rafmacalaba/datause-encoder-data huggingface dataset:
# 6.3 Destination Markets’Demand for Quality

In this section we provide evidence that the risk index, while computed using the EU food safety regulations as a benchmark, does not capture speci...cities of the EU market only (Table 4). Columns with odd numbers report results when controlling for ...rm destination and year ...xed e¤ects and stratifying across HS4. This allows us to recover the main e¤ect of the risk index. Columns with even numbers report results under our preferred speci...cation (column (3) in Table 1). Destination markets are: non European countries only (column 1-2), high income countries only (column 3-4), low income countries only (column 5-6), and ...nally African countries only (column 7-8). First, results indicate that the risk index is not speci...c to the EU market. The coe¢ cient on our interaction term (FDc � sanitary_riskk) is negative and signi...cant at the 1% level when excluding EU countries from the sample (column 1-2). Second, the coe¢ cients on risk and its interaction with ...nancial development are not signi...cant when considering low income or African countries only as destination markets. Suggesting that food safety matters solely for developed countries, restricting the role of ...nancial development for those markets only. Precisely, it re‡ects the stronger concerns of developed economies for human health and food safety issues. Such results ...nd support in the trade and quality literature. Hallak (2006) ...nds some evidence that richer countries have relatively greater demand for high quality goods–quality being measured by unit values.

# 6.4 Survival and Firms’Type

Among exporting ...rms 17% export to African markets only. Obviously, such ...rms face very di¤erent food safety requirements in comparison to ...rms servicing developed countries. We re-estimate our main speci...cation considering ...rms that export only to Africa. The level of ...nancial development does not seem to matter for ...rms that only export to the African region (column 1 in Table 5). Yet, it does for ...rms that export to other regions. In column (2) we rerun estimation dropping ...rms that export only to Africa from the total ...rms sample. The coe¢ cient on our interaction term after dropping the "only-Africa" exporters increases nearly twofold in magnitude (from to �0:26 to �0:46). Additionally, there is evidence of capital ‡ows from multinational ...rm to a¢ liates as potential channels to overcome imperfections in local capital markets (Desai, Folay and Hines, 2009).

23
-28-

# 4. PROJECT BENEFITS AND RISKS

## A. **FINANCIAL ANALYSIS AND MANAGEMENT IMPROVEMENT PLANS**

## **Purpose, Scope, and Methodology**

4.1 This analysis aims to assess the past financial performance and standing of each of the eight project towns<sup>8</sup> to evaluate their potential for future financial improvement and autonomy using the available data and Management Improvement Plans (MIPs)<sup>9</sup> , and to measure the financial impact of the proposed investments and actions on their finances and the sustainability of their operations. Similarly, it attempts to assess the viability of the piloted system and the sustainability of the proposed investments in the water and sanitation sub-sector, using the available data from Arusha, Moshi, and Tanga, where semi-autonomous Urban Water and Sewerage Departments (UWSDs) were recently established (para. 1.26).

4.2 During project preparation, the borrowing and debt carrying capacity of each town was assessed, and attempts were made to establish under what circumstances and at what point in time each town will be financially strong enough to fully finance its operating and maintenance costs, and, in the long term, to be able to borrow for future investments. Thus the project would include such measures that are necessary to monitor and direct the municipalities towards achieving such financial targets. For the water and sanitation subsector, the performance of the three pilot UWSDs was reviewed and was found satisfactory. Hence, the project would assist in replicating the semi-autonomous UWSDs in the other five project towns and in Dodoma (para. 3.6).

4.3 The analysis for the eight municipal councils includes an evaluation of financial performance of each town for the last three years, review of their management capacity and respective future plans, and analysis of individual cash-flow projections during the period of project implementation and two years after its completion. The computation of a financial internal rate of return was not attempted because of the difficulties encountered in isolating and quantifying the incremental revenues. However, since the incremental costs were identifiable and measurable, least cost analysis was the alternative method used. Thus, whenever applicable, the "with project" scenario development followed an exercise of comparison between different feasible and mutually exclusive Arusha, Iringa, Mbeya, Morogoro, Moshi, Mwanza, Tabora and Tanga The MIPs are mechanisms introduced in the eight towns and Dar es Salaam as an integral part of project preparation. In December 1995 the Tanga Municipal Council submitted an MIP which has been recommended by PMO as a model for the other project towns. A standard outline of an MIP has been agreed on during the "technical discussions" at the time of negotiations and is included in the PIP.
60 -

