The Reinstatement of Returnees in District Swat, Pakistan: An Evaluative Study of the Rehabilitation Initiatives

: This study attempted to develop a happiness index tool for evaluating rehabilitation initiatives used to reinstate returnees at district Swat, Pakistan. The Happiness Index (HI) tool compares two periods, i.e., HI before rehabilitation (BR) and HI after rehabilitation (AR). The returnees’ happiness index (RHI) is also compared with Pakistan’s Happiness Index to identify the difference. Data for this study were elicited from 382 respondents through a structured survey questionnaire. The results show that after rehabilitation (AR), the returnees’ happiness index improved from 3.89 to 5.36, which is still less than the world happiness index of Pakistan, i.e., 5.65 in 2019. This study concluded that rehabilitation projects had a signiﬁcantly positive impact on the HI of the returnees. However, more effective and sustainable initiatives are required to align the RHI to the HI of Pakistan. Further, the RHI tool adopted by this study is signiﬁcant for measuring the happiness of the marginalized and affected people in Pakistan and beyond.


Introduction
In the Swat district of Pakistan, the military operation against the militants, Tehrike-Taliban Pakistan TTP, in 2009, followed by a devastating flood in 2010, forced the displacement of approximately 2.5 million (Haider 2009;IDMC 2018). In the mayhem, the Swat district's infrastructure was severely damaged, Agri/Horti-culture was destroyed, and schools, colleges, universities, hospitals, businesses, and mundane life were halted for more than a year (Sayeed and Shah 2017). Consequently, the people of Swat suffered economically, socially, politically, and psychologically. After completing the military operation in 2010, the government started repatriating the IDPs (Internally Displaced Persons) known as returnees or returnee IDPs to their respective areas (Din 2010). However, the returnees of Swat faced tangible challenges as their restoration of normality was not yet thoroughly performed by the government (Sanaullah 2020). Finally, with the help of national and international donor organizations, the government launched rehabilitation projects in various sectors such as transportation, health, education, and agriculture to reinstate the normality of the returnees' IDPs (PDMA 2019).
This study aimed to evaluate the impact of the rehabilitation projects and initiatives on returnee IDPs' reinstatement in district Swat. In this regard, the construct and use of the Happiness Index (HI) tool seemed plausible for evaluating rehabilitation initiatives for IDPs' reinstatement in district Swat. The happiness index (HI) or subjective well-being is an emerging and inclusive tool embodying different aspects of humans' lives. Previous research identified two components of happiness, i.e., the cognitive component (Andrews and Withey 1976) and an effective component, which means both a pleasant and unpleasant effect (Diener and Emmons 1984). Further, HI provided astounding public policy insights to measure social progress (Lepeley 2017). However, as a tool, we observed that in its current nature and set of indicators, it could not be used to evaluate the impact of rehabilitation initiatives. Therefore, a reconstruction of the current HI tool, fitting with the local cultural circumstances, aimed to fill this gap. Further, we believe that this HI tool would help evaluate rehabilitation initiatives for the reinstatement of affected people in Pakistan and beyond, precisely measuring the satisfaction and happiness level of people at pre-and post-rehabilitation initiatives and services.
The current HI aimed to obtain a holistic idea by comparing the returnees' happiness indices in district Swat at the pre-and post-rehabilitation periods. After comparing the two happiness indices, another comparison would be made with Pakistan's world happiness index for 2019. While comparing the two indices, this study's findings would help policymakers chalk up effective and pragmatic policies and rehabilitation plans. The Returnees' Happiness Index (RHI) design implied by this study comprises eleven domains, including traditional and non-traditional, as shown in Figure 1 below. The domains have 37 indicators mentioned in the methodology section. Soc. Sci. 2021, 10, x FOR PEER REVIEW 2 of 14 ant effect (Diener and Emmons 1984). Further, HI provided astounding public policy insights to measure social progress (Lepeley 2017). However, as a tool, we observed that in its current nature and set of indicators, it could not be used to evaluate the impact of rehabilitation initiatives. Therefore, a reconstruction of the current HI tool, fitting with the local cultural circumstances, aimed to fill this gap. Further, we believe that this HI tool would help evaluate rehabilitation initiatives for the reinstatement of affected people in Pakistan and beyond, precisely measuring the satisfaction and happiness level of people at pre-and post-rehabilitation initiatives and services. The current HI aimed to obtain a holistic idea by comparing the returnees' happiness indices in district Swat at the pre-and post-rehabilitation periods. After comparing the two happiness indices, another comparison would be made with Pakistan's world happiness index for 2019. While comparing the two indices, this study's findings would help policymakers chalk up effective and pragmatic policies and rehabilitation plans. The Returnees' Happiness Index (RHI) design implied by this study comprises eleven domains, including traditional and non-traditional, as shown in Figure 1 below. The domains have 37 indicators mentioned in the methodology section.

