rafmacalaba/datause-encoder-data huggingface dataset:
associated with an island or mainland coast), as described in Hoozemans et al. (1993).<sup>13</sup> Five possible responses to SLR, or categories of wetland migratory potential (WMP), were defined for the DIVA database:

|WMP1:|No, or hardly any change|
|---|---|
|WMP2:|A retreat of the coastline, combined with inland migration of coastal<br>ecosystems|
|WMP3:|A retreat of the coastline without the possibility of inland migration due<br>to topography (e.g., coastlines with relatively high relief)|
|WMP4:|A possible retreat of the coastline but increase of flooding area behind<br>the coastline (“ponding”)|
|WMP5:|Total loss of the coastal ecosystem (Hoozmans et al. 1993).|

In the DIVA database, no mangroves occur in areas with the most extreme responses, WMP 1 or WMP 5 (other wetlands may fall in these categories). If mangroves can migrate, category WMP 2, then they may survive in their current location to the extent that natural migration or sediment accretion keeps pace with sea level rise (Alongi 2008). Mangroves in the category WMP 3 cannot migrate, and the human resources associated with them will lose their protection. Mangroves in category WMP 4 are at great risk, but may survive, depending on the effect of flooding behind the coastline. If the flooding is severe enough and persists long enough to seriously disrupt the flow of freshwater and nutrients to mangroves, the mangroves will be severely degraded and may die, putting at risk the population currently protected by them.

Geographic overlays of mangroves with the WMP characteristics of the coastlines from the DIVA database<sup>14</sup> indicates across our study area, 68 percent of the mangroves fall under WMP 2

> 13 The migratory potential of mangroves also depends on a wide range of additional factors that are site‐ specific and highly variable; such as the continued flow of sediment and nutrients from inland stream. Such detailed information was not available on a global scale.

14
(World Bank 2019b). While import duties were reduced, and the overall tariff structure simplified during the 1990s, this trend stopped in the mid-2000s. Table 1 shows that since 2010 average tariffs have been falling gradually from an average of 11.25 percentage points in 2010 to 9.03 percentage points in 2018, yet the decline was temporarily interrupted in 2013 when average tariffs bounced back to 10.95 percentage points on average, as a consequence of a pronounced fiscal and balance of payments crisis. Indeed, during fiscal year 2013 (July 2012-June 2013), the fiscal deficit reached 8 percent of GDP and the current account deficit stood at 1.1 percent of GDP, while external financing dried up as evidenced by the balance of the financial account of the balance of payments going from US$5,097 million in fiscal year 2010, to US$2,103 million in 2011, to US$1,275 in 2012, to a deficit of US$90 million in 2013.

Table 1– Average output tariffs, input tariffs by year

|year|Mean|Output tariffs<br>SD|Mean|Input tariffs<br>SD||
|---|---|---|---|---|---|
||2010|11.25<br>11.27||2.54|2.52|
||2011|10.16<br>11.13||2.27|2.37|
||2012|9.58<br>11.1||2.02|2.3|
||2013|10.95<br>11.46||2.53|2.37|
||2014|9.60<br>9.92||2.14|2.00|
||2015|9.34<br>9.47||2.19|1.77|
||2016|9.49<br>9.82||2.27|1.80|
||2018|9.03<br>9.98||1.88|1.70|
|Betwee|n-sector SD|10.43|||2.07|
|Within|-sector SD|1.68|||0.53|

Source: UN TRAINS. No data available for 2017. Note: SD stands for standard deviation.

Figure 3 shows that in 2013 there was a generalized increase in tariffs across most sectors – as a result of a balance of payments crisis. In the following period, sectors with relatively lower tariffs experienced further small increases in tariffs, while sectors with higher tariffs in 2013 experienced larger reductions. This movement towards the reduction of dispersion was driven by the Extended Fund Facility (EFF) agreed with the IMF in August 2013, in which the government committed to the simplification of tariff rates, moving to four slabs (which implied some increases and some reductions in tariffs).<sup>6</sup> Over the period 2013-2018, the use of customs duty exemptions became even more prevalent. In 2012, for example, 34 percent of imports claimed some form of exemptions (World Bank 2019b). In 2016/17 the exemptions reached 50% of imports. Exporters are entitled to import duty exemptions on intermediates and capital equipment used to produce exportable products. There are two main forms of accessing them; either by being eligible for a duty suspension mechanism, for those firms that are mainly export oriented (export-oriented units), or by being eligible for a refund of the paid duty (for example, the duty tax remission for exporters, DTRE). Most firms use the latter and argue that the process of securing the refund is long and cumbersome.

