Table 1. Spherons’ spheral indices

          Each spheral class is a separate SPHERON covering part of the population employed in o­ne of the four spheres of social production. In sum, the number of people of the four SPHERONS is the total population. This is expressed by the formula: P = P1 (P1n + P1w) + P2 + P3 + P4 or

Population/People=Spheron-1 (nonworking + working in this sphere) + Spheron-2 + Spheron-3 + Spheron-4. This is the fundamental formula of the population/people of any community from the family, town, and country to humanity as a whole in the Spherons’ spheral indices.

The principal advantage and excellence of the spheral indicators is that they ensure the global digitalization of all national statistical indices, constitute their common denominator, logically organize/systematize/integrate them and open the way for the digital economy, for digital global peace through digital/conscious prevention of all wars and nonviolent resolution of all conflicts. The spheral indices aggregate and disaggregate the indices of traditional statistics o­n special digital technology that goes beyond the boundaries of our study. We do not touch upon these technical questions here, because they have been studied in detail in previous works [5; 6; 6a-d].

Spheral indices together with their digital statistical technologies of composition, decomposition, calculation, modeling and forecasting constitute an innovative digital global statistics (GLOBSTAT), suitable for all countries, industries, corporations, cities and other units of the world social production as a necessary tool for its digital economy and global peace.

4. Sources of Statistics o­n Employment and Unemployment of the Population in Russia

            Because the classification of population according to SPHERONS is an unknown statistic, the number can be determined indirectly by available statistics.

            In this study, we used three main sources of statistics o­n Russian employment and unemployment:

•Data of Russia Population Census 2010 round (RPC-2010), published in 11 volumes [7; 8; 9; 10; 11];

•The Distribution of Employees of Enterprises and Organizations o­n Economic Activity Kinds o­n average for 2010. [2];

•Sample Data o­n the Number of Organizations’ Staff by the Activities Kinds and Staff Categories [2].

            In 2010, the census enumerated the resident population of Russia. The census was carried out at each person’s place of permanent (usual) residence, which might or might not coincide with the address where the person is registered. In each dwelling existed the people who (usually) live there all the time, including those who at the moment of census were temporarily absent for a period of up to o­ne year.

            The resident population is categorized by households:

•private households;

•collective households;

•homeless households.

           The population living in private households, distributed by gender and age, were allocated as:

•Children and adolescents aged 0–14 years;

•The population aged 15–72 years; it is among the employed population in this age group that random observations about employment and professional composition of employees in the period after the census were conducted;

•Persons aged 73 years and older.

            This source allows defining the number of people in the population who are employed, self-production or non-working (inactive), which is part of the Socioclass, expressed by spheral index P1n.

5. Classification by Branches of Economic Activity and Categories of Personnel

             In this study were used three statistical classifications, according to:

1.Economic activity;

2.Industries/branches/types/kinds of economic activity (activities);

3.Personnel/staff categories.

These classifications allow us to define the number of SPHERONS employed in four spheres, according to the number of employees in the industries of each sphere.

Classification of economic activity was used from the RPC-2010 materials.

            The economically active population includes persons aged 15–72 years who, for the week before the census (from 7 to 13 October 2010), were employed in economic activity or looking for work and were ready to start. This economically active population was divided into employment in the economy and the unemployed. (Unemployed busy self-production are included in P1n of Socioclass/Spheron-1).

            The employed in economy are those who, for the week before the census (from 7 to 13 October 2010), were working independently of the timing of direct payment or income for their work activity (at least o­ne hour per week) or receiving income regardless of whether it was permanent, temporary, seasonal, casual or other work (including self-employment and the provision of various services to individuals, paid social work, work without pay for a family business, work o­n a peasant/farm and various kinds of underworking both periodic and o­ne-off).

            The employed were considered also the persons who performed household work o­n the production of goods or services, including agriculture, forestry, hunting, fishing and its processing, if the products are intended for market.

              The persons who owned their a company or business and working o­n it but in the survey week were missing work for any reason were considered employed if in the period of their absence the business continued to operate.

             The employed in economy were considered also the temporarily absent from work due to illness or injury, care, annual leave or unpaid leave of their own accord, weekend time off, reimbursement of overtime or work o­n holidays (weekend) days of work o­n a special schedule, spent in reserve, statutory maternity leave, maternity leave and child care, education or training outside their workplace, educational leave, leave without pay or leave with pay by the administration.

            The students and pensioners who had any work during the period are also among the employed in the economy.

Data o­n the average annual number of employed in the economy after the census period formed by the main job of the civilian population o­nce a year in the preparation of the balance of labor resources o­n the basis of organizations’ information, materials of sample survey o­n employed population and the data of executive bodies. The annual average number of employees includes working foreign nationals who are permanent residents and those temporarily residing in the territory of Russian Federation.

            During the census were not considered employed in economy those who from 7 to 13 October 2010 o­nly studied in an educational vocational institution full-time; produced products in private farms for their own consumption; provided services in their own household cleaning house, cooking, and similar services; provided services free of charge for any individuals or charities, parent councils, committees, veterans hospitals or nursing homes, etc.; owned shares of any company or society without direct participation in the economic activity of the organization; begging (even if it generated revenues) or collection of bottles, etc.

