rafmacalaba/data-use-mentions-extended huggingface dataset:
For information related to survey data, we focused on schemata based on the **Data Documentation Initiative (DDI)** , an international standard for documenting survey data (DDI, 2021), in particular the fields used in the Harvard Dataverse and the International Household Survey Network (IHSN) template of the World Bank Microdata Catalog. The **Harvard Dataverse** is an accredited, cross-disciplinary data repository that enables any researcher or institution to publish and archive data and code. J-PAL and IPA maintain a Dataverse data collection called the “Datahub for Field Experiments in Economics and Public Policy” that currently hosts over 200 RCT datasets. The repository builds on a suite of tools for the publication of scholarly data (King, 2007), and its metadata schema is mapped to the DDI Codebook. The schema is organized in blocks that can be used to customize the metadata documentation in individual data collections (Harvard Dataverse, 2021). We considered all blocks used by the J-PAL/IPA Datahub.

The **World Bank Microdata Library** hosts survey and other data from multiple institutions, including the World Bank’s own research departments, which publish their data under the World Bank’s Open Data Policy. It aggregates multiple named collections, including those of the Development Economics Research Group (DECRG), the Development Impact Evaluation Unit (DIME), and the Strategic Impact Evaluation Fund (SIEF),<sup>6</sup> and all metadata can be accessed through an Automated Programming Interface (API) (The World Bank, 2022). The World Bank’s IHSN microdata template has four sections compatible with DDI – Document Description, Study Description, Datasets, and Variables Groups – and an External Resources section compatible with the Dublin Core metadata standard (IHSN, 2022). We considered all of these for the schema and adopted many usage recommendations from Dupriez et al. (2021).

Most existing data catalogs do not cover fields related specifically to the design of RCTs. For these
Policy Research Working Paper 11026

# **Abstract**

Longitudinal surveys are an invaluable source for analyzing the current state of and changes in human populations over time. However, maintaining the accuracy of estimates from a panel sample becomes more difficult as the length of the panel survey increases. Key concerns are the lack of sample representativeness, due to the sample erosion caused by deaths and movers and the impact of new births, and migration flows. Moreover, sample fatigue introduces an increasing measurement error. Correct design, implementation, and use of a panel survey considers a set of methods to deal with these problems at different stages of the statistical process: the sampling design, the data collection, and the estimation. This paper focuses on the case of panels with a rotating sample design. This case represents a powerful hybrid solution for facing the impact of panel dynamics on sample representativeness. Empirically, the paper focuses on the estimation procedures. Using data from the Uganda National Panel Survey, the longest Living Standards Measurement Study panel survey, it experimentally evaluates the suggested technique. In summary, the findings show that the calibrated generalized weight share method base estimator yields individual-level statistics that appear to be more accurate than those produced by the current Uganda National Panel Survey estimator. Additionally, the calibrated generalized weight share method base cross-sectional estimates on the transition matrix show a generally higher degree of stability when the sample is changed compared to the current Uganda National Panel Survey estimates.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gponzini@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.
. Besides, to enhance comparability of consumption data over the four waves, all expenditures have been deflated across both space<sup>2</sup> and time and expressed in 2005 constant prices – as well as converted, when necessary, from Ghanaian second cedi (GHC) to Ghanaian third cedi (GHS), i.e. for GLSS-3 to GLSS-5.

A summary of distributional statistics obtained from the GLSS data sets is given in Table 1. Besides the growth of the real mean and median consumption expenditures, the most notable feature is the picture that emerges across different indicators of inequality. The consumption shares of the poorest percentiles of the population decreased between approximately 0.9 and 1.4 % a year in the period examined, in contrast to what is observed for the richest percentiles, whose shares experienced average yearly increases of around 0.2 %. Inequality in household consumption was initially constant, but widened considerably between 1998/99 and 2005/2006 – a jump of about 7 % in the Gini’s coefficient and 20 % in the Theil’s index.<sup>3</sup> Inequality has remained constant at the higher level after 2005/06, but

> 1 We use adult equivalent scales because also the official consumption, poverty and inequality figures are expressed in adult equivalent terms.

> 2 The price deflator differs across the ten regions in which Ghana is divided and within each region by urban and rural areas.

> 3 Running a simple _t_ -test of the difference between Gini and Theil indices from the 1998/99 and 2005/06 samples yields a _p_ -value of around zero, which confirms the finding that points to increasing inequality over the 19982005 period at any of the usual significance levels.

4
The remainder of the paper is organized as follows. Section II presents the data and the empirical framework. Section III presents our main results. Section IV discusses robustness checks and extensions. Section V concludes.

# **II. Data and Empirical Framework**

This section describes the data and sources used in the empirical analysis. The section also presents the empirical framework used to explore the effect of the COVID-19 oil price crash on the stock returns of U.S. oil firms.

