rafmacalaba/datause-encoder-data huggingface dataset:
# **BDM Sample**

Recall that the BDM exercise was only done with firm owners who had expressed enough interest in business training to both answer our phone survey, and then show up and attend the demonstration session. Despite this, Table 7 shows that when training was offered for free, attendance was not particularly high. While 76% of those offered the course for free attended at least 1 class, only 59% attended at least 5 of the 10 classes of the course, and only 18% attended all 10 classes. The average number of classes attended by those offered the course for free was 5.4 classes.

Table 7 then shows that the overall effect of charging a higher price for training is to reduce attendance dramatically. While 76 percent of entrepreneurs attend at least one class when offered for free, only 43 percent do when charged one-quarter of the cost (J$5,000), 29 percent when charged half the cost, and only 11 percent when the course is priced at full cost. This overall effect shows that the strong demand effect of a higher price reducing purchase overwhelms any selection and effort effects in terms of absolute numbers trained out of a given sample.

In Table 8, we then test for selection effects by studying how WTP correlates with attendance, before examining the causal effect of prices on attendance. We first conduct the analysis using the full sample. We see that conditional on price paid, either as a continuous measure in Column (1) or binary categories in Column (2), WTP is positively and significantly correlated with attending at least one class of the course. Similarly, there is a significant negative effect of prices on attendance. Overall, these results confirm the patterns in demand found above. However, they do not directly provide evidence for selection or sunk-cost effects since the effects can be in part mechanical due to the BDM rule (only those with WTP no less than price are entitled to come to the course).

In Columns (3) and (4), we therefore restrict the sample to those who purchased the course according to the BDM rule, regardless of whether they paid. We still see that conditional on the randomly chosen offer price, those with higher WTP are more likely to attend at least one class. Among those who purchased the course and have the lowest category of WTP (less than J$3,000), the attendance rate was only 25 percent. Attendance jumps significantly by 35 percentage points for the next WTP category (willing to pay from J$3,000 to J$5,000). It then exhibits smaller monotonic increases for the higher WTP categories, except for those who reported a WTP above the actual cost of the course. This indicates that incentivized measures of WTP can be a good way of screening for those who are less likely to show-up to the course. The same is true for the extensive margin of how many classes they do 21
Table 5 shows the distribution of firms by world region. Two out of five MNEs are based in Asia (43.1%), while this share drops to a quarter for large firms (27.8%) and SMEs (26%). By contrast, the percentage of European firms is much lower for MNEs (28.1%) compared with SMEs (42%) and large firms (36.4%). Only 12.8% of MNEs are based on Northern America, while this percentage drops further for other types of firms. The share of large firms (23.4%) and SMEs (22.8%) in Latin America and the Caribbean, by contrast, is much higher compared to MNEs (10.8%). Oceania and Africa play a minor role for all firms.

**Table 5: Distribution of firms by region**

||**_All_**||**_MNE_**|**_s_**|**_Lar_**|**_ge_**|**_SM_**|**_Es_**|
|---|---|---|---|---|---|---|---|---|
|**Region**|**Obs**|**%**|**Obs**|**%**|**Obs**|**%**|**Obs**|**%**|
|Africa<br>|45|2.2%<br>|10|1.7%|31|2.6%<br>|4|1.8%|
|Asia<br>|649|32.1%<br>|259|43.1%|333|27.8%<br>|57|26.0%|
|Europe<br>|698|34.6%<br>|169|28.1%|437|36.4%<br>|92|42.0%|
|Latin America & the Caribbean<br>|396|19.6%<br>|65|10.8%|281|23.4%<br>|50|22.8%|
|Northern America<br>|175|8.7%<br>|77|12.8%|86|7.2%<br>|12|5.5%|
|Oceania<br>|57|2.8%<br>|21|3.5%|32|2.7%<br>|4|1.8%|
|**Total**<br>|**2,020**|**100.0%**<br>|**601**|**100.0%**|**1,200**|**100.0%**<br>|**219**|**100.0%**|

