rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Appendix S5: 26 Refusers under Clinic condition</u>**

||Rejected<br>vaccination<br>|Accepted<br>vaccination<br>|Difference<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Sample size|26|585||
|_Amount of Cash Incentives:_||||
|C300|0.339|0.353|0.014 (0.112)|
|C800|0.316|0.427|0.111 (0.103)|
|_Baseline Characteristics:_||||
|Age|22.952|25.3|2.348 (1.074)**|
|Muslim|0.477|0.466|-0.011 (0.093)|
|Any education|0.499|0.545|0.046 (0.081)|
|Marital status = Single|0.193|0.162|-0.031 (0.091)|
|Currently pregnant|0.372|0.175|-0.197 (0.104)*|
|Have children|0.659|0.776|0.117 (0.099)|
|Has paid work|0.521|0.443|-0.078 (0.074)|
|Monthly HH earning per capita (naira)|7478.626|5851.607|-1627.019 (2100.44)|
|Distance to clinic (km)|1.551|1.6|0.049 (0.032)|
|Transport to clinic (naira)|100.085|116.481|16.396 (27.450)|
|Opportunity costs to clinic (naira)|2.641|3.902|1.261 (1.352)|
|_Baseline behaviors:_||||
|Ever used clinic|0.528|0.695|0.167 (0.067)**|
|Received tetanus vaccine before|0.443|0.384|-0.059 (0.051)|
|_Baseline perceptions:_||||
|Likely to contract tetanus|0.537|0.371|-0.166 (0.087)*|
|Number of people who die of tetanus|31.303|29.619|-1.684 (4.015)|
|Very worried about Tetanus|0.519|0.336|-0.183 (0.062)***|
|Tetanus is very bad|0.29|0.411|0.121 (0.076)|
|Very important to be protected from tetanus|0.416|0.479|0.063 (0.069)|
|Vaccine efficacy|26.459|22.397|-4.062 (5.461)|
|Vaccines protect from disease|0.85|0.903|0.053 (0.054)|
|<br>Needles are scary|0.442|0.587|<br>0.145 (0.067)**|
|Vaccines have side effects|0.641|0.649|0.008 (0.074)|
|Vaccines give diseases|0.285|0.239|-0.046 (0.090)|
|Vaccines give HIV|0.174|0.168|-0.006 (0.087)|
|Heartrate (beat/min)|85.724|86.475|0.751(2.679)|

_Source:_ Authors estimate _Notes:_ These are based on the sample of 611 respondents who visited the clinic under Clinic condition. 26 refused to receive the vaccination, and 585 accepeted the vaccine upon their arrival at the clinic. Transportion and opportunity costs to clinic are the costs for one-way. Transportation costs for 66 observations are missing. "Likely to contract tetanus" is a binary variable; "Number of people who die of tetanus" is the number of people out of 100; "Very worried about tetanus", "Tetanus is very bad", and "Very important to be protected from tetanus" are binary variables. "Vaccine efficacy" is the difference between the hypothetical number of unvaccinated people whom each respondent thinks get tetanus and the number of vaccinated people who get tetanus (range: -100 to 100). 150 naira = $1 approximately. Standard errors are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
Ahn, Hyungtaik and James L. Powell. 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," _Journal of Econometrics_ , 58(1/2):3-29 Andrabi, Tahir, Jishnu Das and Asim Khwaja. 2007. Students Today, Teachers Tomorrow? The Rise of <u>Affordable Private Schools</u> **. Processed.**

- ------------. 2008. “A Dime a Day: The Possibilities and Limits of Private Schooling in Pakistan,” _Comparative Education Review_ , 52(3):329-355.

Andrabi, Tahir, Jishnu Das, Asim Ijaz Khwaja, Tara Vishwanath and Tristan Zajonc. 2009. “Learning and Educational Achievement in Punjab Schools (LEAPS): Insights to Inform the Policy Debate,” _Oxford University Press,_ **Forthcoming** Angrist, Joshua. 1995. “Conditioning on the Probability of Selection to Control Selection Bias,” National Bureau of Economic Research Working Paper No. 181 (October). Available online: <u>http://www.nber.org/papers/t0181.pdf</u> <u>ASER 2008 – Rural Annual Status of Education Report. January 13, 2009. Pratham Resource Center:</u> Mumbai Bacolod, Marigee and Priya Ranjan. 2008. “Why children work, attend school or stay idle: The roles of ability and household wealth,” _Economic Development and Cultural Change_ , 56:791-828.

