Socio-demographic Structures of Families as Predictors of Time Management by Working Housewives in the Federal Capital Territory (FCT), Abuja, Nigeria

 

Amfani-Joe, Comfort E.¹
Okonkwo, Lovina²
Osagede, Esther A.³

1Department of Home Science and Management Faculty of Agriculture, Nasarawa State University, Keffi, Nasarawa State-Nigeria 2,3Department of Home Science and Management University of Agriculture, Makurdi, Benue State-Nigeria.	Abstract The dual role of the working wife increases her responsibilities that require effective management of her time to accomplish and have reserves for her personal requirement and that of the spouse. Time management by working wives is not just a factor of marital satisfaction but also a major factor of reducing stress in their daily activities. The demographic structures of the household determine to a large extent the effective management of time by the working wife. The study therefore dwelled on the demographic structure of the household as predictors of time management by the working wife in the Federal Capital Territory (FCT), Abuja, Nigeria. A total of 435 working women were selected through a stratified random sampling procedure across the study area. A structured questionnaire was employed in data collection which includes; socio demographic structure, time spent at work and the effectiveness of time management. The results revealed that test of relationship between the variables of educated working housewives’ time management is positively and significantly related to the spokes person(s) of the household. The observed correlation coefficient (r) was 0.72 and the p-value was 0.000 (p<0.005) at 433 degree of freedom. The presence of children or related who are working in the house were found to be negatively but significantly correlated to time management of educated housewives (p<0.05). Sharing of kitchen by wife was significantly but negatively correlated with time management by wife (r=0.107, p=0.026). Test of relationship showed that husband contribution to the routine activities of household significantly contributed to wives effective time management (r=0.124, 0.010) but the children’s contribution to household finances was found not to be significant (r=0.066, p=0.173). The observed pseudo-R-square values for the test were 13% for Cox and Snail, 53:4% for the Negelkarde and 38:9% for Mcfadden respectively. These observed Psue-R-square values clearly point to be significant association of wives’ time management with their marital satisfaction. It can be concluded that the level of income along with help from others in the household could greatly predict the effectiveness of time management by educated working housewives in the study area. It is recommended that a study of their husbands be conducted to determine if husbands within this population of study are satisfied with their wives working and if education has made any difference in the time use of the household.
Licensed: This work is licensed under a Creative Commons Attribution 4.0 License.
Keywords: Demographic structures Time management Personal requirement Families Predictors Housewives.
(* Corresponding Author)

1. Introduction

It is often said that women’s work is never done. This saying arises from the involvement of women in cooking, cleaning, arranging the daily mess as well as taking care of their kids, family members and occasionally guests to the house. Most importantly, they ensure that all desires and wishes of their family members are fulfilled. The effectiveness of these tasks requires efficient time management. The number of hours in a day is the same for everyone, but some manage to use them much better than others. The amount of time spent in any activity fashions the quality of work, the relationships and comfort obtained (Goldschmid, 2015). In the Federal Capital Territory of Abuja, Nigeria, apart from the café for family, women have to travel long distance to get to their working places. The urban nature of the area does not allow for practices of the traditional time allocation where wives can perform certain task at leisure. The lack of adequate accommodation is another added constraint to the problem of time available for the accomplishment of tasks by the working wives in the area. The demographic structures of the household constitute a major factor to the effective management of time by working wives. From a study on factors affecting time management and Nurses performance, Mohammad and Sumaya (2014) observed significant relationship between time management obstacles and nurses performance. In relating the problem of time management, Huffington (2015) observed that there were obvious and ever-present constraints to today’s demand at the workplace is the need to accomplish more, better, faster and cheaper, inevitably leading to more stress. Treating everything as urgent, rushing from one activity to another all day long, not enjoying anything and without ever feeling satisfied is a straight path to tension and ultimately to burnout, a phenomenon he described as the disease of our century. This is stated as the result of the inevitable constraints imposed by time because if we want to improve quality, we need more time or we have to reduce the number of objectives. If we want to increase the number of things we want to accomplish, quality may suffer or we’ll be short on time. If we are given less time, there is pressure on the quantity and quality of our production and all in the face of other factors affecting the amount of time available to us. Therefore, having specific daily objectives and being ever conscious of their respective priorities is a hallmark of successful people (Covey, 2013). Thus Tracy (2007) observed that uninterrupted and conducive time schedule leads to better time management for efficiency. This study therefore, examined the impact of family socio-demographic structure on time management by working wives in the area.

