rafmacalaba/datause-encoder-data huggingface dataset:
# Table 2 Summary table of model inputs

|**Category**|**Parameter**|**Unit**|**Côte d’Ivoire**|**Mali**|**Senegal**|**Kenya **|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|---|---|
||**Country ID**|ISO3|CIV|MLI|SEN|KEN|TZA|UGA|
||**Regional level**|GADM GID|2|2|2|3|2|2|
||**Regional nodes level**|GADM GID|1|2|2|2|1|2|
||**Spectrum cost(<1 GHz)**|USD/MHz/pop|0.04|0.04|0.04|0.1|0.1|0.1|
|**Country and**|**Spectrum cost(>1 GHz)**|USD/MHz/pop|0.03|0.03|0.03|0.08|0.08|0.08|
|**scenario**|**Taxation(High)**|%|40|40|40|40|40|40|
|**information**|**Taxation(Baseline)**|%|25|30|30|30|30|30|
||**Taxation(Low)**|%|10|10|10|10|10|10|
||**Cost Inputs(High)**|%|+20|+20|+20|+20|+20|+20|
||**Cost Inputs(Baseline)**|%|0|0|0|0|0|0|
||**Cost Inputs(Low)**|%|-20|-20|-20|-20|-20|-20|
||**3G frequencies**|MHz|1800|1800|1800|1800|1800|1800|
|**Spectrum**|**3G bandwidth**|MHz|2x10|2x10|2x10|2x10|2x10|2x10|
|**portfolio**|**4G frequencies**|MHz|800|700|800|700|700|800|
||**4G bandwidth**|MHz|2x10|2x10|2x10|2x10|2x10|2x10|
||**WACC**|%|15|15|15|15|15|15|
||**Return Period**|Years|10|10|10|10|10|10|
|**Cost model**|**Discount rate**|%|5|5|5|5|5|5|
|**parameters**|**Opex as aproportion of capex**|%|10|10|10|10|10|10|
||**Profit margin**|%|10|10|10|10|10|10|
||**Adminproportion of network**|%|10|10|10|10|10|10|

20
On the surface, it seems that the HR has no effect when coupled with an SSR, as this rule already saves most of the transitory windfalls in the short run. However, the HR prevents the government from increasing consumption over time as the financial returns on the invested assets start to improve the fiscal budget.<sup>26</sup>

# **III. General Baseline Calibration**

In this section, we describe how to calibrate the LTGM-NR to a generic oil exporter for a “business-as-usual” baseline simulation. The discussion below intends to be comprehensive and provide a detailed description of the calibration. The general reader that is more interested in how the LTGM-NR works in practice can jump ahead to the Angolan exercise in section IV.

The baseline simulation runs from 2021 to 2050 (though the simulation horizon can be changed). “Flow” variables are usually calibrated to a 20-year average (2000-2019) to reduce year-to-year volatility—also, 2019 is before COVID-19— and slow-moving “stock” variables are calibrated to the most recent year available. Many of the parameters are common to the standard LTGM (indicated with a red cross on Table 2) and so are not discussed further here (see Loayza and Pennings 2018). However, other similar parameters need to be adjusted, as aggregate data sets like the Penn World Tables (PWT) do not include a resource sector. The user has the ability to override the default calibration if they have better data. If the data are missing, we suggest using an average within the same income group or region. Table 2 provides an overview of the calibration and data sources.

> 26 An equilibrium in the External-Balance submodel is defined as a collection of 12 endogenous trajectories 0 , R 0 D � plus 14 endogenous variables (that are specific to this submodel) tt ttii ttii ttjj tt tt tt tt, c tt ttii ttjj 0 tt , e � where with each endogenous variable specified as a function of the exogenous paths �YY , QQ , RR , KK , KK DDPP , YYpp , iigg , ii , ii , MMRRPPKKpp gg, MMRRPPKKRR gg gg tt tt tt�����tt tt tt tt tt tt tt tt tt tt h �ii , ss bb , dd , dd , dd , zz , zz , zz pp , cc , ii , bb , ff jj∈ } and � as well as initial conditions {0,1, ... , NN}AAjj NN ωω ρρ ii ii ii ii pp tt 0 tt tt 0 tt 0 tt0 tt 0 tt 0 tt 0 tt 0 0 0 0tt� that satisfy equations (1)—(12) and equations tt tt tt ����� (16)—(29) for every period{gg̅ , gg̅ , gg̅ , gg̅jj , gg̅ , DD�jj , pp̅ii , pp�ii _t_ . For HRs, the SBI inequality must also hold for a specified path , QQ� pp �ss̅gg , ffddıı , ηη̅ , bb� �RRDDPPat all periods. , RRDDPP , KK , KK , RR , pp , NN , ωω , ρρ , dd , dd tt SSBBII