Banerjee, Abhijit, V. Shawn Cole, Esther Duflo and Leigh Linden. 2007. “Remedying Education: Evidence from Two Randomized Experiments in India,” _The Quarterly Journal of Economics_ , 122(3):1235-64. Barro, Robert J. and Jong-Wha Lee. 2000. “International Data on Educational Attainment Updates and Implications,” National Bureau of Economic Research Working Paper No. W7911 (November). Available at SSRN: http://ssrn.com/abstract=243657 Becker, Gary S. 1985. “Human Capital, Effort, and the Sexual Division of Labor,” _Journal of Labor Economics_ ,

- 3(1/2: Trends in Women's Work, Education, and Family Building):S33-S58.

26
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# **1.** READING

|**FOR JOB INCUMBENTS**: **How often does your job**<br>**involve...**<br>**FOR OCCUPATION EXPERTS**: **How often does the job**<br>**involve...**|Never|Less than once a month|**Fr**<br>At least once a month|**eque**<br>At least once a week|**ncy**<br>Once a day|Several times a day|Hourly or more often|
|---|---|---|---|---|---|---|---|
|**B.1.1 reading letters, memos, e-mails, newspapers, or**<br>**magazines? **|1|2|3|4|5|6|7|
|**B.1.2 reading books, professional journals, or scholarly**<br>**publications?**|1|2|3|4|5|6|7|
|**B.1.3 reading instruction or operating manuals?**|1|2|3|4|5|6|7|
|**B.1.4 reading bills, invoices, bank statements, or other**<br>**financial statements? **|1|2|3|4|5|6|7|

# **B.1.5 What is the longest document normally read at work?**

- 1 DO NOT READ AT WORK 2 ONE PAGE OR LESS

- 3 2 TO 5 PAGES

- 4 6 TO 10 PAGES

- 5 11 TO 25 PAGES

- 6 MORE THAN 25 PAGES

40
## **CREATING A POVERTY MAP FOR AZERBAIJAN**

# **1. Introduction**

‘Poverty maps’, that is graphic representations of spatially disaggregated estimates of welfare, are being increasingly used to geographically target scare resources (Bigman and Deichmann 2002). There is also growing recognition that location itself is an important determinant of welfare, with the local agro-ecological resource endowment, access to input and output markets, and availability of educational and health facilities all influencing the well being of households. Conversely, household welfare may also have an important effect on the locality, for example local levels of consumer demand, patterns of cropping and deforestation. The development of detailed poverty maps in many settings is, however, hampered due to data constraints. Data on income or consumption are often unavailable and, where they are, direct survey estimates for small areas are likely to yield unacceptably large standard errors due to limited sample sizes. Census data offer the required level of coverage but do not generally contain the appropriate information. This has led to the development of a range of alternative methods aimed either at combining survey data with unit record data from the Census to produce estimates of income or expenditure for small areas or at developing alternative welfare rankings, such as asset indices, using existing census data<sup>2</sup> .

This study aims to develop a set of poverty maps for Azerbaijan which can be used by different users. Two alternative approaches are adopted. Firstly, a map is derived using imputed household consumption, combining information from the 2002 Household Budget Survey (HBS) with 1999 Census data (Section 2). Secondly an alternative map is constructed using an asset index based on data from the 1999 Census to produce reliable estimates of welfare at the district

> 2 <u>http://www.worldbank.org/poverty/health/wbact/health_eq_tn04.pdf.</u>

> See also <u>http://www.healthsystemsrc.org/publications/Issues_papers/Measuring_healthpoverty.pdf</u>

2
conception of privacy as a fundamental human right reflects its own history and cultural trajectory which other countries might not share.

To ensure that the personal data of EU citizens is not abused abroad, data can be transferred out of the EU only under certain conditions. One is that the country meets the EU’s “national adequacy” requirement by enacting a national privacy law essentially equivalent to that of the EU. So far only five countries have received a positive national adequacy finding (Argentina, Uruguay, Israel, New Zealand, and Canada (commercial organizations)).

