rafmacalaba/data-use-mentions-extended huggingface dataset:
Singapore’s ranking in the Global Entrepreneur Monitor actually fell in 2003. It was ranked 15<sup>th</sup> in a group of 22 OECD/East Asian countries, compared with 11<sup>th</sup> a year earlier.<sup>39</sup>

**Table 2    Number of patents in Asian countries over time**

|Country|19|95|19|96|19|97|199|8|19|99|20|00|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Dom|Foreign|Dom|Foreign|Dom|Foreign|Dom|Foreign|Dom|Foreign|Dom|Foreign|
|Singapore|20|1730|30|3300|20|3100|30|2360|50|4360|110|4980|
|Japan|94,804|14,296|187,681|27,419|129,937|17,749|125,704|15,744|133,960|16,099|112,269|13,611|
|South Korea|12,512|5,937|16,516|8,195|24,579|10,082|52,900|17,000|62,635|19,321|34,894|12,006|
|Taiwan|20,717|8,990|19,410|10,059|19,551|9,805|16,417|8,634|18,052|11,092|23,737|14,928|

_Sources_ : Websites of patent offices of respective countries There are a few questions on the effectiveness of the strategic supply push approach
avoid electoral consequences. Conflict resolution theory has similarly highlighted the importance of joint decision making, typically analyzed as a process of negotiation (Filley 1975, Zartman 1977). Management and organizational science has stressed the importance of managers employing joint decision making, typically to prevent workplace frustration boiling over into conflict (Greenhalgh and Chapman 1995). Lastly, the medical literature has explicitly advanced ‘Shared Decision Making’ (SDM) as a risk management tool for doctors seeking to avoid potential malpractice lawsuits. This literature highlights how a lack of shared decision making leads to decisional conflict, with risk managers encouraging joint decision making between doctors and patients to enhance a practice’s legal protection (Hoffmann et al. 2014, Kremer et al. 2007).

— In what follows, we explore the empirical relevance of this multidisciplinary concept joint decision making as a conflict-reducing strategy—for women’s experience of intimate-partner violence in SubSaharan Africa.

# **3. Data and Methodology**

## **3.1 Data**

The DHS is a nationally representative population-based household survey that has been conducted since 1984. The DHS includes data on family planning, maternal and childcare, gender, fertility, and nutrition and has been collected in over 90 countries. The DHS also asks married women aged 15-49 about their decision making roles (“who usually makes decisions over [X]”) across different areas, including family planning, use of one’s own earnings, the respondent’s own health care, ability to visit family and friends, as well as major household purchases.

9
crisis, helping the world distinguish selected anecdotes and rumors from a representative picture of labor force adjustment.

To meet these goals, the rest of the paper is organized as follows. The next section describes the data. The following section uses the data to document the impacts of the financial crisis on employment and off-farm earnings. The third and fourth sections report the correlates of personal characteristics with unemployment and trace the plight of those that were laid off between September 2008 and April 2009. A concluding section summarizes our findings.

# **2. Data**

The data for this study were collected as the 2008/9 wave of a panel dataset. The dataset includes information from 58 randomly selected villages in 6 provinces of rural China selected as representative of China’s major agricultural regions.<sup>1</sup> Henceforth, we call this dataset the 2008 China National Rural Survey, or 2008 CNRS dataset.<sup>2</sup> To reflect accurately varying income distributions within each province, one county was selected randomly from within each income quintile for the province, as measured by the gross value of industrial output. Two villages were selected randomly within each county. The survey teams used village rosters and our own counts to choose twenty households randomly, both those with their residency permits (hukou) in the village and those without. A total of 1160 households were surveyed (6 provinces x 5 counties x 2 villages x 20 households—minus the 40 households in two earthquake damaged villages in Sichuan).<sup>3</sup> When we aggregate our data to produce national estimates, we weight according to the population of the province (and its region).

5
to December) and then during the low malaria prevalence months. With the information available at hand, policymakers could do even better in targeting by using information from the previous year. They know which health facility areas/months had the highest level of malaria in the previous year and can target health facility areas in months according to their ordering the previous year.

The cell phone data allow for even more eective targeting. The piece of information currently not available to policymakers is the number of travelers entering an area from any given other district in the country. The cell phone data provide this information and combined with the incidence from 2012, it makes it possible to estimate which health facility areas are most at risk from travelers in certain months. This can be especially eective because travel choices are not random. There are often high movement corridors between certain communities in a country. Therefore, it is likely that there are pockets that will be more aected by travelers coming from high malaria areas than other parts of the district. By focusing on the pockets of areas most aected by travelers from high malaria settings, limited resources can be spent more eectively training health professionals and tracking travelers only in these areas.

