rafmacalaba/datause-encoder-data huggingface dataset:
association in either direction. In interpreting magnitudes, we follow Greenwald et al. (2003) and use the Cohen’s _d_ benchmarks of 0.2, 0.5 and 0.8 to denote small, medium and large effect sizes. The mean D-scores on the “advances” and “safety” IAT instruments are 0.243 and 0.181, respectively; both are significantly greater than zero (p _<_ 0.000).

The magnitude of our results is in line with results on traditional gender norms. For comparison, Nosek et al. (2009) find IAT D-scores for respondents’ association of gender with STEM fields ranging between 0.25-0.65 in online samples across countries, and Beaman et al. (2009) report an IAT D-score of 0.11 in Indian respondents’ association of gender and leadership roles. In Appendix E we show that the IAT results are not driven by more general sexism: controlling for implicit bias against women in the workplace ( _gender-career_ IAT) has no on these results.

# **5 Do Women Riders Value the Women-Reserved Space?**

## **5.1 Revealed Preference Elicitation**

We elicit revealed preferences from our sample of riders through our crowdsourcing app to estimate the value participants place on riding in the women-reserved space. In this setting, riders always receive a monetary compensation for reporting data about their rides. We add to this by offering a series of incentivized choices in which riders face a monetary opportunity cost for riding in the reserved space, relative to the public space.

Our design yields within-respondent variation in the choice of ride (reserved space vs public space), while retaining the simplicity and incentive compatibility of the take-it-orleave-it single price offer method of eliciting willingness to pay (Lee et al., 2016; Ashraf et al., 2010; Cohen and Dupas, 2010). Figures 1a and 1b illustrate this choice as it was presented to riders in the app. Riders start with a series of at least 5 rides for which they are offered a $4.50 pay-out to ride, regardless of whether they choose the public or the women-reserved space (Figure 1a). Next we introduce variation in opportunity costs for riding the reserved 17
30 1980s, enrollments fall back to 70 per cent, indicating that progress in Africa is actually being reversed. This Ls an al_,rming development particularly in the light of the fact that enrollment levels in Africa remaln the lowest in the world. By 1985 East Asia, EMENA, and LAC had virtual universal primary enrollment. South Asia benefits from India's performance and reached 87 per cent (but only 65 without India).

Although GERs provide indication on the capacity of an educational system, one has to refer to net enrollment ratios (NERs) as regards the coverage by the education system of the relevant population group. NERs give the number of primary school-age children (typically 6-11 years old) in school as a percentage of the number that age in the population, with an adjustment for repeaters. Net enrollment ratios rose steadily for low and lower income countries, and culminated at high levels for upper middle income countries during the 1960s and the 1970s. Although progress has been substantial, low income countries still hardly reached 50 per cent NERs by 1985, up from 28.8 in 1965; lower and upper middle income countries revolved around 90 per cent by 1985, virtually reaching universal enrollment, up from 67 and 85 per cent in 1965.

Net enrollment ratios confirm the picture rendered by GERs. NERs in Africa peak in 1980 with a weak 59.7 per cent, then decrease to 55.7 per cent in 1985. What is worse, completion rates (Table Education 3) indicate that only 67.2 per cent of children in African primary school actually complete their cycle, down from 67.5 in 1980. In other words, compared to other regions, Africa has the lowest enrollment rates and fewer of its enrolled
while the richest 20 percent commanded more than 60 percent of global national income. If we compare the two extremes, the ratio of the top vintile’s GNI per capita to the bottom decile’s GNI per capita is 37 to 1. The population-weighted Gini coefficient of international inequality in 2002 was 53.8, a value that is slightly below inequality in the most inegalitarian countries in the world, such as Nicaragua (55.1), Brazil (58.5) (World Bank 2004b) or South Africa (58) (Hoogeveen and Ozler 2004).<sup>4</sup> It is important to remember that, as described above, our measure of international inequality does not take into consideration within country inequality; hence, “true” global inequality is much higher than what we have described here. Thus, if the world were a single country, it would be one of the most unequal countries in the world.

**Figure 1: 2002 International Distribution of GNI per capita (1995 PPP Dollars)**

<!-- Start of picture text --> Lorenz Curve of GNI, PPP (2002)<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>World<br>0.4<br>Sweden<br>0.3<br>India<br>0.2<br>Brazil<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Cumulative Population<br>Cumulative GNI<br><!-- End of picture text --> Source: Authors’ estimates based on data from World Bank (2004b). Country-specific curves are smoothed curves based on deciles information.

