rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9: Principal Components Analysis of Level 2 variables**

||Sum|mary Stati|stics|Component loadings for first five|principal|compone|nts|||
|---|---|---|---|---|---|---|---|---|---|
||Eigen-|Diff-|Prop.|Variable|Comp1|Comp2|Comp3|Comp4|Comp5|
|**SDI**||||||||||
|Comp 1|3.76|2.02|0.18|Teacher interacts with students as a group|0.21|0.28|0.24|-0.17|-0.24|
|Comp 2|1.74|0.27|0.08|Teacher interacts with a small group of children|0.05|0.12|0.00|-0.32|0.21|
|Comp 3|1.47|0.10|0.07|Teacher interacts with children one on one|0.14|0.19|-0.37|0.23|0.02|
|Comp 4|1.36|0.12|0.06|Teacher reads, lectures, or demonstrates to the pupils|0.12|-0.46|0.11|-0.04|0.03|
|Comp 5|1.24|0.10|0.06|Teacher supervises pupils writing on the board|0.15|-0.13|-0.07|-0.18|-0.62|
|||||Teacher leads kinesthetic group learning activity|0.05|-0.04|0.05|-0.22|0.09|
|||||Teacher writing on blackboard|0.26|-0.04|-0.04|**0.31**|-0.09|
|||||Teacher listening to pupils read/recite|0.05|-0.12|0.30|-0.47|**0.33**|
|||||Teacher waiting for pupils to complete task|0.17|0.07|-0.37|0.00|**0.40**|
|||||Teacher testing students in class|0.03|**0.34**|**0.35**|0.20|-0.11|
|||||Teacher maintaining discipline in class|0.08|0.21|**0.40**|0.09|-0.04|
|||||Teacher doing paperwork|-0.01|0.04|0.02|0.11|0.21|
|||||Teacher in class, not teaching|0.05|0.18|**0.36**|0.23|0.14|
|||||Teacher not in class, learning activity ongoing|0.04|-0.30|0.12|**0.37**|0.21|
|||||Teacher not in class, no learning activity ongoing|-0.48|-0.07|-0.03|-0.02|-0.07|
|||||Break|-0.01|-0.11|0.21|**0.30**|0.12|
|||||Good Teacher Demeanor|**0.43**|-0.01|-0.03|-0.01|0.13|
|||||Good Pedagogical Practise|**0.43**|-0.14|0.05|0.00|0.07|
|||||Classroom Environment|0.05|**0.41**|-0.02|-0.25|0.18|
|||||Materials and Infrastructure|0.06|**0.35**|-0.28|0.11|-0.04|
|||||Use of materials|**0.42**|-0.11|-0.03|-0.07|-0.17|
|**Stallings**||||||||||
|Comp 1|2.67|0.67|0.18|Teacher involved in reading activity|-0.56|0.13|0.01|0.03|0.05|
|Comp 2|1.99|0.68|0.13|Teacher involved in lecture activity|0.00|-0.18|**0.46**|-0.48|0.18|
|<br>Comp 3|1.32|0.10|0.09|<br>Teacher involved in discussion|0.19|0.00|0.19|**0.72**|-0.21|
|Comp 4|1.22|0.08|0.08|Teacher involved in practice activity|0.21|0.20|-0.45|0.05|0.15|
|<br>Comp 5|1.14|0.01|0.08|<br>Teacher involved in class work activity|0.25|-0.41|-0.36|-0.25|-0.01|
|||||Teacher giving instructions on blackboard|0.12|0.04|**0.33**|-0.23|-0.49|
|||||Teacher giving verbal instructions to students|0.15|**0.34**|0.12|-0.10|-0.21|
|||||Teacher involved in social interactions|0.19|**0.43**|0.06|-0.11|0.10|
|||||Teacher involved in dicipline related activity|0.17|**0.44**|0.12|-0.18|0.12|
|||||Teacher involved in classroom management related activit|0.19|**0.37**|0.03|0.04|0.26|
|||||Teacher involved in classroom management related activit|-0.09|-0.15|**0.31**|0.16|**0.62**|
|||||Teacher uninvolved|-0.02|0.08|-0.04|0.03|**0.33**|
|||||Teacher not present in the classroom|-0.54|0.20|-0.14|0.01|-0.11|
|||||Materials and Infrastructure|**0.33**|-0.18|0.05|0.13|0.15|
|||||Classroom environment|-0.03|-0.08|**0.40**|0.18|-0.07|

continued...

