Elements Affecting the Household Size: a Case of the UK - Coursework Example

A practical analysis of the elements affecting the household size: a case of the UK Institutional affiliation The study seeks to analyzes some of the factors that influence household composition. With the provided data set, the chosen independent variables include age, sex, education, and marital status, area of residence, atomic activity, and place of birth, ethnicity and the level of income. General summary The analysis reveals that 54597 individuals took part in the survey, such that 45.9 were females whereas males comprised 54.1 percent of the participants in the survey. Table 1 depicts these findings. Sex of the respondents Frequency Percent Valid Percent Cumulative Percent Valid male 25044 45.9 45.9 45.9 female 29553 54.1 54.1 100.0 Total 54597 100.0 100.0 Of importance to the study is to establish the age distribution of the respondents. The study chose the 10-age interval in the analysis to reduce cases of research bias. The table depicts a summary of the results. Age group: 10-year intervals Frequency Percent Valid Percent Cumulative Percent Valid 10-19 years old 3816 7.0 7.0 7.0 20-29 years old 7768 14.2 14.2 21.2 30-39 years old 9309 17.1 17.1 38.3 40-49 years old 10385 19.0 19.0 57.3 50-59 years old 8585 15.7 15.7 73.0 60-69 years old 7653 14.0 14.0 87.0 70 years or older 7081 13.0 13.0 100.0 Total 54597 100.0 100.0 Typically, majority of the participants in the survey claimed to be 40-49 years and 30-39 years at 19.0 and 17.1 percent respectively. Next were those between 50-59 years, 20-29 years and 60-69 years at 15.7%, 14.2% and 140% respectively. Averagely, 57.3 of the respondents were between 10-49 years while the rest were 50 years or above. The results depict that the chosen population was mostly comprised of a youthful generation. Next in line was the need to establish the level of education of the respondents. Education is an important economic variable that finds its use in the prediction of various socio-economic and political phenomena. Table 3 is a summary of these results. Highest qualification Frequency Percent Valid Percent Cumulative Percent Valid Missing 1256 2.3 2.3 2.3 Don't know 76 .1 .1 2.4 Degree 11258 20.6 20.6 23.1 Other higher degree 5814 10.6 10.6 33.7 A-level etc 10914 20.0 20.0 53.7 GCSE etc 11383 20.8 20.8 74.5 Other qualification 5419 9.9 9.9 84.5 No qualification 8477 15.5 15.5 100.0 Total 54597 100.0 100.0 The data set recorded the highest level of education that the respondents had attained at the time of the study. The chosen levels were Degree, A-level, GCSE, among others. The findings show that the majority of the respondents have an education. Such that, 20.8% representing the highest composition had graduated at the GCSE level, followed by 20.6% and 20.0% at the degree an A-level stage, with 2.4 percent remaining indifferent. Arguably, only 15.5% of the participants had no education compared to 84.5% that were educated. The dependent variable in the study was the number of children in the households. The number of children in a given household is regarded as a vital predictor in most socio-economic surveys. Correlational analysis The summary above is not enough to predict the relationship between the variables under consideration and the size of the households. The correlation analysis uses a mix of crosstabs, histograms, charts and regression analysis to identify the link between the forecaster variables and the dependent variable. The aim of the analysis is to identify which of the stated variables and the extent to which they influence the size of the household. The correlation will also be useful in sieving out the significant variables in the study. In line with the objective of the study, of importance to the study was to seek the relationship between the size of the household and the area of residence. A crosstab is plotted to summarize the distribution of the two variables. Number of own children in household * Urban or rural area, derived Cross tabulation Urban or rural area, derived Total missing urban area rural area Number of own children in household 0 Count 73 29112 9865 39050 % of Total .1% 53.3% 18.1% 71.5% 1 Count 11 5419 1345 6775 % of Total .0% 9.9% 2.5% 12.4% 2 Count 16 4712 1400 6128 % of Total .0% 8.6% 2.6% 11.2% 3 Count 5 1587 415 2007 % of Total .0% 2.9% .8% 3.7% 4 Count 2 391 82 475 % of Total .0% .7% .2% .9% 5 Count 0 115 14 129 % of Total .0% .2% .0% .2% 6 Count 0 16 4 20 % of Total .0% .0% .0% .0% 7 Count 0 7 1 8 % of Total .0% .0% .0% .0% 