rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> 550<br>500<br>450<br>400<br>350 Correlation 0.984<br>350 400 450 500 550<br>HLO Score<br>IRT Score<br><!-- End of picture text --> <!-- Start of picture text --> SUPPLEMENT FIGURE III<br>Comparison to Item Response Theory (IRT) Linking (2000-2010)<br><!-- End of picture text --> _Notes:_ We compare our data to the _Linking International Comparative Student Assessment_ (LINCS) project which uses Item-Response Theory (IRT) methods to link test score data. IRT methods are used to create scores for the underlying international and regional assessments used. However, to compare across assessments, IRT would require enough overlap in the test items. This is not true for a significant enough set of tests and time intervals to create a globally comparable panel data set. However, for a subset of tests, this is the case, such as a series of international studies focused on reading at primary school. The LINCS project produces scores on a globally comparable scale using this subset of data (Steinmann, Strietholt **,** and Bos 2014). We compare results on the HLO as well as the LINCS data for primary school reading on average between 2000-2010.

Supplement Figure III compares scores for the same subject (reading), schooling level (primary) and time period (2000-2010), with a correlation of .984. This comparison indicates that as we expand coverage to 164 countries, we maintain high consistency with alternative measures where there is overlap.

Finally, we compare our primary linking approach using regression and linear linking with two alternative approaches and compare robustness across them. First, we use simple mean linking which introduces a constant adjustment between tests matched with rounds, and which we average across testing rounds. This approach assumes constant standard deviations across
and there is clear evidence of selection, given the statistical differences in characteristics between refugees and their host communities in Chad. Secondly, the cost of aid to refugees only depends on poor refugees (which is why it is based on the poverty gap indicator), which is a non-linear transformation of the welfare indicator. Using a standard OLS regression with a linear approximation of the welfare indicator may not reliably recover the real cost of hosting refugees. Despite a significant body of literature on the value of small area estimation techniques for estimating poverty headcount ratios from OLS (Rao & Molina, 2015), there is a lack of corresponding evidence for other poverty indicators, such as the poverty gap.

Here, an alternative methodological approach is followed to estimate the cost of hosting Sudanese refugees in Chad. The key objective of our framework involves constructing a counterfactual distribution of the welfare indicator for these refugees under the counterfactual that they are fully integrated into the home country system. Specifically, the welfare indicator is assumed to be a function of some observable characteristics, while letting unspecified the functional form dictating the relationship between the welfare indicator and its assumed correlates. As a result, the framework offers greater flexibility compared to, for example, the OLS modeling approach.

The proposed methodology is especially well-suited for evaluating counterfactual nonlinear transformations of the welfare indicator, such as the poverty gap. For instance, this approach is more ambitious as it seeks to estimate the counterfactual distribution of the welfare indicator for Sudanese refugees. Notably, this methodological framework was initially applied to analyze the impact of changes in labor market institutions on the distribution of wages (DiNardo, Fortin, and Lemieux, 1996).

Let the per capita consumption of the Chadian be determined by: . 1)<sup>llll</sup> In (E . 1) , is the actual per capita consumption of the Chadian, which is expressed as a function of , yy<sup>l</sup> = YY<sup>l</sup> (
VaR-implied correlation and showed that it outperformed the linear correlation using data of the US, the UK, France, and Germany. Cotter and Longin (2011) investigated the impact of portfolio weights, the type of position, the frequency of data and the probability level on VaR-implied correlations by using the equity indexes of the US and the UK. Mittnik (2014) extended the pairwise method used in these papers to joint estimation.

