rafmacalaba/datause-encoder-data huggingface dataset:
comparison with utility revenues; amounting to between 20 and 60 percent of utility revenues in most cases, and exceeding 100 percent of utility revenues in a few cases (figure 24b).

In countries such as the Democratic Republic of Congo, the Republic of Congo, Nigeria, Namibia, and Cameroon—to name only a few—transmission and distribution losses (technical and nontechnical) are the lead cause of hidden losses (figure 24b). In Côte d’Ivoire, Niger, Ghana, Uganda, and Botswana—to cite some examples—under-collection of bills is the main offender, though it is observed that unpaid bills are from the government or other public enterprises. Finally, over-manning seems to be an issue for countries such as Cape Verde and Chad.

<!-- Start of picture text --> Figure 24. Monetary costs of overlooked inefficiencies<br>a. % of GDP b. % of revenue<br>90% Congo DR<br>Cote d'Ivoire<br>80% Nigeria<br>70% Congo, Rep.<br>Niger<br>60% Ghana<br>50% Uganda<br>Mali<br>40% Malawi<br>Botswana<br>30%<br>Cape Verde<br>20% Namibia 2006<br>Cameroon<br>10% Tanzania<br>0% Mozambique<br>Chad<br>Ethiopia<br>Benin<br>Burkina Faso<br>Lesotho<br>Rwanda<br>Senegal<br>Kenya<br>Transmission and distribution Losses Zambia<br>Madagascar<br>South Africa<br>Collection Inefficiencies<br>0 20 40 60 80 100<br>Over-manning<br>Transmission and Distribution Losses<br>Collection Inefficiencies<br>Over-manning<br>Percentage of Countries (%)<br><!-- End of picture text --> _Source_ : Briceño-Garmendia, Foster, and Smits 2008 In fact it is not infrequent that the financial burden of the myriad operational inefficiencies is higher than the cost of subsidizing via under-pricing. In about half of the countries in our sample, the magnitude of these operational inefficiencies is higher than the magnitude of price subsidies (figure 25).

Figure 25. Weight of underpricing vis-à-vis operational inefficiencies 35
- The southern end of the break-bulk terminal of Kingstown was refurbished, but the funds to rebuild the rest of the pier, reportedly in the range of EC$35 million ($13.5 million), were reappropriated to construct the Argyle International Airport. There is the danger that eventually the unfinished portion of the pier will collapse.

- Dominica:

- The Port of Roseau is in danger of losing 150 meters or up to the entire length of the pier due to deteriorated pylons. Reinvestment is urgently needed and—according to the latest estimates— amounts to $12 million.

- St. Kitts:

- The government of St. Kitts is advancing a sophisticated PPP for another cruise ship terminal.

