rafmacalaba/data-use-mentions-extended huggingface dataset:
This paper addresses the information gap by developing and applying an algorithm that generates species maps directly from GBIF occurrence data. The algorithm can automatically update maps as new occurrence data become available. We generate maps for all species whose data currently satisfy our computational criteria (over 600,000). Overlaying these maps with a high-resolution grid provides a view of global biodiversity that broadens the traditional focus on vertebrate animals to include more representation for invertebrates, other animals, plants, fungi and other non-plant and non-animal species.

We believe that the new maps are complements rather than substitutes for in-depth biodiversity assessments. Where more information is available, the policy process will undoubtedly benefit from incorporating it. At the same time, we believe that our approach can contribute in three ways. First, for currently-mapped species, fast updates using our algorithm can help identify cases where newly-reported occurrences suggest alteration of map boundaries. Second, for currently-unmapped species, our approach can provide useful new information for global biodiversity assessments. Third, our mapping exercise can yield new insights about the global distribution of endemic species and species that are particularly vulnerable to human encroachment.

The remainder ofthe paper<sup>isorganizedasfollows.Section2describestheGBIFoccurrencedata</sup> and<sup>the</sup> application of our mapping algorithm. In Section 3, we explore overall results for the 610,694 species maps generated by our exercise. Section 4 extends the results to a new view of global endemism, while Section 5 explores the distributions of small-occurrence region species that are particularly vulnerable to human encroachment. Section 6 summarizes and concludes the paper.

The Global Biodiversity Information Facility provides a constantly-updated open-source repository of geolocated, date-stamped reports of species occurrences from many public institutions and NGOs. These In addition, the GBIF identified reporting locations.

Species are traditionally identified as endemic if they are 100% resident in one country. We also explore the implications of lowering the endemism condition to 95% and 90%. ~~a a,~~ respectively. Our mapping algorithm requires at least this
time. Finally, this paper reviews several proposals for using alternate thresholds for income categories based on the current GNI per capita indicator. It also cites alternative methods of classification based on other variables, such as poverty levels or multidimensional indices.

The paper reflects information gathered during interviews and discussions with a number of key internal users of the current system, such as chief economists and selected directors and managers. It incorporates existing literature, including papers prepared for the Board of Executive Directors on the operational guidelines, and views of external users gathered through various channels, including blog posts and online discussions.

# **2. Main findings**

The paper finds that per capita GNI continues to be a readily available and reasonable measure for the purpose of classifying countries for analytical purposes. It correlates well, especially in terms of rankings, with a number of accepted indicators of development outcomes. Consequently, the income classification is widely used within and outside the World Bank for comparing attributes between groups of countries.

While local currency conversion factors based on purchasing power parities (PPP) are preferable to the use of market exchange rates for comparing per capita GNI using a common numeraire, the lack of consistent annual time-series estimates from the International Comparison Program (ICP) limits their use for the annual classification of economies. Had they been used, the classification would likely be subject to substantial revision when new benchmarks are published, which is not a desirable feature of such a system.

A known shortcoming with the use of market exchange rates is instability from one year to the next, and this can also introduce undesirable volatility in the classification system. The current method employed to mitigate this is known as the “Atlas” method, since it was first used in the World Bank’s _Atlas of Global Development_ publication. It uses an average of three exchange rates: the current year, and the previous two years inflated to current year prices. We find that the method is effective in achieving its objective of reducing volatility of the income classification.
.003)|(0.002)|(0.002)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Gini|0.370|0.370|0.358|0.356|0.393|0.357|0.348|0.381|0.357|0.368|
||(0.003)|(0.004)|(0.004)|(0.005)|(0.006)|(0.004)|(0.004)|(0.005)|(0.004)|(0.005)|
|Theil L index|0.221|0.224|0.212|0.208|0.259|0.213|0.205|0.246|0.219|0.231|
||(0.008)|(0.007)|(0.006)|(0.006)|(0.009)|(0.006)|(0.006)|(0.008)|(0.006)|(0.007)|
|Theil T index|0.249|0.241|0.227|0.227|0.294|0.230|0.216|0.265|0.226|0.247|
||(0.010)|(0.007)|(0.008)|(0.008)|(0.014)|(0.009)|(0.009)|(0.013)|(0.007)|(0.011)|

