rafmacalaba/data-use-mentions-extended huggingface dataset:
0.126**|0.126**|0.126**|0.126**|0.126**|
||(0.0418)|(0.0418)|(0.0418)|(0.0535)|(0.0535)|(0.0535)|(0.0535)|(0.0535)|(0.0535)|
|Trust||||0.0476|0.0476|0.0470||||
|||||(0.0459)|(0.0459)|(0.0459)||||
|Observations|6,396|6,396|6,396|4,037|4,037|4,037|4,037|4,037|4,037|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: Firm size and ownership controls and city fixed effects are also included in the regression. Robust standard errors clustered at household level in brackets. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

A-7
.

- Harris, John, and Michael Todaro, “Migration, Unemployment and Development: A Two-Sector Analysis”, American Economic Review 60:1 (1970), 126-142.

- Hasan, Rana, and Karl Jandoc, “The Distribution of Firm Size in India: What Can Survey Data Tell Us?”, ADB Economics Working Paper 213 (2010).

Henderson, Vernon, “Cities and Development”, Journal of Regional Science 50 (2010), 515-540.

- Hsieh, Chang-Tai, and Peter Klenow, “Misallocation and Manufacturing TFP in China and India”, Quarterly Journal of Economics 124 (2009), 1403-1448.

- Hsieh, Chang-Tai, and Peter Klenow, “The Lifecycle of Plants in India and Mexico”, Working Paper (2011).

- Iyer, Lakshmi, Tarun Khanna, and Ashutosh Varshney, “Caste and Entrepreneurship in India”, Working Paper (2011).

- Kanbur, Ravi, “Avoid Informality Traps”, in Ejaz Ghani (ed.) Reshaping Tomorrow (Oxford, UK: Oxford University Press, 2011).

- Kanbur, Ravi, “Conceptualising Informality: Regulation and Enforcement,” Indian Journal of Labour Economics 52:1 (2009), 33-42.

20
In the December 2018 survey wave, 394,422 respondents who are administrators of Facebook-designated SME pages responded to the survey. Of the respondents, 39 percent were female, and 61 percent were male.

The sample for this paper includes all respondents with complete gender and industry information. Out of the total number of observations 91 percent of the respondents (357,877) responded to the gender question and, within this subset, 35 percent of the respondents (122,543) responded to the industry question. Since this paper is focused on female-owned and male-owned firms, we conduct the analysis only for respondents who name themselves as owners and/or managers in the survey and exclude employees from our analysis.<sup>12</sup> The owners and managers make up 54 percent of this remaining sample (66,908).<sup>13</sup> Within this subsample, we were able to clearly identify whether the firm in question was in a male-dominated sector or not for 84 percent. Therefore, the total sample size which we base the analysis in this paper is comprised of 55,932 observations. This sample will be used to define the male-dominated sectors and female-concentrated sectors as described in the following section.

The size of the sample changes depending on the outcome that is being analyzed (see Table 1). Questions that were asked later in the survey suffered from greater non-response (such as generalized self-efficacy or entrepreneurial identity indices). The household income and profits variables were reported in local currency and translated by the research team into USD using an average of the exchange rate for the survey period.

**Table 1. Sample Size for key outcomes**

|Number of employees|31,756|
|---|---|
|Total Average Monthly Profits|17,351|
|Generalized self-efficacy index|26,033|
|Error management Score|28,663|
|Entrepreneurial identity index|25,359|
|Career Fit Confidence: More Committed
-

> 19From a bias perspective, differences between migrants and non-migrants would bias the estimated differences between Hindu migrants and Muslim migrants if selection of migrants is differential by religion.

> 20The NFHS-3 survey asked about the location of migrants’ prior residence and current residence. The sample is limited to respondents who moved from the “countryside” to a “city,” “large city,” or “capital.”

> 21For evidence on the historical importance of water and sewerage infrastructure for health in the United States, see work by Alsan and Goldin (2019).

