rafmacalaba/datause-encoder-data huggingface dataset:
# using non-grain feedstocks.

<!-- Start of picture text --> 880.00%<br>2005 Value/2010 Target Value 2010 Value/2010 Target Value<br>320.00%<br>250.00%<br>200.00%<br>112.11% 121.82% 112.00%<br>95.07%<br>61.58% 25.20% 36.36% 28.00% 53.33% 46.67% 25.00% 18.50%<br>0.00% 0.00%<br><!-- End of picture text --> Fig. 1. Achievement of 2010 targets for various renewable energies in China Source: the 2005 data are from _Medium and Long-term Development Plan for Renewable Energy in China_ ; the 2010 data of solar PV power generation is from Shi et al. (2011), non-grain fuel ethanol is from Liu et al.(2011), and biogas consumption is from DSERE(2011); other data of 2010 are from Zhao et al.(2011).

There are numerous challenges facing the development of biofuels in China, including the potential impact of biofuels production on the grain security and agriculture development (Huang, et al., 2009; Huang, et al., 2010; Fu, et al., 2011), uncertainty of available land potential for biofuels development (Tian, et al., 2009), economic supply of feedstock (Zhang, et al., 2009) and potential ecological environment problems caused by large-scale cultivation of energy crops (Yan and Zhao, 2009). Much of the debate has now been focused on what the impact of meeting the 2020 targets. Some existing studies have analyzed this issue. For example, Shi, et al.(2010) projected that to meet the target of 15% non-fossil energy by 2020, set by the State Council, about 11 million tonnes of ethanol and 2 million tonnes biodiesel will be required. Qiu et al. (2011) argued that the target of 10 million tonnes of fuel ethanol by 2020 seems to be a prudent target, causing no major disturbances in China's food security. And the results of Wu et al. (2011) presented that the achievement of 2020 fuel ethanol target is necessary to keep the oil independent ratio lower than 60%.

The objective of the paper is to make an assessment of potential technology pathways and policies that might help achieve the 2020 targets. The paper is organized as follows. Section 2 presents current status of biofuels development in China, followed by discussions on potential technological pathways to meet the biofuels targets in 2020 regarding their respective resource potential, supply cost and challenges in Section 3. In Section 4, after the review of current policies and major barriers in China based on broader literature analysis, policy options needed to achieve the target are provided. Section 5 presents an overview of results from the biofuels projection scenarios, and provides insights into the comparison of results. Section 6 summarizes the results in this paper.

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2003.<sup>22</sup> The three inputs exhibit different dynamics throughout the sample period. Median employment growth is positive in all industries in 1999-2003, and is highest in the pharmaceuticals and ready-made garments industries, with growth rates of about 3% per year.<sup>23</sup> Overall, there is evidence of an expansion in the workforce of firms across industries during the sample period. The median growth in materials is positive in all industries in 2002-2003 but is negative in all but the pharmaceuticals industry in years prior to 2002. The trends up to 2002 could signal cheaper raw materials becoming available to firms. Finally, the median growth rates of the capital stock are negative and large in magnitude - between - 5% and -10% - in all industries. This negative trend is due to the fact that, in all industries, many firms make only small investments during the sample period.<sup>24</sup> Such small investments are not sufficient to compensate for depreciation and thus the median capital stock declines over time. Increased capacity utilization rates during the sample period are a potential rationale for this decline. If firms have overinvested in the 1990s, as aggregate data on investment flows in Bangladesh suggests, after 1999 they could be making more intensive use of their existing machines instead of purchasing new machines. For firms in our sample, capacity utilization increases from an average of 79% in 2002 to an average of 80% in 2003.<sup>25</sup> In the pharmaceuticals industry, the increase is stronger - from 65% to 69% in 2002-2003- and may account for the large increase in TFP in that industry shown in Figure 1.

