rafmacalaba/data-use-mentions-extended huggingface dataset:
the impacts of climate change and human activities, where unilateral actions may have unintended transboundary effects.

<mark>In this context, we believe that GBIF data can play a critcal role in expanding the informaton base for species that are critcal for biodiversity conservaton. As noted above, a major result of our species-mapping project has been a greatly expanded view of occurrence regions for invertebrates and plants that are endemic, inhabit small regions, or confront elevated extncton threats. The efectveness of biodiversity protecton can only be enhanced by introducing this informaton into the design and implementaton of transboundary treates, river basin initatves, and stabilizaton measures for fragile/confict states.</mark> Biodiversity and natural resources also offer a unique, neutral platform for dialogue and confidence-building between divided groups, as evidenced by several cases worldwide. The Nile Basin Initiative encourages dialogue among Nile River countries, promoting integrated water resource management and sustainable development. The "Peace Parks" in Southern Africa, transboundary conservation areas like the Great Limpopo Transfrontier Park, have been created to promote peace and cooperation among Mozambique, South Africa, and Zimbabwe (Wolmer 2003). By focusing on shared environmental objectives, such initiatives help to build trust, reduce historical tensions, and foster a sense of shared responsibility among nations that may have previously experienced conflict. Similarly, in the Middle East, environmental issues have provided common ground for dialogue among Israelis, Palestinians, and Jordanians. Through initiatives like the Red Sea-Dead Sea Water Conveyance Project and joint efforts to rehabilitate the Jordan River, environmental peacebuilding has emerged as a valuable tool for fostering cooperation and understanding (Aggestam and Sundell 2016; <mark>Markel, Alster and Beyth 2013).</mark> Recently, a report by the World Bank (2022)<sup>11</sup> has highlighted biodiversity as a valuable entry point for intergroup dialogue and cooperation. Again, however, reliable and comparable biodiversity data are critical for the success of such initiatives. Accurate data can provide common ground for all stakeholders by promoting
recall length. Similarly, African Union’s (AU) Comprehensive Africa Agriculture Development Programme (CAADP) was conceived in 2003 to achieve economic growth, eliminate hunger, and reduce poverty through concerted investments in African agriculture. CAADP adopted the Malabo Declaration in 2014 in which AU member states resolved, among other things, to double agricultural productivity and pledged to allocate at least 10 percent of public expenditure towards agriculture. The CAADP results framework monitors progress towards its goals through a set of priority indicators and guides member countries’ strategic investment decisions. CAADP target 2.1 – Increased agriculture production and productivity<sup>14</sup> is comprised of five priority indicators, focusing on growth in agricultural production volume and value added as well as land and labor productivity. For instance, priority indicator 2.1.5 measures yield for the five AU priority commodities, of which maize is one; indicator 2.1.2 tracks the level of agricultural production.

The results of our analysis show that recall error can lead to unreliable measurement of all these indicators at an economically significant level. This is especially true when it comes to monitoring their evolution over time. For illustration, maize yields in Africa (reflected in CAADP 2.1.5) have grown at an average annual rate of 0.8 percent between 2000 and 2017, according to FAOSTAT. Maize production (reflected partially in CAADP 2.1.2) has grown by an average of 3.9 percent per year during the same period. In comparison, our analysis indicates that an additional month in recall length can lead to a change of between 2 and 5 percent in reported maize yields and 2 and 7 percent in the reported maize harvest quantity. Further, an annual growth rate of around 7 percent would be required to achieve SDG target 3.2 of doubling labor productivity within about 10 years. We find an additional month of recall length is associated with a 2 to 3 percent increase in reported output per unit of labor.
This version: 15 Feb 2024 56 Victor, Peter. 2019. “Chapter 10: The disappointments of economic growth”. In: _Managing without Growth_ . Edward Elgar Publishing.

