rafmacalaba/data-use-mentions-extended huggingface dataset:
news media. On the civil society side, a particularly important component is the Ghana Integrity Initiative, the local chapter of Transparency International. It brings together a variety of organizations, religious groups, media representatives, legal practitioners and individuals committed to reform.

The question of how much, and how best, to coordinate activities with the government is a complex one in Ghana. The new Government has indicated they do not intend to nominate anyone to GACC but will negotiate a framework for liaison with the Coalition, either through the Attorney General or the Office of Accountability. Still, the Coalition has asked the Chairman of the Public Accounts Committee to become a partner, and has made similar approaches to the Auditor General. Former President Jerry Rawlings did, however, approve the coalition's request for a World Bank diagnostic survey on corruption. This survey was conducted by the CDD from March to July, 2000, with additional support from the United Kingdom's Department for International Development, the Canadian International Development Association, and the United Nations Development Program. These agencies have also agreed to assist GACC with post-survey analyses, dissemination of findings, and the formulation of specific policy proposals.

The GACC agenda offers an ambitious set of purposive incentives, including: • Creating a forum for interaction among the three arms of government, public and private sector institutions, and civil society groups to work on anti-corruption strategies

- Encouraging the exchange of information and joint sponsorship of programs, where appropriate, to achieve effective utilization of resources

• Enhancing transparency and reducing opportunities for corruption in government, public and private institutions, and civil society organizations

- Sponsoring initiatives leading to appropriate legal and institutional reforms

- Strengthening the capacity of its constituent organizations and other anti-corruption bodies where necessary

- Serving as the primary organization for the implementation of Ghana’s anti-corruption plan

Projects contributing to those goals have included, first and foremost, the diagnostic survey mentioned above; in addition, GACC has prepared an action plan that will serve as the basis for national debate and consensus building.
2000-2015 (IFR 2018). Furthermore, robots are measured at the industry-country-year level therefore we cannot assess differences in the use of robots across establishments within an industry. However, one key

> 9 Note our final sample of countries is 59, after dropping jurisdictions with missing robot information, those who are not represented in the trade data and those lacking data on industrial employment, value added and number of establishments over the sample period.  We also exclude Canada and Mexico as robots for these countries are jointly recorded with the United States for the earlier period.

11
2003-2018 period. We empirically estimate a model of cropland changes accounting for the impact of biophysical, socio-economic, and institutional contexts. To our knowledge, our study is the first to embed a high-resolution analysis of global cropland change in an econometric setting. Closest to our approach is the paper by Haney and Cohen (2015), who developed a grid cell level model of cropland transitions explained by global historical trends and global population dynamics. However, our paper significantly departs from their approach given our consideration of pixel and national level determinants—as opposed to assessing how each pixel responds to global demographic dynamics—and given the breadth of the variables included in our analysis. More importantly, we use measures of actual cropland changes rather than modeled data. Our paper is as follows: In sections 1 and 2 below, we present the datasets used in our analysis as well as stylized facts regarding patterns of cropland distributions and transitions into and out of cropland. Section 3 details our empirical approach and discusses how we deal with potential endogeneity concerns. Section 4 presents the results, and Section 5 concludes.

# **Section 1.  Data**

We measure land cover at the scale of the world using satellite imagery. This provides a global and consistent measure of cultivated land. Our “direct” approach circumvents the issues that emerge with the aggregation of disparate national-level statistics for the whole world. Above all, it allows us to focus on actual cropland location, which would be impossible from national-level statistics as published in a database such as FAOSTAT. We use the MCD12Q1 product from Moderate Resolution Imaging Spectroradiometer (MODIS) available since 2001 (see Sulla-Menashe and Friedl, 2018), which we chose for its relatively high temporal resolution and high level of accuracy (Potapov et al., 2021). For the analysis, we regroup the 16 land cover classes based on the Annual University of Maryland (UMD) Classification into 8 categories. These categories include: water bodies, forests, shrub and grasslands, wetlands, croplands, cropland
4 Overall, our findings are robust to a variety of tests and extensions. Importantly, we show that results are stable over time and across countries (EMs and AEs), are not driven by extreme values or crisis events (such as the Global Financial Crisis) or by the presence of key countries – such as the US – in the sample. They are also robust to an alternative measure of sentiment based on a “Term Frequency - Inverse Document Frequency” (“TF-IDF”) weighting of words. Since we control for the number of articles published every day, the effect we capture is also unlikely to capture the volume of news, as opposed to its tone.

