rafmacalaba/data-use-mentions-extended huggingface dataset:
# _3. The teacher responds to students’ needs_

There is a growing body of theory and research on the important role that teacher responsiveness plays in the classroom. For instance, one study established that studentperceived emotional support was more important than instructional support in predicting students’ social skills and academic competence (Malecki & Demaray, 2003). Sensitivity and responsiveness appears to be even more important for students at risk for low engagement or achievement (Hamre & Pianta, 2005).

Being responsive to students’ needs may take a variety of forms. Responsive teachers show fairness and justice toward their students, demonstrate compassion toward students as individuals, and provide the emotional or physical support that students need in class (Bishop, Berryman, & Richardson, 2002; McGee & Fraser, 2001; Klem & Connell, 2004; Wharton-McDonald et al., 1998).

Overall, students whose needs are met enjoy the long-term benefits of being more engaged in school and performing better academically, socially, and behaviorally (Connell & Wellborn, 1991; Corno, 2008; Malecki & Demaray, 2003). Thus it is important for teachers to recognize individual students’ needs and respond appropriately.

# _4. The teacher does not exhibit gender bias and challenges gender stereotypes in the classroom_

Teacher behaviors such as calling on students of different races and genders equally and using inclusive language are central features of a positive learning environment and are linked with improved student achievement (Pittinsky, 2016; Pittinsky & Montoya, 2016). Unfortunately, it is still common that children of different genders are treated differently, and that teachers reinforce gender stereotypes in the classroom. For example, in a qualitative observational study of Zimbabwean students (equivalent to Grade 11 and 12 in the United States), classroom observations revealed that compared with girls, boys received significantly more teacher-initiated contact overall, more direct questions, more teacher feedback, and greater attention (Mutekwe & Modiba, 2012). Stark gender contrasts also arose in the educational materials used, where more masculine characters were depicted
; grade points in each subject; age and gender of the student; and unique school ID. We match this data with a dataset on the geolocation of each school using the school IDs. Figure 3 shows the geographic footprint of primary schools and coffee mills in Rwanda.<sup>7</sup> NESA uses a nine-point grading system in order from best to worst- 1 (Excellent) to 9 (Poor). Since we are interested in students’ aggregate performance in all subjects as well as individual subjects, we use the grades in the respective subjects to generate a standardized grade point. Specifically, following Denteh et al. (2022), we reverse the scales so that a high grade means better performance. Essentially, we re-order the ranking of the grades such that 1 means Poor

> 6It is noteworthy to emphasize that Rwanda introduced 6-year compulsory primary education. However, in 2009 the country extended the program to a 12-year free and compulsory education system. 7Our data on the location of coffee mills was obtained from Rwanda Geoportal

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leads to significant underreporting of employment in livestock-related activities and household non-farm enterprises (in management and worker capacity) – with stronger effects for women and under a recall period of 12 months, as opposed to 7 days. The analysis also reveals underreporting in wage employment among men under the business-as-usual approach. Looking at how the choice of survey approach affects time allocation to employment, we find that most differences arise within agriculture, with the business-as-usual approach underestimating weekly hours spent by men and women in livestock activities as well as weekly hours devoted by men to crop agriculture. We find that the higher incidence of proxy reporting in the IHS4 is likely a significant contributor of underreporting, as well as potential difficulties, given less time spent with enumerators, that the IHS4 respondents may have faced in interpreting and reporting on concepts related to businesses or enterprises (as evidenced by greater discrepancies in reporting in the IHS4 across the labor and non-farm enterprise modules). Finally, household wealth is significantly associated with the extent of reporting differences across the individual interview and business-as-usual approaches.

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The survey data described above was processed and harmonized to create the HP. The HP consists of four datasets recorded at different units of observation.

- Plot-crop-level dataset (based on the agriculture questionnaire);

- Plot-level dataset (based on the agriculture questionnaire);

- Household-level dataset (based on the household and individual questionnaires/modules);

- Individual-level dataset (based on the individual questionnaire/modules).

We group variables in the HP into four categories: (i) unit and linking identifiers (ii) crop production and agricultural variables that are defined at the plot-crop, plot or household levels (iii) socio-economic variables that are at the household or individual level and (iv) geospatial variables that are integrated using geocoordinates. A more comprehensive description of each variable can be found in the variable directories provided alongside the dataset (see Supplementary Tables 1 to 5). The variable directories also flag cases where entire variables are missing in some country-years due to missing data or absent questions in the raw data files, and describe additional data cleaning operations for each variable.