             The unemployed are those who in the survey week did not have a job (gainful employment) and:

- Were seeking work and were available for work during the survey week. Finding a job is considered a public or commercial employment service; so is use of or placing ads in the media, direct appeal to the administration of a company or potential employer, and use of personal connections to promote o­neself, and the organization of o­ne’s own business;

- Found a job and proceeded to it two weeks after the survey week;

- Found a job and expected a response from the administration or the employer with a response waiting period not to exceed o­ne month.

For those who organize their own business, the period of the job search was considered an activity to registration of business. Activity after the registration was deemed to be busyness o­n own enterprise.

The pupils, students and pensioners were counted as unemployed if they, in the survey week, were seeking work and were ready to start.

             Economically inactive population are people aged 15–72 years who were not employed in the economy or unemployed during the survey week [3].

             Classification of economic activity was used to highlight the following categories of the population aged 15–72 years:

•Economically active population (employed and unemployed);

•Economically inactive population (scholarship holders, pensioners, persons receiving benefits except unemployment benefits, engaged in private farming, etc.);

•Persons who have not indicated economic activity.

              Grouping of economic units (enterprises) by the kinds of economic activity (KEA) was done using the Russian Classification of Economic Activities (RCEA), adopted and entered into force o­n January 1, 2008. RCEA is based o­n harmonization with the official version in Russian of Statistical Classification of Economic Activities in the European Community (NACE Rev. 1) by maintaining the names of respective positions without changing the scope of the concepts. These are the following types of activities [12]:

Table 2. Economic Activities

            During the sample survey of payroll, the number of organizations by professional groups and economic activities conducted o­n October 31, 2012 is identified by the following categories of staff:

•Heads of organizations and their structural units (services);

•Specialists of higher qualification;

•Specialists of mid-level qualifications;

•Employees engaged in the preparation of information, documentation, accounting and service;

•Service workers, housing and communal services, trade and related activities;

•Skilled workers in agriculture, forestry, hunting and fishery activities;

•Skilled factory workers, construction, transport, communication, geology and exploration of mineral resources;

•Operators, machinists of systems and machinery;

•Unskilled workers.

            The above categories of staff were grouped into four groups to highlight four spheral classes or SPHERONS:

1. The heads of organizations and their structural units (services) – P3, Orgclass;
2. Specialists of higher and mid-level qualifications engaged learning and health care personnel – P1w, Socioclass;
3. Specialists and employees involved in preparation of information, documentation and accounting – P2, Infoclass;
4. Other categories of staff, except heads and employees involved in the preparation of information, documentation, accounting and service – P4, Technoclass.

          In this case, the category of employees engaged in the preparation of information, documentation, accounting and service groups include the following personnel:

•Stenographers and typists;

•Operators using special equipment for the preparation and transmission of information (printer, telex, facsimile, cryptographic equipment and e-mail);

•Operators of computers (PCs), employed various information input;

•Operators of computers (counting machines, calculators, texture machine);

•Secretaries and clerks;

•Employees engaged in accounting transactions and accounting;

•Employees engaged in the processing of statistical and financial information;

•Employees engaged in the receipt and delivery of goods;

•Employees engaged in dispatching production.

•Employees engaged in accounting and o­n duty transport;

•Employees engaged in card files and libraries;

•Coders, proofreaders and related occupations;

•Other employees engaged in the preparation of information, documentation and accounting;

•Cashiers, including tickets cashiers;

•Cashiers of banks and related occupations;

•Employees to obtain payments o­n accounts, overdue and other cash amounts;

•Employees engaged in receiving and informing visitors (receptionists, attendants of pass office, o­n the issuance of certificates and others);

             This group includes the specialists of scientific and design organizations, cultural institutions, art, media, advertising and the like.

Due to the fact that the selected above categories are involved in the preparation of information, documentation, accounting and service, employees who do not have special vocational training are necessary to correct this size. Such adjustments are associated with the addition to the number of employees a number of specialists with secondary and higher vocational education who are employed with processing and analysis of information. Unfortunately, data o­n the number of specialists with secondary and higher education employed processing and analysis of information is missing in the statistics of Russian Federation.

6. Technique of Definition of Number of Russia SPHERONS Based o­n Statistics

             The technique of determining the number of SPHERONS of employed (working, economically active) persons in Russia is a superposition and cumulative use of two traditional statistical classifications (KEA and categories of staff) o­n the structure of SPHERONS. Or, in other words, the statistical information of employment in KEA and staff categories are differentiated and grouped by four SPHERONS structure (except for the non-working population engaged in self-production). This technique can be expressed by the following table.

Table 3. KEA by Personnel Categories

Application of this technique to the statistics of employed (economically active) persons of Russia with taking into account the non-working (economically inactive) population will allow us to define the size of SPHERONS of Russia in full in 2010 according to the census.

7. Mid-year Population of the Russian Federation for 2010

Mid-year population of the Russian Federation for 2010 amounted to 142.938 million. This is calculated by the average chronological quarters of 2010. By the average annual number of indicators, we calculate the average per capita (income, housing, consumption, etc.). Therefore, this study used the average annual population and average number of employed as the most robust data for the year [1; 4].

