rafmacalaba/datause-encoder-data huggingface dataset:
profits than female non-crossovers, respectively. These differences are 90, 53 and 63 percent in the developing economies.

Similarly, in developed countries male-owned firms have more employees than female-owned businesses regardless of sector. In the developed nations, female crossovers do not have more employees than female non-crossovers. However, in developing economies, the number of employees follows the hierarchical pattern found with profits and the female crossovers have statistically significant larger firms than female non-crossovers.

|**Table 3. Business Pe**|**rformance: Deve**<br>Number of<br>|**loped versus De**<br>employees<br>|**veloping**<br>Profits in the l<br>|ast month(IHS)<br>|
|---|---|---|---|---|
||Developed|Developing|Developed|Developing|
||(1)|(2)|(3)|(4)|
|Female Crossover [A]|11.393|32.970*|0.542*|0.628***|
||(31.70)|(17.33)|(0.31)|(0.15)|
|Ml i fld B|58.158***|30.559***|1.297***|0.534***|
|ae n emae-concentrate sector[]|(17.91)|(9.05)|(0.17)|(0.08)|
|Ml i lditd t C|35.428*|64.150***|1.337***|0.900***|
|ae n mae-omnae secor []|(20.39)|(10.40)|(0.19)|(0.09)|
|Constant|90.969***|71.916***|6.345***|6.517***|
||(12.53)|(7.00)|(0.12)|(0.06)|
|Observations|7,369|24,387|3,927|13,424|
|Region FE|NO|NO|NO|NO|
|Developed countries|YES|NO|YES|NO|
|Developing countries|NO|YES|NO|YES|
|Mean Female Non-Crossover|90.97|71.92|6.34|6.52|
|P-value A=B|0.14|0.89|0.01|0.52|
|P-value A=C|0.47|0.08|0.01|0.07|
|P-value B=C|0.27|0.00|0.83|0.00|
|Adjusted R-squared|0.00|0.00|0.02|0.01|
|Standard errors in parentheses *** p<0.01, **|p<0.05, * p<0.1||||

_Exploring the Factors Correlated with Crossing Over_ In this section we address the question of whether the women who break into male-dominated sectors are different from women who remain in traditionally female sectors. To examine this, we explore potential differences in demographic characteristics, reasons to start a business, role-models and skills between female crossovers and female non-crossovers.

Table 4 presents the OLS regressions of being a female crossover. We recognize the limitations of this analytical approach particularly since the women have already sorted into sectors and we are only able to understand retrospectively what may have influenced their sector choice. However, we believe that this analysis is helpful in identifying potential variables that correlate with the likelihood of being a female 11
variable twice: once interacted with an indicator for African countries and once interacted with an indicator for non-African countries.

# **_<u>Appendix Table C2. Impacts of US preference margins</u>_**

||<br> <br>Data at country-HS 6-digit-year level (<br>used<br>Dependent variable|<br>including zeros) is<br>is:|
|---|---|---|
||Log (imports + 1)<br>Log (imports + 1)|Log (imports + 1)|
||(1)<br>(2)|(3)|
|Preference margin|0.007<br>(1.01)||
|Preference margin * Africa|1.650***<br>(8.90)||
|Preference margin * Non-Africa|-0.0003<br>(-0.39)||
|Preference margin * Africa * Agriculture||0.370***<br>(3.44)|
|Preference margin * Africa * Apparel||3.360***<br>(8.23)|
|Preference margin * Africa * Manufacturing||1.567***<br>(9.40)|
|Preference margin * Africa * Mining||-1.257<br>(-0.74)|
|Preference margin * Non-Africa * Agriculture||0.358<br>(1.95)|
|Preference margin * Non-Africa * Apparel||-2.423***<br>(-3.95)|
|Preference margin * Non-Africa * Manufacturing||0.0002<br>(0.37)|
|Preference margin * Non-Africa * Mining||6.317***<br>(5.58)|
|Country-product fixed effects|Yes<br>Yes|Yes|
|Country-year fixed effects|Yes<br>Yes|Yes|
|Product-year fixed effects|Yes<br>Yes|Yes|
|Observations|21,931,483<br>21,931,483|21,931,483|

Notes: Robust t-statistics in parentheses, clustered by HS 6-digit product. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Estimates for Eq. (D1) are shown.

