rafmacalaba/datause-encoder-data huggingface dataset:
29

# Blomass Gasiflers

## Heat Gasiflers

## 72.

72. A recent Earthscan studyc' estimated the capital costs of heat gasifiers to vary from USS 15,000 to US$ 35,000 per GJ/hr of rated boiler output. The higher end of the range represents highly mechanized systems while the lower end represents manually operated systems. The principle economic assumptions and results of this study are tabulated below.

|Capital Cost ($/GJ/hr)|15,000|25,000|35,000|
|---|---|---|---|
|Lifetime (years)|12|12|12|
|Discount Rate 10% (CRF=.147)|10%|10%|10%|
|Annualized Cap Cost ($/GJ/hr)|2,205|3,675|5,145|
|O&M ($/yr)|1,500|2,500|3,500|
|Fixed Costs ($/GJ)|1.85|2.85|3.95|
|Woodfuel Cost ($/mt)|20|20|20|
|Energy Content (GJ/mt)|15|15|15|
|Conversion Efficiency|65%|65%|65%|
|Woodfuel Cost ($/GJ)|2.05|2.05|2.05|

## Total Costs ($/GJ)

3.9 4.9 6.0

## 73.

73. By way of comparison, the conversion efficiency in a conventional fuel oil boiler is approximately 85% and the energy content of fuel oil is 37.6 MJ/l. At 20o/iiter (US$ 31.75/bbl) the energy cost of fuel oil steam is roughly US$ 6.26/GJ. As the case presented in Figure 4 is for a heat gasifier retrofit to an existing fuel oil boiler, the total costs of the retrofit gasifier are compared to the energy costs of the fuel oil boiler over a range of fuel oil prices.

Foley, G. and **Banard,** G., Biomass Gasification in Deeloping Countries, Earthscan, 1983.

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The rest of the paper is structured as follows. Section (2) discusses the related literature and puts the contributions of this paper in perspective. Section (3) is devoted to the conceptual framework that describes the measure of sibling correlation and the decomposition methods for estimating the share of intergenerational transmission in sibling correlation. A special focus here is on the Bingley and Cappalleri (2019) approach. Section (4) discusses the advantages of the Demographic and Health Surveys for our cross-country analysis and provides a brief discussion of the estimation methods. Section (5), arranged in a number of subsections, reports and discusses the estimates of sibling correlation. Section (6) discusses the estimates of the share of the intergenerational transmission across regions and countries, and traces out the evolution over time from the 1970s birth cohort to the 1990s birth cohort. The paper ends with a summary of the results and the contributions of the paper in the conclusions.

# (2) Related Literature

The economics literature on intergenerational mobility is grounded on the seminal contributions of Becker and Tomes (1986) that developed a model of intergenerational persistence in permanent income focusing on the role of human capital. The inequality of opportunity (IOP) strand of the literature builds on the foundation of the theory of distributive justice developed by Roemer (see Roemer (1998), and Roemer and Trannoy (2016)). The inequality of opportunity (IOP) refers to the circumstances a child is born into, and emphasizes that inequality due to the circumstances is unjust and should be the focus of policy interventions. Although these two approaches grew largely independently, there has been an increasing appreciation that they deal with fundamentally the same question.<sup>18</sup> These two approaches can be best viewed as complementary. The IOP provides a theory of justice foundation, but does not identify the economic mechanisms which could be the policy levers. The Becker-Tomes model identies a set of such economic mechanisms. The sociological literature uses occupational prestige and class mobility with a focus on the long-term factors including the role of formal and informal institutions, especially in the labor market (see Torche (2015a), Breen (2010)). In the recent decades, many sociologists adopted the regression-based approach of economists and appeal to the Becker-Tomes model for theoretical underpinning of their results.

> 18See, for example, the discussion by Deutscher and Mazumder (2022), and Bjorklund and Jantti (2020). We discuss the dierences between these two approaches later in the paper.

7
4.s

- 76 -

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# 4.c Annual Dat-a

,4.d Quarterly date (f rau Machinea and Fanelli (1987), and Central Bank of Argentine) DEFOP : **Operational** Public sector deficit (with real interest payments).

