rafmacalaba/data-use-mentions-extended huggingface dataset:
quality of courts have been positively associated with better credit availability and the development of credit markets. The consensus also extends to the mechanism behind this effect: prevention of moral hazard through proper enforcement of the credit contracts and increasing competition between lenders. However, caution must be exercised in examining how increasing competition among lenders ultimately affects borrowers’ welfare, which would depend on the presence of well-developed insurance markets. The papers that address this issue (Horioka and Sekita, 2011; T. J. Besley, Burchardi, and Ghatak, 2012; Kranton and Swamy, 1999) present cautionary evidence regarding the effects of competition between lenders on credit outcomes.

Cristini, Moya, and Powell (2001) analyze how variations in the effectiveness of the legal system across different provinces in Argentina have affected the development of credit markets. They find strong results that provinces with poor legal enforcement have less credit available to borrowers and banks’ non-performing loans are higher. The authors conclude that improvements in Argentina’s legal system would result in a significant increase in the availability of credit. Jappelli, Pagano, and Bianco (2005) also evaluate this relationship by using panel data from Italian provinces. The authors show that provinces with longer trial durations or larger backlogs have less developed credit markets and lower credit availability.

Also in Italy, Schiantarelli, Stacchini, and Strahan (2016) use detailed firm level panel data to show that firms are more likely to default against banks when legal enforcement is weak. A similar conclusion is reached by Ponticelli and Alencar (2016), who measure the effects of proper enforcement of Brazilian bankruptcy reform on firm performance using congestion in courts as the enforcement variable. However, since congestion can be endogenous, the authors use "extra jurisdiction" as the instrument. “Extra jurisdiction” is a measure of the number of neighboring municipalities that do not qualify to become an independent judicial district, increasing the congestion of existing courts. The authors find that municipalities that are one standard deviation below the mean with
banks mostly readjusted prices to tighten the supply of credit and promptly correct for pre-crisis risk underpricing where margins were falling precipitously. More recently, margins continue to be the instrument of choice to restrict credit conditions, but there is also a significant role for shortening maturities, and decreasing loan size.

**Combined, tightening supply and demand factors triggered a downward trend of longterm loan flows since 2007 which are currently at their lowest since 2010, well-below shortterm loan flows.** This process accelerated by the end of 2011 and flows have become negative in 2012, marking the break of a pre-crisis pattern in which longer-term flows were dominant (Exhibit II.3). Medium-term flows have already been negative since 2010Q4. Shorter-term flows have behaved more pro-cyclically―they were hit disproportionately in 2009 and rebounded strongly in early 2011 and 2012 when credit conditions temporarily improved.

**Falling demand mainly drove the decrease in flows. However, under extreme financial conditions supply factors contributed significantly to the drop in long-term lending volumes.** A key question is whether the fall in long-term loan flows was driven by supply or demand factors. To gauge this, Exhibit II.5 shows the results of an illustrative regression analysis that disentangles both factors using ECB bank lending survey data. The simple model is able to explain almost 90 percent of the variation in quarterly long-term loan flows.<sup>9</sup> The exhibit displays the supply- and demand-driven parts of the flows that can be explained by the model. The results imply that falling demand mainly drove the decrease in flows. However, a sizeable portion of the fall in loan flows was supply driven during stress peaks in 2007Q3-2009Q3 and 20011Q4.

