Expert answer:Easy homework

  

Solved by verified expert:Read the two articles World Happiness Report (excerpts) and Life, Liberty and the Pursuit of Happiness: Measuring What Matters. (The Gallup Cantrill Scale article is background to the World Happiness Report and explains their methodology). Read them for generalities as opposed to specifics, focusing less on the data itself and more on how the data are presented and discussed.Write a 300-500 word essay, using MLA format and citations as appropriate, addressing the following:1. What is the perspective of each of the authors’?2. What is the context from which they are writing? 3. Which approach/methodology do you think is more effective and why?Your essay should also include an introductory and concluding paragraph/statement.
world_happinesss_report_data.pdf

gallup_cantrill_scale.pdf

Don't use plagiarized sources. Get Your Custom Essay on
Expert answer:Easy homework
Just from $10/Page
Order Essay

life__liberty_and_the_pursuit_of_happiness_measuring_what_matters.pdf

Unformatted Attachment Preview

World Happiness Report 2018
Technical Box 1: Detailed Information About Each of the Predictors in Table 2.1
1.
2.
GDP per capita is in terms of Purchasing
Power Parity (PPP) adjusted to constant
2011 international dollars, taken from
the World Development Indicators
(WD I) released by the World Bank in
September 2017. See A ppend ix 1 for
more details. GDP data for 2017 are not
yet available, so we extend the GDP
time series from 2016 to 2017 using
country-specific forecasts of real GDP
growth from the OECD Econom ic
Outlook No. 102 ( Edition November
2017) and the World Bank’s Global
Economic Prospects (Last Updated:
06/04/2017), after adjustment for
population growth. The equation uses
the natural log of GDP per capita, as
this form fits the data significantly
better than GDP per capita.
The time series of healthy life expectancy
at birth are constructed based on data
from the World Health Organization
(WHO) and W DI. WHO publishes the
data on healthy life expectancy for
the year 2012. The time series of life
expectancies, with no adjustment for
health, are available in W DI. We adopt
the following strategy to construct the
time series of healthy life expectancy
at b irth: first we generate the ratios
of hea lthy life expectancy to life
expectancy in 2012 for countries
with both data. W e then apply the
country-specific ratios to other years
to generate the healthy life expectancy
data. See Appendix 1 for more details.
3.
Social support is the national average
of the binary responses (either O or 1)
to the Gallup World Poll (GWP)
question “If you w ere in trouble, do
you have relatives or friends you can
count on to help you whenever you
need them, or not?”
4.
Freedom to make life choices is the
national average of binary responses to
the GWP question “Are you satisfied o r
dissatisfied with your freedom to
choose what you do with your life?”
5.
Generosity is the residual of regressing
the national average of GWP responses
to the question “Have you donated
money to a charity in the past month?”
on GDP per capita.
6.
Perceptions of corruption are the average
of binary answers to two GWP questions:
“Is corruption widespread throughout the
government or not?” and “Is corruption
widespread within businesses or not?”
Where data for government corruption
are missing, the perception of business
corruption is used as the overall
corruption-perception measure.
7.
Positive affect is defined as the average
of previous-day affect measures for
happiness, laughter, and enjoyment for
GWP waves 3-7 (years 2008 to 2012,
and some in 2013). It is defined as the
average of laughter and enjoyment for
other waves where the happiness
question was not asked.
8 . Negative affect is defined as the average
of previous-day affect measures for worry,
sadness, and anger for all waves. See
Statistical Appendix 1 for more details.
Ranking of Happiness by Country
Figure 2.2 (below) shows the average ladder
score (the average answer to the Cantril ladder
question, asking people to evaluate the quality of
their current lives on a scale of 0 to 10) for each
country, averaged over the years 2015-2017. Not
every country has surveys in every year; the total
sample sizes are reported in the statistical
appendix, and are reflected in Figure 2.2 by the
horizontal lines showing the 95% confidence
regions. The confidence regions are tighter for
countries with larger samples. To increase the
number of countries ranked, we also include four
that had no 2015-2017 surveys, but did have one
in 2014. This brings the number of countries
shown in Figure 2.2 to 156.
The overall length of each country bar represents
the average ladder score, which is also shown in
numerals. The rankings in Figure 2.2 depend only
on the average Cantril ladder scores reported by
the respondents.
