rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 2: Comparison of key features across methods

||||ML method||
|---|---|---|---|---|
|Feature|EBP|Cubist|XGBoost|BRF|
|Prediction target|Census data without<br>assets|Census data without<br>assets|Census data without<br>assets|Census data without<br>assets|
|Functional form|Single linear function|Multiple linear functions<br>(one at each decision tree<br>node)|Decision Trees|Decision Trees|
|Assumed data<br>generation process|Specifed linear model<br>with nested error<br>structure|Average linear prediction<br>based on specifed<br>predictors at each decision<br>tree node|Sum of decision trees<br>based on specifed<br>predictors and residuals|Sum of decision trees<br>based on specifed<br>predictors and residuals|
|Objective (Loss)<br>function|Minimize mean squared<br>error of predictions|Minimize standard error<br>of dependent variable<br>when determining each<br>split. Minimize mean<br>squared error at each node|Minimize mean squared<br>error of predictions|Maximize penalized<br>heterogeneity in the<br>dependent variable when<br>determining each split.<br>The penalty favors<br>balanced splits.|
|Random efect<br>conditioned on<br>survey data|Yes|No|No|No|
|Predictors used|All selected by LASSO|Selected through growing<br>decision trees|Selected through growing<br>decision trees|Selected through growing<br>decision trees from<br>randomly selected subset<br>of candidate predictors at<br>each node|
|Use diferent<br>subsamples of the<br>data for growing<
Table 1. Population coverage by household surveys, 1988-2008 (in percent)

||1988|1993|1998|2002|2005|2008|
|---|---|---|---|---|---|---|
|Africa|48|76|67|77|78|75|
|Asia|93|95|94|96|94|98|
|Latin America and the|87|92|93|96|96|95|
|Caribbean|||||||
|Post-communist countries|99|95|100|97|93|92|
|Rich world (Western|92|95|97|99|99|97|
|Europe, North America<br>and Oceania)|||||||
|_World_|_87_|_92_|_92_|_94_|_93_|_94_|
|_Number of countries with_|_103_|_122_|_124_|_122_|_122_|_116_|
|_household surveys _|||||||

Note: Post-communist countries include Eastern European countries (many of which are members of the EU), and former Soviet republics. This is not an ideal classification, and in the future it may have to be changed.

11
While all auditors were offered more expensive products, experienced auditors, especially those that claimed to have a lower offer from a competing bank, appear to receive a cheaper credit product.

Why do staff provide little information voluntarily and offer relatively more expensive products? Table 7 tries to address this question by reporting results from the staff survey. About 60 percent of the staff in each country have university degrees. We asked the same interest rate question as in Lusardi and Mitchell (2014) and Atkinson and Messy (2012) and found that 62 percent answer the question correctly. Despite servicing about 18 clients per day, 96 percent of the staff claims to voluntarily provide all the information related to products offered. Yet, their income is subject to incentive schemes that reward the number of sales of certain products. About 46 and 41 percent of the staff report being influenced by their incentive schemes when deciding which credit and savings products to offer respectively. This explains why clients are rarely offered the cheapest product that met their needs.

# **7. Conclusions**

This study follows the protocols in Giné and Mazer (2017) to assess the quality of information that financial institutions in Colombia provide to potential customers seeking savings and credit products as well as the adequacy of products that are offered and finds similar results to those in Mexico, Peru and Ghana. We find that consumers who do not actively engage the staff, are not provided enough cost information. In contrast, experienced auditors who were instructed to ask specific questions about the product if the staff did not disclose the information voluntarily end up better informed. This finding underscores the gap between enacted regulation and its enforcement and how this gap may persist as financial institutions have strong incentives to undermine disclosure initiatives.

In any event, financial disclosure regulation in Colombia could be improved. For example, basic guidance could be provided to consumers on the key questions to ask when looking for a credit or savings product. In addition, information could be provided more transparently as tested in a follow-up
Policy Research Working Paper 9531

# **Abstract**

This paper explores the interplay between concentration of wealth and policies, namely regulation and taxation. The paper exploits variation in exposure to international commodity prices. Using a global panel data set of the net worth of billionaires, the results point to a positive relationship between commodity prices and the concentration of wealth at the top. Regulation especially pertaining to competition is found to limit the effects of commodity price shocks on the concentration of wealth, while taxation has little effect. Moreover, commodity price shocks crowd out non-resource tax revenue, hence limiting the scope for income transfers and redistribution. The results are consistent with the primacy of ex ante interventions over ex post ones for addressing wealth inequality.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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://www.worldbank.org/prwp. The authors may be contacted at aislam@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
Policy Research Working Paper 10952

# **Abstract**

This paper provides an overview of the data sources and aggregation methodology for the Worldwide Governance Indicators (WGI). The WGI report six aggregate governance indicators measuring Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption in a sample of 214 economies over the period 1996–2023. The aggregate indicators combine information from 35 different existing data sources, capturing subjective perceptions of the quality of various dimensions of governance reported by experts and survey respondents worldwide. The paper briefly discusses how to use reported margins of error when interpreting cross-country and over-time differences in the aggregate indicators. The paper also updates and extends earlier analysis on three key issues relating to the WGI methodology: (a) the effect of correlated perception errors, (b) the robustness of the aggregate indicators to alternative weighting schemes, and (c) the existence on trends in global averages of governance.