-

Table 3.3a: Share of Rice Area Irrigated and Planted to Modern Varieties in Selected Countries of Asia, 1975-1979

|Country|Share of Area Planted<br>to Modern Rice Varieties<br>(percent)|Share of Rice Area<br>Irrigated<br>(percent)|
|---|---|---|
|Southeast Asia|||
|Indonesia|50|84|
|Malaysia|38|71|
|Philippines|68|42|
|Thailand|10|26|
|South Asia|||
|Bangladesh|15|12|
|Burma|6|17|
|India|||
|Central Indiaa|34|18|
|East Indiab|21|28|
|North Indiac|82|89|
|South Indiad|66|83|
|West Indiae|50|23|
|Pakistan|43|100|
|Sri Lanka|61|64|

8 Central India: Madhya Pradesh, Maharasthra, Uttar Pradesh. bEast India: Assam, Bihar, Orissa, West Bengal, Manipur, Tripura, and Nagaland.

CNorth India: Jammu and Kashmir, Punjab, Haryana, Himachal Pradesh, Delhi. dSouth India: Tamil Nadu, Andhra Pradesh, Karnataka, Kerala, Pondicherry. eWest India: Gujarat, Maharashtra, Goa, Damon, and Diu.

Source: Randolph Barker, Robert W. Herdt, with Beth Rose, The Rice Economy of Asia (Washington, D.C.: Resources for the Future, 1985), p.218
(or IGE in the case of income persistence). As discussed above, compared to IGRC (or IGE), IGC and rank correlation progressively purge the effects of changes in marginal distributions across generations. Many authors argue that this is desirable as this allows a researcher to focus on the fundamental structure of dependence between parents and children. For example, Bjorklund and Jantti (2009) note that IGC provides a measure of mobility that is not affected mechanically by changes in inequality across generations. But others such as Mazumder (2015) and Mitnik et al. (2014) point out that IGRC or IGE are valuable precisely because they contain information about the marginal distributions and relate to cross sectional inequality directly.

# **Conclusions**

Following the influential contribution of Chetty et al. (2014), many researchers working on intergenerational mobility, both in developed and developing countries, have adopted the rank-based measures. There is an emerging body of evidence that rank-based measures are more reliable as they are less sensitive to measurement error and life-cycle biases. We focus on the implications of a common data limitation faced by researchers for estimates of rank-based relative and absolute mobility: sample truncation due to the fact that household membership is defined in terms of a set of coresidency criteria.

We utilize two exceptionally rich household surveys from Bangladesh and India where information on non-resident children was collected, and the subset of children coresident at the time of survey implementation was identified. The evidence shows that truncation results in downward bias in the slope estimate and upward bias in the intercept estimate. The truncation bias in rank correlation is relatively low and similar in magnitude to that in intergenerational correlation (IGC), and the downward bias in coresident samples is substantially higher in the estimates of IGRC. The evidence thus suggests that a researcher working with coresident samples should report both rank correlation and IGC to understand relative mobility. Our results also show that the magnitude of bias in rank-based absolute mobility proposed by Chetty et al (2014) is usually small, and in most cases, suffers 20
# **Table A4: Panel A – Multinomial Regressions, 2006**

Log Odds ratio between Renters and Mortgage holders (Marginal Effects)

