Population Density and Human Fertility

Total Page:16

File Type:pdf, Size:1020Kb

Population Density and Human Fertility Population Density and Fertility Maria Rita Testa Ecological factors, such as population density, which are prominent in animal ecology, have played only a marginal role in the demographic analysis of human fertility. This is because human reproduction is primarily seen as socially determined, especially under the condition of conscious family planning during the late phases of the demographic transition. However, population density may be an important psychological determinant of fertility, if it is considered as perceived living space and not as a factor operating only through direct biological mechanisms. A recent analysis on determinants of human population growth has shown that population density -- beyond the well-studied social and economic determinants -- may be a significant factor for the levels and trends of human birth rates (Lutz and Qiang, 2002). The authors found out a significant negative relationship between population density and fertility on a world scale. The negative correlation does hold even after controlling for several background variables, which may play a confounding effect, such as female labor force participation rate, female literacy, urban population, GDP per capita, and food production index. The results are interpreted with the hypothesis of possible psychological reactions of people to perceived increasing population density. In our paper we study the association between population density and fertility on national scale, by looking at one developing country, Indonesia, and one developed country, Italy. In each of these two countries we implement a territorial analysis by using data disaggregated at provincial level. The aim is to better understand the effects of population density on human fertility, and particularly, the possible mechanisms of causation. In Indonesia population density is found to be significantly and inversely correlated with fertility levels, the Pearson correlation coefficient is equal to - .69 (see graph below). Such a relationship appears even stronger once is controlled for other variables, such as infant mortality, sex ratio, immigration level, population growth and size. Indeed, the partial correlation of Total Fertility Rate with population density, holding all the variables mentioned above constant, is - .73. The regression analysis implemented confirms the population density as an important determinant of fertility rates in Indonesia. This analysis will be repeated with the inclusion of other key socioeconomic indicators, such as urban population and female literacy, which might influence simultaneously fertility and population density playing a role of possible confounding. Relationship between Population Density and Fertility All Indonesian Provinces NusaTenTimur Nu s a Te n B ar a t 3 Sum.Utara Papua Sul.Tenggara Sum.Barat 8 M a l u k u Ka l .T eng ah Kal.Barat J a m bi 2. R i a u B e n g k u l u 1999 Sul.Tengah n Sum.Selatan Nanggroe i L a m p u ng e Sul.Selatan t 6 a 2. Kal.Timur JawaBarat R Kal.Selatan y t i l i t 4 r e 2. Sul.Utara JawaTengah F l a t o 2 T 2. JawaTimur 2 B a l i DI Yogyakarta D.Jakarta 2 4 6 8 10 Population Density in 1995 (LOG.) Source: Authors' elaborations based on Indonesian Statistics In Italy the correlation between population density and fertility is weak and no longer significant, but before we jump to any conclusion we need to repeat the same analysis controlling for some background variables, which may affect the results. If the interpretation of psychological reactions to the perceived living space is corrected, we should expect to observe an impact of population density not only on actual childbearing, but also on ideal fertility. Therefore, the relationship between population density and desired fertility is investigated by looking at the regions of the European Union Countries and using Eurobarometer and EUROSTAT data. A significant negative correlation is found between population density and ideal fertility in the regions of the European Union: the higher the density, the lower the mean ideal family size (see graph in the next page). The Pearson correlation coefficient is equal to -.43 and is highly significant. The relationship is still significant also when the average fertility ideals of women in the main reproductive ages are considered, instead of the mean