rafmacalaba/datause-encoder-data huggingface dataset:
An ongoing research project by the World Bank is compiling data on _actual_ trade policies in services. To date surveys have been conducted in 32 developing countries and comparable information obtained for 24 OECD countries, covering five key sectors: financial services (banking and insurance), telecommunications, retail distribution, transportation, and professional services (Gootiz and Mattoo, 2008).<sup>13</sup> In each sector, the survey covered the most relevant modes of supplying that service: cross border trade in services (mode 1 in WTO parlance) in financial, transportation and professional services; commercial presence or FDI (mode 3) in each services sector; and the presence of service supplying individuals (mode 4) in professional services. Survey results to date are summarized in Annex Figure 1.<sup>14</sup>

# **Annex Figure 1: Restrictiveness of services trade policies by income group, 2007**

<!-- Start of picture text --> Restrictiveness of Services Trade Policies<br>by income groups<br>56.5<br>60.0<br>50.0 43.7<br>40.0<br>25.5<br>30.0<br>15.2<br>20.0<br>10.0<br>0.0<br>Low income Lower middle Upper middle High income<br>Index of restrictiveness<br><!-- End of picture text --> _Source_ : Gootiiz and Mattoo (2008)

> 13 The sectors are further disaggregated into banking (retail and merchant), insurance (life, non-life, and reinsurance), road transport, railway shipping, maritime shipping and auxiliary services, air transport (freight and passengers), accounting, auditing, and legal services.

> 14 Results of the survey are summarized in an index of restrictiveness. For each sector and mode of supply the openness of policy towards foreign suppliers is mapped on a 5-point scale ranging from 0 (for no restrictions) to 1 (very restricted), with three intermediate levels of restrictiveness (0.25. 0.50 and 0.75). Sectoral results are aggregated across modes of supply using weights that reflect judgments of the relative importance of the different modes for a sector. For example, mode 4 (temporary movement of suppliers) is important for professional services, but not for telecommunications, where mode 3 is the dominant mode of contesting a market. Sectoral restrictiveness indices are aggregated using sectoral GDP shares as weights. The country income group indices are derived using GDP weights for the countries in the sample.

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for potential emigrants (e.g., Massey et al., 1993; Beine, Docquier and Ozden, 2011).<sup>12</sup> Past migration also measures other intangible factors unrelated to income, such as trust, cultural proximity, and social openness to migrants of the destination as perceived by emigrants of the source country. Moreover, we employ indicators for geographical factors ( _Distij_ , _Contigij_ ) and linguistic proximity ( _ComLangij_ ), which are typically used in the literature on migration as additional instruments.

To further address potential endogeneity bias, we use the total emigration rate in 1960 instead of _TotalMigij,t-1_ as an instrument, which, however, cannot be readily observed at the time of analysis. We thus construct a proxy for the total emigration rate. Denote by _NetMigi,1960_ the total net emigration rate (the number of emigrants minus the number of immigrants divided by population size) in country _i_ in 1960, provided by the United Nations Population Division.<sup>13</sup> Our measure of bilateral total emigration rates in 1960 is defined by where _Popi,1960_ is population size in the source _i_ and _Popj,1960_ is the population size in the destination _j_ in the year 1960 .<sup>14</sup> As suggested by Beine, Docquier and Rapoport (2001), one may use countries’ population sizes to reflect immigration quotas.

_NetMig i_ ,1960 × _Pop j_ ,1960 is thus a proxy for the net stock of emigrants from country _i_ received in country _j_ in 1960. Because our empirical strategy focuses on emigration rates rather than stocks, we divide this measure by (100 times) the population size of source country _i_ to obtain an estimate for the past bilateral emigration rate.<sup>15</sup> The fraction of high-skilled migrants before 1960 was comparatively low; therefore, potential 18
women also rate their own level of financial literacy more conservatively; self-assessment of financial literacy in the overall population tends to be more inflated.) This is true across almost all countries in both the developed and developing world. One exception is Bucher-Koenen and Lusardi (2011), which finds no differences by gender in East Germany.

In Africa, the FinScope surveys likewise indicate disparities by gender in terms of access to financial services, which could also translate into disparities in levels of financial literacy.<sup>4</sup> In Malawi, for instance, 17 percent of females are banked compared to 21 percent of males. A similar difference is found in many other countries, including Mozambique, South Africa, and Zambia, although the picture varies by type of service and country. When they do have access to finance, females are often more likely than males to rely on informal versus formal services. The InterMedia (2010) survey of youth in Ghana and Kenya (described in greater detail in the next section) also finds disparities between males and females in terms of access to financial services.