8. Definition of Number of Non-working Population (P1n) in Russia for 2010

The 2010 population census of the age and gender composition of the population living in private households identified the following age groups and calculated the structure of the non-working population employed in self-production [3; 7; 8; 9; 10; 11]:

Table 4. Employed self-production (non-working, economic inactive), millions

             Calculated according to the RPC – 2010, Volume 7, Table 1, Population of private households by age groups, gender and economic activity [11]: http://www.gks.ru/free_doc/new_site/perepis2010/croc/perepis_itogi1612.htm

9. Number of SPHERONS in Russia for 2010

Table 5. Number of SPHERONS of Population in RUSSIA for 2010, in million people

         Total in Table 5 are 32 indices + statistical error.

*The number of employed by KEA is given except for the organizations’ heads (P3) and employees working with information (P2).

**The number of employed by KEA in P and Q branches was not published; it is very small, so it is not considered.

          The calculations were performed o­n the average number of employees for 2010.

Note. o­n the basis of available publications and statistical data, we could not allocate the employed in culture (libraries, archives, museums, radio and television, etc.) of economic activity “OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICE ACTIVITIES” to exclude this population from Sociosphere and to include in Infosphere.

10. Number of SPHERONS in Russia for 1991 and 1996

           For comparison, we publish a similar table of SPHERONS of Russian population for 1991 and 1996, made in other state statistical classification of economic activities and published in 2002 [5, 71–72].

Table 6. Number of SPHERONS in Russia for 1991 and 1996 in million people

11. Conclusion: Problems and Significance of the Study

            The problems of this statistical study are caused by its novelty (nobody has conducted similar studies) and its colossal labor input. These problems become apparent when expanding the content of research, for example, as proposed in its Program [6a], including an analysis of the economic situation of SPHERONS, their income, their participation in government, their education and, similarly, their qualities in dynamics for years, in different countries and at different levels from municipal to global, starting from schools, hospitals, small businesses and settlements to large corporations, cities and countries.

           The significance of this study is difficult to overestimate.

            First, the statistical analysis of the number of Russian SPHERONS in 2010 opens the fundamental new possibility of creating an evolution of global (spheral) statistics as a common denominator for the bad comparable national statistics of traditional branch nature, which are now developing in this direction poorly for a very long time. The branches and activities within spheres appear and disappear, change names, unite with others and are divided into several new branches and activities, which are different in different countries and in different within o­ne country. The currently used inferior ways are constantly changeable, while spheres and their classes – SPHERONS – are constant and eternal for any country and for all its historical periods. This will ensure stability and uniformity of the global/spheral statistics for a single global world in the future.

            Second, if this study were possible for o­ne country at different times with different statistical classifiers, it is equally possible for any other country despite the differences in statistical classifications and systems of indices in each country. But it is extremely time-consuming, innovative work that requires efforts of relevant scientific institutions. Now statistical studies of spherons are conducted in other countries, for example, in Ghana, India and Pakistan.

             Third, it opens the possibility of quantifying the causes and sources of all wars and armed conflicts, paving the way for their intelligent, non-violent and peaceful solution. Global/spheral statistics is an essential element of global peace science (GPS), without which it cannot exist and the necessary technological tool for its implementation in the process of conscious building global peace. In place of baseless and eternal accusations by warring parties, the SPHERONS method is able to put accurate, statistical assessment of the scale of mutual contradictions and disharmony and indicate ways to eliminate or minimize them for beneficial mutual interests and nonviolent way.

            Fourth, development of other social sciences depends from the global/spheral statistics of SPHERONS; for example, forecasting, without which “the future loses certainty” and humanity is deprived of scientific wisdom. Academicians Viktor Sadovnichiy and Daisaku Ikeda present this idea. In particular, they wrote: “Today, the mathematical theory of forecasting (which is inseparable from statistics – L.S.) has neither enough depth in the theory of forecasting nor satisfactory breadth of coverage in the range of application areas that are particularly important for practice. These factors cannot but affect the credibility and long-term impact of both discussed and proposed forecasts”[13]. Now “Global Peace Science” (GPS) from the spherons structural harmony crystallizes in its social genome a “deep theory of forecasting.”[6].   This science ensures the reliability, durability and worldwide breadth of forecasting, but require the development of new mathematics of harmony and global statistics suitable for the expression and calculation of spheral indices for the spherons’ structural harmony.

           They define scientific wisdom as “diverse harmony” in the words of Daisaku Ikeda. The future lost certainty in traditional social science of dying industrial civilization, which has no future. But the future finds certainty through the GPS definitions in the new social science based o­n GPS. This future is a harmonious civilization of SPHERONS, conscious/scientific global peace without wars, conservation of nature, gender, economic and political equality, justice and love.

            Fifth, this first statistical proof and the study of Russian spherons has great scientific, political, economic and peacemaking significance for the country. But it requires expansion both in time and in depth, in the subjects of the Russian Federation and in large cities, which will create spheral macro statistical map of its state and development with all its positive and negative trends, including trends in the growth of social tensions between the spherons in different regions and at different stages. This knowledge will allow the country's leadership to find harmonious ways to overcome these disharmonies and ensure a stable peace from the structural genetics of national spherons. But this requires a lot of innovative scientific and practical work in the future, which is just beginning and which will undoubtedly reach the level of conscious building global peace from its structural genetics.

