rafmacalaba/data-use-mentions-extended huggingface dataset:
**References**

Acosta P., Muller, N., Sarzosa, M. (2020). Adult’s Cognitive and Socioemotional Skills and Their Labor Market Outcomes in Colombia. _Revista de economía del Rosario_ 23(1), 109-148.

Ali, D. A., Bowen, D., & Deininger, K. (2020). Personality traits, technology adoption, and technical efficiency: Evidence from smallholder rice farms in Ghana. _Journal of Development Studies_ , 56(7), 13301348.

<mark>Alloy, L. B., Abramson, L. Y., Hogan, M. E., Whitehouse, W. G., Rose, D. T., Robinson, M. S., ... & Lapkin, J. B. (2000). The Temple-Wisconsin Cognitive Vulnerability to Depression Project: lifetime history of axis I psychopathology in individuals at high and low cognitive risk for depression.</mark> _<mark>Journal of abnormal psychology</mark>_ <mark>,</mark> _<mark>109</mark>_ <mark>(3), 403.</mark> Almlund, M., Duckworth, A., Heckman, J. and Kautz, T. (2011). Personality Psychology and Economics. _National Bureau of Economic Research (NBER) Working Paper_ , No. 16822.

<mark>Amanatullah, E. T., and Morris, M. W. (2010). Negotiating gender roles: Gender differences in assertive negotiating are mediated by women’s fear of backlash and attenuated when negotiating on behalf of others.</mark> _<mark>Journal of personality and social psychology</mark>_ <mark>,</mark> _<mark>98</mark>_ <mark>(2): 256.</mark> Anthony, D., and Horne, C. (
# **Figure 1: with the Overall Framework of the GIDD Model**

<!-- Start of picture text --> Box1 - Population Projection Box 2 - Education Projection<br>by Age Groups by education groups<br>Box 3 - New Population Shares<br>(Recalibrated Sampling Weights in Household Survey)<br>Box 4 - Simulation Results<br>from Global CGE model<br>Box 5 - Simulated Household Survey<br><!-- End of picture text --> Source: Bussolo, de Hoyos, and Medvedev ( 2008 ) Box 1 in the figure contains population projection from the United Nations for nearly 200 countries from 2000 to 2090 in five-year intervals. The population projections are disaggregated into age groups and gender. Box 2 contains the projection of educational attainment based on the population projection in Box 1. The education projection in Box 2 is arrived at using changes in the demographic structure over time. The basic idea in projecting education into the future based on population projection is that the average educational attainment of the population changes through the “pipeline effect” as the population ages. This means that the old and unskilled today will be replaced by the young and more educated skilled individuals as the population age advances. As a result, the overall skill endowment of the population in time t+1 increases as the educational attainment improves.

Information in Box 1 and Box 2 are used in Box 3 to recalibrate the base year weights in the household survey to a new set of weights consistent with the population projection in Box1 and the education attainment projection in Box 2. The basic idea is to search for a new set of sampling weights in the household survey that is consistent with the projected population and 7
||||
|terest Facet|13.290|13.230|13.200|0.064|0.086|16,575|
||_(3.073)_|_(3.057_|_(3.089)_||||
|Frustration Reaction|11.033|11.124|11.062|-0.091|-0.029|16,575|
||_(2.535)_|_(2.528)_|_(2.526)_||||
|Motivation Framework|20.862|20.921|20.869|-0.059|-0.007|16,575|
||_(2.873)_|_(2.865)_|_(2.835)_||||
|Locus of Control|17.665|17.648|17.646|0.017|0.019|16,575|
||_(2.299)_|_(2.329)_|_(2.330)_||||
|Present Bias|4.203|4.216|4.237|-0.013|-0.034|16,575|
||_(0.991)_|_(1.006)_|_(0.988)_||||
|S/E Skills Index|0.053|0.073|0.084|-0.020|-0.031|16,575|
||_(0.993_|_(1.001)_|_(1.009)_||||
|GPAs|3.559|3.490|3.548|0.069|0.010|16,575|
||_
al., 2020) or to augment traditional data sources to provide better measures of international migrant stocks (Rampazzo et al., 2021). Developing sound approaches to detect migration events in big data (see for example Chi et al., 2020) can create great opportunities for using this type of data for sampling purposes. One possibility could be, for instance, to use these data to identify primary sampling units or to combine them with data on climate change exposure for determining ‘climate migration hotspots’ to be oversampled in addition to the usual, nationally representative survey sample. All this comes, of course, with non-trivial privacy and ethical issues that need to be addressed as well as with technical challenges, which opens room for new, promising methodological streams of research. However, as the value of leveraging this new data to fill many of the gaps of more traditional data sources gets increasingly recognized by social scientists, it is possible that progress on these fronts will speed up.<sup>35</sup>

