rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2 Education Opportunities**

**Figure 3. Education Opportunities of Child**

<!-- Start of picture text --> Opportunities for Education in 1985 Opportunities for Education in 1998<br>Opportunities<br>Opportunities<br>HOI Attendance 6-12 HOI Attendance 13-15<br>HOI Attendance 6-12 HOI Attendance 13-15 HOI Start 1rst on time HOI Finish 6 grade<br>HOI Finish 6 grade Coverage Coverage Confidence Interval<br>Confidence Interval<br>Source : Authors ' calculation with Household Survey 1998<br>Source : Authors ' calculation with Household Survey 1985<br>Opportunities for Education in 2002 Opportunities for Education in 2008<br>Opportunities<br>Opportunities HOI Attendance 6-12 HOI Attendance 13-15<br>HOI Attendance 6-12 HOI Attendance 13-15 HOI Start 1rst on time HOI Finish 6 grade<br>HOI Start 1rst on time HOI Finish 6 grade Coverage Confidence Interval<br>Coverage Confidence Interval Source : Authors ' calculation with Household Survey 2008<br>Source : Authors ' calculation with Household Survey 2002<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Figure 3 presents HOI and coverage for educational opportunities. The HOI is significantly different from coverage rates across all opportunities in all years (that is, the difference between HOI and access rates is statistically significant across all categories and years). Interestingly, both coverage and HOI are much higher for the opportunities related to attendance than for starting primary school on time (ages 6 to 7) or completion of primary school by age 13 to 15. This is combined with the fact that attendance in school is higher for 6- to 12-year-olds than for 13- to 15-year-olds. The results are similar for 1985, although the 1985 results present a better picture of primary school completion.

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**Table 3: Key Characteristics of Commodity Markets**

||_Maize_|_Wheat_|_Rice_|_Soybeans_|_Palm oil_|
|---|---|---|---|---|---|
|_Global production (million mt), 2011-12_|857|674|457|251|49|
|_Global exports (million mt), 2011-12_|103|145|37|91|38|
|_Exports as % of production, 2011-12_|12.0|21.5|8.1|36.1|77.1|
|_H-index, exports (%), 2010-12_|11.9|21.2|15.2|32.0|40.0|
|_H-index, imports (%), 2010-12_|2.7|2.9|6.3|41.4|9.3|
|_Price volatility, 1975-2004_|5.4|4.9|5.4|5.4|7.7|
|_Price volatility, 2005-09_|8.0|8.3|9.1|6.9|9.4|
|_Price volatility, 2010-12_|6.6|8.8|4.4|4.4|5.2|
|_Global TRI (%), 1965-99_|6|12|48|6|na|
|_Global TRI (%), 2000-04_|3|4|43|6|na|

**_Source_ :** Authors’ calculations based on data from World Bank, US Department of Agriculture, and Anderson and others (2009).

**_Note_ :** ‘mt’ refers to metric tons. Exports as a share of production is a measure of market thinness. _H_ is the Herfindahl index of market concentration. Price volatility has been calculated as 100*STDEV[log _p(t)_ ) - log _p(t-1)_ ], where STDEV denotes standard deviation, _p(t)_ is the current, and _p(t-1)_ is the lagged monthly average price of each commodity. Global TRI (Trade Restrictiveness Index) was taken from Anderson and others (2009, p. 489, Table 12.11).

— 22 —
- 54 -

## Characteristic Expenditure on VR Measures Required to Reduce CAT III Hurricane PML by 50 % Expressed as % of Overall Building Value

||**_New Constru_**|**_ction_**|**_Retrofittink_**|
|---|---|---|---|
|**_Construction Categories:_**||Dwellings||
|A. Reinforced concrete & masonry|1.2||1.8|
|B. Lightweight roof & masonry walls|1.5||2.2|
|C. All lightweight construction|2.2||3.4|
||**Com**|**mercial/ Indus**|**trial/Public**|
|A. Reinforced concrete & masonry|1.3||2.0|
|B. Lightweight roof & masonry walls|1.7||2.8|
|C. Steel structure & lightweight cladding|2.8||3.8|

As an example, a $ 1,000,000 value Class B commercial structure, without deliberate VR measures, could typically be assessed at a 10% ($100,000) PML for a Category III hurricane.

Estimates suggest that VR measures costing $17,000 (new) or $28,000 (retrofitted) would reduce the PML by 50% to $50,000.

