rafmacalaba/datause-encoder-data huggingface dataset:
scenarios, schools are closed for the time period based on actual data for February 2020 – February 2022 from the <u>UNESCO school closures tracker,</u><sup>10</sup> assuming no additional future school closures. Differences between the optimistic, intermediate, and pessimistic scenarios are defined as follows:

- Optimistic: Mitigation measures have a medium level of effectiveness.<sup>11</sup> Partial closures are assumed to affect 75% of the student population.

- Intermediate: Mitigation measures have a low level of effectiveness.<sup>12</sup> Partial closures are assumed to affect 85% of the student population.

- Pessimistic: Mitigation measures have a low level of effectiveness. Partial closures are treated as full closures (that is, to affect 100% of the student population).

UNESCO describes partial school closures as follows: “Partially open: Schools are (a) open/closed in certain areas only, and/or (b) open/closed for some grade levels/age groups only; and/or (c) open but with reduced in-person class time, combined with distance learning (hybrid approach). It also includes the countries where national governments have deferred decisions on re-opening to other administrative units (e.g. region, municipality or individual schools), and where a variety of re-opening modalities are being used” (UNESCO <u>2021). Since the information on partial closures from the UNESCO calendar is qualitative in nature, we treat</u> partial closures differently across the simulation scenarios.

Table 1 below outlines the key differences and similarities between the current and previous simulations. It is important to notice that the income<sup>13</sup> and World Bank lending classifications<sup>14</sup> used in this paper are as of July 1, 2021, and differ from the previous papers.

> 10 In the absence of actual data on school closures, the simulations in Azevedo (2020) and Azevedo, Hasan et al. (2021) assumed that schools are closed only for 3-9 months in a 10-month school year. A few regional simulations (for the East Asia and the Pacific and the Middle East and North Africa regions only) relied on World Bank internal data from Task Team Leaders on actual closures in those regions to inform this parameter. 11 Under the optimistic scenario, mitigation effectiveness is assumed to be 10% for low-income, 14% for lower-middle-income, 20% for upper-middle-income, and 30% for high-income countries.

> 12 Under the intermediate and pessimistic scenarios, mitigation effectiveness is assumed to be 5% for low-income, 7% for lowermiddle-income, 10% for upper-middle-income, and 15% for high-income countries.

> 13 See income classifications here.

> 14 See World Bank lending classifications here.

5
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_25_ 47. Related to the idea of ecosystem health, James Lovelock is the father of the controversial, increasingly respected "Gaia Hypothesis" that the Earth is a self-organizing, self-reguladng living system in which life actively develops and maintains the environmiital conditions which sustain it. He has proposed a new science of "geophysiology," based on the marriage of biology, geochemistry, and atmospheric sciences.<sup>42</sup> 48. The positive vision of ceo-development is for _"green growth"_ and integrated coevoludonary development of humans and nature.4<sup>3</sup> The idea of co-evolution comes from studying the evolution of complex ecosystems with a high degree of speies-specific symbiosis, or mutual dependence (e.g., tropical rainforests and coral reefs). Its application to the theory of environmental management and development is based on the recognition that man and nature are not nearly so separate as Westem philosophy and approaches to govemance have supposed. In fact, all human cultures have been altering ecosystems for millenia, while nature simultaneously exerted evolutionary pressure on human biology and on social systems. In the past few decades, however, humans have succeeded in altering ecosystems to a far greater extent, and in the process, have begun to degrade their capacity to function effectively. Eventually, perhaps quite soon given the strong likelihood of accelerating, discontinuous chinges in the ozone layer and climate, the circle will close, leading to a "natural" degradation of human civilizations' funtioning capacities.

