rafmacalaba/data-use-mentions-extended huggingface dataset:
analyzes five indicators: access to clean water, sanitation, and electricity; completing sixth grade on time; and attending school from age 10 to 14. The analysis focuses on children because, unlike adults, children cannot be expected to make the efforts needed to access these goods and services, therefore, these five indicators can be considered as proxies for opportunities available to a child. Barros and others (2009), and the updated Barros and others (2010) edition, _Do Our Children Have a Chance?_ , analyze these five indicators for 19 LAC countries using the HOI, exploring both changes over time within countries and comparing changes among countries.

The HOI measures how far a society is from universal provision of basic services and goods, such as sanitation, clean water, education, and the extent to which those goods and services are unevenly distributed.<sup>2</sup> A key feature of HOI is that it not only takes into account the overall coverage rates of these services, but also how _equally_ the coverage is distributed. Coverage is determined by measuring the extent to which those _without_ coverage are concentrated in groups with particular circumstances (for example, economic status, gender, parental education, and ethnicity), which are conditions established at birth. More specifically, _HOI is an inequality-sensitive coverage rate_ that incorporates (i) the average coverage of a good or service, which society accepts should be universal (which implies that the _individual_ is not held responsible for lack of access), and (ii) whether it is allocated according to an equality of opportunity principle.

# **2.2 Unpacking the HOI**

The HOI is defined as the difference between two components: the overall coverage rate of the opportunity ( _C_ ), and a ―penalty‖ for the share of access to opportunities that is allocated in violation of the equality of opportunity principle ( _P_ ). HOI = _C_ – _P_ , which implies that the maximum value HOI for a particular opportunity is the average
|**Che**|**-0.33**|-0.21|0.09|**-0.39**|**-0.22**|
|---|---|---|---|---|---|
||**_-3.17_**|_-1.86_|0.97|**_-3.04_**|**-2.29**|
|**NOBS**|571|418|524|429|577|
|**R-Squared**|0.22|0.21|0.29|0.39|0.32|
|**F-statistic**|22.96|15.8||||
|**Prob >F**|0.0000|0.0000||||

_t-scores are shown below parameter estimates. Estimates significantly different from zero at .01 level are shown in bold and italics; at .05 level in italic._

# <u>Results for Brazil</u>

Table 12 shows the corresponding analysis of the Brazil data set. Here the picture is far more mixed, as in all industry groups no single variable is significant. There is also less consistency in the results as, for example, **_age_** has a positive effect on productivity for apparel and a negative effect for electronics.

19
|p=.006 / b=-0.298<br>IPV often index<br>Goal Arm Treatment Effect|
|---|
|p=.079 / b=-0.198<br>IPV in last year index|
|p=.313<br>Sexual activity index|
|p=.070 / b=-0.120<br>With same partner as baseline|
|p=.015 / b=0.218<br>Sex partner quality index|
|p=.447<br>Violence attitudes index (Males)|
|p=.575<br>IPV often index (Males)|
|p=.897<br>IPV in last year index (Males)|
|p=.935<br>Sexual activity index (Males)|
|p=.832<br>SRH perceptions index (Males)|
|0<br>-0.5<br>-0.4<br>-0.3<br>-0.2<br>-0.1<br>0.1<br>0.2<br>0.3<br>0.4<br>0.5|

**Figure 5** ITT Effects of _Goal_ Treatment

_Notes._ This figure presents estimates of _β_ 2 from equation 1 for separate regressions with the outcome indices specified on the y-axis. Indices are centered around females (males) in ELA only communities who were not assigned (whose connected female was not assigned) to the _Goal_ treatment. Bolded markers are statistically significant at _p <_ 0 _._ 1 _. p_ -values and coefficient estimates are displayed beside each marker.

_Source._ Female and male baseline and endline data, balanced panel.

46
and LIC countries. Column (2) shows that high levels of debt lengthen the lead time to production by 16 percent respectively. Higher inflation also lengthens the lead time (by about 8 percent) but the results are only marginally significant. On its own, the commodity price cycle measure is not statistically significant. However interacted with copper mine size, shows that copper mines tend to get developed faster when commodity prices are rising. These results are robust to the inclusion of additional variables in Columns (3) and (4). Finally, governance variables indicate that when governance improves (indicated by higher values of the corruption index), expected times to production fall by nearly 10 percent. The quality of government index is not statistically significant on its own, but when interacted with the variable indicating a copper deposit, shows that times to production of a copper mine fall by nearly 30 percent when governance improves.

