rafmacalaba/datause-encoder-data huggingface dataset:
- 33 -

also held that some migration routes passed out to sea, beyond sight of the flow edge. Aerial surveys confirmed both claims and found whales were moving as far as 300 km from land (Freeman 1988).

# 4.2.2. The Eskimo Walrus Commissioa

The bowhead whaling controversy was soon followed by another, involving Inuit hunting of walrus, only this was complicated by a commercial element in the form of an escalating demand for walrus ivory.

The Marine Mammal Protection Act (MMPA) of 1972 prohibited trade in marine mammal products but permitted native use for subsistance, barter and the fabrication of craft objects as long as this did not endanger the population. By 1976, the black market price of walrus ivory had tripled, but the walrus population was widely considered to be healthy and increasing. Despite this, the State of Alaska argued that federal authorities had done nothing to prevent this illicit trade, assumed management responsibilities under the MMPA and imposed quo>tas upon walrushunting communities.

In 1978, these communities followed the example of the AEWC and set up the Eskimo Walrus Commission (EWC). Its objectives were to develop management plans which represented the native interest and integrated subsistence with other forms of utilisation and to deal with the problem of illicit ivory hunting and trade within the native community. A technical committee was formed to conduct education on the issue and on traditional practice.

In 1981, federal agents seized over half a million dollars worth of ivory and arrested 20 persons in five states. The EWC claimed that the abnormally high prices offered in this sting operation had entrapped hunters who had not offended before.

In 1984, the EWC submitted a walrus management plan which proposed methods for limiting pollution, over-hunting and harassment. It also allowed for sports hunting, once subsistence requirements had been met. In dealing with default, the plan relied upon education and compliance.

The absence of enforcement measures reflects controversy within the walrus-hunting community. Some of the older hunters were worried about non-subsistence hunting while support for a sports hunt was stronger among the younger hunters. Some hunters objected to the EWC as much as to government agencies. But above all, the reliance upon compliance rather than coercion, remained a distinctive approach to securing good conservational practice,

# 4.2.3. Caribou Management Boards

In the Canadian Arctic, a controversy over caribou emerged about the same time as the Alaskan issues over bowhead and walrus. Until quite recently the relationship between hunters and biologists was marked by a form of rivalry about who possessed authority over wild animals. Interestingly, it was by treating the issue primarily as a problem in perception and cow'unication that a resolution became attainable. Government biologists described the situation as follows:
# **1. Introduction**

On March 11, 2011, a 9.0-magnitude earthquake off Japan’s Pacific coast triggered a tsunami that swept over Honshu Island in Japan, killing more than 19,500 people. The tsunami damaged the power supply to the cooling system of three reactors of the Fukushima Daiichi nuclear power plant, causing them to melt. This nuclear accident was rated the highest, level 7, on the International and Radiological Event Scale (World Nuclear Association 2023).

Although no deaths or cases of radiation sickness have been reported, the response to the Fukushima catastrophe was transformative at a global scale (Latre et al. 2017).<sup>2</sup> Japan suspended 21 of its 54 nuclear energy units (Mounfield 2017). Germany decided to shut down all its nuclear power plants by 2022, and Taiwan, China, decided to do so by 2025. Italy reversed its decision to resume its nuclear energy program. France, where nuclear power accounts for about 70 percent of power generation, decided to reduce nuclear dependence to 50 percent of its energy mix by 2025. The Swiss parliament decided to phase out nuclear energy altogether by 2034, and Spain confirmed its plans to close all nuclear stations by 2035. Public opinion was instrumental in supporting these often-costly reforms, which altered the global energy landscape.<sup>3</sup> This paper examines the relationship between how well-informed people are about major global events and their attitudes toward climate change. We assess respondents’ awareness using responses to questions about how many people died in four disasters: the 1984 Bhopal chemical plant accident, the 1986 Chernobyl nuclear power plant accident, the 2010 Haiti earthquake, and the 2011 Fukushima nuclear power plant accident.<sup>4</sup> The question asks, “Could you estimate how many people died from these disasters?” and presents respondents with six response choices regarding the number of deaths in each accident – less than 100, less than 1,000, 10,000, 100,000, Million or more, and Do not know.