Taken together, our results are consistent with shocks to investors sentiment, affecting both advanced and emerging countries, during which local news and their tone affect investor sentiment and prices momentarily before returning to their fundamental values. From a theoretical perspective, this finding is consistent with the presence of noise (or liquidity) traders (DeLong et al (1990), Campbell (1993)). Although we are not able to identify which theory prevails, using capital flows data reveals that such traders are more likely to be foreigners than locals. The response of equity prices to global news sentiment shocks is, however, more ambiguous. The longer and more permanent impact on world stock markets is consistent with global news containing genuinely new (good or bad) information on global fundamentals that is only slowly incorporated into stock prices. An alternative explanation is that sudden changes in global news sentiment are strong enough to cause drifts in investors’ sentiment that do not reverse in the short run, even in the absence of new “hard” information about fundamentals. The fact that global news shocks have a stronger impact in troubled times, when investors are more anxious, support this interpretation (Garcia, 2013).

This paper relates to the growing body of research investigating the link between the news media, investor sentiment, and asset prices, in particular Tetlock (
<!-- Start of picture text --> 50<br>TM<br>4<br>Ni 40 s¥’ iso<br>Ka sYC<br>z us<br>i eo. VAN<br>3S cPV<br>& 30<br>Fs ETH NEWA<br>s Tz4 BDI RIVA<br>8 38MB / LS<br>3 20 KENTGO<br>: “yee<br>x ZMB a<br>=) CODs| ae<br>= tat Moz Oil export<br>eo 10 BCR Hl Exporters<br>oa Snot Resource-intensive non-oil<br>3 a Rest of the region<br>£ Comparator countries<br>oF<br>0<br>0 10 20 30 40 50<br>Share of foreign value added in countries’ exports,<br>average 1991-95<br>Sources: Eora database; and IMF staff calculations.<br><!-- End of picture text -->

Sources: Eora database; and IMF staff calculations.
the ES using its own implied weights are expectedly larger.

# **8 The Underlying Sources of Misallocation**

The results presented above clearly revealed that resource misallocation is quite large in our sample countries. Thus improving the allocation of resources is a critical element in order to boost manufacturing productivity in these countries. To offer specific suggestions on how these countries can improve their productivity by reducing allocative inefficiency, it is important to identify the specific factors that have contributed to the observed misallocation. Nevertheless, identifying the specific policies or institutions that may have contributed to such misallocation is notoriously difficult. In principle, many factors – observable and unobservable – may reasonably contribute to resource misallocation across firms. For example access to finance, tax rates and regulatory requirements may favor particular firms because of political connections or family ties.<sup>22</sup> Identifying specific distortions is perhaps more difficult in Africa since the region is often characterized by a high cost of doing business. While our census data do not allow us to pin down the reasons behind the substantial level of resource misallocation in our sample countries, we believe that this pattern is associated to the heterogeneous business environment faced by firms. Our goal in this section is to uncover the potential underlying sources of misallocation by examining the role of different business environment bottlenecks experienced by firms. To this end we merge our data with the ES data, which provide various measures of business climate indicators.

> 22Hsieh and Klenow (2009) argue that the substantial domination of SOEs and cumbersome licensing requirements contribute to misallocation of resources in China and India, respectively.

26
between the poverty rate with and without the tax or transfer. If positive, it captures a poverty reduction effect, corresponding to a lower poverty rate after the tax or transfer.