|**Table 6.4**<br>**Country**<br>|**Capacity a**<br>**Port**<br>|**nd Demand of Select Ports, 2012**<br>**Capacity**|**Dema**|**nd (as of 2012)**|
|---|---|---|---|---|
|**Antigua**<br>**and**<br>**Barbuda**|**St. John’s**<br>|40,000<br>TEU/yr<br>500,000<br>tonnes/yr(general cargo)<br>200,000<br>tonnes/yr(drybulk)<br>Not reported tonnes/yr(liquid bulk)|13,485<br>288,986<br>30,700<br>890,677*|TEU/yr<br>tonnes/yr(general cargo)<br>tonnes/yr (dry bulk)<br>tonnes/yr(liquid bulk)|
|**Dominica**|**Woodbridge**<br>**Bay/**<br>**Roseau**|Not reported|19,905<br>27,142<br>211,679<br>48,536|TEU/yr<br>tonnes/yr(general cargo)<br>tonnes/yr(drybulk)<br>tonnes/yr(liquid bulk)|
|**Grenada**|**St.**<br>**George’s**<br>|13,920<br>TEU/yr<br>421,568<br>tonnes/yr(general cargo)<br>Not reported tonnes/yr(drybulk)<br>81,061<br>tonnes/yr(liquid)|13,920<br>461,479<br>251,266<br>81,061|TEU/yr<br>tonnes/yr(general cargo)<br>tonnes/yr(drybulk)<br>tonnes/yr(liquid bulk)|
|**and Nevis**|**Basseterre**<br>|Not reported|7,801<br>105,313<br>104926<br>81,296|TEU/yr<br>tonnes/yr (general cargo)<br>tonnes/yr (dry bulk)<br>tonnes/yr(liquid bulk)|
|**St. Kitts**|**Long Point**|30,454<br>TEU/yr<br>Not reported<br>tonnes/yr(general cargo)<br>26,506<br>tonnes/yr(drybulk)<br>14,599<br>tonnes/yr(liquid)|2,665<br>3,661<br>29,049<br>14,599|TEU/yr<br>tonnes/yr(general cargo)<br>tonnes/yr(drybulk)<br>tonnes/yr(liquid bulk)|
|**ucia**|**Castries**<br>|146,000<br>TEU/yr<br>Not reported tonnes/yr(general cargo)<br>Not reported tonnes/yr(drybulk)<br>Not reported tonnes/yr(liquid bulk)|37,672<br>82,627<br>3,859<br>4,060|TEU/yr<br>tonnes/yr(general cargo)<br>tonnes/yr(drybulk)<br>tonnes/yr(liquid bulk)|
|**St. L**<br> <br>|**Vieux Fort**<br>|493,000<br>TEU/yr<br>Not reported tonnes/yr(general cargo)<br>Not reported tonnes/yr(drybulk)<br>Not reported tonnes/yr(liquid bulk)|45,668<br>28,787<br>93,682<br>38,291|TEU/yr<br>tonnes/yr(general cargo)<br>tonnes/yr(drybulk)<br>tonnes/yr(liquid bulk)|
|**. Vincent**<br>**and the**<br>**enadines**|**ampden**<br>**Park/**<br>**ingstown**|Not reported|16,827<br>34,272<br>46,796|TEU/yr<br>tonnes/yr(general cargo)<br>tonnes/yr(drybulk)|
|**St**<br> <br>**Gr**|**C**<br>**K**||63,453|tonnes/yr(liquid bulk)|

_Source:_ Authors’ own elaboration on primary data.

_Note:_ * Numbers for liquid bulk from Antigua need further review and validation. OECS = Organization of Eastern Caribbean States; TEU/yr = twenty-foot equivalent units per year.

30
Table 10: Linear treatment effect in full sample and restricted sample

|||Full sa|mple||School|s with a sing|le class pe|r grade|
|---|---|---|---|---|---|---|---|---|
||Nati<br>languag|ves’<br>e score|Nat<br>math|ives’<br>score|Nati<br>languag|ves’<br>e score|Na<br>mat|tives’<br>h score|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Sh f t|-3.08***|-2.88***|-1.55|-1.49|-3.60***|-3.34***|-1.48|-0.980|
|are o recen<br>migrants in grade|(0.981)|(0.977)|(0.913)|(0.906)|(1.28)|(1.38)|(1.27)|(1.19)|
|Grade-level controls||✓||✓||✓||✓|
|Grade effects|✓|✓|✓|✓|✓|✓|✓|✓|
|School-by-year effects|✓|✓|✓|✓|✓|✓|✓|✓|
|Number of observations|12,053|12,053|12,053|12,053|8,188|8,188|8,188|8,188|

_Notes._ ***: significant at the 1% level, **: significant at the 5% level, *: significant at the 10% level. Robust standard errors clustered at the school level are reported in parentheses. Controls for grade-level characteristics include: the share of students by level of parental education, the share of female students in the grade, the share of disadvantaged students according to the Dutch weighting system, the share of students that repeated a grade, the average class size in the grade, teacher’s years of experience, the total number of students in the grade and its square. Controls for linear school trends are also included. Estimates that do not control for linear school trends are quantitatively very similar and available upon request.
Table A9: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose digital inclusion field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.404|–1.927|–1.860|
||(0.862)|(1.967)|(1.900)|
|_R_ <sup>2</sup>|0.702|0.817|0.814|
|Observations|978|978|978|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|-0.139|-2.015|-0.414|
||(1.783)|(2.131)|(2.052)|
|_R_ <sup>2</sup>|0.694|0.804|0.807|
|Observations|916|916|916|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined digital inclusion field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil_ , and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
more restrictive at the departmental level. Defined by a number of decrees, departmental transfer amounts are based on population formulas, and implicit prioritization of social and productive infrastructure is used for allocating transfer resources. The discretionary use of transfers is broader at the municipal level. See Inchauste (2009) for a more detailed discussion of the transfer allocation formula.