The results suggest an important role for the commodity price cycle, sound macroeconomic management and the quality of governance. Higher commodity prices, on average, are not significant determinants of lead times, probably because of the significant sunk costs involved. However, for copper deposits, an upswing in copper prices at the time of discovery—the crucial

> 11 Regression results are robust to the use of decadal dummies which could help control for the decelerating time to production since the 1950s.

> 12 The importance of the policy environment is borne out in anecdotal evidence. For instance, the Oyu Tolgoi mine in Mongolia—despite being one of the largest copper deposits in the world—took nearly a decade to become operational in 2013, following initial exploration in the early 2000s, lengthy feasibility studies and negotiations between the government and Rio Tinto over the financing of the mine’s construction.

15
The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Unemployment<br>(rate)|Share of men in the _{d, e, a}_ cell that are unemployed|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|_Panel B: Delegati_<br>Area|_on d level variables_<br>Geographical area of each delegation _d_ in km<sup>2</sup>.<br>Douz<br>North and Douz Sud are combined into one administra-<br>tive division.|Author’s calculations based on<br>HDX-OCHA<sup>[</sup><sup>_b_]</sup>|
|Convicted<br>terrorists|Number of individuals indicted for terrorist crimes com-<br>mitted between 2004 and 2018 from delegation _d_. The<br>records include their addresses, which allows us to geolo-<br>cate them.|National List of Persons, Or-<br>ganizations<br>and<br>Entities<br>As-<br>sociated with Terrorist Crime<br>(CNLCT, 2021)<sup>[</sup><sup>_c_]</sup>|
|Emigration<br>(rate)|Share of men from delegation _d_ that emigrated abroad.|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|

24
# Figure 3: A more than 310% increase in the value of declared foreign assets

## (a) Relative values

<!-- Start of picture text --> Wealth value<br>(2015=100)<br>2009 2013-15 2016<br>Amnesty Amnesty Amnesty<br>500<br>400<br>Assets located<br>300 abroad<br>200<br>Assets located<br>in Argentina<br>100<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text -->

## (b) Absolute values

<!-- Start of picture text --> Billions of<br>2015 pesos 2009 2013-15 2016<br>Amnesty Amnesty Amnesty<br>1500<br>1250<br>Assets located<br>in Argentina<br>1000<br>750 Assets located<br>abroad<br>500<br>250<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> _Notes:_ this figure plots the total value of declared domestic and foreign wealth in constant 2015 pesos. Panel (a) expresses the series indexed to equal 100 in 2015, while Panel (b) reports the absolute values. The value of domestic and foreign assets is remarkably stable between 2002 and 2015. However, while domestic assets continued in the same trend, the value of foreign assets quadrupled from Arg$250 billion in 2015 to Arg$1 trillion in 2016 and Arg$1.25 trillion in 2019. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

26
’ firm-level data. However, there is no

> 2 The numbers are based on World Development Indicators of the World Bank.

> 3 In the rest of this paper, we use SSA and Africa interchangeably.  Africa does not include the North Africa region in this paper. It is important to keep in mind that all firms in the World Bank Enterprise Survey that we use consist of formal firms. In a few African countries, informal firms were also surveyed, but the number of countries is too small to merit a full scale cross-country firm-level study.

2
.103*|0.0828|||0.0523|0.0408|
||||(0.0545)|(0.0504)|||(0.0820)|(0.0833)|
|**Information from follo**|**w-up surveys**||||||||
|Changed practices||||-0.0975*||||0.00701|
|||||(0.0499)||||(0.0772)|
|Receive gov support||||-0.270||||0.318|
|||||(0.249)||||(0.256)|
|Observations|2918|578|578|514|2918|578|578|514|

