rafmacalaba/data-use-mentions-extended huggingface dataset:
_Figure A.2 Comparison of Country-level SOE figures. World Bank’s Global BOS vs. IADB database_ <!-- Start of picture text --> Comparison number of firms with state participation WB SOE vs. IADB database<br>(IADB =1)<br><!-- End of picture text --> <!-- Start of picture text --> Comparison number of firms with state participation WB SOE vs. IADB database<br>(IADB =1)<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Paraguay Uruguay Peru Bolivia Costa Rica Argentina Chile Ecuador Colombia<br><!-- End of picture text --> <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Paraguay Uruguay<br><!-- End of picture text --> <!-- Start of picture text --> Peru<br><!-- End of picture text --> <!-- Start of picture text --> Bolivia Costa Rica Argentina Chile<br><!-- End of picture text --> <!-- Start of picture text --> Ecuador Colombia<br><!-- End of picture text --> Source: Author’s compilation based on Global BOS database and IADB (2019) data Furthermore, the database fills an important gap in the coverage SOEs in developing countries. The number of SOEs captured through this methodology is significantly larger than the number
37 payments in the past 12 months, % of adults who used a formal account to receive or send money to family members living elsewhere in the past 12 months, % of adults who used a mobile phone to pay bills or send or receive money in the past 12 months), _Insurance_ (% of adults who personally purchased private health insurance, % of adults who work in farming, forestry or fishing and personally paid for crop, rainfall or livestock insurance)

**<u>Financial Soundness Indicators</u>** database, hosted by the IMF, disseminates data and metadata on selected financial soundness indicators (FSIs) provided by participating countries.

**<u>World Development Indicators</u>** is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

**<u>International Financial Statistics</u>** (IMF) provides is a standard source of international statistics on all aspects of international and domestic finance. It reports, for most countries of the world, basic financial and economic data on international banking, money and banking, interest rates, prices, production, international transactions, international liquidity, government accounts, exchange rates, and national accounts.

**<u>Bank for International Settlements</u>** (BIS) statistics were used for the aggregate data on bond statistics, including domestic debt securities by residence and type of instrument (bonds and notes vs. money market instruments, issued by financial and non-financial corporates; based on publicly available or country reported data). Domestic debt securities (Quarterly Review Table 16) for a given country comprise issues by residents in domestic currency targeted at resident investors, whereas international debt securities (i.e. Quarterly Review Table 11) are the ones targeted at non-residents (a) in domestic currency on the domestic market, (b) in
Payday loans are only available to banked individuals since, in a traditional payday loan, a customer writes a personal check made out to the lender. The lender agrees to hold the check for about two weeks before depositing it. In exchange, the payday lender advances a cash payment to the customer that is somewhat less than the amount of the check. The difference, which is the “finance charge,” in combination with the maturity of the loan determines the annualized interest rate. In the states where payday lending thrives, lenders typically charge $15 to $25 for each $100 that they advance. That is, in a typical transaction, a borrower might write a check for $235 that the lender agrees to hold for two weeks and the lender would provide the borrower with a $200 cash advance. The annualized interest rate on such a loan is 455 percent.

Prior to the maturity of the loan, the borrower can pay the lender the face value of the check in cash, extinguishing the debt and concluding the transaction. If the borrower does not repay the loan by its maturity, the lender may deposit the check. Assuming that the check clears, the loan is fully repaid and the transaction is complete. If a borrower does not want to repay a loan at maturity, or cannot, a lender will frequently allow the borrower to renew the loan by “rolling it over”. In a rollover, the borrower pays the lender the finance charge due at maturity and the lender agrees to hold the check for another specified period of time.

Survey data (Caskey 2002) indicate that most payday loan customers are not officially poor. Rather they belong to moderate-income households, with incomes between $18,000 and $50,000, but they struggle to pay their bills on time. The vast majority of payday loan customers do not have access to convenient lower-cost credit from mainstream lenders because they have severely impaired credit histories or because they have reached the limit of the credit lines these lenders
no assumptions about data distribution, and are unaffected by outliers. As such, we will use decision trees to investigate the presence of nonlinearities in the characteristics influencing citizen participation. Additionally, we will compute the variable importance metric, which assesses each variable’s relevance within the broader citizen participation model.

Given the lack of compelling evidence outside the United States—and the fact that we have no reason to believe that these relationships must necessarily follow simple, linear trends—allowing for nonlinearities relaxes restrictive assumptions made by standard econometric techniques about the relationship between political trust and citizen engagement.