13
evaded, a greater redistributive role is found for indirect tax instruments (Boadway et al., 1994; Huang and Rios, 2016).<sup>48</sup> This discussion suggests that an extended model with direct tax instruments, even constrained, would lead to less optimal redistribution through consumption taxes. Jointly studying the optimal direct and indirect tax instruments over development would, however, require additional empirical moments, including how opportunities to evade income taxes vary along a country’s income distribution and as the country develops. Such an undertaking is beyond the scope of this paper.

# **7 Conclusion**

In this we how with household income both paper study consumption patterns vary within and across countries and derive implications for the optimal design and redistributive potential of consumption taxes. We consider two channels for redistribution: the de facto tax exemption of informal expenditure and the de jure tax exemption of necessities, in particular food. To measure informal expenditure, we harmonize expenditure surveys across 31 developing countries which contain information on the place of for each transaction. We each of to the informal or purchase assign place purchase formal sector using a robust assignment rule, and calculate the informal budget share at the household level. This enables us to characterize Curves: we Informality Engel find that informal budget shares decrease with household income in every country. This implies that the de facto exemption of the informal sector is progressive. We then extend the standard optimal commodity tax model to allow for informal consumption and calibrate it to our data to the effects of different tax on study policies inequality. Contrary to the existing consensus, we find that consumption taxes are redistributive, lowering inequality by as much as personal income taxes in developing countries. This effect is mainly driven by the existence of the informal sector. Once informal consumption is accounted for, reduced rates on necessities only have a limited impact on inequality.

Our findings have sharp implications for the use of reduced rates on necessities and on food items in particular. We find that differentiating rates
cent studies by Akresh, Halim, and Kleemans (2018) and Mazumder, Rosales-Rueda, and Triyana (2019), which are also based on the same program. Departing from the literature that precedes us and these concurrent articles, we examine a broader set of developmental indicators as well as provide evidence regarding the role played by parenting practices on the transmission of policy impacts across generations.<sup>3</sup> We do so both in terms of activities parents carry out with their children but also in terms of interactions parents have with their children. Our analyses parallel those that focus on time use data, except that instead of focusing on the amount of time spent with the child (Andrabi et al., 2012; Bono, Francesconi, Kelly, & Sacker, 2016; Kalil, Ryan, & Corey, 2012) we focus on also characterizing the _quality_ of parent-child interactions.

Our identification strategy follows Esther Duflo’s seminal study on the causal impacts of INPRES (Duflo, 2001) by leveraging the timing and geographic (district/kabupaten) differences in intensity of this major school construction project launched in 1973 by the Indonesian government. INPRES led to the construction of 61,807 new primary schools within five years of its launching (Breierova & Duflo, 2004), corresponding to approximately one additional school per 500 Indonesian children on aggregate. With this historic context in mind, we use the caregiver survey module collected in 2013 as part of the Indonesia Early Childhood Education and Development (ECED) Project. Importantly, the survey contains detailed information about mothers of young children and captures where mothers were born and whether they had moved from their place of birth before completing primary schooling. As such, we are able to locate where the mothers in our data set grew up and characterize whether they potentially benefited from the expansion of primary education as a result of the INPRES program.

Our empirical estimates confirm that the INPRES program produced significant gains in educational attainment among the mothers in our sample. Mothers who grew
reports that it analyzes big data via either of the following methods: the enterprise’s own data collected with smart devices or sensors, data gathered from geolocation data from the use of portable devices, generated from social media, and data collected from other external sources.<sup>22</sup> The E-commerce survey includes information on two separate measures of AI technologies, notably machine learning and natural language processing. Similar to the construction of the other technology variables, we create a binary variable equal to 1 for firms using AI in either of these forms and zero otherwise.