A national law imposes the same standard on all firms in the country, regardless of where they sell, at home or abroad. That could adversely affect poorer countries. Prematurely stringent privacy laws could hurt the development of markets by inhibiting the flow of information. For example, the reporting of personal credit histories is critical to consumer credit, and privacy laws could create significant asymmetries of information and affect the efficiency of markets (Kitchenman, 1999).

If a country’s national law fails the EU adequacy test, as happened in the case of India, their firms will be required to use either Binding Corporate Rules (BCRs), designed for multinational companies to move data globally, or Standard Contractual Clauses (SCCs) for each business deal. Both instruments require the levels of protection, oversight and access for individuals that would be offered in the EU. Both also require a data controller or processor, who can be held liable for breach, to be established in an EU Member State.

Both routes are costly and time-consuming. The requirement of a presence in the EU increases costs and limits the benefits of seamless cross-border digital trade, especially for smaller firms. A survey in India of the impact of the earlier, less-stringent EU Data Protection Directive revealed that the BCR process took over six months, and 90 percent of the respondents used SCCs which also involved a complex process and took on average more than 3 months (NASSCOM-DSCI, 2013). As many as two-thirds of the surveyed services exporters claimed a significant loss of business opportunities because of the requirements.

Is it possible to satisfy the EU’s legitimate needs without obliging other countries to accept nation-wide EU standards or to incur the substantial compliance costs associated with SCCs and BCRs?

The EU-US Privacy Shield Agreement In 2016, the United States and the EU concluded the Privacy Shield – an arrangement that the EU Commission has deemed “adequate” under the Data Directive – thereby enabling the transfer of personal information from the EU to businesses in the United States participating in the Privacy Shield (EC 2017). The Privacy Shield replaced the EU-U.S. Safe Harbor framework, which in 2015 the CJEU found did not provide an adequate level of privacy protection due to an 17
> v The SI data track establishments, rather than _firms_ .  A recent BPS study has suggested that less than 5% of establishments in the Manufacturing Census are owned by a multi-establishment firm (see Blalock et al. 2008 for discussion).  For this reason, we believe that our results for establishments are likely to generalize to firms and we will use the terms “firms” and “plants’ interchangeably in the remainder of the text.

vi We demean minimum wage interactions such that we can continue to interpret the coefficients on the composition of the production workforce as being indicative of average gender pay differentials. vii Note, export status and ownership do vary over time and are appropriate to include in the fixed effects specification.

viii The results presented below are also robust to using measures of total factor productivity to control for productivity. Results are omitted to conserve space, but available from the authors upon request. ix The RIF function serves to invert proportions of the sample below a particular quantile back into the quantiles themselves by dividing these proportions by estimates of the density of the dependent variable.

> x It should be noted that RIF estimates are to be interpreted as a local approximation for the effect changes in the distribution of minimum wages on firm-level average wages.

> xi Note that due to data limitations quantiles are based on average wages at the firm-level, not at the level of individual workers.

xii Given the size of some of the coefficients, this approximation in interpreting the coefficients as percent point changes is not strictly accurate. The exponential values can be calculated; our emphasis here is on the general significance and overall size of the effects.

xiii As education variables are only available for some years, we also tested the robustness of the results on the subsample of those years compared to the whole sample. Results are robust and are available upon request.

47
# **1. Introduction**

As the COVID-19 pandemic continues to spread around the world, the lives of millions of people from diverse communities have been affected. The pandemic has impacted not only physical health but also caused socioeconomic disruptions in individuals' lives, including but not limited to putting individuals at risk of losing their livelihoods and causing deteriorations in their mental health.<sup>1</sup> While economic consequences of COVID-19 are widespread and affect all groups, lesbian, gay, bisexual, transgender, and intersex (LGBTI) people are particularly vulnerable and face unique challenges due to existing systemic and social barriers.