> 6 See Pakistan 2013 Article IV Consultation, and August 2013 Letter of Intent (IMF 2013).

6
* ESUB_G is distributed uniformly between 1 and 3.

- ESUB_MM is distributed uniformly between 4 and 6.

* ESUBC is distributed uniformly, with a range equal to the point estimate assumed above plus or minus 50% of that point estimate.

* ETRN_DX is distributed normally, with a mean equal to the point estimate assumed in the base model (2.9) and a standard deviation of 1.3 from Faini [1988].

- ETRN_G is distributed uniformly between 1 and 3.

- ETRN_XX is distributed uniformly between 4 and 6.

In all cases we assume that elasticities generated by random perturbation must be non-negative, resulting in some truncation at zero for those generated assuming a normal distribution.

Additional tables of basic data employed in the model or calculated from our benchmark data are presented at the end of this appendix. The SOE model is generated with the GAMS/MPSGE software developed by Brooke, Kendrick and Meeraus [1992] and Rutherford [1992a][1992b]. It is then solved using the MILES algorithm developed by Rutherford [1993].

# 3. **Systematic Sensitivity Analysis**

To calibrate our model estimates of elasticities must be assembled for primary factor substitution, import demand, import source, and domestic demand, amongst the more important for our purposes. In the base model all elasticity values are assigned a _priori_ to values which we believe are plausible central tendency estimates. Since elasticity estimates are subject to a margin of error, our "remedy" for this problem, which is endemic to any large-scale model of this kind, is to undertake systematic sensitivity analyses of our major results with respect to A-15
according to their electrification status. Thus, the sample contains roughly 50-50 split between those with and without electricity. The domestic, commercial, industrial and irrigation samples were selected based on actual distribution within rural Bangladesh – that is 85 percent domestic, 11 percent commercial, 2.5 percent irrigation, and 1.5 percent industrial samples were selected. As for the control group, samples were drawn in equal proportion from three types of areas: villages with electricity, project villages without electricity (these villages were expected to get electricity during the ongoing project period), and non-project villages without electricity (these villages were not expected to get electricity during the ongoing project period). In addition, a small number of households (1,000) were purposely selected in the sample areas that own solar home system (SHS). The final collected sample shown in Table 1, and as usually the case it varied slightly from planned sample (see BETSBUP 2006 for more detailed description).

Electrification status by village and household in all six regions, as found in the data, is reported in Table 2.<sup>5</sup> While Sylhet region has the highest proportion of villages that have electricity (63.9 percent), proportion of households in electrified villages is highest in Chittagong region (68.2 percent). Chittagong region overall has also the highest proportion of households with electricity (40.3 percent). Barisal region on the other hand has the lowest household electrification rate (23.1 percent). Overall, about 57 percent of Bangladeshi villages have electricity, 58.2 percent of households in those electrified villages have electricity, and 33.1 percent rural households in whole Bangladesh have electricity.<sup>6</sup> We begin by comparing cases with and without electricity both at village and household-level. The main variables of interest for this study are consumption expenditure and income at household level, and completed schooling years and study hours at individual level. Compared to the households in villages without electricity, those in villages with electricity have significantly better schooling outcomes (Table 3). However, the differences in economic outcomes are not consistent. The village level electricity benefits may also include direct benefits to households and (indirect) spillover benefits, such as street lighting

> 5 During data cleaning 16 households were dropped, keeping 20,900 households for analysis. Since electricity users are overdrawn in the sample, all figures and estimations used in this paper are weighted using actual PBSlevel distributions of users and nonusers.

> 6 This figure is close to the finding of a recent study based on a survey of rural households conducted in 2004 (World Bank 2008).

