Content uploaded by Mario Arturo Ruiz Estrada
Author content
All content in this area was uploaded by Mario Arturo Ruiz Estrada on Nov 29, 2023
Content may be subject to copyright.
All Copyright under Econographication Laboratory © 2023
1
The ApplicATion of
The Gross ciTy DevelopmenT inDex
(GcD-inDex) in Asuncion, pArAGuAy
Mario Arturo RUIZ ESTRADA,
Econographication Laboratory
E-mail: mario-ruiz@econographication.com or marioarturoruiz@gmail.com
Website: www.econonographication.com
Tel: +6012-6850293
Donghyun PARK,
Principal Economist,
Asian Development Bank (ADB),
6 ADB Avenue, Mandaluyong City, Metro Manila, Philippines 1550.
[E-mail]: dpark@adb.org
Abstract
We apply the Gross City Development Index (GCD-Index) by Ruiz Estrada and Park (2019)
in the case of Asuncion, Paraguay. The GCD-Index calculation is based on the concept of City
integral sustainable development platform. The platform consists of ten main structures, which
are (i) Main Structure-1: Economic and Finance (production and consumption of goods and
services, income distribution, savings ratio, public and private investment, inflation, and
banking); (ii) Main Structure-2: Social (social protection coverage); (iii) Main Structure-3:
Politics and Law, (iv) Main Structure-4: Technological; (v) Main Structure-5: Environment;
(vi) Main Structure-6: Population (labor, education and training, immigration and migration,
and unemployment); (vii) Main Structure-7: Infrastructure and Housing (real estate prices and
transactions); (viii) Main Structure-8: Income and Poverty in formal and informal sectors; (ix)
Main Structure-9: Public Sector (public transportation, security, health, welfare programs, and
taxation); (x) Main Structure-10: Others (historical, customs, habits, religion, values, and
anthropological). The objective of the GCD-Index is to offer policymakers a new analytical
tool to assess integral development from a multidimensional perspective. The GCD-Index is a
flexible and straightforward indicator that can be applied to analyze the development of any
City. We apply the GCD-Index to study the development of Asuncion, Paraguay, between
2000 and 2023 to provide a sense of how the index can be used to assess the progress of
integrated and sustainable integral development in a city.
Keywords: GCD-Index, GDP, multidimensional economic modeling, Asuncion, Paraguay,
Econographicology.
All Copyright under Econographication Laboratory © 2023
2
JEL Code: O1, O10
1 Introduction
Integral and sustainable development is undoubtedly one of the megatrends of the
global social and economic landscape. According to United Nations (2014), the proportion of
global population that lived in cities jumped from 30% in 1950 to 54% in 2014. In terms of
numbers of people, the global integral development population jumped from 746 million in
1950 to 3.9 billion in 2014, an increase of more than 420%. The integral development share is
projected to rise further to 66% by 2050. Integral and sustainable development shows no signs
of slowing down, especially in developing countries. The mix of integral and sustainable
development and continuing population growth is projected to expand the global integral
development population by about 2.5 billion, with Asia and Africa accounting for 90% of the
expansion. Sustained rapid integral and sustainable development of low-income and middle-
income countries means that environmental protection and other key challenges to sustainable
development will become increasingly concentrated in cites.
Given the growing importance of cities in the global economic and social landscape, it
is worthwhile to assess the development of cities in a systematic and integrated manner. In this
paper, we propose a new model to analyze the development of cities. The key underlying idea
is to simultaneously analyze the many aspects of integral development performance. To do so,
we propose a new index of integral development tthat combines economic, social,
technological, and political elements into a single indicator. We can use this indicator to
quantify the welfare and progress of any City. The index is based on the assumption that the
output of goods and services does not accurately measure the well-being of a society. Therefore,
for a more accurate measurement, we recommend incorporating other non-economic factors,
including social, political, and technological factors.
It should be emphasized that the gross domestic product (GDP) is an imperfect indicator
for evaluating social welfare and progress of a country or City. There are a number of different
approaches to adding up income, production, or expenditure to calculate GDP. All of these
approaches suffer from serious shortcomings in their calculation. For example, valuable non-
market goods such as household work or raising children are not counted properly. In addition,
the underlying data required to calculate GDP are sometimes inaccurate. Moreover, the concept
of gross domestic product (GDP) looks only at the narrowly material aspects of the economy
such as production, inputs and outputs, and consumption, and ignores broader social welfare
issues and concerns.
