Operational Manual for “A Macroeconomic Framework for Quantifying Growth and Poverty Reduction Strategies in Niger” Nihal Bayraktar* and Emmanuel Pinto Moreira** First complete draft: February 2, 2005 This version: October 09, 2005 Abstract This operational manual provides detailed information on the simulation of a macroeconomic model linking aid, public investment (disaggregated into education, health, and infrastructure), and growth, developed by Agénor, Bayraktar, and El Aynaoui (2005) and applied to Niger by Pinto Moreira and Bayraktar (2005). The manual explains how the model is specified, the parameters are calibrated, and the program is run. It also explains the different steps to follow to introduce policy shocks, analyze the output table, and derives policy implications. In order to help readers not familiar with Eviews to get started, we provide some basic information on EViews 4.0. ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ *Penn State University - Harrisburg and World Bank. E-mail address: [email protected]. **World Bank.
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Operational Manual for “A Macroeconomic Framework
for Quantifying Growth and Poverty Reduction Strategies in Niger”
Nihal Bayraktar* and Emmanuel Pinto Moreira**
First complete draft: February 2, 2005 This version: October 09, 2005
Abstract
This operational manual provides detailed information on the simulation of a macroeconomic model linking aid, public investment (disaggregated into education, health, and infrastructure), and growth, developed by Agénor, Bayraktar, and El Aynaoui (2005) and applied to Niger by Pinto Moreira and Bayraktar (2005). The manual explains how the model is specified, the parameters are calibrated, and the program is run. It also explains the different steps to follow to introduce policy shocks, analyze the output table, and derives policy implications. In order to help readers not familiar with Eviews to get started, we provide some basic information on EViews 4.0.
⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯
*Penn State University - Harrisburg and World Bank. E-mail address: [email protected]. **World Bank.
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Table of Contents I. INTRODUCTION
II. INPUT DATA FILE
III. PARAMETERS 1. Econometric Estimation of Some Parameters
2. Given Parameter Values
3. The Parameter Calibration Part of the Program
IV. EXOGENOUS VARIABLES PROJECTED WITHIN THE PROGRAM
V. CALCULATION OF RESIDUALS TO EQUATE ESTIMATED VARIABLES WITH THEIR
ACTUAL VALUES
VI. PARTIALLY ADJUSTED VARIABLES
VII. THE SIMULATION PROGRAM
1. How to Install the Simulation Package
2. The Setup of the Simulation Package
3. Details about the EViews Simulation Program
3.1. The Basic Information about Running the Program in EViews
3.2 EViews Commands Used in the Program
4. Details about the Excel Output Files
5. Details about the Summary Table File
VIII. Simulating Shocks
SHOCK 1 - Increase in Foreign Aid
SHOCK 2 - Reallocation of Public Investment
SHOCK 3 - Reduction in Tariffs
The Non-Neutral Case – Shock 3a
The Neutral Case: Adjustment in Direct Taxation – Shock 3b
The Neutral Case: Adjustment in Indirect Taxation – Shock 3c
IX. SENSITIVITY ANALYSIS
X. LINKING THE MODEL WITH THE MILLENNIUM DEVELOPMENT GOALS XI. LINKING THE MODEL WITH THE DECOMPOSITION OF PUBLIC CAPITAL EXPENDITURE TABLE APPENDIX A – Definitions APPENDIX B – List of Variables and Parameter Estimates APPENDIX C – Estimation Results APPENDIX D – EViews Commands Used in the Program and Their Meanings APPENDIX E – List of Equations APPENDIX F – Simple Example Model APPENDIX G – Tables of Simulation Results APPENDIX H – Calculation of Variables and Projection of Exogenous Variables
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I. INTRODUCTION, BACKGROUND, and OBJECTIVES
Pinto Moreira and Bayraktar (2005) applied a macroeconomic model which
analyzes the linkages between aid, public investment, and growth developed by
Agénor, Bayraktar, and El Aynaoui (2005) to Niger.1 The macroeconomic model has
been simulated using the software Eviews 4.0. The simulation program has been
used to create the baseline results and to investigate the effects of alternative policy
shocks on the economy, including an increase in aid/GDP ratio, a reallocation of
public investment toward investment in infrastructure, and neutral and non-neutral
reduction in effective tariff rate.
The objectives of this operational manual is twofold: (i) help the user of the
Niger’s model understand the technical aspects of the modeling exercise carried out
in Niger and, (ii) familiarize a reader interested in macro-modeling with some basics,
including modeling procedure, methods, and requirements using the Niger’s model
as an example.
The remainder of the manual is organized as follows. Section II presents
information on the input data file. Section III describes the calculation procedures and
methods used to compute the parameters used in the model. Section IV, presents
the methods used to project exogenous variables within the model. Section V
explains how the residuals are defined. Section VI describes the procedure of
introducing partially adjusted variables in the model. Section VII presents detailed
information about the simulation program written in Eviews 4.0. Section VIII describes
how shocks are run in the model. Section IX concludes.
PRELIMINARY REMARKS TO GET STARTING
Required Programs and the Files in the Package
• Two software programs are required to run this simulation program:
1 This paper applies the dynamic macroeconomic framework developed originally by Agénor, Bayraktar, and El Aynaoui (2005) to Niger.
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(a) EViews Version 4 or higher2.
(b) Microsoft Excel.
• Four different files are used to simulate the model. .
a. “Niger-Data.xls” Excel data file: This is the input data file. It contains
initial values of both exogenous and endogenous variables, and
projections for exogenous variables.
b. “Niger-Simulation.prg” EViews program file: This file is used to run the
simulation program.
c. “Niger-output.xls” Excel data file: It reports the simulation results
created by “Niger-Simulation.prg”. The names of the output files are
“OUTPUT-NIGER.xls” for the baseline output data; “OUTPUT-NIGER-
SHOCK1.xls” presenting the “Shock 1” output data; “OUTPUT-NIGER-
SHOCK2.xls” presenting the “Shock 2” output data; “OUTPUT-NIGER-
SHOCK3A.xls” presenting the “Shock 3A” output data; “OUTPUT-
NIGER-SHOCK3B.xls” presenting the “Shock 3B” output data;
“OUTPUT-NIGER-SHOCK3C.xls” presenting the “Shock 3C” output
data.
d. “Niger-table.xls” Excel data file: This table summarizes the simulation
results. The names of the output files are “NIGER-Output Table -
BASELINE.xls” for the baseline summary table; “Niger-Output Table-
SHOCK1.xls” for “Shock 1” summary table; “Niger-output-Aid-Shock-
Table 4.xls” presenting the deviation of the “Shock 1” values from the
baseline values; “Niger-Output Table-SHOCK2.xls” for “Shock 2”
summary table; “Niger-output-Aid-Shock-Table 5.xls” presenting the
deviation of the “Shock 2” values from the baseline values; “Niger-
Output Table-SHOCK3A.xls” for “Shock 3A” summary table; “Niger-
output-Aid-Shock-Table 6.xls” presenting the deviation of the “Shock
3A” values from the baseline values; “Niger-Output Table-
SHOCK3B.xls” for “Shock 3B” summary table; “Niger-output-Aid-Shock-
2 This software is created by Quantitative Micro Software. The new versions of EViews have tools for programming and solving simulation models.
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Table 7.xls” presenting the deviation of the “Shock 3B” values from the
baseline values; “Niger-Output Table-SHOCK3C.xls” for “Shock 3C”
summary table; “Niger-output-Aid-Shock-Table 8.xls” presenting the
deviation of the “Shock 3C” values from the baseline values; “NIGER-
Output Table – BASELINE-table 9.xls” for the baseline summary table
in case of lower public investment efficiency; “Niger-Output Table-
SHOCK4.xls” for “Shock 4” summary table (aid shock with lower public
presenting the deviation of the “Shock 4” values from the baseline
values.
II. INPUT DATA FILE Data entry and Location • The input file is named as “Niger-Data.xls” (an Excel file). Variables in the model
are separated into two groups: exogenous and endogenous variables. While the
exogenous variables are determined outside the model, the endogenous
variables are determined inside the model.3 The values of these variables and
parameters are presented in the input file.
• The location of the endogenous variables is on “ENDO” sheet, the exogenous
variables on “EXO” sheet, and the parameters on “PARAM” sheet. Data sources
of the variables are given in Appendix H.
• While the names of the variables are reported in column A, their definitions are
given in column B. In the following columns, the data points are presented starting
from 1999. The base year is 2004 in the model. But the data file starts in 1999
due to the presence of lagged variables in the model. All data points between
1999 and 2004 are actual numbers.
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Base year and Simulation period
• The base year is 2004 in the model. But the data file starts in 1999 due to the
presence of lagged variables in the model. All data points between 1999 and 2004
are actual numbers.
• The model is simulated for the years starting from 2004 until 2015. As
specified above, the simulated values of endogenous variables for this period are
determined within the model. But we have to project the values of exogenous
variables for these years since they are determined outside the model.4 The
projected values of exogenous variables are also reported on “EXO” sheet for the
years starting from 2005 until 2015. Detailed information on projections is reported in
Appendix H.
III. PARAMETERS
Three types of procedures have been used to determine the values of the
parameters in the model:
• Estimation running regression equations;
• Use of parameters provided in various studies as given; or,
• Calibration within the model5.
1. Econometric Estimation of Some Parameters
• The parameters of the three “fiscal” equations and private investment equation
(IP), which are listed below, are obtained by running econometric regressions. The
estimation technique is the ordinary least squares. But in order to correct for serial
correlation, the equations are estimated with autoregressive processes of order one
3 Exogenous and endogenous variables are listed in Appendix B and the list of equations is given in Appendix E. 4 It should be noted that some exogenous variables (DB, FP, ERROR_OMM, AID, LE_G, and WG) are projected within the model since they are projected as a constant share of endogenous variables. 5 Different types of parameters are used in Pinto Moreira and Bayraktar (2005). Their definitions are given in Appendix A. In this appendix, we also define the production and transformation functions used in the model.
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and/or two, denoted AR(1) and AR(2) below, where needed. All regressions are
based on annual data for the period 1982-2002. The E-views program used to run
the regression equations are given in “niger-regression-ig-indtxr-ip.prg”. The input file
where λY (lambdaY in the simulation program) is the adjustment parameter. This
parameter captures a low propensity to adjust total output in the short run. Its value is
0.4, which means that the adjustment rate is 40 percent per year.
Similarly, real imports, M, and domestic sales, DOM, are assumed to follow a
partial adjustment process. These equations are redefined as follows
DOM = λDOM*(X/(((PX/PD)*((1 - βDE)/ βDE))σDE)) + (1-λDOM)*DOM-1,
M = λM*(DOM*(((1 - βDM)/ βDM)*(PD/PM))σDM) + (1-λM)*M-1,
where λM=0.9 and λDOM=0.2 are the partial adjustment parameters.
It is assumed that PD exhibits a disequilibrium price mechanism, adjusting
partially towards its equilibrium value, EQPD:
PD = λPD.EQPD + (1-λPD).PD-1, (43)
where λPD is a parameter measuring the speed of price adjustment towards its
equlibrium value. λPD is taken as 0.4.
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VII. THE SIMULATION PROGRAM
The simulation program is written and run in EViews and it is connected to
outside input and output Excel files. This section describes: (i) how the simulation
package is installed; (ii) how it is coded; and (iii) how it is run.
1. How to Install the Simulation Package
There are 4 files in the package: “Niger-data.xls”, “Niger-Simulation.prg”,
“Niger-Output.xls”, and “Niger-Output-Table.xls”. The package is installed following
these two steps.
a. Create a directory named “Niger” on the C drive of your computer.
b. Copy all these files into the newly created directory.
2. The Setup of the Simulation Package The execution of the simulation program consists of the following steps:
Step 1: The data for the variables are put in the excel file named “Niger-
Data.xls”. When we run the simulation program, the values of exogenous and
endogenous variables will be imported into the program. The details about the input
file are given in Section II.
Step 2: Running of the simulation program is the second phase of the
simulation process. It is executed in EViews.
Step 3: When the simulation program is completed, the output file, in which
the simulated variables are stored, will be created. It is named as “Niger-Output.xls”.
It should be noted that this output file is automatically generated by EViews and after
each execution, the program overwrites the existing output file.