## **_Hurricane Vulnerability Reduction (VR) Measuresfor Structures_**

# **Cost/Benefit Yield Estimates**

The methodology for determining VR cost estimates to yield a 50% reduction in Probable Maximum Loss (PML) from a Category III hurricane (Saffir-Simpson scale, above 120 mph wind speed) is illustrated below. This category storm is generally recognized by insurance markets for damage exposure estimations.

The approach used'<sup>2</sup> was first, to express the Category III maximum wind speed as one minute pounds per square foot pressure. As the mean value, the figure of 2.25% was employed, representing the estimate of the incremental VR measures' cost for a new dwelling structure. This figure was deduced from 20 years' of professional experience on both design and retrofitting consulting as well as many on-the-spot surveys of posthurricane damage scenarios. Original estimates were compared and adjusted using other information sources including Munich Re's published (worldwide) materials. In particular findings showed:

> **12** Source: CEP Engineering (T. Gibbs, Barbados).
**Figure 5. Comparing Egypt’s total exports from Enterprise Survey and UN COMTRADE, 2016**

ES vs COMTRADE: 2013, 2016, 2020 <!-- Start of picture text --> 34 12725524<br>20 3419136 92929282 3 2 312913 1 2218 1827 2 26176 2 2161 254 1 7 2 5272581741518 18<br>3334 2 36191 23<br>2 0 20223 322 2322 36<br>32<br>35 16<br>0 5 10 15 20 25<br>ln(COMTRADE ROW Imports)<br>2020 2016<br>2013 45-degree line<br>25<br>20<br>15<br>10<br>ln(ES Exports)<br>5<br>0<br><!-- End of picture text --> Notes: Only Manufacturing sectors are included. The vertical axis measures log export sales in value from the Enterprise Surveys while the horizontal axis measures the log imports by the rest of the world from Egypt.<sup>13</sup> Sectors groupings are in ISIC rev 3.1: 15 = Manufacture of food products and beverages; 16 = Manufacture of tobacco products; 17 = Manufacture of textiles; 18 = Manufacture of wearing apparel; dressing and dying of fur; 19 = Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness and footwear; 20 = Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials; 21 = Manufacture of paper and paper products; 24 = Manufacture of chemicals and chemical products; 26 = Manufacture of other non-metallic mineral products; 28 = Manufacture of fabricated metal products, except machinery and equipment; 31 = Manufacture of electrical machinery and apparatus n.e.c.; 34 = Manufacture of motor vehicles, trailers and semi-trailers; 36 = Manufacture of furniture; manufacturing n.e.c. Source: Authors’ elaboration using data from the Enterprise Survey for Egypt and UN COMTRADE.

> 13 We focus on total imports from Egypt rather than total exports by Egypt because imports tend to be better measured.

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sense that it proxies for the way political pressure groups are formed). We interact both of them with the Africa dummy in formulation **5** . Neither variable has a different effect in Africa compared to the rest of the world although the Africa dummy declines and (possibly because of collinearity) ceases to be significant.

We move next to checking whether dependence on commodity production and exports, where rents are presumably more easily appropriated, may account for high inequality in Africa. Easterly (2002) argues that commodity endowments predict well middle-class share, that is, inequality. Birdsall and Hamoudi (2002) have created a variable which, based on the export data for the period 1980-84, divides the countries into highly commodity-dependent, medium commodity dependent and least commodity dependent.<sup>29</sup> The results are given in formulation **6** . Both commodity-dependence dummies are significant but with different signs: being medium commodity-dependent country contributes to inequality, being highly commodity-dependent _reduces_ inequality (compared to the omitted category of least commodity dependent).<sup>30</sup> The latter is a rather unexpected result contradicting Easterly (2002) and the literature on the link between commodity dependence and inequality. It may be explained by the fact that other variables, specifically regional dummies, included here have swept away some of the commodity dependence effect, or that the effect is non-existent or not robust enough.<sup>31</sup> Yet the important thing for us is that the interaction between ethnic fractionalization and Africa dummy becomes strongly significant, while the Africa dummy is not.<sup>32</sup> We shall therefore keep formulation **6** and contrast it with others.

29 To be highly commodity dependent, a country’s share of primary commodity and natural resource exports in total exports needs to exceed a cut-off point of approximately 90 percent in four out of five years, 1980-84 (the exact annual cut-off points are explained in Birdsall and Hamoudi, 2002, p. 11). If a country’s share is below this cut-off point in _all_ years, it is classified as “least commodity dependent.” Those in-between are classified as medium commodity dependent. The data were kindly provided by Amar Hamoudi.

