rafmacalaba/datause-encoder-data huggingface dataset:
|Coral Reef Targeted Research and CapacityBuildingfor Man|agemenHarideepSingh|
|---|---|
|Coral Reef Targeted Research and CapacityBuildingfor Man|agemenHarideepSingh|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|HIV/AIDS Response Project|Humberto Albino Cossa|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Arid Lands Resource Management Project Phase Two|Johannes Woelcke|
|Afghanistan Reconstruction Trust Fund|Syed Waseem Abbas Kazmi|
|Afghanistan Reconstruction Trust Fund|Syed Waseem Abbas Kazmi|
|Afghanistan Reconstruction Trust Fund|Syed Waseem Abbas Kazmi|
|Afghanistan Reconstruction Trust Fund|Syed Waseem Abbas Kazmi|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Support for Poor and Disadvantaged Areas Project|Natasha Hayward|
|Social Protection Sector Adjustment Loan - DDO|Theresa Jones|
32 Table 10: Estimates of NCMS impact on household health spending

|Outcome|Changes<br>everyone i<br>count|among<br>n NCMS<br>ies|Changes<br>NC<br>house|among<br>MS<br>holds|Changes<br>non-N<br>househo<br>NCMS c|among<br>CMS<br>lds in<br>ounties|Diffs-in-<br>matc|diffs no<br>hing|Diffs-i<br>matc<br>propensi<br>weig|n-diffs<br>hing<br>ty score<br>hts|Diffs<br>k|-in-diffs m<br>ernel wei|atching<br>ghts|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|%<br>change|
|Household health care expenses<br>last 12 months|160.330|14.77|161.072|16.64|158.126|4.75|2.946|0.12|35.095|1.23|27.128|0.59|11%|
|Catastrophic payments (> 10%<br>of income)|-0.005|-1.21|-0.001|-0.27|-0.018|-2.07|0.016|1.59|0.028|2.34|0.019|0.87|5%|
|Catastrophic payments (> 20%<br>of income)|0.003|0.92|0.011|2.65|-0.021|-3.30|0.032|3.93|0.039|4.09|0.036|1.74|20%|
|Catastrophic payments (> 40%<br>of income)|0.005|2.18|0.004|1.53|0.007|1.87|-0.003|-0.64|0.007|1.19|0.003|0.37|5%|
|Net out-of-pocket payments per<br>outpatient visit|6.852|3.69|7.028|3.17|6.277|1.90|0.751|0.17|2.195|0.52|1.898|0.41|18%|
|Net out-of-pocket payments per<br>hospital stay|34.076|2.88|37.557|2.70|23.190|1.04|14.367|0.52|57.326|1.86|43.346|1.38|49%|
|Net out-of-pocket payments for<br>deliveries|-5.211|-2.02|-4.229|-1.34|-7.883|-1.97|3.654|0.61|8.222|1.19|5.841|1.06|13%|
|Out-of-pocket payments for<br>self-treatment|0.641|0.97|1.087|1.47|-0.764|-0.53|1.851|1.20|2.613|1.42|2.791|1.59|78%|
|Net out-of-pocket payments<br>associated with ambulatory<br>care from formal providers|33.595|3.94|33.627|3.27|33.435|2.35|0.192|0.01|3.780|0.19|2.522|0.11|4%|
|Net out-of-pocket payments for<br>inpatient care|34.906|2.96|38.667|2.80|23.116|1.04|15.551|0.56|58.083|1.89|44.109|1.40|50%|
|Additional hospital<br>expenditures|7.625|3.56|7.445|3.29|8.208|1.54|-0.763|-0.15|5.070|1.03|4.029|0.93|38%|
|Total out-of-pocket payments<br>incurred by individual|71.497|4.28|76.473|3.92|56.262|1.75|20.211|0.52|77.574|1.80|59.071|1.33|29%|
|<br>Insurance contributions|33.454|10.87|37.210|9.83|19.945|4.45|17.265|2.40|16.320|2.74|18.724|2.69|66%|
|Individual health care expenses<br>last 12 months|104.431|9.97|113.342|9.16|75.118|3.89|38.224|1.56|91.622|3.41|75.777|1.64|32%|
Table 3b: Industry-level urbanization rates for India's manufacturing sector, continued

