rafmacalaba/datause-encoder-data huggingface dataset:
28 We would like to follow up with some more questions about your general health over the last few weeks. Response options: 1. No never 2. Not more than usual 3. More than usual 4. Much more than usual Do you find it difficult to sleep because you are nervous or preoccupied? <mark>Do you feel under continuous pressure? Do you feel capable of overcoming</mark> <u><mark>your problems?</mark></u> <mark>Do you feel sad and that there is no way out? Have you lost your self-confidence?</mark> The question “Do you feel capable of overcoming your problems?” is negatively phrased in the PPCS (“Do you feel incapable of overcoming your problems?”). In analysis of the Facebook data, this item has been reverse coded so the higher end of the response scale indicates more severe outcomes, in alignment with the rest of the module.

<mark>Do you see yourself as a useless person?</mark>

# **<mark>Depression</mark>**

_<mark>English text of Facebook questionnaire</mark>_ 29 In the past two weeks, how often were the following statements true for you? Response options: 0. Never 1. A little of the time (rarely) 2. Slightly less than half the time 3. More than half of the time 4. More often 5. Always <mark>You were happy with a good mood You were feeling calm and relaxed You were feeling energetic You woke up active and relaxed Your days were full of things you liked</mark> _<mark>Notes on comparability</mark>_

|**C**<br>_English text of Facebookquestionnair_|**OVID-19 exp**<br>_e_|**osure**<br>_Notes on comparability_|
|---|---|---|
|31<br>Have you ever had, or do you believe that<br>you have ever had Coronavirus?|Yes / No / I<br>don’t<br>know|In the PPCS, the first question in this module offers<br>only Yes / No response options.|
|In the last 14 days, do you think you have<br>met (seen) anyone who you think had the<br>coronavirus whenyou met them?|Yes / No / I<br>don’t<br>know||
|Do you think your area has a high incidence<br>of coronavirus?|Yes / No / I<br>don’t<br>know||
|Do you personally know someone who has<br>died from the coronavirus in your area?|Yes / No / I<br>don’t<br>know||

20
total number of Nikkei visiting the 15 locations during the sampling period was almost 14,000.

Although 11 out of the 15 locations were in two Nikkei neighborhoods: Liberdade and Saúde, only 18 percent of the sample lived in these neighborhoods, with individuals traveling into events, and to work, shop, or visit friends.<sup>9</sup> In fact, individuals reported living in over 150 distinct neighborhoods, with a few living outside of São Paulo state.

**Table 5: São Paulo Intercept Survey**

|**Intercept Point**|Number of <br>interviews|Number of<br>refusals|Refusal<br>rate (%)|Time spent<br>in location|Approximate<br>number in location|
|---|---|---|---|---|---|
|**_Fixed point locations_**||||||
|Coopercotia Atlético Clube|34|23|40.4|8.5 hours|368|
|Estação Metrô São Joaquim|49|37|43.0|14 hours|1436|
|Feira da Liberdade|34|3|8.1|5 hours|1282|
|Feira Livre da Rua Carneiro|34|3|8.1|7 hours|1635|
|Hospital Santa Cruz|42|12|22.2|8 hours|374|
|Mercearia Marukai|54|76|58.5|13 hours|2583|
|Mercearia Satsuyama|36|69|65.7|11 hours|1922|
|Sociedad Brasileira de Cultura Japonesa-Bunkyo|34|25|42.4|9 hours|311|
|Agencia Sudameris|34|24|41.4|8 hours|186|
|**_Events_**||||||
|Cinema Bunkyo|34|19|35.8|4 hours|97|
|ACAL Toyo Matsuri - Festival Oriental|30|22|42.3|9 hours|824|
|ACESA Motitsuki Matsuri (Festival Gastronômico)|29|1|3.3|8 hours|424|
|Fundação Mokiti Okada -Exposição de Obras de Arte|4|2|33.3|9 hours|67|
|Coral Bunkyo - Concerto de Natal|34|3|8.1|4 hours|704|
|Grupo the Friends-Koohaku Utagassen 2006(Festival musical)|34|6|15.0|11 hours|1731|
||516|325|38.6|128.5 hours|13944|

At each location, individuals were asked whether or not they had visited any of the other fixed point locations during the past two weeks, and whether they had attended or were planning on attending the six events. Only 19 percent of individuals had visited only their location, and on average individuals had visited 3.18 of the 15 locations during the two week period specified. 12 percent of individuals had visited 6 or more of the locations, with one individual going to 13 out of the 15.

