rafmacalaba/datause-encoder-data huggingface dataset:
Despite this tremendous growth in biofuel production, the share of biofuels in total transport fuel demand was above 2% in 2004 in just three countries – Brazil, Cuba and Sweden (IEA, 2006a), and global output accounted for approximately 1% of total road transport fuel consumption in 2005 (Doornbosch and Steenblik, 2007). In 2007, ethanol production still only amounted to about 4% of the global gasoline consumption of 1,300 billion liters (REN21, 2008).

# _3.2 Trade_

Global trade in biofuels relative to production remains modest; only about one-tenth of total biofuel production by volume is traded internationally (Kojima et al., 2007). Global trade in fuel ethanol is estimated at about 3 billion liters per year in 2006 and 2007, compared to less than one billion liters in 2000 (F.O.Licht, 2007). Even as US ethanol production increased by 20 percent in 2006, with dozens of new production plants becoming operational, blending mandates led to ethanol imports increasing six times to about 2.3 billion liters (REN21, 2008). The US, the world’s largest ethanol importer, received more than half of its ethanol imports from Brazil, the world’s leading exporter, at 3.5 billion liters annually.<sup>6</sup> Brazil was responsible for about half of the ethanol exports to the EU, the second largest importer of ethanol in 2006. China, the second largest exporter of ethanol at 1 billion liters annually, exports mainly to Japan, South Korea and other Asian countries (OECD, 2008).

About 12%, or 1.3 billion liters, of total biodiesel production in 2007 was internationally traded. The EU, at more than 1.1 billion liters per year, is by far the largest importer, while Indonesia and Malaysia are the main exporters, combining to export about 800 million liters (Kojima et al., 2007). While the US also appears to be a major biodiesel trader, this was in fact due to the importing of biodiesel to be blended with small quantities of conventional diesel for export to Europe in order to take advantage of a tax credit (EIA, 2009). The loophole has since been closed.

> 6 Exact ethanol trade statistics are difficult to gather since fuel and non-fuel ethanol typically share the same tariff and are reported together (OECD, 2008). The share of non-fuel ethanol in the international trade in ethanol is estimated to have dropped from 75% at the turn of the century to around 50 to 60% in recent years. Ethanol trade statistics here refer to the total of fuel and non-fuel ethanol.

9
bxodi DATA AND METHoDs Figure A2. Percentage _of_ Total Fertility Assigned to Different Ages, by Level of Total Fertility <!-- Start of picture text --> 20-<br>'4-<br>20<br>10U .> 2o4 22 4 s 4 44<br>¶4<br><!-- End of picture text --> points. Countries wlich had initial sharp declines in fertility tended to continue decHning rapidly, while those that started with more gradual declines continued along the same path. Rapid and slow decline vwere AfoUnd in all regions. 'Me rate of decline was predicted best by the rate in preceding periods; socioeconomic factors added little to this prediction. Other than the mortality threshold, socioeconomic factors also failed to predict the start of fertility transition.

# _Projecging Migration_

E!stimates of future net migrants by quinquennium up to the year 2000 are those prepared by Arnold (1989) as part of an examination of data and sources. These estimates are strongly affected by official policies and plans in the major receiving countries. Ile flgures were designed to total zero in each quinquennium for the whole %,. Id. After the year 2000, the number of net migrants is assumed to approach zero linearly in eacl country, at the estimated rate of change in their number from the period 1990 95 to the period 1995-2000, or at a rate that would make their number zero by 2025-30, whichever rate is faster. Fcir these later periods, a zero total for worldwide net international migration was obtained by adjusting initial estimates of net migrants in three maJor receiving countries--the United States, Australia, and C:anada-upward or downward as necessary by a proportion constant acrcss these countries, but varying by quinquennium. These adjustments were of I or 2 percent for the earlier periods, negligible compared to the volume of migration assumed for these countries, but were up to 10 percent for later periods.

