rafmacalaba/datause-encoder-data huggingface dataset:
attempting to combine the best of both production methods. In a hybrid system, the bioreactor is usually used to cultivate the inoculums that will be established on the ponds.

After the biomass has been produced it needs to be harvested to be used in biofuel production. Low biomass concentration in the culture (due to limited light penetration) and the small size of algal cells complicate harvesting leading to high costs (Li et al. 2008).

**Table 5: Oil yields of algae and other oilseeds**

|Plant source|Oilyield (L/ha/yr)|Plant source|Oilyield (L/ha/yr)|
|---|---|---|---|
|Soybeans|446|Palm|5,950|
|Rapeseeds/canola|1,190|Algae<sup>a</sup><br>|12,000-98,5000|
|Jatropha|1,892|Algae<sup>b</sup>|58,700-136,900|

Source: Chisti (2007), and Schenk et al. (2008).<sup>a</sup> Range from 10 g/m<sup>2</sup> /d at 30% Triacylglycerids (TAG) to 50 g/m<sup>2</sup> /d at 50% TAG (Schenk et al 2008),<sup>b</sup> Range from 30% to 70% oil by weight in biomass (Chisti 2007).

Microalgal can reach large productivity levels. Its yield per hectare has been reported to be several times that of the most productive tropical oilseeds such as palm (Table 5). The two crucial parameters determining algal yield are biomass productivity and oil content of the algae. These two parameters differ widely across algae strains and hence different strains can achieve very different biomass and oil yields.<sup>10</sup> The algae yield scenarios reported by Schenk et al. (2008), included in Table 5, are based on existing production systems and their potential.<sup>11</sup> In terms of potential, Li et al. (2008) reports that the maximum theoretical yield for algal biomass production has been calculated at 365 tons of dry biomass per hectare per year (100 g/m<sup>2</sup> /d). For these of yields to be achieved, high concentrations of CO2 would need to be fed.

In summary, significant barriers are still standing before the commercialization of algal biofuels is economically viable and the risks remain high. Despite this, algae could be a promising feedstock for biofuels. As of late 2008, Darzin (2008) indicated that seven US government laboratories, thirty US universities, and around sixty biofuels companies were conducting research in this area. Intense efforts are also taking place in other parts

> 10 Oil contents between 16% and 77% have been reported by Chisti (2007) for different strains. 11 The examples given are Seambiotic Israel with current production levels of 20 g/m2/d at 8-40% TAG and HR BioPetroleum INC Hawai which aims at obtaining 50 g/m<sup>2</sup> /d at 30% TAG. Schenk et al. (2008) actually report liters of biodiesel per hectare.

14
<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>1-in-20 1-in-100 1-in-500<br>Data: JAXA EORC, 2018 (land use maps), Braese et al. 2020 (costal flood maps), Fathom Braese et al. 2020 (costal flood maps), Fathom 1 (riverine flood maps).<br>Note: Shown percentages are percentages of flooded crop area for a 1-in-500-year flood. A flood threshold of 25cm has been<br>applied,<br>Figure 4.2: Rice production in the Red River and Mekong Deltas are particularly exposed to flood risks, especially for coastal<br>flooding.<br>a) Rice production area exposed to coastal flood risk<br>200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Return period: 1-in-25 1-in-100 1-in-500<br>49%<br>27%<br>42%<br>98%<br>95%<br>49%<br>36% 32% 25% 33%<br>68% 24% 25%<br>70% 39% 27% 42% 32% 26%<br>(hectares, thousands)<br>Exposed agriculture land 24% 25% 32% 29% 44%<br>39% 32% 9% 13%<br>Quang Ninh Hai Phong City Thai Binh Nam Dinh Ninh Binh Thanh Hoa Nghe An Ha Tinh Quang Binh Quang Tri Thua Thien-Hue Da Nang City Quang Nam Quang Ngai Binh Dinh Phu Yen Khanh Hoa Ninh Thuan Binh Thuan Ba Ria-Vung Tau Ho Chi Minh City Tien Giang Ben Tre Tra Vinh Soc Trang Bac Lieu Ca Mau Kien Giang<br>44%<br>82%<br>99%<br>99%<br>57%<br>100%<br>70% 99%<br>99% 94% 52% 100%<br>93%<br>Exposed rice production (hectares, thousands) 55% 59%<br>55%<br>39%<br>5% 1% 3% 1% 1% 4% 1% 1% 24% 67% 100%<br>Quang Ninh Hai Phong City Thai Binh Nam Dinh Ninh Binh Thanh Hoa Nghe An Ha Tinh Quang Binh Quang Tri Thua Thien-Hue Da Nang City Quang Nam Quang Ngai Binh Dinh Phu Yen Khanh Hoa Ninh Thuan Binh Thuan Ba Ria-Vung Tau Ho Chi Minh City Tien Giang Ben Tre Tra Vinh Soc Trang Bac Lieu Ca Mau Kien Giang<br><!-- End of picture text --> Data: <mark>JAXA EORC, 2018 (land use maps), Braese et al. 2020 (costal flood maps), Fathom Braese et al. 2020 (costal flood maps), Fathom</mark><sup>1</sup> <mark>(riverine flood maps).</mark> Note: Shown percentages are percentages of flooded crop area for a 1-in-500-year flood. A flood threshold of 25cm has been applied,

