rafmacalaba/datause-encoder-data huggingface dataset:
32 Apparently there is som ground for concern about the number and the training of those working in the sector.

101. Under the now structure, staff working in PI4 are widely dispersed in the Bank. Many PHN staff members express feelings of isolation and concern about Odeprofessionalization. The opportunitios for on-the-job training, advice and support that were available in the old centralized department no longer exist. It is not as easy as lt used to be to keep up with developments in the field, or even in other parts of the Bank. Channels of professional coomunication are mainly informal, based on former contacts.

Table 1: HIGHUE-LEVML STAFFa IN PHI DIVISIONS BY PHU BACKGROUND AND EXPERIENCE

|**Dlvisies**|Total<br>**La**PU<br>**ep"rtisa**<br>**_MA_**<br>**technieal**<br>**iwisi.ce**|**Total**<br>wLth**PM3**<br>**eaperleee**<br>_-udler_<br>**qallifiatle-oel**|**Total**<br>**with**<br>**experienee**<br>La<br>PMLaBank<br>**before**<br>vies|**Total**<br>**with**<br>**ezperine**<br>**in**<br>Pn before<br>JOlni<br>Bank|**Total**<br>wlth<br>PWA-related<br>academic<br>qualiflcations|
|---|---|---|---|---|---|
|AFUICA||||||
|Hunanosouroes||||||
|D&isioens<br>M)|36|14|1i|7|10|
|TechnLcalDlvisIon||||||
|(Palonly)|9|7|7||A<br>4|
|**ASIA**||||||
|Human Resources||||||
|Divislons (5)|39|1O|9|5|A|
|TechnicalDlVliLon||||||
|(PRR)|17|A|A|A|A|
|Human Resources<br>Divislons(3)|18|8|7|2|A|
|TchnicalDlivslon||||||
|(P1R)|I4|5|5|3|3|
|LAC||||||
|Human Resources||||||
|Divisions(2)|11|A|3|3|2|
|TechnicalDLviLein||||||
|(Pni)|19|3|3|2|1|
|VPR||||||
|PIIRII|11|10|9|A|6|
|TOTAL|174|65|60|34|38|

a **Based** On a review of staff profiles. lacludes operatLons assLstants and reseirch assistants. Does b not include support staff or consultants. Postgraduate tralnLng Ln public a.slth, sociology, anthropology, sedLcine, deaogr:phy, he-lth econonLrsf N umbers in p&rontheses indlcote number of divlsLons.
# **De-Tax**

_De-Tax_ is a proposal combining two elements: (i) participating governments would waive the value-added tax (VAT) by 1 percent on the overall price of any good or service sold by sellers associated with the initiative; (ii) sellers would—on a voluntary basis—waive a share of their profit on designated transactions. A special fund would benefit from the 1 percent of VAT from governments as well as related seller contributions.

See also: Global Taxes

# **Debt Reduction Facility (DRF) for IDA-Only Countries**

The World Bank’s Debt Reduction Facility (DRF) for IDA-Only Countries was established in July 1989. It provides grants to eligible countries to buy back at a deep discount their public and publicly guaranteed commercial external debt. To date, the DRF has supported 24 operations in 21 IDA-only countries, extinguishing about US$4.8 billion of external commercial debt principal and an estimated US$4.2 billion of associated interest arrears and penalties. The DRF has become one of the key instruments to facilitate the participation of commercial creditors in the delivery of HIPC Initiative debt relief.

Further information: <u>http://go.worldbank.org/2CRHS4N500</u> See also: Debt Relief

# **Debt Relief**

“Debt relief is any form of debt reorganization which relieves the overall burden of debt.”<sup>90</sup> Since World War II, industrialized countries have entered into debt rescheduling agreements. Later, through the Paris Club, they also agreed to provide concessional treatment (i.e., a treatment that implies a reduction in the Net Present Value of the consolidated debts) to debt owed by highly indebted countries. In recent years, two major debt relief initiatives have been launched namely, the Heavily Indebted Poor Countries (HIPC) Initiative and the Multilateral Debt Relief Initiative (MDRI). These two initiatives are the first ones to include multilateral debt relief. Debt relief instruments include debt swaps and debt buy-backs.

Further information: <u>http://go.worldbank.org/KNZR2IIQG0</u> See also: Debt Reduction Facility Debt Swaps Heavily Indebted Poor Countries (HIPC) Initiative Multilateral Debt Relief Initiative (MDRI)

# **Debt for Nature Swaps**

In debt-for-nature swaps, a portion of a country’s foreign debt is forgiven in exchange for local investments in conservation measures. Usually, an international NGO purchases debt titles on the secondary market. The NGO transfers the debt title to the debtor country. In exchange the country agrees to either carry out environmental policies or endow a government bond with the aim of funding conservation programs.

