rafmacalaba/data-use-mentions-extended huggingface dataset:
“formal” and “informal wage” sectors respectively.<sup>24</sup> The remaining 32.6 million workers (69.0 percent) were self-employed. In 2004, Vietnam’s workers on average had attained 7.7 years of education, and worked 34.8 hours per week,<sup>25</sup> and its wage workers earned .94 million dong per month. Formal sector workers worked longer hours (41.8 hours on average) than those who worked in the informal sectors (35.1 hours and 33.0 hours for those in the informal wage and self-employment sectors respectively). On average, workers in the formal sector were more educated with average total education of 11.1 years, whereas the corresponding figures were lower for the informal wage sector (6.6 years) and self-employed workers (7.1 years). In 2002, applying the sampling weight in the VHLSS 2002, only 29.6 percent of total workers had wage jobs: the increase in the proportion of wage workers in total workers is attributable to the rise in the proportion of workers employed in the formal sector from 14.2 percent in 2002 to 16.6 percent in 2004.

One limitation of using the VHLSS is that self-employed individuals cannot be included as they have no wage information. Whereas it is possible to calculate income at the household level, it turns out to be difficult to attribute household income to individual household members. However, as the VHLSS includes wage information for informal wage workers, this enables the researcher to extend the sample coverage beyond the formal sector. The data are also limited in that they are likely to exclude a substantial number of migrants who hold a temporary or no

> 24 The classification of employment is based on the response to a question “for whom has [name] worked?” in the VHLSS 2004: those who responded to work in “foreign-invested sector”, “state”, “collective economic sector
data gives broadly similar results (see Annex 6, Table A6.1 – A6.4). The exception is when we restrict our attention to high-density clusters and use the _LandScan_ data, in which case the Latin American dummy remains significantly positive (indicating “over-urbanization”) in the regression of urbanization on the share of the agricultural sector in a country’s GDP. Finally, for the cluster method using the _WorldPop_ data, Caribbean countries continue to have significantly higher levels of GDP per capita than predicted given their levels of urbanization (see Annex 6, Table 6.5). However, again, the sample size for this regression is restricted to only 40 countries. As an additional observation, it will be noted that the basic stylized relationships between urbanization, development and structural transformation break down when the cluster method is used in conjunction with either the _GHS-Pop_ or _WorldPop_ data. Insofar as the basic stylized facts are more stable across different input population data sets, this suggests that the AI results are more robust than those for the cluster method. Given that the AI also relies on additional sources of data – in particular, travel time estimates – this should perhaps come as no surprise.

# **5. Conclusion**

When levels of urbanization are measured based on national definitions of urban areas, countries in the LAC region are seen to depart significantly from the basic stylized facts of urbanization, development and structural transformation. Most notably, Latin American countries appear to be “over-urbanized” relative to their levels of development and the importance of the agricultural sector to their economies. However, when levels of urbanization are instead measured using a consistent set of criteria across countries, this apparent “over-urbanization” is revealed to be an “urban myth.” This finding tends to hold irrespective of whether the Agglomeration Index or a cluster-based methodology is adopted as the approach for consistently defining urban areas across countries. It, furthermore, tends to hold irrespective of the choice of gridded population data used as input into these two
discusses these issues in more detail.

The data on relative factor endowments and factor intensities is taken from Cadot, Tumurchudur, and Shirotori (2009). While finding proxies for factor endowments at the country level is relatively straightforward (physical capital constructed according to the perpetual inventory method, years of schooling, hectares of arable land, number of workers), factor intensities at the product level are not easily observable. Cadot,

> 12 The resulting metrics _distanceck_ is time-varying as countries’ relative factor endowments evolve over time, with the time subscript in Equation 1 omitted for simplicity.

9
<!-- Start of picture text --> K K<br>BM<br>Bu<br>Bu<br>S<br>M<br>b b<br>S<br>z z<br><!-- End of picture text --> Figure 4: Financial Choice without transaction costs (left) or perfect enforcement (right).

other parameters so as to maximize the likelihood that a household obtains the reported expected net income from its financial choice in the particular diagram it faces.

# **4 The Data**

The data used in this paper are the Townsend-Thai data set and come from a specialized but substantial cross sectional survey conducted in two provinces in the Northeast and two in the Central region of Thailand in May l997. It contains a wealth of precrisis socio-economic and financial data on 2,612 households.<sup>16</sup> The survey instruments collected current and retrospective information on wealth (household, agricultural, business and financial) and access and use of a wide variety of formal and informal financial institutions (commercial banks, agricultural banks, village lending institutions, moneylenders, as well as friends, family and business associates). The data also provide detailed information on household demographics, education and other characteristics.

Because these data provide rich and detailed information about the household and the financial intermediaries, they are particularly well suited for the present study. Appendix A describes how the variables are constructed from the original data. We now turn to a brief description of some of the salient features of the data and constructed variables.