9
10 the New York Cotton Exchange has addeeC additional serial months to the Cotlook World Cotton Futures contract- -for which settlement is based on the Cotlook A Index- -to increase the trading and hedging OPPortunities for market users. In addition to the regular cycle months of March, May, August, October, and December, two spot or serial months from the January, February, April, September, and November cycle will also be available. The Exchange anticipates that the addition of rolling spot months will increase the contract'Is liquidity and af ford hedgers and speculators a more viable trading vehicle. However, the present very low level of liquidity of the contract is likely to discourage use of this contract for hedging purposes.
framework for a survey of public servants that we then use to guide our measurement choices.

This requirement is particularly important given the comparative nature of the data we are harmonizing. By making public much of the survey data we – and governments – have collected, we have faced the challenges of integrating surveys that were not always implemented with integration in mind. By including a core module of questions consistent across settings and enumerating it to a similar set of central administrators in our own surveys, the scope of integration is increased. Towards this end, we have developed a conceptual framework to define the topics and questions we include in this core module.

<mark>The conceptual framework of the Global Survey reflects its dual aims: to better understand how public administrations work, and to provide actionable evidence to governments to improve management of public servants. To this end, the core questionnaire of the Global Survey contains measures which reflect core scholarly interests in comparative public administration on the one hand, and government interests on the other.</mark> <mark>As further detailed in Mikkelsen, Schuster, and Meyer-Sahling (2021), from a scholarly perspective our core questionnaire follows public administration literature in focusing on assessing ‘ideal types’ of modes of governing - “the actual operating modes and administrative arrangements by which rulers govern” (Roth 1968, 156). We distinguish and measure in the GSPS indicators related to three ‘ideal types’: patrimonialism, Weberian bureaucracy, and (new) public management.</mark><sup>1</sup> <mark>Weber (1978) coined the term ‘patrimonialism’ for an administrative system in which rulers treat the state as private property and govern it as ‘patrons’ through informal connections with – and loyalty and reciprocity of – public employees (their ‘clients’). Patterns of (neo)patrimonial rule persist in many countries in the global south in particular (cf. Fukuyama 2004), underscoring the conceptual importance of patrimonialism for a global</mark> 5
## <u>Food</u>

ANY( _alimentos_ , _comida_ ) AND OR( _precio_ , _reforma_ )

# <u>Additional Iteration</u>

After consultation with domain experts an additional term was included because of its relevance in the design of the reform _tarjeta_

# **3.3 Time Period**

Tweets were extracted from the following periods corresponding to the dates of the surveys conducted by _La Prensa Grafica_ , the week the subsidy was first introduced as well as a control week September 2013:

- Jan 2011

- May 2011

- August 2011

- 1st-7th September 2011

- May 2012

- August 2012

- September 2013

# **3.4 Geolocation**

Individual tweets were geolocated to a country level using string matching on the user’s declared location and comparing to an open-source database of place names<sup>2</sup> . In addition a small proportion of accounts include an automated GPS location which was extracted and the corresponding country was identified. Only content which was identified to have originated from El Salvador was included.

# **3.5 Crowdsourcing**

In order to identify the subject matter and the sentiment expressed in the social media content, it is necessary to first identify by hand the subject matter and sentiment in a

> 2 `http://www.geonames.org/`

6
If (4) is the preferred model, there are two routes to estimation. One obvious choice is to form a likelihood from (4) and estimate the and the (. ) directly. Alternatively, one could cc proceed in three steps. First, maximize the likelihood (3) and estimate ff(∙| ∙) ππ and the . Then, cc use Bayes theorem to estimate firm-specific class membership probabilities for each firm, ff(∙| ∙) ππ . ccii Finally, use regression techniques to estimate the functions , using as data the ππ� cc and . ππ (. ), cc= 1, . . . CC ccii ii ππ� There is a large literature, both theoretical and applied, reflecting on the choice between the ZZ two routes to estimation. From theory, there are procedures to obtain consistent estimates of and (. ) using the 3-step process (Vermunt 2010, Bakk et al. 2013, Bakk et al. 2014). The applied ff(∙| ∙) cc literature suggests that using the 3-step process is advisable unless one has great confidence in the ππ specification of (4), especially understanding which to include and exclude (Nylund-Gibson ii and Masyn 2016).<sup>52</sup> For that reason, we use the 3-step process because many of the relevant ZZ 's ii remain unmeasured or unknown when using cross-country data. This is the case because of the ZZ inherent difficulty of collecting cross-country data on transaction-related activities and because the discriminating-alignment research program—identifying the 's—is still a work in progress. ii Our decision to use the 3-step procedure also rests on our objective of understanding the ZZ

