rafmacalaba/datause-encoder-data huggingface dataset:
19 This paper provided a preliminary assessment of the experience with rural biogas in China, which is by far the country with the strongest record of biogas promotion. Using purposively collected data with a sample size of 2,700 households from 225 villages in 5 provinces, this paper found tentative ground for optimism. Rural biogas in the sampled areas of China appears to deliver on many if not all of its benefits. There was widespread awareness and acceptance of the technology with clear indications of benefits on the ground. These were: time savings from reduced fuelwood collection and easier cooking that disproportionately benefit women; indications of productive use of biogas’ by-products; signs of partial displacement of fuelwood and crop residues in response to biogas adoption, with mixed indications regarding displacement of coal; and some suggestions of benefits to respiratory health.

Moreover, problems with suspension, whether due to technical or human factors, were not evident to a major degree, with suspension of short duration and most equipment breakdown getting repaired with modest delay and expense. This was refreshing and reassuring, given the uneven record of implementing household energy interventions in developing countries (for a recent overview, see Bruce, Rehfuess and Smith, 2011).

The findings in this paper contribute in important ways to discussions of rural energy and sustainable development. First of all, the findings identify (tentatively) a case of a successful sustainable household energy technology and suggest that China has been able to address some of the technical and managerial problems in its earlier biogas programs. Second, unlike much of the evaluation literature concerned with NGO-supported pilot programs, the findings reported here pertain to a program of massive, national scale—findings apply (potentially) to millions of households. Third, our sample is much larger than the sample used in most of the household energy studies that have conducted purposeful surveys, enabling us to control for placement and supply side effects. Fourth, our findings are rooted in a comprehensive analysis of fuel demand unlike some of the energy and development literature that has taken a more partial approach.
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE price. Given the country’s large infrastructure funding gap, it will be important to explore the potential for cross-border finance arrangements that could potentially cover these costs. If such arrangements could be implemented, the infrastructure funding gap would narrow by a further $750 million.

While the DRC’s infrastructure challenge appears daunting today, there are reasons for hope. True, the analysis has shown that the DRC faces one of the most formidable infrastructure challenges in Africa. The financing needed to rebuild the country’s basic infrastructure to a level consistent with the rest of the developing world is very large both in absolute ($5.3 billion) and in relative terms (75 percent of GDP). Moreover, the DRC’s recent infrastructure spending—though significant relative to GDP—is little more than 10 percent of that needed to make an impact over the next decade. Even if the country could address the significant inefficiencies that are currently wasting at least $430 million each year, an infrastructure funding gap of about $4 billion would remain. Nevertheless, intelligent technology choices and creative use of cross-border finance could squeeze the funding gap to $2 billion a year. Furthermore, the public resource envelope is projected to grow significantly in the medium term. Therefore, by combining new resources with an improved policy and institutional environment, it should be possible to make substantial progress toward redressing and reversing the DRC’s serious infrastructure deficits.

In any case, given the magnitude of the challenges and the difficulties of completely closing the funding gap, DRC will need to have a clear basis for prioritizing infrastructure spending over the coming years. A recent World Bank 2009 study provided a framework for this prioritization based on a spatial analysis of the impacts of infrastructure investments on the broader economy, including the mining, forestry, agriculture and manufacturing sectors. The study found that by far the single highest impact infrastructure investments for DRC relate to rehabilitating and expanding power infrastructure so as to increase the availability and reduce the price of power on the domestic market; bringing an internal rate of return of some 120 percent. In the transport sector, the highest impact single intervention is the rehabilitation of the CFMK rail link; with an internal rate of return of 80 percent. More generally, returns to transport investments are highest when these are packaged together in such a way as to promote mobility along key economic corridors in the country.

Business as usual is certainly not a tenable option. Simulations indicate that if the DRC were to maintain recent infrastructure spending levels and not improve its policy environment, the country would take more than a century to meet the infrastructure policy goals identified here. This is clearly an unacceptable outcome and underscores the urgency of action in this area.