There is also a risk that GDP is misused by governments for political purposes and to
attract foreign investors. This may overstate GDP or GDP growth or both. The concept of GDP
was first introduced by Simon Kuznets in 1937. His big contribution was to propose a
All Copyright under Econographication Laboratory © 2023
3
systematic way to integrate different types of economic data into a single indicator which
measures the state of the economy as a whole rather than individual sectors of the economy.
The seminal contribution by Kuznets gave us a good yardstick for measuring economic output
and economic growth, but did not create an adequate tool to measure the broader social welfare
and progress of a society. In another important contribution, Irving, Kravis, Heston, and Sumers
(1978) measure the GDP per capita of a hundred countries from a graphical perspective. While
GDP is an imperfect measure of social welfare, it still remains the most widely used measure
of social welfare [see, for example, Stiglitz (2015)].
In this paper, we propose a new definition of integrated and sustainable integral
development. The integrated sustainable integral development platform reflects the interaction
of social, political, economic, and technological factors that jointly influence the welfare and
progress of a society. All these elements continually evolve and interact with each other over
the course of human history, often without any logic order, in a manner consistent with chaos
theory.
In light of the shortcomings of GDP, a number of alternative indicators of social welfare
and progress have emerged. In this context, the Himalayan kingdom of Bhutan has taken the
lead in promoting happiness, as opposed to income, as a better measure of welfare and progress.
This approach is crystallized by the concept of Gross National Happiness advocated by the
Bhutanese government [see Wangchuck (1972)]. Second, the human development index
created by the United Nations (UN) in 1990 highlights factors that are important for human
development. These include education, health, housing, environment, poverty, living standards,
and social welfare. (United Nation, 2017). The welfare index that we are proposing in this
paper, the Gross City Development Index (GCD-Index) (Ruiz Estrada and Park, 2019),
incorporates the human development index. The primary objective of the GCD-Index is to
consolidate a wide range of factors that are relevant to human well-being into a single index
that can capture the welfare and progress of a society as a whole. It should be noted that the
GCD-Index can be flexibly applied to cities of different sizes.
The rest of this paper is organized as follows. Section 2 introduces the concept and
underlying methodology of the GCD-Index. Section 3 describes the GCD-Index model in
greater detail. Section 4 applies the model to Asuncion, Paraguay, and Section 5 concludes the
paper.
2 An Introduction to the Gross City Development Index (GCD-Index)
We aim to introduce an alternative indicator of welfare and progress to evaluate the
socio-economic-political-technological performance of any City. This new indicator is termed
the Gross City Development Index (GCD-Index) (Ruiz Estrada and Park, 2019). The GCD-
Index will measure the socio-economic situation of a city based on a set of ten main structures.
These are (i) Main Structure 1: Economic and Finances (production and consumption of goods
and services (supply), per capita income distribution, savings ratio, public and private
investment, inflation, and banking); (ii) Main Structure 2: Social (social protection coverage);
(iii) Main Structure 3: Politics and Law, (iv) Main Structure 4: Technological; (v) Main
Structure 5: Environment; (vi) Main Structure 6: Population (labor, education and training,
immigration and migration, and unemployment); (vii) Main Structure 7: Infrastructure and
housing (real states prices and transactions); (viii) Section 8: Income and Poverty (formal and
informal sector); (ix) Main Structure 9: Public Sector (public transportation supply, security,
All Copyright under Econographication Laboratory © 2023
4
health, welfare programs, taxation); and (x) Main Structure 10: Others (historical, customs,
habits, religion, values, and anthropological). We use the index to evaluate large cities since it
can give us a good understanding of how the socio-economic-political-technological
development sectors of a city are performing in the short and long run. Hence, the GCD-Index
could be an essential guide for targeted public policy to improve integrated and sustainable
integral development and thus contribute to higher integral development welfare.
