rafmacalaba/datause-encoder-data huggingface dataset:
Table 8. Main features of global recoveries (by region)

||Glo|bal reco|veries (in|itial years|)|Globa|l recover|ies (first t|hree year|s)|All|
|---|---|---|---|---|---|---|---|---|---|---|---|
||1976|1983|1992|2010 A|verage|1976-78 1|983-85 1|992-94 2|010-12 A|verage|years|
|**East Asia and Pacific**||||||||||||
|Output|3.4|7.5|10.9|9.8|7.9|6.7|8.3|11.0|8.6|8.6|7.0|
|Output per capita|1.4|5.8|9.5|8.9|6.4|4.8|6.5|9.6|7.8|7.2|5.4|
|Industrial production|...|...|10.4|14.5|12.5|...|...|13.0|11.6|12.3|9.9|
|Trade|2.6|7.7|16.0|18.1|11.1|8.6|9.3|17.8|12.3|12.0|8.6|
|Output (PPP)|3.9|7.1|10.5|9.6|7.8|6.8|7.8|10.6|8.4|8.4|6.9|
|Output per capita (PPP)|1.9|5.4|9.0|8.8|6.3|4.9|6.0|9.2|7.6|6.9|5.3|
|**Europe and Central Asi**|**a**|||||||||||
|Output|6.1|4.3|-9.4|4.6|1.4|4.9|2.7|-7.3|4.6|1.2|3.2|
|Output per capita|5.2|3.4|-9.8|4.2|0.8|4.0|1.9|-7.6|4.1|0.6|2.6|
|Industrial production|...|...|-1.7|9.5|3.9|...|...|-0.7|7.6|3.4|3.3|
|Trade|5.9|0.9|-22.4|11.9|-0.9|6.8|5.3|-7.1|8.4|3.3|5.0|
|Output (PPP)|6.1|4.3|-9.8|4.5|1.3|4.9|2.7|-7.8|4.4|1.1|3.2|
|Output per capita (PPP)|5.2|3.4|-10.1|4.2|0.7|4.0|1.9|-8.1|4.1|0.5|2.6|
|**Latin America and the C**|**aribbea**|**n**||||||||||
|Output|6.0|-2.5|2.5|6.7|3.2|4.8|1.8|4.0|4.6|3.8|3.9|
|Output per capita|3.5|-4.6|0.7|5.5|1.3|2.4|-0.4|2.2|3.5|1.9|1.8|
|Industrial production|...|...|0.6|7.9|4.2|...|...|4.2|3.2|3.7|1.7|
|Trade|1.9|-5.3|12.1|17.6|6.6|5.1|1.6|10.5|9.8|6.8|5.5|
|Output (PPP)|5.4|-2.4|2.9|6.7|3.1|4.6|1.6|4.2|4.7|3.8|3.8|
|Output per capita (PPP)|3.0|-4.5|1.0|5.5|1.2|2.2|-0.6|2.3|3.5|1.9|1.8|
|**Middle East and North**|**Africa**|||||||||||
|Output|15.7|-1.5|5.8|4.8|6.2|6.1|-0.4|3.2|4.5|3.3|5.0|
|Output per capita|12.5|-4.8|3.5|2.6|3.4|3.1|-3.7|1.1|2.1|0.7|2.4|
|Industrial production|...|...|...|...|...|...|...|...|...|...|...|
|Trade|6.2|-3.9|6.6|1.3|2.6|2.4|-7.0|2.3|4.8|0.6|5.1|
|Output (PPP)|15.6|-0.5|5.3|4.1|6.1|5.9|0.1|2.9|3.4|3.1|4.9|
|<br>Output per capita (PPP)|12.4|-3.8|3.0|2.0|3.4|2.9|-3.2|0.7|1.3|0.4|2.4|
|**South Asia**||||||||||||
|Output|2.3|6.7|5.3|9.6|6.0|4.9|5.4|5.3|7.3|5.7|5.0|
|Output per capita|-0.1|4.1|3.1|8.0|3.8|2.5|2.9|3.1|5.8|3.6|3.0|
|Industrial production|...|...|...|...|...|...|...|...|...|...|...|
|Trade|8.0|9.0|14.6|14.6|11.6|9.3|4.4|12.7|13.7|10.0|6.6|
|Output (PPP)|2.3|6.7|5.3|9.6|6.0|5.0|5.5|5.3|7.2|5.7|5.0|
|Output per capita (PPP)|-0.1|4.2|3.1|8.0|3.8|2.5|3.0|3.1|5.8|3.6|3.0|
|**Sub-Saharan Africa**||||||||||||
|Output|4.9|-1.5|-0.8|6.5|2.3|2.3|1.2|0.3|5.2|2.3|3.8|
|Output per capita|2.1|-4.3|-3.5|3.7|-0.5|-0.4|-1.7|-2.5|2.4|-0.6|1.1|
|Industrial production|...|...|...|...|...|...|...|...|...|...|...|
|Trade|-1.0|-7.2|-3.8|7.4|-1.2|4.1|-0.9|3.5|6.3|3.2|4.3|
|Output (PPP)|5.1|-1.5|-0.6|6.8|2.5|2.5|1.2|0.3|5.3|2.3|3.9|
|Output per capita (PPP)|2.3|-4.3|-3.4|4.0|-0.3|-0.2|-1.7|-2.5|2.5|-0.5|1.1|