||Ur|banization|rate||Industry tr|aits in 2000|||Urban-rural|ratio in 200|0|
|---|---|---|---|---|---|---|---|---|---|---|---|
||1994|2000<br>output|2005|Labor<br>intensity|Capital<br>intensity|Materials<br>intensity|Land<br>intensity|Labor<br>intensity|Capital<br>intensity|Materials<br>intensity|Land<br>intensity|
|15 Food products and beverages|0.48|0.45|0.45|0.040|0.239|0.808|0.045|0.884|0.606|1.024|0.603|
|16 Tobacco products|0.72|0.62|0.60|0.084|0.112|0.515|0.027|1.903|1.227|0.826|1.198|
|17 Textiles|0.60|0.53|0.49|0.067|0.475|0.736|0.084|2.090|0.777|0.921|1.119|
|18 Wearing apparel; dressing and dyeing of fur|0.96|0.81|0.80|0.063|0.219|0.602|0.045|0.606|0.319|0.919|0.405|
|19 Leather tanning; luggage, handbags, footwear|0.70|0.72|0.78|0.047|0.179|0.773|0.044|1.487|1.460|0.987|1.343|
|20 Wood and wood products; straw and plating articles|0.55|0.45|0.39|0.063|0.369|0.759|0.051|0.987|0.460|0.969|0.539|
|21 Paper and paper products|0.52|0.48|0.42|0.058|0.659|0.730|0.087|1.335|0.657|0.969|0.701|
|22 Publishing, printing and media reproduction|0.93|0.86|0.84|0.078|0.441|0.591|0.068|2.168|0.289|1.069|0.178|
|23 Coke, refined petroleum and nuclear fuel|0.67|0.60|0.47|0.052|0.378|0.796|0.037|1.296|1.018|0.963|0.755|
|24 Chemicals and chemical products|0.66|0.57|0.53|0.052|0.528|0.670|0.064|1.392|0.849|1.003|0.866|
|25 Rubber and plastic products|0.71|0.53|0.48|0.048|0.469|0.712|0.064|1.716|0.816|1.034|1.005|
|26 Other non-metallic mineral products|0.40|0.36|0.35|0.066|0.931|0.619|0.119|1.468|0.690|1.055|0.691|
|27 Basic metals|0.65|0.60|0.51|0.046|0.410|0.809|0.058|1.529|0.810|1.006|0.850|
|28 Fabricated metal products, except machinery|0.85|0.69|0.69|0.076|0.300|0.717|0.041|1.741|0.681|0.925|0.704|
|29 Machinery and equipment, n.e.c.|0.83|0.72|0.72|0.096|0.314|0.654|0.065|1.447|0.642|1.003|0.602|
|30 Office, accounting and computing machinery|0.71|0.84|0.76|0.035|0.403|0.755|0.034|1.830|3.293|1.255|0.988|
|31 Electrical machinery and apparatus, n.e.c.|0.84|0.88|0.66|0.096|0.348|0.704|0.067|1.966|0.823|0.974|0.918|
|32 Radio, television, and comm. equipment|0.61|0.67|0.60|0.065|0.412|0.718|0.049|1.650|0.676|1.084|0.876|
|33 Medical, precision and optical instruments, watches|0.73|0.72|0.76|0.110|0.350|0.591|0.074|1.460|0.873|1.349|1.000|
|34 Motor vehicles, trailers and semi-trailers|0.83|0.65|0.53|0.075|0.594|0.680|0.079|1.532|0.905|0.945|0.553|
|35 Other transport equipment|0.65|0.62|0.55|0.072|0.291|0.759|0.065|1.952|0.816|0.962|0.944|
|36 Furniture, manufacturing n.e.c.|0.78|0.77|0.83|0.055|0.189|0.730|0.039|0.989|0.552|1.017|0.607|
|Totals and wtd averages|0.64|0.59|0.56|||||||||
|Unweighted averages|0.70|0.64|0.60|0.07|0.39|0.70|0.06|1.52|0.87|1.01|0.79|

