rafmacalaba/datause-encoder-data huggingface dataset:
violated, a point that is shared by most of the studies (see, for instance, Sherret, 2005 and CIP, 2004), and it is precisely the misuse of the herbicide that causes harmful health and environmental effects. For instance, among the many instructions stated by the manufacturer of the formula used in Colombia, it is stated that Roundup should be applied at distances not longer than 2 to 3 meters from the tallest plant, a recommendation that is many times violated, not only because of difficult topographic conditions, but most of the times to reduce the probability of airplanes used for spraying campaigns being hit by gunfire from the illegal organizations that benefit from the cocaine production and trafficking business. As a result of being unable to spray coca crops from the recommended height, oftentimes the herbicides end up affecting legal crop plantations, water sources, and other sites not targeted by the aerial eradication campaigns.

The study by Sherret (2005) also emphasizes the fact that the most harmful environmental effects, so far identified by scientific evidence, are on aquatic organisms and amphibians, when glyphosate formulations are, perhaps mistakenly, applied to aquatic ecosystems.

The environmental costs of cocaine production are also an important side effect of anti-drug policies. More precisely, the criminalization of coca cultivation and cocaine production also create environmental costs because. Because cocaine production is illegal, it is an activity which is not subject to government environmental regulations. According to John Walters, the director of ONDCP, “600 million liters of so-called precursor chemicals are used annually in South America for cocaine production. To increase yields, coca growers use highly poisonous herbicides and pesticides, including paraquat. Processors also indiscriminately discard enormous amounts of gasoline, kerosene, sulfuric acid, ammonia, sodium bicarbonate, potassium carbonate, acetone, ether, and lime onto the 35
18 World Bank data. Because of the<sup>conceptual and empirical differences</sup> associated with using either own<sup>currency prices (associated ith World Bank</sup> and 1MF data) or ICP prices (associated<sup>with the Summers and Heston data set),</sup> researchers should make specific arguments<sup>for using one set of data rather</sup> than the other and may even want<sup>to compare results using both sets of data.</sup>

# E. Sensitivity Analysis & Fragile<sup>Results</sup>

Doing sensitivity analysis means<sup>addressing the question: "Do the</sup> conclusions withstand slight alterations<sup>in the right-hand-side variables, in</sup> functional form, serial correlation<sup>assumptions, measurement error processes,</sup> distributional assumptions, sample<sup>period, and the weighting of observations?</sup> We must subiect our econometric studies<sup>to systematic sensitivity analyses to</sup> determine whether the results<sup>are fragile or robust: i.e.. sensitivity</sup> analyses helD determine the extent<sup>to which we believe econometric studies.</sup> While pioneers in the field of sensitivity<sup>analyses such as Edward</sup> Leamer may complain about the<sup>haphazard way we study the fragility of</sup> empirical results, many cross-country<sup>empirical studies do not suffer from</sup> this criticism because they have<sup>failed to analyze the robustness of their</sup> results, haphazardly or otherwise.<sup>For example, B.rro (1989, 1990, 1991), Ram</sup> (1986), and Landau (1983, 1986)<sup>focus on the relationship between government</sup> expenditures and growth, but they<sup>never test whether their findings would</sup> change if they included proxies for<sup>tax policy, trade policy, or financial</sup> policy. Similarly, Tyler (1981),<sup>Feder (1983), and Moschos (1989) study the</sup> growth effects of trade policy,<sup>but they do not examine the sensitivity of the</sup> results to the inclusion of variables<sup>that represent fiscal, monetary, or</sup> exchange rate policies. A quick<sup>glance at Table 1 indicates that most studies</sup>
Table 3: Ex-post vs. ex ante financing instruments.

||Immediate<br>hours/days|Short term<br>1-3 months|Medium term<br>3-9 months|Long term<br>Over 9 months|
|---|---|---|---|---|
|**Financial needs for**<br>**post disaster**<br>**operations**|||||
|Relief|||||
|Recovery|||||
|Reconstruction|||||
|Financing tools|||||
|Ex-post financing|Budget<br>contingencies|Relief<br>Budget<br>reallocation|Domestic/<br>external credit|Donor assistance<br>Tax increase|
|Ex-ante financing|Reserve fund|Parametric<br>RT,<br>Contingent<br>debt|Traditional RT||

Source: Cummins and Mahul, 2009.

# **_3.3 Step 3:  Assessment of financial vulnerability and the “resource gap”_**

Using the information on direct risks to the government portfolio and financial resilience, financial vulnerability can be evaluated. We define financial vulnerability as the (probabilistic) availability of government finances for paying for relief and reconstruction. The resource gap is the difference between the cost of a disaster (step 1), and the funds available to the government to rebuild and provide relief and assistance with recovery efforts.<sup>7</sup> Figure 7 illustrates the calculation of this metric for a hypothetical case.