Note: All variables show percent changes. See footnote of Table 6. Regional aggregates include EMDEs only.

56
proportion of boys combining working with studying was twice as large compared to girls (18 versus 9 percent). About 6 to 7 percent of boys and girls were neither working nor studying.

# **6. Results: Short-Term Effects of the Ban of December 1998**

This section presents the results for the short-term effects of the ban. By short term we mean the effects for a few months after the change in the law. To estimate the impact of the ban in September 1999, nine months after the increase in the minimum legal age, we use the PNAD of 1999. Given that those who turned 14 in 1999 would still be out of the formal labor force in 2000, we could pool the surveys of 1999 and 2000 for greater efficiency. However, the year of 2000 was a census year and the PNAD was not collected. We thus pool the years 1999 and 2001 and perform the analysis with a narrower bandwidth.

# _Visual Check: Local Linear Regressions_

For the local linear regressions we use a twelve-month bandwidth. Estimates are obtained with a triangle kernel with a one-month bandwidth. The forcing variable is defined in months.<sup>23</sup> The cut-off value is defined at December 1998, that is, the forcing variable takes the value of 0 that month, 1 in January 1999, and so on. All figures show local linear regression with a 95 percent confidence interval.

Figures B.1 to B.9 are shown in the appendix. Figures B.1, B.4, and B.6 suggest that the proportion of boys doing paid work, only paid work, and paid work and studying, dropped. There is some strong indication that the proportion of those only studying increased. Based on these figures it seems that boys traded off work experience for more education. If that was a rationale behind the change in the law, it seems that it paid off at least in the very short term.

The parametric results discussed below confirm what is suggested by the local linear regressions.

> 23 We followed Lee and Lemieux (2010) and used a size of bin that gives a good visualization of the potential local effect.

16
12 measures when they take safeguard action. The fact that VERs are ruled out, combined with relaxation of the nondiscrimination, compensation, and retaliation rules, may encourage the use of safeguards in general, and quantitative restrictions in particular.

# IV. Global Trade Implications: The Round and NTMs

Given the global importance of NTMs (Table 1 indicates that $125 billion of OECD imports from developing countries face these measures), this section seeks to quantify the extent to which the Uruguay Round results will influence the level and structure of nontariff protection. Using published details on the agreement (GATT 1994), Table 3 provides an indication for all developing countries and regional eountry groups (i.e., developing countries in South Asia, Latin American and the Caribbean, Sub-Saharan Africa, etc.). The top quarter of the table shows the 1992 values of OECD imports from each region - - in total and for four product groups: all non-oil goods; ores, minerals and metals; chemicals; and all other manufaures. Directly below these import values, the pre-Uruguay Round NTM coverage ratios are shown for each product and regional group.

The lower half of the table shows estimated post-Uruguay Round NTM trade coverage indices under two alternative assumptions concerning trade values. The first assumption allows for no change - in the value of OECD imports from developing countries this clearly overestimates the importance of remaining NTMs since there will be an import response in the cases where NTMs are removed. In order to quantify the effects of the Round, NTM-ridden trade in agriculture, tectiles and clothing, along with VERs on all other products, is subtracted from the total trade (in all sectors) covered by NTMs and the remainder is then expressed as a ratio to the total value of OECD imports. The second estimate - see - the lower quarter of the table attempts to project the expansion of non-OECD trade that should
reduced job insecurity, may have increased the leverage of workers to demand that the labor contract be formalized. The second driver has to do with a more active role of two Brazilian institutions, the Brazilian Public Prosecutor’s Office, or PPO ( _Ministério Público_ ), and the Ministry of Labor and Employment (MLE). Corseuil, Almeida and Carneiro (2012) present evidence of increased enforcement from these two institutions. They document a causal relationship between changes in the frequency and effectiveness of labor inspections and increases in formal employment from 1996 to 2006, using municipality-level data on labor inspection intensity and job flows.

