rafmacalaba/datause-encoder-data huggingface dataset:
The 16 industries consist of 6 R&D-intensive industries and 10 low R&Dintensity industries.<sup>3</sup> The R&D intensity of the 16 industries is based on US data. An industry’s R&D intensity was calculated as R&D expenditures divided by the value added of that industry. They are grouped in low (10 industries) and high (6 industries) R&D intensity industries.<sup>4</sup> The TFP index is calculated as the difference between value added and factor income, with inputs weighted by their income shares, i.e., _lnTFP_  _lnY_   _ln L_  _(_ 1   _)ln K_ , with  equal to the year-country-sector specific labor’s share. Labor share equal to wages over value added. Value added, wages, and fixed capital formation are reported in current US dollars, and all are deflated using US GDP deflators (1990=100). The capital stocks are derived from the deflated fixed capital formation series using the perpetual inventory model with a 5% depreciation rate.

The R&D flow data are taken from the ANBERD 2000 (OECD) database (DSTI/EAS Division). The database covers 15 OECD countries from 1973 to 2002 at either the two-, three- or four-digit level.<sup>5</sup> R&D flows cover all intramural business enterprise expenditures, converted into US dollars, and then deflated using US GDP

> 3 The 10 low R&D-intensity industries are: (1) 31-Food, Beverage & Tobacco; (2) 32-Textiles, Apparel & Leather; (3) 33-Wood Products & Furniture; (4) 34-Paper, Paper Products & Printing; (5) 355/6-Rubber & Plastic Products; (6) 36-Non-Metallic Mineral Products; (7) 371-Iron & Steel; (8) 372-Non-Ferrous Metals; (9) 381-Metal Products; and (10) 39-Other Manufacturing. The R&D-intensive industries are (1) 382-NonElectrical Machinery, Office & Computing Machinery; (2) 383-Electrical Machinery and Communication Equipment; (3) 384-Transportation Equipment; (4) 385-Professional Goods; (5) 351/2-Chemicals, Drugs & Medicines; and (6) 353/4-Petroleum Refineries & Products.

> 4 The R&D intensities for the 16 industries with ISIC codes 32, 33, 34, 31, 371, 381, 36, 355, 372, 39, **_351, 353, 382, 385, 383, and 384_** are, in percentage terms, 0.4, 0.6, 0.7, 0.8, 1.1, 1.3, 1.8, 2.2, 2.4, 2.8, **_7.9, 8.1, 8.1, 11.0, 11.6 and 18.5_** , respectively (with the R&D-intensive industries and their R&D intensity in bold italics). The average R&D-intensity of the “high” cluster is 10.9% while that of the “low” cluster is 1.4% (with respective standard deviations of 3.6% and .9%), i.e., the “high” cluster is more than 8 times more R&D intensive than the “low” cluster. Assuming a normal distribution, the hypothesis that any of the industries in the “high” R&D intensity cluster belongs to the “low” cluster is rejected at the 1% significance level.

5 The 15 OECD countries are: Australia, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Norway, Spain, Sweden, United Kingdom, and United States.

6
<!-- Start of picture text --> 0.4% mlnterquartile range Median<br>0.3%<br>)<br>Oot |.<br>00%<br>-0.1% | + |<br>-0.2%<br>Oil exporters Oil importers<br><!-- End of picture text -->

<!-- Start of picture text --> 0.05% interquartile range —* Median<br>0.00%<br>-0.05%<br>-0.10%<br>-0.15%<br>Coal exporters Coal importers<br><!-- End of picture text --> <!-- Start of picture text --> 0.15% mlnterquartile range Median<br>Dobe. ()<br>0.00%<br>Ono |<br>0.15%<br>-0.20%<br>Large Small<br><!-- End of picture text -->

<!-- Start of picture text --> 0.8% mlnterquartile range Median<br>0.6%<br>0.4%<br>0.2%<br>0.0% TT Ee<br>-0.2%<br>Gas exporters Gas importers<br><!-- End of picture text --> <!-- Start of picture text --> 0.3% alnterquartile range Median<br>0.2%<br>0.1%<br>0.0%<br>0.1% | @ |<br>-0.2%<br>Surplus Deficit<br><!-- End of picture text -->