7
22 Table 3 Comparison between Historic and Simulated Series (1961-82)

|Variables|correlation<br>coefficient|Theil's<br>coefficient|
|---|---|---|
|lJlam|0.901<br>|334|
|oh|0.825|0.314|
|6g|o0.840|0.466|
|ip|0.780|0.203|
|OLp/P|0.757|0.544|
|_ALgIP_|0.687|0.497|
|Ob|0.770|0.532|

table 4s Direct and Total ElastLcLtLes of the Real Rates of Interest,

|Effect|Om|Oh|o|i L|OL,/P|OL6/P|<br>Ob|
|---|---|---|---|---|---|---|---|
|Dlrect|0.104|-0.034|-0.372|||||
|Total|-0.073|-0.160|0.258|<br>-0.047|-0.284|0.376|-0.459|

1. Evaluated at sample means
statistics on respondents’ confidence using the formal financial services. The figure plots responses to the question “ _how comfortable are you making a transaction at a bank branch or mobile money agent?_ ”. Figure 2, panel (a) shows that a large majority of respondents in our sample are not comfortable using bank branches or mobile money agents, and Figure 2, panel (b) shows that this is even more true for respondents in the low-experience sample who have not used a bank or mobile account before.

# **3.2 Administrative Data**

In addition to the survey data, we obtained detailed transaction data for all participant accounts in our study. With the consent of participants, our partner bank and mobile money provider shared data on monthly account balances and the universe of transactions made in all accounts linked to our study. Each transaction record in this data set includes the transaction time, location, as well as information on the transaction type and fees charged. In total, this data set covers 903 bank accounts, 1,093 mobile accounts and records 82,055 individual transactions over a period of approximately two years. These data enable us to study consumer learning by comparing account usage over time between those receiving a payroll account versus a simple account. Moreover, access to the administrative data allows us to supplement and validate the survey responses. Notably, the administrative data reflects the full universe of workers receiving any account and is free from attrition concerns and concerns of enumerator demand effects. Having access to the complete transaction data for all accounts linked to our study allows us to test for these possibilities and verify the quality of the survey data.

We also received personnel data from the partner firms. While these outcomes are not the focus of our analysis, we report a summary of treatment effects in Appendix Table D.12.

# **3.3 Main Outcomes of Interest**

The focus of our study is the impact of payroll accounts on account use, consumer learning, and consumer protection. To measure how actively consumers engage with the account, we use both the self-reported survey data and the administrative transaction-level data. Intuitively, we would expect to see more active engagement with accounts under the payroll accounts treatment, as workers in this treatment condition receive direct deposit wage payments into their account by default and thus face much stronger incentives to actively engage with the account than workers in the bank or mobile account condition, for whom use of the account is optional.

We use two key outcomes to track how consumers learn to navigate the financial technology. Our first outcome, _outside transactions_ , is an indicator for whether a transaction was performed outside the workplace. As Figure 2 illustrates, most employees in our sample report being either “uncomfortable” or “very uncomfortable” using a bank or mobile money agent without assistance.

13
_Appendix 2: Institutional environment indicators in the East Caribbean States_<sup>_6 0_</sup>

# **Statistical Table 1 Rule credibility indicator and components (East Caribbean)**

||Record<br>managemen|Internal<br>t audit|Performance<br>appraisal|<br>Project<br>evaluation|Rule<br>Credibility|
|---|---|---|---|---|---|
|Antigua and Barbuda|8.4|3.5|4.3|3.7|_5.0_|
|Grenada|8.8|3.6|9.3|6.1|6.9|
|St. Kitts and Nevis|7.1|6.5|5.6|_5.0_|6.1|
|St. Lucia|8.6|5.5|8.1|4.1|6.6|
|St. Vincent And Grenadines|8.9|6.9|4.2|_5.1_|6.3|
|East Caribbean|8.6|5.2|6.6|4.7|6.3|