> 9 19.8 percent of the individuals interviewed in intercept locations in Liberdade and Saúde were from these two neighborhoods.

23

-  -
- 12 -

developing world for the past three years. Two dummy variables are also potentially included for projects in China and projects assisted by JBIC. In China, there are a number of prospective firms, thus likely leading to a larger number of _n_ , holding everything the same. Also, potential bidders may have different preferences to aid projects by multi- and bi-lateral donors.

Finally, the auctioneer, namely executing agency, is supposed to decide whether to adopt the substantial technical evaluation system, depending on technical specifications of each project _Xj_ as well as the expected degree of competition ( _nj_ ).

Therefore, the system has three endogenous variables: ln _b_ , ln _n_ and _D_<sup>_TE_</sup> in three equations (1) to (3).

The data are collected from 44 procurement auctions for 21 electricity projects in 13 countries assisted by the World Bank and a bilateral donor agency, Japan Bank for International Cooperation (JBIC)<sup>13</sup> for the period: 2000 to 2007.<sup>14</sup> The sample coverage is by no means unbiased by country; this is mainly because our sample partly comes from the available procurement data on large electricity projects financed by the Japanese development assistance, which has been concentrated in several countries in Asia (Table 2).

> 13 JBIC ODA Operations will be merged with the Japan International Cooperation Agency (JICA) in October 2008.

> 14 All bids and engineering cost estimates are normalized to the constant 2005 U.S. dollar terms.
Data on the use of mortgages show a large difference between income groups: in high-income economies 24 percent of adults report having an outstanding loan to purchase a home or apartment, while only 3 percent do in developing economies. Even within the European Union there is large variation in the use of mortgages, with very low rates of use in some of the new member states. For example, while 21 percent of adults in Germany have an outstanding mortgage, only 3 percent in Poland do (map 3.3). Such differences may in part reflect differences in housing finance systems across economies—such as in product diversity, types of lenders, mortgage funding, and the degree of government participation, all of which have been shown to affect <!-- Start of picture text --> Mortgages outstanding<br>Adults with an<br>outstanding loan<br>to purchase a home<br>or apartment (%)<br>0–5<br>6–10<br>11–20<br>21–30<br>31+<br>No data<br>Source: Demirguc-Kunt and Klapper 2012.<br>3.3<br>MAP<br><!-- End of picture text --> the availability of loans to individuals.<sup>4</sup> Collateral and bankruptcy laws that define legal rights of borrowers and lenders have also been shown to affect housing finance.<sup>5</sup> And to develop in the first place, a mortgage market requires formal property rights and an efficient framework to record ownership of property.<sup>6</sup>

1. Information is collected on the ownership of credit cards but not their use.

2. Djankov, McLiesh, and Shleifer 2007.

3. Data on the main purpose of outstanding loans were gathered only in developing economies because Gallup, Inc. enforces a time limit for phone interviews conducted in high-income economies, limiting the number of questions that can be added to the core questionnaire. Respondents chose from a list of reasons for borrowing so it is possible that reasons not listed (borrowing to start a business, for example) are also common.

4. IMF 2011.

5. Warnock and Warnock 2008.

6. De Soto 2000.

**40**

MEASURING FINANCIAL INCLUSION
dominant activity in the energy sector (Figures 2.3 and 2.4). Natural gas output is now about eight times the magnitude of oil output. In addition, growth in production of petrochemicals has mirrored growth in the production of natural gas. T&T has become the world’s leading exporter of ammonia and methanol, which together with urea make up the main petrochemical products in the country. We can still interpret this whole process as a confirmation that diversification, at least vertical diversification within the energy sector, has happened and can continue to happen in T&T. Looking ahead, there may also be useful knowledge to extract from what worked in the past and allowed the transformation of the energy sector. Whether the continuation of the diversification process for T&T should continue in the vertical path or whether it should also extend to the horizontal path, is a question policy makers ought to reflect on.