> 9 The regression is based on data from 2003Q1-2012Q3. The independent variable is long-term flows to nonfinancial corporations in Europe, taken from the ECB. The explanatory variables are the cumulative net percentage of banks reporting tightening conditions, the cumulative net percentage of banks reporting higher demand, taken from ECB bank lending surveys. The regression also includes
<br>51|
|A.12 Effects on Self-Reported Bureaucratic Knowledge<br>. . . . . . . . . . . . . . . . . . . . .|. .<br>52|
|A.13 Effects on Village Transfers and Budgets . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>52|
|A.14 Effects on Public Goods Provision: Index Components (2021 Administrative Data) . .|. .<br>53|
|A.15 Public Goods Provision, Heterogeneity by Nepotistic Networks . . . . . . . . . . . . .|. .<br>54|
|A.16 Effects on Old-Serving Nepotistic Appointees . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>54|
|A.17 Dynamic Effects on Public Goods Provision . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>55|
|A.18 Effects on Citizens’ Perceptions of Service Provision: Index Components . . . . . . . .|. .<br>55|
|A.19 Effects on Village Taking Action on Public Goods: Index Components. . . . . . . . . .|. .<br>56|
|A.20 Citizen Demographics, by Social Distance to Village Government . . . . . . . . . . . .|. .<br>56|
|A.21 Alignment with Citizens’ Preferences, by Social Distance to Village Government<br>. . .|. .<br>57|
|A.22 Robustness Checks on Public Goods Provision (Table 7) . . . . . . . .
respondents. Among them, some did not complete the computer-adaptive test, and some answered “Don’t know” to the socioemotional skills questions. Therefore, the sample is 3,285 respondents for the cognitive index and 3,264 respondents for the socioemotional skills index. Regressions in even columns control for village fixed effects, while regressions in odd columns control for province fixed effects. Standard errors are in parentheses (clustered at the village level).

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average for this region, only 5% of the rural population in the region gets water piped to the premises. It is also estimated that more than a quarter of the population in several countries in the region uses more than 30 minutes to make one water collection round trip (WHOUNICEF, 2010).<sup>2</sup> We base our data on four rounds of Demographic Health Surveys (DHS) for Ghana, which provide information on access to water infrastructure along with socio-demographic data for a large number of households. We use spatial identification through GPS coordinates to build a panel of 405 clusters/communities followed over four periods of time: 1993-94, 1998-99, 2003, and 2008. We use panel data techniques for fractional dependent variables to estimate the impact of time to haul water on girls’ school attendance. Our findings indicate that reducing by half the time to haul water would increase the proportion of girls aged 5-15 who attend school by 2.4 percentage points on average, with stronger impacts in rural than in urban communities.

In Section 2 we discuss related work where the impact of infrastructure on children’s school enrollment or work activities is measured. We describe the model and empirical strategy in Section 3. In Section 4 we present the data along with descriptive statistics for main variables of interest. Estimation methodology and results are discussed in Sections 5 and 6, respectively. Section 7 concludes.

# **2. Related literature**

The role of infrastructure in development, and in particular for school attendance and educational achievement, has been the focus of a number of studies. A recent paper, closely related to ours, is by Koolwal and van de Walle (2013; forthcoming). These authors investigate the relationship between the time to walk one-way to the source of drinking water, participation in income earning market-based activities, and children’s school attendance and health (as measured by anthropometric indices of growth status). Their empirical study uses cross-sectional surveys from Sub-Saharan Africa (Madagascar,
models, the simulated random component is no longer common to urban and rural areas in the same municipality. This underestimates uncertainty in the model by incorrectly assuming that the income of urban and rural households in the same municipality are independent. To address this, we estimate the covariance across municipalities of the estimated urban and rural poverty rates and account for it when estimating the variance of the municipal poverty estimates. This in turn overestimates the uncertainty associated with the estimates, which counteracts the underestimated uncertainty due to assuming the variance is known rather than estimated. However, the MSE and coverage rate is still slightly below the baseline estimates, which use spatially deflated welfare and a national model, because the latter allow for positive covariance between urban and rural areas of a municipality.

