 8
Session 9
General methodology
C0 C1 C2 C3Costs
PV of
Costs
Technique of discounting
r Is referred to as the discount rate (in real
terms).
Is referred to as the discount factor.
3
3
2
21
0
)1()1()1( r
C
r
C
r
C
C
=
t
r)1(
1
9. 8/21/2017 9
Session 9
General methodology
PV =
More generally
Suppose a flow of benefits (or costs) Bt from t = 0 until t = T, then the
present value of this flow is:
T
T
r
B
r
B
r
B
r
B
B
)1(
...
)1()1()1( 3
3
2
21
0
Tt
t
t
t
r
B
0 )1(PV =
10. 8/21/2017 10
Session 9
General methodology
PV of costs =
So, for any given project or policy, we will have:
PV =
T
T
r
C
r
C
r
C
r
C
C
)1(
...
)1()1()1( 3
3
2
21
0
Tt
t
t
t
r
C
0 )1(
T
T
r
B
r
B
r
B
r
B
B
)1(
...
)1()1()1( 3
3
2
21
0
PV of benefits
=
Tt
t
t
t
r
B
0 )1(
PV =
11. 8/21/2017 11
Session 9
Outline of presentation
2.1 What is T?
1 General methodology
2.2 What if T is infinite?
2.3 What about inflation? (nominal vs. real?)
2.4 Which discount rate to use: theory and
practice
2 Four questions
12. 8/21/2017 12
Session 9
Outline of presentation
2.1 What is T?
1 General methodology
2.2 What if T is infinite?
2.3 What about inflation? (nominal vs. real?)
2.4 Which discount rate to use: theory and
practice
2 Four questions
13. 8/21/2017 13
Session 9
Four questions
What is T?
In theory, all benefits and costs of a project should be measured, even
when they extend indefinitely.
However, at least in the case of infrastructure project, the common practice
is set the time horizon of the analysis (T) to coincide with the expected
lifetime of the key (most important) component of the infrastructure project.
For example:
• Expected lifetime of a road (before it needs major repair – so major that it
may as well be considered a new road): 40 years
• Expected lifetime of a bridge: 75 to 100 years
• Expected lifetime of a coal-fired power plant: 25 to 40 years
And so, the time horizon of the analysis is set to coincide with these
expected lifetimes.
14. 8/21/2017 14
Session 9
Four questions
What is T?
Or in situation where the Government will provide a concession to build and
run a project (e.g. a power plant, a forestry or mining concession), then very
often the time horizon of the cost-benefit analysis is set to coincide with the
duration of the concession. For example if:
• Duration of a concession of a build and operate power plant 25 years.
Or if:
• Duration of a mining concession: 40 years
• Then the time horizon of the analysis is set to coincide with these
durations (25 years or 40 years respectively).
15. 8/21/2017 15
Session 9
Four questions
What is T?
Warning:
As a principle, this approach for setting the time horizon of the economic
analysis is wrong.
The selection of the time horizon for the CBA should be guided by one
question: How long will the impacts (positive or negative) last? And the
answer to this question may or may not coincide with the lifetime of the
infrastructure or duration of the concession.
This may or may not matter in terms of assessing the economic desirability
of the project or policy. If the infrastructure lifetime or duration of the
concession is long enough, and if the discount rate being used is relatively
high, then whatever happens after the CBA is truncated (after say 40 years)
may not matter very much.
16. 8/21/2017 16
Session 9
Outline of presentation
2.1 What is T?
1 General methodology
2.2 What if T is infinite?
2.3 What about inflation? (nominal vs. real?)
2.4 Which discount rate to use: theory and
practice
2 Four questions
17. 8/21/2017 17
Session 9
Four questions
$A $A $A $A $A $A $A
Year
$A
What if T is infinite?
Suppose the following:
0 1 2 3 4 5 … ∞
For example, if A = $100 and if r = 10%, then the present value of A is:
100*(1.1)/0.1 = $1,100.
PV =
PV =
)1(
...
)1()1()1( 32
r
A
r
A
r
A
r
A
A
r
r
A
)1(
*
18. 8/21/2017 18
Session 9
Four questions
$A $A $A $A
Year
$A
What if T is infinite?
Suppose the following:
0 1 2 3 4 5 … ∞
How would you calculate the PV of $A?
19. 8/21/2017 19
Session 9
Four questions
$A $A $A $A
Year
$A
What if T is infinite?
Suppose the following:
0 1 2 3 4 5 … ∞
Discounted to Year 0, it will be: r
r
A
)1(
*
)1(
3
r
PV to Year 3 =
A *
(1+r)
r
20. 8/21/2017 20
Session 9
Outline of presentation
2.1 What is T?
1 General methodology
2.2 What if T is infinite?
2.3 What about inflation? (nominal vs. real?)
2.4 Which discount rate to use: theory and
practice
2 Four questions
21. 8/21/2017 21
Session 9
Four questions
What about inflation? (nominal vs. real?)
