rafmacalaba/data-use-mentions-extended huggingface dataset:
machine learning (‘applied machine learning is basically feature engineering’ (Ng, 2011)). A brute force data mining approach such as Blumenstock et al. (2015) would algorithmically extract indicators while being agnostic towards the outcome variable. However, such approaches can pick up spurious correlations that make them unreliable in practice (Lazer, Kennedy, King, & Vespignani, 2014). With infinite data and variation, a machine learning approach would drop spurious variables on its own; but when data and variation are finite, focusing on features with intuitive or theoretical links to the outcome of interest can improve stability. Intuition can also suggest more nuanced indicators which a brute force method would neglect. Our approach generates indicators intuitively linked to repayment, which may pick up how consumers manage usage over time, social connections, and consumers’ potential capacity to repay. Indicators that have an intuitive link are also more palatable to implementation partners who can be wary of ‘black box’ methods.

This paper show that these behavioral indicators can reduce the uncertainty around a person’s type. They can derisk interactions between large firms and the poor, at scale. The largest promise of these new technologies is not to duplicate the familiar loan offerings that evolved under the constraints of physical interaction, but to enable new types of formal lending that would not be feasible under historical constraints.

This paper demonstrates the method with data from a telecom in a middle income South American country that is transitioning subscribers from prepaid to postpaid plans. In this country, only 34% of adults have bank accounts but 89% of households have mobile phones.

It tends to be difficult to quantify the performance of scoring rules in mature applications because of a ‘missing labels’ problem: one only observes repayment among people who were granted credit in the first place. As a result, if a lender already uses a sophisticated selection rule that fully incorporates all available information, little can be learned about the performance of different rules. In this regard, our setting
# **3.2 Data from the Private Employment Service Provider**

The private employment service provider collects two sets of data on program participants. The first set contains information regarding the participants’ work experience, skills, and potential barriers to employment. These data are gathered during the profiling interview whereby details on formal education, main and alternative occupations, and self-identified obstacles related to these occupations are recorded. Additionally, information on other skills, such as foreign language proficiency and possession of a driving license, is also collected. Based on the information gathered during the profiling interview, the private provider’s counselors categorize beneficiaries as "less employable," "employable," or "highly employable", respectively. This categorization is based on the counselor’s assessment of the relevance of jobseekers’ skills (technical and soft skills), the relevance of their education to their preferred occupation, and prevailing labor market conditions.

The second set of data includes complete administrative records of the services provided by the counselors to each participant. These activities include conducting a profiling interview, developing an individual action plan, counseling, monitoring, and (depending on the jobseeker) matching. Data on services provided is available for 1,496 jobseekers. However, data on employability assessments is available for 1,399 individuals (or 93.5 percent of the sample). There is no differential attrition between treatment and control groups.

# **3.3 Survey Data**

In addition to the two sources of administrative data, a baseline and end-line survey of program participants was conducted. These surveys collected detailed information on the jobseeker and their household and included modules on: (i) Household demographics and socioeconomic status; (ii) jobseeker’s employment history; (iii) experience with the PES and private employment service providers; and (iv) a battery of questions to characterize each of the jobseeker’s “Big 5” personality traits.<sup>5</sup> The baseline survey had
remittances received from abroad. Unfortunately, we do not know of a similar natural experiment used to study the effect of remittances arising from internal migration.

# **Consequences for income distribution at the place of origin**

Several studies investigate whether remittances increase or decrease inequality in rural areas. Stark, Taylor and Yitzhaki (1986) compare two Mexican villages: one which has had much internal out-migration, and another one which has been subject to more international out-migration to Southern California. Using household data, the authors are able to simulate the effect of small changes in the different types of income (nonremittance income, remittances from internal migrants, and remittances from international migrants) and to provide a fine analysis of household inequality in each village. Obviously, the impact of marginal changes in remittances upon inequality depends upon where the recipients of remittances are located within the village income distribution, on the share of remittances in village incomes, and on the distribution of remittances themselves. The authors find that in the village where many households have links with internal migrants, remittances from internal migrants have an equalizing impact on the village income distribution, whereas remittances from international migrants have an unequalizing impact. Stark, Taylor and Yitzhaki (1988) show the robustness of these findings using extended Gini decompositions. Other studies on Pakistan (Adams, 1994) and the Philippines (Rodriguez, 1998) also find that internal migration is more likely to decrease inequality, in contrast to international migration. The postulated explanation is the selectivity of migrants among the two types of migration. Furthermore, it has also been argued that the effect could also depend on the phase of migration when considered over time. Studying international migration from Mexico to the United States, McKenzie (2005) finds that when the pool of migrants is small, the upper-middle range of the wealth distribution benefit more from migration and remittances. When the pool has grown bigger, it gradually benefits poorer individuals. As already mentioned, the same is likely to apply to internal migration in developing countries since landlord
# **Background: Why is employment growth important for South Asia?**

