rafmacalaba/data-use-mentions-extended huggingface dataset:
MODIS Data."</mark> _<mark>Remote Sensing of Environment</mark>_ <mark>158: 331–47.</mark>

- <mark>Michaels, G., F. Rauch, and S. J. Redding. 2012. “Urbanization and Structural Transformation.”</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>127 (2): 535–86.</mark>

- <mark>Mills, E. S. 1967. “An Aggregative Model of Resource Allocation in a Metropolitan Area.”</mark> _<mark>American Economic Review</mark>_ <mark>57 (2): 197–210.</mark>

- <mark>Muth, R. F. 1969.</mark> _<mark>Cities and Housing: The Spatial Patterns of Urban Residential Land Use.</mark>_ <mark>Chicago: University of Chicago Press.</mark>

- <mark>NOAA (National Oceanic and Atmospheric Administration). 2014. Global Radiance Calibrated Night Lights, National Geophysical Data Center, h</mark> ttps://ngdc.noaa.gov/eog/dmsp/download_radcal.html

- <mark>NSSO (National Sample Survey Office). 2012. “The 68th Round of National Sample Survey of India.” National Sample Survey Office, Ministry of Statistics and Programme Implementation, Government of India.</mark>

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br>department etc). Budgets are government plans for expenditure, (not<br>details of actual expenditure in the past which is covered in the spend<br>category).|
|**ODB.2013.D6**|Spend|Government spend data|Records of actual (past) national government spending at a detailed<br>transactional level; at the level of month to month government expenditure<br>on specific items (usually this means individual records of spending<br>amounts under $1m or even under $100k). Note: A database of contracts<br>awarded or similar is not sufficient for this category, which refers to detailed<br>ongoingdata on actual expenditure.|
|**ODB.2013.D7**|Company|Company registration<br>data|A list of registered (limited liability) companies in the country including<br>name, unique identifier and additional information such as address,<br>registered activities. The data in this category does not need to include<br>detailed financial data such as balance sheet etc.|
|**ODB.2013.D8**|Legislation|Legislation data|The constitution and laws of a country, including national laws and statutes<br>but excluding case-law and administrative regulations.|
|**ODB.2013.D9**|Transport|Public transport<br>timetable data|Details of when and where public transport services such as buses and rail<br>services are expected to run. Please provide details for both bus and rail<br>services if applicable. If no national data is available, please check and<br>provide details related to the capital city.|
|**ODB.2013.D10**|Trade|International trade data|Details of the import and export of specific commodities and/or balance of<br>trade data against other countries.|
|**ODB.
SOEs such as company names, unique company ID, 4-digit NACE code, financial variables such as revenue, employment,  profit and loss for  2019, percent of state ownership stake, and different layers of the ownership chain. The latter two variables allow us to partition SOEs into our analytical categories of ownership degrees and control levels, as  in Table 1 below. The categories in Table 1 are matched to the Romanian MoF firm-level data using the unique tax ID  available in both datasets. Of the 1,416 Romanian SOEs in the BOS database, 1,399 were matched to the MoF firm-level data, with only 17 SOEs not having a match. Of these 17 SOEs, 3 and 11 are central SOEs and local SOEs, respectively, with the other 3 being indirectly owned.

Table 1: SOE Analytical Categories

|Types|Analytical cat|egories|Definitions|
|---|---|---|---|
|**Ownership degrees**|Minority<br>owned|_10-24.9%_|Firms where national or municipal governments hold 10 to<br>24.9 percent stakes.|
|||_25-49.9%_|Firms where national or municipal governments hold 25 to<br>49.9 percent stakes.|
||Majority<br>owned|_50% or_<br>_more_|Firms where national or municipal governments hold 50<br>percent or more stakes.|
|**Control levels**|National vs.<br>subnational|_Centrally_<br>_owned_<br>_SOEs_|Firms where the national government hold 10 percent or more<br>stakes.|
|||_Locally_<br>_owned_<br>_SOEs_|Firms where municipal governments hold 10 percent or more<br>stakes.|

> 6 Subsidies in the dataset are presented as a single continuous variable with the amount granted for any of the following objectives: (i) employment creation; (ii)
and Young, forthcoming). Pagliari, Phillips, and Young (forthcoming) show for US survey data that individuals with larger asset positions are more likely to be in favor of looser financial regulations. Taken together, we expect that these reinforcing mechanisms lead to an amplification of the initial e↵ect of CBI on income inequality dynamics.