This paper is a product of the Office of the Chief Economist, Prosperity Practice 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://www.worldbank.org/prwp. The authors may be contacted at akraay@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<
_The Cultural Trade Index_ Page | **3** of **14**

words, they comprise the cycle of creation, production and distribution of goods and services that use intellectual capital as their primary input. Today’s creative industries involve the interplay of traditional, technology-intensive and service-oriented subsectors. They range from folk art, festivals, music, books, paintings and performing arts to more technology-intensive subsectors such as the film industry, broadcasting, digital animation and video games, and more serviceoriented fields such as architectural and advertising services. All these activities are intensive in creative skills and can generate income through trade and intellectual property rights.”<sup>4</sup> In a broader context, “the interface among creativity, culture, economics and technology, as expressed in the ability to create and circulate intellectual capital, has the potential to generate income, jobs and export earnings while at the same time promoting social inclusion, cultural diversity and human development. This is what the emerging creative economy has already begun to do as a leading component of economic growth, employment, trade, innovation and social cohesion in most advanced economies.”<sup>5</sup> The contribution of cultural trade cannot be underestimated in the mandate to promote economic diversification, boost shared prosperity, and to reduce extreme poverty in a sustainable manner. This because even low-income countries have a wealth of cultural diversity that could be a viable source of export earnings.<sup>6</sup>

# **II. Why the Cultural Trade Index?**

At the outset, one big caveat deserves attention: indices incorporate tradeoffs. And these have to be made explicit. In this case, for instance, if country X’s total movie trade is divided by X’s GDP or population, the two methods might render totally different results. GDP’s great merit is that since trade-offs are measured by market prices, we know the terms at hand. That noted, the immediate purpose
The ultimate goal of VAT reforms is to have a broad based tax that will cover most goods and services. Exemptions and reduced rates should be kept at a minimum and classified in broad categories as opposed to long lists of items defined at the sub‐product level. For example, all medicines, medical equipment, food, books and periodicals can be exempt. Provision of exemptions or reduced VAT rates for inputs and machinery used by certain types of firms is hard to justify on policy grounds. These incentives would make little difference to the ultimate tax burden of firms as VAT on such purchases is creditable. If the objective is to relieve exporting firms of the cash flow and compliance burden, then the solution lies in improving VAT administration and provision of prompt refunds to exporters. Multiple cascading indirect taxes should be replaced by a single levy. If properly implemented, the announced reforms of indirect taxes in Bangladesh and India could offer useful lessons for other countries in the region. Sound evaluation of the design and impact of these reforms will be necessary.

Simpler and broad based corporate and VAT taxes will be easier to administer and comply with. Tax systems that have similar rates and rules for most taxpayers reduce opportunities for evasion and encourage voluntary compliance. Reform of tax administration can make compliance less costly. Priorities for reform include streamlining business processes, reducing processing times, cutting the number of annual tax payments (which is currently among the highest in the world), making greater use of IT systems (including online filing) and providing taxpayer education services. Countries that frequently change tax rules and procedures should consider instituting a cap on the number of SROs that can be issued throughout the year. Several measures can be taken to better detect fraud and non‐compliance, including through regular risk based audits and use of third party information (e.g. from banks, customs, land registration records) to prevent tax evasion.

Broadening the tax base and strengthening public administration will improve revenue collection. This will create space for reducing corporate
# **Table 1**

# **Course characteristics**

||2008|2009<br>Panel|2010<br>A. Stock T|2011<br>rading Cou|2012<br>rses|2013|2014|2015|2016|Total|
|---|---|---|---|---|---|---|---|---|---|---|
|N. of Courses|21|78|141|200|178|83|56|71|48|876|
|Average students|17.14|17.81|18.02|17.61|12.92|16.08|12.14|17.45|16.17|16.14|
|Average hours|10.00|10.00|10.00|11.20|12.57|15.53|25.34|24.34|23.23|14.19|
|Tuition (USD)|82.72|94.28|92.16|113.86|169.26|174.07|240.98|286.66|400.99|162.70|
|Age > 30 years (%)|61.04|70.32|72.40|59.49|53.86|44.60|40.80|31.55|23.36|54.58|
|Females (%)|43.93|29.51|27.94|31.32|31.20|32.13|31.95|34.74|28.90|31.15|
|||Pane|l B. Experi|enced Stud|ents||||||
|% per class|20.61
one based on Euclidean distance and another “least cost path” derived from elevation and slope data. Our main results rely on the linear, Euclidean distance instrument but are robust to using the least-cost path instrument instead. It is also important to note that though the GQ highway project is a relatively recent investment, parts of it follows historical roads (Figure AF.1).