31
Table A4: Balance of baseline outcomes and covariates for those who completed baseline survey

||_Mean_|_Mean_|_Mean_|_Norm.Dif._|_Norm.Dif._|_Dif=0_|_Dif=0_|
|---|---|---|---|---|---|---|---|
||Drama<br>N=1791<br>(1)|Document.<br>N=1783<br>(2)|Control<br>N=1655<br>(3)|Drama<br>vs.Control<br>(4)|Document.<br>vs.Control<br>(5)|_(p-value)_<br>Drama<br>vs.Control<br>(6)|_(p-value)_<br>Document.<br>vs.Control<br>(7)|
|**Panel A: Outcomes**||||||||
|Baseline index|0.002|-0.007|-0.008|0.014|0.002|0.552|0.942|
|Father determines sex|0.266|0.278|0.280|-0.022|-0.004|0.356|0.858|
|Stricter control daugthers|0.543|0.544|0.540|0.004|0.005|0.873|0.832|
|Women should be virgin|0.730|0.727|0.758|-0.045|-0.050|0.081|0.053|
|Justify beating if unfaith|0.356|0.362|0.346|0.015|0.025|0.548|0.325|
|Women wear whatever|0.950|0.956|0.955|-0.017|0.007|0.507|0.792|
|Ban kitchen during period|0.359|0.371|0.370|-0.016|0.001|0.528|0.965|
|Tell anyone if friend beat|0.859|0.856|0.870|-0.023|-0.028|0.380|0.272|
|Climate change is a threat|0.602|0.609|0.627|-0.035|-0.025|0.146|0.291|
|Work in the future|0.706|0.744|0.716|-0.015|0.044|0.530|0.068|
|**Panel B: Controls**||||||||
|Age (years)|20.915|20.980|20.915|-0.000|0.024|0.997|0.327|
|English self-assess (0-10)|5.496|5.478|5.468|0.006|0.002|0.792|0.925|
|Survey in english|0.366|0.397|0.379|-0.018|0.026|0.446|0.273|
|Female|0.242|0.246|0.248|-0.009|-0.002|0.712|0.918|
|Primary|0.067|0.079|0.067|-0.000|0.031|0.994|0.196|
|Secondary|0.430|0.421|0.451|-0.031|-0.044|0.206|0.070|
|University|0.477|0.472|0.454|0.033|0.026|0.165|0.278|
|HH-head primary|0.188|0.189|0.178|0.019|0.021|0.425|0.389|
|HH-head secondary|0.332|0.337|0.341|-0.013|-0.006|0.595|0.791|
|HH-head university|0.374|0.348|0.358|0.024|-0.015|0.318|0.541|
|Hindu|0.813|0.794|0.811|0.003|-0.031|0.912|0.202|
|Muslim|0.132|0.144|0.126|0.014|0.037|0.561|0.124|
|Christian|0.013|0.009|0.011|0.013|-0.014|0.594|0.575|
|Sikh|0.025|0.033|0.030|-0.024|0.012|0.312|0.630|
|General caste|0.529|0.546|0.535|-0.009|0.015|0.723|0.543|
|OBC caste|0.290|0.278|0.275|0.023|0.005|0.333|0.831|
|SC caste|0.135|0.123|0.146|-0.021|-0.046|0.376|0.057|
|Student|0.611|0.646|0.627|-0.023|0.028|0.342|0.249|
|Employed|0.130|0.125|0.140|-0.021|-0.032|0.387|0.192|
|Self-employed|0.106|0.085|0.083|0.056|0.005|0.019|0.840|
|Live in Delhi|0.555|0.581|0.554|0.001|0.038|0.957|0.111|
|Member of organization|0.175|0.178|0.168|0.014|0.019|0.568|0.422|
|Currently dating|0.332|0.333|0.331|0.001|0.003|0.973|0.900|
|Married|0.068|0.063|0.062|0.019|0.003|0.440|0.886|
|With sisters|0.685|0.698|0.663|0.034|0.054|0.164|0.026|
|Daily freq. social media|15.576|15.382|15.943|-0.022|-0.034|0.400|0.198|
|Daily freq. watch videos|2.314|2.283|2.318|-0.003|-0.022|0.914|0.355|
|Male friend beating|0.142|0.158|0.143|-0.000|0.030|0.985|0.218|
|Female friend beated|0.136|0.149|0.161|-0.051|-0.025|0.035|0.304|

Notes: Table shows sample means at baseline for respondents who completed (at least) the baseline survey. The normalized difference in columns 4 and 5 is the difference in the sample means of treatment and control groups divided by the square root of the sum of the sample variances.

29
**Table 4: Summary Fit Statistics**

|Equation|Mean of<br>Dependent<br>Variable|Standard Error<br>of Regression|R-Squared|Durbin-<br>Watson|
|---|---|---|---|---|
|Variable Cost|331.2|32.90|0.9935|0.6793|
|Carbon Permits|4.512|0.5289|0.9888|0.6406|
|High Carbon Fuels|38.38|7.217|0.9706|0.6266|
|Low Carbon Fuels|21.28|4.968|0.9710|0.5968|