Other variable definitions, ibid., Figures 3, **4a**<sup>**and**4b</sup>
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In comparing risk indicators that are predictive of the IEG outcome ratings for PSM and non-PSM projects, the development objectives rating is a highly significant predictor for both types of projects. By contrast, it is striking that the “country record” flag is associated with PSM projects having a 15 percentage point lower average likelihood of being rated MS+, but does not have predictive power for non-PSM projects.<sup>78</sup> This difference raises the question of why PSM projects should be particularly vulnerable to contexts where the Bank’s overall lending portfolio has a poor track record. While the answer is not obvious, one possible interpretation could be that the Bank’s lending portfolio performs particularly poorly if World Bank–client government relations are difficult (for example, because objectives are misaligned). Projects with significant PSM components may be particularly affected by such difficulties, as they seek to affect the—possibly resistant—government administration directly. Other projects may operate in stronger isolation from government systems.

# **7.4 Project Management Correlates of Project Performance**

None of the project management indicators observable at the time of a PSM project’s approval—the committed amount, lending preparation costs, time to approval, or time to effectiveness—are statistically significant predictors of project performance, when controlling for country context and project content characteristics (see annex table A.7). But when not controlling for context and content characteristics, longer gaps between project approval and project effectiveness (“time to effectiveness”) predict slightly lower chances of a project receiving MS+ IEG outcome ratings (see model 3 in annex table A.7). An increase in the time to effectiveness of PSM projects by 100 days is on average associated with a decrease in the likelihood of being rated MS+ by about 2.5 percentage points.

One interpretation of this finding is that long times to effectiveness can signal a lack of government commitment to a given PSM project. It seems plausible that times to effectiveness do not improve the model when country context is controlled for, because one would expect them to be caused, at least in part, by country context factors. Overall, including the four above potential predictors of project performance does not improve the fit of the model (see annex table A.7), suggesting that these characteristics are not useful risk indicators for PSM projects.

> 78 See Blum (2014) for detailed statistics.

38
specific outputs (=products) and desired results (=outcomes)—all operating within a given environment: <!-- Start of picture text --> Resources Products Results<br>Functions Processes<br>(Inputs) (Outputs) (Outcomes)<br><!-- End of picture text --> Powerful social, economic, political, ecological and biological contexts determine the environment, which strongly affects inputs, functions, process, outputs and outcomes. This focus on the effects of contexts helps to avoid presupposing linear, sequential and/or mechanical relationships, instead of complex and iterative dynamics.

Systems Theory has shown its usefulness in explaining conceptually complex relationships and processes of a social system operating within its particular environment, including those relationships and dynamics in contemporary health systems<sup>11</sup> .

In the Study, a very simplified Systems framework is suggested as a descriptive tool for capturing the basic dynamics and relationships between the different system components of the HIV/AIDS response in Colombia. Thus, the framework is not suggested as a model of causality or a predictive tool, but rather as a map to aid in the observation and research of a number of interrelate aspects of implementation efficiency. It has proven useful in enabling the Team to approach the research questions iteratively, and to develop a holistic understanding of the HIV/AIDS response in Colombia.

> 11World Health Organization (2000) _World Health Report 2000._ Health systems: improving performance. Geneva, World Health Organization; Murray CJL and Frenk J (2000) A framework for assessing the performance of health systems. _Bulletin of the World Health Organization_ 78(6):717-731; Black N and Gruen R (2005) Understanding Health Services, Maidenhead, UK: Open University Press; Shakarishvili G (2009) _Building on Health Systems Frameworks for developing a common approach to health systems strengthening_ . Paper prepared for the World Bank, Global Fund Fight AIDS, Tuberculosis and Malaria and GAVI Alliance, Technical Workshop on Health Systems Strengthening, Washington DC, June 25-27, 2009.

15
Data collected before program implementation, presented in Table 2 (see section 3.2 below for a description of the database), suggest that taxes were more important for formal businesses for two reasons: first informal businesses were less likely to pay taxes, and second when they were paying taxes, they represented a lower share of their profit.<sup>8</sup> Slightly more than half (55 percent) of the informal businesses targeted were paying some form of tax in 2013. For formal businesses, it was the great majority (84 percent). One explanation for this difference is that informal businesses can possibly avoid paying taxes if their businesses are closed when tax inspectors visit the area. For formal businesses, it is more difficult because the tax administration knows that their businesses exist and should pay something. On average, the amount of taxes paid by formal businesses in 2013 represented 13 percent of annual profits for formal businesses, and only 5 percent for the informal ones. If we only consider businesses which paid some taxes in 2013, the amounts paid represented 17 percent of annual profits for formal businesses and 9 percent for informal ones.