<!-- Start of picture text --> 0.10% mlnterquartile range Median<br>0.05%<br>0.00%<br>-0.05%<br>“0.10%<br>-0.15%<br>More open Less open<br><!-- End of picture text -->
**Table A12: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Theil Index)**

||**NCR Luzon **<br>**Bo**|**Visayas **<br>**th Sexes**|**Mindanao**|**NCR **|**Luzon **<br>**A**|**Visayas **<br>**ll Men**|**Mindanao**|**NCR **|**Luzon **<br>**All**|**Visayas **<br>**Women**|**Mindanao**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Theil index|0.18<br>0.21|0.29|0.27|0.15|0.19|0.24|0.24|0.23|0.26|0.35|0.32|
|**Residual**|**0.13**|**0.30**|**0.24**||**0.02**|**0.00**|**0.00**||**0.10**|**-0.41**|**-0.63**|
|**Personal Attributes**|**0.19**|**0.40**|**0.54**||**0.23**|**0.49**|**0.72**||**0.32**|**0.67**|**1.06**|
|_Gender_|-0.09|-0.02|-0.04||--|--|--||--|--|--|
|_Experience_|0.04|0.04|0.10||0.10|0.11|0.15||-0.02|0.06|0.25|
|_Dummies for the highest_<br>_degree completed_|0.24|0.38|0.48||0.13|0.38|0.57||0.34|0.62|0.81|
|**Sector**|**0.45**|**0.12**|**0.04**||**0.74**|**0.54**|**0.32**||**0.05**|**0.01**|**-0.16**|
|**Occupation**|**0.23**|**0.18**|**0.17**||**0.00**|**-0.03**|**-0.05**||**0.53**|**0.65**|**0.63**|
|**Year**|**0.00**|**0.01**|**0.01**||**0.00**|**0.00**|**0.00**||**0.00**|**0.07**|**0.09**|

25
-15-

Procedure Used to Fstimate the National Accounts Concept of Gross Value Added (Contribution to GDP at factor cost) for Establishments Covered by the Brazilian Industrial Census

- 1) Gross Value of Output = "Valor de Producao";

- 2) US Value Added Concept = "Valor de Transformacao Industrial"; "Valor de Transformacao Industrial" = "Valor de Producao" minus "Despesas, com as operac6es industriais";

- "Despesas, com as operacoes industriais" = US Cost of Materials concept

- 3) In order to arrive at the present national accounts concept of gross value added (i.e. the contribution to GDP at factor cost before deduction for imputed financial services), we must deduct 15 of the 20 items which the Brazilian census calls "Despesas Diversas"'. These are shown only for the 24 major industry groups (not for individual industries), so we had to use branch ratios, i.e. the ratio of the value of the 15 to the 20 items, to derive a rough estimate of these inputs for industries within each branch.

- The. 5 15 items are:

- a) ' lugeis e Arrendamentos" (rents);

- b) "Royalties" (royalties);

- c) "Manutencao e Reparac&o de Equipamentos e Instalac6es" (repair and maintenance);

- d) "Manutencao de meios de transporte proprio"

- (maintenance of the enterprise's own transport equipment);

- e) "Publicidade e Propaganda" (advertising);

- f) "Despesas com comunicacao" (expenses for communications); g) "Fretes e carretos" (freight and carriage);

- h) "Servicos Professionais e de Assistencia Tecnica"

- (professional services and technical assistance);

- i) "Premios de Otros Seguros" (insurance for other risks);

- J) "Despesas com viagens e representacao"

- (travel and entertainment costs);

- k) "Indenizacao por dispensa" (reimbursement of expenses);

- 1) "Imposto Predial e Territorial Urbano" (urban real estate taxes);

- m) "Impostos e taxas" (excise duty and other indirect taxes);

- n) "Combustiveis e Lubrificantes consumidas no transporte proprio" (gasoline and oil consumption for enterprise vehicles);

- o) "Outros despesas" (other costs);

- 4) In order to arrive at the former national accounts concept of value added we must further deduct "Juros e correcao monetaria e despesas bancarias" (interest and monetary correction payments and bank service charges);

- ' See table 15 of Censo Industrial (1981a), which gives the figures for firms with 5 employees or more (or a gross output more than 640 times the minimum wage). Table 35 gives similar information in more aggregated form for firms with less than 5 employees (or with a gross output less than 640 minimum wages)
population groups in these countries experience significant poverty reductions and the national poverty headcount rises in all of the African countries in their sample.

