rafmacalaba/datause-encoder-data huggingface dataset:
and meeting user needs. Improvements in performance can be represented as a virtuous data cycle that can become self-sustaining.

# _Further description of SPI pillars and dimension_

A quick primer on names. We refer to the 5 rows in the framework in Figure 7 as pillars. We refer to the 22 cells in the framework in Figure 7 as dimensions. Finally, each dimension may be composed of multiple indicators. For instance, the dimension on censuses and surveys is made up of indicators on whether population censuses have been conducted, agriculture censuses, labor force surveys, etc.

# **Data use**

The data use pillar is segmented by user type. The tiles on the Dashboard provide an indicator of use of statistics respectively by the legislature, executive, civil society (including sub-national actors), academia and international bodies. A mature system would score well across the tiles. Areas for development would be highlighted by weaker scores in that domain enabling questions to be asked about prioritization among user groups and why existing services are not resulting in higher use of national statistics in that segment.

# **Data services**

The data services pillar is segmented by service type. The tiles on the Dashboard provide an indicator of the quality of data releases, the richness and openness of online access, the effectiveness of advisory and analytical services related to statistics and the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including ethical consideration of proposals and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.

# **Data products**

The data products pillar is segmented by topic and organized into social, economic, environmental and institutional domains using the typology of the Sustainable Development Goals. This approach enables comparisons across countries and anchors the system in the 2030 agenda so that a global view can be generated while enabling different emphasis to be applied in different countries to reflect the user needs of that country.

# **Data sources**

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# **Appendix A: Additional Detail on Models and Data**

Section 4 of this report briefly describes the models and data used in this study using an XLRM framework. This appendix provides more details on these elements.

## **Measures of Risk (M)**

As described in Section 4, risk can be defined as a product of three components – hazard, exposure, and vulnerability. For our study:

- _Hazard_ is a probabilistic measure of a particular rainfall and tide event that may result in flooding;

- _Exposure_ refers to the poor population, non-poor population, or economic assets that are exposed to the hazard; and

- _Vulnerability_ is a measure of how the exposed populations or economic assets are affected by the particular hazard.

Suppose, for example, that an extreme rainfall event with a 2-year return period has a rainfall intensity of 80mm (the hazard). This rainfall inundates an area of Ho Chi Minh City in which 4 million people live in total, of which 3.6 million are non-poor and 0.4 million are poor (the exposure). Suppose further that the resulting inundation adversely affects 10% of the non-poor but 25% of the poor because the poor are more vulnerable to inundation. In this case, the annual risk from a 2-year event is: We use this approach to calculate annual risk to the poor and non-poor, and risk to the economy as a fraction of GDP from 2, 5, and 10-year events rainfall events. We describe this further in our discussion below of modeling hazard.

## **Relationships and Models (R)**

This project integrates several modeling components shown in Figure 4.1 into an Analytica model that computes population and economic risk. Figure A.1 shows a schematic of the Analytica model developed for the purpose of calculating population risk. The model developed for calculating economic risk uses a similar structure. First, data related to hazard (inundation depth from SWMM), exposure (GIS data and population and poverty data), and vulnerability (depth-damage curves) are input into the model (orange trapezoids on the left). From this data, we calculate the percent of the poor and non-poor affected in each subcatchment, the number of poor and non-poor in each sub-catchment, and, in combination, the risk to the poor and non-poor in each event (blue rectangles). As described next, risk is calculated for extreme rainfall events with 2, 5, and 10-year return periods and using different infrastructure operation rules. We assume infrastructure operators will apply the operating rules that best reduces risk in each event
macroprudential tool especially in developing and emerging markets (Federico _et al._ , 2014), (ii) collecting time series data on RR is, in principle, “easier” than collecting data on other prudential tools such as capital requirements (especially for long time spans), (iii) as is the case when using cyclically-adjusted revenue measures to assess changes in tax policy (e.g., Romer and Romer, 2010; Vegh and Vuletin, 2015; and Riera-Crichton _et al._ , 2016), total banks’ reserves (calculated as the ratio of banks deposits at the central bank to bank deposits) are not valid proxies for changes in policy instruments such as RR (Federico _et al._ , 2014).<sup>7</sup> In particular, we push the empirical frontier on several crucial dimensions. First, in terms of measuring RR, while building upon existing data from Federico _et al._ (2014) that accounts for the different types of RR in terms of maturity and currency denomination, we now construct a novel metric of effective RR which also takes into account the structure of deposits. Second, in terms of the identification of exogenous RR shocks, we develop a Romer and Romer (2010) type of narrative for the nature of RR changes. When shocks to RR are properly identified as exogenous to the business cycle by using a narrative approach, we show that results change dramatically. In other words, we use changes in RR exogenous to the business cycle to analyze their macroeconomic effects, as opposed to those RR changes associated with the policy reaction function of the monetary authority to current or prospective output fluctuations. This is the quintessential problem of reverse causality. Third, since it proves impractical to analyze macroprudential policy in isolation, we also jointly analyze the macroeconomic effects of the central bank policy interest rate (IR, hereafter) on the macroeconomy. To properly identify exogenous IR shocks, we follow Romer and Romer’s (2004) approach and purge the policy rate from systematic responses to information about future economic developments. Given the time-consuming efforts needed to carry out the analysis along these several critical dimensions, we focus our analysis on three Latin American countries (Argentina, Brazil, and Uruguay) since the early 1990s.

