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Advances in Social Sciences Research Journal – Vol. 8, No. 7
Publication Date: July 25, 2021
DOI:10.14738/assrj.87.10542. Bechtel, G. G. (2021). The Cause and Benefits of GDP. Advances in Social Sciences Research Journal, 8(7). 413-423.
Services for Science and Education – United Kingdom
The Cause and Benefits of GDP
Gordon G Bechtel
Warrington College of Business, University of Florida Gainesville, USA
ABSTRACT
It has recently been shown that world GDP FP-Causes world life expectancy at birth,
where FP denotes fractional polynomial (Bechtel, 2021). This article shows that
American and Chinese GDPs FP-Cause world life expectancy even more strongly than
world GDP does. These striking results beg the question: what FP-Causes American
and Chinese GDPs? The answer is globalization, which is also shown here.
Furthermore, it is demonstrated that American and Chinese GDPs also FP-Cause
world literacy and employment rates. It remains to be seen if the World Bank or
International Monetary Fund can show that the GDPs of the other (less affluent) 18
nations in the G20 Fp-cause their country’s life expectancy, as well as their literacy
and employment rates. Yearly, pre-pandemic, within-nation increments in these
variables can then be compared with subsequent decrements to assess an
endogenous viral effect on each country’s well-being. These comparisons will
expose inequalities across nations due to the varying wealth of nations. The results
in this article are brought by Royston and Altman’s (1994) generalization of
polynomial regression, which estimates both coefficients and their powers.
Keywords:Dollar, Temporal, and Percent Indicators, FP-Causation, Ratio Scaling, R2
Invariance with respect to Dollar, Temporal, and Percent Calibration, The KOF Index of
Globalization.
INTRODUCTION
The United Nation’s Secretary General Antonio Guterres has recently advocated reforms of the
World Bank, the International Monetary Fund, and the UN Security Council (Aljazeera, accessed
on 19 July, 2020). Guterres stresses that events have overtaken us
(www.nelsonmandela.org/content/page/annual-lecture-2020):
• The corona virus has brought the world to the breaking point and exposed deep
demographic inequalities.
• A new UN governance would give each nation an equal vote and no veto.
• A new social contract would create equal opportunity at all institutional levels.
• An inclusive and balanced multilateral trading system would provide sustenance and
sustainability.
Guterres reiterated these issues at the United Nations 75th anniversary on September 21, 2020.
Pursuing “the future we want, the United Nations we need”, the UN passed a Declaration of
International Collaboration advocating an egalitarian reformation of the UN as well as the
entire global establishment. Ten weeks later Guterres implicitly admonished some of the G20
nations for their rejection and ignorance of the World Health Organization’s information and
guidance in addressing poor nation’s poverty, hunger, and survival in the covid-19 pandemic:
“When countries go in their own direction, the virus goes in every direction” (Aljazeera,
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December 3, 2020). Pivoting to climate change, Guterres then requested governments to
declare climate emergencies in their own countries: “This is a moment of truth and it is also a
moment of hope” (Aljazeera, December 12, 2020).
Echoing Guterres at the World Economic Forum in Davos, Switzerland on 1/25/2021, Xi Jin
Ping said: “We must build an open world economy, firmly safeguard the multilateral trade
system, and refrain from making discriminating and exclusive standards, rules, and systems, as
well as high walls that separate trade, investment, and technology”
(https://www.euractiv.com).
These recent moments of truth and hope, along with the Covid19 crisis, point toward important
human benefits; namely, life expectancy at birth (X), employment (E), and literacy (L). This
article demonstrates that these benefits are brought by GDP (G), which is the paramount
economic indicator. G is then shown to be driven by the KOF Index of Globalization (K).
Variables X, E, and L have been supplied by the World Bank in Washington
(http://beta.data.worldbank.org). Variable K has been provided by the Swiss Economic
Institute in Zurich (https://doi.org/10.1007/s11558-019-09344-2).
Sections 2, 3, and 4 define world X, E, and L (http://beta.data.worldbank.org). Section 5 details
American and Chinese Gs and Section 6 demonstrates that these Gs FP-cause world X, E, and L.
Section 7 describes K and Section 8 then shows that K FP-causes both American and Chinese
Gs. Section 9 emphasizes the advantages of dollar, yearly, and percentage indicators, like G in
trillions of current US$, X in years, and E and L in percentages, in the new era of data science.