However, the main objective of the Government of Benin with the _entreprenant_ program is not to increase the tax collected in the short term. When they formalize, businesses can benefit from tax exemptions under certain conditions. Businesses which also register to the CGA (an association providing business counselling and account certification) can benefit from a full tax exemption for the first year after formalizing, in addition to a reduction of 40% in the amount of taxes due for the following 3 years. As a result, the amount of taxes paid in the very short term may not be sizeable for many businesses.

# **3. Evaluation design**

# **3.1. Program description**

The _entreprenant_ status can be considered as a package of incentives to formalization that the government offers to small business. The implementation of the _entreprenant_ status in Cotonou offers the ideal conditions to study the impact of different packages of incentives on formalization decisions, the legal environment in which firms operate, and how businesses interact with public authorities. Also, it shows the impact of different business and entrepreneur

> 8 It is difficult to compare informal and formal businesses since levels of profits are very different. Indeed, the difference in tax rates may only reflect progressivity with profits. However, when we compare tax rates at similar levels of profit, the correlation between tax rate and formality is strongly negative.

8
# **Short-Term Effects of India’s Employment Guarantee Program on Labor Markets and Agricultural Productivity**

Klaus Deininger<sup>1</sup> Hari K Nagarajan<sup>2</sup> Sudhir K Singh<sup>1</sup> 1 World Bank, Washington DC

> **2** Institute of Rural Management Anand, Gujarat, India

Key words: Labor markets, Agricultural productivity, Gender, rural development JEL Codes: J20, J21, J22, J23, J24, Q10, Q11, Q12

## **_________________**

> <mark>We thank participants at the Indian Statistical Institute (New Delhi), the Tata Institute of Social Sciences, and the Madras Institute of Development Studies (MIDS), in particular Indira Rajaraman and Farzana Afridi for insightful comments and J.P. Singh for his help in data collection and cleaning.</mark>
Table 5: Interaction with status in concurrent trial of health facility financing modalities and knowledge on vignette

||Larger|trial|Know|ledge|
|---|---|---|---|---|
||(1)<br>Across cases|(2)<br>Across<br>responses|(3)<br>Across cases|(4)<br>Across<br>responses|
|Reward|9.82***|2.73**|4.09***|3.08***|
||(2.71)|(1.18)|(1.48)|(0.66)|
|Penalty|4.34*|1.40|3.04*|2.78***|
||(2.54)|(1.13)|(1.55)|(0.67)|
|DFF|5.04**|3.62***|||
||(2.05)|(0.99)|||
|PFP|6.07***|5.04***|||
||(2.10)|(1.00)|||
|Reward _×_ DFF|-5.96*|-0.55|||
||(3.06)|(1.48)|||
|Reward _×_ PFP|-5.40*|0.09|||
||(3.15)|(1.52)|||
|Penalty _×_ DFF|-1.23|0.66|||
||(2.91)|(1.44)|||
|Penalty _×_ PFP|0.00|1.31|||
||(2.99)|(1.48)|||
|Knowledge _≥_median|||1.92|3.10***|
||||(2.05)|(0.87)|
|Reward _×_ Knowledge _≥_median|||0.49|-1.94**|
||||(2.06)|(0.97)|
|Penalty _×_ Knowledge _≥_median|||0.61<br>(2.08)|-1.73*<br>(0.94)|
|Constant (Information _×_ Control)|48.06***<br>(1.80)|52.43***<br>(0.77)|52.20***<br>(1.26)|54.84***<br>(0.52)|
|Facility fxed efects|No|No|Yes|Yes|
|P-values from tests of coefcients|||||
|Control: Penalty v Reward|0.043|0.277|||
|DFF: Penalty v Reward|0.124|0.425|||
|PFP: Penalty v Reward|0.089|0.440|||
|Penalty v Reward|||0.447|0.666|
|N respondents|1,363|1,363|1,363|1,363|

* p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. Unadjusted OLS models; s.e. clustered at facility level. Estimates by partograph case are available in Table A.6.

