Final Project Update

finalpart2

saaradhaa

Part 2

Author

Saaradhaa M

Published

November 11, 2022

Code

# load libraries.
library(tidyverse)
library(ggplot2)
library(summarytools)
library(interactions)
library(lmtest)
library(sandwich)

knitr::opts_chunk$set(echo = TRUE)

Part 1: Intro

Prior research literature in the social sciences has continually stressed the need for more research on the Global South. However, few papers actually focus on it. Hence, I am interested to learn more about this region. A data source that lends itself useful for this is the World Values Survey, a global survey with an easily accessible database.

I am specifically interested in understanding what drives subjective well-being, which can be interpreted via happiness and life satisfaction (Addai et al., 2013).

Part 1: Research Questions

A. What predicts happiness and life satisfaction in the Global South?

B. Do predictors of happiness and life satisfaction differ between the Global North and South?

This project will be useful to better understand motivations and desires in the Global South, reduce inter-cultural tensions and enhance cross-cultural cohesion. Governments can also benefit from this research in terms of policy prioritization to maximize citizens’ well-being.

Part 1: Hypotheses (Edited Based on Feedback)

Past researchers have studied happiness and life satisfaction in the Global South via the World Values Survey (Addai et al., 2013; Ngamaba, 2016). The studies focused on Ghana and Rwanda respectively. The common predictors of happiness and life satisfaction across both countries were satisfaction with health and income.

Alba (2019) found that happiness was generally greater in the Global North than the Global South, and indicated that future research should attempt to cover the factors behind this, which gave me the impetus for this project.

Edited based on feedback: I refer to Maslow’s hierarchy of needs, where physical and safety needs come first. My thinking is that happiness and well-being in the Global North may depend on more subjective measures, given that health and income-related problems should be relatively more accounted for.

Given the above, we can frame our hypotheses as follows:

H_0A

Health and financial satisfaction will not positively predict happiness and life satisfaction in the Global South.

H_1A

Health and financial satisfaction will positively predict happiness and life satisfaction in the Global South.

H_0B

Health and financial satisfaction will not have a greater impact on happiness and life satisfaction on the Global South than the Global North.

H_1B

Health and financial satisfaction will have a greater impact on happiness and life satisfaction on the Global South than the Global North.

Part 1: Read In Data

I will be working with the most recent wave of the World Values Survey, Wave 7, which was conducted from 2017 to 2022. The data is freely available for non-profit purposes. It must be cited properly and not re-distributed (Haerpfer et al., 2022).

Representative samples of the population aged 18 and above were collected from 59 countries. Data was mostly collected by interviewing respondents at their homes (“WVS Database”, 2022).

I am using the version of Wave 7 released in May 2022.

I will indicate my comments in each code chunk to keep track of my progress.

Code

# read in dataset.
wvs <- read_csv("~/Desktop/2022_Fall/DACSS 603/General/Final Project/WVS/4. Data/WVS_Cross-National_Wave_7_csv_v4_0.csv", show_col_types = FALSE) %>% select("B_COUNTRY_ALPHA", "G_TOWNSIZE", "H_SETTLEMENT", "H_URBRURAL", "O1_LONGITUDE", "O2_LATITUDE", "Q1", "Q2", "Q3", "Q6", "Q46", "Q47", "Q48", "Q49", "Q50", "Q57", "Q171", "Q260", "Q262", "Q263", "Q269", "Q270", "Q271", "Q273", "Q274", "Q275", "Q279", "Q287", "Q288", "Q288R", "Q289", "I_WOMJOB", "I_WOMPOL", "I_WOMEDU", "Q182", "Q184")

The dataset originally had 552 columns. I have selected a subset of columns based on variables used in past papers, as well as some variables I am interested to examine. These include place/area of residence, literacy, demographics, importance of various social aspects, happiness and wellbeing indicators, trust, religiosity, equality of gender/sexual orientation and abortion attitudes.

I will first create a dummy variable for Global North/South. The Global South comprises low- and lower-middle income countries, as defined by the World Bank (“World Bank Country and Lending Groups”, 2022). Global South countries surveyed include Ethiopia, Philippines, Indonesia, Bangladesh, Iran, Kenya, Bolivia, Kyrgyzstan, Lebanon, Tajikistan, Tunisia, Ukraine, Mongolia, Morocco, Egypt, Myanmar, Vietnam, Nicaragua, Zimbabwe, Nigeria and Pakistan.

Code

# create dummy.
wvs <- mutate(wvs, NS = case_when(B_COUNTRY_ALPHA == "ETH" | B_COUNTRY_ALPHA == "PHL" | B_COUNTRY_ALPHA == "IDN" | B_COUNTRY_ALPHA == "BGD" | B_COUNTRY_ALPHA == "IRN" | B_COUNTRY_ALPHA == "KEN" | B_COUNTRY_ALPHA == "BOL" | B_COUNTRY_ALPHA == "KGZ" | B_COUNTRY_ALPHA == "LBN" | B_COUNTRY_ALPHA == "TJK" | B_COUNTRY_ALPHA == "TUN" | B_COUNTRY_ALPHA == "MOR" | B_COUNTRY_ALPHA == "UKR" | B_COUNTRY_ALPHA == "MNG" | B_COUNTRY_ALPHA == "EGY" | B_COUNTRY_ALPHA == "MMR" | B_COUNTRY_ALPHA == "VNM" | B_COUNTRY_ALPHA == "NIC" | B_COUNTRY_ALPHA == "ZWE" | B_COUNTRY_ALPHA == "NGA" | B_COUNTRY_ALPHA == "PAK" ~ "1"))

# replace "NA" with "O" (for Global North).
wvs$NS <- replace_na(wvs$NS, "0")

# change to factor.
wvs$NS <- as.factor(wvs$NS)

# check counts of levels.
wvs %>% select(NS) %>% summary()

 NS       
 0:59178  
 1:28644

Code

# sanity check.
wvs %>% filter(B_COUNTRY_ALPHA == "ETH" | B_COUNTRY_ALPHA == "PHL" | B_COUNTRY_ALPHA == "IDN" | B_COUNTRY_ALPHA == "BGD" | B_COUNTRY_ALPHA == "IRN" | B_COUNTRY_ALPHA == "KEN" | B_COUNTRY_ALPHA == "BOL" | B_COUNTRY_ALPHA == "KGZ" | B_COUNTRY_ALPHA == "LBN" | B_COUNTRY_ALPHA == "TJK" | B_COUNTRY_ALPHA == "TUN" | B_COUNTRY_ALPHA == "MOR" | B_COUNTRY_ALPHA == "UKR" | B_COUNTRY_ALPHA == "MNG" | B_COUNTRY_ALPHA == "EGY" | B_COUNTRY_ALPHA == "MMR" | B_COUNTRY_ALPHA == "VNM" | B_COUNTRY_ALPHA == "NIC" | B_COUNTRY_ALPHA == "ZWE" | B_COUNTRY_ALPHA == "NGA" | B_COUNTRY_ALPHA == "PAK") %>% nrow()

[1] 28644

Code

# rename columns.
names(wvs) <- c("B_COUNTRY_ALPHA", "G_TOWNSIZE", "H_SETTLEMENT", "H_URBRURAL", "Long", "Lat", "FamImpt", "FriendsImpt", "LeisureImpt", "ReligionImpt", "Happiness", "PerceivedHealth", "FOC", "LS", "FS", "Trust", "AttendReligious", "Sex", "Age", "Immigrant", "Citizen", "HHSize", "Parents", "Married", "Kids", "Edu", "Job", "SocialClass", "Income", "IncomeR", "Religion", "I_WOMJOB", "I_WOMPOL", "I_WOMEDU", "homolib", "abortlib", "NS")

The sanity check shows that the creation of the dummy was successful, with 28,644 data points from the Global South.

Part 1: Exploratory Analysis

Code

# check rows, columns and variable types.
str(wvs)

tibble [87,822 × 37] (S3: tbl_df/tbl/data.frame)
 $ B_COUNTRY_ALPHA: chr [1:87822] "CYP" "CYP" "CYP" "CYP" ...
 $ G_TOWNSIZE     : num [1:87822] 6 6 6 6 6 6 6 6 6 6 ...
 $ H_SETTLEMENT   : num [1:87822] 4 4 4 4 4 4 4 4 4 4 ...
 $ H_URBRURAL     : num [1:87822] 1 1 1 1 1 1 1 1 1 1 ...
 $ Long           : num [1:87822] 34.8 34.8 34.8 34.8 34.8 ...
 $ Lat            : num [1:87822] 32.4 32.4 32.4 32.4 32.5 ...
 $ FamImpt        : num [1:87822] 1 1 1 1 1 1 2 1 1 1 ...
 $ FriendsImpt    : num [1:87822] 1 3 2 2 NA 1 2 2 1 2 ...
 $ LeisureImpt    : num [1:87822] 1 1 1 1 2 1 2 1 1 2 ...
 $ ReligionImpt   : num [1:87822] 1 1 1 1 1 3 2 1 3 1 ...
 $ Happiness      : num [1:87822] 2 1 2 2 3 2 2 1 2 3 ...
 $ PerceivedHealth: num [1:87822] 4 2 1 3 3 1 1 1 1 4 ...
 $ FOC            : num [1:87822] 10 5 5 5 3 7 5 5 5 NA ...
 $ LS             : num [1:87822] 8 7 9 5 5 8 4 7 8 9 ...
 $ FS             : num [1:87822] 8 5 5 5 5 7 3 5 8 4 ...
 $ Trust          : num [1:87822] 2 2 2 2 2 2 2 2 2 2 ...
 $ AttendReligious: num [1:87822] 7 2 3 2 4 4 2 4 4 2 ...
 $ Sex            : num [1:87822] 1 2 2 2 2 1 1 2 1 2 ...
 $ Age            : num [1:87822] 61 61 42 64 52 39 61 25 36 77 ...
 $ Immigrant      : num [1:87822] 1 1 2 1 2 2 1 1 1 1 ...
 $ Citizen        : num [1:87822] 1 1 1 1 1 1 1 1 1 1 ...
 $ HHSize         : num [1:87822] 2 4 6 2 8 1 2 3 2 3 ...
 $ Parents        : num [1:87822] 1 1 1 1 1 1 1 1 1 1 ...
 $ Married        : num [1:87822] 1 1 1 1 5 3 1 1 2 1 ...
 $ Kids           : num [1:87822] 2 2 4 2 3 2 2 1 2 3 ...
 $ Edu            : num [1:87822] 1 1 4 3 3 3 2 3 1 0 ...
 $ Job            : num [1:87822] 1 1 5 1 1 1 1 7 1 5 ...
 $ SocialClass    : num [1:87822] 3 4 3 3 5 5 5 5 NA 4 ...
 $ Income         : num [1:87822] 5 5 3 5 3 5 3 5 7 3 ...
 $ IncomeR        : num [1:87822] 2 2 1 2 1 2 1 2 2 1 ...
 $ Religion       : num [1:87822] 3 3 3 3 3 3 3 3 3 3 ...
 $ I_WOMJOB       : num [1:87822] 0.75 0.5 1 0.75 0.5 0.5 0.5 0.75 0.5 0.5 ...
 $ I_WOMPOL       : num [1:87822] 0.66 NA 1 0.66 NA 0.33 0.66 0.66 NA 0.33 ...
 $ I_WOMEDU       : num [1:87822] 0.66 1 1 0.66 0.33 1 0.66 0.66 0.66 0.66 ...
 $ homolib        : num [1:87822] 1 1 5 1 1 3 1 4 1 1 ...
 $ abortlib       : num [1:87822] 1 1 1 1 1 3 1 3 1 1 ...
 $ NS             : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...