> 90 http://stats.oecd.org/glossary/detail.asp?ID=558.

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# **Figure 13**

Forecasted share of fossil energy in t+30 by energy project and according to year of publication Note: US scenarios are depicted as large squares <!-- Start of picture text --> 95<br>90<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Department of the Interior EUOGEP Energy Research and Development Agency European Union / DGTREN<br>Ford Foundation Greenpeace Harvard Business School IEA<br>IIASA IPCC OECD PEW<br>Shell International BV Union of Concerned Scientists WEC WEC and IIASA<br>Fossil energy share (%x100)<br><!-- End of picture text -->
Table 5: How do Borrowers Interpret the Offer?

|Question|M<br>|ean<br>|Difference|SE|N|
|---|---|---|---|---|---|
||Regulator offer|Relationship offer||||
||(1)|(2)|(3)|(4)|(5)|
|[1]_Eligibility for forbearance offers_||||||
|Believes offer given to all customers|0.49|0.39|-0.11|(0.07)|190|
|Believes offer given to least creditworthy|0.22|0.18|-0.03|(0.06)|190|
|Believes offer given to most creditworthy|0.29|0.43|0.14<sup>_∗∗_</sup>|(0.07)|190|
|[2]_Does acceptance of the offer signal creditworthiness?_<br>Accepting sends no signal|0.17|0.31|0.14<sup>_∗∗_</sup>|(0.07)|158|
|Accepting sends positive signal|0.32|0.21|-0.11|(0.07)|158|
|Accepting sends negative signal|0.51|0.48|-0.03|(0.08)|158|
|[3]_What is the main rationale for timely loan repayment?_||||||
|Reason to repay loans is morality|0.15|0.09|-0.06|(0.05)|147|
|Reason to repay loans is penalties|0.17|0.11|-0.06|(0.06)|147|
|Reason to repay loans is future loans from lender|0.03|0.21|0.18<sup>_∗∗∗_</sup>|(0.05)|147|
|Reason to repay loans is credit score|0.65|0.59|-0.06|(0.08)|147|

Notes: The table shows the results from a follow-up survey that elicited beliefs about the offer and beliefs about loan repayment and creditworthiness from a random sample of survey participants who had received either the relationship or regulator forbearance offer.

39
Where _DYc,t_ is the relative economic activity in country _c_ in day _t_ , _Speedc,t_ is the daily change in the smoothed stringency index, S S 2 is the squared terms of the speed of reopening, _Pc,t_ is the daily number of deaths per million due to COVID-19, δcc,tt c are country fixed effects and is i.i.d. SS innovation term.<sup>7</sup> In this specification, the coefficient β1 reflects the impact of the speed of lifting cc,tt εε the restrictions on economic activity. We introduce the square term of the speed of reopening to capture the non-linearities in the implementation of the reopening policies. Similar to specification (2), we add health measures of pandemic progression _Pc,t_ to control for the direct effects of the pandemic on economic activity.

Measures of pandemic progression, such as daily death rates, could be endogenous in specifications (2) - (4) since unobserved factors might affect both the demand for electricity and the health impact of the pandemic. To address these concerns, we instrument the death rate in (2) - (4) with the daily predictions from a standard SIR epidemiological model that assumes an unmitigated spread of the disease (no NPIs implemented). The cross-country variation in these predictions comes from pre-pandemic characteristics like the country’s demographic profile, the number of ICU beds, and pre-reopening epidemiological situation, with a basic reproduction number ( _R0_ ) of 2.5.<sup>8</sup> Finally, we explore the effects of trust in government and people’s perceptions about the pandemic on the recovery process. Governance differences across countries may matter for the effects of government policies on economic outcomes. One of the aspects of governance that may be particularly relevant is that of trust in government institutions. Most of the social distancing measures implemented during the pandemic rely, to some extent, on voluntary compliance by citizens. High levels of trust in government (and interpersonal trust) are associated with better compliance with social distancing (Giuliano and Rasul, 2020). In order to incorporate the mediating factor of trust in government institutions, we add interaction terms between the trust in government institutions and reopening sequencing in specification (2):

> 7 Using the daily number of infected individuals per million does not change the main results.

> 8 The model was initially developed by a team at the University of Basel. The online tool providing the countryspecific scenarios is available at https://covid19-scenarios.org/.

