rafmacalaba/data-use-mentions-extended huggingface dataset:
Applied in 2009**

For headquarters (HQ) based activity of the WBG (including the International Finance Corporation, IFC, the WBG private-sector arm), preliminary calculations of flight records for calendar year 2009 (CY-09) were provided by the American Express (AMEX) travel office. These data consisted of 127,514 records<sup>23</sup> , containing about 189,000 trips by air, covering 447 million miles. The data are limited to trips booked by AMEX in Washington D.C., and do not include travel booked in the WBG’s country offices (CO). The best current estimate is that, overall, HQ-based booking comprises 64% of all WBG air travel in terms of traveled distance.

> 21 One may question the rationale for including such measures in standard footprint calculations. An argument for doing so is that it can give incentives to book flights to a larger degree on efficient aircraft. If many organizations do so, it may give airlines additional incentives to phase out inefficient equipment. This may be a desirable incentive effect when airlines themselves do not face the full global cost of their own emissions.

> 22 For some aircraft, at least or more than half of the total maximal gross aircraft weight is the weight of fuel when tanks are filled up. In particular, the Boeing 747-8 has a maximal take-off weight of 450 tons, of which 230 tons are the fuel carried. This excess weight implies a large “drag” on fuel consumption when tanks need to be filled up from the start of the flight.

> 23 1,685 records representing rail travel found in the Amex data were excluded in the calculations, as well as some pertaining to 2010 travel.

16
6 crisis, the program was expanded in 2009 to 65 districts to reach 2.5 million households in 2012 (World Bank, 2009). The target areas are concentrated in the Southern Highlands, the Northern Highlands, and the Western Region and focused on agro-ecological zones suitable for the target crops.

The input package distributed consisted originally of three vouchers: 1) one for two bags of urea, 2) one for one bag of Di-ammonium Phosphates (DAP) or two bags of Minjingu Rock Phosphate (MRP) with nitrogen supplement, and 3) one for 10 kilograms of hybrid or open-pollinated maize seeds or 16 kilograms of rice seeds, sufficient for half a hectare of maize or rice. Vouchers for each input had a face value equivalent to 50 percent of the market price of the respective input. The remaining 50 percent was to be paid by the farmers.<sup>5</sup> The central government allocates the vouchers to the target regions,<sup>6</sup> taking into consideration the total area of maize and rice under cultivation, the regional maize and rice output as well as the soil and rainfall patterns. To distribute the vouchers further voucher committees were established in the seat of the different local government councils.<sup>7</sup> The Regional Voucher Committees estimate the demand for inputs using historical production data and allocate the vouchers to the districts. The District Voucher Committees (DVC) are responsible for the distribution of the vouchers to the villages. They collect and review information about their maize and rice production, their input use, and other related information. Vouchers are then allocated to the selected villages based on the number of farmers who grow maize and rice and the average land size per farmer. The village council, in consultation with the village assembly, organizes the election of the Village Voucher Committee (VVC), which draws up a list of beneficiary farmers for approval by the village assembly.<sup>8</sup> After approval,
Table 2: Migration Network: Dependent Variable - Number of Migrants in 2011 (1,000)

||All VDCs|in Nepal|NLSS|VDCs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Share of migrants 2001|3.758***|3.779***|6.606***|6.714***|
||(0.101)|(0.101)|(0.676)|(0.678)|
|Constant|0.013***|0.001|0.037***|-0.005***|
||(0.001)|(0.001)|(0.006)|(0.002)|
|District Fixed Eect||Yes||Yes|
|Observations|100880|100880|11648|11648|
|_R_<sup>2</sup>|0.145|0.161|0.060|0.097|

Data Source: 2001 and 2011 Nepal Census. Standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 05 , _∗∗ ∗∗∗ p <_ 0 _._ 01 , _p <_ 0 _._ 001 . Standard errors are clustered at VDC level. Estimated model is _Mj,d,_ 11 = _β_ 0 + _β_ 1 _Yj,d,_ 01 , where _Mj,d,_ 11 is number of migrants, in 1,000, to destination country _d_ from a VDC _j_ in 2011. _Yj,d,_ 01 is share of migrants to destination _d_ from a VDC _j_ in 2001. Share of migrants is calculated as _Yi,d,t_ = _P
1

# **Acknowledgments**

We are grateful to Maxime Émile Armand Roche and Benoît Simon who assisted in the processing of the microdata in this phase of the project, to Gabriel Leung and Irene Wong for help with the Hong Kong data, and to Rachel Lu for help with the Taiwan data. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.