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Step 4: In this stage, the simulated variables stored in “Niger-Output.xls” will
be used to generate tables summarizing the simulation output. This excel file is
named as “Niger-Output-Table.xls”. When you open this file, you are asked whether
you want to update the information in the file. If you choose “update”, the summary
table will be updated by using newly created values of the simulated variables from
“Niger-Output.xls”.
These steps are presented in Figure 1.
3. Details about the EViews Simulation Program
• Before explaining the setup of the simulation program, the following points
must be emphasized related to programming in EViews.6 EViews can work with
square systems. It means that each equation in the model must have only one
endogenous variable assigned to it. Thus the number of independent equations
excluding exogenous variables which are projected within the model must be equal
to the number of endogenous variables in the model. The solution provided by an
EViews program consists of values for endogenous variables given exogenous
variables.
• EViews is a quite user friendly program. If your only aim is to investigate the
effects of shocks on the economy or to recalibrate the model with new values of
variables and parameters, it is not necessary for you to be familiar with Eviews
programming. But if you want to make any structural change, you may need to have
more experience with Eviews programming.
• Our EViews program is executed by double-clicking on the “Niger-
simulation.prg” file. It will be automatically launched and the simulation starts
immediately. After the completion of running of the program, EViews generates a
workfile named “Niger-Simulation.wk1”, in which simulated variables are stored.
5 A simple example model is presented in Appendix F.
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3.1. The Basic Information about Running the Program in EViews
This subsection provides basic information on how we can run our simulation
program in EViews.
FIGURE 8
Figure 8 shows how the simulation program looks like when you open the
simulation program file in EViews. In order to run the program, you click on the “run”
bottom (shown in a black circle in Figure 8). When you click on this bottom, the
following window opens. After you click on “OK” bottom, the program starts running.
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• If there is no error in the program, the workfile of our program will be opened
automatically right after the program stops. The name of this work file is “Niger-
Simulation.wf1”. A sample workfile created by the simulation program is presented in
Figure 9.
FIGURE 9
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• In this file, one can see the list of all variables and parameters. Baseline
variables (exogenous or endogenous) which are simulated within the program are
named with “_0” extension. Our model, which is named as “Niger” also appears in the
list. When you double click on “Niger”, you can see the details about our model. First
of all, the list of equations appears as default. This is shown in Figure 10. By clicking
on the “solve” icon (shown in a black circle in Figure 10), you can change your
solution method. Figure 11 shows the “solve” window. Our model is solved by using
the deterministic simulation technique.7
FIGURE 10
7 See the EViews Help Manual for details.
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FIGURE 11
• As specified in the EViews Help Manual, the following steps are taken while
running a deterministic simulation model in EViews:
a) The block structure of the model is analyzed.
b) The variables in the model are restricted to series in the workfile.
c) The equations of the model are solved for each observation in the solution
sample. During this process, an iterative algorithm is used to compute
endogenous variables.
d) The results will be rounded to their final values.
• As it can be seen in Figure 11, “Dynamics” option is used to specify how the
values of the lagged endogenous variables are determined. This means that the
lagged endogenous variables in the model are calculated using the solutions
calculated in previous periods, not from actual historical values.
• If you change the simulation type or the options related to dynamics, the
model will be simulated again by clicking on “OK” bottom in Figure 11. The old values
of the simulated variables will be replaced by the new values of them and these new
values will be stored in the workfile. If you want to store new values, you should save
the workfile by clicking on “save” bottom as shown in a black circle in Figure 9.
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• You may want to run our program using different parameter values. In this
case, you need to specify these new values in the simulation program file. If you
make any changes in the program, you should save it before you run it again. In
order to save a file, you need to click on the “save” icon (shown in a red circle in
Figure 8).
3.2 EViews Commands Used in the Program
• We use different EViews commands in the simulation program. These
commands and their meanings are given below. Detailed information is presented in
Appendix D. It should be noted that since Eviews reads codes only in the text format,
the program can be written either in the Eviews environment directly or in Microsoft
Word but then saved as a text file with a “prg” extension, which stands for EViews
program.
a. The Create Command
• Whenever one runs an EViews program, a workfile will be created, which
contains data we used and all results created by the program. Detailed information
on workfiles is given in Appendix D. In order to create this workfile, one uses the
create command. The general syntax for this command is as follows:
create workfile_name frequency start end
• Any workfile name can be chosen. The frequency of data can be annual,
monthly, etc or undated. While “start” specifies the starting date of the data, “end” is
the last year in our data file. In the program, this command is coded as follows create Niger-simulation U 17
• Here “Niger-simulation” is chosen as name of our workfile, which will be
created by our simulation program after we run it. “U” stands for undated data
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frequency. Since our data file and the simulation program covers the years starting in
1999 up to 2015, the number of observations is 17.
b. The smpl Command
• The “smpl”, which stands for sample, command specifies the time period that
we are working on. It is generally used after the “create” command. The general
syntax of this command:
smpl sample_name start end
• It is optional to name your sample. The sample range must be given using the
starting and ending dates. One example of “smpl” command in our code is
smpl 1 17
• Note that we have not given any sample name. This code specifies that we will
work with the sample covering the periods from 1 to 17. This means that all the
following calculations and simulations will be done for this period as far as we do not
change our sample range. Some of our calculations require a smaller sample range.
In this case, we redefine our sample range such as “smpl 3 17”.
c. The Read Command
• As it is specified before, we need to use an external data file. When this is the
case, we use the “read” command to import data from an external file. The general
syntax is:
read(options) path\file_name variable names
• After the “read” command, we have to specify our options. Our external data
file is an Excel file. Thus the options are defined in a way that the program is
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importing data from an Excel file. We explain these options below. Then we have to
specify the location of our data file and its name. At the end, we write the names of
the variables that we want to import. The order of the names of the variables must
match with their order in the Excel file.
The “read” command, which imports exogenous variables in the program, is
specified as follows:
read(ae4, t, s=EXO) C:\Niger\Niger-Data.xls AID$ CG DB ER ERROR_OMM FP kappa kappa_edu kappa_hea kappa_inf kappa_oth LAND LE_G n PMstar PXstar RD RGstar RPstar tm UTR$ WG tmnew
• We want to import data from an excel file; thus we need to specify our options
accordingly. Options will be presented within the parenthesis.
a) Provide information about the coordinates of the upper left cell of the data
matrix (excluding names and other definitions) in the Excel spreadsheet. In our
example, c4 stands for the cell number, at which point data that we want to import
starts.
b) Write “t ” when our data series are in rows rather than in columns. In this
columns. In this way, the observations will be read in rows. s=sheet_name option
shows the sheet in the Excel workbook from which data is to be read. Thus, s=EXO
means that we want to import data from the sheet named “EXO”. The location of the
data file is “C:\Niger\” and the name of the file is “Niger-Data.xls”.
c) List the name of variables that will be imported. These will be the names
that will be used if we need to refer to them in our program. It should be noted that
the order of the variables must follow that of the listing of the variables in the Excel
file. Figure 3 shows how the EXO sheet of our data file looks like. The projected data
are highlighted in blue.
• We follow exactly the same procedure to import endogenous variables using
this command: read(ae4, t, s=ENDO) C:\Niger\Niger-Data.xls AID CP DdebtG delta_LE_N Delta_NFA DITAX DITXR DOM FdebtG FdebtP FdebtTot FG GBAL GTOT IG IGedu IGhea IGinf IGoth INDTAX INDTXR IP J KGedu KGhea KGinf KGoth KP LE LE_P LR M NGDP PD PM POP PQ PQT PX PY Q Qd SP T TAX X Y Ydisp YTOT Z KGZ
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• Now, we refer to the sheet name “ENDO” and we have a new list of variables.
Figure 4 shows how the ENDO sheet of our data file looks like. Since these variables
will be determined within the model after 2004, we do not project them.
d. The Write Command
• The “write” command exports variables from EViews into an external file. The
way it is coded is quite similar to the “read” command. The general syntax:
• Here we want to export variables for the period 3 to 17 corresponding to the
years from 2001 to 2015. The first of the options specified in the code is t=xls, which
means that the type of file, in which we want to write the outcome, is an Excel file.
Then we write the coordinate of the cell, at which the exported series will start in the
output file. If we want to export our series in rows, we have to include “t” while
defining our options. Then we specify the desired location of the file that will be
created and the name of the file. If this file does not exist, the program is going to
create it automatically. On the other hand, if it already exists, the program is going to
overwrite it. At the end, we list the names of the series that we want to export. Figure
4 shows an output file.
e. The Model Command
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• The “model” command creates a model. We have to declare our model before
we start coding our equations. The general syntax:
model model_name
In our simulation program, the name of our model is “Niger” and it is declared
as follows
model Niger
f. The Append Command
• We use the “append” command to specify our equations. The general syntax
is:
model_ name.append equation
• For instance, population is defined by the following equation in the program:
niger.append POP = (1+n)*POP(-1)
• It specifies that the equation POP = (1+n)*POP(-1) is going to be added to the
model “Niger”.
g. The Solve Command
• The “solve” command triggers Eviews to solve a model. While running this
command, the Eviews will find a solution to a simultaneous equation model using
available data. This command needs to be placed after equations are listed. The
general syntax is:
solve(options) model_name
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• There are many options that we can use within the “solve” command. Details
are given in Appendix D. In our simulation program, we use the default solution,
which is dynamic simulation.
• The name of the model that will be solved must be specified. The solution
method may be modified by changing the options. The code in the program is:
smpl 7 17
solve(m=100000, c=.001) niger
• We set our sample range between 7 and 17. This means that the model will be
solved for the years 2005 to 2015. “m = integer ” indicates the maximum number of
iterations to be executed. “c = number ” specifies the convergence criterion for the
solution of the dynamic simulation. “Niger” is the name of our model.
h. The Statusline Command
• This command enables a message to be displayed on the status line at the
bottom of the Eviews window. The general syntax is:
statusline message
We use this command as follows
Statusline iteration number: !IDX
• This means that the current iteration number for the current period will be
written as Eviews runs the simulation program.
i. The genr Command
• This command generates new series, which are calculated using available
series. The general syntax is:
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genr ser_name = expression
• For instance, the following statement generates the T series using different
series and parameters:
genr T = AT*(beta_T*(LE_P^(-rho_T)) + (1 - beta_T)*((Kghea/(POP^theta_H))^(-rho_T)))^(-(1/rho_T))
4. Details about the Excel Output File
• All simulated endogenous variables and exogenous variables projected within
the program are named with “_0” extension. For example, AID_0 is the simulated AID
series. All exogenous variables and historical endogenous variables preserve their
original names; they do not take any extension. The output file is created for the
period from 3 to 17. This corresponds to the years from 2001 to 2015. The historical
values of the variables will be reported between 2001 and 2004. After these years,
the simulated values of endogenous variables and the projected values of the
exogenous variables will be presented.
5. Details about the Summary Table File
• This table is directly linked to the “Niger-Output.xls” file. It has to be updated if
we have a new output file. In order to update this table, the file must be opened and
then the “update” option must be chosen when the Excel program asks whether you
want to update this file or not. In this table, variables are presented either in levels (in
millions of LCU) or in percent of other variables, especially in percent of NGDP.
VIII. Simulating Shocks
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• This section explains how we can implement shock in the simulation program.
In Pinto Moreira and Bayraktar (2005), there are three different types of shocks
introduced:
1. Shock 1: Permanent increase in the ratio of foreign aid to GDP by 5
percent.
2. Shock 2: 12 percentage point reduction in investment in “other”
category, which is fully reallocated to investment in infrastructure.
3. Shock 3: Permanent cut of 10 percentage point in the effective tariff
rate.
a. Case 1 - The Non-Neutral Case: No change in the indirect and
direct tax rates.
b. Case 2 - The Neutral Case (Adjustment in Direct Taxation): the
effect of the tariff cut on revenue is offset, ex ante (that is, at
initial baseline values), by an increase in direct taxation.
c. Case 3 - The Neutral Case (Adjustment in Indirect Taxation): the
effect of the tariff cut on revenue is offset, ex ante (that is, at
initial baseline values), by an increase in indirect domestic
taxation.
• In order to run these shocks we have to make some simple changes in the
simulation program. All we need to do is to open some of the lines in the program,
which need to be closed during the baseline simulation, and to close some of the
lines if they will not be used while running the program to investigate the effects of
the shocks. In order to open a line in EViews, all we need to do is to remove the “ ' ” sign at the beginning of the line. We do the opposite to close a line: add “ ' ” at the
beginning of the line. In this way, the program is not going to read these lines when it
is executed.