'Me age-sex distributions of migrants are determined from alternative models based on their sex ratios. If migration is beavily male, migrants are assumed to be concentrated in the age group 15-30, with few children and elderly. If migration is more balanced between males and females, proportionally more migrants are assumed to be children and elderly (Hill, 1990).
Table 29. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Involved in GVCs. All workers. 2011-2015

|Dependent variable:|L|abor force pa|rticipation ra|te||Employm|ent rate|||Unemplo|yment rate||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.149||0.125|0.107|0.122||0.103|0.0818|0.0333||0.0294|0.0373|
||(0.118)||(0.105)|(0.1000)|(0.103)||(0.0919)|(0.0854)|(0.0352)||(0.0330)|(0.0407)|
|Share of hhlds with computer||-0.0567|-0.0707|-0.0436||-0.0465|-0.0580|-0.0273||-0.00847|-0.0118|-0.0235|
|||(0.0253)**|(0.0320)**|(0.0448)||(0.0240)*|(0.0298)*|(0.0389)||(0.0130)|(0.0132)|(0.0225)|
|IDN exports per worker *||||-0.0777||||-0.0881||||0.0336|
|Share of hhlds with computer||||(0.111)||||(0.0902)||||(0.0507)|
|Observations|165|165|165|165|165|165|165|165|165|165|165|165|
|R-squared|0.960|0.966|0.963|0.965|0.972|0.975|0.973|0.975|0.966|0.968|0.967|0.966|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province, year fixed effects, and linear province time trends. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

46
# **Figure 5**

**Mexico's SDS Exports to the US:  Levels and Share of Total US SDS Imports**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Exports ($ Billion) Share of US Imports<br><!-- End of picture text -->

**Table 1 - U.S. Sales of Laundry Detergents 2002 ($ millions)**

||**Powder**|**Liquid**|**Total**|**Share**|
|---|---|---|---|---|
|Procter & Gamble|802.81|1307.15|2109.96|58%|
|Lever Brothers|126.15|470.51|596.66|16%|
|Dial Corp.|42.18|244.37|286.55|8%|
|Church & Dwight|77.42|116.28|193.70|5%|
|USA Detergents|16.14|131.17|147.31|4%|
|Colgate-Palmolive|18.90|111.65|130.55|4%|
|Private Label|35.79|66.46|102.25|3%|
|Huish Detergents|21.48|22.30|43.78|1%|
|Fabrica de Jabon La Corona|14.97|10.03|25.00|1%|
|RedocBrands|4.62|9.99|14.61|0%|
|**Total Market**|**1165**|**2502**|**3667**|**100%**|

Notes: includes sales from supermarkets, drug stores, and mass merchandisers, excl. Wal-Mart Source: Information Resources Inc.

Multinationals operating in the SDS sector in Mexico are not actively engaged in exporting to the United States. Proctor & Gamble does not export detergents to the 19
redistributive effect was negative (i.e., pro-rich) in both years but was much smaller in 1998 than 1993, in part because of the reduction in regressivity but in part because of the reduced share of out-of-pocket payments in pre-payment income (the reduction in t).

# **3. How much progressivity and income redistribution is fair?**

Measuring the progressivity and redistributive effect of health care payments on pre-payment income does not tell us whether or not they are equitable _per se._ To answer this question one needs to adopt positions with respect to both the definition of ATP and the appropriate link between payments and ATP.

The WHO's 2000 WHR (World Health Organization 2000) does both. It argues that ATP should be defined as the household's non-food spending, this being argued to be a good indicator of a household's long-term "normal" living standards. One can think of this approach as taking the household's pre-payment income, deducting its food expenditure (as a proxy for non-discretionary expenditure), and then deducting (or adding) any income windfalls (or shortfalls) compared to the household's "normal" income. Denote ATP by y and any deductions allowed in moving from pre-payment income to ATP by _D(x)._ Thus we have: (4) _y = x - D(x)._ Using some results from the tax literature, we can explore this issue further and link the concept of ATP to the concepts of progressivity and redistributive effect.

### **_3.1. Progressivity, redistributive effect and A TP: Some theoretical results_**

## **3.1.1. Progressivity and ability to pay**

Following Pfahler (1990), the index of progressivity of health care payments on pre-payment income, _.rT_ can be decomposed into two parts: a part capturing the progressivity of payments on ATP; and a part capturing the progressivity of deductions on pre-payment income: Here ;TR measures the progressivity of payments on ATP, defined as _8_
# **Wave Reduction by Mangroves during Cyclones in Bangladesh**

## **Implementing Nature-Based Solutions for Coastal Resilience**

Alejandra Gijón Mancheño<sup>1</sup> , Vincent Vuik<sup>1,2</sup> , Bregje K. van Wesenbeeck<sup>1,3</sup> , S.N. (Bas) Jonkman<sup>1</sup> , Roelof Moll<sup>1</sup> , Swarna Kazi<sup>4</sup> , Ignacio Urrutia<sup>4</sup> , Mathijs van Ledden<sup>4</sup> 1Delft University of Technology 2Royal Haskoning 3Deltares 4World Bank

**Keywords** : building with nature, mangroves, wave attenuation, flood risk reduction, hybrid flood protection, embankments, climate change, resilience, Bangladesh.