Code

# check basic descriptive statistics.
summary(wvs)

 B_COUNTRY_ALPHA      G_TOWNSIZE     H_SETTLEMENT     H_URBRURAL   
 Length:87822       Min.   :1.000   Min.   :1.000   Min.   :1.000  
 Class :character   1st Qu.:3.000   1st Qu.:2.000   1st Qu.:1.000  
 Mode  :character   Median :6.000   Median :3.000   Median :1.000  
                    Mean   :5.312   Mean   :3.066   Mean   :1.318  
                    3rd Qu.:8.000   3rd Qu.:5.000   3rd Qu.:2.000  
                    Max.   :8.000   Max.   :5.000   Max.   :2.000  
                    NA's   :1274    NA's   :207     NA's   :32     
      Long              Lat            FamImpt       FriendsImpt   
 Min.   :-156.34   Min.   :-43.26   Min.   :1.000   Min.   :1.000  
 1st Qu.:   7.66   1st Qu.:  6.99   1st Qu.:1.000   1st Qu.:1.000  
 Median :  39.94   Median : 24.75   Median :1.000   Median :2.000  
 Mean   :  36.16   Mean   : 21.35   Mean   :1.112   Mean   :1.721  
 3rd Qu.: 100.27   3rd Qu.: 35.70   3rd Qu.:1.000   3rd Qu.:2.000  
 Max.   : 156.89   Max.   :100.35   Max.   :4.000   Max.   :4.000  
 NA's   :27098     NA's   :27094    NA's   :146     NA's   :289    
  LeisureImpt     ReligionImpt     Happiness     PerceivedHealth
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.000  
 Median :2.000   Median :2.000   Median :2.000   Median :2.000  
 Mean   :1.788   Mean   :1.938   Mean   :1.857   Mean   :2.194  
 3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000  
 Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :5.000  
 NA's   :473     NA's   :831     NA's   :574     NA's   :254    
      FOC               LS               FS             Trust      
 Min.   : 1.000   Min.   : 1.000   Min.   : 1.000   Min.   :1.000  
 1st Qu.: 6.000   1st Qu.: 6.000   1st Qu.: 5.000   1st Qu.:2.000  
 Median : 7.000   Median : 7.000   Median : 6.000   Median :2.000  
 Mean   : 7.203   Mean   : 7.043   Mean   : 6.172   Mean   :1.765  
 3rd Qu.: 9.000   3rd Qu.: 9.000   3rd Qu.: 8.000   3rd Qu.:2.000  
 Max.   :10.000   Max.   :10.000   Max.   :10.000   Max.   :2.000  
 NA's   :800      NA's   :393      NA's   :545      NA's   :1198   
 AttendReligious      Sex             Age           Immigrant    
 Min.   :1.000   Min.   :1.000   Min.   : 16.00   Min.   :1.000  
 1st Qu.:2.000   1st Qu.:1.000   1st Qu.: 29.00   1st Qu.:1.000  
 Median :4.000   Median :2.000   Median : 41.00   Median :1.000  
 Mean   :4.139   Mean   :1.525   Mean   : 42.85   Mean   :1.059  
 3rd Qu.:6.000   3rd Qu.:2.000   3rd Qu.: 55.00   3rd Qu.:1.000  
 Max.   :7.000   Max.   :2.000   Max.   :103.00   Max.   :2.000  
 NA's   :1034    NA's   :62      NA's   :339      NA's   :344    
    Citizen          HHSize          Parents         Married    
 Min.   :1.000   Min.   : 1.000   Min.   :1.000   Min.   :1.00  
 1st Qu.:1.000   1st Qu.: 2.000   1st Qu.:1.000   1st Qu.:1.00  
 Median :1.000   Median : 4.000   Median :1.000   Median :1.00  
 Mean   :1.022   Mean   : 3.945   Mean   :1.353   Mean   :2.65  
 3rd Qu.:1.000   3rd Qu.: 5.000   3rd Qu.:2.000   3rd Qu.:5.00  
 Max.   :2.000   Max.   :63.000   Max.   :4.000   Max.   :6.00  
 NA's   :5164    NA's   :852      NA's   :1438    NA's   :504   
      Kids             Edu             Job        SocialClass   
 Min.   : 0.000   Min.   :0.000   Min.   :1.00   Min.   :1.000  
 1st Qu.: 0.000   1st Qu.:2.000   1st Qu.:1.00   1st Qu.:3.000  
 Median : 2.000   Median :3.000   Median :3.00   Median :3.000  
 Mean   : 1.766   Mean   :3.546   Mean   :3.13   Mean   :3.255  
 3rd Qu.: 3.000   3rd Qu.:5.000   3rd Qu.:5.00   3rd Qu.:4.000  
 Max.   :24.000   Max.   :8.000   Max.   :8.00   Max.   :5.000  
 NA's   :1201     NA's   :818     NA's   :1143   NA's   :2302   
     Income          IncomeR         Religion        I_WOMJOB     
 Min.   : 1.000   Min.   :1.000   Min.   :0.000   Min.   :0.0000  
 1st Qu.: 3.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:0.2500  
 Median : 5.000   Median :2.000   Median :3.000   Median :0.5000  
 Mean   : 4.859   Mean   :1.841   Mean   :3.005   Mean   :0.5075  
 3rd Qu.: 6.000   3rd Qu.:2.000   3rd Qu.:5.000   3rd Qu.:0.7500  
 Max.   :10.000   Max.   :3.000   Max.   :9.000   Max.   :1.0000  
 NA's   :2330     NA's   :2330    NA's   :2485    NA's   :648     
    I_WOMPOL         I_WOMEDU         homolib          abortlib      NS       
 Min.   :0.0000   Min.   :0.0000   Min.   : 1.000   Min.   : 1.000   0:59178  
 1st Qu.:0.3300   1st Qu.:0.6600   1st Qu.: 1.000   1st Qu.: 1.000   1:28644  
 Median :0.6600   Median :0.6600   Median : 2.000   Median : 2.000            
 Mean   :0.5427   Mean   :0.6649   Mean   : 3.864   Mean   : 3.407            
 3rd Qu.:0.6600   3rd Qu.:1.0000   3rd Qu.: 6.000   3rd Qu.: 5.000            
 Max.   :1.0000   Max.   :1.0000   Max.   :10.000   Max.   :10.000            
 NA's   :2222     NA's   :1250     NA's   :5691     NA's   :1979

Code

print(dfSummary(wvs, varnumbers = FALSE, plain.ascii = FALSE, graph.magnif = 0.30, style = "grid", valid.col = FALSE), 
      method = 'render', table.classes = 'table-condensed')

Data Frame Summary

wvs

Dimensions: 87822 x 37
Duplicates: 45

Variable

Stats / Values

Freqs (% of Valid)

Graph

Missing

B_COUNTRY_ALPHA [character]

1. CAN

2. IDN

3. CHN

4. USA

5. TUR

6. NLD

7. HKG

8. BOL

9. SGP

10. PAK

[ 49 others ]

4018	(	4.6%	)
3200	(	3.6%	)
3036	(	3.5%	)
2596	(	3.0%	)
2415	(	2.7%	)
2145	(	2.4%	)
2075	(	2.4%	)
2067	(	2.4%	)
2012	(	2.3%	)
1995	(	2.3%	)
62263	(	70.9%	)

0 (0.0%)

G_TOWNSIZE [numeric]

Mean (sd) : 5.3 (2.4)

min ≤ med ≤ max:

1 ≤ 6 ≤ 8

IQR (CV) : 5 (0.5)

1	:	8337	(	9.6%	)
2	:	8407	(	9.7%	)
3	:	7237	(	8.4%	)
4	:	6240	(	7.2%	)
5	:	10100	(	11.7%	)
6	:	8447	(	9.8%	)
7	:	15461	(	17.9%	)
8	:	22319	(	25.8%	)

1274 (1.5%)

H_SETTLEMENT [numeric]

Mean (sd) : 3.1 (1.5)

min ≤ med ≤ max:

1 ≤ 3 ≤ 5

IQR (CV) : 3 (0.5)

1	:	18302	(	20.9%	)
2	:	17840	(	20.4%	)
3	:	14116	(	16.1%	)
4	:	14500	(	16.5%	)
5	:	22857	(	26.1%	)

207 (0.2%)

H_URBRURAL [numeric]

Min : 1

Mean : 1.3

Max : 2

1	:	59862	(	68.2%	)
2	:	27928	(	31.8%	)

32 (0.0%)

Long [numeric]

Mean (sd) : 36.2 (68.1)

min ≤ med ≤ max:

-156.3 ≤ 39.9 ≤ 156.9

IQR (CV) : 92.6 (1.9)

5482 distinct values

27098 (30.9%)

Lat [numeric]

Mean (sd) : 21.4 (20)

min ≤ med ≤ max:

-43.3 ≤ 24.8 ≤ 100.3

IQR (CV) : 28.7 (0.9)

3911 distinct values

27094 (30.9%)

FamImpt [numeric]

Mean (sd) : 1.1 (0.4)

min ≤ med ≤ max:

1 ≤ 1 ≤ 4

IQR (CV) : 0 (0.3)

1	:	78979	(	90.1%	)
2	:	7722	(	8.8%	)
3	:	793	(	0.9%	)
4	:	182	(	0.2%	)

146 (0.2%)

FriendsImpt [numeric]

Mean (sd) : 1.7 (0.7)

min ≤ med ≤ max:

1 ≤ 2 ≤ 4

IQR (CV) : 1 (0.4)

1	:	38026	(	43.4%	)
2	:	37657	(	43.0%	)
3	:	10105	(	11.5%	)
4	:	1745	(	2.0%	)

289 (0.3%)

LeisureImpt [numeric]

Mean (sd) : 1.8 (0.8)

min ≤ med ≤ max:

1 ≤ 2 ≤ 4

IQR (CV) : 1 (0.4)

1	:	35509	(	40.7%	)
2	:	37328	(	42.7%	)
3	:	12046	(	13.8%	)
4	:	2466	(	2.8%	)

473 (0.5%)

ReligionImpt [numeric]

Mean (sd) : 1.9 (1.1)

min ≤ med ≤ max:

1 ≤ 2 ≤ 4

IQR (CV) : 2 (0.6)

1	:	42455	(	48.8%	)
2	:	18300	(	21.0%	)
3	:	15396	(	17.7%	)
4	:	10840	(	12.5%	)

831 (0.9%)

Happiness [numeric]

Mean (sd) : 1.9 (0.7)

min ≤ med ≤ max:

1 ≤ 2 ≤ 4

IQR (CV) : 1 (0.4)

1	:	27071	(	31.0%	)
2	:	47564	(	54.5%	)
3	:	10659	(	12.2%	)
4	:	1954	(	2.2%	)

574 (0.7%)

PerceivedHealth [numeric]

Mean (sd) : 2.2 (0.9)

min ≤ med ≤ max:

1 ≤ 2 ≤ 5

IQR (CV) : 1 (0.4)

1	:	19021	(	21.7%	)
2	:	38932	(	44.5%	)
3	:	24210	(	27.6%	)
4	:	4434	(	5.1%	)
5	:	971	(	1.1%	)

254 (0.3%)

FOC [numeric]

Mean (sd) : 7.2 (2.3)

min ≤ med ≤ max:

1 ≤ 7 ≤ 10

IQR (CV) : 3 (0.3)

1	:	2162	(	2.5%	)
2	:	1243	(	1.4%	)
3	:	2445	(	2.8%	)
4	:	3500	(	4.0%	)
5	:	10992	(	12.6%	)
6	:	9521	(	10.9%	)
7	:	13833	(	15.9%	)
8	:	16714	(	19.2%	)
9	:	7937	(	9.1%	)
10	:	18675	(	21.5%	)

800 (0.9%)

LS [numeric]

Mean (sd) : 7 (2.3)

min ≤ med ≤ max:

1 ≤ 7 ≤ 10

IQR (CV) : 3 (0.3)

1	:	2484	(	2.8%	)
2	:	1284	(	1.5%	)
3	:	2875	(	3.3%	)
4	:	4064	(	4.6%	)
5	:	10620	(	12.1%	)
6	:	10040	(	11.5%	)
7	:	14730	(	16.8%	)
8	:	17517	(	20.0%	)
9	:	8939	(	10.2%	)
10	:	14876	(	17.0%	)