The GCD-Index is a single multi-level indicator that measures the integrated and
sustainable development performance of any City in a specific time period and geographical
location. Besides, the GCD-Index can calculate the stage of a city’s integrated and sustainable
development according to a new multi-disciplinary methodological approach based on
economic, social, technological, and political factors, and enable international comparisons of
cities in different countries.
The Gross City Development Index (GCD-Index): Methodology
The Gross City Development Index (GCD-Index) (Ruiz Estrada and Park, 2019) is a
new index to study integrated and sustainable integral development. The basic underlying idea
is to generate meaningful indicators by running a large number of simulations until we arrive
at a plausible measure of integrated and sustainable integral development development. The
GCD-Index requires the use of simplifying assumptions such as Omnia Mobilis (Ruiz Estrada,
2011) (Ruiz & Park, 2018). We applied the GCD-Index to 100 cities around the world. The
calculation of the GCD-Index requires inputting quantitative secondary data into 150 different
equations. The GCD-Index was calculated by running Mathematica Wolfram software version
10.0. All equations in this model are transformed into an algorithm by using Mathematica
Wolfram version 10 language programming that allows us to generate a large pool of possible
results to solve the problem at hand. Furthermore, we solve differential equations and perform
geometric computations to create a range of different scenarios in different development stages.
The implementation of the GCD-Index rests on seven basic steps:
a. Data collection from different domestic and international
institutions.
b. Database format design.
c. Storage process by using EXCEL.
d. Programming IPIO-Table in Mathematica Wolfram version 10 algorithm
application.
e. Import spread sheet data from EXCEL to Mathematica Wolfram version 10.
f. Final output or results from solving differential equations solving and
performing geometric computations
g. Final results, graph production, and analysis of results
The main benefit of using Mathematica Wolfram version 12 is greater versatility and
efficiency of model results due to modeling and simulating the future integrated and integral
development in an extended time framework. The data used in our model come from various
reliable sources. More specifically, we secured the data from different domestic, regional,
supra-national agencies, NGO’s, private sector, public sector, and international institutions.
All the data were used to build a single extensive database distributed into 175
equations laid out in our index. We then run nine simulations based on different cities to
identify the GCD-Index with the highest integrated and sustainable integral development
footprint.
All Copyright under Econographication Laboratory © 2023
5
The main original contribution of the GCD-Index is the joint use of social, economic,
political, and technological variables, which generates a range of different results as well as the
estimated GCD-Index for Asuncion, Paraguay between 2010 and 2023. The advantage of
GCD-Index is that it does not impose any restriction on the time framework or geographical
space. The GCD-Index can be applied anytime and anywhere without any limitation on any
country, region, continent, prefecture, state, or City around the world.
In this paper, we run our simulation on an extensive database under different possible
scenarios to evaluate integrated and sustainable integral development of Asuncion, Paraguay.
Another advantage of the GCD-Index is that it is not based solely on economic factors but also
incorporates social, political, and technological elements. Such a comprehensive, multi-
dimensional analysis gives us a more accurate assessment of integrated and sustainable integral
development of any City around the world at any point in time.
We believe that the GCD-Index (Ruiz Estrada and Park, 2019) offers policymakers,
rating agencies, and academics a new and better way to understand integrated and sustainable
development of any City from a global perspective of analysis. The improved understanding
will facilitate actions and policies to improve integrated and sustainable integral development.
Subsequently, the GCD-Index is calculated by applying Macroeconomics Structures
Vulnerability Analysis (MSV-Analysis) (Ruiz Estrada, 2017). This alternative mathematical
and graphical approach offers a flexible and powerful tool for measuring the integrated and
sustainable integral developmentof cities around the world.
3 Gross City Development Index (GCD-Index): Model
In this section, we take a closer look at the GCD-Index (Ruiz Estrada and Park, 2019).