SHOCK 1 - Increase in Foreign Aid
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• Our first shock on the economy is a permanent increase in the aid-to-GDP
ratio by 5 percentage points (i.e. 0.05). The original value of the ratio of aid to GDP
was 10.67 %. Its value will increase to 10.67% + 5% = 15.67% after the shock to aid
is introduced. In order to apply this shock:
a) Open the line named as “line1shock1” in the simulation program under the
SHOCKS section. When we open this line, EViews reads this line. The following
example shows how we can open this line.
A CLOSED Line 'line1shock1'scalar AID_const = 0.1067 + 0.05 How to OPEN the line 'line1shock1' scalar AID_const = 0.1067 + 0.05
How to re-close the line 'line1shock1'scalar AID_const = 0.1067 + 0.05
• In order to prevent the program from overwriting on the existing output file, we
assign a new name to the output file that will be generated after we run the program.
In order to do this, we need to open the following line:
• The preparation of the program to run this shock is quite similar to the
changes that we made to run the previous shocks.
• In the “Shocks to tm and DITXR” section (given below), the last two lines must
be opened. The first line helps us reduce the value of the tariff rate to half of its
original value. The last line increases the direct tax rate to 4.89% in order to
compensate the reduction in tax revenue caused by decreased tariff rates.
'---------------------------------------------------------------------------------------------------------- 'Shock to tm and DITXR (INDTXR fixed) '---------------------------------------------------------------------------------------------------------- 'NOTE: When tm drops to tmnew, the new value of DITXR must be equal 'to 0.048850383, keeping total tax revenue and INDTXR fixed. 'It is calculated for 2004. 'line1shock3b'genr tm = tmnew 'line2shock3b'scalar DITXR_ALT = 0.048850383
• We also need to open the last line of the following section. This line equates
the value of DITXR to its new higher value.
'---------------------------------------------------------------------------------------------------------- 'Shock to tm and DITXR (INDTXR fixed) '---------------------------------------------------------------------------------------------------------- 'NOTE: Before running this shock, don't forget to close the DITXR equation above. 'line3shock3b'niger.append DITXR = DITXR_ALT
• Since DITXR is defined using the new equation specified above, we need to
close the line containing the original equation determining DITXR. The line that we
need to close is:
37
niger.append DITXR = DITXR_const
• Its location is the section, in which we list the equations. In addition to these
changes, we must also open the line that generates our new output file, which is
• The preparation of the program to run this shock is quite similar to the
changes that we made in the model to run the previous shocks.
• In the “Shocks to tm and INDTXR” section (given below), the last two lines
must be opened. These two lines help us reduce the value of the tariff rate to the half
of its original value. The last line increases the indirect tax rate to 4.93% in order to
compensate the reduction in tax revenue caused by decreased tariff rates.
'---------------------------------------------------------------------------------------------------------- 'Shock to tm and INDTXR (DITXR fixed) '---------------------------------------------------------------------------------------------------------- 'NOTE: When tm drops to tmnew, the new value of INDTXR must be equal 'to 0.049305362, keeping total tax revenue and DITXR fixed. 'It is calculated for 2004. 'line1shock3c'genr tm = tmnew 'line2shock3c'scalar INDTXR_ALT = 0.049305362
• We also need to open the last line of the following section. This line equates
INDTXR to its original value.
'---------------------------------------------------------------------------------------------------------- 'Shock to tm and INDTXR (DITXR fixed) '---------------------------------------------------------------------------------------------------------- 'NOTE: Before running this shock, don't forget to close the INDTXR equation above. 'line3shock3c'niger.append INDTXR = INDTXR_ALT
39
• Since INDTXR is redefined using this new equation, we have to close the line
containing the original equation determining INDTXR. The line that we need to
One should not forget to close the line containing the original write command.
• The results are presented in Table 10 in Appendix G.
As it is noted before, after the new output file is created, all the changes that
we have done must be turned back to their original setup.
X. LINKING THE MODEL WITH THE MILLENNIUM DEVELOPMENT GOALS (MDGS)
This section explains how we can link the macroeconomic framework to the
MDGs.8 It is also shown how we can run the simulation program to create simulated
data files which are used to construct the MDG tables.
8 Details are given in Agenor, Bayraktar, Pinto Moreira, and El Aynaoui (2005).
41
Six of the MDG indicators are integrated: the poverty rate, the literacy rate,
infant mortality, malnutrition, life expectancy, and access to safe water. Because the
model can directly calculate values for the poverty and the literacy rates, we only ran
regressions to estimate the equations for infant mortality, malnutrition, life
expectancy, and access to safe water. The estimation method is ordinary least
squares. We use cross-section data, obtained by taking average values of variables
for each country for the period 1965-2003, depending on the availability of data
series. Our sample consists of Sub-Saharan countries. The regression results are
presented in the following table.
42
Cross-Section Regression Results(All sub-Saharan countries are included unless otherwise indicated)
Dependent variables MALNUTRITION ln(MORTALITY) ln(LIFE_EXP) WATER 2/ Constant term 75.415
(6.055) 5.485 (10.761)
3.428 (27.187)
6.711 (0.299)
HEA_P_GDP 1/ -4.790 (-3.961)
-0.091 (-1.949)
0.048 (2.802)
…
ln(CPPC2003$) -7.951 (-4.126)
… … …
POVERTY 0.144 (1.635)
0.011 (3.247)
-0.002 (-2.771)
…
ln(GDPPC2003$) … -0.191 (-2.820)
0.078 (4.189)
6.921 (2.458)
INF_GDP … … … 1.702 (1.718)
ln(POP_DENSITY) … … … 4.076 (1.551)
Number of observations
28 31 20 31
Adjusted R2 0.552 0.479 0.739 0.292 Note: The estimation technique is OLS. Data points of independent variables in each country correspond exactly to the years in which dependent variables are available. First, averages at the country level are calculated, then the regression equations are run using these cross sectional data. t-statistics are reported in parenthesis. MALNUTRITION is malnutrition prevalence, weight for age (% of children under 5); HEA_P_GDP is public health expenditure in % of GDP; CPPC2003$ is private consumption per capita (in constant 2003 dollars); POVERTY is the percent of population living under $2 per day; MORTALITY is infant mortality rate (per 1000 live births); GDPPC2003$ is GDP per capita (in constant 2003 dollars); LIFE_EXP is life expectancy at birth, total, years; INF_GDP is public infrastructure expenditure in percent of GDP; WATER is percentage of population with access to safe water; POPDEN is population density (people per km square). 1/ While the data source of public heath expenditure is Government Financial Statistics in the life-expectancy regression, the data source of public heath expenditure is World Bank African Database in other regressions. 2/ Due to insufficient number of data points for sub-Saharan African countries, all developing countries are included depending on data availability.
After estimating these coefficients, we calculate the residuals of each
regression equation for Niger, which are going to be used in calculating predicted
values of the MDG indicators. For example, in case of malnutrition prevalence, the
estimated equation is:
ACTUAL value of MALNUTRITION = - 4.79*(HEA_P_GDP) -
• In order to run this shock the following lines in the simulation program should
be closed as indicated in the program: 'NOTE: Close the following line when running the debt relief shock (Shock 6) niger.append GTOT = WG*LE_G + PQT*(CG + IG) + RGstar*ER*FdebtG(-1) + RD*DdebtG(-1)
'NOTE: Close the following line when running the debt relief shock (Shock 6) niger.append IG = (-0.174921+0.649353*(TAX(-1)/(NGDP(-1)))+1.549799*(AID/NGDP)-3.26115*(AID/NGDP)^2)*NGDP/PQT + IG_RES
'NOTE: Close the following line when running the debt relief shock (Shock 6) niger.append delta_NFA = PXstar*X - PMstar*M - RGstar*FdebtG(-1) - RPstar*FdebtP(-1) + UTR$ + (AID$ +AID_RES) + FG + FP+ERROR_OMM
'NOTE: Close the following line when running the debt relief shock (Shock 6) niger.append FdebtG = FG + FdebtG(-1)
- partial efficiency.xls” shows the deviation from the summary baseline table.
XI. LINKING THE MODEL WITH THE DECOMPOSITION OF PUBLIC CAPITAL EXPENDITURE TABLE
55
REFERENCES Agénor, Pierre-Richard, Nihal Bayraktar, and Karim El Aynaoui, “Roads out of
Poverty? Assessing the Links between Aid, Public Investment, Growth, and Poverty Reduction,” World Bank Working Paper No: 3490, (January 2005).
Agénor, Pierre-Richard, Nihal Bayraktar, Emmanuel Pinto Moreira, and Karim El Aynaoui, “Achieving the Millennium Development Goals in Sub-Saharan Africa,” World Bank Working Paper No: ???, (October 2005).
Chiang Alpha C., Fundamental Methods of Mathematical Economics, 3rd Edition, McGraw-Hill/Irwin, (1984).
Pinto Moreira, Emmanuel and Nihal Bayraktar, “A Macroeconomic Framework
for Quantifying Growth and Poverty Reduction Strategies in Niger” World Bank Working Paper No: 3506, (January 2005).
56
APPENDIX A – Definitions
This appendix defines the types of functions and parameters used in the manual. Chiang (1984) gives detailed information on them.
1. Constant Elasticity of Substitution (CES) Production Function
An example of this type of function is given below:
Q = A[βK(-ρ) + (1-β).L(-ρ)](-1/ρ) (A3-1)
where Q is output, K is the capital stock, and L is labor. A is the shift or efficiency parameter, β is the share parameter, and ρ is the substitution parameter. Each input has a constant substitution parameter. The major properties of this type of functions are that they are homogenous of degree of one and display constant returns to scale.
2. Substitution parameter (ρ)
It determines the elasticity of substitution. See the definition elasticity of
substitution below.
3. Elasticity Substitution (σ) From the first order condition obtained by maximizing production function (e.g.
Equation (A3-1)) given the cost of production function, the elasticity of substitution (σ) is equal to 1/(1+ ρ) for CES production functions. It measures the effect of a change in the price ratio of inputs on the least-cost input combination in order to produce the same level of given output. Assuming that w is the price of labor and r is the price of capital, the elasticity of substitution between K and L can be defined as
σ = Relative change in (K/L)/ Relative change in (w/r)
As w increases, the K/L ratio also increases since K, which is relatively cheaper
now, will be substituted for L.
4. Shift parameter (A) For given values of inputs, the magnitude of A will proportionately affect the
level of output. It is also named as efficiency parameter as an indicator of the state of the technology.
5. Distribution parameter (β)
It shows the relative shares of inputs in the production.
6. Constant Elasticity of Transformation (CET) Functions
57
These functions define the allocation of any output between alternative uses. In the example below output Y is allocated between exports, X, and domestic sales, DOM, according to a CET function
Y = A[βX-ρT + (1 - β)DOM-ρT](1//ρT) (A3-2) Their properties are similar to the properties of CES functions. ρT is the
transformation parameter.
7. Transformation parameter (ρT) It determines the elasticity of transformation.
8. Elasticity of Transformation (σT)
From the first order condition obtained by maximizing output function (e.g. Equation (A3-2) given the total cost of products, the elasticity of substitution is equal to 1/(1+ ρT) for CET functions. It measures the effect of a change in the price ratio of alternative outputs on the optimal output combination in order to produce the same level of given input. Assuming that PD is the price of DOM and PX is the price of X, the elasticity of transformation between DOM and X can be defined as
σT = Relative change in (X/DOM)/ Relative change in (PD/PX)
As PD increases, X/DOM drops, since DOM, which is relatively more expensive
now, will be produced more compared to X.
9. Types of Variables
a. Exogenous variables: Variables determined out of the model. These are given variables.
b. Endogenous variables: Variables determined within the model. They can be a function of exogenous and endogenous variables.