References

1.Regions of Russia. Socio-economic Indicators 2013. Statistical Collection. M: 2013

2.Methodological Guidelines for Conducting Sample Surveys of Population o­n Employment (Labor Force Survey). M: 2010: http://www.gks.ru/free_doc/new_site/population/trud/Untitled452-10.pdf

3.Methodological Clarification to the National Population Census 2010. M: 2010

4.Statistical Yearbook of Russia 2013. –M: 2014

5. Semashko, Leo (2002) Tetrasociology: Responses to Challenges. –St Petersburg: http://peacefromharmony.org/?cat=en_c&key=145

6.Semashko, Leo and GHA 75 coauthors (2012). The ABC of Harmony for World Peace, Harmonious Civilization and Tetranet Thinking. New Delhi, Doosra Mat Prakashan. http://peacefromharmony.org/?cat=en_c&key=478

6a. Semashko Leo and 173 coauthors from 34 countries. Global Peace Science…Joint publication of GHA and IASE University, India. World textbook. The first publication in English, Smaran Publication, New Delhi. 2016. 616 p. ISBN 978-81-929087-8-6: http://peacefromharmony.org/docs/global-peace-science-2016.pdf

6b. Semashko, Leo (2018) The ABC/Paradigm of Tetranet Thinking for "New Women" harmonious feminism: http://peacefromharmony.org/?cat=en_c&key=824

6c. Kashina, Olga, Semashko Leo (2014). Statistics of SPHERONS of Russia for 2010: Methodology, Sources and Number. (In the book: 6a) and here: http://peacefromharmony.org/?cat=en_c&key=593

6d. Kashina, Olga (2006) Demography and population statistics / Ed. I.I. Elisseeva.-M: Finance and Statistics, Ch. 13

6e. Maturana, Humberto & Varela, Francisco ([1st edition 1973] 1980). Autopoiesis and Cognition: the Realization of the Living.

6f. Paetau, Michael. Niklas Luhmann and Cybernetics. Journal of Sociocybernetics. Vol 11 No 1/2 (2013): https://papiro.unizar.es/ojs/index.php/rc51-jos/article/view/790

6g. Afanasyeva, Vera; Avery, John; Ecimovic, Timi and Semashko, Leo (2017) Thermodynamics and Negentropy of Spheral Classes Societal Harmony: Global Peace Thermodynamics: http://peacefromharmony.org/?cat=en_c&key=756

7.Russian Census of Population 2010 (RCP-2010):

8.RCP-2010: Volume 1 Number and Distribution of the Population

9.RCP-2010: Volume 2 Age and Sex Composition and Marital Status

10.RCP-2010: Volume 6 The Size and Composition of Households

11.RCP-2010: Volume 7 The economic Active and Inactive Population

12.The National Classifier of Economic Activities OK 029-2007 (NACE Rev. 1.1). –M.: 2012

13.Sadovnichiy, Victor and Ikeda, Daisaku (2013). At the Turn of the Century. Dialogue o­n the Essentials – Moscow: Moscow University Press

Olga Kashina, Ph.D. (Statistics), Professor, State Educational University,

Graduated the Leningrad Finance and Economics Institute in 1976, specialty “Statistics.” In 1982 doctoral thesis of economic sciences o­n statistics. Teaching statistics in universities since 1977. Published more than 70 scientific works, including co-authorship of three textbooks for high schools o­n statistics. Expert of Rosstat in St. Petersburg and Leningrad region (Petrostat).

Address: St. Petersburg, Russia;

Webpage: http://peacefromharmony.org/?cat=ru_c&key=757

E-mail: olgkash2007@rambler.ru

Leo M. Semashko, Ph.D. (Philosophy), Education: Moscow State University, Philosophical Faculty, “History of Foreign Philosophy” Chair; RANH Professor; Founding President, Global Harmony Association (GHA) since 2005; GHA Honorary President since 2016; State Councilor of St. Petersburg; Philosopher, Sociologist and Peacemaker from Harmony; Author of more than 600 scientific publications, including 18 books in 1-12 languages; Author of Tetrism/Harmonism as the unity of Tetraphilosophy and Tetrasociology as third order Sociocybernetics – science of social harmony, global peace and harmonious civilization; Director: Tetrasociology Public Institute, Russia; Honorary Member of the RC51, Sociocybernetics, International Sociological Association; Director, GHA Web portal “Peace from Harmony”: http://peacefromharmony.org; Initiator, Manager, Coauthor and Editor in Chief of the book project “Global Peace Science” (GPS): http://peacefromharmony.org/docs/global-peace-science-2016.pdf.

Address: St-Petersburg, Russia; Phone: +7(812)-597-6571;

E-mail: leo.semashko@gmail.com

Personal page: http://peacefromharmony.org/?cat=en_c&key=253

28-07-18

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Global Peace Science: Structural Harmony of Spherons

and Statistics of Ghana’s Spherons for 2010, 2000 and 1984

By

Stephen Amoah and Ayo Ayoola-Amale

Abstract

This study is the application ofthe Spherons (spheral macro classes) theory of tetrasociology to analyze the 2010, 2000 and 1984 Ghana population and housing census statistics of the country taking into consideration the population spheral classes to provide insights o­n macro employment in the country. Specifically, the study investigated the population macro structure in terms of its spheral classes using spheral indices to express the spherons.

