rafmacalaba/data-use-mentions-extended huggingface dataset:
Mozambican households are fairly well insulated from a variation in market prices because of the large part of food consumption that is home produced, poor households living in Maputo city will suffer the most, because consumption of own-produced food is non-existent. Arndt et al. (2008) predict that the poorest households in Maputo lose the most while middle-income groups in the rural north and center gain the most.

While a rural/urban HIV prevalence breakdown is unavailable for Mozambique, data from other African countries show that most people living with HIV/AIDS in Africa live in urban areas

> 2 The NBR does not allow for behavioral changes on the part of producers or consumers and measures only first order impacts. As households and firms modify the structure of their consumption and production, second order adjustments should dampen any negative first-order impacts.

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Similarly, effective rates were determined for PEMEX,<sup>23</sup> for the atricultural sector,<sup>24</sup> and for the remaining four sectors in the economy.<sup>25</sup> the resulting tax rates were 4 percent on capital and 7 percent on labor in lV83-84 and 4 percent on capital and 6.9 percent on labor in 1985. The dectoral indirect tax rates were: Table S.5. Indirect Tax Rates Sector

||1|2|3|4|5|5|7 A/|
|---|---|---|---|---|---|---|---|
|1983|0|0.053|0.286|0.053|0.053|0.053|0.0798|
|1984|0|0.051|0.277|0.051|0.051|0.051|0.0710|
|1985|0|0.051|0.256|0.051|0.051|0.051|0.0798|

_Al'_ The tariff rate for sector 7, imports, is derived from CIEHEX-WHARTON (1986) Tables 8, 9, 15.

23/ See CIENEX-WHARTON (1986),'Table 15; Sistema de Cuentas Consolidates de la Nacion (1985), Table 26; (1986), Table 41.

24/ See Sistema de Cuentas Consolidades de la Nacion, Table 5.

25/ See Sistema de Cuentas Consolidades de la Nacion (1985), Tables 3, 5-9; (1986) Table 21.
Box 3: Data for Disability in Education

## **In Indonesia, there are three data sources that capture information on students with disabilities:**

- Dapodik by MoEC: Dapodik captures the prevalence of disability variables among students on visual/auditory/motor-sensory dimensions, as well as gifted children, and those with learning difficulties, Downs syndrome, and autism.

- EMIS by MORA: EMIS currently includes data on children with disabilities in all MoRA schools along the following dimensions: physical impairments including visual, auditory, motor-sensory. Data are also collected on behavioral and learning challenges, such as the ability to concentrate, as well as behavioral issues ( _lamban belajar, sulit belajar dan gangguan komunikasi_ ).

- SUSENAS: SUSENAS also captures data on visual/auditory/motor-sensory dimensions for students, in addition to behavioral and learning challenges. Additionally, SUSENAS tracks both “inability to understand communication” and “self-care” ( _kesulitan/ gangguan berbicara dan atau memahami/ berkomunikasi dengan orang lain_ and _kesulitan/ gangguan untuk mengurus diri sendiri_ ).

**However, data verification across the three sources is difficult, as different terms are used to categorize disabilities. Furthermore, data quality issues exist as a result of unclear guidelines and a lack of understanding on the part of data operators to properly record disabilities.** For example, Dapodik may not properly classify children with Down syndrome in the right category, Susenas may include children with Down syndrome in “inability to understand communication”, and EMIS may put them in the “other health problem” category. Similarly, children with the same issue may be classified by one school under “behavioral issues” and another school may classify them under “inability to concentrate.” There also do not appear to be technical guidelines for operators to classify students, and even if there were technical guidelines, it is not clear that operators are qualified to make such classifications.<sup>1</sup>

**To address data quality and verification issues, MoEC and MoRA are
Purchased consumption items are identified from the survey. The first- and second-round effects of VAT and excise taxes applied to those items are simulated using the SAM. We first calculated the price burden of all goods and services by using their effective tax rate. We then estimated the price burden on consumers resulting from indirect taxes paid for inputs of production to estimate how taxes on petroleum and coal affect the prices of final goods and services. For VAT, the second-round effects are estimated for exempted items.

