rafmacalaba/data-use-mentions-extended huggingface dataset:
_- 56 -_ GDP estimate is from World Tables 1995 and relates to 1992. Wage bill estimate is from Syed Nizamuddin, (SAI CO) paper of February 27,1996 A Review of the Waoe and non Wage composition of the Recurrent Budget and relates to 1992. Average Government wage is taken from the same report and relates to 1992.

Cambodia

# Military employment include provincial forces (estimated at 50,000 personnel).

China Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to 1994 and source IV: _Official estimates._ It probably underestimates the level of unemployment. Data on paid employment in nonagricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1993. Data on Central, Non Central, Education and health employment are taken from Maurice Gervais (ASTTP) draft Report on the Public Administration (March 1996) and relates to 1994. Non Central Government employment includes employees of Provinces, Prefectures, Townships and Counties as well as districts and village/urban neighborhood committees. Education and Health services are delivered through a network on Non Profit Organizations, which remain part of the public administration and is in part financed by the budget. Data on State Owned enterprise includes employment in Collective Owned enterprises. Military employment data include conscripts (estimated at 1,275,000 personnel) but do not include paramilitary forces (i.e. the People's Armed Police, under the control of the Ministry of Defense, estimated at 600,000).

GDP, Govemment wage bill and Average wage rate estimate are from Maurice Gervais' draft Report on Public Administration of 30 March, 1996 and are for 1994. Data on wages in manufacturing (monthly basis) are taken from the United Nations' Statistical Yearbook for Asia and the Pacific 1995 and refer to 1992.

Fiji Unemployment data are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1994.

Central and Non Central Government and State Owned enterprise
**Figure 10: Rates of Increase per Year of Age in COVID-19 Mortality vs. Vital Statistics Performance Index by Country**

<!-- Start of picture text --> CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>ESPSWE<br>ITA KORDEU<br>PRT NOR HUN<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>MYS COL<br>ZAF<br>PER<br>PAK<br>MEX<br>0 20 40 60 80 100<br>VItal Statistics Performance Index<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Source: Author’s analysis. LMICs are shown in red and HMICs in blue. The Vital Statistics Performance Index (Phillips, Lozano et al. 2014) is based on analysis of the 2013 Global Burden of Disease data.

The relative rate of infection of older people may vary across countries. Intergenerational households are much more common in low- and middle-income countries, which may mean that older people are more exposed to infection risk, particularly given the often crowded dwellings. Roughly 20 percent of households in LMICs have at least one household member under age 20 and one over age 60, as compared to just 5 percent in high-income countries (Winskill, Whittaker et al. 2020). In 54 LMICs with recent Demographic and Health Surveys, just half of the households have no more than two people per sleeping room (Brown, Ravallion et al. 2020). On the other hand, in HICs a greater share of older people are long-term care facility residents, who appear to be particularly at risk. Approximately a quarter of all COVID-19 deaths in the United Kingdom have been residents of care facilities and by one estimate 43 percent of
|342|
|R2|-|-|
|Akaike Information Criterion|63.26|97.39|

Source: International Organization for Migration Displacement Tracking Matrix.

Note: Separable Temporal Exponential Random Graph Model (STERGM) estimated coefficients and standard errors (in parentheses) are reported. *, **, *** indicate statistical significance at the 10, 5 and 1 percent level. Column (1) and (2) report respectively values for the network formation and dissolution model fitted by STERGM.

23
communities in the immediate and longer term.

There were several challenges associated with conducting a representative survey of the host community population and the forcibly displaced, discussed in detail in Krishnan et al. (2018). In particular, there were no reliable sampling frames for either the resident population or the forcibly displaced.