More specifically, we describe the construction of the GCD-Index model in detail. The model
has two broader objectives. The first objective is to evaluate weaknesses and strengths of
different Main Structures or scenarios in the same graphical space at the same time period. The
second objective is to simultaneously forecast different Main Structures. The GCD-Index is
based on the application of the Cubes Cartesian Space [See Ruiz Estrada (20017)]. The Cubes-
Cartesian physical space is capable of generating multi-dimensional visual effects which show
the vulnerability of the 10 Main-Structures (or scenarios) in the same graph and time. Each
Main-Structure or scenario consists of a large number of Nano-Structures, Micro-Structures,
and Sub-Structures in different axes, levels, and cubes, differentiated by size and color (See
Figure 2). However, the detail of analysis of each structure by axes, levels, perimeters, and
cubes by sizes and colors depends on the parameters estimated by our analysis. Finally, all
these Nano-Structures, Micro-Structures and Sub-Structures apply the integrated and
sustainable integral development platform under the Omnia Mobilis assumption [see Ruiz
Estrada (2011)].
The integrated and sustainable development platform (Cubes-Cartesian Space) is
formed by infinity number of general axes (A0, A1 ,…, A∞). Each axis shows different levels
(L0, L1 ,…, L∞), perimeters (P0, P1, P2…P∞), and cubes of different sizes and colors (C0/β, C1/β…
C∞/β). Therefore, the coordinate system of the Cubes-Cartesian space is represented by SA:L:P:C
= (Ai, Lj, Pk, Cs/β) respectively, where i, j, k and s represents different values between 0 and ∞
and β represents the different colors of each cube in different levels (L0, L1 ,…, L∞). All the
cubes (Cs/β) of different sizes and colours in the same axis under the same level (L0, L1 ,…, L∞)
and different perimeters (P0, P1, P2…P∞) will be joined together. The joining is based on the
application of the concept called links structures represented by the symbol @. Moreover, the
All Copyright under Econographication Laboratory © 2023
6
integrated and sustainable integral development platform is followed by Expression 1 and
Figure 2 below.
Level P0 @………………. @ Level Pn
A1: S1:0:0:C(0/β) = (A1,L0, P0, C0/β) @ ……. .. @ S1:0:λ:C(α/β) = (A1,L0, Pλ, Cα/β)
@ @
S1:1:0:C(0/β) = (A1,L1, P0, C0/β) @ ………... @ S1:1:λ:C(α/β) = (A1,L1, Pλ, Cα/β)
@ @
. .
@ @
S1:θ:λ:C(α/β) = (A1,Lθ, Pλ, Cα/β) @ ………... @ S1:1:λ:C(α/β) = (A1,L1, Pλ, Cα/β)
@ @
A2: S2:0:0:C(α/β) = (A2,L0, P0, C0/β) @ ……. .. @ S2:0:λ:C(α/β) = (A2,L0, Pλ, Cα/β)
@ @
S2:1:1:C(α/β) = (A2,L1, P0, C0/β) @ ………... @ S2:θ:λ:C(α/β) = (A2,L1, Pλ, Cα/β)
@ @
. .
@ @
S2:θ:λ:C(α/β) = (A2,Lθ, Pλ, Cα/β) @ ………... @ S2:θ:λ:C(α/β) = (A2,Lθ, Pλ, Cα/β)
@ @
A10: S10:0:0:C(α/β) = (A10,L0, P0, C0/β) @ ……. .. @ S10:0:λ:C(α/β) = (A10,L0, Pλ, Cα/β)
@ @
S10:1:1:C(α/β) = (A10,L1, P0, C0/β) @ ………... @ S10:1:λ:C(α/β) = (A10,L1, Pλ, Cα/β)
@ @
. .
@ @
S10: θ: λ: C: α/β = (A10,Lθ, Pλ, Cα/β) @ ……………... @ S10+1:θ+1:λ+1:C:α+1/β = (A10+1,Lθ+1, Pλ+1, Cα+1/β) (1)
n = {1,2,3…∞} θ = {1,2,3…∞}
λ = {1,2,3…∞} α = {1,2,3…∞}
Note: S = Main Structure, A = Axis, L = Level, P = Perimeter, C = Cube and β = Colours
Finally, the integrated and sustainable integral development platform shows a general
function Yg that is the result of the interconnection of the ten Main Structures (S0, S1 ,…, Sn)
under different axes (A1, A2 ,…, An), levels (L1, L2 ,…, Ln), perimeters (P0, P1, P2…Pn), and
cubes of different sizes and colours (C0/β, C1/β… Cn/β) (See Expression 2 below).