45
**Table 1: Financial system indicators for El Salvador**

|Year|Credit to private sector<br>to GDP (%)|Demand and term<br>deposits to GDP (%)|Number of<br>institutions|
|---|---|---|---|
|1991|23.4|23.8|13|
|1992|27.5|25.7|13|
|1993|27.7|28.4|13|
|1994|30.9|31.1|17|
|1995|34.8|29.9|21|
|1996|36.9|31.3|21|
|1997|40.3|32.6|18|
|1998|41.9|33.2|17|
|1999|43.8|34.4|16|
|2000|41.4|34.0|15|
|2001|39.0|36.0|14|

Source: Central Bank of El Salvador and Financial System Superintendency of El Salvador.

20
Nkonya, E., Pender, J., Kato, E., Omobowale, O., Phillip, D., & Ehui, S., 2011. Options for Enhancing Agricultural Productivity in Nigeria. Background Paper No. NSPP 011, International Food Policy Research Institute. Washington, DC.

Oaxaca, R. L., 1973. Male-Female Wage Differentials in Urban Labor Markets. International Economic Review. 14, 693–709.

Oaxaca, R. L., 2007. The challenge of measuring labor market discrimination against women. Swedish Economic Policy Review. 14(1), 199.

Oladeebo, J. O., & Fajuyigbe, A. A., 2007. Technical Efficiency of Men and Women Upland Rice Farmers in Osun State, Nigeria. Journal of Human Ecology. 22(2), 93–100.

Peterman, A., Behrman, J. and Quisumbing, A.R., 2010. A Review of Empirical Evidence on Gender Differences in Non-Land Agricultural Inputs, Technology and Services in Developing Countries. Discussion Paper No. 00975, International Food Policy Research Institute. Washington, DC.

Peterman, A., Quisumbing, A., Behrman, J., & Nkonya, E., 2011. Understanding the complexities surrounding gender differences in agricultural productivity in Nigeria and Uganda. Journal of Development Studies. 47(10), 1482-1509.

Phillip, D., Nkonya, E., Pender, J., & Oni, O., 2009. Constraints to Increasing Agricultural Productivity in Nigeria: A Review. NSSP Background Paper 6, International Food Policy Research Institute. Washington, DC.

Porter, G & Phillips-Howard, K. 1997. Contract Farming in South Africa: A Case Study from Kwazulu-Natal. Geography: Journal of the Geographical Association. 82(3&4), 1–38.

Quisumbing, A.R., 1994. Gender Differences in Agricultural Productivity: A Survey of Empirical Evidence. Discussion Paper Series No. 36, The World Bank. Washington, DC.

Quisumbing, A.R., 1996. Male–female differences in agricultural productivity: methodological issues and empirical evidence. World Development. 24(10), 1597–1595.

Saito, K. A., Mekonnen, H., & Spurling, D., 1994. Raising the Productivity of Women Farmers in Sub Saharan Africa. Discussion Paper Series 230, World Bank. Washington, DC.

The Food and Agriculture Organization (FAO), 2011. The State of Food and Agriculture. Women in Agriculture: Closing the gender gap for development. FAO, Rome.

Timothy, A. T., & Adeoti, A. I., 2006. Gender Inequalities and Economic Efficiency: New ‐ Evidence from Cassava based Farm Holdings in Rural South western Nigeria. African Development Review. 18(3), 428-443.

22
missing information that is not being generated by other sources, such as private firms themselves. This is the focus of the next section.

# **3. MONITORING NEEDS DUE TO POTENTIAL CRISIS-INDUCED ABUSES OF WTO COMMITMENTS**

The global financial crisis of 2008–2009 resulted in a severe economic recession in many countries that heightened political pressure to respond with new policy initiatives. In the face of increased political pressure to respond quickly, it is likely that governments increased the amount of policymaking activity that would both adversely affect foreigner’s market access and violate WTO commitments, and that this activity would be above even the ‘normal’ levels that take place under calmer economic conditions. Below we consider three different categories of crisis-induced policies – 1) trade remedies 2) other import border measures, and 3) subsidies and other behind-the-border measures. To guide the discussion, we turn to examples of trade barriers identified by initiatives such as the Global Antidumping Database, Global Trade Alert, and WTO Secretariat; from which it may turn out that there are dozens of examples of countries imposing policies in potentially WTO-inconsistent and economically meaningful ways.