In Lebanon, survey implementation first required cartographic division of the country into geographic areas small enough for a full listing operation, which provided the sampling frame for the first stage sample. In Kurdistan, the scope of the survey was expanded to include internally displaced persons (IDPs), and a representative sample of the host community, of Syrian refugees in camps and outside camps, and of IDPs in camps and outside camps was selected. In Jordan, the original design based on a nationally representative sample proved infeasible because of inaccessibility to the new sample frame based on the 2015 Population and Housing Census. The design was then amended to include a representative sample of the Za’atari and Azraq camps, which account for

> 4 Lebanon and Jordan ranked first and second in the world in 2018 in number of refugees hosted relative to national population. Lebanon counted 156 refugees per 1,000 national population and Jordan 72 refugees per 1,000 residents  (UNHCR, 2018). These figures are based on official UNHCR registration numbers and do not reflect the unknown number of unregistered refugees.

> 5 The main objective of this paper is to provide a quantitative account of Syrian refugees’ departure conditions and subsequent living conditions among host communities. Qualitative evidence is mostly summarized in footnotes.

2
Palmer and Truong (2017) argue, based on findings of Dangelico and Pujari (2010) and Berrone et al. (2013), green technologies lead to increased profitability for firms.<sup>14</sup> Using 1,020 press releases on green technology products introduced by 79 global firms between 2007 and 2012 and their operational profitability, Palmer and Truong (2017) show a positive relationship between green firm profitability. Palčič and Prester (2020) argue that investments in green technologies or innovations benefit firms as it creates a positive image, which eventually leads to an improvement to their market valuation. Using content analysis of corporate disclosures and financial data of 162 companies listed on the Frankfurt Stock Exchange during the 2007-2016 period, Przychodzen et al. (2018) show firms with green information technology receive higher subsequent returns on assets and the market-to-book values of assets ratios. Saha et al. (2017) suggest that investment in green operations and preservation technology can significantly improve the retailer's financial performance.

Some studies report that green/clean investments do not necessarily improve the financial performance or market values of firms. Investors, such as venture capital firms, would be interested in financing green/clean technologies. But Gaddy et al. (2017) find the other way around. Using the investment data from venture capital firms for the 2006-2011 period, they find clean technologies poorly suited for venture capital investment. They find that materials and hardware industries have a longer payback period than venture capital firms normally expect. The size of capital requirement is normally big for large-scale green/clean technology development or adoption. Venture capital firms see the green/clean technology market as risky and yield low returns because the potential acquirers of these technologies (e.g., utilities and large industrial corporations) are reluctant to buy risky startups. Table 6 provides selected empirical studies on the relationship between green investment and the financial performance of firms or industries.

**Table 6. The selected empirical literature on indicating the relationship between
G-5 currencies, a few regional currencies, SDR and ECU visA-vis the US dollar were obtained from the IFS data base (line code rf). Exchange rate data for Taiwan Province of China (POC) were obtained from the Central Bank of China, Taiwan District, Financial Statistics, various issues. To obtain meaningful regression results, data observations with values of log first differences greater than 0.1 (approximately a 10.5 percent change in both directions) were removed. This procedure was taken because countries often devalue their currencies to accommodate persistent differences in inflation rates vis-&-vis their reference-currency country. Without eliminating the effects of such discrete currency devaluations (or revaluations), the regression results could be too unstable to conclude the presence or absence of target/reference currency.

9 In other papers, Frankel and Wei [1993, 1995] use the SDR as a numeraire currency, but Kawai and Akiyama [1998] did not follow this procedure because the SDR was regarded as a potential candidate for a reference currency.

7
Prospects for Assortative Mating.” _Journal of Marriage and Family_ 66 (4): 1015-28.

- United Nations. 2000. _Further Actions and Initiatives to Implement the Beijing Declaration and the Platform for Action_ . Resolution S-23/3 adopted by the General Assembly on 10 June 2000. <u>https://www.un.org/womenwatch/daw/csw/outcome.htm.</u>

- UNHLP. 2016.  Leave No One Behind: A Call to Action for Gender Equality and Women's Economic Empowerment. TechnicalReport, United Nations Secretary General’s High Level Panel on Women’s Economic Empowerment (UNHLP), New York NY. <u>https://www2.unwomen.org//media/hlp%20wee/attachments/reports-toolkits/hlp-wee-report-2016-09-call-to-actionen.pdf?la=en&vs=1028.</u>

- World Bank. 2012. _World Development Report 2012: Gender Equality and Development_ . Technical Report, World Bank, Washington DC. https://openknowledge.worldbank.org/handle/10986/4391.