# _Unit and linking identifiers_

The HP provides two sets of key identifiers: unit identifiers and linking identifiers. Unit identifiers are created during data processing and consist of numeric identifiers which can be used to track units longitudinally. All unit identifiers have variable names that end with _id_obs_ (such as _plot_id_obs_ or _hh_id_obs_ ). Linking identifiers are included to allow merging with the raw data, so they are the same as in the raw LSMS-ISA files that can be downloaded from the World Bank’s Microdata Library. All linking identifiers have variable names that end in _id_merge_ (such as _plot_id_merge_ or _hh_id_merge_ ). To find the names of the specific variables in the raw data that can be merged with the linking identifiers, users may consult the following entries of the variable directories (Supplementary Tables 1 to 5): _plot_id_merge, hh_id_merge, indiv_id_merge, plot_manager_id_merge, respondent_id_merge, ea_id_merge_ .

Moreover, we include a common set of identifier and geographic variables, across the all four datasets
or some periods, data on some of the infrastructure indicators were scanty and thus we used alternative measures. There is paucity of data on road density and electricity production for some WAEMU countries and/or for more recent periods. Given the data are organized into five-year periods, data for the most recent year is taken as representative for the period. For instance, if data on road density were available only for 2011 and 2014, the average of the two would be taken to represent road coverage in 2011-2016. Similarly, electricity production data were not available for a few countries and thus we used alternative measures (such as electricity consumption) as a proxy. It should be noted upfront that we are fully aware of the problems with the 8
The results are presented in Table 5 and indicate that increases in all income sources are positively correlated with increases in consumption, as expected, with the exception of agricultural wage income. Agricultural income growth is more strongly correlated with consumption growth than the other sources of income growth, and the correlation is larger for the bottom 40 percent (column 2). These results suggest that agricultural income growth has been more important for poverty reduction during this period than other types of income growth.

This confirms the finding of other work undertaken on this period in Uganda. Christiaensen and Kaminski (2014) undertake decomposition analysis using the same panel data set and find that agricultural income growth contributed to 18 percent of consumption growth from 2006 to 2010. They also estimate that agricultural growth contributed to 70 percent of the poverty reduction observed from 2006 to 2010, confirming the greater importance of agricultural income growth among the poorest households. Decomposition analysis undertaken using the Uganda National Household Survey<sup>10</sup> for the Uganda Poverty Assessment also indicates that it was poverty reduction among those households that cited agriculture as their main source of income that accounted for most of the poverty reduction in Uganda throughout the period 2006 to 2013 (World Bank 2016). These findings are also consistent with a literature that points to agricultural income growth as a major source of poverty reduction in the country (Kassie et al. 2011, Dorosh and Thurlow 2012, Government of Uganda 2014).

**Table 5. Relationship between income and consumption, 2006-2010**

||(1)|(2)|
|---|---|---|
|Dependent variable: log of per capita consumption|All households|Bottom 40 percent|
|Log of per capita real crop gross income|0.0324***<br>(0.00805)|0.0416***<br>(0.0103)|
|Log of per capita real livestock gross income|0.00573*
**Figure 12. Backward and Forward Integration to Manufacturing GVCs by Sources and Destinations of Imports and Exports of Intermediate Inputs: Africa, Non-Oil Resource Rich Group of Countries (1995 vs. 2015)**

<!-- Start of picture text --> 100<br>90<br>85<br>80<br>70<br>65.5<br>60<br>50<br>40<br>35<br>30 29.4<br>20<br>10<br>5.9<br>0<br>-10 West Europe East Asia China India USA Germany<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

**Figure 13. Backward and Forward Integration to Manufacturing GVCs by Sources and Destinations of Imports and Exports of Intermediate Inputs: Africa, Oil Exporting Countries (1995 vs. 2015)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>54.3<br>50<br>40 40.4<br>30<br>20<br>10<br>0<br>-10 West Europe East Asia China India USA Germany<br>-20<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.
spend a longer time browsing gender websites as opposed to other topics.<sup>16</sup> Second, study participants were provided with the option to publicly display

> 14Specifically, the material is the following: Treatment 1: Sex Ki Adalat (E1) Male child, (E2) Virginity, (E3) Menstruation; Treatment 2: WEvolve clips; Control: Carbon.

> 15We believe that content comprehension is unlikely to drive our results considering that urban youth on social media tend to speak and understand both Hindi and English. Four in 10 respondents decided to fill surveys in English and the overall respondents’ self-assessment of English comprehension was 5.5 on a 1-10 scale. We control for this covariate in all the regressions.

