rafmacalaba/datause-encoder-data huggingface dataset:
- 11 -

exhibits some properties of elementary education in the modem world. Many routinized efforts are made in the current period, mostly by bodies linked to UNESCO, to bring together standard case-by-case descriptions of national school curricula. In earlier periods this business was done witl nore enthusiasm, in international conferences and compendia. But throughout the modern period, education is seen as a national enterprise, and unlike tLd, situation in the United States, national curricular standards are explicitly formulated, preserved, reported, and eventually collected in comparative reports.

Moreover, the reports are written to be interpreted on a worldwide basis, with less idiosyncrasy in language and style tnan might be expected. Thus, our main sources are international and comparative media, not national case reports. Exacdy as others have found with historical enrollment data on primary education (Benavot and Riddle, 1988), curricular discourse has a universalistic and worldwide character, rather than a local and primordial one. As a consequence, the data we collected are organized in categories that have clear general meaning and comparability -- they are, as it were, written to be coded. This makes our research task easier and tells us a good deal about the nature of national educational systems. Still, questions may be raised about several methodological and substantive points as well as how to interpret our results. We touch upon these issues below.

For the contemporary period (the 1980s) we have data on 92 less-developed countries and 2 colonies (Hong Kong and Namibia -- soon to be independent), although for five of these cases our information indicates only whether a given subject is or is not taught. The data come from UNESCO's International Bureau of Education (microfiche); UNESCO's Regional Office for Education in Asia and the Pacific (1984); Massialas and Jarrar (1983); Fafunwa, Babs and Aisuku (1982); and al-Misnad (1985). In a number of instances we use information gathered direcly from inquiries sent to national ministries of education. For the 1960s period, we have relatively complete data on 53 less-developed countries. Our main sources are The World Survey of Education (UNESCO, 1958) and
**Appendix Table A1. Control variables for table 5, columns,** **<u>(4), (8), and (9)</u>**

|Dependent Var.|Lgrow|ln(LP)|TFP|
|---|---|---|---|
||(1)|(2)|(3)|
|Biggest owner share|1.840***|-0.397***|-0.115**|
||(0.321)|(0.076)|(0.054)|
|Small firm (21-50)|2.222***|0.151***|-0.055*|
||(0.322)|(0.037)|(0.029)|
|Medium firm (51-200)|3.059***|0.330***|-0.093**|
||(0.471)|(0.043)|(0.039)|
|Large firm (201+)|3.981***|0.316***|-0.308***|
||(0.674)|(0.056)|(0.053)|
|Firm age: 6-10|-4.516***|0.063*|0.006|
||(0.908)|(0.033)|(0.028)|
|Firm age: 11+|-7.693***|0.071*|-0.010|
||(1.347)|(0.037)|(0.032)|
|Exporter|0.159|0.253***|0.219***|
||(0.232)|(0.044)|(0.036)|
|Foreign owner share|-1.714|0.462***|0.310***|
||(1.038)|(0.102)|(0.088)|
|Foreign firm (foreign share > 0)|-0.047|0.000|0.069|
||(0.784)|(0.082)|(0.083)|
|_City population category (Base group_|_: population < 50k)_|||
|City Population: capital|0.481|0.161**|0.226***|
||(0.519)|(0.067)|(0.059)|
|City Population: more than 1m|0.511|0.034|0.148*|
||(0.427)|(0.057)|(0.085)|
|City Population: 250k~1m|-0.216|0.063|0.111*|
||(0.392)|(0.057)|(0.063)|
|City Population: 50k~250k|-0.493|0.069|0.063|
||(0.413)|(0.043)|(0.058)|
|ln(Population)|0.038|0.084*|0.047|
||(0.188)|(0.049)|(0.040)|
|ln(GDP pc)|0.403|0.366***|0.377***|
||(0.451)|(0.076)|(0.058)|
|ln(Population density)|<br>-0.647***|<br>-0.048|<br>-0.144**|
||(0.235)|(0.066)|(0.065)|
|**All BE indicators**|Yes|Yes|Yes|
|Year, Industry FE|Yes|Yes|Yes|
|Observations|45716|42880|26592|
|Adjusted_R_<sup>2</sup>|0.066|0.337|0.221|