The paper proceeds as follows. Section 2 describes the novel RR dataset and some broad features of the data. Section 3 shows empirical evidence on the macroeconomic effects of macroprudential and monetary policies relying on traditional time-identifying assumptions. In line with the predictions of any standard macro model, we find a negative effect of central

> 7For example, as will be discussed later in the paper, while total banks’ reserves may fluctuate due to non-policy factors (including the structure of deposits), RR changes due to central bank policy take place, on average, every 2 years in developing and emerging markets.

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research design for both Phase 1 and 2. In addition, we also re-estimate equations (3) and (4) using quantile regressions which allows us to explore both cross-school and cross-student heterogeneity.

In a third part of our empirical analysis we estimate whether the impacts are sustained beyond the intervention period. In order to do this, we re-estimate equations (2) and (4) replacing the dependent variable with the math test scores that were collected from Phase 2 students in late 2015—almost 9 months after they would have been covered by the intervention.

In the last part of our empirical analysis we explore various pathways through which impacts might have been generated. In order to do so, we re-estimate models (1)-(4) replacing the dependent variables with measures of teacher attendance, teacher pedagogical behaviors, and student attendance.

# **3. Results**

## **3.1 Balance at baseline**

Before turning to the analysis of impacts, we first establish that our experimental design is valid by examining the first moments of selected characteristics of schools, teachers, and students in control and treatment groups, using baseline data for phases 1 and 2 (Table 2). We report the p-value of an F-test corresponding to a null hypothesis that there is no difference across the three (in phase 1) and four (in phase 2) arms – clustered at the level of treatment assignment which is the school. In phase 1, the p-values for the test are all, with 1 exception out of the 23 variables, above 0.1 suggesting that the samples are very likely balanced. In phase 2, the p-values for all but 5 variables out of the 23 variables are above 0.1. We nevertheless believe that the samples are well balanced since the absolute magnitudes of the differences across these samples are small in magnitude. For example, the share of schools with electricity is 0.313 in the control group and 0.391 in the arm in which teacher incentives were withdrawn. The largest difference in magnitude is for the variable “headmaster rewards teachers who perform better” where the share is lowest in the student incentives arm (0.533) and highest in the teacher incentives arm (0.677). Importantly, the balance tests suggest that there are no statistically significant differences in the school average baseline test scores of students in the various study arms—either in phase 1 or in phase 2.

## **3.2 Average impacts on student achievement**

Impacts on student learning outcomes in Kiswahili, Math, English, as well as averaged across the three subjects, are reported in Table 3. In phase 1, we find positive and statistically significant learning impacts for the schools that received both teacher and student incentives. These impacts are in the range of 0.14 to 0.18 standard deviation. In phase 2, we find positive and significant learning impacts of teacher incentives 12
NUMBER OF DUMPING AND SAFEGUARD APPLICATIONS AND REAL EXCHANGE RATE <!-- Start of picture text --> itcr<br><!-- End of picture text --> Finally, two aspects related to safeguard investigations may be highlighted. On the one hand, the figures evidence a greater relative use of the so-called _special safeguard_ as compared to the WTO safeguard. Although the number of WTO safeguard investigations is almost equal to those of the _special safeguard,_ the period during which the two instruments have been in force indicates a greater dynamism in the application of the latter. In the ten years of existence of the WTO safeguard, there has been, on average, one investigation per year, while the average number of investigations under the _special safeguard_ has been 2 per year between 1999 and 2004. The preference for this last type of instrument could be the result of less stringent requirements to prove the disruption and of shorter periods of investigation and decision-making than those contemplated in the WTO safeguard.

