rafmacalaba/data-use-mentions-extended huggingface dataset:
and Starks, 1997; Nofsinger and Sias, 1999; Grinblatt and Keloharju, 2000; Sias, 2004; and Choi and Sias, 2009).

In this paper, we exploit new data and analyze the investment behavior of pension funds, for which relatively little is known although they have played a crucial role across countries. The few studies available on pension fund investment behavior are very informative, but they only use part of the data available on pension funds in a given country. They typically use quarterly data for a subsample of pension funds and focus exclusively on equity holdings. Lakonishok et al. (1992), Badrinath and Wahal (2002), and Ferson and Khang (2002) analyze US data, while Blake et al. (2002) and Voronkova and Bohl (2005) analyze data from the UK and Poland, respectively. The limited scope of the data and the emphasis on equity holdings present important shortcomings because most pension funds worldwide invest a large fraction of their portfolios in bonds and other types of assets, and their trading patterns might differ significantly across asset classes.

We use data from Chile, which was the first country to embrace the new mandatory, privately managed, defined-contribution (DC) pension fund model, by replacing the public, defined-benefit (DB) pension system with a DC one in May 1981. Many developed and developing countries have followed suit and reformed their pension regimes, establishing very similar pension funds.<sup>1</sup> For example, the UK moved toward a multi-pillar pension system in 1986. Sweden modified in 1994 the pension system from a pay-as-you-go DB to a second-pillar system that includes a voluntary DC system. In the US, proposals to reform the social security system were also recurrently considered. Following Chile’s example, many developing countries adopted similar reforms, including Argentina, Bolivia, Colombia, Costa Rica, the Dominican

> 1 These changes reached even the corporate sector, entailing a shift away from defined-benefit schemes toward defined-contribution schemes to transfer risk from corporations
Working Paper No. w21978.

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conventional and Islamic banks is therefore that either opposing effects of Sharia-compliant banking cancel each other out or that the differences between these two models are smaller than often assumed. However, there are certainly regulatory and supervisory challenges for countries that see an increasing entry of Islamic banks, and our preliminary results suggest that conventional banks that operate in countries with a larger share of Islamic banks are more costeffective but less stable.

We hope that our theoretical discussions and empirical findings stimulate more research in this area. First, disaggregated data on specific products below balance sheet and income statements are necessary to better understand the differences in financial service provisions between conventional and Islamic banks. This would also allow us to include Islamic windows in our analysis. Second, future work can also assess the impact of varying market shares for Islamic banks on the outreach of the banking system and ultimately the access to and use of banking products by firms and enterprises.

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To this end, we exploit the Ghana Urban Household Panel Survey (GUHPS), a data set unique in the African context and one of the very few longitudinal datasets in either the developing or advanced world containing several measures of subjective satisfaction with work, life and finances. By income, Ghana is broadly representative of sub-Saharan Africa and offers insight into low and low middle income countries more generally.<sup>4</sup> The GUHPS permits us to study job satisfaction across sectors, potentially controlling for unobserved heterogeneity, and thereby to better characterize developing country labor markets and assess how well the developed country literature generalizes to them. Using conventional ordered categorical data models, we first test for the existence of self-employment and small firm satisfaction premia. Since studies of job satisfaction in developing countries are scarce (see Clark and Senik, 2010a, for a review of the literature) it is unknown whether this finding translates to poor countries where such jobs often constitute the vast majority of employment, sometimes accounting for 70-80% of the labor force (see Kingdon et al., 2005).<sup>5</sup> The few studies of job satisfaction in developing countries that we are aware of tend to focus on a select subset of workers (Mulinge and Mueller, 1998), and rely exclusively on cross-sectional datasets (Pratap and Quintin, 2006; Perry et al., 2007; B ́oo et al., 2010; Pag ́es et al., 2008). In particular, we test whether the relative satisfaction premium for self-employment and informal salaried employment are more consistent with the competitive view emerging from the OECD literature, or a dualistic conceptualization of the informal sector: whether there is a positive satisfaction premium associated with formal jobs that would signal that markets are not competitive, as well as identifying which characteristics may be associated with it.

We then confront heterogeneity in conditional satisfaction head on by means of mixed (random parameter) ordered probit models. These models extend the mixed logit model to
.14|6,482|5,249|11,731|
|Single caregiver|2.52|54.78|20.36|843|9,477|10,320|
|Couple without children|9.46|1.43|6.72|3,159|248|3,407|
|Unaccompanied children|0.93|1.53|1.13|310|265|575|
|Siblings|1.24|1.51|1.34|415|262|677|
|Extended and other|2.48|4.12|3.04|827|713|1,540|
|Polygamous|0.02|0.02|0.02|6|3|9|
|All Nontraditional<br>_Source:_Own calculations based on ProGre|36.06<br>s and JD‐HV database.|93.74<br>|55.75|12,042|16,217|28,259|

_Note_ : PA = principal applicant. PA is determined by UNHCR at the time of refugee registration. HH = Household Nontraditional=all family types apart from couples without children. This table contains data on PAs 54,408 (26 percent) of all tracked Syrian refugees.

