rafmacalaba/data-use-mentions-extended huggingface dataset:
The World Bank Economic Review 13

# Attitudes

Table 4 presents results on attitude indicators. Columns (1) and (2) present results on attitudes towards the use of condoms, (3) and (4) on conservatism with respect to age of initiation of sexual activities, (5) and (6) on attitudes toward denouncing and seeking help in the event of sexual abuse, and columns (7) − and (8) show results for an overall index of attitudes, containing all variables used in columns (1 6).

For the general attitudes index the study finds significant effects of 0.24 standard deviations in terms of attitudes one week after the intervention and 0.17 standard deviations six months after. Significant effects were again found for each subindex six months after the intervention, and the pattern of positive effects on all partial indices is again apparent.

The training was successful in generating more positive attitudes towards the use of condoms at the first follow-up (0.17 standard deviation) and at the second follow-up (0.10 standard deviation). In the sexually conservative attitudes subindex composed of the following variables: (a) indicates that individuals their age should not have multiple sexual partners in the same month; (b) thinks it is too early for individuals of their age to engage in sexual activities; and (c) feels confident he/she will be able to wait to have sex until emotionally prepared to do so, teens scored 0.13 standard deviations higher six months after the intervention. Column (6) shows that treated teens are 0.11 standard deviations more likely to agree with the need to report cases of sexual abuse to the authorities and the need to seek medical attention in such situations. For attitude indicators, there was no consistent evidence of spillovers across classrooms.<sup>18</sup>

# Sexual Activity and Condom Demand

Table 5 reports results on sexual activity and condom voucher redemptions. Specifically, columns 1–3 report effects of the
.87|100|6.55|7.87|28|57.6|100|
|**Complete Senior High**|||||||||||
|Column|9.27|11.2|11.4|14.5|10.3|17.1|16.1|17.5|14.2|15.5|
|Row|56.7|13.5|17.9|11.9|100|6.45|7.48|33.5|52.6|100|
|**University**|||||||||||
|Column|7.52|18.5|24.3|35.2|13.9|21.4|24.9|27.5|27.7|27.1|
|Row|33.9|16.5|28.3|21.3|100|4.6|6.62|30|58.8|100|
|**Total**|||||||||||
|Column|100|100|100|100|100|100|100|100|100|100|
|Row|62.9|12.4|16.2|8.43|100|5.82|7.2|29.6|57.4|100|

_Source:_ Authors’ computations based on Economic Census and Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo data.

_Note: Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and
started or increased their use of digital technologies, and 61% report having online sales. Only 22% of firms report having received support from the government, which varied widely across the three rounds.<sup>13</sup>

# **5. Results**

# **5.1 Informal firms tend to be smaller, in sectors with low entry costs (retail and services) and also younger**

Informality is highly correlated with small businesses (see, for example, La Porta and Shleifer, 2014; Ulyssea, 2018, 2020). The BPS data for South Asia shows that the share of microenterprises (fewer than nine employees) in the formal sector, after controlling for firm characteristics such as sector, country, time, and age, is 38%. This contrasts sharply

> 13During the first round, on average, only 5% of the firms received support, while for the second round, support increased to around 20% of the firms.

11
both Shanghai and Shenyang.<sup>2</sup> If current trends persist, we project large cumulative losses in human life through 2020. Figure 2 illustrates the grim stakes: Beijing could lose nearly 80,000 people, Chongqing 70,000 and other major cities could suffer losses in the tens of thousands.

> **2** These estimates are based on the dose-responsr **e** lation established for atmospheric S02 in Beijing and Shenyang by Xu, et. al. (1994). This relation is combined with monitoring data on S02 concentrations to produce estimated individual probabilities of premature mortality by city. City-specific probabilities are multiplied by **current** population estimates to produce estimated annual deaths.