The value of in-kind education and health services to individuals is estimated based on total government spending on these services. Copayments are deducted when the beneficiary paid any fee or contribution to use them. We used 2016/17 regional and federal administrative spending data to estimate the cost of providing education by level (primary, secondary, higher) and health services. To fill the data gap for 2018/19, we deflated the 2016/17 spending using the average annual growth rate of spending for each region.<sup>3</sup> We assume that each student enrolled in a public school in each region receives the education benefit. We calculate per-pupil education cost for each region by dividing total spending by the number of primary and secondary students enrolled. For tertiary education, total federal spending is divided by the number of students enrolled in post-secondary education. Most spending on tertiary education is capital spending, notably investments in infrastructure for the recent expansion in higher education; because the benefit is expected to accrue over several years, the analysis considers only a portion of the spending.<sup>4</sup> The per-beneficiary health benefit is obtained by dividing total health spending by the number of public health service users.<sup>5</sup> Indirect subsidies applied to wheat in urban areas and kerosene in all parts of the country<sup>6</sup> are estimated based on what the household spends on these items. The total value of the subsidy is calculated based on subsidy rates per
they show high levels of formal

> 8 Table 1, Column 9, shows attrition rates that range from 18.5 (Card et al., 2011) to 46 percent (Cho et al. 2013). Alzua et al. (2016) reported no attrition because their reliance on administrative data.

> 9 Gers are traditional Mongolian dwellings. They are portable, round tents covered with skins or felt that are used by several distinct nomadic groups in Central Asia. Mongolia is divided into Ulaanbaatar, the capital, and twenty-one provinces (aimags). Ulaanbaatar is divided into six main and three small suburban districts (khoroos), which are further divided into smaller administrative units called khesegs. Poor khesegs are called gers, or impoverished neighborhoods, which, in most cases, lack water, drainage, and electric infrastructure.

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where, as before, the relative change in welfare is represented by a change in consumption (the last term in the left hand side of the equation), by a change in explicit wage earnings and by profit generated via the activity run by the household (the term in squared brackets). This is estimated as the difference between sales (holding constant the quantity shares of the different goods sold _θjO_ ) and input costs (again without changing the structure of input quantities _θjI_ ).

Finally, it should be noted that the consumption of home production (autoconsumption hereafter) has been explicitly excluded from the computations in both equations (1) and (2) given that price changes – in the short run, and those of the order of magnitude considered here – do not affect it. In terms of equation (2), this means that not only does final consumption need to exclude auto-consumption, but also that input costs have to be netted of the costs related to production for auto-consumption.

# **_<u>Household survey data preparation and brief description of the pre-liberalization income distribution</u>_**

The household survey used for the computations is the LSMS 2001 for Nicaragua. At the individual level, the active employed population aged more than 12 years are classified into skilled and unskilled according to their education level. The employed population is also classified into wage-workers and self-employed. For wageworkers, the entire factor-related income is either unskilled or skilled labor income.

In the first stage, we assume that income reported by the self-employed is assumed to have both a labor and a capital component. To separate these two components, a wage for the self-employed is imputed based on a wage equation that is estimated for the wage-workers separately for rural and urban areas. The wage equation is a simple Mincerian wage equation with log wage earnings as the dependent variable and education, education squared, age, age squared, and additional regional and sectoral dummies as explanatory variables. The coefficients of these wage equations are then
for grandparents who live with their children to take care of the grandchildren. In the 2016 VHLSS, in approximately 21% of households, grandparents live with their children. We test whether formal childcare in a center can substitute for informal childcare from grandparents by running a regression of a dummy variable “living with grandparents” on childcare attendance. The effect of childcare attendance is minimal and is not statistically significant. There are no significant effects on parental migration and household size, or the interaction terms between childcare and households having an urban</mark>

> 17 We further explore whether the poverty-reducing impacts of childcare are stronger for certain disadvantaged groups such as ethnic minorities or unemployed individuals by interacting these variables with childcare attendance. Estimation results (available upon request), however, are not statistically significant.