49. It is easy to think of environmental management as a remedial cost. However, there are great economic and social benefits, not just environmental ones, that would accrue, particularly from the types of changes that a redefinition of development along the lines of good resource management and/or ecodevelopment would help promote. In the words of Ignacy Sachs, The existence of tradeoffs between environmental management and economic growth can not be denied, but their pervasiveness and intensity have been overrated, to the detriment of a search for the best of two worlds.44 In many cases, insdtutional and both individual and organizational behavior factors are morp important than the economic ones cited in preventing the development of more ecologically sound

- 42 Lovelock, James, 1979. _GAIA: A New Look at Life on Earth,_ Oxford University Press, New York. 1988. _The Ages of Gaia: A Biography of Our Living Earth._ Norton, New York.

- 4 Norgard, Richard B., Suzanne Easton, George Ledec, and LaIel Prevetti, 1987. "Social Orgnization for Susnaiing Renewable Resources." Paper prepared for the World Bank, 134 pp., and

- Norgaard, Richard B., 1988. "Sustainable Development A Co-Evolutionary View," _Futures. 20:6;_ 606-620.

- 44 Sachs, Ignacy, 1984. "Developing in Harmony with Naue: Consumption Patters, Time and Space Use, Resource Profiles, and Technological Choices", in Bt<hard Glaeser, ed., 1984, _Ecodevelopment: Concepts, Policies. Strategies,_ Pergammon Press, New York.
Policy Research Working Paper 9854

# **Abstract**

What does the macroeconomy look like in the aftermath of tariff changes? This paper estimates impulse response functions from local projections using a panel of annual data that spans 151 countries over 1963–2014. Tariff increases are associated with persistent, economically and statistically significant, declines in domestic output and productivity, as well as higher unemployment and inequality, real exchange rate appreciation and insignificant changes to the trade balance. Output and productivity impacts are magnified when tariffs rise during expansions and when they are imposed by more advanced or smaller (as opposed to developing or larger) economies; effects are asymmetric, being larger when tariffs go up than when they fall. While firmly establishing causality is always a challenge, the results are robust to a large number of perturbations to the baseline methodology, and hold using both macroeconomic and industry-level data.

This paper is a product of the Development Policy Team, Development Economics. 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 dfurceri@imf.org, sahmed@imf.org, jostry@imf.org, and bizaro@nus.edu.sg.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Figure 23. SSA: Investment needs**

_Sub-Saharan Africa’s investment needs are high across a wide range of sectors. There has been progress in improving infrastructure in the region, but progress has been slow, especially in energy and transport._

# **A. Total infrastructure spending needs**

# **B. International perspective on Africa's infrastructure deficit**

<!-- Start of picture text --> US$, billions Normalized units SSA middle-income countries<br>Other middle-income countries<br>100<br>SSA low-income countries<br>Operation and maintenance 1,500 Other low-income countries<br>80 Capital expenditure 1,200<br>900<br>600<br>60<br>300<br>40 0<br>20<br>0<br>Irrigation ICT Transport WSS Power Total<br>C. Selected health care indicators D. Selected education indicators<br>Per capita Percent of population Percent of per capita<br>5,000 120 income, ratio<br>4,000 100 50 RangeAE<br>80 40 EMDEs<br>3,000 SSA<br>60<br>2,000 Range 30<br>AE 40<br>1,000 EMDE 20 20<br>SSA<br>0 0 10<br>Health Improved Improved<br>expenditure sanitation water 0<br>(LHS) (RHS) source Government Pupil-teacher ratio<br>(RHS) expenditure<br>Paved-road density Total road density Main-line density Mobile density Internet density Generation capacity Electricity coverage Improved water Improved sanitation<br><!-- End of picture text --> Source: Haver Analytics; Pierce, and Foster 2008; Regional Economic Outlook, International Monetary Fund; World Bank; Yepes (2008).

A. ICT=information and communication technology; WSS=water supply and sanitation. Estimates by Foster and Briceno-Garmendia (2010).

B. Road density is measured in kilometers per 100 square kilometers of arable land; telephone density in lines per thousand population; generation capacity in megawatts per million population; electricity, water, and sanitation coverage in percentage of population.

C. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 47 SSA economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 47 SSA economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 47 SSA economies. AE are advanced economies. Latest data available during 2011-15. D. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 29 SSA economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 44 SSA economies. Latest data available during 2011-15.