### **JEL Classification** : Q54, Q56, Q57.
Figure 3 <!-- Start of picture text --> I- PUEBUC TRANSPORTATIO ELECTR!CITY<br>i4H* 0.19<br>0.17<br>0.16<br>-<br>1s 0.15<br>0.14-<br>0.13<br>0.12<br>0.11<br>S* ~0.1<br>0.09<br>(9<br>o 02460.08 8<br>0.07-<br>0.06<br>ft * ~0.05-<br>0.04-<br>* ~0.03<br>0.02<br>i ~0.01I<br>0~~<br>la* 0 0.2 0.4 0.6 0.8<br><!-- End of picture text -->
|**HS**||||Year|||
|---|---|---|---|---|---|---|
|<br>**code**<br>|**_Type of products_**|2001|2002|2003|2004|2005|
|**2 digits**|||||||
|76||15.1|15.0|15.3|15.4|15.1|
|||_(5.0)_|_(5.0)_|_(5.3)_|_(5.4)_|_(5.0)_|
|||13<br>|13<br>|13<br>|13<br>|13<br>|
|78||5.0|5.0|5.0|5.0|5.0|
||_Lead and its products_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||12|12|12|12|12|
|79|_Zinc and its products_|5.0|5.0|5.0|5.0|5.0|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|80|_Tin and its products_|8<br>5.0|8<br>5.5|8<br>12.9|8<br>5.5|8<br>5.0|
|||_(0.0)_|_(1.3)_|_(9.1)_|_(1.4)_|_(0.0)_|
|||78|78|78|78|78|
|81|_Non-ferrous metals, cermets and their_|14.2|14.2|14.2|14.2|14.2|
||_products_|_(2.7)_|_(2.7)_|_(2.7)_|_(2.7)_|_(2.7)_|
|||108<br>|108<br>|108<br>|108<br>|108<br>|
|82|_TOOLS AND CUTLERY_|9.2|9.2|9.2|9.2|9.2|
|||_(6.0)_|_(6.0)_|_(6.0)_|_(6.0)_|_(6.0)_|
|||50<br>|50<br>|50<br>|50<br>|58<br>|
|83||19.8|19.8|19.4|19.4|18.3|
||_Other metallic products_|_(1.0)_|_(1.0)_|_(2.2)_|_(2.2)_|_(4.9)_|
|||1,136|1,150|1,134|1,168|1,205|
|84|_MACHINERY/APPLIANCES_|7.8|7.7|7.8|7.7|7.4|
|||_(4.1)_|_(4.2)_|_(4.2)_|_(4.5)_|_(4.5)_|
|||745|745|735|739|787|
|85|_Electrical equipment, audio- and video_|11.7|11.7|11.7|11.7|11.1|
||_equipment_|_(8.2)_|_(9.7)_|_(9.7)_|_(11.7)_|_(9.6)_|
|||41|47|44|44|44|
|86|_Railroad locomotives and cars_|7.2|6.6|7.0|7.0|7.0|
|||_(3.4)_|_(3.6)_|_(3.3)_|_(3.3)_|_(3.3)_|
|||249|283|266|266|272|
|87|_EHILE AD PART_|12.1|19.1|26.0|20.2|18.6|
||_VCS N S_|_(7.1)_|_(44.8)_|_(90.7)_|_(42.6)_|_(38.7)_|
|||36|36|36|36|36|
|88||15.4|15.4|15.4|15.4|15.4|
||_Aircrafts_|_(7.0)_|_(7.0)_|_(7.0)_|_(7.0)_|_(7.0)_|
|||39|39|39|39|39|
|89|_Boats, ships_|12.2|12.2|12.2|12.2|12.2|
|||_(7.1)_|_(7.1)_|_(7.1)_|_(7.1)_|_(7.1)_|
||_Optical equipment_|320|320|317|317|321|

31
variables results strongly indicate that the genocide had a negative impact on exposed children’s schooling outcomes. The magnitudes of the coefficients for the instrumented years of schooling regressions are each negative and significant at the 10 percent level, which is consistent with the Table 7 results. Increasing the war intensity measure by one standard deviation leads to a decline of 0.29 or 0.22 years of school using war intensity Measure A or C (columns 1 or 3), respectively and moving a child to one of the three most war impacted provinces lowers schooling by 0.84 years. Examining completion rates for grades 1 to 6 shows that exposed children are worse off, as the coefficients are always negative and most coefficients are significant at the 5 or 10 percent level. The only exception is for the first grade completion rate that shows no statistically significant impact of the genocide. As seen in previous tables and in these instrumented regressions, the largest negative impact is for child completion rates in grade 4. Overall, the instrumented regressions provide a further robustness check reinforcing the conclusion that the genocide had a negative impact on exposed children’s educational attainment.

