rafmacalaba/datause-encoder-data huggingface dataset:
work. The site appears to be operated by three women — project manager, a project administrator, and a podcast presenter (as of March 2015).

This platform is reviving African poetry via digital technology without hiring an army of staff. Moreover, the catalog can always be transcribed into the text with new voice recognition technology. Nonetheless, it could be argued that a level of ‘digital literacy’ is needed to effectively benefit from Badilisha as a contributor and also as a user. Generally speaking, however, the issue of digital literacy is a broad one; it also affects other fields where the problem of the so-called “digital divide” is prevalent. It would be useful indeed to have printed copies of African poetry. But given the current evolution of the book publishing industry and African literature in particular, Badilisha seems to be filling a gap much needed not only in Africa but also elsewhere. One can only conclude that this example shows how digital technology can be used to promote creative work. Badilisha’s effort provides lessons for organizations and policy makers who seek to promote such creative output in the developing world.

***

# _Artsy in the Digital World_

In the developed world, meanwhile, Artsy an online art resource whose “mission is to make all the world’s art accessible to anyone with an Internet connection” is worth noting. Founded in 2012, Artsy “features the world’s leading galleries, museum collections, foundations, artist estates, art fairs, and benefit auctions, all in one place.” Its “growing database of 250,000 images of art, architecture, and design by 40,000 artists spans historical, modern, and contemporary works, and includes the largest online database of contemporary art. Artsy is used by art lovers, museum-goers, patrons, collectors, students, and educators to discover, learn about, and collect art.”<sup>29</sup> Based in New York, Artsy is trying to position itself as a middleman in the global art market of more than 65 billion dollars. “The art industry is an old-school, relationship-driven one, and it won’t be easily turned on its head by software from outsiders.”<sup>30</sup> But this ‘soft gallery’ is gaining traction. The so-called “Artsy app has been downloaded by collectors 300,000 times. And while the company does not track inquiries which result into actual purchases, the figure behind the requests themselves is impressive: In 2014 “potential buyers inquired on $5.5 billion worth of art using Artsy—four times as much as the year prior.”<sup>31</sup>

# _Artsy in Education_

Money aside, Artsy is also invested in promoting arts education — in our context this strategy seems more germane to universal human development. This is because the art world itself seems to be more exclusive than inclusive, catering for those with deep pockets

> 29 Artsy, “The Art World Online,” accessed March 28, 2015, <u>https://www.artsy.net/about.</u>

> 30 Erin Griffith, “A Bold, New Direction for the Fine Art Industry: Online Sales,” _Fortune_ , accessed March 28, 2015, <u>http://fortune.com/2015/01/22/artsy-galleries/.</u> 31 Ibid.

10
_iii. Experience and education requirements_ On average, firms specified a minimum requirement of 2.5 years of experience. However, overall, only around 58 percent of vacancies actually specify an experience requirement at all. There is large heterogeneity across industries: Table 2 shows that management positions are associated with higher experience requirements (≥3 years) while sales positions tend to be associated with lower experience requirements (≥1 year). These findings suggest that higher experience requirements are specified in cases where job profiles may be more complex, e.g. for leadership positions.

Table 2: Summary statistics of minimum years of experience

|Industry<br>Mean|SD|Min.|Max.|Median|
|---|---|---|---|---|
|Administration<br>2.94|1.75|1|8|2|
|Architecture & Construction<br>2.84|1.56|1|8|3|
|Art & Design<br>2.22|1.23|1|5|2|
|Banking & Insurance<br>2.11|1.20|1|5|2|
|Consulting<br>4.86|2.30|1|8|5|
|Economics<br>2.96|1.49|1|5|3|
|Education<br>2.81|1.46|1|8|3|
|Finance & Accounting<br>2.80|1.59|1|8|2|
|Gastronomy & Hotelling<br>2.11|1.19|1|5|2|
|Health & Biotech<br>1.77|0.93|1|5|2|
|IT<br>2.37|1.28|1|8|2|
|Language<br>2.59|1.09|1|5|2|
|Law<br>3.04|1.62|1|8|3|
|Logistics & Transport<br>2.37|1.45|1|7|2|
|Management<br>3.17|1.59|1|8|3|
|Manual occupations<br>2.00|1.19|1|8|2|
|Manufacturing<br>2.43|1.18|1|5|2|
|Marketing, PR & Media<br>2.34|1.51|1|8|2|
|Others<br>2.67|1.57|1|8|2|
|Public sector<br>3.14|1.58|1|8|3|
|Sales<br>1.62|0.94|1|6|1|
|Science and Research<br>2.20|1.17|1|4|2|
|Telecommunication<br>2.21|1.28|1|5|2|
|Unclassified<br>2.78|1.03|1|5|3|