Note: This table is based on Equation (1) in Section 3 of the paper. Columns (1) and (5) present the results for the tax administrative data. Columns (2)-(4) and (6)-(8) present the results for the survey data.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. 1 The omitted category is manufacturing. 2 The omitted category is Small. _Manager experience:_ Defined as the number of years experience of the top manager in the firm. _Competitive market:_ Dummy variable for whether a firm has had more than five competitors entering their market in the previous two years. _Introduced new products:_ Dummy variable for whether a firm introduced new products in the previous three years. _Limited finance:_ Dummy variable for whether a firm stated that access to finance was a
a false impression of a disconnect between money supply and the different components of inflation. With respect to the magnitude of the shortterm response of prices to money growth, econometric results suggest that a 1 percent increase in money growth increases food prices by 0.13 percent and non-food prices by 0.05 percent. This response may appear weak but, considering the large increases of money supply particularly over the last ten years (by over 20 percent per year according to the NBE data), the impact on prices is indeed very strong.

Even though these results are in line with economic theory, they are different from those of some previous influential studies that did not find any long-term effect of excess money supply on inflation in Ethiopia, notably Durevall et al. (2013). The reason might be that this study covers a longer sample period. The behavior of monetary aggregates as presented in Section 3 suggests that they were stable during most of the period covered by the Durevall et al. study (January 2000 to December 2009). The longer and more updated sample period covered by this study (up to September 2020) captures a period of rapid increase in money stock and money velocity relative to the previous period.

17
**Figure 2: Intra-regional intra-industry trade (Grubel-Lloyd index, 2008)**

<!-- Start of picture text --> North America 0.553<br>Western Europe 0.457<br>Southeast Asia and Pacific 0.357<br>Northeast Asia 0.27<br>Eastern Europe 0.204<br>South America 0.202<br>Central America and Caribbean 0.118<br>Western Asia 0.033<br>Eastern Africa 0.027<br>Central Africa 0.022<br>Central Asia, Caucasus and Turkey 0.012<br>South Asia 0.006<br>Northern Africa 0.003<br>Western Africa 0.003<br>Southern Africa 0.002<br>0 0.1 0.2 0.3 0.4 0.5 0.6<br><!-- End of picture text --> _Source: Brulhart (2009)_ According to research by World Bank (2010), if the scale economies of the East African Common Market are fully realized, the exports of goods and services would grow from US$12 to US$20 billion over the next decade, contributing to a doubling of formal employment and GNI per capita. While intra-regional trade has significant growth potential, clearly the majority of this export growth will need to come from global markets. As suggested above, regional integration is complementary with the aims of promoting competitiveness and growth in global markets.

However, while this combination of regional and global integration should contribute to faster growth EAC-wide, its effects will not be felt evenly across the region. As goods, capital, and labor markets open up in the EAC, economic activity is likely to concentrate increasingly in a few key areas where scale and access to global markets can be best exploited. Unlike in the ASEAN case, the unique geography of the EAC (3 of its 5 member states and 40%
highlights (i) the observed value ranges of selected financial risk indicators, (ii) recommended value ranges suitable for consideration in internal govemance and, where appropriate or warranted, (iii) suggested threshold values with respect to external regulation for each of the three categories of MFIs. The recommended and threshold values are neither absolute nor arbitrary, and it is emphasized that practical applications should take into account specific country conditions.

57. This paper has used values of certain indicators of financial and operating performance of MFIs which were readily available from published work, simply for the purpose of illustrating the application of risk management principles discussed in this paper to differern **t** ypes of MFIs. The basis for selecting the MFIs whose financial indicator values are being used for illustrative purposes was the closeness by which they _approximated the conceptual types of MFIs in the continuum_ discussed in this paper. However, no claim is being made that the MFIs selected confoirm strictly to the characteristics of each type of MFI in the continuum, and neither is it the intention to "force" these MFIs into any classifications. For illustrative purposes, the values of key indicators derived from the 1993 USAID-sponsored survey of eleven successful microfinance programs, the database developed by the Microfinance Program of the

> 22 David C. Richardson, "PEARLS - Financial Stabilization Monitoring andl Evaluation", World Council of Credit Unions Research Monograph Series, No. 4, August 1997 edition (processed).