Literature Review
The concept of 'happiness' has multifaceted meanings and usage. For example, happiness is used for subjective well-being, life satisfaction, and quality of life (Al-Qawasmi et al. 2021;Land et al. 2011). Recently, the satisfaction with life scale (SWLS) was developed to assess satisfaction with the respondent's life as a whole but does not assess satisfaction with life domains such as health or capital (Diener 2009). The SWLS focuses on psychopathology or emotional wellbeing using the person's criteria. It is an umbrella term and can be used in various ways, but overall, it denotes both individual and social welfare as a notion of what is good. However, in the past decade, the happiness index (HI) scale became popular and attracted the attention of economists and mass media because of its inclusiveness, as being happy may also make an individual healthier and earn more (Lepeley 2017). However, being healthier and wealthier is ultimately only valuable if they provide more happiness (Nanyang 2015).

Literature Review
The concept of 'happiness' has multifaceted meanings and usage. For example, happiness is used for subjective well-being, life satisfaction, and quality of life (Al-Qawasmi et al. 2021;Land et al. 2011). Recently, the satisfaction with life scale (SWLS) was developed to assess satisfaction with the respondent's life as a whole but does not assess satisfaction with life domains such as health or capital (Diener 2009). The SWLS focuses on psychopathology or emotional wellbeing using the person's criteria. It is an umbrella term and can be used in various ways, but overall, it denotes both individual and social welfare as a notion of what is good. However, in the past decade, the happiness index (HI) scale became popular and attracted the attention of economists and mass media because of its inclusiveness, as being happy may also make an individual healthier and earn more (Lepeley 2017). However, being healthier and wealthier is ultimately only valuable if they provide more happiness (Nanyang 2015).
On the contrary, Graham et al. (2004) indicated that though a wealthier individual is happier than a poor one on average after a minimum income level, more money does not make people much more comfortable. Therefore, during the last two decades, the topic "happiness" has become very popular among economists and contemporary researchers (Kahneman and Krueger 2006;Clark et al. 2008). A global happiness council started the HI, a team of independent academic happiness specialists, in 2012. Since then, they have been publishing world happiness reports every year. Happiness specialists became inspired by this idea and started ranking all countries accordingly. The world happiness report (WHR) has defined the HI as a high weighted rate of respondents reporting 'very happy,' low weighted rate of respondent rate 'not very happy', plus 100. According to this idea, the happiness scale ranged from 0 to 200 (Helliwell et al. 2018). The concept of determining the happiness index was changed and converted to the happiness ladder in 2018. The ladder consists of '0' to '10' steps, where step' 0' signifies the worst possible life; however, step 10 represents the best possible life. It means a respondent imagines his life as a ladder with '0' at the bottom and '10' at the top (Helliwell et al. 2019).
The government must take action for citizens' happiness (Ramesh 2011). This implies the government is responsible for putting in place a policy framework where individuals, businesses, and governments operate. For example, the fourth king of Bhutan first declared the Gross National Happiness (GNH) in 1972 to substitute the Gross Domestic Product (GDP) as it provides the developmental progress of the country from a holistic view (Bates 2009). The GNH is the best way to compare happiness among developing countries, along with other factors such as wealth, comfort, and economic growth (Frey and Stutzer 2009). According to Helliwell et al. (2015), the happiness index is the most convenient process to implement appropriate policies on the country's citizens. Implementing the happiness index (HI) has provided Bhutan's economy with effective results. It helped the government develop a measurement tool for making policies and guidelines for governmental purposes and the businesses in the country (Bates 2009). The HI is in accordance with the level of happiness of its people in the country.