14
Table 6: Multivariate estimations of urbanization and 1-person establishment development

||Overall<br>growth<br>1989→2010<br>|Overall<br>growth<br>2000→2010<br>|1-Person firm<br>growth<br>1989→2010<br>|1-Person firm<br>growth<br>2000→2010<br>|Average 1-<br>person firm<br>employment<br>share 1989-2010<br>|Tradables 1-<br>person firm<br>growth<br>1989→2010<br>|Tradables 1-<br>person firm<br>growth<br>2000→2010<br>|Tradables 1-<br>person firm<br>employment share<br>1989-2010<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|||||Panel A. A|ll firms in district||||
|Share of population that is urban|0.112|0.129|0.116|0.078|-0.295|0.171|0.176|-0.150|
||[0.029]***|[0.044]***|[0.040]***|[0.034]**|[0.058]***|[0.081]**|[0.117]|[0.076]**|
|Observations|216|216|216|216|216|216|216|216|
|Adjusted R-squared|0.166|0.081|0.126|0.083|0.390|0.060|0.103|0.270|
|||||Panel B. Ur|ban firms in distric|t|||
|Share of population that is urban|0.130|0.070|0.178|0.172|-0.094|0.088|0.151|-0.092|
||[0.044]***|[0.046]|[0.076]**|[0.085]**|[0.048]*|[0.057]|[0.079]*|[0.060]|
|Observations|198|198|198|198|198|198|198|198|
|Adjusted R-squared|0.160|0.078|0.157|0.083|0.292|0.107|0.050|0.286|
|||||Panel C. R|ural firms in distric|t|||
|Share of population that is urban|0.070|0.084|0.078|0.120|-0.153|0.399|0.075|-0.012|
||[0.028]**|[0.047]*|[0.044]*|[0.153]|[0.074]**|[0.164]**|[0.136]|[0.090]|
|Observations|203|203|203|203|203|203|203|203|
|Adjusted R-squared|0.042|0.088|0.054|0.047|0.300|0.085|0.073|0.234|

Notes: See Table 5. Estimations include districts for which all eight outcome variables are observed. Outcome and explanatory variables are expressed in unit standard deviations. Unreported district covariates include the traits shown in Table 5. Appendix Tables 3a-3c report the complete regression results. Estimations report robust standard errors. * significant at 10% level; ** significant at 5% level; *** significant at 1% level.
The analysis proceeds by estimating equation (1) with the two Landsat indices above. Since these indices are already normalized and well bounded, there is no need to apply the natural logarithm. Table 2, Panel B summarizes the results. In column (1), both indices are statistically significant in explaining differences in the (log) number of enterprises between communes. While a higher measure of built-up areas is positively correlated, the coefficient on NDVI is negative and indicates that the presence of vegetation predicts fewer enterprise in the cross-section. This is not surprising as high NDBI and low NDVI values indicate the presence of cities. NDVI is often used as a measure of agricultural productivity, but as self-employed farmers are not covered by the enterprise survey, the vegetation measure does not reflect this. The R-squared measure of the model is 0.341, suggesting a rather high prediction power with the use of only two indices. This is about four times larger than in the nightlights model.

The coefficients in the regression on employment are largely similar, indicating that employment is higher in more urban places. The Landsat indices explain 25.7% of the variation in employment and thus about 2.5 times as much as the nightlight approach (10.3%). The coefficients in the expenditure model are much smaller in absolute value, but still highly significant at the 0.1% level. Again, the difference in predictive power of both remote sensing approaches is striking. The simple Landsat model explains about 10 times as much of the variation as the sum of light measure ( _R_<sup>2</sup> = 0 _._ 274 versus _R_<sup>2</sup> = 0 _._ 023). Figure 2 summarizes the predictive power of the models by plotting the actual values versus the predicted values for all three economic outcome variables.

When controlling for commune size, the coefficients on NDBI and NDVI are reduced in absolute value, but remain highly statistically significant. This indicates some correlation between commune area and vegetation and built-up area respectively, highlighting the endogenous nature of commune boundaries. However, the correlation is less pronounced than in the nightlight regression. The increase in R-squared measures after controlling for 14
45

