rafmacalaba/datause-encoder-data huggingface dataset:
# **Development of Biofuels in China: Technologies, Economics and Policies**

Chang Shiyan<sup>a,b</sup> , Zhao Lili<sup>b</sup> , Govinda R. Timilsina<sup>c</sup> , Zhang Xiliang<sup>b,d</sup>

**Key words** : Biofuels, Energy policies, Renewable energy

## **JEL Classification** : Q28, Q42

**Sectors:** Energy and Mining, Environment, Agriculture and Rural Developments

aLow Carbon Energy Laboratory, Tsinghua University, Beijing, (changshiyan@mail.tsinghua.edu.cn); bInstitute of Energy, Environment, and Economy, Tsinghua University, Beijing, (zhaoll@mail.tsinghua.edu.cn); cCorresponding author, The World Bank (gtimilsina@worldbank.org); dChina Automotive Energy Research Center, Tsinghua University, Beijing, (zhang_xl@mail.tsinghua.edu.cn)
# **Determinants of Globalization and Growth Prospects for Sub-Saharan African Countries**

Hippolyte Fofack<sup>1</sup> _The World Bank Group, Washington DC_ JEL Classifications: E66, C33, F14, N10, O33. **Keywords** : Economic diversification, globalization, globalizers and globalizees.

> 1 This paper has benefited greatly from suggestions and comments by Pierre-Richard Agénor, Shanta Devarajan, Ibrahim Elbadawi, Leonce Ndikumana, Roumeen Islam, Raj Nallari, Yaw Nyarko and Etienne Yehoue; I would like to thank them all for their contribution which helped improved earlier drafts. In addition, I would like to thank seminar participants at the African Development Bank Economic Conference held in Tunis in November 2008 and particularly Ron Leung, the discussant, for their constructive comments and suggestions. I would also like to thank Nihal Bayraktar for excellent research assistance.
erosion across countries (regions). He concludes that the preferences, on average, equal 12 percent of the total value of eligible exports, three-quarters of which is generated by EU programs. Echoing others in the literature, almost half of the total benefits (by export value) generated by the EU is due to the EU Sugar Protocol.<sup>34</sup> Yamazaki’s conclusions that the impact of preferences are highly concentrated—a recurrent finding in the literature has been well known for decades—also suggests that problems of preference erosion are concentrated.

There are two crucial questions: the potential magnitude of erosion (a key input into any determination of the overall level of financing that may be needed to address it) and the sectoral composition of the potential impacts (a key factor determining the incidence and distribution of adjustment costs within countries). Using a partial equilibrium framework, IMF (2003) examines the overall impact on the exports of LDCs from preference erosion arising from trade liberalization by the Quad. Assuming a 40 percent cut in protection in the Quad and that LDCs have free access to these markets (a strong assumption given the literature cited above), it concludes that the potential loss at the aggregate level amounts to just 1.7 percent of total LDC exports. However, individual LDCs may suffer a more significant loss from preference erosion due the concentration of their exports in products that enjoy deep preferences. Of these, Malawi, Mauritania, Haiti, Cape Verde, and São Tomé and Príncipe are found to be the most vulnerable to preference erosion. Malawi was predicted to experience a loss of 11.5% of total exports, the next four countries between 5 and 10%, and another 10 countries between 3% to 5%. The total (aggregate) value of export revenue that would be lost was around $530 million (of which two-fifths would be accounted for by Bangladesh). Note that these are small numbers—equivalent to only one percent of ODA flows.

Earlier research by Yeats (1995), also using a partial equilibrium framework, estimates that the complete elimination of MFN tariffs by the EU, Japan and the United States would result in total erosion costs for 29 Sub-Saharan African countries of some US$4.2 billion of exports (in present discounted value terms), with the reductions in exports to the EU and Japan on the order of 1-2 percent, and an _increase_ in exports to the

> 34 Also significant were preferences for tobacco. As noted by Alexandraki and Lankes (2004), given that Yamazaki’s dataset ends in 1992, bananas are not covered as the EU banana regime began in 1993.

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**Table 2: Public Finances and Economic Growth, 1995--98**

|Year|Primary<br>deficit<br>(percent of<br>GDP)|Interest<br>Percent of<br>GDP|payments<br>Percent of<br>revenues<sup>a/</sup>|Governm<br>$US<br>billion|ent debt<sup>b/</sup><br>Percent of<br>GDP|Real GDP<br>growth<br>(percent a<br>year)|
|---|---|---|---|---|---|---|
|1995|2.2|3.6|28|170|50|-4.0|
|1996|2.5|5.9|47|201|48|-3.4|
|1997|2.4|4.6|38|218|50|0.9|
|1998<sup>c/</sup>|1.3|4.6|43|242|75|-4.9|

<u>a/ Cash plus noncash basis.</u> b/ Domestic plus foreign, end of period. c/ Does not capture the subsequent debt renegotiation. _Source_ : Table 9.1, Pinto, Gurvich and Ulatov (2005).

