rafmacalaba/datause-encoder-data huggingface dataset:
bound” on the number of jobs that could feasibly be performed entirely from home, as it “neglects many characteristics that would make working from home difficult.”<sup>5</sup> For many jobs, one of the principal constraints on performing them from home is internet access. Even when a job is in principle amenable to working from home (teleworking), that option may not be available in practice if the worker does not have internet access at home. To properly measure this constraint and account for the importance of ICT, we first need to split the telework jobs identified by the DN2020 index into two categories - jobs that require internet and those that do not require it. Then, for those telework jobs that require internet, we must identify which workers actually have access to internet and for which workers the lack of access to internet constitutes a constraint. Our final objective is to classify jobs as amenable to being performed from home only if they do not require internet or if they require internet and are held by workers who have internet access.

Our first step is identifying telecommutable jobs that require internet access using detailed information on occupation characteristics from the O*NET surveys. We use two specific questions on the importance and frequency of computer and email use in the performance of the tasks. The answers to these questions are scored on a 5-point scale with higher numbers indicating greater dependence on computers and email use. We consider an occupation as requiring internet access if the combined average score exceeds 8 (of a total of 10). This leads to 55% of all SOC 8-digit occupations in O*NET as being classified as requiring internet. By combining this measure with the DN2020 index, we can now distinguish four different types of occupations: (a) those that can be performed from home and require internet; (b) those that can be performed from home without the use of internet; (c) those that cannot be performed from home and do not require internet; and (d) those that cannot be performed from home but do require internet. In the United States, 33.3 percent of all jobs can be done from home and require internet -e.g. fall into group (a), while a further 3.3 percent can be performed from home without internet usage -e.g. fall into group (b).

> 5 In addition, it is not clear how the tasks required to perform an occupation are the same across economies at different levels of development. Using PIAAC (Programme for the International Assessment of Adult Competencies) data at the country level to assess the variations in the task content of occupations, Hatayama, Viollaz and Winkler (2020) observe changes in the ranking of countries in terms of their jobs’ amenability to working from home. However, both measures are highly correlated and most of the lower income developing countries do not have data on the task content of their jobs (the PIAAC survey covers mostly OECD countries), we apply the DN2020 index to all countries. The results from PIAAC countries using the Hatayama, Viollaz and Winkler’s (2020) methodology are very similar and available upon request.

7
amount of violence experienced in the regions they fled reported in Figure 6, even with the post-displacement escalation, was very small on average compared to other displaced households. The average number of monthly fatalities after displacement for households from no-violence districts was less than one-third that of other displaced households in Figure 6; and the number of monthly fatalities was approximately one-half that experienced in the months after the latest survey of non-displaced households. Consistent with having a much stronger aversion to a poor security situation, the households that fled regions before violence occurred were also fleeing from what turned out to be much smaller post-displacement violence on average than other displaced households.<sup>20</sup>

# **Section 6. Conclusion**

Using unique displacement data from the ongoing conflict in the Republic of Yemen, we find that violence was strongly associated with displacement. But we also find that there was significant heterogeneity in how different types of violence forced displacement and in the amount of violence experienced before becoming displaced. Importantly, the heterogeneity in the ways different types of violence force displacement is not emphasized in many of the previous studies analyzing the relationship between violence and forced displacement (e.g., Stanley 1987; Engel and Ibanez 2007; Williams 2008; Lozano-Garcia et al. 2010; Bohra-Mishra and Massey 2011; Adhikari 2013; etc.), and the results suggest a potentially sharper difference among the population in in the tolerance of violence than is typically assumed (e.g., World Bank 2017a).

However, there are several caveats to this analysis. First, the present analysis only addresses households with access to mobile phones, leaving a portion of displaced and non-displaced households excluded from the analysis. Although mobile phone penetration is high in the country (even among displaced populations), other survey modalities need to be used to reach the population without access to mobile phones.