# 9. Conclusions

Our main objective in this paper was to develop ways of testing<sup>the</sup> hypothesis of perfect competition everywhere<sup>in the quota-restrained</sup> market for apparel. We examined a broad implication<sup>of this hypothesis in the light</sup> of US clothing imports from Hong Kong,<sup>specifically that if the hypothesis</sup> were true and the products homogeneous,<sup>then the Hong Kong price inclusive of</sup> the license price and adjusted for tariffs and<sup>transportation</sup> costs, should equal the US price, The two prices may deviate<sup>if there exists monopsony power</sup> in the market for apparel, which may<sup>or may not be combined with some kind of</sup> market power in the license market. Noting<sup>that siwple product market power is</sup> not sufficient to generate this difference,<sup>we were careful to distinguish</sup> between "rent appropriation" by product market power and "rent sharing",<sup>which</sup> arises because of market power on the part<sup>of the buyers. Rent appropriation</sup> affects the potential license price which<sup>is the difference in the US price</sup> and the Hong Kong price, adjusted for tariffs<sup>and transport costs but</sup> exclusive of the license price. Rent sharing<sup>determines the distribution of</sup> these potential rents between the license<sup>owners and the buyers.</sup> We found significant evidence of rent sharing in the<sup>data we collected</sup> on ten apparel groups. Recognizing that this could<sup>be due to compositional</sup> differences in US production compared<sup>to Hong Kong exports, we developed a way</sup> of testing for the existence of such differences.<sup>We did not find these</sup> differences to be significant on the whole. We<sup>also attempted to incorporate</sup> some notion of product differentiation and found that this did not entirely close the gap between the US and Hong Kong<sup>prices.</sup> Our work gives some support for the existence<sup>of buying power in the</sup> quota-constrained market for apparel imports from Hong Kong together<sup>with some</sup>
EDUCijs = θj + λRs + γ CONFEXPijs + XijsR<sup>_′_</sup> φ+ μijs . _(2R)_ This second identification strategy has three important advantages compared to the traditional approach based on variation in exposure by birth cohort and district for individuals observed at a single point in time. First, it has the advantage of comparing cohorts that are born only five years apart but have experienced very different degrees of conflict (i.e., a 10-year-old in 2001 in district _j_ was exposed to much less conflict than a 10-year-old in 2006 in district _j_ but was born only five years earlier), which reduces concerns regarding potential confounders, including differential migration patterns. Second, when the sample is restricted to children aged 14 years and under, the concern regarding selection bias due to voluntary migration decreases because most migrants are men aged 15–44 years, who typically leave their wives and children behind (Lokshin and Glinskaya 2009). Third, I can further test the robustness of my findings to migration bias by excluding from the sample children surveyed in 2006 whose mothers moved to their current location after 1996.<sup>9</sup> All specifications are estimated using linear district fixed-effects panel data models. All models allow for error terms to be correlated in an arbitrary fashion within a district to avoid overrejection of the null hypothesis of zero treatment effect due to serial correlation, following Bertrand et al. (2004).

# **<<B>>** <u>Preliminary Analysis</u>

An inspection of the data shows that although the legally mandated age for beginning schooling is six years old and there are five years of primary schooling, a sizeable proportion of children are enrolled in primary school before age six (70.1 percent at five years) and until age 14 (16.1 percent) in the 2006 DHS, with numbers subsequently decreasing sharply (7.9 percent at 15 years and 2.85 percent at 16 years).<sup>10</sup> Therefore, an analysis of the long-term effect of conflict on primary schooling completion should consider children aged at least 14 years at the time of the survey, and control cohorts should have been at least 15 years at the beginning of the conflict (and preferably 16
**Figure 6** : Percent of facilities with handwashing facilities, toilets, and acceptable waste disposal by urban/rural location in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Kenya Sierra Leone<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Facilities with Facilities with Facilities with Facilities with Facilities with Facilities with<br>handwashing for toilets acceptable waste handwashing for toilets acceptable waste<br>patients disposal patients disposal<br>Urban Rural Urban Rural<br>Tanzania<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Facilities with Facilities with Facilities with<br>handwashing for toilets acceptable waste<br>patients disposal<br>Urban Rural<br><!-- End of picture text -->

# Availability of personal protective equipment

Personal protective equipment (PPE) is imperative to ensure that health care workers are protected during a time of high risk. As described previously, health workers and health facility staff experience high rates of exposure to disease during a pandemic, and PPE can help protect them from infection. While information on protective eyewear and face shields is not available, the Sierra Leone SDI collected data on availability of disposable gloves, apron, face mask, and protective boots. Encouragingly, disposable gloves, face mask, and apron were each available in more than 95% of facilities. Protective boots were less common, occurring in just 71% of facilities. All four pieces of protective equipment were present together in only 66% of facilities. Figure 7 shows that the average availability of these four PPE items varies based on facility level, most notably for boots. Notably, the Sierra Leone survey occurred in 2018, following the West African Ebola epidemic in 2013-2016. The high rates of PPE ownership may be partly attributable to the epidemic response there and PPE availability may be lower in other African countries that have not experienced a recent similar event.