# **Accessing the database**

A portal version of the 2019 database with visualization functionality can be accessed at <u>http://datatopics.worldbank.org/health-equity-and-financial-protection/. The data set itself can be</u> accessed and downloaded, indicator by indicator, or in its entirety, from <u>https://datacatalog.worldbank.org/node/142861 from which model Stata ‘do’ files can be downloaded</u> to replicate the datapoints in the HEFPI data set.

# **Citing the database**

The reference citation for the data is: Wagstaff, Adam, Eozenou, Patrick, Neelsen, Sven and Smitz, Marc. 2019. The Health Equity and Financial Protection Indicators Database 2019. World Bank: Washington, DC.

* Corresponding author: Adam Wagstaff. Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA. Tel: +1 202 473 0566, awagstaff@worldbank.org
a unique experiment embedded into a national agricultural survey in Malawi, this paper investigates the extent, drivers, and persistence of NCME in farmer-reported plot areas. We also examine how farmers learn and adjust to demonstrably accurate information about plot size, both in terms of correcting pre-existing measurement error in plot size as well as their reporting behavior (responses) on non-land agricultural inputs.

Prior to the information treatment, farmers’ self-reported plot areas exhibit considerable NCME. Measurement error is negatively correlated with true plot size, which accounts for close 29
harmonizes product codes across the HS 1996, 2002, and 2007 versions (used in the raw exporter-level datasets).<sup>10</sup> Export values are measured in US dollars (USD) converted from local currency when necessary using official exchange rates from the IMF’s _International Financial Statistics_ . Export values for all LAC countries are Freight on Board (FOB) figures

> 7 For some countries, namely Brazil, Egypt, Estonia, Laos, New Zealand, Norway, Portugal, Spain, Sweden, and Turkey, the indicators were calculated without having permanent access to the customs data at the exporter-product-destination-year level. 8 The firm coding system was changed in Mexico in 2007, thus it is not possible to calculate exporter dynamics indicators such as exporter entry rates in 2007 for Mexico.

> 9 The “special trade system” refers to the recording of transactions where the goods enter or exit the domestic territory in free circulation (customs warehouses, free zones and goods under contract for inward or outward processing). In contrast, the “general trade system” refers to the recording of all other transactions of goods passing the border in their trade statistics, except for transit, trans-shipment and temporary admission transactions (see p. 32 of United Nations, 2008). The exclusion of special trade regime transactions has a minor effect on total exports in those countries given that sales to free zones/customs warehouses are generally negligible.

> 10 See Cebeci (2012) for the details on this consolidation. A list of the unique 4767 consolidated codes and concordances are available at http://econ.worldbank.org/exporter-dynamics-database.

7
.S.** market returns during each of these three crises. Given the proximity **to** Mexico, and the importance of economic links between the two countries, it is not surprising that the link between Latin-American portfolios and **U.S.** returns is strongest during the Mexican crisis. Interestingly, the contagion trading statistic is negative, and is significant at both the manager and investor levels. This suggests that, during the Mexican crisis, managers and investors tended to sell Latin American equities when **U.S.** returns were high, and vice versa. One interpretation is that strong **U.S.** returns in the face of Mexico's crisis bodes well for Mexican equities, which induces a portfolio shift away from the rest of Latin America.

In closing this section on contagion trading, it is worthwhile re-emphasizing the qualitative difference between the results above and the existing contagion literature. The difference is that we measure quantities, as well as prices, and address their joint behavior, whereas much of the literature focuses on correlation in prices only.

**18**
Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles were created using per capita household expenditure. Lower- and upper-bound elasticities show average differences of −0.2 and +0.2, respectively, with the medium-bound elasticity. Demographic controls include area, macroregion, and year fixed effects.

# **Mortality and morbidity**

Data on mortality, years of life lost, and morbidity have been obtained from the Global Burden of Disease Study. Ischemic heart disease, stomach cancer, and stroke are among the most prevalent diseases among men and women in Russia. In 2016, approximately 243,252 deaths were attributed to tobacco-related diseases, of which nearly 86 percent were men (208,451) (table 2). Similarly, more than 2.5 million cases of tobacco-related disease were reported in Russia the same year (table 3). A total of 5,979,509 years of life lost were estimated for all tobacco-related deaths in 2016 (table 4).

> 9 To make this paper comparable to the other cases analyzed by the authors in the past (Chile, Ukraine, Moldova, South Africa, Indonesia, Bangladesh and Bosnia Herzegovina) it was decided to use household level data to estimate price elasticities. However, a full replication of the analysis using individual level data is also performed and included in the annex. Although there is a difference in levels, both analyses yield similar and consistent results in terms of trends and incidence.

> 10 Only partial elasticity of consumption is estimated and used here unless specified otherwise. Using the full elasticity, including the effect on participation, would make the results more precise, but, because of computational difficulties, this step was omitted on the assumption that participation in smoking is less sensitive to relatively small changes in prices.