The findings in Sections 2.3.1.-2.3.2 are based on a sample of manufacturing firms, rather than a full census of manufacturing firms in Bangladesh. Given the size distribution of firms in our sample, we can only claim that our findings on TFP growth rates are representative of the growth rates for larger manufacturing firms.

# **_2.3.3. Industry TFP and Allocative Efficiency_**

For any given industry and year, aggregate TFP may grow (decline) because all firms become more (less) productive or because output is reallocated towards the more (less) productive firms. Olley and Pakes (1996) propose an interesting decomposition of

> 22 Within the ready-made garments industry, median output growth is positive and very strong in knitwear and sweater in all years but is negative in woven until 2002.

> 23 Within the ready-made garments industry, median employment growth is highest in knitwear.

> 24 Average and median real investment rates are shown in Appendix Table 2.

> 25 The survey did not collect data on capacity utilization rates prior to 2002.

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advice based on relationships observed from data in other countries may be quite risky, if not misleading, unless the same relationship is also validated with country-specific data. DML estimates from the Republic of Congo suggest that HAZ and stunting have a direct causal effect on early childhood development. In contrast, in São Tomé and Príncipe only HAZ has a positive effect on the ECD index, but otherwise the growth measures do not have a relationship with child development outcomes. This is very much in line with the conclusion of Tran et al (2019) that “...overall, growth is not a sensitive and therefore suitable indicator of child development.” The reasons for the absence of a relationship (causal or not) between stunting and child development outcomes in São Tomé and Príncipe are not clear, but certainly deserving of more investigation.

**Table 6: Summary of the effects of a child stature and maternal education on early childhood development based on the DML approach**

||**Dependent variable: ECD**<br>|**Index(standardized)**<br>|
|---|---|---|
||**Republic of Congo **|**São Tomé & Príncipe **|
|**Specification A**|||
|Child HAZ score|Positive effect|Positive effect|
|Mother’s years of education|No direct effect,<br>(but indirect effect through improved<br>child stature)|Positive effect<br>(no effect on child stature)|
|**Specification B**|||
|Whether child is stunted|Negative effect|No effect|
|Mother’s years of education|No direct effect,<br>(but indirect effect through improved<br>child stature)|Positive effect<br>(no effect on child stature)|
||**Dependent variable: Binary variable(0,1)**|**on whether child is on track in ECD**|
||**Republic of Congo**|**São Tomé & Príncipe**|
|**Specification A**|||
|Child HAZ score|No effect|No effect|
|Mother’s years of education|No direct effect,<br>(indirect through better child stature)|Positive effect<br>(no effect on child stature)|
|**Specification B**|||
|Whether child is stunted|Negative effect|No effect|
|Mother’s years of education|No direct effect,<br>(indirect through better child stature)|Positive effect<br>(no effect on child stature)|

In the Republic of Congo, maternal education does not have any direct effect on early childhood development and the main effect of maternal education on ECD is an indirect one through improved child HAZ or reduced incidence of stunting in the first two years of a child’s life. In contrast, in São Tomé and Príncipe maternal education has a direct effect on child ECD. One important caveat is that all of the 10 questions on the developmental status of children 36-59 months used to construct the ECDI are addressed to and responded by the caregiver of the child, usually the mother. This suggests that socio21
From Necessity to Opportunity: Lessons for Integrating Phone and InPerson Data Collection for Agricultural Statistics in a Post-Pandemic World Alberto Zezza<sup>*</sup> , Kevin McGee*, Philip Wollburg<sup>*</sup> , Thomas Assefa<sup>‡</sup> , Sydney Gourlay<sup>*1</sup>

**JEL Classification:** Q10 Agriculture; C83 Survey Methods, Sampling Methods; R2, Household analysis **Keywords:** Phone surveys; Agriculture; Household surveys; Living standards; Data