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|-0.2284 ***|-0.0160|-0.0111|-0.1279 ***|-0.0670 ***|-0.0076|-0.2734 ***||-0.4526 ***|-0.0476|
|Estonia|-0.2921 ***|0.0025|-0.0402 ***|-0.0518|-0.0820 ***|0.0031|0.0381 **|-0.4777|-0.1999|-0.0637 **|
|Hungary|-0.1530 ***|-0.0100|-0.0096|-0.0226|-0.0358 ***|-0.0125|-0.12 ***|0.0507|-0.0791|-0.0319 *|
|Latvia|-0.2663 **|0.0084|0.0040|-0.0093|-0.0333 *|-0.0339|-0.1296 ***|-0.3599|-0.1874|-0.0767 *|
|Lithuania|-0.1655 ***|-0.0277 ***|-0.0157 **|-0.0308 ***|-0.0326 ***|-0.0030|0.0128||-0.0037|-0.0015|
|Poland|-0.2196 ***|0.0210|-0.0181 ***|-0.0839 ***|0.0306|0.0523 **|-0.5958 ***|-0.0408|-0.5135 ***|0.0207|
|Slovak Republic|-0.5318 **|-0.0251 **|-0.0032|0.0750|-0.0199|0.0381 *|-0.1114 ***|-0.8940|-0.1563|-0.0329|
|Slovenia|-0.1245 ***|0.0003|-0.0373 ***|-0.0004|-0.0670 ***|0.0705 ***|-0.0872 ***|0.7900 ***|0.9712 ***|0.1010 ***|
|_EU-15 and Cyprus_|||||||||||
|Austria|-1.2767|0.0092|-0.0250 *|-1.9602 ***|-0.0505 *|-0.0135|-0.4781 ***|-1.0000|-1.0000|-0.0432|
|Belgium|-0.3224 **|0.0334|-0.0247|-0.1925 **|-0.0442|0.0456|-0.5021 ***|0.0336|0.0135|-0.1744 ***|
|Cyprus|0.0159|0.0072|-0.0146|-0.1968 ***|0.0602 *|-0.0471|-0.2319 ***|0.9305 ***|0.8724 ***|0.0973 *|
|Denmark|0.1007 *|-0.1098 ***|0.0819 ***|0.2343 ***|0.0018|-0.1940 ***|0.5194 ***||-0.0015|0.1702 ***|
|Finland|-0.2824 *|0.0002|-0.0118|-0.2720 ***|0.0220|0.0718 *|-0.3208 ***|0.8040 ***|0.9368 ***|-0.1473 ***|
|France|-0.4545 ***|0.0638 ***|-0.0158|-0.1641 ***|-0.0471 *|0.0100|-0.4982 ***|0.9064 ***|0.9407 ***|-0.0610|
|Greece|-0.3211 ***|-0.0430 **|-0.0815 ***|0.0121|-0.0714 **|0.0288|-0.1654 ***|0.8618 ***|0.2360|0.0159|
|Ireland|-0.4601 ***|0.0561 **|0.0185 *|-0.1235 ***|-0.0341|0.0055|-0.6479 ***|0.3920|0.7084|-0.1598 ***|
|Italy|-0.3815 ***|-0.0126|-0.0235 ***|-0.0924 ***|-0.0583 ***|0.0278|-0.1128 ***|-0.4355 **|-0.2274 *|0.0399 **|
|Luxembourg|-0.1814 *|-0.0490|0.0282|-0.2724 ***|0.0264|-0.0171|-0.5028 ***|0.8920 ***|-0.6124 **|0.0037|
|Netherlands|0.3258 ***|-0.0363 *|-0.0217|0.1451 **|0.0272|0.0883 *|0.3812 ***|-0.8784 ***|-0.9380 ***|0.2257 ***|
|Portugal|-0.2731 ***|0.0216|-0.0094|-0.1075 ***|-0.0072|-0.0314|-0.0537 **|0.0818|0.7502 **|-0.0836|
|Spain|-0.1729 ***|0.0239 *|-0.0057|-0.0771 ***|-0.0669 ***|-0.0572 **|-0.0449 ***|-0.2863|0.0998|0.0512 **|
|Sweden|-0.4284 **|0.0035|-0.0061|-0.1266|-0.0721 **|-0.0389|-0.5426 ***|-0.7485|-0.7752 **|-0.1163 ***|
|United Kingdom|-0.8167 ***|0.0295 *|0.0088||-0.0820 ***|0.0237|-0.4289 ***|-0.9062 ***||-0.4009 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