# _6.4 Using 2016 sample data_

The analysis up to this point has all used a single household survey, the 2014 MCS-ENIGH, to estimate municipal-level poverty. While this sample was drawn to generate official measures of poverty, it is also useful to check that the results are robust to the use of an alternative sample. We therefore repeat the analysis using the 2016 MCS-ENIGH instead of the 2014 round, which contains a different set of selected AGEBs. This also eliminates any possible mechanical correlation between the small area estimates and the benchmark CONEVAL estimates, which occurs because both use the 2014 MCS-ENIGH survey to estimate the empirical best prediction model. Table 7 reports the results when using the 2016 sample for the baseline specification. The main difference is that the estimates are moderately less accurate, due to the use of survey data that differs from that used to generate the benchmark. The correlation with the benchmark is now only 0.81, as opposed to 0.86 when using the 2014 survey. The same pattern of results holds, however, when ranking across methods. For in-sample areas, the household model gives moderately more accurate estimates than the sub-area model (correlation of 0.78) while the arealevel model and direct estimates give less accurate estimates (correlation of
which provides limited banking services. This is a point that might have been missed if a standard template was imposed, or in the case of if a limited set of financial questions being added to a general household survey.

Econometric analysis of the survey data threw light on a number of issues that might have remained hidden if the results had simply been tabulated, as is so often the case. For example, branch location may be less important than the precise type of housing (overcrowding) as a predictor of whether a household will have access. In addition to income, education, employment status and gender prove to be reliable predictors of financial access both on the deposit and lending side.

In contrast to the Brazil case, rural households were the focus of the recent household survey in two provinces (Andhra Pradesh and Madhya Pradesh) of _India_ (Basu, 2004). Actually, India had been a pioneer in surveying rural access with the All-India Rural Credit Survey of 1951-2, which was repeated at intervals, but discontinued in 1991. In the 2003 survey 6000 households were surveyed and the questions focused especially on credit allowing a time-series dimension building on the earlier surveys. Source, reason,

> 16 Parallel work is under way for Colombia and Mexico (cf. Caskey et al. 2004).  The list is by no means comprehensive.  In addition to other nationwide household surveys focusing on credit and finance. there have been several confined to rural areas, for example Chaves et al. (2001) for Romania.

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**Figure 12. Inequality (Dissimilarity Index) in Nutrition by Year**

<!-- Start of picture text --> 35<br>30<br>30<br>25<br>20 2003/4<br>2006/7<br>15<br>2011<br>10<br>5 5<br>5 3 3<br>1 1<br>0<br>Stunted Underweight Wasted Iodized salt<br>Dissimilarity index<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04, MICS 2006/07, and ENPSF 2011.

Although overall there are no great inequalities in malnutrition, children do have very different chances of being stunted, underweight, or wasted depending on their circumstances. Looking at 2011, the least advantaged child has a 33 percent chance of being stunted, compared to a 5 percent chance for the most advantaged. Likewise in 2006/07, the least advantaged child has a 1 percent chance of access to adequately iodized salt compared to a 38 percent chance for the most advantaged child. These are large disparities in healthy physical growth and nutrition early in life for children with different circumstances.

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bags), or stocks of PPE (only on whether a least one pair of disposal latex gloves or one gown/apron is available, etc.).<sup>25</sup> In sum, no major survey exists, at present, that collects adequate information on facility pandemic preparedness, which reflects a clear blind spot in standard measurement tools.

# Innovations to further the measurement agenda

To address these gaps, the SDI Team and partners from within and outside of the World Bank have undertaken several innovative lines of work. First, in the immediate term, the data from the health SDI surveys done to date (in nine countries in Sub-Saharan Africa) can serve as sample frames for rapid phone surveys to collect additional information on COVID-19 preparedness. Not only does the SDI collect contact information for facilities (allowing for rapid re-contacting of the sample), but also, only a limited set of phone survey questions would be required to supplement the broad set of items from the SDI to generate a comprehensive view of pandemic preparedness.