> 1 Author ordering randomized using American Economic Association’s randomization tool (confirmation code: Yj0CGbPwsdua).<sup>*</sup> Living Standards Measurement Study, Development Data Group, World Bank.<sup>‡</sup> University of Georgia. Authors may be contacted at pwollburg@worldbank.org. This is a publication of the 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. For more information on the Initiative, visit 50x2030.org. The authors thank Daniel Ali, Chiara Brunelli, Andrew Dillon, Neli Georgieva and Diego Zardetto for their valuable comments on an earlier draft, as well as participants in IFAD’s 2022 conference on “Jobs, innovation and rural value chains in the context of climate transition: Bridging the gap between research and policy” for their useful feedback. The authors are solely responsible for the contents of the paper.
forced and voluntary eradication respectively in Peru) with technical and financial support from the US government. Figures 13 (a, b, and c) show the number of eradicated hectares, as reported by each of the governmental entities in charge of eradication in Bolivia, Colombia and Peru respectively, together with the estimated number of hectares cultivated with coca bush for the each of three producer countries, as reported by UNODC.

As the aerial eradication campaigns in Colombia were intensified after the implementation of Plan Colombia, those individuals and organizations who benefit from coca cultivation (and cocaine production) have figured out ways of counteracting these campaigns. Because spraying with aerial herbicides is prohibited in national parks in Colombia, there has been a rapid increase in coca cultivation in National Parks.<sup>34</sup> The same pattern has been observed in Bolivia and Peru, where farmers have sought remote or protected areas for coca cultivation as a result of the government’s pressure to reduce coca cultivation in the existing centers of cultivation. According to UNODC (2005b), between 2003 and 2004, cultivation in national parks in Bolivia increased by more than 70% and, during the same time, cultivation increased by 53% in protected and forest areas in Peru (see UNODC (2005b) for Bolivia and Peru).

Individuals involved in the cocaine production business have created many other ways of counteracting eradication campaigns: prune operations right after aerial spaying (where the coca plant is cut at one foot above the ground and then grows rapidly), intermingling coca crops with legal crops to avoid being detected, spraying coca plants with substances such as molasses to prevent the herbicide from destroying the leaves of the plant, and the development of genetically modified coca plants that supposedly are resistant to the herbicides currently being used.

> 34 However, at the time this chapter was being written, the Colombian government was evaluating the possibility of lifting the ban on aerial eradication campaigns in national parks.

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obtained from a German bank in DM. This estimate assumes that Levs were exchanged at the parallel market exchange rate. This rate indicates an increase in the real opportunitv# cost of domestic credit from -1.5 p<sup>0</sup> rcent in 1986 to 92 percent in 1989, which then fell to 3: percent in 1990. Therefore, this indicates that in fact the real interest computed using the official price index understates the true gain enterprises obtained from having access to low-cost credit from the domestic financial system. The authorities, bv providing enterprises access to low-cost credit, encouraged the financial indiscipline in the enterprise sector: the greater the losses the more lowcost (subsidized) credit the enterprise received.

Enterprises had difficulties paying their loans, even though they were exempted from the foreign exchange risk and real interest on loans were negative starting in 1989. Moreover, the priority sub-sectors were the ones with greatest problems. These sectors included mineral extraction, metallurgy, energy generation and agriculture. An indication of this difficultv was the 2- year general moratorium granted by the Government in 1987. This moratorium enabled enterprises to renegotiate their payment conditions with banks. Some enterprises renegotiated their loans and became creditworthy, while others defaulted on their loans and limited their access to credit from the financial system. Because defaulting enterprises were concentrated in the priority sectors, the Government started channelling credit to these sectors a. preferential conditions through an extrabudgetary fund, SCIF (Specialized Credit Investment Fund). (See Taole 4 for a breakdown of the credits granted by the SCIF and Table 6 for an estimate of real interest rates.) .Jn addition, in March 1990, the Government wrote off about 2.2 billion lev of agricultural and agro-industry cooperative loans (see Table 5).