31
_-9-_ Table 1. Bangladeshi Hacroeconomic Effects of Raising One Percent of Real Revenue by Different Excise Taxes

||(1)<br>Relative<br>Increase in<br>Excise Tax<br>Rate<br>(Pct.)|(2)<br>Relative<br>Increase in<br>Consumer<br>Prices<br>(Pct.)|(3)<br>Relative<br>Increase in<br>Trade<br>Deficit<br>(Pct.)|(4)<br>Relative<br>Increase in<br>Excise Tax<br>Revenue<br>(Pct.)|(5)<br>Relative<br>Increase in<br>CDST<br>Revenue<br>(Pct.)|(6)<br>Distributio<br>Effect<br>(Sign)|(7)<br>Relarive<br>n Loss of<br>_SWF_<br>(Pct.)|
|---|---|---|---|---|---|---|---|
|Rice|0.18|-0.17|-0.39|3.31|-0.02|-|2.35|
|Wheat|2.73|-0.08|-0.37|3.31|-0.03||2.06|
|Jute|4.10|-0.95|-0.76|3.38|-0.05|-|4.79|
|Cotton|7.02|-0.14|-0.23|3.55|-0.13|_7/-_|1.38|
|Tea|16.57|-0.05|-0.11|3.27|-0.01|_?1-_|0.57|
|Other Crops|0.61|-0.19|-0.40|3.37|-0.05|2/-|2.25|
|Livestock|1.84|-0.20|-0.41|3.32|-0.03|7/-|2.19|
|Fisheries|0.99|0.17|-0.38|3.31|-0.03|7/-|2.11|
|Forestry|2.72|-0.06|-0.13|3.38|-0.06|0|0.66|
|Sugar|3.50|-0.09|-0.26|3.53|-0.12|0/-|1.44|
|Edible Oil|2.82|-0.11|-0.36|3.41|-0.07|0/-|2.00|
|Tobacco|7.27|-0.10|-0.36|3.32|-0.03|<br>0/-|2.41|
|Cther Food|3.14|-0.15|-0.37|3.35|-0.04|0/-|1.77|
|Cocton Yarn|3.90|-0.24|-0.42|3.64|-0.17|0/-|2.45|
|Cloth|1.11|-0.10|-0.32|3.41|-0.07|0/-|1.87|
|Jute Textiles|2.04|-0.01|-0.03|3.26|-0.00|+|0.13|
|Paper|8.08|-0.16|-0.27|3.55|-0.13|0/-|1.51|
|Leather|2.91|-0.18|-0.33|3.31|-0.03|0/+|1.93|
|Fertilizer|2.98|-0.13|-0.38|3.33|-0.03|7|2.07|
|Pharm. & Chemical|s 2.28|-0.12|-0.32|3.54|-0.13|0/-|1.68|
|Cement|11.13|0.03|0.01|3.47|-0.10|+|-0.03|
|Basic Mct ls|1.85|0.01|0.01|3.73|-0.21|+|0.03|
|Metal Products|1.20|-0.01|-0.05|3.30|-'.02|0|0.23|
|Wood/-':her<br>Ind.|1.32|-0.06|-0.17|3.47|-0.10|0/-|0.84|
|Urban-HBldg.|8.78|-0.01|-0.04|3.27|-0.01|0|0.19|
|Rural-HBldg.|3.45|-0.00|-0.01|3.26|-1.00|0|0.07|
|Other Constr.|1.40|-0.01|-0.02|3.26|-0.00|0|0.09|
|Petroleum|0.48|-0.13|-0.35|3.41|-0.07|0/-|1.85|
|Electricity/Gas|<br>3.32|-0.13|-0.20|3.39|-0.06|0/-|1.16|
|<br>Tranap. Services|<br> <br>1.11|-0.19|-0.37|3.40|-0.06|0/-|2.05|
|<br>Pousing|<br>2.14|-0.16|-a.36|3.30|-0.02|<br>0/-|2.02|
|<br>Health|6.51|-0.07|-0.26|3.31|-0.03|-|0.88|
|Education|3.29|-0.04|-0.12|3.27|-0.01|0|0.46|
|Public Admn.|1.35|-0.01|-0.02|3.27|-0.01|0|0.11|
|Trade&Other|0.30|-0.20|-0.38|3.44|-0.08|-|2.03|
- 43 -

low enrollment levels -- the following pattems may be noted. Fast developing countries with strong primary school systems continue to place more emphasis on national languages and less emphasis on official languages than slow developing countries with weak primary education systems. Both types of countries have increased curricular time allocations to math, science, social science (mainly social studies) and hygiene. In several peripheral subjects, divergent trends are found. For example, countries in the former category placed greater emphasis on moral education and lesser emphasis **:)n** aesthetic, physical and vocational education while countries in the latter category did just the opposite. These changes in subject emphases are generally small, except for pre-vocational education where there was a sharp decline among countries with negative growth rates and low enrollment levels. Overall the constructed typology does not appear to be a salient mechanism for explaining shifts in curricular emphases during this period.

