rafmacalaba/datause-encoder-data huggingface dataset:
HIV-negative at baseline – the group that constitutes 95% of our sample. However, the HSV-2 prevalence at follow-up among the small group of young women who received HIV-positive test results was 23 percentage points higher than HIV-positive individuals who did not receive HIV tests. While this result warrants some caution given the small sample size (N=73), the finding is at odds with most previous studies, which suggest that those who learn they are HIV-positive adopt safer sex practices.<sup>7</sup> Consistent with economic theory, the perverse effect of testing is stronger among those surprised by HIV-positive tests, i.e. those who reported no chance of being infected with HIV at baseline. Also in line with theory, we find that testing can increase investments in one’s own human capital. Those surprised by HIV-negative tests, i.e. those who report some chance of HIV infection at baseline but discover that they are HIV-negative, perform significantly better in achievement tests – a finding supported by an increase in self-reported school enrollment among the same subgroup.

Our results suggest that home based HIV testing and counseling can lead to important changes in human capital investments by providing vital health information and add to a larger literature on the effects of information-interventions (Nguyen 2008; Dupas 2010; Jensen 2010; Goldstein et al. 2013; Oster, Shoulson, and Dorsey 2013). Furthermore, our finding of increased STI risk among HIV-positive individuals who receive HIV tests serves as a caution to the assumption that such individuals will adopt safer sexual practices upon learning their serostatus. Finally, our study points to the importance of using objective outcome measures rather than self-reported behavior change when studying treatments that may differentially alter social desirability bias.

In the next section, we describe the setting of our study. Sections III and IV describe the methods and our findings. Section V provides a concluding discussion that includes the limitations of our study.

> 7 The lone exception is a recent study by Gong (2013), who used biomarker data from a VCT experiment in a highrisk urban sample in Kenya and Tanzania and found a similar increase in STI incidence among those infected with HIV.

5
_will all be exposed to the virus eventually. The best way to prepare yourself is to get fully vaccinated because it greatly reduces the risk of hospitalization and death’_ ).

**Variants framing (** **_k = 6_ ,** **_n_ = 3,336).** This framing focused on the vaccine’s efficacy against new variants, to account for concerns at the time about whether the vaccines would be protective against different strains of the virus (e.g., _‘New variants like Omicron and Delta can be worrying, but the best evidence so far indicates that vaccines are still highly protective against serious illness and death from COVID-19’_ ).

# **2.4. Measures and Indices**

## **_Primary Outcome_**

**Vaccine Intention** **_._** The outcome of interest is self-reported vaccination intention, which was measured for respondents who reported that they were not yet vaccinated at the time of the study. Vaccine intention was measured as a categorical outcome by asking a question “ _Are you willing to/do you plan to get the COVID-19 vaccine_ ?” Response options included “Yes,” “Unsure,” and “No”. For our analysis, we recode these responses into a binary classification of “Yes” or “No/Unsure”.

## **_Potential Individual-Level Moderators_**

**Gender.** Participants were asked to indicate whether their gender was male, female, or other. In some country contexts, it was considered inappropriate to include the ‘other’ category, and participants were simply asked if they were male or female.

**Age.** Age was measured categorically, asking participants to indicate whether they were 18-29, 30-39, 40-49, 50-59, or 60+.

**Education.** Participants were asked to indicate the highest level of education they had completed – No Education, Primary Education, Secondary Education, or Tertiary Education.