18
Policy Research Working Paper 7013

# **Abstract**

This paper analyzes the impact of introducing credit information-sharing systems on firms’ access to finance. The analysis uses multi-year, firm-level surveys for 63 countries covering more than 75,000 firms over the period 2002–13. The results reveal that credit bureau reforms, but not credit registry reforms, have a significant and robust effect on firm financing. After the introduction of a credit bureau, the likelihood that a firm has access to finance increases, interest rates drop, maturity lengthens, and the share of working capital financed by banks increases. The effects of credit bureau reforms are more pronounced the greater the coverage of the credit bureau and the scope and accessibility of the credit information-sharing scheme. Credit bureau reforms also have a greater impact on firms’ access to finance in countries where contract enforcement is weaker. Finally, there is some evidence that the effects of credit bureau reform are more pronounced for smaller, less experienced, and more opaque firms.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mmartinezperia@worldbank.org and ssingh13@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Control<br>Sample Cash<br>Sample CWLP (N=1,000)<br>Grant Study<br>Study (N=4,000)<br>(N=2,000) T1: No Gender<br>T: Cash Grant<br>Dialogue (N=498)<br>+ Training<br>(N=1,000)<br>T2: Gender<br>Dialogue (N=502)<br><!-- End of picture text --> – Figure A.3 Experimental Design Notes: Women in the cash grant study were recruited from the treatment and control groups of the Community Works and Local Participation (CWLP) randomized experiment, which evaluated the impact of a public-works program. Out of the 2,000 women in the cash grant study, 710 had been assigned to the treatment group of the CWLP program. Random assignment in the cash grant study was not stratified based on prior exposure to the CWLP program.

39
<!-- Start of picture text --> 100%<br>90% Others<br>UK<br>80% USA<br>Sri Lanka<br>70%<br>Switzerland<br>Spain<br>60%<br>Netherlands<br>Japan<br>50%<br>Italy<br>40% India<br>Germany<br>30% France<br>Denmark<br>20%<br>China<br>Canada<br>10%<br>Austria<br>Australia<br>0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> <!-- Start of picture text --> Source: Nepal Tourism Statistics, Ministry of Culture, Tourism and Civil Aviation.<br><!-- End of picture text --> Table 2 shows the evolution of exports (in thousands of dollars) for the products considered in this analysis, both for Nepal, and for all countries (total world trade). Of particular note is the evolution of exports of shawls and pashmina-related products (chapter 62 in HS2), which were negligible toward the beginning of the 1990s, and increased substantially thereafter.

**Table 2: Trade Patterns by Product**

|**HS Code**|**Description**||**1990/1994**|**1995/1999**|**2000/2004**|**2005/2011**|
|---|---|---|---|---|---|---|
|90830|Cardamoms|Nepal's Exports|2585.4|3257.1|8971.3|13826.1|
|||World Trade|50142.5|72260.0|136102.0|187357.0|
|121110|Liquorice roots, of a kind<br>used in perfumery|Nepal's Exports|||0.031||
|||World Trade|29047.6|33493.4|30677.8|9213.4|
|121120|Ginseng roots, of a kind used<br>in perfumery, pharmacy|Nepal's Exports|30.8|28.5|5.7|1.3|
|||World Trade|177406.0|276204.0|220284.0|219201.0|