58
Appendix B - List of Variables and Parameter Estimates Endogenous Variables Variable EViews
Name Definition
AID AID Total aid measured in domestic-currency terms
CP CP Total private consumption in real terms CG CG Real public spending on consumption DdebtG DdebtG Domestic public debt stock (direct
borrowing)
∆NFA Delta_NFA Change in net foreign assets of the central bank
DITAX DITAX Direct tax revenue DITXR DITXR Effective direct tax rate DOM DOM Domestic sales EQPD EQPD Equilibrium value of PD
FdebtG FdebtG Stock of public foreign debt
FdebtP FdebtP Stock of private foreign debt FdebtTot FdebtTot Total external debt FG FG Flow of government borrowing abroad GBAL GBAL Government budget balance GTOT GTOT Total government expenditure IG IG Real public investment IGedu IGedu Real public investment in education
IGhea IGhea Real public investment in health IGinf IGinf Real public investment in infrastructure IGoth IGoth Real public investment in other
categories
INDTAX INDTAX Indirect tax revenue INDTXR INDTXR Effective indirect tax rate IP IP Real private spending on investment J J Composite input from the supply of
composite input T and private capital, KP
KGedu KGedu Stock of public capital in education KGhea KGhea Public capital in health KGinf KGinf Public capital in infrastructure KGZ KGZ Composite public capital in education KP KP Private capital LE, ∆LEN LE,
delta_LEN Total educated labor (stock and flow)
LEP LE_P Quantity of educated labor used in private production
LR LR Raw labor M M Demand for imported goods (in real
59
terms) NGDP NGDP Nominal gross domestic product PD PD Price of the domestic good PM PM Domestic-currency price of imports POP POP Size of the population PQ, PQT PQ, PQT Composite price index (before and after
indirect taxes)
PX PX Domestic-currency price of exports PY PY GDP deflator Qd Qd Total demand for goods sold on the
domestic market (which includes both imports and domestically-produced goods)
Q Q Domestic sales SP SP Private savings T T “Effective” labor; composite input from
the supply of educated labor, LE, and the stock of public capital in health, Kghea
TAX TAX Total tax revenue X X Exports (in real terms) Ydisp Ydisp Households’ disposable income in
nominal terms
Y Y Aggregate supply of domestic goods (in real terms)
YTOT YTOT Total income before taxes Z Z Composite public education input
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Exogenous Variables Variable EViews Name Definition
κh Kappa_h Share of public investment in h with h = edu, hea, inf, oth and Σκh = 1
χ Kappa Share of teachers in LE_G AID$ AID$ Total aid measured in US dollars
terms
DB DB Flow of direct domestic borrowing ER ER Nominal exchange rate ERROR_OMM ERROR_OMM Errors and omissions FP FP Private capital inflows LAND LAND Land (normalized to unity)
LEG LE_G Quantity of educated labor employed by the public sector
n n Growth rate of population and raw labor
NMCG NMCG Real public spending on other goods and services
PM* PMstar World price of imports PX* PXstar World price of exports RD RD Interest rate on domestic public
debt
RG* RGstar Interest rate on public foreign debt RP* RPstar Interest rate on private foreign
borrowing
tm tm Tariff rate UTR$ UTR$ Private unrequired transfers WG WG Average effective wage in the public
θKGI theta_KGI Parameter capturing congestion effects in the education system
θH theta_H Parameter determining the strength of congestion effects in the provision of health services
θI theta_I Parameter capturing congestion effects on infrastructure capital
ADE ADE Shift parameter in production of domestic goods Ys
ADM ADM shift parameter in production of Qs AE AE Shift parameter in flow production of
educated labor LE
AJ AJ Shift parameter for composite input J AKGZ AKGZ Shift parameter for composite input KGZ
AT AT Shift parameter for composite input T
AY AY Shift parameter for in the production function
AZ AZ Shift parameter for in Z
βDE beta_DE Share parameter between exports EXP and domestic sales DOM in production of domestic goods Ys
βDM beta_DM Share parameter between imports M and demand for domestic goods DOM
βE beta_E Share parameter between educated labor LE and public capital in education, Kgedu in flow production of LE
βJ beta_J Share parameter between the supply of T and the stock of private capital, KP in production of J
βKGZ beta_KGZ Share parameter in production of KGZ βT beta_T Share parameter between the supply of
educated labor, LE, and the stock of public capital in health, Kghea in production of T
βY beta_Y Share parameter between the supply of J and public capital in infrastructure, Kginf in production of Ys
βZ beta_Z Share parameter in equation Z δh delta_h Depreciation rate of public capital with h
62
= edu, hea, inf δP delta_P Constant rate of depreciation of private
capital
ρDE rho_DE transformation parameter in production of domestic goods Ys
ρDM rho_DM Substitution parameter in Qs ρE rho_E Substitution parameter in flow production
of LE
ρJ rho_J Substitution parameter in production of J ρKGZ rho_KGZ Substitution parameter in production of
KGZ
ρT rho_T Substitution parameter in production of T ρY rho_Y Substitution parameter in production of
domestic output
ρZ rho_Z Substitution parameter in production of Z s s Marginal propensity to save σDE sigma_DE = 1/(1-ρDE); elasticity of transformation
between exports and domestic sales
σDM sigma_DM = 1/(1+ρDM); elasticity of substitution between imports and demand for domestic goods
σE sigma_E = 1/(1+ρE); elasticity of substitution between LR-1 and KGedu/(LR-1)θE
σJ sigma_J = 1/(1+ρJ); elasticity of substitution between T and KP
σKGZ sigma_KGZ = 1/(1+ρKGZ); elasticity of substitution between KGedu and KGinf
σT sigma_T = 1/(1+ρT); elasticity of substitution between LE and Kghea/POPθH
σY sigma_Y = 1/(1+ρY); elasticity of substitution between J and KGinf/Y-1
θI
σZ sigma_Z = 1/(1+ρZ); elasticity of substitution between between LE_G and KGedu
63
APPENDIX C – Estimation Results This appendix reports the estimation results for Equations (1) to (4). The estimation technique is ordinary least squares. The regressions are corrected for serial correlation with autoregressive processes of order one and/or two, denoted AR(1) and AR(2). The definitions of the equations and variables are given in Pinto Moreira and Bayraktar (2005). Estimation results for 11 PQT·IP/NGDP = IP((∆Y/Y-1) -2, KGinf/Y, ER·FP/NGDP) Dependent Variable: PQT.IP/NGDP Method: Least Squares Sample(adjusted): 1982 2002 Included observations: 21 after adjusting endpoints Convergence achieved after 120 iterations
R-squared 0.812020 Mean dependent var 0.032714Adjusted R-squared 0.710799 S.D. dependent var 0.014028S.E. of regression 0.007544 Akaike info criterion -6.653767Sum squared resid 0.000740 Schwarz criterion -6.255853Log likelihood 77.86455 F-statistic 8.022309Durbin-Watson stat 1.893319 Prob(F-statistic) 0.000717Inverted AR Roots .41 -.43i .41+.43i
Note: Since private investment variable fluctuates a lot in year 1987 and years between 1992 and 1995, dummy variables are used for these years. Dummy-87 is 1 in 1987, 0 otherwise. Dummy-92_95 is 1 in 1992-95, 0 otherwise.
11 Note that the coefficient of (KGinf.PQT/NGDP) is taken as 0.15304 in the simulation program since the lower value of the coefficient was producing lower IP.
64
Estimation results for INDTXR = INDTXR(INDTXR-1, AID/NGDP) Dependent Variable: INDTXR Method: Least Squares Sample(adjusted): 1986 2002 Included observations: 17 after adjusting endpoints Convergence achieved after 7 iterations
R-squared 0.702030 Mean dependent var 0.058565Adjusted R-squared 0.553044 S.D. dependent var 0.017584S.E. of regression 0.011756 Akaike info criterion -5.768968Sum squared resid 0.001382 Schwarz criterion -5.479247Log likelihood 52.15174 F-statistic 4.712076Durbin-Watson stat 1.888750 Prob(F-statistic) 0.017978Inverted AR Roots .26 -.59i .26+.59i
12 Note that the coefficient of (TAX/NGDP)-1 is taken as 0.649353 in the simulation program since the higher value of the coefficient was producing extremely sensitive results to changes in the tax to NGDP ratio. Similarly, the coefficient of (AID/NGDP)2 is taken as -3.26115 in order to reduce the negative effect of the square term of aid on IG.
65
APPENDIX D – EViews Commands Used in the Program and Their Meanings
The following definition of the EViews command used in the simulation program for Niger is presented in this appendix. These definitions are taken from the help menu of EViews Version 4.1. CREATE Command
Create a new workfile. Syntax Command: create optional_name frequency start end You may provide an optional name for your workfile. If you do not, EViews will
create an untitled workfile. You must specify the frequency, and the starting and ending dates of your data.
For undated data, you should specify the starting and ending observation numbers. Options You must choose one of the following options to specify the frequency of your
workfile: a Annual s Semi-annual q Quarterly m Monthly w Weekly d Daily (5 day week) 7 Daily (7 day week) u Undated or irregular Examples create a 1880 90
creates an annual workfile from 1880 to 1990. create m 1990:1 2010:12
creates a monthly workfile from January 1990 to December 2010. create w 2/10/1951 3/17/1994
creates a weekly workfile from the week starting February 10, 1951 to the week starting March 17, 1994.
66
create u 1 5000
creates an undated workfile with 5000 observations.
SCALAR Command Declare a scalar object. The scalar command declares a scalar object and optionally assigns a value. Syntax Command: scalar scalar_name Command: scalar scalar_name=assignment The scalar keyword should be followed by a valid name, and optionally, by an
assignment. If there is no explicit assignment, the scalar will be assigned a value of zero.
Examples scalar alpha
declares a scalar object named ALPHA with value zero. equation eq1.ls res c res(-1 to -4) x1 x2 scalar lm=eq1.@regobs*eq1.@r2 show lm
runs a regression, saves the as a scalar named LM, and displays its value in the status line at the bottom of the EViews window.
READ Command Read data from a foreign disk file. The "read" command may be used to read multiple series into a workfile from a
file on disk. When used as a procedure, read imports data directly into pool and matrix objects.
Syntax
67
Command: read(options) path\file_name name1 name2 name3 Command: read(options) path\file_name n Coef Proc: coef_name.read(options) path\file_name Pool Proc: pool_name.read(options) path\file_name n1? n2? n3? Matrix Proc: matrix_name.read(options) path\file_name You must supply the name of the source file. If you do not include the optional
path specification, EViews will look for the file in the default directory. The input specification follows the source file name. Path specifications may point to local or network drives. If the path specification contains a space, you may enclose the entire expression in double quotation marks.
In the command proc form of read, there are two ways to specify the input
series. First, you may list the names of the series in the order they appear in the file. Second, if the data file contains a header line for the series names, you may specify the number n of the series in the file instead of a list of names; EViews will name the series as given in the header line. If you specify a number and the data file does not contain a header line, EViews will name the series as SER01, SER02, SER03, and so on.
For the pool proc form of read, you must provide a list of ordinary or pool series. Options File type options t=dat, txt ASCII (plain text) files. t=wk1, wk3 Lotus spreadsheet files. t=xls Excel spreadsheet files. If you do not specify the "t" option, EViews uses the file name extension to
determine the file type. If you do specify the "t" option, then the file name extension will not be used to determine the file type.
Options for ascii text files na= Specify text for NAs. Default is "NA". byper Panel data organized by date/period. Default is data organized by
cross-section (only for pool read). bycross (default) Panel data organized by cross-section (only for pool read). t Read by series (or transpose the data for matrix objects). Default is to read by
observation with series in columns. d=t Treat tab as delimiter.
68
d=c Treat comma as delimiter. d=s Treat space as delimiter. d=a Treat alpha numeric characters as delimiter. custom= Specify symbol/character to treat as delimiter. mult Treat multiple delimiters as one. name Series names in file. label= Number of lines between the header line and the data. Must be used
with the "name" option. rect(default) Treat file layout as rectangular. norect Do not treat file layout as rectangular. skipcol= Number of columns to skip. Must be used with the "rect" option. skiprow= Number of rows to skip. Must be used with the "rect" option. comment= Specify character/symbol to treat as comment sign. Everything to
the right of the comment sign is ignored. Must be used with the "rect" option. singlequote Strings are in single quotes, not double quotes. dropstrings Do not treat strings as NA; simply drop them. negparen Treat numbers in parentheses as negative numbers. allowcomma Allow commas in numbers (note that using commas as a
delimiter takes precedence over this option). currency= Specify symbol/character for currency data. Options for spreadsheet (Lotus, Excel) files _number (default=b2) Coordinate of the upper-left cell containing data. s=_name Sheet name for Excel 5-8 Workbooks. byper Panel data organized by date/period. Default is data organized by
cross-section (only for pool read). bycross (default) Panel data organized by cross-section (only for pool read). t Read by series (or transpose the data for matrix objects). Default is to read by
observation with each series in columns. Examples read(t=dat,na=.) a:\mydat.raw id lwage hrs
reads data from an ASCII file MYDAT.RAW in the A drive. The data file is listed by observation, NA is coded as a "." (dot or period), and there are three series, which are to be named ID, LWAGE, HRS in this order from left to right.
read(a2,s=sheet3) cps88.xls 10
reads data from an Excel file CPS88 in the default directory. The data are organized by observation, the upper left data cell is A2, and 10 series are read from a sheet named SHEET3.
read(a2, s=sheet2) "\\network\dr 1\cps91.xls" 10
reads the Excel file CPS91 from the network drive specified in the path.