Section 10 reviews the major discoveries here; namely, American and Chinese Gs FP-cause
world X, E, and L, and K FP-causes American and Chinese Gs.
These findings are brought by the three data definitions and four fractional-polynomial
regressions in Sections 5 and 6. It will be interesting to see if the G7 and G20 nations meeting
this year will strive for similar findings regarding GDP, life Expectancy, and globalization.
LIFE EXPECTANCY AT BIRTH (X)
Life expectancy at birth indicates the number of years a newborn infant would live if prevailing
patterns of mortality at the time of its birth were to stay the same throughout its life.
Source. (1) United Nations Population Division. World Population Prospects: 2019 Revision,
or derived from male and female life expectancy at birth from sources such as: (2) Census
reports and other statistical publications from national statistical offices, (3) Eurostat:
Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics
Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of
the Pacific Community: Statistics and Demography Programme.
Development Relevance. Mortality rates for different age groups (infants, children, and
adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are
important indicators of health status in a country. Because data on the incidence and prevalence
of diseases are frequently unavailable, mortality rates are often used to identify vulnerable
populations. And they are among the indicators most frequently used to compare
socioeconomic development across countries.
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Limitations and Exceptions. Annual data series from United Nations Population Division's
World Population Prospects are interpolated data from 5-year period data. Therefore they may
not reflect real events as much as observed data.
Long Definition. Life expectancy at birth indicates the number of years a newborn infant
would live if prevailing patterns of mortality at the time of its birth were to stay the same
throughout its life.
Periodicity. Annual
Statistical Concept and Methodology. Life expectancy at birth used here is the average
number of years a newborn is expected to live if mortality patterns at the time of its birth
remain constant in the future. It reflects the overall mortality level of a population, and
summarizes the mortality pattern that prevails across all age groups in a given year. It is
calculated in a period life table which provides a snapshot of a population's mortality pattern
at a given time. It therefore does not reflect the mortality pattern that a person actually
experiences during his/her life, which can be calculated in a cohort life table. High mortality in
young age groups significantly lowers the life expectancy at birth. But if a person survives
his/her childhood of high mortality, he/she may live much longer. For example, in a population
with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy
at birth may be low due to the high childhood mortality so that once a person survives his/her
childhood, he/she may live much longer than 50 years.
WORLD EMPLOYMENT RATE (E)
Employment to population ratio is the proportion of a country's population that is employed.
Employment is defined as persons of working age who, during a short reference period, were
engaged in any activity to produce goods or provide services for pay or profit, whether at work
during the reference period (i.e. who worked in a job for at least one hour) or not at work due
to temporary absence from a job, or to working-time arrangements. Ages 15 and older are
generally considered the working-age population.
Source. International Labour Organization, ILOSTAT database. Data retrieved on June 15,
2021.
Aggregation Method. Weighted average
Development Relevance. Four targets were added to the UN Millennium Declaration at the
2005 World Summit High-Level Plenary Meeting of the 60th Session of the UN General
Assembly. One was full and productive employment and decent work for all, which is seen as
the main route for people to escape poverty. Employment to population ratio is a key measure
to monitor whether a country is on track to achieve the Millennium Development Goal of
eradicating extreme poverty and hunger by 2015. And it continues to be a priority in the
Sustainable Development Goal of promoting sustained, inclusive and sustainable economic
growth, full and productive employment and decent work for all.
General Comments. National estimates are also available in the WDI database. Caution should
be used when comparing ILO estimates with national estimates.
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Limitations and Exceptions. Data on employment by status are drawn from labor force
surveys and household surveys, supplemented by official estimates and censuses for a small
group of countries. The labor force survey is the most comprehensive source for internationally
comparable employment, but there are still some limitations for comparing data across
countries and over time even within a country. Comparability of employment ratios across
countries is affected by variations in definitions of employment and population. The biggest
difference results from the age range used to define labor force activity. The population base
for employment ratios can also vary. Most countries use the resident, non-institutionalized
population of working age living in private households, which excludes members of the armed
forces and individuals residing in mental, penal, or other types of institutions. But some
countries include members of the armed forces in the population base of their employment
ratio while excluding them from employment data. The reference period of a census or survey
is another important source of differences: in some countries data refer to people's status on
the day of the census or survey or during a specific period before the inquiry date, while in
others data are recorded without reference to any period. Employment ratios tend to vary
during the year as seasonal workers enter and leave. This indicator also has a gender bias
because women who do not consider their work employment or who are not perceived as
working tend to be undercounted. This bias has different effects across countries and reflects
demographic, social, legal, and cultural trends and norms.
