rafmacalaba/data-use-mentions-extended huggingface dataset:
Malaysia|Census of Manufacturing<br>Sector|Census of all industrial frms<br>with no size cutof but<br>restricts analysis to 10<br>workers or more|
|Peru|National Economic Census|Census of all frms with no<br>size cutof but restricts<br>analysis to 10 workers or<br>more|

Note: The table shows selection criteria for countries in the Amadeus database. Keep in sample if 1) the ratio of aggregate manufacturing employment in Amadeus to aggregate manufacturing employment in Eurostat is greater than or equal to 80 percent, and 2) the ratio of manufacturing employment between Amadeus and Eurostat in each bin of Eurostat’s size distribution above the worker threshold (10–20, 20–50, 50–250, 250+) is also greater than or equal to 80 percent.

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# **VI. Concluding remarks**

This note uses a multidimensional approach to estimate the determinants of international tourism and to produce a partial equilibrium analysis of the tourism sector in STP. The tourism industry is analyzed using different determinants highlighted in the literature as important. The main empirical findings, general and specific to STP, can be summarized as follows: The first set of general findings confirms most of the results found in the literature. GDP in the issuing country and the exports of the host country are positively associated with tourism. Higher flight connectivity increases tourist arrivals but does not change expenditures per tourist. Tourist decisions are influenced by relative price variations, which means that a real exchange rate depreciation has a positive effect on arrivals and a negative effect on expenditures. Finally, tourist arrivals and expenditures per tourist have a persistent effect, meaning that increases in one year tend to be maintained in future years.

A causal analysis is performed to evaluate the effect of decriminalization of LGBT and large positive effects are observed. Using a difference-in-differences approach of a dummy variable that takes the value 1 if being LGBT is not a crime in the country and zero otherwise, the dummy is positively associated with tourist arrivals although there is no relationship with per capita spending.

The data obtained on digital presence indicators are partial and from a short period, which limits the ability for meaningful statistical analysis. The preliminary data analysis using Instagram posts reveals high visibility for STP, however with great variability across time. As the literature speculates on the positive effects derived from digital presence, this should be encouraged at all levels. The empirical evidence for other destinations reveals that tourism is positively related to digital presence. As such, investing in digital visibility is likely to have a positive effect on the arrival of tourists.

Using a comprehensive list of determinants, the empirical analysis reveals that STP can do better in terms of the number of tourist arrivals, but it has a good performance in terms of expenditures per tourist. Using a hedonic model of tourist arrivals
# IV. Relationship between government spending and the Human Capital Index

There is a large body of analytical work studying the relationship between spending and human capital outcomes adopting a sector-specific view. For instance, the 2018 World Development Report (WDR) finds that the relationship between changes in public education spending and student learning is often weak. In health, flagship work in the United States using Medicare data has highlighted that higher spending is often associated with poorer outcomes (“Dartmouth Atlas Project” 2019). Given the multidimensional nature of human capital and the possible interactions between different types of spending, in this note we will try to account for the multisectoral nature of both outcomes and spending. In terms of outcomes, our preferred measure is the newly designed HCI. For spending, we create a measure of spending that includes health, education and social protection as defined above. Since the definition of spending varies across the three sectors, the sum of the three might be plagued by substantial measurement error issues.

Using a cross-section of the most recent data, we observe a weak correlation between HCI scores and social spending in Figure 3 (Pearson correlation coefficient ρ = 0.38). Measurement error might partly explain it. Moreover, countries differ along many different dimensions that can be potentially correlated with both spending and human capital outcomes. For example, higher-income countries might have better institutions that could make social spending more effective. In order to partly control for some of these differences, we plot the relationship between spending and HCI for different country income levels (Figure 4). When stratified by income levels, none of the correlation coefficients capturing the relationship between HCI and spending is statistically significantly different from zero. While the correlation between HCI and spending is clearly positive for HICs, there is no evidence that the correlation monotonically declines when we consider less affluent countries. A closer look at both Figures 3 and 4 suggests the presence of extreme outliers that make the interpretation of any correlation particularly problematic. Teasing out the potential role of GDP in
, Tsukayama, E., Goodwin, G. P., Patrick, S., and Duckworth, A. L. (2017). A tripartite taxonomy of character: Evidence for intrapersonal, interpersonal, and intellectual competencies in children. _Contemporary educational psychology_ , 48:16–27.

- Patton, J. (1995). Factor structure of the barratt impulsiveness scale. _Journal of Clinical Psychology_ , 51(6):768–74.

- Peterson, C. and Seligman, M. E. (2003). Character strengths before and after september 11. _Psychological Science_ , 14(4):381–384.

- Peterson, C., Seligman, M. E., et al. (2004). _Character strengths and virtues: A handbook and classification_ , volume 1. Oxford University Press.