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Table 1: Overview of data sources

|**Source**|**Year**|**Aggregation**|**Key indicators**|
|---|---|---|---|
|Population & housing census (census)|2019|Sector and county|Formal & informal employment|
|Gross County Product (GCP)|2019|Sector and county|Gross County Product|
|Census of establishments (CoE)|2017|Sector or county|Number of formal sector establishments|
|Micro, small & medium sized<br>enterprises survey (MSMEs)|2016|Firm-level|Main input source and buyer|
|Census of industrial production|2010|Sector and county|Sales of multi-establishment frms|

All data are collected and published by the Kenya National Bureau of Statistics. **Sources:** 2019 Kenya Population & Housing Census KNBS (2019); Gross County Product KNBS (2022); Census of Establishments KNBS (2017); Small & Medium-Sized Enterprises Survey KNBS (2016); Census of Industrial Production 2010 (KNBS, 2010).

of the regional economic size captured by the Gross County Product (KNBS, 2022).<sup>14</sup> The employment-based measure, which later serves as a key input for predicting the revised network with informal firms, offers two distinct advantages. First, it enables joint disaggregation of informal activity by sector and region. Second, it allows us to distinguish between private and public sector employment, a distinction unavailable in alternative measures, but based on which we can rule out that this measure of informality captures a proportion of public sector activity. The graph on the right in Figure 2 shows the geographical dispersion of informal activity as per the employment-based measure derived from the labor force module of the 2019 census. The measure correlates strongly ( _ρ_ = 0.83, Table A3) with a measure of regional formal sector shares that relies on the administrative data
<!-- Start of picture text --> Figure 1: Percentage of Firms Registered with different<br>Government Entities by Firm Size<br>100<br>90<br>80<br>P 70<br>e<br>r 60<br>c<br>e 50 DS<br>n 40 PS or UCMC<br>t<br>30 VAT<br>EPF/ETF<br>20<br>10<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 to 15 to<br>14 20<br>Number of Paid Employees<br><!-- End of picture text -->

# **Figure 2: Timeline**

<!-- Start of picture text --> Dec 2008 Feb – July Aug March Dec 2011<br>2011<br>2009<br>Baseline 2010 Round 4 survey<br>Round 3<br>Formalization<br>Round 2 (n=424 in person<br>(n=520) treatments<br>survey Survey<br>offered<br>+ proxy reports<br>(n=465) (n=445) on 59)<br><!-- End of picture text --> 24
tax morale more salient through careful messaging, with mixed results. Kettle et al. (2016) finds that both letters emphasizing national pride and those emphasizing social norms improve compliance in Guatemala, but not significantly more than a letter invoking a heightened probability of audit. In richer countries, randomized studies of letter or e-mail campaigns typically find that attempts to emphasize the social contract or civic duty either have little impact or are marginally effective (Coleman 1996; Blumenthal et al. 2001; Torgler 2004; Ariel 2012; Fellner et al. 2013; Castro and Scartascini 2015; Meiselman 2018) with some exceptions (Hallsworth et al. 2017). Krause (2020) finds that messages that emphasize the social pressure mechanism in Haiti might even have a negative effect on tax compliance. This is consistent with results from psychology research which have shown that in contexts with low rates of pro-social coordination, a mechanism known as antisocial punishment could be at play (Herrmann et al. 2008).

A recent meta-analysis (Antinyan and Asatryan 2020) of studies of nudges for tax compliance finds that deterrence nudges are on average more effective than tax morale nudges. The metaanalysis also finds that nudges seem to work better for the sub-samples of late payers, which is the sample we focus on in this paper.

# **3 Experiment and data collection**

## **3.1 Baseline data and randomization**

The frame for this experiment is a list of 241,200 properties for which, as of June 1, 2018, no property tax had been paid to the TRA for the 2017/2018 tax year. The deadline for property tax payments to be completed was June 30th. After June 30 had passed, the TRA extended the deadline for another two weeks, although continued to accept payments after this point.