fertility ideals of all people aged 15 or more. We will look more in-depth at this relation in a multivariate analysis, in order to verify whether population density is one of the most important factors associated with differences and trends in fertility ideals. Relationship between Ideal Family Size and Population Density All Regions of the European Union countries 5 3. SoutheIRL NortheIRL DonegIRL e 3 z i WestIRL SouthGRE S Mi d IRSoL uthwIRL SardITA y PohjoisFIN NorthwIRL l EastIRL i CampanITA Ce n te r GR E No rt h GB SoutheGB m AbruzITA VäliFIN No rt h GRSoE uthe FRA 5 a SveaSWEMi d e I RL BassinFRA SoWeutshNotSPAFRrthANoIRLrthPOR No rS W E ItäFI N EastFSoRAutSSohicuilIthTwAGB BrussBEL 2. EtelaFIN AngliaGB F GötaSWJyEl landDENNortheITA NorthwGB SouthPOR FyWanDleEsNGB CopenDEN SoutCehwntFWeeRrPAOstRLUX StockhSWE NoScrthoLtGUXB SjaellNoandrtDh EN NE T IleFRA NorthSPAMeUpd iptFeRrAAEaUSsCatSnPaAriaEaSLoPsWetAmNSoIEsTtTmAutGhNBET TirolfAUS CeUunsteimrITaFAIN CenterLUX deal CenterSPA YorkBG Mad ri d S P A NortheSPA LiSoLasCabuzoatilhoeILPFTRUOAXAR WestNET I WalloB EL StyrAUS BadenWVlaaGEndBEL 2 MeLocwkel rebEAGUES ScSahlelarWGEE ean CarysAUS ThuringEGE EmilITA WestfWGE M BranderEGENo rt hwITA S_AnBahayeltrESnGWaEchGEsenEGE HessenWGE 5 Vienna AUS 1. Berlin 2 4 6 8 10 Population Density (LOG) Data Source: Eurobarometer 2001 and Eurostat 2001 NOTE: **94 EU regions - whole EB sample Finally, several possible mechanisms of causation are discussed in the paper: (a) direct biological factors, such as decreasing fecundability due to “density stress”, that seem rather unlikely candidates for the human population, especially in a technologically advanced stage of development; (b) psychological factors, which receive a support from the so called “island factor” in the onset of fertility decline, i.e. the fact that in otherwise comparable socioeconomic settings, small islands -- where the spatial limitations are obvious – began their fertility transition earlier. With respect to the contemporary European fertility levels, it is conspicuous that the very- low-density regions of Northern Scandinavia have significantly higher fertility than the high-density areas of Central and Southern Europe. The analysis is of particular relevance for the assumptions of long-term fertility levels. Ecological factors, which will probably be very important for the future of human life span and health, may also play an increasingly important role with respect to human fertility, as evidenced in our results. Basic References: Lutz Wolfgang and Qiang Ren. 2002. Determinants of Human Population Growth. The Royal Society. 357, pp.1197-1210. .
Recommended publications
  • Globalization and Infectious Diseases: a Review of the Linkages
    TDR/STR/SEB/ST/04.2 SPECIAL TOPICS NO.3 Globalization and infectious diseases: A review of the linkages Social, Economic and Behavioural (SEB) Research UNICEF/UNDP/World Bank/WHO Special Programme for Research & Training in Tropical Diseases (TDR) The "Special Topics in Social, Economic and Behavioural (SEB) Research" series are peer-reviewed publications commissioned by the TDR Steering Committee for Social, Economic and Behavioural Research. For further information please contact: Dr Johannes Sommerfeld Manager Steering Committee for Social, Economic and Behavioural Research (SEB) UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR) World Health Organization 20, Avenue Appia CH-1211 Geneva 27 Switzerland E-mail: [email protected] TDR/STR/SEB/ST/04.2 Globalization and infectious diseases: A review of the linkages Lance Saker,1 MSc MRCP Kelley Lee,1 MPA, MA, D.Phil. Barbara Cannito,1 MSc Anna Gilmore,2 MBBS, DTM&H, MSc, MFPHM Diarmid Campbell-Lendrum,1 D.Phil. 1 Centre on Global Change and Health London School of Hygiene & Tropical Medicine Keppel Street, London WC1E 7HT, UK 2 European Centre on Health of Societies in Transition (ECOHOST) London School of Hygiene & Tropical Medicine Keppel Street, London WC1E 7HT, UK TDR/STR/SEB/ST/04.2 Copyright © World Health Organization on behalf of the Special Programme for Research and Training in Tropical Diseases 2004 All rights reserved. The use of content from this health information product for all non-commercial education, training and information purposes is encouraged, including translation, quotation and reproduction, in any medium, but the content must not be changed and full acknowledgement of the source must be clearly stated.