Notes: See Table 2. Land intensity includes land and buildings. Urban-rural ratio provides ratio of mean values for urban plants in the industry divided by mean values for rural plants in the industry.
|risk_health check|-11.627|-28.990|-2.674|-10.501|||
|---|---|---|---|---|---|---|
||(1.85)*|(2.03)**|(0.45)|(0.58)|||
|risk_religion|28.130<br>|66.962<br>|26.888<br>|87.209<br>|||
||(1.53)|(1.34)|(1.54)|(1.43)|||
|risk_trust|10.119|36.900|0.313|6.796|||
||(2.02)**|(2.90)***|(0.06)|(0.41)|||
|risk2_worker||-0.126||0.014|||
|||(0.31)||(0.03)|||
|risk2_smoking||0.061<br>||0.155<br>|||
|||(0.21)||(0.52)|||
|risk2_fincome||0.000||0.000|||
|||(2.63)***||(0.43)|||
|risk2_education||-1.163||0.084|||
|||(2.51)**||(0.17)|||
|risk2_Rcancer||-1.084||-0.079|||
|||(1.96)*||(0.12)|||
|risk2_uncertainty||0.807||-0.503|||
|||(1.23)||(0.62)|||
|risk2_health check||0.650||0.151|||
|||(2.18)**||(0.47)|||
|risk2_religion||-0.799||-0.858|||
|||(1.25)||(1.18)|||
|risk2trust||-0.346||-0.045|||
|_||(0.44)||(0.22)|||
|Worker|||-271.764|-250.159|-241.520|-0.066|
||||(2.07)**|(1.56)|(2.15)**|(0.47)|
|Smoking|||205.419|257.786|101.575|0.009|
||||(1.50)|(1.57)|(0.89)|(0.06)|
|Fincome|||0.010|0.013|0.005|0.311|
||||(1.31)|(1.48)|(0.93)|(2.98)***|
|Education|||224.738|482.622|98.078|0.585|
||||(0.91)|(2.37)**|(0.45)|(2.07)**|
|Rcancer|||595.629|531.328|507.543|0.582|
||||(1.90)*|(1.43)|(1.91)*|(2.39)**|
|Uncertainty|||-224.828|-340.606|-158.991|-0.452|
||||(1.69)*|(1.85)*|(1.29)|(2.01)**|
|Health check|||-33.921|9.362|-67.210|0.229|
||||(0.26)|(0.06)|(0.62)|(1.64)*|
|Religion|||-177.525|-431.596|54.066|0.061|
||||(0.91)|(1.70)*|(0.24)|(0.24)|
|Trust|||401.842|368.080|403.771|0.425|
||||(2.82)***|(2.19)**|(3.40)***|(2.99)***|
|Constant|||-330.964|-599.908|-164.013|0.602|
||||(0.87)|(1.51)|(0.53)|(0.61)|
|Observations|833|833|833|833|833|833|
|Log-likelihood|-7378.16|-7350.87|-7099.72|-7295.66|-7304.20|-1748.22|
|R-squared|_-0.14_|_-0.08_|_0.05_|_0.06_|_0.04_|_0.06_|
|**Typical person’s WTP-ri**|**sk coefficient**||||||
|Constant|0|0|785.886|738.3830|762.3307|4.9817|
|Level term|28.2843|81.0879|-0.9486|11.8542|2.4805|0.0199|
|Squared term|0|-1.2815|0|-0.2464|-0.0882|-0.0003|
|Marginal WTP(Yuan)|282,843|810,879|-9,486|72,616|8,366|25,787|
|Average WTP(Yuan)|282,843|572,023|833,824|794,692|834,589|183,385|
|Robust t statistics in parent<br>R=9,3194,WTP=180,2293|heses.significant<br>,|at 15%, * significant|at 10%, ** signific|ant at 5%; *** sig|nificant at 1%.||