> 8In many instances, governments use credit guarantee schemes or deploy only a small subsidy on loans. In the case of a guarantee, these do not have to be included at face value of the loan in budgetary balances, e↵ectively masking a government’s real financial exposure (Hoek-Smit, 2008; Bachmair, 2016).

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in GDP, estimated from the IMF’s WEO database at 037 over the period 2000-10. These numbers imply from the budget constraint that the share of spending on other items,  , is 0736. The cash transfer per child, <sup></sup> , is set equal initially to 005, with the total amount of spending taken to be subsumed in other spending, <sup></sup> <sup>,asnoted</sup> earlier.

Dabla-Norris et al. (2011, Table 1.a) estimate the efficiency parameter for public investment in Brazil, , at 078.<sup>28</sup> Thus, according to these numbers, in Brazil more than 20 percent of public spending on infrastructure investment is “wasted,” in the sense that it does not turn into public capital. In the absence of data specific to the education and health sectors, the efficiency parameters for spending on education and health,  and , respectively, are also set at the same value. Using (29), the equilibrium public-private capital ratio can be computed to give  = 0147. Thus, by this measure, public capital remains a relatively scarce factor in Brazil.

Finally, the elasticity of output of health services with respect to public spending on health is set at  = 08. There are no good benchmarks in the literature but this value appears to be a reasonable starting point, given that most of these services are measured based on salaries paid to medical workers, and the correspondence between elasticities and shares in the Cobb-Douglas function with constant returns to scale. By implication, the elasticity with respect to public capital in infrastructure is 02.

The benchmark parameter values are summarized in Table 1. Based on these parameter and initial values–from which it can be established that Π2 = 0289, which implies that
Such disaster-related infrastructure disruptions are widespread in developing countries. From descriptive studies of survey results, we know that households in both affected and not affected areas suffer from indirect impacts of disasters. For example, in Ghana, 29% of all households that reported being affected either indirectly or directly by the 2015 flood in Accra, Ghana, experienced electricity, water or road disruptions (Erman et al., 2018). Preliminary results from a household survey in Dar es Salaam, show that 62% of households that reported being affected by floods in any way stated that roads were inaccessible because of floods and 75% and 22% reported experiencing electricity and water disruptions respectively (Erman et al., 2019).

Building infrastructure reliability by improving operation and maintenance help mitigate the impacts of natural disasters. Picarelli, Jaupart, & Chen (2017) provide evidence that performant infrastructure, and in particular high-quality drainage systems, help reduce the impact of climatic shocks on health. In Dar es Salaam, heavy rainfalls are associated with high rates of cholera incidence, but the impact is lower in neighborhood with a functional drainage system that prevents water from stagnating. Well-maintained roads of high-voltage transmission line corridors might play the role of firebreaks –gap in vegetation that slows down or even stops a fire progression– or have an influence on flood flows (Jones et al., 2000.

All the evidence presented in this analysis tend to conclude that building resilient infrastructure would directly benefit households, improving health conditions, educational, and professional outcomes as well as building household resilience to natural disasters. There is also widespread evidence that resilient infrastructure allows firms to thrive (Braese, Rentschler, & Hallegatte, 2019). Therefore, in addition to closing the infrastructure gap, policies should seek to improve the quality of local infrastructure. While building consensus and acknowledging the strategic importance of infrastructure in mitigating the impacts of natural disasters, we now need evidence on large-scale policy intervention to understand priorityinvestments that increase resilience.

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levels relative to the satellite measures. This relative underestimation is largely due to the low-cost monitors’ under-measurement of dust. Adjusting the data from the low-cost monitors, especially using a context-specific formula, improves the alignment of the data with both the data from the regulatory-grade monitors and the estimates from the satellite.