    [Show full text]
  • Litter Pollution in Densely Versus Sparsely Populated Areas: Dog River Watershed
    LITTER POLLUTION IN DENSELY VERSUS SPARSELY POPULATED AREAS: DOG RIVER WATERSHED Gabrielle M. Hudson, Department of Earth Sciences, University of South Alabama, Mobile, AL 36688. E-mail: [email protected]. It is commonly known that when humans populate an area that area is usually subject to some environmental degradation. One of the more common aspects of environmental degradation is litter. This type of degradation is no stranger to the Dog River watershed. For years residents have seen the rivers in this watershed covered in trash, specifically after rain events. The vast majority of the trash is a result of litter from roadsides being carried into the river via drainage pipes. This paper is a comparative study of litter in areas of varying population densities in the Dog River watershed. It seeks to distinguish between the amount of litter found in densely populated areas and sparsely populated areas, and to find out if there is a correlation between population density and litter. I utilize GIS to map population density of the Dog River watershed, and analyze and compare the amounts of litter in areas of sparse and dense populations. The results show that there is no correlation between population density and litter. It also shows that there is no difference in the amounts of litter found in densely and sparsely populated areas. Keyword: litter, population density, watershed Introduction Pollution has long been an issue in the Dog River watershed, in particular litter pollution. The extent of the pollution has not gone unnoticed. There are groups of people and organizations who have taken increased interest in the Dog River watershed with intentions of reducing pollution, including Dog River Clearwater Revival and its numerous volunteers.
    [Show full text]
  • A Non-Linear Threshold Estimation of Density Effects
    sustainability Article Population Dynamics and Agglomeration Factors: A Non-Linear Threshold Estimation of Density Effects Mariateresa Ciommi 1, Gianluca Egidi 2, Rosanna Salvia 3 , Sirio Cividino 4, Kostas Rontos 5 and Luca Salvati 6,7,* 1 Department of Economic and Social Science, Polytechnic University of Marche, Piazza Martelli 8, I-60121 Ancona, Italy; [email protected] 2 Department of Agricultural and Forestry Sciences (DAFNE), Tuscia University, Via San Camillo de Lellis, I-01100 Viterbo, Italy; [email protected] 3 Department of Mathematics, Computer Science and Economics, University of Basilicata, Viale dell’Ateneo Lucano, I-85100 Potenza, Italy; [email protected] 4 Department of Agriculture, University of Udine, Via del Cotonificio 114, I-33100 Udine, Italy; [email protected] 5 Department of Sociology, School of Social Sciences, University of the Aegean, EL-81100 Mytilene, Greece; [email protected] 6 Department of Economics and Law, University of Macerata, Via Armaroli 43, I-62100 Macerata, Italy 7 Global Change Research Institute of the Czech Academy of Sciences, Lipová 9, CZ-37005 Ceskˇ é Budˇejovice, Czech Republic * Correspondence: [email protected] or [email protected] Received: 14 February 2020; Accepted: 11 March 2020; Published: 13 March 2020 Abstract: Although Southern Europe is relatively homogeneous in terms of settlement characteristics and urban dynamics, spatial heterogeneity in its population distribution is still high, and differences across regions outline specific demographic patterns that require in-depth investigation. In such contexts, density-dependent mechanisms of population growth are a key factor regulating socio-demographic dynamics at various spatial levels. Results of a spatio-temporal analysis of the distribution of the resident population in Greece contributes to identifying latent (density-dependent) processes of metropolitan growth over a sufficiently long time interval (1961-2011).