_Note:_ Standard errors clustered at the country level. The missing values of the firm-level control variables are imputed with city-industry-size mean. Firm size is in terms of number of employees. BE = business environment; FE = fixed effects. * p < 0.1, ** p < 0.05, *** p < 0.01 38
49 A,oendix table A3. Chile: Financing of school feeding grooras **(PAB).** (1988 US$m) <!-- Start of picture text --> Year Central Govt Municipal Govt<br>1981 47.3 0.5<br>1982 41.4 4.6<br>1983 43.7 5.7<br>1984 45.3 5.0<br>1985 47.7 4.1<br>1986 44.0 5.6<br>1987 34.7 7.5<br>1988 34.9 8.6<br><!-- End of picture text --> Based on CH$245.048: US$1.00 Source: World Bank unpublished documents

# Appendix table C4. Cost ver beneficiary and ver calorie of maior interventions.

|PROGRAM|Total<br>Annual<br>Cost (USSM)|K Cal.<br>Delivered<br> (Million)|**_I_**of<br>Benef.<br> ('000)|Cost/Ben<br>per year<br>(USS)|Cost/Kcal<br>(US cents)<br>_|Cal/Benef<br>per day<sup>1</sup>|
|---|---|---|---|---|---|---|
|PNAC|39.7|135,457|1,042.0|<br>38.1|0.0293|130.2|
|PAR|35.0|80,244|557.3|<br>62.8|0.0436|394.0|
|JUNJI|16.0|10,571|55.9|300.0|0.1510|518.0|

I Average calories received per beneficiary per day, assuming the total amount of calories provided were distributed in 365 days. Sources Muchnik and Vial (1990).
given country, weighted by the share of trade in each industry. We use the classification of goods _k_ at the 2-digit SITC level in order to obtain a broad classification of goods, thus allowing us assess the effects of IIT as related to trade of related but different goods rather than those of products with some degree of horizontal differentiation. For example, “optical glass and elements of optical glass” and “glass mirrors, unframed, framed” belong to the same 2-digit SITC category (industry code 66, “non-metallic mineral manufactures”), but are not in the same 4-digit SITC category (the former is classified as industry code 6642 and the latter as 6648). The broader classification may capture and is arguably more indicative of possible technology diffusion and learning spillovers than the stricter classification at the 4-digit level, which can be associated with the love for variety as in Krugman (1979).

Our measure of countries’ insertion into GVCs is proxied by the degree of upstreamness embedded in traded goods. Antrás et al. (2012) calculate a measure for the United States by exploring input-output matrices for 426 industries in the 2002.<sup>22</sup> The constructed indicator ranges from 1 (final use goods, such as cars and footwear) to 4.65 (goods associated with the processing of raw materials, such as petrochemical manufactures and copper and iron ore mining). We use this classification of US industries as a benchmark and apply it to the basket of other countries’ exported goods. In particular, we calculate the share of exports classified within a given range of this upstreamness indicator: goods used at the beginning of GVCs (e.g. exports of primary products), goods used in the middle of GVCs (e.g. exports of intermediate goods), and goods used at the end of GVCs (e.g. exports of final goods).<sup>23</sup> We consider two alternative definitions for these three ranges of goods. First, we split evenly the 426 industries into five groups. The first group corresponds to goods used at the beginning of GVCs, the three following groups correspond to goods used in the middle segments of GVCs, and the last group corresponds to goods at the end of GVCs. Second, as robustness, we evenly split industries in three groups, each one corresponding to one of these segments of GVCs.

The second set of variables analyzed is related to the composition of countries’ trading partners, exploring the position of these partners in the global trade network. Even though Few countries occupy central places in the global trade network, there is no widely accepted definition of

> 22 The classification by Antrás et al. (2012) is based on the HS 2002 classification at the 10-digit level. We convert this classification to the 4-digit level SITC Revision 2, which is the classification available for the trade database of Feenstra et al. (2005).