Each of these bars is divided into seven
segments, showing our research efforts to find
possible sources for the ladder levels. The first
six sub-bars show how much each of the six
key variables is calculated to contribute to that
country’s ladder score, relative to that in a
hypothetical country called Dystopia, so named
because it has values equal to the world’s lowest
national averages for 2015-2017 for each of the six
key variables used in Table 2.1. We use Dystopia as
a benchmark against which to compare each
other country’s performance in terms of each of
the six factors. This choice of benchmark permits
every real country to have a non-negative
contribution from each of the six factors. We
calculate, based on the estimates in the first
column of Table 2.1, that Dystopia had a 20152017 ladder score equal to 1.92 on the O to 10
scale. The final sub-bar is the sum of two
components: the calculated average 2015-2017
life evaluation in Dystopia (=1.92) and each
country’s own prediction error, which measures
the extent to which life evaluations are higher or
lower than predicted by our equation in the first
column of Table 2.1. These residuals are as likely
to be negative as positive.12
It might help to show in more detail how we
calculate each factor’s contribution to average
life evaluations. Taking the example of healthy life
expectancy, the sub-bar in the case of Tanzania
is equal to the number of years by which healthy
life expectancy in Tanzania exceeds the world’s
lowest value, multiplied by the Table 2.1 coefficient
for the influence of healthy life expectancy on
life evaluations. The width of these different
sub-bars then shows, country-by-country, how
much each of the six variables is estimated to
contribute to explaining the international ladder
differences. These calculations are illustrative
rather than conclusive, for several reasons. First,
the selection of candidate variables is restricted
by what is available for all these countries.
Traditional variables like GDP per capita and
healthy life expectancy are widely available. But
measures of the quality of the social context,
which have been shown in experiments and
national surveys to have strong links to life
evaluations and emotions, have not been
sufficiently surveyed in the Gallup or other
global polls, or otherwise measured in statistics
available for all countries. Even with this limited
choice, we find that four variables covering
different aspects of the social and institutional
context – having someone to count on, generosity,
freedom to make life choices and absence of
corruption – are together responsible for more
than half of the average difference between each
country’s predicted ladder score and that in
Dystopia in the 2015-2017 period. As shown in
Table 19 of Statistical Appendix 1, the average
country has a 2015-2017 ladder score that is 3.45
points above the Dystopia ladder score of 1.92.
Of the 3.45 points, the largest single part (35%)
comes from social support, followed by GDP per
capita (26%) and healthy life expectancy (17%),
and then freedom (13%), generosity (5%), and
corruption (3%).13
Our limited choice means that the variables we
use may be taking credit properly due to other
better variables, or to other unmeasured factors.
There are also likely to be vicious or virtuous
circles, with two-way linkages among the variables.
For example, there is much evidence that those
who have happier lives are likely to live longer,
be more trusting, be more cooperative, and be
generally better able to meet life’s demands.14
This will feed back to improve health, GDP,
generosity, corruption, and sense of freedom.
Finally, some of the variables are derived from
the same respondents as the life evaluations and
hence possibly determined by common factors.
This risk is less using national averages, because
individual differences in personality and many
life circumstances tend to average out at the
national level.
To provide more assurance that our results are
not seriously biased because we are using the
same respondents to report life evaluations,
social support, freedom, generosity, and
corruption, we tested the robustness of our
procedure (see Statistical Appendix 1 for more
detail) by splitting each country’s respondents
randomly into two groups, and using the average
values for one group for social support, freedom,
generosity, and absence of corruption in the
equations to explain average life evaluations in
the other half of the sample. The coefficients on
each of the four variables fall, just as we would
expect. But the changes are reassuringly small
(ranging from 1% to 5%) and are far from being
statistically significant.”
The seventh and final segment is the sum of
two components. The first component is a fixed
number representing our calculation of the
2015-2017 ladder score for Dystopia (=1.92). The