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investment incentives are systematically lower on sharecropped than on owned land, the total productivity effect of _barga_ tenure will be the sum of Marshallian inefficiency and investment effects. For observable investments such as irrigation that enter into the production function, multiplying relevant coefficients is one way to estimate total productivity impacts.

# **3. Data and descriptive evidence**

We use basic information at the household level that is available for all households in the village before focusing specifically on plot-level outcomes for owner-cum cultivators only. For the full sample, our data support the notion that issuance of share contracts virtually came to a halt after 1978 and that, while effectively targeting the poor, land reform did not trigger a massive exodus out of poverty. At the plot level, data for owner-cum tenants are consistent with the hypothesis of tenancy-induced differences in effort supply between owned and sharecropped parcels. Levels of input use, productivity, investment, and exogenous plot characteristics (e.g. salinity) that change slowly over time in response to cultivators’ effort are all lower on tenanted as compared to owned parcels, justifying econometric investigation.

# **3.1 Household-level evidence from listing data**

Our data are from a 2008/9 World Bank/FAO survey in 200 randomly selected villages from 10 districts in West Bengal. Villages were selected randomly with probability of selection proportional to the number of beneficiaries from 1978 land reforms, based on official lists obtained from the State Institute of Panchayats & Rural Development (Chakraborti, Mukhodpadhyay, and Roy 2003). The data is thus representative of the universe of West Bengal’s land reform beneficiaries. A complete listing of all village residents provided, for the approximately 96,000 households in our 200 sample villages, basic information on current and past household structure, key asset endowments, and the extent and nature to which households were affected by land reform. This was followed by an in-depth follow-up survey of some 1,800 owner-cum-tenants to compare productivity, intensity of input use, and investment between owned plots
find that these results are driven by the impact of policies implemented in upper-middle-income and high-income countries<sup>25</sup> which is where the implementation of various forms of wage subsidy policies was more frequent.

Assessing rigorously the impact of policies is complex based on our cross-sectional data and the fact that a firm has access to policy support cannot be considered exogenous. Given our available information, it is not easy to identify an instrument that would work for different types of policies. For this reason, we present here some descriptive evidence based on simple OLS regressions but try to compare firms that are as comparable as possible. Specifically, we compare firms that applied and received policy support with those that also applied but did not receive it. Additionally, we always control for country, sector, size and time fixed effects, to make sure that we are narrowing our comparison to firms that are as similar as possible. We take these results as indicative evidence about the possible effect of policies while realizing we are not identifying precisely their causal impact as our results may still be affected by a selection bias.

We focus on four firm-level outcomes of interest, some are outcomes that we expect could be directly affected by the policies (i.e. likelihood of laying off workers, expected future sales growth, likelihood of falling into arrears), while the remaining one could be at the same time affected by policy but also operate as a mechanism that in turn influences future firm-level outcomes (i.e. probability of investing in digital technology and solutions). The results are presented in Table 3 where we separate our analysis for different groups of policies (each policy is separately analyzed in different columns). We observe that certain groups of policies appear to be more effective than others. Specifically, monetary transfers and access to credit which may be relaxing short-term credit-constraints and liquidity problems are correlated with higher future expected sales growth, as well as with higher probability of investing in digital solutions. Wage subsidies, in line with our
growth, all but China were G7‘s industrial countries. By 2009, all except the United States were emerging economies—with China as the top contributor (Figure 1.2).

The replacement of G7 by the G20 as the prime economic forum in the world since the eruption of the global crisis in 2008 has underscored the emergence of a multipolar world. The sheer size of the large emerging economies combined with dynamic and sustained growth has elevated Brazil, Russia, India, and China (BRIC) to the largest contributors to economic growth in the world (Figure 1.2). In 2006-10, almost 45 percent of global GDP was generated in the BRIC. The era of a multi-polar world is firmly established.

**<u>Figure 1.2: The 21st. Century is marked with the emergence of a multipolar world</u>**

<!-- Start of picture text --> 120% Contribution to World GDP growth<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1981–85 1986–90 1991–95 1996–2000 2001–05 2006–10<br>-20%<br>HICs DCs ex-BRICs Brazil Russia India China<br>Source: World Development Indicators, 2012<br><!-- End of picture text -->

# **_Catch-up made the world less poor but not in the poorest countries_**

The narrowing of the huge income gap with the developed countries conferred on China dividends in the form of absolute poverty reduction (Figure 1.3). In the early 1980s, many African countries were more prosperous than China and India. In 1981 for instance, over 835 million Chinese were below the poverty line of less than $1 a day. In comparison, there were just over 212 million poor in Sub-Saharan Africa. Twenty-five years later, China had turned the income-poverty
rate for household grid|80% (60% in Moscow and St-Petersburg)|
|Final sample used in the model||
|Individuals|12,554|
|Households|4,849|
|Population with grossing-up weight*||
|Individuals|143,454,148|
|households**|61,168,334|
|Population according to Census||
|Individuals|146,804,000|
|households**|61,168,333|

# Notes:

* Weights are used in order to adjust the results for sample design and non-response, as well as for deviations from the census. In addition, weights provided with the original data were scaled up to the total population.