47
The training material was prepared by a team with expertise on entrepreneurship, organizational behavior and work psychology. The first five classes focus on personal initiative, a psychological intervention aimed at making business owners more proactive with respect to new ideas, and more persistent in overcoming challenges (Frese and Gielnik, 2014; Campos et al., 2017; Ubfal et al., 2019). The second five classes cover content from a standard training program that teaches a set of best business practices based on the ILO’s Improve your Business (IYB) training, adapted to the Jamaican context. The cost of providing the course is approximately J$20,000 Jamaican dollars (US$150) per participant.

# **2.3 Sampling Firms**

The population of interest for JBDC is the set of micro, small and medium enterprises (MSMEs) in a targeted region who they can reach through their outreach activities, and who might potentially be interested in taking part in business training. We therefore worked closely with them to recruit the types of firms they would typically be interested in serving. Our recruitment process was divided into two phases. In the first phase, which was used for selecting firms to take part in the BDM elicitation method, we targeted firms in the Western regions of Jamaica (the parishes of St. James, Westmoreland, Trelawny, Hanover, Manchester and Saint Ann). These parishes include the second-largest city in Jamaica, Montego Bay. The second phase, used to select firms for the take-it-or-leave-it (TIOLI) approach, recruited mainly around the capital city of Kingston (72%), with the remainder from Montego Bay and surrounding parishes.

A variety of communication methods were used to contact entrepreneurs in both locations and gauge their interest in learning more about the business training. These included emails to the existing client database of JBDC; advertisements via social media, newspaper and radio; and messages from other MSME support organizations. The goal was to reach 2,000 MSMEs, with a target of having 1,000 of them attend a demonstration workshop, where the content of the course and the methodology to determine the price would be explained and willingness to pay for the course would be elicited. A short baseline survey was conducted on the phone or online upon enrollment in the study to provide basic background on the firm, including characteristics that can be used to examine what factors help predict willingness to pay.

This process launched in late June 2017 and was extended until May 2018. Overall, 1,823 entrepreneurs showed interest in the training and answered the baseline survey. We dropped 8 respondents who reported not having a business, and one respondent who did not provide any contact details. We also removed from the sample 20 outliers in terms of monthly 7
# TABLE 7: Impact of leaders’ exposure on intermediate outcomes

||(1)<br>Nutrition|(2)<br>Stimulation|(3)<br>Health|(4)<br>Micronutrients|(5)<br>Proteins|(6)<br>Attitudes|(7)<br>Hygiene|
|---|---|---|---|---|---|---|---|
|**_Panel A: ITT of leaders’ exposure to text messages_**||||||||
|ITT Leader|-0,14<sup>_∗_</sup><br>(0,08)|0,02<br>(0,06)|-0,00<br>(0,04)|-0,16<sup>_∗∗_</sup><br>(0,06)|-0,23<sup>_∗∗_</sup><br>(0,09)|-0,00<br>(0,06)|-0,11<sup>_∗_</sup><br>(0,06)|
|Observations|2062|2062|2073|2059|2062|1970|2068|
|**_Panel B: Heterogeneity by caregiver education_**||||||||
|ITT Leader|-0,01|0,10|-0,01|-0,23<sup>_∗∗∗_</sup>|-0,18<sup>_∗_</sup>|0,00|-0,08|
||(0,12)|(0,11)|(0,07)|(0,09)|(0,09)|(0,10)|(0,09)|
|ITT Leader X Medium educ (4-6 years of education)|-0,04|-0,04|-0,01|0,08|0,03|0,04|-0,05|
||(0,10)|(0,12)|(0,08)|(0,09)|(0,09)|(0,11)|(0,10)|
|ITT Leader X High educ (more than primary education)|-0,34<sup>_∗∗∗_</sup><br>(0,13)|-0,21<br>(0,13)|0,04<br>(0,10)|0,10<br>(0,10)|-0,20<br>(0,12)|-0,06<br>(0,13)|-0,04<br>(0,10)|
|Observations|2062|2062|2073|2059|2062|1970|2068|
|**_Joint signifcance test_**||||||||
|ITT Leader + ITT Leader X Medium Educ|0,58|0,46|0,66|0,08|0,12|0,61|0,16|
|ITT Leader + ITT Leader X High Educ|0,00|0,12|0,67|0,16|0,01|0,60|0,13|