There are fewer surveys that focus specifically on women, particularly in developing countries. One exception in this regard is MasterCard Worldwide (2011), which reports on an index of financial literacy for women in 24 countries across Asia, the Middle East and North Africa, and Sub-Saharan Africa. The index measures knowledge of money management (weighted 50 percent), financial planning (30 percent), and investment (20 percent), and sampled approximately 10,500 women (although it is not clear how the sample was chosen). Interestingly, they find that financial literacy is not necessarily correlated with the level of income in each country; within Asia, women in Thailand scored the highest and women in Japan and Korea scored the lowest. However, women in African countries and some Middle Eastern countries generally had lower scores than those in Asia.

The gender gap in financial literacy is of particular concern as women are also more likely than men to become economically vulnerable due to longer life spans, shorter work experiences, and other factors. Fonseca et al. (2009) finds that the gap between men and women in the U.S. is explained less by differences in covariates (i.e. difference in characteristics) than in coefficients (i.e. differential effects of characteristics), with household decision-making roles allocated more by relative levels of education than by gender. Lusardi and Mitchell (2008) show

> 4 In their analysis of formal bank account ownership, however, Honohan and King (2009) find that the coefficient on gender is insignificant when they control for variables such as financial sector knowledge, education, and other demographic factors.

10
Figure A3.4 shows an analogous chart for FY-12, to figures A3.1-A3.2 above. We here need just one figure (as we have dropped first class as a separate category due to very few flights in this class). Depending on average load factors in premium classes, the combined carbon footprint from all air travel in that year is shown to vary from about 85,000 tons CO2 (for average load factors of 0.9; clearly an overestimate); and up to about 220,000 tons CO2 (for average load factors of 0.3; clearly an underestimate).

Figure A3.5 shows the distribution of flights by flight length and by flown class, for the two years in question, and corresponding footprints per passenger mile flown; in similar way as for figure A3 above, again assuming average load factors in premium classes of 0.6.

# **Figure A3.5: Footprint for CY 2009 and FY 2012 per Distance Class Compared, With Average Fuel Consumed per Passenger Mile From Figure 3.**

<!-- Start of picture text --> 40,000,000 0.700<br> 35,000,000 0.600<br> 30,000,000<br> 0.500 FY 2012 First<br> 25,000,000 FY 2012 Business<br> 0.400<br> 20,000,000 FY 2012 Coach<br> 0.300<br>CY 2009 First<br> 15,000,000<br>CY 2009 Business<br> 0.200<br> 10,000,000<br>CY 2009 Coach<br> 0.100<br> 5,000,000 Avg Footprint per Pax/Mile<br> - -<br>1 3 5 7 9 11 13 15<br>Distance Class<br>(Kg)<br>CO2 Footprint (Kg)<br>Average CO2 Footprint per Passenger Mile<br><!-- End of picture text --> Table A3.6 below shows the distribution of footprints as calculated by distance class for HQbooked trips in CY-09 and FY-12, for the sample of trips in our data set. The most interesting issue here is the substantial change in the distribution of flight activity over this period, with fewer flights in distance classes 10-11 (in the range 3,450 - 4,600 miles), and more flights in particular in class 16 (the longest flights, above 6,900 miles).

Future improvements in calculations of the carbon footprint load of WGB air travel can be made on several fronts, including the following. (a) Match the American Express data set with the purchased scheduling data published by companies such as Diio or OAL to find the equipment 34
**The fall in foreign lending activity—consisting of cross border claims and claims of local affiliates—was supply driven during periods of extreme European financial stress.** Exhibit VI.4 provides an illustrative exercise which assesses the extent to which financial shocks in Europe were transmitted to EMDEs via European banks’ cross-border and local affiliates activity.<sup>34</sup> The exhibit is based on a panel regression model which disentangles supply from demand.<sup>35</sup> Supply factors are proxied by the 3-month EURIBOR-OIS spread which reflects interbank stress. Demand factors are captured by country-level quarterly GDP growth in EMDEs. The exhibit displays the portions of European foreign bank claims growth that can be explained by supply and demand factors for the _average_ EMDE country.<sup>36</sup> Claims growth is mostly driven by demand for credit in EMDEs, with the exception of a significant slowdown in 2009. Average EMDE demand explained 2 percentage points of pre-crisis claims growth and vanished in 2009. EMDE demand picked up in 2010 but has been falling and only explained about 1-1.5 percentage points. The exhibit clearly shows supply shocks propagated to EMDEs in 2007, 2008, and 2011 when European interbank stress conditions spiked.

**As European banks deleveraged in 2008 and their foreign claims contracted, the** **_domestic_ credit supply in recipient countries was directly negatively affected,** even after taking into account the impact of local demand effects.<sup>37</sup> A 1-percent reduction in foreign claims resulted in roughly a 0.5-0.7-percent decline in domestic credit. This suggests many economies have not been able to fill the gap European banks left behind with likely ramifications on domestic economic growth.