The least cost path is calculated based on a time cost raster method using the Cost Connectivity algorithm available in ESRI ArcGIS as the minimum time result from off road speeds on land. We construct the
**Table 8 – Multidimensional poverty versus income poverty**

|**Quintiles of**<br>**Income per**<br>**capita**||**CWI Poverty**||
|---|---|---|---|
||**FGT0**|**FGT1**|**FGT2**|
|**1**|**28.3**|**10.0**|**5.4**|
|**2**|**17.1**|**4.8**|**2.1**|
|**3**|**9.0**|**2.8**|**1.3**|
|**4**|**4.3**|**1.0**|**0.4**|
|**5**|**2.0**|**0.3**|**0.1**|
|**Total**|**12.1**|**3.8**|**1.9**|

Source: Calculations based on data from the NSLS 2006-2007.

33
special credit institutions. This category is supposed to capture non-bank financial institutions that specialize in certain lending activities that might be more attractive for small and mediumsized enterprises, such as leasing and factoring. The final category is deposit-taking or commercial banks.

We use the FSAP data to construct two indicators. The **asset share** is calculated as each type’s assets relative to the sum of low-end financial institutions, specialized non-bank financial institutions and commercial bank financial assets gauges the importance of each segment within the financial system by dividing the total assets of each category by total financial assets of these three segments in the country. The three asset shares add up to 100.<sup>6</sup> The **average size** is computed by dividing the total amount of assets per category by the number of institutions per category.

Both indicators vary widely across our sample countries. The share of banks varies from almost 99% in Ukraine to 61% in Colombia. The share of specialized lenders varies from 38% in Colombia to less than one percent in Senegal, Ukraine, Bolivia, and Madagascar. The share of low-end financial institutions varies from 21% in Burkina Faso to less than one-half percent in Chile and Latvia. The average size of banks in USD ranges from 3.5 billion in Turkey to 10 million in Guinea-Bissau. The average size of specialized lenders varies from 350 million USD in Chile to less than one million in Mongolia. The average size of low-end financial institutions varies from 800 million in Turkey to less than one million in Mongolia.

We combine the financial structure data with data from the World Bank/IFC Enterprise Surveys. The Enterprise Surveys collect firm level-data from key manufacturing and service sectors in over 120 countries since 2002.<sup>7</sup> Countries are surveyed every three to four years but not simultaneously. To ensure data consistency and inter-country comparability we only use data from countries in the standardized dataset 2006-2010 which contains data for 100 countries.<sup>8
<sup>11</sup> Interestingly, reform chatter is not correlated with FDI inflows. This finding suggests that the support for the current reforms matters for FDI decisions. In other words, “talk is not cheap”. FDI inflows also correlate with the GCC’s GDP growth and trade openness in the same year.

# V. Conclusion

This paper finds that GCC reform efforts have intensified in recent years, as indicated by the rapidly rising number of passed laws and decrees. Nevertheless, the GCC countries tend to pass more laws and decrees in stronger policy areas. Using data from the social media platform Twitter, the paper discovers increasing engagement from the population. In addition, the number of economic reform tweets per engaged user has risen dramatically. Nevertheless, support for economic reforms from the population is declining, suggesting either growing discontent about the current economic situation or shrinking support for the current direction of economic reforms. Data from news media show that international media is more engaged but less positive about the GCC’s economic reforms than local media. These findings suggest a relative disengagement from economic reform discussions by local media (and potentially domestic participants). The more negative tone from international media, which possibly reflects international stakeholders’ views, is noteworthy. Sentiment in international media and social media matters because it is found to be correlated with FDI inflows into the GCC.

> 11  The literature on the determinants of FDI inflows is very broad, covering different regions, time periods, data sets, and employing different methodologies. Some examples include Gammoudi and Cherif (2016) who study the MENA region, Hintosova et al. (2018) for the ‘Visegrad’ countries in Eastern Europe, and Asongu et al. (2018) on the BRICS and MINT countries. Our estimations follow the literature in including variables for GDP growth, inflation, trade openness, and institutional quality, in addition to the media-based variables.