2. Methodology

2.1. Areas of Study

The federal capital territory is divided into districts and the districts were grouped into development pluses. These comprised four (4) phases, each of which were further divided into districts and cadastral zones. Most districts in phase 1 and phase 2 are highly developed with infrastructure in place. Some districts in phase 3 and 4 are also developed while others are in the process of development. All the districts in phase 1 except Grozabe are fully developed with infrastructure in place. These include Asokoro, Garki I, Garki II, Guzape, Wuse II, Maitama cadastral zones. In phase 2, some districts are highly developed while others are in the process of development. These include Gudu, Durumi, Utako, Jabi, Wuye, Dakibiya, Kaura, Duboyi, Gadiwa, Kado, Mabuchi and Dutse. Districts in phase 3 are Gwarinpa, Galadimawa, Dakwa, Lokogoma, Sarayi, Kabu Sa, Okanji, Pyakasa, Nbora, Karma. Most districts in phase 4 are being developed. They include Karsama, sabo Couda, Idu, Idogwari, Kaba, Kajini, Kete, Shetti Cheche, Warupaina, Gwari, Bude, Chafe, Jaire, Manusa, Burom, Parfon. The sub-urban districts are not in the federal capital territory but they have attracted development because of the many people who work in the FCT but live in some of these sub-urban districts these include Kubuwa, Lugbe, Chika, Kudin Gwore, Npape and Dei-Dei.

Abuja is a municipal area in Nigeria located by the coordinates 90­ 4N70 29E. It is 1,769km2 have a density of 937,06 (2015) and have a population of 1,763,800 (2015) males 415,957 and females 360,347 (2016).

FCT climate under Koppen climate classification features a tropical wet/dry climate (Koppen). The FCT expenses three weather conditions annually. This included a warm humid rainy season and a blistering dry season. In between the two, there is a brief interlude of harmattan accessioned by north last trade wind with the main feature of dust and dryness.

The FCT vegetation falls within the type of the guinea forest savanna Mosai zone of west African sub-region. Mercy drops of rains forest however, occur in the Gwagh plains especially in the rugged terrain south-south East parts of the country with a landscape of gullies and rough terra is found (http://enwikipedia.org.wiki/abujaclimate10/17/2017,11am).

2.2. Population

The populations of the study were educated working housewives who have children, spouses and other related or non related persons living with them as family members.

2.3. Sample Size

Five hundred (500) questionnaires were administered but only four hundred and thirty five (435) were useable. The subjects comprising educated working housewives responded to the open ended questions which boarders on demographic factors, personal activities of the respondents, presence of children including pre-school children, management techniques and marital satisfaction.

2.4. Sampling Technique

A stratified sampling technique was employed. From the pulse 1 of the FCT there were eight (8) districts from which by ballot sampling technique four (4) districts ( Garki Gozape, Wuse and Wuse II) were selected. In phase 2, there were fourteen (14) districts from which seven (7) (Coudu, Durumi, Utako Fabi, Wuye, Katanpe, Jahi and Duboyi) districts were selected. From phase 3 Gwarinpa, Dakwo Central area, Lugbe, Nbora, Karma were selected from the eleven (11) districts. In phase 4, there were seventeen (17) districts out of which nine (9) districts were selected and these included Idu, Idogwari, Kubua, Dutse, Bode, Galadima, Mamusa, Burum, Purfun. Other districts selected from the suburban districts included Gwagwalada, Kubuwa, Nyanya. Overall, thirty two districts were selected which were used for data.