Note: i) Authors’ estimation from VHLSSs. ii) Standard
The measurement of time-related underemployment relies on thresholds against which to compare working time/hours worked, as well as questions on desire and availability to work. Desire and availability to work are still not included in many socioeconomic surveys, although some LSMS-ISA surveys (including the 2013-14 Uganda survey, presented in Table 2 and Figure 4 below) include variants of these questions. Accurate reporting of hours worked can be challenging when unpaid activities are conducted simultaneously with paid work — as in the case of contributing family workers, whom the ILO LFS pilot studies found to report, on average, the least number of hours compared to other employed groups (Benes and Walsh, 2018c). Better counting of employment, as discussed in Section II, can help address some of these issues. Proxy reporting (and in particular, women proxies) can also lead to lower estimates of hours worked, as discussed further in this section. Table 2, which presents data from the 2013-14 Uganda LSMSISA, also shows that women proxies from poor households (who are mostly rural) are significantly less likely to report desire to work than male proxies, even though the difference between own-reporting men and women is not significant (for the non-poor sample, gender gaps emerge in both own and proxy reporting). As with the issues raised in Section II around proxy reporting of market activity in agriculture, further investigation is needed across countries on how women proxies, particularly in poorer contexts, might affect reporting.

Table 2. Uganda National Panel Survey, 2013-14: share of women and men who would like to work more paid hours, by own- versus proxy reporting, and poverty status

||W<br>Own<br>reporting|omen (W<br>Proxy|)<br>Diff. (own-<br>proxy)|<br>Own<br>reporting|Men (M)<br>Proxy|Diff. (own-<br>proxy)|Gender gap<br>(W-M,
.We use these value added elements</sup> to construct proxies for country _i_ ’s domestic value added embodied in foreign production of each sector _s ∈S_ in trading partner _j_ = _i ∈C_ ( _DV A_<sup>_j_</sup> _sit_<sup>)andforeignvalueadded</sup> embodied in country _i_ ’s domestic production of _s_ ( _FV A_<sup>_i_</sup> _st_<sup>).Specifically,foragivengood</sup><sup>_x_,</sup> _DV A_<sup>_j_</sup> _xit_<sup>_≡V Aj_</sup> _xit_<sup>and</sup><sup>_FV Ai_</sup> _xt_<sup>_≡_�</sup> _c_ = _i∈C_<sup>_V A_</sup> _xct_<sup>_i_.</sup> We compute value added content using the disaggregated 40 country version of the WIOD data set. We then aggregate value-added content across EU countries to form the EU composite, because EU countries have common external tariffs and trade policy.

> 60The global value chain traces backward through the production chain from final goods to identify the sources of value added in those goods. This is different than the value-added export decomposition developed by Johnson and Noguera (2012), which traces value added forward through the production chain to determine where value added from each country is ultimately consumed. It is also different than the decomposition of gross exports advanced by Koopman, Wang and Wei (2014).

50
for contemporary research and policy. Some of the policies of importance were only temporary; others have arguably had effects that persist to this day. Along the way, we also highlight both our main themes and important venues for additional research, especially given the troves of historical data, digitization, and electronic archives increasingly being made publicly available to researchers.<sup>66</sup>

# **4.1 Pre-GATT 1947 tariff levels, and tariff trends over the GATT period**

The GATT negotiations began in 1947 with 23 countries - referred to as ‘Contracting Parties’ - ultimately signing the agreement. The initial activity consisted of a round of reciprocal tariff cutting negotiations between those countries, as well as the establishment of a set of principles, rules and exceptions, set out as distinct ‘Articles’ in the agreement, that together launched the new multilateral trading system.<sup>67</sup> The US, Canada, Australia, the countries of Western Europe, and the other Contracting Parties then repeatedly convened under GATT negotiating rounds over the next five decades to bargain for additional tariff reductions and sometimes consider adoption

> 66The Data Appendix includes a detailed introduction and discussion of the GATT archival data utilized in this Section.

> 67For a description of the key GATT Articles - the rules and exceptions - that we have introduced and referenced throughout this chapter, see Table Appendix A.

38
90 percent of the main effect in column 1. This suggests that a heritage of high human capital could be one reason for more aid going to mission areas. However, there seems to be room for other explanations as well, since the coefficient on Mission _ik_ is not reduced to zero.

In column 5, the baseline specification is estimated in the subsample of cells for which a DHS measure of religion is available. Although religion seems to play an independent role in aid allocation, the coefficient on Mission _ik_ does not change when a measure of Christian religion is included in the regression. This means that there is no evidence in the data that Christian favoritism is the mechanism behind the correlation uncovered in Section 2.

> 34The question about religion is not included in all countries for all rounds.

38
tegrity barely changes, but standard errors become larger and the coefficient is no longer statistically significant (p-value = 0.11). As in the model chosen by LASSO, we find that a higher number or radio headquartered in a district is a strong predictor of better health services, as is scores at the UCE national examination.