> 23 A detailed discussion of financial performance and risk management indicators for banking institutions can be seen in (i) Jan Hendrik Van Greuning, "The Implementation of a Risk-Based Approach to Bank Supervision as a Micro-Economic Component of Monetary Policy", unpublished doctoral dissertation, Pretoria, South Africa: University of Pretoria, 1993; (ii) Chris Baltrop and Diana McNaughton, Banking Institutions in Developing Markets: Interpreting Financial Statements, vol. 2, Washington, DC: The World Bank, 1992 (1996) and (iii) for MIFIs in particular, the CGAP Appraisal Format
were available and spanning the complete 200-year picture.

In the literature on investor returns, several earlier papers calculate internal rates of return (IRRs) to assess how creditors have fared in sovereign debt markets (i.e., until the bond is extinguished). IRRs take the perspective of a buy-and-hold investor and discount cash flows over the life span of a debt instrument (from issuance until maturity or sample end).<sup>15</sup> IRRs have the benefit that they do not require collecting market prices, so that they can be calculated for all types of instruments, even for bonds not traded on the market. The main drawback of IRRs is that, despite requiring much data work, they provide only one data point per instrument, namely the bond’s return over the entire holding period. Indeed, IRRs assume that investors buy a sovereign bond instrument at issuance and never sell it. This is unlikely to be true for most investors, today and also prior to WW2 when sovereign bond maturities typically exceeded 30 years. Price-based returns are a more flexible measure and can be used to address a much broader range of

> 15 Specifically, IRRs (or I ) can be extracted from the cash flow data to yield a net present value of zero, as follows: C 0 = N 0 + ∑ ~~,~~ where is bond maturity, C are debt payments (coupon and amortization) in II I )<sup>tt</sup> TT rr tt year , and NN= −NN0 is the purchasing price, that is, the issue price of the bond.  tt=1 (1+rr II TT tt tt NN

5
J., A. Mattoo, and G. Narciso (2008) “Services Inputs and Firm Productivity in SubSaharan Africa: Evicdence from Firm-Level Data”, Journal of African Economies, Vol. 17, No.4, pp. 578-599.

Aw, Y.-B., X. Chen, and M. Roberts (2001), “Firm-level evidence on productivity differentials and turnover in Taiwanese manufacturing”, Journal of Development Economics, Vol. 66, No.1, pp. 51-86.

Blalock, G. and P. Gertler (2008), “Welfare gains from foreign direct investment through technology transfer to local suppliers”, Journal of International Economics, Vol. 74, No. 2, pp. 402-421.

Blalock, G. and P.Gertler (2009), “How firm capabilities affect who benefits from foreign technology”, Journal of Development Economics, Vol. 90, No. 2, pp. 192-199.

Blalock, G. and D. Simon (2009), “Do all firms benefit equally from downstream FDI? The moderating effect of local suppliers’ capabilities on productivity gains”, Journal of International Business Studies, Vol. 40, pp. 1095-1112.

18
# **Annex 1: Administering the River Basin Survey (Report prepared by INBO)**<sup>**12**</sup>

About 200 questionnaires were sent by e-mail, fax or surface mail to RBOs, national administrations in charge of water, and international commissions.<sup>13</sup> Some answers were clear:

- positive in some cases, as some administrations forwarded the questionnaire to the relevant bodies,

- or negative from the international commissions, those usually being a small coordination unit not able to answer the questions that were actually relevant to the different countries concerned, as members of the International Commission. In the case of the Danube River for instance, 13 countries are involved and compiling data from all these countries is a difficult task.

The only positive answers from International Commissions came from the Zambezi River Basin Authority and the Niger Basin Authority.<sup>14</sup> As far as the national administrations are concerned, if we except some countries like Burkina Faso, the Czech Republic and Romania for instance, the answers were mainly negative (Ivory Coast, South Africa, Bulgaria, etc.) as the creation of RBOs was planned but not effective at that time.

# **Difficulties encountered**

The first difficulty is that people usually are not keen about answering any kind of questionnaire or do not give it first priority. Therefore several reminders (three) were necessary to have any kind of reply.

Another difficulty encountered is that there are several kinds of RBOs with different responsibilities, therefore one single questionnaire could not cover them all, or some questions were outside RBOs responsibilities.

There are: _RBOs that have existed for a long time_

> 12 RBOs with no entries in the table were either not approached nor been followed.

> 13 INBO Members have commented about this questionnaire and found it non adapted to practical RBOs in the field and, therefore, faced numerous difficulties in answering it.