HBL also consider a “high productivity” scenario for 2030 (again just 10% probability of this occurring) in which world prices fall as a result of climate change. Here, the results are largely reversed, with poverty falling amongst urban and wage earning households, and rising amongst those households earning their living in farming. The authors’ most likely climate change scenario for 2030 (the mean outcome from this uncertain distribution of climate shocks) suggests little change in world prices due to modest productivity effects and offsetting impacts in Northern (higher yields) and Tropical (lower yields) latitudes. Consequently the poverty impacts are muted.

However, as previously noted, gradual climate change may be less onerous for the poor than the occurrence of extreme climate events such as flooding, drought and heat waves. The poor tend to be more vulnerable to extreme events since they live in more exposed locations, spend more of their income on food, and rely heavily on farming for their incomes. Ahmed, Diffenbaugh and Hertel (2009) use the same basic modeling framework as do HBL to explore the impacts of extreme (1 in 30 chance) adverse climate events under current (1970-2000) climate as well as under future (2070-2100) climate. These authors find that current climate volatility has a particularly severe impact on poverty in Bangladesh, Southern Africa and Mexico, with urban wage earning households experiencing the sharpest increase in poverty. In attempting to predict the impact of increasingly intense extreme climate events in the future, the authors focus on the impact of drought (measured as the longest period of consecutive dry days) which increases in 11 of the 16 countries under examination. They find the largest rises in 21
# **2. The data**

This section describes data on the size of bank liabilities relative to national economies as well as other variables used in this study.

# **2.1. The size of bank liabilities**

In this study, we consider an international sample of banks over the 1991-2008 period. Accounting data on bank liabilities and other variables are taken from Bankscope. Our sample of banks excludes banks that are categorized as investment banks or securities houses. Also, we restrict ourselves to banks that are publicly-listed to ensure data quality and to enhance comparability across countries. The largest banks in most countries tend to be publicly-traded.

As a measure of systemic size, we take the ratio of a bank’s total liabilities to national GDP, denoted Liabilities (see the Appendix for variable definitions and data sources). This reflects that in practice often a bank’s total liabilities, rather than just insured deposits, are honored in a bank bail-out. For 2008, we have identified 30 publicly-listed banks worldwide with liabilities in excess of half of their country’s GDP. These major banks are listed in Table 1. UBS, a Swiss bank, had a liabilities-to-GDP ratio of 3.7, followed by ING of the Netherlands with an analogous ratio of 2.2, and Credit Suisse of Switzerland with a ratio of 2.1. Among these systemically large banks, the largest banks in terms of absolute liabilities were Barclays, BNP Paribas, Deutsche Bank and Royal Bank of Scotland, each with liabilities exceeding 2.5 trillion US dollars.

The world’s largest banks tend to be international banks with large shares of assets and liabilities located in foreign branches and subsidiaries. In these instances, the home-country fiscal authorities tend to remain responsible wholly or in part for insuring the bank’s liabilities and for paying for any bail-out. In the European Economic Area (including the European Union, Iceland, Liechtenstein, and Norway), bank deposits located at foreign branches are formally covered by the deposit insurance scheme of the home country, according to the EU directive on deposit insurance adopted in 1994. Furthermore, the EU directive on the reorganization and winding-up of credit institutions adopted in 2001 requires that domestic and foreign bank creditors are treated equally in bankruptcy proceedings, preventing selective bail-outs of only 7
|**Dietary intake**<br>**variables**|**Indicator definition**|**Data source**|**Year**|**Data**<br>**availability**<sup>**1**</sup>|**Notes**|
|---|---|---|---|---|---|
||sugar, salt, total and saturated fat, and red<br>and processed meat.||||The score ranges from zero to<br>nine expressed as an average<br>score.|
|**All-5**|% of adults ≥ 15 years who consumed all<br>five food groups typically recommended<br>for daily consumption in food-based<br>dietary guidelines around the world|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries|Proportion of the population<br>who consumed all five food<br>groups [_as well as each one_<br>_individually_] at least one<br>**vegetable**, at least one**fruit**,<br>at least one**pulse, nut or**<br>**seed**, at least one**animal-**<br>**source food**, and at least one<br>**starchy staple**in the previous<br>day or night.|
|**Zero vegetable or fruit**<br>**consumption**|Proportion of the population who<br>consumed no vegetables or fruits during<br>the previous day or night.|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries||
|**Soft drink**<br>**consumption**|Proportion of the population who<br>consumed a sugar-sweetened soft drink<br>during the previous day or night. Sugar-<br>sweetened soft drinks include soda,<br>energy drinks, and sports drinks.|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries||