> 34 Note that foreign claims do not include cross-border flows between the parent bank and its affiliates. Moreover, the foreign claims data are not available in currency-adjusted form because the currency composition of cross-border claims in unknown.

> 35 The regression is based on country panel data from 200Q1-2012Q2. The dependent variable is the quarterly growth rate of European consolidated foreign claims on EMDEs as provided by the BIS. The independent variables are quarterly changes in EURIBOR-OIS, GDP growth (YoY), and country- and season-fixed effects. The supply and demand factors are highly statistically significant using robust standard errors clustered on the country level. The model’s R-squared is 0.08. For a similar analysis using currency-adjusted locational claims, see Takáts (2010).

> 36 Since there is substantial heterogeneity between EMDEs, these results should be interpreted as illustrative only.

> 37 Aiyar and Jain-Chandra (2012).

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Given that perceptions of exclusion from access to justice are associated with increased risk of violence, ‘exclusion’ may constitute a covariate that moderates policy impact. The World Bank, for example, recently concluded the establishment of the WEI, which may be employed as data indicating selectivity of the provisional benefits (and costs). The WEI represent, across 181 countries from 1900-2018, denial of individuals’ access to services or participation in governed spaces based on their identity or belonging to a particular group. The indicators measure exclusion across four dimensions: exclusion from civil liberties; exclusion from access to public services; exclusion from access to state jobs; and exclusion from access to state business opportunities. Exclusion in these dimensions is grouped by five indices: socio-economic group; by gender; by rural/urban location; by political group; and by social group (including caste, ethnicity, language, race, region, religion, migration status, or some combination thereof). The WEI, therefore allow for exclusion, for example by gender, within a country or across a region, for example the Middle East across the four dimensions (Pillai, 2019). Of particular relevance to justice processes are the dimensions of exclusion of social groups in their access to civil liberties and the state service of justice. However, the efficacy of equal access to other state services, to state jobs, and state business opportunities may inform, in particular, identification of social group-specific exclusion in CBA modeling of justice processes to resolve equal access in those dimensions.

A further covariate example that may moderate policy impact may be ‘governance’. The World Bank’s WGI reports aggregate and individual governance indicators for over 200 countries and territories from 1996, for six governance dimensions: Voice and Accountability; Political Stability and Absence of Violence; Government Effectiveness; Regulatory Quality; Rule of Law; and Control of Corruption. These aggregate indicators, based on over 30 individual data sources, combine the views of a large number of enterprise, citizen, and expert survey respondents.

As data emerges specific to _how_ different justice process exclude social groups, frameworks can be developed and vetted to inform what constitutes optimal social (such as inclusion) and economic net benefits. The United Nations and World Bank, on this issue specific to processes of accountability for atrocity crimes, note: “Weighing the equality of accountability processes against the imperative to bring perpetrators to book is critical to the challenge of advancing stabilization and justice in conflict-affected environments under SDG 16. Accountability processes may exacerbate 20
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 11. ICT indicators, benchmarked against Sub-Saharan African fragile and low- income countries**

|||Fragile states|C|AR|Low-income<br>countries|
|---|---|---|---|---|---|
|Indicator|Unit|2008|2000|2008/2009|2008|
|GSM coverage|% population under signal|68|19|50|63|
|Mobile phone|subscribers/100 people|20.0|0.3|15.2|24.4|
|International bandwidth|bits/capita|47.2|0.06|3.5<sup>[1]</sup>|24.8|
|Internet|users/100 people|2.7|0.06|1.4|3.5|
|Landline|subscribers/100 people|0.7|0.3|0.2|0.8|
|||Fragile states|C|AR|Low-income<br>countries|
|_US dollars_||2008|2002|2009|2008|
|Price of monthly mobile b|asket|12.0|—|12.4|11.0|
|Price of monthly fixed-line|basket|16.0|—|13.9|10.4|
|Price of monthly fixed bro|adband|72|—|671<sup>[2]</sup>|287|
|Price of a call to the Unite|d States per minute|0.62|4.53|0.62|0.69|
|Price of an inter-Africa ca|ll per minute|0.74|—|0.62|0.94|

_Source:_ Adapted from AICD, OTH, ART, SOCATEL, Orange, and World Bank ICT At-a-Glance. [1] = 2007.

[2] = 2010, WiMAX. — = Data not available.