# 3. Data

The data used in this study comes from the Latinobarómetro Survey for 2020 (Latinobarómetro Corporation 2021), the latest year available. The

> 7 Ridge uses the same reasoning as Lasso, but adds a penalty in the form of the sum of the squared beta coefficients; as a result, it never sends the coefficients to zero but instead shrinks them (Le Cessie and Van Houwelingen, 1992). Elastic net, in turn, incorporates both types of penalty, allowing it to benefit from both the Lasso and ridge approaches. In the context of our study, however, given the low correlation between regressors (see table B1 in Annex B) and given that the number of observations exceeds the number of regressors, the Lasso approach is more fitting to our sample than the other techniques.

8
: World Bank). A low value thus characterizes a country where governance is poor; The index of weak protection of investors is the rank of the variable Strong investor protection in the Doing Business database and is comprised between 1 and 215 (Source: International Finance Corporation, World Bank). A high value of this index reflects situations where investors are poorly protected; The tenure security index is constructed by the French Development Agency (AFD). This is the first projection on the axis of a Principal Component Analysis of the variables contained in the land based Institutional Profiles (Source: Ministry of Finance and AFD). The index is interpreted as a measure of security of tenure enjoyed by local people. A low value implies high levels of tenure insecurity.

22
where

# **Intergenerational Persistence Equation**

Combining equations (3) and (8) above, we get the following relationship between the education of the father and that of a son, determined by the optimal investment decision: where Equation (10) is consistent with the almost universally used specification for intergenerational schooling persistence in the literature, but it allows for possible differences in educational opportunities across the farm and nonfarm households. Some examples of studies that use this linear-in-levels specification are: Neidhofer et al. (2018), Narayan et al. (2018) and Hertz et al. (2007) on cross-country analysis, Azam and Bhatt (2015) and Emran and Shilpi (2015) on India, and Emran and Sun (2015a) on China. However, we are not aware of any studies on intergenerational educational mobility in developing countries that derive the estimating equation from a theoretical model.

It is standard to assume homothetic functional forms in the analysis of intergenerational income mobility, as the estimating equation is log-linear (see, for example, Solon (1999, 2004)). The reliance on the linear in levels (years of schooling) specification in the literature on intergenerational educational persistence partly reflects the fact that a substantial proportion of fathers have no schooling. This is especially relevant in the rural areas of developing countries such as India where about 40 percent of fathers have no schooling (estimate based on REDS 1999 data). The estimating equation (10) is also consistent with the common assumption in the literature that the omitted ability is captured in the error term of the intergenerational persistence regression, and can lead to ability bias in the OLS estimates of the parameters.<sup>19</sup> An important implication of equation (10) is that both the slope and the intercept of

> 19Although most of the existing studies on intergenerational mobility adopt this additively separable specification for the impact of ability, there is little evidence on the validity of this assumption. In a recent paper, Ahsan et al
.00|
|**Educat**<br>**ion**|0.00|0.00|0.02|0.04|0.06|0.06|0.06|0.06|0.06|0.07|0.07|
|**Health**|0.00|0.00|0.02|0.05|0.07|0.07|0.07|0.07|0.07|0.07|0.08|
|**Other**<br>**Servic**<br>**es**|0.00|0.01|0.07|0.14|0.22|0.22|0.22|0.22|0.23|0.23|0.23|

_Table B. 3. Annual spending associated with investments and households’ consumption under the ISUI2 Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricu**<br>**lture**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.
In addition to generating awareness for the program, its potential target group, aand the characteristics of demand, the process of beneficiary selection also provides a basis for local authorities to integrate land reform into a broader program of capacity building and social assistance at the municipal level. This could contribute to the resolution of at least some aspects of the potential conflict between the dual objectives of equity and efficiency which is to some extent unavoidable if land reform is to make a longterm sustainable contribution to poverty reduction.

# **Creating the basis for a functioning land market**

The availability of large amounts of unutilized or underutilized land in large holdings implies that, in a reasonably fluid land market there would be plenty of supply to enable potential beneficiaries to choose the most suitable lands and negotiate to obtain a competitive price. In practice, however, land markets have found to be thin, highly segmented, characterized by high transaction costs, and often pushed into inforrnality (FAO, 1994). Credit market imperfections, lack of market information by potential sellers, and the lack of farmn models suited to the specific needs and factor endowments of small agricultural producers, have prevented such an outcome and contributed to the fact that beneficiaries under the oldstyle reform program often acquired marginal lands at highly exaggerated prices without making productive use of it.<sup>2 5</sup> In the pilot municipios, a procedure similar to the identification of demand for land is being followed on the supply side as well. The first step is to determine ecologically suitable zones and, based on cadastral information, establish an inventory of the land according to- size classification that could be used to identify target areas for agrarian reform. Areas where land reform would result in environmental hazards, where soil fertility is insufficient, or where the existing ownership structure is already characterized by small to medium-sized holdings, are thus eliminated a _priori._ This gives beneficiaries a better idea of where to focus their efforts, helps to set realistic goals, and puts into perspective the
debts. We show that debt data revisions are systematically upward biased for almost all debtor regions and income groups. Across all countries and years, we identify USD 1 trillion in “hidden” sovereign borrowing that is added to debt statistics only in hindsight, more than twelve percent of total sovereign borrowing by all countries in our sample. This amount is a lower bound for the true magnitude of hidden debt since not all unreported debt is eventually revealed. In the cross-section of countries, hidden debt levels are highest in countries with weak institutions and low capacity, however, even the countries with the strongest institutions in our sample exhibit systematic downward bias in their debt reporting. Comparing across creditor groups, we document that non-bond private loans and bilateral loan instruments are the most prone to under-reporting.