30 Note that the “overall” omitted category is a WENAO country with a presidential system and low commodity-dependence. The United States is one such country.

31 The average Gini of highly commodity dependent countries (43.6) is higher than that of least commodity dependent (37.6) but not that of medium commodity dependent (50.6).

32 If we replace interaction between fractionalization and the Africa dummy with ethnic fractionalization squared—arguing that there may, in general, be convexity in the relationship between ethnic 19
volatility, latent diversification has a negative impact on ToT volatility, as expected from Table 3, and ToT volatility increases GDP per capita growth volatility.

The estimates presented in Table 4 suggest a large impact of latent diversification on the volatility of GDP per capita growth. An increase from the 25<sup>th</sup> to the 50<sup>th</sup> percentile of the latent export basket distribution in our restricted sample of 95 countries reduces ToT growth volatility by 1.37 percentage points, or one fourth of the average standard deviation in the sample. In turn, this implies a 0.8 percentage point reduction in the volatility of GDP per capita growth.

# **7. Sectoral Distribution of Latent Export Baskets: The Role of Factor Endowments**

Thus far the analysis has highlighted the importance of latent export baskets in mitigating external volatility. This section provides preliminary evidence on another important policy question—which types of goods does a country add to its latent export basket over time?

To answer this question we explore the importance of the cross-sectoral composition of cross-sectional export baskets in a given point in time on the cross-sectoral composition of the goods added to the latent export basket from that point onwards. In particular, we focus on the role of initial factor endowments as a potential determinant of cross-sectional and latent export baskets. We do so by splitting a country’s export basket into the ten broad industry clusters proposed by Leamer (1995), which group products according to factor intensities,<sup>25</sup> and analyze the relationship between the share of goods in the crosssectional export basket belonging to an industry cluster and the share of goods added to the latent export basket belonging to that industry cluster. To avoid the problem of truncation present in the trade data, the analysis focuses on the four industry clusters with the highest number of export lines in them, namely labor intensive goods, capital intensive goods, chemicals and machinery.

We begin our analysis by estimating for each year from 1980 to 2005 the following equation:

> 25 Leamer (1995) proposes a set of 10 industrial clusters. The distinguishing feature of each of these clusters is that the products included in each tend to be exported by similar countries in terms of their endowments of labor, land and natural resources. The ten broad clusters are: petroleum; raw materials; forest products; tropical agriculture; animal products; cereals; labor intensive goods; capital intensive goods; machinery; and chemicals.

27
<!-- Start of picture text --> 120<br>s<br>100 ‘ H<br>haarsoa 11’* ey' 1<br>4 peitoefurt og Shyrig<br>80 fh at art oa 4 '<br>ty wh yey<br>7 7a | t, " Vota<br>Vv a]8 iT]¥ v se<br>Han ====-Toteél events<br>60 ' ! vv‘ ‘4| u4as<br>‘ yi va wr) —— Lethal events<br>40 1 ‘ \5 iW 7 ~<br>NPA FE ANY ——— Linear (Total events)<br>20 Mfow VY<br>PAN ——— Linear (Lethal events)<br>oO<br>SweaSo“4 m9aa woFORaa aael Na— woRRaa co“aa DBaa4a BOATasta “a~~a Soaa oaa_ to“aAa aASSaa Ccm~a woTMN<br><!-- End of picture text -->
**Figure 25: Actual and model predicted current account balance (5 year average)**

<!-- Start of picture text --> 4%<br>2%<br>0%<br>-2%<br>-4%<br>-6%<br>-8% actual CA predicted CA<br>-10%<br>1986-87 1988-92 1993-97 1998-02 2003-07 2008-12 2013-17<br>Source: Author’s calculations.<br>Actual and predicted Current Account Balance (Percent of GDP)<br><!-- End of picture text -->

**Additional estimates as robustness checks are reported in Table 3, where the baseline specification is reported in the first column.** To examine the sensitivity of baseline results to changes in the gold trade balance, we add the lagged changes in world gold prices (which have a negative correlation with the gold trade balance (-0.30) and positive correlation with gold imports (0.48)) as an additional explanatory variable (Column 2); and estimate the baseline specification using as the dependent variable a measure of the current account that excludes the gold trade balance (Column 3) and that excludes the oil trade balance (Column 4). The coefficient of gold prices has an expected negative sign but not significant (Column 2). More importantly, estimates of main explanatory variables are not significantly different from baseline estimates.