Despite some social advancement achieved in the past two decades in some countries, LGBTI people still face discrimination and violence globally. This discrimination and violence often adversely impact their lives -- excluding them from communities and diminishing their well-being through lack of access to basic needs such as education, housing, health, employment, and financial services (Kattari et al. 2016; Koehler et al. 2017; Conron, Goldberg, and Halpern 2018; Ojanen et al. 2019; Badgett, Choi, and Wilson 2019). Existing evidence indicates that LGBTI people suffer from a high rate of unemployment and violence (Conron, Goldberg, and Halpern 2018; Flores et al. 2020). As a result, LGBTI people are more likely to live in poverty and are likely to be overrepresented in the bottom 40 percent of the population (Sexual Orientation and Gender Identity Task Force (SOGI) and Koehler 2015). A World Bank study by Koehler et al. (2017) in Serbia suggests that in addition to experiencing low socioeconomic status, the risk of poverty increases for the LGBTI people suffering discrimination and exclusion. Any disproportionate impact resulting from the COVID-19 pandemic might lead to a further decline in their socioeconomic status and risk the limited gains of the last decades.

> 1 See: Blake and Wadhwa (2020); World Health Organization (2020); Vindegaard and Benros (2020); Jackson et al. (2021); Dang and Nguyen (2021).

1
19

# F. **P** LSector and Research Work

## Sector worSk

49. Sixteen sector reports were completed in FY88 (see Annex 8), compared with eight in FY87 (table 9) and an average of 13 for the preceding five years. Performance in VY88 put a healthy stop to the decline in sector work from FY85 on. The Africa region, a major contributor to PHN sector work, accounted for half of FY88 output. Most sector work addressed issues within a specific sector, such as population (Botswana, Niger, Algeria) or health (Chile), or such subsectoral issues as family planning (Burkina Faso), financing and efficiency within the health sector (Nigeria, Indonesia), and considerations of the equity and economic viability of social programs, including social security (Argentina, Brazil, Jamaica). With few exceptions the issues covered were similar: (a) internal efficiency and the rationalization of services within a sector, (b) resource mobilization, particularly the potential for selective increases in cost recovery through user fees, and (c) developing the institutional capability and administrative efficiency to formulate and implement programs.

# Taole 9: PHN SECTOR REPORTS COMPIE!ED BY REGION, FY81-88

||||Nu|mber o|f Repo|rts|||Tyo|e of Re|port|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Region|FY81|FY82|FY83|FY84|FY85|FY86|FY87|FY88|Broad|Focused|Total|
|AFRICA<br>ASIA|<br>1<br>1|5<br>1|6<br>4|8<br>4|8<br>3|6<br>2|4<br>0|7<br>1|35<br>8|10<br>9|45<br>17|
|EMENA|0|0|0|5|1|1|1|2|7|3|10|
|LAC|**Q**|Q|3|3|2|4|3|6|14|6|**2Q**|
|TOTAL|2|6|13|20|14|13|8|16|64|28|92|

50. In countries where governments have launched stabilization and structural reform measures (Bolivia, Mozambique), where the Bank or Fund is supporting a SAL (Zaire, Madagascar) or where the condition of the poor has been deteriorating and SALs are planned (Jamaica, Argentina, Brazil), sector work has reviewed government social programs, identified their limitations and-recommended how to make them more responsive to human resource development and other social needs. Five studies (Madagascar, Mozambique, Zaire, Pakistan, Bolivia) were general PHN sector reviews, outlining the principal issues and constraints within the sector and providing recommendations as a
dominated occupations and no statistical difference in male-dominated occupations. More recently, Darolia et al. (2016) found no strong evidence of gender-based discrimination when studying almost 9,000 applications across different occupation categories in the United States. Overall, correspondence studies examining gender-based discrimination show mixed findings. Further research is therefore needed for two reasons. First, the number of studies is small. And, second, the literature suffers from a developed country bias in the sense that research conducted in the developing world is extremely scarce—a problematic situation because women in developing countries exhibit overall worse labor market conditions.

Students’ perceptions of employer-based discrimination in Tunisia: Qualitative evidence This study was motivated by qualitative focus group discussions conducted with 57 female and male students in their last year of tertiary studies at the four main universities in Tunis.<sup>8</sup> A questionnaire was administered as well.