As some taxpayers own multiple parcels, we collapsed these data to the taxpayer level (237,699 taxpayers), as indicated by the taxpayer identification number associated with the property. We use two sources of
**Figure 8: Perception of corruption in the government, 2019 (% of Adults)**

<!-- Start of picture text --> Oil exporting economies 73%<br>Upper-middle income economies 72%<br>Nigeria 87%<br>Malaysia 54%<br>Kazakhstan 55%<br>Iraq 90%<br>Indonesia 80%<br>Colombia 82%<br>Corruption is widespread throughout the government in this country<br><!-- End of picture text --> Source: Gallup World Poll, 2019.

**Figure 9: Perceptions of corruption in government and business over time (% of adults)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>pre-ISIS ISIS control post-ISIS<br>Corruption widespread within businesses located in this country<br>Corruption widespread throughout the government in this country<br><!-- End of picture text --> Source: Gallup World Poll, 2019.

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<!-- End of picture text --> _Source:_ GTZ 2008. _Note:_ Diesel retail prices for Central African countries as of mid-November 2008.

The CAR’s infrastructure network would benefit from closer interconnection and collaboration with neighboring countries. The Central African Backbone has the potential to connect the CAR to the submarine cable landing in Cameroon, reducing the cost of telecommunications in the CAR. Upgrading the Cameroonian portion of the Bangui-Douala corridor is critical, as it could decrease the time and cost of transport goods from the CAR to the port of Douala. As far as the energy sector is concern, estimates suggest that as long as the CAR is in a position to develop its own significant domestic hydropower, the country will not necessarily benefit from regional trade.

The main achievements and challenges in each of the CAR’s major infrastructure sectors are summarized in table 1. The table outlines the need for upgrading infrastructure in all subsectors, the obstacles to enhancing the coverage and quality of infrastructure services, and the ambitious reform efforts in this area. In particular, there is a need to improve the operational and financial performance of state-owned enterprises (SODECA, ENERCA, and SOCATEL). The following section discusses these achievements and challenges in more detail, by sector.

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<mark>supported national longitudinal household survey, there are two potential sources of bias in the resulting data: (i) selection bias associated with not being able to call LSMS-ISA households that do not own mobile phones and (ii) non-response bias associated with not being able to interview households that are targeted for phone interviews. Without correcting for these two sources of bias, results of our analysis are likely to underestimate the negative impacts of COVID-19, since poorer households are less likely</mark> than wealthier households <mark>to have phones, and less likely to respond even if they do have phones.</mark> <mark>In our analysis, we use the phone survey weights that are provided in public use datasets and that are computed in an attempt to address the aforementioned potential sources of bias using wellestablished sampling techniques.</mark><sup>_7,9_</sup> <mark>. The phone survey weights in each country build on the sampling weights for the associated LSMS-ISA-supported survey. They are calibrated to address the selection bias introduced from LSMS-ISA households not owning a mobile phone and nonresponse bias from not answering the phone. This latter issue is overwhelmingly due to nonworking phone numbers or prospective respondents not answering calls (as opposed to refusals). To calculate survey weights for the phone survey, we implement the following steps in Ethiopia, Malawi, and Uganda</mark><sup>_8_</sup> <mark>.</mark> <mark>1. We begin with the existing sampling weight for each household, as computed for the associated LSMS-ISA-supported survey (</mark> wi,pre <mark>), which are available in those datasets.</mark> <mark>2. We calculate the probability of selection into the phone survey for each household (</mark> pi <mark>), as the total number of LSMS-ISA households for which contact was attempted in that household’s region (</
study, to our knowledge, to both probe the underlying mechanisms driving impacts on wellbeing and offer a benchmark against a standard cash transfer program.

# **2 Research Context**

## **2.1 Recent Events**

The Rohingya are an ethnic group that, prior to the genocide of 2017, lived predominately in Rakhine State along the western coast of Myanmar (also known as Burma) (Blakemore, 2019). The community traces their origins back to the 15th century, when thousands of Muslims settled in the former Arakan Kingdom, which was conquered by the Burmese Empire in 1784 (Albert and Maizland, 2020). The Rohingya have since faced multiple waves of discrimination and suppression, with the first major campaign of ethnic cleansing occurring in 1978 when the Burmese military, tasked with performing a census of the border regions to determine citizenship, conducted indiscriminate attacks across Rohingya villages in Rakhine state. This led to an estimated quarter million people fleeing into neighboring Bangladesh. Subsequent ethnic cleansing campaigns in 1992 and 2012 sent additional waves of Rohingya into Bangladesh (Watch, 1996).