_Source:_ World Bank calculations based on job portal data _._ We explore this aspect more in depth below. The extent of under-specification differs across industries as shown in Figure 40 in Appendix C, while jobs in Finance & Accounting, Law and the Public Sector specify experience requirements in more than 50 percent of the cases, hardly any requirements are found among jobs posted in Gastronomy and Hoteling or Telecommunication. For management jobs the specification rate is

- 24 -
8 government facility and the person pays 100 percent of this cost, he/she has paid the same total amount as at the government facility. The basic problem here is directly related to the definition of total health expenditures in a country. The truth of the matter is that estimates or expenditures for traditional care vary widely from country to country 'Vogel 1989a) an" from 3 analyst to analyst. These traditional health expenditure estimates make up part of the "total" for each country in Vogel (1989a), but they appear unreliable. To the extent that they ."r.not be used in the base for healthinsurance ratio comparisons, the percent of health expenditures that is insured becomes distorted. For the share (ratio) comparisons in the last three columns of Table 3, traditional health care expenditures are excluded from both the category "out-of-pocket" and from the base, simply because they appear to be too variable and unreliable (Brunet-Jailly, 1988).

Estimates on tie percentage share of out-of-pocket expenditures, including user fees and private expenditures, in the total range from 7.3 and 7.4 percent in Malawi and Mozambique, respectively, to i0 1 and 72.1 percent in Uganda and Mali<sup>4</sup> . Part of this variability may be due to imprecision in the measurement of the total-expenditure base of the ratio. Although every effort was made to include only those foreign aid expenditures in the total that were clearly for recurrent expenditures, some investment expenditures may have slipped into the estimates in the reportt for each country that were used as sources.

3As an example, see the discussion in Brunet-Jailly (1988).

'These estimates come from the source documents listed in the last column in Table 4.
The main dependent variable in our regressions is a measure of social capital obtained from cross-country data on national levels of trust from the World Values Survey (WVS). Using a nationally representative sample<sup>5</sup> , the WVS provides a measure of “trust” given by the percentage of the population who answer yes to the question: “In general, do you think that most people can be trusted?” against the alternative that “you can’t be too careful when dealing with people”. We use data from the latest waves of the survey, which includes six Sub-Saharan African countries (only Nigeria and South Africa were available in previous surveys). We list all the countries included in this study in Appendix A.

An alternative aggregate measure of social capital proposed by Temple (1998) is the “social capability” index which is an assessment of a “society’s suitability for institutional and economic development”. Nonetheless, this measure was constructed in the early 1960s and therefore would not be suitable for our purposes because the first cases of AIDS where identified in the early 1980s. Another measure for social capital is participation in voluntary associations but it is inadequate for our objectives because they are not widely available across countries and they might be outcomes rather than indicators of social capital.

Furthermore, the empirical work by Bjonskov (2003) lends support to our choice of “trust”. He applies principal component analysis on data provided by the World Value Survey (WVS) and shows that although trust, norms and networks tend to co-vary, there are distinct elements. More interestingly, he explores the sensitivity of these components by including each one as regressors in cross-country regressions and finds that only the social trust component is robust to different specifications. He obtains similar qualitative results when using the variable ”trust” from the WVS, which was also used by Knack and Keefer (1997) and Zack and Knack (2001) in their growth regressions.