3
# **Decomposing the Great Trade Collapse: Products, Prices, and Quantities in the 2008-2009 Crisis**

**Mona Haddad, Ann Harrison, and Catherine Hausman**<sup>**1**</sup>

> 1 The authors can be contacted at mhaddad@worldbank.org, ann.harrison@berkeley.edu, and calmirall@berkeley.edu. We thank Hiau Looi Kee, Brent Neiman, Daria Taglioni, and Hans Timmer for valuable comments. We thank Caroline Freund and Matias David Horenstein for the use of the Tradewatch Data. This paper is part of a World Bank research project on exports and growth supported in part by the governments of Norway, Sweden and the United Kingdom through the Multidonor Trust Fund for Trade and Development. This article reflects the views of the authors and not the World Bank.
_Results: significant at the 5%_<br>_level for both number of_<br>_privatisations and proceeds from_<br>_privatisation_|-average annual electricity<br>consumption (kWh) (SE)<br>_Result: insignificant_<br>a dummy for the political<br>orientation of a county’s<br>privatisation (CL)<br>_Result_:_significant and_<br>_positive at the 10% level_<br>_for number of_<br>_privatisations_<br>an institutional credibility<br>index (CL)<br>_Result: significant at the_<br>_5% level for the percentage_<br>_of privatised stock_<br>and financial market<br>liquidity indicators (CL)<br>_Result: significant at the_<br>_10% level for privatisation_<br>_proceeds_|Privatisation data on 48<br>power generation companies<br>from 19 developed and<br>developing countries for the<br>1977-1997 period. Source:<br>Privatisation International<br>(1997) and original<br>documents for some countries<br>not included in this source.|
||_H1: vertical integration has_<br>_a negative impact on_<br>_aggregate proceeds of_<br>_privatisation._|-the per country aggregate<br>proceeds from total sales<br>(PI) (US$ 96)||||
||_H2: a more regulated_<br>_environment is conducive to_<br>_privatisation; therefore,_<br>_regulation has a positive_<br>_impact on the number of_<br>_privatisations._|-total number of sales in<br>electricity generation in a<br>given country (PI)|-a regulatory index (which takes<br>the maximum value when a<br>country’s regulatory setting<br>entails: regulated TPA, a<
examining in intra-day trading patterns by level of active management and by documenting the potential costs of passive investing.

In our paper, instead of trying to identify informational advantages of local versus foreign investors,<sup>2</sup> we document a particular type of disadvantage —the cost of index investing in a small equity market. We would expect for similar findings in other developing countries, or in small (illiquid) stocks in developed countries. Overall, our results challenge the conventional wisdom that passive funds are, unconditionally, the best alternative for investors. While this could be the case for U.S. households investing in their home country (French, 2008), it is possible that for global strategies, index investing might deliver disappointing results.

> 2Other examples in this literature rely on lower frequency data, e.g. monthly or quarterly holdings. Most recently, Ferreira et al. (2015) use a sample of 32 countries to compare the performance of institutional investors in stocks of their home country to the performance of money managers located in other countries.

4
Figure 4. Composition of debt, external debt and credit ratings <!-- Start of picture text --> A. Composition of debt in advanced economies B. Composition of debt in EMDEs<br>Percent of GDP Percent of GDP<br>320 210<br>External debt Domestic debt External debt Domestic debt<br>240<br>140<br>160<br>70<br>80<br>0 0<br>2010 2012 2014 2016 2018 2020 2010 2012 2014 2016 2018 2020<br>C. External debt D. Sovereign credit rating downgrades<br>Percent of GDP World Percent of countries<br>180 Advanced economies 50<br>2010-19 2020<br>EMDEs<br>40<br>120<br>30<br>20<br>60<br>10<br>0<br>0 Advanced EMDEs LICs<br>2000 2005 2010 2015 2020 economies<br><!-- End of picture text --> Sources: Kose et al. (2017, 2021); World Bank.

A.B. Nominal GDP weighted averages for up to 39 advanced economies and 152 EMDEs with available data for government, private, and external debt. Domestic debt is defined as a difference between total (i.e., government and private) debt and external debt.

