rafmacalaba/datause-encoder-data huggingface dataset:
18<sup>22</sup> . The table indicates that the energy balance of second-generation biofuels has the potential to be much higher than that of their first-generation counterparts, with the exception of sugarcane ethanol and perhaps biodiesel from waste grease or oil. It is worth noting that the numbers presented should be looked as a rough approximation, and care should be exercised when comparing them to each other. As Farrell et al. (2006) indicated, different studies will in general rely on different assumptions and system boundaries, and thus direct comparison of the results of their LCAs is often misleading. For example, the US Natural Resources Defense Council (NRDC 2006) surveyed studies of fossil energy balances, reporting figures between 4.4 and 6.6 (units of energy produced for every unit of energy used) for lignocellulosic ethanol.<sup>23</sup>

**Table 18: Fossil energy balance of a sample of the fuel types**<sup>a</sup>

|Fuel|Approximatefossilenergy balance<sup>b</sup>|
|---|---|
|Cellulosic ethanol|2-36|
|Corn ethanol|~1.5|
|Wheat ethanol|~2|
|Sugarbeets ethanol|~2|
|Soybeans biodiesel|~3|
|Sugarcane ethanol|~8|
|Rapeseed biodiesel (EU)|~2.5|
|Waste vegetable oil biodiesel|~5-6|
|Palm oil biodiesel|~9|

Source: WWI (2007).<sup>a</sup> For the full list of fuels/feedstocks and references see table 10.2 in WWI (2007)<sup>b</sup> Ratio of energy contained in the fuel to the fossil energy used to produce it.

# **4.2    GHG emission reductions**

By displacing fossil fuels, second-generation biofuels have the potential to reduce GHG emissions (EPA 2007). Several studies have estimated the GHG mitigation potentials of different feedstocks, conversion, process technologies, and handling of co-product. Table 19 compares GHG mitigation potentials of various biofuels using LCA techniques.

> 22 The fossil energy balance is a measure of the amount of energy obtained as a biofuel per unit of fossil energy utilized in the biofuel production process.

> 23 For the sake of completeness, we should mention that there is a study (Pimentel and Patzek 2005) which contradicts these consensus findings, reporting that both corn and lignocellulosic ethanol would result in negative energy balances. Major departures from other studies are that Pimentel and Patzek (2005) did not credit co-products for the energy they contain, and assume higher levels of inputs and lower productivities than most authors.

34
37 This pace of price adjustments may not be , however, optimal for policy makers. The same issue analyzed in the context of political economy yields a different outcome: from the point of view of the government it might be better to avoid frequent price increases. Any decision to raise prices is likely to be costly for the authorities in terms of the loss of popularity and the cost is clearly minimized by lowering the frequency of adjustments. These political costs may be thought of as an important part of menu costs introduced earlier in the paper. Just like standard menu costs, they make (administered) prices more sticky in that they discourage the government from making frequent inflation-driven adjustments. Keeping in mind that all governments whether populist or not are subject to those same constraints, it may turn out that the economically rational option of small repeated adjustments is not politically feasible. Therefore, with only limited frequency of administered increases available, the revised policy recommendation could suggest bigger adjustments because only they can ensure that necessary relative price realignments take place. In other words, when political economy rules out the feasibility of regularly repeated increases, sizable adjustments might be preferred to small ones.

# Section 8: SUMMARY AND CONCLUSIONS

This paper has been designed as an attempt to estimate the magnitude of relative price shifts on the overall price level in Poland during the transition period 1989-1997. For that purpose, the theoretical model has been found that builds on menu costs and trend inflation to derive a positive relationship between variance and skewness of the distribution of relative price changes and the general inflation. The model allowed to estimate the effect of relative price shifts within the framework controlling for nominal and real shocks. Using Polish data, a set of 3 versions of the model were estimated. All of them yielded high explanatory power and statistically significant coefficients on most variance and skewness variables thus giving a strong empirical suppor **t** o the theoretical relationship. Larger shifts in relative prices accompanying the adjustment process and detected by higher variance in the equation were proven to exert substantial upward pressure on inflation that persists over time. On the other hand, high positive skewness reflecting the domination of the adjustment process by few large increases was
Table 4. Conflict and land devoted to poppy cultivation