393 (0.4%)

FS [numeric]

Mean (sd) : 6.2 (2.4)

min ≤ med ≤ max:

1 ≤ 6 ≤ 10

IQR (CV) : 3 (0.4)

1	:	4989	(	5.7%	)
2	:	2834	(	3.2%	)
3	:	5059	(	5.8%	)
4	:	6455	(	7.4%	)
5	:	14206	(	16.3%	)
6	:	11865	(	13.6%	)
7	:	13932	(	16.0%	)
8	:	13170	(	15.1%	)
9	:	5783	(	6.6%	)
10	:	8984	(	10.3%	)

545 (0.6%)

Trust [numeric]

Min : 1

Mean : 1.8

Max : 2

1	:	20326	(	23.5%	)
2	:	66298	(	76.5%	)

1198 (1.4%)

AttendReligious [numeric]

Mean (sd) : 4.1 (2.2)

min ≤ med ≤ max:

1 ≤ 4 ≤ 7

IQR (CV) : 4 (0.5)

1	:	11941	(	13.8%	)
2	:	16094	(	18.5%	)
3	:	8890	(	10.2%	)
4	:	13579	(	15.6%	)
5	:	4512	(	5.2%	)
6	:	10835	(	12.5%	)
7	:	20937	(	24.1%	)

1034 (1.2%)

Sex [numeric]

Min : 1

Mean : 1.5

Max : 2

1	:	41654	(	47.5%	)
2	:	46106	(	52.5%	)

62 (0.1%)

Age [numeric]

Mean (sd) : 42.9 (16.4)

min ≤ med ≤ max:

16 ≤ 41 ≤ 103

IQR (CV) : 26 (0.4)

85 distinct values

339 (0.4%)

Immigrant [numeric]

Min : 1

Mean : 1.1

Max : 2

1	:	82299	(	94.1%	)
2	:	5179	(	5.9%	)

344 (0.4%)

Citizen [numeric]

Min : 1

Mean : 1

Max : 2

1	:	80826	(	97.8%	)
2	:	1832	(	2.2%	)

5164 (5.9%)

HHSize [numeric]

Mean (sd) : 3.9 (2.2)

min ≤ med ≤ max:

1 ≤ 4 ≤ 63

IQR (CV) : 3 (0.6)

33 distinct values

852 (1.0%)

Parents [numeric]

Mean (sd) : 1.4 (0.6)

min ≤ med ≤ max:

1 ≤ 1 ≤ 4

IQR (CV) : 1 (0.4)

1	:	61004	(	70.6%	)
2	:	20796	(	24.1%	)
3	:	4048	(	4.7%	)
4	:	536	(	0.6%	)

1438 (1.6%)

Married [numeric]

Mean (sd) : 2.6 (2.1)

min ≤ med ≤ max:

1 ≤ 1 ≤ 6

IQR (CV) : 4 (0.8)

1	:	49193	(	56.3%	)
2	:	6782	(	7.8%	)
3	:	3614	(	4.1%	)
4	:	1909	(	2.2%	)
5	:	4770	(	5.5%	)
6	:	21050	(	24.1%	)

504 (0.6%)

Kids [numeric]

Mean (sd) : 1.8 (1.7)

min ≤ med ≤ max:

0 ≤ 2 ≤ 24

IQR (CV) : 3 (1)

23 distinct values

1201 (1.4%)

Edu [numeric]

Mean (sd) : 3.5 (2)

min ≤ med ≤ max:

0 ≤ 3 ≤ 8

IQR (CV) : 3 (0.6)

0	:	4690	(	5.4%	)
1	:	10721	(	12.3%	)
2	:	12179	(	14.0%	)
3	:	22178	(	25.5%	)
4	:	8235	(	9.5%	)
5	:	7446	(	8.6%	)
6	:	15158	(	17.4%	)
7	:	5402	(	6.2%	)
8	:	995	(	1.1%	)

818 (0.9%)

Job [numeric]

Mean (sd) : 3.1 (2.1)

min ≤ med ≤ max:

1 ≤ 3 ≤ 8

IQR (CV) : 4 (0.7)

1	:	31351	(	36.2%	)
2	:	7467	(	8.6%	)
3	:	12868	(	14.8%	)
4	:	10126	(	11.7%	)
5	:	12073	(	13.9%	)
6	:	5034	(	5.8%	)
7	:	6783	(	7.8%	)
8	:	977	(	1.1%	)

1143 (1.3%)

SocialClass [numeric]

Mean (sd) : 3.3 (1)

min ≤ med ≤ max:

1 ≤ 3 ≤ 5

IQR (CV) : 1 (0.3)

1	:	1453	(	1.7%	)
2	:	18048	(	21.1%	)
3	:	33302	(	38.9%	)
4	:	22649	(	26.5%	)
5	:	10068	(	11.8%	)

2302 (2.6%)

Income [numeric]

Mean (sd) : 4.9 (2.1)

min ≤ med ≤ max:

1 ≤ 5 ≤ 10

IQR (CV) : 3 (0.4)

1	:	6904	(	8.1%	)
2	:	5163	(	6.0%	)
3	:	9612	(	11.2%	)
4	:	11839	(	13.8%	)
5	:	20733	(	24.3%	)
6	:	13216	(	15.5%	)
7	:	9950	(	11.6%	)
8	:	4966	(	5.8%	)
9	:	1417	(	1.7%	)
10	:	1692	(	2.0%	)

2330 (2.7%)

IncomeR [numeric]

Mean (sd) : 1.8 (0.6)

min ≤ med ≤ max:

1 ≤ 2 ≤ 3

IQR (CV) : 1 (0.3)

1	:	21679	(	25.4%	)
2	:	55738	(	65.2%	)
3	:	8075	(	9.4%	)

2330 (2.7%)

Religion [numeric]

Mean (sd) : 3 (2.6)

min ≤ med ≤ max:

0 ≤ 3 ≤ 9

IQR (CV) : 4 (0.9)

0	:	19919	(	23.3%	)
1	:	16027	(	18.8%	)
2	:	6508	(	7.6%	)
3	:	7762	(	9.1%	)
4	:	235	(	0.3%	)
5	:	23807	(	27.9%	)
6	:	569	(	0.7%	)
7	:	5556	(	6.5%	)
8	:	2777	(	3.3%	)
9	:	2177	(	2.6%	)

2485 (2.8%)

I_WOMJOB [numeric]

Mean (sd) : 0.5 (0.3)

min ≤ med ≤ max:

0 ≤ 0.5 ≤ 1

IQR (CV) : 0.5 (0.7)

5 distinct values

648 (0.7%)

I_WOMPOL [numeric]

Mean (sd) : 0.5 (0.3)

min ≤ med ≤ max:

0 ≤ 0.7 ≤ 1

IQR (CV) : 0.3 (0.6)

4 distinct values

2222 (2.5%)

I_WOMEDU [numeric]

Mean (sd) : 0.7 (0.3)

min ≤ med ≤ max:

0 ≤ 0.7 ≤ 1

IQR (CV) : 0.3 (0.4)

4 distinct values

1250 (1.4%)

homolib [numeric]

Mean (sd) : 3.9 (3.3)

min ≤ med ≤ max:

1 ≤ 2 ≤ 10

IQR (CV) : 5 (0.9)

1	:	37570	(	45.7%	)
2	:	4967	(	6.0%	)
3	:	4037	(	4.9%	)
4	:	3211	(	3.9%	)
5	:	9115	(	11.1%	)
6	:	3866	(	4.7%	)
7	:	2893	(	3.5%	)
8	:	3366	(	4.1%	)
9	:	2090	(	2.5%	)
10	:	11016	(	13.4%	)

5691 (6.5%)

abortlib [numeric]

Mean (sd) : 3.4 (2.9)

min ≤ med ≤ max:

1 ≤ 2 ≤ 10

IQR (CV) : 4 (0.9)

1	:	39931	(	46.5%	)
2	:	6502	(	7.6%	)
3	:	5802	(	6.8%	)
4	:	4207	(	4.9%	)
5	:	10290	(	12.0%	)
6	:	4357	(	5.1%	)
7	:	3450	(	4.0%	)
8	:	3748	(	4.4%	)
9	:	2019	(	2.4%	)
10	:	5537	(	6.5%	)

1979 (2.3%)

NS [factor]

1. 0

2. 1

59178	(	67.4%	)
28644	(	32.6%	)

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-11-11

The dataset has 87,822 rows, each representing one participant, and 37 columns. Referring to the codebook, these are some noteworthy descriptive statistics:

Respondents tended to come from more urban settings (H_URBRURAL).
On average, family was more likely to be perceived as important than friends, leisure time and religion (FamImpt, FriendsImpt, LeisureImpt, ReligionImpt).
On average, people were “quite happy” (the second-highest option for Happiness).
Life satisfaction tended to be 7/10 (LS).
People tended to err on the side of caution when it came to trusting others (Trust).
Households had 4 people on average, with maximum household size being 63 (HHSize)!
The interquartile range for education was lower secondary to short-cycle tertiary education (Edu).
For the survey variables (FamImpt to abortlib), missing data ranged from 0.2% to 6.5%, which is acceptable.
67.4% of respondents were from the Global North (NS).

Let’s check if life satisfaction and happiness differ between the Global North and South.

Code

t.test(Happiness ~ NS, wvs)


    Welch Two Sample t-test

data:  Happiness by NS
t = 4.1272, df = 49878, p-value = 3.677e-05
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 0.01164249 0.03270103
sample estimates:
mean in group 0 mean in group 1 
       1.863945        1.841774

Code

t.test(LS ~ NS, wvs)


    Welch Two Sample t-test

data:  LS by NS
t = 13.283, df = 47990, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 0.1962795 0.2642322
sample estimates:
mean in group 0 mean in group 1 
       7.117885        6.887629

The Welch’s two-sample t-tests show that there is a significant difference in happiness and life satisfaction between the Global North and South, where the former has higher mean values for both, p < .001. This echoes Alba (2019)’s finding on happiness, and adds new knowledge to the literature regarding life satisfaction.

We can also create graphs to visualize the latitude and longitude of countries in the Global North and Global South.

Code

ggplot(wvs) + geom_bin2d(mapping = aes(x = Long, y = Lat)) + facet_wrap(vars(NS))

Warning: Removed 27098 rows containing non-finite values (stat_bin2d).

The graph above shows that the Global North (“0”) and South (“1”) are not neatly divided by physical location, due to the existence of developed countries physically located in the South (e.g., South Korea) and developing countries physically located in the North (e.g., Ukraine).

Part 2: Intro

In the previous post, I discussed the prior literature on the topic (predictors of happiness and life satisfaction in Global South vs. North), my RQs and hypotheses, and explored the dataset (WVS Wave 7). I was given feedback to explain my hypotheses better and have edited that section accordingly.

I have added one variable, SocialClass, which was a significant predictor in past papers that I missed out previously. I have also removed several variables I feel are not useful for my RQs (e.g., number of women in parliament).

The response variables are Happiness and LS (life satisfaction). They will be measured separately, as done in prior papers.

The main explanatory variables are PerceivedHealth, FS (financial satisfaction) and NS (country type: North vs. South). A potential interaction between the explanatory variables will be included.

Some notes before commencing analysis:

It is important to note that NS cannot be transformed, since it is categorical. However, if required, log/quadratic transformations can be done for PerceivedHealth or FS.
Since each row represents one participant, the unit of analysis is at the participant level. The NS dummy refers to where the participant comes from, either the Global North or the Global South.
I will be treating FamImpt, FriendsImpt, LeisureImpt, ReligionImpt and Happiness as continuous. I will attempt to verify this for RQ B by running two regressions with FamImpt as categorical vs. continuous.
I went through the variables again and realized I have to change many of them to factors, and reverse code some of them. I will do this in the code chunk below before generating the models.