**Health worker.** Participants were asked to indicate whether they were employed as a health worker or not.

## **_Potential Study-Level Moderators_**

Based on discussion with public health and behavioral science experts and a review of the literature, we explore the following potential moderators: the month of study launch, the GDP per capita of the study country, the vaccination rate in the country at the time of the study, and trust and vaccine perception measures from the Wellcome Global Monitor Survey (2018). The Wellcome Global Monitor survey is a survey of attitudes toward science and health based on a survey with over 140,000 people in more than 140 countries. Since these data from the Wellcome Global Monitor survey are measured at the individual level, we aggregate these measures to the country level by taking their mean value. This approach is consistent with practices in cross-cultural and health research (e.g., Gelfand et al., 2011; Weinberg et al., 2021), including research estimating generalized trust at the country level (e.g., Marozzi, 2014; Reeskens & Hooghe, 2008; Robbins, 2012). See Appendix D for more details on the empirical rationale for country-level aggregation of the Wellcome Global Monitor survey indicators. The Wellcome Global Monitor 2018 data were not available for Belize, Djibouti, Jamaica, Papua New Guinea, and West Bank and Gaza.

10
|||5.2.5<br>Policy option 3: nuclear families, school-attendance subsidies . . .<br>82|
|---|---|---|
||5.3|Other Variations and Robustness . . . . . . . . . . . . . . . . . . . . . . .<br>83|
|||5.3.1<br>Mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>84|
|||5.3.2<br>The transmission factor_z_ . . . . . . . . . . . . . . . . . . . . . . .<br>87|
|||5.3.3<br>A delayed policy response . . . . . . . . . . . . . . . . . . . . . .<br>89|
|**6**|**Con**|**clusions**<br>**92**|
|**7**|**App**|**endices**<br>**95**|
||7.1|An Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>95|
|||7.1.1<br>Household decisions . . . . . . . . . . . . . . . . . . . . . . . . .<br>95|
|||7.1.2<br>Steady states<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>97|
||7.2|Proof of Proposition 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>97|
||7.3|Proof of proposition 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>100|
||7.4|Proof of Proposition 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>103|
||7.5|Proof of proposition 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>105|
|**8**|**Figu**|**res**<br>**109**|

4
no significant difference between male and female owners of restaurants/businesses in the tourism sector (which includes teashops and restaurants), as can be seen in Table 4.

**Women entrepreneurs own significantly fewer bank accounts.** While the banking system in Myanmar has improved considerably since the country opened up, a very high proportion of SMEs do not have a bank account, more so amongst women. On average, 20 percent more male-owned businesses have a bank account than female-owned businesses. As having a bank account is a requirement to apply for a bank loan, this also affects women unfavorably when trying to access credit to expand their businesses. Indeed, this highlights a gender-gap in the use and access of financial instruments in Myanmar. Overall, as compared to men, women face structural barriers as they borrow considerably less, are more likely to be asked for higher collaterals (61 vs 50 percent of men who have borrowed), are less likely to have bank accounts and have smaller businesses.

**There are no major differences in the adoption of ‘good’ business practices between male and female entrepreneurs.** When it comes to adopting business practices such as advertising, negotiating with suppliers, record keeping, and preparing a budget and cash flow statements, there is no gender-gap. This is an interesting finding, as in a previous study conducted in 2017,<sup>29</sup> focusing only on the manufacturing sector, it was found that female entrepreneurs are less likely to apply as many beneficial business practices as their male counterparts. Overall, in our sample, the adoption of good business practices is high, with over 50 percent of businesses adopting at least one.

**Women entrepreneurs spend twice as much time as male business owners doing household chores.** In a month, women owners will spend 24 hours more than men doing household chores, again indicating the burden of cultural norms imposed on female entrepreneurs. This is consistent with studies on the gender barriers in the country. One of the most consistently raised issues by women trying to enter the business ecosystem in Myanmar is their excessive burden of domestic and household responsibilities.<sup>30</sup>

**Despite women business owners facing discrimination, gender is not perceived as a barrier to growth by women entrepreneurs themselves.** This can probably be imputed to the fact that gendered roles are so engrained in Myanmar society that the different opportunities one has depending on their gender is accepted as the inviolable norm. Despite women entrepreneurs carrying out twice as much household related work than men, they do not report their gender as a barrier to the growth of their business (Table 3b Appendix A). In our sample, only 18% of women reported their gender as some sort of barrier, with the vast majority reported gender as not being a barrier at all.