15
**Table 11:** Correlates of city mobility indices, uncongested mobility and congestion factor

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Panel A: Uncongeste|d mobili|ty|||||||
|log population|-0.16<sup>_a_</sup>|-0.15<sup>_a_</sup>|-0.15<sup>_a_</sup>|-0.14<sup>_a_</sup>|-0.14<sup>_a_</sup>|-0.15<sup>_a_</sup>|-0.14<sup>_a_</sup>|-0.13<sup>_a_</sup>|
||(0.017)|(0.016)|(0.015)|(0.016)|(0.015)|(0.017)|(0.014)|(0.017)|
|log area|0.17<sup>_a_</sup>|0.15<sup>_a_</sup>|0.13<sup>_a_</sup>|0.13<sup>_a_</sup>|0.13<sup>_a_</sup>|0.14<sup>_a_</sup>|0.13<sup>_a_</sup>|0.11<sup>_a_</sup>|
||(0.017)|(0.017)|(0.018)|(0.018)|(0.017)|(0.019)|(0.017)|(0.020)|
|log roads||0.013<sup>_a_</sup>|0.013<sup>_a_</sup>|0.014<sup>_a_</sup>|0.012<sup>_b_</sup>|0.015<sup>_a_</sup>|0.015<sup>_a_</sup>|0.017<sup>_a_</sup>|
|||(0.0050)|(0.0049)|(0.0045)|(0.0050)|(0.0053)|(0.0044)|(0.0045)|
|log income|||0.15<sup>_c_</sup><br>|0.15<sup>_c_</sup><br>|0.13<br>|0.14<br>|0.14<br>||
||||(0.084)|(0.083)|(0.089)|(0.093)|(0.086)||
|log<sup>2 </sup>income|||-0.026|-0.029|-0.022|-0.023|-0.029||
||||(0.026)|(0.025)|(0.027)|(0.028)|(0.026)||
|Network / shape||||0.21<sup>_c_</sup>|0.047|0.020|||
|||||(0.11)|(0.063)|(0.032)|||
|Pop. growth 90-10|||||||0.11<sup>_a_</sup><br>(0.030)||
|share w. car||||||||0.55<sup>_a_</sup><br>(0.14)|
|share w. motorcycle||||||||0.016<br>(0.051)|
|R-squared|0.43|0.45|0.48|0.50|0.49|0.50|0.53|0.53|
|Panel B: Congestion|factor||||||||
|log population|0.024<sup>_b_</sup>|0.024<sup>_b_</sup>|0.026<sup>_a_</sup>|0.025<sup>_a_</sup>|0.024<sup>_b_</sup>|0.023<sup>_b_</sup>|0.029<sup>_a_</sup>|0.030<sup>_a_</sup>|
||(0.0096)|(0.0096)|(0.0095)|(0.0097)|(0.0099)|(0.010)|(0.0095)|(0.0098)|
|log area|0.018<sup>_b_</sup>|0.017<sup>_c_</sup>|0.0071|0.0084|0.0096|0.011|0.0044|0.0068|
||(0.0086)|(0.0095)|(0.010)|(0.011)|(0.011)|(0.011)|(0.010)|(0.011)|
|log roads||0.00068|0.00094|0.00068|0.0017|0.00081|0.0017|0.0028|
|||(0.0028)|(0.0029)|(0.0029)|(0.0029)|(0.0031)|(0.0027)|(0.0028)|
|log income|||-0.079|-0.080|-0.064|-0.091|-0.081||
||||(0.060)|(0.060)|(0.061)|(0.065)|(0.057)||
|log<sup>2 </sup>income|||0.037<sup>_b_</sup>|0.038<sup>_b_</sup>|0.032<sup>_c_</sup>|0.041<sup>_b_</sup>|0.036<sup>_b_</sup>||
||||(0.019)|(0.019)|(0.019)|(0.020)|(0.018)||
|Network / shape||||-0.046<br>(0.045)|-0.061<sup>_c_</sup><br>(0.035)|-0.035<sup>_b_</sup><br>(0.016)|||
|Pop. growth 90-10|||||||0.054<sup>_a_</sup><br>(0.018)||
|share w. car||||||||0.34<sup>_a_</sup><br>(0.091)|
|share w. motorcycle||||||||-0.096<sup>_b_</sup><br>(0.042)|
|R-squared|0.54|0.54|0.60|0.60|0.61|0.61|0.63|0.63|
|Observations|153|153|153|153|153|142|153|152|

_Notes:_ OLS regressions with a constant in all columns. The dependent variable is the city fixed effect estimated for uncongested mobility in panel A, and the congestion factor in panel B. Robust standard errors in parentheses. _a_ , _b_ , _c_ : significant at 1%, 5%, 10%. See the footnote of table 10 for further details about the explanatory variables.

32