> 20As mentioned above, it is also possible that some households are able to better predict future violence than others. However, it is unlikely that this explanation can fully explain the differences between households that become displaced before violence occurs and others that become displaced only after. The vast majority of displaced households coming from regions of significant violence do so long after the violence has already begun. Additionally, the escalation of violence in non-violent regions was identical for types of violence, including the types that might be more difficult for households to predict than others (e.g., coalition air strikes and terrorism).

20
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Impact on Employment 38. In very general terms, the employment in a port may be expressed as: Employment E = k + a.tl + b.t2 + c.t3 + d.t4 where k is a fixed number of management and administrative staff, only loosely related to the tonnage handled, tl, t2, t3 and t4 are the annual tonnages respectively of general cargo, containerized cargo, dry bulk cargo and liquid bulk cargo.

a, b, c and d are labor productivity indices for each type of cargo.

39. The overall port productivity, expressed in terms of the tonnage handled per employee year, is given by: so that overall port productivity is affected both by any increases in the - efficiency with which each specific type of cargo is handled and probably - more significantly by changes in the proportions of the different types of cargo. The subject is worthy of much more detailed attention, but an overall picture can be obtained from the figures given in the ILO report on the impact of new cargo techniques, published in 1986 and with data referring mostly to 1983/4 and to the mid 1970's (Couper, 1986). From the data of that report (and with many qualifications concerning manifest and undetected errors) a graph has been prepared showing changes in overall port productivity between the mid 1970's and the early 1980's, related to the percentage of containerized cargo at the two dates. For ease of study the graph is in two sections (figures la and lb). It will be appreciated that the data of the report did not permit a separate identification of bulk cargoes, so that any changes in their relative tonnage will distort the results. Nevertheless, the overall trend, of increasing productivity related to an increasing proportion of containerization, is clear.

40. In the graphs, a nearly vertical line indicates that increases in productivity have been achieved without a significant increase in the proportion of containerized cargo. This applies to the ports of Inchon, Antwerp, Tai Chung, Port Kelang and Sydney, though it should be noted that there are substantial tonnages of bulk traffics in all these ports, so that the results must be treated with caution.

41. By contrast, a nearly horizontal line indicates that the percentage of containerized traffic increased significantly but without any increase in productivity. These include the ports of Bombay, Colombo and Freetown, among others. The ports of Venezuela actually experienced an
These requirements may include none, all or some of the following: (1) draft by laws, (2) intended organizational chart, (3) first 3-year financial projections, (4) financial information on main potential shareholders, (5) background/experience of future directors, (6) background experience of future managers, (7) sources of funds to capitalize new bank and (8) intended differentiation of new bank from others. This index is constructed using the data collected and methodology proposed by Barth, Caprio, and Levine (2001).

_Host-Country Restrictions on Bank Activities_ is also an index developed by Barth et al. (2001). This index, ranging between 1 to 16, tries to capture the extent to which banks can engage in (a) the business of securities underwriting, brokering, dealing, and all aspects of the mutual fund industry, (b) insurance underwriting and selling, (c) real estate investment, development and management, and (d) whether banks can own non-financial firms. For each of these subcategories, a value between 1 and 4 is assigned depending on whether the activity is unrestricted (1), permitted (2), restricted (3), or prohibited (4).

_Host-Country Regulations on Foreign Bank Branches_ refers to an index that captures the degree to which the host-country restricts the operation of foreign banks as branches in the country, relative to its treatment of foreign subsidiaries. This variable was constructed on the basis of 2002 bank regulations and legislation in each of the twenty host countries. Details on this index are shown on Table 4.