Next, we use the three data sets to evaluate pollution externalities of the set of mobility-reducing policies enacted during the COVID-19 pandemic. We use a difference-in-differences identification strategy to analyze the impact of the policies on the level of air pollution. Overall, we find the policies lead to a positive externality in the form of a reduction in air pollution, similar to research conducted in highincome settings (Brodeur _et al._ , 2021). Importantly, we find that all three data sources display similar patterns of PM2.5 reductions due to the policy. Using PM2.5 data at the hourly level, the impact of the policy in levels is smaller when using the data from the low-cost monitors than the other data sources because the low-cost monitors tend to underestimate PM2.5 levels. However, the impact of the policy in percent is similar across all data sources.

Finally, we demonstrate the value of co-locating at least one regulatory-grade monitor in a network of low-cost monitors. We demonstrate that for a given day, relying on only a regulatory-grade monitor or only the satellite data will provide no spatial variation. However, relying on only the network of low-cost monitors will provide spatial variation but severely underestimate the level of PM2.5. Using a co-located regulatory-grade monitor to develop a context-specific calibration formula to be applied to the data from the network of low-cost monitors improves the alignment in the level of PM2.5 while preserving spatial variation. Given the tight budget constraints that policy makers in LMICs face, investing in one well-maintained regulatory-grade monitor complemented with a dense network of low-cost monitors may strike the right balance. Barring any regulatory-grade monitors, calibration of PM2.5 measurements from low-cost monitors using satellite based estimates of PM2.5 can serve
in Table 11 shows, up to 1994 the Northern Region was in line with the rest of the country by degree of decentralization of the "Social Protection" expenditures. But in 1994, it did not follow the countrywide rapid expansion of local governments' involvement in that sphere. And even though the average share of local governments in consolidated regional expenditures on "Social Protection" in the North Region consequently doubled to 36.5% in 1996, it was still half of the national average.

**Table 11. Average Shares of Local Governments in "Social Protection" Expenditures in the Northern Region and in the Russian Federation, %**

|**Aierage Shares / Year**|**1992**|**1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|**1.**Average Share of Local Govemrnents in<br>Consolidated Regional Expenditures on Social||||||
|Protection in the Northern Region|18.38<br>(7.86)|15.19<br>(3.61)|17.45<br>(4.34)|34.84<br>(12.3)|36.52<br>(13.85)|
|2. Average Share of Local Govemnrents in||||||
|Consolidated Regional Expenditures on Social||||||
|Protection in the Russian Federation|22.34<br>(2.17)|26.31<br>(2.36)|30.77<br>(2.2)|67.46<br>(1.97)|70.79<br> <br>(2.13)|

Standard error of the mean
Gas for instance, contributed 126 GWh in 2004 and 2,003 GWh in 2005 due to imports through regional interconnections with Algeria.<sup>33</sup> In 2014, for the first time, windgenerated electricity surpassed hydroelectric power as the biggest contributor to renewable energy generation (Figure 5).

_Figure 4: Electricity Generation in GWh (1990-2017)_ <!-- Start of picture text --> 35,000 0.70<br>30,000 0.60<br>25,000 0.50<br>20,000 0.40<br>15,000 0.30<br>10,000 0.20<br>5,000 0.10<br>0 0.00<br>Hydro Geothermal Other renewables<br>Coal Oil Gas<br>Biofuels and waste Nuclear Total final consumption<br>Fuel type concentration index<br>GWh<br>Fuel Type Concentration Index<br><!-- End of picture text --> _Source: Authors based on data from ONEE_

> 32 (Camos, Bacon, Estache, & Hamid, 2018)

33 Morocco plans to expand the share of natural gas in the longer term. Several new gas supply routes are being discussed, including imports of natural gas from Nigeria, and the construction of an LNG terminal, which can provide access to contract and source flexibility (IEA, 2019, p. 12).

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marketing (Acquisti et al. 2016; Tucker et al. 2018). The constraints imposed by privacy regulations, such as the GDPR, CCPA, and recent changes by Apple and Google, pose significant challenges (Goldfarb and Tucker 2011 _b_ ; Johnson et al. 2020; Wernerfelt et al. 2022).<sup>8</sup> However, they also underscore the need for innovative approaches that respect consumer privacy while leveraging data for effective targeting and measurement (Godinho de Matos and Adjerid 2022; Goldfarb and Tucker 2011 _a_ ; Rafieian and Yoganarasimhan 2021). By integrating privacy considerations into our experimental methodology, our approach highlights how first-party data, directly collected from users on social media, can inform targeting and help reach the most valuable segments of the population—in our context, those most at risk of malaria.