Code

# change the following variables to factor type.
wvs <- wvs %>% mutate(across(c(B_COUNTRY_ALPHA,H_SETTLEMENT,H_URBRURAL,Trust,Sex,Immigrant,Citizen,Parents,Married,Job,Religion), as.factor))

# reverse code the following variables, such that the largest number reflects agreement.
wvs$FamImpt <- 5-wvs$FamImpt
wvs$FriendsImpt <- 5-wvs$FriendsImpt
wvs$LeisureImpt <- 5-wvs$LeisureImpt
wvs$ReligionImpt <- 5-wvs$ReligionImpt
wvs$Happiness <- 5-wvs$Happiness
wvs$PerceivedHealth <- 6-wvs$PerceivedHealth
wvs$AttendReligious <-8-wvs$AttendReligious
wvs$SocialClass <- 6-wvs$SocialClass

Part 2: Regression Models (RQ A)

Recap: H_1A

Health and financial satisfaction will positively predict happiness and life satisfaction in the Global South.

To test this hypothesis, I will:
- Filter the dataset to only include observations from the Global South.
- Create plots of health and financial satisfaction against happiness and life satisfaction.
- Run a regression model with these variables. Previous papers did not test an interaction between the variables, so I will not do so. There also does not seem to be a meaningful reason to do so.

Code

# create subset of dataset.
subset <- wvs %>% filter(NS == "1")

# i first made bar plots with facet wrapping, but this was not ideal. i also tried a jitter plot (code below), but it also didn't work. i'll use boxplots in the end.
# ggplot(subset, aes(x = FS, y = LS)) + geom_jitter(stat = "identity", width = 0.2, height = 0.1, na.rm = T)

# generate boxplots.
boxplot(LS ~ PerceivedHealth, subset)

Code

boxplot(LS ~ FS, subset)

Code

boxplot(Happiness ~ PerceivedHealth, subset)

Code

boxplot(Happiness ~ FS, subset)

Looking at the boxplots, there seems to be a roughly linear positive relationship between all 4 variables. No transformations should be required to run the regression models.

Code

# run regression model for Happiness.
ModelA_H <- lm(Happiness ~ PerceivedHealth + FS, subset)
summary(ModelA_H)


Call:
lm(formula = Happiness ~ PerceivedHealth + FS, data = subset)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.78824 -0.37097  0.01499  0.47464  1.95803 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     1.701889   0.018399   92.50   <2e-16 ***
PerceivedHealth 0.262885   0.004639   56.67   <2e-16 ***
FS              0.077193   0.001582   48.78   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6944 on 28499 degrees of freedom
  (142 observations deleted due to missingness)
Multiple R-squared:  0.2025,    Adjusted R-squared:  0.2024 
F-statistic:  3618 on 2 and 28499 DF,  p-value: < 2.2e-16

Code

# run regression model for Happiness with demographic controls.
ModelA_H_controls <- lm(Happiness ~ PerceivedHealth + FS + Sex + Age + Immigrant + Citizen + HHSize + Parents + Married + Kids + Edu + Job + SocialClass + Income + Religion, subset)
summary(ModelA_H_controls)


Call:
lm(formula = Happiness ~ PerceivedHealth + FS + Sex + Age + Immigrant + 
    Citizen + HHSize + Parents + Married + Kids + Edu + Job + 
    SocialClass + Income + Religion, data = subset)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.85651 -0.38630  0.02411  0.50703  2.13673 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.629e+00  3.488e-02  46.707  < 2e-16 ***
PerceivedHealth  2.608e-01  4.956e-03  52.615  < 2e-16 ***
FS               7.508e-02  1.700e-03  44.172  < 2e-16 ***
Sex2             8.090e-02  9.718e-03   8.324  < 2e-16 ***
Age             -4.815e-05  4.261e-04  -0.113 0.910044    
Immigrant2       5.271e-02  5.848e-02   0.901 0.367467    
Citizen2        -8.387e-02  8.628e-02  -0.972 0.331025    
HHSize           6.807e-03  2.053e-03   3.316 0.000914 ***
Parents2         6.084e-03  1.274e-02   0.478 0.632937    
Parents3         2.492e-02  1.878e-02   1.327 0.184554    
Parents4        -7.208e-02  5.673e-02  -1.270 0.203939    
Married2        -2.291e-02  1.926e-02  -1.190 0.234094    
Married3        -1.878e-01  2.980e-02  -6.302 2.99e-10 ***
Married4        -1.990e-01  3.518e-02  -5.658 1.55e-08 ***
Married5        -1.065e-01  2.120e-02  -5.024 5.09e-07 ***
Married6        -7.052e-02  1.496e-02  -4.715 2.43e-06 ***
Kids             4.017e-03  3.149e-03   1.276 0.202050    
Edu             -1.390e-02  2.447e-03  -5.679 1.37e-08 ***
Job2            -3.250e-03  1.648e-02  -0.197 0.843711    
Job3            -1.926e-02  1.238e-02  -1.555 0.119967    
Job4             2.946e-02  2.234e-02   1.318 0.187377    
Job5            -8.471e-02  1.505e-02  -5.630 1.82e-08 ***
Job6            -2.189e-02  2.068e-02  -1.058 0.289894    
Job7            -5.892e-02  1.607e-02  -3.667 0.000246 ***
Job8            -3.689e-02  4.336e-02  -0.851 0.394827    
SocialClass      3.316e-02  4.753e-03   6.977 3.10e-12 ***
Income          -1.856e-04  2.287e-03  -0.081 0.935317    
Religion1        6.950e-02  1.742e-02   3.991 6.61e-05 ***
Religion2        7.609e-02  2.102e-02   3.620 0.000295 ***
Religion3       -1.818e-02  2.265e-02  -0.803 0.422249    
Religion4       -1.593e-01  8.440e-02  -1.887 0.059135 .  
Religion5        1.240e-02  1.533e-02   0.809 0.418618    
Religion6       -3.304e-02  4.661e-02  -0.709 0.478414    
Religion7       -1.247e-02  2.091e-02  -0.597 0.550772    
Religion8        1.105e-01  2.569e-02   4.302 1.70e-05 ***
Religion9        3.676e-02  4.022e-02   0.914 0.360654    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.69 on 26907 degrees of freedom
  (1701 observations deleted due to missingness)
Multiple R-squared:  0.2118,    Adjusted R-squared:  0.2107 
F-statistic: 206.5 on 35 and 26907 DF,  p-value: < 2.2e-16

Code

# run regression model for LS. for whatever reason, logging PerceivedHealth produced a slightly higher adjusted R^2, but i did not include that because (1) it did not improve the diagnostic plots and (2) PerceivedHealth is not count data (e.g., population/income) that traditionally improves with logging.
ModelA_LS <- lm(LS ~ PerceivedHealth + FS, subset)
summary(ModelA_LS)


Call:
lm(formula = LS ~ PerceivedHealth + FS, data = subset)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.3728 -1.1136  0.0639  1.0965  6.6294 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     2.515388   0.054654   46.02   <2e-16 ***
PerceivedHealth 0.338881   0.013790   24.57   <2e-16 ***
FS              0.516304   0.004706  109.72   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.064 on 28509 degrees of freedom
  (132 observations deleted due to missingness)
Multiple R-squared:  0.3396,    Adjusted R-squared:  0.3395 
F-statistic:  7329 on 2 and 28509 DF,  p-value: < 2.2e-16

Code

# run regression model for LS with demographic controls.
ModelA_LS_controls <- lm(LS ~ PerceivedHealth + FS + Sex + Age + Immigrant + Citizen + HHSize + Parents + Married + Kids + Edu + Job + SocialClass + Income + Religion, subset)
summary(ModelA_LS_controls)


Call:
lm(formula = LS ~ PerceivedHealth + FS + Sex + Age + Immigrant + 
    Citizen + HHSize + Parents + Married + Kids + Edu + Job + 
    SocialClass + Income + Religion, data = subset)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.0351 -1.1901  0.0939  1.1606  7.0551 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      2.252840   0.103753  21.714  < 2e-16 ***
PerceivedHealth  0.374906   0.014743  25.429  < 2e-16 ***
FS               0.501032   0.005058  99.058  < 2e-16 ***
Sex2             0.111760   0.028898   3.867  0.00011 ***
Age              0.005052   0.001267   3.987 6.71e-05 ***
Immigrant2       0.024794   0.172774   0.144  0.88589    
Citizen2         0.012178   0.256561   0.047  0.96214    
HHSize          -0.003993   0.006105  -0.654  0.51306    
Parents2         0.137374   0.037897   3.625  0.00029 ***
Parents3         0.127139   0.055866   2.276  0.02287 *  
Parents4        -0.175944   0.169331  -1.039  0.29879    
Married2         0.038214   0.057252   0.667  0.50448    
Married3        -0.281920   0.088449  -3.187  0.00144 ** 
Married4        -0.363767   0.104647  -3.476  0.00051 ***
Married5        -0.136939   0.062998  -2.174  0.02974 *  
Married6        -0.234748   0.044475  -5.278 1.31e-07 ***
Kids             0.003537   0.009349   0.378  0.70518    
Edu             -0.010311   0.007279  -1.417  0.15663    
Job2             0.125980   0.049015   2.570  0.01017 *  
Job3            -0.061988   0.036841  -1.683  0.09247 .  
Job4            -0.043148   0.066305  -0.651  0.51521    
Job5            -0.108559   0.044760  -2.425  0.01530 *  
Job6            -0.098398   0.061520  -1.599  0.10973    
Job7            -0.220107   0.047815  -4.603 4.18e-06 ***
Job8             0.376925   0.128739   2.928  0.00342 ** 
SocialClass      0.003458   0.014134   0.245  0.80673    
Income           0.019709   0.006803   2.897  0.00377 ** 
Religion1        0.153145   0.051783   2.957  0.00310 ** 
Religion2       -0.323565   0.062492  -5.178 2.26e-07 ***
Religion3       -0.405155   0.067274  -6.022 1.74e-09 ***
Religion4       -0.218467   0.251068  -0.870  0.38423    
Religion5       -0.039050   0.045606  -0.856  0.39187    
Religion6        0.236925   0.138646   1.709  0.08749 .  
Religion7        0.021554   0.062184   0.347  0.72888    
Religion8        0.397010   0.076415   5.195 2.06e-07 ***
Religion9        0.440675   0.119625   3.684  0.00023 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.053 on 26916 degrees of freedom
  (1692 observations deleted due to missingness)
Multiple R-squared:  0.3474,    Adjusted R-squared:  0.3466 
F-statistic: 409.4 on 35 and 26916 DF,  p-value: < 2.2e-16

To summarise, I ran 4 regression models above - 2 each for Happiness and LS (1 with just the main predictors, and 1 with demographic controls). What’s important to note is that in all the models, even with the addition of demographic control variables, PerceivedHealth and FS positively predict Happiness and LS in the Global South, p < .001. The addition of demographic controls also did not improve adjusted R² by much - just ~0.01. For RQ A, we can reject the null hypothesis.

Code

# diagnostic plots for the models above.
par(mfrow = c(2,3)); plot(ModelA_H_controls, which = 1:6)

Code

bptest(ModelA_H_controls)


    studentized Breusch-Pagan test

data:  ModelA_H_controls
BP = 1111.1, df = 35, p-value < 2.2e-16

Code

par(mfrow = c(2,3)); plot(ModelA_LS_controls, which = 1:6)

Code

bptest(ModelA_LS_controls)


    studentized Breusch-Pagan test

data:  ModelA_LS_controls
BP = 1753, df = 35, p-value < 2.2e-16

The diagnostic plots generally seem fine except the scale-location plot, which indicates heteroskedasticity. The Breusch-Pagan test helps to confirm this. One way to correct this would be to use robust standard errors.