_Note:_ Sample includes only non-leader households in 92 villages with opinion leaders. Five villages without leaders were excluded from the estimation. All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. Impacts on each of the individual items used to construct the indices are reported in Tables A17, A18, A19. All regressions include controls for the householdlevel treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 41
# **Table 3: Descriptive statistics**

# a. Circumstances

||**BRAZIL**|**COLOMBIA**|**ECUADOR**|**GUATEMALA**|**PANAMA**|**PERU**|
|---|---|---|---|---|---|---|
|Gender|||||||
|_male_|47.4|46.4|48.8|47.2|48.5|47.6|
|_female_|52.6|53.6|51.2|52.8|51.5|52.4|
|Ethnicity|||||||
|_majority_|59.8|90.8|88.3|69.3|92.2|72.3|
|_minority_|40.2|9.2|11.7|30.7|7.8|27.7|
|Father's occupation|||||||
|_agricultural worker_|35.0|missing|51.9|49.5|37.1|missing|
|_other_|65.0||48.1|50.5|62.9||
|Father's education|||||||
|_none or unknown_|50.2|36.2|27.9|67.3|21.7|30.9|
|_primary_|40.2|49.0|56.1|17.3|54.2|32.1|
|_primary complete / secondary_|9.7|14.8|16.1|15.5|24.1|37.0|
|Mother's education|||||||
|_none or unknown_|53.1|31.7|29.3|76.8|24.5|48.7|
|_primary_|37.9|53.9|56.4|12.2|54.5|24.9|
|_primary complete / secondary_|9.0|14.4|14.4|11.0|21.1|26.4|
|Birth region|||||||
|_Region 1_|17.6|45.1|31.3|27.5|30.4|45.4|
|_Region 2_|47.1|45.8|50.6|20.9|21.1|35.7|
|_Region 3_|35.3|9.1|18.1|51.6|48.5|18.8|

# b. Economic outcomes

||**BRAZIL**|**COLOMBIA**|**ECUADOR**|**GUATEMALA**|**PANAMA**|**PERU**|
|---|---|---|---|---|---|---|
|Currency unit|Reais 1996|Pesos<br>2003|USD<br>2006|Quetzal<br>2000|Balboas (USD)<br>2003|Sols<br>2001|
|Individual earnings|905.8|544,800|341.4|1,734.2|477.0|809.9|
||[1,460.1]|[838,400]|[516.2]|[4,195.4]|[620.0]|[1,542.9]|
|Per capita total|391.9|329,300|199.0|667.0|255.3|376.8|
|household income|[708.2]|[546,400]|[256.1]|[1,237.6]|[376.2]|[682.9]|
|Per capita||341000|123.0|603.5|180.3|307.4|
|consumption||[485,300]|[131.8]|[701.8]|[187.5]|[353.2]|
|Means and standard devi<br>capita income.|ations for economi|c outcomes in t|he population.|Sources: all six su|rveys, samples for a|nalysis of per|

36
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rights, have a relatively larger effect on financial intermediation in high-income countries, while improving credit registries has a relatively larger impact on financial deepening in low-income countries.<sup>17</sup> A second piece of evidence on reform prioritization comes from the transition economies of Eastern Europe and Central Asia. Focusing on enforcement mechanisms for simple contractual arrangements such as collateral recovery can result in more benefits than reforming more complicated multi-stakeholder conflicts such as bankruptcy.<sup>18</sup> In addition, legal system reforms have to be context-specific, particularly with respect to the legal tradition. In spite of their shortcomings and deficiencies, court systems in the former British colonies still have a reasonable reputation. They can rely on a large body of case law and precedents, from London and other parts of the former British Empire. What courts in many common-law countries in Africa are lacking are capacity and financial sector–specific skills. The introduction of commercial courts might be helpful in this context. The situation in most Civil Code countries in Africa is different, as courts in these countries have deficiencies along many dimensions and suffer from very low reputations. In these countries, establishing alternative dispute resolution systems might be more helpful.