<mark>World Bank. 1993.</mark> _<mark>East Asian miracle: Economic growth and public policy</mark>_ <mark>. Washington, DC: Oxford University Press. -</mark> <u><mark>http://documents1.worldbank.org/curated/en/975081468244550798/pdf/multi page.pdf</mark></u> <mark>World Bank. 2019.</mark> _<mark>Making Ends Meet</mark>_ <mark>. Malaysia Economic Monitor. World Bank, Kuala Lumpur.</mark> <mark>World Bank. 2020.</mark> _<mark>Aspirations Unfulfilled: Malaysia’s Cost of Living Challenges</mark>_ <mark>. World Bank, Kuala Lumpur.</mark> <mark>World Bank. 2023.</mark> _<mark>Raising the Tide, Lifting All Boats</mark>_ <mark>. Malaysia Economic Monitor. World Bank, Kuala Lumpur.</mark> World Bank. 2024. Poverty and Inequality Platform (version 20230919_2017_01_02_PROD) [data set]. pip.worldbank.org. Accessed on February 16, 2024.

56
the welfare gains. In the following section, we capture some supply responses and second round effects.

It should be pointed out that the present paper only explores the poverty alleviation effect of cotton production in rural areas by smallholders. There are vertical linkages in cotton production that suggest that significant additional poverty effects could be secured through the domestic production of textiles and garment products for exports. These important issues, that deserve further consideration and analysis, are not the focus of our investigation.

# **3. Cotton Production and Household Outcomes**

In this section, we explore the impacts of cotton production on household outcomes. If free trade and cotton liberalization bring about renewed incentives for cotton production in Zambia, we expect farmers to switch from subsistence to cotton production (and, more generally, to market-oriented agriculture). In what follows, we explore these supply responses with the help of matching methods: by matching households in subsistence agriculture with households in cotton, we estimate the average effects of participating in cotton markets on several household outcomes. We focus on income differentials, children anthropometry, and education outcomes.<sup>11</sup>

## **3.1 The Method**

Our aim is to estimate the differences in outcomes linked to the production of a cash crop such as cotton, and to explore the poverty alleviation effects of allowing for an expansion of cotton activities among Zambian farmers. We use matching methods based on the propensity

> 11The estimation of supply responses has proved very difficult. The survey in Winters, McCulloch and McKay (2004) highlights these issues and reports some of the available methods and results. For the case of income gains, see Lopez, Nash and Stanton (1995) and Heltberg and Tarp (2001). For non-monetary outcomes, see Edmonds and Pavcnik (2004).

16
Heavy administrative burdens on firms, such as frequent and lengthy inspections, bribe extortions, and excessive involvement of the political elite in business, also tend to weaken property rights, threaten profits, and reduce a country’s attractiveness to domestic and foreign investors. In countries with weak institutions like Tajikistan, overcoming these kinds of constraints may take a long time. If leading cities could improve their business environment, this could initiate transformation of the private sector. But firms in Tajikistan’s two leading cities (Dushanbe and Khujand) face obstacles in both their internal and external operating environments.

The latest BEEPS surveys raise major concerns about the internal business environment for firms operating in Tajikistan. Just 17 percent of firms in Tajikistan reported having an internationally recognized quality certificate; this compares with 43 percent in Chad, one of the world’s poorest countries. Only 24 percent of the interviewees in Tajikistan reported using technologies licensed from foreign companies, compared with 36 percent in Mozambique; and only 21 percent of interviewees in Tajikistan reported having their own Web sites, compared with 63 percent in Armenia. Of firm employees interviewed, only 22 percent in Tajikistan were offered some kind of formal training, compared with 63 percent in Kenya. The BEEPS indicators provide a broad look at the external environment in which firms in leading Central Asian cities are operating. The most challenging issues appear to be permits and licensing, regulation and tax issues, and corruption (Table 3).

Another constraint to industrial expansion, particularly in the food and textile industries, appears to be fragmentation of the production chain into small-scale, poorly equipped operators. Such fragmentation leads to low quality and low quantity of raw materials for the food industry, and therefore to safety and quality issues. Fragmentation has also blocked development of a shadow textiles sector, whose production and market knowledge cannot be harnessed to scale up the sector.

While Tajik food products have so far enjoyed easy access to CIS markets, the increasing share of supermarkets in Russia’s retail trade will soon make food
# **2.2 Trade data**

Our export and import data is a monthly product-country data series obtained from the Philippine Statistics Authority (PSA). The data ranges from January 2019 to December 2020. Products are classified at 10-digit Harmonized System (HS) level.