The matched employer-employee data comes from the Annual Survey of Hours and Earnings, provided by the ONS, and comprises panel data on the wages of 1% of UK workers. Since the data is a sample of workers, this prevents us aggregating to the firm-level, and limits us to worker-level analysis. We examine the demand for workers that perform data analytics (or business intelligence) tasks , those that perform other data tasks (specifically, database, data stores or software creation) and those that do not perform data tasks, using occupation information on time use from Corrado et al. (2022).<sup>23</sup> Details on the Annual Investment Allowance policy over time are provided by UK Tax Authority (HMRC). This data contains information on investment thresholds of the allowance, eligible investment, when the policy was introduced (2008) and details on changes in the thresholds over time up to 2019. Measures of IT capital investment, as well as historic (pre-AIA) total investment in plant and machinery and the date of each firm’s financial – – year end which we use to identify our set of treated firms are taken from the Annual Business Survey (provided by the ONS). Finally, data on firm control variables, age, multi– establishment status and foreign ownership are sourced from the UK business registry the annual Business Structure Database.

> 22The E-commerce survey defines big data and big data
10 households, e.g. type/size of dwelling, ownership of vehiclcs, number/type of appliances, neighborhood, etc. With spatial stratification, use of functional rather than administrative regions may be more appropriate when trying to link consumption patterns with ecological zones. However, it may be difficult to stray too far from official administrative division, as many statistics are only available on this basis. Finally, it should be noted that the more a sample is stratified, the more it requires a larger sample to ensure that results obtained from the sample are generalizable to the larger population Sample Size Should Be Calculated. When using random survey techniques, the sample size should be calculated using statistical methods which determine sampling error and confidence level. However, if resource constraints limit the sample size, then its reliability and precision should be determined by working backwards from the feasible sample. Confidence levels and sampling error should always be given to clarify the validity of published results. Simply selecting a sample based on 1% or 0.1% of the population does not guarantee that results will be representative.

# Sample Design in Morocco

A household energy consumption survey has been designed in Morocco under a USAID/ESMAP project. It will involve three rounds to capture winter, spring, summer and crop-related seasonality. The objective of the survey is to provide data to analysts on fuelwood developmcnt, household energy conservation, cooking equipment, fuel prices, petroleum products and rural electrification so that a comprehensive household energy strategy can be developed.

Sampling methodology. A random stratified sample was drawn from the Statistics Departmen.'s master sample. This is a large sample which is designed to meet all household survey needs for the 1984-92 period. It is based on the 1982 census and is already divided into rural and urban areas, and five strata refleciing housing types. Sampling involved the following phases: (a) review of existing data; (b) selection of zones to be surveyed; (c) familiarization with the master sample; (
finds that furloughing was more prevalent in the United Kingdom (43%) than in the United States (31%), a country that strongly relied on the expansion of unemployment benefits to respond to the crisis. The study also finds that in all countries, people who can work from home are less likely to lose their jobs. This is also the case for people with permanent contracts, fixed hours and in salaried jobs. In the United States and the United Kingdom, less educated workers and women were found to be more likely to lose their jobs during the pandemic. This is not the case in Germany. Based on a survey covering China, Japan, Korea, the United States, the United Kingdom, and Italy, Belot et al (2020) also find that young people are severely affected by the crisis in these countries.

Focusing on Denmark, another country that introduced significant measures to encourage job retention, Bennedsen et al. (2020) provide additional evidence on the strong impact of these policies in helping firms keep their workers. Estimates presented in this study suggest that the policies introduced by the Danish government contributed to a reduction in layoffs by 81,000 jobs and increase in furloughs by 285,000. Employment subsidies seem to have a stronger correlation with job retention, while the correlation is weaker for cost subsidies and the evidence for tax subsidies is mixed. The authors conclude that labor subsidies meet their objective of preserving employer-employee relationships, while the impact of the other policies is less clear.

Sweden is another example of a country that has leveraged on strong job retention interventions to respond to the crisis. Hensvick et al. (2020) study the impact of the COVID-19 crisis on job search using real-time data from the job board of the Swedish Public Employment Service. They find that between March and May employers posted 8
Table 9: Heath care data