8 Count 0 4 0 4 % of Total .0% .0% .0% .0% 9 Count 0 1 0 1 % of Total .0% .0% .0% .0% Total Count 107 41364 13126 54597 % of Total .2% 75.8% 24.0% 100.0% The result depicts that a relationship exists between the area of residence and the size of the household in terms of the number of own children. If we were to compare the areas of residence of the persons in relation to age, it would appear that on average; most of the respondents are urban dwellers. However, comparing the ages reveals that though to a low extent, most young persons below 50 years preferred living in the urban areas as compared to the older persons. Age group: 10-year intervals * Urban or rural area, derived Crosstabulation Urban or rural area, derived Total missing urban area rural area Age group: 10 year intervals 10-19 years old Count 5 2982 829 3816 % of Total .0% 5.5% 1.5% 7.0% 20-29 years old Count 28 6459 1281 7768 % of Total .1% 11.8% 2.3% 14.2% 30-39 years old Count 34 7497 1778 9309 % of Total .1% 13.7% 3.3% 17.1% 40-49 years old Count 14 7940 2431 10385 % of Total .0% 14.5% 4.5% 19.0% 50-59 years old Count 8 6254 2323 8585 % of Total .0% 11.5% 4.3% 15.7% 60-69 years old Count 11 5305 2337 7653 % of Total .0% 9.7% 4.3% 14.0% 70 years or older Count 7 4927 2147 7081 % of Total .0% 9.0% 3.9% 13.0% Total Count 107 41364 13126 54597 % of Total .2% 75.8% 24.0% 100.0% Given this insight, it is easier to explain the relationship between the number of children in a given household and the place of residence. Urban dwellers mostly being a youthful generation in this case between 10-19 years spend most of their time working or studying, making them have less number of children (Kiriti, & Tisdell, 2005, 493). On the other hand, persons above 70 years also tend to have few children since they are mostly retirees depending on the pension or government aid. The remaining group comprises of middle-aged persons. The persons have matured up enough, are at the helm of their careers, have just bought a house on the outskirts of town (Marteleto, & de Souza, 2012, 1495). The age group between 20-69 is mature up and ready to take care of their families, an aspect that explains the relatively higher number of children. Correlations Number of own children in household Urban or rustic region, the established count of biological kids in household Pearson Correlation 1 -.031** Sig. (2-tailed) .000 N 54597 54597 Urban or rural area, derived Pearson Correlation -.031** 1 Sig. (2-tailed) .000 N 54597 54597 **. Correlation is significant at the 0.01 level (2-tailed). In building up the dependent variable, the study had to determine the relationship between the number of children in a given household and their levels of income. The level of savings is very significant in estimating the purchasing power of an individual or a household. The provided data set had several forms of savings that could be applicable to the assessment. The analysis opted to use the mean monthly saving option as a measure of the purchasing power of a household. Experts prefer the use of monthly savings since it takes into account the effects of tax and other deductions (Kiriti, & Tisdell, 2005, 497). The figure depicts the graphical relationship between the mean usual net income per month against the number of own children per household. The findings depict that on average families with less number of children had relatively higher savings, such that from the graph, families that had zero to two children recorded higher levels of savings. However, further movements to the left where the number of own household children increased implied lower levels of savings. The findings are concurrent with economic theory, which records that the higher the family size, the more expenditure (Marteleto, & de Souza, 2012, 1497). Families with relatively more children have to allocate more funds for expenditures such as school fees, insurance and food. As such, such expenses have a negating effect on the levels of income. Even though the graph presents a good summary of the relationship between the two variables, it is imperative to ascertain the authenticity of the relationship by determining whether the results are significant or not. As such, a correlation analysis is carried out to determine if the results are significant. Correlations The family's number of biological kids monthly total saved Number of own children in household Pearson Correlation 1 -.016** Sig. (2-tailed) .000 N 54597 54597 monthly amount saved Pearson Correlation -.016** 1 Sig. (2-tailed) .000 N 54597 