49
can lead to the emergence of zombie firms, low aggregate investment, and debt overhang (Caballero et al., 2008; Schivardi et al., 2017; Kalemli-Ozcan et al., 2019; Xiao, 2020; Chari et al., 2021). But public programs can also reduce bankruptcies and firm failures (Carletti et al., 2020; Demmou et al., 2020; D ́ıez et al., 2021; Cros et al., 2021; Gourinchas et al., 2021).

What makes our paper unique is that we study how a sizable and sudden government credit program available to most firms has worked in practice. By analyzing the universe of bank loans and formal firms in a country, which we match with their credit history and firm-level attributes, we can obtain a systemic view. That is, rather than focusing on a single segment of firms (e.g., either SMEs or publicly listed firms), we can speak to the allocation of credit across the entire firm distribution and the program’s aggregate impacts on both banks and the government.

Having the complete set of bank transactions, firms, and credit history can be important for evaluating the behavior of the supply and demand of credit since firms and banks can substitute across lenders, borrowers, and loan types. Moreover, it provides precise estimates of the macroeconomic impacts of the loans granted by characterizing the ex-post credit exposure of firms with different risk of default. It also enables us to identify different contributors and mitigating factors of aggregate risk accumulation. Calibrating our model to these data allows for counterfactual simulations of different policies and scenarios. Our data on loan applications and approvals allow us to estimate the actual behavior of firm demand and bank supply of public guaranteed credit and what determines the equilibrium outcome. Although we find adverse selection, as the existing literature does, we show that its contribution to aggregate risk can be contained.

# **3 Data**

We use three administrative data sets from various sources in Chile, depending on what is appropriate for the type of analysis we perform at different points. These data sets cover the entire formal private sector in Chile in rich detail. Below we describe the data sources, sample selection, and key variables.

First, we use granular confidential bank-to-firm information compiled by the Financial Market Commission (the financial supervisory agency) for all firms using the entire banking system. We have information on stocks and flows of credit. For stocks, we have data on the amount of debt each firm has with each bank in the system every month. We also know the number of days each loan in the system is past due. For flows, we have transaction-level data 6
- Morales-Arilla, J. and Traettino, S. (2023). Vulnerability, fear of discrimination and clientelism: Organizer identity and the backfire of solidarity. _Working Paper_ .

- Murphy, T. E. and Rossi, M. A. (2020). Following the poppy trail: Origins and consequences of Mexican drug cartels. _Journal of Development Economics_ , 143:102433.

- National Survey on Living Conditions (2021). Encovi 2014–2021.

- OCHA (2023). Venezuelan shapefiles at administrative levels.

- Parsons, C. and V ́ezina, P.-L. (2018). Migrant networks and trade: The Vietnamese boat people as a natural experiment. _The Economic Journal_ , 128(612):F210–F234.

- Portes, L. S. V. (2009). Remittances, poverty and inequality. _Journal of Economic Development_ , 34(1):127.

- R4V (2024). La plataforma de coordinaci ́on interagencial para refugiados y migrantes.

- Resultados con VZLA (2024). Resultados Elecciones Presidenciales – Venezuela 2024.

- Rios-Avila, F., Sant’Anna, P. H. C., and Naqvi, A. (2021). DRDID: doubly robust difference-

- in-differences estimators for stata. `https://www.stata.com/symposiums/economics21 /slides/Econ21_Rios-Avila.pdf` . SSC Symposium Presentation.

Sabbatino, M. (2018). Global oil & gas features database.

- Sahoo, S., Gupta, P. K., and Srivastav, S. K. (2020). Inter-calibration of DMSP-OLS and SNPP-VIIRS-DNB annual nighttime light composites using machine learning. _GIScience & Remote Sensing_ , 57(8):1144–1165.

- Sant’Anna, P. H. C. and Zhao, J. (2020). Doubly robust difference-in-differences estimators. _Journal of Econometrics_ , 219(1):101–122.

- Spilimbergo, A. (2009). Democracy and foreign education. _American Economic Review_ , 99(1):528–543.