The public expenditure data used in this paper was derived from the Accounting Department of the Ministry of the Economy and Public Finance of Bolivia. Expenditure data in Bolivia is typically recorded by program and project, and aggregated at the national level. The expenditure data on agriculture and rural development was disaggregated by function (research, extension, irrigation, rural roads, etc.), economic classification (current and capital) and level of government (national, departmental and municipal) for a period of 13 years (1996-2008). This provides a rich panel for analysis within and across levels of government or categories of spending. Annex 2 presents the definitions of categories used in this analysis.

# **5. Analyzing the Effects of Agricultural Spending on VAM**

A simple correlation of the municipal distributions of agricultural spending and probabilities of each category of vulnerability to food insecurity indicates that total per capita agricultural spending and vulnerability to food insecurity are only weakly correlated at the municipal level. The correlation between per capita agricultural spending (in Bolivianos) and VAM is only 0.03 in 2007. The relationship between VAM scores across the 327 Bolivian municipalities and per capita total agricultural spending in 2007 in these municipalities is almost horizontal (Figure 5).

**Figure 5. Per Capita Agricultural Expenditure and Probability of Being in Each VAM Category**

<!-- Start of picture text --> Total Agr Expenditure pc and Prob of being in each VAM cat<br>Prob VAM 1 Prob VAM 2 Prob VAM 3<br>.5 .6 .7 .8 .9 .5 .6 .7 .8 .9 1 .5 .6 .7 .8 .9<br>Prob VAM 1 Prob VAM 2 Prob VAM 3<br>Tot Exp pc 07 Fitted values Tot Exp pc 07 Fitted values Tot Exp pc 07 Fitted values<br>Prob VAM 4 Prob VAM 5<br>.5 .6 .7 .8 .5 .6 .7 .8 .9<br>Prob VAM 4 Prob VAM 5<br>Tot Exp pc 07 Fitted values Tot Exp pc 07 Fitted values<br>600 600 600<br>400 400 400<br>200 Tot Exp pc 200 Tot Exp pc 200<br>0 0 0<br>600 600<br>400 400<br>200 Tot Exp pc 200<br>0 0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database. Note: each figure depicts the association between the estimated probability of vulnerability across municipalities pertaining to the respective vulnerability category, 1 to 5 (as described in footnote 4 above).

9
wife but which the migrant has less reason to underreport. For the subset of couples for which this data is available, wives underreported hours worked by 10 percent. We find that the ratio of hours worked is uncorrelated with the reported earnings gap (p-value = 0.73) when included as an additional variable in the empirical specification. Consequently, a possible predisposition by the wives to underreport unobserved variables does not seem to play a role in explaining the reported earnings ratio.

The remaining variables in column 2 behave as expected. Wives’ years of education and the presence of wives who are employed are positively correlated with the reported income gap. More optimistic belief held by the wife about overseas income is also positively associated with the earnings ratio. In contrast, being unaware of the husband’s expenses in Qatar, having disagreement with the household over the uses of remittances and spending on temptation goods by the migrant are negatively associated with the reported income ratio. With the exception of the wife’s unawareness of her husband’s expenses, all the predictors of the earnings gap are statistically significant at the 10 percent level or lower. Overall, the effects of asymmetric information on reporting behavior predicted by our model are supported by the empirical evidence that cannot be explained by reporting biases.

We also separately examined the role of migrant networks on the wife’s ability to verify foreign earnings or to influence her expectations of it. Access to a larger migrant network could increase the availability and quality of information that recipients have about migrant earnings abroad, potentially lowering the cost of verification (Chort et al. 2012 and De Weerdt et al. 2014). At the same time, larger networks could be associated with greater negative selection of migrants with respect to education and earnings as stronger networks help reduce the transaction costs associated with finding jobs abroad (McKenzie and Rapoport 2007, 2010). Using the KMS 23
_information and collateral registries frameworks_ which reduces agency frictions. Eighth, _revisit direct policy interventions_ by assessing the design and impacts of, inter alia, reserve requirements frameworks, interest rates restrictions, and directed credit and interest subsidy programs.