69
SERIES Command Series of observations. An EViews series contains a set of observations on a
variable. To declare a series, use the keyword series, followed by a name, and
optionally, by an "=" sign and a valid series expression: series y series x=3*z If there is no assignment, the series will be initialized to contain NAs. Series Views bar bar graph of the series. bdstest BDS independence test. cdfplot distribution (cumulative, survivor, quantile) functions. correl correlogram, autocorrelation and partial autocorrelation functions. edftest empirical distribution function tests. freq one-way tabulation. hist descriptive statistics and histogram. kdensity kernel density estimate. label label information for the series. line line graph of the series. qqplot quantile-quantile plot. seasplot seasonal line graph. sheet spreadsheet view of the series. spike spike graph. statby statistics by classification.
70
stats descriptive statistics and histogram. testby equality test by classification. teststat simple hypothesis tests. uroot unit root test. Series Procs displayname set display name. hpf Hodrick-Prescott filter. seas seasonal adjustment only for quarterly and monthly time series. resample resample from the observations in the series. smooth exponential smoothing. tramoseats seasonal adjustment using Tramo/Seats. x11 seasonal adjustment by Census X11 method only for quarterly and
monthly time series. x12 seasonal adjustment by Census X12 method only for quarterly and
monthly time series. Series Data Members (i) i-th element of the series from the beginning of the workfile (when used
on the left-hand side of an assignment, or when the element appears in a matrix, vector, or scalar assignment).
Series Element Functions @elem(ser, j) function to access the j-th observation of the series SER, where j
identifies the date or observation. Series Examples You can declare a series in the usual fashion: series b=income*@mean(z) series blag=b(1)
71
Note that the last example, above, involves a series expression so that B(1) is treated as a one-period lead of the entire series, not as an element operator. In contrast,
scalar blag1=b(1)
evaluates the first observation on B in the workfile. Once a series is declared, views and procs are available: a.qqplot a.statby(mean, var, std) b To access individual values: scalar quarterlyval = @elem(y, "1980:3") scalar undatedval = @elem(x, 323) GENR Command Generate series using pool objects. This procedure allows you to generate multiple series using the cross-section
identifiers in a pool. To generate values for a single series, see series. Syntax Pool Proc: pool_name.genr ser_name = expression You may use the cross section identifier "?" in the series name and/or in the
expression on the right-hand side. Examples The commands pool pool1 pool1.add 1 2 3 pool1.genr y? = x? - @mean(x?)
are equivalent to generating separate series for each cross-section: series y1 = x1 - @mean(x1)
72
series y2 = x2 - @mean(x2) series y3 = x3 - @mean(x3) Similarly, pool pool2 pool2.add us uk can pool2.genr y_? = log(x_?)-log(x_us)
generates three series Y_US, Y_UK, Y_CAN that are the log differences from X_US. Note that Y_US=0.
The pool genr command simply loops across the cross-section identifiers,
performing the appropriate substitution. Thus, the command pool2.genr z=y_?
is equivalent to entering series z=y_us series z=y_uk series z=y_can
so that the ordinary series Z will contain Y_CAN, the last series associated with the "Y_?".
SMPL Command Set sample range. The smpl command sets the workfile sample to use for statistical operations
and series assignment expressions. Syntax Command: smpl start1 end1 start2 end2 ... if_condition Command: smpl sample_name List the date or number of the first observation and the date or number of the
last observation for the sample. Rules for specifying dates are given in Date Formats. smpl may contain more than one pair of beginning and ending observations.
73
The smpl command also allows you to select observations on the basis of conditions specified in an if statement. This enables you to use logical operators to specify what observations to include in EViews' procedures. Put the if statement after the pairs of dates.
You can also use smpl to set the current observations to the contents of a
named sample object; put the name of the sample object after the command smpl. Special keywords for smpl The following "@-keywords" can be used in a smpl command: @all The whole workfile range. @first The first observation in the workfile. @last The last observation in the workfile. Examples smpl 1955:1 1972:12
sets the workfile sample from 1955:1 to 1972:12 smpl @first 1940 1946 1972 1975 @last
excludes observations (or years) 1941-1945 and 1973-1974 from the workfile sample.
smpl if union=1 and edu<=15
sets the sample to those observations where UNION takes the value 1 and EDU is less than or equal to 15.
sample half @first @first+@obs(x)/2 smpl half smpl if x>0 smpl @all if x>0 The first line declares a sample object named HALF which includes the first half
of the series X. The second line sets the sample to HALF and the third line sets the sample to those observations in HALF where X is positive. The last line sets the sample to those observations where X is positive over the full sample.
MODEL Command
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Set of simultaneous equations used for forecasting and simulation. Declare an object by entering the keyword model, followed by a name: model mymod
declares an empty model named MYMOD. To fill MYMOD, open the model and edit the specification view, or use the append view. Note that models are not used for estimation of unknown parameters.
Model Views block display model block structure. eqs view of model organized by equation. label view or set label information for the model. msg display model solution messages. text show text showing equations in the model. trace view of trace output from model solution. vars view of model organized by variable. Model Procs addassign assign add factors to equations. addinit initialize add factors append append a line of text to a model. control solve for values of control variable so that target matches trajectory. displayname set display name. exclude specifies (or merges) excluded series to the active scenario. makegraph make graph object showing model series. makegroup make group out of model series and display dated data table. merge merge other objects into the model. override specifies (or merges) override series to the active scenario.
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scenario set the active, alternate, or comparison scenario. solve solve the model. solveopt set solve options for model. spec Displays the text specification view. Model Examples The commands model mod1 mod1.append y=324.35+x mod1.append x=-234+7.3*z mod1.solve(m=100,c=.008)
create, specify, and solve the model MOD1. The command mod1(g).makegraph gr1 x y z
plots the endogenous series X, Y, and Z, in the active scenario for model MOD1. APPEND Command Append a specification line to a model, system, sspace, or var. Syntax Object Proc: object_name.append text Var Proc: var_name.append(options) text Type the text to be added after the append keyword. For vars, you must specify
the text type in the options argument. Options for Vars One of the following options is required when using append as a var proc: svar Text for identifying restrictions for structural VAR.
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coint Text for restrictions on the cointegration relations and/or adjustment coefficients.
Examples model macro2 macro2.merge eq_m1 macro2.merge eq_gdp macro2.append assign @all f macro1.append @trace gdp macro2.solve The first line declares a model object. The second and third lines merge existing
equations into the model. The fourth and fifth line appends an assign statement and a trace of GDP to the model. The last line solves the model.
system macro1 macro1.append cons=c(1)+c(2)*gdp+c(3)*cons(-1) macro1.append inv=c(4)+c(5)*tb3+c(6)*d(gdp) macro1.append gdp=cons+inv+gov macro1.append inst tb3 gov cons(-1) gdp(-1) macro1.gmm show macro1.results The first line declares a system. The next three lines appends the specification
of each endogenous variable in the system. The fifth line appends the list of instruments to be used in estimation. The last two lines estimate the model by GMM and display the estimation results.
vector(2) svec0=0 sspace1.append @mprior svec0 This command appends a line in the state space object SSPACE1 to use the
zero vector SVEC0 as initial values for the state vector. STATUSLINE Command Send text to the status line.
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Displays a message in the status line at the bottom of the EViews main window.
The message may include text, control variables, and string variables. Syntax Command: statusline Examples statusline Iteration Number: !t Displays the message "Iteration Number: !t" in the status line replacing "!t" with
the current value of the control variable in the program. SOLVE Command Solve the model. solve finds the solution to a simultaneous equation model for the set of
observations specified in the current workfile sample. Syntax Command: solve(options) Model Proc: model_name.solve(options) Note: When solve is used in a program (batch mode) models are always solved
over the workfile sample. If the model contains a solution sample, it will be ignored in favor of the workfile sample.
You should follow the name of the model after the solve command or use solve
as a procedure of a named model object. The default solution method is dynamic simulation. You may modify the solution method as an option.
solve first looks for the specified model in the current workfile. If it is not
present, solve attempts to fetch a model file (.DBL) from the default directory or, if provided, the path specified with the model name.
Options solve can take any of the options available in solveopt. Examples
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solve mod1
solves the model MOD1 using the default solution method. nonlin2.solve(m=500,e)
solves the model NONLIN2 with an extended search of up to 500 iterations. WRITE Command Write series to a disk file. The write command creates a foreign format disk file containing any number of
series. You should use write when you wish to export EViews data to another program.
Syntax Command: write(options) path\file name1 name2 name3 ... Pool Proc: pool_name.write(options) path\file n1? n2? n3? ... Coef Proc: coef_name.write(options) path\file Matrix Proc: matrix_name.write(options) path\file Follow the write keyword by a name for the output file and list the series to be
written. The optional path name may be on the local machine, or may point to a network drive. If the path name contains spaces, enclose the entire expression in double quotation marks. To write matrix objects, simply provide a filename; the entire matrix will be exported.
Note that EViews cannot, at present, write into an existing file. The file that you
select will, if necessary, be replaced. Options Options are specified in parentheses after the write keyword and are used to
specify the format of the output file. File type t=dat, txt ASCII (plain text) files. t=wk1, wk3 Lotus spreadsheet files. t=xls Excel spreadsheet files.
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If you omit the "t=" option, EViews will determine the type based on the file extension. Unrecognized extensions will be treated as ASCII files. For Lotus and Excel spreadsheet files specified without the "t=" option, EViews will automatically append the appropriate extension if it is not otherwise specified.
ASCII text files na=text Specify text for NAs. Default is "NA". dates Write dates/obs and (for pool) cross section identifiers. nodates (default) Do not write dates/obs and (for pool) cross-section
identifiers. names (default) Write series names. nonames Do not write series names. id Write cross-section identifier. d=s Single space delimiter (default is tab). d=c Comma delimiter (default is tab). byper Panel data organized by date/period. Default is data organized by
cross-section (only for pool write). bycross (default) Panel data organized by cross-section (only for pools). t Write by series (or transpose the data for matrix objects). Default is to read by
obs with series in columns. Spreadsheet (Lotus, Excel) files letter_number Coordinate of the upper-left cell containing data. dates (default) Write dates/obs and (for pool) cross-section identifiers. dates=first Write date in Excel date format converting to the first day of the
corresponding observation if necessary (only for Excel files). dates=last Write date in Excel date format converting to the last day of the
corresponding observation if necessary (only for Excel files). nodates Do not write dates/obs and (pool) cross-section identifiers. names (default) Write series names. nonames Do not write series names. byper Panel data organized by date/period. Default is data organized by
cross-section (only for pool write). bycross (default) Panel data organized by cross-section (only for pools). t Write by series (or transpose the data for matrix objects). Default is to write by
obs with each series in columns. Examples write(t=txt,na=.,d=c,dates) a:\dat1.csv hat1 hat_se1 Writes the two series HAT1 and HAT_SE1 into an ASCII file named DAT1.CSV
on the A drive. The data file is listed by observations, NAs are coded as "." (dot), each series is separated by a comma, and the date/observation numbers are written together with the series names.
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write(t=txt,na=.,d=c,dates) dat1.csv hat1 hat_se1
writes the same file in the default directory. mypool.write(t=xls,per) "\\network\drive a\growth" gdp? edu?
writes an Excel file GROWTH.XLS in the specified directory. The data are organized by observations and are listed by period/time.