Summarizing, between 1990 and 2004 Colombia has made a relatively equal use of the antidumping and safeguard arrangements. To a great extent this trend is the result of the recurrent use of the Andean safeguard, although if it is excluded from the statistics, an important number of 22
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number of firms who can be characterized as fringe bidders. However, second, they tend to exhibit aggressive behavior in bidding. Normally several incumbent bidders are expected be present in a competition.<sup>8</sup> Then, the “low bid and broke winner” concern will emerge. It is important to establish a sound selection mechanism, e.g., prequalification and two-stage evaluation of prices and technical attributes, to exclude firms who are unlikely to deliver the reasonable quality of work.

Contrary to prior expectations, there is no statistically significant evidence that entrant bidders are less efficient or less inefficient than incumbents. In the electricity industry, entrants seem have the disadvantage, because one of the coefficients is significantly positive. This may be reasonable with the advanced technical nature required by the sector taken into account.

The number of contracts obtained by each bidder in the past has negative coefficients across sectors, meaning that firms who received prior procurement awards are likely to be efficient enough to submit low bids. In the last column model with water project procurement data, the coefficient turns out statistically significant. This is good news and bad news. It can be interpreted to mean that the past auctions achieved the efficient allocation. The contractors who had been selected were actually efficient as far as their bidding behavior in the subsequent period is considered. Bad news is this: the result suggests that past contractors are likely to be awarded repeatedly, making it difficult to invite new entrant bidders. However, once they decide to participate in the bidding process, they have a tendency toward aggressive bidding.

Finally, high concentration of incumbent bidders in an auction has an adverse effect on efficiency. The coefficient of the share of incumbents is consistently positive and very significant. This indicates the classical risk of anticompetitive behavior among market

> 8 It is noteworthy that the relatively low market concentration of incumbent bidders measured by our measure of the share of incumbents in each auction is subject to our tentative incumbent definition assumed for the analytical work. However, as mentioned, it is open to discussion who is the incumbent.
The more limited individual-level Gallup International results are generally consistent with the level-1 findings from the WVS. For example, Table 5 reveals that women are more supportive of wealthier nations giving more financial help to poorer nations. Remembering that the dependent variable in the GI analysis is a dichotomous variable (“should give more” / “giving enough”), interpretation of effect sizes requires transformation of the estimated coefficient. For example, in models 5, compared to men, being a woman is expected to increase the odds of favoring more aid by 35% ((EXP[.30×1]-1)×100). An alternative interpretation focuses on the change in the predicted probability of favoring more aid. An unemployed male, who has never contacted a public official, does not trust NGOs, and has average education, age, etc. has a predicted probability of favoring aid of .72 (calculated by: 1/(1+EXP-[.95])). If that individual were instead female, her predicted probability would be .78, leading to a change in the predicted probability of .06 – a fairly strong effect.

Although the GI survey has fewer individual-level variables in general, it does have an expanded set of questions on trust. As in the WVS analysis, TRUST has a positive and significant effect on support for more foreign aid. Declaring trust in others increases the odds of favoring aid by approximately 50%. Models 3-5 show substantial differences in the effect of trusting particular institutions -- GOVERNMENT, THE UNITED NATIONS AND THE WORLD BANK, and NGOS. Trust in one’s own government does not significantly increase an individual’s support for aid. Contrast the lack of effect of trust in one’s own government to the significant, but small, effect of trust in international donor institutions (the UN and World Bank) and the significant and large coefficient for trust in NGOs. Each one-unit increase in expressed trust in international donors leads to a 7% increase in the odds of favoring more aid to poor countries. Each one-unit increase in trust in NGOs, however, leads to a 57% increase in the odds of favoring more aid to poor countries.

Table 6 introduces country-level effects on support for foreign aid as well as the individual-level effects. Theory suggests that existing levels of aid from donor countries may have differing effects in the WVS and GI analyses, i.e. the ODA coefficients may have opposite signs. The WVS question asks respondents whether they favor provision of economic aid to poor countries, while the GI question asks whether wealthy nations 15
in school management - TCHPARTI indicates a Dev7 mean value of -0.2169 compared to 1.6445 for Vietnam. Principals in Dev7 are more likely to say that they communicate and act on school goals (LEADCOM), but there is much higher prevalence of public posting of school achievement data (SCORE ~~P~~ UBLIC) in Vietnam. Interestingly, even Dev7 countries have high levels of achievement tracking data by authorities (80% of schools report this - SCORE ~~A~~ UTHRITS). Finally with regard to the school climate, indices described further in the PISA documentation, STUDCLIM (student climate) is roughly even between Vietnam and Dev7, but TEACCLIM (teacher climate), that includes variables such as teacher absenteeism and teacher expectations of students, is higher for Vietnam.