**Table 5: Partial Adjustment Elasticities of Demand and Marginal Cost  (asymptotic t-ratios in parentheses)**

||Carbon<br>Price|Coal & Oil<br>Prices|Natural Gas<br>Prices|Nuclear<br>Generation|Renewable<br>Generation|Total<br>Output|
|---|---|---|---|---|---|---|
|Carbon|0.004|-0.009|0.005|-0.899|-0.122|1.720|
||(2.3)|(1.9)|(1.0)|(30.4)|(10.3)|(31.3)|
|Coal & Oil|-0.005|-0.012|0.017|-0.906|-0.184|1.745|
||(1.9)|(1.7)|(1.7)|(18.8)|(9.0)|(21.2)|
|Natural Gas|0.004|0.019|-0.023|-0.936|0.038|1.807|
||(1.0)|(1.7)|(1.6)|(14.9)|(1.4)|(15.5)|
|Marginal Cost|0.258|0.220|0.348|-0.243|-0.029|0.318|
||(12.7)|(31.3)|(21.2)|(37.2)|(10.5)|(15.5)|

28
9 regions show greater variability. The City Sofia again scores favorably with the highest minimum score of 0.87, while the lowest minimum score is found in a municipality in the region of Mihailovgrad.

**Table** 3: Sumusy of **EfIci,y** Scors by **RqIk**

|Region|**Mean**Eflicicncy|**Standard**Deviation|Mininmn||Maximum|
|---|---|---|---|---|
|All9 rcgions (199 observatlons)|0.793|0.129|0.430|1.000|
|City Sofia (5 observed)|0.951|0.067|0.870|1.000|
|Burgas (IS observcd)|0.307|0.121|0.591|1.000|
|Varma (20 observed)|0.782|0.137|0.539|1.000|
|Lovec**(30**obscrved)|0.774|**0.111**|0.618|1.000|
|**Mihailovgrad**(23observed)|0.782|0-131|0.430|1.000|
|Plovdiv(;2 observed)|0.775|0.109|0.631|1.000|
|**Razgrmd**(21observed)|0._44|0.118|0.601|1.000|
|Sofa (33 observed)|0.783|0.141|**_0.465_**|X.000|
|Haskovo (35 observed)|0.786|0.154|0.465|1.000|

The question arises what determined the observed variation in efficiency scores in Bulgarian municipalities. To address that question, we use multivariate regression analysis. The choice of the explanatory variables is, obviously, partly dictated by the availability of data. Nevertheless, we were able to collect data on several variables which intuitively should be related to the efficiency of use of Bulgarian classrooms. First, the percentage of students in the population under age 20 is expected to be positively related to the use of classrooms, directly through the availability of students and indirectly through the class size. Second, the number of teachers is anticipated to be positively
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small, with the ITT showing only a 0.04 standard deviation improvement in employment outcomes as a result of being selected for training.

Table 4 uses the social security data to measure impacts on formal employment. Consistent with our survey data, the first column shows a statistically significant 2.6 percentage point impact on the likelihood of having found a formal job by the time of the follow-up survey. However, the next three columns show that this effect disappears over time. There is no significant effect on formal employment status or formal income by August 2013, and no impact on whether they have ever found a formal job by the end of November 2013. Figure 2 examines the trajectory of impacts in more detail, plotting the proportion of treatment and control who have ever found a formal job month by month. We see that the treatment group is more likely to have found a formal job than the control group by May 2012, and that this gap lasts for about a year, including the time of the follow-up survey, before closing again.<sup>15</sup> By November 2013, 66 percent of both groups have found a formal job at some stage during the post-training period. However, there appears to be a lot of churn in employment, with the last column of Table 4 showing that 50 percent of both groups have also left a formal job at some point during this period. As a result, there is no lasting impact of training on formal employment.

# **5.2 Heterogeneity of Employment Impacts by Age and Gender**

Existing experimental evaluations of vocational training programs in developing countries have focused just on youth, and Attanasio et al. (2011) find different impacts by gender, with young women benefiting more from their training in Colombia than young men. Recall that our randomization stratified by gender*age group within each course. We estimate equation (3) with _X_ as the vector of four gender*age group strata and report the results in Table 5. A test of equality of treatment effects across the four subgroups enables us to determine whether there is significant heterogeneity in employment effects by age or gender. Lee bounds control for the differential attrition, which is largely only an issue for males.

The key result from Table 5 is that we cannot reject the null hypothesis of equality of employment treatment effects across the four age*gender groups. This is despite the four groups

> 15 The control group was about 5 percentage points more likely than the control group to take a non-ISKUR training course.  If those individuals entered the labor force later, it could at least partially explain the lag in the control entering the labor force.  Another possible explanation is that the impact of training interacted with changing labor market conditions.