# **5. Conclusions**

In this paper, we investigate the impact of the 1994 genocide in Rwanda on the schooling outcomes of children. We compare the changes in educational achievement for younger cohorts of school-aged children in one nationally representative cross-sectional survey conducted two years prior to the genocide (in 1992) with the same aged children in a second cross-sectional survey conducted six year after the genocide (in 2000), and we contrast this with the changes in schooling rates for older cohorts of children in both surveys. The empirical identification strategy uses the pre-war data to control for baseline schooling levels for a given age group and exploits variation in which cohorts of children were still in school when they were exposed to the war.

18
**Table 3: Descriptive statistics for sales transactions**

||**Total**|**By type**|**of land**|**Co**|**mmercial land with da**|**ta on ..**|
|---|---|---|---|---|---|---|
|||**Commercial **|**Personal **|**Registry**|**Buyer char’s (BC)**|**Reg & BC**|
|**Parcel characteristics**|||||||
|Parcel area (ha)|2.42|3.07|1.27|2.74|3.00|2.86|
|Sale price ($/ha)|1,439|1,132|1,986|1,153|1,527|1,897|
|<br>Median sale price ($/ha)|959|977|902|995|1,155|1,475|
|Buyer legal entity|0.065|0.078|0.042|0.092|1.000|1.000|
|**Field Characteristics**|||||||
|Parcel located within field|0.839|0.928|0.679|1.000|0.876|1.000|
|if yes, field area (ha)|72.34|83.66|44.70|102.93|109.60|116.02|
|<br>area share not mapped|0.048|0.037|0.075|0.029|0.031|0.028|
|unregistered land in RRP||||0.790||0.800|
|area share not registered||||0.087||0.069|
|<br><1% unregistered area||||0.173||0.189|
|1-10% unregistered area||||0.458||0.489|
|<br>>10% unregistered area||||0.160||0.121|
|<br>Only owner registered||||0.322||0.304|
|area % w owner; no user||||0.058||0.040|
|<1% only owner reg. area||||0.051||0.058|
|1-10% owner reg. area only||||0.228||0.218|
|<br>>10% only owner reg. area||||0.043||0.028|
|<br>% of field used by buyer||||0.060||0.716|
|area share of largest user||||0.805||0.834|
|<br>Buyer is the largest farm||||0.067||0.808|
|No of registered users||||2.332||2.147|
|5 or more registered users||||0.085||0.071|
|<br>**Buyer & village characteristics**|||||||
|Buyer's reg. area (ha)|||||498.76|600.50|
|<br>Buyer's cult. area (ha)|||||446.12|526.16|
|Buyer's share of village land|||||0.371|0.540|
|Buyer's sh. of v. land <33%|||||0.352|0.160|
|Buyer's sh. of v. land 33-50%|||||0.155|0.174|
|Buyer's sh. of v. land >50%|||||0.385|0.622|
|<br>No. villages buyer operates in|||||23.10|17.74|
|<br>Sales of comm. land in village|27.66|28.41|20.35|43.65|47.54|54.87|
|Sales of pers. land in village|20.69|10.97|24.22|13.32|11.48|13.42|
|<br>Year 2021|0.163|0.147|0.192|0.139|0.000|0.000|
|Year 2022|0.114|0.109|0.123|0.108|0.000|0.000|
|Year 2023|0.386|0.380|0.396|0.385|0.000|0.000|
|Year 2024|0.337|0.363|0.289|0.367|1.000|1.000|
|Number of observations|270,762|173,651|97,111|25,719|9,166|2,135|

_Source:_ Own computation based on land monitoring data reported by SGC. _Note:_ All distances are in km.

24
titling regulations were key barriers which appeared to prevent firms formalizing, and more work on the impacts of policy efforts to improve these types of regulations is needed.

