rafmacalaba/datause-encoder-data huggingface dataset:
respect to their impact on people with disabilities overall and by sex, age group, residence and other potential factors of intersectional disadvantage. More research is needed on program or policy evaluations. Not all such assessments need to be quantitative and large scale in nature. Qualitative, mixed methods and participatory studies are necessary on social protection programs and more broadly to complement the statistics produced in this paper by trying to understand the results in their complex contexts and by listening to voices and perceptions (e.g. Chaudhry 2019, Diaz et al (2015), Kuper et al 2016).

Consistent with earlier research (Washington Group on Disability Statistics (2016)), this paper shows that data sets with the WGSS and similar questions can be used to monitor the disability related targets of the 2030 Agenda. The Washington Group’s recommendation is to consider those who have at least a lot of difficulty in at least one functional domain as a threshold for disability. Our results suggest that analyses should try to incorporate the degree of functional difficulties through different degree categories or a functional score (Mitra 2018; Loeb 2020). Analyses that focus on persons experiencing a lot of difficulty leave out persons with some difficulty who, as shown in this paper, are also at risk of deprivations.

In addition, the range of development indicators on the radar screen of international and national organizations to monitor the SDGs for persons with disabilities should be expanded to more comprehensively monitor disability inequalities. Results in this paper suggest that considering indicators in addition to the SDG indicators such as the employment population ratio is necessary to monitor disability inequalities. Further, there is a need to include information on environmental barriers that could answer the question of why disparities exist.

Finally, for progress to continue in research and data analysis, internationally comparable disability questions need to become standard in censuses and general household surveys in LMICs, as well as in the monitoring systems of NGOs and governments. Results in this paper stress the need to produce disability prevalence and disability disaggregated statistics in LMICs.

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this class of crisis (and crises of a nominal nature), we accordingly drop inflation crises (which is more highly correlated with the other indicators than currency crises) from the set of crisis history indicators that we consider.

Our exclusion of external debt crises is motivated by a different rationale. External debt refers to debt that falls under foreign jurisdiction, and so a crisis of this sort—typically involving default, restructuring, or repudiation—does not directly concern domestic markets, and is less likely to lend itself to the learning experience of banks within the country. Just as important, the impact of external debt crises on banks’ balance sheets is unclear. Unlike the default of domestic debt obligations—which unambiguously imposes a burden on banks holding such instruments as assets— external debt default may (paradoxically) _strengthen_ bank balance sheets: if banks mainly hold domestic debt, the selective default of foreign obligations can result in an enhanced ability to repay domestic creditors, which in turn improves bank balance sheets.<sup>16</sup> The upshot is that domestic banks can largely avoid the consequences of external debt crises, with little change to the crisis experience of their home country. In contrast, domestic debt crises are directly related to the domestic banking sector, may include— _inter alia_ —deposit freezes or the forcible conversion of foreign currency deposits into local currency, and hence are far more likely to directly affect bank balance sheets. For these reasons, we do not feature external debt crises in our set of crisis history measures.

Finally, we merge the aggregated crisis histories with the bank ownership data to obtain our crisis experience variable. Although matched to banks, this measure is at the country level: thus, a bank established in Mexico but owned by a U.S. institution will be endowed with the crisis experience of the United States. Since organizational learning experiences are typically embedded in operational procedures disseminated from a bank’s home base, this attribution strikes us as perfectly reasonable.

Additional details regarding variable construction, along with definitions and sources, are provided in the appendix (Subsection A.1 and Table A.1), along with summary statistics for the main variables of interest (Table A.2).

# **4.2 Empirical model**

Our baseline model is the following cross-sectional specification: where _lijk_ is the loan-to-asset ratio of bank _i_ in (host) country _j_ and owned by an entity established in (home) country _k_ , _crisisXPk_ is the stock of crisis experience corresponding to the bank’s home

> 16Of course, banks are just as likely to hold external sovereign debt. However, in this case an external debt crisis does not present an opportunity for learning attributable to the home country of the bank. For example, consider a case where Deutsche Bank holds Greek paper. An external debt crisis resulting from a Greek default does not add to the stock of learning by Greece (although it might for Germany).

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results in a substantial devaluaticn in<sup>the real exchange rate in 1999, which is partially</sup> reversed in 2000 by a nominal exchange<sup>rate appreciation.2 9</sup> Results for the simulation of the consolidated<sup>public sector debt path during 1999-2002 are</sup> presented in Table IV.2. The model simulates<sup>the values of total net debt in the last line of</sup> the table and then distributes it according<sup>to the relative shares of net domestic and net</sup> foreign debt observed in the preceding year.<sup>Under the assumptions of Scenario 1, the ratio</sup> of net public debt to GDP rises tc 50.1<sup>percent in 1999 and then declines slightly to about</sup> 49.1 percent in 2002. This scenario highlights the key role played<sup>by the sustained</sup> improvement in the fiscal stance in stabilizing<sup>the net debt to GDP ratio in the medium</sup> term, even as the federal govermnent recognizes<sup>a substantial amount of hidden and</sup> contingent liabilities every year.