Whatever the exact causes for the increase in formalization, the fact is that the proportion of workers employed under formal contracts increased by almost a fifth, from 48 percent to 57 percent, over the complete period, as shown in Table 1. This came at the expense of both informal employees and self-employment. Interestingly, Table 1 also shows that the increase in the proportion of workers earning below the minimum wage during 19952003 occurred despite this rising formalization: doubling the real level of the minimum wage proved to be too large an increase to sustain coverage, even as informality as a whole was in retreat.

The third group of factors affecting earnings inequality is related to the demographic composition of the labor force. As shown in Table 1, there was a substantial increase in female labor force participation between 1995 and 2012, as a result of which the proportion of female workers increased by 10 percent, from 38 percent to 42 percent. This trend is associated with increases in women’s educational attainment levels but, more recently, there were also large increases in the provision of public childcare. From 1991 to 2007, the proportion of 0-6 year old children attending childcare increased from 27% to 44.5%, according to the IBGE.<sup>14</sup> It is likely that such a large increase might also have had an enabling impact on female labor force participation. Table 1 also shows that the proportion of nonwhite workers (mostly Afro-Brazilians and people of mixed race) in the working-age population increased by 8 percentage points, to just over 51 percent of the total.

The fourth set of candidate explanatory factors relates to the spatial distribution of the labor force. Table 1 points to the continued trend towards a more urban labor force, with the rural share of the working-age population decreasing by 38 percent, from 16 percent to 10 percent of the total. On the other hand, changes in the regional composition of the labor force were not particularly pronounced, as seen in the same table.

> 14 See <u>http://seriesestatisticas.ibge.gov.br/series.aspx?vcodigo=CAJ318.</u>

11
Policy Research Working Paper 8735

# **Abstract**

Somalia is highly data-deprived, leaving policy makers to operate in a statistical vacuum. To overcome this challenge, the World Bank implemented wave 2 of the Somali High Frequency Survey to better understand livelihoods and vulnerabilities and, especially, to estimate national poverty indicators. The specific context of insecurity and lack of statistical infrastructure in Somalia posed several challenges for implementing a household survey and measuring poverty. This paper outlines how these challenges were overcome in wave 2 of the Somali High Frequency Survey through methodological and technological adaptations in four areas. First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability-based representative sample. Therefore, geospatial techniques and high-resolution imagery were used to model the spatial population distribution, build a probability-based population sampling frame, and generate enumeration areas to overcome the lack of a recent population census. Second, although some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. To address security concerns, the survey adapted logistical arrangements, sampling strategy using micro-listing, and questionnaire design to limit time on the ground based on the Rapid Consumption Methodology. Third, poverty in completely inaccessible areas had to be estimated by other means. Therefore, the Somali High Frequency Survey relies on correlates derived from satellite imagery and other geo-spatial data to estimate poverty in such areas. Finally, the nonstationary nature of the nomadic population required special sampling strategies.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at upape@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
||b�2007<br>2.705<br>6.434<br>2.475<br>1.897<br>2.986<br>2.478<br>4.198<br>6.046<br>3.413<br>1.669<br>2.025<br>2.611<br>2.191<br>4.140<br>2.444<br>2.863|||
|---|---|---|---|
||b�2000<br>2.566<br>8.600<br>2.671<br>1.901<br>2.683<br>2.201<br>3.955<br>5.165<br>3.405<br>1.894<br>1.905<br>3.758<br>2.114<br>2.626<br>2.398<br>2.569|is||
||b�IV<br>2007<br>2.826<br>6.561<br>2.539<br>1.892<br>2.552<br>3.967<br>4.083<br>6.313<br>3.418<br>1.570<br>2.208<br>2.639<br>2.213<br>4.212<br>2.434<br>2.619|ID: �ID <br>i¤s, and||
||b�IV<br>2000<br>2.678<br>8.779<br>2.745<br>1.896<br>2.316<br>3.411<br>3.848<br>5.385<br>3.411<br>1.762<br>2.067<br>3.806<br>2.135<br>2.663<br>2.388<br>2.363|imating �<br> input tar|0.01.|
|Varieties|s.e.<br>(0.017)<br>(0.170)<br>(0.066)<br>(0.069)<br>(0.110)<br>(0.434)<br>(0.183)<br>(0.066)<br>(0.062)<br>(0.154)<br>(0.166)<br>(0.118)<br>(0.027)<br>(0.076)<br>(0.050)<br>(0.061)|d when est<br> upstream|5; *** p<|
|eign Input|�ID<br>0.351***<br>0.553***<br>0.296***<br>0.176**<br>0.403***<br>0.283<br>0.525***<br>0.891***<br>0.426***<br>0.142<br>0.202<br>0.287**<br>0.273***<br>0.396***<br>0.286***<br>0.335***|ways include<br>change rates,|.10; ** p<0.0|
|and For|s.e.<br>(0.019)<br>(0.211)<br>(0.072)<br>(0.058)<br>(0.112)<br>(0.529)<br>(0.180)<br>(0.066)<br>(0.059)<br>(0.121)<br>(0.155)<br>(0.091)<br>(0.024)<br>(0.069)<br>(0.048)<br>(0.057)|ects are al<br>ighted ex|nts. * p<0|
|en Domestic|�IV<br>ID<br>0.376***<br>0.566***<br>0.309***<br>0.175***<br>0.315***<br>0.568<br>0.506***<br>0.938***<br>0.427***<br>0.121<br>0.238<br>0.292***<br>0.278***<br>0.405***<br>0.284***<br>0.291***|Firm ...xed e¤<br>ding import-we|hese instrume|
|betwe|sD<br>2007<br>0.710<br>0.885<br>0.722<br>0.732<br>0.717<br>0.742<br>0.793<br>0.771<br>0.771<br>0.694<br>0.730<br>0.768<br>0.644<br>0.852<br>0.728<br>0.765|ntheses. <br>nts inclu|tails of t|
|stitution|sD<br>2000<br>0.661<br>0.921<br>0.770<br>0.734<br>0.603<br>0.620<br>0.769<br>0.690<br>0.770<br>0.802<br>0.664<br>0.882<br>0.571<br>0.580<br>0.713<br>0.691|ed in pare<br> instrume|for the de|
|Table 9: Estimated Elasticity of Sub|e<br>s & spirit (16-24)<br> products (28-38)<br>& rubber (39-40)<br>s & skins (41-43)<br> articles (44-46)<br>wood (47-49)<br>(50-63)<br> & headgear, etc. (64-67)<br>aster, cement, etc. (68-70)<br> metals (71)<br>tals (72-83)<br>ry, mechanical electrical & equipmt (84-85)<br> & aircraft (86-89)<br>photographic, etc. (90-92)<br>nufacturing (94-96)|tandard errors (with 500 repetitions) are report<br>g OLS, while �IV<br>ID is estimated using 2SLS with|of FDI in the same industry. See the Appendix|
||pl<br>ge<br>al<br>s <br>de<br>& <br> <br> <br>ar<br>pl<br>s <br>e<br>e<br>s <br>, <br>a|s<br>n|l|
||Industry<br>whole sam<br>04: bevera<br>06: chemic<br>07: plastic<br>08: raw hi<br>09: wood <br>10: pulp of<br>11: textiles<br>12: footwe<br>13: stone, <br>14: preciou<br>15: base m<br>16: machin<br>17: vehicle<br>18: optical<br>20: misc m|Bootstrapped<br>estimated usi|the (log) leve|