> 3A general form of the exceedance correlation between two variables X and Y at thresholds δ1 and δ2 is

4The coefficient of upper tail dependence is and the coefficient of lower tail dependence is 3
# Unadjusted and adjusted changes in risk associated with simultaneous access to nutrition drivers for infant and early childhood stunting (24-60 months of age)

<!-- Start of picture text --> Unadjusted Adjusted<br>Any 1 domains (Definition 5) 0.78> _0.9<br>Any 2 domains (Definition 5) 0.57~~ —0.73<br>Any 3 domains. (Definitiona 5) 0.42a —_0.56<br>Any 4 or 5 domains (Definition 5)}_ —*—0.27 —_——0.43<br>Female P +<br>Single birth aa a<br>Urban —<br>Total HH size (N) 1.03°<br>Total children under five in HH (N) +<br>Total HH income per month (1 = 1000 PHP) (log) 0.78~~<br>In poorest quintile of total HH income per capita _——1.11<br>Mother has high school or tertiary education ost<br>Mother is married r-°<br>Mom wasa teenager at time of birth aa<br>Mother has a job or business eal0.93<br>0.25 0.50 0.75 1.00 1.25 0.25 0.50 0.75 1.00 1.25<br>Relative Risk (95% Cl)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). statistically significant predictors are labeled.

Notes: Only the relative risk estimates for
Further, we find that commodity price shocks reduce non-resource taxes, both direct and indirect. Our findings relate to the volatility of public budgets due to commodity price volatility (Robinson et al., 2017) and the resource curse in terms of public finances (Borge et al., 2015). James (2015) establishes a negative relationship between resource and non-resource revenues as the expression of a crowding out effect between these sources of revenue in US states. Our findings further contribute to this literature by documenting that certain institutional arrangements can help curb the rise in top incomes.

The remainder of the paper is structured as follows. Section 2 presents the data. Section 3 describes the estimation strategy. Section 4 presents the main results and robustness checks. Section 5 presents additional results. Section 6 concludes.

# **2. Data**

This section presents the data used in the empirical investigation.

# _2.1 Top Incomes_

Data on billionaire net worth (in USD) are used to proxy for top incomes. The data are obtained from Forbes magazine’s updated database of billionaires (2001 to 2018). Billionaires are identified based on their first name, last name, and their profile in Forbes magazine. Information from Wikipedia is used to fill in missing information on billionaire characteristics such as country of citizenship. The number of billionaires in the sample rose from approximately 565 in 2001 to 2,208 in 2018. Forbes magazine’s billionaire database has been used in the literature to study wealth distribution (Piketty, 2014; Bagchi et al., 2016), the international mobility of billionaires (Sanandaji, 2014), the emergence of Russian billionaires (Treisman, 2016), and statistical regularities at the top end of the wealth distribution (Klass et al., 2006) among others. Summary statistics for the sample of analysis are provided in Table A1.

# _2.2 Commodity Windfalls_

Data on commodity price shocks are obtained from the IMF (Gruss and Kebhaj, 2019). The commodity terms
under normal circumstances, include most of the information needed to deploy the proposed stress testing approach at a reasonable level of granularity and with the necessary bank-by-bank information. For instance, based on prevailing accounting standards (including the IFRS), audit reports normally have a classification of lending to individual borrowers, companies, other financial institutions, and the state and state owned enterprizes (SOEs). This would make it possible to differentiate across the banks with regards to the structure of their credit exposures. In addition, the level of secured loans and collateral can be obtained and applied (correspondingly or judgmentally) to classify credit exposures by their level of security and their associated LGDs.

Overall loan exposures or exposures in selected asset classes can be constructed from historical audit reports. The most recent credit growth period can be determined from aggregate historical data published by the central bank or the supervisor. Credit growth at the individual bank level in the period of interest and in selected asset classes can be determined based on the in credit in external year-to-year change exposures published audit reports, and then used to compute the individual PDs based on the scaling factor that is proposed in Section 4.6. Indirect credit risks from FX exposures can also be estimated based on data from audit reports. If no data at the asset class level is available, this information should be available at least at the individual bank level and can be incorporated into the proposed framework as outlined in the paragraph following Equation (6). General as well as specific provisions or loan loss reserves and Tier 1 and Tier 2 capital can also be obtained from the audit reports.

Similarly, a bank level approximation of off-balance sheet exposures can be taken from the audit reports and added (either proportionately or judgmentally) to the individual onbalance sheet exposures. Lending concentration and residual maturity can be aggregated 66
contracts are from sellers that produce intermediate goods), indicating that the buyers are mostly at the end of the value chain.