Nonetheless, one has to acknowledge that this particular measure of social capital has been subject to a number of criticisms in the literature. One concern relates to the fact that it reflects individual perceptions of society and that one needs to take into

> 5 Sabatini (2006) observes that urban areas and better educated persons are usually overrepresented in the WVS but Delhey and Newton (2005) argue that these problems do not seriously affect the randomness of the sample.  Knack and Keefer (1997) provides empirical validity for the use of WVS data when they found a strong correlation between  WVS trust and  the number of wallets that were ‘lost’ and  returned intact  in an experiment carried out in Europe and the United States.

9
vulnerable. In a 30-30 shock scenario this increases to 14.9 percent and in a 50-50 scenario it would reach 19.7 percent.

**Exhibit 8. DaR1 and DaR2 under different shock scenarios to earnings and receivables by region**

|||**Da**|**R1**|**D**|**aR2**|
|---|---|---|---|---|---|
||**N**|**(0,0)**|**(30,30)**|**(0,0)**|**(30,30)**|
|**EAP**|4,702|78.3|88.4|15.9|29.2|
|**ECA**|1,445|59.4|69.1|7.2|14.8|
|**LAC**|610|56.7|79.6|3.6|11.4|
|**MNA**|193|91.6|95.6|47.2|63.8|
|**SA**|2,493|43.0|97.9|10.6|39.5|
|**SSA**|207|75.8|93.8|8.8|16.5|
|**Vulnerable sectors**|582|73.7|78.9|9.1|14.9|
|**_All firms_**|_9,650_|_63.9_|_87.9_|_12.4_|_28.5_|

Note: (0,0) = no shocks to earnings and receivables. (30,30) = 30 percent shock to earnings, 30 percent shock to receivables. Vulnerable sectors = Aerospace & Defense, Oil & Gas Producers, Oil Equipment, Services & Distribution, and Travel & Leisure.

6. Corporate Balance Sheet Vulnerabilities and Corporate Insolvency Framework **The most vulnerable countries should prepare for a rise in defaults in the corporate sector, which may lead to an increase in bad loans as a result of the COVID-19 crisis.** To mitigate these negative effects and ensure a stable recovery, it is critical that countries rely on a strong legal and institutional framework for corporate insolvency. Indeed, the insolvency system can assist countries in responding to the COVID-19 crisis with a critical framework for collective action to save viable firms, maximize debt recoveries and preserve employment. The readiness of the insolvency system to deal with widespread default in the corporate sector is therefore essential in understanding the implications of the acute corporate vulnerabilities identified in our analysis. However, corporate insolvency institutions may weaken during the crisis due to the disruptions to basic operations of courts and insolvency agencies.

**We find that a significant fraction of countries in our sample exhibit both elevated corporate vulnerabilities and weaknesses in their corporate insolvency frameworks, setting the stage for a ‘perfect storm’ of firm failure.** While these vulnerabilities were already present by the end of 2019, COVID-19 related shocks may exacerbate these. For example, the insolvency system may become more inefficient due to case overload. Exhibit 9 presents country-level data on DaR1 and DaR2 and an index of corporate insolvency that measures the quality and efficiency of the insolvency system. Of the 56 countries in the figure, a significant fraction of countries has over 60 percent (15 percent) of corporate debt considered financially vulnerable according to at least one indicator (both indicators) and a corporate insolvency rank of over 50 percentile prior to the COVID-19 crisis (blue dots). A stress test scenario (orange dots) consisting of 30 percent shocks to both corporate earnings and receivables further raises DaR1 and DaR2 for these countries. Firms

14
ditionally withdraw international migrants (i.e., mainly relatively wealthy Malian migrants to Ivorian regional cities such as Korhogo), a slight attenuation is again observed, but the BDs still range between +0.8 and +1.2.