STOP Command Break out of program. The stop command halts execution of a program. It has the same effect as
hitting the F1 (break) key. Syntax Command: stop
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APPENDIX E - List of Equations Production of Goods
This appendix gives information about a simple model and how we can code a program to simulate this model in Eviews. If you do not have any experience in Eviews, it would be best to go over this appendix before you study the actual model.
The simple model defined in this appendix is a two-equation Keynesian model.
The first equation is the income (Y) function:
Y = Consumption + I + G
where Consumption is private consumption, I is private investment, and G is total government spending. The second equation is the consumption function:
Consumption = alpha_con + beta_con * Y where alpha_con is autonomous consumption, and beta_con is the marginal propensity to consume. In this model, I and G are exogenous variables, which are determined outside of the model, and alpha_con and beta_con are parameters. The variables determined within the model, or endogenous variables, are Y and Consumption.
The starting period or base year is 1 in this example. The model is simulated between period 2 and period 5. The starting and projected values (between period 2 and 5) of exogenous variables are given in EXOG.xls file. Different values can be assigned as projected values of exogenous variables. In this example, period 1 values are used as projected values throughout our simulation period. The starting values of endogenous variables are given in ENDO.xls file. After we run the simulation program, the results will be written in output.xls file.
The simulation program written in Eviews is in Model.prg and also written below.
ENDO.xls
EXOG.xlsExogenous Variables Time Period -1 1 2 3 4 5
ENDOGENOUS Time Period -1 1 2 3 4 5variable name Definition starting valuesY income 1750.00Consumption aggregate consumption 1450.00
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' ----------------------------------- ' Demonstration Model: Keynesian Model ' coded in EViews by Nihal Bayraktar ' ----------------------------------- create C:\Niger\simple-simulation-program\KeynesianModel U 5 smpl 1 5 '------------------------ 'IMPORTING DATA: EXOGENOUS VARIABLES AND PARAMETERS read(e3, t) C:\Niger\simple-simulation-program\exog.xls I G ' IMPORTING DATA: ENDOGENOUS VARIABLES read(e3, t) C:\Niger\simple-simulation-program\endo.xls Y Consumption '------------------------ 'PARAMETERS scalar beta_con = 0.8 ' marginal propensity to consume '------------------------ 'CALIBRATED PARAMETER series Y series Consumption scalar alpha_con = Consumption(1) - beta_con*Y(1) ' autonomous consumption '------------------------ model KC '----------------------S I M U L A T I O N ------------------------- ' AGGREGATE INCOME IDENTITY KC.append Y = Consumption + I + G ' CONSUMPTION FUNCTION KC.append Consumption = alpha_con + beta_con * Y '------------------- smpl 2 5 solve(m = 20000, c = 0.001) KC smpl 1 5 write(e3, t) C:\Niger\simple-simulation-program\output.xls Y_0 Consumption_0 I G
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The commands used in this simple program and their meanings are summarized below. Similar commands are used in the actual model.
create C:\Niger\simple-simulation-program\KeynesianModel U 5
• Create an EViews workfile named as KeynesianModel • “U” stands for undated data frequency. Since our data file and the simulation
program covers the period between 1 and 5, we write 5 after ‘U”. smpl 1 5
• Sample range scalar beta_con = 0.8
• The parameters are defined as a constant number in the simulation program. The scalar command guarantees that the parameters are kept constant throughout the simulation period. series Y
• In order to calculate a scalar number (it is alpha_con in our case), variables that are used in calculating the scalar need to be defined as “series”.
• After Y is defined as series, Y(1) corresponds to the value of Y in Period 1.
read(e3,t) C:\Niger\simple-simulation-program\exog.xls I G
• Importing exogenous variables and parameters from EXOG.xls • Options
– “e3”: coordinates of the upper left cell of the data matrix in the Excel spreadsheet.
– “t ”: when our data series are in rows rather than in columns. read(e3, t) C:\Niger\simple-simulation-program\endo.xls Y Consumption
• Importing endogenous variables from ENDO.xls model KC
• Creates a model named as KC
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KC.append Y = Consumption + I + G KC.append Consumption = alpha_con + beta_con * Y
• “Append” command to specify our equations
• NATIONAL INCOME IDENTITY: Y = Consumption + I + G
• CONSUMPTION FUNCTION: Consumption = alpha_con + beta_con * Y solve(m = 20000, c = 0.001) KC
• Trigger Eviews to solve a model. • Eviews finds a solution to a simultaneous equation model using available data. • Options
– “m = integer ”: maximum number of iterations to be executed. – “c = number ”: convergence criterion for the solution of the dynamic
Memorandum itemsReal GDP per capita at factor cost (% change) 2.3 0.6 2.1 3.2 3.8 4.1 4.1 4.0 3.9 3.6 3.4 3.1Real GDP per capita at market prices (% change) -1.5 5.4 3.8 4.6 5.1 4.9 4.7 4.4 4.0 3.7 3.4 3.1Real disposable income per capita (% change) -0.4 0.7 2.3 3.5 4.3 4.5 4.6 4.4 4.1 3.8 3.5 3.1Private savings rate (% of GDP) 10.0 9.6 9.4 9.3 9.3 9.2 9.2 9.2 9.2 9.2 9.3 9.3Real private consumption per capita (% change) -4.9 0.8 2.4 3.5 4.1 4.4 4.4 4.2 3.9 3.6 3.3 3.0Private investment (% of GDP) 8.2 8.0 7.8 7.4 7.1 6.9 6.6 6.4 6.1 5.9 5.7 5.5Private investment (% of total investment) 61.6 60.4 62.0 61.8 62.0 62.1 62.1 62.0 61.8 61.5 61.3 61.1Public investment (% of total public expenditure) 12.7 12.9 11.8 11.3 10.8 10.4 10.0 9.7 9.5 9.2 9.0 8.8 Health (% of public investment) 17.7 17.7 17.7 17.7 17.7 17.7 17.7 17.7 17.7 17.7 17.7 17.7 Infrastructure (% of public investment) 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.4 Education (% of public investment) 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 Other (% of public investment) 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2Aid (% of total revenue) 50.3 51.4 51.4 51.3 51.3 51.2 51.2 51.1 51.0 50.9 50.8 50.8Total public investment (% of aid) 48.2 49.6 44.8 42.8 41.0 39.3 37.8 36.6 35.6 34.7 33.8 33.1Domestic debt (% of GDP) 7.9 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 9.0 9.3 9.5External debt (% of GDP) 65.1 62.0 59.4 56.4 53.3 50.5 48.1 46.0 44.2 42.7 41.4 40.4Interest payment on external public debt (% of exports) 3.8 3.6 3.4 3.2 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3Degree of openness (total trade in % of GDP) 45.5 42.0 41.5 41.1 40.7 40.4 40.2 40.0 40.0 40.0 40.0 40.1Educated labor (in % of population) 18.3 19.2 19.9 20.8 21.7 22.6 23.5 24.4 25.3 26.1 26.9 27.6Real imports (in billions of current CFA francs) 1064.5 1032.1 1080.6 1155.2 1248.0 1353.8 1468.3 1587.8 1709.6 1831.8 1953.1 2072.6Real exports (in billions of current CFA francs) 1145.3 1239.8 1361.3 1493.9 1630.8 1767.6 1903.6 2039.1 2174.6 2311.1 2449.1 2588.9Real public investment (in billions of current CFA francs) 83.6 93.5 90.4 93.2 96.5 99.8 103.6 107.8 112.1 116.7 121.3 126.0
Note: The real exchange rate is defined as the growth rate of nominal exchange rate plus the growth rate of the import price index minus the growth rate of composite good price after indirect taxes.The “adjusted” elasticity formula proposed by Ravallion (2004) is -9.3*(1-Gini)^3 = -1.13 where Gini index is 50.5 for Niger.
Memorandum itemsReal GDP per capita at factor cost (% change) 0.00 0.00 0.00 0.24 0.51 0.75 0.94 1.07 1.16 1.21 1.23 1.23Real GDP per capita at market prices (% change) 0.00 6.18 0.08 0.83 1.05 1.24 1.37 1.42 1.39 1.43 1.40 1.36Real disposable income per capita (% change) 0.00 0.97 0.80 1.01 1.25 1.43 1.56 1.63 1.62 1.65 1.61 1.57Private savings rate (% of GDP) 0.00 -0.40 -0.34 -0.32 -0.30 -0.28 -0.26 -0.25 -0.23 -0.21 -0.19 -0.17Real private consumption per capita (% change) 0.00 0.92 0.71 0.89 1.07 1.24 1.36 1.43 1.47 1.48 1.46 1.42Private investment (% of GDP) 0.00 -0.35 -0.23 -0.18 -0.14 -0.11 -0.08 -0.06 -0.04 -0.03 -0.02 -0.01Private investment (% of total investment) 0.00 -12.90 -12.40 -12.42 -12.42 -12.44 -12.48 -12.53 -12.63 -12.74 -12.86 -12.99Public investment (% of total public expenditure) 0.00 6.51 6.05 5.95 5.86 5.78 5.72 5.67 5.64 5.62 5.61 5.61 Health (% of public investment) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Infrastructure (% of public investment) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Education (% of public investment) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Other (% of public investment) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Aid (% of total revenue) 0.00 10.69 10.83 10.97 11.09 11.18 11.23 11.27 11.29 11.30 11.29 11.27Total public investment (% of aid) 0.00 4.73 4.15 4.33 4.39 4.48 4.57 4.66 4.79 4.94 5.10 5.28Domestic debt (% of GDP) 0.00 -0.53 -0.64 -0.78 -0.91 -1.03 -1.16 -1.27 -1.37 -1.47 -1.56 -1.65External debt (% of GDP) 0.00 -5.85 -8.24 -10.46 -12.36 -14.02 -15.50 -16.82 -17.99 -19.06 -20.03 -20.91Interest payment on external public debt (% of exports) 0.00 -0.03 -0.10 -0.20 -0.29 -0.38 -0.47 -0.55 -0.62 -0.70 -0.77 -0.83Degree of openness (total trade in % of GDP) 0.00 -2.37 -2.60 -2.96 -3.30 -3.60 -3.88 -4.12 -4.34 -4.54 -4.71 -4.86Educated labor (in % of population) 0.00 0.00 0.00 0.10 0.27 0.49 0.75 1.04 1.34 1.66 1.99 2.32
Years
Table 10Niger: 5 Percent Increase in Aid to GDP Ratio, 2005-15, Lower Efficiency of Public Investment
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APPENDIX H – CALCULATION OF VARIABLES AND PROJECTION OF EXOGENOUS VARIABLES ENDOGENOUS VARIABLES *** = calibrated series AID Total aid. The series are named as Official Development Assistance, Grants
total (from all donors) in the Development Assistance Committee database. It includes both food aid (relief food aid and food aid excluding relief food aid) and nonfood aid. Unit: in current LCU File: "Niger-AID-OECD.xls"; "Sheet: AID-TRANS"; "line 4", (Aid) Note: The original series are multiplied by ER to express in current LCU. Source: OECD.
***CP Total private consumption in real terms
Unit: in constant LCU (2004) From: Equation (A19) CP = Qd – IP – IG – CG
***DdebtG Domestic public debt stock
Unit: in current LCU From: Equation (A27) DdebtG(t) = DdebtG(t+1) - DB(t+1) Note: In 2003, actual value of DdebtG is taken, which is 0.0773 of NGDP. In 2004, DdebtG in 2004 = DdebtG in 2003 + DB in 2004.
***delta_LE_N Total educated labor flow
From: Equation (A11) Delta_LE_N = LE – LE(-1)
Delta_NFA Change in reserves Unit: in US$ File: “Niger-BOP.xls”, line 32, = overall balance Note: The original series are divided by ER to convert into $US. Source: IMF.
DITAX Direct tax revenue (domestic) Unit: in current LCU File: [Niger-BUDGET.xls, line61] Note: Total tax revenue is taken from the “budget” file. The share of direct tax revenue in total tax revenue is calculated using data from World Bank sources. (Niger-Wbafrican.xls; line=124; Direct taxes (Cur. Loc. Curr.)) Definition of Direct tax revenue in WB Africa Database: Direct taxes on goods and services include all taxes and duties levied on production, extraction, sale, transfer, leasing, or delivery of goods and rendering of
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services, or in respect of the use of goods, or permission to use goods or to perform activities, are covered. Examples include all general sales taxes, value added taxes and excises. Source: IMF and World Bank.