There is no doubt that the happiness index (HI) calculation occurs by asking questions about the happiness level, but happiness is more than just a number. Many factors play a significant role in making people happy or not happy. These factors include: (1) GDP Per Capita: The GDP is the country's total production divided by its whole population, whereas GDP per capita measures its wealth. The wealth of a country is arguably highly related to its happiness (Goyal 2018).
(2) Social Support: Social support is the help a citizen can avail if in trouble from his fellow citizens such as family members or friends. Countries that rank high in social support tend to have a higher happiness rating. Nevertheless, to some extent, this relation is linear, as some countries have low social support and are not very happy (Kim et al. 2008 Similarly, the perception of corruption, freedom to make life choices, and generosity are also determinants of the happiness index (Goyal 2018). (4) Unexplained Happiness: The WHR (World Happiness Report) explains the key factors essential in determining a country's happiness. The happiness index rating can be calculated by adding all the elements, but there is some unexplained happiness besides these factors. Unexplained happiness is also known as residual happiness and includes stable factors that affect happiness and might include cognitive bias. It is seen that many countries in the world are happy without any reason (Graham et al. 2004). Unexplained happiness is also a part of the factors determining the world happiness index by WHR.
The GNH (Gross National Happiness) has holistically indicted sustainable development and has been considered equally important as the non-economic aspects of well-being. The idea of GNH has also impacted the social and economic policies of countries (Munro 2016). Therefore, the tool can be sought to create the GNH index, which is essential for policy initiatives for businesses, governments, and NGOs in the country. Hence, the GNH index has included both traditional and non-traditional and areas of socio-economy. Education, living standards, and health are traditional areas, whereas psychological well-being and culture are non-traditional (OPHI 2018). GNH has four main pillars: Sustainable socio-economy development, cultural preservation, environmental conservation, and good governance. These pillars are divided into eleven subdomains that are essential for reflecting GNH values' holistic range (Lepeley 2017). All the domains have been equally weighted as the domains have been considered equally important, having intrinsic significance for the GNH (Lepeley 2017). Thus, the GNH consists of both traditional and non-traditional concerns that reflect the growth and wellbeing of a country (OPHI 2018). According to Frey and Stutzer (2009), the GNH is ranked under nine categories: Psychological wellbeing, health, education, time-use, community vitality, living standards, good governance, cultural diversity, and ecological diversity. However, Musikanski et al. (2017) used ten happiness domains: Psychological wellbeing, health, time balance, community, social support, education, arts and culture, environment, government, and material well-being. The happiness index domains used in both mentioned studies are almost identical except for the two new domains used in the second study. The new domains are, in fact, an adjustment of the domains and corresponding indicators.
Overall, the abovementioned HI and GNH tools are used to improve the health, standard of living, and educational requirements for a particular country ranked with other countries based on the psychological and physical happiness of the population. However, neither of these tools have previously been used to measure returnee IDPs' happiness level and their satisfaction with the rehabilitation initiatives. In this regard, this study would fill this gap by reconstructing an HI tool to evaluate the rehabilitation initiatives in the reinstatement of the IDPs in Pakistan and beyond.