** Estimated by dividing the population by the average household size. Source: RUSMOD for 2017 policy year and 2016 RLMS-HSE data

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ulation census of 2018: Barranquilla, Bogot ́a, Medell ́ın (and their metropolitan areas)—three of the largest cities in Colombia—and a fourth “region” of smaller cities.<sup>8</sup> Figure 2 illustrates the geographic distribution of the sample and the number of Venezuelan migrants in the 2018 population census, which can be taken as a proxy of the overall distribution of Venezuelan migrants in the country. Forced migrants in the sample fulfilled the following criteria: (i) were aged 18 or older; (ii) were the household head or partner; (iii) were undocumented at the time of arrival in Colombia; and (iv) arrived in Colombia between January 1, 2017 and December 2018.

We constructed the sampling frame separately for RAMV and non-RAMV forced migrants. For the former, we drew the sample directly from the RAMV census, which had information on 442,462 Venezuelan forced migrants in Colombia. Using the census, we drew a representative sample of 13,083 forced migrant households in the four regions mentioned above, from which we surveyed 1,135 households. For non-RAMV forced migrants, for whom there was no administrative data available, the sampling frame was constructed by combining databases shared by associations of Venezuelan migrants in the four regions above with referrals from forced migrants who were surveyed as part of the RAMV sampling frame. The non-RAMV sampling frame included data from 12,554 non-RAMV households, 81 percent of which were obtained from the organizations. Using this sampling frame, we surveyed a random sample of 1,097 migrant households in the same four regions mentioned above: 527 households referred by the organizations and 570 referred by other irregular migrants. As discussed below, key outcomes were elicited in each household from the household head and partner or another randomly selected adult member. This produced an overall sample of 3,896 forced migrants surveyed in 2,232 households, including 1,947 RAMV and 1,708 non-RAMV individuals.

To assess whether forced
the AS1 group, and had issued title certificates for 1,481 (covering 410 hectares). The remaining 815 unfinished cases were to be completed by the Lands Commission of Ghana. In addition to this, plans for 5,729 land parcels, covering 5,039 hectares had been produced.

The nature of the project and the interventions which accompanied this pilot program were aimed at some of the structural barriers to land registration. The fact that the program created a new registration district and an office with modern equipment for land data collection, processing and storage removes some of the bottlenecks associated with previous attempts. The program also created incentive structures to nudge officials to maintain a constant workflow. Finally, the program facilitated negotiations with chiefs and family heads who hold allodial titles to land in order to ensure consent for the issuance of titles.

# **4.2 Evaluation Design**

We build our analysis on three survey waves conducted in 20 communities that are located around a main road that forms a closed circuit. This main road geographically divides many of the sample communities into two halves. This circuit also corresponds to the boundary of the Land Titling Program. Households within the loop were eligible to participate in the pilot while households outside of the loop were not eligible in the first phase of the program which spans our entire evaluation period. The physical demarcation of the road dividing communities into two forms the basis for our empirical evaluation strategy.<sup>7</sup> More precisely, households located within the loop of the road and not more than 200 meters away from the road are considered as the treatment group in our study. On the other side of the main road, i.e. outside of the loop and outside of the pilot area, we sampled households such that they form two types of control groups: a short-term control group and a long-term control group. At the time of project initiation, it was anticipated that households just outside the loop would be the most likely future recipients of
been published in peer reviewed journals. Therefore, most of the evidence presented below on comparisons across alternative models should be interpreted as tentative priors based on limited evidence from specific contexts.

This review is divided into three sections. The first section begins by very briefly describing some of the many publicly available geospatial indicators. It then reviews selected studies from a rapidly growing literature evaluating the accuracy of small area estimates of wealth and poverty using geospatial data, documenting strong correlations across several studies when compared with census-based estimates. I then briefly touch on three related issues: The sensitivity of accuracy to the nature of the training data; the more limited ability of geospatial data to predict variation across time in welfare than variation across space; and the important distinction between sampled and non-sampled target areas when considering the accuracy of estimates. The second section focuses on comparisons between different types of statistical methods for cross-sectional predictions, including the nature of the geospatial features and different types of models used for prediction. The third section briefly discusses an important recent paper describing how survey and geospatial data were combined to target poor households in Togo (Aiken et al, 2022). The final section concludes with a summary of key points and suggestions for further research.