South Asia’s impressive GDP growth of the last decade has also created employment. Yet, the region has been afflicted by asymmetrical structural change. Performance differed widely across informal versus formal status, across firm sizes, across sectors (manufacturing and services), and across location (leading versus lagging regions) in recent decades, with adverse effects on employment creation. Even though available data indicate that enterprise dynamics have improved over the past two decades, and South Asian economies are more able to reallocate resources to more productive activities, several challenges remain (see The World Bank, 2011; Dutz et al, 2011).

**Small and large firms make for most employment, while medium sized companies are underrepresented in South Asia.** There is evidence of a persistent “truncated competition”, with the constrained market selection process having generated a lack of medium sized firms. This may point to difficulties of new entrants and microenterprises to grow into the 10 to 99 worker size category. Industrial survey data for Bangladesh, India and Sri Lanka for the manufacturing sector exhibit this bimodal distribution, with the lion’s share of workers engaged in micro enterprises (less than 10 employees) and in large firms (over 100 employees) – characteristic of a “missing middle” employment problem. Employees in microenterprises make up 18 to 30 percent of total employment, while employees in large firms account for between 35 and 65 percent (Dutz et al, 2011).

**South Asia requires jobs to accommodate a young workforce that already struggles on the labor market.** The demographic pyramid already shows a significant share of young people who are already demanding jobs. Current employment rates, however, are low, and the situation is expected to worsen since the labor force is forecasted increase significantly by 2050 (Bloom et al. 2010). In addition, the region is struggling with post conflict and conflict situations. Employment opportunities are central
# Does Patient Demand Contribute to the Overuse of Prescription Drugs?

Carolina Lopez and Anja Sautmann and Simone Schaner<sup>_∗_</sup> JEL Codes: I12, I18, O12 Keywords: malaria treatment, demand for prescription drugs, healt care overuse, doctor-patient interaction, Mali

> _∗_ We are grateful to Pascaline Dupas, Rema Hanna, seminar participants at the University of Toronto, Loyola Marymount University, MIT, USC, Rice University, UCSD Rady, the Barcelona Summer Forum, the BREAD pre-conference, the Stanford Rosenkranz Global Health Policy Symposium, McGill, Brown, Microsoft Research, FGV Sao Paulo, PUC Rio, the University of Cape Town, the University of Gothenburg, and the Stockholm School of Economics for many helpful comments. We thank the staff of IPA Mali and especially Alassane Koulibaly for expert field coordination. This project would not have been possible without the advice and support of Dr. Seydou Doumbia and Dr. Issaka Sagara (Malaria Research and Training Center, University of Bamako) as well as Dr. Seydou Fomba (Programme National de Lutte contre le Paludisme (PNLP)). We thank the Ministry of Health and the participating community clinics for allowing us to conduct this research. The study was funded by ESRC/DFID Development Frontiers Award ES/N00583X/1. The research protocol was approved by the IRB of La Facult de Mdecine de Pharmacie et d’Odontostomatologie de Bamako (FMPOS) (protocol No. 2016/47/CE/FMPOS) and registered in the AEA RCT registry (AEARCTR-0001707). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. Carolina Lopez: Brown University, carolina <u>lopez@brown.edu.</u> Anja Sautmann: World Bank, asautmann@worldbank.org
60 obtain district-level tea production data from the Sri Lanka Tea Board’s annual reports for 20192022.

Production is measured in kilograms (kg) and cultivated area is measured in hectares (ha). For crops with harvests in both Yala and Maha seasons (maize, rice, and groundnuts), we compute total production and total cultivated area in each year and district by summing across the two seasons. For each crop, we obtain yields (in kg/ha) as the ratio between production and cultivated area.

To estimate fertilizer coefficients in our model’s Cobb-Douglas agricultural production functions (see Section VI.A.3), we use national production data for each crop in 2022, in metric tons (MT), from the Central Bank of Sri Lanka’s Economic and Social Statistics 2023 report.<sup>100</sup> We use national production data for cinnamon, cloves, groundnuts, maize, onions, potato, rice, and tea.