# **2.10 Preliminary conclusions from comparison of endowments**

In summary, the mean comparisons between Vietnam and Dev7 students finds a number of potentially insightful results. Consider the four-fold classification of factors presented in the conceptual diagram of Figure 3 - students, parents, teachers and the school, the findings are summarized below.

**Students** : Students in Vietnam are more likely to have attended pre-school and less likely to have repeated grades in the past. They are likely to behave more disciplined at school, skip fewer classes, and assume greater responsibility for their own learning. Vietnamese students are less likely to brag about their abilities and experience and yet work harder, especially out of school, in extra classes. They tend to have lower anxiety about mathematics and higher about the usefulness of mathematics in their future.

**Parents** : Parents in Vietnam are likely to be more involved in the school life of their children than parents of students in Dev7 countries. Though time spent on homework help is similar in both groups, Vietnamese parents are more likely to volunteer and take part in fund-raising for the school and help the teachers as classroom assistants. Vietnamese parents are also more likely to seek to meet the teacher to discuss their child’s progress or the child’s behavior on their own initiative. Principals in Vietnam report higher levels of parental pressure.

**Teachers** : Teachers have similar levels of formal education in both groups, but Vietnamese teachers may have had more recent professional development activities. There are more specialist mathematics teachers at high schools in Vietnam, and teachers overall are also more likely to be certified. The performance of teachers is more likely to be monitored in Vietnam, with higher emphasis on student achievement and on making information about that achievement public. Teachers also tend to have lower autonomy, more likely to be sub-

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**Table 6. Business entry and regulations affecting entry and exit**

|VARIABLES|(1)<br>newbiz|(2)<br>newbiz|(3)<br>newbiz|(4)<br>newbiz|(5)<br>newbiz|(6)<br>newbiz|(7)<br>newbiz|(8)<br>newbiz|
|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|1.999***|2.000***|1.754***|1.955***|1.975***|1.778**|1.916**|1.470**|
||(2.921)|(2.913)|(2.814)|(3.073)|(2.903)|(2.523)|(2.573)|(2.037)|
|FDI||-0.00206|-0.00344|-0.00353|-0.00397|-0.00398|-0.00320|0.00271|
|Political freedom and civil rihts||(-0.624)|(-1.147)|(-1.171)|(-1.299)|(-1.301)|(-1.025)|(1.058)|
|g<br>(FH)|||0.149|0.168|0.217|0.230|0.251|0.281|
||||(0.948)|(1.058)|(1.236)|(1.306)|(1.254)|(1.647)|
|Trade (X and M)||||0.0110**|0.0101**|0.0101**|0.0121**|0.00665|
|||||(2.407)|(1.993)|(1.982)|(2.128)|(1.225)|
|Corruption (CPI)|||||0.0939|0.0965|-0.0533|-0.0208|
||||||(0.765)|(0.786)|(-0.344)|(-0.147)|
|Lagged GDP growth||||||0.0257|0.0289|0.0353|
|||||||(1.071)|(1.030)|(1.418)|
|Country risk (ICRG)|||||||0.00835|0.0246|
||||||||(0.241)|(0.786)|
|Credit to private sector||||||||0.0165***<br>(5.212)|
|DTF (entry/exit)|0.0274**|0.0274**|0.0354***|0.0371***|0.0400***|0.0398***|0.0355**|0.0341**|
||(1.980)|(1.969)|(2.780)|(2.875)|(2.946)|(2.933)|(2.308)|(2.431)|
|Constant|-16.50***|-16.50***|-15.23***|-18.13***|-18.98***|-17.36***|-18.50***|-16.00**|
||(-2.772)|(-2.763)|(-2.818)|(-3.229)|(-3.190)|(-2.829)|(-2.756)|(-2.484)|
|Observations|917|914|905|891|856|855|717|619|
|R-squared|0.055|0.056|0.065|0.073|0.075|0.077|0.080|0.159|
|Numberofcod2|123|123|122|121|120|119|94|93|
|t-statistics in parentheses|||||||||
|*** p<0.01, ** p<0.05, * p<0.1|||||||||

**Notes:** ***(**)(*) denotes significance at the 1(5)(10) percent level. Standard errors reported in parentheses. This table reports results from OLS panel regressions of new business creation on the distance to the entry/exit regulatory frontier. Business creation is defined as number of new businesses per 1,000 people in working-age. Country fixed effects are included in order to capture unobserved between-country differences. The estimations reported include controls for lagged GDP per capita growth, per-capita GDP, trade openness, corruption, credit to the private sector, FDI inflow, economic and political risk as well as civil freedoms. All specifications include time dummies to control for any global changes in the macroeconomic environment.

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