**In column 5, we exclude the crisis dummy variable.** Not surprisingly given the short period, the average fit of the model worsens to some extent when these extreme observations are not explicitly controlled for. The crisis dummy exclusion, however, does not have a significant effect on the estimated coefficients for the remaining variables of interest. Finally, we allow for nonlinearity in the effect of the past net foreign liabilities accumulation on the CAB by adding to the baseline specification the interaction term for periods when the negative NFA is above 40 percent of GDP (Column 6). The interaction term captures the hypothesis that the strength of the effect on the CAB may change at higher levels of accumulated foreign liabilities (Catao and Milesi-Ferretti, 2014). The estimated coefficient for the interaction term, however, is not statistically significant, suggesting that the size of accumulated liabilities in the past was not sufficiently high to generate proportionally stronger effects on the CA. Indeed, Catao and MilesiFerretti (2014) documented that the risk of a balance of payments crisis increases when the stock of net foreign liabilities exceeds 50 percent of GDP, the size which has been reached only in 2017 in the case of Turkey.

25
<!-- Start of picture text --> Decadal Productivity Convergence<br>Manufacturing<br>1970-1980 1980-1990<br>wo wo<br>= @mes 2<br>35 ta Sr @rogrey<br>5 so)Qu Ome. Oms, |20 Op non Ore<br>x ror) ‘Stor, Qo . S<br>fo} Dw ro oe OE aaaes<br>= Yi 6 8 10 12 MA 6 8 10 12<br>(0)<br>a<br>=<<br>=o<br>r= 1990-2000 2000-2010<br>S © _ ces w<br>o Se ‘= ox on<br>Oo> Sg e co"© arma ete, Sg e oun ongs ai e o s<br>OF.i Dw _ Orme OreOu owmee Orme —<br>6 8 10 12 6 8 10 12<br>@ African countries @ East Asian countries @ African countries @ East Asian countries<br>In(VA per worker)<br><!-- End of picture text -->

<!-- Start of picture text --> Decadal Productivity Convergence<br>Wholesale retail & trade<br>1970-1980 1980-1990<br>on @ Tame own 5 one<br>Lc = attain g° onfone ORS<br>Q 3a ew econO cae FC verezieiasii 38% NFda@ i twicgin e s Oc “Cia "<br>— @vim OFxr<br>fo) T O teresa v Orem @ eames<br>= 7 8 9 10 11 12 7 8 9 10 11 412<br>oO<br>8<br><x<br>=<br>£ 1990-2000 2000-2010<br>£& “7 ee eon<br>2 satay Tatwen core Ss O neta<br>Oo Q': OG... OO ceces a" @ Sronpere<br>7 8 9 10 11 12 7 8 i) 10 11 12<br>In(VA per worker)<br><!-- End of picture text -->
In addition, we test for co-integration of the variables of interest. The lower panel of Appendix Table A3 reports the results of the two-step Engel-Granger co-integration test. Given our inability to construct a complete series of poaching rates, we aggregate the poaching data at the global level and obtain a time series of 14 consecutive years. The price series on the other hand are much longer with local ivory price data going back to 1970. The data on ivory price in China however have missing information for four non-consecutive years. When testing for co-integration of gold prices with ivory prices, we reject the null of _no co-integration_ at the 1 percent level. Furthermore, despite having only 13 observations to run the co-integration test, we are still able to reject the absence of co-integration between global poaching and gold prices. Gold prices are thus shown to be co-trending with ivory prices in both African range states and China and global poaching rates. However, when looking at co-integration between poaching and Africa (13 observations) and China (7 observations) prices, we fail to reject the null of _no co-integration_ .

# **4.4 Other data**

**Ivory seizures** We use ivory seizure data collected by the Elephant Trade Information System (ETIS) to control for the amount of ivory seized originating from a given country. Underwood et al. (2013) give a detailed description of the data and attempt to correct for reporting biases. The seizure variable, although potentially endogenous (e.g. higher seizure might be due to higher production) might capture some law enforcement intensity.

**Socio-economic data** We will use the London fix price of gold an instrument for ivory prices. We take the annual average of morning prices in the London Bullion Market denominated in current U.S. dollars, and deflate them by the consumer price index (CPI) published by the U.S. Bureau of Labor Statistics.<sup>8</sup> Our real GDP, population, and consumption data for all the African countries and China come from the Penn World Table (version 9.0). The number of conflicts are based on the _UCDP/PRIO Armed Conflict Dataset_ (Melander et al., 2016; Gleditsch et al., 2016). We transform the original dataset from conflict-year level to country-year level by counting both major and minor conflicts a given country was involved in during a given year. We count civil wars as a single conflict within each year.