Figure 6 demonstrates the full cost curves, from a strategy where only one health district area-month is treated, all the way up to all health district area-months being treated. The benet at each point is calculated based on the earlier model to calculate the total primary and secondary cases attributed to travelers. I assume that all travelers are targeted, but only 94% of them agree to take the test and contribute to the benet from targeting.<sup>27</sup> Depending on the resources available to the government, it is possible to determine for any given budget how many cases would be treated or averted depending on the targeting strategy chosen. Looking across the entire distribution in the top panel, the targeted strategy based on information from mobile phones is consistently
RCI (rental cost index) = the ratio of per square rental cost in this region to the national average; SDI (student disability index) = the ratio of the percentage of students with physical disabilities in this region to the national average; PFI (poor family index) = the ratio of the percentage of students from low-income families in this region to the national average.

The weights attached to the four factors should add up to one, i.e., a, + a2 + a3 + a4 = **1.** These weights can be derived from an econometric estimation using cross-region or penal data (cross region and time series) from the past years. Shah (1994a) provides an example of such an estimation. Many countries try arbitrary values of weights based on the designers' intuition about the importance of different factors in affecting the costs of services. Assigning these weights can also be a method for the designers to emphasize certain factors in grant distribution.

Figures used to calculate the indices (WI, RCI, SDI, and PFI) are those of the previous year or past few years' averages.

# (2) Health

# Measurement unit = total population in this region

# Average per unit cost = the country's per capita public expenditure on health care

# Adjustment index = a1HPI + a2IMI + a3ILEI + a4IPDI

- where HPI (health price index) = the ratio of health care cost in this region to the national average; IMI (ifant mortality index) = the ratio of infant mortality rate in this region to the national average;

- ILEI (inverse life expectancy index) = the ratio of national average life expectancy to life expectancy in this region;

IPDI (inverse population density index) = the ratio of national average population density to that in this region; a, +a 2 +a 3 +a 4 = 1.

# (3) Transportation

# Measurement unit = total length of roads in this region

# Average per unit cost = the
Nowcasting Economic Activity in Times of COVID-19: An Approximation from the Google Community Mobility Report James Sampi and Charl Jooste<sup>_∗_</sup>

> _∗_ We are extremely grateful to Jorge Araujo for his invaluable support in this project. We thank to Pablo Saavedra and Stefano Curto for valuable advice, and Julio Velasco, Barbara Cunha, Marek Hanusch, Gabriel Zaourak and Luigi Butron for helpful discussions. Corresponding authors: jsampibravo@worldbank.org and cjooste@worldbank.org
income. An Egyptian household’s expenditure profile in the HIECS cannot provide any information on the composition of its income. For the same reason, the team is unable to say anything about the savings or current asset profiles of HIECS households: a current consumption expenditure profile provides no information on either investment spending or on the returns accruing to any household assets.

22 While a CEQ Assessment typically includes an alternative scenario where public contributory pensions are treated as a government transfer (rather than deferred income), information regarding total pension payouts (in 20
As the GTAP 6 data base contains data for 2001, but the AfT policies is designed for the year 2010, we follow the methodology described in Arndt _et al._ (1997) to provide a status quo projection of the global economy in the selected year. The approach is based on a two-stage procedure. Firstly, we have generated “pseudo-calibration” from 2001 to 2010 by calibrating the technical parameters related to population growth, capital and labour stock change, labour and land productivity change, so that we achieve growth in regional GDP consistent with the World Bank projections. Figure 2 shows the convergence results to the real data in terms of GDP. The resulting scenario in this first stage is called “baseline”. Subsequently, conventional comparative analysis is conducted simulating the AfT scenarios for 2010.

**Figure 2. Gross domestic product (GDP) convergence**

<!-- Start of picture text --> 12,000<br>11,000<br>10,000<br>9,000<br>8,000<br>7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>Model Baseline Calibration World Bank World Development Indicators Data<br>US $ billion<br>USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA ROW<br><!-- End of picture text -->

**Source: Our calculation from World Development Indicators &  authors’ modeling results.**

**14**
**12** **<u>│</u>**

|**Pillar**|**Area**|**Indicator**<br>**type**|**Strategic question**|**Indicator’s description**|
|---|---|---|---|---|
|||Micro-<br>Based|Do foreign-owned firms source<br>inputs more intensively from<br>abroad?|Wedge between foreign-owned and<br>domestically owned firms in their<br>intensity of sourcing domestic<br>inputs|

Source: Authors’ elaboration of an earlier version prepared jointly with the OECD for the Investment Committee in October 2016.