Cross-country comparisons using Doing Business indicators show that Africa has made progress in reforming the contractual and information framework. The average cost of contract enforcement across Sub-Saharan Africa has dropped from 56 percent of a typical debt contract in 2003 to 49 percent in 2008, while it has increased from 28 percent to 29 percent in the rest of the world. Such diverse countries as Ghana, Mozambique, and Rwanda have implemented reforms to reduce the number of procedures and the time it takes to enforce a contract. While there has been only little progress in establishing new credit registries over the past years, the existing

> 17 Djankov et al. 2007. 18

> Haselmann and Wachtel 2007.

18
distributional impact of the program in question. In section 4, we will present more elaborate ways of modeling behavior and interaction in the context of fiscal incidence analysis. For now we focus on some basic tools for taking behavioral response into account when analyzing the incidence of public spending. These simple methods rely on reduced form relationships between interventions and outcomes. They do not attempt to bring out all behavioral inter-linkages that may explain the outcome (van de Walle 2003).

There are two situations where behavioral approaches are particularly relevant. The first concerns the construction of a _counterfactual_ distribution of welfare against which the impact of a spending program is to be judged. The second situation pertains to the assessment of the distributional implications of a program expansion. This latter case is known as _marginal incidence analysis_ .

Fundamentally, the incidence of an expenditure program (or any public policy for that matter) entails a comparison of the distribution of a welfare indicator (such as income) _with_ and _without_ the given program (or policy). Reliable information on the counterfactual, that is the distribution of welfare that would have prevailed in the absence of the expenditure program, is necessary for an accurate estimation of incidence. A reliable counterfactual is also needed if we are to make any causal inference about the expenditure program for policy purposes. This is due to the fundamental fact the effect of a cause can be understood only in relation to another cause (Holland 1986). The behavioral responses of socioeconomic agents to public policy must therefore be accounted for properly to improve the representation of the state of the world that would have prevailed in the absence of the policy under consideration. Indeed there is evidence that an expenditure program can affect such behavior as savings, labor supply and schooling choices. It can even crowd out private transfers received by households. Behavioral approaches are considered as attempts to resolve the counterfactual problem (van de Walle 1998).

Regression analysis of the type described above in the context of the Malaysia study represents the simplest way of accounting for behavioral responses and hence improving the definition of the counterfactual. One can specify a linear regression model where a welfare indicator is a function of program participation and individual characteristics (both observable and non-observable). To be meaningful, this 34
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Table 6 Central Government DeficitQr Snrplua (average of annual figures, percent of GDP)

|Reschedulers|1979-82|1983-86-|
|---|---|---|
|Argentina*|-5.4|-8.4|
|<br>Brazil*|-2.0|-3.7|
|Chile|3.0|-2.2|
|Costa Rica|-4.5|-2.2|
|Ecuador|-2.8|-1.9|
|Mexico*|-7.1|-3.1|
|Morocco*|-11.4|-7.5|
|Peru*|-1.7|-5.7|
|Philippines|-2.4|-2.6|
|Uruguay|-2.6|-3.0|
|Yugoslavia|-0.4|0.0|

# NonReschdulrs

|Colombia|-1.9|na|
|---|---|---|
|Egypt|-13.2|-11.0|
|India*|-6.3|-7.7|
|Indonesia|-2.1|_-1.5_|
|Israel*|-15.0|-14.7|
|Korea|-2.6|-0.9|
|Malaysia|-10.4|-10.0|
|<br>Venezuela*|-0.9|1.4|

Notes: Asterisks denote 1983-85 rather than 1983-86. For Yugoslavia, deficit or surplus is measured as percent of gross material product.

Source: IFS Yearbook (IMF) except: Brazil 1985: Government Finance Statistics Yearbook (GFSY) 1987 Ecuador 1979 and 1980: GESY and IFS Monthly 1984 to 1985: IFS Montbly Uruguay 1979: IFS Monthly 29
# 3.3. Results

Table 2 and Table 3 present the main results for this section for events. Table 2 shows results for events with SS equal or higher than 1, while Table 3 for events with SS equal or higher than 3. Results are standardized in order to make all the indexes comparable.<sup>19</sup> Column (1) shows results from equation (4) estimated using OLS. These results have potential issues of serial and spatial autocorrelation. Spatial autocorrelation is a concern given that countries are closely located. Serial autocorrelation is a lesser concern since our study period covers approximately 30 years and bias decreases inversely to time. Therefore, column (2) shows results correcting for serial and spatial autocorrelation in line with Hsiang (2010) and Conley (2008). Column (3) estimates the model using panel data and the control variables. Given that we model it dynamically (with the lagged dependent variable as one of the regressors), it potentially have a systematic bias in our coefficient of interest. Therefore, column (4) shows the panel data results correcting for dynamic models in line with Strobl (2012) and Bruno (2005). Column (2) and Column (4) reports the most credible specifications.