As high as international inequality was in 2002, it appeared even greater in the two preceding decades, as measured by standard inequality indices. Bourguignon et al. (2004)

> 4 Hoogeveen and Ozler (2004) use consumption Gini for South Africa, but presumably, income Gini would be even higher.

4
26

# **VI.** IMPORTS, EFFICIENCY **AND** DE-INDUSTRIALIZATION

_50._ Thus at one time or another, during the early or the latter part of the decade, manufactures imports surged in the three large economies. Meanwhile, the growth of industrial production generally slowed down: among major industrial countries, only Germany's and Japan's industrial production increased as mush as in the 1970s: the index of industrial production (relating to the "volume" of production) rose by 19% in Germany, as against 18% in the 1980s; by 49% in Japan, as against the previous decade's 5O%21. Elsewhere, production lagged, imports surged and (with the exceptions again of Japan and Germany, and also of Italy) manufacturing trade balances deteriorated.

51. When the share of imports soars, and import and export increases are also considerable relative to the overall domestic market size, there must be at least a suspicion of considerable competitive strains on domestic manufacturing, and of substantial restructuring. In some cases, this was partially disguised, for a time, by surges in overall domestic demand, causing domestic production to rise even when its market share was falling. Such may have been the case in the United Kingdom during the early 1980s, when its economy was fuelled by Thatcherian reforms and rising oil revenues, and in the United States during the same period. It also happened in Germany in the late 1980s and early 1990s, when imports rose under the impact of the rising budget deficit associated with reunification. Even when attenuated by currency appreciation, as has now happened in Germany and earlier in the US, the salve provided by domestic demand can be quite soothing. However, it is rarely sustainable when it is accompanied by growing balance of payment deficits.

_52._ The relationships of import penetration, export growth and industrial restructuring are not the topic of this paper. They are undoubtedly complex. There is considerable evidence, notably rising unemployment despite stagnant or falling real manufacturng wages, that in few industrial countries was the relationship a happy one in the 1980s. One may then wonder if freer trade always speedily leads to greater domestic welfare.

53. The classical economists' argument, in the simplest terms, goes thus: if an industry cannot compete, if it has no _com;parafive advantage,_ then the factors of production employed in it can be more productively and more remuneratively employed in other industries. The national economy will gain from such changes imposed by competition; and so will the factors of production, including labor, once it overcomes the psychic cost of moving from one sector (or location) to another.

ts SOURCE: Intmational **Fuanial** Statigtici (EPS), Yeaibook 1992
taxpayers and the tax authorities. The taxpayers perceive it as an increase in the authorities’ effort to recover their money; therefore, the relative return of avoidance decreases.

The problem with advocating for individual-specific interventions such as auditing or doorto-door visitations is that those are costly programs. There has been some disagreement in the literature regarding such interventions. On one hand, Slemrod and Yitzhaki, 2002 argue that due to the potentially significant costs of auditing, the optimization of audit numbers and their effective distribution emerge as a pivotal policy dilemma.

On the other hand, many studies have found high returns to investing in tax auditing. Tax authorities’ official statements seem to corroborate this understanding. The Congressional Budget Office CBO, 2020 estimated that modest investments in the IRS would generate somewhere between $60 billion and $100 billion in additional revenue over a decade. Sarin and Summers, 2020 suggest that the methodology used seriously underestimates the potential revenue. Moreover, Boning et al., 2023 find that the direct revenues collected during an audit exceed the costs of the audit by a factor of more than 2:1. The returns to audits of high-income taxpayers are substantially greater than the returns to audits of low-income taxpayers. If sticky behavioral responses are considered, the return of auditing is much higher. H. Kleven et al., 2011 conducted a seminal study in Denmark, which showed that audited taxpayers increased their reported liabilities by 55% of the audit adjustment in the subsequent year. This suggests that audits not only recover immediate lost revenue, but can also have a positive influence on future compliance. Advani et al., 2023 uses microdata from the UK to find a persistent impact of audits on increasing reported tax liabilities for five years after the audit. Moreover, effects are longerlasting for more stable sources of income, and only individuals found to have made errors respond to audits.

Similarly to our setting, Hebous et al., 2023 show that dynamic effects seem to be present despite the absence of penalties. For those found non-compliant from the audits, there was an improvement in the future up to six post-audit years. The main caveat: This study took place in Norway. It is imperative to note that the impact of audits can be contingent on the specific socioeconomic and cultural context. On the one hand, there are questions on the external validity of 6
change in overfishing status.<sup>30</sup> We then calculate the percent change in overfishing status by FAO Region by comparing the predicted overfishing status in each scenario to the current overfishing status.