# **II.1 Data**

The daily data for oil and stock prices used in the analysis covers January 2, 2020, to June 30, 2020, for 43 U.S. oil firms. Daily crude oil prices are obtained from the U.S. Energy Information Administration. We use oil prices for the Western Texas Intermediate (WTI) benchmark. Daily data on stock prices for U.S. oil firms are obtained from Compustat. The list of U.S. firms used in the empirical analysis is shown in Appendix Table A1.

Data for debt and other firm characteristics are from 2019, meaning they are _pre-determined_ before the period of analysis. Debt data are obtained from the Compustat database. The debt variables used in the analysis include total debt, long-term debt, short-term debt, and cash as shares of assets. Debt variables are measured in the last quarter of 2019—before COVID-19 was discovered. Shortterm debt/assets is the ratio of current liabilities to total assets (DLC/AT). Current liabilities are those that mature within a year. Long-term debt/assets is the ratio of total long-term debt to total assets (DLTT/AT). Long-term debt is the liabilities with maturities longer than one year. Finally, total debt/assets is the ratio of total debt, including short-term and long-term debt, to total assets ((DLC+DLTT)/AT).

Other firm characteristics used as controls include the logarithm of the firm’s assets, beta, market-to-book
Lopez, 1997). By reducing normal food consumption and nutrient adsorption, diarrheal diseases are also a significant cause of malnutrition, leading to impaired physical growth and cognitive development (Guerrant et al., 1999), reduced resistance to infection (Baqui et al., 1993), and, potentially, long-term gastrointestinal disorders (Schneider et al., 1978). Improvements in sanitation thus can help reduce the transmission of pathogens that cause diarrhea by preventing human fecal matter from contaminating environments.

In January 2007, the Water and Sanitation Program (WSP) launched the Scaling Up Rural Sanitation program<sup>3</sup> to address the precarious sanitation conditions of large rural populations in the developing world. With technical support from WSP, local and national governments are implementing the program in three countries: Indonesia, India, and Tanzania.

The Scaling Up Rural Sanitation program focuses on learning how to combine the approaches of Community-Led Total Sanitation (CLTS), behavior change communications, and social marketing of sanitation to generate sanitation demand and strengthen the supply of sanitation products and services at scale, leading to improved health for people in rural areas. The project

> 2 A recent four-county study estimate found that the annual economic impact of poor sanitation is approximately US$1.4 billion (1.5 percent of GDP) in the Philippines, US$780 million (1.3 percent of GDP) in Vietnam, and US$450 million (7.2 percent of GDP) in Cambodia (WSP, 2008).

> 3 For more information on the Scaling Up Rural Sanitation program, see www.wsp.org/scalingupsanitation.

2
# 4. Leave room for judgment

Our caution is against using data reductively: as a replacement or substitute for managing. Management _must_ be about more than measuring. A good manager needs to be able to accommodate the immeasurable, since so much that is important to human thriving is in this category; dashboards et al certainly have their place, but if these were all that was needed then ‘managing’ could be conducted by machines. We all know from personal experience that the best managers and leaders are those that take a holistic interest in their staff, making the time and effort to understand the subtle, often intangible processes that connect their respective talents. As organizational management theorist Henry Mintzberg (2015) wisely puts it, Measuring as a complement to managing is a fine idea: measure what you can; take seriously what you can’t; and manage both thoughtfully. In other words: If you can’t measure it, you’ll have to manage it. If you can measure it, you’ll especially have to manage it. Have we not had enough of leadership by remote control: sitting in executive offices and running the numbers—all that deeming and downsizing?<sup>37</sup> Contrary to the “what can’t be measured can’t be managed” idea, we _can_ manage the less measurable if we embrace a wider set of tools and leave space for judgment. The key for practitioners is to begin with a recognition that measurability is not an indicator of significance and that professional management involves far more than simply “running the numbers”, as Mintzberg puts it. Perhaps the most compelling empirical case for the importance of ‘navigating by judgment’ in public administration has been made by Honig (2018), in which he shows – using a mix of quantitative data and case study analysis – that the more complex the policy intervention, the more necessary it becomes to grant discretionary space to front-line managers, and the more necessary such discretion is to
2015; Duflo, Dupas, & Kremer, 2021). These two dimensions of education cannot be considered entirely separately. Improving the quality of education has more impact if more children go to school for longer, and programs that increase years of schooling lead to more learning if the underlying education system is of a higher quality.

In this paper, we analyze over 150 educational policies and interventions across 46 countries and identify the most efficient policies and interventions to improve education outcomes. We use a recently developed, unified measure—learning-adjusted years of schooling (LAYS)—that combines improvements in access and quality. By doing so, we make it possible to compare the effectiveness of a broad range of education interventions using a concrete and policy-salient metric.<sup>2</sup><sup>_,_3</sup> For a subset of interventions for which cost data are available, we include cost-effectiveness analysis and comparisons, a critical component to assess trade-offs of the most efficient policy and program to invest in.