2.5. Instrument

The instrument was formulated and structured into five (5) sections. Section A comprised questions about the background information of the respondents. Section B comprised open ended questions on the personal activities of the respondent. In section C questions boarded on the presence of children and pre–school children. Section D comprised questions on the management techniques of the educated working housewives. Section E were questions on the respondents marital satisfaction stated on a five (5) power likert scale structured into Agree (A) Disagree (DA) Strongly Agree (SA) Strongly disagree (SD) and undecided (UI)

2.6. Validity

The instrument was validated by professionals in the area of Home Economics and pilot tested.

2.7. Reliability of the Instrument

Alpha Cronbach reliability index was obtained at 0.758.

2.8. Data Analysis Procedure

Data analysis procedure adopted were descriptive statistics, spearman correction and multinorminal logistic regression (Chao-Ying & Nichols, 2003).

3. Result and Discussions

3.1. Socio-Demographic Structures

All the respondents were educated with a minimum of secondary schools certificate. The average age of the respondents was 43.58years with a standard deviation of 9.19years. Most (94.7%) were from monogamous household while 5.3% were from polygamous homes. The relatively low number of women from polygamous homes is attributable to the cultural and religious adherence in the traditional society which is extended to the area in spite of its urbanization. The male is the main decision maker in 88.0% of the home. Most (63.0%) of the respondents live in rented houses. Only 37.0% said they live in their own houses. Nineteen (18.5%) percent of the respondents can becategorized as low income level household. 9.0% were between level 1 and 3, 14.8 were between 4 and 6 while 31.9% were between 7 and 9 with 19.9% in levels 10 to 13. Only 5.9% were in level 13 and above. On the average, the respondents have between 1 to 5 children with between 1 and 3 of Nursery/primary school age and between 2 and 3 of secondary schools with 1 and 2 in tertiary institution. Of the total, 66.1% were civil servants, 2.8% were artisans, 21.0% were traders, while 10.1 were involved in otheroccupations, possibly as a means of sustenance.

Most (81.2%) of the respondents starts their work in morning hours and work on the average 6 hours on working days of the week. But 18.8% said they work at their leisure hours but mostly in the afternoon. Only 31.9% of the respondents said they do not go to work late but 57.1% said they occasionally go to work late while 10.9% reported they frequently resume at work place late. For 11.8%, caring for children was their reason for going to work late while 17.4% attributed their lateness to electricity failure, 3.9% car breakdown, 14.8% wake up late and 10.1% said when they were sick. Of the 273 who said they allow time for helping their children with homework, 157(57.5%) said they perform such task on a daily basis, 14(5.1%) revealed twice a week and 102(37.4%) indicated when it is necessary. Only 27.5% of the respondents said children of relatives live with them and 10.4% said such relative contributed to the finances of the household, of the respondents that have houses, 119(49.0%) have only 1 each while 37(15.2%) have 2 each and 6(2.5%) have 3 with 1(0.4%) having four house and 2(0.8%) having five houses. Only 13.2% said they rent out their houses. As for the kind of fuel used for cooking, 60.5% use gas, 2.8% Electricity, 22.4% Kerosene, 9.5% Charcoal, 2.8% Firewood. Reasons for choice of fuel used included cost, availability and proximity along with safety and preference for time. For technological devises that saves time and stress, only 40.1% said they such devices. Only 8.4% of the respondents said they share their Kitchen with others. Of the total respondents, 18.2% were not satisfied with their marriages, 7.0% were barely satisfied, 33.1% were satisfied and 41.7% were very satisfied. For the time management, 72.3% were not satisfied with the way they management their time, 23.5 were barely satisfied. But 2.8% were satisfied while 1.4% were very satisfied with their time management.