Consider the following example of a 50% reduction in subsidies and FAO Region 87 (Southeast Pacific), where Chinese vessels accounted for 70.3% (1.02 million hours) of fishing in 2020 (Figure A10). The current overfishing status in FAO Region 87 is _F/Fmsy_ = 1 _._ 28 (so overall fishing in that region of ocean is about 28% larger than the biologically sustainable benchmark). By how much would this value decrease if China was to reform its distant water fuel subsidy program? With an elasticity of 2.177 and a 50% reduction in Chinese fuel subsidies, we calculate the reduction in fishing by Chinese vessels as ((1 _−_ 0 _._ 5)<sup>2</sup><sup>_._177</sup> _−_ 1) _×_ 100 = _−_ 77 _._ 9%. Since Chinese vessels account for 70.3% of fishing hours in the area, the total reduction in fishing in this FAO area would be 77.9% _×_ 70.3% = 54.8%. Affecting only the numerator in the _F/Fmsy_ ratio, this change in fishing hours implies that _F/Fmsy_ would decrease from 1.28 to 0.58 (because 1 _._ 28 _×_ (1 _−_ 0 _._ 548) = 0 _._ 58). Thus, we estimate that reducing Chinese distant water fuel subsidies by 50% would be sufficient to eliminate biological overfishing in the Southeast Pacific. Of course, each region has a different fraction of Chinese fishing hours and different baseline levels of overfishing status, so the calculations need to be repeated worldwide.

Our scenarios hold fishing by non-Chinese vessels constant. If other countries do not simultaneously reduce fuel subsidies or otherwise limit fishing (e.g., through fisheries management), then our calculations could be viewed as upper bounds on the changes in overfishing because it is possible that non-Chinese vessels would increase their fishing in response to the reduction in fishing by Chinese vessels. But viewed from the perspective of WTO negotiations, in which countries simultaneously reduce fisheries subsidies, then our calculations could be viewed as lower bounds in the sense that reductions in Chinese fuel subsidies would likely co-occur with reductions in subsidies by other countries. Our _ceteris paribus_ scenarios are intermediate to these two cases.

> 30This portion of the analysis uses fishing hours by all Chinese-flagged fishing vessels, as identified by publicly available data from GFW.

35
- Cost classification – what are the relevant direct and indirect costs?

- Cost assignment – what are the most appropriate and cost-effective methodologies to assign costs to the cost objects?

- Calculate, validate and confirm – apply the methodologies, validate the calculations and assumptions and confirm consistency with the cost purpose.

The _Guide_ emphasizes that ‘costing is done for the purpose intended’ and therefore that specific costing exercises should be formulated by utilizing methodologies to suit the particular needs of that costing exercise. For example, it identifies a range of common cost applications for departmental program management purposes – including cost recovery, make-or-buy and level-of-service decisions – which are intended principally to support how services are most effectively funded and delivered.

As discussed earlier, the _Guide_ is intended to support the costing of new budget initiatives through the Treasury Board Submission process. These submissions are required to set out details on program design, relevant direct and indirect costs, expected results and outcomes, and program delivery and implementation. In particular, the submission should identify all costs by category, their phasing over the budget and forward years, the total cost of the proposed initiative and, where relevant, the source of funds.

The guidelines for preparing Treasury Board Submissions provide an illustration of how cost information can be presented for Budget submissions. In particular, the guidelines require the use of a standard ‘Cost, Funding Requirements and Source of Funds Table’ to accompany Budget submissions with cost impacts (Treasury Board Secretariat 2014).<sup>18</sup> Exhibit 4 is the template for this standard table and Exhibit 5 outlines the range of costs that should normally be considered in constructing cost schedules.

Calculated on a cash accounting basis, Exhibit 4 illustrates how at the organizational level total costs are itemized by vote, input factor (such as ‘personnel’ or ‘operating and maintenance’), accommodation costs and other statutory items. (If the submission seeks to acquire capital assets and/or land through new funding, a separate table is required to present the accrual information: see discussion below). Total costs are estimated for the five year fiscal forecast period. Importantly, the standard table also obliges submissions to calculate the

> 18 The Treasury Board Submission Guidance was revised in April 2014, and the costing guidelines are available at the Treasury Board Secretariat of Canada website: http://www.tbs-sct.gc.ca/tbs-pct/dgw-ddir/dgw-ddir- <u>eng.asp#Toc370738882. The template for the ‘Cost, Funding Requirements and Source of Funds Table’ is</u> available for download as an Excel spreadsheet at the same location.