Should we include inflation in this work? If we do, then what discount rate to
use?
So, suppose that prices are expected to rise at rate ‘g’ for the duration of
the project.
Suppose that the nominal discount rate is m (the real discount rate is r).
22. 8/21/2017 22
Session 9
Four questions
PV =
What about inflation? (nominal vs. real?)
Then, the present value of a flow of benefits from t = 0 to t = T is:
According to Fisher’s Law: (1 + m) = (1 + r)(1 + g). So we have:
T
T
T
m
gB
m
gB
m
gB
m
gB
B
)1(
)1(
...
)1(
)1(
)1(
)1(
)1(
)1(
3
3
3
2
2
21
0
PV = TT
T
T
gr
gB
gr
gB
gr
gB
gr
gB
B
)1()1(
)1(
...
)1()1(
)1(
)1()1(
)1(
)1)(1(
)1(
33
3
3
22
2
21
0
Which is the same as:
PV =
T
T
r
B
r
B
r
B
r
B
B
)1(
...
)1()1()1( 3
3
2
21
0
23. 8/21/2017 23
Session 9
Four questions
What about inflation? (nominal vs. real?)
If all costs and benefits are measured in nominal values, then use nominal
discount rate. If all costs and benefits are measured in real values, then use
real discount rate. It does not matter.
However, in practice, most of the time we do the analysis in real terms. In
doing so, there is one less variable we need to make projections for, the
annual inflation rate for each year of the time horizon of the analysis.
Note: Doing the analysis in real terms does not necessarily in constant
prices.
.
Message is:
24. 8/21/2017 24
Session 9
Four questions
Adjust past value for US
inflation to find today’s US
value
Taking values from the
past
What about inflation? (nominal vs. real?)
Today
Do analysis in real
terms
Taking values from the
past
Adjust past value for national
inflation to find today’s value
Use exchange rate today to transfer in
national value today
Do analysis in real
terms
Do analysis in real
terms
25. 8/21/2017 25
Session 9
Outline of presentation
2.1 What is T?
1 General methodology
2.2 What if T is infinite?
2.3 What about inflation? (nominal vs. real?)
2.4 Which discount rate to use: theory and
practice
2 Four questions
26. 8/21/2017 26
Session 9
Four questions
Which discount rate to use: theory
Consumers have utility function defined over consumption today and in the
future.
Where and stand for consumption today and tomorrow.
Two things about this function:
In a perfect world:
1 The higher or , the higher is utility.
2 The marginal utility of consumption is declining. It implies that
consuming everything today or tomorrow will not maximize utility
(consumption smoothing is preferred).
27. 8/21/2017 27
Session 9
Four questions
Which discount rate to use: theory
The Marginal Rate of Substitution (MRS) tells us by how much future
consumption must be increased to compensate for a loss of consumption
today.
Where U is marginal utility with respect to C and U with respect to C
The MRS is also interpreted as the individual’s rate of time preference.
In a perfect world:
It is the slope of the indifference between present and future consumption.
1100
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The slope of this Indifference curve is the marginal rate of substitution.
However, if I have a limited
amount of C0 and you are taking
one unit away, I will need a large
increase in C1 to be
compensated.
If I have lots of C0 and you take
away one unit of C0, I do not
need a large increase in C1.
In a perfect world:
C0
C1
-1
-1
Which discount rate to use: theory
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We can transform today’s consumption into future consumption.
In a perfect world:
If we invest one unit of C0, we will obtain additional units of C1 according
to the marginal productivity of capital.
Which discount rate to use: theory
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The slope of this Indifference curve is the marginal rate of substitution.
The slope of this
function is the marginal
rate of transformation.
In a perfect world:
C0
C1
Which discount rate to use: theory
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The optimal consumption path (which maximizes lifetime utility) is
such that the marginal rate of transformation is equal to the
marginal rate of substitution.
In a perfect world:
Marginal rate of
transformation
Which discount rate to use: theory
If we introduce perfectly competitive capital markets, we can show
that a competitive equilibrium in capital markets is such that the
market interest rate (which is the opportunity cost of capital)
equals the marginal productivity of capital.
Individual’s rate of time
preference
Opportunity cost of
capital
Marginal productivity of
capital
=
=
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And so now we finally have:
In a perfect world:
Market interest rate
Which discount rate to use: theory
Opportunity cost of
capital
Individual’s rate of
time preference
=
= Marginal productivity
of capital
=
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Suppose G comes in and wants to undertake a public project. The
project will decrease consumption and/or private investment today,
and increase consumption tomorrow.
In a perfect world:
Which discount rate to use: theory
What should be the discount rate to evaluate the benefits of future
additional consumption?
Answer is:
The project will either displace consumption or private investment.
Hence, the discount rate to use will depend on what G project is
displacing.
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If the project displaces current consumption, the discount rate should be
the rate of time preference.