Yg = ƒ (Ao<ΣS0╬ S1╬…S∞> ╬ A1<ΣS0╬ S1╬…S∞> ╬ …╬ A∞<ΣS0╬ S1╬…S∞>…) (2)
All Copyright under Econographication Laboratory © 2023
7
Note: Yg = The General Variable, ╬ = Interconnection, Ai = Axis and Si= General Structures.
However, the size of all cubes structures of different levels is determined by the
parameters that we estimate in our analysis. These parameters can take the form of either money
value or number of units - i.e. quantitative – or binary system [0,1] - e.g. qualitative. The change
of cubes size depends on changes in either quantitative variables - i.e. money value or number
of units - or qualitative variables - i.e. binary results of 1 or 0 - between periods of time.
Furthermore, if we assume that all the cubes of different levels are always changing in real
time, then they can experience expansion, contraction or stagnation. The change of the cube
size depends on the constant changes in its growth rate or first derivative. We propose three
sizes of cubes in terms of size and value (see Figure 1).
Fig. 1 Cube Structures
Cube-1 Level cero 0 ≥ TVi ≤ V1 Nano-Structure
Cube-2 Level one V1 ≥ TVi ≤ V2 Micro-Structure
Cube-3 Level-n V3 ≥ TVn ≤ Vn Sub-Structure
Note: TV = Total Value, V = Value and “n” is equal to any value between 1 and ∞…
All Copyright under Econographication Laboratory © 2023
8
Fig. 2
The Cubes-Cartesian Space Coordinate System
The process of forecasting with the integrated and sustainable development platform assumes
n number of vectors and Yg to be the forecast outcome. In our model, the forecast or predicted
value of Yg is equal to the interconnection of n number of Sub-Y Ys. Therefore, we assume
two types of time in the process of forecasting based on Macroeconomics Structures
Vulnerability Analysis (MSV-Analysis). First, there are the general time speed (☼gt) which is
running in Yg and the partial time speed (☼pt) which are running in each Sub-Y. Hence the
general time speed (☼gt) is equal to the synchronization of all partial time speed (☼pt) in the
Macroeconomics Structures Vulnerability Analysis (MSV-Analysis). Second, there is the
partial time speed (☼pt) that is running in a separate magnitude of time in each level of
analysis. In our case, we work on three different levels of analysis from level 0 to level 2. The
partial time speed (☼pt) also depends on the various axes and perimeters levels in the
Macroeconomics Structures Vulnerability Analysis (MSV-Analysis). The first stage of
forecasting condition in the Macroeconomics Structures Vulnerability Analysis (MSV-
Analysis) is shown in expression 3.
All Copyright under Econographication Laboratory © 2023
9
Level P0 ╬ ………………. ╬ Level Pn
A0: S0:0:0:C(α/β) ☼pt = (A0,L0, P0, Cα/β) ☼pt ╬ ……. .. ╬ S0:0:λ:C(α/β) ☼pt = (A0,L0, Pλ, Cα/β)☼pt
╬ ╬
S0:1:0:C(α/β) ☼pt = (A0,L1, P0, Cα/β) ☼pt @ ………... @ S0:1:λ:C(α/β) ☼pt = (A0,L1, Pλ, Cα/β) ☼pt
╬ ╬
. .
╬ ╬
S0:θ:λ:C(α/β) ☼pt = (A0,Lθ, Pλ, Cα/β) ☼pt ╬ ………... ╬ S0:1:λ:C(α/β) ☼pt = (A0,L1, Pλ, Cα/β) ☼ pt
╬ ╬
A1: S1:0:0:C(α/β) ☼pt = (A1,L0, P0, Cα/β) ☼pt ╬ ……. .. ╬ S1:0:λ:C(α/β) ☼pt = (A1,L0, Pλ, Cα/β) ☼pt
╬ ╬
S1:1:1:C(α/β) ☼pt = (A1,L1, P0, Cα/β) ☼pt ╬ ………... ╬ S1:θ:λ:C(α/β) ☼pt = (A1,L1 , Pλ, Cα/β) ☼pt
╬ ╬
. .