> 14 Questionnaires from RBOs of transboundary rivers were eliminated
# **IV. Results – Pooled Sample**

Our first set of results uses the pooled sample of eight STEP countries for which we have comparable measures for nine noncognitive skills.<sup>51</sup> We examine if men and women in this sample have different returns to the same covariates, and we explore the effect of covariates on selection into remunerated work. We then explore if the results vary along the earnings distributions for this same sample. In order to allow for non-linearity in the returns to schooling, we use spline variables that define three education segments corresponding roughly to basic education (less than or equal to nine years of schooling), secondary education (10 to 13 years of schooling), and tertiary education (greater than 13 years).<sup>52</sup> For cognitive skills, we use plausible values computed from literacy assessment scores. We include individual noncognitive skills, and we test for the joint significance of those skills.

> 51 This set of results excludes Serbia which does not have data for three measures of noncognitive skills.

> 52 Previous studies find that the returns to each school cycle do differ—on average, higher for the tertiary level than for the lower cycles—but that these returns can shift with massive increases in school enrollments and educational levels (Montenegro and Patrinos, 2014).

33
1965,<br>1970-71, and 1979 censuses<br>and 1979 postcensaL|Bank est.|
|||||demographic<br>survey||
|Cameroon|Apr 76|7090 F|Bank projection from census,<br>adjusted for 7.4X undercount|1980-85, World Fertility<br>Survey; 1985-90, Bank<br>projection assuning<br>reduced<br>sterility|Bank assessment of World<br>Fertility<br>Survey child<br>survival rates|
|Canada|Jun 86|25309 J|UNPVSR 1/87 (official<br>est.)|Official<br>est., Population<br>Projections<br>for Canada,<br>1984-2006|Official<br>est. (population<br>projections<br>for Canada,<br>1984-2006)|
|Cape Verde<br>|Jun 80<br>|<br>296F<br> <br>|Bank projection from census<br> <br>|Bank assessment of official<br>CBR est.|Bank assessment of official<br>CDR est.|
|Caymn Islands|Oct 79|<br>17 F|UNPVSR 1/88 (official<br>est.)|--||
|CentralAfrican<br>Rep.|Dec75|<br>2055 F|Bank projection<br>from census,<br>adjusted for 11.3X undercount|Bank est.|Bank assessment of census<br>dataon child survival|
|Chad|--|---|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|
|Charnel Islands|Mar 86|136 F|UNPVSR<br>4/87|Based on UNPVSR<br>7
# **_4.1 Prenatal risk factors_**

In the prenatal period, fetal growth is governed by the mother’s nutrition, endocrine and metabolic signals, and placental development. Newborn birth weight and gestational age reflect the intrauterine environment. While low birth weight (LBW, defined as < 2500g) is used as a proxy for undernutrition in utero, it really encompasses two related phenomena that have distinct underlying causes: small for gestational age (SGA)<sup>14</sup> and preterm delivery. SGA describes a fetus that is smaller than expected for the number of weeks in pregnancy and is closely associated with fetal growth restriction (FGR). Preterm denotes live births that occur before 37 weeks of completed gestation. Filipino newborns are among the world’s most likely to be born too small or too soon. An estimated 15 percent of live births in LMICs are LBW, with LBW prevalence highest in South Asia (25.6 percent), Sub-Saharan Africa (14.2 percent) and Southeast Asia (12.2 percent).<sup>15</sup> In the Philippines, the 2017 Demographic and Health Survey (DHS)<sup>16</sup> estimated 14.5 percent of live births were LBW, suggesting a significant drop since 2011 when LBW prevalence was estimated at 21.4 percent. Using data from the INTERGROWTH-21st project,<sup>17</sup> a recent study examined the global prevalence and burden of smallfor-gestational-age (SGA) and estimated that in 2012, 25.6 percent of Filipino newborns were SGA and that 22.7 percent of neonatal deaths were attributable to SGA (either term or preterm), placing the Philippines among the 10 countries globally with the highest SGA burden.<sup>18</sup> Recent global and national estimates<sup>19</sup> suggest preterm birth prevalence in the Philippines was in the range of 13.3 percent in 2014 and more than doubled since 2000