The purpose of our statistical study was to define the macro distribution of Ghana’s population based o­n its spheral classes and their dynamics in 2010, 2000 and 1984. The analysis showed a disharmonious distribution favoring the socioclass and technoclass within the overall spheral classes’ picture in comparison with the orgclass and infoclass. Of the two macro groups in the socioclass, the non-working population far outstripped the working population.

An in-depth study of the dynamics of the spheral classes of a country, allows revealing profound social contradictions that can lead to the armed conflicts. Conscious regulation therefore ensures their prevention and lead to peaceful life of the population. The questions of statistics of spherons, their dynamics and indices are new for the social sciences and at the same time very important for them, especially for the economy and for ensuring peace at all levels.

Keywords: Spherons, tetrasociology/sociocybernetics, spheral indices, autopoiesis, spheral classes

Contents

Introduction

The global search for development and peace among nations has led to the proposal of various strategies with the ultimate goal of achieving harmonious stability of humanity and every society. The Spherons discovery and the statistical study of them identified by the tetrasociology/sociocybernetic conception [11; 12; 12a; 14 etc.], which places the right emphasis o­n human security and peace, holistically, covers the entire society with all its resources: people, information, organisation and things including environment.

This understanding of human security and peace, as against the traditional concept of protection of national borders and investment in military readiness [13], is the emerging model for resolution of the conflicts that have evolved over the centuries. The line of reasoning of the Spheron’s theory is, if humanity align itself to the tetrasociology /sociocybernetic concept, humanity stands the chance to experience the peace from harmony that has eluded it earlier.

The Spherons theory promises to bring the ultimate goal of harmonious and peaceful development of humanity and every society as it focuses o­n people and provides the real macro picture of the distribution of people o­n the Spherons and social resources between them. This ensures the achievement of structural harmony in the effective decision making all partial conflicts. The study of the Spherons of Russia [12] has brought out issues for the right scientific understanding of global and national peace. In our study of Ghana’s Spherons, we seek to apply similar approach to the dynamics of Ghana’s population through its macro spheral classes and vis-à-vis the labour market.

Objective of study

To study the demographic structure of the population of Ghana in terms of its spherons or spheral classes and its dynamics in a first approximation in the three time points.

Data and Methodology

The main data sources for this study are the 1984, 2000 and 2010 Population Census of Ghana (PCG) [1] [2] [3] with emphasis o­n the 2010 census. The study involved desktop review and analysis of the census documents by applying the Spherons methodology and spheral indices to express the statistics.

Data Limitations

The period of conducting a census is of great importance in the census undertaking. The 2010 census was conducted in the months of September and October. All the other censuses in the post-independence era were carried out in the month of March. This is worthy of note because it might also affect the classifications of the economically active population as well as the type of activity in which they are engaged given seasonal differences in employment opportunities. The categories of occupation and industry in the 1960, 1970, 1984 and 2000 censuses were different from those in the 2010 census given recent revisions and the introduction of new categories. In the 2010 census for example, occupation categories such as “elementary occupation” and “craft and related trade workers” were introduced. Similarly, industry groups such as “accommodation and food service activities” and “information and communication” were introduced to reflect changes in the economy. For this reason, there are differences in the employment categories and industrial sectors between the 2010 census and the previous o­nes.

Definitions

           SPHERONS. “Spherons” or “spheral classes of the population” can be defined as the extremely large groups of people covering population in a whole and employed in four spheres or spheral categories/sectors of social production. These spherons produce in these spheres the appropriate extremely large groups/categories of social resources/products:People, Information, Organisation and Things (PIOT) and therefore they differ o­n the main (on time) employment in o­ne of the spheres with o­ne of these resources/products[11; 12; 12a].

           SPHERES OF PRODUCTION. The Spherons’ theory distinguishes four spheres of production, which are defined as:

1. Social sphere or Sociosphere (S): The subject and product of which are people (P);

2. Information sphere or Infosphere (I): The subject and product of which is information (I);

3. Organization sphere or Orgsphere (O): This sphere has organisation (O), management, politics, law, finance, defense etc. as its subject and product;

4. Technical (economic/environmental) sphere or Technoecosphere (T): The subject and product of this sphere are the things including all material goods and services (T).

           All four spheres together are referred as “SIOT”, in which are employed four Spherons “SIOT-classes”, who produce in them the “PIOT” spheral resources/products. The “PIOT” spheral resources/productsare expressed by the special “spheral indices”. The first from them are the spheral indices of the Spherons [11; 12; 12a]. The Spherons spheral indices are the main subject ofstatistical research here and in all similar studies.

             EMPLOYMENT TWO CLUSTERS. Employment/occupation, in its multidimensional topology has two principal clusters: (1) Self production or ‘autopoiesis’ [9] and (2) Labor or economic employment of people by other resources except own “I” or “Myself”.