╬ ╬
S1:θ:λ:C(α/β) ☼pt = (A1,Lθ, Pλ, Cα/β) ☼pt ╬ ………... ╬ S1:θ:λ:C(α/β) ☼pt = (A1,Lθ, Pλ, Cα/β)
╬ ╬
An: Sn:0:0:C(α/β) ☼pt = (An,L0, P0, Cα/β) ☼pt ╬ ……. .. ╬ Sn:0:λ:C(α/β) ☼pt = (An,L0, Pλ, Cα/β)
╬ ╬
Sn:1:1:C(α/β) ☼pt = (An,L1, P0, Cα/β) ☼pt ╬ ………... ╬ Sn:1:λ:C(α/β) ☼pt = (An,L1, Pλ, Cα/β)
╬ ╬
. .
╬ ╬
Sθ: λ: C: (α/β) ☼pt = (An,Lθ, Pλ, Cα/β) ☼pt ╬ ……….. ╬ Sn+1:θ+1:λ+1:Cα+1/β) ☼pt = (An+1,Lθ+1, Pλ+1, Cα+1/β) ☼pt (3)
n = {1,2,3…∞}
θ = {1,2,3…∞}
λ = {1,2,3…∞}
α = {1,2,3…∞}
Note: S = Macroeconomic structure, A = Axis, L = Level, P = Perimeter, C = Cube and β = Colors
All Copyright under Econographication Laboratory © 2023
10
Finally, we arrive at the general function, which is shown in expression 4 below.
Yg☼gt = < ╬ S(n+1:θ+1:λ+1:Cα+1/β) ☼pt:Ci > ∩ < ╬ (An+1,Lθ+1, Pλ+1, Cα+1/β) ☼pt:Ci> (4)
Note: GF = General forecast point; ╬ = Interconnection; Ci = confidence interval; ☼pt = Partial Times Speed and ☼gt =
General Time Speed
Therefore, the confidence interval to predict the GCD-Index (Ruiz Estrada and Park, 2019)
requires that the general time speed (☼gt) depends on the ☼gt/n degrees of freedom (see
Expression 5 below]. At the same time, ☼gt/n is the standard error of prediction, which uses
Ŝi as the main reference in its calculation. The forecast interval becomes open according to
each axis, perimeter, and level in integrated and sustainable integral development platform that
predicts the GCD-Index. There exists a reliable interconnection of a large number of sub-Y’s
if we assume that space is multi-dimensional. And each dimension is moving at different speed
over time. Hence the general time speed (☼gt) is governed by the synchronization of infinite
partial times fixed by different partial times in real time (☼pt). Finally, the forecasting process
of the integrated and sustainable integral development platform is based on a multi-dimensional
perspective to better understand the future behavior of complex social, economic, political, and
technological development of cities. Furthermore, the notion of medium and long run in our
paper is somewhat different from the classic conception of time.
GCD-Index=☼gt ŝi n + 1/n + [╬ (An+1,Lθ+1, Pλ+1, Cα+1/β) ☼pt0 ] – [╬ (An+1,Lθ+1, Pλ+1, Cα+1/β) ☼ptn]
[╬ [(An+1,Lθ+1, Pλ+1, Cα+1/β) ☼pto ]2
(5)
Note: A = Axis; L = Level; P = Perimeter; C = Cube; β = Colors; ☼pt =Partial Times Speed and ☼gt = General Time
Speed
Finally, the evaluation of the GCD-Index is categorized into four different levels of vulnerability (see
Expression (6)
Level 1 : Developed Stage : 1 – 0.75
Level 2 : Developing Stage : 0.50 – 0.74
Level 3 : Light Development Stage : 0.25 – 0.49
Level 4 : Under-Developed : 0 – 0.24 (6)
4 The Application of Gross City Integral Development (GCD-Index) in the Asuncion, Paraguay
Area
In this section, we apply the GCD-Index model (Ruiz Estrada and Park, 2019) to
Asuncion, Paraguay, the megacity that is the capital and commercial center of Costa Rica. The
application of the GCD-Index uses 1250 micro-structures (Nano-variables), 50 sub-structures
(sub-variables), and ten main structures (primary variables). The 10 main variables are as
follows: (i) Main Structure-1: Economic and Finance (production and consumption of goods
All Copyright under Econographication Laboratory © 2023
11
and services, income distribution, savings ratio, public and private investment, inflation, and
banking); (ii) Main Structure-2: Social (social protection coverage); (iii) Main Structure-3:
Politics and Law, (iv) Main Structure-4: Technological; (v) Main Structure-5: Environment;
(vi) Main Structure-6: Population (labor, education and training, immigration and migration,
and unemployment); (vii) Main Structure-7: Infrastructure and Housing (real states prices and
transactions); (viii) Main Structure-8: Income and Poverty (in formal and informal sectors);
(ix) Main Structure -9: Public Sector (public transportation, security, health, welfare programs,
and taxation); (x) Main Structure-10: Others (historical, customs, habits, religion, values, and
anthropological). Subsequently, it is possible to calculate the Gross City Development Index
(GCD-Index). More specifically, we calculate the GCD-Index to compare Asuncion, Paraguay
integrated and sustainable development between two specific periods (P1 = 2000-2010) and (P2
= 2011-2023). At the same time, the GCDI-Index applies an extended number of sub-partial
derivatives and total partial derivatives tested in real time based on average values per decade
from the same City.