<u>Figure 1</u> shows the distribution of answers to that question. The majority of LiTS respondents underestimated the fatalities in the Bhopal chemical leak accident of 1984 (almost 70 percent of respondents) and the Haiti earthquake of 2010 (about 61 percent). At the same time, the

> 2 In 2018, the Fukushima nuclear plant worker died from lung cancer believed to be the result of radiation exposure (BBC 2018).

> 3 For example, Jarvis et al. (2022) estimate that the total costs of phase-out of the nuclear energy for Germany could be up to €160 billion.

> 4 Background information on these events is included in Appendix A.

2
tually granted at customs and not ‘on paper’). The advantage is that actual preferences capture the effects of non-trade barriers (agriculture and textile & clothing quotas, rules or origin, etc...) on the value of the preference granted “on paper”.

To calculate the tariff preferences at the 3-digit level of the ISIC classifi cation we proceeded as follows. We obtained data on duties and import values for each product exported by Latin American countries at the 8-digit level of the HS classi fi cation for the period 1997-2000. The average across this four year period was calculated by country and by HS 8-digit line. For the products exported by Latin American countries, we also collected data on duties and import values from the world, but that entered the US under the MFN regime. The actual tariff collected on each of these 8-digit tariff line products was then calculated for US imports from Latin America, but also from the world under the MFN regime by simply dividing duties collected by the value of imports. This provided us with two tariffs: the (potentially) preferential tariff on each Latin American country, denoted t<sup>F</sup> , and the MFN tariff, denoted t, at the 8-digit HS level.

Because the contribution data is only available at the 3-digit ISIC level, we needed to fi lter our tariff preferences from the 8-digit HS level to the 3-digit ISIC level. The tariff data was aggregated to the 3-digit ISIC classi fi cation using the HS 8-digit exports of each Latin American country to the US within a 3-digit ISIC classi fi cation as weights (both for the Latin American tariff and for the MFN tariffs).<sup>11</sup> Then, we de fi ne the US tariff preferences to Latin

> 11We used the same weights in order to avoid aggregation bias when moving from the tariff line level to the 3-digit ISIC level.

7
Table A2 presents the results of balance checks of baseline characteristics across the different treatment groups and control group. Overall, it shows that all groups are relatively well balanced with respect to observable characteristics: the number of tests that are statistically significant is close to what should be expected due to chance (2 out of 15 tests for the joint tests of all coefficients are equal to zero are significant at the 10% level).

# **3.4 Data**

Three main sources of data are used for this study: administrative data on formalization and program implementation, in-person quantitative surveys with business owners, and qualitative data with study participants and implementing agencies.

Our main measure of formalization is based on monthly administrative data on business registration provided by the GUFE. This database includes the complete list of all newly registered businesses for all legal statuses. Since most businesses in the control group would not have been aware of the new _entreprenant_ status, this measure will capture any alternative legal status they registered under. Appendix 3 describes the matching process used to identify whether firms in the GUFE database came from our sample.

Other main outcomes on business performances (profits and turnover) and intermediate outcomes like business knowledge and practices, taxes and banking were measured through inperson interviews with business owners. The baseline survey of the selected sample of businesses was conducted in March-April 2014 prior to program implementation. Two followup surveys were conducted in April-June 2015, and in May-June 2016. Attrition rates at first and second follow-up surveys were 11.8 percent and 15.9 respectively and were not correlated with treatment status. Two years after the baseline survey, 8.6 percent of the businesses had closed their operations, and business closure was also not correlated with treatment status. Table A3 presents survey rates, closure rates and attrition rates by groups.