    [Show full text]
  • Global Typology of Urban Energy Use and Potentials for An
    Global typology of urban energy use and potentials for SPECIAL FEATURE an urbanization mitigation wedge Felix Creutziga,b,1, Giovanni Baiocchic, Robert Bierkandtd, Peter-Paul Pichlerd, and Karen C. Setoe aMercator Research Institute on Global Commons and Climate Change, 10829 Berlin, Germany; bTechnische Universtität Berlin, 10623 Berlin, Germany; cDepartment of Geographical Sciences, University of Maryland, College Park, MD 20742; dPotsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; and eYale School of Forestry & Environmental Studies, New Haven, CT 06511 Edited by Sangwon Suh, University of California, Santa Barbara, CA, and accepted by the Editorial Board December 4, 2014 (received for review August 17, 2013) The aggregate potential for urban mitigation of global climate (9, 10), and also with GHG emissions from the residential change is insufficiently understood. Our analysis, using a dataset sector (11). The recent IPCC report identifies urban form of 274 cities representing all city sizes and regions worldwide, as a driver of urban emissions but does not provide guidance on demonstrates that economic activity, transport costs, geographic its relative importance vis-à-vis other factors. In comparative factors, and urban form explain 37% of urban direct energy use studies, cities are often sampled from similar geographies (5, 12, and 88% of urban transport energy use. If current trends in urban 13) or population sizes (8). In these studies, causality is difficult expansion continue, urban energy use will increase more than to establish, and self-selection (14) as well as topological prop- threefold, from 240 EJ in 2005 to 730 EJ in 2050. Our model shows erties and specific urban form characteristics (15) partially ex- that urban planning and transport policies can limit the future plain the relationship between urban population density and increase in urban energy use to 540 EJ in 2050 and contribute to transport energy consumption.
    [Show full text]
  • Handout: Ecological Footprints from Around the World
    Ecological Footprints From Around the World Handout Map Your Eco-Footprint lesson plan support material Green Star! Newsletter May/June 2006 Handout: Ecological Footprints From Around the World (Adapted from: “How big is your footprint,” Energy for a Sustainable Future — Education Project, www.esfep.org/ ) Ecological Footprints From Around the World: Where Do You Fit In? How Much Land Do You Need to Live? If you had to provide everything you use from your own land — how much land area would you need? This land would have to provide you with all of your food, water, energy and everything else that you use. The amount of land you would need to support your lifestyle is called your Ecological Footprint. The ecological footprint is one way of measuring the impact a person has on the environment. Is the World Big Enough for All of Our BIG Feet? The size of a person’s Ecological Footprint will depend on many factors. Do you grow your own food? Do you walk or drive? Do you use renewable or non-renewable energy sources? Everyone has an ecological footprint because we all need to use the earth’s resources to survive. But we must make sure we don’t take more resources than the earth can provide. Different people in the same country will have different sized ecological footprints. Different countries also have different ecological footprints. For example, a person with the average Canadian lifestyle has an ecological footprint of 8.56 hectares. A person living in Ethiopia, Africa, has an average ecological footprint of 0.67 hectares.
    [Show full text]
  • The Socio-Spatial Determinants of COVID-19 Diffusion
    Sigler et al. Globalization and Health (2021) 17:56 https://doi.org/10.1186/s12992-021-00707-2 RESEARCH Open Access The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population Thomas Sigler* , Sirat Mahmuda, Anthony Kimpton, Julia Loginova, Pia Wohland, Elin Charles-Edwards and Jonathan Corcoran Abstract Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results: The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion.