> 16See Townsend et al. (1997) for more details on the sampling methodology and the data. From the original data of 2,880 households we dropped those did not report expected income.

14
a period starting 12 months before the hazard and until 12 months after. The second analysis moves the focus from specific events to a national level and hazards that occurred between June 2012 and March 2018. To find any short and medium‐term impacts that each disaster type has, a fixed effect regression with 12 monthly lags is performed.

Overall, the results are not particularly significant for either analysis. Two case studies of typhoons in the Philippines exhibited the strongest results, where one found a clear drop in mean nightlight during the month that Haiyan struck and similarly a clear drop when Rammasun struck in July 2014. The 2017 floods in Thailand also exhibited a drop, but compared to noise in other months it was rather small, whereas the 2016 Aceh Earthquake exhibited no difference between cells hit by it versus those that were not affected. Part of the reason for these results could be that an influx of aid and personnel would lead to a net effect of zero, but given the noise that is exhibited overall in the VIIRS data it would be hard to draw any more confident conclusions in this regard. The issue of noise in the underlying monthly data has been well illustrated in Hu and Yao (2019), where the monthly VIIRS data showed much lower coorelation with GDP than annual VIIRS and DMSP data.<sup>1</sup> The fixed effects regressions were also inconclusive, as there was little if any significance across disaster types and months. Several methods were tried to correct for the noise, such as dropping any negative values, setting any value below a certain threshold to zero (both for

> 1 The annual VIIRS data were not used in this study, as they are currently only available for 2 years

3
44 for additioual factors. The data are taken largely from USAID's Contraceptive Prevalence Surveys (CPS) and Demographic and Health Surveys (DHS), although information from more modest efforts are included vhere possible. It should be pointed out, however, that this effort is very preliminary and draws only on accessible data and undertakes little analysis.

Source of Contraceptives in Developing Countries All Methods. In theory, individuals can obtain contraceptives from a range of sources. The availability of various sourc-s differs across countries and regions, and neither the quality nor convenience of sources can be generalized. More important to the task at hand, many potential sources are not clearly distinguished in data collection, vhich makes comparisons even more difficult. For example, some countries have data on use of NGO sources vhile others have lumped these into an "other" or "private" category. None of the surveys make it possible to distinguish between users of a social marketing and a commercial product.8 Table III.1 provides figures on contraceptive method source among current users for those countries for vhich 1980s data are available. Where data exist for multiple years, the most recent year is used. Contraceptive prevalence in each of the countries is listed to indicate vhat proportion of vomen of reproductive age in the country contracepts at all. The data in the table suggest no particular pattern to sources of family planning. Some very poor countries rely heavily on government facilities, like Nepal (73.9 percent), Pakistan (66.8) and El Salvador (81.5), while in other countries--such as Paraguay (100 percent), Bolivia (93 percent) and Egypt (69.4 percent)--couples 8. CSM programs nave been successfully established in Bangladesh, Colombia, Dominican Republic, Eastern Caribbean, Egypt, El Salvador, Honduras, India, Jamaica, Nepal, Mexico, Sri Lanka, and Thailand.

**al.fpt?auily** Pluming Paper (whole)
We find that the general measure of business-friendly regulations, as well as the measures of getting credit and enforcing contracts are negatively associated with the poverty headcount. However, these associations are statistically significant only in the case of getting credit and enforcing contracts. In terms of comparative statics, a 10% improvement in the enforcing contracts indicator results in a 2 percentage points’ reduction in the poverty headcount, a sizable effect.

We next investigate the likely conduit for the association between lower poverty and the ease of doing business (Tables 4a and 4b). Djankov et al. (2002) offer suggestive evidence that the relationship goes through increased rates of new business formation in countries with improved regulation.

We test this hypothesis, using World Bank data on the number of newly created businesses. We first demonstrate that business regulation is highly correlated with new business formation, both using the overall Doing Business index, as well as the separate measures on starting a business and acquiring business licenses (panel A). Second, we show that new business formation is negatively related to poverty, though this association is statistically insignificant (panel B). Further work is needed to collect data on all formal businesses, not just limited liability corporations but also sole proprietorships. Such data may provide a more robust link between business regulation and poverty. For now, the conduit from business regulation to poverty through new business formation is only suggestive.