<!-- Start of picture text --> Figure 2.3: Trinidad and Tobago: GDP growth Figure 2.4: Crude Oil and Natural Gas Production<br>45 (2000=100)<br>900000<br>40 Real GDP Growth - Total<br>Natural Gas<br>Petroleum Sector 800000<br>35<br>Non-Petroleum Sector 700000 Crude Oil<br>30<br>600000<br>25<br>500000<br>20<br>400000<br>15<br>300000<br>10<br>200000<br>5<br>100000<br>0<br>0<br>-5<br>-10<br>Source: Central Bank of Trinidad and Tobago Source: Central Bank of Trinidad and Tobago<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Production (Barrels of Oil quivalent per Day) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

# _<u>2.2 Data</u>_

To put the experience of T&T into perspective, an attempt is made to construct a group of comparable countries and develop a consolidated net commodity exporter category. Countries that fit into this category are defined as net exporters of fuel, metals and ores for at least five years in the 2000s<sup>6</sup> . The list of control countries is restricted to non-OECD and non-European Union member countries as these countries share particular institutional characteristics. Additionally, given the limited size of T&T’s economy, countries with a population of more than ten million were excluded. This generated a list of fifteen control group countries located across regions (see Table 2.2). The rationale behind the creation of this comparison group is to have a set of countries similar to T&T at hand so as to: i) assess whether commodity-rich countries share particular characteristics in terms of diversification, and ii) evaluate whether specific policies have a differentiated impact between commodity exporters and commodity importers.

> 6 Commodity exports data come from the World Development Indicators database.

7
Figure A13: FDI Concentration by sector (Developing countries) <!-- Start of picture text --> 30<br>Manufacturing<br>Services<br>25<br>20<br>15<br>10<br>2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br>Top 10 manufacturing industries Top 10 services industries<br>Manufacture of motor vehicles Utilities<br>Manufacture of chemicals Construction of buildings<br>Manufacture of electronics Accommodation<br>Manufacture of electrical equipment Financial services activities<br>Manufacture of basic metals Land transport<br>Manufacture of coke Telecommunications<br>Manufacture of non-metal products Real estate activities<br>Manufacture of metal products Warehousing and support activities<br>Manufacture of rubber Retail trade<br>Manufacture of machinery Information service activities<br>0 20 40 60 80 0 20 40 60 80<br>Concentration Ratio Concentration Ratio<br>(5 largest investing MNEs) (5 largest investing MNEs)<br>2003-2007 2016-2022<br>Concentration Ratio<br>(5 largest investing MNEs)<br><!-- End of picture text --> Note: The analysis relies on greenfield FDI capital expenditure, without any exclusion from the full sample. The concentration ratio is calculated for two different groups of industries (manufacturing and services) in each year, without applying country weights (upper panel); and separately for industries at the 2-digit ISIC rev. 4, in sub-periods of several years, without applying country weights (lower panels).

25
14 of services provided, owing to the inadequacy of human resources in the sector and poor physical and financial access to health facilities, notably in rural areas.

**_B. Trends and Likelihood:_ a low probability of achieving MDG4** . Despite some progress – especially in child vaccination – during the 1990s, trends are not encouraging. Between 1988 and 2001, chronic malnutrition rates in children stagnated. Between 1990 and 2000, infant mortality diminished from 126 to 87 per 1,000 live births, but under-5 mortality rate remained virtually stagnant. In particular, progress slowed down since 1996 for child mortality and from 2000 for infant mortality. If past trends are maintained, Mauritania will remain very far from reaching the child mortality target in 2015, but closer to reach the infant mortality target (Figure 8).

**Figure 8. MDG 4: Current Situation and Trends**

<!-- Start of picture text --> Child mortality Infant mortality<br>145<br>130 137134.5 132 129.5127 124.5 128.5 125.25131.75 135 134.3133.6132.9132.2131.5130.8130.1 129.4128.1287 125110 126.0 122.1 118.2 114.3<br>115 122 113.4 110.4 106.5 102.6<br>95 98.7<br>10085 98.8 84.2 80 94.8 90.9 87.0 86.3 85.6 84.9 84.283.5 82.882.1 81.480.7 80.0 72.0<br>65 64.0<br>70 69.6<br>56.0<br>55 Past performanceGovernment planProjected status quo 55.0 50 Past performanceGovernment planProjected status quo 48.0<br>MDG-4 MDG-4 40.0<br>40 35<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> _Source:_ Authors’ calculations. _Source:_ Authors’ calculations.