Periodicity. Annual
Statistical Concept and Methodology. The employment to population ratio indicates how
efficiently an economy provides jobs for people who want to work. A high ratio means that a
large proportion of the population is employed. But a lower employment to population ratio
can be seen as a positive sign, especially for young people, if it is caused by an increase in their
education. The series is part of the ILO estimates and is harmonized to ensure comparability
across countries and over time by accounting for differences in data source, scope of coverage,
methodology, and other country-specific factors. The estimates are based mainly on nationally
representative labor force surveys, with other sources (population censuses and nationally
reported estimates) used only when no survey data are available.
WORLD LITERACY RATE (L)
The following passages are taken the World Bank’s description of World Literacy Rate, which
is sourced from the UNESCO Institute for Statistics:
Adult literacy rate is the percentage of people ages 15 and above who can both read and write
with understanding a short simple statement about their everyday life.
Outcome. Literacy rate is an outcome indicator to evaluate educational attainment. This data
can predict the quality of a future labor force and can be used in ensuring policies for life skills
for men and women. It can be also used as a proxy instrument to see the effectiveness of an
education system; a high literacy rate suggests the capacity of an education system to provide
a large population with opportunities to acquire literacy skills. The accumulated achievement
of education is fundamental for further intellectual growth and social and economic
development, although it doesn't necessarily ensure the quality of education. The term literate
women implies that women can seek and use information for the betterment of the health,
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nutrition and education of their household members. Literate women are also empowered to
play a meaningful role.
Measurement. In practice, literacy is difficult to measure. Estimating literacy rates requires
census or survey measurements under controlled conditions. Many countries report the
number of literate people from self-reported data. Some use educational attainment data as a
proxy but apply different lengths of school attendance or levels of completion. And there is a
trend among recent national and international surveys toward using a direct reading test of
literacy skills. Because definitions and methods of data collection differ across countries, data
should be used cautiously.
GROSS DOMESTIC PRODUCT (G)
History of GDP. In the great depression Simon Kuznetz formulated American national accounts
in terms of dollars, which evaluated different commodities in a common unit. He added up
various national income sources and reported his result to the United States Senate in January,
1934 (Masood, 2016, Prologue, Chapters 2 and 3). “In 1940, six years after Simon Kuznetz had
presented his national income estimates to the Senate, Keynes had written down in a table the
basis for what today is the formula for GDP” (Masood, 2016, p. 26). This formula adds up three
macro indicators, household expenditure, domestic savings, and government expenditure, which
constitute Keynesian GDP.
Importance of GDP. In 1999, mindful of Simon Kuznets original accounting of distinct goods
like cars and cereal boxes by their dollar values, the United States Commerce Department
proclaimed the GDP formula as the U.S. government’s greatest invention of the 20th century
(Masood, 2016, Introduction). The calibration of GDP’s three indicators in current US dollars
for all nations signals a continuing American control of the global economy. In the plethora of
global indexes, GDP looms as the composite most fundamental to the global economy. GDP is
so basic, longstanding, and prestigious that market traders, analysts, and policy planners track
it daily on worldwide television and internet. The new empirical economics is dominated by
GDP’s “Making the Modern World” (Masood, 2016, Preface), it’s fostering human development,
and it’s availability in most national accounts.
Indicators of GDP. Here we view GDP’s components, household expenditure, domestic savings,
and government expenditure, as separate time-varying indicators
(http://beta.data.worldbank.org):
Household final consumption expenditure (current US$): “Household final consumption
expenditure (formerly private consumption) is the market value of all goods and services,
including durable products (such as cars, washing machines, and home computers), purchased
by households. It excludes purchases of dwellings but includes imputed rent for owner- occupied dwellings. It also includes payments and fees to governments to obtain permits and
licenses. Here, household consumption expenditure includes the expenditures of nonprofit
institutions serving households, even when reported separately by the country. Data are in
current U.S. dollars.”
Gross domestic savings (current US$):“Gross domestic savings are calculated as GDP less final
consumption expenditure (total consumption). Data are in current U.S. dollars.”