54597 **. Correlation is significant at the 0.01 level (2-tailed). The study establishes that a negative correlation exists between the two variables. Such that it is possible to argue that, the correlation is significant at the held level of significance. In that an increase in the size if the family leads to a substantial decrease in the size of the family. Another important relationship in the study is the association between the size of the number of children in the family and age. Age is similarly a very important demographic variable in the determination of trends in economic and social phenomena (Ingrid, & O'Flaherty, 2007, 498). From the summary, majority of the respondents in the study were picked from a youthful of generation of less than 50 years. It is thus interesting to determine whether age determines the distribution of the number of own children in a family. Number of own children in household * Age group: 10-year intervals Crosstabulation Age group: 10 year intervals Total 10-19 years old 20-29 years old 30-39 years old 40-49 years old 50-59 years old 60-69 years old 70 years or older Number of own children in household 0 Count 3735 5580 3071 4585 7429 7580 7070 39050 % of Total 6.8% 10.2% 5.6% 8.4% 13.6% 13.9% 12.9% 71.5% 1 Count 78 1178 2093 2571 795 50 10 6775 % of Total .1% 2.2% 3.8% 4.7% 1.5% .1% .0% 12.4% 2 Count 3 728 2765 2330 282 19 1 6128 % of Total .0% 1.3% 5.1% 4.3% .5% .0% .0% 11.2% 3 Count 0 227 1017 696 65 2 0 2007 % of Total .0% .4% 1.9% 1.3% .1% .0% .0% 3.7% 4 Count 0 45 268 149 13 0 0 475 % of Total .0% .1% .5% .3% .0% .0% .0% .9% 5 Count 0 7 77 42 1 2 0 129 % of Total .0% .0% .1% .1% .0% .0% .0% .2% 6 Count 0 2 11 7 0 0 0 20 % of Total .0% .0% .0% .0% .0% .0% .0% .0% 7 Count 0 1 4 3 0 0 0 8 % of Total .0% .0% .0% .0% .0% .0% .0% .0% 8 Count 0 0 2 2 0 0 0 4 % of Total .0% .0% .0% .0% .0% .0% .0% .0% 9 Count 0 0 1 0 0 0 0 1 % of Total .0% .0% .0% .0% .0% .0% .0% .0% Total Count 3816 7768 9309 10385 8585 7653 7081 54597 % of Total 7.0% 14.2% 17.1% 19.0% 15.7% 14.0% 13.0% 100.0% From the crosstab, it is apparent that the trend is that the number of children tends to increase as the age increases. From the table, 71.5% of the families have no children. However, 12.4% and 11.2% of he folks claim to either have one or two children. Developed nation like the UK people would give preference to having less number of children compared to developing countries (Ingrid, & O'Flaherty, 2007, 498). As such, even though slight variations are detected in the distribution of the number of children per household across the ages of the respondents, the results show that on average most persons had preference to fewer children. Nonetheless, as seen in the earlier analysis, persons younger than 20 years and those above 70 years preferred fewer children. On the other hand, those between the age brackets of 20 to 69 had relatively more children. Another relationship important to the study was that one between the number of own children in a household and the level of education of the respondents. Education entails the passing on information from one entity to another. Through education, an individual receives various forms of knowledge such family planning and other important information (Abdur, et al, 2014, 244). As such, the variable is very crucial in the study. Number of own children in household * Highest qualification Crosstabulation Highest qualification Total Missing Don't know Degree Other higher degree A-level etc GCSE etc Other qualification No qualification Number of own children in household 0 Count 1037 59 7189 3934 7824 7582 4195 7230 39050 % of Total 1.9% .1% 13.2% 7.2% 14.3% 13.9% 7.7% 13.2% 71.5% 1 Count 103 10 1726 812 1422 1653 564 485 6775 % of Total .2% .0% 3.2% 1.5% 2.6% 3.0% 1.0% .9% 12.4% 2 Count 84 5 1718 778 1207 1468 458 410 6128 % of Total .2% .0% 3.1% 1.4% 2.2% 2.7% .8% .8% 11.2% 3 Count 26 2 510 231 361 505 151 221 2007 % of Total .0% .0% .9% .4% .7% .9% .3% .4% 3.7% 4 Count 4 0 94 44 82 126 38 87 475 % of Total .0% .0% .2% .1% .2% .2% .1% .2% .9% 5 Count 2 0 19 13 17 39 10 29 129 % of Total .0% .0% .0% .0% .0% .1% .0% .1% .2% 6 Count 0 0 1 2 0 8 2 7 20 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% 7 Count 0 0 0 0 0 2 1 5 8 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% 8 Count 0 0 1 0 1 0 0 2 4 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% 9 Count 0 0 0 0 0 0 0 1 1 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% Total Count 1256 76 11258 5814 10914 11383 5419 8477 54597 % of Total 2.3% .1% 20.6% 10.6% 20.0% 20.8% 