> 1We calculate this based on a measure of expected years of schooling using source data from the UNESCO Institute for Statistics (UIS) compiled for the World Bank Human Capital Index 2020.

> 2In previous work, the concept of LAYS has been applied to characterizing the differences in quantity and quality of education across countries (Filmer, Rogers, Angrist, & Sabarwal, 2020), which we refer to in this study as “macro-LAYS” to distinguish them from intervention- and policy-level effects expressed in terms of LAYS, which we refer to as “micro-LAYS.”

> 3Since we adjust any increase in years of schooling for quality, one implication is that in the combined measure, improved access is valued in large part to the extent that schooling leads to at least some improvements in learning. This reflects an explicit value judgement that a central goal of schooling is to produce learning, and that other benefits of schooling (such as enhanced socio-emotional skills and reduced fertility) are not the only ones to
26 Transfers, and Schooling in Nicaragua **.”** _World Bank Economic Review_ 22(2): 271-290.

- Grosh Margaret, Carlo del Ninno, Emil Tesliuc and Azedine Ouerghi. 2008. _For Protection and Promotion: The Design and Implementation of Effective Safety Nets_ . The World Bank.

- Henderson, Pamela W., and Robert A. Peterson. 1992. "Mental accounting and

- categorization." _Organizational Behavior and Human Decision Processes_ 51(1): 92-117.

- Hirst, D. Eric, Edward J. Joyce, and Michael S. Schadewald. 1994. "Mental Accounting and Outcome Contiguity in Consumer-Borrowing Decisions." _Organizational Behavior and Human Decision Processes_ 58: 136-152.

- Hoddinott, John, and Lawrence Haddad. 1995. “Does Female Income Share Influence Household Expenditures? Evidence from Côte d'Ivoire.” _Oxford Bulletin of Economics and Statistics_ 57: 77-96.

- Hsieh, Chang-Tai. 2003. “Do Consumers React to Anticipated Income Changes? Evidence from the Alaska Permanent Fund.” _American Economic Review_ 93(1): 397-405.

- Hsieh, Chang-Tai, Satoshi, Shimizutani, and Masahiro, Hiro. 2010. “Did Japan’s Shopping Coupon Program Increase Spending?” _Journal of Public Economics_ 94: 523-529.

- Islam, Mahnaz, and John Hoddinott. 2009. “Evidence of Intra-household Flypaper Effects from a Nutrition Intervention in Rural Guatemala.” _Economic Development and Cultural Change_ 57(2): 215-37.

- Jacoby, Hanan. 2002. “Is There an Intra-household ‘Flypaper’ Effect? Evidence from a School Feeding Programme.” _Economic Journal_ 112: 196-221.

- Kazianga, Harounan, and Christopher, Udry. 2006. “Consumption Smoothing? Livestock,

- Insurance and Drought in Rural Burkina Faso.” _Journal of Development Economics_ 79-2: 413-446.

- Kooreman, Peter. 2000. “The Labeling Effect of a Child Benefit System.” _American Economic Review_ 90(3): 571-583.

- Lazarus, Richard S., and
" the invention in the United States or "importing" the invention into the United States. Note that while there are several patenting offices in the world, we have chosen to only use U.S. patent data. This is because different patenting agencies have different criteria as to what constitutes a novel and original innovation. By using only U.S. patent data, we are adhering to a consistent set of minimum standards for an innovation.

The data set, which we obtained from the USPTO website, provides information on the number of U.S. patents distributed by the country of origin, which is determined by the residence of the inventor listed first on the patent application. The patent data set

> 34 Psacharopoulos (1985) attributes the observed inversely relationship between the rate of returns to education and per capita income to the relative scarcity of human-to-physical capital within each group of countries.
# **Figure C7 High school-dropouts wage skill premium and terms of trade**

<!-- Start of picture text --> ARG BOL BRA CHL<br>50 100 150 100 120 140 60 80 100 120 100 150 200<br>COL CRI ECU HND<br>90 100 110 120 80 100 120 90 100 110 120 80 100 120<br>MEX NIC PAN PER<br>90 95 100 105 80 100 120 80 100 120 100 120 140 160<br>PRY SLV URY VEN<br>90 100 110 120 80 100 120 90 100 110 120 50 100 150 200<br>Terms of trade (2000=100)<br>.7 .5 1 .5<br>.6 .45 .9 .45<br>.5 .4 .8 .4<br>.4 .35 .7 .35<br>.3 .3 .6 .3<br>.6 1<br>.55 .4<br>.55 .5 .38 .9<br>.5 .45 .36<br>.45 .4 .34 .8<br>.4<br>.35 .32 .7<br>.8 .7 .6 .35<br>.7 .6 .55 .3<br>.6 .5 .25<br>.5<br>.5 .45 .2<br>.4 .4 .4 .15<br>Wage Premium, High School-Dropouts<br>.8 .6 .3 .45<br>.55 .25 .4<br>.6 .45.5 .2 .35<br>.4 .4 .15 .3<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