This paper is organized as follows. Section 2 is the conceptual framework, which describes the relevance of lending rates and spreads for economic development and discusses the main drivers of lending rates and spreads according to the literature. Section 3 shows the main trends and patterns of lending interest rates in the 15-year period 2003-2017, presents an illustrative econometric analysis of common correlates of lending rates and spreads, and proposes decomposition of level and change of the average lending rate in EMDEs during the decade 20072017. Section 4 presents eight considerations which should be assessed holistically to formulate a coherent policy mix that deals with the roots of high and persistent lending interest rates and spreads in some EMDEs.

# 2. Why do bank interest rates and spreads matter? A conceptual framework

## **A. Relevance for development**

Bank interest rates are fundamental to macroeconomic and financial sector outcomes. Banks dominate credit intermediation and savings mobilization in many EMDEs and constitute a primary vehicle for monetary policy transmission. Bank interest rates therefore influence the available capital in the economy (e.g., cost and volume), the pool of viable investments (e.g., size and composition), and the health of public and private balance sheets. As a result, bank interest rates have a significant impact on private sector-led investment, economic growth, job creation, and the overall progress towards the twin goals.

Bank lending rates reflect nominal benchmark interest rates which are determined by macroeconomic factors. In a small, closed economy, nominal benchmark rates are determined by the real interest rate and expected inflation. However, when the capital account is fully open and 4
**Figure 6: Observed and Predicted Frequencies of PSP Counts**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 5 10 15 20 25 30<br>Observed Frequencies NBREG Predictions<br><!-- End of picture text -->

# **6 - Results and Interpretation**

The detailed results for the regression of the different models can be found in Annex A. The following table summarises the results in percent change in expected PSP project count for one standard deviation increase in X, holding other variables constant.

|**Percent change in expected pr**<br>|**oject count for o**<br>|**ne standard dev**<br>|**iation incre**<br>|**ase in X**<br>|||||
|---|---|---|---|---|---|---|---|---|
|**Variable**|**Transf.**|**Basic Model**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|Population|Natural Log|178.8***|148.6***|142.9***|144.9***|140.1***|233.6***|179.9***|
|GDP per capita|Natural Log|138.4***|158.5***|152.1***|169.5***|156.2***|171.1***|164.9***|
|Debt/GNI|Natural Log|40*|47.3**|51**|41.3*|47.2**|57.8**|57.3***|
|ICRG Economic Risk Index|Natural Log|39*|31.1|23|34.8*|31.2|24.2|24.7|
|Urban Coverage Gap|Raw|44***|45.3***|44***|35***|46.6***|42.8***|55***|
|Rule of Law|Raw||35.4**||||||
|Government Effectiveness|Raw|||49.6***|||||
|Political Stability|Raw||||31*||||
|Control of Corruption|Raw|||||39.3**|||
|Contract Enforcement|Raw||||||-28.7*||
|ICRG Investment Profile|Natural Log|||||||42.7***|
|_*** Significant at 1%; ** signif_|_icant at 5%; * sig_|_nificant at 10%_|||||||

The results should be read (looking at the first cell for the basic model): an increase of one SD of the log of the population variable increases the expected mean of project signature count by 178.8% with 99% confidence. Likewise, looking at model 1, an increase of one SD of the rule of law index (which corresponds, for example, to the difference between the scores of Angola and Argentina) increases the expected mean of projects signature count by 35.4% with 95% confidence.

Overall, the results of the regression provide strong support for our hypotheses. The basic components of demand for water services, the size of the population and its ability to pay are significant at the 1% level and take the expected positive sign in all the models. We also observe that ability-to-pay has an impact on PSP that is comparable to the effect of market size.

Turning next to the factors which drive government demand for the involvement of the private sector, we find that level of indebtedness of a country is significant in all the models, generally at the 5% level.

We understand this relationship to operate through the following mechanism: governments of more highly indebted countries find it more difficult to access credit, putting pressure on the government to engage in fiscal stabilization. Governments then reduce their investment expenditure on infrastructure as part of their 14
processes, make decisions, and generate outcomes independently, without human intervention.

2. **Novelty and Cost** : We documented the year of invention and the cost of each technology and studied their correlation with the experts’ rankings. Although novelty and cost do not define sophistication, more sophisticated technologies tend to be newer and more expensive.

3. **Large language models (LLMs)** : We conducted two exercises to validate our ranking through LLMs. First, we asked ChatGPT to rank the technologies based on their levels of sophistication. We replicate this exercise following specific definitions of sophistication based on functionality, integration, and automation. Second, we asked ChatGPT to identify a specific task for each of the 14 business functions and estimate the time required to perform the task with each technology.

