rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3.

In Table 7, we repeat the interactions with Intangible Capital/Assets in Table 3 but re-estimate _ROIC_ using different treatments of cash. In columns 1-3, we use the firm’s total cash holdings in computing ROIC, _ROIC_<sup>CASH</sup> , in columns 4-6, we consider excess cash to be any cash over 1% of sales, _ROIC_<sup>1per</sup> , and in columns 7-9 we consider excess cash to be any cash over 10% of sales, _ROIC_<sup>10per</sup> . Across the columns, we obtain similar results wherein large firms with high intangible capital and firms with high markups and high intangible capital are less likely to be in the top 10% of ROIC in a year.

# **5 Conclusion**

We assess the performance of publicly-listed star firms in the United States. When we use financial statement data as conventionally presented, a small percentage of firms seem to be pulling away over time from other firms in the economy in terms of their return on capital. In particular, star firms in highly skilled industries and industries with high levels of intangible capital seem to be pulling away from the others.

However, conventional financial statements do not capitalize R&D expenditures or organizational capital. Once we adjust firms’ returns to capital to address these shortcomings, there is little evidence that the most profitable 10% of firms are pulling away from the rest of the economy, and the differences in firm returns in highly skilled and other industries shrink dramatically. Furthermore, once we adjust markups based on operating expenses for investment in intangible capital, we only find a modest increase in market power especially in high skilled industries. Star firms tend to be larger, younger, and have higher markups. While they may have more pricing power than other firms, at each level of markup star firms tend to produce more than other firms. We also find no evidence that star firms are differentially affected by import competition compared to other firms in the economy.

Overall, our results indicate that while star firms have some pricing power, this is used to support the intangible assets that are costly to acquire and so we see little evidence that these high 29
|41-9021.00|Real Estate Brokers|209<br>|5.26|19|1<br>Non-green|
|---|---|---|---|---|---|
|29-2054.00|Respiratory Therapy Technicians|208<br>|5.26|19|1<br>Non-green|
|47-2141.00|Painters, Construction and Maintenance|210<br>|5.26|19|1<br>Non-green|
|49-2092.00|Electric Motor, Power Tool, and Related Repairers|212<br>|5.13|39|2<br>Non-green|
|11-9013.01|Nursery and Greenhouse Managers|213<br>|5.00|20|1<br>Non-green|
|49-3043.00|Rail Car Repairers|215<br>|5.00|20|1<br>Non-green|
|49-9052.00|Telecommunications Line Installers and Repairers|216<br>|5.00|20|1<br>Non-green|
|39-2021.00|Nonfarm Animal Caretakers|220<br>|4.76|21|1<br>Non-green|
|19-1029.02|Molecular and Cellular Biologists|217<br>|4.76|21|1<br>Non-green|
|39-1011.00|Gaming Supervisors|219<br>|4.76|21|1<br>Non-green|
|19-3093.00|Historians|218<br>|4.76|21|1<br>Non-green|
|29-1126.00|Respiratory Therapists|224<br>|4.55|22|1<br>Non-green|
|23-1011.00|Lawyers|223<br>|4.55|22|1<br>Non-green|
|45-2092.01|Nursery Workers|226<br>|4.35|23|1<br>Non-green|
|45-3021.00|Hunters and Trappers|227<br>|4.35|23|1<br>Non-green|
|17-2171.00|Petroleum Engineers|225<br>|4.35|23|1<br>Non-green|
|47-3013.00|Helpers--Electricians|232<br>|4.17|24|1<br>Non-green|
|25-1053.00|Environmental Science Teachers, Postsecondary|230<br>|4.17|24|1<br>Non-green|
|53-2022.00|Airfield Operations Specialists|233<br>|4.17|24|1<br>Non-green|
|51-9122.00|Painters, Transportation Equipment|235<br>|4.00|25|1<br>Non-green|
|25-1064.00|Geography Teachers, Postsecondary|234<br>|4.00|25|1<br>Non-green|
|37-3013.00|Tree Trimmers and Pruners|240<br>|3.85|26|1<br>Non-green|
|29-1069.05|Nuclear Medicine Physicians|239<br>|3.85|26|1<br>Non-green|
|25-1051.00|Atmospheric, Earth, Marine, and Space Sciences<br>Teachers,Postsecondary|237<br>|3.85|26|1<br>Non-green|
|19-4099.01|Quality Control Analysts|236<br>|3.85|26|1<br>Non-green|
|47-2081.00|Drywall and Ceiling Tile Installers|241<br>|3.85|26|1<br>Non-green|
|25-1052.00|Chemistry Teachers, Postsecondary|238<br>|3.85|26|1<br>Non-green|
|11-9141.00|Property, Real Estate, and Community Association<br>Managers|242<br>|3.70|27|1<br>Non-green|
|29-2011.01|Cytogenetic Technologists|245<br>|3.33|30|1<br>Non-green|
|45-3011.00|Fishers and Related Fishing Workers|246<br>|3.33|30|1<br>Non-green|
|49-9031.00|Home Appliance Repairers|247<br>|3.23|31|1<br>Non-green|
|51-4072.00|Molding, Coremaking, and Casting Machine<br>Setters,Operators,and Tenders,Metal and Plastic|248<br>|3.03|33|1<br>Non-green|
|25-9041.00|Teacher Assistants|249<br>|2.94|34|1<br>Non-green|
|49-9012.00|Control and Valve Installers and Repairers, Except<br>Mechanical Door|250<br>|2.56|39|1<br>Non-green|
|51-9071.01|Jewelers|252<br>|2.50|40|1<br>Non-green|