8
Table 9: EFFECTS ON TRANSFERS FROM EMPLOYERS

||Househol|d received trans|fers from...|
|---|---|---|---|
||any<br>employer(s)|female<br>respondent’s<br>employer(s)|male<br>respondent’s<br>employer(s)|
||(1)|(2)|(3)|
|Treatment|-0.039***|-0.035***|-0.013***|
||(0.008)|(0.008)|(0.004)|
|Mean in control|0.091|0.081|0.032|
|Adjusted R-squared|0.068|0.059|0.039|
|Observations|13464|13464|7778|
|_Panel B:_|_Sample of _|_wage-workers in_|_all 3 surveys_|
|Treatment|-0.040***|-0.026|-0.019|
||(0.013)|(0.016)|(0.015)|
|Mean in control|0.133|0.138|0.073|
|Adjusted R-squared|0.079|0.071|0.077|
|Observations|6336|4488|1332|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance
education; and creative economy.<sup>31</sup> Even though it is mandatory that the pedagogical support field is chosen by all schools, schools can then choose the specific subjects of the extracurricular supported activities (either Portuguese, mathematics, sciences). Activities can be carried out inside or outside the school area, often by establishing partnerships with NGOs or public/private entities.

Not all students within each participating school are necessarily part of _Mais Educação_ . Student participation depends on his or her interests and on criteria used by the school for student participation, which may differ across schools. We note that each school can select which and how many students will participate in _Mais Educação_ in a given year. Although each school is free to set its own student selection criteria, MEC provides general guidance on student selection, which focuses on students with disadvantaged backgrounds and/or with a higher probability of dropping out.<sup>32</sup> In general, the majority of students enrolled in a participating school end up joining the program. Data from MEC and INEP show that between 2008 and 2011 the average share of participating students per school is 77 percent and the median is 82 percent.

The program began in 2008 and has expanded gradually since then. Table 1 reports the coverage of _Mais Educação_ nationwide and by regions between 2008 and 2011, with the number of schools enrolled in _Mais Educação_ and the share of participating schools in Brazilian public schools.<sup>33</sup> Results show great heterogeneity in the proportion of participating schools across regions. For example, in 2011, the year with maximum program coverage over the analyzed period, whereas in the Midwest 11.2 percent of the public schools were enrolled in _Mais Educação_ , in the Northeast this percentage was only 5.2 percent. Wide variation is also seen in program coverage over time. In 2008, 1,380 schools were enrolled in _Mais Educação_ , with 924,584 students participating in the program. A great expansion
positive social impact.

The collected digital presence data proved to be very volatile, and only Belize and the Maldives showed some persistent improvement in this metric. Following the relatively new literature about the digital presence effects on tourism and other industries, consider now the Instagram posts. As discussed above, these correspond to daily data, aggregated on a monthly basis that mentions a given country. Figure 25 reports the evolution of posts for 2016 to the latest 2018 available data. The series are very volatile and, therefore, it is difficult to extract clear trends and patterns. This may signal that it is difficult to maintain a consistent digital presence or a great influence of one-off factors such as campaigns or events. From the data, some preliminary conclusions can be extracted. First, there is a significant increment in volume for all destinations (but only Belize and Maldives seem to have achieved some persistent digital presence). There is, however, a relative decline for the latest data collected. Second, STP has a considerable increment around 2018, when it reaches the numbers of other locations with many posts. This is not maintained for subsequent periods, however. Unfortunately, the frequency and nature of the data cannot be used to further investigate the effect of digital footprints on tourist arrivals or to relate measures. The digital presence cannot be used in the subsequent regression analysis because the tourism data set only has observations up to 2016, and, therefore, it overlaps with this data in only one year.

> 11 The OECD Tourism Trends and Policies 2018 recognizes that the increased use of digital means is a recent trend: “ _An increasing focus on digital strategies, with digital platforms opening new partnership opportunities and routes to market with reduced costs compared to traditional marketing approaches_ ”.

16
# 1. Introduction

An estimated 5.5 million people were internally displaced in Afghanistan in the summer of 2021, according to the International Organization for Migration (IOM, 2021). The 2020 Global Report ranks Afghanistan among the top five countries with high levels of internal displacement due to conflict and violence. It ranks first with the highest number of Internally Displaced Persons (IDPs) due to natural disasters (IDMC, 2020a, 11-12). Important geographic disparities can be observed, with regions such as Herat, Helmand, Badghis, or Kandahar hosting many IDPs (Figure 1). Since 2017, 350,000 – 480,000 people have been displaced annually due to ongoing conflict and violence (UNOCHA, 2020; UNOCHA, 2021). A developing drought that threatens millions with food insecurity worsens a precarious political context. At the same time, fragile contexts like Afghanistan have relatively scarce on-the-ground data due to constraints on data collection.