Note that a progressive tax does not necessarily reduce inequality, F<sup>12</sup> which would instead require a positive redistribution effect, or poverty, which would require a positive poverty reduction effect. Similarly, a tax can reduce inequality but, at the same time, increase poverty. 12F<sup>13</sup> For a complete picture, measures of progressivity must therefore be combined with marginal contributions to evaluate the effects of fiscal interventions on poverty and inequality.

# 2.2 Data sources

The study relies on a variety of data sources including household surveys, government expenditures from administrative sources, and findings from earlier studies. We describe them in detail below.

## A. Household Surveys

**_PNAD-C._** The national household survey ( _Pesquisa Nacional por Amostra de Domicílios Contínua_ , PNAD-C) is the main source of information in Brazil about labor market outcomes, educational attainment indicators and households’ income sources. We use the 2019 13F<sup>14</sup> annual release of the survey data as published by the Brazilian national statistical office ( _Instituto Brasileiro de Geografia e Estatística_ , IBGE). The survey comprises 150,667 households and 443,790 individuals interviewed during the 2019 calendar year.

**_PNS._** The national health survey ( _Pesquisa Nacional de Saúde_ , PNS) 2019 interviewed 100,541 households. PNS was designed to collect information about the population’s health conditions, surveillance of chronic noncommunicable diseases (NCDs) and their associated risk factors. It also aimed to verify the performance of the national health system regarding both access and use of available services and

12 In the literature, this is known as the Lambert conundrum (Lambert 2001; Lustig & Higgins, 2018). Taxes, for instance, can be regressive according to the Kakwani index
. Using these data, we could also check that our results on firm size are not specific to the proxy we use. We explored two alternative measures of firm size from the Worldscope data: total assets and net sales, taking the end-of-year values reported by firms. Both sets of results with the alternative measures are qualitatively and quantitatively similar to the ones reported in the paper.

> 6 This statement also holds when considering as “listed firms” subsidiaries owned by listed parent companies.

11
to generate meaningful improvements in long-run human capital and that sustainable management of forests should be part of the menu of interventions targeting stunting in rural communities.

The rest of the paper is organized as follows: section 2 explains the data sources and the process of combining the health outcomes with the geospatial information to create a rich micro database, section 3 describes our empirical strategy. Section 4 analyzes the results, and conducts falsification and robustness checks to demonstrate that our results are driven by the water channel. Section 5 concludes.

# **2. Data**

Analyzing the impact of upstream forest cover loss on downstream health outcomes requires combining household surveys with high-resolution spatial data. We use data from DHS and link the spatial coordinates of the household clusters with geospatial data on forest cover (Hansen et al., 2013), upstream and downstream watersheds (Lehner et al., 2008) and climate (Hersbach et al., 2018).

