rafmacalaba/data-use-mentions-extended huggingface dataset:
measures also often have non-intuitive units. For example, the squared poverty gap is measured in squared percent deviations from the poverty line, while the Watts poverty index is measured in log-point differences from the poverty line that cannot easily be interpreted as percent differences unless they are small. While these complications of interpretation and units are of little concern to mathematically minded users, they represent only a small proportion of the audience for welfare measures. For the wider audience of policymakers and the public, “average income in dollars” or “the number of poor people” are far more understandable, and therefore far more effective in influencing policy and public opinion despite their shortcomings. This dilemma is well-summarized by the succinct remark in Watts (1969) that the headcount has “ _little but its simplicity to recommend it.”_ To address this problem, we propose a new distribution sensitive welfare index with a simple mathematical formulation and intuitive units. Our basic measure, described more formally in Section 2.1, is the average across all individuals of z/yi, where yi is the income of individual i and z is a reference income level. As an average, its formulation is simple to describe. Moreover, the average has very intuitive units. Each term in the average is the factor by which the income of the corresponding individual needs to be multiplied to attain the reference level of income, and the index simply is _the average factor by which individual incomes need to be multiplied to reach the reference level of income_ .

In addition to being extremely simple to explain, this measure has several desirable properties. In Section 2.3, we show that it satisfies all three definitions of distribution sensitivity in the literature: Pigou-Dalton sensitivity, transfer sensitivity, and growth sensitivity. In addition, the new index is subgroup decomposable with population weights, which means for example that its value at the world level is simply a population-weighted average of its value in each country in 2
# **Table 10. The Effect of Titling Firms’ Political Connections on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable and firms’ political connections, measured by either the total number of local bureaucrats on the firms’ boards or the ratio of Communist Party members as the percentage of total employees. The independent variable is the averaged investment between 2013 and 2017 net of investment between 2010 and 2012. _Bureaucrats on Boards_ is the total number of board members of the firms with experience of serving in the government. The _Party Member Ratio_ is the ratio of the total number of employees with communist party membership divided by the number of total employees. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

|||∆(Investm<br>|ent/PP&E)<br>||
|---|---|---|---|---|
||Bureaucrat|s on boards|Party Mem|ber Ratio|
||(1)|(2)|(3)|(4)|
|Titling Firm|0.186***||0.211***||
||(2.682)||(3.093)||
|Bureaucrats on boards|-0.035|-0.035|||
||(-1.251)|(
|**Metric**|**Description**|**Strengths**|**Limitations**|
|---|---|---|---|
||• Includes 14 foods, 7 beverages and 33<br>nutrients|• GDD data can be used to estimate<br>disease burdens attributable to poor<br>diet quality||
|**Global Burden of Disease**<br>**– 13 dietary risk factors**|• Derived from modelling approach using<br>multiple sources of dietary data (24-hr<br>recall, supply utilization, FFQ, sales data,<br>Household Budget Surveys)<br>• Dietary risk factors are those with<br>convincing or probable evidence of health<br>effect|• Validated with health outcomes<br>• GBD data can be used to estimate<br>disease burdens attributable to poor<br>diet quality|• For countries without 24-hour<br>dietary recall data, GBD estimates<br>are modeled based on existing data<br>sources (data on food availability<br>or food sales)<br>• Modeled data may be unclear to<br>non-experts|

Adapted from WHO and UNICEF (2021) 30
�</sup> , and �<sup>�</sup> . Following Uchida and Nelson (2010) and World Bank (2008), it has become standard to set �<sup>�</sup> �50,000, �<sup>�</sup> �150 people per km<sup>2</sup> , and �<sup>�</sup> �60 minutes.<sup>22</sup> In other words, an area is defined as urban if it has a population density of at least 150 people per km<sup>2</sup> and falls within a 60 minute travel time radius of a settlement which itself has a population of at least 50,000. A country’s level of urbanization is then given by the sum of the population living within all such areas.

# 2.2.2. The cluster method

In contrast to the AI, the cluster method adopts a more spatio-demographic approach to the consistent cross-country measurement of urbanization. In doing so, it follows a long tradition of using population size and density thresholds to define urban areas. As noted above, over a hundred countries use population size to define urban areas, while 11 use population density. The US Census Bureau began using population size, population density, and contiguity criteria to standardize the definition of metropolitan areas as far back as the early 20th century (Berry, Goheen and Goldstein, 1969).