Table 1 displays descriptive statistics for our trade data. The average monthly productcountry of destination exports from the Philippines between January 2019 and December 2020 was US$ 553,417. The average monthly product-country of origin imports to the Philippines was US$ 232,370 over the same period. Whereas the average monthly exports exceeded imports, reflecting a surplus trade balance, the Philippines has perennially had a trade imbalance. Thus, our surplus could indicate a massive drop in imports than exports during the lockdown period.

Table 1 also shows that the average lockdown dummy for both exports and imports was 43%. Statistics of the level of stringency are also presented in Table 1. It is a binary variable that is constructed from the aforementioned government stringency index which ranges from 0 to 100. Following Majune (2020), a country is classified as having high stringent measures if its average score is greater than the median (53 in our case) and vice versa for a low stringent country. We find that over half of the Philippines’ trading partners had strict lockdown policies with export destinations being slightly sterner than importing countries.

**Table 1:** Descriptive Statistics

|**Variable**|**Obs.**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Export trade**||||||
|Export value|243,526|553,417|5,570,730|25|458,000,000|
|Lockdown dummy|243,
., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V. & Midgley, P. M. (eds.) Climate Change 2013. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

CIESIN, IFPRI, the World Bank & CIAT (2011) Global Rural-Urban Mapping Project, Version 1 (GRUMP v1): Population Density Grid. Data and Applications Center (SEDAC), Columbia University, New York, USA.

Diaz, D.B. (2016) Estimating global damages from sea level rise with the Coastal Impact and Adaptation Model (CIAM) Climatic Change, 137: 143-156. https://doi.org/10.1007/s10584016-1675-4 Dronkers, J., Gilbert,J.T.E., Butler, L.W., Carey, J.J., Campbell, J., James, E., McKenzie, C. , Misdorp, R., Quin, N., Ries, K.L., Schroder, P.C., Spradley, J.R. , Titus, J.G., Vallianos, L., von Dadelszen, J. (1990) Strategies for adaptation to sea level rise. Report of the IPCC Coastal Zone Management Subgroup: Intergovernmental Panel on Climate Change. (Intergovernmental Panel on Climate Change, Geneva).

Ericson J.P., Vörösmarty C.J., Dingman S.L., Ward L.G., Meybeck M. (2006) Effective sea-level rise and deltas: Causes of change and human dimension implications. Global Planet. Change, 50: 63–82.

Fankhauser, S. (1995) Protection versus Retreat: The Economic Costs of Sea-Level Rise. Environment and Planning A, 27,
if �� ��<sup>�</sup> �� and �∑�∈� �����<sup>�</sup> ��, where �<sup>�</sup> �� denotes a population density threshold and �<sup>�</sup> �� a population threshold. In addition, the cell must be spatially contiguous with at least one other cell within the same cluster. In other words, a spatially contiguous group of grid cells will be classified as constituting an urban cluster if each of these cells satisfies a certain minimum population density threshold and the aggregate population of these cells also exceeds some specified threshold value. Given this, a country’s level of urbanization can then be defined as the share of the overall national population living in urban clusters. Dijkstra and Poelman (2014) set values of �<sup>�</sup> �� �300 people per km<sup>2</sup> and �<sup>�</sup> �� �5000. Within urban clusters, they also define a sub-set of “high density clusters”. These high density clusters are defined similarly to urban clusters, but instead use threshold values of 1,500 people per km<sup>2</sup> for population density and 50,000 people for overall population.<sup>23</sup>

# 2.2.3. Comparison between the two approaches

While it is not the intention of this paper to draw any conclusion as to which of the above two methods is to be preferred, some remarks on their similarities and differences are nevertheless in order. Hence, it will be noted that both crucially rely on a source of gridded population data for their implementation. Furthermore, while the AI may well be considered preferable to the cluster method to the extent that it more closely approximates an attempt to identify FUAs, it suffers the disadvantage of being more data-intensive
patient satisfaction with in-patient services at Jimma University Specialized Hospital, Southwest Ethiopia. BMC Res Notes 8:285.

WHO (2020a). Global report on the epidemiology and burden of sepsis: Current evidence, identifying gaps and future directions. Geneva: World Health Organization.