Second, we show that hidden debt tends to accumulate when growth is strong and be revealed in economic downturns. For example, the COVID-19 recession was followed by the largest hidden debt revelation in 50 years.<sup>1</sup> We document that the procyclicality of hidden debt accumulation and revelation is mainly driven by time-varying monitoring efforts. In our

> 1Recent World Bank efforts to strengthen debt reporting are discussed in World Bank (2022a).

1
9

**Figure 1. Municipal and regional government spending, total**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1994 1995 1996 1997 1998 1999 2000 2001<br>year<br>municipal government regional government<br><!-- End of picture text --> Income can be measured using per capita GRP in real terms<sup>9</sup> . An alternative (purchasing power parity [PPP] adjusted) measure of income takes into account the fact that the purchasing power of the Russian rouble differs greatly across regions. These differences are reflected in highly varying figures of the regional subsistence minimum, a minimum monthly per capita income required to survive (according to the Russian Statistical Agency, see Table 1). Since the bundle of goods underlying the calculation of the subsistence minima is the same across regions, by dividing per capita GRP by the subsistence minimum one obtains a PPP adjusted measure of per capita income (expressed as a multiple of the annual subsistence minimum). Both indicators are used to check the robustness of the results.

Ethnolinguistic fractionalization [ELF] is measured using the conventional ELF index, a number between 0 and 1 showing the probability that two randomly selected individuals belong to different ethnolinguistic groups. Higher values of the index correspond to higher population heterogeneity. The ELF index was used by Mauro (1995) and by many subsequent cross-country

> 9 Converted to 2000 prices using region-specific consumer price indices since regional specific GRP deflators are unavailable.
and Svensson (2006) that measured stock-out rates in combination with the PETS/QSDS in Uganda, and another study in Kenya that followed the PETS methodology and reported on stock-outs at health care facilities (reviewed in Gauthier 2006). An African initiative of civil society organizations to monitor and pressure governments to reduce stock-outs can be found online (www.stopstockouts.org). This initiative includes periodic visits to facilities in Kenya, Uganda, Zambia and Zimbabwe to determine whether essential medicines are available.

A joint effort by WHO and Health Action International (HAI) in 2008 created a survey tool that is the best currently available for measuring the prices and availability of drugs. The survey tool gathers information on prices paid for key medicines, as well as differences between brand and generic prices, differences between national and international reference prices, the availability of drugs at facilities and pharmacies, and the variation of prices and availability across sectors and regions within countries. The tool is specifically designed to allow cross-country comparisons and has been conducted in more than 55 countries.

In the survey, data are collected on the availability of a selected group of medicines from a sample of outlets in the public and private sectors (both for- and non-profit) in six regions of a country or – in the case of large countries – of a state or province. Up to 50 medicines are chosen, generally comprising: 14 global core medicines; 16 regional core medicines; and 20 supplementary medicines. The global and regional core medicines lists are part of the standard methodology provided by WHO and HAI and are chosen to assist countries in making international comparisons. Price data can be collected quite readily in a randomized representative survey of sites at which medicines are sold. However, measuring stock-out rates is only possible where good stock records exist – which is somewhat more common in the public sector than in the private sector, according to HAI. Applying the survey as designed by HAI and WHO typically costs between US$10,000 and US$20,000
- Mahler, Daniel Gerszon r Nishant Yonzan r Christoph Lakner. 2022. “The Impact of COVID19 on Global Inequality and Poverty.” Policy Research Working Paper 10198; Washington, DC: World Bank Group.

- Martinez, Luis R. 2022. "How Much Should We Trust the Dictator’s GDP Growth Estimates?" _Journal of Political Economy_ 130(10): 2731-2769.

- Nicoletti, Cheti, Franco Peracchi, and Francesca Foliano. 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error." _Journal of Business & Economic Statistics_ 29(1): 61-72.