Table 4: Discontinuities at the border of Cˆote d’Ivoire with Mali and Guinea

||Nat.|Linea|r RD|Polyn|omial|Matc|hing|
|---|---|---|---|---|---|---|---|
|Bandwidth in km:||75|100|75|100|75|100|
||(1)|(2a)|(2b)|(3a)|(3b)|(4a)|(4b)|
|1993 Cˆote d’Ivoire / Mali||||||||
|Log. Expenditures p. c.|+0.89***<br>(0.07)|+0.91<br>(0.59)|+1.41**<br>(0.68)|+0.90**<br>(0.34)|+1.06*<br>(0.59)|+1.52*** <br>(0.64)|+2.02***<br>(0.49)|
|Head ”Mande-Voltaic”<sup>_a_</sup>||+0.93|+1.51**|+0.87**|+1.18**|+1.39**|+1.88***|
|||(0.55)|(0.66)|(0.30)|(0.54)|(0.61)|(0.49)|
|Head ”Mande-V.” & national<sup>_a_</sup>||+0.84|+1.45**|+0.76**|+1.10**|+1.21*|+1.70***|
|||(0.56)|(0.67)|(0.35)|(0.55)|(0.61)|(0.50)|
|Stunted 6-59 m. (%)|+2|-13*|-3|-21***|-16***|-20**|-16**|
||(2)|(7)|(9)|(6)|(6)|(7)|(8)|
|Head ”Mande-Voltaic”<sup>_a_</sup>||-14**|-7|-21***|-16***|-18**|-16**|
|||(7)|(9)|(5)|(5)|(8)|(8)|
|Head ”Mande-V.” & national<sup>_a_</sup>||-13*|-4|-17*|-15**|-18**|-16|
|||(7)|(12)|(9)|(7)|(8)|(11)|
|Electricity connection (%)|+36***|+10|+9|+15|+16|-1|+1|
||(2)|(11)|(13)|(15)|(12)|(6)|(12)|
|N C. d’Iv. / N Mali|468/473|13/11|32/31|13/11|32/31|13/11|32/31|
|1993 Cˆote d’Ivoire / Guinea||||||||
|Log. Expenditures p. c.|+0.44***|-0.03|-0.28|-0.08|+0.31|-0.07|-0.42|
||(0.07)|(0.53)|(0.64)|(0.68)|(0.62)|(0.32)|(0.38)|
|Stunted 6-59 m. (%)|+12***|+23|+29|+14|+5|+11|+17|
||(2)|(22)|(22)|(16)|(19)|(11)|(17)|
|Electricity connection (%)|+21***|+30|+14|+11|+35*|+10|+9|
||(3)|(20)|(22)|(14)|(21)|(6)|(8)|
|N C.d’Iv. / N Guinea|468/312|27/22|37/28|27/22|37/28|27/22|37/28|

_Source_ : Authors’ analysis based on data described in the text. _Coverage_ : PSUs in the bandwidth window (50 or 75 km from the corresponding border). _Notes_ : In the cases of “Border RD” and “Matching RD” and for 100 km bandwidth (col. 2b & 4b), sample sizes allow for using a quadratic in distance to border rather than a simple linear specification. Col. (2a) to (4b): controls for longitude (Mali) or latitude (Guinea), rainfalls, elevation, and distance to river are always included. a: Head from a Mande or a Voltaic linguistic group. National: excluding foreign immigrants. Positive numbers indicate differences in favor of Cˆote d’Ivoire. Standard errors in parentheses. ***: _p < ._ 01; **: _p < ._ 05 ; *: _p < ._ 10 Here, monetary welfare comparisons are facilitated by the fact that the two countries share the same currency, namely the CFA franc. We acknowledge that the BDs in nominal consumption may mix discontinuities in real terms and in 27
from oil revenue is needed. The simulations also reveal that for oil revenue as a share of GDP reaching above approximately 6 percent there will be a potential increase in the share of oil revenue that can be consumed (since the sink of the trajectory will drop below 100 percent investment share. This will be the case until the population has aged substantially, and capital is crowded out sufficiently as to increase the necessary investment share.

**Figure 13. An Increase in Life Expectancy from 73 to 83 Years Could Lead to Higher** **<u>(Efficient) Consumption of Oil Revenue if Oil Revenue is a Large Share of GDP</u>**

<!-- Start of picture text --> 98%<br>97%<br>96%<br>95%<br>94%<br>93%<br>92%<br>Age<br>91%<br>69 71 73 75 77 80 82 84 87 89 91 93 96 98 100<br>Investment sShare (Oil Revenue/GDP Equals 10%)<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

Population aging is associated with two demographic phenomena; an increase in life expectancy (as studied above) and a drop in the fertility rate. The former part has been studied above while the latter can be simulated in the same manner. By simulating various population growth rates (which in the model are the same as fertility rates) the required reaction of the efficient investment share can be computed: With a population growth rate of 47 percent over a generational period, combined with oil revenue being 10 percent of GDP, we find that 97.8 percent of oil revenue must be invested. This is, obviously, in line our results from the baseline scenario analyzed above which assumes these two calibrations.