Because there are dozens and perhaps hundreds of new crisis-era policies alone that _could be_ subject to the additional analytical scrutiny of ‘monitoring’ by the technical experts (economists, lawyers and political scientists) best suited to advising developing country exporters, we also provide an analytical discussion of which policies _should be_ subject to such additional scrutiny. From the demand-side perspective, we focus on 20
# **Table 7: Prediction models of the number of deposit accounts in commercial banks**

This table shows the estimates of OLS regressions of the number of deposit accounts in commercial banks per thousand adults on several covariates. Each column is represents a different regression. The dependent variable in each regression is ln(Number of accounts in commercial banks/1000 adults).

||Model<br>(1)|Model<br>(2)|Model<br>(3)|Model<br>(4)|Model<br>(5)|
|---|---|---|---|---|---|
|ln(GDP/capita)|0.41**|0.44***|0.63***|0.68***|0.83***|
||(0.16)|(0.084)|(0.18)|(0.090)|(0.069)|
|High Inc. OECD (dummy)|-0.59**|-0.55*|-0.67*|-0.61*|-0.62*|
||(0.29)|(0.28)|(0.34)|(0.33)|(0.33)|
|ln(Population Density)|0.090|0.14**|0.049|0.12*|0.17***|
||(0.062)|(0.053)|(0.073)|(0.064)|(0.058)|
|ln(Private Credit/GDP)|0.18|0.17|0.33**|0.35***||
||(0.13)|(0.11)|(0.15)|(0.13)||
|ln(Branches/100k Adults)|0.53***|0.56***||||
||(0.10)|(0.093)||||
|ln(Value Deposits/Population)|0.041||0.062|||
||(0.12)||(0.14)|||
|Constant|1.48*|1.21|1.11|0.84|-1.06*|
||(0.87)|(0.77)|(1.04)|(0.94)|(0.60)|
|Observations|62|73|64|75|80|
|R-squared|0.824|0.824|0.738|0.733|0.708|

Note: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 In these regressions and subsequent predictions, we leave out countries with fewer than 100,000 adult populations and extreme outliers.

45
propensity score for non-treated individuals that is very close to the propensity score of treated individuals. We omit further details here for brevity and refer to the literature on matching methods (e.g., Rosenbaum and Rubin, 1983; Dehejia and Wahba, 2002; Heckman et al., 1998; Caliendo and Kopeinig, 2005; Smith and Todd, 2005).

We include covariates that satisfy the following conditions: (i) the covariates are not affected by the income shocks; (ii) are time invariant or are relatively stable over time; (iii) are derived from the same source and from the same environment (Caliendo and Kopeining, 2008, Heckman et al, 1999). Hence, we include the following covariates to predict the household’s propensity of being crisis-affected: demographic variables, educational attainment of household heads, size of the household, and households’ asset index (the asset index is created and calculated following Filmer and Pritchett, 2000).

# <u>5.3. Fixed effects model</u>

As a final robustness check we run fixed effects parametric regressions. As noted above, the longitudinal dataset allows us to implement fixed effects and thus, to account for the omitted variable bias. A key concept of the fixed effects is the existence of time-invariant characteristic that may influence the outcome variable. If one accounts for the existence of this time-invariant characteristic, then any changes in the outcome variable must be due to variables other than these fixed characteristics.

# <u>5.4. Identifying vulnerable groups</u>

To identify vulnerable groups we follow the approach taken by Glewwe and Hall (1998) and we regress the percentage change in household consumption on various vulnerability characteristics. Table 2 reports the results. The results show that female headed households, households with large number of children (higher than 3), large households, and households whose head has a low level of educational attainment are particularly vulnerable to shocks. Households with a higher number of elderly people are less susceptible to shocks, which, although counterintuitive at first, is explained by significant transfers (either in a form of pensions or social assistance).<sup>9</sup>

> 9 Nevertheless, when doing our analysis, we still confine parts of it to the households with higher number of elderly people.

12