Second, for the longer term, pandemic preparedness experts from the GPs and the SDI Team have developed a novel pandemic preparedness/disaster response module for inclusion in future SDIs. This module is visible in Appendix 6 and aims to fill the gaps described above in the current tools. Specifically, the module includes a more comprehensive set of questions on infrastructure, equipment, and supplies needed in the case of a pandemic (including PPE and infection control items), in addition to questions on preparedness plans/guidelines, drills, and trainings. An earlier version of this module was piloted in Bhutan (in preparation for that country’s first health SDI) in January. The team welcomes feedback on this module (please see Appendix 6 for additional details). In addition, future iterations of the SDI will include questions for health care providers (based on the Brief Resilience Scale<sup>26</sup> ) to assess their ability to cope with external shocks, such
<br>informal informal<br>a. By “high-risk” sectors b. By gender<br>0.71<br>0.68<br>0.51<br>0.47<br>0.43 0.45<br>0.39<br>0.28 0.27<br>0.21<br>IME Textile Transport Tourism Construction Commerce Agriculture Other masculin feminin<br>informal informal<br>.8 .8<br>.6 .6<br>.4 .4<br>% informal jobs % informal jobs<br>.2 .2<br>0 0<br>.8 .8<br>.6 .6<br>% informal jobs .4 % informal jobs .4<br>.2 .2<br>0 0<br><!-- End of picture text --> Source: Author’s calculations based on EBCNV 2015 8
3 United States). Moreover, the presence of a new, detailed nationally-representative household survey in Guatemala makes it possible to empirically analyze the relationship between internal remittances, international remittances and poverty in that country.

The paper proceeds in five further parts. Section 1 presents the data set. Section 2 discusses the need to use predicted income functions for identifying household income excluding remittances. Section 3 then takes the results of the predicted income equations to examine the impact of internal and international remittances on poverty in Guatemala in three situations: excluding remittances, including internal remittances and including international remittances. This section finds that internal and international remittances have a greater impact on reducing the severity as opposed to the level of poverty in Guatemala. To pinpoint the reasons for this finding, Section 4 analyzes the distributional effect of internal and international remittances on different decile groups of households. Section 5 summarizes the findings.

# 1.  Data

Data for the study come from a national household survey done by the Instituto Nacional de Estadistica in Guatemala during the period July to December 2000.<sup>4</sup> The survey included 7276 urban and rural households and was designed to be statistically representative both at the national level and for urban and rural areas. The survey was comprehensive, collecting detailed information on a wide range of topics, including income, expenditure, education, financial assets, household enterprises and remittances.<sup>5</sup> It should, however, be emphasized that this survey was not designed as a migration or remittances survey. In fact, the survey collected very limited information on
collection relative to ordinary business taxes are then justified as mirroring what a rights holder collects when contracting with an extractor.

# D. Governments Tax Only a Small Proportion of Mining Project Rents

There is a general proposition that regular corporate taxes are insufficient to tax all resource rents. As a result, mining projects are charged with special additional taxes like royalties to soak up these untaxed rents as they manifest as surpluses during operations. Yet there is still the feeling that even these regimes leave large quantities of rents in the hands of the investors, with calls for additional special taxation instruments.

Rents cannot be measured. What one can measure is profits and the taxes on profits. There is often the claim that mining company profits are taxed at lower effective rates than companies in other sectors because they manage to shield profits from taxation through various accounting profit-shifting exercises.<sup>5</sup> This may be the genesis of the idea that rents, in turn, are not sufficiently taxed. The data shows otherwise. In 2021, 25 mining companies belonging to the International Council on Mining and Metals (ICMM) states that “current CIT and royalties charged in 2021 was 32.6% of adjusted profit before tax”<sup>6</sup> corresponding to payments of US$30.2 billion in income taxes and royalties to host countries from their operations, even after this alleged profit shifting. About 24% of that tax is derived from corporate income tax (CIT). The additional 9 percentage points is because of special royalty charges that are unique taxation instruments applied to mineral and energy extraction in an effort to tax resource rents. According to ICMM, its member companies paid about 38% in effective profits taxes from 2013 to 2021.

Data published by Damodaran on listed company effective tax rates according to companies’ financial reports supports this relatively high level of profits taxation. The different versions of effective tax rates calculated by him for the profitable mining companies as a sector varies between 25.8%
, the time it takes for shocks to transmit is independent of the portion of the shock that is passed through to domestic prices.