_Host-Country Corporate Taxes_ refers to the top corporate tax rate in the host-country as reported by the Heritage Foundation. _Host-Country Size_ is measured by the log of constant dollar GDP in the host-country. This variable is intended to capture the scope for scale economies in the country. _Host-Country GDP per capita,_ measured as the dollar GDP per capita, is included to capture the degree of economic development in the host-country. This variable may also help to 11
PRE Working Paper Series

||DAL|AuIho|Da|Contact<br>for paper|
|---|---|---|---|---|
|WPS469|Modeling Economic Behavior in<br>Peru's Informal Urban Retail Sector|J. Barry Smith<br>Morton Stelcner|August 1990|M. Abundo<br>36820|
|WPS470|_Wh_.Do Alternative Measures of<br>Comparative Advantage Reveal<br>About the Composition of Developing<br>Countries' Exports?|Alexander J. Yeats<br>|August 1990|J. Epps<br>33710|
|WPS471|The Determinants<br>of Farm<br>Investment and Residential<br>Construction in Post-Retorm China|Gershon Feder<br>Lawrence J. Lau<br>Justin Lin<br>Xiaapeng Luo|August 1990|C. Spooner<br>30464|
|WPS472|Gains in the Education of Peruvian<br>Women, 1940 to 1980|Elizabeth M. King<br>Rosemary Bellew|August 1990|C. Cristobal<br>33640|
|WPS473|Adjustment, Investment, and the<br>Real Exchange Rate in Developing<br>Countries|Riccardo Faini<br>Jaime de Melo|August 1990|R. Sugui<br>37951|
|WPS474|Methods for Measuring the Effect<br>of Adjustment Policies on Income<br>Distribution|Anne Maasland|August 1990|P. Dixon<br>39175|
|WPS475|Does Divestiture Matter? A<br>Framework for Learning from Experie|Ahmed Galal<br>nce|August 1990|G. Orraca-Tetteh<br>37646|
|WPS476|Health Insurance in Sub-Saharan<br>Africa: A Survey and Analysis|Ronald J. Vogel|August 1990|K. Brown<br>35073|
|WPS477|Private Participation in the Delivery<br>of Guinea's Water Supply Services|Thelma<sup>A. Triche</sup>|August 1990|M. Dhokai<br>33970|
|WPS478|Interrelations Among Child Mortality,<br>Breastfeeding, and Fertility in<br>Egypt, 1975-80|John Marcotte<br>John B. Casterline|August 1990|S. Cochrane<br>33222|
|WPS479|Conversion Factors: A Discussion<br>of Alternate Rates and Correspondin<br>Weights|Michael Hee<br>g|August 1990|E. Zamora<br>33706|
|WPS480|An Evaluation of Neutral Trade Policy<br>Incentives Under Increasing Returns<br>to Scale|Jaime de Melo<br>David Roland-Holst|August 1990|R. Sugui<br>37951|
|WPS481|The Effects of Trade Reforms on<br>Scale and Technical Efficiency:<br>New Evidence from Chile|James Tybout<br>Jaime de Melo<br>Vittorio Corbo|August 1990|R. Sugui<br>37951|
|WPS482|Membership in the CFA Zone:<br>Odyssean Journey or Trojan Horse?|Shantayanan Devarajan<br>Jaime de Melo|August 1990|R. Sugui<br>37951|
Due to identification issues, the most widespread statistical software programs estimate a “traditional” parametrization of model (1) in which _z i_ = _βx i_ + _e i_ and _e i_ ∼ logistic( _x i ,_ 1). However, estimating all the thresholds in the same scale of income requires a slightly different parameterization (Gelman and Hill 2007). Using this parameterization, we can directly interpret the cutpoints _c_ 1 _._ 5, _c_ 2 _._ 5 , and _c_ 3 _._ 5 of model (1) as thresholds on the same scale of income and the standard deviation of the error terms _σ_ as the gradual transition from one class to the next.