50
For the poverty targeting simulations, we examine impacts on the poverty gap rather than the headcount poverty rate (P0) because targeting areas based on their poverty headcount rates maximizes the impact of a transfer program on the poverty gap (Besley and Kanbur, 1991; Kanbur, 1986). Reporting the headcount poverty rate, on the other hand, could give a misleading picture of targeting effectiveness by rewarding methods that successfully identify households whose predicted consumption is just under the poverty line and would therefore exit poverty as a result of the transfer.

A key difference between the poverty targeting measure and all others previously considered is that it is averaged across individuals rather than areas. This implicitly weights areas according to their population, rewarding methods that more accurately estimate poverty rates for areas that have larger population. Weighting areas according to their population during model estimation could yield estimates better suited for the purpose of minimizing this indicator, and would be a useful topic for further research.

A final caveat is useful. The final performance of a specific ML method may depend on a combination of different factors related to data sources (including data-specific features), prediction targets, the prediction method, and performance metrics. While one method may work better for a specific application or according to a specific evaluation metric, it may perform worse for another. To better focus the comparison, we present in Table 2 a general comparison of the key features of the four methods that we use in this paper. To compare their performance, we use the same prediction targets (i.e., imputing into census data without any assets or poverty data) and performance metrics (as discussed above). However, they differ by design regarding the functional form (including whether random effects are employed), (assumed) data generation process, objective function, and other features such as how they select the predictor variables and whether they use different subsamples of the data in growing trees and making predictions. Finally, these ML methods offer various degrees of transparency and ease of use.

# **3 Results**

We first examine the accuracy of the four candidate estimators, starting with basic average accuracy statistics.<sup>15</sup> Table 4 presents two separate indicators of accuracy: pearson and spearman (rank) correlations. The values are averages across all areas and the 100 samples.

Although the relative performance of different methods varies across cases, consistent patterns emerge. When looking at average Pearson correlations, XGBoost and Cubist nearly always outperform traditional EBP, which has been the workhorse of small area prediction for decades. In all nine cases (seven for assets and two

> 15Section 3.3 considers alternative measures of accuracy

18
**Table 3: Decomposition of export market shares growth into composition and country specific performance: G-3, BRICS and MIST (2006q1– 2013q1)**

||Export|**Export market**|Specialization<br>effec|composition<br>ts|Export marke<br>com|t shares gr<br>position ef|owth without<br>fects|
|---|---|---|---|---|---|---|---|
||rowth|**share chane**|||Vl|||
||g|**g**|Geographical|Sectoral|aues<br>(overall)|Price|Volumes|
|G-3|4,5|-2,6|-0,3|0,0|-2,3|-0,7|-1,6|
|USA|6,1|-1,1|0,9|0,4|-2,4|-0,7|-1,7|
|Japan|2,3|-4,9|1,9|-1,5|-5,3|0,3|-5,6|
|EU-27:|4,6|-2,5|-1,2|0,2|-1,6|-0,9|-0,7|
|EA17:|4,4|-2,7|-1,1|0,2|-1,8|-1,0|-0,8|
|France|2,6|-4,6|-1,2|1,2|-4,6|-1,1|-3,5|
|Germany|4,1|-3,0|-0,8|-0,1|-2,1|-1,3|-0,7|
|Italy|4,4|-2,8|-0,5|-0,7|-1,6|-0,5|-1,2|
|UK|2,1|-5,1|-1,5|0,9|-4,5|-1,1|-3,4|
|BRICS|11,1|4,0|0,2|-2,2|6,0|0,8|5,2|
|Brazil|9,2|2,0|1,6|2,0|-1,7|1,2|-2,8|
|Russia|8,8|1,6|0,4|4,0|-2,8|1,6|-4,4|
|India|15,3|8,1|1,2|1,1|5,9|-1,0|6,9|
|China|11,2|4,0|-0,2|-4,8|9,0|0,9|8,1|
|South Africa|13,1|5,9|0,9|5,1|-0,1|-3,0|2,9|
|MIST|8,6|1,4|0,3|-1,1|2,2|0,3|1,9|
|Mexico|7,9|0,7|-3,3|0,1|3,9|0,7|3,2|
|Indonesia|9,0|1,8|0,7|1,6|-0,4|0,6|-1,1|
|South Korea|7,8|0,6|2,8|-2,8|0,7|-0,2|0,9|
|Turkey|12,3|5,2|0,4|-2,3|7,0|0,4|6,6|