||l|og (Poppycult|ivationper 1,|000 inhabitant|s)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Conflictt-1|0.000540|0.000385|0.0107|-0.00158|0.00817|
||(0.0395)|(0.0395)|(0.0397)|(0.0395)|(0.0398)|
|Conflictt-2|0.0640*|0.0613*|0.0640*|0.0603*|0.0630*|
||(0.0365)|(0.0365)|(0.0369)|(0.0365)|(0.0369)|
|Conflictt-3|0.0450|0.0429|0.0530|0.0450|0.0539|
||(0.0530)|(0.0528)|(0.0525)|(0.0526)|(0.0524)|
|Conflictt-4|0.0674|0.0667|0.0626|0.0676|0.0637|
||(0.0614)|(0.0610)|(0.0593)|(0.0610)|(0.0595)|
|Troopst-1||-0.00704||-0.00729||
|||(0.00910)||(0.00903)||
|ISAFt-1|||0.0105||0.00877|
||||(0.0124)||(0.0122)|
|ANAt-1|||-0.0478**||-0.0444**|
||||(0.0186)||(0.0187)|
|AIDt-1||-5.10e-06**|-4.79e-06**|||
|||(2.41e-06)|(2.42e-06)|||
|Securityt-1||||-0.000274***|-0.000233**|
|||||(9.41e-05)|(9.14e-05)|
|Other aidt-1||||-5.05e-06**|-4.77e-06**|
|||||(2.41e-06)|(2.42e-06)|
|District Fixed Effects|Yes|Yes|Yes|Yes|Yes|
|Time Fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|3,582|3,582|3,582|3,582|3,582|
|Adj. R2|0.805|0.805|0.805|0.805|0.805|
|Robust standard errors in paren|theses|||||
|*** p<0.01, ** p<0.05, * p<0.1||||||

18
|**ability?**<br>is the average reliability<br> variable is the average<br>fned as the proportion<br>lity loan was disbursed.<br> January 2008 and the<br>`ty Dummy` thus refer to<br> their loan on or before<br> sample. `Entire group`<br> `group lives in same`<br>rhood, 0 otherwise. In<br>mple consists of group<br>cusing on neighborhood<br>(6)<br>0.274***<br>(0.058)<br>-0.001<br>(0.078)<br>0.378***<br>(0.095)<br>Yes<br>0.427<br>471<br>0.620|
|---|
|**milar Reli**<br>nt variable<br> dependent<br>ability is de<br> group liabi<br>it between <br> `Reliabili`<br>ho received <br>ches in the <br>se. `Entire `<br>me neighbo<br>-(6), the sa<br>orhood, fo<br>(5)<br>0.355***<br>(0.060)<br>-0.240***<br>(0.065)<br>-0.047<br>(0.053)<br>0.615***<br>(0.105)<br>No<br>0.346<br>471<br>0.620|
|**oups of Si**<br>the depende<br>er group, the<br>) to (3), reli<br>h before the <br>avings depos<br> `Level` and <br>borrowers w<br>o MFI bran<br>e, 0 otherwi<br>ve in the sa<br>-(3) and (5)<br> same neighb<br>evel.<br>(4)<br>0.340***<br>(0.043)<br>-0.204***<br>(0.042)<br>-0.013<br>(0.040)<br>-0.029<br>(0.032)<br>0.601***<br>(0.063)<br>No<br>0.325<br>833<br>0.646|
|**ls Join Gr**<br>vel, where<br>lar borrow<br>columns (1<br> the mont<br>mplusory s<br>`liability `<br>bility loan <br>ne of the tw<br>p are femal<br>er group li<br>columns (2)<br> live in the <br>wer group l<br>(3)<br>0.287***<br>(0.058)<br>-0.026<br>(0.036)<br>0.153***<br>(0.041)<br>Yes<br>0.570<br>471<br>0.281|
|**Individua**<br>individual le<br> to a particu<br>ividual. In <br>ary 2008 and<br>sing any co<br>bles `Own Re`<br>s. Group lia<br>ummy for o<br>orrower grou<br> the borrow<br> clients. In <br>up members <br>at the borro<br>(2)<br>0.409***<br>(0.064)<br>-0.195***<br>(0.043)<br>-0.036<br>(0.026)<br>0.340***<br>(0.058)<br>No<br>0.480<br>471<br>0.281|
|**Table 2: Tests for Assortative Matching: Do**<br>This table reports OLS regressions with cross-sectional data at the<br>of other group members. That is, for each individual who belongs<br>reliability of the rest of the group members, not including the ind<br>of compulsory savings deposits that were not made between Janu<br>In columns (4) to (6), reliability is defned as a dummy for mis<br>month before the group liability loan was disbursed. The varia<br>each individual’s own measure of reliability, given these defnition<br>January 2008 are excluded in these regressions. `Behrampura` is a d<br>`is female` is a dummy variable equal to 1 if all members of the b<br>`neighborhood` is a dummy variable equal to 1 if all members of<br>columns (1) and (4), the sample includes all group liability loan <br>liability loan clients who belong to a borrower group where all gro<br>where there is more than one such group. Robust SEs, clustered <br>(1)<br>Own Reliability Level<br>0.414***<br>(0.046)<br>Reliability Dummy<br>Behrampura<br>-0.148***<br>(0.027)<br>Entire group is female<br>-0.012<br>(0.021)<br>Entire group lives in same neighborhood<br>-0.041*<br>(0.021)<br>Constant<br>0.326***<br>(0.039)<br>Neighborhood FEs<br>No<br>R-squared<br>0.435<br>N<br>833<br>Mean of Dep Var<br>0.315<br>* _p <_0_._10, ** _p <_0_._05, *** _p <_0_._01|