Code

# obtain robust standard errors for models.
coeftest(ModelA_H_controls, vcov = vcovHC, type = 'HC1')


t test of coefficients:

                   Estimate  Std. Error t value  Pr(>|t|)    
(Intercept)      1.6290e+00  3.6811e-02 44.2526 < 2.2e-16 ***
PerceivedHealth  2.6076e-01  5.5823e-03 46.7122 < 2.2e-16 ***
FS               7.5078e-02  1.8435e-03 40.7247 < 2.2e-16 ***
Sex2             8.0897e-02  9.7539e-03  8.2938 < 2.2e-16 ***
Age             -4.8146e-05  4.3017e-04 -0.1119 0.9108859    
Immigrant2       5.2707e-02  5.7875e-02  0.9107 0.3624530    
Citizen2        -8.3868e-02  8.0561e-02 -1.0411 0.2978586    
HHSize           6.8075e-03  2.3891e-03  2.8494 0.0043833 ** 
Parents2         6.0841e-03  1.3093e-02  0.4647 0.6421611    
Parents3         2.4917e-02  1.8159e-02  1.3722 0.1700264    
Parents4        -7.2076e-02  6.2383e-02 -1.1554 0.2479456    
Married2        -2.2913e-02  2.0262e-02 -1.1308 0.2581384    
Married3        -1.8780e-01  3.2263e-02 -5.8210 5.918e-09 ***
Married4        -1.9903e-01  4.1600e-02 -4.7843 1.725e-06 ***
Married5        -1.0652e-01  2.2111e-02 -4.8175 1.462e-06 ***
Married6        -7.0524e-02  1.5482e-02 -4.5552 5.256e-06 ***
Kids             4.0168e-03  3.3643e-03  1.1939 0.2325112    
Edu             -1.3898e-02  2.4639e-03 -5.6406 1.711e-08 ***
Job2            -3.2496e-03  1.5661e-02 -0.2075 0.8356224    
Job3            -1.9258e-02  1.2231e-02 -1.5744 0.1153957    
Job4             2.9459e-02  2.2008e-02  1.3386 0.1807214    
Job5            -8.4713e-02  1.4778e-02 -5.7324 1.001e-08 ***
Job6            -2.1886e-02  2.0707e-02 -1.0569 0.2905639    
Job7            -5.8923e-02  1.7249e-02 -3.4160 0.0006365 ***
Job8            -3.6892e-02  5.0343e-02 -0.7328 0.4636812    
SocialClass      3.3162e-02  4.9316e-03  6.7244 1.799e-11 ***
Income          -1.8561e-04  2.3702e-03 -0.0783 0.9375819    
Religion1        6.9505e-02  1.8209e-02  3.8170 0.0001354 ***
Religion2        7.6088e-02  2.2839e-02  3.3315 0.0008650 ***
Religion3       -1.8177e-02  2.1665e-02 -0.8390 0.4014735    
Religion4       -1.5928e-01  8.7627e-02 -1.8177 0.0691182 .  
Religion5        1.2403e-02  1.5230e-02  0.8144 0.4154368    
Religion6       -3.3038e-02  3.8184e-02 -0.8652 0.3869116    
Religion7       -1.2473e-02  2.0610e-02 -0.6052 0.5450570    
Religion8        1.1052e-01  2.5547e-02  4.3261 1.523e-05 ***
Religion9        3.6764e-02  4.3284e-02  0.8494 0.3956905    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Code

coeftest(ModelA_LS_controls, vcov = vcovHC, type = 'HC1')


t test of coefficients:

                  Estimate Std. Error t value  Pr(>|t|)    
(Intercept)      2.2528402  0.1064129 21.1707 < 2.2e-16 ***
PerceivedHealth  0.3749064  0.0162830 23.0244 < 2.2e-16 ***
FS               0.5010321  0.0062128 80.6452 < 2.2e-16 ***
Sex2             0.1117604  0.0289195  3.8645 0.0001116 ***
Age              0.0050525  0.0013206  3.8258 0.0001306 ***
Immigrant2       0.0247937  0.1779629  0.1393 0.8891987    
Citizen2         0.0121777  0.2740275  0.0444 0.9645543    
HHSize          -0.0039932  0.0065037 -0.6140 0.5392264    
Parents2         0.1373743  0.0386397  3.5553 0.0003782 ***
Parents3         0.1271390  0.0562443  2.2605 0.0237995 *  
Parents4        -0.1759444  0.1602356 -1.0980 0.2721988    
Married2         0.0382137  0.0594300  0.6430 0.5202275    
Married3        -0.2819201  0.0976985 -2.8856 0.0039096 ** 
Married4        -0.3637666  0.1143297 -3.1817 0.0014656 ** 
Married5        -0.1369391  0.0649089 -2.1097 0.0348923 *  
Married6        -0.2347479  0.0454759 -5.1620 2.460e-07 ***
Kids             0.0035369  0.0101206  0.3495 0.7267341    
Edu             -0.0103113  0.0071848 -1.4352 0.1512529    
Job2             0.1259797  0.0478372  2.6335 0.0084555 ** 
Job3            -0.0619882  0.0358968 -1.7268 0.0842075 .  
Job4            -0.0431482  0.0646541 -0.6674 0.5045419    
Job5            -0.1085591  0.0442199 -2.4550 0.0140953 *  
Job6            -0.0983980  0.0588659 -1.6716 0.0946227 .  
Job7            -0.2201066  0.0511284 -4.3050 1.676e-05 ***
Job8             0.3769250  0.1471414  2.5617 0.0104230 *  
SocialClass      0.0034580  0.0152135  0.2273 0.8201950    
Income           0.0197092  0.0074973  2.6289 0.0085720 ** 
Religion1        0.1531451  0.0503513  3.0415 0.0023560 ** 
Religion2       -0.3235648  0.0648191 -4.9918 6.019e-07 ***
Religion3       -0.4051554  0.0654854 -6.1870 6.222e-10 ***
Religion4       -0.2184666  0.2857574 -0.7645 0.4445656    
Religion5       -0.0390497  0.0411714 -0.9485 0.3429004    
Religion6        0.2369245  0.1113743  2.1273 0.0334058 *  
Religion7        0.0215541  0.0577008  0.3735 0.7087426    
Religion8        0.3970097  0.0738658  5.3747 7.733e-08 ***
Religion9        0.4406754  0.1037644  4.2469 2.175e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

I generated robust standard errors for the models above. Although the t-values have reduced, the predictors are still positive and significant. Hence, we can still reject the null hypothesis.

Part 2: Regression Models (RQ B)

Recap: H_1B

Health and financial satisfaction will have a greater impact on happiness and life satisfaction on the Global South than the Global North.

I will first run a correlation matrix with all potential numeric variables that might be relevant.

Code

# run correlations for numeric variables (except DVs, which are Happiness and LS).
matrix <- wvs %>% select(PerceivedHealth, FS, G_TOWNSIZE, FamImpt, FriendsImpt, LeisureImpt, ReligionImpt, FOC, AttendReligious, Age, HHSize, Kids, Edu, SocialClass, IncomeR, I_WOMJOB, I_WOMPOL, I_WOMEDU, homolib, abortlib)
cor <- cor(matrix, use="complete.obs")

In the correlation matrix, I am concerned about correlations where r ≥ 0.5 (more conservative than a cut-off of r ≥ 0.7). This is observed between AttendReligious and ReligionImpt; and homolib and abortlib. I will try a few different models to figure out which combination of variables might work best.

Code

# try model with "AttendReligious" and "abortlib".
summary(lm(Happiness ~ PerceivedHealth*FS*NS + G_TOWNSIZE + H_URBRURAL + FamImpt + FriendsImpt + LeisureImpt + FOC + Trust + AttendReligious + Sex + Age + Immigrant + Citizen + HHSize + Parents + Married + Kids + Edu + Job + SocialClass + Income + Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, wvs))


Call:
lm(formula = Happiness ~ PerceivedHealth * FS * NS + G_TOWNSIZE + 
    H_URBRURAL + FamImpt + FriendsImpt + LeisureImpt + FOC + 
    Trust + AttendReligious + Sex + Age + Immigrant + Citizen + 
    HHSize + Parents + Married + Kids + Edu + Job + SocialClass + 
    Income + Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, 
    data = wvs)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.84562 -0.35131 -0.02747  0.46085  2.53907 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             1.0023646  0.0485735  20.636  < 2e-16 ***
PerceivedHealth         0.2417281  0.0091906  26.302  < 2e-16 ***
FS                      0.0752496  0.0055272  13.614  < 2e-16 ***
NS1                    -0.3760471  0.0494237  -7.609 2.81e-14 ***
G_TOWNSIZE             -0.0109366  0.0013678  -7.996 1.31e-15 ***
H_URBRURAL2             0.0398046  0.0071450   5.571 2.54e-08 ***
FamImpt                 0.1207622  0.0070036  17.243  < 2e-16 ***
FriendsImpt             0.0249529  0.0034003   7.339 2.18e-13 ***
LeisureImpt             0.0239952  0.0032088   7.478 7.64e-14 ***
FOC                     0.0324218  0.0011304  28.682  < 2e-16 ***
Trust2                 -0.0242134  0.0061949  -3.909 9.29e-05 ***
AttendReligious         0.0077179  0.0013120   5.882 4.06e-09 ***
Sex2                    0.0448632  0.0053685   8.357  < 2e-16 ***
Age                    -0.0003398  0.0002341  -1.452 0.146603    
Immigrant2              0.0048528  0.0121882   0.398 0.690517    
Citizen2                0.0346332  0.0198507   1.745 0.081045 .  
HHSize                  0.0049448  0.0013326   3.711 0.000207 ***
Parents2               -0.0080024  0.0073853  -1.084 0.278566    
Parents3                0.0269953  0.0116900   2.309 0.020932 *  
Parents4               -0.0226720  0.0315412  -0.719 0.472263    
Married2                0.0392866  0.0093657   4.195 2.74e-05 ***
Married3               -0.1071227  0.0130624  -8.201 2.43e-16 ***
Married4               -0.0757703  0.0165779  -4.571 4.87e-06 ***
Married5               -0.0653700  0.0119429  -5.474 4.43e-08 ***
Married6               -0.0475473  0.0081482  -5.835 5.39e-09 ***
Kids                    0.0104236  0.0019030   5.477 4.33e-08 ***
Edu                    -0.0049675  0.0014208  -3.496 0.000472 ***
Job2                    0.0321825  0.0093302   3.449 0.000562 ***
Job3                    0.0065385  0.0076058   0.860 0.389974    
Job4                    0.0400777  0.0098986   4.049 5.15e-05 ***
Job5                    0.0308835  0.0085066   3.631 0.000283 ***
Job6                    0.0239344  0.0116517   2.054 0.039965 *  
Job7                   -0.0086893  0.0095815  -0.907 0.364470    
Job8                    0.0333899  0.0243857   1.369 0.170928    
SocialClass             0.0325389  0.0029396  11.069  < 2e-16 ***
Income                  0.0007032  0.0014035   0.501 0.616341    
Religion1               0.0519833  0.0083318   6.239 4.43e-10 ***
Religion2               0.0046457  0.0107478   0.432 0.665564    
Religion3              -0.0727053  0.0104960  -6.927 4.34e-12 ***
Religion4              -0.1164567  0.0458200  -2.542 0.011036 *  
Religion5              -0.0820015  0.0084759  -9.675  < 2e-16 ***
Religion6              -0.0568803  0.0292758  -1.943 0.052031 .  
Religion7              -0.0326901  0.0113023  -2.892 0.003825 ** 
Religion8               0.0617715  0.0152881   4.040 5.34e-05 ***
Religion9               0.0269946  0.0157294   1.716 0.086132 .  
I_WOMJOB                0.0183741  0.0090170   2.038 0.041582 *  
I_WOMPOL               -0.0080608  0.0094226  -0.855 0.392295    
I_WOMEDU               -0.0738107  0.0093836  -7.866 3.72e-15 ***
abortlib               -0.0091560  0.0009459  -9.679  < 2e-16 ***
PerceivedHealth:FS     -0.0042135  0.0014208  -2.966 0.003022 ** 
PerceivedHealth:NS1     0.0915541  0.0132100   6.931 4.23e-12 ***
FS:NS1                  0.0522839  0.0080080   6.529 6.67e-11 ***
PerceivedHealth:FS:NS1 -0.0115482  0.0020588  -5.609 2.04e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6226 on 68392 degrees of freedom
  (19377 observations deleted due to missingness)
Multiple R-squared:  0.2346,    Adjusted R-squared:  0.2341 
F-statistic: 403.2 on 52 and 68392 DF,  p-value: < 2.2e-16