51
# **3. Sector-level bilateral input-output table**

## **3.1 EORA: World MRIO data**

The multi-region input-output table (MRIO) that is part of the Eora database provides an in-depth window on the GVC integration of sub-Saharan countries. There exists several alternative input-output databases, but this is the only version where there is enough country detail to study African countries individually.<sup>9</sup> Unavoidably, this level of detail comes at a cost, which is the greater reliance on imputations and proportionality assumptions to complement the relatively sparse data for some countries. The underlying data that we use is freely downloadable and the construction of the various components is described in detail in Lenzen et al. (2012, 2013).<sup>10</sup> We use the global MRIO table that contains information for 190 countries over the 19902015 period for a simplified set of 26 harmonized sectors. These dimensions imply that just the matrix of bilateral input coefficients at the country-industry level is a (190*26) by (190*26) square matrix that contains more than 24 million coefficients. In addition, there are columns for 6 final demand components that add another 5.6 million pieces of information. Moreover, this information is available for 26 years.<sup>11</sup> Clearly, in order to learn something from this gigantic source of information, we will need to aggregate and zoom in on particular areas of interest. Along the three dimensions—time, industry, country—we made the following choices.

### <u>Time</u>

We observe a different IO table for 26 consecutive years, but the structure of the economy and the bilateral trading relationships only change gradually over time. Therefore, we only used the information for three equidistant years: 1995, 2005, 2015. In the analysis we will look both at the current composition of input-output relationships using the most recent IO table for 2015 and we will consider changes over time. The data file that we make available contains all indicators calculated for all three years.

### <u>Industries</u>

The next step in the analysis is to reduce the sectoral detail. In total there are 26 harmonized industries, but we combine them into the following nine more broadly defined

> 9 A widely-used alternative is the World Input-Output Database (WIOD) (http://www.wiod.org/home) constructed and maintained by researchers at the Groningen Growth and Development Centre. The 2016 release covers 43 individual countries (including the 28 EU member states) and a rest-of-the-world aggregate, the period 2000 to 2014, and a decomposition into 56 sectors and 5 final demand components. A second alternative is the Trade in Value Added (TiVA) database (http://www.oecd.org/sti/ind/measuring-trade-in-value-added.htm). The most recent 2016 edition of this resource covers more countries (63) but at a lower sectoral detail (34) for the period 1995 to 2011.

> 10 http://worldmrio.com/

> 11 In total, the entire database consists of almost 770 million values, most of which are naturally indistinguishable from zero, but not exactly zero.