- Pinkovskiy, Maxim, and Xavier Sala-i-Martin. 2016. “Lights, Camera ... Income! Illuminating the National Accounts-Household Surveys Debate *.” _The Quarterly Journal of Economics_ 131 (2): 579–631.

- Pokhriyal, Neeti, and Damien Christophe Jacques. 2017. "Combining Disparate Data Sources for Improved Poverty Prediction and Mapping." _Proceedings of the National Academy of Sciences_ 114(46): E9783-E9792.

- Prydz, Espen Beer, Dean Jolliffe, and Umar Serajuddin. 2022. “Disparities in Assessments of Living Standards Using National Accounts and Household Surveys.” _Review of Income and Wealth_ 68: S385-S420 _._

-

- Roberts, David R., Volker Bahn, Simone Ciuti, Mark S. Boyce, Jane Elith, Gurutzeta Guillera ‐

- Arroita, and Severin Hauenstein. 2017. "Cross Validation Strategies for Data with Temporal, Spatial, Hierarchical, or Phylogenetic Structure." _Ecography_ 40(8): 913-929.

- Roy, Sutirtha, and Roy Van Der Weide. 2025. “Estimating poverty for India after 2011 using private-sector survey data.” _Journal of Development Economics_ 172, 103386.

- Sherwood, B., & Maidman, A. (2017). rqPen: Penalized quantile regression. R package version, 2.

- Soergel, Bjoern, Elmar Kriegler, Benjamin Leon Bodirsky, Nico Bauer, Marian Leimbach, and Alexander Popp. 2021.
two income groups, the monthly SHS cost is about 3.5 times the kerosene cost, while it is about 6.4 times the kerosene spending for the top two income groups.

The comparison of SHS with grid-electricity shows that the average monthly cost of SHS is about 3.4 times the cost of grid-electricity. In reality, however, SHS is not directly comparable with grid-electricity because of significant differences in the quality of services provided from the two electrification technologies.

> 8 The case study of  the RERED project shows that poor households tended to purchase SHS  of lower capacity while better-off households opted for larger capacity SHS. Among the 320,000 SHSs installed by 2009, about 20% households chose a SHS of 40 Wp, 45% chose a SHS of 50 Wp, 20% in the range of 60-65 Wp and only 9% in the 85 Wp range. In estimating the monthly SHS cost,  we assume that the bottom two deciles purchase only SHS of 2030 Wp; the 3rd and 4th deciles purchase SHS of 40 Wp; 5th to 7th purchase SHS of 50 Wp, 8th and 9th for SHS 6065 Wp and the top deciles purchase SHS of 85 Wp and above. Battery costs are assumed to range from Tk 3,200 to Tk 11,400, depending on the size of the SHS (RERED report).  This amounts to about between 20-24% of a SHS price accordingly, which will be factored in the SHS prices.

10
ESEs can provide key support HIV/AIDS services—such as nutrition, social care, pharmacy, and diagnostics—in the same building, which benefits patients notably by saving them time and expenses that would otherwise be incurred by travelling to multiple locations.

Persons from urban areas who are affiliated with the contributory insurance regime receive the second best care. Persons from urban areas who are affiliated with the

> 46 MPS (February 2011) Encuesta Nacional de Demografía y Salud 2010, Bogotá: MPS.

45
Consequently, this has resulted in diminishing benefit adequacy over time, with GMI indicators as a percentage of the AROP poverty line decreasing from 21.4 percent in 2016 to 14.3% in 2021 (Figure 3). Given this, the design of the GMI-linked basic income support has long called for improvement. Possible policy choices included anchoring the GMI indicator to a poverty line (preferably an absolute poverty measure) or a basic consumption basket and establishing ad-hoc indexation rules to establish changes over time. Such a parametric reform was expected to result in higher coverage of the poor, better targeting, and potentially higher impacts on poverty. The magnitude of the impacts naturally depended on the parametric design features.

**Figure 3. Monthly GMI Indicator compared to at-risk-of-poverty line**

<!-- Start of picture text --> 80 30.0<br>60 21.4 21.4 20.0<br>18.2<br>40 16.6 14.9 14.3<br>10.0<br>20<br>0 0.0<br>2016 2017 2018 2019 2020 2021<br>Monthly GMI Indicator<br>GMI Indicator (% AROP) or adequacy relative to the poverty Line<br>GMI Indicator (BGN) GMI Indicator (% AROP)<br><!-- End of picture text --> 8
: We use tabulations from the 2000 and 2010 Census of Population and Housing and the 2015 Population Count to obtain labor market indicators, as well as demographic characteristics of the population at the municipal level.<sup>2</sup> Given that

> 2Even though the 2015 population count is a survey, its sample size is large enough to obtain statistics representative at the municipality level. See Enamorado et al. (2016).

11