rafmacalaba/datause-encoder-data huggingface dataset:
Brazil. The 262 sampled firms who reported getting a construction permit reported on average a time to get a construction permit of 85 days. For the 80 countries with both DB and ES results the average _excess_ of the DB days to get a construction permit versus the ES average is 177 days, almost three times larger than the firm reported average time of 64 days.

Third, we examine the associated between DB and the firms who report very quick times (the ―favored‖ firms) and those who report very slow times (the ―disfavored‖ firms). Using the 10<sup>th</sup> or 25<sup>th</sup> percentile of firm reported times we find that for these firms the DB numbers mean nothing at all. In every country the ―favored‖ firms report very little delay in any of the three indicators (clearing customs, getting construction permits, or operating licenses)—and hence are _massively_ under-water. The median DB time to get a construction permit is 242 days, about eight _months._ The median 10<sup>th</sup> and 25<sup>th</sup> percentiles are one _week_ and two _weeks_ respectively. For time to import goods the results are, if anything, more dramatic, the median DB time is 23 days, the 10<sup>th</sup> percentile is 1 day, 25<sup>th</sup> percentile is 2 days. Moreover, there is almost no variation across countries in the ES ―favored firm‖ responses. The standard deviation across countries in ―favored firm‖ (25<sup>th</sup> percentile) time to get a permit was 12 days (compared to a standard deviation of 119 days in the DB indicator).

In contrast, there is a set of firms that do find regulations an obstacle, and the extent of delay for those firms does vary widely across countries. The ―disfavored‖ firms—those in the 90<sup>th</sup> percentile—show huge differences across countries, the standard deviation for getting a construction permit of the 90<sup>th</sup> percentile is 106 days. Even though there are large differences, these are not well explained by a simple linear association with DB. Rather, there is a sharply non-linear pattern in which increases in DB days from low levels are associated with a rapid increase in the time reported by ―disfavored firms‖ (90<sup>th</sup> percentile), but for increases above the first tercile of the data (and at least by the median) there is no association between the DB indicator and the ES 90<sup>th</sup> percentile responses.

Fourth, several countries have repeated ES surveys that span a change in the corresponding DB indicator. While these are many substantial _reductions_ in the DB reported time, there were almost no instances in which this was associated with a fall in the ES reported time for compliance. In fact, the change in average responses reported by firms often _rose_ . This 5
The target population provides an excellent setting for testing variations in expectations elicitation methodology. The survey respondents are boat owners, who are broadly representative, in terms of literacy and education levels, of the typical respondent in many developing country field surveys. In a survey conducted in July 2009, we asked expectations about the value of fish a boat owner expected to catch in a day in the month of August 2009. We can then use the realized distribution of daily catches during August to compare the elicited distribution to the realized distribution at an individual level. This is a unique feature of this setting which makes it particular suitable for testing methodology, since in many other cases one only observes one realization at the individual level (e.g. income in the month, whether they live or not to a given age) and not the distribution.

Our results should offer considerable comfort to researchers employing visual aids to elicit subjective expectations. We find respondents to be willing and able to answer questions using this format, and that almost all the answers received obey basic laws of probability. Similar distributions are obtained with 10 beans as with 20 beans, although we do find the distributions with 20 beans are more accurate, with respondents able to use more intervals of the pre-determined distribution to express their beliefs. The distributions elicited with a pre-determined support and many intervals are remarkably consistent with the self-anchored distribution with a small number of intervals, and the ordering of the minimum and maximum does not significantly change responses. We therefore conclude that the elicited distribution is robust to many of the key elicitation design decisions a researcher must make. Nevertheless, we do find the most accurate results are obtained using 20 beans and a pre-determined support, suggesting this design could serve as a default for future studies.