# IX. Cross-National Variation in Annual Instructional Time

Up to this point, our study of the curricular content of primary education has examined information referring to either the presence or absence of subjects taught during primary schooling or the relative emphasis of each subject in the official curriculum. These analyses suggest that there is a good deal of official consensus at the world level as to what subject areas primary school curricula should contain and how much emphasis should be placed on each subject in official weekly time allocations. It is clear, however, that such general indicators of organization of the primary curriculum mask significant country differences of other key curriculum-related variables: for example, the amount of actual time spent on instruction; the specific content that is taught to pupils in each subject category; the proportion of students exposed to the same curicular content in the country; the knowledge or skills that pupils are required to learn for examinations; and the textbooks and other instructional materials that may or may not be accessible to pupils. A comparative assessment of these aspects of primary schooling demands a much more extensive and
**Table A.5. Attrition and missing information on survival by follow-up horizon**

|Years<br>since<br>baseline|Percent<br>attrited|Percent<br>missing|Percent<br>attritted +<br>missing|
|---|---|---|---|
|0.25|1,1%|0,1%|1,2%|
|0.5|2,4%|7,1%|9,5%|
|0.75|6,1%|0,0%|6,1%|
|1|4,8%|0,0%|4,8%|
|1.25|9,2%|0,7%|9,9%|
|1.5|6,3%|0,4%|6,7%|
|1.75|10,0%|0,6%|10,6%|
|2|10,0%|12,0%|21,9%|
|2.5|7,0%|9,5%|16,5%|
|3|9,5%|3,6%|13,1%|
|3.5|11,6%|0,0%|11,6%|
|4|5,7%|0,0%|5,7%|
|4.5|5,6%|0,0%|5,6%|
|5|15,4%|0,0%|15,4%|
|5.5|9,0%|0,0%|9,0%|
|6|4,1%|0,0%|4,1%|
|7|20,0%|0,0%|20,0%|
|8|15,5%|13,8%|29,3%|
|9|20,0%|0,0%|20,0%|
|10|7,4%|0,0%|7,4%|
|11|8,0%|0,0%|8,0%|
|12|20,0%|0,0%|20,0%|
|13|20,0%|0,0%|20,0%|
|14|20,0%|0,0%|20,0%|
|15|19,3%|12,4%|31,6%|
|16|20,0%|0,0%|20,0%|
|17|20,0%|0,0%|20,0%|

**Table A.6. Reason for closure by** **<u>gender</u>**

|Reason for closure|Male|Female|Total|
|---|---|---|---|
|Making a loss|49.34%|35.50%|590|
|Sickness|9.79%|15.69%|197|
|Care for family|2.45%|18.07%|180|
|Better wage job|8.10%|5.63%|95|
|Better business|7.91%|3.14%|71|
|opportunity||||
|Other|22.41%|21.97%|322|
|Total|531|924|1,455|

72
**Root for the Tubers: Extended-Harvest Crop Production and Productivity Measurement in Surveys**

Talip Kilic<sup>‡</sup> , Heather Moylan<sup>ǂ</sup> , John Ilukor<sup>+</sup> , Clement Mtengula<sup>†</sup> , and Innocent Pangapanga-Phiri<sup>#</sup> 0F<sup>1</sup>

**JEL Codes:** C83, Q12.

**Keywords:** Root Crops, Tuber Crops, Tree Crops, Extended-Harvest Crops, Cassava, Production Measurement, Yield Measurement, Harvest Diaries, Recall, Crop Cutting, Household Surveys, Malawi, Sub-Saharan Africa.