Effect	Model Fitting Criteria	Likelihood Ratio Tests
	-2 Log Likelihood of Reduced Model	Chi-Square	df	Sig.
Intercept	360.182a	0.000	0
Who speaks for the family	364.092b	3.910	3	.271
Number of wives/mates	370.592b	10.410	6	.108
Number of persons in the household	427.707	67.524	24	.000
Ownership of house	363.907b	3.725	3	.293
Income level of wife	419.170b	58.988	15	.000
Husband help in performing household activities	408.865b	8.683	3	.042
Children help in performing household activities?	410.230b	8.761	3	.041
Presence of school aged children	364.265b	4.083	3	.253
Presence of other sources of income	368.483b	8.301	18	.974
Presence of children / relatives who are working	362.992b	2.810	3	.422
Children / relatives contribute financially to the running of household activities	362.268b	2.085	3	.555
Ownership of other houses	389.348b	29.166	15	.015
Type of cooking fueling	399.867b	39.685	15	.001
Ownership of time saving devices	423.594b	9.412	3	.018
Do you share your kitchen with others?	363.979b	3.796	3	.274

Source: Field survey, 2017

From the result in Table 1, the spoke person of the house hold and number of wives were not found to be significant predictors working wives, time management (P > 0.05). This conclusion goes for ownership of the house, presence of school aged children, and available other sources of income for the household. Other socio-demographic variables whose impact did not significantly impact the working wives’ time management were the presence of children / relatives who are working in the household, Children / relatives contribute financially to the running of household activities and sharing of Kitchen by the respondents (P > 0.05). Socio-demographic variables whose impact could significantly predict the management of working wives observed in the test were number of persons in the household, Income level of wife, whether husband help in performing household activities, whether Children help in performing household activities, Ownership of other houses, Type of cooking fueling and Ownership of time saving devices (P < 0.05). From of the multinomial logistic regression parameter estimates with the Wald statistics, it was found that respondents whose income were high had relatively higher time management than was obtained for those whose income was low. Therefore the fewer the number of persons in the house the better the odds of effective management by the wife. Provision of help by husband and children was found to have better effects on the time management by the wife than where such help is not provided. Ownership of other houses was found to encourage time management. This is consistent with the contribution of income observed in the test. Respondents who use faster cooking fuel and devices were found to effectively management their time more than others with slower cooking fuel. This same goes for the use of technological time saving devices (p < 0.05).

Objective 1

To determine the effect of household structure on the time management of the educated working housewives

Hypothesis 1

There is no significant impact of household variable s on the time management of the educated working housewives

3.2. Effects of Household Structure on Time Management by Wife

The test of relationship between the variables revealed that working wives time management is positively and significantly related to the spokes persons of the household. The observed correlation coefficient (r) was 0.172 and the p-value was 0.000 (P < 0.05) at 433 degree of freedom. But the number of wives present in the home was not found to significantly impact on the management of time by the working wife (r = 0.084 P = 0.082). The number of persons in the household which included husband, wife, children and relative was found to be significantly correlated with the working wives’ time management (r=0.096, p=0.044). The presence of school age children was not found to be a significant predictor of the working wives’ time management(r=0.078, p=0.103). The presence of children or relatives who are working but live in the house was found to be negatively but significantly contributed to time management by the working wives (r=-0.103, p=0.032)

Objective 2

To determine the impact of household economic structure on the time management of the educated working housewives

Hypothesis 2

There is no significant impact of household economic structure on the time management of educated working housewives.