**Figure 4.2: Rice production in the Red River and Mekong Deltas are particularly exposed to flood risks, especially for coastal flooding.**

_b) Rice production are exposed to riverine flood risk_ 21
# **What Constrains Africa's Exports?**

## Caroline Freund<sup>*</sup> Nadia Rocha ς

> * Development Economic Research  Group, The World Bank.

> ς Economic Research and Statistics Division, World Trade Organization.

We would like to thank Allen Dennis for providing us with disaggregated data from the Doing Business report. We are also grateful to GPS team for providing us with detailed GPS data on SubSaharan Africa travel distances and times. In addition, we would like to thank seminar participants at the World Bank seminar, the Geneva Trade and Development Workshop and the European Trade Study Group (ETSG) conference. This paper received financial support from the governments of Finland, Norway, Sweden and the United Kingdom through the Multidonor Trust Fund for Trade and Development. The views presented in the paper are those of the authors and do not reflect the views of World Bank or the World Trade Organization.
TABLE 4

# Cross-Country Growth Regressions Dependent Variable is Average Annual Growth Rate of Per Capita Income

|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|
|Period<br>1960-89|1960-85|1960-89|1960-85|1960-85|
|Data Set<br>WB|SH|WB|WB|SH|
|Independent Variables<br><br>|||||
|Constant<br>-.83|2.01|. 86|47|2.95|
|(.85)|(.83)|(.89)|(1.18)|(1.16)|
|Initial GDP/Capita<br>-.35¢#<br><br>|-.69#<br>|-.304<br>|-. 408<br>|-.564<br>|
|RGDP60<br>(.14)|(.12)|(.11)|(.13)|(.13)|
|Investment Sh.<br>17.498|9.314|16.778|13.4468|10.32¢|
|INV<br>(2.68)|(2.08)|(2.62)|(3.13)|(2.50)|
|Popul.Growth<br>-.38|.08|-.53#|-.15|-.02|
|<br><br>GPO<br>(.22)|(.18)|(.18)|(.19)|(.19)|
|Secondary Enroll.<br>3.174|1.21||-63|.95|
|SEC<br>(1.29)|(1.17)||(1.26)|(1.25)|
|Primary Enroll.|1.794||91|1.92|
|PRI|(.58)||(.73)|(.79)|
|Government Share|-6.374||-.59|-6.756|
|GOV|(2.03)||(3.73)|(2.39)|
|Growth Gov Share||-.08|||
|GSG||(.06)|||
|Socialist Econ|-.25||-.21|-.15|
|$0c|(.38)||(.45)|(.44)|
|Revolution&Coup|-1.76#||-.86|-1.788|
|<br>REVC|(.52)||(.62)|(.60)|
|AfricaDummy|1.2464||-1.36#|-1.82¢|
|<br>AFRICA<br>|(.37)<br>||(.48)<br>|(.46)<br>|
|Latin America Dummy|-1.18#||-1.348|-1.824|
|LAAM<br><br>|(.33)||(.38)<br>|(.37)<br>|
|Growth of Dom.<br>Credit||0194|.013|208|
|GDC||(.009)|(.008)|(.007}3|
|Stan.<br>Dev.<br>Dom.<br>Cred.||-.0094|-.0064|-.003|
|STDD||(.003)|(.003)|(.0C3)|
|ExportShareGrowth||090|.023|-.027|
|<br>XSG||(.052)|(.047)|(.046)|
|CivilLiberties||-.22|.O1|.15|
|<br>CIVL||(.11)|(.13)|(.13)|
|Number of Obs.<br>101<br><br>|103<br>|83<br>|84<br>|84<br>|
|Re<br>46|68|61|.67|73|

# f=significant at 52, Standard errors in parentheses

Regressions 1,3,& 4 use World Bank data for growth and national accounts while numbers 2 & 5 use Summers-Heston (1988) data 38
In addition, greater exposure to international competition could substantially improve firms’ efficiency. Russia advanced to 31st place in the World Bank’s 2019 global ease of doing business ranking, representing an improvement from the 35th place last year and 40th place two years ago. However, there is room for further improvement in several areas, including Trade Across Borders, Resolving Insolvency and Protecting Minority Investors. Progress in easing barriers to trade would also support productivity growth.