The indicators presented in this study are all compiled at the level of production establishments. They adopt the first approach of measuring the differences between foreign multinational and domestic firms by employing two thresholds of foreign ownership: 10 percent and 50 percent.

# 3.1 The data: Foreign-owned firms in World Bank Enterprise Surveys

The World Bank Enterprise Survey (WBES) is a unique source of economic data, which has a pool of 131,000 firms from 139 countries, mostly developing nations. The data have been collected since 2006 using a harmonized methodology to ensure cross-country comparability.

The surveys only represent a country's non-agricultural economy. Thus, the sectoral scope of the surveys is not comprehensive. Manufacturing and services are the primary sectors of focus, with services including construction, retail, wholesale, hotels, restaurants, transport, storage, and telecommunications. Mining and financial services, which attract significant foreign investment, are excluded. This also excludes firms that employ fewer than five workers, as well as wholly state-owned enterprises. All these factors affect the benchmark for evaluating the differences between foreign multinational and domestic firms.

But how accurate is the WBES when it comes to foreign multinationals? The sampling follows frames provided by national
United States were selected as the industrial countries, and China, Brazil, India, the Republic of Korea, Malaysia, Mexico, South Africa, and Turkey were selected as the developing countries.<sup>2</sup> Most market share analyses have been carried out using only exports, and testing whether the exports of a specific country have expanded at the same rate as world exports (Balassa (1984); Fosu (1990); Ng and Yeats (1997 and 2003); Mayer (2004); Hanson and Robertson (2009) etc). These exercises take the world trade growth as given and estimate the relative performance of counties against this trend, and not what determines that trend. The contribution of market share changes versus demand increases in explaining trade growth has not received much attention.<sup>3</sup> One simple way of analyzing this issue is to see whether the growth of imports in selected markets are driven by the demand increases or they have expanded by gaining market shares in the importing countries. This can be done by estimating the shares of imports in domestic absorption and then measuring the changes in these shares. These changes in shares show the contributions of share changes while import increases under constant market shares show the contribution of demand increases. This decomposition can also shed light on the likely outcomes for world trade growth under different global demand growth scenarios.

A related issue involves the role of developing (especially emerging market) countries in world manufacturing trade. Many have also argued that developing country exports are taking a big share of the markets in industrial countries and that within manufacturing a significant portion of production is moving from industrial to developing countries. Some have even argued that this development is leading to deindustrialization of many industrial countries. Along with the decomposition exercise, this paper also estimates the magnitude of developing country exports (aggregate and 3 digit) within the domestic absorption and production in these selected countries (both industrial and developing) and their evolution over the last two decades
27 without taking diversion into account, LPG price subsidies for households are regressive and benefit the rich disproportionately (Kojima 2011).

In all cases, diversion makes less subsidized fuel available for the intended beneficiaries, enables black markets to flourish, and all too often forces the poor—especially in rural areas—to pay more. In the extreme, the prices paid can be markedly higher than what would have prevailed in a deregulated market. An example is kerosene in Nigeria, for which the official price has been ₦50 ($0.32) a liter for years but which has been known to cost as much as six-times the official price in some regions (All Africa 2011).

Large enough financial incentives may attract well-organized mafias, who may use physical force and violence to silence investigators and whistle-blowers ( _Hindu_ 2011). To stem diversion, smuggling (into or out of the country, depending on price levels in neighboring countries), and tax evasion, some countries require the use of chemical markers or dyes (Kenya, Senegal, South Africa, Thailand, Togo, Tanzania, Turkey, Uganda), tagging of vehicles (Repúblicana Bolivariana de Venezuela), and even electronic tracking of delivery vans (Tanzania). But when the financial incentives are powerful, those engaged in commercial malpractice find ways to get around these restrictions over time.

A clean-burning fuel for cooking and heating may be a merit good with health benefits not fully recognized by the users. One way of ensuring continuing consumption of such a merit good is to ear-mark cash transfers for the purchase of that specific fuel. In this way, market prices are not affected, and the poor receiving cash can use it only to buy the clean fuel. This is the principle behind bonogas cards for LPG in the Dominican Republic. In this regard, recent developments in biometric databases of personal information merit attention; presumably it would be more difficult to escape detection loaning cards with biometric information than loaning cards without. The government of India is proceeding with what
Equation (2) for worker employment and wages from 2006 until 2017.<sup>28</sup> The estimates in Appendix Figure C3 show an insignificant impact of the GFC shock on worker employment and wages in 2006 and 2007 before the GFC, which supports the causality of the impacts we estimate from 2009 onward. The significant negative impact that is estimated in 2008 is not surprising as the GFC was already unfolding.