# **A.C.2 Remote Sensing Estimates**

We produce novel remote sensing estimates of rice and tea yields and of cultivated rice areas at a highly granular level. For rice, the methodology described in Ozdogan et al. (2024) relies on an expert-based image classification algorithm on satellite observations from Landstat and Sentinel2 enhanced to isolate the rice signal based on the premise that paddy rice has specific growing conditions in Sri Lanka. The presence of rice-growing areas is determined by factors related to climate, water availability, topographic position, and farmer decision-making and technical expertise. The methodology monitors rice-related vegetation and water management from space and distinguishes it from other land use types using spectral (color) and temporal information included in the satellite signal. Since water and vegetation indexes are more visible for rice than for other crops, remote sensing tools can more accurately map crop type and predict yields for the former than for the latter (Dong et al., 2016). To estimate rice yields, we use a statistical model that correlates
In addition to a full food security module<sup>9</sup> , the survey also included a full displacement module between August 2015 and December 2018. The module collected information regarding a household’s district and whether the household was currently displaced. Furthermore, if the household was currently displaced, the module also inquired about the month and year of displacement, and the origin governorate. Because of the panel component of the survey, we are able to identify the origin and migration districts of 1,116 households that became displaced only after their first interview, and these are the households on which the analysis primarily focuses.<sup>10</sup> Importantly, previous work illustrates that there is little observable difference in food access following displacement of these 1,116 displaced households for whom we can observe more detailed displacement information and the 6,970 displaced households that were only first reached by the survey after they had become displaced (e.g., D’Souza et al. 2022).

We further merge the WFP survey with data on violence from the conflict. For the 1,116 displaced households for which we observe the origin and migration district, we merge the number of violent incidents and the number of fatalities occurring in both the origin and migration districts at and around the time of displacement; and for nondisplaced households we merge the same violence outcomes in the district in which the household lives at the time of the most recent survey. Prior to 2016, we use conflict data from the Uppsala Data Program; and after 2016, we used data from the Armed Conflict Location and Event Data Project (ACLED). From these data sources, we use number of fatalities caused by violent incidents and the number of violent incidents as our main indicators of violence. However, for 2016 and on, we are also able to disaggregate each indicator by major types of violence- ground battles, remote violence (e.g., airstrikes), and thus we are unable to
|**Australia**|0.945|–<br>0.575|0.998|–0.067|0.990|–<br>0.126|0.954|–<br>0.242|0.946|–<br>1.302|
|**U**nited<br>States|0.893|–<br>0.243|0.961|0.169|0.972|–<br>0.262|0.596|0.284|0.250|0.124|
|**Switzerland**|0.771|0.393|0.971|0.041|0.900|–<br>0.312|0.899|–<br>0.006|0.818|0.010|
|**Chile**|0.688|0.059|0.997|0.183|0.897|0.038|0.498|–<br>0.080|0.376|0.009|

_Note:_ A cutoff of 250 migrants is implemented for calculating the log ratios since they can be highly skewed by the predictions of very small corridors.

_Source:_ Authors‘ calculations based on data described in text.

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the estimated treatment coefficients from regression (1).

11 We do see an uptick in sales beginning in the month of April, prior to the redistribution activities. Households may be selling off more of the grains they have in home storage at this time; alternatively, this result may indicate recall error. We do not measure sales every month and instead rely on a retrospective sales history. Retrospective agricultural data have been shown to be heavily influenced by current agricultural experiences (see for example, Godlonton _et al._ , 2018); a similar issue with recall bias may influence these timing results.

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# **1. Background and Introduction**

Integrated water resources management (IWRM) and organizing it primarily at the river basin level are two of the most common and widely repeated recommendations in the water resources literature of the last decade if not longer (Allee 1988; Galloway 1997; McDonald and Kay 1988; World Bank 1993). Basin management is often associated with the concept of decentralization, of managing water resources at the “lowest appropriate level.” (See, e.g., International Conference on Water and the Environment 1992; Mody 2001). Several conceptual arguments have been presented in favor of decentralization in water resource management, and basin-level management in particular, including that the whole array of resources and use patterns in the basin will be taken into account, management decisions will be based on better knowledge of local conditions, and incentives for stakeholders to actively participate in management will be stronger.

Empirical studies of river basin management systems provide opportunities to examine the claims made for basin-level integrated resources management, and to explore factors that appear to influence its implementation and outcomes. In this research project the project team has searched for those factors and their relationships to river basin management in two ways: with a survey of river basin organizations throughout the world, and with case studies of eight river basins analyzed in greater detail. Some of those eight cases have long histories of basin-scale institutions for water resource management, such as the Guadalquivir river basin in Spain and the Murray-Darling river basin in Australia. Others have emerged recently, as in the Fraser basin in British Columbia, Canada where the Fraser Basin Management Board was established in the early 1990s, succeeded in 1997 by the Fraser Basin Council.