- World Development Indicators. 2020. <u>https://databank.worldbank.org/source/world-developmentindicators.</u>

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predicted disbursement as the instrumental variable, the coefficient on public investment is 1.6 (in the case of _h_ =0) and The cumulative is statistically significant at the 5% level of significance (table 2; panel B).<sup>12</sup> impact of public investment on private investment rises to a peak of 2.4 after two years. The estimates have two economic meanings, as highlighted in section 2. Specifically, recall that a positive coefficient on public investment in the private investment equation means that government investment is an Edgeworth-Pareto complement to private investment, suggesting that an increase in public investment raises the marginal product of private investment. Additionally, the estimated positive coefficient implies the crowding-in of private investment following an increase in public investment. In particular, the results

> 11 Total disbursements average 77 percent of public investment and 2.8 percent of GDP for country-year pairs. 12 In annex C we look at the impact of alternative censoring rules applied to the loan-level data.

11
being taken into account and prices are generally assessed at market levels rather than at their shadow level.

**Table 2.** Social rates of return on World Bank Projects Unweighted Average 1960-2000

|**Region**|**Energy/Mining**|**Telecoms &**<br>**Information**|**Transport**|**Urban**|**Water &**<br>**Sanitation**|
|---|---|---|---|---|---|
|**Africa**|14.1|20.6|25.5|21.3|7.5|
|**East Asia**|18.3|19.5|24.8|20.3|10.5|
|**Eastern**<br>**Europe**|30.9|31.1|25.8|15.7|9.8|
|**Latin America**|12.8|16.6|22.4|19.2|11.0|
|**Middle East**|12.3|26.9|25.1|16.5|7.8|
|**South Asia**|23.2|22.0|24.1|14.9|9.8|
|**Developing**<br>**World**|18.4|21.5|25.4|19.2|9.2|

Source: World Bank- Operations and Evaluation Department Database There are several other striking results – first, the returns to the “market enlarging” sectors – ICT and transport – are considerably higher than the more service/welfareoriented sectors, like water and sanitation, which is roughly consistent with the conclusions
>3 Federation<br>3<br>2 EAP ECA Brazil EAP ECA<br>LAC MENA 2 LAC MENA<br>1 SAS SSA SAS SSA<br>BRICS 1 BRICS<br>0 0<br>0 5 10 15 20 25 0 5 10 15 20 25<br>GDP per capita, US $ thousands GDP per capita, US $ thousands<br><!-- End of picture text --> Sources: World Bank, World Economic Outlook.

Note: EAP is East Asia and Pacific, ECA is Europe and Central Asia, LAC is Latin America and the Caribbean, MENA is Middle East and North Africa, SAS is South Asia, and SSA is Sub-Saharan Africa. BRICS are Brazil, Russia, India, China and South Africa.

A. Data 2014. Population living in slums is the proportion of the urban population living in slum households, defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.

B. Data for 2015. Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.

C. Data for 2015. The score is from 1 to 7. Higher score indicates better quality. Quality of Transportation Infrastructure surveyed countries on the question of “How would you assess general infrastructure (e.g., transport, telephony, and energy) in your country?” The score is from 1 to 7. Higher value indicates better quality.

D. Data for 2015. The score is from 1 to 7. Higher score indicates better quality. Quality of Port Infrastructure surveyed countries on the question of “In your country, how would you assess the quality of seaports? (For landlocked countries: How accessible are seaport facilities?”.

