rafmacalaba/data-use-mentions-extended huggingface dataset:
|||Yes|4,011||
|Gymnasium|||Yes|16,778||
|Gymnasion (disability)|61,243|3.6|Yes|1,361||
|High school|||Yes|5,354||
|High school (disability)|||Yes|621||
|Vocational school|||Yes|7,629||
|Vocational school (disability)|||Yes|1,688||
|Tertiary schools|14,477|0.8|Yes|19,403|134%|
|Other education|14,761|0.9|No|||
|_Healthcare_|79,734|4.6|Yes|63,199|79%|
|Health insurance fund|61,653|3.6|No|||
|Other health|18,081|1.1||||
|_Other social spending_||||||
|Other expenditure(non-social)|277,301|16.1||||

Sources: Eurostat, GUS, and own estimates based on HBS 2014.

9
# Life Satisfaction, Social Capital and the Bonding-Bridging Nexus

Maurizio Pugno<sup>_∗_</sup> and Paolo Verme<sup>_†_</sup>

**Abstract**

The paper investigates the relation between social capital and life satisfaction focusing on the distinction between bonding and bridging. Using the latest version of the combined World and European Values Surveys, the authors first address the question of measurement of social capital by means of a multi-step factor analysis. Through this procedure, they find that proxies typically used for social capital tend to polarize around two dimensions interpreted as bonding and bridging. These two dimensions are in fact associated with a single latent variable with opposite signs suggesting that they describe two sides of the same latent variable rather than two independent latent variables. The authors call this latent variable the _locus of socializing_ and use it to explore the relation between social capital and life satisfaction across world citizens and across groups of similar countries. The results indicate that people with extreme bonding or bridging attitudes are less happy than people with more balanced attitudes. Unlike the literature on social capital and economic growth that finds bridging attitudes more desirable than bonding attitudes, they find that bonding attitudes are at least as important as bridging attitudes for life satisfaction. This suggests that the social capital dimensions important for economic growth may not necessarily coincide with the social capital dimensions important for life satisfaction.

**JEL:** A13, D6, I3, Z1. **Keywords:** Life Satisfaction, Social Capital, Bonding, Bridging. **Sector Board** : Poverty (POV)

> _∗_ Department of Economic Sciences, University of Cassino, Italy.

> _†_ The World Bank and Department of Economics “S. Cognetti de Martiis”, Torino, Italy.

1
0.00547|
||(0.0197)|(0.0172)|(0.00905)|(0.00674)|(0.00302)|(0.00123)|(0.0168)|(0.0189)|
|Trade Openness|-0.0321|0.312|0.0135|-0.0956|0.0162|0.0186|-0.0617|0.351|
|_(% of GDP, logs)_|(0.112)|(0.233)|(0.0515)|(0.0939)|(0.0172)|(0.0164)|(0.0954)|(0.257)|
|Financial Openness|0.00798|-0.0176|0.00113|-0.00529|-0.00257|-0.00304|0.00942|-0.00635|
|_(Chinn‐Ito Index)_|(0.0295)|(0.0404)|(0.0136)|(0.0131)|(0.00452)|(0.00224)|(0.0251)|(0.0446)|
|_External Factors_|||||||||
|Foreign Growth|0.0130|-0.00301|-0.000937|0.00827|-0.00503*|-0.00317**|0.0189|-0.00282|
||(0.0176)|(0.0170)|(0.00809)|(0.00883)|(0.00270)|(0.00145)|(0.0150)|(0.0188)|
|VIX Index|-0.0588|-0.384*|-0.00399|0.0775|-0.
two scenarios. The average years of schooling and human capital growth rates remain similar in the scenarios of the forced repatriation case compared to the voluntary mobility case as there is little change in the population distribution by age groups.

## _4.1.4 TFP_

Using the Kim and Loayza (2019) empirical model, we assume a gradual rebuilding of the TFP determinants index which was estimated at 15.98 in 2018. Under the optimistic scenario, we increase this index to 35.42 by 2028 and 75.76 by 2048 based on the trajectory of the Republic of Korea’s index over the 30‐year period, 1985‐2014. Korea is the best performer in the sample of countries used in Kim and Loayza (2019). This gives average annual TFP growth of 1.4 percent over 2019‐2048 (0.1 percent over 2019‐2028, 2.1 percent over 2029‐2038, and 2.0 percent over 2039‐2048).