## **2.1. Demographic and Household Surveys (DHS) Data**

The DHS Program, sponsored by the United States Agency for International Development (USAID), provides technical assistance for the implementation of nationally representative, stratified, two-stage cluster sample household surveys that collect data on population, health, and nutrition for over 90 developing countries around the world. We focus our analysis on 91 surveys representing 46 developing countries<sup>2</sup> for the 2000-2020 period. To be included in our sample, each DHS survey must georeference clusters (shown in Figure 1), so that we can match the health outcomes and household characteristics to the environmental and climate data. Following Herrera et al. (2017), we restrict the sample to rural communities, as they are directly dependent on watershed conditions, and focus only on households that have either always lived in their current location or have migrated to their current location before the child was conceived (the rural non-migrant sample). This ensures that health outcomes are measured in the same location of conception
|**Country**<br>This table presents c<br>EPFR data. TIC data i<br>residents in a country)<br>the absolute value of <br>by market capitalizati<br>Bloomberg, US Treasu|**Equity EPFR/TIC**<br>**Correlation**<br>**Country**<br>**Table 8**<br>**Correlation between TIC and EPFR data**<br>ountry correlations between capital inflows <br>s the absolute value of monthly inflows (purch<br>normalized by equity market capitalization o<br>monthly inflows into a country by global equ<br>on or total debt. * denotes significance at the <br>ry, BIS.|**Bond EPFR/TIC**<br>**Correlation**<br> <br> measures from TIC and<br>ases minus sales from US<br>r total debt. EPFR data if<br>ity/debt funds normalized<br> 1% level. Source: EPFR,|
|---|---|---|
|Argentina|0.4422*<br>Argentina|0.2211|
|Brazil|0.1846<br>Brazil|-0.0704|
|Chile|-0.03<br>Chile|0.0922|
|China|0.1475<br>China|-0.1972|
|Czech Republic|-0.0691<br>Hungary|0.1152|
|Egypt|-0.0892<br>Indonesia|-0.1646|
|Hungary|0.1475<br>Poland|0.1454|
|India|0.0839<br>Mexico|0.2493|
|Indonesia|-0.0055<br>Peru|-0.0794|
|Israel|0
specification are informal workers more likely to self-assess worse health than formal workers. Finally, the association between informal employment and household poverty or life satisfaction is not robust to changes in econometric specification of the sample. It seems the self-employed informals -but not the salaried informals- do find themselves in poorer households and have less life satisfaction, but further research is needed here. This leads the authors to believe that informality in Russia is mostly a problem of fiscal sustainability, labor contract enforcement and labor productivity; but worsening wages and some association to household poverty may turn it also into a social equity problem.

The rest of this paper is organized as follows. Section II provides a review of the international and Russian literature on informal employment, a description of the various data sources used and the levels and trends in informal employment derived from them. Section III describes the methodology used for the regressions that estimate the impact of informal employment on wellbeing. Section IV presents the main results. Section V summarizes and concludes.

# **II. Measures of Informal Employment in Russia**

This paper focuses on informal employment rather than the informal sector. Informal aspects of the economy are notoriously difficult to measure and there are several definitions of what is meant by “informal”. There is broad agreement that several criteria must be considered, which include registration, legal incorporation, firm size, coverage by statistical surveys, borderline of activity, illegal activity, location and terms of employment. The word “sector” refers to production activities and not a grouping of institutional units (i.e. firms, households, government) in the System of National Accounts (SNA) sense. In addition to the production side and firms’ perspective, employment can also be characterized as formal or informal. It is possible for formal production units to have informal employees, and for informal units to have formal employees. Finally, there is a distinction between a job and an employee as one employee can hold several jobs. Hence informal employment refers to
_Figure 6. Cost decomposition of landed duty-paid value of exports in the US (2015)_

# **Woven men’s trousers (cotton)**

**Knitted women’s trousers (synthetic)**

<!-- Start of picture text --> 2016 2016<br>BangladeshCambodia China Ethiopia Kenya Lesotho Vietnam BangladeshCambodia China Ethiopia Kenya Lesotho Vietnam<br>unit value (fob) cif duty unit value (fob) cif duty<br>80 100<br>80<br>60<br>60<br>40<br>40<br>Price per unit (US$) Price per unit (US$)<br>20<br>20<br>0 0<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from USITC There is considerable variation among export destinations in this context. Lesotho has by far the greatest variety of exports going to South Africa, with only Namibia and Botswana exporting more. While the overall number of products has decreased between 2010 and 2015, the number of exports greater than $100,000 in value has increased. More significantly, while the number of products going to the European Union is greater than to the United States, over 30 products are greater than $100,000 for the United States, and only nine go to the European Union – this is a decline of 50% compared to 2010.

Most apparel export products to the United States had stagnant or declining market share over the last decade. For one, Lesotho has been losing market share in the United States in 10 of its most exported products against Asian and Latin American competitors such as Vietnam, Malaysia, Bangladesh and Cambodia. The potential expiration of AGOA could also result in a significant disadvantage for Lesotho’s apparel exports, as they would revert to the MFN tariff - which is slightly higher than 20% on average - since none of these products is
34 Note that foreign claims do not include cross-border flows between the parent bank and its affiliates. Moreover, the foreign claims data are not available in currency-adjusted form because the currency composition of cross-border claims in unknown.