- While oversampling is the recommended (and perhaps only) direct method to get around the ‘rare event’ problem, a complementary solution can come from **looking at ‘surrogate outcomes’** (McKenzie, 2022) – that is, at immobility as opposed to only mobility responses. As already pointed out, the existing climate migration literature shows that climate-induced immobility is at least as important as climate-related migration (Letta et al., 2023). For that reason, when considering the reference statistical population for climate migration studies, ‘trapped’ or ‘potential’ migrants should be included to supplement the (typically small) ‘actual’ migrant sample. Multi-topic household surveys such as the LSMS hold many comparative advantages for investigating the causal links between climatic shocks and the ‘failure to migrate’, especially as they contain rich information on a variety of topics that allow for precisely assessing the transmission mechanisms and contextual factors at play. To maximize the research potential for studying climate migration
Page 39 space and the amenities of low rise living. ‘Affordable’ formal housing is now required to be in cramped apartment living—most likely an unintended, but still very real consequence of the multiple design standards in the GDCR.

The AMC’s large stock of well laid out vernacular housing fortunately offers good affordable housing options in the city. **But this stock cannot now expand legally. This cuts off a path to meeting the needs of the large population of low and middle income households with growing purchase power** . The GCDR sets standards that are really only suited to the highest income households. For example, the GCDR requires all formal apartments to build a parking space---whereas automobile ownership in Ahmedabad is 12 vehicles per 1000 population<sup>26</sup> , below the average for India of 15 automobiles per thousand persons in 2006, where India is ranked 127 out of 138 countries for car ownership.<sup>27</sup> The large gap that has been introduced between this more modest standard of legacy urban development and new formal housing not only raises the costs of new Greenfield development. As discussed above, the high GCDR standards raise the costs of regularizing and redeveloping existing urban development for a very large share of the housing stock. Full regularization of tenure requires demolition of illegal structures and redevelopment according to the costly GCDR standards—and most likely a reduction in living space.

> 26 <u>http://www.ourcitiesourselves.org/exhibition/city/ahmedabad referenced on November 18, 2011</u> 27 World Bank Development Indicators referenced 11 August 2011. Data is for 2006. Includes commercial  vehicles, automobiles, vans and SUVs but excludes motorcycles and other two wheelers.
By creating hypothetical tradeoffs between specific job benefits and wages, and keeping everything else equal between alternative job options, we are able to calculate quantitative estimates of workers’ valuations of specific benefits, and identify which benefits are valued the most. In addition, by including formal, informal, and self-employed workers in the survey, we can examine the extent to which preferences for specific attributes differ by individual characteristics and type of current employment.

This study also contributes to the policy discussion regarding informality. While many employment protections, such as basic occupational safety and paid leave, are supposed to apply to all workers, in reality such protections are more likely to be enforced in larger, formal firms (and even there not on a uniform basis) and government agencies may not have sufficient resources to extend enforcement efforts to numerous, small firms. Moreover, attempting to extend additional formal protections or to enforce all existing protections on informal firms may encourage them to become even less visible to regulators. This study therefore views formality as a continuum, and our findings shed light on which benefits attached to formal employment might be more valuable to workers, and thus should have higher priority in policy discussion.

The rest of this paper is organized as follows. Section 2 provides a brief overview of the survey methods, while Section 3 describes the choice experiment design. Section 4 presents results, and Section 5 concludes.

# **2. Survey Methods**

We conducted a survey of approximately 2,000 workers in the two major administrative divisions of Bangladesh.<sup>v</sup> We used a two-stage sampling design to select workers to interview in four districts: Dhaka, Gazipur, and Narayanganj districts in the 8
Taking advantage of a quasi-experiment in historical land settlements and restrictions in Sri Lanka, we provide evidence on the e¤ects of land market restrictions on the equilibrium wage and its spatial pattern. We use household survey data for the year 2002 (Sri Lanka HIES 2002) for our empirical analysis.

The restrictions on land sales, mortgage, and rental raise the cost of labor mobility. The inalienability of land rights implies that in the case of migration, households will lose the right to the net present value of future earnings to land. The increased migration cost leads to lower migration and thus to higher labor endowment in a location with higher incidence of land restrictions. This reduces the land-labor ratio compared to the counterfactual where there are no land even after the Hukou restrictions were relaxed starting from mid 1980s. The young men left their family members back in rural areas to keep the entitlement to the land. See also Iyer et al (2009).