9.9% 15.5% 100.0% Analysis shows that no particular group referenced a limit in the number of children. For instance, just like over 70% of the respondents preferred not to have children, about 13 percent of them were university graduates. It is evident that people preferred lesser number of children. The results are further strengthened with averagely the same number of persons having GCSE documentation or no quantifiable education at all. However, since the crosstab fails to reveal an explicit relationship between the two variables, a correlation analysis is vital in establishing whether there is a significant relationship between the two variables. Correlations Number of own children in household Highest qualification Number of own children in household Pearson Correlation 1 -.066** Sig. (2-tailed) .000 N 54597 54597 Highest qualification Pearson Correlation -.066** 1 Sig. (2-tailed) .000 N 54597 54597 **. Correlation is significant at the 0.01 level (2-tailed). The analysis establishes a negative correlation between the explanatory variable and the number of own children in a given household. The negative relationship implies that as the level of education of an individual increases, the resultant effect is a decrease in the number of children a family has. Scholarships are of the view that as persons seeks for higher education, most of their time is spent studying and hence limited time is available to fend for children (Dittgen, 2005, 260). Further, through education, views and campaigns such as those of family planning are easily inculcated on them, which eventually drive them to prefer less number of children. Having established the relationship between the level of educating and the number of children in a given household, an issue that emerges is the relationship between the type of economic activity and the size of the family. Studies agree that most educated persons prefer top jobs that are also demanding in terms of time (Dittgen, 2005, 263). The results show that on average, respondents with paid employment had little number of children followed by retirees. For instance, for the respondents that reported having zero children, 29.1 percent and 21.7 percent of them were in paid employment and retirement respectively (see Appendix A). As claimed by similar studies, paid forms of employment entail lots of sacrifice in terms of time (Dittgen, 2005, 263). As such, most employed individuals spend substantial amounts of their time on either company trips or hunting for more markets, with less time left for their families, leading to them opting to have less number of children. Regression analysis Further, as despite the crosstabs and the graphs a presenting the trend of the results, they fail to show the nature of the relationship between the explanatory variables and the number of won children in a given household. As such, a regression is critical in determining the nature of the relationship between the variables described. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.179 .014 86.571 .000 Age group: 10 year intervals -.126 .002 -.243 -55.845 .000 Urban or rural area, derived -.015 .006 -.010 -2.440 .015 Highest qualification .001 .001 .003 .679 .497 monthly amount saved -3.309E-5 .000 -.013 -3.001 .003 a. Dependent Variable: Number of own children in household The predictors are significant at the held significant level. From the table, it is apparent that expect the highest level of education attained, all the other variables have a negative relationship with the dependent variable. The negative relationship is an indication that the number of children in a given household tends to decrease with an increase in the independent variable except for the highest level of education. Rather, the highest level of education has a positive relationship with the size of the household. The model summary table below displays the extent to which the independent variables predict the dependent variable. At 0.59, the results show that the tested variables, though significant indicators, they are very weak determinants of the number of own children in a given household. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .244a .059 .059 .904 a. Predictors: (Constant), monthly amount saved, Urban or rural region, deduced, Highest Education, Age Bracket: 10-year intervals a. Predictors: (Constant), monthly amount saved, Urban or rural area, derived, Highest qualification, Age group: 10-year intervals . Dependent Variable: Number of own children in household Discussions and conclusions The study sought to explore the elements that decide the size of a household. Using the dataset provided the Understanding Society survey. Leaving the amount of own kids in a household as the dependent variable. Various variables in the form of age, savings, area of residence, highest qualification, and the type of economic activity were chosen as the independent variables. The analysis depicted that a negative relationship exists between age and the number of children. From literature, mothers of infants are more youthful now than their similitudes were 20 years ago. In the final quarter of the 20th C, teenagers had a bigger quota of all births relative to females aged 35 and older. However, in the year 2008, the reverse occurred — 10% of births were to teenagers, likened with 14% to females aged 35 or older. Many females had at acquired some level of college by 2009, a significant increment relative to 1990 (Vinovskis, 2013, 289). According to this study, among maidens of neonates 35 or older, 71% had at least some college education. The trends provided here forth are a reflection of the complex mix of demographic and behavioral aspects in the society. For instance, the bigger share of college-educated maidens arises both from their increasing birth rates and from female’s raising educational achievement. In conclusion, it is possible to deduce that though a lower relationship exists between the various variable discussed, some level of significance the study has established some significance. It is apparent from the analysis that education has a positive relationship with the size of a family. The government may use the information regarding family sizes by use of education. Reference list Abdur Rehman, Wang Jian, Zhang Runqing 2014, "Estimation of Urban-Rural Expenditure and Household Size Elasticities of Food Items in Pakistan: - Evidence From PSLM Survey", Asian Economic and Financial Review, vol. 4, no. 2, pp. 183-190. Dittgen, A. 2005, "Housing and Household Size in Local Population Dynamics: The example of the UK", Population, vol. 60, no. 3, pp. 259-298. Ingrid, G.E. & O'Flaherty, B. 2007, "Social programs and household size: evidence from New York City", Population Research and Policy Review, vol. 26, no. 4, pp. 387-409. Kiriti, T.W. & Tisdell, C. 2005, "Family size, economics and child gender preference: a case study in the Nyeri district of Kenya", International Journal of Social Economics, vol. 32, no. 6, pp. 492-509. Marteleto, L. J., & de Souza, L.,R. (2012). The changing impact of family size on adolescents' schooling: Assessing the exogenous variation in fertility using twins in Brazil. Demography, 49(4), 1453-77. doi:http://dx.doi.org/10.1007/s13524-012-0118-8 Vinovskis, M.A. 2013, "From Household Size to the Life Course Some Observations on Recent Trends in Family History", The American Behavioral Scientist (pre-1986), vol. 21, no. 2, pp. 263-306 Appendix A: Number of own children in household * current economic activity Crosstabulation current economic activity Total refused don't know self employed paid employment(ft/pt) unemployed retired on maternity leave family care or home full-time student lt sick or disabled got training scheme paid, family business doing something else Number of own children in household 0 Count 0 4 2417 15900 2130 11830 30 945 3722 1772 54 33 213 39050 % of Total .0% .0% 4.4% 29.1% 3.9% 21.7% .1% 1.7% 6.8% 3.2% .1% .1% .4% 71.5% 1 Count 0 2 670 4369 439 38 153 775 97 193 7 3 29 6775 % of Total .0% .0% 1.2% 8.0% .8% .1% .3% 1.4% .2% .4% .0% .0% .1% 12.4% 2 Count 1 0 637 3872 346 8 114 938 75 112 1 2 22 6128 % of Total .0% .0% 1.2% 7.1% .6% .0% .2% 1.7% .1% .2% .0% .0% .0% 11.2% 3 Count 0 0 224 1030 144 1 38 485 30 43 2 2 8 2007 % of Total .0% .0% .4% 1.9% .3% .0% .1% .9% .1% .1% .0% .0% .0% 3.7% 4 Count 0 0 42 183 39 0 4 184 5 13 0 0 5 475 % of Total .0% .0% .1% .3% .1% .0% .0% .3% .0% .0% .0% .0% .0% .9% 5 Count 0 0 9 46 11 0 2 56 1 2 1 0 1 129 % of Total .0% .0% .0% .1% .0% .0% .0% .1% .0% .0% .0% .0% .0% .2% 6 Count 0 0 1 2 4 0 0 12 0 1 0 0 0 20 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% 7 Count 0 0 0 1 1 0 1 4 0 1 0 0 0 8 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% 8 Count 0 0 1 0 0 0 0 3 0 0 0 0 0 4 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% 9 Count 0 0 0 0 0 0 0 1 0 0 0 0 0 1 % of Total .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% Total Count 1 6 4001 25403 3114 11877 342 3403 3930 2137 65 40 278 54597 % of Total .0% .0% 7.3% 46.5% 5.7% 21.8% .6% 6.2% 7.2% 3.9% .1% .1% .5% 100.0% Read More