The remainder of the paper is structured as follows. Section 2 outlines the survey setting and the design of the methodological experiment. Section 3 then describes the basic results in terms of whether the results obtained obey basic laws of probability, and how the distributions elicited vary with differences in elicitation design. Section 4 then 4
significantly and positively associated with higher account ownership rates. These coefficients suggest opportunities for multi-pronged programs that drive employment or educational opportunities in combination with financial inclusion for women. For example, a government workfare program in India reached more than 100 million people and paid women government benefits directly into their own account. An impact analysis showed that the program increased women’s financial control, influenced gender norms preventing women from working, and incentivized women to find employment, compared with those paid in cash (Field, et al., 2021).

# **3. The benefits of direct payments into and out of an account**

Account ownership is a clear prerequisite to using financial services, but ownership alone is not enough to drive development outcomes. Owners must use their accounts to benefit from them. For example, an experimental study to test the impact of expanding access to basic bank accounts in Chile, Malawi, and Uganda showed that giving free bank accounts to previously unbanked adults had no impact on savings or welfare (Dupas, et al., 2018). The takeaway from this and other research suggests that expanding access to basic accounts is unlikely to deliver development outcomes without policies, products, and incentives to increase the use of accounts.

Globally, digital payments are the most used financial service. In developing countries, on average in 2021, 57% of account owners made or received a digital payment,<sup>3</sup> compared to 24% who used their account to save and 22% who formally borrowed. Digital payment systems allow both individuals and micro, small, and medium-sized business entrepreneurs to pay for goods or services electronically using a mobile phone, the internet, retail points of sale, and other broadly available channels, instead of using cash. The share of adults making or receiving digital payments in developing economies grew to 57 percent in 2021, from 35 percent in 2014.

When account owners _use_ their accounts for digital payments, savings, and appropriate credit, they experience a range of positive benefits, including greater security and greater privacy for their transactions. For example, a field experiment in Bangladesh found that factory workers

> 3 Digital payments are defined as using a mobile money account, a debit or credit card, or a mobile phone or the internet to make a payment from an account, or the use of a mobile phone or the internet to send money to relatives or friends or to pay bills. Digital payments also include in-store or online merchant payments; paying utility bills; sending or receiving domestic remittances; receiving payments for agricultural products; or receiving wages, government transfers, or a public pension directly from or into an account. Global Findex 2021 survey respondents were prompted to answer whether they used digital payments based on their experience during the 12 months prior to the survey.

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26
mostly by a small group of insiders.<sup>17</sup> To identify the class of security that is underlying the ADR, we read through 20-F filings, which are mandatory SEC filings of listed ADRs. Also, while the ADR Ratio is usually reported by major databases (e.g. Datastream, Compustat and BONY website), it requires adjustment for some of the issuers that have ADR ratios greater than 1,000. In such cases the underlying security’s trading volume is reported in units of thousands and to convert the ADR trading into equivalent underlying trading volume this convention needs to be accounted for.

The liquidity estimation process is illustrated by using the example of Videsh Sanchar Nigam Limited (VSNL), an Indian firm. For the year 2000-01, the average number of shares outstanding for VSNL was 28,500,000. On the Bombay Stock Exchange, the average daily trading volume (in 2001) of VSNL’s underlying shares was 352,500 shares. Thus the _Liquidity-Underlying_ for VSNL is 0.0124 (352,500/28,500,000). Furthermore VSNL has issued an ADR that is listed on the NYSE. One VSNL ADR provides the holder a right to own two underlying shares. The daily ADR trading volume on the NYSE for VSNL is 183,090 ADRs. This is equivalent to 366,180 underlying shares. The _Liquidity-Relative_ for VSNL is thus 1.0388 (366,180/352,500).

We also include a number of other firm-specific characteristics.<sup>18</sup>

> 17 Voting rights (and dividend rights) for holders of preferred shares are severely limited. Similarly, Mexican firms have many different classes of stocks and typically have a complex underlying security (usually referred to as a CPO or a unit) that is a bundle of different classes of stock (e.g. Grupo Televista has an ADR on an underlying CPO that represents 1 A Share, 1 L Share and 1 D Share). Also some firms have multiple ADRs based on multiple underlying stocks in Mexico (e.g., Telefonos de Mexico, America Movil, Transport Maritima Mex) and Chile (e.g. Sociedad Quimica, Embotelladora Andina). All Chinese ADRs are based on H class shares (different from domestic class shares which can only be held by Chinese investors) which trade on the Hong Kong stock exchange.