6
themselves from crime, and also the use of digital technology (Islam et al., 2020). Another significant disparity between female-owned and male-owned businesses is the degree of capital investment. Data from 14 impact evaluations show that the average capital investment by femaleowned firms is more than six times lower than the average for male-owned firm in Africa (World Bank, 2019b). A key question to answer is, therefore, whether highly inclusive digital technology, like mobile money, can benefit women enterprises by increasing their investments.

A recent study across four East Africa economies uncovered a positive relationship between mobile money use and investment by firms (Islam et al, 2018). This study revisits this relationship by exploring whether this relationship varies by gender of the business owner, and also extending the sample of study from 4 economies in East Africa to 16 economies across Sub-Saharan Africa. The analysis is based on a unique firm-level data set with a mobile money module that is implemented using a consistent methodology and the same survey instrument for about 4,700 formal firms across 16 countries in Sub-Saharan Africa. The findings are striking. The positive relationship between mobile money use and investment is confirmed; more importantly, the relationship is largely driven by female-owned firms and is statistically insignificant for maleowned firms. The identification strategy employed is unlikely to obviate all concerns of simultaneity bias between mobile money use and investment given the cross-sectional nature of the data. However, the fact that the relationship is uncovered for female-owned but not maleowned firms attenuates some of the concerns of reverse causality. Furthermore, this study provides external validation for experimental evidence that shows mobile money savings leading to greater capital investments by female-owned micro-business in Tanzania (Bastian et al., 2018) and smallholder farmers in Mozambique (Batista and Vincente, 2020).

We explore potential channels that may convey this effect. Female-owned firms that use mobile money to transact with suppliers are more likely to invest. The importance of supplier relationships uncovered by this paper is consistent with the results of Beck et al. (
0<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <!-- Start of picture text --> EMDEs AEs World<br>Percent of EMDEs 10<br>100<br>8<br>80<br>6<br>60<br>40 4<br>20 2<br>0 0<br>E. Share of world investment F. Change in TFP and investment growth, 2010‐15<br>Percent EMDE excluding BRICS Percentage points<br>Other BRICS 30<br>50 China 20<br>10<br>40<br>0<br>30<br>-10<br>20 -20<br>-30<br>10<br>-40<br>-15 -10 -5 0 5 10<br>0<br>1993-1999 2000-08 2010-15 Change in TFP growth<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2010 2011 2012 2013 2014 2015 2016<br>growth<br>Change in investment<br><!-- End of picture text -->

## **F. Change in TFP and investment growth, 2010‐15**

Sources: Consensus Economics; Haver Analytics; International Monetary Fund; Oxford Economics; Penn World Tables; World Development Indicators, World Bank.

A. Weighted averages. Long‐term average starts in 1991 for EMDEs due to lack of earlier data.

B. Weighted averages. Includes 28 EMDEs with available quarterly data. Long‐term averages start in 1991 for EMDEs and are based on annual data. Last observation is for Q2 2016.

- C. Long‐term averages are country‐specific and refer to 1990‐2008. Latest year is 2015.

- D. Each line shows five‐year‐ahead Consensus Forecasts as of the latest available month in the year denoted
the National Institute of Statistics, Economic, and Demographic Studies (INSEED). Survey estimates are based on the subsample of households with valid GPS coordinates.

Typically, small area estimation applications combine survey data with covariates from census (or other population) data. However, except for Burkina Faso, the last time a census was conducted in these countries was between 2009 and 2014. Using out-of-date census data to update small area estimates can lead to biased estimates for example, if the distribution of the census covariates used for prediction has changed over time. This is an issue that is often not discussed in applied poverty mapping work. Literature on approaches to update poverty estimates in the intercensal period includes Isidro et al. (2016), Koebe et al. (2022) and AriasSalazar (2023). In this paper we rely on using contemporaneous geospatial covariates, as first illustrated by Battese et al. (1988) (see also Nguyen, 2012), to produce small area poverty estimates in countries that lack recent census data.

Advances in processing of geospatial data and the richness of geospatial data sources make their use as auxiliary information in small area models appealing. Newhouse et al. (2023) summarize recent literature on the use of geospatial data for small area estimation of wealth and poverty. Jean et al. (2016), Yeh et al. (2020), and Chi et al. (2022) show that satellite data are predictive of wealth indices. The present paper utilizes a method commonly used in small area estimation based on the empirical best predictor (EBP) under a nested error regression model (also referred to as mixed model) (Molina and Rao, 2010). When applied to predicting headcount poverty rates using geospatial covariates, this method has yielded predictions that are highly correlated with up-to-date census-based estimates in Mexico, Sri Lanka, and Tanzania (Masaki et al., 2022; Newhouse et al., 2022). The methodology we use in this paper deviates from the official approach endorsed
The impact on household production, combined with qualitative work, provides some insights into the mechanisms behind these opposing trends. While improvements in road infrastructure offer better economic opportunities in agriculture, only some households can take advantage of these opportunities by increasing crop production in response (Table A.6). Male-headed households benefit from greater availability of one of the critical inputs into agricultural production: household labor. Female-headed households are predominantly households of widows or separated women and have on average one less working age adult compared to male-headed households.