On August 25, 2017, the Rohingya insurgent group Arakan Rohingya Salvation Army launched an attack on security force outposts in northern Rakhine, killing twelve security personnel. Within hours, Myanmar security forces responded. Satellite imagery documented the destruction of at least 392 villages (40 percent of all settlements in northern Rakhine), with 80 percent burned within three weeks. By October 2018, over 750,000 Rohingya refugees found themselves in a veritable city of makeshift tents along the southern tip of Bangladesh, stretching from Teknaf to Cox’s Bazaar. The largest and most densely populated refugee camp on earth was constructed in a matter of weeks (Hussam, 2019).

There are currently 34 camps in Bangladesh, each subdivided into blocks ranging in population density from 60 to 130 households. Each block is represented by a local leader (a _majhi_ ) who is responsible for organizing distribution efforts and serving as a liaison between humanitarian organizations and the
gender differences in returns to education (Morduch 2000) or in home production (Edmonds 2006). Second, our first stage estimates that provide strong evidence of the link between negative shocks experienced in utero or during early childhood on subsequent cognitive ability contribute to the fetal origins hypothesis literature by highlighting a potential mechanism, reduced cognitive ability, through which observed impacts on adult outcomes such as earnings and occupation can be explained. We are also able to estimate placebo regressions showing that rain shocks prior to when the child was in utero show no relationship with ability, further strengthening our interpretation of a causal relationship. Third, because of the unique household survey data collected by the authors, this paper is the first to examine the relationship between cognitive ability and child labor, focusing not only on total hours of child labor, but also changes in the specific tasks that each child performs.<sup>5</sup> We find that higher ability children, compared to their lower ability siblings, are more likely to be enrolled in school and perform fewer hours of child labor. We also find the child labor they do is less concentrated in tasks requiring long, continuous blocks of time to complete. Rainfall more than one standard deviation below the village‟s long-run historical average that is experienced by a child in utero is correlated with 0.24 standard deviations lower cognitive ability z-scores and compared to their siblings, that corresponds to a 38 percent lower likelihood of enrollment and a 49 percent increase in the number of hours of child labor. The negative

> 5 The current paper builds on previous research (Akresh et al., 2012), which also explores the relationship between a child‟s cognitive ability and educational investments, but there are three significant differences between the current paper and the earlier work. First, the econometric identification strategy in the previous work only allows for an examination of correlations between child ability and enrollment, while in this paper we are able to measure a causal impact of ability on schooling by implementing an
American countries have significantly lower GDP per capita than expected based on their urbanization levels and are also “over-urbanized” relative to the size of their agriculture sectors. Roberts et al. (2017) demonstrate that these patterns are an illusion resulting from systematic biases in the measurement of urbanization levels, and disappear when adopting a consistent definition of urban areas across countries.

> 13These new methods tend to adopt either a functional or, more commonly, a morphological approach to delineating urban areas (Duranton, 2021). Functional approaches identify a city’s geographical extent based on the strength of spatial economic interactions with the typical focus being on delineating cities based on the strength of commuting flows. By contrast, morphological approaches identify a city’s geographical extent based on its physical extent. Examples of more functional based approaches to the globally consistent definition of urban areas include Uchida and Nelson (2009; see also World Bank, 2009) and Moreno-Monroy et al. (2021). Meanwhile, examples of morphological approaches include those that identify a city’s physical extent using built-up area data (Heinrigs, 2020; OECD/SWAC, 2020; OECD/UN ECA/AfDB, 2022), gridded population data (Dijkstra and Poelman, 2014; Dijkstra et al., 2021), and nighttime lights data (Balk et al., 2006; Brecht et al., 2013; Ch et al., 2021; Dingel et al., 2021; Ellis and Roberts, 2016; Zhang and Seto, 2011; Zhou et al., 2015).

6