However, as the population growth rate falls, the efficient investment share increases (Figure 14). This is because there will be fewer workers to save and more relatively more retirees to dis-save and spend a relatively larger share of the transferred oil revenue on consumption.

Since the oil revenue transfers are assumed to be transferred equally among workers and retirees — one dollar per person — there will be a large fraction of the population in retirement, and these individuals save less, or not at all, and as such the economy’s capital accumulation deteriorates. This must be made up for by increased investments of oil revenue; so the conclusion to the population aging 26
Table 10: **Relative intensity indices in 2007 (based on forward linkages)**

||Primary|Text./<br>Cloths|Machin.|oth.<br>Mfc.|Trade/<br>Transp.|Com.<br>Ser.|Pub.<br>Ser.|
|---|---|---|---|---|---|---|---|
|Australia|0.406|0.005|0.038|0.107|0.128|0.288|0.028|
|New Zealand|0.315|0.018|0.044|0.145|0.183|0.273|0.022|
|Japan|0.015|0.007|0.322|0.176|0.187|0.229|0.064|
|Korea|0.038|0.021|0.348|0.165|0.148|0.234|0.046|
|Indonesia|0.502|0.069|0.065|0.179|0.101|0.078|0.006|
|Malaysia|0.255|0.009|0.215|0.154|0.188|0.176|0.005|
|Philippines|0.118|0.035|0.434|0.103|0.162|0.141|0.007|
|Singapore|0.019|0.003|0.230|0.164|0.280|0.285|0.019|
|Thailand|0.158|0.044|0.224|0.199|0.223|0.141|0.011|
|China|0.184|0.087|0.172|0.255|0.128|0.160|0.013|
|Hong Kong|0.032|0.033|0.038|0.040|0.538|0.318|0.001|
|Taiwan|0.023|0.028|0.342|0.198|0.162|0.225|0.022|
|India|0.197|0.050|0.036|0.150|0.193|0.364|0.010|
|Rest of South Asia|0.207|0.286|0.011|0.044|0.236|0.149|0.068|
|Canada|0.255|0.007|0.145|0.206|0.114|0.251|0.022|
|United States|0.082|0.010|0.196|0.200|0.166|0.298|0.048|
|Mexico|0.176|0.019|0.314|0.136|0.165|0.186|0.004|
|Central America, Carib.|0.264|0.065|0.072|0.083|0.253|0.230|0.033|
|Argentina|0.466|0.012|0.056|0.104|0.131|0.209|0.022|
|Brazil|0.305|0.020|0.090|0.183|0.159|0.232|0.011|
|Chile|0.359|0.003|0.014|0.267|0.128|0.222|0.007|
|Rest of South America|0.595|0.020|0.026|0.117|0.084|0.149|0.009|
|EU12|0.090|0.023|0.180|0.208|0.106|0.371|0.022|
|EU3|0.065|0.013|0.191|0.248|0.102|0.362|0.020|
|Central European EU|0.101|0.037|0.223|0.245|0.084|0.293|0.017|
|Former Soviet Union|0.480|0.004|0.030|0.092|0.190|0.196|0.007|
|Rest of Middle East|0.635|0.022|0.062|0.089|0.079|0.104|0.010|
|Rest of Sub-Sah. Africa|0.485|0.012|0.023|0.155|0.150|0.157|0.017|
|Rest of World|0.421|0.020|0.059|0.108|0.161|0.205|0.026|

18
# **References**

Thomas Glaessner, Valerie McNevin, and Tom Kellermann. October, 2003. _“Electronic Safety and Soundness: Securing Finance in a New Age: Public Policy Issues.”_ World Bank, Washington, D.C.