The estimated cut-points (intercepts) make it possible to estimate the probability of being Poor, Vulnerable, Middle Class, and Prosperous. For example: Prob{yi = poor|xi} = Prob{xi + ei ≤ c1.5} = Φ(c1.5 −xi) ,

e<sup>w</sup> where Φ(w) = is the logistic distribution function.<sup>8</sup> 1+e<sup>w</sup> To reduce uncertainty in estimating the thresholds we incorporate into the second class the 441 respondents (3.7%) who identify themselves as poor, ending up with three social classes—Poor/Vulnerable, Middle Class, and Prosperous—and two thresholds, those for the lower and upper bounds of the middle class, to be estimated. Estimation of model (1) in 2013 yields a lower income threshold of 474,000 tenge and an upper of 1,772,000 tenge, which correspond to the 56th and the 99th percentiles of the weighted income distribution, and a size of 43.5 percent.<sup>9</sup> These values correspond to US$14.00 and US$52.20 per day in 2011 international dollars. Using the consumption price index for 2013 with the base in 2011 as deflator, annual values in tenge at current prices have been converted to 2011 international dollars using the 2011 PPP conversion factor for private consumption (World Bank, International Comparison Program database), and then divided by

> 8 Depending on the assumption on the distribution of the error terms, logistic or Gaussian, we estimate a logistic or a probit regression model. Logit and probit differ in how the function that links the categorical outcome and the linear predictor is defined. The logistic model uses the cumulative distribution function of the logistic distribution, while the probit model uses the cumulative distribution function of the standard normal distribution. Both methods yield similar, although not identical, inferences.

> 9 We fit the ordered logistic model using the bayespolr (bayesian “proportional odds logistic regression”) function, which is part of the ARM package in R (Gelman et al., 2016). For robust estimation the model was fitted with trimming: we trimmed the top and the bottom 2% for each social class. Estimation of the probit model leads instead to a lower threshold equal to 474,900 tenge and an upper threshold of 1,639,100 tenge corresponding to a size of the middle class equal to 43.2 percent.

11
Figure 2: Percentage of patents mentions by technology within each WIPO green sector, 20002021 <!-- Start of picture text --> Construction Energy Farming and Forestry<br>80 A i r Cond i t i oning 80 B i ofue l s 80 Soiless Ag r icu l ture<br>LED Technology Geothermal Energy Transgenic Crops<br>Sensors Inverter Technology Water Saving Irrigation<br>60 Construction Material 60 Solar Energy 60<br>Li-ion Battery<br>40 40 40<br>20 20 20<br>0 0 0<br>PMP Transportation<br>80 Ca r bon Capture 80 Dr o nes<br>Insulating Glass Hybrid/Electric Vehicles<br>60 60<br>40 40<br>20 20<br>0 0<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>% of patents mentions<br><!-- End of picture text --> Notes: The figure depicts the share of patents that belong to each of the selected technologies within each WIPO Green sector in the period 2000-2021.

17
**Table S1.1.** Continued.

|**INDEX**|**INDIVIDU**|**AL variables**|**MEAN**|**SD**|**MIN**|**MAX**|**Q25**|**Q75**|**N**|
|---|---|---|---|---|---|---|---|---|---|
||Respondent’s answer to:|Age at which women should<br>start having sex|19.577|3.296|10|30|18|20|4501|
|||Age at which men should<br>start having sex|18.449|3.248|10|30|16|20|4509|
||Respondent is confident he/s<br>tionally ready|he will have sex only when emo|- 1.411|0.776|0|2|1|2|4525|
|Sexual abuse<br>reporting<br>attitudes index|Respondent disagrees with th<br>olence the person must not te|e idea that in case of sexual vi-<br>ll anyone.|0.983|0.131|0|1|1|1|4481|
|variables|Respondent thinks that when|Must tell his/her family|0.713|0.452|0|1|0|1|4502|
||a teenager is suffering from|Must tell the authorities|0.741|0.438|0|1|0|1|4502|
||sexual violence he/she:|In case of rape, must seek<br>medical help|0.596|0.491|0|1|0|1|4502|
|||Must tell someone such as<br>teachers, friends, etc.|0.021|0.144|0|1|0|0|4502|

_Source_ : Authors’ analysis from student surveys.