Given losses due to a certain event, such as the 100 year event (public sector loss of 4,000 local currency units (LCU)), the algorithm evaluates the sources for funding these losses. An implicit ordering of these sources is assumed according to the availability and marginal opportunity costs of the sources: grants from donors and international financial institutions (IFIs) would have the least costs associated as these are donations; thus they would be used first. Second, diversions from the budget could be used, then domestic credit, followed by borrowing from IFIs and the international markets (bonds). While in this illustration a 100 year event could be financed, for a 200 year (public sector loss of 10,000 LCU), there would be lack of (ex-post) sources and consequently a resource gap. Ghesquiere and Mahul (2007) added another important dimension related to the timing of resource flows.

> 7 The term resource or financing/resource gap has been heavily used in the economic growth modeling literature as the difference between required investments in an economy and the actual available resources. In this report, this tradition is followed and the financing gap is understood as the lack of financial resources to restore assets lost due to natural disasters and continue with development as planned.

18
**Figure 5: Public expenditure on education and share of enrolment in private secondary schools**

<!-- Start of picture text --> Public expenditure and enrolment in private schools in LAC<br>BOL<br>JAM<br>MEX<br>PAR<br>CRC<br>ARG<br>BRA<br>VEN<br>CHI<br>NIC<br>SAL<br>PER<br>URU<br>DOM<br>0 10 20 30 40 50<br>% enrolment in private schools<br>6<br>5<br>4<br>3<br>2<br><!-- End of picture text --> Source: Authors ́ calculations. Data from the Ministry of Economy Planning and Development, UNESCO The DR is at the moment devoting just around 2.3% of its GDP (13.6% of public spending) to education. The economic and financial crisis of 2003 and the need to implement austerity measures resulted in lower public spending in education, both in terms of percentage of GDP and public budget. Public spending in education increased in 2009 and 2010 in relative terms, but it is still far below regional averages (4.7% of GDP and 15-16% of public budget). Some of the implications of the lack of resources are double shifting, high teacher/pupil ratios, teacher absenteeism, inadequate teacher supervision and training and the poor quality of infrastructures, furniture, etc.

Apart from budget/input indicators, the Dominican Republic seems to have fallen behind also when we look at other dimensions of the education system. Gross primary (104%) and secondary (75%) enrolment were below the regional averages (116% and 89% respectively) in 2008. Adopting a socioeconomic approach, the _middle_ and _high classes_ are characterized for enjoying higher formation than the lower strata; even if it would be necessary to control for specific factors, this observation probably highlights inequalities and barriers in the access and completion of secondary and superior education for those with limited resources.

[11]
an individual’s borrowing. The negative coefficients in the case of men’s and women’s past loans associated with household non-land assets are clear evidences of village diseconomies. While positive externalities can be seen for boys’ schooling in case of female’s past borrowing, negative externalities are evident for non-land asset in case of the past borrowing of both males and females.

# <u>7. Multiple program membership and MFI competition</u>

As we have mentioned, multiple program membership (also known as overlapping) is a relatively new phenomenon. While there was no evidence of multiple program membership in the 1991/92 survey, some 9 percent of households were concurrent members of more than one program by 1998/99, and this figure more than doubled to 32 percent by 2010/11. What are the reasons behind such a growth in multiple memberships? Does multiple program membership reflect a higher demand for credit by borrowers that is not met by a single source, or is it for paying the loan from one source by borrowing from another, or is it simply a matter of programs chasing the less creditrisky clients in an area? The extent of multiple program membership could also be an outcome of market saturation, measured by the average of village-level borrowing. Even if market saturation leads to diminishing returns to borrowing (i.e., village diseconomies), this can increase an individual’s demand for credit and, consequently, lead to a higher incidence of multiple program membership if the extra credit demanded is not coming from a single source.<sup>28</sup> In this case, the village-level intensity of program participation would be seen as a positive phenomenon. That is, the higher the amount of village-level borrowing, the higher the marginal effect of individual borrowing on the incidence of multiple program membership. Conversely, if the additional demand for credit is met by a single source of microcredit, we would observe a negative effect of the village-level borrowing.

> 28 In the case of village economies (market saturation with increasing returns), the demand for credit would induce multiple membership if the additional demand for credit is not met by a single source.

24
30 number of days worked by each permanent worker. However, there is some indication that the use of temporary workers has been increasing. The proportion of the temporary wage bill to the permanent wage bill doubled from 1980 to 1984. Moreover, since temporary workers receive few fringe benefits, their wage bilL relative to the wage bill of permanent workers, underestimates their relative use.