1 Data are also available for Palestine 42
<!-- Start of picture text --> GNI per capita,<br>current USD<br>13000<br>12000, | ————— :::_hh_<br>11000 High income: 12,056<br>10000<br>9000<br>8000 *<br>7o00 a<br>6000 oe? ” Co *<br>5000 a *<br>Had<br>4000 Upper-middle-income: 3,896 a<br>3000 ee at .<br>2000 . aa _----7"<br>- oo ———=<br>1000 . are ee<br>a Lower-middle-incame: 996<br>a a 2 a@« 44 a4 a a4 a4 8 wm a om m ¢ ¢ ¢ = | &<br>o =I i=} o =I oS o a oS o i=] oS ao i=] a a a a a a a<br>ia | i | tl ia | i | el el el el el el el el el el a a el a a el<br>=etiee Conflict ——Counterfactual (no conflict) = = = Pessimistic Moderate ---«--- Optimistic<br><!-- End of picture text -->

Notes: GNI per capita based on the World Bank Atlas Methodology for Syria is only avaible up to 2007. We impute future values based on actual GDP per capita growth up to 2010 (at 2010 prices), our projections for GDP per capita growth over the conflict and post conflict periods (both also at 2010 prices). We account for inflation up to 2019 based on US inflation rates. On using GDP growth rates to proxy GNI growth rates, our calculations based on pre-conflict data suggest the the growth rates of GDP per capita in current USD and GNI per capita based on the Atlas Methodology on average are quite close (e.g. 1998 - 2007: 7 percent versus & percent; 1993-2007: 5 percent versus 4 percent).

The post-conflict scenarios (pessimistic, moderate and optimistic) reflect the voluntary mability case.
(FGT0) is 57.4% for the reference distribution.<sup>9</sup> Not surprisingly, the reduced consumption and the median summary statistic overestimate poverty by several percentage points due to the under‐estimation of consumption (Figure 4 and Table 4 in the appendix). The average summary statistic and the regression techniques underestimate poverty since they overestimate consumption. The multiple imputation techniques over‐estimate poverty but only by 0.5 percentage points (or about 1 percent) performing significantly better than the reduced consumption approach with a more than two times larger bias. The reduced consumption and the median summary statistic as well as the multiple imputation techniques deliver good results for the FGT1 and FGT2 emphasizing that not only the headcount can be estimated reasonably well but also the distributional shape is conserved. Except for the median summary statistic, these techniques also perform well estimating the Gini coefficient with a bias of less than 0.5 percentage points. The relative standard errors show similar results as for the estimation of the consumption (Figure 5 and Table 4 in the appendix). While the relative standard error of the reduced consumption for FGT0 is double compared to the multiple imputation techniques, the relative standard errors for FGT1 are comparable but larger for FGT2 and Gini for the multiple imputation techniques.