Table 6 shows the distribution of firms by the country’s income status. Half of the firms in the sample are based in OECD member states across types of firms. A third of all MNEs are located in non‐OECD countries that do not receive development aid through the OECD Development Assistance Committee (DAC), while these shares are much smaller for large firms (14.3%) and SMEs (12.3%). Only 14.6% of MNEs are located in upper‐middle‐income countries which, however, are more relevant to large firms (27.3%) and SMEs (22.4%). Low‐income and lower‐middle‐income countries play no role for MNEs, while a significant portion of SMEs is located in lower‐middle‐income countries (15.5%).

**Table 6: Distribution of firms by country’s income status**

||**_A_**|**_ll_**|**_MN_**|**_Es_**|**_Lar_**|**_ge_**|**_S_**|**_MEs_**|
|---|---|---|---|---|---|---|---|---|
|**Income/Country status**|**Obs**|**%**|**Obs**|**%**|**Obs**|**%**|**Obs**|**%**|
|DAC‐LDC|5|0.2%<br>|2|0.3%|3|0.3%||0.0%|
|DAC‐LMICT|136|6.7%<br>|9|1.5%|93|7.8%|34|15.5%|
|DAC‐OLIC|2|0.1%||0.0%|1|0.1%|1|0.5%|
|DAC‐UMICT|465|23.0%<br>|88|14.6%|328|27.3%|49|22.4%|
|Non‐OECD / Non‐DAC|401|19.9%<br>|202|33.6%|172|14.3%|27|12.3%|
|OECD|1,011|50.0%<br>|300|49.9%|603|50.3%|108|49.3%|
|**Total**|**2,020**|**100.0%**<br>|**601**|**100.0%**|**1,200**|**100.0%**|**219**|**100.0%**|

Note: OECD = Organisation for Economic Co‐operation and Development, DAC = Development Assistance Committee, i.e. a country receives development aid through the OECD DAC, LDC = Least Developed Countries, LMICT = Lower Middle Income Countries and Territories, OLIC = Other Low Income Countries and Territories, UMICT = Upper Middle Income Countries and Territories.

10
teacher. In contrast, process aspects of quality refer to the quality of the interactions that children experience in the classroom, e.g., the teacher’s promotion of critical thinking skills during learning activities, sensitivity to children’s needs, and the promotion of a positive classroom culture to enable development and early learning. In general, process quality elements, and the “quantity and quality of interactions between a young child and his or her primary caregivers, including ECE providers, are the most important factors in early development” (Burchinal, 2018, p. 3).

Teach ECE captures information regarding the process quality of classrooms during two 15-minute segments of class time. Observers collect information on whether the teacher is engaging the class in a learning activity and the proportion of the class that is actively engaged three times during each segment to measure the Time on Task of the classroom. Observers then code 28 behaviors indicative of quality during the lesson which fall under nine elements. These nine elements fall under three primary areas of process quality—Classroom Culture, Guided Learning, and Socioemotional Skills. **Figure 3** shows the structure of the Teach ECE measures of process quality (Ding et al., 2023; World Bank, 2021). In addition to the process quality of classrooms, observers also collect information regarding the structural aspects of quality, including the teacher’s level of education, teacher-child ratio, attendance and composition of enrolled children, language-match in the classroom, availability of learning materials, and safety and hygiene issues. Consistent with the literature emphasizing the importance of process quality in ECE, the majority of this paper focuses on these process quality scores from the Teach ECE classroom observation tool.

_Figure 3. Teach ECE Framework of Areas and Elements_ <!-- Start of picture text --> Quality of Teaching Practices Time on Task<br>Socioemotional<br>Classroom Culture Guided Learning Skills Time on Learning<br>Supportive Facilitation of<br>Learning Autonomy<br>Environment Learning<br>Positive Behavioral<br>Feedback Perserverance<br>Expectations<br>Social &<br>Critical Thinking Collaborative Skills<br>Checks for<br>Understanding<br><!-- End of picture text --> 8
enterprises in 2,131 households in the 2018 wave.<sup>11</sup> For each enterprise, there are questions about enterprise characteristics, household members’ participation, employment of non-household members, net revenues and expenditures.