For the pessimistic scenario, we repeat the exercise but based on index of the United Arab Emirates (UAE), the best performer for the sub‐sample of MENA countries. This would imply an increase in Syria’s index to only 21.72 by 2028 (still below pre‐conflict level) and 32.74 by 2048. The corresponding average annual TFP growth over 2019‐2048 would be 0.3 percent (‐0.9 percent over 2019‐2028, 0.9 percent over 2029‐2048). For the moderate scenario, we assume TFP growth rates that are the average of the rates under the optimistic and pessimistic scenarios.

# **4.2 Simulation of the post‐conflict growth outlook**

For the simulations in this section, we keep the constant parameters (labor share and depreciation rates) unchanged from the values used in the pre‐conflict and conflict projections. Default initial conditions as of 2018 (GDP level, GDP per capita level, and K/Y
# **3 Characteristics of Electricity Sectors in Sub-Saharan Africa**

This section presents aggregate data on the characteristics of electricity sectors in the 48 countries of SSA unless otherwise noted. Detailed country-level data on the characteristics of electricity sectors in the countries studied in this paper can be found in annexes 2-7. Additional electricity sector characteristics related to operational efficiencies are discussed in section 0. Apart from the access data based on the Global Tracking Framework of the Sustainable Energy for All initiative (SE4ALL) and installed capacity data, all other data are for the reference year for the 39 countries covered in this study and for the most recent year for which data are available for the countries excluded from the study. As such, regional averages presented in this section do not represent averages of data in the same year but the data are from different reference years (except for installed capacity for which data was available for a common reference year).

## 3.1 Access

The regional access rate of 35 percent is less than half percent of the global average levels of access of 85 percent (SE4ALL 2015a). Mauritius and the Seychelles have 100 percent access to electricity, followed by Gabon (89 percent) and South Africa (85 percent). A further nine countries have access rates above 50 percent. Rural areas remain the most underserved in the world. In some countries, less than 5 percent of rural population has access to electricity.

## 3.2 Installed capacity and power mix

Installed capacity in Figure 2 consists of all domestic grid-connected capacity, including IPPs, gridconnected power supply for exports and industry, and emergency power. It excludes enclave-generation such as mines. To avoid double counting, country-specific estimates exclude capacity from imports.

**Figure 2: Power mix on installed capacity, 2014, percent of total**

<!-- Start of picture text --> 1% 0% 4% 4% 0%<br>6%<br>4% 0%<br>Hydropower<
were provided by the implementer, with random ID numbers replacing parents’ phone numbers and the child’s name and school.

We use IVR provider records to measure engagement with the calls, that is, whether the call recipient actually starts the exercises. Uliza’s records show every contact with the parent’s registered phone number, along with the length of each call in seconds. We define a call as successful if the parent started the first exercise, which requires tapping a phone key to confirm. We define a parent as having engaged in one of the twice weekly exercise sets if the IVR made at least one successful call in that set. Since there are 9 exercise sets per wave, engagement can take values between 0 and 9. Call records are available immediately, and they are complete and accurate.

The implementer measures children’s ORF scores during the midterm and endterm examination periods. In 2-3 hour periods set aside for the fluency test, a teacher examines each child by counting the number of words on a list that a child can read correctly in one minute of time (see Rodriguez-Segura et al. (2021) for the use of this measure to assess reading and literacy). The teacher then submits the scores to the school’s grade record system. ORF scores range between 0 and 85 correct words per minute (cwpm) based on the length of the provided word list.<sup>14</sup> There are a number of issues with ORF measurement, which were partly revealed only after wave 1 of the experiment had already started. Figure 1 shows the high variation in ORF scores between midterm and endterm. Among the non-missing scores, an unusually high proportion are multiples of five, and in some classrooms, there are implausibly many very high scores. In addition, a high percentage of scores are missing or submitted late to the recording system: ORF scores were available for only 73.5%-88.9% of children depending on the exam.<
One way to explore whether there are systematic differences in the income gaps with the leading areas between men and women is to estimate regression models (4) and (5) at the individual level for men and women, estimate (6) by gender, and compare the female and male income gaps in each country.<sup>22</sup> The results suggest that the differences between the male and female urban income gaps were relatively muted in the early 2000s (Figure 8, Panel A), except in Brazil, Mexico, and Uruguay. By the end of the 2010s, female urban income gaps widened in Bolivia, Colombia, Ecuador, Peru, and Paraguay, but only in Colombia, they deteriorated relative to the early 2000s.

The growing gender disparities in the income gaps with the leading areas cannot be explained with growing gender disparities among the skilled workers (Figure 8, Panel B). Among the B40, female income gaps became significantly larger than male income gaps only in Colombia, Mexico, and Uruguay (Figure 8, Panel D). In Colombia and Mexico, where we have information on individuals residing in rural areas, this development can be attributed to the increase in the female rural income gaps with the leading area. Female rural income gaps also opened in Paraguay and grew in Ecuador, Panama, and Peru. These results are consistent with evidence from the migration literature, which documents no change in the preferences of young migrants for large cities (Rodríguez and Busso 2009; Rowe, 2013), but a shift in the sex and education composition of domestic migrants to leading areas towards migrants who are male and educated (Rodríguez, 2004).