15
# **Tables**

Table 1: Summary Statistics

|||Panel A: Mo|bility Samp|le|
|---|---|---|---|---|
||Rural (N|OB:10239)|Urban (N|OB: 5238)|
||Mean|SD|Mean|SD|
|Father’s Years of Education|5.66|3.49|7.54|3.76|
|Child Years of Education|9.05|3.22|10.18|3.38|
|Child Age|29.47|6.25|28.88|6.37|
|Child Raven Test Score|67.14|24.20|71.55|23.23|
|Child Memory Test 1|55.74|16.00|57.65|15.84|
|Child Memory Test 2|46.40|17.39|48.30|17.23|
||Panel|B: Education|al Expense|Sample|
||Rural (N|OB: 9797)|Urban (N|OB: 8530)|
||Mean|SD|Mean|SD|
|Average Monthly Expenses|0.64|1.77|1.51|2.82|
|Father Edu|5.31|3.67|7.48|3.95|
|Child Edu|9.47|3.12|10.82|3.32|
|Child Raven Test Score|69.73|23.28|76.11|21.39|
|Child Memory Test 1|56.36|15.53|59.50|15.29|
|Child Memory Test 2|47.19|17.03|50.34|16.52|
|Number of School Age Children|2.21|1.16|2.31|1.16|
||Pane|l C: Permane|nt Income|(Father)|
||Rural (N|OB: 2253 )|Urban (N|OB: 1740)|
||Mean|SD|Mean|SD|
|Permanent Income|199.32|139.53|334.79|258.47|
|Father’s Years of Education|523|362|743|376|
||.<br>Panel|.<br>D: Permanen|.<br>t Income (|.<br>Children)|
||Rural (N|OB: 2419)|Urban (|NOB: 977)|
||Mean|SD|Mean|SD|
|Permanent Income|208.93|149.75|269.39|227.92|
|Child’s Years of Education|8.38|3.49|10.02|3.65|

Notes: Author’s calculation based on the Indonesian Family Life Survey. In Panel A, Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. The sample in Panel A is based on the 18 to 40 years old in the fifth wave of the IFLS. In panel B, Rural and Urban are defined based on the location of the household in the survey. The summary statistics of Panel B are based on the merged sample of first four waves of the IFLS. Average monthly expense was calculated using the inflation adjusted educational expenditure of the first four waves. In Panel C, Rural and Urban are defined based on household location in the first wave (1993). Father’s permanent income was calculated using the inflation adjusted household consumption expenditure in first three waves (1993, 1997, and 2000). In panel D, Rural and Urban are defined based on location of birth (same as in Panel A). Children’s permanent income was calculated using the inflation adjusted household consumption expenditure in last two waves (2007, 2014). Inflation rates are calculated using the price of rice separately for rural and urban areas. The school expense and permanent incomes are expressed in 10,000 rupiahs.

33
(as credible information is less available and more limited) and riskier (with higher mortality rates, lower growth, and less profitability), and they usually do not have adequate collateral. Consequently, their access to bank financing is more restricted. Factoring helps them overcome a number of these constraints, allowing them access to short-term financing, mostly for working capital. These operations offer smaller firms financing without collateral, albeit small guarantees might be charged in some cases, as the underlying credit risk of the transaction belongs to the issuer of the invoice. In addition, factoring can lower the cost of capital for SMEs, because, in many emerging markets, issuers are larger firms with lower credit risk (due, at least in part, to a better credit history) than the SMEs seeking financing.

Factoring is an expanding industry, particularly in LAC countries and emerging markets more broadly. According to the International Factors Group, the worldwide industry turnover in 2008 was estimated at €1.2 trillion (the total amount of assigned receivables), and it has been growing—worldwide volumes increased 3.75 percent in 2008 and 15 percent in 2007.<sup>12</sup> This expansion in factoring volumes, although slowed during the global financial crisis of 2007–08, has been concentrated mostly in emerging markets and particularly in China, Eastern Europe, and LAC7 countries. Nevertheless, factoring is typically less important in emerging markets than in developed countries. In LAC7, for example, factoring represented 2.6 percent of GDP in 2008–09 compared to about 4 percent for developed countries (figure 18a).

Chile and Mexico are notable examples in the LAC region where factoring services have developed significantly in recent years and where invoices can actually be traded on organized exchanges or online markets. Factoring in Chile, for example, is one of the largest among emerging markets. In 2009, it had an accumulated volume of €12 billion (10.7 percent of GDP) and about 14,000 users of factoring services, according to the International Factors Group and the Chilean Association of Factoring. Moreover, nonbank factoring companies represent almost 10 percent of this total, according to the Central Bank's Financial Stability Report (2008). In Mexico, total industry turnover was estimated to be almost €11 billion in 2007 (almost 2 percent of GDP). Nonetheless, factoring is still relatively small compared to bank loans or credit lines.

As an alternative to the factoring services typically offered by banks in Chile, Bolsa de Productos is a new initiative that might actually become an important source of SME financing

> 12 The statistics, however, were significantly influenced by a strong euro. Most notably, a large market, such as that in the United Kingdom, actually increased when expressed in British pounds, while it decreased by 4.84 percent when expressed in euros.

20