<mark>One‐third of Syrian refugee households have female principal applicants with the corresponding two‐ thirds of households having a male principal applicant. There are clear differences between the characteristics of male and female principal applicant households’ in terms of education, marital status,</mark> and family type (Table 1).

<mark>The average age of male and female principal applicants is similar; however, female principal applicant households are more likely to be seniors (over 65 years old) than male principal applicants. Most female principal applicants are married (60 percent) but they are more likely to be living without their spouse than a
informal sector. It starts with a description of the distribution of employment across a formality-employment type continuum in the 26 countries and finds that approximately 56 percent of urban jobs are informal, though this statistic rises to 70 percent if we exclude the two countries with the largest urban populations: Nigeria and South Africa. It then profiles urban workers within each formality-employment type category. The paper turns to firms, using data from three countries to unpack informal sector firm characteristics and heterogeneity therein. The next section focuses on the relationship between the urban environment and different types of informal and formal jobs. It starts with a profile of urban infrastructure and services for each employment type, using data from household surveys and the IES. It then uses geospatial data and a Census extract in one country to understand the role played by urban variables that are not measurable in surveys. Finally, it shares policy-related reflections that emerge from the descriptive analysis.

The paper makes three main contributions to the existing descriptive literature on informality in SSA. First, it limits its analysis to informality in the urban space. Second, the analysis considers heterogeneity within the urban labor market by exploring different categories of employment (i.e. self-employed, employer, or wage employee) in the informal and formal sectors. This is in line with recent literature that differentiates by employment categories within the informal sector to explore questions of dualism, differential job quality, transitions between employment types,<sup>10</sup> and heterogeneity within informal enterprises.<sup>11</sup> Third, the paper jointly considers employment types and urban variables, such as access to public services and urban density.

The analysis presents five main results. First, the urban informal sector is large and persistent in SSA. More than half of urban SSA workers can be considered informal, reaching 70 percent if we drop the two most populous countries in the sample. Of these, 52 percent are self-employed, while the others are informal paid (33 percent) or unpaid wage employees
exit rate for large firms in the United States, according to the Statistics of U.S Businesses database.<sup>18</sup> Note that in the model, large firms do not exit because of lack of profitability; they exit because of this exogenous shock. Hence, _δ_ directly determines the annual exit rate of these firms. Next, I parameterize the distribution _G_ of productivity draws so that all firms enter with the same level of productivity, _z_ = 0. In the full model, firms will also enter with _τ_ = 0.

Using data from the Business Dynamics Statistics, Atkeson and Bursteinn (2010) find that the size of the median firm in the U.S. employment-based firm size distribution is 500 employees. In the model, firm size in terms of number of employees is a normalization. I set the level of employment _L_ so that entering firms’ employment will be 6 percent of the median employment in the economy, as reported by Luttmer (2007). Finally, I normalize _fe_ = 1 and set _f_ = 0.2, which implies an exit rate of 6 percent.<sup>19</sup>

> 18 These statistics can be found at <u>www.sba.gov.</u>

> 19 Notice that the exit rate is determined by the fixed cost of producing _f_ , the entry cost _fe_ that determines the minimum productivity required to stay in the market, and the stochastic process. In a model with rent-seeking, the stochastic process for the firm-specific revenue tax or subsidy will also play a key role in determining survival. In that case, firms with low productivity could stay in the market if they had subsidies large enough.

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appear to be motivating many countries and voters to contribute more (given that some of the richest OECD donors do not contribute the most, as measured by share of GDP).<sup>5</sup> Moving to the more nudging indicator may well be worth the loss in precision.

A similar argument might be made for carbon dioxide emissions, which while not an MDG target, has been suggested as a target for MDGs 2.0. Thousand metric tons of carbon dioxide equivalent emissions, per capita or per dollar of national GDP, is a difficult-to-grasp indicator. Even measures of global average temperature may not be intuitive – an increase of two degrees centigrade in the average temperature of the world does not seem like much to the untrained ear. Rather, the contributions of an average person in a country to the rise in sea levels, in millimeters, over a defined time period, would be a more palpable target. That indicator can be visualized. It also embeds within it the causal narrative between carbon emissions and some of its potentially catastrophic consequences. It is true that estimating current and future sea levels as a function of carbon emissions is an ongoing area of research, and the estimates would invariably have a confidence interval around them. But again, if the goal is nudging, and if the confidence intervals are not too large, this would be an acceptable cost for a motivational device.