23
Policy Research Working Paper 8371

# **Abstract**

This paper utilizes information from the 2015 Nigeria National Water and Sanitation Survey to identify the extent and timing of the failure of water schemes in the country and the factors affecting it. Around 46 percent of all the water schemes in Nigeria are nonfunctional, and approximately 30 percent are likely to fail in the first year. The results indicate that during the first year of operation, factors that can be controlled in the design, implementation, and operational stages contribute to the failure of 61 percent of the water schemes. As water schemes age, their likelihood of failure is best predicted by factors that cannot be modified. The influence of operational factors, such as repairs and maintenance, decreases slightly over time.

This paper is a product of the Water Global Practice. 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 Landres@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
35 into poverty. The two indicators are therefore complements, and the impoverishment indicator sheds light on the poverty SDG (target 1.1). Both indicators are widely used in the literatures on financial protection in health (Wagstaff and van Doorslaer 2003; van Doorslaer et al. 2006b; van Doorslaer et al. 2007; Wagstaff et al. 2018a; Wagstaff et al. 2018b).

Some studies decide whether health expenditures are catastrophic by comparing them to total consumption, while others compare them to total income. Similarly, some studies decide whether health expenditures are impoverishing by comparing income net of health expenditures and income gross of health expenditures, while others compare consumption net and gross of health expenditures. There is no right or wrong approach. However, it is important to keep in mind that total consumption includes health expenditures, and will therefore increase when a health shock occurs if the household finances part of the health expenditure through borrowing or dissaving rather than entirely through cutting back consumption on other budget items. A household with a large health expenditure may therefore appear to be richer (in terms of consumption) than one that incurs only small health expenditures. As a result, we may end up with the rather perverse result that large health expenditures (possibly catastrophic ones, too) are more common among ‘rich’ households, and less common among ‘poor’ households (WHO and World Bank 2017). By contrast, income is not directly affected by health expenditures. (It may be affected indirectly in the sense that the same health shock that causes the health expenditures may also reduce labor income.) Thus, when computing catastrophic and impoverishing health expenditures, and especially when looking at inequality in catastrophic health expenditures, it is probably preferable to use income rather than consumption (WHO and World Bank 2017).

# <u>Data search and data sources</u>

As with the health equity side of the HEFPI database, our goal is to assemble as large a data set as possible of surveys suitable for the analysis of financial protection. Again. we undertook inventories of the microdata catalogs of the International Household Survey Network and the World
Gregorio Impavido, Alberto R. Musalem, and Thierry Tressel

- [17] **Demirguc-Kunt, Asli and Enrica Detragiache** (1997) The Determinants of Banking Crisis: Evidence from Developed and Developing Countries, mimeo World Bank.

- [18] **Demirguc-Kunt, Asli and Enrica Detragiache** (1998) Financial Liberalization and Financial Fragility, mimeo World Bank.