> 35 The regression is based on country panel data from 200Q1-2012Q2. The dependent variable is the quarterly growth rate of European consolidated foreign claims on EMDEs as provided by the BIS. The independent variables are quarterly changes in EURIBOR-OIS, GDP growth (YoY), and country- and season-fixed effects. The supply and demand factors are highly statistically significant using robust standard errors clustered on the country level. The model’s R-squared is 0.08. For a similar analysis using currency-adjusted locational claims, see Takáts (2010).

> 36 Since there is substantial heterogeneity between EMDEs, these results should be interpreted as illustrative only.

> 37 Aiyar and Jain-Chandra (2012).

24
preferential partner.

We estimate the model’s structural equation for the equilibrium trade policy using detailed product level data for the EU. There are several compelling reasons for focusing on the EU to analyze whether there is a clash of liberalizations. First, a key concern with PTAs is their potential to harm nonmembers. Given that the EU is the world’s largest trader, its trade policy surely affects non-members. Second, as we discuss below, the EU’s preferential agreements are quite diverse, which allows us to theoretically derive and test a rich set of predictions. Finally, although the EU accounts for a fi fth of world trade, there is hardly any empirical evidence on the formation of the EU’s trade policy in general and none that analyzes how its PTAs affect its MTL.<sup>5</sup>

> 4 Bagwell and Staiger (1998) analyze two opposing effects of PTAs on the equilibrium multilateral tariff level in a self-enforcing model. They show that PTAs are a stumbling block if countries are very patient and a building block otherwise. Winters (1999) surveys this literature and Panagariya (2000) the broader issue of regionalism. Another approach to the PTA vs. MTL issue is due to Krugman (1991) who analyzes the welfare path for exogenously expanding trading blocs.

> 5 Constantopoulos (1974) and Riedel (1977) examine determinants of industry level protection of individual members before accession to the EU. Tavares (2001) is an exception in that she analyzes the determinants of the EU’s common external tariff, also at the industry level. We are not aware of any paper that either estimates the determinants of

2
countries is expected to be 57% in 2019. This 4.3 percentage point jump in baseline (pre-COVID-19) Learning Poverty is due to the use of updated learning data and reporting windows described in Table 1. In a post-COVID-19 intermediate scenario of low mitigation effectiveness for the effects of school closures, the simulations show that Learning Poverty may increase from its baseline level of 57% to 70% for low- and middle-income (Part 2) countries – an increase of 13 percentage points, as shown in Figure 1, implying an increase of 23% relative to the Learning Poverty baseline value.

**Figure 1. Results of simulation: Learning Poverty**

<!-- Start of picture text --> % Change<br>Baseline 57.0% -<br>Optimistic 68.2% 20%<br>23%<br>Intermediate 70.0%<br>Pessimistic 71.2% 25%<br>0.0% 20.0% 40.0% 60.0% 80.0%<br>Share of Students in Learning Poverty<br><!-- End of picture text --> _Note: Includes low- and middle-income (Part 2) countries only. The estimates are population-weighted averages._ Most of this increase is expected to occur in South Asia and Latin America and the Caribbean, where countries may experience an increase in Learning Poverty of 18.2 and 26.8 percentage points respectively under the intermediate scenario, as shown in Table D1 in the annex. Regions that had the highest levels of Learning Poverty before COVID-19, such as Sub-Saharan Africa, are likely to have smaller absolute and relative increases in Learning Poverty because the learning crisis was already so severe in those countries before the pandemic. This result aligns with the findings in Samiego et al. (2022), who document greater losses in human capital accumulation in developed countries than in developing countries. But children with the weakest foundational literacy before school