||Problems|Solutions/Assumptions|
|---|---|---|
|1|Most recent official beneficiary and expenditure<br>information refers to 2008, while household-level<br>LCMS information refers to 2010.|**Main Assumption:**2008 spending is representative of<br>2010 spending.<br><br>We deflate the private expenditure data for 2010<br>(from the LCMS survey) and the government<br>expenditure data for 2008 (from official sources) and<br>express both in 2009 prices.<br><br>We assume that government expenditures (both in<br>terms of real value and distribution) remained<br>constant between 2008 and 2010.<br><br>We assume that the number and distribution of<br>beneficiaries by welfare level remained constant<br>between 2008 and 2010.<br><br>We assume that households‘ healthcare expenditures<br>(both real value and distribution) remained equal<br>between 2008 and 2010.|
|2|From the survey we know:<br>a) The number of people who were ill or<br>injured during the 15 days before the survey.<br>b) The amount spent on healthcare for each<br>individual during the 15 days before the<br>survey.<br>c) If subject visited a public health facility for<br>treatment.<br>But we do not know:<br>a) If a reported visit actually took place during<br>the 15 days before the survey or earlier.<br>b) If a patient visited a health facility other than<br>the reported visit to a hospital<br>c) The number of visits to hospitals or health<br>posts during the 15 days before the
Tools and Data for Global Diet Quality Monitoring.” In _22nd IUNS-ICN International Congress of Nutrition, Tokyo_ .

Kent, Seamus, Francesco Fusco, Alastair Gray, Susan A. Jebb, Benjamin J. Cairns, and Borislava Mihaylova. 2017. Body mass index and healthcare costs: a systematic literature review of individual participant data studies. _Obesity Reviews_ 18: 869-879.

- Konstantinidi, Melina, and Antonios E. Koutelidakis. 2019. “Functional Foods and Bioactive Compounds: A Review of Its Possible Role on Weight Management and Obesity’s Metabolic Consequences.” _Medicines_ 6 (3): 94. https://doi.org/10.3390/medicines6030094.

- Krasevec, Julia, Xiaoyi An, Richard Kumapley, France Bégin, and Edward A. Frongillo. 2017. “Diet Quality and Risk of Stunting among Infants and Young Children in Low- and Middle-Income Countries.” _Maternal & Child Nutrition_ 13 (October): e12430. https://doi.org/10.1111/MCN.12430.

- Lane, Melissa M., Jessica A. Davis, Sally Beattie, Clara Gómez‐Donoso, Amy Loughman, Adrienne O’Neil, Felice Jacka, et al. 2021. “Ultraprocessed Food and Chronic Noncommunicable Diseases: A Systematic Review and Meta‐analysis of 43 Observational Studies.” _Obesity Reviews_ 22 (3). https://doi.org/10.1111/obr.13146.

- Li, Man, Xiaojie Li, Yanfang Zhao, Lu Zhang, Jing Yang, Maigeng Zhou, and Zhuoqun Wang. 2021. “The Burden of Ischemic Heart Disease and Type 2 Diabetes Mellitus Attributable to Diet High in SugarSweetened Beverages in China: An Analysis for the Global Burden of Disease Study 2017.” _Journal of Diabetes_ 13 (6): 482–93. https://doi.org/10.1111/1753-0407.13132.

Liu, Dan, Qingyang Shi, Gaiping Cheng, Qiaorong Huang, and Sheyu Li. 2022.
neutral measure of geographical distance, and obviously the lived experience of dispersion (or remoteness) is likely to be more impactful in Tuvalu than Fiji, given is vastly smaller population. Our dispersion-adjusted measures of population sparsity reflect this perspective. FSM and Kiribati stand out as being extremely sparsely distributed (with the sparsity of settlement patterns equivalent to 1 person per 20 km<sup>2</sup> ), as is Tuvalu (1 person per 5 km<sup>2</sup> ). Sparsity for the populations of Tonga, Solomon Islands and Vanuatu is somewhat more moderate (around 1-2 people per km<sup>2</sup> ), while the population of Fiji is by far the most highly concentrated across geographical space (16 people per km<sup>2</sup> ).