More specifically, the cluster method classifies cells in a population grid according to density, and then groups them into “urban clusters.” Again using the notation �� to denote the population of a grid cell and �� its population density, a grid cell � is classified as belonging to an urban cluster �

> 20 Although, as demonstrated by Day _et al._ (2017) for Sri Lanka, the approach can also be feasibly implemented using polygon data which correspond to sub-national administrative units.
in this regard.

The bilateral trade prediction for upstream products ( _k ∈ V_ ) takes the form: Variation in _τj_<sup>_k_affectsbilateraltradethroughsubstitutionaccordingto</sup><sup>_−σk_,but</sup> tions of minimum efficient scale are likely to be particularly important in small economies like PR’s. 25One possible example of industry composition effects due to the JA is the absence of viable sugar cane growing and processing sectors on the island. Global agronomic data (https: //gaez-services.fao.org/) shows that much of PR has high yield potential in the production of cane sugar if modern production technologies are used (e.g. the application of physically heavy fertilizers and the use of bulky capital goods). While the increasing returns to scale model proposed here may not apply directly to the sugar cane growing sector, it offers a reasonable treatment of sugar cane processing. Trade data from IEPR (2021) show that exports of processed sugar from PR are almost non-existent.

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# **III. Data and Methodology**

## **3.1 Data**

## **_3.1.1 GVC data_**

Intermediate inputs increasingly cross-national borders multiple times as production processes become more fragmented across different countries. Because of the growing importance of doublecounting, traditional trade statistics have become increasingly less reliable as a measure of the value contributed by each country. Value-added trade statistics help to identify and net out the “double-counting” effect of GVCs on gross trade figures. This is done by mapping out how value addition moves between countries along the Global Value Chain before final consumption of endproducts. The GVC data used in this study were obtained from the UNCTAD-Eora GVC database. This database provides value-added trade statistics for 187 countries (including 45 Sub-Saharan African countries) from 1990 to 2015 covering between 25 and 500 industries depending on the country under consideration.

To calculate both domestic and foreign value-added in trade, the Eora data set provides a global multi-region input-output (MRIO) table. A simplified MRIO table is presented in _Figure 1_ to illustrate how value-added is calculated in the manufacturing industry<sup>3</sup> for trade between two countries. Consider a situation where country produces a manufactured good x (say, wood products) which can be used domestically to satisfy final demand or to serve as an intermediate input in the production of another good in the same industry. Alternatively, the same good x can be exported to country B where it can be used as a final product or as an intermediate input in the production of another good in the same industry. The first row of _Figure 1_ summarizes how gross output X produced in country A (X�) can be shared between country A and country B as an intermediate input or final product. Conversely, the same good can be imported from country B and used in country A as an intermediate input or final product. This is summarized in the second row of _Figure 1_ . As a result, total exports of
provide reconstruction and relief and potentially increasing the economic consequences of the disaster. The route followed by different governments concerning the combination of expenditures, revenues and borrowing will likely depend on the access to lending, its cost, and on the demand for government services. For instance, countries that can borrow at low cost and face the burden of reconstruction and relief may prefer that route to increasing revenues through taxation or restraining expenditures. And countries where private insurance markets share a large fraction of the reconstruction costs (e.g. by financing the reconstruction of private and public capital) may focus on emergency relief, face smaller funding requirements, and expanding expenditures moderately.

This paper estimates the impact of natural disasters on fiscal sustainability by characterizing how government expenditures and revenues typically respond to different types of disasters, and how these responses relate to a government’s ability to borrow and to the availability of private financial sources for private and public reconstruction. Following Raddatz (2009), we do this by estimating the parameters of a Panel Vector Autoregression (PVAR) model that includes real output, government expenditures, government revenues, measures of the occurrence of geological, climatic, and other disasters, as well as other external shocks and standard macroeconomic variables like inflation and interest rates.<sup>1</sup> The three categories of natural disasters we consider follow Skidmore and Toya (2002) and are defined as follows: geological disasters including earthquakes, landslides, volcano eruptions, and tidal waves; climatic disasters including floods, droughts, extreme temperatures, and windstorms; and other disasters including famines, epidemics, insect plagues, wild fires, miscellaneous accidents, industrial accidents, and transport accidents.