Balance checks of baseline characteristics across the different groups on the sample of businesses successfully surveyed at the two years follow-up survey show that, overall, the postattrition sample is relatively well balanced with respect to observable characteristics. Results are presented in table A4 and are very close to those presented in table A2 on the whole sample. The number of statistically significant tests is also close to what should be expected due to 11
<!-- Start of picture text --> — Early stopping<br>35 —— Truncation<br>— TrEst<br>0 x PMT baseline<br>2<br>20<br>15<br>10<br>0<br>0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64<br><!-- End of picture text --> <!-- Start of picture text --> — Early stopping<br>35 —— Truncation<br>— TrEst<br>0 x PMT baseline<br>2<br>x<br>20<br>15<br>10<br>0<br>0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54<br><!-- End of picture text --> <!-- Start of picture text --> — Early stopping<br>35 —— Truncation<br>— TrEst<br>0 x PMT baseline<br>x<br>20<br>15<br>10<br>0<br>0.32 0.34 0.36 0.38 0.40 0.42 0.44<br><!-- End of picture text --> <!-- Start of picture text --> — Early stopping<br>35 —— Truncation<br>— TrEst<br>0 x PMT baseline<br>x<br>20<br>15<br>10<br>0<br>0.26 0.28 0.30 0.32 0.34 0.36 0.38<br><!-- End of picture text --> <!-- Start of picture text --> — Early stopping<br>35 —— Truncation<br>— TrEst<br>0 x PMT baseline<br>2<br>x<br>20<br>15<br>10<br>5<br>0<br>0.22 0.24 0.26 0.28 0.30<br><!-- End of picture text -->
Table 5. Linear regression model estimates (education level and country fixed effects)

|Dependent variable:<br>|Log CO2 e<br>va<br>|missions per i<br>lue-added (20<br>|ndustry real<br>12)<br>|Average<br>emissions<br>added (<br>|annual growt<br>per industry<br>2010-2019),<br>|h in CO2<br>real value-<br>% point<br>|
|---|---|---|---|---|---|---|
|Skill measure domain|Literacy|Numeracy|Problem<br>solving|Literacy|Numeracy|Problem<br>solving|
|Skill measure|-0.05***<br>(0.01)|-0.03***<br>(0.01)|-0.05***<br>(0.01)|-0.12***<br>(0.02)|-0.13***<br>(0.02)|-0.11***<br>(0.03)|
|female|-0.6***|-0.6***|-0.55***|-0.12***|-0.15***|-0.28***|
||(0.01)|(0.01)|(0.01)|(0.03)|(0.03)|(0.04)|
|Education level (relative to no education)|||||||
|Primary|-0.12**|-0.13**|-0.11|-1.21***|-1.21***|-1.88***|
||(0.06)|(0.06)|(0.07)|(0.15)|(0.15)|(0.24)|
|Lower secondary|-0.12**|-0.13***|-0.14***|-0.03|-0.03|-0.51***|
||(0.05)|(0.05)|(0.05)|(0.14)|(0.14)|(0.15)|
|Vocational upper secondary|-0.12**|-0.14***|-0.18***|0.37***|0.38***|-0.11|
||(0.05)|(0.05)|(0.05)|(0.13)|(0.13)|(0.13)|
|General upper secondary|-0.33***|-0.35***|-0.39***|0.07|0.08|-0.47***|
||(0.05)|(0.05)|(0.05)|(0.13)|(0.13)|(0.12)|
|Technical post-secondary|-0.57***|-0.6***|-0.66***|0.25*|0.27*|-0.37***|
||(0.05)|(0.05)|(0.05)|(0.15)|(0.14)|(0.12)|
|General post-secondary|-0.79***|-0.83***|-0.85***|-0.21|-0.19|-0.77***|
||(0.05)|(0.05)|(0.05)|(0.15)|(0.14)|(0.12)|
|Other controls (age, age square, dummy variables<br>for each country)|yes|yes|yes|yes|yes|yes|
|Constant|13.88***|14.01***|13.85***|73.29***|73.28***|82.86***|
||(0.2)|(0.2)|(0.22)|(0.96)|(0.95)|(1.08)|
|obs|67812|67812|44446|67877|67877|44508|
|r2|0.25|0.25|0.26|0.21|0.21|0.23|
|Countries|21|21|17|21|21|17|

Estimated using ten plausible values for each skill measure following OECD (2013); jackknife standard errors reported in parentheses. Statistical significance at the 1, 5 and 10 percent levels denoted by ***, **, and * respectively.