> 23 We also tried a number of alternative classifications involving 4, 5, or even 10 groups of industries based on the characteristics of the industries and the number of goods within each category. The results are qualitatively similar to the ones presented in the paper.

13
Policy Research Working Paper 10306

# **Abstract**

Understanding how e-commerce platforms are affecting the small, informal firms that sell on them is a question of growing importance to researchers and policy makers in developing countries. This paper examines this question using data from surveys of firms selling on two e-commerce platforms in South Asia. The businesses selling on these platforms range widely in terms of size, degree of formalization, and other characteristics. However, these firms—even the micro and small ones, which tend to be informal— are from a selected group, being owned and managed by individuals who are more educated and younger than the owners and managers of more typical firms in this setting. The sellers’ main reason for joining the platforms is to access more customers. Most of the sellers report an expansion of their business after joining the platforms. They also report an increase in their incentive to register their business and their visibility to tax authorities. Other, less widespread channels of impact reported by the firms include the adoption of new or improved business practices and technologies, better access to finance, and greater flexibility in balancing home and work life. In general, these reported impacts do not vary significantly by firm size or degree of formalization, suggesting that even informal, small firms that have (selectively) joined e-commerce platforms can benefit from the greater market access facilitated by the platforms. Finally, given size and age, firms that have been selling on the platform for a longer period are more likely to experience these impacts, suggesting that firms learn how to use the platform more effectively over time.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, akshay_dixit@g.harvard.edu, amgolla@yahoo.com, ananyakotia@gmail.com, jlee20@worldbank.org, prema.narasimhan@berkeley.edu, and ssharma1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
because of changes in the eligibility criteria, with more generous replacement ratios and changes in indexation rules.

**Table 1: Changes in primary deficit and items of public expenditures between 2008 and 2017 (% of GDP)**

|**Country**|**Primary deficit**|**Public wages**|**Social benefits**|**Interest**|
|---|---|---|---|---|
|Argentina|6.2%|3.3%|5.0%|0.5%|
|Peru|6.1%|0.9%|-0.3%|-0.5%|
|Chile|5.9%|1.5%|-0.5%|0.3%|
|Brazil|5.5%|0.1%|2.1%|0.5%|
|Costa Rica|5.3%|1.7%|-0.1%|0.6%|
|Ecuador|4.9%|2.2%|2.4%|1.0%|
|Panama|3.1%|1.9%|-0.6%|-1.4%|
|Colombia|2.1%|-0.2%|0.4%|-0.1%|
|Uruguay|1.6%|0.6%|4.0%|0.3%|

_Source_ : Authors’ own calculations based on data from IMF, World Bank and national institutes of statistics.

To estimate how these measures of budget rigidities affect the probability of countries getting into fiscal distress and the likelihood of doing a fiscal adjustment, we use the following strategy. First, we identify periods of adjustment needs based on two different methodologies: one based on consecutive years of primary gaps above certain threshold, and the other based on the level of debt. Second, we identify periods of fiscal adjustment as years where there was at least a 0.1% of GDP improvement in the cyclically adjusted primary balance for two consecutive years (Escolano et al., 2014). Then, we estimate a linear probability model using a panel of 182 advanced and developing countries controlling for economic and political factors that previous literature has identified as potential determinants of governments having fiscal adjustment needs and the ability to do so.