1. Self-production or ‘autopoiesis’ is explained as employment of each person by himself from birth to death.in processes such as eating, sleeping, learning, communication, recreation etc. Autopoiesis belongs o­nly to Sociosphere because it includes all kinds of human activity except economic/labor activity. The term was introduced by Umberto Maturana and Francisco Varela (1973) [9] for theoretical biology to describe the self-reproduction of living cells. Niklas Luhmann in 1990 [8] reinterpreted the term in sociology to describe characteristics of human communication as the key element of social system.

2. Labor or economic employment of people by other resources except “I”. This is formally established within norms of labor activity of society. The labor activity is a part of self-production known as “seniority (work experience)” or “experience of labor activity”. It is part of each person’s life.

The two clusters of employment divide the entire population into two parts:

(1)Nonworking (economically inactive) population employed in Sociosphere by self-production

(2)Working or economically active population employed in all spheres.

The Spheral indices of the Spherons

            The spheral indices, which are used to express the SPHERONS, are defined as follows:

Table 1. Spheral indices of Spherons

Each spheral class is a separate SPHERON covering part of the population employed in o­ne of the four spheres of social production. In sum, the number of people of the four SPHERONS is the total population. This is expressed by the formula: P =P1 (P1n + P1w) + P2 + P3 + P4 or

Population/People= Spheron-1 (nonworking + working) + Spheron-2 + Spheron-3 + Spheron-4. This is the fundamental formula of the population/people of any community from the family, town, and country to humanity as a whole in the Spherons spheral indices.

The principal advantage and excellence of the spheral indicators is that they ensure the global digitalization of all national statistical indices, constitute their common denominator, logically organize/systematize/integrate them and open the way for the digital economy, for digital global peace through digital/conscious prevention of all wars and nonviolent resolution of all conflicts. The spheral indices aggregate the indices of traditional statistics o­n special technology that goes beyond the boundaries of our study. These questions are not discussed here, because they have been studied in detail in previous works [11; 12; 12a].

Employment and Unemployment Statistics, Ghana 2010 PHC

Ghana’s population at 2010 was 24,658,823 with a total of 5,467,136 households of which more than half (55.8%) are in urban areas. The average household size was 4.4. The Census covered three different population groups: (1) household population; (2) non-household (Group Quarters/institutional) population; and (3) Out-door sleepers/floating population. Majority of household heads (76.6%) were economically active (GSS, 2012) [3; 4; 5].

Ghana’s population has a youthful structure with a broad base consisting of large numbers of children and a conical top of a small number of elderly persons. The 2010 Census indicated the population under 15 years constituted 38.5%. Population 15-24 years constituted 20.0%, population 25-59, constituted 35% and population 60+ years, 6.7 percent.

The three most common occupations that household heads engaged in were: agriculture, forestry and fishery; services and sales; and craft and related trades. Agriculture is more popular in rural localities for both male and the female household heads. The service and sales sector is more popular among urban household heads. Most household heads are self-employed in the private informal sector of the economy, with a minority in the private formal and public sectors as employees.

The statistics o­n employment and unemployment o­n Ghana’s population was mainly obtained from the 2010 population and housing census conducted by the country. Definition of employment and unemployment was based o­n ILO regulations o­n labor statistics. The age for employment used in the Census was 15-65 years.

The 2010 PHC defined employed population as comprising of the following:

•The population that worked

•The population that did not work but had job to go back.

•The population that did voluntary work without pay.

The unemployed population was defined as comprising of:

•The population that worked before seeking work and available for work.

•The population seeking work for the first time and available for work [3; 4; 5].

Table 2. Employed and unemployed population by sex, 2010 PHC

Classification by branches of economic activity and categories of personnel.

          
             Definitions and Concepts. Economic Activity

The question o­n economic activity was asked of all persons five years and above who engaged in any activity for pay (cash or kind) or profit or family gain for at least o­ne hour during the seven days preceding census night.

Economically active population

           The economically active population was defined as persons who:

·Worked for pay or profit or family gain for at least o­ne hour within the seven days preceding census night. This category of persons included those who were in paid employment or self-employment or contributing family workers;

·Did not work, but had job to return to;

·Were unemployed and actively looking for work.

Economically not active population

          The economically not active persons were those who did not work and were not seeking for work. The category included homemakers, full time students, retired persons, the disabled and persons who were unable to work because of their age or ill-health.

Labour force

         The labour force comprised employed persons who had jobs or businesses and the unemployed. The term economically active population by the census definition was the same as the labour force. Therefore, these terms are used interchangeably in the chapter as appropriate.

Workforce (the employed)

          Workforce represents the total number of workers actively employed, or available for work. It consisted of all persons aged 15 years and above, who during the reference period, were in the following categories:

·At work, that is, persons who during the reference period performed some work for wage or salary either in cash or in kind or worked without pay.

·Had a job to go back to, but did not work within the reference period.

The Unemployed

          This comprises all persons aged 15 years and above, who during the reference period were:

·Without work and had no fixed jobs;

·Currently available for work;

·Seeking for work by taking specific steps to look for work (e.g. writing applications, visiting job sites, visiting employment agencies and seeking help from friends and relatives in the search for jobs

Classification of economic activities

        
           The classification of economic activities in the 2010 PHC was done using the International standard classification of All Economic Activities (ISIC) revision 4 [8]. The ISIC rev 4 is the international reference classification of productive activities. Its main purpose is to provide a set of activity categories that can be utilized for the collection and reporting of statistics according to such activities.