# _C. Robustness Checks_

We subject our main results in Figure 1 to a series of robustness checks. First, as an alternative to the use of the worker panel database, we estimate Equation (2) separately for each worker cross-section from 2009 onward. The corresponding point estimates shown in Appendix Figure C4 are very close to those in Figure 1. Second, given the important commodity boom experienced by Latin America during the 2003–2013 period, we estimate Equation (2) excluding commodity-related sectors from the sample.<sup>29</sup> Third, Brazil experienced a domestic economic recession from 2014 onward. While the sector-year and region-year fixed effects included in our specifications go a long way towards accounting for domestic economic cycles, we also estimate Equation (2) excluding the period of domestic recession from our sample. Fourth, we estimate two variants of Equation (2): dropping the vector of firm covariates and including separate 2-digit sector, region, and year fixed effects. The results from these estimations presented in Appendix Table C2 show that the impact of firm GFC shocks on workers’ months worked and real wages is qualitatively unchanged, relative to the main results.

As alternatives to the worker outcome variables described in Section III.A we consider as dependent variables in Equation (2) employment and wage measures defined following Yagan (2019).<sup>30</sup> The estimates in Appendix Table C3 are qualitatively similar to those in Figure 1. Moreover, we estimate the impact of GFC firm shocks on worker job displacement by constructing two indicator variables for a worker
infection and childcare options could be important factors explaining the persistent rates of labor inactivity in the US.

# **3. Data**

The analysis in this paper relies on a new country-level data set of expenditure on social protection measures implemented in response to the COVID-19 pandemic put together by the authors. Our database uses information from the Global Database on Social Protection Responses to COVID19 (GDSPRC) (Gentilini et al. 2022). The GDSPRC contains information on the duration, target population, and other technical aspects of these measures. We collected detailed budget 7
)|
|Maximum Coverage Registry or Bureau||**-0.03**|**-0.04**|**0.15**|**0.01**|**-0.04**|**-0.04**|**-0.05**|**0.13**|**-0.02**|**-0.01**|
|||[0.44]|[0.71]|[2.53]**|[0.11]|[0.83]|[0.71]|[0.88]|[2.03]**|[0.34]|[0.14]|
|||(0.37)|(0.60)|(2.41)**|(0.08)|(0.70)|(0.59)|(0.73)|(1.97)*|(0.27)|(0.12)|
|Legal Rights Index|||**1.94**|||||**1.55**||||
||||[1.97]*|||||[1.56]||||
||||(1.43)|||||(1.14)||||
|Time to Register Property||||**-0.14**|||||**-0.12**|||
|||||[3.54]***|||||[3.28]***|||
|||||(2.69)***||
historical standards, since it is unlikely that the steady state is associated with extreme values. Therefore, we calibrate the rest of the world price at $176/ton, the average US export price in 2005—2007, a period before the very high prices of the food crisis but during a time when prices were higher than in the beginning of the decade.<sup>14</sup> The steady-state MENA price is defined by assuming that the price difference between the regions reflects transport costs, which is set at $35.55/ton based on a recent survey (World Bank, 2012).

Picking appropriate elasticities is a challenge given the large variation in elasticities reported in the literature. As a guide, we follow the literature on commodity price dynamics, which shows that observed price volatility is consistent with relatively low demand elasticities ( Roberts and Schlenker, 2009; Cafiero et al., 2011), thus, we assume the demand elasticity to be equal to –0.12, which is toward the lower end of commonly used elasticities. We consider a supply elasticity of 0.2, in line with commonly used supply elasticities for wheat.<sup>15</sup> The cost function is assumed to be quadratic, making the marginal cost function linear. We express the marginal cost function as The parameters �<sup>�</sup> and �<sup>�</sup> determine the supply elasticity (see Table 2) assuming that marginal cost equals the steady-state price.

� We specify two sets of uncorrelated supply shocks, �<sup>�</sup> ~��1, �� �. Based on historical production data, we derive an estimated distribution of supply shocks, where production in MENA is more volatile than in the rest of the world.

Interest rates and per unit storage charges are assumed to be the same for each region at 5 percent per annum and $22.40 per ton per annum. The
al. 2023) by showing that the GBIF’s radical expansion of georeferenced species information has significant implications for global conservation planning. From a static perspective, spatial analysis suggests that broadening species representation also broadens the critical domain for biodiversity conservation. The concurrent availability of new risk indicators also broadens the scope for stakeholder participation because, as our results show, identification of high-priority by adding a dynamic perspective as well. Continued rapid growth of the GBIF database will both expand the number of mapped species and alter the estimated boundaries of existing maps. As this process continues, the global stakeholder community will be best served by a georeferenced database that supports continually updated estimation of species occurrence maps and the associated extinction threat indicators.