More broadly, our study relates to the work emphasizing the importance of marketing techniques to address social issues (Chandy et al. 2021; Kotler and Levy 1969; Kotler and Zaltman 1971), with

> 6Traditional media (television/radio/print) campaigns have also been shown effective for public health outcomes such as the prevention of childhood obesity and COVID-19 (Croker et al. 2012; Ghosh Dastidar et al. 2023. For a complete review of the effects of mass media on health outcomes, see Orozco-Olvera and Malhotra (2023).

> 7Brand Lift Studies are commonly used to measure ad effects on outcomes such as ad recall, brand sentiment, and purchase intent. Companies such as Meta and Google provide Brand Lift experiments to advertisers, typically executed via a brief three-question survey administered to respondents one to two days following exposure to the advertisement.

> 8Regulatory measures such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States have been enacted to protect consumer privacy and limit the extent to which personal data can be used without explicit consent. Concurrently, tech giants like Apple have restricted user tracking through device IDs on
<!-- Start of picture text --> 2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br>Average Ransom Average Negotiation Length<br>4<br>250<br>3 200<br>2 150<br>1 100<br>Average Ransom (Mil.$)<br>Average Negotiation Length (Days)<br>50<br>0<br><!-- End of picture text --> Figure 4: Ransom paid and duration of captivity Note: The figure plots the annual average ransom paid (in million dollars) and the duration of captivity (in days) of hijacked vessels.

Data source: UNODC-WB.

<!-- Start of picture text --> 0 2 4 6 8 4 6 8 10 12<br>Log(Negotiation Length) Log(Vessel Size)<br> 95% CI Fitted Value 2005 2006 2007 95% CI Fitted Value 2005 2006 2007<br> 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012<br>(a) Ransom and duration of captivity (b) Ransom and size of the vessel<br>4 4<br>2 2<br>0 0<br>Log(Ransom) Log(Ransom)<br>−2 −2<br>−4 −4<br><!-- End of picture text --> Figure 5: Ransom, Duration of Captivity, and Vessel Size Note: The figure plots the log of ransom against the log of duration of captivity and the size of the vessel of each negotiation. The date associated with each point is the year of attack. Data source: UNODC-WB.

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crucial for agricultural production. A one standard deviation increase in monsoon rainfall in a district improves the yield of rice by 2.7 percent, improving total production by 4.5 percent and total rice cultivated area by 1.8 percent. Similarly, maize production increases by 5.9 percent and area of cultivation by 6.8 percent in response to one standard deviation higher rainfall in monsoon<sup>15</sup> .

Consequently, increased rainfall in the monsoon months increases farming income and consumption of farming households. I investigate the impact of rainfall directly on measures of income and consumption using three waves of cross-sectional surveys conducted between 1995 and 2010. I find that one standard deviation of rainfall increases farming income by Rs. 2,400 (about 16 percent of total farm income in 2010) and increases consumption in the subsequent year by almost the same amount (Rs. 2,300 or about 7 percent of total consumption in 2010) for households who do not receive any remittance income. Details of this exercise are described in Appendix B.

## **Data on rainfall**

The rainfall data that I use for this paper are provided by the Department of Hydrology and Meteorology (DHM) for years 1972 to 2010. The dataset contains average daily rainfall for each of the months from 1972-2010 collected from over 300 rainfall stations throughout the country. The distribution of these stations is shown in Figure 3. I interpolate these rainfall measures to each of the 452 villages in my dataset using inverse of distance from the stations as weights. For each of the villages in my sample, I compute normalized monsoon rainfall in standard deviation units using historic (spanning 1972-2002) mean and standard deviation of monsoon rainfall in the village. The measure that I use for my analysis is the normalized monsoon rainfall in the year preceding the survey.

There is considerable variation in rainfall between years as well as within a given year. Figure 5 plots the histogram of normalized rainfall for the three survey years. Within each year
the Foster–Greer–Thorbecke family, and the associated elasticities with respect to growth and inequality. Any member of this family for a given group can be written as a weighted sum of poverty within all the subgroups. The weights are equal to the population shares. Based on this fact, SimSIP Poverty provides decompositions of poverty outcomes in three components. The first component represents change in within-group poverty. The second term measures the effects of population shifts between groups. The last component captures the interaction between inter- and within-group effects (see Ravallion and Huppi 1991 for details). It is also possible to decompose changes in poverty over time into the following contributing factors: growth, inequality, and a residual. These decompositions require two sets of observations on the distribution of income or expenditure. This is the same approach discussed above in the case of POVCAL.