Using the parameters of the model we can predict the dynamic response of each of the variables of interest to the occurrence of any type of disaster the same year the disaster occurs and in the years following the disaster. We estimate the model using annual data for high and middle-income countries during the period 1975-2008. While low-income countries are also of
Another issue that could be considered relates to the use of administrative data in the PMT process. One possibility is to compare the profile of current Samurdhi beneficiaries from administrative data with those stated beneficiaries in a representative household survey. This examination may reveal discrepancies that provide hints about implementation failures. Another possibility is to use administrative data that are aggregated at pre‐defined geographic variables. Possibilities include share of households with access to piped water, average classroom size, access to roads, etc. The advantage of including administrative data in the PMT formula would be that it would be less subject to manipulation by households and more timely than household surveys. However, for this to function effectively, mechanisms need to be in place to ensure that the administrative data are complete, vetted, and fed into the PMT process in a timely manner.

Finally, further study needs to be conducted on whether PMT can be complemented with additional targeting methods for short‐term targeting, for example, to respond to natural disasters. One possibility is to use data from satellite imagery – which can be potentially obtained more frequently than household surveys – to narrow pockets of poverty within disaster‐affected areas (Engstrom et al., 2017). The PMT could be subsequently used to identify beneficiaries of emergency assistance within these areas. Another possibility is to potentially incorporate variables derived from remote sensing data in the PMT formula. The advantage of these variables would be that they may be less costly to collect than household surveys, less subject to manipulation by households, and provide more timely information on household welfare. The capacity of government agencies in countries like Sri Lanka may not be currently sufficient to process remote sensing data, but this may change in the due course of time. This may also be an avenue of collaboration between government agencies and development partners.

21
volatility<br>across fixed and flexible<br>exchange rate regimes.|The paper combines<br>three datasets: Grilli and<br>Yang (1988), Boughton<br>(1991) and IMF’s_IFS_.<br>The paper examines a<br>_real_commodity price<br>index, defined as the<br>ratio of the chosen<br>nominal commodity<br>index deflated by a<br>manufacturing unit<br>value (MUV) index.|ANOVA tests are<br>performed on long-<br>run data to test equal<br>variance across fixed<br>and flexible exchange<br>rate periods. Dummy<br>variables in a<br>(T)GARCH (1,1)<br>framework are used to<br>test the presence and<br>persistence of a<br>volatility shock.|Strong evidence supporting<br>the conjecture that the<br>flexible exchange periods<br>have been associated with<br>higher real commodity price<br>volatility than the fixed<br>exchange periods.|
|Leon J., Soto R.<br>(1995)|The paper revises the long and<br>short run time series structure<br>of commodity prices in order<br>to answer the questions of the<br>secular decline and the long-<br>run persistence of shocks.|Monthly prices of the<br>twenty-four most-traded<br>commodities in 1900-<br>92.|The paper applies the<br>non-parametric<br>estimator of<br>persistence proposed<br>by Cochrane (1988<br>and 1991) and<br>extended by Lo and<br>McKinley (1989) and<br>Chow and Denning<br>(1993).|15 of the 24 commodity<br>prices have
"treated" after 2014. Forward-looking firms make entry, investment and hiring decisions, for example, with the goal of maximizing firm value, taking into account the entire flow of future after-tax profits, not only the present period. In that sense, we can consider this staggered adoption as treating all firms but with somewhat different intensities.

At the same time in 2014, the general corporate tax rate was lowered from 30% to 25%. Thus, half of the onshore-offshore rate differential was eliminated in 2014. This convergence process continued in 2019 when the tax rate for offshore firms was further increased to 13.5% and was finalized in 2021 when both the onshore and offshore tax rates were set to 15%. We do not include this further convergence period in the analysis for two reasons. First, it was arguably not as unexpected as the 2014 reform, which came on the heel of many years of deferral. Second, these additional reforms coincided with the Covid period, which would make the identification of impacts particularly challenging. It is also worth noting that onshore firms in a number of sectors faced a higher rate (35%) than the general rate throughout the period. These sectors, including for instance credit, insurance, oil refining, car dealership and hyper-markets, comprise a relatively small share of firms, which we exclude from the subsequent analysis.

According to data from Tunisia’s Ministry of Finance, the tax reform was associated with a reduction in corporate income tax revenues for the fiscal year 2014 (and collected in 2015). Overall tax revenues from non oil companies dropped by 28% to TD 1.6 billion dinars mainly driven by the rate reduction for onshores, which still represented the bulk of CIT collection.<sup>7</sup> Revenues from offshore represented around 4.4% of total CIT revenues, a share which eventually increased to 9.4% by 2018.