For fair trade, which is another element of the “global partnership for development” (MDG 8), an indicator might be the total cost of each country’s tariff and non-tariff barriers on the income of the poorest countries. This could be easily comparable among rich countries, and the numerical benchmark of zero – no tariffs and non-tariff barriers on the poorest countries – is easily understood. It is also consistent with the readily grasped “do no harm” principle (more on that below).

The health targets in the MDGs were to reduce by two-thirds the under-five mortality rate and by three quarters in
define as self-employed all individuals that contributed to old age provision from independent work. In order to obtain information on an individual’s education, we are adding the Swiss Structural Surveys from 2010–2017 to the data. The Structural Survey is a cross sectional dataset collected by the FSO that includes permanent residents aged 15 and older. Around 200,000 persons are randomly sampled every year to participate with an online questionnaire. The information we are using from the Structural Surveys is the highest completed level of education. Since this data is only available starting in 2010 and only for a subsample of individuals, we are assuming education to be constant over time and we are running most specifications first with the larger sample excluding the Structural Surveys and then with the smaller sample including the Structural Surveys.

The analytical sample used in our empirical analysis includes all refugees and migrants that arrived after 1998 since we only observe the labor market outcomes from 1998. We are restricting the sample to the working age population (18-64 years old)<sup>18</sup> and we only consider refugees and migrants who arrived after the age of 17, excluding those who would have had access to primary or secondary education in Switzerland.For the comparison of refugees and migrants we are further restricting the migrant group to those who are coming from a non EU-15 country to make them more comparable. Robustness checks with the universe of the migrant population are available from the authors.

The final sample includes 111,382 refugees, 701,371 migrants from non EU-15 countries and 1,293,071 natives. The sample that includes the Structural Surveys is smaller and comprises 12,379 refugees, 93,150 immigrants and 390,071 natives. Table 2 presents the summary statistics of the main variables used in the analysis.<sup>19</sup> Figure 3 shows the top 10 origin countries for refugees arriving in Switzerland between 1998 and 2018. Most refugees in this period came from Eritrea, the second biggest group came from Syria,
first name, and either the same phone number, or the same sector of activity and an address in the same neighborhood.” Using this definition, the likelihood that a business in the program database was considered as formal, whereas the business was in reality not formal, was very low. The opposite case (i.e. a business was considered as informal, whereas it is in reality formal) is however possible, so this measure of formalization may underestimate the actual number of businesses which formalized in all groups. We therefore also supplement the administrative measure of formalization with survey measures.

58
participants report having better attitudes toward school by 0.17 standard deviations and spending 16% more time (20.4 minutes approximately) each day doing their homework. Moreover, 7.9% report that they pay more attention during classes, compared to the C group. This improvement in attitudes is also confirmed using administrative data: treated students are absent 1.6 days fewer per week than students in the comparison group. This implies a reduction of 23% in school absenteeism. These effects shed light that the ASP directly affects students’ positive attitudes toward school as the program may allow them to be involved in a different, potentially more interesting learning approach, or to be exposed to a new category of role models—tutors—along with their teachers.

Then, we estimate the ITT effect on misbehavior and violence-related outcomes, using measures from student and teacher reports. As we can see in columns (5) – (9) in panel A, after seven months of intervention, students report having committed fewer delinquent actions and being less violent compared to reports of students in the C group (in magnitudes of 0.19 and 0.14 standard deviations, respectively). Similar effects are found using teacher reports. Students randomly assigned to participate in the ASP reduced both their bad behavior at school by 0.17 standard deviations and their probability of having a misbehavior report by 6.4 percentage points.<sup>19</sup> Although our two sets of measures are not completely comparable since they differ by report source and the items and domains included—family, school, or community—results from both are consistent with an increase in participant willingness to reduce bad behavior and tendencies to violence. Combining these two groups of results, the effects we find from the intervention are similar to those previously identified in the literature. For example, Durlak et al. (2010) find a reduction in criminal behavior by 0.19–0.30 standard deviations in a meta-analysis of ASPs
End of picture text --> <!-- Start of picture text --> EACR Decomposition (Turkey as base country)<br>2011 2014<br>50<br>0<br>‐50<br>Three different sources of disparity amongst the equity adjusted<br>coverage rates of selected countries with respect to Turkey<br>Change<br>Composition Scale<br>Equalization<br><!-- End of picture text --> <!-- Start of picture text --> EACR Decomposition (Turkey as base country)<br>2011 2014<br>50<br>0<br>‐50<br>Three different sources of disparity amongst the equity adjusted<br>coverage rates of selected countries with respect to Turkey<br>Change<br>Composition Scale<br>Equalization<br><!-- End of picture text -->