**_C. Financial Needs:_ 0.6 percent of GDP on average per year.** The financing gap to reach MDG4 is the third widest gap, after poverty reduction (MDG1) and maternal mortality (MDG5). The cost of achieving this target for the period 2008-2015 amounts to 1.1 percent of GDP on average per year, corresponding to US$ 202.5 million in total. Once the allocation of government expenditure is taken into account, the country needs an additional total financing of 0.6 percent of GDP on average per year (Figure 1), which translates into US$ 110.9 million (Table 7). For a detailed, yearly cost breakdown, see Annex 1.

**Figure 9. MDG 4: Financing Gap**

<!-- Start of picture text -->
-13.0 -0.55<br>2008 2009 2010 2011 2012 2013 2014 2015<br>-17.0 -16.35 -17.46 -0.57 -0.58 -0.58<br>-17.57<br>-0.59<br>-21.0 -0.59 -19.04 -21.86 -0.61<br>-0.62 -24.49<br>-25.0 -0.64<br>-0.64 -27.40<br>-29.0 -0.67 -30.59 -0.67<br>Millions of US$<br>% of non-oil GDP (secondary axis) -0.69<br>-33.0 -0.70<br>Source:  Authors’ calculations.<br><!-- End of picture text -->

**Table 7. MDG 4: Cost, Budget and Financing Gap (2008-2015)**

|**Total Cost**|US$ 202.5 m<br>MRO 55,816.45 m|
|---|---|
|**Budget**|US$ 91.60 m|
|**Allocation**|MRO 25,280.47 m|
||US$ 110.90 m|
|**Financing**<br>**gap**|MRO 30,535.98 m<br>0.62% of non-oil GDP|
||US$ 6.56 p.c.|

_Source:_ Authors’ calculations.
Policy Research Working Paper 10572

# **Abstract**

This study investigates how the landscape of sex work in Dar es Salaam, Tanzania, evolved in the context of the COVID-19 epidemic. Using a mixed-methods approach, the analysis triangulates data from quantitative and qualitative sources to quantify shifts in income, demand, and client frequency and describe female sex workers’ perspectives on their work environment. The COVID-19 restrictions introduced in early 2020 resulted in dramatic decreases in sex work income, leading to extreme financial vulnerability, food insecurity, and challenges in meeting other basic needs such as paying rent. However, in a 2021 follow-up survey, sex workers reported the summer of 2021 as a key turning point, with the demand for sex work rebounding to closer to pre-pandemic levels. Notably, despite the average number of unique weekly clients not yet having fully rebounded, by 2021 the price per client and the total monthly sex work income had returned to pre-pandemic levels. This may potentially be explained by an increased number of repeat clients, which represented a larger proportion of all clients during the COVID-19 pandemic.

This paper is a product of the Development Research Group, Development Economics. 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/prwp. The authors may be contacted at at mbalampama@pactworld.org; ddewalque@worldbank.org; wdow@berkeley.edu; and rebeccahemono@berkeley.edu.

_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
<!-- Start of picture text --> Effect of domestic lockdown on imports<br>Import Value<br>2 2000<br>$&8 1000<br>is}<br>z 0<br>=§ 5000-1000<br>17 -15 -13 -11 9 7 5S -3 41 1 3 5 7 9 1°13 15 17<br>Months Relative to Lockdown<br>Import Quantity<br>© 2000<br>S8 1000<br>sy<br>F -10000<br>= 5000<br>7 -15 13-11-99 FT 5S 3 A 1 3 5 7 9 1° =13° 15 17<br>Months Relative to Lockdown<br>* Point Estimate —— 95% Cl<br><!-- End of picture text --> <!-- Start of picture text --> Effect of external lockdowns on imports<br>Import Value<br>@ 200<br>s3 100<br>oo<br>z petesiuueee wwawaass<br>258 -100-200 }<br>-22 -20 -18 -16 -14 -12 -10 8 6 4 2 0 2 4 6 8 10 12 14 16 18 20<br>Months Relative to Lockdown<br>Import Quantity<br>@ 200<br>gEa 100<br>oo<br>5 -100 peeeaturers WHEESEETSTTTT T TS<br>&§ -200 }<br>-22 -20 -18 -16 -14 -12 -10 8 6 4 2 0 2 4 6 8 10 12 14 16 18 20<br>Months Relative to Lockdown<br>* Point Estimate —— 95% Cl<br><!-- End of picture text -->
# **Table 7: Reasons for Avoiding Wage Cuts: The importance of product market competition**