**Figure 4: Average Bias for FGT0, FGT1, FGT2 and Gini coefficient.**<sup>**8**</sup>

**Figure 5: Average Standard Error for FGT0, FGT1, FGT2 and Gini coefficient.**<sup>**8**</sup>

<!-- Start of picture text --> 5% 5%<br>3% 4%<br>3%<br>1%<br>2%<br>‐1% FGT0 FGT1 FGT2 Gini<br>1%<br>‐3%<br>0%<br>‐5% FGT0 FGT1 FGT2 Gini<br>Reduced Item List Median Reduced Item List Median<br>Mean OLS Regression Mean OLS Regression<br>Tobit Regression Chained Equations Tobit Regression Chained Equations<br>Multivariate Normal Multivariate Normal<br><!-- End of picture text --> In summary, the average summary statistic and the regression approaches cannot deliver convincing estimations. While the reduced consumption and the median summary statistic perform considerably better, they both over‐estimate poverty by construction. Only the multiple imputation techniques can

> 9 The FGT0 is calculated based on the US$ 1.90 PPP (2011) international poverty line converted into local currency in 2013.

13
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Ministère de la Communications et des Technologies de l’Information et de la Communication. 2008.

- Document de politique et de stratégie du secteur de Télécommunications, des TIC et de la Poste. Available at: http://www.atrpt.bj/reference.php

- Thisday (Nigeria). 24 June 2008. ―Glo Benin Signs up over 600,000 Subscribers in 2 Weeks.‖ Available at: http://www.cellular-news.com/story/31953.php

# Irrigation

FAO (Food and Agriculture Organization). 2004. Aquastata: Benin Country profile. Downloadable from: http://www.fao.org/nr/water/aquastat/main/index.stm

- Heidecke, Claudia. 2006. Development and evaluation of a regional water poverty index for Benin. IFRPI, Discussion Paper 145.

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- Helio International 2009. Energy systems: Vulnerability – Adaptation – Resilience (VAR) 2009: Regional Focus Benin. Paris

- Keener, S., and S. G. Banerjee. 2005. ―Ghana: Poverty and Social Impact Analysis of Electricity Tariffs.‖ ESMAP Technical Paper 088, World Bank, Washington, DC.

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- Tallapragada Prasad, Maria Shkaratan, Ada Karin Izaguire, Jaakko Helleranta, Saifur Rahman and Sten Bergman. 2009. Moniotring Performance of Electric Utilities. Washington DC

- World Bank. 2009a. Increased Access to Modern Energy Project in Benin. Project Appraisal Document. Washington DC. May 28, 2009

46
13 across regions (or even cities) within a country and in the quality of the good, whereas price data tend to be collected at a higher level of aggregation and for a representative (average) item in each category. But it is hard to think that these problems are so severe that choosing arbitrary weights across “dimensions” such as the availability of cooking utensils and, say, the ownership of certain means of transport, would yield a preferable metric. In this regard, Ravallion’s (2011) arguments for a greater reliance on market prices seem overwhelmingly compelling.

The true value of the analysis of multidimensional poverty (or well-being) lies in the existence of certain aspects of well-being that we deem important, but for which there can truly be no sensible estimate of relative prices. It is reasonable to include in this category items such as political and personal freedoms, health and, arguably, education. While various inputs into the production of health and education are marketable, that is not true of all of them. Health is influenced by environmental quality and a number of other public goods. “Education”, once embodied in human capital, generates so many externalities that it is difficult to think that school fees or costs are suitable approximations to its true shadow price. These things – not the material from which a ceiling is made, or what kind of stove one uses – are true _dimensions_ of welfare.

It is in these cases that multidimensional analysis becomes particularly relevant. If, for the purpose at hand, the analyst decides that the price of selecting weights is worth paying (in order to obtain a complete ordering), then the question is whether one can choose relative weights that at least attempt to represent the existing trade-off between the different components of deprivation (or well-being). There are various approaches for setting these weights, some based exclusively on the observed distribution of attributes, others based on people’s opinions, and others using both sorts of information (Decancq and Lugo, forthcoming). Whatever the weighting scheme and precise functional form chosen, multidimensional indices of deprivation (or well-being) should be allowed to be sensitive to the one thing the multidimensional approach is called upon, that is, to changes in the degree of dependency between its components.

# _4. Conclusions_

There is widespread agreement that poverty is a multifaceted phenomenon. Income shortfalls, which translate into an inability to consume certain basic commodities, are central to this phenomenon. But income poverty is typically associated with deprivations in other realms, such as health, education, social status and political power, which are harder to price. These associations, or correlations, between the constituent dimensions of poverty vary over time and from place to place, and are often thought to matter. Recent advances in multidimensional