_Note_ : Variables with range [1–4] are coded as: totally agree, almost agree, sort of disagree, and totally disagree. Variables with range [0–2] are coded as: 0 – not so sure, 1 – almost sure, 2 – totally sure.
Gregorio Impavido annuitant population matures. Since assets closely follow the pattern of liabilities, assets too grew rapidly until 2001 and less rapidly since then due to the decreased income after that year as previously reported for premiums and technical provisions. In the first five years since the reform, total assets reached MXN 60,000 million while in the last four years they increase about only MXN 21,000 million.

Figure 2 shows the evolution of total assets and its sources of financing: liabilities and capital. Notice that the drastic increase in capital registered in 2003 was due the decision of one company to transfer to capital excess resources and results in participation in subsidiaries. The same company in 2004 registered another increment in capital as a result of its participation in subsidiaries. In 2005, the same company disinvested around MXN 7 billion. Due to the fact that capital excess resources were stocks not listed on the Mexican stock exchange market, there was no effect on the solvency of the annuity market.

**Figure 2: Evolution and Financing of Total Assets (Real 2005 MXN mln.)**

<!-- Start of picture text --> 90,000<br>80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Libilities Paid up Capital<br><!-- End of picture text -->

**Source** : CNSF.

Investment of technical provisions of pension annuity companies is subject to the same rules applicable to other insurance companies in Mexico. The regulatory framework for investments of technical provisions is contained in the RIRT (CNSF 2000) and _Circulares_ S-11.1 to S-11.6, which in turn, expand on the general provisions contained in Articles 56, 57, 58, 59, 81 (Cap II, III and IV), 82 (Cap XIV), 91 and 92 of the Insurance Law (LGISMS). Such framework regulates the formation and operations of the investment committee, the list of assets that are admitted in representation (to cover) liabilities, rates for private companies’ values, currency and duration matching, custody requirements, investment administration and investment limits per issuer or security type. In what follows, we focus on admitted assets, investment limits, liquidity requirements,

- 14 -
studies the poverty trend from 2015 to 2017/18 and its decomposition. Section 4 quantifies the impacts of the non-standard features of the official methodology. Section 5 provides concluding comments.

# **Section 2: Description of Egypt’s official poverty lines**

In this section, we briefly present the data and methodology used to produce poverty figures for Egypt. More complete information on Egypt’s official poverty measurement methodology can be found in World Bank (2011) or CAPMAS (2019).

# **Data**

Living standards in Egypt are monitored through the Household Income, Expenditure, and Consumption Survey (HIECS), which captures income, expenditures, and consumption at the household level. These surveys are conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS).

Our analysis is based on two rounds of these surveys. First the 2015 round, which covered a representative sample of 23,976 households for the period January to December 2015.<sup>4</sup> Second, the 2017/18 round, which covered a representative sample of 24,971 households from October 2017 until September 2018.

# **Total household expenditures**

The consumption indicator that we use is annual household total expenditures. Total expenditures include all spending on consumption of goods and services, including food and beverages; clothes and footwear; housing; housing services (water, electricity, gas, and fuel); furniture and household equipment and maintenance; health care; transportation; wired and wireless communication; culture and recreation; education; restaurants and hotels; and miscellaneous goods and services. The implicit value of housing is also added.<sup>5</sup> In the remainder, we sometimes use “consumption” or “expenditure” interchangeably.

# **Official poverty lines**

The definition of the official poverty lines is relatively complex, which makes the interpretation of the poverty trend challenging. The main complexity comes from the way in which the poverty lines are computed and updated.

The constitutive elements of Egypt’s official poverty lines are related to the cost of basic needs approach (Ravallion 1998, 2015a). These lines are computed as the sum of two components: a food poverty line and a non-food poverty line. For a household h living in region r, the poverty line zhr is thus computed as F The food poverty line zhr for household h living in region r is computed as:

> 4 The publicly available microdata of HIECS 2015 only contains 50 percent of the total sample.

> 5 As we explain in Appendix 2, the households’ total expenditures slightly differ from their official welfare aggregates, because the latter also include (small) food subsidies.

4