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The World Bank’s update of John Maynard Keynes final indicator, added during World War II
(Keynes, 1940), is:
General government final consumption expenditure (current US$): “General government
final consumption expenditure (formerly general government consumption) includes all
current government expenditures for purchases of goods and services (including compensation
of employees). It also includes most expenditures on national defense and security, but
excludes government military expenditures that are part of government capital formation.
Data are in current U.S. dollars.”
This dollar denomination of variables counted in different units (automobiles, cereal boxes,
etc.) allows the ratio scaling of GDP up to a multiplier calibrating GDP in single, thousands,
millions, billions, or trillions of current US dollars. This ratio scaling also allows daily exchange- rates to multiply one nation’s currency into another’s (e.g. dollars into yuan).
G AS AN FP-CAUSE OF X, E, AND L
Recalling that G denotes either American or Chinese GDP (cf. Section 1), this article shows that
G FP-causes world X, E, and L, where FP denotes fractional polynomial (cf. Royston and
Altman,1994; Granger, 2001).
Definition 1. Vector Greplicates a constant GtMt times,where Mt denotes American or Chinese
population size in year t = 1991 ... 2018.
Vector G containsStMt values andis calibrated in singles, twenties, fifties, hundreds, thousands,
millions, billions, or trillions of current US$ .
Definition 2. Vector X replicates a constant Xt Nt times, where Nt is world population size in
year t = 1991 ... 2018.
Vector X containsStNt values and is calibrated in hours, days, weeks, months, or years.
Definition 3. Vector E replicates a constant Et Nt times, where Nt is world population size in
year t = 1991 ... 2018.
Vector E containsStNt values and is calibrated in percentages.
Definition 4. Vector L replicates a constant Lt Nt times, where Nt is world population size in
year t = 1991 ... 2018.
Vector L containsStNt values and is calibrated in hours, days, weeks, months, or years.
Definition 5. If fractional polynomial regressions of world X, E, and L on G over t = 1991 ...
2018 return R2s 3 .95, then G FP-causes world X, E, and L.
The following Tobrina algorithms return importance-weighted fractional polynomial
regressions in Stata syntax (StataCorp., 2011):
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fracpoly regress X G [iweight = POPmillions], adjust(G:mean) degree(2) noscaling (1)
fracpoly regress E G [iweight = POPmillions], adjust(G:mean) degree(2) noscaling (2)
fracpoly regress L G [iweight = POPmillions], adjust(G:mean) degree(2) noscaling (3)
Table 1 exhibits the R2s for fractional polynomial regressions of X, E, and L on G over t = 1991
... 2018.
Table 1. FP Regression R2s of X, E, L on G
--------------------------------------------------------
Nation X E L
--------------------------------------------------------
USA .9941 .9722 .9858
China .9944 .9883 .9899
---------------------------------------------------------
These R2s give strong empirical confirmation that American and Chinese Gs FP-cause world X, E,
and L.
Finally, It is important to note that the above R2s are invariant with respect to the units in which
G, X, E, and L are calibrated.
THE KOF INDEX OF GLOBALIZATION (K)
K was inspired by Visions of Governance for the 21st Century in Cambridge, MA, USA. It was
introduced in 2002, published in 2006, updated and detailed in 2008, and revisited by Savina
et al. (2019). The index is produced by the KOF Swiss Economic Institute at ETH Zurich, who
defines globalization as the process of creating networks of connections among actors at multi- continental distances, mediated through a variety of flows including people, information and
ideas, capital and goods. Globalization is conceptualized as a process that erodes national
boundaries, integrates national economies, cultures, technologies and governance and
produces complex relations of mutual interdependence (http://globalization.kof.ethz.ch/).
Table 2 lists the six sub-indicators that make up K. The Swiss Federal Institute of Technology
describes the standardization of these indicators: each sub-indicator is transformed to a scale
of one to one hundred, where one hundred is the maximum value for a specific indicator and
one is the minimum value. Higher values denote higher globalization and lower values denote
less globalization. The data are transformed according to the percentiles of the original
distribution. These percentiles constitute an identity scale; i.e. a scale whose transformation is
restricted to multiplication by one.
