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Determinants of Productivity among Senior Citizens in
Marikina City
Determinants of Productivity among Senior Citizens in
Marikina City
Presented by:Ines Alcantara-de Guzman
October 18, 2007
Background of the StudyBackground of the Study
Increasing number of senior citizens
Outright discrimination in the workplace
Limited access to adequate health services
Inadequate retirement plans
Economic difficulties-reduced income
Statement of the ProblemStatement of the Problem
1. What is the socio-demographic and health profile of senior citizens in Marikina City?
1.1. Age1.2. Gender1.3. Civil Status1.4. Educational attainment1.5. Occupation upon retirement1.6. Living arrangement1.7. Health status
2. Which among the variables are possible determinants of productivity among senior citizens?
2.1. Economic activities2.1.1. Employment status2.1.2. Workplace
2.2. Volunteer Work2.2.1. Membership in organization2.2.1. Involvement in community affairs
2.3. Life activities 2.3.1. Activities of daily life2.3.2. Instrumental activities2.3.3. Household activities
Statement of the ProblemStatement of the Problem
Conceptual ParadigmConceptual Paradigm
Engaged in economic activitiesMembership in civic org.Involvement in volunteer workPerformance of daily life activitiesAbility to adjust to new ways of living
•Inability to cope with retirement and reduced income•Non-participation in age-group activities•Non-involvement in social and civic obligations•Inability to adjust to new living arrangements
PRODUCTIVE NON-PRODUCTIVE
Productivity(Economic Activities, Life Activities, Volunteer Work)
SENIOR CITIZENSHealth Status
Socio-Demographic Profile
Objectives of the StudyObjectives of the Study
The study is aimed at accomplishing the following objectives:
1. to describe the socio-demographic and health profile of senior citizens in Marikina City;
2. to identify the variables that are possible determinants of productivity among senior citizens in Marikina City; and
3. to develop a productivity scorecard for senior citizens.
MethodologyMethodology
• Locale: Barangka and Concepcion/Tumana, Marikina City
• Sampling Design: 354 male and female senior citizens aged 60-79
• Data Gathering Instrument: Survey questionnaire, face-to-face interview, and Focus Group Discussion (FGD)
• Data Analysis: Frequency, Cluster Analysis and Logistic Regression
Study ResultsStudy Results
DemographicsDemographics
• Majority of the respondents belong to the 60-69 age group.
CIVIL STATUSSingle5%
Widow/Widower42%
Married51%
Separated2%
• Majority of the respondents are married.• Majority of the respondents are females.
EducationEducation
EDUCATIONAL ATTAINMENT
None3%
Elementary
49%
High School22%
VocTec/Undergrad
10%
College/Grad units
15%
Graduate Degree
1%
• Majority are elementary graduates.
• 22 percent are high school graduates.
• 10 percent are college graduates.
• 10 percent are Vocational/Technical graduate
• 1 percent with graduate degrees
EmploymentEmployment
TYPE OF EMPLOYMENT (Base = All Employed)
Self-employed
24%
Private62%
Government14%
EmploymentEmployment
EMPLOYMENT STATUS
Otherwis34% Employed
17%
Retired49%
EmploymentEmploymentEMPLOYMENT
37.6
13.0
11.6
9.0
7.3
5.9
3.4
3.1
2.0
1.7
0.3
1.1
1.1
1.1
0.6
0.3
0.3
0.3
0.3
No Employment
Laborer
Shoemaker
Professional
Service Worker
Manager/Supervisor
Clerk
Sales Worker
Machine Operator
Vendor
Unskilled Worker
Farmer
TechnicianStore
Owner/OperatorUnderwriter
CarinderiaCorporateExecutive
Fisherman
Shop Worker
Living ArrangementsLiving Arrangements
LIVING ARRANGEMENT
with Child/Chil
dren36%
with Spouse
12%
with Other
Relatives8%
Alone8%
with Spouse/Ch
ildren36%
• Majority live with spouse/children
• And with a child/children
• 12 percent live with spouse
• 8 percent with other relatives
• 8 percent live on their own.
HealthHealth
MEDICAL FACILITY
39.836.7
18.4
11.6 11.6
GovernmentHospital
HealthCenter
PrivateClinic
Home PrivateHospital
HealthHealth
HEALTH SELF-ASSESSMENT
Good to Excellent
75%
Poor to Fair 25%
HealthHealth HEALTH PROFILE
56.2
29.9
23.7
14.4
14.4
10.5
9.0
5.6
5.1
4.8
3.1
2.8
1.1
0.8
0.6
Poor Vision
Arthritis
Hypertension
Poor Hearing
CataractHeart
DiseaseDiabetes
AsthmaKidney
ProblemOsteoporosis
PulmonaryDisease
Ulcer
Tuberculosis
Emphysema
Glaucoma
Respondents ProfileRespondents Profile
SMOKING & DRINKING PATTERNS
11.6
10.5
5.4
3.7
2.3
Smoke
DrinkAlcoholicBeverages
Beer
Wine
Liquor
• A small percentage smoke and drink alcoholic beverages mostly beer.