# **Statistical Table 2 Policy credibility indicator and components (East Caribbean)**

||Consistency|Policy<br>coordination|Micro-<br>management|Policy<br>Credibility|
|---|---|---|---|---|
|Antigua and Barbuda|4.1|_5.3_|4.2|4.5|
|Grenada|3.7|5.4|3.8|4.3|
|St.Kitts andNevis|7.3|6.9|3.3|5.8|
|St. Lucia|6.0|6.4|3.5|5.3|
|St. Vincent And Grenadines|4.2|_5.1_|3.8|4.4|
|East Caribbean|4.8|5.7|3.7|4.8|

# **Statistical Table 3 Indicator of resource adequacy and predictability and components (East Caribbean)**

||Lost man-day<br>from natural<br>disasters|s skills<br>available|Institutional<br>capacity|Resource<br>adequacy and<br>predictability|
|---|---|---|---|---|
|Antigua and Barbuda|4.1|5.5|7.7|_5.8_|
|Grenada|3.9|7.4|7.9|6.4|
|St. Kitts and Nevis|5.3|5.9|7.4|6.2|
|St. Lucia|5.3|6.5|6.2|6.0|
|St. Vincent And Grenadines|5.3|6.1|6.3|5.9|
|East Caribbean|4.5|6.3|7.1|6.0|

60 See footnote 48.

46
# **_3.2 More Recent Evidence on the Effects of Levels of and Growth in Years of Schooling_**

To provide a basic representation of the association between years of schooling and economic growth on the most recent version of available data, we use a slightly extended version<sup>31</sup> of the education data by Cohen and Soto (2001), representing the average years of schooling of the population aged 15 to 64, together with data on real GDP per capita in 1960-2000 from the latest update (version 6.1) of the Penn World Tables by Heston, Summers, and Aten (2002).<sup>32</sup> Figure 3.1 plots the average annual rate of growth in GDP per capita over the 40-year period against years of schooling at the beginning of the period for a sample of 92 countries. Both growth and education are expressed conditional on the initial level of output, to account for the significant conditional convergence effect.<sup>33</sup> The regression results depicted by Figure 3.1 imply that each year of schooling is statistically significantly associated with a long-run growth rate that is 0.58 percentage points higher.<sup>34</sup> The positive association is substantially larger in the sample of non-OECD countries (at 0.56) than in the sample of OECD countries (at 0.26), which is in line with the pattern of larger returns to education in developing countries discussed above.<sup>35</sup> However, after controlling for the influence of openness and the security of property rights, the association becomes substantially smaller and turns insignificant, and it is close to zero when the total fertility rate is controlled for. Thus, while there is a clear positive association between years of schooling and growth in the latest available data, it is also somewhat sensitive to model specifications.

31 See Jamison, Jamison, and Hanushek (forthcoming) for details of the extension.

32 As discussed below, one line of investigation has been the impact of mismeasurement of the quantity of education on growth. The Cohen and Soto (2001) data improve upon the original quantity data by Barro and Lee.

33 Added-variable plots show the association between two variables after the influences of other control variables are taken out. Thus, both of the two variables are first regressed on the other controls (in this case, initial GDP). Only the residual of these regressions, which is the part of the variation in the two variables which cannot be accounted for by the controls, is used in the graph. In so doing, the graph makes sure that the depicted association between the two variables is not driven by the control variables. The procedure is numerically equivalent to including the other controls in a multivariate regression of the dependent variable (growth) on the independent variable under consideration in the graph.

34 The association is somewhat lower (at 0.32), but still significant when regional dummies are added to the regression.

35 Similarly, the association is larger in the sample of countries below the median of initial output than above the median.

22
# Fiscal and Quasi-Fiscal Deficits, Nominal and Real: Measurement and Policy Issues

<!-- Start of picture text --> by<br>Roberto de Rezende Rocha<br>and<br>Femando Saldanha<br><!-- End of picture text -->

# Table of Contents

|I.|Introduction|2|
|---|---|---|
|II.|The Deficit of the Non-Financial Public Sector|4|
|111.|The Quasi-Fiscal Deficit of the Central Bank|1 2|
|IV.|The Consolidated Public Sector Deficit|2 3|
|V.|Some Stylized Examples|2 5|
||Case 1: Primary deficits financed by Interest rate subsidies<br>Case 2: Private sector activities financed by interest rate subsidies<br>Case 3: Forebgn<br>exchange losses at the central bank|2 5<br>2 7<br>3 0|
|VI.|Conclusions and Further Thoughts|4 0|
|Appe|ndix|4 3|
||A.1 Above and below the line deficit estimates<br>A.2 Money finance|4 3<br>4 6|
||<br>A.3 Domestic debt finance|5 4|
||A.4 Extemal debt finance|5 4|
|Refe|rences|5 8|
# **3. Impact evaluation design**