second component is the 2015-2017 residual for
each country. The sum of these two components
comprises the right-hand sub-bar for each
country; it varies from one country to the next
because some countries have life evaluations
above their predicted values. and others lower.
The residual simply represents that part of
the national average ladder score that is not
explained by our model; with the residual
included, the sum of all the sub-bars adds up
to the actual average life evaluations on which
the rank’1ngs are based.
What do the latest data show for the 2015-2017
country rankings? Two features carry over from
previous editions of the World Happiness Report.
First, there is a lot of year-to-year consistency in
the way people rate their lives in different countries.
Thus there remains a four-point gap between the
10 top-ranked and the 10 bottom-ranked countries.
The top 10 countries in Figure 2.2 are the same
countries that were top-ranked in World Happiness
Report 2017, although there has been some
swapping of places, as is to be expected among
countries so closely grouped in average scores.
The top five countries are the same ones that
held the top five positions in World Happiness
Report 2017, but Finland has vaulted from
5th place to the top of the rankings this year.
Although four places may seem a big jump, all
the top five countries last year were within the
same statistical confidence band, as they are
again this year. Norway is now in 2nd place,
followed by Denmark, Iceland and Switzerland in
3rd, 4th and 5th places. The Netherlands, Canada
and New Zealand are 6th, 7th and 8th, just as
they were last year, while Australia and Sweden
have swapped positions since last year, with
Sweden now in 9th and Australia in 10th position.
In Figure 2.2, the average ladder score differs
only by 0.15 between the 1st and 5th position,
and another 0.21 between 5th and 10th positions.
Compared to the top 10 countries in the current
ranking, there is a much bigger range of scores
covered by the bottom 10 countries. Within this
group, average scores differ by as much as 0.7
points. more than one-fifth of the average
national score in the group. Tanzania, Rwanda
and Botswana have anomalous scores, in the
sense that their predicted values based on their
performance on the six key variables, would
suggest they would rank much higher than
shown by the survey answers.
Despite the general consistency among the top
countries scores, there have been many significant
changes in the rest of the countries. Looking at
changes over the longer term, many countries
have exhibited substantial changes in average
scores, and hence in country rankings, between
2008-2010 and 2015-2017, as shown later in
more detail.
When looking at average ladder scores, it is also
important to note the horizontal whisker lines at
the right-hand end of the main bar for each
country. These lines denote the 95% confidence
regions for the estimates, so that countries with
overlapping error bars have scores that do not
significantly differ from each other. Thus, as already
noted, the five top-ranked countries (Finland,
Norway, Denmark, Iceland, and Switzerland) have
overla.pping confidence regions, and all have
national average ladder scores either above or
just below 7.5.
Average life evaluations in the top 10 countries
are thus more than twice as high as in the bottom
10. If we use the first equation of Table 2.1 to look
for possible reasons for these very different life
evaluations, it suggests that of the 4.10 point
difference, 3.22 points can be traced to differences
in the six key factors: 1.06 points from the GDP
Figure 2.2: Ranking of Happiness 2015-2017 (Part 1)
1.
Finland ( 7.632)
2.
Norway (7.S94)
3.
Denmark (7.SSS)
4.
Iceland (7.49S)
s.
Switzerland (7.487)
6.
Netherlands ( 7.441)
7.
Canada (7.328)
8.
New Zealand (7.324)
9.
Sweden (7.314)
10.
Australia ( 7.272)
11.
Israel (7.190)
12.
Austria (7.139)
13.
Costa Rica (7.072)
14.
Ireland (6.977)
1S.
Germany (6.96S)
16.
Belgium ( 6.927)
17.
Luxembourg ( 6.910)
18.
United States ( 6.886)
United Kingdom (6.814)
19.
20. United Arab Emirates ( 6.774)
21.
Czech Republic (6.711)
22.
Malta (6.627)
23. France ( 6.489)
24. Mexico (6.488)
2S.
Chile ( 6.476)
26. Taiwan Province of China (6.441)
27.
Panama (6.430)
28.
Brazil ( 6.419)
29. Argentina (6.388)
30. Guatemala (6.382)
31. Uruguay (6.379)
32. Qatar (6.374)
33. Saudi (Arab ia (6.371)
34. Singapore ( 6.343)
35. Malaysia (6.322)
36. Spain (6.310)
37.
Colombia (6.260)
38. Trinidad & Tobago (6.192)
39. Slovakia (6.173)
40. El Salvador (6.167)
41.
Nicaragua (6.141)
42. Poland ( 6.123)
43. Bah rain ( 6.lOS)
44. Uzbekistan ( 6.096)
4S. Kuwait (6.083)
46. Thailand ( 6.072)
47.
Italy (6.000)
48. Ecuador ( 5.973)
49. Belize (S.9S6)
so.
Lithuania (5.9S2)
51.
Slovenia ( 5.948)
S2. Romania (5.94S)
0
11
6
2
7