25
_4. Prioritizing SDGs_

**Figure 7 Distribution of densities for a sample of countries**

<u>Source: Authors’ calculations</u>

> The _shape_ of the distribution matters. A narrow probability distribution means that most SDGs have similar levels of density for the country in question. A broad probability distribution means that the density differs significantly between SDGs. Hence, the country has a higher potential to achieve some of the goals, but a lower potential to achieve others, suggesting an SDG network analysis would be very helpful in prioritizing

> SDG initiatives. With the _average_ density in each country, it is possible to rank countries

> in terms of their potential for SDG _improvement_ (not SDG levels achieved). Table 5 presents the top 15 and bottom 15 countries.

**Table 5 Average density in selected countries**

|TOP 15||BOTTOM 15||
|---|---|---|---|
|Country|Average<br>density|Country|Average<br>density|
|Bulgaria|0,703|Micronesia|0,161|
|Panama|0,672|Afghanistan|0,159|
|Ecuador|0,671|Andorra|0,141|
|Peru|0,663|Tuvalu|0,118|
|Colombia|0,663|São Tomé and Príncipe|0,116|
|Chile|0,656|San Marino|0,112|
|Costa Rica|0,655|Palau|0,099|
|Mexico|0,641|Marshall Islands|0,092|
|Argentina|0,641|Faroe Islands|0,085|
|Vietnam|0,632|Yemen, Rep.|0,081|
|Madagascar|0,629|Morocco|0,075|
|Uruguay|0,628|Liechtenstein|0,061|
|Korea, Rep.|0,627|Greenland|0,058|
|Germany|0,623|Isle of Man|0,017|
|Hungary|0,621|Chile|0,008|

Source: Authors’ calculations 13
**Figure 19 Latvia’s Education in Global Context**

<!-- Start of picture text --> 10.00<br>9.00<br>F in land<br>Sw eden<br>8.00 D enm ark<br>S lo venia<br>L A T V IA R ussia<br>7.00 E stonia<br>L ith uania H u ngar<br>Slovakia y B ulgari<br>6.00 P olana Ireland<br>R om aniaC zech . R ep.d<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>0.00<br>0.00 1.00 2 .00 3 .00 4.00 5 .00 6 .00 7 .00 8 .00 9 .00 10.00<br>1995<br>Most Recent<br><!-- End of picture text --> _Source_ : WBIKAM

- With respect to the ICT index, Latvia is in the middle rank of NMS’s, but still does reasonably well in absolute terms.

**Figure 20 ICT in Global Context**

<!-- Start of picture text --> 12.00<br>D en m ark<br>10.00<br>F inland<br>Ireland S w eden<br>E stonia S lovenia<br>8.00<br>S lovakia<br>C zech R ep.<br>L ithuania P olandH ungary<br>L A T V IA<br>6.00<br>R o m ania B ulgaria<br>R ussia<br>4.00<br>2.00<br>0.00<br>0.00 2.00 4.00 6.00 8.00 10.00 12.00<br>1995<br>Most Recent<br><!-- End of picture text --> _Source_ : WBIKAM 31
27

**Table 1.  Summary of Data on Non-Remittance and Remittance-Receiving Households,** **<u>Indonesia, 2000 and 2007</u>**

|Variable|2000<br>Receive no<br>remittances|2000<br>Receive<br>remittances|2000<br>t-test<br>(Receive<br>remittances vs.<br>no remittances)|2007<br>Receive no<br>remittances|2007<br>Receive<br>remittances|2007<br>t-test<br>(Receive<br>remittances vs.<br>no remittances)|
|---|---|---|---|---|---|---|
|Mean age of<br>household head<br>(years)|50.17<br>(29.13)|55.03<br>(14.22)|2.19***|52.80<br>(12.82)|56.53<br>(15.26)|4.26***|
|Number of<br>children below<br>5 years in<br>household|0.38<br>(0.59)|0.40<br>(0.65)|0.37|0.28<br>(0.53)|0.35<br>(0.60)|1.93*|
|Number of<br>children<br>between 6 and<br>18 years old in<br>household|1.38<br>(1.22)|1.22<br>(1.17)|-1.63|1.03<br>(1.08)|1.05<br>(1.06)|0.33|
|Number of<br>household<br>members with<br>primary<br>education|1.46<br>(1.15)|1.20<br>(.82)|-0.07|1.32<br>(1.10)|1.33<br>(1.06)|0.12|
|Number of<br>household<br>members with<br>high school and<br>university<br>education|0.74<br>(1.17)|0.59<br>(.93)|-2.16**|.93<br>(1.25)|0.61<br>(0.93)|-3.78***|
|Area (0=rural,<br>1=urban)|0.33<br>(0.47)|0.21<br>(0.41)|-3.15***|0.38<br>(0.48)|0.28<br>(0.45)|-3.26***|
|Mean annual<br>per capita<br>household<br>expenditures<br>(000<br>Indonesian<br>rupiah) at 2000<br>prices|702.5<br>(861)|614.2<br>(471)|-1.34|1007.8<br>(3573)|931.3<br>(1240)|-.032|
|Remittances as<br>percent of total<br>per capita<br>household<br>expenditure|NA|26.0<br>(42)|NA|NA|29.0<br>(63)|NA|
|N|5132|169||5122|179||