Our starting sample consists of all firms that participated in NaN in 2018, in three out of six SEBRAE-PR offices. We limited the number of offices participating in the intervention to simplify the logistics. From the starting sample, we dropped firms that (i) had participated in a previous round of NaN (before 2018), (ii) adopted all 29 business practices, as measured by the questionnaire applied during the first NaN visit, or (iii) whose firm identification number (CNPJ) was not found in the 2015 RAIS database, which we use to measure employment, as described below. RAIS data is available with a time lag, and 2015 was the latest year we were able to access at the time of randomization. This selection criterion thus restricts our sample to firms that were at least three years old.

The final sample includes 866 firms. We randomly assigned these firms to a control and four treatment groups. The control group received the standard six-page report described in section 2. The treatment groups received the standard six-page report, plus one of the four versions of the information sheet. Appendix figure A1 provides an overview of the five experimental groups.

Randomization was done by computer, in weekly batches, to enable returning the report one to two weeks after the first NaN visit. Randomization was also stratified by SEBRAE-PR office and by whether the firm had previously used any SEBRAE service.

# **4. Data, timeline, and baseline summary statistics**

Our data comes from four different sources. Appendix figure A2 shows the timeline of the intervention and data sources. First, we have administrative data from SEBRAE-PR on use of SEBRAE services between January 2017 and March 2020, corresponding to about 12 months before and 18 months after the intervention. As outcome variables, we code dummies indicating whether a firm used a SEBRAE service within three, six, twelve, and eighteen months relative to month zero, which we define to be the month when the firm received the six-page report (and information sheet, for treatment firms).

SEBRAE-PR offers many different services, which we classify into two broad groups: material and training, constructing separate dummy variables for each group. Material means a firm went to a SEBRAE-PR service center with a specific question and received written materials or media (DVDs, CDs) with content related to the question. Is also covers visits to the SEBRAE website where the firm logged into their SEBRAE account and accessed online materials. Training consists of training sessions for groups of firms, one-on-one consulting, and networking opportunities, such as trade fairs and match-making events for buyers and suppliers.

7
of cooperative decision making is relatively well understood. In water resource management especially, where there can be so many indicators of water resource conditions and the performance of management efforts, forums for information sharing are vital to reducing information asymmetries and promoting cooperation.

Since information will not automatically be perceived the same way by all stakeholders, and the implications of information about resource conditions will differ among these groups, it is arguably as important that there also be institutionalized or other regular forums in which basin stakeholders can communicate. _All other things being equal, we expect to find successful decentralized water resource management more likely where information sharing and communication among stakeholders are more apparent_ .

<u>Mechanisms for conflict resolution</u> are needed to prevent disagreements from arising. Resource users can and will disagree about how well their interests are being represented and protected, about how well the resource management program is working and whether it is time for a change, about the distribution of benefits and costs, and manifold other issues.

The success and sustainability of decentralized resource management efforts therefore also depend on the presence of forums for addressing conflicts. _All other things being equal, we would expect successful implementation of decentralized water resource management more likely in settings where forums for conflict resolution exist_ .

# **3. The data collection and quality assurance process**

The above analytical framework was applied, using a comprehensive dataset of 83 river basin organizations. A survey instrument was developed and applied to river basins of the International Network of Basin Organizations (INBO) as well as to other river basin organizations that have been identified The instrument (Annex 4) provides the necessary data to be used in the empirical analysis.

# **3.1 River basin organization data collection process**

The survey instrument was developed and was pre-tested on 25 RBOs prior to being modified and finalized. The survey questions correspond to the list of the variables that were presented in the Methodology section. The survey was administered by INBO (Annex 1), although several questionnaires were administered directly by the team data processors. Questionnaires in English, 16