- [19] **Demirguc-Kunt, Asli and Enrica Detragiache** (2000) Does Deposit Insurance Increase Banking System Stability? An Empirical Investigation, mimeo World Bank.

- [20] **Demirguc-Kunt, Asli and Harry Huizinga** (1999a) Determinants of Commercial Bank Interest Margins and Profitability: some International Evidence, _The World Bank Economic Review,_ vol. 1 3 No.2, 379-408.

- [21] **Demirguc-Kunt, Asli and Harry Huizinga** (1999b) Financial Structure and Bank Profitability, mimeo World Bank.

- [22] **Demirguc-Kunt, Asli, and Ross Levine** (1999) Bank-Based and Market Based Financial Systems: Cross-Country Comparisons, mimeo, World Bank.

- [23] **Demirguc-Kunt, Asli, and Vojislav Maksimovic** (1996) Stock Market Development and Financing Choices of Firms, _The World Bank Economic Review,_ vol.10, no.2.

- [24] **Dewatripont, M., and J. Tirole,** (1994) _The Prudential Regulation of Banks,_ The MIT Press.

- _[25]_ **Diamond, D., and Dybvig** (1983), Bank Runs, Deposit Insurance, and Liquidity, _Journal of Political Economy,_ 91, 401-419.

- [26] **Diamond, Douglas W.** (1991) Monitoring and Reputation: The Choice between Bank Loans and Directly Placed Debt, _Journal of Political Economy,_ vol. 99, no. 4.

- [27] **Freixas, Xavier, and Jean-Charles Rochet** (1997) Microeconomics of Banking, The MIT Press.

- [28] **Greene William H.** (1990) Econometric Analysis, Second Edition, Prentice Hall.

- [29] **Grossman, S. and J. Stiglitz,** (1984) On the impossibility of informationally efficient markets, _American Economic Review,_ 70, 393-408.

- [30] **Harwood, A., Litan, R., and M. Pomerleano** (eds.) Financial Markets and Development - The Crisis in Emerging Markets. 1999, Brookings Institution Press, Washington D.C.

- [31] **Holmstrom, Bengt, and Jean Tirole** (1993) Market Liquidity and Performance Monitoring, Journal of Political Economy, vol. 101, no. 4.

- [32] **Impavido, G., and A. R. Musalem** (2000), Contractual Savings, Stock and Asset Markets, _World Bank Policy Research Paper_ 2490.

- [33] **Impavido, Gregorio, Musalem, Alberto R., and Thierry Tressel** (2001), Contractual Savings, Capital Markets and Firms' Financing Choices, _World Bank Policy Research Paper_ 2186.

- 14 -
Table 16 presents the maximum cost values of each of the technical and vocational courses. Observing the monthly costs, we can state that the maximum monthly cost of a general TEC course should be R$ 2,398.3 (assuming the course lasts for one year). Any amount above that would exceed the present value of the benefits provided in the form of income by these courses.

Table 16 – Maximum cost calculation – vocational education

||Maximum annual cost|Maximum monthly cost|
|---|---|---|
|TEC general|R$ 28.779,2|R$ 2.398,3|
|TEC after general education|R$ 25.460,0|R$ 2.121,7|
|TEC concurrently with general<br>education|R$ 22.002,0|R$ 1.833,5|
|Technical education at the<br>tertiary level|R$ 196.774,8|R$ 16.397,9|
|FIC|R$ 7.363,2|R$ 613,6|

Source: Authors based on PNAD/IBGE (2007). We used a discount rate of 5%. We assume that vocational courses last 1 year.

Vasconcellos et al. (2010) calculate a proxy for the student-cost in general upper secondary and technical schools using data from INEP<sup>15</sup> and the _Sistema Informacional Custo Aluno_ – SICA – (Student-Cost Information System). This database (SICA) contains information on costs submitted by the state schools in the state of Minas Gerais. The authors estimate that the student-cost in general upper secondary education is R$ 2,213.5 per year (in 2009 values) and that the student-cost of upper secondary technical education is R$ 2,550.5 per year (in 2009 values). Thus, the annual student-cost of upper secondary technical education is only 15.22% higher compared with regular education.