***DITXR Effective direct tax rate
From: Equation (A30) DITXR = DITAX/YTOT
***DOM Domestic sales
Unit: in constant LCU (2004) From: Equation (A21) DOM = [PQ.Q-PM.M]/PD.
FG Flow of government borrowing abroad
Unit: in US $ File: "Niger-AID-OECD.xls";"Sheet: AID-TRANS"; "line 6", (Loans Total Net) Note: Since the original series are in millions of US dollars, we multiply them by 1,000,000. Source: OECD.
***FdebtG Stock of foreign debt
Unit: in US$ From: Equation (A37) FdebtG(t) = FG(t) + FdebtG(t-1) Note: 2004 value is the actual value.
***FdebtP Stock of private foreign debt
Unit: in US$ From: Equation (A36) Note: FdebtP in 2002 = [Total external interest payment in $US from BOP in 2004]/RP* in 2003 and 2004. Then FdebtP in 2004 = FP in 2004 + FdebtP in 2002. FdebtP for t < 2002 is calibrated as follows: FdebtP(t) = FdebtP(t+1) - FP(t+1).
Unit: in current LCU File: "Niger-BUDGET.xls";"Sheet: Summary for model Final"; "line 29"; Overall balance including grants Source: IMF.
GTOT Total government expenditure
Unit: in current LCU
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File: "Niger-BUDGET.xls";"Sheet: Summary for model Final"; "line 15" Source: IMF.
IG Real public spending on investment
Unit: in constant LCU (2004) From: IG = Public FCF/PQT File: "PUBLIC INVESTMENT STRUCTURE_ NIGER (1967-2002).xls" Note: In 2003 and 2004, IG is calculated by multiplying total capital expenditure (from Niger-Budget) in 2004 by the share of IG in total capital expenditure (from Niger-Budget) in 2002. Source: IMF.
IGedu Real public investment in education
Unit: in constant LCU (2004) From IGedu = Public FCF in Education/PQT File: "PUBLIC INVESTMENT STRUCTURE_ NIGER (1967-2002).xls" Source: IMF.
IGhea Real public investment in health
Unit: in constant LCU (2004) From: IGhea = Public FCF in health/PQT File: "PUBLIC INVESTMENT STRUCTURE_ NIGER (1967-2002).xls" Source: IMF.
IGinf Real public investment in infrastructure
Unit: in constant LCU (2004) File: "PUBLIC INVESTMENT STRUCTURE_ NIGER (1967-2002).xls" From: IGinf = Public FCF in infrastructure /PQT Source: IMF.
IGoth Real public investment in others Unit: in constant LCU (2004) File: "PUBLIC INVESTMENT STRUCTURE_ NIGER (1967-2002).xls" From: IGoth = Public FCF in others /PQT Source: IMF.
INDTAX Indirect tax revenue (domestic)
File: [Niger-BUDGET.xls, line62] Note: Total tax revenue is taken from the “budget” file. The share of indirect tax revenue in total tax revenue is calculated using data from World Bank sources. Total indirect tax revenue = Indirect [Niger-Wbafrican.xls;line=543; Indirect taxes (Cur. Loc. Curr.)] – [Niger-Wbafrican.xls; line=1014,Taxes on int'l trade (Cur. Loc. Curr.)] Definition of Indirect taxes in WB Africa Database: Indirect taxes are the sum of indirect taxes less subsidies. Indirect taxes are those taxes payable by producers that relate to the production, sale, purchase or use of the goods and services. Subsidies are grants on the current account made by general government to private enterprises and unincorporated public enterprises. The
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grants may take the form of payments to ensure a guaranteed price or to enable maintenance of prices of goods and services below costs of production, and other forms of assistance to producers. Note: This definition includes taxes on international trade. Since our definition includes only domestic indirect taxes, taxes on international trade is subtracted. Source: IMF and World Bank.
Unit: in constant LCU (2004) File: "Niger-WBafrican.xls";"line=373”; GDFI-priv From: IP = Private FCF/PQT Source: World Bank.
***J Composite input from the supply of composite input T and private capital, KP Unit: in constant LCU (2004) From: Equation (A2) J(T, KP) = AJ·[βJ·T-ρJ + (1 - βJ)KP-ρJ]-1/ρJ
***KGedu Stock of public capital in education Unit: in constant LCU (2004) From: Equation (A33) KGedu(t) = (1-deltaedu).KGedu(t-1)+IGedu(t-1)
Unit: in constant LCU (2004) From: From: Equation (A18)
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KP(t) = (1-deltaP).KP(t-1)+IP(t-1) LE Educated labor level
File: "Niger-WDI-GDF-all.xls";"line=475", (Literacy rate, adult total (% of people ages 15 and above)) Note: Its value in 2004 is calibrated by multiplying LE in 2002 by 0.035 (n). From: LE = POP*(literacy rate) Source: World Bank.
***LE_P Quantity of educated labor used in private production
From: Equation (A13) LE_P = LE - LE_G
***LR “Raw” labor
From: = POP - LE M Demand for imported goods
Unit: in constant LCU (2004) File: "Niger-BOP.xls"; Line 10 + Line 14 From: M = Nominal imports.(1+tm)/(PM) Source: IMF.
NGDP GDP at market prices
Unit: in current LCU File: "Niger-WBafrican.xls";"line=390" Note: The 2003 value is taken from the file named [IMFMACROFRAMPETERLASTJUNE23.xls]; Sheet = Selected ind -TAb 4; line = 75]. The 2004 value is taken from the file named [IMFMACROPRGF2.xls]. Source: World Bank.
POP Size of the population
File: "Niger-WDI-GDF-all.xls"; line=645 Note: 2003 value = POP in 2002 * (1+0.031). 0.031 sent by Emmanuel. 2004 value = 3.3 % sent by government authorities. Source: World Bank.
PQ Composite market price (or consumer price index) Unit: 2004 = 1 File: consumer price index "Niger-WDI-GDF-all.xls"; line=107; consumer price index Note: The CPI level in 2003 is calculated by multiplying CPI in 2002 by the CPI inflation rate in 2003, which is taken from File = “IMFMACROFRAMPETERLASTJUNE23.xls”; Sheet = Selected ind -TAb 4; Line 16. Similarly, the CPI level in 2004 is calculated by multiplying CPI in 2003 by the CPI inflation rate in 2004 given in the file named as [IMFMACROPRGF2.xls]. Then the CPI series is re-indexed in a way that the 2004 value = 1.
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Source: World Bank and IMF.
***PQT After tax PQ From: Equation (A41) PQT = (1+INDTXR)·PQ
***Qd Total demand for goods sold on the domestic market (which includes both
imports and domestically-produced goods) Unit: in constant LCU (2004) From: Equation (A5) and (A21) Qd= (PY.Y (i.e. nominal GDP at factor cost) – PX.X (i.e. nominal exports from BOP) + PM.M (i.e. nominal imports from BOP times (1+tm)))/PQ
Unit: in current LCU From: Equation (A47) SP = Ydisp.s
***T “Effective” labor; composite input from the supply of educated labor, LE, and
the stock of public capital in health, Kghea Unit: in constant LCU (2004) From: Equation (A1) T = AT[betaT.LE^(-rhoT) + (1-betaT).(Kghea/(POP^thetaH))^(-rhoT)]^(-1/rhoT)
TAX Total tax revenue
Unit: in current LCU File: “’[Niger-BUDGET.xls]Summary for model Final'!line=9” From: TAX = Tax revenue + non-tax revenue Source: IMF.
X Exports (in real terms)
Unit: in constant LCU (2004) File: "Niger-BOP.xls"; line= (9 + 13) From: Exports in real terms = Nominal exports/PX Source: IMF.
Y Aggregate supply of domestic goods (in real terms)
Unit: in constant LCU (2004) File: "Niger-WBafrican.xls";"line=385"; GDP at factor cost Note: The original series is in constant 1987 LCU. In order to change the base year to 2004, the Y series is re-indexed. It is calculated by dividing real GDP at
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factor cost in 1987 prices by the ratio of real GDP at factor cost in 1987 prices to nominal GDP at factor cost in 1987 prices in 2004. Source: World Bank.
***Ydisp Households’ disposable income in nominal terms
Unit: in current LCU From: Equation (A15) Ydisp = YTOT-DITAX
Unit: in constant LCU (2004) From: =(Total gov. expenditure-interest payment-public FCF-wages)/PQT File: "Niger-BUDGET.xls";"Sheet: Summary for model Final"; "line 15" (Expenditure - government), "Niger-BUDGET.xls";"Sheet: Summary for model Final"; "line 18" (Interest Payments - government),"Niger-BUDGET.xls";"Sheet: Summary for model Final"; "line 21" (Public FCF) Source: IMF.
DB Flow of direct domestic borrowing
Unit: in current LCU File: "Niger-BUDGET.xls";"Summary for model Final"; "line 34" Source: IMF.
ER Nominal exchange rate
Unit: LCU per US$ File: "Niger-WDI-GDF-all.xls";"line=573); Official exchange rate (LCU per US$, period average) Note: In 2003 and 2004, ER is taken from File = “Niger-IFS-all.xls”; Line = 12. Source: World Bank and IMF.
ERROR_OMM Errors and omissions
Unit: in US$ From: = capital account balance (incl. errors and omissions) - (FP+FG) File: “Niger-BOP.xls”; Line = 27 Source: IMF.
FP Private capital inflows
Unit: in US $ File: Between 1975-95, Foreign direct investment, net (BoP, current US$), "Niger-WDI-GDF-all.xls", Line = 242. Between 1996-2002, Foreign direct invest. (Net, cur. US $), "Niger-WBafrican.xls"; Line = 359 Note: In 2003 and 2004, the source of FP is File = “Niger-BOP.xls”; Line = 28. This data point is divided by ER to convert it into $US. Source: World Bank.
***Kappa_edu Share of public investment in education
From: Equation (A29)
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Kappa_edu = IGedu/IG ***Kappa-hea Share of public investment in health
From: Equation (A29) Kappa_hea = IGhea/IG
***Kappa-inf Share of public investment in infrastructure
From: Equation (A29) Kappa_inf = IGinf/IG
***Kappa-oth Share of public investment in other
From: Equation (A29) Kappa_oth = IGoth/IG
***LAND Land (normalized to unity) LE_G Quantity of educated labor employed by the public sector
File: “Effectifs Fonction Publique (SKILLED UNSKILLED).xls”; Line = 21 Source: Government Authorities.
***n Growth rate of population and raw labor
From: n = (POP-POP(-1))/POP(-1) Note: In 2003, n is taken as 3.1%. In 2004, n is taken as 3.3%.
***RD Interest rate on domestic public debt
From: = [Total domestic interest payment = RD.DdebtG(t)]/DdebtG(t-1) File: "Niger-BUDGET.xls";"Summary for model Final"; "line 54", RD.DdebtG-1, Public interest payment, domestic, in billions of LCU Source: IMF.
***RG* Interest rate on public foreign debt
From: = [Total external interest payment in $US = RG*.FdebtG(t)]/FdebtG(t-1)] File: "Niger-BUDGET.xls"; Sheet = "Summary for model Final"; Line = 53; RG*.FdebtG-1, Public interest payment, external, in billions of LCU Source: IMF.
***RP* Interest rate on private foreign borrowing
From: In 2004, its value is assumed to be equal to 0.021, the money market rate in the Euro Area (assuming expected depreciation is zero and there is no capital control in Niger). This value of RP* is used to calibrate total private foreign debt stock in 2003 such that FdebtP in 2003 = [Total external interest payment in $US]/RP* in 2003. Then other values of FdebtP are calibrated. Then using this information, RP* before 2004 is calibrated such that RP*(t) = [Total external interest payment in $US in period t]/FdebtP(t-1) where t = 1998 and 2002.
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File: The data source of Total external interest payment is File = "Niger-BOP.xls"; Line 16 + Line 17; Income (net) + Of which: gross official interest payments. Source: IMF.
tm Tariff rate
From: = tax revenue from international trade (current LCU)/total imports(current LCU) File: [Niger-BUDGET.xls, line63] Note: Total tax revenue is taken from the “budget” file. The share of international tax revenue in total tax revenue is calculated using data from World Bank sources. The data source of Taxes on international trade is File = "Niger-Wbafrican.xls"; Line = 1014; Taxes on int'l trade (Cur. Loc. Curr.). Definition of taxes on international trade in WB Africa Database: Taxes on international trade include import duties, export duties, profits of export or import monopolies, exchange profits, and exchange taxes. Current revenue includes all revenue from taxes and nonrepayable receipts (other than grants) from the sale of land, intangible assets, government stocks, or fixed capital assets, or from capital transfers from nongovernmental sources. It also includes fines, fees, recoveries, inheritance taxes, and nonrecurrent levies on capital. Source: World Bank and IMF.