> 8We will also use silver as an alternative commodity – see the Online Appendix for details.

15
**Figure 1c: Sri Lanka Microenterprise profits – sample size 300**

<!-- Start of picture text -->
-.4 -.2 0 .2 .4<br>Difference in average profits (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>10<br>8<br>Baseline 64<br>2<br>0<br>2.5<br>2<br>1.5<br>1<br>6 months later<br>.5<br>0<br><!-- End of picture text -->

# **Figure 2a: ENE Labor Income Data – sample size 30**

<!-- Start of picture text -->
-1 0 1<br>Difference in average income (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>4<br>3<br>2<br>Baseline<br>1<br>0<br>1.5<br>1<br>.5<br>6 months later<br>0<br><!-- End of picture text -->
# **Introduction**

# _Background_

COVID-19 has had profound effects on the global economy, health, and wellbeing of households worldwide.<sup>1,2</sup> Marginalized groups and individuals working in the informal sector, including female sex workers (FSW), have been disproportionately affected by the pandemic due to lower demand for their activity either because of fear of contagion or lockdowns, reduced financial protection, and a limited ability to navigate income shocks.<sup>3–5</sup> Previous assessments have found that in addition to disrupting sex work, COVID-19 lockdowns increased mental health challenges, food insecurity, and exacerbated barriers to accessing health care services including reducing HIV/STI prevention and treatment.<sup>6,7</sup> In addition, FSW were usually not covered by government social protection programs and were left with few economic prospects, increasing their vulnerability to violence.<sup>3,6,8.</sup> The acute impacts of the COVID-19 pandemic, particularly the effects of “shelter-in-place” measures, curfews, and closures of businesses and other essential services in early 2020, have been well documented.<sup>7–13</sup> However, public health restrictions evolved throughout the different waves of the pandemic, and these changes in regulation likely play an important role in how FSW continue to be affected. Despite this, little is known about the economic impacts for FSW after the initial public health protection measures were eased or lifted, and how the landscape of sex work subsequently changed. We conducted a mixed-methods analysis of the effects of the COVID-19 pandemic on FSW in Dar es Salaam, Tanzania to determine how their activities shifted over the course of the pandemic and to understand how FSW were affected economically.

# **Study setting**

# _COVID-19 in Tanzania_

The Director-General of the World Health Organization (WHO) declared COVID-19 a global pandemic on March, 11 2020.<sup>14</sup> The first case of COVID-19 was reported in Tanzania five days later,<sup>15</sup> and was followed by WHO-recommended public health restrictions,<sup>16</sup> including government closures of schools and universities. Public gatherings were banned with the exception of churches and mosques, many businesses were temporarily or permanently closed, and travelers were required to quarantine for a 14-day period.

Government reporting of COVID-19 data ended in April 2020 after 509 cumulative cases and 21 deaths.<sup>17</sup> The late president, John Magufuli, declared in June 2020 that COVID-19 had been 2
# **Table A16:  Gallup World Poll (GWP)**

**Data Provider** The Gallup Organization **Description** Commercial survey firm based in Washington, United States **Website** www.gallup.com **Data Source** Gallup World Poll **Type** Survey **Respondents** Households **Frequency** Annual starting in 2006 **Coverage** Global sample of countries **Public Access** Full dataset is commercially available.  Averages of sub-indicators are publicly available in this spreadsheet. **Description** The Gallup World Poll is a new survey polling representative samples of households in a large sample of countries.  The core survey instrument asks a wide range of questions, including some related to governance as indicated below.  Note that in 2007 we have carried forward scores for those countries that were covered in 2006 but not in current year.

|**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|
|Confidence in honesty of elections|X|X|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|Satisfaction with public transportation system|X|X|..|..|..|..|..|..|..|
|Satisfaction with roads and highways|X|X|..|..|..|..|..|..|..|
|Satisfaction with education system|X|X|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|Confidence in the police force|X|X|..|..|..|..|..|..|..|
|Confidence in judicial system|X|X|..|..|..|..|..|..|..|
|Have you been a victim of crime?|X|X|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||||
|Is corruption in governmnent widespread?|X|X|..|..|..|..|..|..|..|
|**Country coverage**|**140**|**122**|**..**|**..**|**..**|**..**|**..**|**..**|**..**|
|**Year of Publication**|**2007**|**2006**|**..**|**..**|**..**|**..**|**..**|**..**|**..**|

54