2
(0.00)|(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Observations|41459|41459|41459|41459|9459|9459|9459|9459|99613|99653|99653|99653|

_Note:_ **SE stands for self-employment without employees; Employer are self-employed with employees.** Robust standard errors in parentheses. Marginal effects in the Logit/Multinomial logit model are reported. Sample is restricted to males aged 20-59. To combine the HIES and the RMS sample in Column 1, sample weights are applied. Data source: Bangladesh HIES 2016-17 for nonmigrants and RMS 2018-19 for return migrants; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. * p _>_ 0.1, ** p _>_ 0.05, *** p _>_ 0.01.
Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan<sup>∗</sup> Emily L. Aiken<sup>†</sup> Guadalupe Bedoya<sup>‡</sup> Joshua E. Blumenstock<sup>†</sup> Aidan Coville<sup>‡</sup> _Keywords_ : Targeting; Machine Learning; Mobile Phone Data; Afghanistan _JEL_ : I32, I38, O12, O38, C55

> ∗We thank Seungmin Lee, Maria Camila Ayala and Thomas Escande for excellent research assistance. This work was supported by DARPA and NIWC under contract N66001-15-C-4066, the NSF under grant IIS-1942702, and by the World Bank’s Knowledge for Change Program. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes not withstanding any copyright notation thereon. The views, opinions, and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense, the U.S. Government, the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †School of Information, University of California, Berkeley

- ‡Development Impact Evaluation Department, World Bank
# 5. **Evaluation results**

This section focuses on the presentation and the discussion of the key evaluation results, with a focus on the outcomes of interest related to social behavior and crime. Before discussing the main results, the first subsection describes the socioeconomic context of Port Moresby, and presents descriptive evidence on the extent of crime and anti‐social behavior from the baseline sample.

# _5.1 Context_

PNG is disproportionately young, as 67% of the national population is below 35 years of age and 25% are aged 16 to 35.<sup>12</sup> Urban youth face several challenges. For example, in the 2009/10 Household Income and Expenditure Survey, 58% of urban youth had dropped out of school or tertiary education. 60% of urban youth were unemployed, and less than 7% of those employed are in formal wage jobs5. Furthermore, PNG’s rapid GDP growth has had a limited effect on the well‐being of PNG’s youth. Despite real GDP annual growth of almost 8% in the last decade, urban wage employment decreased by 11 percentage points between 1996 and 2009/10. Rapid increase in rural‐urban migration, high rates of poverty and inequality, and high levels of unemployment create community stress that contributes to crime and violence, which is highly prevalent in the main urban centers. For example, Port Moresby and Lae City reported 33 and 66 homicides per 100,000 persons, respectively, between 2010 and 2013. According to surveys in Port Moresby, Youth aged 19 to 29 are the most active in crime; in fact, criminal activity is so pervasive that 54% of youth report to have been a victim of crime, and 35% report to have been engaged in crime at some point.<sup>13</sup> The national government is well aware of these challenges, and established “Human Capital Development, Gender, Youth, and People Empowerment” as one of the 7 pillars
Several quality control measures are taken. Riders take a photograph of their check-in and check-out station. The app geo-tags and time-stamps each observation when a sub-task is started. Riders take a photo and record the car number on which they ride. The app also included checks against riders changing the time settings on their phone. The different spaces are internally connected and riders are allowed to switch spaces after reporting on the space they boarded.<sup>13</sup> Riders are paid for each ride shortly after completion, and can choose to discontinue participation at any time. As a result, some riders only experimented with the application for a few rides. Table A1, panel A, shows the number of riders that progress through each of the study stages. Appendix D presents robustness checks to attrition in two forms: early attrition from the experiment and attrition (non-response) as a function of treatment assignment; we conclude that reporting through the app is unrelated to treatment assignment and, therefore, that attrition is unlikely to bias our estimates across all phases of the study.

# **Survey questions**

We administer two short surveys through the smartphone application. An initial demographic survey includes standard questions on age, employment, education, marital status, self-assessed socioeconomic status, home location and commuting patterns (timing, lines and frequency of riding SuperVia). Once a woman rider finishes her pipeline of ride tasks, she is invited to take an exit survey, which includes questions on topics that were not included in previous interactions to avoid priming effects. It includes a set of questions on riders’ stated preference as well as stated willingness to pay for the reserved space. Finally, we ask about the perceived risk of harassment across spaces, as well as the stated attitudes toward commuters using either space.<sup>14</sup>

> 13Upon checking out, riders are asked whether they switched spaces; they report doing so on 4-6 percent of rides across phases. We revisit this as a margin of adjustment
the Data Analysis Group. Kilic and Murray were in the Data Generating Group and had full responsibility for extracting the remote sensing data and matching it to the household records in the LSMS-ISA data to create a number of different paired weather-LSMS-ISA data sets.<sup>3</sup> In these data sets, the source of the weather data and the obfuscation methods was anonymized prior to sharing with the Data Analysis Group. Josephson and Michler made up the Data Analysis Group and had full responsibility for cleaning the LSMS-ISA production data, running the regressions, and conducting and writing the analysis. The pre-specified analysis was carried out on the anonymized data sets and these results were posted to arXiv.org prior to unblinding (Michler et al., 2021).<sup>4</sup> The generation of data sets in this manner preserves the objectivity of any findings regarding differences in outcomes between different remote sensing products and types of obfuscation.