The answer to this puzzle can be found in movement of the real exchange rate. The real interest rate, _r_ , is a composite of that paid on ruble and dollar-denominated debt, with the weights determined by the relative shares of these currencies in total government debt. Here’s what happened over 1995-97: even though real interest rates on ruble debt were high and growing, it accounted for a relatively small share of total debt; even in 1997, ruble debt was just 37 percent of total debt. With the real exchange rate appreciating rapidly over this period, Russia enjoyed substantial valuation gains on dollar-denominated debt. Thus, Kharas, Pinto and Ulatov (2001) estimate that in 1996 alone, the effect of real appreciation was to lower the ratio of government debt by 8 percentage points of GDP, which would offset a substantial fiscal deficit. But there is a problem if the real exchange rate is becoming overvalued, which we shall argue later was the case in Russia.

How did nonpayments feed into the government’s debt dynamics? Directly, as it turns out. The biggest implicit subsidy providers to the manufacturing sector via nonpayments—oil, gas, electricity, railways—became delinquent on their own tax payments in order to compensate themselves. At the same time, manufacturing companies ran their own tax arrears. The combined effect led to chronic tax shortfalls and hence, higher primary fiscal deficits and greater 12
**Table 2: Intergenerational Persistence and Coresident Sample Bias: India (All Children)**

||**Inter**<br>|**generational R**<br>**Coefficients (IG**|**egression**<br>**RC)**<br>Test of|**Inte**|**rgenerational Cor**|**relations(IGC)**<br>Test of|
|---|---|---|---|---|---|---|
||**Full**|**Co-resident**|Equality<sup>2</sup>)|**Full**|**Co-resident**|Equality<sup>2</sup>)|
|**Father's Education**|**0.49*****|**0.43*****||**0.44*****|**0.41*****||
|(Standard Error)|(0.0120)|(0.0134)|109.23|(0.0108)|(0.0128)|38.87|
|Observations|14,877|9,132||14,877|9,132||
|**Mother's Education**|**0.57*****|**0.47*****||**0.37*****|**0.33*****||
|(Standard Error)|(0.0178)|(0.0172)|88.47|(0.0115)|(0.0121)|27.62|
|Observations|14,877|9,132||14,877|9,132||
|**Parent's Education**|||||||
|**(average)**|**0.66*****|**0.56*****||**0.46*****|**0.42*****||
|(Standard Error)|(0.0152)|(0.0164)|54.56|(0.0106)|(0.0123)|23.41|
|Observations|14,877|9,132||14,877|9,132||
|**Biases**||**Bias**<br>**(IGRC)**|Co-residency<br>Rate||**Bias (IGC)**||
|Father's Education||**14%**|61%||**7%**||
|Mother's Education||**21%**|61%||**12%**||
|Parent's Education|||||||
|(average)||**18%**|61%||**10%**||

NOTES: (1) * significant at 10%; ** significant at 5%; *** significant at 1% (2) The Bias is defined as [(Estimate from Full Sample – Estimate from Coresident Sample) *100] / Estimate from Coresident Sample

# **Table 3: Intergenerational Persistence between Father- Sons and Mother-Daughters (Bangladesh and India)**

||**Ban**<br>**Bias**|**gladesh**<br>Test of<br>No Bias<sup>2</sup>)|**Bias**|**India**<br>Test of<br>No Bias<sup>2</sup>)|
|---|---|---|---|---|
|**Father-Son Persistence**|||||
|Intergenerational Regression Coefficient|**30%**|69.54|**9%**|49.59|
|Intergenerational Correlation|**9%**|7.95|**2%**|9.18|
|Coresidency Rate|52%||79%||
|**Mother-Daughter Persistence**|||||
|Intergenerational Regression Coefficient|**46%**|69.54|**24%**|31.75|
|Intergenerational Correlation|**11%**|7.95|**13%**|8.27|
|Co-residencyRate|26 %||39%||
Further to these main outcomes, I discuss results for some additional outcomes below.<sup>31</sup> As additional proxies of intra-household bargaining, these include the wife’s recent experience of physical violence perpetrated by the husband against her, the couple’s time use, and outcomes from two small economic games that I implemented as part of the survey. To further explore mechanisms, I also investigate other expenditures of the household and measures of the women’s attitudes and self-beliefs.