Our data is a snapshot of outstanding receivables as of December 1, 2005. Importantly, this snapshot is before PrimeRevenue started factoring the receivables.<sup>6</sup> Also, PrimeRevenue allows firms to post whatever trade credit contract they choose, and does not limit the choice to a set of standardized options for firms. Buyers generally post invoices for all ̳important‘ suppliers, which is estimated by PrimeRevenue to capture over 90% of total inputs to the buyer. Our database includes information for 29,019 contracts, the full set of contracts in PrimeRevenue at the time, which includes 56 large buyers and 24,140 suppliers. The data includes complete information on contract terms: the contract size, net days, discount days, discount, and currency in which the contract is denominated. Most buyers interact with most sellers only once, though there is a fraction of repeat buyers and sellers who have multiple buyers.

For buyers, we can control for firm size using buckets based on their total sales<sup>7</sup> , location (North America or Europe), sector, and whether the buyer is investment grade. For suppliers, we know their size (sales buckets) and whether the supplier is investment grade. Information is not provided on supplier location or sector, since 95% of buyers purchase inputs via local distribution centers to avoid any import duties, such as tariffs and taxes. Apart from missing information about net days for 832 out of 29,019 contracts, we have complete information on contract terms.

Empirical work on trade credit thus far has been hampered due to a lack of firm-level data on trade credit contract terms. Most studies have used the Federal Reserve‘s Survey of Small Business Finances (SSBF) database of U.S. firms, which has only limited data on credit terms and firm characteristics (e.g
foundations behind the new index and defines a set of verifiable, comparable, and transparent indicators for index construction.

The following section summarizes the recent literature on Green IT measurement. Section 3 describes the specific features of IT systems in government institutions that justify the need for the government-specific Green IT index. Section 4 provides a theoretical framework for the new index. Section 5 discusses the index dimensions and aggregation approaches. Section 6 presents the approach for the practical implementation of the GGIT Index and develops the survey instrument, and Section 7 concludes.

# **2. Measuring Green IT: Literature review**

Over the last two decades, studies related to Green IT have been consolidating into a wellestablished research stream (Ribeiro et al. 2021). But, despite the importance of ICT for the green transition agenda, the limited quantitative and longitudinal research represents a significant gap in this area (Singh and Sahu 2020). The literature on the integrated indexes of Green IT is sparse, and most of the Green IT metrics have focused on the energy efficiency of the hardware side of IT. Only a few papers developed frameworks for measuring the environmental impact of software products (e.g., Bozzelli et al. 2014).

Several indicators and frameworks were recently proposed to measure the “greenness” of IT organizations and departments. <u>Molla et al. (2009)</u> introduced the Green IT Readiness (Greadiness) model that conceptualizes Green IT from the infrastructure and capability perspective. The analysis in the paper is based on a survey of CIOs of more than 2,100 private companies from the US, Australia, and New Zealand. The paper identifies four components of the Green Readiness assessment: attitudes, policies, practice, and governance. These main components comprise eight subcomponents and 32 indicators. The averaging of the components produces the summary G- readiness index to identify areas needing improvement. While the G-readiness index was one of the first to measure the environmental sustainability of IT infrastructure, it is
26 2/ SOURCE: Official announcement October 3, 1984 and Budget statement June 1986, Government of Tanzania. Official prices announced on October 3, 1984 were unchanged as of April 1986 for gasol'ne, kerosene and diesel. Hence, it is assumed that no significant retail price changes occurred between October 1984 and April 1986. 1985 prices converted to $US at $17 TSH1 = $1 (Oct. '85 Avg); July 1986 prices at 42 Tsh = $1. Prices quoted for diesel are for "diesel gas oil" in Tanzania. 1985 LPG and heavy fuel oil prices are wholesale.

JQ/ SOURCE: 1985 prices from Swaziland Energ Assessment, Yellow Cover, World Bank. July 1986 prices from personal communicatin with USAID regional economist for Southern Atrica. Prices quoted for diesel are for "bus rate" diesel fuel as almost all diesel pumps in the country are set at teh bus rate. Industrial diesel 1985 price is for "mid-duty" diesel fuel. November 1985 prices converted to $US at 2.7E = $1; July 1986 prices at 2.2 E = $1.