C. Nominal GDP weighted averages for up to 174 countries, including 35 advanced economies and 139 EMDEs. D. Share of countries in each country group with sovereign credit downgrades during 2010-19 (average downgrade per year) and in 2020 (i.e., downgrade from 2019). Sample includes up to 144 countries, including 41 advanced economies, 103 EMDEs, and 6 LICs, with available data for foreign currency long-term sovereign debt ratings in two consecutive years.

24
results for contracts should be viewed as suggestive because the test there is lower-powered and some of the point estimates are large. Each survey wave was collected in all four quarters of the year, allowing us to separately examine short-term effects (in the remainder of 2013) and medium-term effects (in 2015 and 2016) of the reforms that followed the event. While we believe these medium-term results are important, we acknowledge that by several years after Rana Plaza, it is more likely that there has been a shock that differentially affected garment workers in treated districts, and so the medium-term results should be viewed as somewhat more suggestive than the 2
variability in work across weeks). Table 3 shows that agricultural labor does not take place every day, nor does it even necessarily take place every week. However, these facts alone need not undermine the success of inference if there is a regular cycle or pattern to the work that respondents can use as a relatively accurate rule of thumb.

To uncover what, if any, labor patterns exist during the season, we examine the weekly data.<sup>18</sup> First, we calculate the modal days spent farming during those weeks in which there was any farm work. In table 4, for each mode of days worked, we show the distribution of days worked. The distribution of workdays is essentially bimodal: many people generally work in agriculture once a week (24 percent), while another group works six times a week (29 percent). Even though farming is the predominant activity in the region, the farming workweek is short, and the majority of people farm little each week.<sup>19</sup> There is also substantial deviation from the modal work pattern. For example, of those with a modal farm workweek of six days, fewer than half (42 percent) of their weeks entailed six days of work. For these people, 15 percent of their working weeks consist of five working days, and 9 percent entail only one working day. The proportion of all workweeks conforming to the people’s modal workday (represented by the diagonal in bold in the table) usually represent under half of the weeks, except for mode 1 persons, who work one day a week in 56 percent of their working weeks. The proportion of weeks not conforming to the modal work pattern are relatively evenly spread from one to seven working days. From these data, it is clear that even a person’s typical workweek is not that typical and that their work patterns in an atypical week vary widely.

The case with respect to hours, however, is somewhat
# **4. Discussion**

The sampling strategies described in this paper were designed with the aim of producing a comprehensive longitudinal household survey of social, economic, and health indicators from a representative sample of Rohingya displaced and the host Bangladeshi population in Cox’s Bazar district. This effort was important to set an infrastructure that allowed for the representative tracking of both hosts and displaced in later years. In particular, with the COVID-19 pandemic, the existence of this representative survey with detailed contract information for all respondents allowed to implement a series of phone-based follow-ups that were critical to inform about the impacts of the pandemic in Cox’s Bazar.<sup>36</sup> This paper describes the strategy implemented to design a representative survey of this nature, using a combination of census data and publicly available humanitarian and geospatial data. Two different data collection exercises were carried out to assess the prevalence of Rohingya displaced outside the camps to inform the sampling strategy for Rohingya displaced. Administrative data from humanitarian agencies were used to design the sampling frame within the camps. Drone imagery and digital maps were used to implement the listing within camps and host communities. Two different open-source data sets were used to inform the design of the host strata and help generate the host enumeration areas. Government data such as the 2011 population census and administrative shapefiles were also used.

This paper aims to share these different strategies and lessons learned for researchers interested in designing representative surveys in displacement contexts. This is particularly relevant when attempting to do such exercises close to the time the displacement occurs, as it is unlikely that national sampling frames are updated. The analysis also highlights the importance of listing as a way to update population counts, and some of the potential risks of key informant approaches to estimate population counts. We also highlight the value of humanitarian data registries to inform ex-ante selection probabilities in contexts where those displaced are largely located in camps or areas covered by those data.