Methodology of the Study
This study's target population is the returnee IDPs of the district Swat, Pakistan. Specifically, we chose the Kabal tehsil of Swat, using the purposive sampling technique. The Kabal region of Swat was the most damaged area, as it had 100% displacement and the government provided the highest rehabilitation to the returnees of this region (Bangash 2012;Mackey and Gass 2015), so the selection of Kabal tehsil was more appropriate than other regions. The researcher obtained IRB approval "ref UTM.K.55.01.03/13.11/1/4" from the University of Technology Malaysia before the data collection process. Oral consent was sought from all respondents before filling out the questionnaire survey. However, the participants had the freedom to withdraw themselves or their views during the data collection process. The survey questionnaire was distributed amongst 400 respondents, where some of the answers were discarded due to ambiguity, and the final 382 respondents were considered.
This study used a quantitative study design. Data were collected from n = 382 samples of n = 47,943 population using Krejcie and Morgan's (1970) sampling model. The stratified random sampling was used where each village was considered as a substratum (Clark and Creswell 2014). In addition, a self-administered structured questionnaire was used as a tool of data collection from the (male) head of the household (HH). The questionnaire consisted of a 5-point Likert scale, where option 1 reflected the lowest level of happiness or satisfaction and option 5 represented the highest level of agreement to satisfaction.
Differentiating a 'happy' from an 'unhappy' respondent, the public opinion method was used and assigned a threshold value to each indicator, as shown in Table 1 below. According to Mercer et al. (2018), a public opinion method is an appropriate tool for a sample size larger than 100. Therefore, considering the response of the original respondents, the threshold was assigned. Accordingly, all the indicators' threshold value is 80% except for three, i.e., 7, 19, and 23, which is 60%. The threshold value depends on the nature of the indicator and the type and scale of measurement. The total 100% sufficiency is equally divided in the response options of the 5-point Likert scale. For example, (1. SD = 20%; 2. D = 40%; 3. N = 60%; 4. A = 80%; 5. SA = 100%), where 'SD' stands for strongly disagree; Soc. Sci. 2021, 10, 476 5 of 13 'D' for disagree; 'N' for neutral; 'A' for agree; and 'SA' for strongly agree. The indicators with an 80% threshold mean that those respondents who responded to option '4' and '5' achieved happiness sufficiency, whereas options '1',' 2', and '3' are considered lower than the sufficiency threshold. Similarly, for indicators whose sufficiency is 60%, options' 3', '4', and '5' achieve the sufficiency level where options' 1' and '2' are lower than the threshold. However, some indicators are negative, such as a worried life, feeling rushed, government corruption level, eating mutton, feeling lonely, and discrimination. The opposite meaning's response option was used in the negative indicators, i.e., '1' means strongly agree, whereas option '5' means strongly disagree (1. SA = 100%; 2. A = 80%; 3. n = 60%; 4. D = 40%; D = 20%). Therefore, for the 80% threshold, the options '1' and '2' achieve sufficiency, whereas for 60% sufficiency, the options '1', '2', and '3' achieve sufficiency. We used Microsoft Excel for the reverse coding schemes and swapped the highest value with the lowest one, whereby '5' is changed to '1', and '4' to '2'.

Data Collection
The data collection took a six-month time period, i.e., from February 2019 to July 2019. Using the indicator's code of Table 1, the frequency and percentage of happy people are presented in Table 2 below. The rows show the domains, whereas the columns present the corresponding indicators. The corresponding codes are used to present that data. For example, the first domain is SWL, with the indicators SWL1, SWL2, and SWL3 such that for BR, the number of happy respondents in SWL1 is 34, SWL2 is 55, and SWL3 is 54, whereas for AR, the SWL1 is 269, SWL2 is 248, and SWL 3 is 255. The maximum number of indicators in the community (COM) domain is six, representing the data from COM1 to COM6. All other domains have fewer indicators, and therefore the value in the table cell is empty, filled with "-".