6
shock in our parlance) captures the disease environment effect, which, according to the discussion above, is likely to reduce the child’s weight. At this point in the agricultural calendar, however, the household cannot yet avail itself of the increased purchasing power induced by the higher than normal rainfall; this windfall is realized only after the harvest, which has yet to take place. Hence, the income effect of a contemporaneous rainfall shock on the child’s weight is essentially zero.<sup>5</sup> In contrast, excess rainfall during the _last_ completed monsoon season is likely to operate through both the income _and_ disease environment channels. High past monsoon rainfall has a negative impact on previous period nutritional status through _Et-1_ , which is then carried through to the current period by _Ht-1_ in equation (1). Moreover, plentiful rainfall during the last monsoon results in a better harvest, increasing _It._<sup>6</sup> In sum, the reduced form impact of excess rainfall in the last monsoon on child weight consists of two opposing effects: a (negative) lagged disease environment effect and a (positive) income effect; the reduced form impact is thus a _lower bound_ on the income effect of rainfall shocks on child weight. Indeed, in principle, _the reduced form effect of excess rainfall in the last monsoon can be negative._

# **3. Data**

# _Anthropometrics_

Child and household-level data are drawn from three rounds of the nationally representative Demographic and Health Surveys conducted in Nepal in 2001, 2006 and 2011. The DHS samples were selected using a stratified, two-stage cluster design. The 2001 round consists of 8602 households in 257 clusters of which 84% are rural; 2006 and 2011 rounds are larger with, respectively, 9036 households in 260 clusters (68% rural) and 10,826 households in 289 clusters (71% rural).

> 5 Excess precipitation could have a _contemporaneous_ effect on household income via changes in the local agricultural
for the tenure security dimension and therefore abstain from an additional macro-check.

To the best of our knowledge, there are no comparable data sets at macro-level for overcrowding and structural quality for a large enough sample. The results [ _available in supplementary material_ ] show negligible differences which can likely be attributed to the differences in definition across surveys and macro-level data. Hence, we are comfortable that our harmonization efforts across the 64 household surveys measure the concept accurately.

> 12 JMP provides an adequacy ladder of drinking water which includes safely managed drinking water located within the premises of the HH; basic drinking water, which is an improved source within 30 minutes roundtrip; and limited drinking water for which collection time exceeds 30 minutes. Since not all HCES data allow for the calculation of time to the nearest water source, we collapse these three categories of JMP for our robustness check instead of reverting to WDI data, which only includes the first category. For more information, see: washdata.org.

> 13 <u>https://www.iea.org/articles/defining-energy-access-2020-methodology</u>
and services it also incorporates a basic ―basket of insurance‖ against ―unacceptable risks‖. The main argument is that by embedding risk in the measurement of poverty, such a measure becomes a wider concept of vulnerability that captures the welfare consequences of exposure to risk, not only that of having been subject to shocks.

Longitudinal data for Chile, Mexico and Peru –described in detail in section 4— allow us to observe the incidence of events that could potentially affect welfare. In Mexico, table 1 shows the incidence of several shocks between 2002 and 2005: death or illness of a household member; economic shock defined as business bankruptcy or unemployment; and loss of dwelling, crops and livestock due to climate-related events. While a higher incidence of loss of crops and livestock is evident for poorest households (1st quintile) –probably due to these activities are mostly rural and where poor population could be more exposed to shocks— an important result is that the occurrence of deaths (8.8 percent), health shocks for which the individual was hospitalized (12 percent), economic shocks (6.6 percent) and, in general, any kind of shock (25 percent), is a somewhat stable through the income distribution suggesting thus that the entire population is prone to negative shocks.

**Table 1: Incidence of shocks by income quintiles; Mexico 2002-2005**

_Percentage of households_

|**Income**<br>**quintiles**|**Any shock**|**Death**|**Health**<br>**shock**|**Economic**<br>**shock**|**Loss of**<br>**dwelling**|**Loss of**<br>**crops**|**Loss of**<br>**livestock**|**Dwelling,**<br>**crops and**<br
be accompanied by a theoretical or agronomic justification and likely warrant robustness checks to ensure results are not driven by the choice of any one specific weather metric.