7
**Table 2 Log consumption and household income sources, 2009**

|**Panel A :Household income source(dummy for income source)**|Urban|Rural|
|---|---|---|
|Aill|-0.31***|-0.08**|
|grcutura wage|(0.07)|(0.05)|
|Fil fi|-0.08***|0.02|
|amy armng|(0.02)|(0.04)|
|Ri|0.23***|0.20***|
|emttances|(0.04)|(0.04)|
|Household enterprise:|||
||0.10***|0.15***|
|Primary Employment|||
||(0.02)|(0.04)|
||0.07***|0.13***|
|Secondary Employment|(0.03)|(0.02)|
||0.70***|0.54***|
|Micro enterprise|(0.05)|(0.06)|
||0.09***|0.16***|
|Private wage|(0.02)|(0.04)|
|Public wage|0.15***|0.35***|
||(0.03)|(0.06)|
|Additional variables included but not shown: demographics, location, and education|x|x|
|R square|0.43|0.28|
|Observations|5219|5600|
|**Panel B :Household incomeportfolios** (familyfarm onlyis excluded category)|Urban|Rural|
|Household Enterrise onl|0.30***|0.20**|
|p y|(0.04)|(0.08)|
||0.31***|0.36***|
|Private or public wage only|(0.04)|(0.07)|
||0.15***|0.14***|
|Family farm and household enterprise|(0.04)|(0.02)|
||0.18***|0.17***|
|Family farm and private or public wage|(0.04)|(0.04)|
||0.30***|0.27**|
|Household enterprise and private or public wage|(0.04)|(0.12)|
||0.23***|0.11***|
|Other|(0.04)|(0.03)|
|Additional variables included but not shown: demographics, location, and education|x|x|
|R square|0.41|0.27|
|Observations|5219|5600|

Source: IOF 2008/09. _Notes:_ Table 2 show coefficients of most interest, full regressions can be found in the appendix. Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1. Standard errors take clusters into account, weights are not used 13 | P a g e
and ‘weak/absent farmer organizations’. Given that respondents suggested very few additional constraints, the list of items was considered to include all substantive supply‐ side constraints.

**Table 3. Ordered mean scores for importance of constraints limiting African smallholder participation in higher­value supply chains:**

|**Constraint**|**Mean**|**Standard**<br>**Deviation**|
|---|---|---|
|Weak/inadequate rural transport infrastructure|1.451|0.371|
|Non‐competitive/high cost transport services|1.622<br>|0.425|
|Weak/inadequate rural marketing infrastructure|1.713<sup>a,b</sup><br>|0.401|
|Lack of scale|1.797<sup>b</sup><br>|0.358|
|Weak/limited availability of technical advisory services|1.831<sup>b</sup><br>|0.366|
|Lack of access to competitively priced finance|1.835<sup>b</sup><br>|0.347|
|Lack of pertinent information on prevailing standards|1.872<sup>b</sup><br>|0.353|
|Lack of timely access to improved inputs|1.881<sup>c</sup><br>|0.386|
|Lack of farmer `quality' consciousness|1.884<sup>c</sup>|0.408|
|Lack of pertinent market information|1.894<sup>c</sup>|0.369|
|Weak or high cost services of market intermediaries|1.919<sup>c</sup>|0.369|
|Limited technical knowledge of farmers|1.937<sup>c</sup>|0.307|
|High costs of standards certification|1.939<sup>c</sup><br>|0.412|
|Limited commercial skills of farmers|1.985<sup>c,d</sup><br>|0.332|
|Lack of business culture|1.989<sup>c,d</sup><br>|0.311|
|Lack of/limited capacity for irrigation|2.044<sup>d,e</sup><br>|0.414|
|Distrust/weak bargaining power vs commercial agribusiness entities|2.138<sup>e</sup><br>|0.357|
|Weak/absent farmer organizations|2.152<sup>e,f</sup><br>|0.358|
|Risk management concerns|2.247<sup>f</sup><br>|0.403|
|Fragmentation of household land holdings|2.312<sup>f</sup><br>|0.348|
|Insecure land tenure|2.361<sup>f</sup>|0.412|
|Insufficient availabilityof household labor|2.858|0.399|

_Note: Mean scores denoted by the same letter are not significantly different from each other at the 5% level on the basis of a Wilcoxon Signed rank test._ Principle component analysis revealed four underlying constructs that collectively accounted for 72 percent of variation across the scaling items (Annex Table 2). The Kaiser‐Meyer‐Olkin measure of sampling adequacy was 0.743 and Bartlett's test of sphericity was significant (χ2 =580.9 (78 df); p=.000). The first factor accounted for 31.8 percent of the variation and can be characterised as **weakness of support services** , with heavy loadings for ‘weak/absent farmers’ organizations’, ‘high costs of standards certification’, ‘weak/limited availability of technical advisor services’, ‘weak bargaining powers of farmers versus commercial agribusiness entities’, ‘weak/inadequate rural transport infrastructure’ and ‘lack of pertinent market information’. The second principal component (PC2), accounting for 21.7 percent of the sample variation can be 11
factor, and hence that its changes are driven by changes in � and in the variance of the error term in equation (1).