Importantly for our analysis, the aggregate data also suggest that the reform was not accompanied
The taxation rate for each country is taken from a global corporation tax database, with the rate being 30% in each country, except Côte d’Ivoire which is 25% (Tax Foundation, 2020). The MNO is then allocated a 10% Net Operating Profit After Taxes Margin (NOPAT) as the return for taking the investment risk associated with the network deployment, which is consistent with data on mobile industry financial performance (Applied Value, 2016). If gross profits are extracted, infrastructure can only be viably deployed in urban and suburban areas, leaving large rural areas uncovered, which is not conducive for universal broadband. Hence, excess capital beyond the 10% profit margin is reallocated to the next most viable region via a process of user cross-subsidization (Curien, 1991; Glass and Tardiff, 2021). This is essentially a universal service obligation. After this reallocation process is completed, any areas which remain unviable will require state funded subsidization, but as state funds are limited, this is therefore a last resort.

Additionally, the private cost to the MNO in the i th region is estimated based on the sum of the network, admin and operations, spectrum, taxes and profit ( PrivateCosti = Networki + Administrationi + Spectrumi + Taxesi + Profiti ). Finally, the net cost to government in the i th region is treated as the required state subsidy minus any revenues gained from spectrum fees and taxation ( GovernmentCosti = Subsidyi −(Spectrumi + Taxi) ). Once the private and government costs have both been obtained, it is possible to estimate the financial cost to society by finding the summation of the two variables ( FinancialCosti = PrivateCosti + GovernmentCosti ). For the individual country assessments, the total market investment to deliver universal broadband is reported, by multiplying the cost per user by the number of users needing to be connected.

A summary of all model input parameters can be found in Table 2.

19
# **Table 1. Definitions of Key Variables**

Definitions FDI share of foreign ownership in a firm averaged at the district level ln(L) Ln(district average of the number of employees in a firm) ln(firm age) Ln(district average of firm age) ln(GDP PC) Ln(city-level GDP per capita evaluated at 2004 Yuan) ln(city distance to port +1) Ln(City's distance to the port+1) City phone density City level average number of phones per 100 people. ln(city average wage) Ln(city-level average wage per employee based on city survey), wage evaluated at 2004 Yuan. district average share of college District-level share of employees with college or above education.

district average share of college District-level share of employees with college or above education. graduate in L City share of good air days Share of days in the city that the quality of air reaches a specified threshold (to be judged as good or ok). dummy: open city Dummy of initial open cities, including Shenzhen, Zhuhai, Shantou and Xiamen. ln(days passing customs) Ln(district-level average of the number of days for import to pass customs plus the

- Ln(district-level average of the number of days for import to pass customs plus the number of days for export to clear customs).

- dist avg of private firm access to The average share of private firms with access to loans within the district. A firm is loans defined to have access to loans if it has borrowed bank loans in the past three years.

- dist avg of private firm ETC The average ETC burden of domestic private firms within the district. ETC burden is

- The average ETC burden of domestic private firms within the district. ETC burden is a firm's expenditure on entertainment and traveling over sales.

- dist avg of foreign advantage in District average ETC burden for domestic private firms over district average ETC ETC burden for foreign firms minus one.
land plots, the paper assumes that the relative change in illicit economic activity is proportional to the relative change in the land surface devoted to opium cultivation. yy<sup>AA</sup> The data on land use are generated by the Ministry of Counter Narcotics (MCN) of Afghanistan in collaboration with the United Nations Office on Drugs and Crime (UNODC). The extent of opium poppy cultivation is assessed on an annual basis through a methodology that combines field work with remote sensing techniques. The latter are needed because field work is dangerous, given the security conditions on the ground and the illegal nature of the crop. Image interpretation of geo-referenced ground and aerial photography allows to generate digital maps of poppy fields (UNODC 2007). The data on opium poppy cultivation used for this study are at the district level, for the period from 2007 to 2016 (UNODC 2016), measured in hectares per 1,000 inhabitants.

_Conflict intensity_ The intensity of conflict is derived from the number of killings, a metric associated with terrorist attacks generated by the Global Terrorism Database (GDT). This is a comprehensive source produced by the CC National Consortium for the Study of Terrorism and Responses to Terrorism, at the United States Department of Homeland Security Center of Excellence. Data on casualties for Afghanistan are available from May 1973 to December 2016. The raw data are georeferenced and available on a monthly basis; for this paper, they are aggregated at the district level. The intensity of conflict is defined as the number of killings and normalized by their standard deviation across the sample.

_Control variables_ Information on the presence of international and Afghan combat troops – and respectively – comes from the NATO ISAF placemat for each Regional Military Command (NATO 2018). Placemats report TT<sup>WW</sup> TT<sup>AA</sup> province-level information on the number of ISAF troops by the contributing nations and on the localization of Provincial Reconstruction Teams (PRTs). Placemat issues also include information on the number