# **2.1.2. Economic Census and Establishment Census**

We use the public release 50% sub-sample of Egypt’s 2012/2013 EcC. Despite being called a census, the EcC is a survey of fixed establishments, with the largest establishments having a sampling rate of 100%. The 50% sub-sample we have access to includes 62,108 establishments. Although the 2017 EsC is truly a census of fixed establishments, we only have access to a 20% random sample of these establishments. Thus, our sample is made up of 772,432 establishments. The EcC and EsC cover all non-governmental establishments and therefore exclude any firms that are not in fixed establishments, i.e. ones that involve mobile workers or ones whose activities take place in a field or open worksite, such as the vast majority of firms engaged in construction, agriculture, and transportation activities.<sup>12</sup> The EcC and EsC include public enterprises, but we exclude those from our analyses since they are by definition formal. We also exclude the very small number of agricultural firms that show up in both data sources for comparability with the ELMPS. Since the ELMPS excludes the Frontier governorates, we likewise exclude these governorates from the EsC and EcC data. Since the EcC and EsC only include firms that operate in fixed establishments but the ELMPS includes firms that operate both in and out of establishments, most of our analysis is limited to firms operating within fixed establishments.

# **2.2. Measuring Formality**

Firm formality can be defined and operationalized in a variety of ways. We test two increasingly expansive measures of firm formality. First, our basic measure of formality is commercial registration. However, in the EcC, the registration question was not asked of firms in a number of industries.<sup>13</sup> In our extended definition, we expand this measure to include firms that paid social insurance premiums (available in EcC and EsC) or taxes (available in the ELMPS).

# **2.3. Firm characteristics**

We have a limited set of harmonizable, common characteristics across all the data sets we use. We compare firms across these characteristics in terms of whether they are describing firms in our universe in the same fashion and whether the different data sources find similar patterns of formality across characteristics. We then conduct some analyses on a broader set of characteristics present only in the EcC or only in the ELMPS.

> 11 For the dynamic analyses we also use the 1998 wave (1,059 households and 1,134 enterprises) and 2006 wave (1,899 households and 2,044 enterprises).

> 12 Firms operating in fixed establishments include firms that operate in private homes.

> 13 The EcC uses 11 different survey instruments for firms in different industries. Some of these did not include a question on commercial registration.

5
Policy ReseaRch WoRking PaPeR 4484-

# **Abstract**

Despite large amounts invested in rural roads in developing countries, little is known about their benefits. This paper derives an expression for the willingness-topay for a reduction in transport costs from the canonical agricultural household model and uses it to estimate the benefits of a hypothetical road project. Estimation is based on novel cross-sectional data collected in a small region of Madagascar with enormous, yet plausibly exogenous, variation in transport cost. A road that essentially eliminated transport costs in the study area would boost the incomes of the remotest households— those facing transport costs of about $75/ton—by nearly half, mostly by raising non-farm earnings. This benefit estimate is contrasted to one based on a hedonic approach.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the poverty impacts of rural infrastructure. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hjacoby@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
To identify the effects of the unconditional cash transfer on expenditures in education, we build a variable that captures expenditures on education for each school age child as our key unit of analysis. Expenditures on education include education enrollment tuition and fees, uniforms, school materials, other school fees and contributions, transportation related expenditures and individual expenses for each student<sup>10</sup> . This measure captures heterogeneity across different age cohorts, as expenditures may differ across educational levels, and intra-household human capital investment decisions may vary at different stages of life. Therefore, to evaluate the impact on education investment, our unit of analysis is children between 6 and 18 years old<sup>11</sup> . Accordingly, we analyze the combined impact on the decision to invest, if the children have matriculated, and the amount spent.