TABLE IV.2. SCENARIO 1 - NET DEBT<sup>PATH, 1998/2002</sup>

|Variables|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|
|Net Domestic Debt|36.0%|39.3%|39.3%|38.9%|38.5%|
|Net Foreign Debt|6.6%|10.8%|10.8%|10.7%|10.6%|
|Total Net Debt|42.6%|_50.1%_|50.0%|49.6%|49.1%|

In order to stress further the inLportance of the<sup>programmed primary surpluses for the</sup> stabilization of the net debt to 3iDP ratio<sup>over the medium term, a second scenario is</sup> considered in which there is a milder adjustment<sup>in the fiscal position of the consolidated</sup> public sector, perhaps because cf delays in<sup>the implementation of fiscal reforms. Table</sup>

> 29 The inflation rate in the debt simulation<sup>exercises should be a proxy for the GDP deflator. Here it is</sup> measured by the average IGP-DI.

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Sales volumes of different fuels present various patterns of growth ( **Figure 2** ), reflecting influences of economic conditions, domestic policies, international markets, and technological invention/adoption. Diesel oil consumption has been increasing more steadily across time, with temporary dips during recessions. Gasoline consumption has been growing with medium-term fluctuations, partially reflecting world commodity cycles/oil crisis. Hydrous ethanol grew rapidly in the 1980s, lost momentum in the 1990s, before trending up again amid short-term fluctuations, likely resulting from policy incentives and the flex-fuel technology introduced in 2003 that enables ethanol to substitute gasoline. The growth of natural gas has been stagnant since 2007/2008 and was surpassed by biodiesel in 2013, the latter of which continues to grow thanks partially to policy support. Overall, the total volume of these fuels grew until the mid-2010s when it started fluctuating, closely mirroring the country’s real GDP.

<!-- Start of picture text --> Figure 2. Varied Paths of Consumption across Time<br>a. Accumulated/Stacked Values (10 3 toe) b. Time Series of Each Fuel (10 3 toe)<br>90000 40000<br>80000 35000<br>70000 30000<br>60000 25000<br>50000 20000<br>40000 15000<br>30000 10000<br>20000 5000<br>10000 0<br>0<br>Diesel Oil Biodiesel<br>Diesel Oil Biodiesel Gasoline Gasoline Anhydrous Ethanol<br>Anhydrous Ethanol Hydrous Ethanol Natural Gas Hydrous Ethanol Natural Gas<br>Data Source: Brazilian Energy Balance (BEN), EPE.<br>1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text --> Fuel prices, another factor that affects fuel consumption and its resultant emissions, present both spatial and temporal variations, which we will exploit to understand the effect of an incremental change in fuel price as a proxy for fuel tax. Final consumer prices in Brazil consist of producer billing price, federal and state taxes, and cost of distribution. These retail fuel prices vary by fuel type, year, and region ( **Figure 3** ). The price of ethanol is more dispersed, probably because it is supplied by numerous ethanol plants. The north and northeast regions are less developed but tend to have higher fuel prices than the richer southeast regions, partially due to logistics cost, resulting in a regressive fuel price system. Notably, FUs with the highest average fuel prices in 2022 are all in the north region, namely Acre, Roraima, Pará, and Rondônia, followed by four FUs in the northeast region, while São Paolo has the lowest average fuel price (Map 1). In addition, we also see rises in nominal fuel prices in recent years before dropping towards the end of 2022, as federal fuel tax rates were temporarily reduced to zero to mitigate the impact of inflation.

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by 2009/10.<sup>43</sup> Column (4) shows estimates for the matched sample. As we can see, the effects are only slightly smaller when matching households on their probability to register with the NREGS. Overall, the effects are of a similar size in most specifications though somewhat lower than the estimates presented in Table 4, column (2). Column (5) shows the estimation results for the full sample without matching. Here, being registered by 2009/10 is the main explanatory variable. As we would expect, households that registered with the NREGS only shortly before or even after deciding on their crop portfolio, did not alter their input allocation in a meaningful way.