UTR$ Private unrequited transfers
Unit: in US$ File: Niger-BOP.xls, line 19 (Private transfers, net) Note: The original series is divided by ER to convert it into $US. Source: IMF.
WG Average effective wage in the public sector
From: = total wage bill/LE_G File: “Niger-BUDGET.xls, line 49, wages and salaries. Source: IMF.
χ Share of teachers in total educated labor in the public sector
From: χ = total number of teachers/LE_G
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PRICES Given share parameters, elasticity of substitution parameters, shift parameters,
and PQ = 1 in the base year, other prices are calibrated as follows: PM Domestic-currency price of imports (index)
From equation (A22) and given • M = Nominal M (from BOP series).(1+tm)/PM; • DOM = Nominal DOM/PD where Nominal DOM = PD.DOM = PQ.Q – PM.M; • PD = {[PQ1-σ
where NM is nominal imports and NDOM is nominal domestic sales.
PD Price of the domestic good (index)
From equation (A43); given the value of PM calculated above.
PD = {[PQ1-σDM – (1 - βDM)·PM1-σ
DM]/ βDM}1/(1-σDM
)
PX Domestic-currency price of exports (index)
From equation (A6) and given
• X = Nominal X (from BOP series)/PX; • DOM = Nominal DOM/PD where Nominal DOM = PD.DOM = PQ.Q (nominal
Q) – PM.M (nominal M); • the value of PD calculated above; • βDE = 0.15; • σ
DE = 0.3;
PX = [PD1+σDE.(NX/NDOM).((1- βDE)/βDE)-σ
DE]1/(1+σDE
) where NX is nominal exports and NDOM is nominal domestic sales.
PY Price of Y
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From equation (A42) and given the values of PX and PD calculated above
PY = [βDE· PX1+σ
DE + (1 - βDE)· PD1+σDE] 1/(1+σ
DE).
PM* World price of imports From equation (A45) and given the value of PM calculated above
PM* = PM/(ER.(1+tm)).
PX* World price of exports
From equation (A44) and given the value of PX calculated above
PX* = PX/ER.
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PARAMETERS σDM = 1/(1+ρDM); elasticity of transformation between imports and demand for
domestic goods; =coefficient from regression = 0.7 ρDM Substitution parameter in Q; calculated with σDM; = 0.43 θKGE Parameter capturing congestion effects in the education system; =0.9 θKGI Parameter capturing congestion effects in the education system; =0.9 θH Parameter determining the strength of congestion effects in the provision of
health services; =0.4 θI Parameter capturing congestion effects on infrastructure capital; = 0.3 ADE Shift parameter in production of domestic goods Ys; calibrated ADM shift parameter in production of Qs; calibrated AE Shift parameter in flow production of educated labor LE; calibrated AJ Shift parameter for composite input J; =1 AKGZ Shift parameter for composite input KGZ; =1 AT Shift parameter for composite input T; =1 AY Shift parameter for composite input Ys; calibrated AZ Shift parameter for composite input Z; =1 βDE Share parameter between exports EXP and domestic sales DOM in
production of domestic goods Ys; calibrated = X/(X+DOM); =0.15 βDM Share parameter between imports M and demand for domestic goods DOM;
calibrated = DOM/(M+DOM); =0.75 βE Share parameter between educated labor LE and public capital in education,
KGedu in flow production of LE; = 0.3 βJ Share parameter between the supply of T and the stock of private capital, KP
in production of J; imposed; = 0.6 βKGZ Share parameter between KGedu and KGinf in production of KGZ; calibrated;
= KGinf/(KGinf+KGedu); 0.79
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βT Share parameter between the supply of educated labor, LE, and the stock of public capital in health, KGhea in production of T; imposed; = 0.85
βY Share parameter between the supply of J and public capital in infrastructure,
KGinf in production of Y; imposed; = 0.85 βZ Share parameter in equation Z; = 0.5 ρDE Transformation parameter in production of domestic goods Y; =4.333333333 ρE Substitution parameter in flow production of LE; =2.333333333 ρJ Substitution parameter in production of J; =2.333333333 ρKGZ Substitution parameter in production of KGZ; =2.333333333 ρT Substitution parameter in production of T; =2.333333333 ρY Substitution parameter in production of Y; = 1.5 ρZ Substitution parameter in production of Z; = 4 σDE = 1/(1-ρDE); elasticity of transformation between exports and domestic sales;
=0.3 σE = 1/(1+ρY); elasticity of substitution between LR-1 and Kgedu in change LE; =
0.3 σJ = 1/(1+ρJ); elasticity of substitution between T and KP; =0.3 σKGZ = 1/(1+ρKGZ); elasticity of substitution between KGinf and KGedu; =0.3 σT = 1/(1+ρT); elasticity of substitution between LE and Kghea/POPqH; =0.3 σY = 1/(1+ρY); elasticity of substitution between J and KGinf; =0.4 σZ = 1/(1+ρZ); elasticity of substitution in Z; =0.2 δh Depreciation rate of public capital with h = edu, hea, inf, oth; = 0.035 δP Constant rate of depreciation; = 0.06 s Saving rate; imposed; = 0.1
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ORDER OF CALIBRATION FOR CALIBRATED SERIES Step 1: All the following variables can be determined individually right after assigning
the values of the actual variables.
DebtG, delta_LE_N, CP, FdebtG, FdebtP, FdebtTot, INDTXR, J, KGedu, KGhea, KGinf, KGZ, KP, LE_P, LR, PQT, Qd, Q, T, Kappa_edu, Kappa_hea, Kappa_inf, Kappa_oth, LAND, n, Z
Step 2: All the following variables are suggested to be determined by the order given below.
PROJECTIONS (2004-2015) Kappa_edu = Kappa_edu in 2004 Kappa_hea = Kappa_hea in 2004 Kappa_inf = Kappa_inf in 2004 Kappa_oth = Kappa_oth in 2004 NMCG = Constant share of GDP (2004 value) DB = 1 percent of NGDP AID$ = Constant share of GDP (2004 value) ER = ER in 2004 FP = Constant share of GDP (2004 value) LAND = 1 n = 2004 value = 3.3% PM* = PM*(t-1)*(1+0.03) PX* = PX*(t-1)*(1+0.03) RD = RD in 2004 RG* = RG* in 2004 RP* = RP* in 2004 tm = tm in 2004 UTR$ = POP* constant share of per capita UTR$ in 2004 ERROR_OMM = Constant share of GDP (2004 value) LE_G = Constant share of LE in 2004 WG = WG(t-1)*[1+(Change in PQ/PQ(t-1)]
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APPENDIX I – MDG Tables Table – Baseline MDG Table
Aid and external debt indicators Foreign aid (in % of GDP) 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 Aid (in % of total government revenue) 51.4 51.4 51.3 51.3 51.2 51.2 51.1 51.0 50.9 50.8 50.8 External debt (in % of GDP) 62.0 59.4 56.4 53.3 50.5 48.1 46.0 44.2 42.7 41.4 40.4 Interest payments on external public debt (in % of GDP) 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.3 Interest payments on external public debt (in % of exports) 3.6 3.4 3.2 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3
Note: The “adjusted” elasticity formula proposed by Ravallion (2004) is -9.3*(1-Gini)^3 = -1.13 where Gini index is 50.5 for Niger. Malnutrition prevalence is in % of children under 5.1/ The observation year is 1993.2/ The observation year is 1992.
Projections
Table 2Niger: MDG Indicators, Baseline Results for 2005-15
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Table – 5 Percent Increase in the Aid to GDP Ratio
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Poverty rate (2003 = 63)(% of the population living below $2 per day) Consumption per capita growth elasticity of -0.5 -0.3 -0.5 -0.9 -1.3 -1.8 -2.4 -2.9 -3.5 -4.0 -4.4 Consumption per capita growth elasticity of -1.0 -0.6 -1.1 -1.7 -2.5 -3.4 -4.3 -5.2 -6.0 -6.7 -7.3 Consumption per capita growth elasticity of -1.5 -0.9 -1.6 -2.5 -3.6 -4.8 -5.9 -6.9 -7.7 -8.4 -9.0 Ravallion's (2004) adjusted elasticity (Gini = 50.5) -0.7 -1.2 -1.9 -2.8 -3.8 -4.8 -5.7 -6.5 -7.2 -7.9
Literacy rate 0.0 0.0 0.2 0.5 0.9 1.4 2.0 2.5 3.1 3.7(% of educated labor in total population)
Infant mortality (2002=155) -10 -10 -11 -12 -13 -14 -15 -16 -17 -17(Infant mortality rate per 1000 live births)
Aid and external debt indicators Foreign aid (in % of GDP) 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Aid (in % of total government revenue) 10.6 10.8 11.0 11.2 11.3 11.4 11.4 11.4 11.4 11.4 External debt (in % of GDP) -5.6 -8.0 -10.1 -12.0 -13.6 -14.9 -16.0 -16.9 -17.7 -18.4 Interest payments on external public debt (in % of GDP) 0.0 -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 -0.2 -0.2 Interest payments on external public debt (in % of exports) 0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.6 -0.7 -0.7
Projections
Table 3Niger: 5 Percentage Point Increase in Aid-to-GDP Ratio, Simulation Results for 2006-15
(Absolute deviations from baseline)
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Table – Cancellation of External Debt in 2006
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Poverty rate (2003 = 63)(% of the population living below $2 per day) Consumption per capita growth elasticity of -0.5 0.0 -0.1 -0.1 -0.2 -0.2 -0.3 -0.4 -0.4 -0.5 -0.5 Consumption per capita growth elasticity of -1.0 -0.1 -0.1 -0.2 -0.3 -0.4 -0.5 -0.7 -0.8 -0.9 -0.9 Consumption per capita growth elasticity of -1.5 -0.1 -0.2 -0.3 -0.4 -0.6 -0.8 -0.9 -1.0 -1.1 -1.2 Ravallion's (2004) adjusted elasticity (Gini = 50.5) -0.1 -0.1 -0.2 -0.3 -0.5 -0.6 -0.7 -0.8 -0.9 -1.0
Literacy rate 0.0 0.0 0.0 0.1 0.1 0.2 0.3 0.3 0.4 0.5(% of educated labor in total population)
Infant mortality (2002=155) -2 -1 -1 -2 -2 -2 -2 -2 -2 -2(Infant mortality rate per 1000 live births)
Aid and external debt indicators Foreign aid (in % of GDP) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Aid (in % of total government revenue) 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 External debt (in % of GDP) -49.8 -44.5 -39.5 -35.2 -31.5 -28.3 -25.7 -23.4 -21.5 -19.8 Interest payments on external public debt (in % of GDP) -0.5 -0.5 -0.4 -0.4 -0.3 -0.3 -0.3 -0.2 -0.2 -0.2 Interest payments on external public debt (in % of exports) -3.4 -3.0 -2.7 -2.4 -2.2 -2.0 -1.8 -1.6 -1.5 -1.4
Projections
Table 4Niger: External Debt Cancellation in 2006, Simulation Results for 2006-15
(Absolute deviations from baseline)
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Table – 0.52 Percent Increase in the Aid to GDP Ratio
Aid and external debt indicators Foreign aid (in % of GDP) 0.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Aid (in % of total government revenue) 0.0 1.3 1.3 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 External debt (in % of GDP) 0.0 -0.6 -0.8 -1.0 -1.2 -1.4 -1.5 -1.6 -1.7 -1.8 -1.9 Interest payments on external public debt (in % of GDP) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Interest payments on external public debt (in % of exports) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Note: The “adjusted” elasticity formula proposed by Ravallion (2004) is -9.3*(1-Gini)^3 = -1.13 where Gini index is 50.5 for Niger. Malnutrition prevalence is in % of children under 5.
Projections
Table 3Niger: Monitoring the MDGs, 2005-15 (5 Percent Increase in Aid to GDP Ratio, 2006-15)