This case has been an extremely valuable addition to the research project, for three main reasons. First, it adds an example of a nongovernmental river basin organization, whereas the other cases were of governmental or inter-governmental structures. The Fraser Basin case is not one of governmental decentralization, but of the creation of a non-governmental body
Gómez-Ibáñez (1993): “Functionally, exactions are at once instruments for shaping the physical environment, for generating public revenue, and for resolving political conflict”.

# _Methodology_

Exactions are backed by a regulatory authority, and some suggest that they border on a taking since owners are not provided just compensation. In the United States, exactions must be able to uphold their force in court since takings are unconstitutional. States are required to authorize local authorities to exact development fees. The following are three characteristics that govern the use of exactions in the United States:

- 1) “Rational nexus:”  the justification for exactions must be determined by an impact from the development that warrants infrastructure investment; the project must comply with the Master Plan, and the exactions must be applied equitably (Altshuler and GómezIbáñez 1993)

- 2) <u>Proportionality: the cost of the infrastructure provided, and thus the amount of</u> exaction, should be proportional to the impact of the project

- 3) <u>Restricted funds: exactions collected can only be used to fund the cost of the</u> infrastructure required by that project (Peterson 2009).

The amount of fees paid varies significantly, not just by state to state, but by jurisdiction. Impact fees are not assumed to fully fund the total cost of infrastructure, so fees may be either set or negotiated. Altschuler and Gómez-Ibáñez (1993) cite a 1991 survey in California which found that municipalities were unable to determine exact fees amounts charged because of such variation.

A major criticism of impact fees is that by acting as a development tax they reduce home construction, especially within the small home market. However, recent empirical work suggests that impact fees may in fact expand housing construction within suburban areas by reducing exclusionary regulations and increasing the percentage of proposed projects receiving local government approval (Burge and Ihlanfeldt, 2006). Burge and Ihlanfeldt (2006) find that in Florida counties, impact fees earmarked for public services other than water and
assigning firms to receive a visit from a municipal inspector did result in an increase in municipal registration, although the impact was much less than anticipated by the government, with only an additional 3 percent of those assigned to treatment formalizing. This low rate results from the inspectors finding some firms closed, not finding others, and from some firms in our sample already being formal to start with. An instrumental variables estimate suggests that the impact of actually receiving an inspector visit is much higher – resulting in a 21 to 27 percentage point increase in registration. We find no evidence of spillovers on neighboring firms, perhaps due to the relatively low increase in inspections and to many firms saying they do not communicate very much with neighboring businesses. Cost-benefit analysis suggests the revenue benefits to the government of bringing these firms into the formal system are likely to outweigh the costs of the added inspections needed to get them to formalize, suggesting that more inspections of informal firms of this size would be beneficial from a fiscal standpoint.

3
Rodríguez, Nohoia. Comité de Prevención y Control del VIH/SIDA de las Fuerzas Armadas y Policía Nacional – COPRECOS Rubio, Martha. UNFPA Salazar, Nicolás. Asociación usuarios Nueva EPS Silva, José. Ministerio de Defensa Nacional (MDN) Simbaqueva, Juan. Recolvih Vera, Mayerlene. Huella de Arte Villera, Rafael. Mutual Ser (EPS) Yopasá, José. Comité de Prevención y Control del VIH/SIDA de las Fuerzas Armadas y Policía Nacional – COPRECOS

**3. Survey for Health Territorial Entities**

# **IMPLEMENTATION EFFICIENCY ANALISIS OF THE HIV/AIDS RESPONSE AT THE DEPARTMENT/DISTRICT LEVEL**

(1) Are there a sufficient number of useful and reliable studies on the HIV/AIDS epidemic in your Department/District?

(a) Is there a common agreement in regard to who are the most vulnerable groups? (MSM; SW; prisoners; MTCT; other)

- (b) What is the HIV/AIDS prevalence rate for these groups?

- (2) Have you developed a Territorial Public Health Plan in your Department/District?

(a) If so, how does that Territorial Public Health Plan tackle the HIV /AIDS response?

- (3) Is there a Departmental Committee for HIV/AIDS in your Department/District?

- (4) What kind of collective actions for prevention and promoting HIV/AIDS have been implemented in your Department/District?

(a) In particular, what collective actions have been undertaken that target vulnerable groups?

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