The annual student-cost calculated by Vasconcellos et al. (2010), R$ 2,550.5, is much lower than our maximum annual cost shown in Table 16 (R$ 28,779.2). This result may be an indication that the economic benefits of secondary technical education are higher than its costs, which would allow public investment in this educational sector.

# 8. Conclusion

Technical and vocational education can contribute to the qualification of a workforce with the skills required for inclusion in the labor market. This type of education may have advantages over regular education by providing possibly shorter duration courses and teaching techniques and skills that may be more directly used at work. Vocational education in Brazil is divided into three 15 INEP stands for Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (National Institute for Educational Study and Research Anísio Teixeira). It is a governmental institute.

40
hazard model. The results are largely similar to the univariate results: firm death is higher for younger firms, especially those in their first year, lower for firms with higher baseline profits, lower for middle-aged owners compared to youth or older owners, and higher for female owners. Business sector, which was not statistically significant in the univariate specification, is significant in both the probit and hazard multivariate models. Manufacturing firms are 2.7 percentage points less likely to die than retail firms, while service firms are 1.7 percentage points less likely to die compared to retail firms. The number of employees also becomes significant at the 10 percent level in the hazard model, suggesting that larger firms are less likely to die. However, the odds ratio of 0.98 shows this effect is small in magnitude.

# **5.4 Do death rates vary with level of development?**

All of the above analysis controls for survey fixed effects, which captures country-level differences across the samples. But it is still of interest to see descriptively whether firm death rates vary with level of development. On one hand, we might expect businesses in less developed countries to face more constraints such as less access to credit, and so be less likely to survive negative shocks. However, there may be fewer other options for employment in poorer economies, suggesting fewer voluntary exits. Figure 5 plots the observed correlation between annualized firm death rates and per-capita GDP. We see a positive relationship (correlation 0.41), with firm death rates higher in richer developing countries. Each log point increase in per capita GDP is associated with a 5.2 percentage point higher annualized firm death rate (p=0.004). This is also consistent with the evidence presented earlier in this paper suggesting that small firm death rates are higher in the U.S. than in our sample.

# **6. Why do firms die?**

The previous section shows that firm death does not simply occur at random, but is more likely to occur for certain types of firms and firm owners than others. We consider three competing theories of why micro and small firms may close down. These theories differ in the extent to which firm closure is a voluntary choice for the owner, and in terms of the relationship that can be expected between firm productivity and firm death. As a result, they lead to different conclusions about how firm death will affect aggregate productivity and individual welfare. We then relate these theories back to our data on which firms die, and use data on cause of death to help understand why firms die.

17
and 6.2% are publicly listed. The foreign ownership share is, on average, 10.7%, while the average government ownership is 0.7%. On average, firms are 17.5 years old.

Finally, we control for industry-level variation in the need for external finance. Specifically, we use the Rajan and Zingales (1998) indicator on the fraction of investment that cannot be financed through internal cash flows, computed over the 1980s for listed firms in the U.S. The underlying assumption in Rajan and Zingales and our work is that for technological reasons some industries depend more heavily on external finance than others and that this industry variation does not differ across countries. We use the self-reported industry categorization by firms in the Enterprise Surveys to match with the Rajan and Zingales classification. Since this variable is only available for manufacturing industries, we lose about a half of our sample. The average fraction of external need for finance across our sample is 0.29, varying from -0.45 (tobacco) to 1.14 (plastic products).

The correlations in Table 2 suggest that there is no systematic relationship between the country-level metrics of financial segment size. Not surprisingly, however, the average asset size of some of the institutional categories is positively and significantly correlated. The log of GDP per capita is, as expected, positively and significantly correlated with the mean asset size of all institutional categories except low-end NBFIs. There are no significant correlations between the asset shares of the different segments of the financial system and access to finance. There are, however, significant correlations between the average size of financial institutions and access to finance. Countries with larger banks have a higher share of firms with loans and overdraft and firms that complain less about financing obstacles. Countries with larger specialized lenders also have more firms with overdraft facilities or loans. Many of the firm characteristics are also correlated with each other. Countries with more small firms, for instance, have fewer listed and younger firms. Our access indicators are also significantly correlated with our industry indicator of external dependence, with firms in industries more reliant on external finance reporting lower financing obstacles and a higher probability of having an account, a loan or an overdraft.

9