Although it was possible to solve many data gaps in this case,
methodology we propose for identifying firm-level demand shocks has some advantages relative to the shocks previously exploited. Although exchange rate movements and innovations leading to patents are difficult to forecast, variation over time is likely to reflect in part economic fundamentals and policy choices. These developments can be monitored and analyzed by firms, which may therefore partly respond in anticipation.<sup>6</sup> Identifying the effects of unexpected exchange rate movements is further complicated by the fact that they are subject to incomplete passthrough, which may be influenced by the currency in which trade transactions are denominated and vary across firms and markets (Amiti, Itskhoki, and Konings, 2014; Gopinath, Boz, Diez, Gourinchas, and Plagborg-Moller, 2020).<sup>7</sup> Unexpected GDP shocks in destinations are arguably less subject to this concern.<sup>8</sup> Our paper further speaks to a recent strand of work highlighting the role of internationally active firms in the international transmission of business cycles. Using French firm-level data, di Giovanni, Levchenko, and Mejean (2018) show that trade linkages with a foreign country are associated with a significantly higher correlation between a firm and that foreign country, which has significant macro implications. In related work, di Giovanni, Levchenko, and Mejean (2020) document that larger French firms are significantly more sensitive to foreign GDP growth. Using a quantitative model, they find that this granularity accounts for 40 to 85% of the impact of foreign fluctuations on French GDP. Focusing on exporting firms, we contribute to this literature by estimating the response of firm-level sales, employment, investment and wages to both expected and unexpected fluctuations in foreign GDP growth. In addition, we use employeremployee data to estimate how each of these shocks impacts the different components of worker compensation within and across heterogeneous exporters. As Grigsby, Hurst, and Yildirmaz (2021) emphasize, measuring this nominal wage adjustment in micro data is key for disciplining macroeconomic theories of nominal wage rigidity.<sup
The data shows interesting regional patterns, especially in productivity differences between the state and non-state sector (see charts in the Appendix). Coastal and high-income provinces tend to have a larger non-state sector. The productivity gap- labor and capital-between state and non-state sectors is larger in coastal provinces, such as Guangdong, Shanghai, Jiangsu, Zhejiang and Shandong, than in the inner provinces.

We also estimate "average" provincial production functions of the Cobb-Douglas type in the state and non-state sectors, and for various years (see table 4). This allows for the computing of the marginal efficiencies of capital and labor. The results for 1996 and 1997 13 and for the pooled 1994-1997 data show that the non-state sector has a higher marginal productivity of labor and capital than the state sector. The gap in capital performance has always been large - in the order of two. The gap on the labor side has been small in the past, but has risen a lot recently, indicating perhaps the difficulty that the private sector is encountering in finding skilled labor.

With the available information, it is possible to compare roughly marginal productivity and the value of compensation, in the two sectors. Comparing tables 2 and 4, it is apparent that workers are overpaid in the state sector, and perhaps underpaid in the non-state sector (compared to their marginal productivity). Based on 1997 data, wage and non-wage income per state worker is 53

> '3 We excluded 1995 estimates due to poor quality of data that complied by China Yearbook. Here we only report 1996 and 1997 estimates, the previous years of labor productivity performance have been estimated in other studies that are not reported here.

10
# **5 Technology and Income Taxes**

Income taxes paid by individuals, companies as well as employees are an important revenue source for African economies, representing about 33 percent of total revenues (UNU-WIDER Government Revenue Dataset, 2021). Unlike Pay-As-You-Earn (PAYE) taxes that are deducted at source on wages and salary, corporate income and other personal income taxes are typically self-declared, providing more opportunity for evasion (Kleven et al., 2011). This makes them particularly vulnerable to broader structural issues like high informality and low tax morale that to an extent also affect other tax categories.