3.3. Effects of Household Economic Structures on Time Management by Wife

The economic structure of the household investigated included ownership of the house, income level of the wife, presence of other sources of income for the family, whether Children / relatives contribute financially to the running of household activities, Type of cooking fueling, ownership of time saving devices and whether respondents share kitchen with others. The ownership of the house where the respondents dwelt was not significantly correlated with their time management (r = 0.051, p=0.291). Income level of wife was found to significantly and negatively correlated with her time management (r=0.150, p= -0.002)). But the presence of other sources of income to the household was not found to have significant relationship with the time management of the wife (r = 0.073, p=0.130).This finding was replicated for the contribution of children or relative to the financial running of the house activities (r = -0.080, p = 0.095). The type of fueling for cooking was found to be positively and significantly correlated with the management of time by the working wives (r=0.186, p = 0.000). The ownership of time saving devices like washing machines and Blender were not found to positively contribute to time management by the wives (r = -0.005, p= 0.910 and r=0.030, p=0.528) for washing machines and blender respectively. The husband occupation was not significantly correlated with the time management of the wife (P > 0.05). Sharing of kitchen by wife was significantly but negatively correlated with time management by the wife (r = -0.107, p = 0.026).

Objective 3

To determine the impact of household help structures on the time management of the educated working housewives

Hypothesis 3

There is no significant impact of household help structures on the time management of the educated working housewives.

3.4. Effects of Household Help Structures on Time Management by Wife

Two variables were assessed here in relation to their impact on the wife manage of her time. These are whether the husbands’ help in performing household activities and whether children and relatives who live within the house help in the routine activities of the house in relation to the time management of the working wife. From the test of the relationship, husband’ contribution to the routine activities of the household significantly contributed to effective time management of the wife (r = 0.124, p = 0.010) but the children’s contribution chores in the household was not found to be significant (r=0.066, p = 0.173).

Objective 4

To determine the predictive nature of the socio-demographic structures on time management by educated working housewives.

Hypothesis 4

There is no significant relationship between socio-demographic structures on time management by educated working housewives.

3.5. Impact of Socio-Demographic Structures on Time Management by Educated Working Wives

To determine the predictive nature of the socio-demographic variables on the wives’ time management, the logistic regression model was used. Table 1 shows the summary of the logistic regression model. The model fitting criteria revealed an observed -2 Log Likelihood intercept of 592.050 and a final value of 360.182 with a Chi-Square value of 231.867 and a probability level of significance of 0.000 obtained at 126 degree of freedom. The observed Pseudo R-Square values for the test were41.3% for the Cox and Snell, 55.4% for the Nagelkerke and 38.9% for the McFadden respectively. These observed Pseudo R-Square values clearly point to a significant association of the wives time management with their marital satisfaction.

4. Conclusion

This study revealed that number of persons in the home, income of wife, provision of help by husband and children, ownership of houses, type of cooking fuel used, ownership of time saving devices could be significant predictors of time management by working wives. This shows that level of income along with helps from others in the household could generally predict the effectiveness of time management by working housewives in the study area.

5. Recommendations

1. A study of husbands of educated working housewives and their level of satisfaction with their wives’ time management at home and at work
2. A study of the relationship between available working space in the household and the use of time management of graduates of home economics who are working housewives.
3. Comparative study of marital satisfaction literate and illiterate housewives and their husbands.

References

Chao-Ying, J. P., & Nichols, R. N. (2003). Using multinomial logistic models to predict adolescent behavioral risk. Journal of Applied of Statistics Methods, 2(1), 177 – 188.

Covey, S. R. (2013). The 7 habits of highly effective people: Powerful lessons in personal change (pp. 170 – 186): Franklin Covey Co.

Goldschmid, M. L. (2015). Time is life: 5 Key factors of time management. Retrieved from https://www.linkedin.compulse/time .

Huffington, A. (2015). Thrive: The third metric to redefining success and creating a life of well-being. New York: Wisdom and Wonder. Harmony.

Mohammad, Q., & Sumaya, S. (2014). Factors affecting time management and nurses’ performance in hebron hospitals. Journal of Education and Practice, 5(35), 2 – 8.

Tracy, B. (2007). Eat that frog!: 21 Great ways to stop procrastinating and get more done in less time (2nd ed., pp. 9 – 14). San Francisco, CA: Berrett-Koehler Publishers.