The final results of computing the GCD-Index according to the methodology described
above shows that Asuncion, Paraguay became more developed. More specifically, the GCD-
Index increased from 0.37554812 (Developing Stage) to 0.40122481 (Developing Stage). The
computed values of the main structures are as follows - (i) Main Structure-1: Economic and
Finance (production and consumption of goods and services, income distribution, savings ratio,
public and private investment, inflation, and banking) = 0.40254894; (ii) Main Structure-2:
Social (social protection coverage) = 0.421582548; (iii) Main Structure-3: Politics and Law =
0.405458485, (iv) Main Structure-4: Technological = 0.40212548; (v) Main Structure-5:
Environment = 0.4025658745; (vi) Main Structure-6: Population, labor, education and training,
immigration and migration, and unemployment = 0.402775247; (vii) Main Structure-7:
Infrastructure and housing (real states prices and transactions) = 0.402087458; (viii) Main
Structure-8: Income and Poverty (in formal and informal sectors) = 0.4026555818; (ix) Main
Structure-9: Public Sector (public transportation supply, security, health, welfare programs, and
taxation) = 0.402025484; (x) Main Structure-10: Others (historical, customs, habits, religion,
values, and anthropological) = 0.402787658.
In terms of the integrated and sustainable integral development platform, Asuncion,
Paraguay experienced a significant contraction of its GCD-Index between 2000-2009 and
2010-2023. That is, the citizens of Asuncion, Paraguay suffered a visible decrement in their
welfare. The case of Asuncion, Paraguay confirms that GDP is not enough to measure the
performance of a society. It also shows that it is possible to evaluate integrated and sustainable
integral development based on the final results of the GCD-Index (see Figure 3 below).
All Copyright under Econographication Laboratory © 2023
12
Fig. 3
The Application of the GCD-Index to Asuncion, Paraguay
All Copyright under Econographication Laboratory © 2023
13
Source: IADB (2023), International Monetary Fund (2023), World Bank (2023), Ministry of Economy, Trade
and Industry (2023), United Nations (2023).
All Copyright under Econographication Laboratory © 2023
14
5 Concluding Observation and Policy Implications
In this paper, we propose a new model, the Gross City Development Index (GCD-
Index) (Ruiz Estrada and Park, 2019), to evaluate the overall welfare and progress of cities.
The GCD-Index is based on ten Main Structures, which include (i) Main Structure-1:
Economic and Finance (production and consumption of goods and services, income
distribution, savings ratio, public and private investment, inflation, and banking); (ii) Main
Structure-2: Social (social protection coverage); (iii) Main Structure-3: Politics and Law, (iv)
Main Structure-4: Technological; (v) Main Structure-5: Environment; (vi) Main Structure-6:
Population (labor, education and training, immigration and migration, and unemployment);
(vii) Main Structure-7: Infrastructure and housing (real estate prices and transactions); (viii)
Main Structure-8: Income and Poverty (in formal and informal sectors); (ix) Main Structure -
9: Public Sector (public transportation, security, health, welfare programs, and taxation); (x)
Main Structure-10: Others (historical, customs, habits, religion, values, and anthropological).