# **_2.2. Unit-Record-Based Approaches_**

# **_PovStat_**

PovStat is an Excel-based tool primarily designed to simulate the poverty implications of alternative growth paths. This simulation tool arose out of the basic idea that the rate and pattern of economic growth determine the evolution of poverty over time. In particular, _it is assumed that per capita consumption for a household grows at the same rate as per capita output in the sector of employment of the head of the household_ . Households are classified into four sectors on the basis of the employment status of the head: (1) agriculture, (2) industry, (3) services, and (4) residual. The residual sector amalgamates households with unemployed or inactive heads and those with unknown occupational status.

A major advantage of PovStat over SimSIP Poverty stems from the use of unitlevel data. This has the potential to improve the precision of the estimates of the poverty and inequality measures. Otherwise, the tool shares in the major limitations that we have flagged for POVCAL and SimSIP Poverty: (1) there is no explicit modeling of the macroeconomic framework, and (2) there is a
exceed value added as measured in the SNA. We therefore normalize the returns to factors to sum to 1 after the disentangling of resource rents and profits has been carried out.<sup>5</sup> Another wrinkle in the calculation is that, because data on the number of people self-employed are lacking in many countries, the growth rate of labor inputs used in our calculation of TFP growth is based on the number of people in formal employment, which may not be a good assumption in many developing countries.

# _Fixed capital_

Turning to fixed capital, the _Wealth of Nations_ database uses capital stock estimates from the Penn World Table, built on the Perpetual Inventory Model, converted to constant 2014 US dollars.<sup>6</sup> To reach the final value, however, the underlying stock of capital at constant local prices and currency units (a measure of volume) is first converted to current international dollars at purchasing power parities (PPPs), then to current US dollars at market prices, then finally to constant 2014 US dollars. The various conversions involving prices and exchange rates lead inevitably to volatility in the final estimates of fixed capital. To avoid this volatility, we use the PWT figures for the volume measure – stock of capital at constant local prices – in our TFP estimation.

One final issue arises, however. The PWT methodology measures the total capital stock, both public and private. As a result of SNA conventions, however, public sector fixed capital is assumed to have zero profits. As a consequence, when measuring the contribution of fixed capital to GDP growth, we weight the growth rate of total fixed capital (g� in the second formula) by the returns to capital measured in the SNA (s� in the second formula), which are returns on productive capital. This approach introduces a potential bias to the extent that public sector fixed capital may grow at a rate different from productive fixed capital (that is, g� may have two components, one
2.5 -.2<br>-.3<br>2.0 1999 2000 2001 2002 2003<br>95 96 97 98 99 00 01 02 03<br>Source: World Bank staff calculations based on Bacen Source: World Bank calculations based on Bacen<br>data data<br><!-- End of picture text --> 22. Moreover, base money grew in lockstep with the faster depreciation of the currency. This pressured inflation, which accelerated between June and December, reaching a peak of 5.8 percent per month in November (Figure 9). Monetary growth and rising inflation increased the government’s revenue from money creation up to the 11
. 427-462.

Ferreira, F., J. Messina, J. Rigolini, L. López-Calva, M.A. Lugo, and R. Vakis (2013). _Economic Mobility and the Rise of the Latin American Middle Class_ (Washington, DC: World Bank).

Frankel, J., C. Vegh, and G. Vuletin (2013). “On Graduation from Procyclicality,” _Journal of Development Economics_ , Vol. 100, pp. 32-47.

Galeano, L., A. Izquierdo, J. Puig, C. Vegh, and G. Vuletin (2021). “Can Automatic Government Spending Be Procyclical? NBER Working Paper No. 28521.

Gasparini, L., F. Gutiérrez, and L. Tornarolli (2007). “Growth And Income Poverty In Latin America And The Caribbean: Evidence from Household Surveys,” _Review of Income and Wealth_ , Vol. 53, No 2, pp. 209-245.

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