We also exploit the World Bank’s subnational geography database to estimate the geographical scale of population dispersion and dispersion-adjusted population sparsity for 110 other countries around the world. This exercise highlights the extreme degree of population sparsity in the PICs, relative to all other countries assessed. Across the global database, the geographical scale of population dispersion ranges from a maximum of 3,600km in the Russian Federation, to a minimum of 40km in Trinidad and Tobago. None of the PICs lie at the extremes of this range, but dispersion for FSM and Kiribati is at the higher end of the distribution, while Fiji and Tonga are at the lower end. On the other hand, after accounting for population size, the PICs stand out as having extremely low levels of dispersion-adjusted population density compared to other countries, and equivalently, an extremely high degree of population sparsity **.** This note proceeds as follows. In the first section we define our three key indicators which reflect, respectively: local-area community-level remoteness; the geographical scale of population dispersion across a country; and, country-wide, dispersion-adjusted population density, or inversely sparsity. The second section presents empirical estimates for each of the PICs and compares
It is difficult to say with certainty why respondents to the Facebook survey dropped out at these points, but the partial data on reported conflict exposure suggest that dropout varied with experience of conflict events (see Table 3). We focus on respondents from Gaza for all analysis of reported conflict exposure because only Gazan respondents were asked these questions in the PPCS, but the pattern of results reported in Table 3 is not different if we use the full national sample. Among respondents from Gaza, respondents who reported no exposure to conflict events were overrepresented among those who dropped out of the survey after partially completing it. This pattern is even more pronounced among respondents who dropped out of the survey immediately after the conflict exposure module. Overall, respondents from Gaza who reported experiencing at least one conflict event were about 6.5 percent more likely to complete the survey than those who did not report experiencing any conflict events, which is consistent with hypothesis H1a.

# **Table 3: Respondents who reported no exposure to conflict events were overrepresented among those who dropped out of the Facebook survey**

||**Respondents who reported no**<br>**exposure to conflict events**|
|---|---|
|_Share of all respondents_|37.6%|
|_Share of respondents who dropped out at any time before_<br>_completing the survey_|40.5%|
|_Share of respondents who dropped out at the end of the conflict_<br>_exposure module_|46.0%|

Note: Restricted to respondents from Gaza who answered at least one conflict exposure question.

Were people more willing to provide answers to sensitive questions in the Facebook survey than in the PPCS?

Systematic variations in response patterns may also have contributed to the differences between the PPCS and Facebook survey estimates. One potential variation of this type is sensitivity bias, which could stem from multiple sources.<sup>6</sup> For example, respondents might underreport aggressive tendencies because
—believed that the program targeted grade 3 and 4 teachers. At midline 91 percent of teachers in the in-kind arm believed that the award had an in-kind component, and 62 percent of teachers in the recognition arm believed that the program included a certificate. Teachers were also relatively well-informed about the inputs to the award function.

# **2.2 Sample and Experimental Design**

The evaluation sample consists of 420 randomly selected primary schools, stratifying by regions and zones (rural-urban), from the population of public French (non-Arabic) speaking schools found in the Education Management Information System (EMIS) database and using the number of teachers in targeted grades to define the selection probability of a given school.<sup>9</sup>

> 9The sampling frame was limited to public schools because they are the schools under direct authority of the MEPU-EC.

6
>_th_ </sup>recovery year||||||109,938***<br>(23,690)|
|7<sup>_th_ </sup>recovery year||||||109,283***<br>(24,808)|
|8<sup>_th_ </sup>recovery year||||||88,871***<br>(15,669)|
|9<sup>_th_ </sup>recovery year||||||84,465***<br>(16,136)|
|10+ recovery years||||||73,528***<br>(8,684)|
|Observations|12,784|12,784|12,763|12,784|12,784|12,784|
|R-squared|0.435|0.467|0.489|0.478|0.483|0.485|
|Country FE|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Country-level data on yearly stocks of refugees are gathered from UNCHR database. In column (1) “25+ battle deaths” is the definition from PRIO dataset of at least 25 battle related deaths in a given year. In column (2)-(6) internal conflict, i.e. variable “1000+ battle deaths”, is defined as a year in which violence involves more than 1000 fatalities. Variable “25-1000 battle deaths” controls for years in which fightings cause a number of deaths between 25 and 1000. “Year before war” is