## **<mark>Step 2. Select Country Comparators</mark>**

<!-- Start of picture text --> 2011 2014 2011 2014<br>The value of this dashboard is the possibility of the user to select comparison countries on<br>the basis of different criterias such as poverty level, GDP per capita, and national savings 50<br>as a share of GDP or on the basis of one of the financial inclusion indicators.<br>Choice of filter indicator (WDI, FINDEX)<br>GDP per capita, PPP (constant 2011 international $) 0<br>Filter: All (highlight countries to select them) ‐50<br>.<br>. TUR<br>17,188<br>. Measures the size of contribution of the selected dimensions to the Three different sources of disparity amongst the equity adjusted<br>dissimilarity index of each country coverage rates of selected countries with respect to Turkey<br>Dimensions Change<br>Age
# **4.2. Life satisfaction**

We construct an index of average satisfaction with life for every country, measured as the simple weighted average of answers of respondents to the statement: _All things considered, I am satisfied with my life now,_ with the following coding: 1. strongly disagree; 2. disagree; 3. neither disagree nor agree; 4. agree; 5. strongly agree. We find that changes in satisfaction with life at the country level are correlated with the severity of the crisis impact, as measured by GDP growth (see Figure 9). Life satisfaction captures, at the individual or national level, many measures of well-being (Layard, 2005). It is worth noting that, because our baseline period is 2006, we are not able to compare changes in life satisfaction just before and after the crisis. The period since 2006 has included both periods of strong growth and the contraction during the crisis. We nevertheless analyze changes in life satisfaction with regards to the crisis, assuming that recent events matter most when answering life satisfaction evaluation questions.

**Figure 9. Crisis severity and changes in life satisfaction**

<!-- Start of picture text --> .5 Azerbaijan<br>Montenegro<br>Mongolia<br>Bosnia-HerzegovinaMacedonia<br>Serbia Turkey<br>Moldova Poland<br>Georgia<br>Bulgaria<br>Tajikistan<br>0 Armenia RussiaCzech Republic<br>Slovak Rep.<br>Albania<br>Ukraine Hungary Kyrgyz Rep. Kazakhstan Uzbekistan<br>Croatia<br>Estonia<br>Slovenia<br>Belarus<br>Latvia<br>Lithuania<br>Romania<br>-.5<br>-10 -5 0 5 10<br>Average GDP growth in 2009-2010 (percent)<br>Change in average life satisfaction (2006-2010)<br><!-- End of picture text --> Source: LiTS I (2006
2019 (i.e., 1980=2019=100).<sup>23</sup> In 1986, 55 percent of the EMDEs recorded real per capita GDP levels that were below those in 1980. By 1990 the situation had not improved, as 58 percent of EMDEs had real per capita GDP

> 22 The discussion that follows is based on the October 2020 World Economic Outlook database and projections.

> 23 As already noted, even before COVID-19, there were a number of countries in crisis.

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ultimately interested in safe toilet uptake, not just any type of latrine ownership. Following the guidelines provided in WHO/UNICEF [2017] to define a safe toilet, we find that 99.6 percent of all reported toilets in our study area belong to any of these categories.<sup>25</sup><sup>_,_26</sup> Therefore, the measure of toilet ownership (as reported by head of household and by interviewer observation) can be interpreted as equivalent to safe toilet ownership in our study context.

Columns 2 and 3 indicate at most small, and statistically insignificant differences in the means of these variables across the treatment and control group. This confirms that the randomisation was successful in creating observationally equivalent groups. Importantly, we find no significant difference in toilet ownership between the two experimental groups prior to intervention roll-out.

# **4 Theoretical Framework**

We develop a simple model to provide guidance on how the intervention may impact household borrowing and investment behaviour. It allows us to not only assess the likely overall effects of the intervention

> in Table OA-B.2 in the Online Appendix.

> 23Reassuringly, we also find no systematic differences in observed characteristics between the two groups when we repeat the same exercise with baseline data collected prior to the intervention roll-out for a sub-sample of clients and their households. These results are available on request.

> 24This retrospective measure of toilet ownership at baseline matches well with data collected in a baseline survey of panel households.

> 25The guidelines define a toilet to be safe when is is an improved facility for which excreta is safely disposed of in situ or off-site. Based on this definition, in our data we consider the following types of toilets safe: Flush/pour flush to piped sewer system, septic tank, pit latrine, VIP, pit latrine with slab, composting toilet, biogas system, urine diversion dehydration.

> 26 We can use the pre-intervention data available for a sub-set of clients as well as the endline data for this