We assumed a gradual acceleration of TFP growth in the reform scenario. In this scenario, TFP will increase by 0.1 percentage point in 2028 from the 2017 level instead of the 0.2 percentage point decline expected in the baseline scenario. This increase would push potential growth up by 0.3 percentage point. The cumulative impact of increasing the TFP growth rate is around 2 percent of potential output. It is worth noting that unlike for capital and labor, a one percentage point increase of TFP growth would increase potential growth by the same one percentage point.

# **VI. Can Russian potential growth match global growth?**

Global growth is expected to remain around 3 percent. As we have seen, in the baseline case, Russian potential growth is expected to remain around 1.3-1.5 percent for the same period. This suggests a relative decline of the Russian economy for the coming decade.

However, simulations of specific reform measures currently being considered by the government, such as pension reform, more migration, higher investment, and gradual acceleration of TFP growth, _can_ increase Russia’s potential growth rate to 3 percent in 2028 (Figure 6). Pension reform, and increases in migration, investment, and productivity contribute 0.4, 0.2, 0.6, and 0.3 percentage point, respectively, to the increase in the potential growth rate. Improvement in the demographic conditions by 2028 contributes to the potential growth rate as well. Even without reforms, the potential employment growth rate in 2028 is 0.2 percentage point higher than that in 2023.

15
_-5-_

# II. TRENDS **IN** THIE BANK'S LENDiNG **TO HIIHER** kDUCATION

_Overnew:_ Overall, the Bank approved 122 higher education projects between 1972-1981 and 131 projects between 1982-1992 in Africa, East and South Asia, EMENA and Latin America/Caribbean. Of the 122 projects identified between 1972-1981, 32 (or 26% of total projects) acknowledged gender as an issue and 13 (or 11 % of the total projects) introduced significant interventions to overcome gender disparity. The number as well as the proportion of projects addressing gender significantly increased during 1982-1992. Fifty-three (or 41 % of the total higher education projects) recognized gender as an issue while 36 (28%) introduced specific interventions.

_Resources for Gender Specific Interventons:_ Ideally, to determine the most costeffective approaches to increasing ger.der equity in higher education, one would have to examine the costs associated with each gender-specific intervention. This was not possible, given the paucity of disaggregated data from project SARs. We can, however, estimate the amount invested in projects with gender components as a part of total higher education investment. Examination of the total investment allocated to specific projects may illustrate the relative weight placed on projects addressing gender equity within the context of higher education projects. Table 1 below compares the distribution of total number and costs of Bank-assisted higher education projects by region for 1972-1981 and 1982-1992.

A. 1972-1981: The total cost of higher education projects approved between 1972 and 1981 was US $4.2 billion.<sup>3</sup> Interestingly, only 24% ($1 billion) of the total investments in higher education funded projects which acknowledged gerder as an issue. Further, only 41 % of the funding for projects acknowledgin **g** ender as an issue was allocated to projects with genderspecific policies ($420 million or 10% of 1! higher education investments). These figures sugges **t** hat projects acknowledging gender disparity did not receive primary attention from the Bank during the 1970s.

3 All **figures** **_arm_** expressed in 1985 dollars.
This pattern is in part explained by poorer households or households with less favorable circumstances participating less in secondary education than children from richer households and with more favorable circumstances. Results also reflect that beneficiaries from poor households or least favorable circumstance group attend public schools in departments with higher provision costs. These higher costs typically reflect higher salaries of teachers in rural and isolated schools and the allocation of centralized expenses of the educational system ( _alcance nacional_ ) across fewer students in smaller departments. In contrast, the most populous and richer regions in the country receive lower than average unitary transfers. This is especially true for primary education. Finally, richer families spend more on fees, texts, school materials, and other costs than poorer families.<sup>25</sup>

**Figure 7: Distribution of Unitary Public Expenditures on** **_Elemental_ Education Net of Private Household Contributions, Paraguay, (ages 5-15), 2009**