Moreover, our respondents told us that the increased opportunities to trade agricultural products spurred two main types of changes: hiring machinery and switching to more commercially lucrative crops that also had longer cropping cycles. These changes required up-front investment, and in case of commercial crops, such as acacia trees, would not bring any returns in the short run. Such long-term or capital-intensive projects are easier to implement for households with greater availability of labor. In addition to serving as a resource for agricultural production, these projects may enable households to rely on multiple sources of income from different household members, while waiting for commercial crops with long growth cycles to yield the first crops.

While in theory female-headed households could substitute capital for labor and still make investments in agriculture, in the absence of well-developed credit markets it is hardly an option.<sup>19</sup> Increase in sales of assets may suggest an attempt by female-headed households to acquire capital for such investments. The decrease in income from agriculture may be indicative of the start of a transition process to longer growth cycle commercial crops.

# _Employment_

Existing literature suggests that roads rehabilitation is associated with increased off-farm employment (Asher and Novosad, 2016; Garz and Perova, 2014). We take advantage of the fact that VARHS contains employment information for each individual living in a household. The questionnaire asks whether each household resident older than 10 years does any work for wages, works on the household farm, is selfemployed, and does any work within the household
that there is no variation in the composition of the household. In other words, the number, age and gender of members of a household remain constant over time. The demographic change is incorporated via calibration of the survey weights. For further details on estimation strategy of parameters see Olivieri, S. et al (2014).

# **Simulation**

The second step consists of _replicating_ macroeconomic simulated changes (i.e., sector of employment, total output or public and private transfers) between the baseline of 2012 and each projected scenario for 2014 of changes in different components of the household income generation model (i.e., labor and non‐labor income). This process is divided into three sub‐steps ordered in the following sequence: population growth, labor market status and income, and non‐labor income.

The population growth adjustment is particularly important in countries with high fertility rates, important immigration flows, or when the last available household survey is relatively far from the projection year. In the first case, the number of labor market entrants rises faster than overall population. In practical terms, this allows us to explicitly take into account changes in the size of the working age population, and hence to distinguish between employment growth driven (or rather absorbed) by demographic trends and net (or additional) employment growth.

In the case of Iraq, we divide this adjustment into two steps: natural population growth between baseline (2012) and projected year (2014); and then internal displacement of 2014 population. The first step is done by re‐weighting households in the baseline data (i.e. IHSES 2012) to replicate demographic changes predicted by gender, age groups and governorates. In most household surveys, the household is the unit that is sampled, thus the probability of including an individual conditional on the household being selected is 1.<sup>15</sup> In other words, the survey weights attached to every individual within the household should
Learning from Developing Country Power Market Experiences: The Case of Colombia

- Report regularly to the operation committee (CNO) regarding planned and actual system operation and the risks involved in supplying demand.

- Report violation of operation procedures.

XM also performs the following functions as wholesale market operator (ASIC):

- Keep an updated registry of long-term energy contracts in force.

- To calculate settlement, bill, collect and pay transactions among Gencos and suppliers in the wholesale market (the pool, with respect to energy and Automatic Generation Control transactions); and perform other related tasks such as developing and maintaining the required information systems.

- To organize and hold auctions for the supply of firm energy, whenever it is deemed necessary by XM with the objective of ensuring supply reliability.

- Report to the SSPD and the regulator (CREG) any improper conduct or violation of the procedures and regulations.

As market operator, XM collects and issues payments arising from the pool, and is forbidden of profiting from these funds. Instead, XM’s services as system and market operator are levied to market participants (Gencos, Transcos, Distcos and suppliers) based on the regulated tariff determined by the CREG. Nevertheless, XM is allowed to pursue other non-regulated businesses.

The operator makes publicly available through its website relevant information such as system and market operation procedures and market prices. However, scheduled and dispatched quantities and settlements are only available to selected market participants. Timely and public dissemination of market data (provided that this does not violate confidentiality of commercially sensitive information) has evolved in Colombia from a rather opaque process a decade ago (Wolak, 2009).

The operator is a Legally-unbundled TSO (LTSO) controlled by the major government Transco ISA, and independent from other market participants (Gencos, suppliers and Distcos).<sup>12</sup> System and market operation functions were transferred from ISA to its subsidiary XM in 2005 by CREG’s decree 848. XM’s corporate governance is based on a board of