16
financial constraints that beneficiary households faced in accessing health services. The expansion in health‐seeking behavior was observed in both rural and urban households.

B.2.3 Education Services In contrast with the program impacts on access to health services, CfW participation had no systematic positive impact on access to education (Table 9). Indeed, student absenteeism appears to have risen among treatment households. The average number of school days missed in the four weeks previous to the survey increased by nearly 51 percent (0.2 days). Also, expenditure data show that there were declines of about 27 percent in household spending on girls and boys schooling in urban areas (see Table 6, columns 9‐10). However, at least in the short run, treated households did not report lower school enrollments among children, only lower school attendance rates.<sup>28</sup> At the same time, there was no expansion in children’s labor market participation. One possible explanation for the rise in school absenteeism is that, because of the substantial increase in labor market participation among adult household members, school‐going children may have been skipping school to help with household chores or to take care of younger siblings. While there was no observed increase in the time children spent on household chores, the IE captured a relatively narrow set of data on time use and the distribution of household responsibilities among members.

# **_B.3 Impacts on Savings and Investment_**

Part of the income gains from CfW participation were clearly being used for welfare‐enhancing consumption (food, household supplies, and consumer durables), especially among rural households (see above). However, the longer‐term poverty reduction potential of this instrument is directly linked to the extent to which CfW income is used to enhance the productive potential and overall resilience of targeted households. It is on this dimension that some of the most interesting program impacts emerge, despite the fact that the CfW program’s emphasis was on
recorded instance of conflict; and information on port blockages and non‐payment of government salaries was obtained from reports of humanitarian and news agencies on the ground in Yemen.

The food security data were obtained from a mobile phone survey of approximately 2,400 households conducted each month by the WFP. The survey, conducted via random digit dialing, continues to survey households in a governorate until a sufficient number of respondents complete the survey, where the governorate targets are based on the governorate’s share of the total population. The survey is representative of the mobile phone‐using population and provides governorate‐level estimates of five commonly‐collected food coping strategies used by households with poor access to food‐ restricting consumption, reducing the number of meals, relying on less expensive foods, borrowing to purchase food, and limiting portion sizes. Thus, the data allow for a regionally‐disaggregated analysis of food access each month over nearly the entire course of the conflict.

However, there are two issues to note with these data. First, the survey can only be representative of the mobile phone‐using population following the beginning of the conflict. However, mobile phone ownership was very high prior to the conflict (even among relatively disadvantaged groups),<sup>13</sup> anecdotal evidence suggests that even struggling households have found innovative ways to maintain mobile phone access despite the lack of access to network electricity and the dramatic decline in overall welfare,<sup>14,15</sup> and a comparison of the number of mobile phones owned in the WFP surveys and the population‐level estimates before the conflict are actually very similar (Tandon and Vishwanath 2019).

Second, there could be sample selection (i.e., non‐random non‐response) of who responds to the survey methodology and the averages analyzed here can only speak to the actual sample as opposed to populations in which we might have more interest.<sup>16</sup> Although this is a
in the wake of the bankruptcy of Lehman Brothers, pre‐crisis average for financial market uncertainty excludes 2008. Last observation is for Q1 2016.

B. All series are normalized to standard deviation of 1.

# **Figure 2.2 Financial market uncertainty and investment in EMDEs**

_Rising global financial market uncertainty, as captured by the VIX, reduces EMDE investment. Accommodative monetary policy by major central banks has reduced financial market uncertainty._

**A. Investment response to a 10 percent increase in the VIX B. Contribution of the VIX to EMDE investment growth**

<!-- Start of picture text --> Percentage points Percent<br>0.0 16 VIX Others Actual<br>12<br>-0.5<br>8<br>-1.0 4<br>0<br>-1.5 -4<br>-8<br>-2.0<br>1 2 3 4 5 6 7 8<br>Quarter 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Bloomberg, Haver Analytics, World Bank estimates.

Note: Vector autoregressions are estimated with sample for 1998Q1‐2016Q2. The model includes, in this order, the VIX, MSCI Emerging Markets Index (MXEM), J.P.Morgan Emerging Markets Bond Index (EMBIG), aggregate real output and investment growth in 18 EMDEs with G7 real GDP growth, U.S. 10‐year bond yields, and MSCI World Index as exogenous regressors and estimated with two lags. Estimates for 2016 are based on the first half in 2016 (annualized).

A. Shows cumulative responses of EMDE investment to a 10 percent increase in the VIX. Solid lines indicate the median responses and the dotted lines indicate 16‐84 percent confidence intervals. B. Indicates historical decomposition to EMDE investment. “Others” indicates other EMDE and global factors