**Figure 1:** FGI versus UFGI

<!-- Start of picture text --> All Countries Developed<br>1992 1997 2002 2007 2012 2017 1992 1997 2002 2007 2012 2017<br>year year<br>Emerging Frontier<br>1992 1997 2002 2007 2012 2017 1992 1997 2002 2007 2012 2017<br>year year<br>FGI UFGI<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text --> Source: Authors’ computation using Datastream.

Data Appendix tables DA7-DA9 provide summary statistics for the FGI and UFGI in developed, emerging, and frontier markets. Our main findings are illustrated in Figure 1, where we plot the average FGI and UFGI for the different market groups. As it is evident from a simple inspection of the figure, the correction for the heteroscedasticity of the global factor affects substantially our measure of globalization, especially during financial crises.<sup>10</sup> We discuss the correlations between UFGI, FGI, and UFGI 10 in section 5 below, where we also look at other measures of integration.

To offer the reader a better idea of the behavior of the FGIs, in Figure 2a we plot the FGI for a developed, Sweden, an emerging, Turkey, and a frontier market, Romania; while in Figure 2b, we plot the average value of the FGI, for developed, emerging, and frontier markets for the 1992-2016 period together with the volatility of the GF we estimated, a measure of financial turbulence. Figure 2 suggests that, once we control for the heteroscedasticity of the global factor, the process of financial globalization has a stop and go pattern, it increases in tranquil times and experiences setbacks during turbulent ones. This reflects the fact that, differently from other measures used in the literature (see section 5, below), our measure cleans the index from the spurious increases in correlation between stock markets due to increases in global volatility.

It is also worth noticing that, while the patterns of financial globalization are similar for developed and emerging markets, frontier markets remain relatively isolated from global dynamics.<sup>11</sup>

> 10 Forbes and Rigobon (2002) also find an increase in conditional but not in unconditional correlations during the 1987 U.S. market crash, the Tequila, and the Asian crisis.

> 11 See Appendix tables DA7-DA9.

4
# AN<sup>1</sup> EX

|Annex 1:|Tentative Policy Matrix ............................|34|
|---|---|---|
|Annex 2:|The Organizational Structure<sup>of the University of</sup>||
||Sana'a ....................................|35|
|ijnnex 3:|The Stracture Organization of<sup>the Faculty of Education</sup>||
||at<br>the University of Sana'a ....<br>.............|.<br>36|

# APPENDIX

|Graph I:|Total Graduate Output from 1974-1989<sup>....</sup><br>...........|38|
|---|---|---|
|Graph II:<br>Graph III:|Female Composition of Student Output<sup>.39</sup><br>Student Admissions<sup>to Sana'a University by Secondary</sup><br>School Graduation Grades 1989/90<sup>.40</sup>||
|Table 1:|The Number of Students at Sana'a<sup>University, 1988/89</sup><br>|41<br>|
|Table 2:|College of Education- Student<sup>Enrollments, 1989-90</sup><br>|42<br>|
|Table 3:|Faculty Position in Faculty<sup>of Education, July 1990</sup><br>|43<br>|
|Table 4:|Member of Faculty of Education<sup>on Fellowships Abroad</sup><br>|43|
|Table 5:|Number of Academic Staff, 1988/89<sup>.44</sup><br>||
|Table 6:|Staff on Overseas Higher Degree<sup>Training, 1988</sup><br>|45|
|Table 7:|Postgraduate Profile According<sup>to Their Major, Sex and</sup><br>||
||Nationality, 1988/89 .46<br>||
|Table 8:|The Output from Different Kinds<sup>of Education, 1979/80-</sup><br>1988/89 ...............<br>.<br>.<br>......................<br>|47<br>|
|Table 9:|Graduate Output from 1973/74<sup>to 1988/89 ....</sup><br>........|48|

# Acknowledgement

The authors acknowledge with<sup>thanks the valuable comments of Dr.</sup> Abubakar Qirby, Douglas Albrecht,<sup>Tom Eisenmon, Birger Fredriksen, John</sup> Middleton, S. Rangachar, Ralph Romain,<sup>Djamalddine Rouag, Jamil Salmi,</sup> Yogendra Saran, Antoine Schwartz, Adriaan<sup>Verspoor, and George Za'rour.</sup>
opposition-reported 2024 election results and are consistent across all Venezuelan municipal elections that occurred during our period of analysis. These effects are not a mechanical consequence of population loss and reflect a change in the remaining vote composition, since they persist even when the electoral census is updated to reflect the population loss.