53
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29
# **II.   BACKGROUND**

# **A.   Similarities and Differences: International Aviation and Shipping**

11. **There are important similarities between these two sectors** . Both industries

- **_Account for a sizable and likely growing share of global emissions_** . In 2007, international aviation accounted for around 1.5 percent of global emissions and international shipping for around 2–3 percent.<sup>4</sup> The share of the sectors in global emissions could expand rapidly.<sup>5</sup>

- **_Are effectively exempt from any charge on their fuel use_** , in contrast to normal practice for domestic transportation activities.

- **_Pay various ‘user fees’ for services received_** . In the aviation sector, such fees include airport landing and take-off fees, slot charges, costs of using airport facilities, air traffic control, and security charges.<sup>6</sup> In maritime, payment is made for an analogous range of services, such as anchorage dues, channeling dues and pilotage charges. User fees of these kinds, though important and appropriate in practice, are essentially a cost of doing business like any other. They are not discussed further here as they are not designed to raise public revenue for the government (net of the costs of ancillary services).

- **_Provide potential tax bases that are hard to allocate to particular countries_** . This and the fact that such activities are currently under-taxed from an environmental perspective have made fuel use in these sectors a prominent candidate as source of international finance.

- **_Are subject to other tax rules that differ from normal practice_** . As discussed in Box 2, aviation receives favorable treatment under VAT regimes, while shipping receives favorable corporate tax treatment. To varying degrees therefore, emissions from both industries are too high for two distinct reasons: the failure to charge for environmental damages and the excessive demand for transportation due to special tax exemptions.

> 4 See (AGF, 2010b) and IMO (2009).

> 5 According to AWG-LCA (2008), CO2 emissions from the aviation and maritime sectors (domestic as well as international) could, if unchecked, account for 10-15 percent of the global total by 2050. Accurately projecting the future emissions growth is difficult however, not least because the industries themselves are taking measures to reduce emissions intensity.

> 6 The  ̳taxes and charges‘ item shown on air tickets is often misleading, as it can contain a plethora of items including surcharges for high fuel prices, landing fees, and airport security services that are not  ̳taxes‘ in the sense of being unrequited payments to a sovereign power.