    [Show full text]
  • Globalization and Outbreak of COVID-19: an Empirical Analysis Mohammad Reza Farzanegan, Mehdi Feizi, Hassan F
    8315 2020 May 2020 Globalization and Outbreak of COVID-19: An Empirical Analysis Mohammad Reza Farzanegan, Mehdi Feizi, Hassan F. Gholipour Impressum: CESifo Working Papers ISSN 2364-1428 (electronic version) Publisher and distributor: Munich Society for the Promotion of Economic Research - CESifo GmbH The international platform of Ludwigs-Maximilians University’s Center for Economic Studies and the ifo Institute Poschingerstr. 5, 81679 Munich, Germany Telephone +49 (0)89 2180-2740, Telefax +49 (0)89 2180-17845, email [email protected] Editor: Clemens Fuest https://www.cesifo.org/en/wp An electronic version of the paper may be downloaded · from the SSRN website: www.SSRN.com · from the RePEc website: www.RePEc.org · from the CESifo website: https://www.cesifo.org/en/wp CESifo Working Paper No. 8315 Globalization and Outbreak of COVID-19: An Empirical Analysis Abstract The purpose of this study is to examine the relationship between globalization, Coronavirus Disease 2019 (COVID-19) cases, and associated deaths in more than 100 countries. Our ordinary least squares multivariate regressions show that countries with higher levels of socio-economic globalization are exposed more to COVID-19 outbreak. Nevertheless, globalization cannot explain cross-country differences in COVID-19 confirmed deaths. The fatalities of coronavirus are mostly explained by cross-country variation in health infrastructures (e.g., share of out of pocket spending on health per capita and the number of hospital beds) and demographic structure (e.g., share of population beyond 65 years old in total population) of countries. Our least squares results are robust to controlling outliers and regional dummies. This finding provides the first empirical insight on the robust determinants of COVID-19 outbreak and its human costs across countries.
    [Show full text]
  • Chapter 2.Pmd
    Unit-II The people of a country are its real wealth. It is they, who are the actual resources and make Chapter-2 use of the country’s other resources and decide its policies. Ultimately a country is known by its people. It is important to know how many women and men a country has, how many children are born each year, how many people die and how? Whether they live in cities or villages, can they read or write and what work do they do? These are what you will study about in this unit. The world at the beginning of 21st century recorded the presence of over 6 billion population. We shall discuss the patterns of their distribution and density here. The World Population Why do people prefer to live in certain Distribution, Density and regions and not in others? The population of the world is unevenly Growth distributed. The remark of George B. Cressey about the population of Asia that “Asia has many places where people are few and few place where people are very many” is true about the pattern of population distribution of the world also. PATTERNS OF POPULATION DISTRIBUTION IN THE WORLD Patterns of population distribution and density help us to understand the demographic characteristics of any area. The term population distribution refers to the way people are spaced over the earth’s surface. Broadly, 90 per cent of the world population lives in about 10 per cent of its land area. The 10 most populous countries of the world contribute about 60 per cent of the world’s population.
    [Show full text]
  • FW 662 – Density-Dependent Population Models in the Previous
    FW 662 – Density-dependent population models In the previous lecture we considered density independent population models that assumed that birth and death rates were constant and not a function of population size. Long-term density independent population growth is unlikely and an unrealistic assumption. Birth and death rates are more likely a function of population density or abundance. Density Dependence births are a decreasing function of density b(N) and deaths are an increasing function of density d(N). births deaths d b or N This results in population growth being a declining function of N ) N ( th (f w o r G on ti a l pu o P N Hence population growth will be zero at some population size. This point is usually referred to as K (or carrying capacity) but let’s develop the model first considering the explicit functions for birth and death. The approach is described in Donovan and Weldon (2002) but I have modified it to match the notation in Gotelli (1998). FW 662 – Density-dependent population models We need two new terms to account for changes in per capita birth and death rates a= the amount by which the per capita birth rate changes in response to an addition of one individual to the population. c= ditto for death rate… Our density independent discrete model was a difference equation expressed as: N t+1 = N t + bN t − dN t We replace b and d (the density independent birth rate and death rate) with: b − aN t and d + cN t Now our new density dependent model looks like N t+1 = N t + ()b − aN t N t − (d + cN t )N t Population growth rate is not easy to visualize from this equation.