We also find that organized crime expanded in Venezuelan municipalities that were more exposed to forced displacement as network connections grew among criminal organizations operating both inside Venezuela and abroad. This criminal expansion provided the regime with a source of illicit revenue and enforcement capacity, reducing pressure for economic and political reform. The exodus facilitated this expansion through two channels. First, criminal groups were more able to enlist, coerce, and exploit displaced populations in highoutflow areas. Second, the departure of displaced individuals conveyed information about weak institutional conditions in their municipalities of origin, allowing criminal organizations to identify and exploit areas of low state presence.

Our quantitative findings align with abundant qualitative evidence from journalists and NGOs in the region, suggesting that forced displacement from Venezuela reduced transaction costs for drug and human trafficking groups and allowed them to grow while benefiting the regime. In particular, reports variously suggest that the Venezuelan government maintained power by leveraging criminal rents, as seen in the _Paraguan ́a Cartel_ and _Cartel of the Suns_ cases (Insight Crime, 2022a,b); that politicians, security forces, and drug traffickers formed symbiotic networks in which officials shielded traffickers from prosecution in exchange for financial support, political influence, and public services; that governors and mayors manipulated military and police appointments to ensure loyalty and facilitate drug trafficking; and that, at the same time, traffickers financed campaigns, mobilized votes, and filled gaps left by a collapsing state.<sup>5</sup>

> 5The Paraguan ́a Cartel is a criminal organization operating primarily in Venezuela’s Falc ́on state, particularly around the Paraguan ́a Peninsula. It is involved in large-scale drug trafficking, smuggling, and money laundering, often taking advantage of the area’s free-trade zones and proximity to Caribbean maritime routes. On other hand, the Cartel of the Suns ( _Cartel de los Soles_ ) refers not to a single cartel but to a network of high-ranking members of the Venezuelan military and security forces allegedly involved in cocaine trafficking

7
public expenditure on the probability of attrition. Moreover the effect is insignificant: a 100 million soles increase in public spending will increase the probability of attrition by 0.01%...

**Table 1: Test for mean differences** **<u>between attrite sample and continuing sample</u>**

|Variable|Conti|nued|Att|rite|T -statistic|
|---|---|---|---|---|---|
||Mean|S.D|Mean|S.D|Ho: diff = 0|
|Probability of being malnourished|0.17|0.01|0.24|0.05|-1.54|
|Z-score (height for age)|-0.78|0.03|-0.81|0.17|0.22|
|Gender|0.50|0.01|0.41|0.05|1.73|
|Birth weight|3201|12|3188|57|0.23|
|Age of child in months|11.54|0.08|11.63|0.38|-0.25|
|Serious illness|0.32|0.01|0.33|0.05|-0.29|
|Age of mother at birth|27|0.15|26|0.68|1.34|
|Mother's height|150|0.12|149|0.63|2.18|
|Maternal mother tongue is not Spanish|0.32|0.01|0.42|0.05|-1.94|
|Maternal education level|7.56|0.03|7.51|0.10|0.10|
|Number of children younger than 5 yrs|0.43|0.01|0.49|0.06|-0.97|
|Household size|5.71|0.05|5.52|0.24|0.76|
|Drinking water piped into household|0.77|0.01|0.72|0.05|1.11|
|Household Wealth score|0.01|0.06|-0.21|0.25|0.81|
|Immunization program in community|0.85|0.01|0.76|0.05|2.57|
|Number of people living in the locality|4.81|0.09|4.68|0.41|0.29|
|Highlands|0.50|0.01|0.59|0.05|-1.64|
|Poverty Index|0.14|0.00|0.14|0.01|-0.73|
|Urban|0.65|0.01|0.72|0.05|-1.37|
|Debt Repayment|11.02|0.60|10.70|2.76|0.11|
|Total Public Spending|29.34|1.28|28.63|5.93|0.12|
|Unanticipated miningrevenues|20.15|23.92|20.97|22.95|-0.31|

Source: Young Lives Study Round 1, ENAHO 2002, and Ministry of Economic and Finance Budgetary Expenditure for 2002. Note: Attrite sample: 90 children. Continuing children: 1962 15