11/ SOURCE: September 1985 prices from Latin America and the Caribbean, Countr-y Programs Department, World Bank. August 1986 prices form Shell OIl, Haiti (new prices effective May 1986). Diesel prices are for "gasoil" in Haiti. All prices converted at G5 = $1.

i2/ SOURCE: August 1986 prices have not changed (in nominal colones/liter) since 1983, as per SDE Costa Rica. August 1986 prices converted to $US at 56.3 CRC = $1; November 1985 prices at CRC 52.8 = $1.

13 SOURCE: Central Bank of Brazil, July 1986 Bulletin. Prices in Cr$/liter for gasoline and diesel oil, and Cr$/kg for fuel oil, assumed to be residual with density of .94 kg/1. October 1985 prices converted to $US at 8.56 Cr$
Unadjusted and adjusted changes in risk associated with underlying pre-natal and postnatal risk factors for infant and early childhood stunting (24-60 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Adequate pre-natal conditionsa: (maternal nutrition,a birth: outcomes) —0.71 —0.72<br>Adequate HH food security & diversity} —*—0.6 —_——0.74<br>Adequate HH water, sanitationPeerinfrastructure & environment. —_0.76 —0.89<br>Adequate child. health care _0.84 ——0.91<br>Female > ——<br>Single birth —_—_0.85 —_—0.8<br>Urban —e<br>Total HH size (N) 1.03.<br>Total children under five in HH (N) 14<br>Total HH income per month (1 = 1000 PHP) (log) —0.78<br>In poorest quintile of total HH income per capita —_——1.12<br>Mother has high school or tertiary education _0.87<br>Mother is married ae<br>Mom wasa teenager at time of birth a<br>Mother has a job. or business. —0.93<br>0.7 0.9 1.1 0.7 0.9 1.1<br>Relative Risk (95% Cl)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for statistically significant predictors are labeled.
0|5|
|9022120000|Computed tomography apparatus|275|0|5|
|1511909908|<br>Palm oil and its fractions|271|0|3|
||Food grinders and mixers, fruit or||||
|8509400000|vegetable juice extractors|268|5|10|
|9018120000|ultrasonic scanning apparatus|264|0|5|
|8422400008|<br>packing and wrapping machinery<br>Flat- rolled products of iron or non- alloy|258|0|5|
|7208512009|steel|249|0|5|
||ozone therapy, oxygen therapy, aerosol<br>therapy, artificial respiration or other||||
|9019200000|<br>therapeutic respiration apparatus|244|0|4|
||machinery for filling, closing,sealing, or||||
|8422300008|<br>labelling bottles<br>Machines for treating metal, including|229|0|5|
|8479810000|electric wire coil- winders|226|0|5|

Note: An applied MFN rate includes both ad valorem and specific components of the tariff. Specific component of the tariff is translated into the ad valorem equivalent according to the formula that is explained in the methodology section and using import statistics of Russia in 2006-2010. For 2011-2020, the ad valorem equivalent is computed using the 2009-2011 import data.

30
owning or operating agricultural land); log values of durable assets, housing and livestock; age of household head; dummy for households in which heads are literate; dummy for households in which heads are married; and, separately, the numbers of men, women and children. We include the household composition variables to control for differences in consumption requirements between children and adults and for economies of scale in consumption.<sup>12</sup> The asset values are intended to control for wealth effects and are assumed to be quasi-fixed in the short run.<sup>13</sup> The value of durable goods is estimated based on a detailed inventory of household assets; it accounts for depreciation and the opportunity cost of the funds tied up in the good. For housing, we estimate a hedonic model for housing based on characteristics of the structure, as well as the location, and derive an imputed rental value from this.<sup>14</sup> All values are in Afghani.

At the district level, we include dummies for topography—plateau and mountainous areas (plains areas make up the excluded category). To control for observable and unobservable time-invariant province-level factors that could confound the results, we include province dummy variables. While this method does not control for time-varying province characteristics, it does control for factors such as instability and conflict that may present in certain provinces throughout the survey year.