Respondents ProfileRespondents ProfileLEISURE ACTIVITIES
84.7
63.3
15.0
6.5
4.0
2.5
2.3
1.1
0.3
WatchingTelevision
Listening toMusic
Needlework
BallroomdancingPlayingBoardgames
Fishing
Handicrafts
Painting
Collectingitems
• Majority indicated watching television as their way of spending leisure time.
IncomeIncome
Majority are receiving an income below 4,000.00Php
MONTHLY INCOME
55.8
12.0
5.8
1.2
0.5
0.0
29.7
Below P4, 000
4,001-8,000
8,001-15,000
15,001-30,000
30,001-50,000
5,0001 and above
No data
IncomeIncomeMONTHLY PENSION
63.3
21.7
6.7
1.7
1.7
1.7
3.3
Below P4, 000
4,001-8,000
8,001-15,000
15,001-30,000
30,001-50,000
50,001 and above
No Data
Majority are receiving a monthly pension below 4,000.00Php
Majority claimed that their income is not sufficient to provide for their basic needs in life.
Preparation for old agePreparation for old age
PREPARATION FOR OLD AGE
71.8
57.9
26.3
5.7
5.4
4.8
28.2
12.1
16.1
1.1
Prepared for Old Age
Sent children to school
Saved money in thebank
Acquired land
Got an insurance plan
Invested in a business
Did not prepare for oldage
Did not see it as a need
No extra income
Busy to think of saving
ADL and IADL EngagementADL and IADL Engagement
ENGAGEMENT IN IADLs
92.1
73.2
92.9
76.3
81.6
Doing lighthousehold
work
Managing ownmoney
Preparing ownmeal
Shopping forown groceriesor personal
item
Using publictransportation
ENGAGEMENT IN ADLs
96.9
96.9
97.5
98.6
Dressingup/Puttingon clothes
Eating
Taking abath
Walkingaround the
house
Respondents ProfileRespondents Profile
HOUSEHOLD ACTIVITIES
80.5
78.0
68.6
54.8
54.8
46.6
37.0
36.7
Cleaning thehouse
Cooking
Washing thedishes
Ironing clothes
Washingclothes by hand
Taking care ofinfants/children
Homedecorating
Washingclothes bywashing
• The top three household activities the respondents are involved in are:
– Cleaning the house– Cooking– Washing the dishes
Volunteer Work / Community InvolvementVolunteer Work / Community Involvement
REASONS FOR NO VOLUNTEER WORK
28.8
18.1
12.1
2.8
1.4
Healthreasons
Lack oftime
Don'tknow howand where
Lack ofnecessary
skills
Lack offunds
VOLUNTEER WORK PARTICIPATED IN
28.2
10.2
5.6
4.8
3.7
2.3
1.7
1.7
1.7
1.1
0.8
0.8
0.6
BarangayActivities
Homeowners'Association
Church choir
Lay Minister
Lector
Feedingprograms
Catechist
LivelihoodprogramsFamily
counselingMotherButler
GawadKalingaTraffic
Enforcer
Acolyte
Cluster Analysis ResultsCluster Analysis Results PRODUCTIVE VS NON-PRODUCTIVE
46%21%38%78%
100%100%99%99%98%89%99%94%94%98%96%60%89%66%50%73%93%76%71%
50%91%
62%13%38%65%94%94%95%98%86%57%86%57%68%62%59%13%19%26%23%36%43%73%56%38%81%
• Cluster Analysis showed that 183 of the respondents are “productive” and 173 are “non-productive”.
• This is based on the 25 variables aligned with the Havighurst’s paradigm.