We find that the actual levels of rigid components of public spending (wages, pensions and interests) are positively associated with the probability of countries getting into fiscal distress and are negatively associated with the ability to consolidate. This result may appear counterintuitive, as many advanced economies with relatively high shares of rigid spending have had successful fiscal consolidations. However, we find more robust evidence by using the share of non-structural rigid spending (when spending on public wages and pensions is above what the fundamentals of their economy would suggest), which may explain why these apparently rigid economies have been able to pursue fiscal consolidations. Moreover, the effect of rigid expenditure seems to be more relevant for economies with higher inequality, government with lower margin of majority and countries with lower institutional

- 7 -
Figure 2: R/M2 and VAR(n(R/M2)

# LATIN AMERICA

<!-- Start of picture text --> ARGENTINA COLOMBIA<br>0.45 3.125 0.60 0175<br>0.40 Ii ~~~~~~~~~~~~~.100 0.55 l.0150<br>0.35 t 9 0 : X . .- O.S0I .o .0125<br>0.35<br>0.0754 0.45 1<br>lolj LO~~ II.011<br>0.15 , , , , 0.35 , .0050 , ' > ,^ ,.- -. J ...... }.WW 025<br>0.20 0~~~~~~~~.025 I '<br>Lj I 0.30 I I02<br>0.15 * . '' 1. .00 0.25 ..- 09<br>1990 1991 1992 1993 1994 1995 1990 1991 1992 1993 .994 1995<br>BRAZIL MEXICO<br>0.440 _ 0.09 0.30 .16<br>/ _. _ -<br>0.400 0.0 W i.14<br>0.360 . .07 0.25 0.12<br>0.320 I .01 o.as<br>0.402gt ^ I _0 0.20 I 1<br>.0.240 0 II<br>02W -0~~~~~~~.031~<br>0.250 O..0OS0<br>0.1S9 19U 1959 1990 1991 1992 1"N 1994 1995 1991 1992 1993 1994 IS90 199<br>0.120 I; .0. 0.10.02<br>3.0 0.05 . .. .. .0.0<br>99 191 192 '1993' 1994 1995 1990 1s99 1992 1993 1994 199<br>CHILE URUGUAY<br>0.70 -01so 0.200 .045<br>0.60 ~~~~~~~~~~II .0~~~~~~125 0.175 ]<br>0.55 I .0~~~ ~ ~~~~~~~ 0.150 II ~ ~~~~100 I.5<br>0.50 I- 4 -I<br>5.45 It1 I<br>0.40 I 0.000 0.100 0015<br>I I ~~~ ~~~~~~~ .015~~~~~~~~~~~0<br>I 007 I .000~~02<br>0.30 III .02 .050.005<br>0.25 1987' 16 'ii 19910191 ii'gi..... 199193 1994 1995 ,Si'S -. 0000 1W ii 92 12 14'99 9<br><!-- End of picture text -->
Learning from Developing Country Power Market Experiences: The Case of Colombia

|**2011**|9,295|14,420|55%|2%|9%|58,062|3%|
|---|---|---|---|---|---|---|---|
|**2012**|9,504|14,361|51%|2%|0%|59,211|2%|
|**2013**|9,383|14,559|55%|-1%|1%|61,182|3%|
|**2014**|9,551|15,489|62%|2%|6%|64,014|5%|
|**2015**|10,095|16,420|63%|6%|6%|66,275|4%|
|**2016**|9,904|16,595|68%|-2%|1%|66,318|0%|
|**2017**|9,996|16,779|68%|1%|1%|66,893|1%|
|**CAGR 2010-17**|**1.4%**|**3.4%**||||**2.4%**||

*Net effective generation capacity in the National Interconnected System by December 31st.

Source: Own elaboration based on data collected by the World Bank.

Despite very high and rising reserve margin, supply reliability in Colombia must be assessed considering its dependence on hydro power plants with little multi-year storage capacity. Power generation from thermal power plants increased sharply during 2015, providing 32% of total generation (see Figure 6-13). Average PLFs increased for thermal power plants while PLFs decreased for hydro power plants, mostly due to the 2015-16 El Niño phenomena (see section 6.1.3). Such trend reversed during 2017 due to normal hydro inflows, highlighting the dependence of Colombia’s generation mix on the prevailing hydrological conditions.