19
# 6. Business Viability

In this section we assess whether DrumNet was profitable from a business standpoint. The monthly cost of the DrumNet main regional office in Kerugoya for an average month during the study was KSh 93,000 (USD 1,200), and included the rental, salaries, transportation, utilities, marketing and communication expenses. In addition, the Kerugoya office benefited from two “market intelligence” offices in the nearby markets of Karatina and Wakulima where the staff would check on local prices and report to Kerugoya. These offices were fully staffed from January until June 2004, and were closed in December 2004. Therefore, the monthly costs for these two offices during the study period was KSh 3,860 (USD 50). These monthly costs do not include a motor vehicle owned by the Kerugoya office nor expenses from the Pride Africa Nairobi national office, even though DrumNet was a project of Pride Africa.

At the time of the study, DrumNet was already operating with some SHGs that were growing passion fruit, French beans and baby corn. By the end of the study, they were working with 43 collection points, 14 of which were established for the study. In order to calculate the cost of the study to DrumNet, we calculate a monthly cost per collection point and multiply it by the number of study collection points.

To compute the sustainability of DrumNet as a business, we compute the annualized cost of running DrumNet per member and compare it to the income generated from the commission that DrumNet charged in each transaction. DrumNet registered 294 farmers in the month of June 2004 for the study, although they did not start generating revenues until September 2004. Unfortunately, we only have administrative data from DrumNet for 2004, so we can only assess business profitability from June to December. Assuming 19
A crucial determinant of sugar production costs is the amount of sugar extracted from a given amount of cane. This reflects the sucrose content of the cane and hence the efficiency of cane farming, the efficiency of sugar cane processing, and the coordination between farming- in particular harvesting - and milling. Between 1964 and 1994, according to FAO statistics, Indian extraction rates averaged 10 percent, compared to a world average of 9.5 percent over the same period ( Data S4). By this measure, India efficiency appears to be about average, but it is well below the averages in the most efficient producing countries. According to Borell (1996) average extraction rates in the mid- 1 990s in Australia, Brazil and Thailand were about 13 to 14 percent.

India is also the largest consumer of sugar in the world, followed by the USA and Brazil. Other large consumers are China and the EU. World sugar consumption grew at an average annual rate of 2.6 percent. during the period 1961-91. The rate of growth in the developing countries was 4.8 percent, but only 0.1% p.a. in the developed countries. The stagnation of demand in the developed countries was due to saturation in per capita demand and substitution of sugar by alternative sweeteners, such as High Fructose Corn Syrup (HFCS). The development of alternative sweeteners-especially in North America- was to a large extent motivated by high protection of the sugar industries and high domestic sugar prices in these markets. During 199093 , per capita consumption in North America was 31-32 kg per annum and in Europe around 3640 kg. By contrast, average per capita consumption in Asian countries was about 12 kg. India and China, the two most populous countries, have per capita consumption of around 13.9 kg and 6.4 kg, respectively.

# _World trade and major exporting and importing countries_

The quantity of sugar traded internationally is about a quarter of world production, although this involves some double counting insofar as raw sugar is imported to centers such as Rotterdam and is exported after processing into refined sugar. Before the mid 1970s , most world trade took place at prices fixed under long term quota agreements to supply protected domestic markets, and the free market share was around 45 percent of total world trade ( Fig 2). The free market share increased sharply in 1974 and has since remained in a range of about 60-73 percent of total world trade. The increase in the free market share after 1991 largely reflects the move of the FSU from managed imports of raw sugar (mainly from Cuba) to free market imports of refined sugar. However, it should be recognized that a substantial part of free market trade takes place under long term contracts which are used by both sellers and buyers to protect themselves against large swings in spot prices. While the prices fixed under these long term contracts take account of spot prices, the quantities actually traded at spot prices in any given year are considerably lower than the total free market trade.