# **3.4 Descriptive statistics**

The typical woman in the samples, for both the promotion and the role model analysis, is in her mid-twenties and lives with her husband and one child in a nuclear family. Women in both samples are similar on a number of dimensions.<sup>32</sup> The women are on average about 26 years old and more than 80% are married. Nearly two-thirds of the women have migrated to Dhaka from their place of birth. On average, they have one child. Interestingly, more than half of respondents with children report that at least one child does not live in the household, but instead lives with relatives.<sup>33</sup> The women are also four to five years younger than their husbands. Despite this age gap, they only have about a third of a year less education than the husbands. They contribute slightly below half of the total household income.<sup>34</sup> It is noteworthy that two-thirds of all husbands also work in the garment industry.

The two samples differ in other aspects which indicate that the women who were nominated or selected for the promotion program come from a higher socioeconomic background and play a larger role in their households than women in the role model analysis sample. For example, 12% of women in the promotion analysis sample consider themselves the household head, compared with only 1% in the role model analysis sample. In addition, the former group has on average about two years of education more than the latter group. The households in the promotion analysis sample also report slightly higher household income, of about 23,500 Taka (roughly 280 USD at the time of the surveys), compared to about 21,000 Taka (roughly 260 USD) in the role model analysis sample.

These differences underline that the sample of selected and nominated women is certainly not representative of all women in the garment industry. I however posit that comparing selected

> 31All are defined in detail in Table C.4 in the Online Appendix.

> 32See the summary statistics in Table C.5 in the Online Appendix.

> 33Note that this is derived from the non-exposed operators in the role model analysis sample, since I don’t have comparable data from the promotion analysis factory baseline.

> 34Note that we only collected individual income data for the wife and husband in the promotion analysis baseline, but total household income during the household surveys. For the promotion analysis baseline, I therefore approximate total household income as the sum of income of both husband and wife. This will only differ from the total household income variable used in Panel B if household members other than the husband and wife also contribute income.

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# **c. Generosity (benefits as % of MYPP)**

# **d. Inequality, poverty, and poverty gap impact**

<!-- Start of picture text --> 50.0 Marginal Contributions to<br>0.01<br>Inequality<br>Change in Poverty Gap 1.39<br>Marginal Contributions to<br>0.40<br>Poverty<br>0.5 0.0 0.0 0.1<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**In the context of evaluating the impact of both Monthly Social Allowance (MSA) and heating allowances, a substantial portion of households in the lowest income brackets, particularly the first and second deciles, would benefit from at least one of these transfers (figure 13, panel a)** . Surprisingly, even households in the fourth decile would receive support. For those in the first decile, the level of generosity would be notably high, reaching approximately 28% of their income (figure 13, panel c). However, while the assistance provided to the first decile is substantial, the overall effect on inequality at the societal level remains relatively unchanged. This observation implies that although MSA plays a crucial role in directing assistance to the most financially vulnerable, the scale of its impact on broader income inequality remains limited.

**Conversely, the impact on poverty levels is more pronounced than alternative scenarios (figure 13, panel d). The reduction in the poverty gap, which measures the depth of poverty, is particularly noteworthy.** Nonetheless, this outcome suggests that to bring about a more substantial reduction in overall poverty rates, a further amplification of MSA or complementary poverty-alleviating measures may be necessary, given that the bulk of MSA benefits primarily the first decile, and its value has only seen marginal increases.

**Figure 13. Simulated performance and poverty and inequality Impacts of the MSA Scheme and Heating Allowance under Policy Scenario 2**

**a. Coverage (% of decile in each decile)**

**b. Benefit incidence (% benefits allocated to each decile)**

<!-- Start of picture text --> 4.3 0.2<br>84.3<br>1<br>53.1 2<br>40.4 3<br>54.7<br>4.2 0.2 0.4 4<br>5<br> 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> 32
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8. No precise estimate of health expenditures was reported for Colombia, but in 1981 the provision of public and private health services represented about 5 percent of the country's GDP. Health expenditures were then covered by the fiscal budget (33 percent) and user charges (40 percent). Other sources of finance included returns on investments, credit, and leasing of property especially by private health institutions. Only about one-sixth of the user fees were collected in the public sector. Governmental budgetary allocations and foreign aid were almost equally important sources for funding health expenditures in Burkina.