> 12 An alternative approach to account for such differences employs equivalency scales that take into account nutritional requirements based on age and, sometimes, gender when calculating per capita measures.  See, for an early example, Buse and Salathe (1978). An advantage of including household composition in the specification, rather than using equivalence scales, is that this method allows the data to specify the parameterization of the scales.

> 13In previous versions of this work, we included quintiles of total per capita consumption in the specification to control for wealth and socio-economic status. A concern with this specification is that
to be over $1.4 billion, even if only one in ten US patients chose to undergo treatment abroad rather than in the US. Of these annual savings, $690 million would accrue to the Medicare program alone.

The scope for trade would be greatly enhanced if quality abroad were further improved, and credibly signaled through international accreditation of export-oriented hospitals and 34 Currently, stages 1 and 2 of the USMLE can be taken abroad, while plans to set up overseas locations for the NCLEX-RN are still in the pipeline (Sources: National Board of Medical Examiners and National Council of State Boards of Nursing).

35 For example, Aetna’s Aexcel program rates physicians by regional network (e.g., the Baltimore/DC/Northern VA region) and medical specialty, after controlling for patient volume, clinical performance, cost-efficiency, and network adequacy (see Aetna, 2004). This is evidence of that fact that insurers are interested in identifying high-quality cost-efficient domestic providers in the US. If foreign hospitals and medical boards were to collect and report outcomes and cost data on treatments provided by their physicians, then insurers could rate foreign providers on a similar basis and thereby maintain quality and cost standards for the overseas network as well.

36 The Guardian, May 11, 2004.

25
*

Equation (1) models changes in food security for all households at the national level; however we may expect these changes to differ based on whether a household cultivates wheat. Deaton (1989, 2000) draws a theoretical distinction between households that produce (and sell) their own food and those that do not. Net buyers depend on the market to meet their food needs, while net sellers produce enough food to consume and to sell on the market. Increases in food prices hurt net buyers, but benefit net sellers whose revenues increase. The NRVA data do not allow us to distinguish directly between these households, thus we use an indicator for households that report owning or operating agricultural land as a broad proxy for this distinction. While not all households that have access to agricultural 18
# **1 Introduction**

**Togo is one of the poorest countries in the world.** With a per capita GDP equal to 437 current US$ in 2008, the economy was ranked within the world’s lowest income decile and in Purchasing Power Parity (PPP) terms it was the world’s 10<sup>th</sup> poorest economy.<sup>1</sup> Similarly and in the same year, Togo was ranked 159<sup>th</sup> among 179 countries appraised in 2008 UN’s Human Development Index.<sup>2</sup> Low aggregate per capita income level has translated into widespread poverty. In 2006, 62% of the population was poor, with poverty headcount rates in the north exceeding 90% in most rural areas (CWIQ, 2006). Furthermore, an additional 20% of the population technically considered non‐poor, was deemed as vulnerable, which raised the fraction of the population that may eventually fall into poverty to over 80% (PRSP, 2009).

<!-- Start of picture text --> Figure 1: GDP per capita development of Togo and LICs 1990‐2007.<br>( $ )<br>400<br>Low Income Avg.<br>350<br>Togo<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Real US$ of 2000<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> _Source:_ Authors’ calculations. DDP Data, World Bank.

**However, Togo has not always exhibited a grim social and economic performance.** In fact, almost half a century ago, Togo began its post‐independence
.....<br>.. E... .... .. .... .............<br>.f. ...........<br>|g. E. EEi**i**i.<br>i2 ESi..||

Sources: The Moscow Langitudinal Household Survey, January 1996; Vladimir and Gorodets Longitudinal Household Survey, December 1996.

The categories of households with the most pronounced intention to iimpro,ve their living conditions are married couples with or without children who have strong reasons to want to move to larger apartments, as well as households consisting of several generations who desire to split the household or who need more space. The mobility of these households is 1.5-2 times higher than the average due to the urgency of their housing problem and their comparative prosperity. For instance, among maried couples with children (about 30%/ of all urban households), 25.9% in Moscow, 40.4% in Vladimir and 25.8% in Gorodets are planning to move to improve their housing.