Notes: N=5301 households. Standard deviations are in parentheses. In 2000, 8422 Indonesian rupiah= US$1.00; in 2007, 9141 Indonesia rupiah=US$1.00. Source: Indonesia Family Life Survey (IFLS), 2000 and 2007. *Significant at the 0.10 level. **Significant at the 0.05 level. ***Significant at the 0.01 level.
- **89** -

The concept of creating regional pools for the purposes of reinsuring local primary insurers, has been debated often in the Caribbean. There are several options which can be considered in the design of regional pools, although key aspects to take into account are whether the creation of a pool gives incentives to primary carriers to reduce dependence on commercial international reinsurers solely for the associated commission income, and to what extent a pool itself will subsequently cede part of its coverage to international reinsurers (i.e., to optimize risk management). Both these issues will determine to some extent, the success and functionality of an insurance pool. The critical criteria should be the extent to which any pool arrangement is able to generate genuine additional capacity (see below) rather than the reshuffling of existing capacity levels.

**CTVURE...**

<!-- Start of picture text --> :,::,SO 4K -T'AN,R CTVURE...<br>Transfer ot f l<br>Cat Risk<br>Primary _ \Pnmarl Public<br>* Local Primary Primary Local Sector<br>Insurer Local Local Insurer Assets<br>Insurer I Insurer<br>Policy Holders - Insured Household<br><!-- End of picture text -->

# **Reinsurance Pool & Risk Funding Arrangements**

A key aspect to consider would be how the pool itself would retrocede14 some of the risk to international commercial reinsurers and whether the pool would be a reinsurer at all versus a primary insurer for catastrophe risk. Sufficient initial capitalization and a minimum ongoing level of working capital would be required of the pool to assure that it retains sufficient coverage to meet its objectives. The result of retroceding too much of its portfolio would likely invalidate much of the benefits of establishing a pool, unless of course, the excess of loss limits for such retrocession were 14 'Retrocession' refers to the reinsurance of an already reinsured portfolio. I'hat is, property which is initially insured by a primary insurer, is then reinsured internationallv and the reinsurer insures again (or retrocedes) the covered portfolio to yet another reinsurer. Some reinsurers that accept rctroceded coverage, generallv specialize in the catastrophe levels of coverage.
TABLE 3: POLICE AND CRIME AGAINST FIRMS (LOSSES DUE TO CRIME/SALES)– INSTRUMENTAL VARIABLES

||% of Losses Due to Crime<br>Over Sales<br>IV Estimates|Police per 100,000<br>persons lagged|
|---|---|---|
||**Second Stage Results**|**First Stage Results**|
||**1**|**2**|
|Police per 100,000 persons lagged|-0.0025**<br>[0.0012]||
|Real GDP per Capita Growth|-0.0889***|-4.6990|
||[0.0250]|[7.7187]|
|Right wing government||-107.1931**<br>[47.0149]|
|International migrant stock (% of population)||8.8599***<br>[3.2290]|
|Firm with Female Owner|0.1107*<br>[0.0594]|-2.7705<br>[6.5694]|
|Real GNI per capita in 100s|-0.0056***|0.8227|
||[0.0013]|[0.6320]|
|GINI|-0.015|0.1940|
||[0.0156]|[4.3247]|
|Population in Millions, Total|0.0029*|-0.4467|
||[0.0016]|[1.1525]|
|Small firms|0.4002***|-1.3706|
||[0.0928]|[2.7090]|
|Medium firms|0.1681***|0.4318|
||[0.0643]|[2.0697]|
|Large or Capital city|0.0949<br>[0.1244]|34.4200*<br>[19.8669]|
|Manufacturing|-0.2027**|-13.8239***|
||[0.0853]|[4.5140]|
|Number of Countries|27|27|
|Number of Observations|12274|12274|
|Instruments|Right wing government,<br>International migrant stock<br>||
||(% ofpopulation)||
|F Statistics for the First Stage||15.00|
|Under-identification test (Kleibergen-Paap LM statistic) P-<br>value:|0.0322||
|Hansen J statistic (over-identification test of all instruments)<br>p-value:|0.7219||

* significant at 10%; ** significant at 5%; *** significant at 1%, Standard errors in brackets clustered at the country level

27