This paper explores how remote sensing data, particularly geospatial data, can be leveraged to monitor displacement flows in Afghanistan. It seeks to draw lessons from the 2018 drought, which up to that point was considered one of the worst in decades. Between mid-2017 and mid-2018, Afghanistan recorded a deficit of 70 percent precipitation and insufficient snowfall, which constitutes an important water source for irrigation (Oxfam, 2018). The 2018 drought was dubbed the worst drought in two decades and affected 22 of the 34 provinces in the country. About 13.5 million Afghan people faced heightened food insecurity in 2018, with lost livelihoods, livestock deaths, and forced displacement into urban areas (FAO, 2019). An estimated 371,000 people left their homes, including 120,000 in Badghis province or about a quarter of the population. They congregated in the <mark>provincial capital Qala-e-Naw to receive emergency humanitarian assistance.</mark> <mark>To identify patterns of displacement, the team combined displacement data from the IOM Displacement Tracking Matrix (DTM) with Nighttime Lights (NTL)
|0.001||
|Unemployment||0.086|0.086|0.115|0.087|0.106|0.108|0.413||
|B) Empirical data||||||||||
|Food & Beverages|0.943|0.717|0.002|0.001|0.005|0.015|0.205|0.059||
|Textiles|0.953|0.005|0.748|0.000|0.011|0.029|0.123|0.093||
|Minerals|1.169|0.018|0.025|0.692|0.046|0.087|0.136|0.076||
|Metals|0.876|0.021|0.024|0.016|0.615|0.170|0.122|0.064||
|Other Manuf.|1.039|0.007|0.009|0.005|0.041|0.713|0.166|0.062||
|Non-Traded|1.020|0.008|0.004|0.001|0.003|0.015|0.908|0.061||
|Unemployment||0.014|0.018|0.002|0.008|0.029|0.374|0.556||
||||Firm Inve|stment||||||
|||||Percenta|ge of Fir|ms||||
|||All rms||Lo|w-_K_ rm|s|H|igh-_K_ rm|s|
||_I_<0|_I_=0|_I_>0|_I_<0|_I_=0|_I_>
This paper investigates the linkages among revenues from these taxes, female property ownership, and gender-based discounts on property-related taxes. Sections 1.2 and 1.3 explore trends in female property ownership and property rights in India, and Section 2 reviews the existing literature on the benefits of female property ownership and tax incentives. An understanding of the legal framework behind levying stamp duty and recurrent (annual) property tax emerged through a review of the stamp and municipal acts of 28 states and Delhi (see Appendix 2 for a summary of the results), and the property tax rules of 20 of the largest cities in the country were reviewed. Provisions pertaining to discounts in tax payments and other benefits provided to female property owners are provided in Section 3.

Section 4 presents the study’s main research objectives and details on the methodology used. Following the legal review, eight cities with different discounts for stamp duties and property taxes were shortlisted, and a methodology was designed for collecting insights from focus group discussions (FGDs) with male and female property owners and taxpayers and one-to-one interviews with government officials in state revenue and land administration departments and ULB property tax administration departments (see Section 5). FGDs were conducted with female and male property owners from six of the eight cities short-listed. Due to differences in the legal review findings and the on-the-ground realities of implementing discounts, as well as difficulties reaching relevant government officials in some cities, interviews took place in ten cities, including four in which FGDs were also conducted. Where possible, gender-disaggregated home ownership figures were also obtained from government departments. Finally, Section 6 provides the main conclusions from the research and the policy implications these suggest.

# 1.2 Female property ownership in India

Various researchers report that gender-disaggregated data on property ownership in India is either not publicly available or not collected (Agarwal, Anthwal, and Mahesh 2020; Lahoti and Swaminathan 2016). This complicates an assessment of the level of property ownership among females. An extraction and analysis of raw data on home
and age, and reporting on the intrahousehold inequalities in deprivation status for individuals in MPI poor households, following from the strategy set forth in Alkire, Ul Haq, and Alim (2019). This paper is the first such study to apply this MPI measurement strategy to adults.

Furthermore, because several criteria are applied – e.g., an individual’s displacement status, gender, residence in an MPI poor household, and eligibility for the relevant indicator’s deprivation cutoff – the number of observations used for cell size comparisons can become small. Point estimates where the _n_ <25 is noted with ‘NA’, and if _n_ <=50, brackets ‘[]’ – this follows the reporting criterion established by DHS (Croft et al. 2018: p.1.65) and cautions against strong interpretation of those results.

# **4 Data**

# **_4.1 Data sources_**

The five household surveys conducted in Sub-Saharan Africa and selected for the purpose of analyzing the MPI constructed in Admasu et al. (2021) are presented in Table 2.<sup>8</sup> These surveys differ in population and geographic coverage, as well as sample design and size, limiting the possibilities for comparisons across countries. A full analysis of these data sources can be found in Admasu et al. (2021).

_Table 2: Summary of the datasets_

|**Country**|**Survey**|**Sample Design**|**Geographical**<br>**coverage**|**Population**<br>**coverage**|**Retaine**<br>**d**<br>**sample**|
|---|---|---|---|---|---|
|Ethiopia|Skills Profile<br>Survey<br>(2017)|Multi-stage<