In column (4) we add the average integrity of bureaucrats as explanatory variable in order to assess if it has independent explanatory power. The coefficient on politicians’ integrity remain unchanged, and that of bureaucrats’ integrity is an order of magnitude smaller and not statistically different from zero. Given the large standard-errors of estimates, however, we cannot reject that these two coefficients are equal (p-value = 0.41). In a very demanding test, in column (5) we include all of bureaucrats’ and politicians’ traits simultaneously as explanatory variables, together with districts’ characteristics. In this model, the coefficient on politicians’ integrity is smaller (0.16) and not statistically different from zero. We should caution, however, that in this specification we are estimating 40 parameters with 75 observations. That is, the F test of a “horse race” between bureaucrat and politician integrity, is a weak test in our setting. We provide other tests below that reveal politicians’ integrity, not that of bureaucrats’, as a robust correlate of service delivery. The available economic theory does not provide any predictions about whether bureaucrat or politician characteristics should matter more. We do not conclude from the results that bureaucrat traits don’t matter, and indeed find that bureaucrats’ altruism does show-up as a significant correlate in several specifications. Other bureaucrat traits might matter as well, but perhaps we are unable to discern it for a variety of reasons (we are underpowered; we have not measured the relevant traits well; local bureaucrats may be strategically appointed by national bureaucrats and politicians, making bureaucrat traits more endogenous to district characteristics which directly predict service delivery.) The one
should be interpreted as an upper-bound of what providers could do; i.e., the vignettes measure what the medical provider knows, but not necessarily whether the provider applies this knowledge in practice.<sup>9,10</sup> The main advantages of using medical vignettes compared to direct observation of real patient consultations (or re-examination of patients outside of the clinic by well-qualified personnel), are that they are far less time-consuming and they increase comparability by avoiding issues related to differences in case mix. Moreover they provide a direct measure of providers’ clinical knowledge, rather than a combined measure of knowledge and effort. Data from medical vignettes is also directly policy relevant as the scores are closely linked to the quality of medical training.

Validation of vignettes typically involves comparing scores from vignettes with scores from direct observations.<sup>9,10</sup> This is less relevant for this study as we focus on provider knowledge, realizing that in practice (some) providers may not always fully utilize their knowledge when diagnosing and treating patients. Still, the vignettes utilized in this study has been (indirectly) validated as a testing method for provider knowledge. One source of validation is the fact that providers with a high share of relevant items performed are much more likely to suggest the correct diagnosis and treatment.<sup>9</sup> That is, better knowledge of which history taking question to ask and examinations to perform is highly correlated with both diagnostic and treatment accuracy. The two figures below, repeat that exercise using the full sample of vignettes and share of items performed (89,824 observations). We estimate a logit model with the binary response variable correct diagnosis [correct diagnosis and treatment] and with share of items performed as a continuous predictor and country and condition categorical predictors as control variables. The figures plot the adjusted probability of reaching the right diagnosis [right diagnosis and treatment]. As the share of items performed increases, the estimated probability of reaching a correct
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey workers from exporting to non-exporting firms. They show that firms that do not engage in exporting activities are precisely the ones that, on average, pay lower wage premia, resulting in wage compression.<sup>14</sup> They estimate that the largest observed increase in commodity prices, between 2002 and 2011, is associated with a reduction in skill premium of 7 log points.<sup>15</sup> They confirm the proposed cost and wealth channels, accounting for an estimated 2/3 of the reallocation across sectors and between exporter and non-exporter Finally, the commodity boom may have impacts on the wage distribution simply due to the resulting currency appreciation. As the value of domestic currency raises, exporting firms suffer losses, which may trigger the mechanism described in the model developed by Helpman et al. (2010). Since exporting firms are larger and pay higher wages, and because interfirm wage dispersion is lower within the nontradable sector, the reallocation of labor from the exporting to the nontradable resulting from currency appreciation should result in decreased dispersion of wages. Messina and Silva (2017) argue that this can be a potential mechanism in explaining decreased wage inequality in Brazil, where 2/3 of the decrease in inequality among workers in the same sector and occupation occurred through a reduction in the variance of wages between firms. However, they do not provide an empirical analysis of this hypothesis. Some evidence against it is provided by Benguria et al. (2018), who investigate the possible confounding effect of commodity prices and exchange rate in their specification, showing that this is not what is driving their results. However, their focus is limited to skill premium and reallocation of workers, and may not account for other mechanism responsible for determining interfirm wage differentials. Further research is necessary to clarify the role of exchange rate appreciation on inequality.