18
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25
Table 11: IV estimates of the effect of ICT use on Wages: Services and Commerce

||Serv|ices|Com|merce|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Dependent variable:** ∆**ln(Wages of w**|**hite-collar w**|**orkers)**|||
|∆Share of labor that uses computers|-0.0263***|-0.0308***|0.0000|0.0011|
||(0.0029)|(0.0065)|(0.0004)|(0.0007)|
|F- frst stage|82.53|21.79|1,067.39|248.45|
|Sargan p-value|0.6023|0.2424|0.1799|0.2324|
|∆Share of labor that uses Internet|-0.0250***|-0.0509***|0.0000|0.0011*|
||(0.0026)|(0.0136)|(0.0004)|(0.0007)|
|F- frst stage|98.06|9.56|1429.109|301.285|
|Sargan p-value|0.6083|0.9829|0.1800|0.2380|
|∆ln(stock of computer equipment/worker)|-0.6635***|-0.7686***|0.1423**|-1.1797***|
||(0.0755)|(0.1337)|(0.0655)|(0.4162)|
|F- frst stage|88.88|31.72|30.77|5.63|
|Sargan p-value|0.9545|0.4930|0.2074|0.7652|
|**Dependent variable:** ∆**ln(Wages of bl**|**ue-collar wo**|**rkers)**|||
|∆Share of labor that uses computers|-0.0104***|-0.0191***|-0.0023***|-0.0007|
||(0.0021)|(0.0053)|(0.0004)|(0.0006)|
|F- frst stage|82.53|21.79|1,067.39|248.45|
|Sargan p-value|0.1567|0.9491|0.1426|0.2411|
|∆Share of labor that uses Internet|-0.0101***|-0.0206***|-0.0021***|-0.0007|
||(0.0019)|(0.0071)|(0.0003)|(0.0005)|
|F- frst stage|98.06|9.56|1429.109|301.285|
|Sargan p-value|0.3960|0.2520|0.1330|0.2363|
|ln(stock of computer equipment/worker)|-0.2563***<br>(0.0493)|-0.3523***<br>(0.0876)|-0.4000***<br>(0.0871)|0.0379<br>(0.1254)|
|F- frst stage|88.88|31.72|30.77|5.63|
|Sargan p-value|0.4801|0.6543|0.8237|0.2851|
|**Controls**|||||
|ln(capital/worker)|No|Yes|No|Yes|
|Age dummies|No|Yes|No|Yes|
|Size dummies|No|Yes|No|Yes|
|Observations|6,241|6,241|8,333|8,333|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

36
Finally, if it were true that family planning centers were more likely to be first established in locations where households have larger family size, assuming a negative relationship between family size and household investment in their children, we would expect this endogenous placement of these centers to weaken the impacts of the instrument and thus bias estimates upward toward zero. Thus our estimation results would provide conservative estimates in this case.<sup>32</sup> For relevance, our review of the literature from other countries suggests that access to family planning facilities is highly relevant to household decisions on family size. Previous studies for Vietnam using data from the 1997 Demographic and Health Survey offer similar findings that increased access to family planning services increases contraceptive use (Thang and Anh, 2002; Thang and Huong, 2003) and reduces unintended pregnancy (Le et al., 2004). Our estimates turn out to consistently show a strong and negative impact of the distance to family planning center on family size.

On the issue of the exclusion restriction, there may be concerns that family planning centers directly affect the investment in children by explicitly promoting the idea of a quantity-quality tradeoff. But given the presence of family planning workers in every commune (GDPFP, 2011) who can provide interested households with detailed information on the benefits of family planning, family planning centers mostly serve as facilities that provide options for restricting family size to the desired number of children. These centers

> families know about and use the services at these centers). Larger impacts for family size in the sample of older centers thus would not necessarily indicate violation of exogeneity.