# **4.3 Remote Sensing Products**

By narrowing in on a single extraction method and a subset of weather metrics that are correlated with outcomes, we can better investigate which remote sensing products mismeasure precipitation and temperature. Specifically, we want to examine the evidence around Hypothesis 3 ( _H_ 0<sup>3- different</sup> measurement technologies for precipitation and temperature have the same impact on estimates of agricultural productivity). As noted previously, mismeasurement in the objective fact of precipitation and temperature may be introduced by inaccuracies in the sensor (e.g. infrared, microwave, optical), the algorithm used to convert sensor data into rainfall or temperature (e.g. reanalysis, interpolation), or the resolution of the data (e.g. spatial, temporal). Our null hypothesis rests on the assumption that these inaccuracies either do not exist (that all remote sensing products accurately report the objective facts) or that any mismeasurement is insubstantial and will not impact the analysis. The alternative hypothesis is that there is substantial mismeasurement in remote sensing weather data, as Figures 1 and 2 seem to imply. If the alternative is true, then care must be taken

> 22See Appendix F for an exploration of linear combinations of rainfall and temperature metrics.

33
how much easier it would be to imagine the impact of a decline in ODA if it were expressed, not as a reduction from 0.5% to 0.27% of GNI, but as a contribution of, say, USD$218 person falling to $132 per person (roughly the decline in Austria from 2007 to 2011). Of course, ODA per person does not measure the same quantity as share of GNI; it would shift benchmarks of donor generosity to the benefit of richer countries. Still, the percentage of GNI target, while more “fair” from a certain point of view, does not

> 4 The finding has been replicated several times. See, for instance, the World Public Opinion survey of 2010: <u>http://www.worldpublicopinion.org/pipa/articles/brunitedstatescanadara/670.php?nid=&id=&pnt=670&lb=. See</u> also this 2012 survey of American public opinion from the Kaiser Family Foundation: <u>http://www.kff.org/kaiserpolls/upload/8304.pdf.</u>

10
To address this issue and the lack of an SOE identifying variable in the ORBIS database, we developed an algorithm that retrieves the ownership structure of firms linked to the state using available information on shareholders and direct shareholding percentages.<sup>18</sup> Similar to a genealogic tree, the algorithm reconstructs the linkages at different ownership layers of all firms with state participation and their subsidiaries (and subsidiaries of those subsidiaries, etc.) applying at each stage the 10 percent threshold defined above.<sup>19</sup> The use of the algorithm allows for the identification of indirect shareholding relationships. The construction of the full ownership trees raises a set of methodological issues, including how to deal with loops or circular ownership links, what the maximum depth level for the tree should be, etc. The answer to these questions determines when the algorithm will stop the search for subsidiaries. These issues are addressed in section 4.2.

The output of the algorithm lays the foundation for understanding governments’ corporate structures through ownership relationships at the national and municipal levels. This algorithm delivers the full extent of the linkages between a company and the state, which is key to understanding the real extent of fiscal risks and liabilities posed by SOEs and their subsidiaries, designing corporate governance forms, formulating transparency and accountability requirements, unveiling the risks of crowding out private investment, and addressing plausible distortions to competition (for example in the case of SOEs that are vertically integrated in upstream/downstream sectors). The algorithm captures all interrelated companies even if those are operating across borders. Figure 1 shows part of Pakistan’s SOE ‘ownership tree’ constructed with the algorithm. The tree depicts a set of firms that are owned by the Federal Government of Pakistan (node labeled as “Pakistan”) and a set of firms linked to local governments (nodes labeled as “Government of Khyber Pakhtunkhwa” and “Punjab Provincial Government”).<sup>20</sup>

> 17 There are cases in the