    [Show full text]
  • Cities in the World a New Perspective on Urbanisation About the OECD
    HIGHLIGHTS Cities in the World A new perspective on urbanisation About the OECD The OECD is a unique forum where governments work together to address the economic, social and environmental challenges of globalisation. The OECD is also at the forefront of efforts to understand and to help governments respond to new developments and concerns, such as corporate governance, the information economy and the challenges of an ageing population. The Organisation provides a setting where governments can compare policy experiences, seek answers to common problems, identify good practice and work to co-ordinate domestic and international policies. About the OECD Centre for Entrepreneurship, SMEs, Regions and Cities The Centre helps local, regional and national governments unleash the potential of entrepreneurs and small and medium-sized enterprises, promote inclusive and sustainable regions and cities, boost local job creation and implement sound tourism policies. About this booklet This booklet contains highlights from the Cities in the World – A new perspective on urbanisation report, which applies consistent definitions of metropolitan areas, cities, towns and semi-dense areas, and rural areas across the world. The report is a joint work of the OECD and the European Commission, Directorate-General for Regional and Urban Policy. It examines the growth of metropolitan areas over the past 40 years, analyses the link between economic development and countries’ urban systems, assesses and quantifies differences in quality of life across the degree of urbanisation, and documents the changing shape of cities over the past four decades. Find out more Find out more about OECD work on Regional Statistics: www.oecd.org/regional/regional-statistics © OECD/EU 2020 This document is published under the responsibility of the Secretary-General of the OECD.
    [Show full text]
  • F 10 Million, the Next Largest City Will Have a Population Of
    AP HUMAN GEOGRAPHY THE GRAND REVIEW Unit I: Geography: Its Nature and Perspective Identify each type of map: 1. 2. 3. 4. Match the following: 5. a computer system that stores, organizes, a. cultural diffusion retrieves, analyzes, and displays geographic data 6. the forms superimposed on the physical b. cultural ecology environment by the activities of humans 7. the spread of an idea or innovation from its source c. cultural landscape 8. interactions between human societies and the d. environmental determinism physical environment 9. a space-based global navigation satellite system e. GIS 10. the physical environment, rather than social f. GPS conditions, determines culture 11. the small- or large-scale acquisition of g. remote sensing information of an object or phenomenon, either in recording or real time Choose the one that does not belong: 12. a. township and range 16. a. major airport b. clustered rural settlement b. grid street pattern c. grid street pattern c. major central park d. natural harbor 13. a. site e. public sports facility b. situation c. its relative location 17. a. Westernization b. uniform consumption preferences 14. a. latitude and longitude c. enhanced communications b. site d. local traditions c. situation d. absolute location 18. a. time zones b. China 15. a. globalization c. United States railroads b. nationalism d. 15 degrees c. foreign investment d. multinational corporations Match the following (some regions have more than one answer): 19. formal region a. Milwaukee 20. functional region b. the Milwaukee Journal Sentinel 21. vernacular region c. Wisconsin d. the South e. an airline hub f.
    [Show full text]
  • Population Dynamics
    LECTURE 3 Population Dynamics The simplest of all the growth processes which provide models for popula- tion dynamics is exponential growth. This is a process of constant proportional growth whereby each time period witnesses the same percentage increase in numbers. If the proportional rate of growth is constant; and, if >0, then the absolute rate of growth must be ever-increasing. When <0, there is a constant proportional rate of decline in the population and a diminishing ab- solute rate. A zero population is approached as time elapses, but it is never reached. Let y 0 represent the size of the population which, for convenience, we take to be a real number instead of an integer. Then the dierential equation governing exponential growth is dy (1) = y, dt where t stands for time. The solution of the equation is the exponential function t (2) y = y0e , where y0, which stands for the size of the population at time t = 0, is described as the initial condition, and where e ' 2.7183 is the so-called natural num- ber. To conrm that this function satises the dierential equation, we simply t dierentiate it via the chain rule to obtain dy/dt = y0e = y. To nd the value of y at time t when and y0 are given, we may take natural logarithms—ie. logs to the base e—of equation (2) to give (3) ln y =lny0+t. Once the value of ln y has been calculated, the value of y may be recovered from a table of antilogarithms. We can also use logs to the base 10.
    [Show full text]