To collect the information in the first two stages, we relied on multiple sources, including the official description of specific leading brands supplying these technologies. For GBFs we collected information from multiple companies websites, including Microsoft, Google, SAP, Oracle, QuickBooks, IBM, Sage, NetSuite, BambooHR, Trello, Salesforce, Workday, Meta, Qualtrics, Survey Monkey, Amazon, Shopify, LinkedIn, among others. These companies have more than 80% of the global market share for technologies used in business administration, such as standard software (e.g, spreadsheet) and enterprise resource planning (ERP) systems, and a large share of the market across GBFs.<sup>5</sup> In addition, we consulted specialized websites (e.g, tech.co; erpresearch.com; getapp.com) that provide comparisons across these products, totaling more than 50 original sources of information. Similar exercises with multiple sources of information were replicated for SSBFs.

We illustrate the validation methodology using the example of business administration, a GBF that includes finance, accounting, and human resources processes. Table 1 summarizes the three-step validation procedure for each technology in business administration. The least sophisticated technology, handwritten processes, can only perform basic manual administration tasks such as transaction entry, bookkeeping, or employee records handling, without any integration or automation features. Standard software like Microsoft Excel or Google Sheets helps with basic functionality to perform mathematical and statistical operations, including charts, and handle financial account, and HR records. However, it requires manual inputs and knowledge to build specific applications, with limited integration and automation. Mobile apps, such as QuickBook online, are pre-designed to perform these tasks with some

> 5Estimates based on Enlyft dataset (Cirera, Comin and Cruz, 2022). These companies are recognized as key players by various specialized sources estimating market potential for ERP (e.g., Research and Market, Fortune Business Insight), even if there are variations in their market share estimations.

8
and quota-free treatment for imports of textiles and apparel articles made in ATPA countries, using yarn or fabric or fabric components wholly formed in the US. Similar to the CBTPA, ATPA also provides some preferential access for apparel made from regional fabric, but no third country fabric provision.

# **3.  Data Description**

A preference database was constructed for this study using trade and tariff data at the US HTS 8- digit level, extracted from the USITC DATAWEB database (http://dataweb.usitc.gov) and the USITC Tariff Database (http://www.usitc.gov/tata/hts/other/dataweb). All US imports in HS chapters 1 through 97 in the year 2003 are included.<sup>9</sup> The USITC records US imports from beneficiary countries by customs value<sup>10</sup> in current US dollars. The import data include the preferences claimed, the value of total imports, dutiable imports (ex post), the duties paid, the quantity imported, and preference eligibility status by country and program. The use of preferences is indicated by the preference claimed when the product entered the United States.<sup>11</sup> MFN and preferential tariffs, including both _ad valorem_ and specific tariffs are converted to _ad valorem_ equivalents (AVEs), using the USITC method.<sup>12</sup> While the ITC tariff database does include AVEs of tariff-rate quotas, it does not include any AVE estimates of import quotas or other types of

> 8 Hereafter, ATPA refers to ATPA or ATPADEA.

> 9 Note that chapters HS61 and HS62 do include the value of apparel entering the US under the production-sharing program (HTS 9802.00.80), though that value is not broken out separately.

> 10 Customs value is equivalent to f.o.b. value.

> 11 There are cases where the claim is later denied, but these represent a tiny proportion of cases.

12 US AVE tariffs for HTS 8-digit items with specific or compound rates are estimated by the following steps. 1) Where there is US MFN import trade for an HTS item, the AVE is estimated by duties divided by dutiable US imports. Trade entering under special tariff preference programs is not included. 2) Where there is no US MFN import trade for an item, the quantity and customs value of all US imports under that item are used and the MFN specific and compound rates are applied to calculate the tariffs that "would have been collected " if the trade had entered as MFN trade rather than under a special program. The duties thus calculated are divided by the customs value to estimate the AVE. 3) Where there is no US import trade at all for an item in a given year, the quantity and customs value of imports (MFN if available, all trade if not) in that item from the previous full year are used and the MFN specific and compound rates for the current year are used to calculate the tariffs that "would have been collected " if the trade had entered as MFN trade in the current year. The duties thus calculated are divided by the 6