_Note: figures are averages of the year on year changes in natural logarithms (delta log) in the period 2006q1-2013q1, which preserve the additivity of its components. For relatively small changes the delta log approximates almost exactly the simple percentage growth rate (e.g. ln(1+5%)=0.0488). To obtain the corresponding growth rate of an indicator or of their sum it is sufficient to compute the exponential. Section 3.2 of the paper and footnote 10 provide additional explanations on this point._ Meanwhile a favorable geographical composition helped US (0.9%) and Japanese exports (1.9%). With the exception of Mexico and China, also the BRICS and the MIST enjoyed advantages due to their geographical specialization. Mexico’s geographical specialization dampened the country’s export growth, with an annual negative contribution equal to 3.3%, while the geographical specialization was only slightly negative for China (-0.2%).<sup>17</sup> Among the BRICS, China, India and Russia’s geographical specialization had mostly a neutral effect on overall export growth while it helped more substantially Brazil (1.6%) and India (1.2%). Sectoral and product specialization, on its part, was favorable to commodity exporters (South Africa, Russia, Brazil, Indonesia and India), mostly neutral for the USA and the euro area, and negative for Japan, China, South Korea and Turkey.

> 17 Figure 4 shows the export performance of the MIST, year over year, from 2005 to the first quarter 2013 for exports both in value and volume terms. Mexico’s geographical specialization was most detrimental in the period before the global crisis,

19
rules, i.e. not restricting trade unnecessarily.

We focus on two types of NTMs to tease out such heterogeneity. The first is _antipest treatment requirements_ (APT), which we use as an example of “virtuous NTM”. Most countries apply this type of measure to eliminate a potential negative externality in agricultural products, i.e. the presence of plant and animal pests or disease-causing organisms. The second type of NTM is _pre-shipment inspections_ (PSI), which we deem an example of “protectionist NTM”. This measure requires the goods to be inspected at the port of departure. Only a handful of mostly low-income countries applies PSI, typically to ensure that the import declaration lists the correct classification of the goods to be imported as a way of detecting improper importing activities. Unlike APT, PSI appear redundant in a country like Indonesia, where the customs agency applies a risk management system aiming to detect suspicious shipment at the border.<sup>4</sup> In the absence of clear benefits, the bulk of the effect of this measure is likely to be the increased compliance costs for imports.

The analysis leverages the unusual richness of Indonesia’s manufacturing data to decompose firms’ revenue-total factor productivity (TFPR) into quantity-total factor productivity (TFPQ) and markup. The results of the analysis are in line with our priors linked to the different features of these NTMs. The introduction of APT on a plant’s inputs results in an increase in both the plant’s technical efficiency and its markup, consistently with an increase in the quality of imported inputs. On the other hand, the application of PSI on inputs reduces the markup and the TFPR of the plant. When applied on the same product produced by the plant, the PSI increases the markup, in line with the increased protection for domestic producers.

We are able to explore in detail the mechanism behind the negative effects of input PSI on plants’ performance. This enables the paper to provide a third contribution by unveiling the difference in impacts between PSI and import tariffs, and adding to the literature on fixed trade cost, trade lumpiness, and the behavior of inventories and markups (e.g. Alessandria et al. (2010)). We find that by increasing the cost of imported goods, tariffs trigger a switch from imported to domestically-produced inputs, which translates in lower technical efficiency. Unlike tariffs, however, PSI generate non-monetary fixed costs per-shipment. These costs are related to the uncertainty around the timing of the

> 4Consistently with this view, Indonesian Customs do not take into account the outcome of the PSI in their decision whether to inspect the shipment at the border.

4