> 29 Myanmar MSME Survey, 2017.

> 30Ibid.

16
likely to be ISO-certified and 17.1% more likely to use the web, while joint ventures are 3.2% more likely to be ISO-certified and 13.4% more likely to use the web in 2005. This finding is consistent with that in Clarke (2001) and is likely to reflect the fact that these firms are embodied in international networks requiring the frequent use of communications technology and that they compete in global markets which require the use of state of the art technological know-how through internationally recognized technical standards. Exporters are also significantly more likely to be ISO-certified or use the web. In column (4), an increase in the firm’s export share by one standard deviation (21.3%) is associated with an 8.1 % increase in the likelihood of web-use, all else constant. This finding likely reflects the fact that exporters learn about new technologies through their interaction with foreign buyers. While the strong positive association between the participation in export markets and technology adoption is found across the three groups of countries in Table 3, the effects of foreign ownership are strong only in CIS countries and in South Eastern European countries and Turkey. In EU-8 countries the knowledge advantage of foreign-owned firms relative to domestic firms is less marked.

Note again that our findings on access to international knowledge do not suffer from multicollinearity problems since the coefficients for the BEEPS 2002 and the BEEPS 2005 samples are qualitatively unchanged when we estimate a variant of Equation (2) where we exclude the factors linked to complementary inputs and market incentives.

# **_IV.4 Input Markets, Market Incentives, and Access to International Knowledge - Panel Results_**

The results discussed so far suggest important correlations between complementary inputs, market incentives, and access to international knowledge on the one hand, and ISO certification or web-use on the other hand. However, since the results are based on cross-sections of firms in ECA countries in 2002 and in 2005, they do not allow us to interpret the estimated effects as causal. Problems of omitted firm characteristics as well as reverse causality could bias the estimated coefficients in Tables 2-4. While we do not have valid instruments to identify causal effects of the correlates of technology adoption, we observe a subset of our firms at two points in time. Therefore, we are able to apply panel data estimation techniques - probit regressions with random effects – to the BEEPS panel. The corresponding results are shown in Table 5. In this case, we estimate the determinants of ISO certification and of web-use controlling for unobserved firm invariant effects. As mentioned earlier, the two drawbacks of this approach are the small panel dimension of the data and the smaller sample size. The probit regressions with random effects allow us to analyze how changes

- 15 -
improvement in child sex ratios only if this is indicated in the comparison of the 19952005 with the 1990-2000 trends as well as in the quinquennial trends, but not if it is indicated only in one quinquennial period.

The data show that most of China’s subnational units experienced a decline in the rate of masculinization of child sex ratios.<sup>13</sup> These are indicated in bold italics in Table 3. Together they comprised nearly 88 percent of China’s population in 2000, and more importantly, include the provinces which have a history of high son preference and correspondingly high child sex ratios.

It is especially striking to note that in many cases, the estimates are _negative_ , indicating not merely a reduced rate of increase of masculinization — it indicates that the child sex ratios actually became more feminine than in the preceding period. This is true of half the provinces in the East region (Zhejiang, Fujian, Shandong); Guangxi in the Central South region; and Beijing, Inner Mongolia, and Liaoning in the North and Northeast. In some of these, the pace of normalization of child sex ratios was really striking. Altogether, the child sex ratios became more feminized in subnational units totaling 24 percent of the country's population in 2000.

In provinces totaling another 64 percent of China’s population in 2000, the rate of masculinization has decreased — in many cases quite sharply. This includes all the provinces in the East and Central South regions which did not register an actual feminization; much of the Southwest, Northwest and North regions; and Jilin in the Northeast.

