Modeling gender evolution and gap in science and technology using ecological dynamics

https://doi.org/10.1016/j.eswa.2012.12.056Get rights and content

Abstract

In this paper a model based on population biology is proposed in order to investigate the evolution of human resources (men and women) in science and technology as a share of labor market as well as the dynamics of their gap. An analytical and a simulation method using the Artificial Bee Colony optimization algorithm are described and used for the determination of the proposed model parameters. The presented model is applied to three case studies; Greece, Portugal and Europe-27.3 The accuracy of the obtained results is confirmed through comparison with actual data. In addition, the model can also be used to accurately forecast future trends. It is illustrated that the gender gap is continuously decreasing, while in the last years, women seem to outperform men in the field of science and technology. The estimation and forecasting ability of the model can be used as an extremely valuable tool for decision and policy makers.

Highlights

► Modeling the gender relationship, gap and HR evolution in science and technology. ► The model is based on analytical and simulation methods of population biology. ► The accuracy of the obtained results and forecasting is confirmed using real European data. ► Gender gap is continuously decreasing and tends to vanish. ► The proposed model is of great importance for decision and policy makers.

Introduction

In the last decades, an unrelieved evolution of technologies is observed. Societies are experiencing new and rapidly changing scientific and technological (S&T) achievements. It is a common belief that the progress in science and technology is usually an indicator of economic growth, environmental well-being and social development. However, a link between the S&T evolution and the socio-economic development is required. The main candidate able to fill in the gap between these activities is the human resources of the specific field (Chou et al., 2011, Kefela, 2010). A workforce with lifelong learning (updated skills) seems to be the key ingredient for the adoption of the rapid S&T changes as well as the development and diffusion of knowledge.

Recently, there has been an increased attention for qualitative and quantitative investigation of human resources in science and technology (Chou et al., 2012, Davó et al., 2011). Statistical information regarding S&T human resources is of great importance and high interest for several different parties from industry, government and public sector to academics. These data are useful in determining the current status and monitoring workforce’s evolution.

Special interest has also been paid for the investigation of gender gap in science and technology. As described in the following section, several studies have been conducted regarding the inequalities in education and/or the occupation of the two genders in the field of S&T. It has been shown that although the differences between males and females are marginal in younger ages, there are obvious discrepancies in older population with males outperforming females.

Although human resources in science and technology (HRST) are extremely significant, their study was incorporated for several years in research and development (R&D) related analyses, creating thus a lack of systematic mechanisms capable for in depth examination and/or future tracking of HRST based issues. It has then become evident that more effort should be made to gather and process data enhancing HSRT knowledge. This endeavor can be exemplified by the numerous programs supported by the OECD and the European Commission. Milestone to this process was the Canberra manual proposed and developed by the organization for economic co-operation and development in 1995 (Nanopoulos, Sirilli, & Tanaka, 1995).

Canberra Manual incorporates the best national and international practice as a result of a wide inventory along with the use of the main standard international classifications. In fact, it provides a framework for processing HRST data, investigating trends and preparing up-to-date series for intended users aiming in the harmonization of data and the use of HRST indicators.

In this work and in contrary to the majority of previous studies that are limited to statistically analyze and forecast human resources in science and technology as a whole or the evolution of each gender separately, the evolution of “population shares” in S&T as well as gender interactions are modeled, investigated and forecasted using the evolutionary theory of population biology and dynamics. In detail, the proposed model is based on Lotka–Volterra model describing the competition between species (Begon et al., 2006, Murray, 2007). Lotka–Volterra model is a widely used model especially in biology.

However, it has also been applied in several other areas, besides biology, providing precise estimates of the dynamics under consideration (Foryś, 2009, Lee et al., 2005, Ying and Shi, 2008). A typical example is telecommunications market where the L–V model is used to examine providers’ competitive behavior-market share (Kim et al., 2006, Lopez and Sanjuan, 2001, Michalakelis et al., 2012). The results obtained by the model can be supportive to other already used techniques providing a comparison reference confirming their results.

The proposed model was applied in the case of two European countries; Greece and Portugal as well as in the case of Europe-27. It was shown that the model gives very good interpolation of the statistical data providing at the same time an accurate forecasting (It was compared to actual data of 2011) using the parameters obtained from both the analytical and simulation methods. The results showed that in the case of Greece and Portugal, human resources of the two genders in S&T as a share of active population will continue to increase in the following years tending to a steady-state. On the other hand human resources in the case of Europe-27 revealed a decreasing oscillatory behavior, possibly due to the contribution of countries with different characteristics, leading again to a steady-state.

Good performance of the proposed methodology would result in a twofold contribution. On the one hand, it would provide an alternative analysis and interpretation method of HRST data as well as of the interaction of the two genders in this area. On the other hand, it would act as valuable tool for policy and decision makers saving money from expensive and frequently unnecessary training of S&T skills.

This paper is organized as follows. At first a literature review is introduced regarding gender inequalities in HRST. Then the proposed model describing the relationship and the evolution of the two genders regarding human resources in science and technology is presented. Subsequently the solution procedure of the set of the nonlinear differential equations is described and details are given for the analytical method for the determination of model coefficients is given and the simulation method based on Artificial Bee Colony optimization algorithm. The linearization of the nonlinear problem as well as a closed form formula for the evolution of males and females in S&T is derived the following subsection. The results obtained by the application of the described model along with the two solving methods are finally presented and discussed before the concluding remarks.

Section snippets

Literature review and definition

Gender inequality has existed since the ancient times. Women were not involved in paid labor (Rossi, 1988) until 1830. Women began to participate in labor after the first industrial revolution that generated a great need for manpower (Ruskin, 2002). However, both technological revolutions brought up once again gender inequality issues.

The European Union (EU) since its establishment anticipated these gender inequality issues and therefore took steps towards equal rights between men and women.

Proposed model and population dynamics

According to population biology, expressing the growth or decline of the population of a given species can be achieved by the rate of its change proportional to its current size. The simplest approach implies absence of any competitors (i = 1) and was given in Boyce and DiPrima (2008):dyi(t)dt=r1-yi(t)kyi(t)where yi(t) is the size of the given species population at time t, constant r is called the intrinsic growth rate and presents the growth rate in absence of any limiting factors and k is the

Methodology and solution procedure

In order to model the evolution of the number of women and men in science and technology as a share of the labor force, one should solve the system of differential equations shown in (2). However, the first step towards this process is to estimate the unknown coefficients aij using actual statistical data.

Results and discussion

In this section the proposed model is applied in order to describe the gender evolution and gap of HRST in the case of two European countries; Greece and Portugal as well as the European-27. These two countries were chosen as their common socio-economic characteristics may facilitate the conduction of common or similar conclusions. Furthermore Europe’s-27 results may provide an indicator of European trends in a very attractive sector.

Calculations were performed on annual data describing human

Conclusions

A model to describe the interactive evolution of men and women in science and technology as a share of labor force was proposed in this work. The model was based on evolutionary ecology while the solution of the described system of differential equations was derived via an analytical method and a simulation optimization method based on ABC algorithm that proved to be equally accurate. The results obtained by solving the presented model in three cases, Greece, Portugal and Europe-27 revealed its

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