> 18 Previous literature has shown a positive relationship between better firm-level attributes and financial and equity performance in developed and emerging markets (see, for example, Gompers, Ishi and Metrick, 2003, and Klapper and Love, 2003).

30
instances the supervisors in Nigeria were present at the plot at the time of measurement). This may influence the comparison of compass and rope with GPS measurement if, for example, an enumerator had to re-visit the plot in order to take the new compass and rope measurement and unintentionally identified the borders differently during the second visit. In Ethiopia, on the other hand, all re-measurements were completed at the same time of the initial measurement as the closing error was calculated before leaving the plot. The instance of closing error greater than 5%, however, was rare and therefore not expected to influence the analysis (in the Ethiopia experiment, only 5% of fields were measured more than once).

Second, the skill level of the enumerators varied. In Tanzania, the enumerators were the agricultural extension officers for the local area. These enumerators were very familiar with the agricultural practices but generally inexperienced in survey administration and the use of the particular measurement tools. These enumerators also happened to be significantly older and several had poor vision (requiring the purchase of glasses in order to read the compass and GPS). Because of the ongoing and intensive nature of the cassava measurement component of the Tanzania experiment, the extension officers were the preferred enumerators for the existing infrastructure and established relationships within the community. In Ethiopia, professional enumerators were hired based on past performance with the Central Statistical Agency and previous experience with computer-assisted personal interviewing. In this particular study the enumerators all held bachelor’s degrees and were relatively young. In Nigeria, staff from the head office of the National Statistics Bureau were trained and sent to the field rather than the enumerators used to conduct the national panel survey. In each of these studies the health, education, and skill level varied, as did the incentive structure and duration of fieldwork. Rather than discredit the comparability of the data collected from each of these studies, the consistency observed in the comparison of methods should lend confidence in the applicability across survey environments.

# **IV. Methods**

The first step in analyzing the different methods is comparing the measurements obtained. To that end we construct two measures of deviation between the GPS and CR measures, defined as follows: The bias is the simple difference between the GPS measure and the CR measure, expressed in acres. The relative bias is the simple difference between the GPS measure and the CR measure, in acres, divided by 14
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of Table 5 shows the results of this decomposition when the groups are represented by either regions or countries.<sup>26</sup> A clear answer emerges: global inequality decreases mainly because, on average, poorer countries are catching up. At the beginning of the period, the contribution of the ‘between-groups’ component to total inequality is more than 50 percent (52 percent when using regions, 57 percent when using countries as groups). However, by the end of the period, the between-country component drops to the less than 50 percent (due to income convergence) while the within-countries component shows a larger contribution to global inequality. This result does not imply that inequality within countries is increasing (more on this below), but that, at least, inequality between countries is decreasing at a faster pace. Poor and large countries, such as India and China, are growing fast to reshaping the global income distribution and contributing to the reduction of global inequality.

The importance of the education wave in the dynamics of inequality within countries can be seen by comparing the results of the education wave with those of the no-wave scenario in the last two columns of Table 5. The larger decreases of the skill premium in the education wave scenario pushes down inequality within countries, while this is not the case in the no-wave scenario. As a result, the within-group component in the no-wave scenario, as well as total inequality, are higher than those in the education wave.

Comparing the global growth incidence curves (GICs) for the education wave and the nowave scenarios is another way of illustrating the change in the global distribution (Figure 10). The GICs differ from the elephant curve of Lakner and Milanovic (2016) in that they do not have the trunk part. The reasons are: the microsimulations are based on labor incomes,<sup>27</sup> and there are no corrections for top incomes (which are not recorded in the underlying household surveys). Moreover, our 2 sectors by 2 levels of skilled workers do not provide enough heterogeneity to capture effects on the top income.