> 16Time spent visiting a website is extrapolated from data on link clicking collected by the Virtual Lab platform. Appendix Figure B2 shows an example of the provided buttons. Visit duration is calculated from the moment the respondents click on one link until the moment they click on the subsequent link. For this reason, we could not measure visit duration for the last link the respondent clicked. Since missing duration tends to be more frequent on the last websites, we focused the attention on those links which were provided at the beginning, namely the PFI and Delhi Green websites, and exclude from the analysis the UN Women India and the UN Environment Program India websites. An additional problem with measuring visit duration arises when the respondents click on one link, then close the browser (or phone) without any further action, and then return to visit another link at a later moment in time. In this case, our measure of visit duration

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an increase in the volatility of the monsoon, which means that the calculations in Giné et al. (2007), which are based on historical data, underestimate expected payouts. (In that paper, we conducted some simple tests suggesting this is not the case; however, those tests were only preliminary in nature.) Notably, returns on livestock insurance are significantly less volatile than those on rainfall insurance and average around 60% of premiums collected over this period.

# 7. Who Buys Rainfall Insurance?

In this section we discuss evidence from Giné et al. (2008) which studies factors determining rainfall insurance participation. In particular, we estimate a simple regression model of the determinants of insurance demand, based on such household characteristics as wealth, landholdings, and risk aversion.

Evidence from this paper relates to a fundamental research question: Why aren’t financial products that help pool important sources of idiosyncratic risk (such as rainfall) widely available and widely used? A first potential answer to this question is simply that these products are too expensive to be attractive to households. Section 6 presents some evidence that rainfall insurance expected payouts on average appear to be significantly smaller than average premiums, presumably reflecting a combination of transaction costs, limited product market competition, and a lack of scale economies. High costs are a persistent feature of financial services offered to the rural poor in India and other developing economies, even for financial products that are widely used. For example, Cull, Demirguc-Kunt, and Morduch (2009) estimate that annual operating costs for nonbank microcredit loans are equal to 17% to 26% of loan value—far higher than corresponding ratios for consumer credit in the developed world.

An alternative view is that, while price is an important factor, other factors, such as financial constraints, trust, and financial literacy, are equally important barriers to increasing
# **List of References**

Blinder, A., and A.B. Krueger (2013), “Alternative Measures of Offshorability: A Survey Approach”, Journal of Labor Economics, Vol. 31/2, pp. S97-S128, http://dx.doi.org/10.1086/669061 Cali, Massimiliano; Francois, Joseph; Hollweg, Claire Honore; Manchin, Miriam; Oberdabernig, Doris Anita; Rojas Romagosa, Hugo Alexander; Rubinova, Stela; Tomberger, Patrick (2016) “The labor content of exports database” Policy Research working paper; no. WPS 7615. Washington, D.C., World Bank Group.

Costinot, A., L. Oldenski, and J. Rauch (2011), “Adaptation and the Boundary of Multinational Firms”, The Review of Economics and Statistics, Vol. 93/1, pp. 298-308, <u>http://dx.doi.org/10.1162/REST_a_00072</u> De Backer, K. and S. Miroudot (2013) "Mapping Global Value Chains", OECD Trade Policy Papers, No. 159, OECD Publishing, Paris, https://doi.org/10.1787/5k3v1trgnbr4-en Del Prete, D., Giovannetti, G., and Marvasi, E. (2016a). “Global Value Chains: new evidence for North Africa.” WP N. 07/2016 University of Firenze.

Dimaranan B. V. and Narayanan B. G. 2014. Skilled and unskilled labor data. Chapter in <mark>Narayanan, G., Badri, Angel Aguiar and Robert McDougall, Eds. 2012.</mark> _<mark>Global Trade, Assistance, and Production: The GTAP 8 Data Base</mark>_ <mark>, Center for Global Trade Analysis, Purdue University</mark> Escaith, Hubert (2014) “International Supply Chains, Trade in Value-Added and Development
face-to-face (F2F) index to highlight geographical differences across communes in Vietnam. F2F is a continuous measure that captures the average intensity of human-to-human interaction required to conduct business in a commune based on its existing sectoral composition of household businesses. An array of empirical estimates find that household businesses in communes with a higher F2F index suffered higher suspension rates, higher closure rates, lower weekly working hours, and lower monthly incomes.

This paper contributes to the literature in several ways. First, our analysis adds to a literature that studies the performance of informal businesses in developing countries by using high-frequency, tax-registration information covering all tax-registered Vietnamese household businesses. Previous work has relied on information from national household and labor force surveys. See, for example, Maloney (2004); Jayachandran (2020); Banerjee (2013); Banerjee et al. (2015); de Mel et al. (2019); Bruhn and McKenzie (2014); McKenzie and Woodruff (2013); McKenzie and Paffhausen (2019); Li and Rama (2015). Specifically related to the business cycle dynamics of informal firms, McKenzie and Paffhausen (2019) examine the characteristics of, and motivations behind, small firm exit, using a 2
within the household achieved through various legal and policy changes that grant women more control over resources. This policy implication is consistent with a number of studies that provide evidence that more control of resources by women leads to an improvement in child health, nutrition, education, and on women’s own well-being. Moreover, women’s participation in the labor market, a key policy lever in the unitary framework itself is endogenous, and is an outcome of intrahousehold bargaining.