> 21 See Bastos (2017), who shows that in Brazil the deficit in formal housing has been growing, and potentially deterring skilled workers who might be more reluctant than unskilled workers to live in poor quality informal housing.

> 22 While the SEDLAC data are representative only at the household level, it is possible to conduct the
and derived from Genaioili et al. (2013), in place of the modeled grid-level disaggregated data. In column 1, we estimate equation 1 on the sub-sample of observed sub-national GDP per capita data. In column 2, we estimate equation 1 using the completed version of the sub-national GDP data that use national data for those countries missing in the Genaioili et al. (2013) data set. In column 3, we estimate equation 1 using the interpolated and extrapolated sub-national GDP data. Finally, in column 4, we estimate equation 1 using sub-national and national filled-in data. Results for rainfall are consistent with previous findings and indicate an increasing concave relationship between GDP per capita growth and precipitation for more than 99% of the sample.

## **3.B Heterogeneity**

For completeness, we explore the relationship between cell-level precipitation, temperature and growth patterns across grid cells with different levels of development (3.C.1) and shares of cropland (3.C.2).

## _3.B.1 Income category_

Table 2 shows that rainfall impacts growth in developing countries, but not in high-income countries. However, temperature remains significant in both high- and low-income countries. One reason could be a greater dependence on agriculture and rainfall sensitive economic activities in developing countries. One way to test for this is to explore if impacts differ across cells by levels of agricultural activity.

## _3.B.2 Agriculture_

Is the relationship between rainfall and GDP explained by agriculture? We use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample into quartiles based on the shares of cropland. Table 3 shows that growth in cells with more than 75% of cropland is found to be more sensitive to rainfall variations than the global average. A clear implication of this finding is that much, but not all, of the variation in GDP growth is a consequence of the effects
experiences an average of 424 storm-induced outages per year, lasting 40 hours and affecting 13,500 consumers on average.

What share of outages are caused by natural hazards?

Unlike in the US, data on overall outages is not readily available for Bangladesh. One reason for this is that outages are occurring at such a high frequency that does not allow for reliable monitoring. For instance, the 2013 Enterprise Survey for Bangladesh suggests that the majority of firms experience more than two outages per day.<sup>17</sup> In order to get a sense of the relative importance of storms as a cause of outages, we retrieve firm-level outage data from the Enterprise Surveys for 2007 and 2013. The survey covers six Bangladeshi divisions – Chittagong, Dhaka, Rajshahi, Khulna, Barisal, and Sylhet– and is representative at the country level. However, to ensure comparability with the BREB dataset, we illustrate the share of outages caused by storms for two selected divisions: Chittagong and Dhaka. Figure 3.7 compares the number of outages due to storms (as reported by BREB) with the average monthly number of all outages (reported in the Enterprise Surveys). In 2007, storms were responsible for approximately 1% of all outages in Chittagong and 24% in Dhaka. In 2013, those percentages increased in both regions: storms caused 4% of all outages in Chittagong and 41% in Dhaka.

**Figure 3.7: Number of outage reports per month in Chittagong and Dhaka**

<!-- Start of picture text --> Chittagong - 2007 Chittagong - 2013<br>120<br>40<br>100 35<br>30<br>80<br>25<br>60 20<br>15<br>40<br>10<br>20 5<br>0<br>0<br>Storm-induced outages Storm-induced outages<br>Annual average (all causes) Annual average (all causes)<br
4International Energy Agency.

- 5International Monetary Fund, International Financial Statistics and Balance of Payments databases, World Bank, Global Development Finance, and World Bank and OECD GDP estimates. 6World Trade Organization, and World Bank GDP estimates.

7United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.

8International Labour Organization.

12United Nations Conference on Trade and Development, Handbook of Statistics, and International Monetary, International Financial Statistics.

13United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.

14Note: Break in series between 1997 and 1998 due to due to change from International Standard Classification of Education (ISCED76) to ISCED97. Recent data are provisional.

Source: World Bank‘s World Development Indicators (2009).