In terms of treatment assignment, we identify the treatment status of a household based on the age of the household member closest to the cut-off age of 65 years. If the household member closest to the cut-off age is younger than 65, the household is considered untreated, and if the member is 65 years old or older, the household is considered treated. For example, a household with two senior people, one who is 64 and one who is 80, is considered untreated, while a household with two senior persons, one who is 65 and one who is 80, is considered treated. In this sense, the results can be interpreted as the impact of having one additional recipient member, and we can more clearly identify the impact of receiving the cash transfer on the allocation of expenditures within the household. Thus, we consider as treated those children who live in a treated household and consider as untreated those children living in an untreated household.

Panel (a) in Figure 2 displays a histogram of individuals at age 50 and above that compares before and after the implementation of Bolivida, which occurred at the end of 2000. There is no evidence of a sudden increase in the population distribution at the age of the threshold of eligibility. Panel (b) in the same figure indicates that there is no change in the share of households that are eligible around the cut-off age. In addition, the conditional probability of being eligible increases at such a

> 10For additional details see the household questionnaire for the 2001 Household survey, section 9 part E: Education expenditures. We exclude from the calculations expenditures on photocopies due to inconsistent information reported and outliers in the sample.

> 11Additional estimations using household per-student educational expenditure as well as including children in school but without expenditures were performed and the results remain robust.

7
<mark>welfare standard widely accepted to ensure a minimum standard of well-being, and (iii) the estimation methodology.</mark>

# <mark> The Reference Group</mark>

<mark>The ‘reference group’ represents a group of households whose expenditure patterns provide an adequate description of what is required to</mark> _<mark>escape</mark>_ <mark>poverty in the given country context. Thus, it typically includes households that are neither among the poorest nor the wealthiest in a country, ideally reflecting the needs of those who are ‘around’ the existing poverty level. One way to ensure this is to select from among households in the lower ‘middle’ of the distribution of expenditure. In practice, this implies excluding households in the bottom decile and sometimes the bottom quintile of the expenditure distribution. Similarly, it is not recommended in practice to include households that are much above the median expenditure level. That said, the choice of a reference group is ultimately subjective and relies heavily on the subjective notions of poverty in the country.</mark> <mark>In Rwanda, the 2001 poverty line was based on the expenditure patterns of households in the bottom three quintiles of the distribution of consumption expenditure. This was done because poverty headcount in 2001 was between 60-65 percent. Similarly, in 2014 NISR used households in the bottom two quintiles as the reference group—as the poverty rate from the last available survey at the time (EICV3) was around 45 percent.</mark> <mark>Including the bottom decile or sometimes the bottom quintile in the reference group is tricky as the tail has a lot more noise than the lower middle of the distribution. As mentioned earlier, it is best to choose a reference group that is around the poverty line, reducing sensitivity to measurement error in extreme values.</mark>

- <mark>The Normative Standard – a Caloric Threshold</mark>

<mark>Absolute poverty lines supplement the consumption basket of the chosen reference group with a normative standard of well-being. The use of an externally established minimum standard of well-being helps to anchor the poverty line such that it becomes consistent with the country’s subjective standards of consumption adequacy. These standards are typically based on thresholds of nutritional adequacy provided by the Food and Agriculture Organization (FAO), and defined in terms of the minimum daily caloric intake of an individual.</mark> <mark>The relevant FAO standard for a country like Rwanda, for example, is about 2,100-2,500 calories per adult equivalent per day. In the estimation of the 2001 poverty line, Rwanda uses a caloric threshold of 2,500 calories per adult equivalent per day which is at the higher end of the regional established minimum (NISR 2015). Ultimately, the decision to choose the final standard is the country’s decision, and despite the 2,500 calorie standard being more aspirational than the usual minimum for the region, Rwanda decided to keep the caloric threshold unchanged from the one used previously. The final 2014 poverty line is therefore based on a caloric threshold of 2,500 calories per adult equivalent per day.</mark> 5
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