Code

# try model with "ReligionImpt" and "abortlib".
ModelB_H <- lm(Happiness ~ PerceivedHealth*FS*NS + G_TOWNSIZE + H_URBRURAL + FamImpt + FriendsImpt + LeisureImpt + ReligionImpt + FOC + Trust + Sex + Age + Immigrant + Citizen + HHSize + Parents + Married + Kids + Edu + Job + SocialClass + Income + Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, wvs)
summary(ModelB_H)


Call:
lm(formula = Happiness ~ PerceivedHealth * FS * NS + G_TOWNSIZE + 
    H_URBRURAL + FamImpt + FriendsImpt + LeisureImpt + ReligionImpt + 
    FOC + Trust + Sex + Age + Immigrant + Citizen + HHSize + 
    Parents + Married + Kids + Edu + Job + SocialClass + Income + 
    Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, data = wvs)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.83838 -0.35003 -0.02486  0.45985  2.58146 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             0.9653591  0.0487346  19.808  < 2e-16 ***
PerceivedHealth         0.2427013  0.0091832  26.429  < 2e-16 ***
FS                      0.0753615  0.0055248  13.641  < 2e-16 ***
NS1                    -0.3875722  0.0493653  -7.851 4.18e-15 ***
G_TOWNSIZE             -0.0104199  0.0013679  -7.617 2.62e-14 ***
H_URBRURAL2             0.0409348  0.0071384   5.734 9.82e-09 ***
FamImpt                 0.1139240  0.0070337  16.197  < 2e-16 ***
FriendsImpt             0.0239321  0.0033986   7.042 1.92e-12 ***
LeisureImpt             0.0218539  0.0032127   6.802 1.04e-11 ***
ReligionImpt            0.0359167  0.0031719  11.323  < 2e-16 ***
FOC                     0.0322169  0.0011295  28.522  < 2e-16 ***
Trust2                 -0.0291083  0.0062043  -4.692 2.72e-06 ***
Sex2                    0.0415730  0.0053662   7.747 9.52e-15 ***
Age                    -0.0002997  0.0002339  -1.281 0.200055    
Immigrant2              0.0069104  0.0121815   0.567 0.570520    
Citizen2                0.0388779  0.0198208   1.961 0.049829 *  
HHSize                  0.0045529  0.0013320   3.418 0.000631 ***
Parents2               -0.0086985  0.0073777  -1.179 0.238390    
Parents3                0.0216617  0.0116667   1.857 0.063356 .  
Parents4               -0.0282459  0.0314608  -0.898 0.369288    
Married2                0.0394323  0.0093613   4.212 2.53e-05 ***
Married3               -0.1125812  0.0130569  -8.622  < 2e-16 ***
Married4               -0.0795349  0.0165963  -4.792 1.65e-06 ***
Married5               -0.0668593  0.0119429  -5.598 2.17e-08 ***
Married6               -0.0500088  0.0081394  -6.144 8.09e-10 ***
Kids                    0.0101190  0.0019017   5.321 1.04e-07 ***
Edu                    -0.0039301  0.0014214  -2.765 0.005694 ** 
Job2                    0.0299734  0.0093275   3.213 0.001312 ** 
Job3                    0.0051187  0.0076043   0.673 0.500861    
Job4                    0.0367885  0.0099071   3.713 0.000205 ***
Job5                    0.0263369  0.0084946   3.100 0.001933 ** 
Job6                    0.0255340  0.0116272   2.196 0.028091 *  
Job7                   -0.0123139  0.0095726  -1.286 0.198321    
Job8                    0.0260058  0.0243860   1.066 0.286237    
SocialClass             0.0331230  0.0029360  11.282  < 2e-16 ***
Income                  0.0007745  0.0014028   0.552 0.580883    
Religion1               0.0356580  0.0084815   4.204 2.62e-05 ***
Religion2              -0.0148305  0.0108456  -1.367 0.171498    
Religion3              -0.0949489  0.0107237  -8.854  < 2e-16 ***
Religion4              -0.1352025  0.0458731  -2.947 0.003207 ** 
Religion5              -0.1116511  0.0090220 -12.375  < 2e-16 ***
Religion6              -0.0804296  0.0294147  -2.734 0.006252 ** 
Religion7              -0.0484974  0.0114371  -4.240 2.23e-05 ***
Religion8               0.0410340  0.0154060   2.664 0.007735 ** 
Religion9               0.0127811  0.0157734   0.810 0.417775    
I_WOMJOB                0.0209233  0.0090190   2.320 0.020349 *  
I_WOMPOL               -0.0063906  0.0094151  -0.679 0.497292    
I_WOMEDU               -0.0730143  0.0093696  -7.793 6.65e-15 ***
abortlib               -0.0075929  0.0009610  -7.901 2.82e-15 ***
PerceivedHealth:FS     -0.0042494  0.0014202  -2.992 0.002771 ** 
PerceivedHealth:NS1     0.0932809  0.0131979   7.068 1.59e-12 ***
FS:NS1                  0.0535637  0.0080021   6.694 2.19e-11 ***
PerceivedHealth:FS:NS1 -0.0117965  0.0020574  -5.734 9.86e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6226 on 68517 degrees of freedom
  (19252 observations deleted due to missingness)
Multiple R-squared:  0.2363,    Adjusted R-squared:  0.2357 
F-statistic: 407.7 on 52 and 68517 DF,  p-value: < 2.2e-16

Code

# run model with "FamImpt" as categorical.
summary(lm(Happiness ~ PerceivedHealth*FS*NS + G_TOWNSIZE + H_URBRURAL + as.factor(FamImpt) + FriendsImpt + LeisureImpt + ReligionImpt + FOC + Trust + Sex + Age + Immigrant + Citizen + HHSize + Parents + Married + Kids + Edu + Job + SocialClass + Income + Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, wvs))


Call:
lm(formula = Happiness ~ PerceivedHealth * FS * NS + G_TOWNSIZE + 
    H_URBRURAL + as.factor(FamImpt) + FriendsImpt + LeisureImpt + 
    ReligionImpt + FOC + Trust + Sex + Age + Immigrant + Citizen + 
    HHSize + Parents + Married + Kids + Edu + Job + SocialClass + 
    Income + Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, 
    data = wvs)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.83836 -0.34998 -0.02473  0.45999  2.60795 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             1.1072339  0.0681742  16.241  < 2e-16 ***
PerceivedHealth         0.2425875  0.0091842  26.414  < 2e-16 ***
FS                      0.0752720  0.0055256  13.622  < 2e-16 ***
NS1                    -0.3882081  0.0493691  -7.863 3.79e-15 ***
G_TOWNSIZE             -0.0104176  0.0013681  -7.615 2.68e-14 ***
H_URBRURAL2             0.0409434  0.0071384   5.736 9.75e-09 ***
as.factor(FamImpt)2     0.0592525  0.0602280   0.984 0.325216    
as.factor(FamImpt)3     0.2033328  0.0549154   3.703 0.000214 ***
as.factor(FamImpt)4     0.3138016  0.0544706   5.761 8.40e-09 ***
FriendsImpt             0.0239401  0.0033987   7.044 1.89e-12 ***
LeisureImpt             0.0218856  0.0032129   6.812 9.72e-12 ***
ReligionImpt            0.0359340  0.0031724  11.327  < 2e-16 ***
FOC                     0.0322200  0.0011296  28.524  < 2e-16 ***
Trust2                 -0.0291466  0.0062056  -4.697 2.65e-06 ***
Sex2                    0.0415226  0.0053664   7.738 1.03e-14 ***
Age                    -0.0002972  0.0002339  -1.270 0.203936    
Immigrant2              0.0069407  0.0121817   0.570 0.568840    
Citizen2                0.0387761  0.0198213   1.956 0.050435 .  
HHSize                  0.0045466  0.0013320   3.413 0.000642 ***
Parents2               -0.0088173  0.0073794  -1.195 0.232147    
Parents3                0.0217277  0.0116670   1.862 0.062561 .  
Parents4               -0.0280973  0.0314612  -0.893 0.371818    
Married2                0.0393610  0.0093636   4.204 2.63e-05 ***
Married3               -0.1123870  0.0130579  -8.607  < 2e-16 ***
Married4               -0.0794961  0.0165964  -4.790 1.67e-06 ***
Married5               -0.0668742  0.0119429  -5.599 2.16e-08 ***
Married6               -0.0499130  0.0081398  -6.132 8.73e-10 ***
Kids                    0.0101127  0.0019017   5.318 1.05e-07 ***
Edu                    -0.0039281  0.0014214  -2.764 0.005719 ** 
Job2                    0.0300083  0.0093277   3.217 0.001295 ** 
Job3                    0.0051541  0.0076045   0.678 0.497914    
Job4                    0.0368074  0.0099073   3.715 0.000203 ***
Job5                    0.0263615  0.0084947   3.103 0.001915 ** 
Job6                    0.0255713  0.0116272   2.199 0.027863 *  
Job7                   -0.0122479  0.0095730  -1.279 0.200754    
Job8                    0.0259292  0.0243864   1.063 0.287665    
SocialClass             0.0331054  0.0029361  11.275  < 2e-16 ***
Income                  0.0007985  0.0014030   0.569 0.569271    
Religion1               0.0355768  0.0084819   4.194 2.74e-05 ***
Religion2              -0.0147994  0.0108457  -1.365 0.172402    
Religion3              -0.0949489  0.0107240  -8.854  < 2e-16 ***
Religion4              -0.1347230  0.0458750  -2.937 0.003318 ** 
Religion5              -0.1115545  0.0090242 -12.362  < 2e-16 ***
Religion6              -0.0803182  0.0294174  -2.730 0.006329 ** 
Religion7              -0.0486226  0.0114391  -4.251 2.14e-05 ***
Religion8               0.0408256  0.0154072   2.650 0.008057 ** 
Religion9               0.0128689  0.0157737   0.816 0.414593    
I_WOMJOB                0.0209234  0.0090190   2.320 0.020348 *  
I_WOMPOL               -0.0064471  0.0094153  -0.685 0.493502    
I_WOMEDU               -0.0729682  0.0093718  -7.786 7.02e-15 ***
abortlib               -0.0075775  0.0009611  -7.884 3.22e-15 ***
PerceivedHealth:FS     -0.0042300  0.0014204  -2.978 0.002902 ** 
PerceivedHealth:NS1     0.0934470  0.0131990   7.080 1.46e-12 ***
FS:NS1                  0.0537264  0.0080034   6.713 1.92e-11 ***
PerceivedHealth:FS:NS1 -0.0118354  0.0020577  -5.752 8.87e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6226 on 68515 degrees of freedom
  (19252 observations deleted due to missingness)
Multiple R-squared:  0.2363,    Adjusted R-squared:  0.2357 
F-statistic: 392.6 on 54 and 68515 DF,  p-value: < 2.2e-16

Code

# try model with "ReligionImpt" and "homolib": adjusted R^2 was lower, so i'm sticking to ModelB_H. significance of main predictors doesn't change either.
summary(lm(Happiness ~ PerceivedHealth*FS*NS + G_TOWNSIZE + H_URBRURAL + FamImpt + FriendsImpt + LeisureImpt + ReligionImpt + FOC + Trust + Sex + Age + Immigrant + Citizen + HHSize + Parents + Married + Kids + Edu + Job + SocialClass + IncomeR + Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + homolib, wvs))