Child sex ratios continue to rise in areas comprising ten percent of the population. These  include the large metropolises of Tianjin and Shanghai perhaps partly because loosened migration policies have resulted in inflows of people from rural areas with higher son preference. They also include Heilongjiang in the Northeast, and the minoritydominated provinces of Guizhou, Qinghai and Xinjiang in the Southwest and Northwest. With the exception of Guizhou, all these provinces/metropolises have consistently recorded child sex ratios well below the national average, and have therefore not been the major contributors to China’s high child sex ratios.

The raw data on child sex ratios in China’s provinces/metropolises are on the righthand side of Table 3, and grey highlighting indicates the period(s) with the highest recorded sex ratio. These data indicate that in over half the provinces, child sex ratios peaked in 1995 or 2000, followed in some cases by a steady decline and in others by fluctuation around the peak.

The provinces in the East and Central South regions of China have had the highest child sex ratios in the country historically (implying the highest levels of son preference), and the ratios soared in the 1990s. With their large populations (57% of China’s population in 2000) these provinces have contributed heavily to China’s total “missing girls”. A reduction or even reversal of this trend in these provinces is thus suggestive of an incipient turnaround for China as a whole.

7
a phone but using a dierent mobile provider. Based on the Listening to Senegal Survey (LSS) done in 2014, 12.3% of adults age 18 and over never use a mobile phone. Sonatel is one of three mobile phone providers. Based on the LSS, Sonatel is the main provider for 80% of those surveyed with a cell phone, and 88% of those with a cell phone have a Sonatel SIM card (Agence Nationale de la Statistique et de la Démographie - Ministére de l'Economie 2014). Therefore, only around 12% of adults with a SIM are excluded. Combining the two types of missing adults, 77.2% of adults are represented in these data.

I conduct checks to see how representative the data are for the two types of users that are not includedthose without a SIM and those with a SIM from a dierent provider. I use the DHS survey to compare mobility patterns between women with and without a cell phone in the household.<sup>17</sup> There is no statistical dierence in whether a trip longer than a month was taken between those with and without a cell phone. The women with a cell phone in the household have only a slightly higher average number of trips taken in the last year. Using the LSS, I compare several indicators between people that have a Sonatel SIM card and those that only have a SIM card from another provider. Based on t-tests, there is no signicant dierence between these categories of individuals based on type of primary activity (p-value=0.344), sector of primary activity (p-value=0.863), source of drinking water (p-value=0.868), non-food expenditures over past month(p-value=0.193), and non-food expenditures over past 12 months(p-value=0.115). The third missing group is children. If children travel, they are likely traveling with adults (though they might be traveling less on average if they do not always travel every time an adult travels). Since there is no data comparing the short term movement of adults and children in Senegal, I use an across the board weight of 1.4 to represent the full population and to get an upper bound on movement. The weighted data overestimates total movement and underestimates the impact of each trip. Unweighted results are provided in the robustness section as an upper bound on the eect size.

> 17The survey does not contain data on men.

14
# **Table 21: The effect of baseline socio-economic status (using individual SES)**

||Math|English|
|---|---|---|
|WSD|-0.14*|-0.08|
||(0.07)|(0.08)|
|Grant|-0.14*|-0.17*|
||(0.08)|(0.08)|
|Child’s SES|0.07*|0.05|
||(0.04)|(0.04)|
|WSD×Child’s 2011 SES|-0.07|0.04|
||(0.06)|(0.34)|
|Grant×Child’s 2011 SES|0.01|0.03|
||(0.04)|(0.06)|
|6<sup>th</sup>Grade Dummy|0.68***|0.73***|
||(0.04)|(0.04)|
|Constant|-|-0.18***|
||(0.17)|(0.06)|
|Observations|2289|2289|

Notes: Robust Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. Child’s 2011 SES is a composite measure of the child’s socio-economic background as measured in 2011. The variables included in the factor analysis are the quality of the housing (floor, roof, walls, electricity), the assets (phone, motorcycle, fridge, car), and the occupation of the father – Higher values of the factor associate with higher economic status. The treatment is not correlated with the measure of SES in 2011.