52
Tee 1000<br>3 5 = “r:: ao ee et eee ee eee<br>zx "0 5 10 15 20 25 30 te) 5 10 15 20 25 30<br>x 1008060 Electricity composition| o<1aa x 1008060 Electricity compositionMEietA CodA 9o< =5mamiPoo8Gasea io<br>40 doa sas 40<br>20 maa [=] Reha 20<br>oO() 5 10 15=BEEBE EEE20 2SB EEE25ES 30 fo)te) 5 10 15 20 25 30<br><!-- End of picture text -->
Financial inclusion can help drive development by helping people escape poverty. Financial services can facilitate investments in health, education, or businesses and make it easier to manage financial emergencies. The ability to manage financial risk is especially important for people earning their living through agriculture. Many farmers only get paid once or twice a year and households need to stretch their earnings across the year by saving or borrowing money. Moreover, agricultural production faces a variety of risks related to both production and markets because of their exposure to weather and disease shocks. Households engaged in agriculture may thus especially benefit from financial inclusion – access to and use of formal financial services.

This paper explores the topic of financial risk management in agriculture – how adults who rely on growing crops or raising livestock as their household’s main source of income manage financial risk and use financial services. The data draw from a module on agricultural risk management added in select economies to the 2017 Global Findex questionnaire.<sup>2</sup> Collected by the World Bank in coordination with Gallup, Inc., the data are based on a nationally representative survey of about 15,000 adults in 15 lowermiddle- and low-income Sub-Saharan African economies.<sup>3 4</sup> All averages, including for the 15 economies, are population weighted.

In the 15 surveyed economies in Sub-Saharan Africa, on average 35 percent of adults reported that their household relied on agriculture by growing crops or by raising livestock as the main source of their household’s income. But there is wide variation among economies (map 1). Ethiopia has the highest share of adults reporting relying on crops and livestock as their household’s main source of income, at 63 percent. Mali is a close second at 55 percent. In Malawi, Mozambique and Tanzania, about 40 percent of adults reported agriculture to be their household’s main source of income, followed by about a third to a quarter of adults in most other economies. The Democratic Republic of Congo has
. (2022) show that the average consumption patterns follow state-specific SNAP-benefit-issuance schedules, we show the MPC is also higher in the weeks in which more SNAP benefits are issued to households, by interacting each eligibility indicator with the fraction of SNAP benefits issued in a given week by state. As shown in Appendix Table N12, the interaction coefficient for the MPC is positive and strongly significant for eligible households in both product groups, and significantly higher for eligible products. As suggested by Goldin et al. (2022) and Baker et al. (2017), consumption is complementary for both eligible and ineligible product groups due to fixed shopping-trip costs. The percentage increase in consumption during weeks with higher SNAP issuance is

> 34The noise in these point estimates could also be attributed to the fact that we are unable to identify household eligibility with complete accuracy given limited data on households selected by the Nielsen Consumer Panel. Note also that the point estimates include the price response; they would be smaller without the price response.

22
odds ratio is 1.500 (p-value = 0.000). This is rather remarkable in that it suggests that female clients continue to transact with female agents, even when they are more difficult to access.

With respect to transaction values, the pattern described in Columns 1 to 6 of Table 6 is also evident when restricting the data set in Columns 7 and 8 to markets with high and low presence of female agents. In both markets with more and less than 25% female agents, females make lower value transactions than male customers overall (coefficients = -0.517 and -0.556 with p-values = 0.000 in Columns 7 and 8, respectively), but transaction amounts are again significantly higher for females transacting at female agents (coefficients = 0.500 and 0.436 with p-values = 0.000 in Columns 7 and 8 of Table 6, respectively).

# **Agent characteristics**

A second threat to validity is unobservable differences in agent and customer characteristics. For example, female clients may transact more with female agents simply because female agents tend to also sell other goods or services more demanded by females. While we lack customer-level data, we were able to obtain rich data from the agent registration records. According to Column 7 of Table 1, there are no differences between male and female agents across almost all dimensions including number of employees, sector, existing client relationship with FINCA, opening days per week, daily revenue, stock value, and minimum POS balance (only opening hours per day are significantly lower for female agents). We conclude that based on the data we have available, male and female agents are largely similar and thus differences in the shops or what they sell cannot mainly account for the assortative matching by gender we observe in our data.

We also run our main specifications on a data set which only includes customers transacting at matched agents (i.e., those for whom registration and transaction data could be matched). We do