nature of the classes. A prime contribution of this paper is the characterization of the most common governance structures (step 1). By estimating the classes in general—independently of the correlates of class membership—we can focus on this goal and provide readers with results unencumbered by any more ambitious objectives. Moreover, by providing the Bayesian posterior probabilities produced at step 2 of the 3-step procedure (the ), we make it possible to pursue ccii the exploratory exercises of Sections V and VI, which suggest an approach that can be taken by ππ�

> 52 Jedidi et al. (1997) make a similar comment in a more general modeling context than LCA.

27
country over‐time fluctuations in harmonized test scores for a given testing program reflect only changes in the test scores themselves, and not changes in the conversion factor between tests.<sup>8</sup> Harmonization is done at the subject x grade level. The country‐level test scores used in the HCI average across subjects and grades.

In most cases, the tests are designed to be nationally representative. There are however some notable cases where they are not, including:

- In a number of countries, EGRA assessments are not nationally‐representative and are identified as EGRANR in the data documentation.

- In India, the 2009 PISA was administered in two states (Himachal Pradesh and Tamil Nadu). However, a comparison with state‐level scores for all of India in the 2012/2013 National Achievement Survey (NAS) suggests that the average NAS score for these two states is quite similar to the national average NAS score, indicating that the 2009 PISA scores probably are roughly representative of India as a whole.<sup>9</sup>

- PISA scores for China in 2009 and 2012 are based only on reported data for Shanghai, and in 2015 for Beijing, Shanghai, Jiangsu and Guangdong (B‐S‐J‐G).  Shanghai and B‐S‐J‐G are both considerably richer than China as a whole.  Since test scores tend to improve with income within and across countries, reported PISA scores are unlikely to be nationally representative. Corroborating evidence can be found in Gao et. al. (2017) who implement PIRLS assessments in Shaanxi and rural Jiangxi and Guizhou provinces, the latter being among the poorest areas in China.  As noted in Gao et. al. (2017), test scores in these areas are among the lowest PIRLS scores observed globally.  Extrapolations using average household per capita income using (a) Shanghai and B‐S‐J‐G PISA scores, and (b) PIRLS scores in Gao et. al. (2017), result in similar estimates of nationally‐representative test scores.  Converting these extrapolations to HLO units and averaging them together results in an HLO of 456 (as opposed to 532 in B‐S‐J‐G alone).  This extrapolated estimate is used as the HLO for China.<sup>10</sup>

> 8 The one exception to this is the 2007 and 2014 PASEC rounds, which were not designed to be intertemporally comparable, and for which different doubloon countries are available in 2007 and 2014.

> 9 See the appendix of Patrinos and Angrist (2018) for details.

> 10 See the appendix of Patrinos and Angrist (2018) for details.

29
**Figure 2: Electricity Expenditure Shares of Poorest**

<!-- Start of picture text --> and Richest Households in ECA<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Poorest Richest<br>20% 20%<br>Albania Armenia Azerbaijan Belarus Bulgaria Georgia Hungary Kazakhstan Kyrgyz Moldova Montenegro Poland Romania Russia Serbia Tajikistan Turkey Ukraine<br><!-- End of picture text --> Source: World Bank staff estimates, as reported in Alam et al (2005) For some households that use only electricity as a source of heating, the impact of higher electricity tariffs may be particularly severe. As shown in Table 2, the share of electricity expenditures in total household budget falls across all social and economic categories as income rises. In all but three instances, the share of electricity expenditures for households in the lowest income quintile group exceeds 10 percent of household expenditures. Particularly vulnerable are poor households with disabled persons and on family material assistance, with electricity expenditures reaching 13 percent of household expenditures, and households headed by an unemployed or retired person, which have electricity expenditures shares four times higher than similar household in the highest quintile group. Thus, at current prices electricity expenditures already exceed what is considered the benchmark affordability level of 10 percent of household expenditures.<sup>9</sup> The next section examines how higher electricity tariffs would affect household welfare and electricity expenditure shares, particularly the burden of higher electricity tariffs for the poor and vulnerable households.