As mentioned before, the NREGS can affect household decisions via different mechanisms. This set of robustness checks seeks to understand if the observed effect of the NREGS is indeed an insurance effect and not due to alternative mechanisms such as the increase in income of participating households or the change in agricultural wages. If, for example, risky crops are also more capital intensive, then observed outcomes could also be driven by increases in income and wealth or better access to credit of participating households. Likewise, if risky crops are also less labor intensive, then observed outcomes could be driven by wage changes due to the NREGS instead of its insurance effect. I therefore test if the NREGS affects on the labor intensity or cost intensity of households’ crop portfolios (c.f. Table 6). Labor intensity per crop is calculated as the share of expenditures on labor in total production costs. Cost intensity is defined as the total production cost that has to be incurred per hectare for each crop.<sup>44</sup> The coefficient on labor intensity is positive and statistically significant at the 10% level in one of the two specifications. This indicates that the NREGS, if anything, increases the labor intensity of crop portfolios (c.f. cols. (1) and (2)). The coefficient on cost intensity is also positive, suggesting that households are able to spend more on their agricultural production. Again, only one out of two specifications is statistically significant at conventional levels (c.f. cols (3) and (4)). I can therefore not rule out the possibility that the observed

> 43This is to exclude all households from the sample that - either because they consider it socially undesirable or because they have other means of risk coping - would probably never register with the NREGS.

> 44Both measures are based on the crop-wise Cost of Cultivation Statistics, published by the Ministry of Agriculture. See appendix B for more details.

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Report 2009 Reshaping Economic Geography (Uchida and Nelson 2009).<sup>31</sup> The surface model, or offpath friction-surface raster,<sup>32</sup> is a grid where each pixel contains the estimated time required to cross that pixel by walking. To create this raster two basic layers are combined: terrain slope and land cover.

The slope raster was calculated from NASA’s Shuttle Radar Topography Mission (SRTM)<sup>33</sup> Digital Elevation Models (DEMs) with a resolution of 90 meters. Even though the original topography data was obtained in February 2000, it was assumed that there has not been any drastic change in DRC’s terrain in the 20th century. Therefore, it is safe to assume that the SRTM 90-meters data set provides a fairly good representation of the elevation terrain circa 1900.

Land cover data is far more challenging. In the last few years, with the surge of remote sensing technology, several land cover and land use data sets have been created. These high-resolution data sets are a very accurate representation of the current state of physical material at the surface of the earth. However, these cannot be used in the analysis because they are not a good representation of the land cover for year 1900. Land has changed rapidly in the last 100 years in DRC: deforestation, open pit mining and urbanization in some cities have drastically transformed the surface. Therefore, other data sets were used. The Oak Ridge National Laboratory (ONRL)<sup>34</sup> has developed a Historical Land Cover and Land Use data estimate (Goldewijk 2010). This data set describes historical land use changes over a 300year historical period (1700-1990) and was modeled based on a deep understanding of the global environment, historical statistical inventories on agricultural land (census data, tax records, land surveys, etc), and different spatial analysis techniques. A shortcoming of this data set is that the resolution is approximately 55km per cell, making a clear tradeoff between space (resolution) and time (representative for 1900). Given the importance of obtaining an accurate picture of the historical land cover, this low resolution, but better account of the land surface types circa year 1900 was chosen (i.e. ORNL Historical Land Cover) over the better resolution but newer data. Figure A1 displays the terrain roughness (left) and land cover (right) for DRC, circa 1900.

> 31 Uchida, H. and Nelson, A. Agglomeration Index: Towards a New Measure of Urban Concentration. Background paper for the World Bank’s World Development Report 2009

> 32 A raster is a geographic map with information (e.g. elevation, landcover) in a matrix of pixels. 33 http://www2.jpl.nasa.gov/srtm/

> 34 ORNL is a multiprogram science and technology laboratory managed for the U.S Department of Energy by UTBattelle,LLC

46
theoretical reasons to believe that it will independently affect how participants evaluate the World Bank. Indeed, as the selection stage in Table A8 demonstrates, _number_ _~~o~~ rg_ is highly significant and positively correlated with the selection variable. The results of the outcome stage also show that our key finding—the positive relationship between the provision of ESW and the World Bank’s level of agenda-setting influence—is robust to this correction for self-selection bias.

Fourth, we conduct a placebo test using the average agenda-setting influence evaluations of three other donors—the European Union, the United Nations Development Program, and United States Agency for International Development—for respondents who evaluated those donors _and_ the World Bank. We find no relationship between the World Bank’s provision of ESW and the reported policy influence of these donors (see Table A9).

Fifth, we take advantage of another unique feature of the _2014 Reform Efforts Survey_ : the fact that those respondents who indicated familiarity with one or more of the World Bank’s core ESW products were asked to independently evaluate the agenda-setting influence of those particular analytical and advisory products (as opposed to the World Bank’s policy influence on the whole).<sup>33</sup> Table A10 demonstrates that our main findings are insensitive to the use of this alternative dependent variable.

> 33The World Bank’s core ESW products include Country Economic Memoranda (CEMs), Development Policy Reviews (DPRs), Country Financial Accountability Assessments (CFAs), Country Procurement Assessment Reviews (CPARs), Poverty Assessments (PAs), and Public Expenditure Reviews (PERs). Participants in the _2014 Reform Efforts Survey_ evaluated four of these ESW products (CEMs, DPRs, CFAs, and PA), so we use only these data. Since this outcome variable is measured at the respondent level, we average across the set of core ESW diagnostic products that a respondent evaluated.