Concerning the credit allocated to the Government sector, it accounted for a significant portion of the credit expansion (see Table 3). This credit was used both for lending to enterprises through the SCIF and to finance the deficit. In 1987-89, on average, financial system's expansion in net credit to the General Government was 4 percent of GDP per annum, which represented 31 percent of total financial system's net credit expansion. However, most of this credit was used to fund the SCIF. On average, in 1987-89 credit extended to the SCIF accounted for 3.6 percent of GDP per annum. In addition, the Government also used the credit to finance its own deficit which was substantial in 1988 and 1990.

Interest rates on Government loans were even lower than those applicable to enterprise credit (see Table 6), that is, they had access to preferential rates. Real interest on Government loans started to be negative in 1988. In 1988-90, real interest rates on Government loans were between -1.2 percent and -38.7.
Region Freq. Percent AFR | 412 24.67 EAP | 265 15.87 ECA | 367 21.98 LCR | 351 21.02 MNA | 122 7.31 SAR | 153 9.16 Total | 1,670 100.00

3.9 Table 5 shows multivariate analysis at the project level, with descriptive statistics in Table 6. Two specifications were estimated, one using outcome rating as the measure of project quality, the other based on quality at entry. Both specifications included also dummies for region, a quadratic in total commitment value (grant plus loan) of the project, and a quadratic in the elapsed time (in days) from concept note to project effectiveness.

3.10 The estimates do not support the hypothesis that project design quality (as imperfectly proxied either by quality at entry or outcome rating) is associated with subsequent replication of project concepts – i.e., ‘influence’. Nor is ease of project preparation (as proxied by time from concept note to effectiveness) associated with ‘influence’. However, there is a very strong and statistically significant association between project size (in current dollars) and ‘influence’ (Figure 10). The increase in ‘influence’ from a tiny ($1 million) to a small ($10 million) project is 4.05; from a $10 million to a $100 million project the increment is 1.42, after which ‘influence’ plateaus. As expected, recently exiting projects have much lower ‘influence’ than those circa 2005, simply as a result of their youth. (Figure 11.) However, the statistically significant and substantial rise in ‘influence’ from 1999 to 2005 is unexplained and may be an artifact of increasingly detailed component descriptions over this period.
# **3.3 Pakistan**

For Pakistan, we use data from the Pakistan Panel Household Survey (PPHS) carried out by the Pakistan Institute of Development Economics (PIDE) in the year 2010. The survey contains detailed data on anthropometric measures for children less than six years old. In addition, the survey includes a detailed consumption module covering all aspects of household consumption in the food and non-food categories. The food consumption module in particular allows us to construct six measures of food security for our analysis: per capita household expenditure, food share of total expenditure, per capita daily caloric availability, starchy staple ratio, starchy staple expenditure ration, and food consumption score.<sup>14</sup> Summary statistics on the nutritional indicators broken down by the main regions as well as gender and age groups are presented in Supplemental Web Appendix tables G1 and G2.

# **3.4 Tanzania**

The data for Tanzania come from the 2008-2009 first round of the National Panel Survey administered by Tanzania National Bureau of Statistics. The NPS is a nationally representative “integrated” household survey which covers a broad range of topics. The survey covers 3,280 households spanning all regions and all districts in Tanzania, both mainland and Zanzibar. The comprehensive questionnaire includes a detailed consumption section which allows us to construct measures of food security. It also collects information on child anthropometric measures for children below the age of five. This allows us to construct measures of nutritional outcomes. Restricting the analysis to children under age 5 for whom anthropometric measurements were collected and excluding z-scores outside a certain range leaves us with an effective sample size of 4,392 children for weight-for-age and 4,374 children for height-for-age. There is a considerable variation in nutritional indicators across different regions in the country. Summary statistics on the nutritional indicators broken down by the main regions as well as gender and age groups are presented in Supplemental Web Appendix tables H1 and H2.