> 9 See “Electricity Poverty and Energy Poverty,” IPA report (October 2003).

5
<u>Table 1: Costs to Customs</u>

|2002 Bangladesh ICA|Mean|Standard Deviation|
|---|---|---|
|Average days to clear customs|8.87|10.69|
|Maximum number of days to clear customs|14.10|13.76|
|Offcial costs (takas)|84935|440869|
|(USD)|1456|7556|
|Unoffcial costs (takas)|32895|221242|
|(USD)|564|3792|
|Value of exports (takas)|139981|321491|
|(USD)|2399|5510|
|Percent that use clearing agent|89||
|Cost of agent as % of the value of freight|0.8132|3.109|

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank Note: The exchange rate in 2002 average is 0.017137961 takas=1 USD Table 2: Timing of payment for sales

|Mean|% of Sales paid|% Sales paid at|% Sales bought|Number|
|---|---|---|---|---|
||before delivery|delivery|on credit|of Obs|
|Non-Exporting|5.57|65.74|28.43|561|
|Firms|||||
|Exporting|2.87|89.42|7.66|421|
|Firms|||||
|Total|4.41|75.89|19.53|982|

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank to industry. It ranges from a month in garments to 4 months for shrimp farming ( **?** ). For firms that export, clearing customs, transiting to overseas destination and clearing foreign customs all add to the amount of time firms have to wait before receiving payment for their goods. The time to clear domestic customs is on average around 9 days, with a maximum of 14 according to the 2002 Bangladesh ICA (see Table 1). For transiting time, Bangladesh exports are shipped using international shipping lines that run on regular schedules. The time in transit consists of: overland to port, a feeder journey from Chittagong or Dhaka to Singapore to meet up with the international shipping line, and then the destination port to customer location. According to **?** , the greatest delays are caused in the exchange from feeder to mainline vessel as schedules may not be synchronized and containers may wait in the transshipment port for several days. His estimate of ocean transit times from Bangladesh, using a variety of shipping companies, is between 25-35 days. Figure 1 shows the transit times for APL shipping (the world’s sixth largest container transportation and shipping company) which corroborates the **?** estimate. Without even counting for time needed to clear foreign customs and inland delivery, selling to European markets from Bangladesh can add an extra month to the product cycle. Considering that production of garments only requires about a month to process, exporting essentially would double the amount of working capital required.

Among exporting firms, Figure 2 shows that the amount of working capital is increasing with the distance of export destination. This relationship is particularly stark with Europe and the EU - destinations that have the longest shipping transit times from Bangladesh. This suggests that if firms require additional working capital the longer the transit time or the greater the distance of the export destination.

5
Figure 8 Other Cities, wage informal Cumulative distribution functions Figure 7 Other Cities, wage formal <!-- Start of picture text --> 100 __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _100<br>90 90 -<br>80 80<br>70 7<br>60 A P, 60 g<br>50 so9189<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>I CD 0 0 0 0 0r<br>o o 0 0) 0 0 i0 0 0<br><!-- End of picture text --> Figure 9 Other Cities, food-crop farmers Cumulative distribution furnctions <!-- Start of picture text --> 100<br>90-<br>80 A P8___ 1 88 8 8<br>70 1988 - - - - 1992<br>60<br>50<br>40 -<br>30<br>20<br>10<br>_____ 198~8 79<br><!-- End of picture text --> <!-- Start of picture text --> 198 ---- 19<br><!-- End of picture text --> Figure 10 Rural Areas, food-crop farmers Figure 1 1 Rural Areas, rural nonfarm Cumulative distribution functions <!-- Start of picture text --> I10010<br>90<br>80<br>70~~~~~~~~~~~~~~~~~~~~~~~~~~~P<br>60<br>30 I<br>20<br>10 /<br>0<br>o 0~~~~~~~~~~~~2<br>1988 -. - 99<br><!-- End of picture text --> <!-- Start of picture text -->
--  _<br>80<br>60<br>K  40/<br>~~~~~~~~~~~~~~20<br>0<br>99  -<br><!-- End of picture text -->