# **3.3 Effects of trade liberalization on regional, racial, and
# 1. Introduction

SARS-CoV-2, the virus that causes COVID-19, has reached nearly every corner of the world, resulting in millions of deaths (Dong et al., 2020). To decrease the spread of the virus, governments around the world have implemented lockdowns and mobility restrictions. These restrictions are apparent in Figure 1, which visualizes the Oxford Stringency Index, broken down by regions. Governments introduced peak stringency immediately following the onset of the COVID-19 pandemic in the first quarter of 2020, after which there was a mild decline in stringency. These restrictions led to massive economic disruption, as the COVID-19 pandemic has resulted in a reversal of a decade-long declining global poverty trend, pushing upwards of 100 million people into extreme poverty in 2020 (World Bank, 2020).

In response to the growing demand for accurate statistics of the economic cost of the pandemic and resulting policies, scholars have adopted three general approaches designed to shed light on the impact of measures like mobility restrictions and lockdowns. One strand of literature used past data to forecast the state of the world's economic well-being during the pandemic. In April 2020, during the peak stringency adopted in most countries, the IMF projected a 3 percent contraction of world economic output, much larger than during the 2008 global financial crisis (IMF, 2020a). The 2021 growth rate was projected to be 6.5 percentage points below the pre-pandemic forecast made in January 2020 (IMF, 2020b). Following the onset of the COVID-19 crisis, other work has used real-time forecasting. Combining pre-pandemic household surveys with the growth projections predicted that nearly 49 million people would fall below the $1.90 poverty threshold in 2020 real terms (Mahler et al., 2020a). An updated set of estimates published in June 2020 painted a gloomier picture, predicting that the number of newly poor individuals would range from 71 million to 100 million by the end of 2020 (Mahler et al., 2020b).

The second strand of literature uses interviews of representative parts of the
into question the positive coefficient for horizontal FDI in other specifications reported elsewhere in this paper, but confirms the positive vertical linkages between domestic suppliers and foreign users of their products.

# **IV. Concluding Comments**

In this paper, we explore the ways in which a range of institutional features, some general and some unique to China, affect the direction and magnitude of FDI spillovers. Specifically, we examine the role played by foreign investors in generating productivity spillovers via horizontal and vertical linkages, as these spillovers affect the reform of state enterprises through joint venture activity. We also explore the different impacts of spillovers that originate from FDI aggregations that embody different mixes of investment from Hong Kong-Taiwan-Macau on the one hand and largely OECD sourced investment on the other. Finally, our study investigates the implications of tariff protection for the nature of productivity spillovers and explores whether the Chinese government‘s targeting through the selective imposition of tax holidays to attract foreign investors is consistent with larger externalities. The focus on the heterogeneity of spillovers across different policies, such as differences in the tax and tariff regime, is a primary innovation of this paper.

We use a firm-level dataset from China for the 1998-2007 period, Across a variety of specifications, and controlling for firm and year effects, we find that positive productivity spillovers from FDI take place through contacts between foreign affiliates and their local clients in upstream (backward) or downstream sectors (forward linkages). We also find evidence that positive productivity spillovers occur through horizontal foreign investment, but these types of spillovers are less robust, and become negative when we instrument for FDI.

We also highlight the different effects played by the sources of sectoral foreign direct investment on domestic firm productivity. While at the firm level foreign equity participation is generally associated with higher productivity, this is not the 27
this iterative process, the participant confirmed her final estimates and they were stored by the survey tool.

In a related paper, Anderson and Zia (2016) explicitly test the precision of various performance estimates obtained through this anchoring and adjustment approach using the electronic survey tool and find the coefficient of variation across a randomly selected sample of businesses is significantly lower than simple recall measures. We acknowledge that other biases may still exist; for instance, treated individuals may deliberately overstate profits. However, the iterative and detailed exercise of tabulating individual costs and sales would require a great degree of sophistication to systematically over-report on revenues and under-report on costs. Indeed, the firm owners responding to this electronic survey would need to remember 100+ numbers in order to purposefully game the survey, not to mention their responses from 6-12 months prior. In addition, our analysis is based on data from two follow-up surveys and, as later sections of this paper will show, we find significant treatment effects only at endline and much smaller impacts at midline. The null/small effects at midline help allay concerns that businesses in our sample were significantly over-reporting outcomes and instead seems to support the view that treated firms were gradually improving performance over time. Moreover, McKenzie and Woodruff (2016) conduct an audit exercise in a similar sample of small businesses in Sri Lanka who were provided business training to precisely test such over reporting, and find no significant differences and very high correlations between self-reported and auditor-recorded estimates.

# **3.4. Empirical Specification**

Based on the random assignment, we measure the impact of finance and marketing training at midline and endline as the difference in average outcomes in the treatment and control businesses using the following intention-to-treat OLS regression: where Yi is the outcome measure for firm _i_ at either midline or endline. The variable Financei indicates whether a business was assigned to the finance training, while Marketingi indicates the same for marketing training. di.s comprises a set of baseline controls for entrepreneur gender, age, 18