Head of the familyEmployedIncomePrepared for old ageDressingEatingBathingWalkingLight ChoresManaging Own MoneyPreparing Own MealShopping for GroceriesRiding a Public TransportCleaning the HouseCookingHome DecoratingIroning ClothesCaring for ChildrenWashing Clothes (by machine)Washing Clothes (by hand)Washing the DishesHealthy (Perceived)Organization (Membership)VolunteerProductive (Perceived)
Development of the Productivity ScorecardDevelopment of the
Productivity Scorecard
Logistic Regression: FULL MODEL (25 variables)Logistic Regression: FULL MODEL (25 variables)
B S.E. Wald df Sig. Exp(B)V6(1) -0.07360 0.55719 0.01745 1.00000 0.89491 0.92904I7EMPSTA(1) 1.63568 0.81396 4.03823 1.00000 0.04448 5.13293I13INCME(1) 0.56825 0.60100 0.89399 1.00000 0.34440 1.76517I16PREPA(1) 1.20478 0.59663 4.07757 1.00000 0.04346 3.33604B6DRESSI(1) 19.85974 7202.85698 0.00001 1.00000 0.99780 421673546.89229B6EATING(1) 9.32784 7202.85853 0.00000 1.00000 0.99897 11246.82413B6BATHIN(1) -2.03175 41.71401 0.00237 1.00000 0.96115 0.13111B6WALKIN(1) 0.98608 41.69646 0.00056 1.00000 0.98113 2.68072B7LIGHT(1) 0.22836 1.86287 0.01503 1.00000 0.90243 1.25654B7MANAGI(1) 1.87337 0.72819 6.61851 1.00000 0.01009 6.51020B7PREPM(1) -3.21565 1.56648 4.21391 1.00000 0.04009 0.04013B7SHOPPI(1) 3.08804 0.87897 12.34281 1.00000 0.00044 21.93399B7PUBTRA(1) 1.65616 0.84654 3.82744 1.00000 0.05042 5.23916B8CLEANI(1) 0.72461 1.19682 0.36656 1.00000 0.54488 2.06392B8COOKIN(1) 3.20908 0.94830 11.45183 1.00000 0.00071 24.75642B8HOMDEC(1) 1.11888 0.64857 2.97615 1.00000 0.08450 3.06142B8IRONIN(1) 4.27764 0.73252 34.10116 1.00000 0.00000 72.06987B8CARING(1) 2.26907 0.57065 15.81079 1.00000 0.00007 9.67039B8WASHIN(1) 1.51751 0.60944 6.20012 1.00000 0.01277 4.56087B8WCLOTH(1) 1.63393 0.60584 7.27355 1.00000 0.00700 5.12397B8WDISHE(1) 1.83901 0.74201 6.14251 1.00000 0.01320 6.29030B10HEALT(1) -2.09528 0.73078 8.22071 1.00000 0.00414 0.12304C1MEMBER(1) 0.66267 0.56741 1.36396 1.00000 0.24285 1.93997C2VOLUNT 0.94411 0.58968 2.56341 1.00000 0.10936 2.57052C6PRODUC 0.11467 0.81185 0.01995 1.00000 0.88767 1.12150Constant -42.25201 10158.07219 0.00002 1.00000 0.99668 0.00000
Logistic Regression: 14 VariablesLogistic Regression: 14 Variables B Sig.
I7EMPSTA(1) 1.4243 0.0349I16PREPA(1) 0.7588 0.1532B6WALKIN(1) 4.5917 0.0132B7MANAGI(1) 1.9924 0.0031B7SHOPPI(1) 2.4659 0.0021B7PUBTRA(1) 1.7397 0.0202B8COOKIN(1) 2.4571 0.0015B8HOMDEC(1) 1.0608 0.0553B8IRONIN(1) 3.6348 0.0000B8CARING(1) 1.9034 0.0002B8WASHIN(1) 1.2632 0.0188B8WCLOTH(1) 1.5427 0.0030B8WDISHE(1) 1.9588 0.0031C2VOLUNT 0.6943 0.1696Constant -19.5457 0.0000
Logistic Regression: 11 VariablesLogistic Regression: 11 Variables B Sig.
B6WALKIN(1) 4.1218 0.0179B7MANAGI(1) 2.0202 0.0019B7SHOPPI(1) 2.2135 0.0028B7PUBTRA(1) 1.8509 0.0125B8COOKIN(1) 2.2796 0.0023B8HOMDEC(1) 1.0704 0.0469B8IRONIN(1) 3.4044 0.0000B8CARING(1) 1.9376 0.0001B8WASHIN(1) 1.1254 0.0294B8WCLOTH(1) 1.5623 0.0022B8WDISHE(1) 1.5934 0.0071Constant -16.5552 0.0000
ScorecardScorecard
Summary and ConclusionsSummary and Conclusions
1. Using the 25 variables, out of 354 respondents, 181 are “productive” and 171 are “non-
productive”.
2. Using the same 25 variables and applying Logistic Regression, 11 variables were found to be predictors of productivity among the respondents.
3. There are two Productivity Models generated:– 14-Variable Productivity Model– 11-Variable Productivity Model
Summary and ConclusionsSummary and Conclusions
4. Based on the Havighurst’s paradigm, the data shows that economic activity, membership in civic organizations, and involvement in volunteer work do not seem to discriminate between “productive” and “non-productive” senior citizens.
5. Life activities are the only specific indicators of productivity among senior citizens.
6. A scoring instrument for the 14-Variable Productivity Model and the 11-Variable Productivity Model
RecommendationsRecommendations• The findings of the study led to the
following recommendations:• Plan and conduct personality development
programs• Formulate public policy , maintenance, monitoring,
and evaluation for the productive and democratic participation of senior citizens in community and nation building
• Possibility of adjusting existing labor practices that apply to ageing workers (optional retirement at age 65)
• Inclusion of topics on the elderly or later maturity in the basic and tertiary education home economics curriculum
RecommendationsRecommendations
•A pilot test on the productivity scoring instrument
•Utilization of the Productivity-Scoring Instruments to assess the senior citizens capabilities and preparedness to undergo programs designed to provide them with sustained stimulating environment towards high levels of attentiveness and productivity
•In-depth study using a larger and more varied samples
•Further study on the 14 Predictor Productivity Model
Thank YouThank You