WHO (2020b). Global antimicrobial resistance surveillance system (GLASS) report: early implementation 2020. Geneva: World Health Organization WHO and UNICEF (2020). Global progress report on water, sanitation and hygiene in health care facilities: fundamentals first. Geneva: World Health Organization; 2020. Licence: CC BYNC-SA 3.0 IGO.

WHO and UNICEF (2022). Progress on WASH in health care facilities 2000–2021: special focus on WASH and infection prevention and control (IPC). Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2022. Licence: CC BY-NC-SA 3.0 IGO Yallew WW, Kumie A, Yehuala FM (2016). Point prevalence of hospital-acquired infections in two teaching hospitals of Amhara region in Ethiopia. Drug Healthc Patient Saf. 8: 71.

Yallew WW (2017). Hospital acquired infections and infection prevention practice in teaching hospitals in the Amhara Regional State, Ethiopia. PhD Thesis. Addis Ababa University, Ethiopia.

Zaidi AK, Huskins WC, Thaver D, Bhutta ZA et al (2005). Hospital-acquired neonatal infections in developing countries. Lancet 365(9465): 1175-88.

28
residential areas using a time use meter (TUM). In this case, daytime consumption (from 6:00 to 21:00 hours) is charged 104.3 MNT/kWh (4.9 cents of US$/kWh) and nighttime (21:00 to 6:00 hours) is charged 77.10 MNT/kWh (3.6 cents of US$/kWh). Finally, for residential use, central heating tariffs are 3,000 MNT/month for areas up to 40 square meters (m<sup>2</sup> ), 5,000 MNT/month for areas between 41 and 80 m<sup>2</sup> and 10,000 MNT for areas above 81 m<sup>2</sup> .

The subsidy to electricity and heating is computed using the price-gap approach, which compares actual consumer tariffs with production costs and then multiplies that differential by the volume of electricity consumed to obtain the level of subsidies. This method ignores implicit subsides that may be due to other tax exemptions or preferential pricing for inputs to public utility companies, or to consumers, and makes use of data approximations about the actual cost of production of the service. The latter may be difficult to collect due to the dearth of accurate data in the companies and difficulties in gauging opportunity cost of these utilities which are seldom traded in international markets.<sup>10</sup> Estimates of average production costs of electricity range from 147 MNT/kWh to 213 MNT/kWh, while for central heating the production costs range 460 to 693 MNT/m<sup>2</sup> . These come from data of reported costs of production, generation and distribution of electricity and central heating companies from the Central System (representing 95% of the total sector costs). Expert estimates indicate that these reported costs of production fail to include three important components: first, the implicit subsidy that companies receive from coal at a price estimated
Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) In the baseline, which incorporates long-term trends and accounts for all the current tariff liberalization commitments within the region (except RCEP) in line with the ITC MacMap database, real income in Vietnam is expected to grow 112.7 percent between 2020 and 2035, with exports and imports increasing by 155.5 percent and 134.8 percent, respectively. With implementation of RCEP, when rules of origin and productivity are included on top of tariffs and non-tariff measures reductions, real income grows faster, with an increase of 123.1 percent between 2020 and 2035. The benefits of the implementation of these measures are also reflected on trade, with exports and imports growing 182.5 percent and 155.5 percent, respectively, and between the same period.

Vietnam’s real income and trade expand faster than the baseline in the scenarios with tariffs, non-tariff measure reductions and rules of origin, and in the productivity kick scenario (see Figure 17). In the productivity kick scenario, where a productivity shock is included, Vietnam has the highest gains of all RCEP member countries. Real income<sup>16</sup> increases by 4.9 percent relative to the baseline, higher than the gains for the bloc as a whole, where real income increases by 2.5 percent (Figure 8). Trade also increases the most in this scenario, with exports expanding by 11.4 percent and imports by 9.2 percent, relative to the baseline.