The survey contains detailed information on household food consumption with a 7-day recall period. Using the consumption module and supplementing it with the information on household expenditure, we construct seven different measures of household food security: per capita expenditure, share of food in total expenditure, starchy staple ratio (SSR), starchy staple expenditure ratio (SSEXR), household dietary diversity score (HDDS), food consumption score (FCS), and per capital caloric availability.

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in incon,e. Thus, transfer programs will be ineffectivm **e** ans to improve nutrition. Similarly, as argued by Alderman (1989), a weak link between income and nutrition implies that nutrition is to some degree buffered from the downswings in the local economy. Under such scenario, and given that definition of N, for income to have an impact on nutrition, policies must be designed to raise the income elasticity of nutrient intake _(cn_ ); some analysts have argued that raising the mother's education c6ould be another way to do it.

Thus, it would seem that such a focus on food intake (n) to improve nutrition (N) is too restricted, failing to capture the complementarity between the privately and publicly provided inputs in the production of health and nutrition, and the substitution between various privdte inputs (n and q).

If, instead, the most important policy objective is to raise the level of nutrition (N) or health (H), the approach developed above suggests that, except in the case of famines, there is a variety of alternative policy instruments available and their relative merits may change according to location (rural or urban), initial conditions of infrastructure, etc. It also suggests that income will affect nutrition N (and health H) through its impact on q, p, and h (and m), even if it has little or no impact on nutrient intake n.

Education undoubtedly plays an important role in the process of raising nutrition. Not only can it raise the level of food intake but one might also expect the nutrition-related and health-related child care to improve with the level of the iaother's education. In his study for India, Padmanabha (1982) finds that infant mortality falls both in rural and urban areas as the literacy and formal educational level of the mother increases. The evidence may not be entirely conclusive as income may be positively correlated with the mother's education and was not controlled. Garcia and Pinstrup-Andersen (1987) **ini** their study on the Philippines find that the mother's edurscion strongly affects the food consumption and nutritional status of pre-schoolers.

'f the concern is the production of health, and taking infant modtality as one indicator of health (H), Padmanabha (1982) argues that in rural areas in India, the main causes of death are tetanus, pneumonia, dysentery, and typhoid, which are mainly conditioned by the absence or availability of basic facilities of reliable water supply, sanitation (k), and basic child care services (m), so that nutrient intake does not seem to be the major factor. This confirms the results obtained by Castaneda (1989) for Chile, where he finds that the most important variable explaining the remarkable reduction of infant mortality in Chile from 107 per 1,000 in 1965 to 19.4 per 1,000 in 1986 was the increase in urban coverage of potable water and sewerage. Differences in such coverage were found to be statistically more important than the positive impact of the available nutritional programs addressed to mothers (which in turn appeared to have more impact than child oriented programs). Thus, governments may have to increase the
# **DEBT SUSTAINABILITY IN SUB-SAHARAN AFRICA:**

# **UNRAVELING COUNTRY-SPECIFIC RISKS**<sup>**a/**</sup>

Bill Battaile<sup></sup> , F. Leonardo Hernández<sup></sup> , Vivian Norambuena<sup>***</sup>

## **KEYWORDS**

Public debt, external debt, debt sustainability, Sub-Saharan Africa

**JEL CLASSIFICATION CODES** E63, F34, H63, H68

> a/ Paper prepared as background for World Bank (2015) “Feet on the Ground, Eyes on the Horizon: Assessing Vulnerability and Resilience in Sub-Saharan Africa” (forthcoming). Cleared by Punam Chuhan-Pole (AFRCE) and Mark R. Thomas (GMFDR).

>  GMFDR, the World Bank. Email: <u>bbattaile@worldbank.org</u> 

> GMFDR, the World Bank. Email: fhernandez@worldbank.org

> ***  Department of Economics, University of Chile. Email: vnorambuen@fen.uchile.cl

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