37
**Table 2 Risk indices for water and sanitation derived from household survey responses. Risk levels are presented in ascending order of risk.**

|**Risk level**<br>**Description**|
|---|
|Risk associated with household source of water|
|0<br>Piped to premises|
|1<br>Public standpipe/tap or borehole within 100m|
|2<br>Public standpipe /tap more than >100m from house|
|3<br>Well more than 100mm from the house|
|4<br>well within 1-10m of a pit latrine|
|5<br>well within 11-40m of a pit latrine|
|6<br>Surface water source|
|Risk associated with household access to sanitation|
|0<br>Flush to sewer no blockages reported|
|1<br>Flush to sewer (reported blockages)|
|2<br>onsite improved + unshared + not broken/leaking|
|3<br>onsite improved + shared + not broken/leaking|
|4<br>onsite improved + unshared + broken/leaking|
|5<br>onsite improved + shared + broken/leaking|
|6<br>unimproved/ambiguous + shared + not broken|
|7<br>unimproved/ambiguous + unshared + not broken|
|8<br>unimproved/ambiguous + unshared + broken|
|9<br>open defecation|

# _Development of a cholera risk map_

To generate a high resolution map showing spatial variation in underlying cholera risk across Lusaka, a log-Gaussian Cox Process (LGCP) model was developed (Diggle et al. 2013). LGCP models are appropriate when the data in question can be considered the result of a spatial point process, denoting the location of events (in this instance, cholera cases), and the objective is to infer an underlying spatial surface (a map) denoting how the intensity of occurrence varies across the study area. Formally, the model was structured as follows: RR(xx, yy) = λλ(xx, yy)exp{Υ(xx, yy)} where is the intensity (in our application, cholera incidence rate) at a spatial location ; is the fixed spatial component; and Υ is a spatial Gaussian Process (a multivariate normal Gaussian distribution RR (xx, yy) λλ characterized by a mean and spatial covariance function). The fixed spatial component is then defined as: λλ } where is a vector of coefficients and is a vector of geospatial covariates selected from those λλ= exp{β described above. Covariate selection was performed via an exhaustive screening process (using the ββ ββ _glmulti_ package in R, (Calcagno 2020)) that formulated every possible model based on combinations of
Infante, Chacaltana, and Higa (2014) estimate that between 2000 and 2011, output per worker grew some 3.3 percent, of which some 2.8 percent was due to large firms and only 0.5 percent to SMEs.

An interesting feature of this process is that the reduction in labor informality was stronger among larger firms than smaller firms (figure 1). Between 2002 and 2012, labor formality rates increased from 12.5 to 15.2 percent among SMEs; in contrast, they increased from 60.9 to 70.5 percent among large firms. This happened despite the implementation of several policy interventions aimed at business and labor formalization of SMEs over this period, with mixed results.

Figure 1: Informality Rates among Wage Earners, by Firm Size, Peru <!-- Start of picture text --> 2004 2006 2008 2010 2012 2014<br>Year<br>Small Medium Large<br>1<br>.8<br>.6<br>.4<br>.2<br>Share of informal employment in each firm size<br><!-- End of picture text --> _Source:_ Labor Ministry of Peru.

Business informality in Peru, defined as SMEs not complying with tax registration, decreased from 67% in 2011 to 53% in 2015 (PRODUCE 2017). Along the business dimension, promotional regimes were introduced during the same period. The _Registro Unico Simplificado_ system simplified and reduced the value-added tax. The _Regimen Especial de Renta_ system reduces income taxes for small businesses. There are also some promotional programs, such as the National Plan to Promote and Formalize SMEs, which aim to enhance SME access to markets. The SME Law of 1998 gives SMEs access to a specific fund for competitiveness and technological upgrading. In 2006, the _Garantía Mobiliaria_ law was approved to help SMEs with financial collateral. More recently, the _Mype-tributario_ , a special tax regime for SMEs, 9