Table 2. Components of the K
Indicator Sub-Indicators
Actual flows in % GDP Trade, Foreign direct investment, stocks, Portfolio investment,
Income payments to foreign nationals
Low Restrictions Hidden import barriers, Mean tariff rate, Taxes on international
trade, Capital account restrictions
Personal Contact Telephone traffic, Transfers, Foreign population, International letters,
Information Flows Internet users, Television, Trade in newspapers
Cultural Proximity Number of McDonalds, Number of Ikea, Trade in books
Political Globalization Embassies in country, Membership in international organizations,
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Participation in U.N. Security Council missions, International treaties
The six sub-indicators of K in Table 2 support Xi Jin Ping’s plea to the 2021 World Economic
Forum in Davos, Switzerland: “We must build an open world economy, firmly safeguard the
multilateral trade system, and refrain from making discriminating and exclusive standards,
rules, and systems, as well as high walls that separate trade, investment, and technology”
(https://www.euractiv.com) (cf. Section 1). This plea offers an investment-policy guide to
foreign nationals, sovereign states, the United Nations, the World Bank, the International
Monetary Fund, and the New Development Bank in Shanghai.
K AS AN FP-CAUSE OF G
The next Tobrina algorithm returns a importance-weighted fractional polynomial regressions
of G on K for the USA and China:
fracpoly regress G K [iweight = POPmillions], adjust(K:mean) degree(2) noscaling (4)
The R2s returned by (4) for American and Chinese Gs over t = 1991 ... 2018 are .9672 and .9555.
These R2s confirm that K FP-causes both American and Chinese Gs. It also remains to be shown
whether K FP-causes the GDP of each of the other 18 G20 nations over 1991-2018.
DOLLARS, YEARS, AND RATES IN DATA SCIENCE
The results brought by Stata commands (1) and (2) override the basic canon of statistical
inference, that there is fundamental uncertainty in all data. Neither denying nor quantifying
uncertainty, we simply ignore it. This approach to sequential populations brings compelling
advantages to social and economic data science. Probabilistic inference is replaced by
parameter computation and random variables give way to real variables. This suggests further
“statistical thinking and new foundational frameworks” that help sort out “the many
philosophical issues data science presents ... “ [Davidian, 2013]. This call has been echoed by
the American National Science Foundation, who has “released a revised version of the
solicitation ‘Critical Techniques and Technologies for Advancing Foundations and Applications
of Big Data Science ... ‘ ” (Vogelius et al., 2015). In view of trade-war, covid-19, and
environmental shocks to all economies, focusing data science on GDP’s predictions of other
important global and national indexes is now compelling.
Pfeffermann observes that “The use of big data does not require a sampling frame,
questionnaires, interviews, and all the other ingredients underlying survey samples [...] this
should be the ultimate target of every country - having sufficiently accurate administrative
records so that no population censuses will be needed” (Pfeffermann, 2015) (pp. 433, 455).
Horrigan also views Big Data as non-sampled data “from electronic sources whose primary
purpose is something other than statistical inference. [...] this type of Big Data typically
comprises the universe and, by definition, can represent (nearly) the entire population [...]
(Horrigan, 2013) (pp. 25-26).” As examples of non-sampled universe files Horrigan mentions
daily price indexes, point-of-sale retail databases, universe data on hospitals, and corporate
data.
Similarly, Stata commands (1) – (4) here exploit the electronic files of the World Bank and the
Swiss Economic Institute. Commands (1) – (4) provide the R2s in Sections 6 and 8, where world
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URL: http://dx.doi.org/10.14738/assrj.87.10542
Savina, G.; Haelg, F.; Potrafke, N.; Sturm, J-E. 2019. The KOF Globalisation Index – Revisited, Review of
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Shapiro, H. 1972. The index of consumer sentiment and economic forecasting: A reappraisal; In Human Behavior
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StataCorp. 2011. Stata Statistical Software, Release 12; StataCorp LP, College Station, TX, USA.
Stevens, S. S. 1946. On the theory of scales of measurement. Science, 677-680.
Suppes, P.; Zinnes, J. L. 1963. Basic measurement theory; In Handbook of Mathematical Psychology, Volume I;
Bush, R. R., Luce, R. D., Galanter, E., Eds.; John Wiley & Sons, New York, NY, USA, 1-76.
Thompson, M. E. 1997. Theory of Sample Surveys; Chapman and Hall; London, UK. Torgerson, W. S. 1958. Theory
and Methods of Scaling; John Wiley & Sons, New York, NY, USA.
Vogelius, M.; Kannan, N.; Huo, X. 2015. NSF big data funding opportunity for the statistics community. Amstat
News, April, 6.