2. Small area estimates of poverty and wealth with geospatial data a. What type of geospatial features are publicly available?

Geospatial data are typically obtained from satellites, mobile phones, or internet activity. Satellite indicators have a few key advantages over mobile phones and internet activity, however, including the public availability of a large number of indicators, in many cases derived from publicly available imagery provided by the Sentinel 2 and Landsat satellites. Proprietary high-resolution satellite imagery--from companies such as Maxar, Planet, Airbus, and others--can also either be used directly as an input into deep learning models, or as inputs to derive interpretable features such as building footprints, roads, and vehicles. Unlike call detail records, satellite-based indicators typically cover the entire country and therefore avoid selection bias. Call
If these initiatives achieve fruition, they would create competing sources of international bandwidth, driving down bandwidth costs and retail broadband prices. Wireless broadband will also create greater competition with Econet having launched 3G and WiMAX<sup>12</sup> networks while Powertel and Tel-One have deployed CMDA<sup>13</sup> 2000-based high-speed wireless networks.

> 11 As in most low-income countries, Internet access is estimated to be negligible in rural areas in Zimbabwe. The 2010 estimate for Internet users is derived from applying the ZAMPS **[[expand acronym?]]** survey data to the urban population 15 years and older using statistics for Zimbabwe from the United Nations. See: unstats.un.org/unsd/demographic/products/socind/.

> 12 Worldwide interoperability for microwave access.

> 13 Code division multiple access.

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# combination of a scale and a distribution effect.

# **2.2** **_HOI Indicators for Egypt_**<sup>13</sup>

This paper measures the living conditions of infants, children, and youth, to provide a richer description than the one given by monetary measures of poverty alone. From this perspective, the HOI is an instrument that allows us to detect situations of inequality and/or exclusion between individuals in the first stages of the life-cycle, specifically associated with circumstances beyond the individual's control. At the same time, the measure of the HOI represents the first step for the recurrent measurement of the living conditions of Egyptian infants, children, and youth and for the evaluation of progress in access or equality of opportunities as a result of public policy programs at the national, regional, and municipal levels.

This study covers 16 opportunity indicators for children, which can be classified into four groups or sectors: education (three), basic housing services (six), early childhood healthcare (four), and nutrition (three) – see Table 1. These 16 indicators cover human development milestones at the three stages of the life cycle between birth and 17 years of age. All 16 opportunities take into account two criteria: relevance to wellness and quality of life for children and youth, and their responsiveness to public policies. These opportunity indicators are measured at the beginning and end of the decade, in 2000 and around 2009. All nine education and basic housing service opportunity indicators are obtained from the 2000 and 2009 Household Income, Expenditure and Consumption Surveys (HIECSs).<sup>14</sup> And all seven early childhood and nutrition opportunity indicators are estimated from the 2000 and 2008 EDHSs .

_Education._ While one of the three opportunities for education is associated with attendance, the other two are associated with quality or performance. The indicator for attendance is school attendance for children between 9 and 15 years of age. The performance indicators include
motivated by bias against sexual minorities and questions on the existence of policies that mandate training for police officers on the identification and investigation of hate crimes.

The data collected from the surveys were then cross-referenced with public legal databases from international organizations and local human rights nongovernmental organizations. When the answers to the surveys were unclear, the experts were contacted again for clarification and the data verified through desk-top research. The findings were subsequently codified and submitted to experts within and outside the World Bank Group for peer review.

Disagreement often arises about which groups constitute a minority in a country. To control for different interpretations and to guarantee uniformity in the data collection, the experts who completed the surveys were given a predetermined list of ethnic and religious minorities in each of the pilot countries drawn from internationally recognized studies of minorities, including work by the International Labour Organization,<sup>21</sup> United Nations Committee on the Elimination of Racial Discrimination,<sup>22</sup> United Nations Office of the High Commissioner for Human Rights (UNOHCHR 2010, 2014),<sup>23</sup> United Nations Universal Periodic Review Process,<sup>24</sup> Minority Rights Group International,<sup>25</sup> and the European Union Agency for Fundamental Rights.<sup>26</sup> Equality of opportunities for minorities in society depends on the interplay of a variety of factors. This paper focuses on only one of those factors: the formal legal and regulatory environment ( _de jure_ ) for six indicators; it does not cover the practice or implementation of the law ( _de facto_ ) more than tangentially through the identification of policies and other enforcement mechanisms seen as proxies of implementation. Also, not everything of importance is covered. For example, although access to institutions includes laws covering quotas in parliament, it does not include political party laws—an important factor in determining minority representation in political parties. The indicator for access to education covers only public—not private