Construction of Returnees Happiness Index (RHI)
Considering both the traditional and non-traditional indicators, the Alkire Foster method is used to calculate the RHI. This method uses two numbers. (1) Headcount ratio, and (2) Breadth percentage. The 'Headcount ratio' is the percentage of happy people, whereas the 'Breadth percentage' is the percentage of domains or indicators in which the unhappy people enjoy sufficiency (Ura et al. 2012). Accordingly, first, the average sufficiency of the unhappy people is multiplied by the total number of unhappy people and then added to the total number of happy people to calculate the final happiness index as shown below: RH I = Hh + (Hn × As) where RH I = Returnees Happinees (Total RH I = 1). Hh = % o f happy people. Hn = % o f not happy people(1 − Hh). As = % average su f f iciency o f the not happy people.
The RHI calculation consists of five steps: (1) Choose indicators and apply the sufficiency threshold; (2) apply the happiness threshold; (3) identify two groups (happy and not happy); (4) among the not happy people, identify what percentage of domains or indicators they lack sufficiency in and at what percentage they enjoy sufficiency; and (5) calculate the final happiness index.

Step 1. Chose Indicators and apply the sufficiency threshold
The RHI consist of 11 domains, further divided into 37 indicators as shown in Table 1 above.

Step 2. Applying the happiness threshold
The study's happiness threshold was adapted from the GNH of Bhutan, where the happiness threshold is 66% (Ura et al. 2012). In contrast, in the current study, the threshold or cut-off of the overall happiness is 67%, which means out of the total 37 indicators, a respondent who achieves sufficiency in 25 indicators or above was happy; otherwise, they were unhappy. The happy and not happy respondents are shown in Table 3 below. For example, in BR and AR, the number of respondents who achieved happiness sufficiency is '0' in indicator 1 and '13' and '0' in indicator 2, respectively. Similarly, in BR and AR, respondents who achieved happiness sufficiency are shown up to indicator 24. This means these are the not-happy respondents who achieved happiness sufficiency in less than 25 indicators. Furthermore, this means achieving sufficiency from 0-24 indicators represents individuals who are not happy (NH). On the other side, the respondents who have achieved happiness sufficiency in 25 or more indicators are happy. Moreover, the table shows that for BR, out of the total 382 respondents, only 106 people are happy and 276 are unhappy, whereas for AR, the number of happy people increased to 166, and the number of unhappy people decreased to 216. The sufficiency of the happy and not happy people is shown in Table 3 below, containing all 37 indicators. The number of NH, achieved sufficnecy in less than 25 indicators.i.e. out of total 37 indicators, how many people achieved sufficiency in how many indicators.
The number of happy people, achieved sufficinecy in 25 or more indicators.
Step 3. Identify the happy and not happy groups Microsoft Excel was used to differentiate between the happy and not-happy groups. First of all, indicators 1 to 37 were presented column-wise in the Excel sheet, where the respondent's response was entered row-wise across all the indicators. i.
The COUNTIF () function was used to count the number of indicators where a respondent achieved the happiness sufficiency threshold. ii. The COUNTIF () function was used to count the number of happy people who achieved sufficiency in 25 or more indicators. iii. The COUNTIF () function was used to count the number of unhappy people who have not achieved sufficiency in 25 or more indicators.
Step 4. Identify the sufficiency among the not happy people The average sufficiency of the not happy people was obtained by using the AVER-AGE () function in Excel, and the result is summarized and shown in Table 4 below. To calculate a respondent's sufficiency, the numbers of indicators where a respondent The number of happy people, achieved sufficinecy in 25 or more Step 3. Identify the happy and not happy groups Microsoft Excel was used to differentiate between the happy and not-happy groups. First of all, indicators 1 to 37 were presented column-wise in the Excel sheet, where the respondent's response was entered row-wise across all the indicators. i.
The COUNTIF () function was used to count the number of indicators where a respondent achieved the happiness sufficiency threshold. ii. The COUNTIF () function was used to count the number of happy people who achieved sufficiency in 25 or more indicators. iii. The COUNTIF () function was used to count the number of unhappy people who have not achieved sufficiency in 25 or more indicators.
Step 4. Identify the sufficiency among the not happy people The average sufficiency of the not happy people was obtained by using the AVER-AGE () function in Excel, and the result is summarized and shown in Table 4 below. To calculate a respondent's sufficiency, the numbers of indicators where a respondent The number of happy people, achieved sufficinecy in 25 or more indicators.