Call:
lm(formula = Happiness ~ PerceivedHealth * FS * NS + G_TOWNSIZE + 
    H_URBRURAL + FamImpt + FriendsImpt + LeisureImpt + ReligionImpt + 
    FOC + Trust + Sex + Age + Immigrant + Citizen + HHSize + 
    Parents + Married + Kids + Edu + Job + SocialClass + IncomeR + 
    Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + homolib, data = wvs)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.82527 -0.34776 -0.02813  0.46170  2.57719 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             9.352e-01  4.939e-02  18.934  < 2e-16 ***
PerceivedHealth         2.391e-01  9.295e-03  25.728  < 2e-16 ***
FS                      7.412e-02  5.575e-03  13.296  < 2e-16 ***
NS1                    -3.655e-01  5.080e-02  -7.194 6.34e-13 ***
G_TOWNSIZE             -7.377e-03  1.393e-03  -5.295 1.20e-07 ***
H_URBRURAL2             4.442e-02  7.274e-03   6.107 1.02e-09 ***
FamImpt                 1.179e-01  7.079e-03  16.661  < 2e-16 ***
FriendsImpt             2.194e-02  3.486e-03   6.293 3.14e-10 ***
LeisureImpt             1.999e-02  3.324e-03   6.013 1.83e-09 ***
ReligionImpt            4.532e-02  3.186e-03  14.224  < 2e-16 ***
FOC                     3.203e-02  1.156e-03  27.711  < 2e-16 ***
Trust2                 -1.941e-02  6.334e-03  -3.064 0.002184 ** 
Sex2                    3.663e-02  5.473e-03   6.693 2.21e-11 ***
Age                    -6.294e-04  2.394e-04  -2.629 0.008555 ** 
Immigrant2              8.568e-03  1.221e-02   0.702 0.482983    
Citizen2                3.916e-02  1.985e-02   1.973 0.048524 *  
HHSize                  2.562e-03  1.377e-03   1.861 0.062696 .  
Parents2               -6.188e-03  7.519e-03  -0.823 0.410536    
Parents3                3.009e-02  1.212e-02   2.483 0.013020 *  
Parents4               -2.584e-02  3.217e-02  -0.803 0.421753    
Married2                3.385e-02  9.428e-03   3.590 0.000331 ***
Married3               -1.082e-01  1.326e-02  -8.162 3.34e-16 ***
Married4               -7.639e-02  1.688e-02  -4.525 6.06e-06 ***
Married5               -6.989e-02  1.231e-02  -5.676 1.39e-08 ***
Married6               -5.380e-02  8.288e-03  -6.491 8.58e-11 ***
Kids                    9.797e-03  1.952e-03   5.019 5.21e-07 ***
Edu                    -6.213e-03  1.460e-03  -4.256 2.09e-05 ***
Job2                    3.939e-02  9.561e-03   4.120 3.80e-05 ***
Job3                    1.194e-02  7.739e-03   1.543 0.122926    
Job4                    3.650e-02  1.012e-02   3.607 0.000310 ***
Job5                    3.441e-02  8.731e-03   3.941 8.11e-05 ***
Job6                    2.356e-02  1.187e-02   1.985 0.047189 *  
Job7                   -2.361e-02  9.918e-03  -2.381 0.017287 *  
Job8                    2.882e-02  2.483e-02   1.161 0.245692    
SocialClass             2.947e-02  2.934e-03  10.046  < 2e-16 ***
IncomeR                 6.624e-03  4.955e-03   1.337 0.181299    
Religion1               3.819e-02  8.529e-03   4.478 7.56e-06 ***
Religion2              -1.602e-02  1.094e-02  -1.464 0.143063    
Religion3              -9.595e-02  1.093e-02  -8.777  < 2e-16 ***
Religion4              -1.403e-01  4.582e-02  -3.061 0.002207 ** 
Religion5              -1.144e-01  9.290e-03 -12.318  < 2e-16 ***
Religion6              -8.127e-02  2.935e-02  -2.769 0.005632 ** 
Religion7              -4.454e-02  1.148e-02  -3.881 0.000104 ***
Religion8               4.505e-02  1.560e-02   2.888 0.003875 ** 
Religion9               1.857e-02  1.585e-02   1.172 0.241385    
I_WOMJOB                8.150e-03  9.281e-03   0.878 0.379913    
I_WOMPOL               -1.148e-02  9.708e-03  -1.182 0.237049    
I_WOMEDU               -6.899e-02  9.770e-03  -7.062 1.66e-12 ***
homolib                 2.897e-05  9.231e-04   0.031 0.974960    
PerceivedHealth:FS     -3.838e-03  1.433e-03  -2.678 0.007418 ** 
PerceivedHealth:NS1     9.339e-02  1.355e-02   6.891 5.60e-12 ***
FS:NS1                  4.705e-02  8.226e-03   5.719 1.08e-08 ***
PerceivedHealth:FS:NS1 -1.121e-02  2.111e-03  -5.309 1.11e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.622 on 65328 degrees of freedom
  (22441 observations deleted due to missingness)
Multiple R-squared:  0.231, Adjusted R-squared:  0.2304 
F-statistic: 377.4 on 52 and 65328 DF,  p-value: < 2.2e-16

Looking at adjusted R², ReligionImpt is preferable to AttendReligious, and abortlib is preferable to homolib.

I also ran another model to see how things change when FamImpt is treated as categorical: adjusted R² remained the same, but the standard errors rose and t-values went down. Hence, I will stick to treating it as numeric (as well as other ordered variables with 4 levels).

Additionally, in past papers, adjusted R² ranged between 0.15 to 0.3 for happiness. Ours is in the higher part of that range (adjusted R² = 0.24).

For the final model I settled on (labelled as ModelB_H above), a three-way interaction between PerceivedHealth, FS and NS was observed. I will plot this graphically.

Code

interact_plot(ModelB_H, pred = PerceivedHealth, modx = FS, modx.values = c(1,10), mod2 = NS)

For people in the Global North (NS = 0), it seems like the magnitude of the relationship between PerceivedHealth and Happiness does not change for different values of FS. However, for those in the Global South, the relationship seems to become less steep for greater values of FS. As FS increases, the impact of PerceivedHealth on Happiness reduces. I think at least partially, this shows that Happiness depends more on PerceivedHealth and FS in the Global South (but I am not 100% sure that my interpretation is correct).

To fully answer the RQ, I now need to re-run the same model with LS as the DV.

Code

# run model.
ModelB_LS <- lm(LS ~ PerceivedHealth*FS*NS + G_TOWNSIZE + H_URBRURAL + FamImpt + FriendsImpt + LeisureImpt + ReligionImpt + FOC + Trust + Sex + Age + Immigrant + Citizen + HHSize + Parents + Married + Kids + Edu + Job + SocialClass + Income + Religion + I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, wvs)
summary(ModelB_LS)


Call:
lm(formula = LS ~ PerceivedHealth * FS * NS + G_TOWNSIZE + H_URBRURAL + 
    FamImpt + FriendsImpt + LeisureImpt + ReligionImpt + FOC + 
    Trust + Sex + Age + Immigrant + Citizen + HHSize + Parents + 
    Married + Kids + Edu + Job + SocialClass + Income + Religion + 
    I_WOMJOB + I_WOMPOL + I_WOMEDU + abortlib, data = wvs)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.0761 -0.9137  0.0669  0.9278  8.1280 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)             0.118394   0.135781   0.872 0.383240    
PerceivedHealth         0.517614   0.025575  20.239  < 2e-16 ***
FS                      0.489514   0.015392  31.803  < 2e-16 ***
NS1                    -0.329556   0.137541  -2.396 0.016575 *  
G_TOWNSIZE             -0.024345   0.003814  -6.384 1.74e-10 ***
H_URBRURAL2            -0.028291   0.019904  -1.421 0.155220    
FamImpt                 0.167848   0.019595   8.566  < 2e-16 ***
FriendsImpt            -0.018183   0.009471  -1.920 0.054868 .  
LeisureImpt             0.018509   0.008955   2.067 0.038750 *  
ReligionImpt            0.058195   0.008839   6.584 4.62e-11 ***
FOC                     0.241036   0.003148  76.576  < 2e-16 ***
Trust2                  0.030117   0.017293   1.742 0.081591 .  
Sex2                    0.055853   0.014955   3.735 0.000188 ***
Age                     0.004196   0.000652   6.436 1.23e-10 ***
Immigrant2              0.048708   0.033967   1.434 0.151591    
Citizen2               -0.005785   0.055315  -0.105 0.916707    
HHSize                 -0.012284   0.003713  -3.308 0.000940 ***
Parents2                0.005514   0.020563   0.268 0.788601    
Parents3                0.026205   0.032533   0.805 0.420553    
Parents4               -0.232785   0.087756  -2.653 0.007988 ** 
Married2                0.209746   0.026089   8.040 9.15e-16 ***
Married3               -0.128045   0.036375  -3.520 0.000432 ***
Married4               -0.107591   0.046250  -2.326 0.020005 *  
Married5               -0.093412   0.033257  -2.809 0.004974 ** 
Married6               -0.101216   0.022679  -4.463 8.10e-06 ***
Kids                    0.019528   0.005297   3.687 0.000227 ***
Edu                    -0.011649   0.003962  -2.940 0.003285 ** 
Job2                    0.095599   0.025989   3.678 0.000235 ***
Job3                   -0.028597   0.021202  -1.349 0.177418    
Job4                    0.069394   0.027600   2.514 0.011932 *  
Job5                    0.052595   0.023680   2.221 0.026348 *  
Job6                    0.054279   0.032410   1.675 0.093984 .  
Job7                   -0.109330   0.026686  -4.097 4.19e-05 ***
Job8                    0.125121   0.068019   1.840 0.065846 .  
SocialClass             0.019626   0.008183   2.398 0.016474 *  
Income                  0.005628   0.003911   1.439 0.150099    
Religion1               0.111241   0.023636   4.706 2.53e-06 ***
Religion2              -0.155857   0.030234  -5.155 2.54e-07 ***
Religion3              -0.269519   0.029872  -9.023  < 2e-16 ***
Religion4              -0.290707   0.127955  -2.272 0.023093 *  
Religion5              -0.314493   0.025148 -12.506  < 2e-16 ***
Religion6              -0.042414   0.081963  -0.517 0.604825    
Religion7              -0.152603   0.031879  -4.787 1.70e-06 ***
Religion8               0.233037   0.042930   5.428 5.71e-08 ***
Religion9              -0.001271   0.043950  -0.029 0.976926    
I_WOMJOB                0.034039   0.025140   1.354 0.175740    
I_WOMPOL                0.081730   0.026240   3.115 0.001842 ** 
I_WOMEDU                0.036452   0.026119   1.396 0.162834    
abortlib               -0.025280   0.002678  -9.439  < 2e-16 ***
PerceivedHealth:FS     -0.027077   0.003957  -6.843 7.81e-12 ***
PerceivedHealth:NS1    -0.027232   0.036778  -0.740 0.459037    
FS:NS1                  0.085490   0.022307   3.832 0.000127 ***
PerceivedHealth:FS:NS1 -0.004343   0.005735  -0.757 0.448892    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.737 on 68623 degrees of freedom
  (19146 observations deleted due to missingness)
Multiple R-squared:  0.411, Adjusted R-squared:  0.4106 
F-statistic: 920.9 on 52 and 68623 DF,  p-value: < 2.2e-16

For the model with LS as the DV, the adjusted R² of 0.41 for life satisfaction is in line with past papers (range: 0.16 to 0.5).

There is no three-way interaction. Only 2 of the two-way interactions are significant - PerceivedHealth*FS and FS*NS. I will plot these graphs to interpret them.

Code

# plot interactions.
interact_plot(ModelB_LS, pred = PerceivedHealth, modx = FS, modx.values = c(1,10))

Code

interact_plot(ModelB_LS, pred = FS, modx = NS)

PerceivedHealth*FS: FS seems to limit the effect of PerceivedHealth on LS, regardless of whether the person is from the Global North/South.
FS*NS: There seems to be a stronger effect of FS on LS for the Global South (NS = 1).