TABLE 46

**Seneqpl Ratio** of **Total** Wage Bl of **Tanpoay** Workers to Total Wagp Bi of Permanent Workers in the Enterprise Sample

||1980|1982|1984|1985|
|---|---|---|---|---|
|Food|0.05S|0.129|0.197|0.163|
|Tobacco an Matches|0.055|.025|0.053|0.041|
|Tcxtiles|0.062|0.118|0.068|0.110|
|Wood|0.112|0.080|0.048|0.049|
|Paper|0.039|0.006|0.050|0.053|
|Prindng|0.121|0.069|0.193|0.103|
|chemicals|0.046|0.025|0.031|0.032|
|Constuction|0.078|0.115|0.149|0.116|
|Mechanical Industries|0.023|0.032|0.030|0.026|
|AVERAGE|0.051|0.087|0.116|0.103|

Source: Enterprise sample
However, the initial transition cost for ETI was estimated KRW263 billion (approximately USD240 million). Transition cost for corporate businesses came to KRW222 billion, of which KRW158 billion accounted for acquisition cost for new IT systems. On the other hand, transition cost for individual businesses was KRW41 billion. In addition, according to the NTS, cost incurred by the NTS for developing ETI systems including the EWS and operating the systems reached approximately KRW27 billion from 2009 to 2014.

These findings suggested that the benefits of ETIs (KRW978 billion) easily exceed initial private sector investment cost (KRW263 billion) and public sector investment cost (KRW27 billion). If tax revenue increases that resulted from curbing tax evasions and frauds through ETI and EWS and cost savings to the NTS from the new invoice administration are taken into account, the benefits would have been significantly higher.

# **Combating Tax Evasion and Fraud**

Since the EWS based on ETI was introduced in 2012, it has triggered a total of 6,822 early warnings and early verifications for tax evasion and fraud including the pilot operations in 2011. Among them, 1,922 cases or 28.2 percent of the total were investigated. As of the end of the first half of 2014, a total of 1,643 cases of tax investigations were completed, and 1,159 cases or 17 percent of the total warnings/verifications led to charges of misconduct to the government prosecution authority and tax levies that reached KRW1,187 billion (see Table 5).

Table 5: Early Warning and Investigation

||Warnings<br>Triggered<br>(A)|Tax<br>Investigations|Investigations<br>Completed|Charges of<br>Wrongdoing<br>(B)|Taxes<br>Levied<br>(Billions)|Charges-<br>Warning<br>Ratio<br>(B/A)|
|---|---|---|---|---|---|---|
|Total|6,822|1,922|1,643|1,159|1,186.8|17.0%|
|First Half,<br>2014|1,021|283|244|198|232.8|19.4%|
|2011- 2013|5,801|1,639|1,399|961|954.0|16.6%|

Source: The National Tax Service, Korea; One area of particular interest is demonstrating to what extent ETI and EWS contributed to the reductions in tax evasions and frauds. According to data provided by the NTS, the number of early warnings since 2012 has risen from 639 in the first half of 2012 to 722 in the second half of 2013, but it fell back to 521 in the first half of 2014. There was an upward trend in early warnings during the initial stage of the ETI and EWS implementation, but it shifted downward thereafter. The most likely explanation for the upward and then downward trend is that it increased following an initial learning curb, but it began to decline as the ETI and EWS began to bite tax evasions and frauds. The number of charges for misconduct also demonstrates a 34
Table B.5a: Comparison of income inequality from the literature, Gini index and mean log deviation

|||Th|ispaper|||Milan|ovic(202|1)||World|Bank(20|16)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||M|LD|||M|LD|||M|LD||
||Gini||Between-|No. of|Gini||Between-|No. of|Gini||Between-|No. of|
||index|<br>Total|country|countries|index|<br>Total|<br>country|countries|index|<br>Total|country|countries|
|1988|||||||||69.7|101.4|80.0|73|
|1993|69.7|98.7|73.7|218|||||69.3|98.3|75.9|102|
|1998|69.3|96.7|72.2|218|||||68.6|94.0|73.5|106|
|2003|68.4|92.6|70.4|218|||||68.7|93.9|72.3|135|
|2008|66.3|85.5|67.8|218|66.4|91.0|61.9|136|66.6|86.3|69.8|136|
|2013|63.0|75.3|65.6|218|61.6|75.9|59.7|131|62.5|74.5|65.2|101|

Table B.5b: Comparison of income inequality from the literature, income shares

|||Thispaper||Worl|d InequalityRep|ort(2022)|
|---|---|---|---|---|---|---|
||Top10%|Middle 40%|Bottom 50%|Top10%|Middle 40%|Bottom 50%|
|2019|48.8|40.2|10.9|52.2|39.3|8.5|
|2020|49.5|39.8|10.7|52.3|39.3|8.4|

_Source_ : PIP; Milanovic (2021); World Bank (2016); Chancel et al. (2022).