Explained by: GDP per capita

Explained by: generosity

Explained by: social support

Explained by: perceptions of corruption

Explalned by: healt hy lffe expcetency

Dystopia (l.92) +residual
Expla ined by: freedom to make llfe choices
H 95″ confidence Interval
World Happiness Report 2018
Figure 2.2: Ranking of Happiness 2015-2017 (Part 2)
53. Latvia (S.933)
S4. Japan (S.91S)
SS. Mauritius (S.891)
56. Jamaica (5.890)
57.
South Korea (5.875)
S8. Northern Cyprus (S.83S)
59. Russia (5.810)
60. Kazakhstan (5.790)
61. Cyprus (S.762)
62. Bolivia (5.7S2)
63. Estonia (S.739)
64. Paraguay (S.681)
65. Peru (S.663)
66. Kosovo (S.662)
67.
Moldova (5.640)
68. Turkmenistan (5.636)
69. Hungary (5.620)
70. Libya (S.S66)
71.
Philippines (5.524)
72. Honduras (5.504)
73.
Belarus (5.483)
74. Turkey (S.483)
7S. Pakistan (5.472)
76. Hong Kong SAR. Chi na (S.430)
77.
Portugal (S.410)
78. Serbia (5.398)
79. Gre ece (S.358)
80. Tajikistan (5.352)
81.
Montenegro (5.347)
82. Croatia (S.321)
83. Dominican Republic (5.302)
84. Algeria (5.295)
85. Morocco (5.254)
86. China (5.246)
87.
Azerbaijan (S.201)
88. Lebanon (5.199)
89. Macedonia (5.185)
90. Jordan (5.161)
91. Nigeria (5.155)
92. Kyrgyzstan (5.131)
93. Bosnia and Herzegovina (5.129)
94. Mongolia (5.12S)
9S. Vietnam (S.103)
96. Indonesia (5.093)
97. Bhutan (S.082)
98. Somalia (4.982)
99. Cameroon (4.975)
100. Bulgaria (4.933)
101. Nepal (4.880)
102. Venezuela (4.806)
103. Gabon (4.758)
104. Palestinian Territories (4.743)
3
0