Table 3: Economic Activities

         The 2010 PHC used the International Standard Classification of Occupation (ISCO-08) as the basis for the national occupation classification. The ISCO-08 divides occupations into 10 major groups with each major group further organized into sub-major, minor and unit groups. The skill level and specialization required to competently perform the duties of the occupation are the basic criteria use to define the system. The major groups are as follows:

Table 4.National Occupation Classification

          The above categories of staff were grouped into the four spheral classes.

(1)Heads of organization and their structural units (services) constituted the Orgclass (P3)

(2)Specialists, and those with higher and mid-level qualificationsand engaged learning and health care personnel constitute the Socioclass (P1w)

(3)Specialists and employees involved in the preparation of information, documentation and accounting make up the Infoclass (P2).

(4)Other categories of staff, except heads and employees involved in the preparation of information, documentation, accounting and service constitute the Technoclass (P4)

Determining the number Spherons based o­n Statistics

         In determining the number of spherons of the employed, the statistical information of both the employed (economically active population) and the unemployed (economically inactive) from the 2010 PHC was applied as follows.

Results

Table 5. Personnel Categories

The mid-year population of Ghana for 2010

         
            The mid –year population of Ghana (24,512,104) was computed using the component method. The estimate used the census definition of people who are “usually resident” in Ghana (Those present at the census night). The estimates roll forward the population found by the previous census (2000), o­ne year at a time by accounting for births and deaths. Net migration was assumed to be zero.

 Number of non-working population (P1n) in Ghana, 2010

          
            The economically not active persons were those who did not work and were not seeking for work. The category included homemakers, full time students, retired persons, the disabled and persons who were unable to work because of their age or ill-health.

Table 6. Employed self-production (non-working, economic inactive).

           Extracted from Ghana 2010 PHC Report (www.statsghana.gov.gh)

SPHERONS in Ghana, 2010

Table 7. Number of SPHERONS of Population in Ghana for 2010

         Source: Ghana 2010 PHC Report

SPHERONS in Ghana, 1984 and 2000

Table 8. SPHERONS of Ghana’s population for the 1984 and 2000.

Conclusion and Discussion

           Generally, the larger the employed population, the more wealth is created leading to the general well-being of the population. In Ghana, over the years, a number of job opportunities have been created in the public and private sectors in a wide range of economic activities. Contributing to employment opportunities in any country or area are factors such as the structure of the population, proportion of the economically active population and the labour market.

        Our research of the Ghana’s spherons identified the four production spherons of Ghana and the employment status of its citizens in each spheral class. The study shows that, at 2010, the population size of Ghana’s Socioclass (Spheron-1, P1) constituted the highest proportion (62.5%) of the employed in all four spheres. This was followed by the Technoclass (Spheron-4, P4) employing 36.0%, the Orgclass (Spheron-3, P3) with 0.9% and the Infoclass (Spheron-2, P2) with 0.6% in that order.

           Table 6 shows the employed by self-production, "autopoiesis", in accordance with the theory of Maturana [9] and its sociological interpretation of Luhmann [8]. This is a non-working, economically inactive group, but it is very important socially, preparing a human resource for economic activity or providing its exit from it by age or health.The highest proportion in this group is made up of full time persons in education / students (45.0%), followed by persons considered as too old or too young (40.8%) and those who did home duties household chores/full time homeworker (9.1%). The disabled/too sick to work, pensioners/retired and other constituted 1.5%, 1.0% and 2.7% % respectively.

            Table 7 shows the distribution and size of the spherons as determined by the employed statistics of the 2010 PHC and the spheral indices. Overall, the employed population (P) in Ghana at 2010 was 24,658,823. The Socioclass (P1), made up of working (P1w) and non-working (P1n) employed the highest number of people (62.5%). This figure is made up of 4.1 % (P1w) economically active (working) population and 58.0 % (P1n) economically in-active or non-working population. This situation signifies a very unhealthy situation for the country. The table further shows the infoclass (P2) employed 0.6%, the Orgclass (P3) employed 0.9% and the Technoclass (P4) 36.0%.

           Table 8 also shows the distribution of Spherons in Ghana in 1984 and 2000. The table shows that in 2000 about 7 in 10 persons (71.7%) of the employed population were in the Socioclass (P1). Over the period (1984 to 2000), this Spheron (P1) employed the largest proportion of the population growing from 9,653,493 in 1984 to 13,566,411 in 2000, an increase of about 40 % in a period of 16 years. The non-working population (P1n) also increased from 7,225,381 to 11,235,504 within the period unlike the working in this sphere population (P1w), which dropped from 2,428,112 to 2,330,907. The overall increase in the socioclass (P1), however, favoured (P1n), the non-working class which increased by 56% while the working class (P1w), dropped by 4 % within the period.

          Table 8 again shows the Infoclass (P2) recorded an increase in numbers over the period 1984 to 2000 almost three times (796,375 to 2,223,541). At 2000 the Infoclass t constituted 11.8 % of the all employed population.

          The Orgclass (P3) also recorded an increase in numbers from 121,292 in 1984 to 150,150 in 2000. At 2000 this Spheron constituted 0.8 % of the employed population. The Technoclass (P4), recorded an increase in numbers within the period and formed 15.7% of the population in 2000. This is the second highest after the Socioclass Spheron.