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- 19 -

borrowers, BoT began to require banks to declare their minimum lending rate (MLR)the rate on term credits to large customers-, its minimum retail rate (MRR)-the rate on small prime customers-, and the widest margins charged above these rates. The initial "fonnula" for the MRR was set so as to reflect commercial banks' total cost of funds given the deposit rates plus the banks' operating costs. Deposits banks also had to declare the rates for general and large deposits.<sup>32</sup> _Relaxation of Portfolio Restrictions and Expanding the Scope of Activities._ Also important in the reform program were those measures that eliminated restrictions on the scope of activity and portfolio of financial institutions. First, prior requirements on portfolio composition of commercial banks were relaxed (by expanding the definition of agricultural credits in which commercial banks are expected to lend no less than 20 percent of their deposits). Second, to bolster the competitive position of domestic financial institutions, finance companies were authorized at end 1991 to conduct leasing business. In March 1992, finance companies were authorized to act as selling agernts for government bonds, to provide economic, financial, and investment information services, and to advise companies seeking listing on the SET. Third, in 1992, commercial banks were allowed to expand their areas of operation to include issuance, underwriting, and distribution of debt securities, to act as supervisors as well as selling agents for mnutual funds, and to become securities registrars. Finally, reserve requirements were converted into liquid asset requirements allowing banks to invest up to 3 percent in government paper.

32 See World Bank, Shadow Financial Sector Report (1997).
# **1. Introduction**

Purchasing Power Parity exchange rates (“PPPs” for short) have been mainly derived from the International Comparison Program (ICP), which collects data on prices across countries. The latest (2005) ICP survey round—thought to be the largest international statistical operation ever—collected prices for a great many goods and services, grouped under 155 “basic headings” (corresponding to the expenditure categories in the national accounts) for each of 146 countries in six regions (Africa, Asia-Pacific, Commonwealth of Independent States, South America, Western Asia and Eurosat-OECD). Region-specific product lists were developed and the regional PPPs were linked through a common set of global prices. The 2005 ICP’s governance structure entailed that each of the six regional ICP offices worked closely with government statistics offices in each country, while the World Bank provided global management and estimated the final PPPs. World Bank (2008a) provides estimates of the PPP for GDP and its main components for 2005. World Bank (2008b) compares the results to those based on the main prior ICP round, for 1993.

Some dramatic revisions to past PPPs are implied by the results of the 2005 ICP. Figure 1 compares the estimates of real (at PPP) GDP per capita for 2005 from the World Bank’s _World Development Indicators_ 2007 database—just prior to the release of the 2005 ICP results—with the estimates implied by the 2005 ICP.<sup>2</sup> Due to the changes in PPPs, real GDP was revised downwards for many low- and middle-income countries, and substantially so for some. In the Asia-Pacific region, real GDP for 2005 was revised down by 30% (World Bank, 2008b).

These large data revisions have been attributed to the changes in data and methods that were introduced in the 2005 ICP round, as described in World Bank (2008a,b). A potentially important difference is that (compared to prior ICP rounds) stricter quality standards were used in the 2005 price surveys, to assure that the ICP was obtaining prices for internationally comparable commodities. This is important given that one expects that lower quality goods are consumed in poorer countries, creating a risk that (without strict standards in defining the products to be priced) one will underestimate the cost of living in poor countries by confusing quality differences with price differences. With better funding of the ICP in 2005, clearer product descriptions were developed.

> 2 The least squares regression line has an intercept of -0.580 (While s.e.=0.236) and slope of 1.057 (s.e.=0.025); R<sup>2</sup> =0.938; n=136. One can reject the null hypothesis that the original estimate is an unbiased predictor of the revised estimate (i.e., intercept=0, slope=1); the F-test is 4.491 with prob 0.013.

2
32 Figure 6: Relative contributions to welfare reduction from agricultural policies (IWRI) of different policy instruments including non-product-specific support<sup>a</sup> , highincome countries, 1980-84 and 2000-04 (percent) <!-- Start of picture text --> 1980–84 2000–04<br>350 350<br>300 300<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>ANZ NA EU EFTA Ja/Ko ANZ NA EU EFTA Ja/Ko<br>Border measures Border measures<br>Production tax Production tax<br>Production subsidy Production subsidy<br>Input subsidy Input subsidy<br>Decoupled payments Decoupled payments<br>Non‐product‐specific (NPS) support Non‐product‐specific (NPS) support<br>NPS to inputs NPS to inputs<br><!-- End of picture text --> Source: Authors’ calculations using data in Anderson and Valenzuela (2008).

a Both ‘decoupled’ measures plus other non-product-specific support.