Figure 10: Time it takes for international troughs to transmit to domestic markets <!-- Start of picture text --> Groundnut Oil Rice<br>Raja Karn<br>Raja<br>KeraAPraTNadMaha RajaPunj UPraDelhiKarnRajaAPraRaja APra PunjAPraTNadTNadKeraUTMPraKarnGu Bihar MeghWBenKerajaTNadUPra HaryAPraRaja TNadRajaAPra UPra MeghAPra NagaMPra HaryRaja Jhar UTKarnGujaMizoMaha OrisHary UPraWBenHPra APraKarn Bihar Punj DelhiKeraUPraTpurAPraGoa APra KeraPunjJKasTNadTNad WBen TNad Kera OrisUPra<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Trough Distance in Months Average International to Domestic Trough Distance in Months<br>Bananas Coconut Oil<br>Megh<br>Raja<br>MizoKeraTNadWBenAPraMPraTNadUPraAPraRajaRajaTNadUPra JharRaja JKasHaryOrisDelhiPunjHPra UPraHaryKeraNaga APra Raja APraKeraAPraPunjTpurUT MahaGoa Bihar Delhi Karn APra Raja BiharHaryMahaAPraTpur KeraKera Goa Guja TNadAPraPunjPunjWBenKeraKarn APra UPraTNadTNad Oris UT UPra MPra<br>TNad<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Trough Distance in Months Average International to Domestic Trough Distance in Months<br>Source: Authors’ calculations<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br><!-- End of picture text --> 9 These four products matter in the consumption basket of Indian households. For example, rice accounts for 6.6 percent of the consumption of non‐poor households and 9.2 percent of that of poor households. Bananas, for about half of a percent for both types of households, and groundnut and coconut oil combined, for 4.95 percent among non‐poor households and 5.5 percent for poor households
# **Appendix 1 –** Variable Definitions

|**Variable**|**Description**|**Source**|
|---|---|---|
|Adult population|Adult population in 2009. When the 2009 data are not available,<br>we use the most recent|World Development Indicators|
|Bank cost/income|Bank Cost‐Income Ratio, 5‐year average (2002‐2007)|Financial Structure Database|
|Banking concentration|Share of deposits in the five largest banks|Barth, Caprio, and Levine<br>(2004)|
|Branches per adult|Number of branches per 100,000 adults|Financial Access Database<br>(2010)|
|Closing business ‐ days|The average time to close a business, 2007|World Bank Doing Business<br>Indicators (2008)|
|Contract enforcement ‐<br>procedures|The average number of procedures to enforce a contract, 2007|World Bank Doing Business<br>Indicators (2008)|
|Contract enforcement ‐ time|The time to resolve a dispute, counted from the moment the plaintiff<br>files the lawsuit in court until payment, 2007.|World Bank Doing Business<br>Indicators (2008)|
||Getting credit, Credit Information Index, 2007. This index measures||
|Credit information index|rules and practices affecting the coverage, scope and accessibility of<br>credit information available through either a public credit registry or<br>a private credit bureau.|World Bank Doing Business<br>Indicators (2008)|
|Domestic credit/GDP|Domestic credit to GDP, 2007|World Development Indicators|
|Expected real growth|Expected real growth rate of GDP per capita, 2009|World Development Indicators|
|Foreign owned banks, share|Percentage of banking system assets in banks 50percent + owned by foreign|Barth, Caprio, and Levine|
, which is quite close to the total population. It indicates approximately 18% of the population is children below 18 years old. However, children below 18 years of age account for around 23%-24% of Armenia’s population in other datasets. So, Armenia’s total number of adult populations from different and official data sources has been investigated to compare the aggregate numbers. **Table 2** reports the findings. The share of the adult population from the 2011 Armenia Census (population 0-17 years old) is 77%, and the adult population share based on the combination of 2022 World Bank data (population 0-14 years old) and the 2011 Armenia Census (population 15-17 years old) is 76%, indicating that election data overstates the number of adult populations by 4%-5%.

Despite these discrepancies, the PSU size measured by the number of adults or voters is not a serious concern since the total and adult populations across strata or administrative units are strongly and positively correlated with the correlation coefficient = 0.996 ( -value = 0.000). ρρ pp 8