<!-- Start of picture text --> 70,000<br> 60,000<br> 50,000<br> 40,000<br>GWh<br> 30,000<br> 20,000<br> 10,000<br> -<br>2010 2011 2012 2013 2014 2015 2016 2017<br> Hydro Thermal<br><!-- End of picture text --> Figure 6-13 Evolution of power generation by technology in Colombia.

Source: Own elaboration based on data collected by the World Bank.

47
Policy Research Working Paper 7253

# **Abstract**

This paper analyzes the impact of the 2012 crisis in Mali on internally displaced people, refugees and returnees. It uses information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination was found to present a good and robust way to monitor the impact of conflict on - hard-to-reach populations who at times live in areas inacces sible to enumerators. Results indicate that better educated and wealthier households as well as those exposed to less violence fled the crisis. Significant amounts of durable goods (20–60 percent) and animals (75–90 percent were lost and the welfare of the displaced declined considerably as a result of the crisis. Yet over time its impact has diminished. By February 2015, most eligible children were going to school and employment levels and number of meals consumed were at pre-crisis levels. The paper finds that different ethnic groups chose different places of refuge. Depending on location, the narrative of the crisis and the solutions that are envisaged differ diametrically.

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jlendorfer@ worldbank.org and jhoogeveen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 5b—Trade Flows by Trading Partner of Tanzania (in percentage)**

|||||I|mports|||||||||Exports|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Kenya|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|Kenya|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|
|**Business Services**|||||||||||||||||||
|Air Transport|0.0|0.0|0.0|1.7|1.4|11.5|42.4|1.7|41.9|0.0|0.0|0.0|0.5|1.0|16.3|40.1|3.1|38.9|
|Communication|0.3|0.0|0.0|2.1|0.0|16.6|46.4|3.9|30.7|0.2|0.0|0.0|0.7|0.6|11.4|58.6|1.7|26.8|
|Insurance|0.0|0.0|0.0|0.3|0.9|19.8|48.7|1.5|28.8|0.0|0.0|0.0|0.8|0.8|33.2|24.9|6.3|33.9|
|Business Services nec|0.0|0.0|0.0|0.0|0.0|9.1|60.0|2.5|28.3|0.0|0.0|0.0|0.4|1.2|10.7|51.6|2.4|33.7|
|Financial Services nec|0.1|0.0|0.0|0.5|0.6|24.9|41.2|2.5|30.3|0.0|0.0|0.0|0.5|0.8|13.0|41.9|2.3|41.5|
|Transport nec|0.0|0.0|0.0|1.8|0.9|7.2|40.0|4.4|45.7|0.0|0.0|0.0|0.5|0.6|16.3|44.7|3.7|34.2|
|Water Transport|0.0|0.0|0.0|1.7|0.5|10.2|19.9|10.7|56.9|0.0|0.0|0.0|0.6|0.8|12.0|42.0|3.9|40.6|
|**Dixit-Stigliz Goods**|||||||||||||||||||
|Chemicals Mineral and Metal Products|8.0|0.6|0.0|1.1|15.6|0.9|21.9|11.8|40.2|0.9|0.4|0.7|0.7|22.0|0.9|3.8|4.2|66.5|
|Energy and Minerals|46.7|0.4|0.0|3.9|13.3|0.4|1.4|2.0|31.9|0.2|0.0|0.0|0.2|7.9|1.5|10.0|47.0|33.1|
|Food Products|11.1|3.1|0.0|0.2|8.5|1.4|11.7|0.8|63.2|5.6|3.5|1.5|0.9|2.2|4.4|52.0|1.7|28.3|
|Petroleum and Coal Products|1.2|0.0|0.0|1.9|2.7|0.4|1.3|1.1|91.4|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Other Manufacturing|2.0|0.1|0.0|1.1|8.7|7.2|41.8|13.9|25.3|12.0|1.7|1.1|2.2|22.7|6.9|24.3|1.5|27.7|
|Textile and Apparel|6.1|0.5|0.0|0.3|2.5|1.0|3.6|48.7|37.4|10.5|2.1|0.3|2.0|12.4|7.1|28.5|3.9|33.2|
|Wood and Paper Products|11.4|0.2|0.0|1.6|19.6|1.2|23.6|9.9|32.4|55.0|1.0|0.5|1.6|3.5|1.5|7.5|6.5|22.8|
|**CRTS**|||||||||||||||||||
|Agriculture and Forestry|6.9|0.2|0.0|0.3|5.5|11.3|2.7|0.6|72.5|4.9|0.2|0.1|0.6|1.7|6.9|41.0|3.5|41.0|
|Other Services|0.2|0.0|0.0|0.8|1.0|27.5|35.9|4.4|30.1|0.1|0.0|0.0|0.7|0.8|20.3|38.0|3.1|37.1|
|Trade|0.0|0.0|0.0|0.6|0.3|13.0|44.3|3.5|38.4|0.0|0.0|0.0|0.5|0.7|12.2|45.6|3.9|37.1|
|Utilities|0.2|0.4|0.0|0.3|3.1|16.5|35.8|3.5|40.1|0.0|0.0|0.0|0.5|0.7|12.2|45.9|3.9|36.7|