32
**Table 1: COVID-19 mortality costs in Côte d’Ivoire over time**

|||**Pa**|**nel A: Central value**<br>**Mortality costs**|**s**|
|---|---|---|---|---|
|**Elasticity**|**Value of a statistical life**|2020|2021|2022|
|1|734 103.81|98 369 910.86|420 641 484.50|90 294 768.92|
|1.4|259 313.96|34 748 070.04|148 586 896.52|31 895 616.53|
||||**Panel B: Low values**||
|**Elasticity**|**Value of a statistical life**||**Mortality costs**||
|1|342 581.78|45 905 958.40|196 299 359.43|42 137 558.83|
|1.4|121 013.18|16 215 766.02|69 340 551.71|14 884 621.05|

Notes: The term elasticity refers to the value of a statistical life (VSL) 's income elasticity, or the proportional change in the VSL brought on by a change in income. All estimates are performed using a central value of VSL=US$ 9 000 000, and USA GNI per capita (PPP, current international US$) = 54 380 for 2013. The low values of VSL are equal to US$ 4,200,000 for the USA. All VSL estimates are adjusted for inflation using the GDP deflator, as well as for potential changes in real income. All estimates are in US$.

**Table 2** indicates the costs of COVID-19 deaths since the pandemic began while taking into consideration the fact that the disease disproportionately affects the very old. Two findings emerge from **Table 2** . The mortality costs decreased, regardless of the discount rate applied. For instance, **Panel B of Table 2** shows that the mortality costs associated with COVID-19 range from US$ 31,100,23.79 (2020) to US$ 2,854,723.33 (2022) using a 5% discount rate, low US VSL estimates, and a constant income elasticity of VSL. Furthermore, the findings of the study support a reduction in the costs of COVID-19 deaths beginning in 2021. This observation holds true regardless of the income elasticity of VSL or the discount rate applied.

6
39 incidence calculated for each group. Note that since the log-variance is exogenously determined, it remains fixed in the counterfactual analysis.<sup>3</sup> ' Table 7 gives the head count ratio for each group and the contribution to total poverty. In general the poverty profile obtained from the model is similar to Kanbur (1990) with some relative differences. The household with the highest poverty incidence is the Savannah household with 63.68 percent of the population living below the poverty line, and contributing to 19 percent of national poverty. The informal household group also has a relatively high poverty incidence while contributing the most to national poverty. Interesting differences with Kanbur (1990) is the high incidence of poverty in the export households 44 percent, compared to 36.5 percent using the 1985 household survey, and for the government household, 3.1 percent in 1985, compared to 8.69 percent in the model.<sup>32</sup> In general the model shows that the non-agricultural poor contribute significantly to national poverty (45 percent of total poverty).

31The poverty indexes obtained for each group is therefore determined in part by the exogenous log-variance and by the adoption of a log-normal distribution. This has important implications for the size of the targeting program needed to eradicate poverty.

**32** It is really not possible to compare both sets of figures since they are based on a different data set, and different methods of calculation, but it does provide an idea of the poverty incidence adopted in the model compared to other studies. For example, the informal and inactive households are more represented in the 1986 survey than in the 1985 data set leading to their higher contribution to poverty [Kanbur (1990), Chia et al. (1991)].
While the point estimate for having a choice between the two types of loans is both statistically and economically significantly greater than only having the conventional option, it is actually lower than only having the sharia-compliant option. While the difference is not statistically significant it does lead to additional questions regarding whether having additional options may complicate the application decision, leading to a type of choiceparalysis seen in the behavioral economics literature (Iyengar and Lepper 2000; Iyengar, Huberman, and Jiang 2004; Bertrand et al. 2010).

_Estimating the Price Elasticity of Demand for Sharia-Compliant Loans_ The estimates for the price elasticity of demand are reported in columns 3 and 4. As the price of the shariacompliant loan increases, the likelihood that an individual would apply for any type of loan decreases. Although this relationship is not statistically significant it is likely due to the fact that in the treatments where the price of the shariacompliant loan varied, the price of the conventional option stayed the same. To overcome this challenge we now turn to considering consumer preferences when choosing between the two types of loans.