In the scenario where only the tariff reduction is implemented, the impact on Vietnam’s economy is negligible, with real income close to zero. Trade too sees a small reduction relative to the baseline, with both exports and imports declining by 0.3 percent. Vietnam is already deeply integrated into the region; therefore, any additional tariff reductions yield only negligible impacts. Like many other RCEP member countries, Vietnam is also part of the CPTPP and ASEAN.
Policy Research Working Paper 9700

# **Abstract**

This paper estimates the impacts of regulating the use of sanitary and phytosanitary and technical barriers to trade measures through preferential trade agreements on exports of firms in Chile, Colombia, and Peru along the firm size spectrum. The analysis exploits novel data from the World Bank Deep Trade Agreements database and customs covering the universe of exporting firms in each country over 1996–2015. The paper uses a firm-product gravity equation with a stringent set of fixed effects and controls for the overall depth of the preferential trade agreements and product-specific bilateral tariffs. The findings show that firms’ exports increase significantly in destination markets with preferential trade agreements, including a larger number of sanitary and phytosanitary and technical barriers to trade provisions, and the effect is stronger for smaller firms. Provisions for the harmonization of sanitary and phytosanitary regulations in preferential trade agreements also have greater benefits for the exports of smaller firms, and so do preferential trade agreements, including stronger transparency provisions for sanitary and phytosanitary and technical barriers to trade regulations. The results are robust to dropping larger exporters and highly concentrated export sectors to address endogeneity. The benefits of sanitary and phytosanitary and technical barriers to trade provisions are mainly driven by sectors with more heavily-regulated products. Entry into new product markets and increases in export quality partly explain the rising exports of smaller firms. Finally, the estimated impacts are similar regardless of the income level of the preferential trade agreement partners.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at afernandes@worldbank.org, klefebvre@worldbank.org, and nrocha@worldbank.org.

_The Policy Research Working Paper Series disseminates the
|[49]||||[.32]|
|N|184|184|184|184|184|184|184|184|184|184|184|184|
|R-squared|0.86|0.90|0.87|0.88|0.83|0.91|0.84|0.85|0.34|1.00|1.00|1.00|
|treatmenteffect||0.32**|-0.15|0.22***||0.40***|=-0.004|0.27***||-0.16|-0.01|-0.18|
|<br>with interaction||(0.18)|(0.14)|(0.14)||(0.24)|<br>(0.07)|(0.17)||(0.24)|(0.17)|(0.21)|
|||[.03}|(40)|.0]||[.01}|[.90)|—L.01]||[54]|[84]|[43]|
|mean outcome|0.38|0.38|0.38|0.38|0.22|0.22|0.22|0.22|12.30|12.30|12.30|— 12.30|
|Outcome is share of students taking Grad|e 12 exam|in private s|chools. Sa|mple is dist|rict panel|, Regions 2-|6, 1998-20|12. Schools|is numbe|r of priva|te secon|dary schools. Low|
|enrollmentandwealthinteractionsaredi|strictchar|acteristicsfr|om1998|households|urvey
Table 1: Summary of Countries and Years with Microdata Available

|Country|Label|Total Observations|Obs./Year|Years|
|---|---|---|---|---|
|Argentina|_Arg_|582,592|64,732|2004-2012|
|Bolivia|_Bol_|130,054|14,450|2001-2002, 2005-2009, 2011-2012|
|Brazil|_Bra_|2,932,482|183,280|1995-1999, 2001-2009, 2011-2012|
|Chile|_Chl_|875,430|125,061|1996, 1998, 2000, 2003, 2006, 2009, 2011|
|Colombia|_Col_|676,177|135,235|2008-2012|
|Costa Rica|_Cri_|76,318|25,439|2010-2012|
|Dominican Republic|_Dom_|185,374|16,852|2002-2012|
|Ecuador|_Ecu_|369,451|36,945|2003-2012|
|Guatemala|_Gtm_|100,989|33,663|2000, 2006, 2011|
|Honduras|_Hnd_|269,539|44,923|2006-2011|
|Mexico|_Mex_|492,068|70,295|1996, 1998, 2000, 2002, 2008, 2010, 2012|
|Nicaragua|_Nic_|19,603|19,603|2005|
|Panama|_Pan_|182,524|30,420|2007-2012|
|Peru|_Per_|669,352|74,372|2004-2012|
|Paraguay|_Pry_|145,748|12,145|1995, 1997, 1999, 2001, 2003-2005, 2007-2011|
|El Salvador|_Slv_|350,962|38,995|2004-2012|
|Uruguay|_Ury_|429,685|61,383|2006-2012|
|Total Obs.||8,488,348|||

_Notes_ : Information is based on SEDLAC (World Bank and CEDLAS). Only individuals in