6For a non-technical discussion of alternative sweetener **s** , ee James Fry ( 1985). 7
- 33 -

In addition, some variables that are critically related to hydrological flows may be omitted from our model. Bergström _et al_ . (2001) point out that a key to successful hydrological modeling is to properly account for soil moisture, which is not included in the above model. There are possibly other omitted variables. In the context of Northern India, for instance, the retreating Himalayan glaciers may have to be taken into consideration.<sup>18</sup> However, there may be a tradeoff; more variables will involve more uncertainty. Particularly, evapotranspiration in a future clime may involve serious uncertainty to be modeled (Bergström _et al_ ., 2001). Mountain areas where hydropower projects are often located may be more complicated because mountains are among the most fragile environments; the rate of warming in mountain systems is expected to be two to three times higher than that recorded during the 20<sup>th</sup> century (Nogués-Bravo _et al_ ., forthcoming).

There is a piece of evidence to support the validity of the used stochastic model. When comparing our dynamic forecasts of temperature and precipitation with the IPCC projections, temperature forecasts seem well comparable (Figure 9). On the other hand, precipitation forecasts are broadly consistent but may be underestimated in some cases, such as the Vishnugad Pipalkoti project. The difference may be attributed to the factors that the IPCC model accounts for and our VAR model does not. Obviously, again, the IPCC projections, as such, may have to be interpreted with some caution.

> 18 The Himalayan glaciers are currently retreating at a speed of 10-15 meters a year (WWF Nepal Program, 2005).
4.11 It is interesting to note that of those categories, only four (subsoil user fees, pollution charges and land payments) are shared across the three levels. Three categories (oil and gas excises and aquatic biological resource user fees) flow to the federal level exclusively. One category (water supply, sewage and wastewater treatment fees) accrues to the local level only.

4.12 These are statutory, not actual rates, however. As Table 20 indicates, actual data show greater shares staying at the regional and local levels, as regions and municipalities often resent remitting all the revenues due to the federal budget.

Table 20. Actual Revenue Sharing Among Levels of Government (1998)

||Fed|eral|Regi|onal|Lo|cal|To|tal|
|---|---|---|---|---|---|---|---|---|
||RUB<br>bn.|%<br>Share|RUB<br>bn.|%<br>Share|RUB<br>bn.|%<br>Share|RUB<br>bn.|%<br>Share|
|CPT|35|38|36|39|21|23|93|100|
|PIT|0|0|26|37|44|62|71|100|
|VAT|105|67|33|21|19|12|156|100|
|Excises|52|77|12|17|4|6|68|100|
|Propertytax|0|0|23|49|24|51|47|100|
|"Greentaxes"|3|14|9|41|10|45|23|100|
|Foreigntradetaxes|37|100|0|0|0|0|37|100|
|Othertaxes|4|8|14|30|30|63|48|100|
|Total taxrevenues|236|44|154|28|153|28|542|100|
|Non-tax revenues|40|45|14|43|10|12|64|100|
|Budget funds|26|59|18|41|0|0|44|100|
|Totalrevenues|302|46|186|29|163|25|650|100|

Source: USGTAT (1998a).

4.13 In contrast to Table 19, which indicated that the law mandates regions to direct the larger share of natural resource tax revenues to the federal government, Table 21 shows that regions and municipalities instead retained as much as 86% in 1998. With respect to the impact of fiscal federalism on the feasibility of a shift to rental payments, two main issues arise. First, would regions raise the tax burden on natural resources if they knew they would have to relinquish part of the resulting revenues to Moscow? As Table 20 suggested, subnational governments do end up retaining the bulk of natural resource revenues. Second and more importantly, could Russia, as a federation, sustain a tax system relying more heavily on natural resources? Revenue disparity across regions is at the heart of the problem.

4.14 A shift from capital and labor taxes to rental payments could be criticized because it risks heightening regional revenue disparity. The argument would go as follows. First, natural resource endowments vary greatly across regions, so that resource-poor regions would find it hard to sustain themselves on natural resource revenues. Second, regions remit relatively smaller shares of their natural resource tax revenues to the federal budget than other tax revenues. Not only do sub-national governments retain a larger share of the 42
.

PoLicy RESEARCH WORKING PapER Transient Poverty In. Rural China.