1 0
<!-- Start of picture text --> Under-5 EFM (no controls)<br>S+<br>=~<br>oS<br>:ow mo . seeeeree Torte eet pectertteete<br>Eo<br>Bo<br>E<br>Sg<br>5°<br>o8<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br>Year of child birth<br><!-- End of picture text --> <!-- Start of picture text --> Under-5 EFM (controls)<br>ge<br>258oS<br>oN a Tree eeeeeeet tte, _— a<br>2 - vesteeneeeete ee oe<br>Eo tetee<br>3G<br><9<br>8<br>°8<br>$<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br>Year of child birth<br><!-- End of picture text --> <!-- Start of picture text --> 2© Months breastfed (no controls)<br>2<br>rs)<br>0 we oe Geetttttteeee,<br>°<br>3 cee er ceeneeetett<br>¢ ee<br>$<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br>Year of child birth<br><!-- End of picture text --> <!-- Start of picture text --> 8 Months breastfed (controls)<br>+<br>° Wott . -<br>- vo ete te,<br>lo ao Mee tea we<br>8<br>?<br>?<br>8<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br>Year of child birth<br><!-- End of picture text --> <!-- Start of picture text --> ° Immunization ( (no controls)<br>re<br>$eoS<br>©<br>a Cette [eee eee,<br>Bo ~ aa<br>E<br>s van aeeettteneeet<br>5 -<br>o<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br>Year of child birth<br><!-- End of picture text --> <!-- Start of picture text --> 2) Immunization (controls)<br>a<br>~<br>9<br>°<br>1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984<br>Year of child birth<br><!-- End of picture text -->
As illustrated in Figure 3, there occurred 3,367 accidents in the energy sector that caused more than 5 fatalities during the 1970-2008 period. Those accidents caused a total 78,199 fatalities. Of the total accidents, 87% occurred in Non-OECD countries. Eighty-nine percent of the total fatalities in the energy sector accidents also occurred in Non-OECD countries.

Coal is the main source accidents and corresponding deaths; 2,481 accidents in the coal sector killed 40,931 people during the 1970-2008 period (see Figure 3). It accounts for 74% of the total energy sector accidents and 52% of the total fatalities. Of the total coal sector accidents, 97% occurred in Non-OECD countries resulting in 95% of the total coal sector accident fatalities. China alone accounted for 85% of accidents and 70% of fatalities in the coal sector in Non-OECD countries (Burgherr and Hirschberg, 2014).

Most of the accidents in the coal sector occurred in coal mining. Table A1 in the Appendix presents selected coal mining accidents since 1950 to present examples of major coal mine accidents around the world. The two main sources of coal mine accidents are underground explosions and flooding of mines. One of largest coal mine accidents was the underground explosion in Laobaidong coal mine, located near Datong in the Shanxi province of China on May 9, 1960. The accident was caused by coal dust and methane explosion and it was the most fatal coal mine disaster since the inception of People's Republic of China in 1949 and killed 684 coal minors.<sup>3</sup> The most recent coal mining accident occurred in Soma, Turkey on 13 May 2014 where one of the pits was engulfed with fire and carbon monoxide killing 301 miners burning alive or suffocating.<sup>4</sup> It was the largest coal mine disaster in Turkey’s history.

Explosions of oil and gas wells, pipelines and tankers are another the major sources of accidental impacts of human health from energy supply chain. During the 1970-2008 period, Burgherr and Hirschberg, (2014) reported 732 accidents in the oil and gas sector (excluding LPG related accidents) causing deaths of 5,189 people. The liquefied petroleum gas (LPG), the most popular fuel for cooking in developing world, caused 128 accidents killing 1,336 people during the 1970-2008 period (Figure 3). Tables A2 and A3 in the appendix present selected of oil and gas production and transportation related explosions since 1950 to show examples of major

> 3 The Globe and Mail, May 14, 2014, https://www.theglobeandmail.com/news/world/turkish-tragedy-among-theworlds-worst-mining-disasters/article18662842/

> 4 BBC News, 13 May 2015. http://www.bbc.com/news/world-europe-32709431

10