In the three treatment arms where we varied the cost of the sharia-compliant loan, individuals had the ability to choose between a conventional loan of fixed price or the sharia-compliant loan at the price of the group they were randomized into. In the analysis of this choice we use an outcome variable which takes the value of one if the individual preferred the sharia-compliant loan<sup>19</sup> and zero if they preferred the conventional loan. The first row in Table 3 shows how the proxy variable for religiosity relates to their preferred loan type. Table 3 shows that religious individuals are 10.8 percentage points (se=2.6) more likely to prefer the sharia-compliant loan (compared to a mean of 75.3 percent in the non-religious group). This shows that there are strong preferences for sharia-compliant products in general, but that there is still significant heterogeneity in product preferences based on religiosity. This result holds when the study considers the half of the sample where we have finer data on religiosity (proxied by if they “mostly” or “always” watch religious TV).

> 19 Surveyors asked everyone in the three treatment arms that were given a choice between loan types which loan they preferred whether or not they applied for the loan. The regressions in Table 3 include the preferences of individuals even if they did not apply. When we restrict to only those individuals who applied for the loan, the estimates remain statistically significant, but the p-value for “Religious vs Non-Religious” increases to 0.038. If we restrict the outcome variable to those that preferred the sharia-compliant loan and applied for the loan we find no significant difference.

16
# **Table 19: GHG Emission reductions of select biofuels compared to gasoline and diesel excluding land use change impacts**

|Biofuel|Emission Reductions (%)<sup>a</sup>|
|---|---|
|Sugarcane ethanol|65 – 105<br>|
|Wheat ethanol|-5 – 90<sup>b</sup>|
|Corn ethanol|-20 – 55|
|Sugarbeet ethanol|30 – 60|
|Lignocellulose ethanol|45 – 112<sup>c</sup>|
|Rapeseed biodiesel|20 – 80|
|Palm oil biodiesel|30 – 75<br>|
|Jatropha biodiesel|50 – 100<sup>d</sup>|
|Lignocellulose diesel|5 – 120|

Source: OECD (2008), WWI (2007), Wang et al. (2007) and Whitaker and Heath (2009) data.<sup>a</sup> Values are approximate, as some reports only reported results in graphical form.<sup>b</sup> Negative numbers mean increases in GHG emissions.<sup>c</sup> Includes forest residues, energy crops (such as short tree rotations (e.g., poplar), and switchgrass), and crop residues (e.g., corn stover).<sup>d</sup> Whitaker and Heath (2009), their base base resulted in 62% GHG emission reductions when compared to diesel. Previous studies by Ecofys BV (2008, commissioned by D1 Oils) and Prueksakorn and Gheewala (2006) reported values within that range (70% and 77% respectively).

Most studies coincide in that most biofuel pathways reduce emissions of GHG when compared to the petroleum energy they displace, especially when land use changes are not included in the analysis. The second-generation biofuels appear to have higher potentials of GHG mitigation as compared to the first-generation biofuels. The inclusion of land use change (both direct and indirect) may reduce some or all GHG emission gains, or even result in net emission increases (Searchinger et al. 2008). Note however that the indirect GHG emissions through land use change would be smaller in the case of second-generation biofuels as compared to that of first-generation biofuels.

# **4.3    Water footprints**

Water scarcity is one of the major constraints of future potential production of secondgeneration biofuels, which has been largely ignored in the literature. The increasing demand for fuels produced from biomass will intensify the pressure on clean water resources because (1) large quantities of water are needed to grow certain feedstocks such as energy crops, and (2) agricultural drainage (containing fertilizer, pesticides, and sediments) is likely to increase with crop production.

The water footprint is a measure commonly applied in the literature to represent the extent of water use. For example, the water footprint of a country refers to the volume of fresh water needed for the goods and services produced to be consumed by the people in the country (Hoekstra and Chapagain 2007). The water footprint of a biofuel is defined 35
Fazio, 2007). Moreover, some researchers found that women’s decision to reenter the workforce was affected not only by permanent and relatively generous pensions, but also by family circumstances (Giles et al 2006b).