Thomas Glaessner, Valerie McNevin, and Tom Kellermann. (2002) _“Electronic Security: Risk Mitigation in Financial Transactions.”_ World Bank, Washington, D.C.

Saarinen, Markku-Juhani (2001) “Attacks Against The WAP WTLS Protocol” University of Jyv. askyl., Finland Stijn Claessens, Thomas Glaessner, and Daniela Klingebiel (2001). _“E-Finance in Emerging Markets: Is Leapfrogging Possible?”_ , World Bank, Washington D.C.

Stijn Claessens, Thomas Glaessner, and Daniela Klingebiel (2000). _“Electronic Finance: Reshaping the Financial Landscape Around the World”_ , World Bank, Washington D.C.

Setsuya Sato and John Hawkins (2001). _“Electronic finance: a new perspective and challenges”_ , BIS Papers No. 7: Electronic finance: a new perspective and challenges, BIS, Switzerland.

Helen Allen, John Hawkins and Setsuya Sato (2001). _“Electronic trading and its implications for financial systems”_ , BIS Papers No. 7: Electronic finance: a new perspective and challenges, BIS, Switzerland.

Rouben Indjikian (2002). _“E-Finance for SMEs: Global Trends and National Experience”_ , Electronic Commerce Branch, SITE, United Nations Conference on Trade and Development (UNCTAD) Goldstein, Andrea, and David O’Connor. (2000). “E-Commerce for Development: Prospects and Policy Issues”, OECD Development Centre, Paris.

KPMG, (2002). _“Global Information Security Survey”_ , KPMG, UK.

E&Y, (2002). _“Global Information Security Survey”_ , Ernst & Young, UK.

3i, (2002). _“E-security – 2002 and Beyond”_ , 3i white paper in association with the Economist Intelligence Unit.

PWC, (2002). _“Information Security Breaches Survey”_ , Technical Report, Price Waterhouse Coopers.

Glaessner, Thomas et-all. Upcoming Paper, _“Access to Financial Services in South Africa”_ , World Bank, Washington, D.C.

45
# <u>1. INTRODUCTION</u>

Annual and seasonal variability in river flow is well known and well documented by riparian communities, scientists, engineers and policy makers (Milly et al., 2005; Milliman et al., 2008). Yet, the projected effects of climate change may render future river flow variability outside the bounds of previously observed runoff events (IPCC, 2007: 31; Milly et al., 2008). These effects may be particularly salient in international river basins where several riparians are affected. For example, rivers such the Jordan and Tigris-Euphrates in the Middle East are expected to experience reductions in stream flow. Meanwhile, other basins such as the Congo in equatorial Africa and the La Plata in South America will experience increases in flow, adding to the salience of issues such as floods and inundation (Arnell, 1999 and 2004; Milly et al., 2005).

Climate change and water variability are also expected to intensify security concerns within or between countries or within river basins (Brown, 1989; Gleick, 1989; Nordås and Gleditsch, 2007). A report titled _National Security and the Threat of Climate Change_ attests that one of the most destabilizing impacts from climate change will be in the form of reduced access to freshwater (CAN, 2007: 13-16). Despite the often cited claim that ‘water is a source of cooperation, rather than violent conflict’ (Wolf and Hamner, 2000), some observers have revived ‘water wars’ prognostications given the effects of climatic change (Working Group II, n.d; Schwartz and Randall, 2003). Whether in the form of heightened political tensions or the more extreme violent exchange, climate change and the projected increase in water variability may further complicate existing shared water management strategies.

Given the links between climate change, water variability, and inter-state tensions, the role of institutions in assuaging potential conflicts between states seems paramount (Salehyan, 2008: 317). Such allocational and institutional mechanisms may confer resilience allowing states to successfully deal with hydrologic changes that may have economic, social, environmental, and political consequences (Adger et al., 2005; Walker et al., 2006). In other words, while the mere existence of institutions may bestow resilience, this research is primarily interested in investigating how particular regime designs fare in the face of variability. Consequently, this research is interested in examining those river basins governed by treaties.

Guided by recent research exploring the utility of particular water allocation mechanism designs as well as various institutional mechanism designs in international water agreements 2