**a.                                                          b.**

<!-- Start of picture text --> Unitary public expenditure on elemental education<br>by quintiles of incomes (2009) Unitary public expenditure on elemental education<br>by quintiles of probability (2009)<br>232 224<br>212 209 224 241 222<br>177 144 116 94 207 175 168 134 202 188<br>39 70<br>-55 -80 -96 -115 -49 -73 -88<br>-168 -133<br>-178<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>Figure 8: Distribution of Unitary Public Expenditures on Secondary<br>Household Contributions, Paraguay, (ages 16-17), 2009<br> a. b.<br>Unitary public expenditure on secondary education Unitary public expenditure on secondary education<br>by quintiles of incomes (2009) by quintiles of probability (2009)<br>162 252 260 280 269 152 236 231 318 331<br>101 152 121 156 98 145 112 143 62<br>-61 -100 -137 -125 47 -54 -91 -119 -173 -269<br>-221<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>200<br>200<br>100<br>100<br>0 0<br>-100 -100<br>-200 -200<br>300 400<br>200<br>200<br>100<br>0<br>US$ 0 US$<br>-100 -200<br>-200 -400<br><!-- End of picture text -->

**Figure 8: Distribution of Unitary Public Expenditures on** **_Secondary_ Education Net of Private Household Contributions, Paraguay, (ages 16-17), 2009**

_Source_ : Authors’ estimates from INEC’s EPH 2009, Ministerio de Educación de Paraguay (2009, 2010), Ministerio de Finanzas de Paraguay (2013), Cuesta and Suarez-Becerra (2013) 25 Some 39 percent of children in the top quintile of the distribution of beneficiaries attend private schools, only 3 percent of children in each of the two bottom quintiles do. Those children from the top quintile who go to public or publically subsidized schools spend on average three times more than children from the bottom two quintiles.

20
ETHIOPIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE while developing its power export potential, which alone accounts for $1 billion of these annual needs. While small relative to power needs, transport and WSS needs are high in absolute terms: each amounts to almost $1 billion annually. In the power sector, the need for investment is particularly high, at $3.1 billion. In the WSS sector, the need for operations and maintenance spending is particularly high, at $355 million. ICT sector spending needs are comparatively modest (table 8).

**Table 8. Indicative infrastructure spending needs in Ethiopia**

|US$ millions per year<br>|<br>||
|---|---|---|
|Sector|Capital expenditure<br>Operation and maintenance|Total spending|
|ICT|72<br>139|211|
|Power|3,105<br>276|3,380|
|Transport|248<br>149|398|
|WSS|846<br>355|1,201|
|Irrigation|6<br>—|6|
|Total|4,277<br>919|5,196|

Source: Briceño-Garmendia and others 2008.

Note: Figures refer to investment except public sector, which also includes recurrent spending. Public sector covers general government and nonfinancial enterprises.

Ethiopia’s total spending requirement is among the highest in Sub-Saharan Africa and is huge relative to the size of Ethiopia’s economy: an untenable 42 percent of the country’s GDP. This is one of the largest infrastructure spending burdens found for any African country in the study (figure 11).

Ethiopia currently allocates $1.3 billion per year to meeting its infrastructure needs. Capital expenditure absorbs 60 percent of total spending (table 9). Operating expenditure is entirely covered from budgetary resources and charges contributed by infrastructure users. Overseas Development Assistance accounts for 40 percent of total capital expenditure and is the largest source of external finance for infrastructure investment in Ethiopia. From the non-OECD financiers, China is an increasingly important source of external finance and currently contributes 20 percent of total capital expenditure. Private participation is conspicuously absent, particularly in ICT, where the absence of a liberalized regulatory framework and competitive market structure hampers provider diversification. Households play a significant role in funding on-site sanitation.

19
IPRs because of PTA provisions, it must extend this treatment to all WTO members. That is, it cannot discriminate between rights-holders from PTA members and others. Indeed, it makes little practical sense for nations to discriminate across the origins of applications for intellectual property protection. Thus, in principle, rights-holders from countries not party to a PTA are affected legally under the same terms as their counterparts from member countries. This fact suggests that the effects of elevated intellectual property norms in PTAs spread beyond the agreements’ members _de jure_ , though it may not prevent _de facto_ discrimination. This insight suggests that the effects of such standards may not display much diversion.

In this paper we study the effects of membership in enforceable IP-related PTAs, negotiated both with strong _demandeur_ countries (the United States and EU/EFTA) as well as with other countries (such as Japan, South Korea, and Australia), on exports and imports of goods that intensively use intellectual property, accounting for levels of economic development. We estimate the impacts on member nations’ aggregate and bilateral trade in IP-intensive sectors, using a difference-in-differences approach comparing various definitions of treatment agreements. We adopt rigorous econometric specifications to deal with endogenous selection into such agreements. In general, we find that the trade effects are modest. However, there is evidence of a trade-expanding impact on specific IP-intensive sectors, such as pharmaceuticals, chemicals, and information technology products, particularly in higher-income emerging countries. We also find evidence that developing countries that join such agreements see significant reductions in their trade in goods that are not IP-intensive, relative to countries that do not join them. These findings imply that strong IPRs chapters may exert a sorting effect, shifting trade from industries that are less sensitive to IPRs protection to those that are more dependent on it.