> 26 The regions are: Sub-Saharan Africa, Middle-East and North Africa, South Asia, East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, High Income countries. These regions correspond to the World Bank definitions.

> 27 Note that household surveys often, if not always, record non-labor incomes. The microsimulations apply the country-specific average growth rate to change these non-labor incomes. The change in the returns of capital from the global CGE could be also used, but this was not done in the current exercise. A main reason is that most of the non-labor incomes are (public and private) transfers and thus do not represents incomes from capital.

24
measuring whether and for how long the central bank has published a financial stability report (FSR), based on Čihák et al. (2012).

We find that placing bank supervision in the central bank decreased the probability of banking crises during 2007–12, when conditioning on past crisis experience and controlling for the quality of microprudential supervision and the quality of financial stability oversight. When conditioning further on macroeconomic and financial variables, we find that placing bank supervision in the central bank continues to diminish the probability of the negative outcome; however, its effect is no longer statistically significant at common levels. The significantly reduced sample size available for estimation after controlling for a large set of macroeconomic and financial variables and the correlation of our variable of interest with some of the macrofinancial variables contribute to the lower significance level. Since the theoretically predicted positive and negative effects from placing bank supervision in the central bank could cancel out in practice, or because either of the two opposite effects could become significant only in certain country circumstances, we further interact the dummy for bank supervision in the central bank with selected macrofinancial variables. We find that placement of bank supervision in the central bank reduced the contribution of financial depth to banking crises.

The results survive several robustness tests of alternative definitions of the dependent variable, an alternative construction of our explanatory variables of interest, and an alternative estimation model, with a different functional form. We use the alternative definition of _banking crises_ by Reinhart and Rogoff (2011) and Reinhart (2010). We also consider a separate class of borderline crises identified by Laeven and Valencia (2013). For an alternative construction of our explanatory variable of interest, we average the annual 1/0 dummies indicating whether bank supervision was/was not in the central bank over different time spans. In addition, we test the robustness of our baseline results against the assumed curvature of the cumulative distribution function in the logit model by estimating a probit model. All robustness tests support and, on occasions, reinforce our baseline results. Interestingly, when we use the 7
and Woessman (2012) however base their analysis on a cross-section of data only, while this research estimates the parameters of a fully specified dynamic value-added model.

The data we use are unique for the developing world. Subject-matter test score data of all (around 45,000) students of a near representative sample of 240 primary schools are tracked across three rounds of measurement, November 2009, May 2011 and May 2012. Figure (1) presents a map of the 20 districts that were randomly selected to take part in the study.<sup>4</sup> The student level test score data can be linked to survey and subject-matter test score data of their respective teacher(s). A feature of this data is that both students and teachers are tested on the same three subjects – math, science and Indonesian language – in each of the three rounds of measurement. The data therefore allows for estimating value-added models on variation within each student-teacher pair, across the three subjects. At the same time, the data permits to account for biases due to measurement error in test scores (under standard assumptions on the nature of the measurement error).

**Figure 1:** Map of Indonesia, with the 20 selected districts highlighted

Kingdon and Teal (2010), and Chu, Loyalka, Chu, Qu, Shi, and Li (2015).

> 4The data was collected in support of a randomized controlled experiment designed to evaluate the effects of Indonesia’s teacher certification program (De Ree, Muralidharan, Pradhan, and Rogers (2015)). The data is nearly geographically representative for the majority public primary schools in Indonesia. (The data on 120 Junior Secondary schools is not used in this paper.) Indonesia spans across 1,700 more or less inhabited islands and is the world’s fourth most populous country, and the distance from the most western school in the sample, on mainland Sumatra, to the most eastern, on one of the remote islands of the South Moluccas, roughly spans the distance between San Francisco and New York. See also De Ree, Al-Samarrai, and Iskandar (2012), Chang, Shaeffer, Al-Samarrai, Ragatz, De Ree, and Stevenson (2013) and World Bank (2015), for analysis based on the same data. For more information on the data and the experiment see De Ree, Muralidharan, Pradhan, and Rogers (2015) and World Bank (2015).

3