In a perfect world:
Which discount rate to use: theory
If the project displaces other investments, the discount rate should be the
marginal productivity of capital.
If the project displaces both consumption and investment, the discount rate
should be a weighed average of the marginal productivity of capital and the
rate of time preference.
But in this perfect world, both the rate of time preference and the marginal
productivity of capital equals the market interest rate.
Thus we get:
Discount rate = Market Interest Rate (risk free)
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We may base the selection of the discount rate either on the rate of
society’s time preference or on the social opportunity cost of capital or on a
weighed average of those two.
In a less than perfect world:
Which discount rate to use: theory
Most financial/treasury/planning institutions would understand using the
social opportunity cost of capital to set the discount rate.
For example, from ADB’s CBA guidelines:
“A project investment is economically justified if the estimated economic
internal rate of return (EIRR) exceeds the economic opportunity cost of
capital (EOCC) for the country.”
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All of welfare economics is based on the presumption that policy choices
aim to maximize society’s welfare.
However:
Which discount rate to use: theory
If this is correct, then upon noting (1) that society’s welfare is made of some
aggregation of individual’s welfare (utility) and (2) that utility is defined over
consumption, then the value of the social discount rate should be based the
rate of society’s time preference – not the opportunity cost of capital.
This approach gives rise to the Ramsey Rule.
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Ramsey Rule (from Ramsey), 1928)
Where:
is the social discount rate
is the rate at which society discounts the utility of present versus future consumption
(impatience)
is the relative aversion to intertemporal inequality.
is the growth rate of per capita consumption.
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Upon applying the Ramsey Rule:
Which discount rate to use: theory
Weitzman (2007) 2% 2 2% 6%
Nordhaus (2005) 1% 2 2% 5%
Stern (2006) 0.1% 1 1.3% 1.4%
U.K. (since 2003) 1.5% 1 2.0% 3.5%
France (since 2005) 0% 2 2.0% 4.0%
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MDBs:
MDBs Discount rates
World Bank 10%
Asian Development Bank 12%
Inter-American Development Bank 12%
European Bank (EBRD) 10%
Note: All projects, irrespective of sectors (be it transport, energy, environment,
health, water supply, agriculture, etc.) and irrespective of countries, must use the
same discount rate.
Which discount rate to use: practice
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MDBs::
MDBs:
MDBs Discount rates
World Bank 10%
Asian Development Bank 12%
Inter-American Development Bank 12%
European Bank (EBRD) 10%
ADB: “Given the difficulty of estimating country-specific economic opportunity costs
of capital (EOCC), the EOCC for all ADB DMCs is 12%.”
World Bank: “ It is justified as a notional figure for evaluating Bank-financed
projects. This notional figure is not necessarily the opportunity cost of capital in
borrower countries, but is more properly viewed as a rationing device for World
Bank funds" (Operational Core Services Network Learning and Leadership Center,
1998).
Which discount rate to use: practice
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Some countries
Countries Discount rates
Philippines 15%
India 12%
Pakistan 12%
Which discount rate to use: practice
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Line ministries (or operational departments in MDBs) would prefer to have
their policies or projects accepted.
Incentives to over-estimate benefits and under-estimate costs.
Set a high discount rate.
Which discount rate to use: practice
Strategic reaction from those who allocate resources:
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Some countries
Which discount rate to use: practice
Countries Discount rates
Philippines 15%
India 12%
Pakistan 12%
New Zealand 10% (until 2008); now 8%
United States OMB: 10% (until 1992); 7% (until now); 3% for sensitivity
(since 2003); NOAA: 3%: EPA: 3%
European Union 6% (until 2006); now 5%
Germany 4% (until 2004); now 3%
France 8% (until 2005); now 4%
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In the USA:
On the selection of 7% in 1992:
“This new rate of 3% is justified by the social rate of time preference. If we take the
rate that the average saver uses to discount future consumption as our measure of
the social rate of time preference, then the real rate of return on long-term
government debt may provide a fair approximation” (OMB, (2003).
The 3% corresponds to the average real rate of return of 10- year Treasury notes
between 1973 and 2003.
Which discount rate to use: practice
On the selection of 3% in 2003:
“7% is an estimate of the average before-tax rate of return to private capital in the
U.S. economy” (OMB (2003).
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U.K. since 2003
Which discount rate to use: practice
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
100 200 300 400 500
Rationale: We cannot presume that future
generations will be better off than today’s
generation.
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Canada:
TBS currently recommends the use of 3% (“social” rate) and 7% (“real” rate) as
CBA discount rates.
TBS now allows for the option to use 3% discount rate as a central value for health
and environmental CBAs.
Which discount rate to use: practice
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Applying the Ramsey Rule in Canada:
Which discount rate to use: practice
Mid - point 1% 1.5 1.7% 3.5%
Range 1 – 2 1.5% – 2.0% 2.5% - 5.0%
Source: Boardman et al (2008)