|<br>Reg|./Agreement|Reduced<br>Effort/Morale|Reputation|Best<br>employees<br>leave|Hiring/training<br>cost|Hiring<br>difficulty|Implicit<br>contract|Employees<br>compare<br>wages|
|---|---|---|---|---|---|---|---|---|
|_Panel A: Competition(Refere_|_nce category:_|_Severe Competitio_|_n)_||||||
|Strong Competition|-0.063**|-0.051*|-0.040|-0.042|-0.061**|-0.055**|-0.082***|-0.085***|
||(0.017)|(0.054)|(0.110)|(0.110)|(0.016)|(0.028)|(0.002)|(0.001)|
|Weak Competition|-0.126***|-0.102**|-0.118***|-0.149***|-0.148***|-0.139***|-0.091*|-0.119**|
||(0.007)|(0.029)|(0.009)|(0.001)|(0.001)|(0.002)|(0.054)|(0.012)|
|No Competition<br>|-0.041<br>(0.614)|-0.361***<br>(0.000)|-0.293***<br>(0.000)|-0.433***<br>(0.000)|-0.346***<br>(0.000)|-0.414***<br>(0.000)|-0.252***<br>(0.001)|-0.504***<br>(0.000)|
|Observations|8720|9018|8834|8907|8677|8821|8524|8597|
|_Panel B: Union Coverage_|||||||||
|Coverage (% of labor force)|0.675***|0.028|0.064**|-0.070**|-0.042|0.021|0.077**|0.018|
||(0.000)|(0.389)|(0.035)|(0.030)|(0.178)|(0.492)|(0.019)|(0.562)|
|Observations|7636|7882|7719|7782|7574|7711|7405|7489|
|_Panel C:Labor Turnover_<sup>_~~1~~_</sup>|||||||||
|Turnover (%)|0.167***|0.083*|0.136***|0.197***|0.071|0.172***|0.060|0.144***|
||(0.000)|(0.073)|(0.002)|(0.000)|(0.103)|(0.000)|(0.182)|(0.001)|
|Observations|8068|8348|8181|8238|8029|8163|7880|7953|

Note: Ordered probit regressions. Each panel includes a different set of regressions. All specifications include country and sector effects, three size dummies, the share of workers with temporary contracts and three indicators of skills: the share of low-skilled blue collars, high- skilled blue collars and low -skilled white collars.<sup>1</sup> Labor turnover is defined as the sum of the workers joining and leaving the firm during the reference year as a percentage of total employment Robust p values in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

28
Masters students. Most of the field staff are located in developing countries, but 20 percent list their location as the U.S., either because they are U.S.-based staff for IPA, or because they are temporarily in the U.S. before heading to a field office.

The baseline survey asked about 12 working papers released in 2010 on the BREAD working papers website, a leading source of working papers in development economics. Even with self-reports of having read the paper, and counting having seen a seminar on the paper as having read it, the majority of survey respondents have not read most papers. The assistant professors in development have on average only read 2.2 out of the 12 papers, and 22 percent have read none. PhD students have read only 1.4 of these papers on average, and field staff and World Bank economists outside the research department less than 1 of the 12 papers. Given these low levels of reading working papers, there certainly seems to be a role for other forms of dissemination about new results.

# _4.2 Self-reported Uses of Economics Blogs in Survey Data_

The second panel of Table 3 provides more detailed information on how individuals read and use blogs. Readership of economics blogs is high among all 5 groups surveyed, with between 76 and 84 percent of those surveyed having read an economics blog in the past 6 months. Female graduate students are significantly less likely to read blogs than males (p<0.02), although there is no significant difference in blog readership by gender among assistant professors, field staff, and World Bank economists. Among those who don‟t read economics blogs, the most common reasons given as the most important for not reading them were lack of time, and lack of knowledge about what economics blogs are out there.

Many of those who read blogs do so only sporadically – only 40 percent of graduate students and 34 percent of assistant professors who read blogs do so at 16