Our results have implications for two distinct streams of literature. The first is for the literature

> 3For example, in one data set the remote sensing weather data product may be matched with the exact household GPS coordinates in the LSMS-ISA, while in another data set the remote sensing weather data may be matched with low-level administrative centroid.

> 4An initial incomplete draft of the paper was posted to arXiv.org on 22 December 2020 (arXiv:2012.11768v1). This draft was a placeholder and posted to satisfy project reporting requirements at the World Bank. On 19 August 2021 the complete anonymized draft was posted (arXiv:2012.11768v2). On 23 August 2021 the Data Generating Group shared the key to de-anonymize the data with the Data Analysis Group. The version of the paper in hand is the updated paper (v3) containing the same analysis as the anonymized v2 of the paper, just replacing the anonymized placeholders with the actual de-anonymized names.

6
**|||||**0.00838**|
|**N**|**2151**|**2151**|**2149**|**1281**|**2149**|
||*** p<0.10**|**** p<0.05**|***** p<0.01**|||

_Source:_ Authors‟ calculation based on Lao PDR customs data Finally, as a robustness check on these results, we ran the regressions in Table 7 and Table 8 on a couple of sub-samples. First we dropped all transactions of firms that had exported copper at one point or another, which we refer to as “the copper industry”. There is a concern, outlined earlier, that this industry has a significant number of extraordinary exports of machinery, which is, most probably, just sent abroad for repairs. This concerns ~300 of the ~5,000 FPD triplets, singular events which would inflate the creation and destruction of FPD flows, and induce a bias. We find that the results, not shown here, are remarkably similar to Table 7 and Table 8. Furthermore, Table 9 shows the same survival regressions on a sample from which all products with an HS classification as garment or footwear were excluded (~2,000 FPDs). Again, this industry is special in that there are a few large firms, established before the expiration of the Multi Fiber Agreement specifically for the export markets and enjoying some special tax exemptions. We see that the results are again, remarkably similar, with the same signs and magnitudes but a few drops in statistical significance due to higher variance. This analysis confirms the fact that grouping transactions into FPDs is a useful way to filter out some of the noise inherent in the data and therefore produce robust, statistically significant and meaningful results.

# **5 CONCLUSIONS**
trading with another country is more costly than trading domestically. Novy (2013) developed a micro-founded measure of aggregate bilateral trade costs using a theoretical gravity equation for the trade cost parameters that capture the barriers to international trade. The resulting solution expresses the trade cost parameters as a function of observable trade data, providing a micro-founded measure of bilateral trade costs. The measure is easy to implement empirically for a number of countries with readily available data. One drawback is that the contribution of the individual cost factors cannot be easily disentangled by simple inspection of the measure. A way proposed in the literature to overcome this is to combine indirect and direct measurements into a single regression (Arvis et al. 2013).

## Determinants of trade costs

To estimate the contribution of different determinants of trade costs, a gravity model is estimated for a panel of up to 23 advanced economies and 72 EMDEs with annual data for both trade costs and all determinants of trade costs over 2007-18. The sample includes 25 industrial commodity (energy and metals) exporters, all of which are
The first question we explore is a routinely asked question in the Bangladesh survey on changes in the level of corruption. The question asks about increases or decreases in the level of corruption over the last quarter. The exact wording of this question is presented in the Appendix.

In Table 1, we present the responses from 1,417 firms surveyed. These results indicate a general stability in the level of corruption, with a slightly higher percentage of managers (21.81%) indicating a decrease in corruption compared with that who indicates an increase in corruption (13.83%). These results provide some insights into changing levels of corruption over time, one of the important drivers in changes in business activities (investment, sales, etc). These general questions on corruption mask important differences in firm managers’ personal experiences with corruption. General levels of corruption may have a low correlation with actual firm experiences with corruption.

**Table 1: Evaluations of Changes in Corruption**

||Number|Percent|
|---|---|---|
|No Response|230|16.23%|
|Much Lower|54|3.81%|
|Somewhat Lower|309|21.81%|
|About the Same|582|41.07%|
|Somewhat Higher|196|13.83%|
|Much Higher|46|3.25%|
|Total|1,417|100%|

An alternative approach is to directly ask firms about their evaluation of the role of corruption in affecting business activity. The second question that we explore is a question on the relationship between a number of different factors and business activity. Along with a number of questions related to economic factors affecting business, one 9