Jyotsna Jalan . . Martin Ravallion

# = 1616

. Panel data for rural China indicate that roughly half the poverty is transient. This ._ severely constrains efforts to aereach the chronically poor using cross-sectional data. But similar processes are at work in creating transient and "chronic poverty.

The World Bank Policy Research Department Poverty and Human Resources Division June 1996 . coh Db
high values are problematic for charge-offs, we look at P90 and max statistics instead of P10 and min, respectively.

# **4.1.1 Bank Conditions**

The bank equity ratio (equity/GTA) and similar ratios are frequently employed in banking research as summary measures for bank health and the ability to absorb shocks. As noted above, it is consistently measured over time and is not subject to temporary easing of what counts as regulatory capital during COVID-19. We find that the equity ratio is stable for the banking industry for the entire set of five quarters. The mean equity ratio for the U.S. banking industry stays in the tight range of less than one percentage point variation between 12.56 and 13.41 percent, suggesting that the industry remained well capitalized throughout. Focusing next on the P10 points to see if there is trouble with the least capitalized set of banks, the figures range between 8.30 and 8.98 percent, showing relatively little movement, no sign of a banking crisis, and much higher than in troubled times of the past.

We next turn to the bank liquidity ratio, which includes cash, due from, and securities normalized by GTA, for potential signs of trouble that might signal a liquidity problem that would require fire sales or reduced credit supply. The mean bank liquidity ratio varies from 30.33 to 35.27 percent, rising over time as banks become more liquid after the crisis starts. The P10 also rose from 12.69 to 17.34 percent over time as the industry becomes more liquid, clearly indicating that the COVID-19 crisis did not induce liquidity problems in the U.S. banking industry.

The U.S. G-SIBs, whose data are shown on the right side of the table, normally maintain significantly lower capital ratios and higher liquidity ratios than other banks. The mean capital ratio ranges between 9.13 and 10.54 percent, with the minimum varying between 6.96 and 8.35 percent. Again, these ratios achieved their minimums in 2020:Q1, and were recovering thereafter.

20
**_Figure 5:  Average WIIW Labor Productivity in the Services Sector_**

<!-- Start of picture text --> Panel A. Unweighted Average Labor Productivity Panel B. Weighted Average Labor Productivity<br>Czech Rep. Slovak Rep.<br>Slovenia Hungary<br>Slovak Rep. Czech Rep.<br>Hungary Slovenia<br>Croatia Poland<br>Poland Croatia<br>Romania Bulgaria<br>Bulgaria Romania<br>Macedonia Macedonia<br>Ukraine Ukraine<br>0 10 20 30 40 50 60 0 10 20 30 40 50 60 70<br>Thousands of PPP USD per Employee Thousands of PPP USD per Employee<br>1997-2000 2001-2004 1997-2000 2001-2004<br><!-- End of picture text --> Source: WIIW database 2005. Note: The averages of labor productivity are taken across the 6 sub-sectors listed in Appendix Table A.1.

Figure 6 shows unweighted (Panel A) and weighted (Panel B) averages of KLEMS labor productivity taken across all NACE 2-digit services sub-sectors in each country in 1997-2000 and in 2001-2004. The messages emerging from the figure are as follows. First, there is a large degree of heterogeneity in average KLEMS labor productivity in services across EU-8 countries. A productivity divide separates EU-5 countries from Baltic countries, which exhibit lower average productivity. Second, weighted averages are higher than unweighted averages of KLEMS labor productivity in all EU-8 countries but Latvia. Thus, in most countries more productive sub-sectors account for a larger share of services value added. Third, unweighted and weighted averages of KLEMS labor productivity increase between 1997 and 2004 in all EU-8 countries with the exception of Slovenia (for both averages) and the Slovak Republic (for the unweighted average). This finding differs from the evidence shown in Figure 5. Another difference relative to Figure 5 concerns the ranking of EU-5 countries according to their average labor productivity: while the Czech Republic (Panel A) or the Slovak Republic (Panel B) rank highest in Figure 5, Hungary ranks highest in both panels in Figure 6. This divergence is expected given that the use of sub-sector specific prices underlying Figure 6 differs substantially from the use of common prices across sub14