The country has benefited from a huge expansion in the coverage and access of mobile communications. Mobile penetration rose from 0.3 subscriptions per 100 people in 2000 to 15.2 in 2009, but is still below the rates of fragile states. Nonetheless, with only around half the population covered by a mobile signal,<sup>12</sup> penetration remains behind the levels of comparable Sub-Saharan peers (table 11). The intensification of competition since 2004 and the actions of strategic investors from the Middle East and Europe in mobile operations<sup>13</sup> should help to boost access. But this may be difficult without universal service support, since it is estimated that only around half the population can afford to pay for service (figure 14) (Mayer and others 2009).

> 12 Figure refers to the largest mobile operator by subscriptions (Orascom Telecom 2010).

> 13 Bintel (a Dubai-registered company) purchased Nationlink, an existing mobile operator in 2007. Likewise, Orascom Telecom of Egypt purchased Telecel, another existing mobile operator, in 2008. France Telecom entered as the fourth mobile operator in 2007 through its Orange subsidiary.

30
In addition, our results may be influenced by more general interactions between labor market outcomes and levels of cohesion in a society. For example, employers in societies with greater generalized levels of trust may be less likely to discriminate on the basis of social stereotypes. This implies that the characteristics of those who are unemployed, as well the social experiences associated with joblessness, may vary across countries with different initial levels of social cohesion.<sup>13</sup> In spite of these concerns we hope that also the simpler comparisons of workrelated differences in social and subjective wellbeing presented in the following sections can provide initial indications of the interaction between social cohesion and labor market outcomes in different parts of the world.

# **V. Jobs and subjective wellbeing**

Recent events in the Arab World and other transition economies suggest that differences in wellbeing between those in and out of work pose increasing challenges to social stability in emerging economies. Yet we know surprisingly little about how jobs affect wellbeing, once we leave the relatively small group of well-researched high income countries.

In high income countries the link between unemployment and low life satisfaction is fairly well established. A ground-breaking study of unemployment in Britain reports that the experience of joblessness “depressed well-being more than any other single characteristic, including important negative ones such as divorce and separation” (Clark and Oswald 1994). Subsequent research indicates that these effects are typically robust to the inclusion of controls for income, suggesting that a large part of the wellbeing effect of unemployment works through non-monetary channels (for overviews and evidence see Di Tella et al 2001, 2003 and Frey et al. 2008). There are also indications that the causal link flows from unemployment to subjective wellbeing. For instance, research carried out on German panel data suggests that the same individuals experience considerable reductions in life satisfaction when they lose their job (Winkelmann and Winkelmann 1998, Frey 2008: 47).

Outside of the group of high income countries evidence tends to be limited to a relatively small group of middle income and transition economies. For example, estimates on a pooled sample of Latin American economies finds a clear and unambiguous effect of unemployment on reported life satisfaction, even when a range of personal and household attributes are accounted for (Graham 2008, see also Graham and Pettinato 2002). Similar results have been presented for Russia and formerly socialist economies in Eastern Europe (Blanchflower and Freeman 1997,

> 13 However, the country fixed effects in our estimation model will account for some of these variations.

12
# 3.3 Consequences

For CBA a monetary estimate of the benefits of a particular health - program_e, service or project is required in order to permit evaluation of widely differing alternative investments. For CEA, however, health output is measured quantatively and comparisons are made between differing strategies to achieve a stated objective. The consequences of any programme, project or service can be summarised as (Drummond and Stoddart 1985):

1. Changes in physical, social or emotional functioning (effects)

-

- 2. Changes in resource use for organising and operating services v .hin the health care sector both for the original and for unrelated conditions (direct benefits); relating to activities of patients and their families, in particular the savings in expenditure or leisure time (direct benefits) and savings in lost work time (indirect benefits).

3. Changes in the quality of life of patients and their families (utility).

In evaluating the consequences of health programmes, projeLts or. services there are two main problems: how to measure health effect and how to value health effects/benefits. For CEA measurement only is required, but for CBA both measurement and valuation is necessary.

Given the difficulty of determining other effects (changes in social/emotional functioning), changes in physical functioning are generally identified as the health effect of interventions. Choosing a measure for this effect is, of course, necessary but difficult. In some cases deaths averted or prevented may be suitable. However they could only be applied to fairly specific objectives such as reducing the deaths from a particular disease (the focus of most CEA analyses). In themselves, therefore, they inevitably bias evaluation away from less severe illnesses wh.cb may still have important, even lifelong, consequences. Further, preventive programmes which seek to reduce incidence (rather than fatality), are also less favoured by use of this measure. It wouid be m're useful to have a measure "that can be used to compare the effects of interventions targeted at different discases which have differing effects on mortality, morbidity and on 16
Graph 4: Decomposition of CSF'R's Nominal Interest Rates Spreads

# By 'Type of User

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