Table 3 Sources of productivity growth by sector for 1996-2006.

|**Sector Mean**|**Technical**<br>**Efficiency**<br>**Change**<br>**(TEC)**|**Technical**<br>**Change**<br>**(TC)**|**Scale**<br>**Efficiency**<br>**Change**<br>**(SEC)**|**Total**<br>**productivity**<br>**Change**<br><br><br>0<br>_G_|
|---|---|---|---|---|
|Argentina|-0.018|-0.598|8.943|8.328|
|Bolivia|0.003|1.131|-31.481|-30.347|
|Brazil|-0.001|0.072|-20.542|-20.471|
|Chile|0.004|-0.978
effects across sub-populations in India. By linking survey responses and administrative records with (potential) ad exposure data, our design enables us to assess the causal impact of online public health messages on offline behaviors and real-world outcomes, such as consistent bed net usage, timely treatment-seeking, and malaria incidence. Additionally, we investigate whether the ad content reached its intended audience by explicitly targeting and oversampling respondents at higher risk of contracting the disease. Our findings show that the ad campaign was effective at improving health outcomes and reducing malaria incidence only among individuals who were ex-ante less likely to contract the disease, underscoring the need for improved measurement and targeting in public health campaigns.

Malaria is transmitted by the bite of infected mosquitoes and disproportionately affects poor and rural people in India (Dev et al., 2004). The ad campaign was designed by the public health NGO Malaria No More (MNM) and used behaviorally-informed ads to provide information and encourage individuals to take preventive measures (e.g., sleeping under mosquito nets) and response actions (e.g., timely seeking treatment).<sup>2</sup> It was run on Meta platforms and aimed to achieve its public health objectives by maximizing engagement with the ads and MNM’s pages. While it succeeded in achieving these engagement metrics (e.g., the ads reached 130 million people and garnered 46

> 1Social marketing entails applying commercial marketing techniques to influence the behavior of target audiences to enhance personal welfare and societal well-being (Andreasen 1994; Kotler and Zaltman 1971).

> 2For example, 75% of households in our baseline survey have mosquito nets, but only 70% of them use them regularly, implying that almost 50% of households remain unprotected to some degree.

2
CHARLS main respondents are a random sample of people over the age of 45, of which both main respondents and their spouses are asked for detailed information on themselves and on their families. In this paper, we are particularly interested in the transfers between the respondents and their adult children.<sup>5</sup> CHARLS has information on all living children of each respondent and spouse, no matter where they live. In order to fully employ the rich information on each of their children, the basic sample of interest are the children of the CHARLS respondents. Specifically we first chose the 789 households in which either the main respondent or his/her spouse was over 60.<sup>6</sup> The 2,667 adult children (25 or older<sup>7</sup> ) of the 789 respondents are then treated as our study sample.

Several sample restrictions are further applied according to different purposes of the study. For the estimation on transfers, we restrict the analyses to non-resident children (2,202 observations) because transfers within the household are not clearly specified conceptually and CHARLS, like other aging surveys, does not attempt to measure them. In analytical models exploiting family fixed-effects, only those families with at least two non-resident adult children are included (2,068 observations).

In order to use individual information on both parent and child, we match individual child and parental characteristics. We choose the information of the main respondent parent

> 5 The incidence of transfers between the respondents and their elderly parents are quite small (only 13.4%), so we did not take them into consideration in our analysis. Family transfers can entail interactions among members of three or even four generations, but it is beyond the scope of this paper to give a comprehensive treatment of this issue. 6 Only 16 main respondents do not have any children in the sample, who are dropped for the purpose of studying parent-child transfers.

> 7 We choose age 25 because many adult
|Country Group<br>This table presents estimations of annual eco<br>weighted average of each individual countr<br>i_growth+i_growth*late=0 where i represents<br>presented in italics. *, ** and *** denotes sig|EM<br>nomic growth (q/q data) <br>y belonging to that grou<br>G7 or China. Both panel<br>nificance at the 10, 5 and 1|EM<br>**The R**<br> of EM against G7<br>p. Growth is wei<br>and country by co<br>% level respective|EM<br>**Tab**<br>**eal Decoupling h**<br> and China growth<br>ghted by PPP-meas<br>untry (taken median<br>ly. Source: IMF IFS|EM<br>C b C<br>**le 2**<br>**ypothesis among EM**<br>. CRB Commodity Index G<br>ured GDP in current price<br>values) are presented. Pane<br>Statistics, IMF WEO, Bloo|EM<br>C b C<br>rowth is the log change in <br>s from a year before. The<br>l estimations have country<br>mberg.|EM<br>C b C<br> the CRB general commod<br> last four rows of the tab<br>fixed effects. P-values from|ity index. G7 growth is a<br>le present a joint test of<br>robust standard errors are|
|---|---|---|---|---|---|---|---|
|Estimations|Panel|Panel|Panel|ountry y ountry<br
# **References:**

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