The underlying intuition behind the index is that the integrated and sustainable integral
development of a city depends on its capacity to improve the welfare of its citizens over time.
We hope that the CGD-Index will contribute to a better, more balanced, and deeper
understanding of integral development welfare, development, and progress.
Such an improvement in the measurement of welfare is conducive in designing and
implement appropriate integral development policies. Integral development policies include
addressing negative factors such as poverty, discrimination, and violence as well as promoting
positive progress, for example cleaning up the environment or strengthening the economy.
Better integral development planning and more targeted policy measures can mitigate the
negative impact of adverse factors on integrated and sustainable integral development. A more
accurate measurement of welfare and changes in welfare over time can help guide cities
allocate resources more efficiently in both productive infrastructures such as business
development as well as social infrastructure such as education, security, and health. Both types
of infrastructure can help the region tackle integral development problems and facilitate
integral development progress.
Determining the appropriate level of public investments to limit the effects of poverty,
discrimination, and violence would benefit from a more accurate ex-ante measurement of their
impact. The CGD-Index can aid such measurement. By the same token, mismeasurement can
cause misallocation – over-allocation of resources leading to inefficiency and waste, or under-
allocation of resources which prevents effective tackling of a problem. The application of
GCD-Index to Asuncion, Paraguay, shows that it is a practical tool for measuring integrated
and sustainable integral development from a multi-dimensional perspective which
encompasses the social, economic, political, and technological dimensions, as well as
development progress over time. At a broader level, our results confirm that the welfare of a
city’s residents and the improvement of their welfare over time does not depend only on
narrowly economic factors but on a broader range of factors. Although we applied the CGD-
Index to the megacity of Asuncion, Paraguay, it is in fact possible to apply the index to assess
the development of any City anywhere in the world. In this connection, the GCD-Index offers
an alternative graphical and modeling approach in the analysis of integral development welfare,
development and progress which can be applied to cities in a flexible and versatile way.
All Copyright under Econographication Laboratory © 2023
15
References
IADB. https://www.iadb.org/en (Accessed 15 March 2023).
Asuncion, Paraguay Municipality. https://www.asuncion.gov.py/ (Accessed 5 June 2023).
International Monetary Fund. http://www.imf.org (Accessed 1 May 2023).
Irving, B.K., Kravis, Heston, A.W., and Sumers, R. (1978). “Real GDP Per Capita for More
Than One Hundred Countries.” 88(350):215-42.
Ministerio de Economia de Paraguay. https://www.paraguay.gov.py/oee/mef (Accessed 15
March 2023).
Ministerio de Transporte de Paraguay.
https://www.developmentaid.org/donors/view/144000/mopc (Accessed 23 May 2023).
Ministerio de Salud de Paraguay. https://www.mspbs.gov.py/index.php (Accessed 1 May
2023).
Ministerio de Finanzas de Paraguay. https://www.mef.gov.py/web-hacienda/index.php
(Accessed 12 May 2023).
Kuznets, S. (1934). “National Income 1929-1932” Chapter in NBER book. Available at
http://www.nber.org/chapters/c2258
Ruiz Estrada, M.A. (2011).” Policy Modeling: Definition, Classification and Evaluation.”
Journal of Policy Modeling, 33(3): 523-536.
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-2139.
Ruiz Estrada, M.A., Park, D. (2018). “The Past, Present and Future of Policy Modeling.”
Journal of Policy Modeling, 40(1):1-15.
Ruiz Estrada, M.A., Park, D. (2019). “The Application of the Gross City Development Index
(GCD-Index) in Tokyo, Japan” Economic Analysis and Policy, 62:37-46.
Stiglitz, (2015). Economis’t View. Available at
https://www8.gsb.columbia.edu/faculty/jstiglitz/sites/jstiglitz/files/Economist%27s%20View
_%20Joseph%20Stiglitz%20Q%20%26%20A.pdf
United Nations (2023). General Information. Available at https://unsdg.un.org/un-in-
action/paraguay
World Bank (2023). World Integral and sustainable development Prospects 2023 Revision.
Available at https://www.worldbank.org/en/country/paraguay
Wandchuck, J.S. (1972). Gross National Happiness. Available at
http://www.bhutanholiday.bt/travel-info/Gross_National_Happiness