Results across all specifications provide similar results and negative as expected (see Table 2).<sup>20</sup> The magnitude of the effects are economically significant. An increase of one standard deviation in the intensity of a hurricane leads to a decrease in total per capita GDP growth of between ‐0.9 (for MSWS) and ‐1.6 percentage points (for IHD). It is important to highlight that the variation is explained by the different magnitude of a standard deviation according to the different indexes. For example, in the case of the IHD, a standard deviation is equivalent to a hurricane with a sustained wind speed of 74 mph (SS Scale 1) hitting an area with 2.3 percent of the population, or one with a sustained wind speed of 111 mph (SS Scale 3) in an area with 0.5 percent of the population. For WEI, a standard deviation is comparable to a hurricane affecting 10.6 percent of the country with a sustained wind speed of 74 mph, or one affecting 7.1 percent of the country’s territory with a sustained wind speed of 111 mph. To illustrate this, consider Hurricane Mitch which hit Honduras in 1998 with a MSWS of 130.6 mph.<sup>21</sup> Given these parameters, due to the hurricane, the model predicts a decrease in per capita GDP growth of 1.1, 3.1, and 1.9 percentage points for MSWS, IHD, and WEI, respectively.

> 19 For example, the IHD index (population‐weighted wind speed) powers wind speed to the 3.8, hence unstandardized coefficients are simply smaller than the MSWS and WEI indexes.

> 20 Column (1), without controlling for serial and spatial correlation, only shows significant results for IHD but not for the other two indices (MSWS and WEI). When correcting, Column (2) results consistent negative and significant estimates.

> 21 Its WEI was 30.41 and its IDH was 699,548.

12
economic and physical soil and climate conditions and crop models. They showed that changes in the reference point by which farmers encoded farm profits as either gains or losses strongly affect what combination of crops turn out to be optimal, if farmers were assumed to optimize their returns as evaluated by a prospect theory value function rather than an expected utility function.

Another variable that differs quite significantly as a function of the reference point for returns (and thus the region of the return distribution that is encoded as a loss and subject to loss aversion) is the value of information (VOI) of available seasonal climate forecasts that tell farmers probabilistically, but with some measure of skill, whether the coming growing season is of an “el nino,” “la nina,” or “normal” type. Whereas the VOI of such climate forecasts is on average positive, in the vicinity of 6- 7% (meaning that farmers’ satisfaction with their returns can be expected to improve by this percentage if they use the climate forecast in an optimal fashion), for some combination of parameter values (high reference points or aspiration levels and large loss aversion), the VOI can actually be negative (Letson et al., 2009). These results suggest more generally that policy makers need to better understand decision makers’ utility function and reference points in order to evaluate the impact of technological innovations and policy interventions.

# **Use of comparisons and regret**

The outcomes obtained by others provide a very salient reference point for relative comparisons. Regret theory, independently first proposed by Bell (1982) and Loomes and Sugden (1982), assumes that people not only make such comparisons after the fact (feeling somewhat good when they fared better than others, and very bad when they fared worse), but that they anticipate these comparisons and incorporate them into their original decision of what to do. If regret about worse and rejoicing about better outcomes were of equal magnitude, anticipation of such emotions would cancel each other out. The assumption that regret is stronger than rejoicing puts regret theory into a the class models that assume that people often operate under asymmetric loss functions, where an error in one direction is seen as resulting in more severe consequences than an error in the opposite direction (Weber, 1994). The experience of strong regret following the mental comparison of an unfavorable outcome following a decision with better outcomes that would have been obtained had a different decision been made has obvious learning functions and can improve the quality of decisions. The prominent use of available climate change adaptation technologies by trusted opinion leaders (e.g., seasonal climate forecasts that help adaptation to the greater climate variability known to accompany climate change, or the use of more drought-resistant seed corn, in the agricultural sector) can be a way of putting experienced and subsequently anticipated regret about worse outcomes to work to help later adopters of such technologies modify their practices in a more timely fashion.

33