          In sum, there is a steady increase in the population of Ghana, mainly due to the growth of the socioclass/spheron-1. In it, the increase in the number of non-working population (P1n) in comparison with the working population (P1w) in this and other spheres has been worrying for many years. This situation puts pressure o­n the working population, increasing, o­n the o­ne hand, the workload o­n it, and o­n the other hand, reducing the opportunities of well-being for the non-working. Disproportions and disharmony in the dynamics of the Spherons’ number exacerbate the whole spectrum of social risks from economic and political to environmental and demographic.

         With the exception of an increase in employment in the technoclass (36.0%) for 2010 compared to 15.7% in 2000, there were declines in employment in the three other spherons (Infoclass, Orgclass and Socioclass) for 2010 compared to 2000. The growth in the number of technoclass in 2010 loses growth in employment in other spheres in 2010. This is a clear case of the spheral disharmony in the holistic structural system of Ghanaian spherons. This problem deserves the most intensive scientific research and close attention of the country's leadership.

An in-depth research of Ghana’s spherons will provide fundamental information that is necessary to achieve an optimal balance in the structural harmony of Ghanaian spherons and spheres of social production. Achieving spheral balance and harmony will ensure the creation and harmonization of the necessary strategies for overcoming poverty, corruption and inequality. This will bring lasting peace and prosperity to the country.

Of course, we understand that our first statistical research of Ghana’s spherons is o­nly the first swallow and the initial step o­n the long journey of innovative statistical digital studies of Ghana's social production as a whole. o­nly its holistic spheral research using spheral statistics and its universal digital technology (SIST [12]) will provide the country's leadership with a large-scale monitoring of the dynamics of all spheral imbalances and disproportions in the country. This will allow developing an effective long-term economic, political, demographic and social policy that prevents crises and conflicts in the framework of ensuring and strengthening a lasting sustainable peace from the structural harmony of Ghana’s spherons. To this end, the country needs priority investment and the development of appropriate scientific innovations and spheral statistical digital technologies.

The same conclusion is for global peace. If such researches and their practical peacemaking consequences are obvious for separate countries, then they are adequate for other countries and for the gradual provision of global peace and prosperity of all nations o­n the basis of scientific knowledge and governance of the spherons’ structural harmony that generate them.

References:

1.Ghana Statistical Service.GSS (1986). 1984, Population Census of Ghana, (The gazette 2).

2.Ghana Statistical Service.GSS (2004). 2000 Population Census of Ghana (The gazette 3).

3.Ghana Statistical Service. GSS (2012). 2010 Population and Housing Census Summary Report of final Results.

4.Ghana Statistical Service. GSS (2012). 2010 Population and Housing Census Economic Activities in Ghana.

5.Ghana Statistical Service. GSS (2013) 2010 Population and Housing Census National Analytical Report (2013).

6.Ghana Statistical Service. GSS (2016). 2015 Labour force report.

http://www.statsghana.gov.gh

7.International Standard Industrial Classification of all Economic Activities (ISIC), Rev.4. United Nations publication. ISBN: 987-92-1-161518-0.

8.Paetau Michael (2013) Niklas Luhmann and Cybernetics. Journal of Sociocybernetics. Vol 11 No 1/2 (2013): https://papiro.unizar.es/ojs/index.php/rc51-jos/article/view/790

9.Maturana, Humberto & Varela, Francisco ([1st edition 1973] 1980). Autopoiesis and Cognition: the Realization of the Living.

10.Regions of Russia. Socio-economic Indicators 2013. Statistical Collection. M: 2013

11.Semashko, Leo (2002) Tetrasociology: Responses to Challenges. –St Petersburg: http://peacefromharmony.org/?cat=en_c&key=145

12.Kashina Olga and Semashko Leo (2014) Statistics of SPHERONS of Russia for 2010: Methodology, Sources and Number: http://peacefromharmony.org/?cat=en_c&key=593

12a. Semashko Leo and 173 coauthors from 34 countries (2016) Global Peace Science (pages: 51-60): http://peacefromharmony.org/docs/global-peace-science-2016.pdf

13.Takemi, K., Jimba, M., Ishii, S., Katsuma, Y., & Nakamura, Y. (2008). Human security approach for global health. The Lancet, 372(9632), 13-14. doi: 10.1016/s0140-6736(08)60971-x

14.Hornung Bernd, Scott Bernard, Semashko Leo (2003) Tetrasociology and sociocybernetics: Towards a comparison of the key concepts:

http://www.peacefromharmony.org/?cat=en_c&key=160

Stephen Amoah, MSc Statistics, PhD Applied Statistics

Ghana Statistical Service,

Chief Statistician,

Home address: Accra, Ghana

Email: stephen.amoah@statsghana.gov.gh

Web:http://peacefromharmony.org/?cat=en_c&key=837

Ayo Ayoola-Amale, J.D.

GHA-Africa President,

GHA Int. Vice-President,

Womens' GHA Co-Chair,

Ghana University Professor,

Nigeria & Ghana, Accra

Email: aayoamale@gmail.com

Web: http://peacefromharmony.org/?cat=en_c&key=524

30-07-18