14
5
7
Explained by: GOP per cepita
Explained by: social support

Explained by: genoroslty

Explai ned by: perceptions of corruption
Explained by: healthy life expectancy
Explained by: freedom to make life choices
• Dystopia (1.92) + residua!
H 95% confidence interval
8
Figure 2.2: Ranking of Happiness 2015- 2017 (Part 3)
105. South Africa (4.724)
106. Iran (4.707)
107. Ivory Coast (4.671)
108. Ghana (4.657)
109. Senegal ( 4.631)
110. Laos ( 4.623)
111.
Tunisia ( 4.592)
112. Albania (4.586)
113. Sierra Leone (4.571)
114. Congo (Brazzaville) (4.559)
115. Bangladesh (4.500)
116. Sri Lanka (4.471)
117. Iraq (4.456)
118. Mali (4.447)
119. Namibia (4.441)
120. Cambodia (4.433)
121. Burkina Faso (4.424)
122. Egypt (4.419)
123. Mozambique (4.417)
124. Kenya ( 4.410)
125. Zambia (4.377)
126. Mauritania ( 4.3S6)
127. Ethiopia ( 4.350)
128. Georgia (4.340)
129. Armenia ( 4.321)
130. Myanmar (4.308)
131. Chad (4.301)
132. Congo (Kinshasa) (4.245)
133. India (4.190)
134. Niger (4.166)
135. Uganda (4.161)
136. Benin (4.141)
137. Sudan (4 .139)
138. Ukraine ( 4 .103)
139. Togo (3.999)
140. Guinea (3.964)
141. Lesotho (3.808)
142. Angola (3.795)
143. Madagascar (3.774)
144. Zimbabwe (3.692)
145. Afghanistan (3.632)
146. Botswana (3.590)
147. Malawi (3.587)
148. Haiti (3.582)
149. Liberia ( 3.495)
150. Syria (3.462)
151. Rwanda (3.408)
152. Yemen (3.355)
153. Tanzania (3.303)
154. South Sudan (3.254)
155. Central African Republic (3.083)
156. Burundi (2.905)
0
4
5
6

Explained by: GDP per capita


Explained by: social support
Explained by: healthy life expectancy



Expla ined by: freedom to make life choices
H 95% confidence Interval
7
Explained by. generosity
Explained by: perceptions of com.1ptlon
Oystopla (1.92) + residual
s
//odd [‘-lapphiess Report 2018
per capita gap, 0.90 due to differences in
social support, 0.61 to differences in healthy
life expectancy, 0.37 to differences in freedom,
0.21 to differences in corruption perceptions,
and 0.07 to differences in generosity. Income
differences are the single largest contributing
factor, at one-third of the total, because, of the
six factors, income is by far the most unequally
distributed among countries. GDP per capita
is 30 times higher in the top 10 than in the
bottom 10 countries.16
Overall, the model explains quite well the life
evaluation differences within as well as between
reg·1ons and for the world as a whole.17 On average,
however, the countries of Latin America still have
mean life evaluations that are higher (by about
0.3 on the 0 to 10 scale) than predicted by the
model. This difference has been found in earlier
work and been attributed to a variety of factors,
including especially some unique features of
family and social life in Latin American countries.
To help explain what is special about social life in
Latin America, and how this affects emotions
and life evaluations, Chapter 6 by Mariano Rojas
presents a range of new evidence showing how
the social structure supports Latin American
happiness beyond what is captured by the variables available in the Gallup World Poll. In partial
contrast, the countries of East Asia have average
life evaluations below those predicted by the
model, a finding that has been thought to reflect,
at least in part, cultural differences in response
style.’8 It is reassuring that our findings about the
relative importance of the six factors are generally
unaffected by whether or not we make explicit
allowance for these reg’1onal differences.19
Changes in the Levels of Happiness
In this section we consider how life evaluations
have changed. In previous reports we considered
changes from the beginning of the Gallup World
Poll until the three most recent years. In the
report, we use 2008-2010 as a base period, and
changes are measured from then to 2015-2017.
The new base period excludes all observations
prior to the 2007 economic crisis, whose effects
were a key part of the change analysis in earlier
World Happiness Reports. In Figure 2.3 we show
the changes in happiness levels for all 141 countries
that have sufficient numbers of observations for
both 2008-2010 and 2015-2017.
Of the 141 countries with data for 2008-2010 and
2015-2017, 114 had significant changes. 58 were
significant increases, ranging from 0.14 to 1.19
points on the O to 10 scale. There were also 59
significant decreases, ranging from -0.12 to -2.17
points, while the remaining 24 countries revealed
no significant trend from 2008-2010 to 2015-2017.
As shown in Table 35 in Statistical Appendix 1,
the significant gains and losses are very unevenly
distributed across the world, and sometimes also
within continents. For example, in Western
Europe there were 12 significant losses but only
three significant gains. In Central and Eastern
Europe, by contrast, these res …
Purchase answer to see full
attachment

Place your order
(550 words)

Approximate price: $22

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more

Order your essay today and save 30% with the discount code ESSAYSHELP