One key obstacle to optimizing income taxes is informality: many individuals and companies operate completely outside the purview of the tax authority. The existence of an informal economy generates horizontal inequities and economic distortions that potentially undermine tax perceptions and the moral fiber of a society (Joshi, Prichard and Heady, 2012). Within the informal sector, most harm to revenue is produced not by micro-enterprises such as street vendors, but by hard-to-tax professionals such doctors, lawyers, architects, etc. (Ogembo, 2020). Even among those formally registered, tax minimizing schemes by professionals and high net worth individuals determine large revenue losses and damage to the fairness of the tax system (Keen, 2012; Kangave et al., 2016; Santoro and Waiswa, 2022).

A second major challenge for income taxes is that even among taxpayers that are formally registered, there is a high level of non-compliance: the vast majority of registered taxpayers in Africa either do not file their returns or zero-file (Santoro, 2021; Mascagni, Dom and Santoro, 2022; Almunia et al., 2022). In addition, African taxpayers’ morale and willingness to pay are remarkably low. Different factors associated with low willingness to pay include: distrust of the state (Bratton and Gyimah-Boadi, 2016; Isbell, 2017), dissatisfaction with public services (Blimpo et al., 2018), perceived unfairness of the tax system (D’Arcy,
then make it possible to provide policy makers with holistic results that lead to more informed resource allocation decisions. It would also enable benchmarking across countries.

A further area of research stems from the scarcity of data characterizing specific health care services provided at known facility locations. While initial efforts have mapped travel time to basic health care globally (Weiss et al., 2020), comprehensive assessments of the availability of specific health care services, such as care for child birth or the treatment of HIV/AIDS, are less common due to lack of data. This knowledge gap represents a substantial opportunity for enumerating and ultimately addressing disparities in health care access. Where high-quality data are available, such as the Malawi 2013-14 Service Provision Assessment (Ministry of Health (MoH) [Malawi] & ICF International, 2014), analyses quantifying spatial disparities in access to services such as pediatric emergency care are possible (Hulland et al., 2019).

Additional research that would inform assessments of disparities in health care include those focused on characterizing health care-seeking behaviors and improving the quality, completeness, and timeliness of data captured in national health care information systems. Refining our understanding of health care-seeking behavior and preferences is required for understanding how far people are willing to travel to seek care (Noor et al., 2006), how care-seeking behavior varies according to the health care service required (for example, because of the desire for privacy), and which facilities care-seekers will utilize (e.g., hospitals vs clinics). Estimating these parameters will require detailed surveys that catalogue patient behavior while also capturing their movements through detailed travel histories or GPS-equipped mobile devices. Methodologically, assessments of care-seeking may draw upon health care catchments modeling (Arambepola et al., 2020; Gao et al., 2016), which could potentially be approximated using URCA or FEA data. Lastly, continued improvement of health care information systems will be critical for accurately estimating the causes and spatial patterns of morbidity and 23
>19</sup> More information on the methodology is provided in the appendix.

A total of 533 interviews were completed. For comparison, the World Bank 2019 Enterprise Survey for West Bank and Gaza consisted of 365 interviews, 205 of which were in the West Bank. Given our main focus on ICT, this industry is over-represented in the survey (table 2), representing 23.6% of the sample or 126 interviews and 14% of the existing establishments. The remaining 407 interviews cover the manufacturing sector (87 interviews), the retail and wholesale trade sector (202 interviews) and other industries (118 interviews).

As in the population of reference, almost half of the ICT firms interviewed are retailers or repairers (table 3), while the other half consists of firms conducting activities such as computer programming, telecommunications, information services, and other ICT related activities.<sup>20</sup>

> 17Sectors excluded from the analysis are: agriculture, mining and quarrying, electricity and water supply, public administration and defense, compulsory social security, activities of households as employers, undifferentiated goods- and services-producing activities of households for own use, activities of extraterritorial organizations and bodies. Retailers of ICT products are excluded from the wholesale and retail trade. Repairers of ICT products are excluded from other industries.

> 18We will refer to the wholesale and retail trade sector as retail.

> 19In the second phase, call-backs and 65 additional interviews were conducted.

20Businesses conducting maintenance and repair often also sell computers and other hardware components as secondary economic activity.

12