Given support available through pensions to relatively young workers during economic restructuring and potential biases against hiring displaced workers relatively close to retirement age, one would expect to see sharp drops in employment of urban women from 1991 to 1997 and 2000, as is shown in Panel B of Figure 1. If reduced employment rates of urban women over 45 were simply the effect of restructuring in the late 1990s, however, we would expect that employment of women in the 45 to 55 age range would return to higher pre-1997 levels by 2009. The fact that older working age women, who have not yet reached mandatory retirement age, continue to have lower employment rates, even after labor markets tightened during the 2000s, raises the possibility that exits from the labor force may be a choice facilitated by higher wealth or by increasing demands on time for non-market activities, such as caring for children, elderly or other ill family members.

Exits from employment among older workers as they approach pension eligibility are not unusual in more developed economies such as the UK and the US, but exit rates in the years before retirement age are not typically as high as one observes in China. Figure 2 highlights differences in employment rates across five countries and shows that 68 and 70 percent of women age 50 to 54 are still employed in the US and UK, respectively. Employment rates of women in this age range are somewhat lower in urban Indonesia at 63 percent, but above the 38 and 30 percent employment rates witnessed in urban Korea and China, respectively.<sup>11</sup> Relative employment patterns of men in the 55 to 59 age range follow a similar pattern: 68 and 67 percent of urban men in this age range in China and Korea, respectively, are employed, while 82 percent of urban Indonesia men of this age are still working.

**Mandatory Retirement Provisions and Retirement Patterns** . Incentives created by gender differences in the mandatory retirement age may encourage early exits from the labor force for women, particularly for women who face the prospect of job search in their 40s. Career changes and job changes later in working life can be difficult in developed and developing economies

> 11Note that this is the urban employment rate for women using the CHARLS sample, in the CHNS sample the rate is higher at 42 percent.

7
130. Stauber, C., Ortiz, G., Loomis, D., & Sobsey, M. (2009). A randomized controlled trial of the concrete biosand filter and its impact on diarrheal disease in Bonao, Dominican Republic. _The American Journal of Tropical Medicine and Hygiene_ , _80_ (2), 286–93. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19190228.

131. Stoller, N., Gebre, T., Ayele, B., Zerihun, M., Assefa, Y., Habte, D., ... Emerson, P. (2012). Efficacy of latrine promotion on emergence of infection with ocular Chlamydia trachomatis after mass antibiotic treatment: a cluster‐randomized trial. _International Health_ , _3_ (2), 75–84. doi:10.1016/j.inhe.2011.03.004.

132. Talaat, M., Afifi, S., Dueger, E., El‐Ashry, N., Marfin, A., Kandeel, A., ... El‐Sayed, N. (2011). Effects of hand hygiene campaigns on incidence of laboratory‐confirmed influenza and absenteeism in schoolchildren, Cairo, Egypt. _Emerging Infectious Diseases_ , _17_ (4), 619–25. doi:10.3201/eid1704.101353.

133. Tiwari, S.‐S., Schmidt, W.‐P., Darby, J., Kariuki, Z., & Jenkins, M. (2009). Intermittent slow sand filtration for preventing diarrhoea among children in Kenyan households using unimproved water sources: randomized controlled trial. _Tropical Medicine & International Health_ , _14_ (11), 1374–82. doi:10.1111/j.1365‐3156.2009.02381.x.

134. UNICEF. (2011). _Impact Evaluation of Drinking Water Supply and Sanitation Interventions in Rural Mozambique: More than Water._ Government of the Netherlands Partnership for Water Supply, Sanitation and Hygiene, UNICEF.

135. URI. (1995). _Contra la morbilidad infantil: Filtros artesanales y educación._ (Evaluacion de Impacto. No. 53).

136. Wang, Z., Shepard, D., Zhu, Y., Cash, R., Zhao, R., Zhu, Z., & Shen, F. (1989). Reduction of enteric infectious disease in rural China by providing deep‐well tap water. _Bulletin of the World Health Organization_ , _67_ (2), 171–80. Retrieved from <u>http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2491231&tool=pmcentrez&rendertype=abstract.</u> 38