Step 3. Identify the happy and not happy groups
Microsoft Excel was used to differentiate between the happy and not-happy groups. First of all, indicators 1 to 37 were presented column-wise in the Excel sheet, where the respondent's response was entered row-wise across all the indicators. i.
The COUNTIF () function was used to count the number of indicators where a respondent achieved the happiness sufficiency threshold. ii.
The COUNTIF () function was used to count the number of happy people who achieved sufficiency in 25 or more indicators. iii.
The COUNTIF () function was used to count the number of unhappy people who have not achieved sufficiency in 25 or more indicators.
Step 4. Identify the sufficiency among the not happy people The average sufficiency of the not happy people was obtained by using the AVERAGE () function in Excel, and the result is summarized and shown in Table 4 below. To calculate a respondent's sufficiency, the numbers of indicators where a respondent achieved the corresponding target threshold are divided by the total number of indicators. For example, for BR, 13 respondents achieved happiness sufficiency in two indicators only. Therefore, it is first divided by 37, as shown in column b, whereas for AR, 0 respondents achieved sufficiency in two indicators. The resulted value is then multiplied by the number of people who achieved it. Therefore, for BR, column 'A' is multiplied by column 'B', whereas in AR, column 'C' is multiplied by column 'D'. The total sufficiency is divided by the total unhappy people and multiplied by 100 to achieve the average percentage sufficiency (As) as As % = ( Total su f f iciency Total not happy people ) × 100. Step

Calculation of the final happiness index
Following the frequency analysis of the variables considered in the happiness index, the researchers computed the happiness indices for returnees before and after incorporating rehabilitation projects. Using the happiness index formula as given in the subsequent section and considering the values of 'As', 'Hn', and 'Hh' of Table 4, the final happiness indices of the IDP returnees for both BR and AR periods are calculated as shown in Figure 2 below. Where, BR (Before Rehabilitation), AR (After Rehabilitation), Hn (Not Happy People), Hh (Happy People), S (Sufficiency), As (Average Sufficiency). Source: Compiled by the researchers. Step

Calculation of the final happiness index
Following the frequency analysis of the variables considered in the happiness index, the researchers computed the happiness indices for returnees before and after incorporating rehabilitation projects. Using the happiness index formula as given in the subsequent section and considering the values of 'As', 'Hn', and 'Hh' of Table 4, the final happiness indices of the IDP returnees for both BR and AR periods are calculated as shown in Figure  2 below.   Figure 2 indicates the BR percentage of happy people was 27.74% while not-happy people were 72.25%. Similarly, the average sufficiency of those not-so-happy people was 15.47%. While considering the happiness index's main elements, it can be seen that the HI was 38.92%. On the other hand, in AR, the total percentage of happy people increased to 43.45% and the percentage of not-so-happy people reduced to 56.54%. Thus, it can be seen that AR, the average sufficiency of the not-so-happy people, also improved to 17.99%. Overall, the AR happiness index experienced an incremental shift of 14.70%, reaching 53.62%. Therefore, the inference can be drawn that in AR, the value increased significantly. It further assumes that the happiness quotients amongst the residents are increased due to rehabilitation projects. This improvement in the HI occurred due to the government and donor organizations' rehabilitation projects. For example, the report of PDMA (2019) states that rehabilitation projects were launched to reinstate returnees such as re/construction of schools, health rehabilitation and medication, counselling, agriculture, water and sanitation systems, and the rebuilding of damaged roads and infrastructure. The purpose of the rehabilitation projects was to facilitate and improve the overall living standard and the returnees' overall well-being. It can be stated that the rehabilitation projects achieved their purpose because they succeeded in reinstating the IDPs and restoring normality after the disaster (Serghiou et al. 2016). Likewise, Elahi (2015) documents that in the district Swat, the rehabilitation projects restored the social conditions and the infrastructure facilities affected by mass displacement, the war on terror, and devastating floods in 2010.
Furthermore, we intended to compare the HI findings of our study with Pakistan's happiness index of 2019, which was 5.65 (Gallup Pakistan 2019). Our study findings depicted returnees' HI in the BR as 38.92 out of 100 or 3.89 out of 10; however, HI in AR rose to 53.62 out of 100 or 5.36 out of 10. This implies that after the rehabilitation, the happiness index of returnees increased by 1.47. However, it is still 0.29 less than the country's happiness index. Drawing on the aforementioned comparison, it is argued that the rehabilitation projects were successful and significantly improved the entire well-being of the returnees. However, more attention is required, specifically in the domains with lower efficiency levels as identified by this study, to level it, at least, to the happiness index of Pakistan.