However, the change in a woman’s bargaining power may affect marriages (and perhaps divorces). The measure of bargaining power used in this paper (assets at marriage) is largely determined by parental wealth, and in this society the norm is that parents arrange marriages for their offspring. Thus, future work perhaps could focus on how the policy change leading to increased female controls of resources could potentially affect household formation and dissolution and not just intrahousehold allocation in a traditional poor rural society.

# **Acknowledgements:**

I thank Richard Blundell, Andrew Chesher, Imran Rasul, Sonia Bhalotra, Giacomo de Giorgi for their valuable comments and suggestions. I also thank Howarth Bouis, Wahid Quabili, and Agnes Quisumbing in helping me accessing the IFPRI data and with numerous queries. All the remaining errors are mine.

# **References**

- Adato, M., de la Bri` _e_ re, B., Mindek, D., Quisumbing, A. R., 2003. The Impact of PROGRESA on Women’s Status and Intrahousehold Relations. In: Quisumbing, A. R. (Ed.), Household Decisions, Gender, and Development. International Food Policy Research Institute, Washington, D.C.

- Barcellos, S. H., Carvalho, L., Lleras-Muney, A., 2010. Child Gender and Parental Investment in India: Are Boys and Girls Treated Differently, mimeo, Rand and UCLA.

- Bardhan, P. K., 1974. On Life and Death Questions.
in a rural area and individual motivation. In the remainder of this section, we focus on the contingent valuation questions and the measurement of motivation.

In the past there has been controversy over how to measure contingent valuations properly.<sup>15</sup> For this reason a committee of experts, appointed by the National Oceanic and Atmospheric Administration (NOAA), following the Exxon Valdes incident, was asked to produce a report on best practice for reliable contingent valuation.<sup>16</sup> The basic conclusions of the NOAA Committee can be summarized as follows: (i) use probability sampling; (ii) avoid mail surveys; (iii) interview people in a place that is related to the question analyzed in the contingent valuation survey; (iv) formulate the question in a specific and realistic context; and (v) use closed-end questions or a variant thereof. In writing our instruments we follow NOAA recommendations as close as possible. First, although the schools are selected by convenience sampling, the students in each school were selected randomly. Second, the questionnaires were administered in the schools, and were filled out in the presence of members of the team. Third, the contingent valuation question was specific and the context was realistic. The salary of reference is the actual salary of health workers when they start their career. In addition since students are close to their graduation, it is very likely that they have already thought about the issue before participating in the survey. Finally, since the sample size is small we use a "payment card" type of question.<sup>17</sup> The question is the following: _Imagine that when you finish your studies you get two jobs as a health worker in the public sector, one in Addis Ababa and one in a rural area 500 km from Addis Ababa. Both contracts are for at least 3 years. Your monthly salary for the job in Addis Ababa would be 700 Birr. Which job would you choose
>64</sup> It has been hypothesized that the mechanism for the intergenerational transmission of exposure to violence is the hormonal imbalance resulting from the maternal experience of stress. The effect for girls was not visible when exposure to violence was in the first trimester of gestation but only when it occurred in late stages, which is at odds with this expectation of the biological channel being more harmful in early fetal development.

I used information about maternal behaviors during pregnancy as an attempt to disentangle other possible channels. The nature of the data warrants care in the interpretation of these results, but the exploratory analysis presented here is worth continuing in future work. I found that exposure to violence led to changes in some maternal pregnancy behaviors. There was a reduction in the chance of attending antenatal care visits, receiving a tetanus vaccination and an increase in the likelihood of drinking alcohol, and these changes were significant for women pregnant with girls, and especially strong in the second trimester of gestation. The hypothesis is that the increase in these behaviors creates a harsher intrauterine environment for girls, leaving them more vulnerable to environmental stressors and thus to scarring during late gestation. In this period, stress hormones should be less harmful for fetuses, leaving more room for other intrauterine environmental factors to affect the pregnancy outcomes.

Although we cannot be conclusive with respect to whether these behavioral results are a reflection of a parental preference for boys over girls, the analysis complements the literature on skewed

> 64 The generalizability of these results for girls is subject to certain limitations. Importantly, the effect was only evident when considering the likelihood of being born with low birthweight, which is a measure suffering from reporting issues, especially when the data come from the mother’s recall.

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|139.7|4.0|
|2016|0.187|0.004|123.4|15.7|0.187|0.011|123.4|24.2|0.187|0.000|123.4|4.7|
|2017|0.195|0.006|128.2|18.4|0.195|0.022|128.2|34.9|0.195|0.001|128.2|5.9|

_Source:_ Based on National Household Survey data.

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