> 1 ‡Senior Economist, Living Standards Measurement Study (LSMS), Survey Unit, Development Data Group (DECDG), The World Bank, Rome, Italy; tkilic@worldbank.org.<sup>ǂ</sup> Corresponding Author. Survey Specialist, LSMS, Survey Unit, DECDG, The World Bank, Rome, Italy; hmoylan@worldbank.org.<sup>+</sup> Survey Specialist, LSMS, Survey Unit, DECDG, The World Bank, Kampala, Uganda; <u>jilukor@worldbank.org.</u><sup>†</sup> Independent Consultant, Zomba, Malawi; <u>cmtengula@gmail.com.</u><sup>#</sup> Lecturer, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi; phiriinnocent@gmail.com.
concentration) and economic development are strong and in the expected direction. Although table 3 reports several instances of relatively high correlations among independent variables, which could potendally be a source of multicollinearity (Hanushek, 1979), these were later checked using a series of regression diagnostics (Belsey, Kuh and Welsch, 1980) and found to have no adverse impact on the main pattern of results reported below.

In the panel regression analyses, three different models were estimated. In the first model, a baseline equation was estimated in which level of economic development in 1985 was regressed on level of economic development in 1960, the primary and secondary enrollment rates for 1960 and the aforementioned control variables. The second model added to this baseline equation a single measure for overall yearly instructional time at the primary level (that is, across all subject areas). The third model estimated the economic effects of the more detailed measures of annual instructional time in eight different subject areas (mathematics, science, language, art/music, social science, religion/moral education, physical education and pre-vocational education) after controlling for the influence of educational expansion, ferility rates, high income oil exporters and commodity concentration. These three models were first estimated for the full sample of countries (table 4) and then for a smaller sample of less-developed countries (table 5). In all models only unstandardized regression coefficients are reported; instances in which these regression coefficients are either 1.5 times or twice their standard error are reported as significant.

The economic impact of interaction effects between curricular time allotments and the level of educational expansion at the primaxy level were also analysed (see tables 6 and 7). Such interaction (or multiplicative) effects measure what percentage of students in each nation were exposed to how many hours of fonnal instruction in each subject area and were constructed by multiplying the primary enrollment rate by the annual hours of instruction in each subject area. These indicators are analogous to measures of the "yield" or "productivity" of an educational system which have been employed recently in the IEA studies of mathematics achievement (McKnight et al., 1987: 61).

The baseline model estimated in table 4 (equation A) shows that educational expansion at both the primary and secondary levels has a significant positive impact on economic growth between 1960 and 1985. This finding confwrms the results of earlier cross-national studies (Meyer et al, 1979; Benavot, 1989). Each of the control variables is significant and in the expected direction 13
reaction time and financial sector policy making activity, respectively. An in-depth analysis of temporary policy measures (for example, the effectiveness and potential unintended consequences of policy interventions) is left for future research.

# _1.1 Overview of policy response surveillance initiatives_

Various surveillance efforts exist which track the policy responses to COVID-19. Some take stock of a broad range of interventions, such as fiscal, monetary and prudential policies (e.g. IMF Policy Tracker, COVID-19 Financial Response Tracker (CFRT) by the Yale Program on Financial Stability, IDB Summary of Policy Measures, and OECD Policy Tracker); others focus exclusively on prudential regulatory measures (e.g. Institute of International Finance’s Regulatory Measures) or containment and economic support measures (e.g. Oxford COVID-19 Government Response Tracker (OxCGRT)).

Compared to these projects, our database offers three distinctive features. First, it tracks the financial sector policy response with a focus on emerging markets and developing economies (EMDEs),<sup>2</sup> although it also covers several advanced economies. Second, the database adopts a tiered classification system which provides a typology of policy measures to facilitate analysis. Third, each measure is dated and accompanied by the relevant authority which implemented the measure. Where available, a link to the primary source is also provided as well as a termination or extension date, if applicable. Taken together, these features enhance the ability of both policy makers and researchers to comprehensively analyze and benchmark policy responses and their impacts at different stages of the pandemic.

# _1.2 Related literature_

This paper contributes to a small, but burgeoning literature that explores the impact of COVID-19 on the financial sector, analyzes drivers of the policy response and evaluates policy effectiveness.

**Impact of COVID-19 on the financial sector.** The financial sector has been put under strain by the COVID-19 crisis. Core markets became dislocated, EMDEs experienced massive capital outflows, and some borrowers faced liquidity and repayment challenges (e.g., BIS (2020), IMF (2020), and Powell and Rojas-Suárez (2020)). Due to a concerted effort by policy makers, market functioning was largely restored,

> 2 In this paper, we define EMDEs as all countries that are not in the high-income group according the World Bank country classification.

3