# **Source:** GTAP 8.1 data set.

60
categories (in order to obtain the same total cost). More variations in costs are introduced in the study by Frederick et al. (2008). Note that feedstock accounts for between 32% and 52% of total costs of production across all studies. This is in marked contrast with firstgeneration ethanol, where feedstock accounts for roughly 55% to over 70% of the total costs of production (IEA 2008).

**Table 15: Production costs for the lignocellulose** **<u>process</u>** <u>($/lt)</u><sup>a</sup>

||Sass|ner et al (20|08)|McAloon et<br>al(2000)|Solomon et al<br>(2007)|
|---|---|---|---|---|---|
||Salix<br>(willow)|Spruce|Corn st|over<br>Cornstover|Switchgrass or<br>Wood|
|Feedstock|0.23-0.28|0.21-0.23|0.21-0|.28<br>0.19|0.20|
|Other costs|0.19-0.26|0.17-0.19|0.18-0|.26<br>0.20|0.22|
|Co-products|-0.09--0.16|-0.1--0.12|-0.09--|0.16<br>-0.02|-0.04|
|Total operating costs|0.32-0.37|0.28-0.3|0.3-0.|37<br>0.36|0.38|
|Capital costs|0.25-0.31|0.24-0.25|0.23-0|.31<br>0.24|0.22|
|Totalcosts|0.57-0.69|0.52-0.55|0.53-0|.68<br>0.60|0.60|
|||Fredericke<br>|t al(200<br>|8)<br>||
||YellowPopl|ar<br>Lobloll<br>(1)|y Pine<br><sup>b</sup>|Loblolly Pine<br>(2)||
|Feedstock|0.15|0.2|3|0.27||
|Other costs|0.12|0.1|1|0.07||
|Co-products|-0.02|-0.0|2|-0.11||
|Total operating costs|0.25|0.3|2|0.23||
|Capital costs|0.14|0.1|2|0.42||
|Totalcosts|0.39|0.4|4|0.65||

Source:<sup>a</sup> Inflation adjusted to 2008.<sup>b</sup> Two different pretreatments of the biomass are considered here.

For the case of jatropha based biodiesel, costs of production have been reported in the range of $0.44-2.87 per liter for developing country settings (see Table 16). Wide ranges are reported even within studies for a given location, reflecting persisting uncertainty regarding jatropha yields and associated feedstock costs of production (Peters and Thielman 2008). Estimates at the lower end of the range seem to be based on fairly optimistic assumptions on production costs. Some authors expect costs to decline as large scale production and oil extraction improves the efficiency of the process and economies of scale are exploited (GTZ 2005). Other authors indicate that stakeholders should be cautious in front of these cost projections, since they may remain high even in large scale operations (Peters and Thielman 2008).

Some support for that view can be gleaned from the Indian experience. The Indian government mandated their state-owned distribution firms to buy biodiesel at the fixed 30