> 32 An additional concern related to exogeneity is that families could have immigrated to their current commune, meaning that they were not necessarily constrained by the current distance to family planning center when making their decision on giving birth. However, this concern does not apply in our context:  we restrict our analysis to rural families only, and fewer than 3 percent of the total population over five years of age move within or to rural areas in Vietnam between 1994 and 1999 (Dang, Tacoli, and Hoang, 2003). Our tabulation of the 2006 VHLSS also shows that 98 percent of the population register their official residence _(hộ khẩu)_ in their current community.  Because Vietnam has a rigid household registration system under which it usually takes years for households to change their official registration after moving (see, for example, Hardy (2001), this finding indicates that few families have moved recently.

19
would expect, although the recall modules still yield somewhat more inequality for this type of consumption.<sup>24</sup> One surprising result from Table 7 is that the highest measured inequality is for the sample given the “usual” month recall. The aim of the “usual” month format is to measure consumption over a longer time frame, averaging over short-term fluctuations in living standards to yield lower inequality. Hence finding higher inequality with this design suggests that another effect is operating and a likely candidate is the heavy cognitive burden of this design. As noted below, the interviews for the “usual” month module took far longer than for any other interview, and the understatement by this module is most apparent for the least educated households. So it may be that some degree of inequality in education (the Gini for years of schooling of household heads is 0.43) is combined with the actual consumption inequality when a cognitively burdensome module like the “usual” month format is used.

Among the other recall modules, the subset and collapsed list (modules 3 and 4) generally yield a more equal distribution. For both module types, this may not be surprising since the relative lack of prompts among the aggregate categories is expected to yield compressed consumption measures due to omissions. If the diversity of consumption goods is greater among wealthier households, this error type will lead to truncated distributions at the upper end. Looking within the diary formats, the personal diary (module 8) yields slightly higher inequality, suggesting that the extent of omission of individual consumption in household diaries is not constant across wealth levels (as we have seen in Table 6 with respect to urban/rural differences and asset-index values) and this variation results in more compressed distributions.

Should these inter-module differences in Gini coefficients in Table 7 be considered large? The difference between the Gini coefficient calculated with the benchmark diary and with module 1 is 14 percent and with module 5 it is 19 percent. These are equivalent to the average gap between consumption-based and income-based Gini coefficients reported by Deininger and Squire (1996), the basis for the current UNU/WIDER WIID database. Thus, just as those authors were careful to identify which inequality estimates came from consumption surveys and which from income surveys, so that investigators might adjust them to a consistent basis, so too would it be worth identifying which Gini coefficients are computed from surveys that use diaries and which recall, and over what reference period and level of respondent. Yet such meta-data are hardly ever reported, forcing investigators who combine inequality measures from various surveys to introduce considerable measurement error into their analyses.

> 24 The pattern of results also holds for Generalized Inequality measures GE(0), GE(1), and GE(2) (not presented but available upon request).
Table 8: Climate Change Impacts from AOGCM Scenarios to Large Farms.

||Land Value:|Net Revenue:|Land Value:|Land Value:|
|---|---|---|---|---|
||All|All<br>Large Farm|Rainfed<br>s|Irrigated|
|Baseline|1135|200|1284|777|
|2020|||||
|CCC|-173** (-15%)|-127** (-64%)|-208** (-16%)|116 (+15%)|
|CCSR|-154** (-14%)|-84** (-42%)|-184** (-14%)|119 (+15%)|
|PCM|-61 (-5%)|-32 (-16%)|-74 (-6%)|-23 (-3%)|
|2060|||||
|CCC|-373** (-33%|-49 (-24%)|-449** (-35%)|323 (+42%)|
|CCSR|-248** (-22%)|-39 (-20%)|-300** (-23%)|231 (+30%)|
|PCM|-138* (-12%)|13 (-6%)|-165** (-13%)|10 (+1%)|
|2100|||||
|CCC|-702** (-62%)|-83 (-42%)|-837** (-65%)|811 (+104%)|
|CCSR|-397** (-35%)|-58 (-28%)|-483** (-38%)|394 (+51%)|
|PCM|-191** (-17%)|-17 (-8%)|-228** (-18%)|35 (+5%)|

Impacts to large farms in sample measured in USD/ha.

* implies a significance at 95% confidence level and ** at 99% confidence level.

35