- There is a need for better measurement of both the regulations facing small-scale enterprises, and of their interactions with these regulations. For example, the _Doing Business_ project has attempted to measure key regulatory burdens facing businesses around the world, but focuses on limited liability companies and not small-scale enterprises. The ease of starting a business measure considers a company with start-up capital of 10 times per capita income, turnover of at least 100 times per capita income, and between 10 and 50 employees (World Bank, 2013), and may not reflect the regulations facing small businesses. Few countries have data on the characteristics of firms registering and whether they are new or existing firms.

- Existing research has largely focused on the private costs and benefits of formalizing for informal firms. Two key public policy rationales for formalization require more research. The first is the claim that informality leads to inefficient resource allocation, since informal firms who do not pay taxes can compete away customers from more efficient tax-paying formal firms. The second is the view that formalization is socially optimal since it increases government revenues and a culture of respect for the rule of law.

# **Summary and Policy Advice**

Efforts to dramatically lower the cost of registration and simplify the registration process have still left most small scale enterprises operating informally in many developing countries. While these firms would gain some benefits from formalizing in terms of being able to advertise more and issue tax receipts, they typically view these benefits as less than the costs of becoming formal and the ongoing taxes they must pay for being formal. Existing evidence suggests most of these firms are making what is a privately optimal decision, and so the policy rationale for trying to bring small scale informal firms into the formal sector should not rely on the belief that doing so will bring benefits to these firms. This does not mean that there is no cost to inefficient and costly regulations – making firm owners spend extra time and hassle in registering their firms is usually a pure social cost, and so efforts to simplify regulations will benefit those who choose to go through this process.

A key issue for policymakers is then whether there is a public rationale for attempting to formalize small-scale firms. There are several compelling reasons to try and bring larger and more profitable informal firms into the formal system. First, the need of most developing countries to widen the tax 13
# **_Climate change and glacier dynamics_**

There is now general agreement among scientists that unlike thickness (mass balance), which immediately responds to climatic changes, flow rate and glacial length (glacier retreat) have longer response times (Kargel et al., 2011). Response times of most Himalayan glaciers are in the range of 10-200 years (Armstrong, 2010; Kargel et al., 2011) and the largest Himalayan glaciers are responding to changes that occurred as many as 100 years ago (Thompson et al., 2011). With a few exceptions, field measurements of Himalayan glacier retreat are short, and thus available data may not be adequate to capture decadal or multi-decadal climate variations, as suggested by Fujita and Nuimura (2011) and Kargel et al. (2011). Climatic factors, such as precipitation amount and type (e.g. summer versus winter accumulation), albedo (Koul and Ganjoo, 2010), and temperature (which effects glacier retreat by controlling the position of the equilibrium line – a theoretical line that divides the accumulation and ablation zones) (Venkatesh et al., 2011), and non-climatic factors (e.g. debris cover, slope, aspect, elevation) both modulate glacier retreat (Scherler et al., 2011). Scherler et al. (2011) point to the role of debris cover in reducing retreat rate: several debris-covered Himalyan glaciers show stable snouts despite rapidly losing mass (Bolch et al., 2011; Scherler et al., 2011).

Unlike glacier retreat or advance, glacier mass balance (i.e., change in thickness/volume) is the direct and immediate response to annual atmospheric conditions (Haeberli and Hoelzle, 1995; Kargel et al, 2011). The existing literature documents negative mass balance across the Himalayas.

Most recently, Fujita and Nuimura (2011) report comparative analysis of three small, relatively debris-free glaciers in Nepal, varying between 0.5 and 0.8 meter water equivalent per annum (m w.e.a<sup>-1</sup> ). They found considerable thinning (negative mass balance) in the two glaciers in the humid climate in recent decades. Higher mass loss rates in the humid region are attributed to the glaciers’ lower altitude, which makes them more sensitive to small changes in temperature and surface albedo (Fujita and Nuimura, 2011).

High-resolution satellite data shows that glaciers in Everest Region have been losing mass since 1970 (Bolch et al., 2011). From 1970-2007, all ten glaciers in the region lost mass at an average rate of -0.32 ± 0.08 m w.e.a<sup>-1</sup> . Interestingly, the thick debris-covered parts of these glaciers lost mass at a still higher rate of -0.39 ± 0.07 m w.e.a<sup>-1</sup> (Bolch et al., 2011). The greatest mass loss is observed at Imja/Lhotse Shar, although this is partly attributed to lake-induced ice 2