The remainder of the paper is organized as follows. Section 2 provides background on the development of PTAs with strong intellectual-property chapters and overviews their scope and coverage, relying on World Bank data. Section 3 discusses the empirical framework and presents estimates of the effects of IP-related PTAs on aggregate and bilateral imports and exports at the sectoral level. Section 4 offers concluding remarks.

# **2. Background**

5
|**Table 9: Trends by re**<br>Tre<br> <br>|**gion ***<br>nd<br>||
|---|---|---|
|Region<br>Decrease|Increase|Total|
|Africa<br>8|2|10|
|(13.6)|(8.3)|(12.1)|
|Americas<br>14|8|22|
|(23.7)|(33.3)|(26.5)|
|Asia<br>20|9|29|
|(33.9)|(37.5)|(34.9)|
|Europe<br>16|5|21|
|(27.1)|(20.8)|(25.3)|
|Oceania<br>1|0|1|
|(1.7)|(0.0)|(1.2)|
|Total<br>59|24|83|
|(71.1%)|(28.9%)||
|* Column percents in pare|ntheses||

**Table 10:  FUAs, 2019 deviations from 5-year means, 2015-2019**<sup>*****</sup> Dependent variable:  Local CO2 Anomaly (ppm)

||2019 De|viation||
|---|---|---|---|
|Significance Level|Negative|Positive|Total|
|Not Significant|446|401|847|
|.05|71|56|127|
|.01|42|29|71|
|.001|16|11|27|
|Total Significant|129|96|225|
|Total|575|497|1,072|

*  FUAs with degrees of freedom ≥ 30

**Table 11:  FUAs, 2021 deviations from 5-year means, 2017-2021**<sup>*****</sup> Dependent variable:  Local CO2 Anomaly (ppm)

||2021 De|viation||
|---|---|---|---|
|Significance Level|Negative|Positive|Total|
|Not Significant|418|431|849|
|.05|76|70|146|
|.01|37|25|62|
|.001|15|12|27|
|Total Significant|128|107|235|
|Total|546|538|21<br>1,084|

*  FUAs with degrees of freedom ≥ 30
Overall, these results suggest that distortions imposed by the voucher are apparent at two margins. First, _voucher_ households purchased some categories of goods at significantly higher rates than _cash_ households. Second, _voucher_ households adjusted at the intensive margin, purchasing more of some goods (primarily salt) than the unconstrained cash households.

# _Food Consumption and Assets_

Since the voucher program distorted _voucher_ households’ purchasing decisions as compared with the cash transfer, a natural question is whether the transfer modality had differential effects on other aspects of well-being. For example, if _voucher_ households incurred significant costs while reselling salt, the value of the transfer could have been significantly lower among _voucher_ households, thereby lowering their purchasing power. In addition, since _cash_ households were able to use the transfer when, where, and how they wished, they could have saved a portion of the transfer, thereby allowing them to better cope with shocks, or they could have arbitraged for better prices across different markets, thereby increasing their purchasing power as compared with _voucher_ households.<sup>34</sup> Since _cash_ households purchased a more diverse set of food items, albeit in relatively small quantities, it is possible that this could have translated into better outcomes.

Table 4 looks at the impact of the transfer modality on households’ food consumption, as measured by diet diversity, the number of meals per day, and the number of months of adequate provisioning. Using the HDDS, _voucher_ households consumed 3.07 food categories, without a statistically significant difference between the two modalities (panel A). There also were no statistically significant differences in the likelihood of consuming particular food groups or in other food security indicators, including the number of meals per day and the number of months of adequate food provisioning (panel B).<sup>35</sup> These results are also robust to using data from all transfer periods (appendix S4, panel B), as well as controlling for baseline values in an ANCOVA specification (appendix S5, panel B).

> 34 If one of the objectives of the _voucher_ program was to encourage greater consumption of food items, these benefits would be mitigated to the extent that households purchased items that were not oriented towards consumption or did not consume what was provided. Resale of the goods would detract from the objective of increasing the consumption of those specific foods, whereas lumpy expenditures would support this objective.

> 35 While milk is included as a category in HDDS, no _voucher_ or _cash_ households consumed milk, so it is excluded.

19