Now I will generate the diagnostic plots for ModelB_H and ModelB_LS.

Code

par(mfrow = c(2,3)); plot(ModelB_H, which = 1:6)

Code

bptest(ModelB_H)


    studentized Breusch-Pagan test

data:  ModelB_H
BP = 3520.9, df = 52, p-value < 2.2e-16

Code

par(mfrow = c(2,3)); plot(ModelB_LS, which = 1:6)

Code

bptest(ModelB_LS)


    studentized Breusch-Pagan test

data:  ModelB_LS
BP = 6066.6, df = 52, p-value < 2.2e-16

Similar to what was observed for RQ A, the scale-location plot indicates heteroskedasticity (confirmed by the Breusch-Pagan test). I will generate robust standard errors for both models.

Code

# obtain robust standard errors for models.
coeftest(ModelB_H, vcov = vcovHC, type = 'HC1')


t test of coefficients:

                          Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)             0.96535914  0.05634466  17.1331 < 2.2e-16 ***
PerceivedHealth         0.24270127  0.01144805  21.2002 < 2.2e-16 ***
FS                      0.07536150  0.00666454  11.3078 < 2.2e-16 ***
NS1                    -0.38757224  0.06503656  -5.9593 2.546e-09 ***
G_TOWNSIZE             -0.01041991  0.00139078  -7.4922 6.856e-14 ***
H_URBRURAL2             0.04093482  0.00730926   5.6004 2.147e-08 ***
FamImpt                 0.11392400  0.00748668  15.2169 < 2.2e-16 ***
FriendsImpt             0.02393210  0.00367876   6.5055 7.798e-11 ***
LeisureImpt             0.02185385  0.00344548   6.3428 2.271e-10 ***
ReligionImpt            0.03591670  0.00323280  11.1101 < 2.2e-16 ***
FOC                     0.03221690  0.00130500  24.6873 < 2.2e-16 ***
Trust2                 -0.02910827  0.00572731  -5.0824 3.737e-07 ***
Sex2                    0.04157302  0.00534434   7.7789 7.418e-15 ***
Age                    -0.00029973  0.00023855  -1.2565  0.208953    
Immigrant2              0.00691044  0.01077333   0.6414  0.521240    
Citizen2                0.03887792  0.01796379   2.1642  0.030449 *  
HHSize                  0.00455286  0.00154128   2.9539  0.003138 ** 
Parents2               -0.00869855  0.00767204  -1.1338  0.256883    
Parents3                0.02166174  0.01212325   1.7868  0.073975 .  
Parents4               -0.02824595  0.03476356  -0.8125  0.416498    
Married2                0.03943227  0.00968852   4.0700 4.707e-05 ***
Married3               -0.11258117  0.01339883  -8.4023 < 2.2e-16 ***
Married4               -0.07953490  0.01853927  -4.2901 1.789e-05 ***
Married5               -0.06685929  0.01270208  -5.2636 1.416e-07 ***
Married6               -0.05000883  0.00828032  -6.0395 1.554e-09 ***
Kids                    0.01011896  0.00206435   4.9018 9.520e-07 ***
Edu                    -0.00393011  0.00142772  -2.7527  0.005912 ** 
Job2                    0.02997341  0.00914822   3.2764  0.001052 ** 
Job3                    0.00511872  0.00782594   0.6541  0.513068    
Job4                    0.03678854  0.00968219   3.7996  0.000145 ***
Job5                    0.02633692  0.00851043   3.0947  0.001971 ** 
Job6                    0.02553402  0.01142623   2.2347  0.025441 *  
Job7                   -0.01231387  0.01055162  -1.1670  0.243209    
Job8                    0.02600579  0.02677949   0.9711  0.331498    
SocialClass             0.03312301  0.00311454  10.6349 < 2.2e-16 ***
Income                  0.00077450  0.00147361   0.5256  0.599183    
Religion1               0.03565801  0.00846033   4.2147 2.504e-05 ***
Religion2              -0.01483054  0.01087377  -1.3639  0.172609    
Religion3              -0.09494892  0.01080759  -8.7854 < 2.2e-16 ***
Religion4              -0.13520247  0.04550734  -2.9710  0.002969 ** 
Religion5              -0.11165106  0.00919236 -12.1461 < 2.2e-16 ***
Religion6              -0.08042960  0.02698805  -2.9802  0.002882 ** 
Religion7              -0.04849738  0.01087110  -4.4611 8.166e-06 ***
Religion8               0.04103402  0.01560114   2.6302  0.008536 ** 
Religion9               0.01278109  0.01514373   0.8440  0.398680    
I_WOMJOB                0.02092325  0.00951658   2.1986  0.027909 *  
I_WOMPOL               -0.00639059  0.01005827  -0.6354  0.525198    
I_WOMEDU               -0.07301433  0.01000503  -7.2978 2.958e-13 ***
abortlib               -0.00759285  0.00097437  -7.7926 6.657e-15 ***
PerceivedHealth:FS     -0.00424937  0.00167731  -2.5334  0.011297 *  
PerceivedHealth:NS1     0.09328088  0.01707000   5.4646 4.655e-08 ***
FS:NS1                  0.05356371  0.00996608   5.3746 7.700e-08 ***
PerceivedHealth:FS:NS1 -0.01179646  0.00251070  -4.6985 2.626e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Code

coeftest(ModelB_LS, vcov = vcovHC, type = 'HC1')


t test of coefficients:

                          Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)             0.11839390  0.15647523   0.7566 0.4492740    
PerceivedHealth         0.51761407  0.03262995  15.8632 < 2.2e-16 ***
FS                      0.48951395  0.01878447  26.0595 < 2.2e-16 ***
NS1                    -0.32955582  0.19624677  -1.6793 0.0930995 .  
G_TOWNSIZE             -0.02434490  0.00386763  -6.2945 3.102e-10 ***
H_URBRURAL2            -0.02829065  0.02086088  -1.3562 0.1750534    
FamImpt                 0.16784807  0.02052719   8.1769 2.962e-16 ***
FriendsImpt            -0.01818347  0.01028087  -1.7687 0.0769533 .  
LeisureImpt             0.01850893  0.00977097   1.8943 0.0581924 .  
ReligionImpt            0.05819543  0.00862565   6.7468 1.523e-11 ***
FOC                     0.24103567  0.00427540  56.3773 < 2.2e-16 ***
Trust2                  0.03011720  0.01557963   1.9331 0.0532263 .  
Sex2                    0.05585260  0.01478280   3.7782 0.0001581 ***
Age                     0.00419594  0.00067759   6.1925 5.957e-10 ***
Immigrant2              0.04870756  0.02822134   1.7259 0.0843677 .  
Citizen2               -0.00578498  0.04589833  -0.1260 0.8997014    
HHSize                 -0.01228380  0.00417933  -2.9392 0.0032919 ** 
Parents2                0.00551351  0.02111411   0.2611 0.7939937    
Parents3                0.02620458  0.03506813   0.7472 0.4549165    
Parents4               -0.23278509  0.08357550  -2.7853 0.0053488 ** 
Married2                0.20974578  0.02636583   7.9552 1.815e-15 ***
Married3               -0.12804540  0.03611182  -3.5458 0.0003917 ***
Married4               -0.10759093  0.04846076  -2.2202 0.0264107 *  
Married5               -0.09341168  0.03561537  -2.6228 0.0087232 ** 
Married6               -0.10121559  0.02296190  -4.4080 1.045e-05 ***
Kids                    0.01952766  0.00589338   3.3135 0.0009219 ***
Edu                    -0.01164860  0.00398015  -2.9267 0.0034272 ** 
Job2                    0.09559875  0.02580836   3.7042 0.0002122 ***
Job3                   -0.02859702  0.02178343  -1.3128 0.1892589    
Job4                    0.06939360  0.02637211   2.6313 0.0085072 ** 
Job5                    0.05259493  0.02402791   2.1889 0.0286067 *  
Job6                    0.05427933  0.03125783   1.7365 0.0824793 .  
Job7                   -0.10932964  0.03054728  -3.5790 0.0003451 ***
Job8                    0.12512155  0.07189406   1.7404 0.0818003 .  
SocialClass             0.01962597  0.00901434   2.1772 0.0294694 *  
Income                  0.00562853  0.00438793   1.2827 0.1995906    
Religion1               0.11124113  0.02298752   4.8392 1.306e-06 ***
Religion2              -0.15585713  0.02994334  -5.2051 1.945e-07 ***
Religion3              -0.26951940  0.02976146  -9.0560 < 2.2e-16 ***
Religion4              -0.29070744  0.12026620  -2.4172 0.0156430 *  
Religion5              -0.31449248  0.02519223 -12.4837 < 2.2e-16 ***
Religion6              -0.04241378  0.07059987  -0.6008 0.5480000    
Religion7              -0.15260268  0.02876183  -5.3057 1.126e-07 ***
Religion8               0.23303695  0.04394884   5.3025 1.146e-07 ***
Religion9              -0.00127116  0.03863632  -0.0329 0.9737539    
I_WOMJOB                0.03403916  0.02709696   1.2562 0.2090483    
I_WOMPOL                0.08173040  0.02906210   2.8123 0.0049208 ** 
I_WOMEDU                0.03645214  0.02900196   1.2569 0.2087994    
abortlib               -0.02528014  0.00273160  -9.2547 < 2.2e-16 ***
PerceivedHealth:FS     -0.02707689  0.00475419  -5.6954 1.236e-08 ***
PerceivedHealth:NS1    -0.02723172  0.05270169  -0.5167 0.6053572    
FS:NS1                  0.08549005  0.02984951   2.8640 0.0041841 ** 
PerceivedHealth:FS:NS1 -0.00434316  0.00765981  -0.5670 0.5707115    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

For ModelB_H, zooming in on the three-way interaction between PerceivedHealth*FS*NS: the magnitude of the t-value reduces, but remains significant, p < .001.

For ModelB_LS, the results are similar: t-values go up, but both PerceivedHealth*FS and FS*NS remain significant.

Summarizing the results for both happiness and life satisfaction:

Perceived health and financial satisfaction seem to have a greater impact on happiness in the Global South.
Financial satisfaction has a greater impact on life satisfaction in the Global South.
However, perceived health did not have a different effect on life satisfaction in the Global North vs. South.

Given the third point, we cannot reject the null hypothesis for RQ B. However, I want to caution that I am not sure if I am interpreting the results correctly.

I am also wondering…is there a function/package in R that can generate the model equations easily for me, especially since I included so many controls?

Moving Forward

This is definitely a work-in-progress and will be edited further upon receiving feedback. Other things I want to try before submitting the final product in December:

I had included variables measuring equality of gender/sexual orientation and abortion attitudes in the regression, just out of curiosity. I would like to interpret whether they had significant effects on happiness and life satisfaction.
There are some outliers indicated in the diagnostic plots. Removing them could potentially improve my final 4 models (ModelA_H_controls, ModelA_LS_controls, ModelB_H and ModelB_LS).

Bibliography

Addai, I., Opoku-Agyeman, C., & Amanfu, S. (2013). Exploring Predictors of Subjective Well-Being in Ghana: A Micro-Level Study. Journal Of Happiness Studies, 15(4), 869-890.

Alba, C. (2019). A Data Analysis of the World Happiness Index and its Relation to the North-South Divide. Undergraduate Economic Review, 16(1).

Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). 2022. World Values Survey: Round Seven - Country-Pooled Datafile Version 4.0. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat.

Ngamaba, K. (2016). Happiness and life satisfaction in Rwanda. Journal Of Psychology In Africa, 26(5), 407-414.

World Bank Country and Lending Groups. World Bank Data Help Desk. (2022). Retrieved from https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

WVS Database. World Values Survey. (2022). Retrieved from https://www.worldvaluessurvey.org/WVSDocumentationWV7.jsp.