_Note_ : Panel a compares estimates of the Gini index and mean log deviation (x100) from various sources with the current paper. The between-country share (in %) according to the mean log deviation is also reported. Panel b reports the income shares (in %) of the top 10%, middle 40%, and the bottom 50% of the respective income distributions. MLD – mean log deviation.

51
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|**T**it3e|Author|**Date**|Contact<br>for paper|
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of the explanatory variables have very low PIPs that are practically indistinguishable from zero or from each other. To the extent that the list of top‐25 percent of explanatory variables includes such variables with very low PIPs, it would not be very surprising to find a lot of instability across outcome variables. We explore this possibility in Table 6, where we consider how our results on instability across outcomes vary for different thresholds used to identify the set of important regressors. The first row of Table 6 contains the benchmark results discussed above: we identify regressors as “important” if they fall in the top quartile of indicators as ranked by their PIPs for each outcome variable. We then report the p‐ values discussed above for the null hypothesis that the event of inclusion in the set of “important” regressors is independent across outcomes, and saw that we could not reject the null only for the least‐ disaggregated GII‐12 and DB‐10 levels of disaggregation. The remaining rows of the table consider other possible rules for identifying “important” regressors. If for example we focus only on the top‐10 percent of regressors we see the same pattern of instability. And if we look at just the top five, three, or even just one indicator as ranked by PIPs, we still see in nearly all cases that we cannot reject the null hypothesis of independence for the more disaggregated sets of indicators (GII‐45, GII‐303, and DB‐41).

We note also that our instability findings are robust to the choice of the key prior parameter g. Recall from the discussion in Section 3 that g plays a key role in determining how the posterior probability mass is distributed across models. When g is chosen to be small as we do, posterior model probabilities are highly sensitive to differences in model fit as measured by R‐squared. As a result, posterior model probabilities tend to be very highly concentrated on a few models with good fit, and the posterior inclusion probabilities of individual right‐hand‐side variables are very concentrated on the few variables included in those models. Small differences in R‐squareds as we move to different choices of outcome variables can then lead to large changes in PIPs. One might therefore reasonably wonder whether our findings on instability are driven by this important sensitivity of BMA to the choice of prior parameter g. It turns out however that this is not the case. First, recall that we have focused throughout on the _ordering_ of right‐hand‐side variables by their PIPs, rather than the magnitudes of the PIPs themselves which are very sensitive to the choice of g. Second and more important, we have also reproduced the BMA analysis for all of the datasets considered here, but using a hyperprior distribution for the prior parameter g, as advocated by Liang et. al. (2008) and Feldkircher and Zeugner (2009). This effectively averages all inferences across a range of alternative values of g, and so smooths out the effect of g on inferences. When we do this, we find that the rank ordering of variables by their PIPs is 25
I1 2) The total debt to total assets ratio is higher for the larger firms, but the difference is not ver large. This is because the ratio of short term borrowing to gross working capital is higher for the small firms as indicated by the ratio of short term debt to inventories. However, the ratio of long term borrowing to fixed assets is significantly lower for the smaller firms, which also suggests that the smaller firms find it more difficult to access long term finance. The debt-equity ratio also shows that larger firms are more leveraged than the small firms. However, of the total liabilities of the small firms almost 40% are in the form of trade dues and current liabilities whereas these form only 25-27% of the total liabilities of the larger firms".

3) For the smaller firms almost two-thirds of their non trade debt consists of short term borrowing whereas for the larger furms the situation is quite the reverse, with two-thirds of their borrowing being long-term (see matl which gives the percentage of long term debt relative to total debt, excluding trade dues). If trade dues are included in total borrowing, short term debt represents more than 80% of total debt for small firms, versus 57% for large firms (see mat2). This also suggests that access to long-term finance is harder for small firms. The maturity structure of debt matches the maturity structure of assets as represented by the ratio of fixed assets to total assets which is 27.3% for the small firms and about 45% for the larger firms.

4) When one compares the large firms with the entire set of public limited companies, there appears to be a positive relationship between the cash flow ratio and the debt maturity ratio. If the cash flow ratio is interpreted as a performance measure, then this suggests a positive correlation between performance and maturity. However, the result is quite weak as the difference between the maturity structure of the debt of the large firms and of all public limited companies is small. Comparison 16 Trade dues and current liabilities are not included in the definition of total borrowing unless specifically indicated.