9. Taking the sample **as** a whole, contributions from the governments were the principal source of funds. User fees played a comparatively minor role in financing public health services, as demonstrated in the table below:

|Source of Funds|**Umz**<br>**I1**<br>(% of Total Expenditures)|9||
|---|---|---|---|
|Government|62.7<br>15- 100|100% in|2 cases|
|UserFees|28.4<br>0 -<br>85|0 in|2 cases|
|ForeignAid& Other|8.9<br>0 -<br>31|0 in|6 cases|

10. The exceptionally high cost recovery levels in Brazil and China deserve a further focus. The Brazilian experience could be attributed almost solely to the performance of the National Social Security Institute for Medical Assistance (INAMPS). INAMPS wa formed by a merger of several social security programs for **various** groups of **workers** under the supervision of the Ministry of Social Insurance and Assistance. It is financed by a payroll tax for wage earners and equivalent taxes for the self-employed. Its subscribers include about 90 percent of Brazil's population. The impressive recovery of costs from users in China is also associated with the manner in which the health system is structured. The users are actively involved in the management of health services. Health services at the brigade level are mostly fi:anced by cooperative uedical care insurance schemes, which are in turn funded jointly by annual prepayments of individual members and by annual appropriations from the brigade's welfare funds. A certain proportion of the brigade's collective income, largely from sales of agricultural produce, is set aside as 'welfare funds" to be used for educational, health, cultural and other public functions. At the coimune level, health centers are mostly established by the coomunes themselves, and operations are financed through user fees and governmental subsidies. It is the "collectivity" aspect of health care provision and finance that allows members to closely monitor the performance and viability of the system.

11. The expenditures and financing of urivate sector health services were available only in 6 cases. Private sector spending for health care ranged from negligible levels (e.g., China and Burkina) to nearly US$80 million per year (e g., Indonesia). On average, about 40 percent of total expenditures (slightly over 59 percent excluding China and Burkina) were recovered through user fees. A comparison of the contribution of user charges in financing health services by sector, based on the six cases, is shown below:
17 confirmed Influenza and Absenteeism in Schoolchildren, Cairo, Egypt’, _Emerging Infectious Diseases_ , 17(4), pp. 619–625. doi:10.3201/eid1704.101353.

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If the impact of public health investment was modest, was it due to inadaquate investment, or to a weak impact of public health investment on mortality, or both? We favor the first hypothesis. That conclusion is based in part on the evidence that 18th century public health environmental efforts had recorded considerable success in reducing English mortality. The conclusion is also based in part on Nichael Flinn's (1965, p. 52) observation that reformers thought that French public health practice was in advance of the British at the time of the Sanitary Report. Perhaps more to the point, as with Riley's (1987) 18th century English evidence, work by Samuel Preston and Etienne van de Valle (1978) suggests that water and sewage improvements played an important role in 19th century French urban mortality declines. These were concentrated among the water-borne diseases, but they think that cleaning up French cities may have lowered death rates by air-borne disease too since sanitation investments improved nutritional status (Preston and van de Walle, 1978, p. 288). Thus, they conclude that "water support rather than food support systems are the key to understanding trends in urban French mortality" (Preston and van de Walle, 1978, p. 284), quite in contrast with McKeown's characterization of 19th century Britain. In another paper, Preston and Verne Nelson have shown that the mortality changes in late 19th century Britain were quite unusual based on the "normal" cause of death patterns among 48 nations betweenl 1861 and 1964, the high share attributed to tuberculosis in particular (Prestcn and Nelson, 1974, p. 24).

In 1985, Preston offered one more piece of evidence which suggests that McKeown has exaggerated the role of nutritional intake and living standards, and, by inference, understated the potential impact of public health. Preston estimated the relationship between per capita income and life expectancy for the 1960s and the 1930s, comparing each with limited observations on the 1900s. Across the 20th century at least, income per capita (standard of living

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Figure 9. Expected impact of reforms on components of performance in Boliviae <!-- Start of picture text --> Reform Interventions affectine three nerformance areas.<br>I M results focus °accountability *employee morale<br>Reform Intervention<br>ensuring staff support for<br>government policies<br>strengthening credibility of<br>niles for record managemnent<br>strengthening credibility of<br>rules for evaluation<br>0% 10% 20% 30% 40%<br>Percentage increase in present level of performance<br>Reform Interventions affectins two performance areas.<br>assuring staff of fair<br>preventing political I<br>Reform Intervention intervention/nmcro- 15% 12%<br>moanagement<br>stengLhening credibility<br>of rules for uaining<br>0% 10% 20% 30%<br>Percentage increase in present level of performance<br>Reform Interventions affectine one Derformance area.<br>Reform I<br>Intervention strengthening credibility of<br>rules for recruitment<br>0% 3% 5%<br>Percentage increase in present level of performance<br><!-- End of picture text --> 56 **The** unit of measurement for the improvement in the performance due to a reform intervention is the percentage change in the present level of performance. For details, see Appendix A6.4.

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