Conclusions and Discussion
Governments frame public policies to cater to progress and social wellbeing. People choose government as they deem it better for their country and their wellbeing. Therefore, the local government should be given ample training to improve performance and take initiative in developing professionals (Suwanda and Suryana 2021). The government should involve the community to participate because it can improve human resource quality at the village level (Udjianto et al. 2021). Moreover, public expenditure positively affects short-term economic growth (Alzyadat and Al-Nsour 2021). In recent times, the happiness index has played a vital role in making governments realize how far they have succeeded in keeping their people happy (Ramesh 2011). For example, all those factors that keep people happy are outcomes of government policies. For instance, health is related to a healthy life expectancy, which is one of the determinants of the happiness index. Therefore, the happiness index can show governments the level of public health in their countries, and based on that data, the government can formulate future policies.
Similarly, the education level, GDP per capita, anti-corruption, equity and equality, and most importantly, employment are governments' obligations towards their citizens (Graham et al. 2004). Governments who think their policies related to these factors are efficient need to compare their claims with the happiness index report in detail. WHR (World Happiness Report) provides governments with a detailed analysis of each significant sector and its outcome in the form of people being happy or not (Ramesh 2011). Efficient governments can utilize these data to craft their future policies. The happiness index highlights the exact strengths and weaknesses of the country, and governments regulate many of them. Good governance itself is one of the determinants of the happiness index.
Thus, a country uses the GNH index to identify the policies' effectiveness under GNH guidelines (Bates 2009). The data shared by the world happiness index plays a vital role in crafting the government's future policies, and the WHR highlights the area that needs particular attention of the governments. In return, the government formulates a new policy of modifying the already prevailing policy to solve the issue. Apart from that, project screening tools are another policy used by strategists to generate implementation plans in agriculture, trade, manufacturing, health, and all over the eleven stated dimensions. The supreme goal of government-based projects and implementations is to increase the country's happiness index (Schubert 2012).
The use of the returnees' happiness index (RHI) in this study has a significant implication on the rehabilitation policy as it gives a road map to the authorities and policymakers. It provides policy-relevant insights and a robust tool that enables policymakers to track the progress and output of rehabilitation projects. It guides the management to focus on those areas and domains where the happiness sufficiency has not been improved. The RHI index divides the returnees of Swat into two groups of happy and not-happy people. Not-happy people are a policy priority for the government. The RHI is intended to guide rehabilitation policy and the relevant authorities and agents across society. Analysis of the RHI suggests areas where policy interventions are needed. Therefore, the domain-wise sufficiency or deprivation suggest the policymaker act accordingly. The findings of this study provide tangible insight that rehabilitation policy could be tailored accordingly to increase the RHI.
It is concluded that this research was a first attempt to find returnees' happiness in the Kabal region of the district of Swat, Pakistan. The data were collected through a structured questionnaire and quantitatively analyzed. A sample of 382 respondents was drawn from the n = 47,943 population. Two happiness indices were established for the returnees, one for BR and another for AR. While comparing the pre-and post-conflict periods in Swat, a significant improvement has been found in people's overall happiness level in the post-conflict period. Although, after providing rehabilitation, the RHI increased from 3.89 to 5.36, yet it is still less than the country's HI. The significant increase in happiness confirms the rehabilitation projects positively impact the returnees of Swat. Still, sustainable development is needed in the region to leverage the returnees' HI to that at the country level. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.