The findings validate the presence of perceived discrimination in Tunisia’s labor market. When asked how important various attributes were when considering a job, an overwhelming 75 percent of students noted that the issue of gender discrimination (or arguably lack thereof) was important when they were considering various occupations. This finding may in turn spill over into the choice of major, and almost 60 percent noted that the matter of gender discrimination crossed their mind when selecting into their current major. In particular, when asked whether “there is a lot of gender discrimination” in their preferred top three occupations, some 30 percent agreed or strongly agreed. However, this may be a conservative downward estimate because perceptions about gender discrimination are endogenous, and, as such, they may have already influenced students’ preferences about their top occupations.

As for the hiring process itself, when compared with men, women reported a lower probability of being invited for an interview after sending in a job application (a difference of 25 percent). In focus group discussions, students also noted that discrimination may appear in the initial salary offered and the progression of increases in salary. In citing other forms of discrimination specific

> 8 The 57 students were selected from four universities roughly corresponding to the majors targeted by the correspondence study: Ecole nationale des ingénieurs de Tunis (ENIT), Ecole supérieure des sciences economiques et commerciales de Tunis (ESSEC), Institut des sciences sociales et humaines de Tunis (ISSHT), and Ecole nationale supérieure de l’informatique de Tunis (ENSIT). Students were not sampled at random, but were selected using ‘convenience sampling’.

16
to the global cement industry and presents our identification and estimation strategies. Section 5 reports the main empirical results and considers potential threats and sensitivity to identification assumptions. Section 6 discusses policy implications.

# **2 Data, Motivating Facts, and Industry Background**

We begin by describing our data and three unique characteristics of cement markets in Africa that motivate this research. A variety of hypotheses could account for these facts, necessitating an economic model to distinguish them. This section closes by providing background on the technology, market structure, and demand of the cement industry as a prelude to the model.

## **2.1 Data**

The sample of cement markets includes 96 economies with available data on cement and input prices, consumption and production quantities, the number of cement firms, and other market characteristics. Over the two years of the panel, 2011 and 2017, our sample has 169 observations.<sup>7</sup> Price data are from the ICP, which in 2020 released the first ever largesample cross-economy panel of average prices of investment goods (World Bank, 2020b). The ICP data are the empirical basis for the measurement of global output by purchasing power parities and represent the statistical best practice in terms of the care taken to ensure prices refer to the same quantity and quality of the good, such that they are comparable across economies.

The cement price used is for one metric ton of ordinary Portland cement in bags or bulk delivery. Given this definition suggests transportation costs may factor into in prices, in our empirical model, we include variables related to transportation costs, including road density and the price of diesel, in the measure of marginal cost. Average prices are calculated from either regular surveys underlying the national consumer and producer price indices, or a special national survey fielded for purpose of the ICP. No alternative database of cement prices that are comparable across economies exists. Prior to 2011, the ICP only collected data from several ‘core’ economies in each continent.

## **2.2 Motivating Facts**

The data reveal three facts about African cement markets that motivate our research.

> 7Appendix A includes a detailed description of the sources, and summary statistics for each variable in the sample are provided in Table A1.

6
As long as a potential targeting variable increases in value as the likelihood of poverty increases (i.e., it is "monotonically increasing with the risk of failure"), then the area under an ROC curve can be used for ranking the efficacy of different targeting variables (Stata Corporation, 2001a). The more a test's ROC curve is bowed toward the upper left-hand corner of the graph, the greater is the accuracy of the test. Since the ROC curves are bounded by the interval [0,1], the maximum value for the area under an ROC curve is 1.0 (in which case the test would predict poverty perfectly and the ROC curve would coincide with the left-hand vertical and top horizontal axes). In contrast, a test with no predictive power would correspond to an area of 0.5 under the ROC curve (which would itself coincide with the 45-degree line in the ROC diagram). Table 7 shows the

**Figure 4. Receiver Operating Characteristic Curves for Selected Targeting Variables**

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It can be seen that the current system for classifying "poor and remote communes" does not perform particularly well in identifying poor people, especially for the "overall" poverty line. Although the poor and remote communes list has a relatively low probability (7.7 percent) of incorrectly identifying a non-poor person as poor, it has an high probability (80.5 percent) of classifying a poor person as non-poor - for the simple reason that the vast majority of poor people in Vietnam do not 32