rafmacalaba/datause-encoder-data huggingface dataset:
countries report outstanding debt net of some financial assets (Arslanalp & Tsuda, 2014b), while others do not. A more widely-agreed view is that the net stock of external wealth should be equivalent to the Net International Investment Position, which is the difference between outstanding external stock of assets and outstanding external stock of liabilities. Gross positions then refer to the outstanding stocks of assets and liabilities separately.

# **A.1.2 External Borrowing of Sectors**

The focus of this is on the differentiation of sector in the domestic paper capital flows by economy. The term “sector” is used here to refer to institutional sectors: general government, central banks, depository corporations except the central bank (“banks”), and other sectors (“corporates”).<sup>42</sup> There are other ways to define the sectors of the economy, but this breakdown is the most common in the data.<sup>43</sup> For much of our analysis, and all analysis using asset flows, we combine the central bank and general government sectors into a single sector called “public sector”.

These broad sectors can sometimes be decomposed into various institutional subsectors (for example, other sectors are sometimes split into other non-bank financial and other nonfinancial sectors in the BOP data). Thus, sectors can also be defined differently depending on the dataset or measure. For instance, several datasets such as the WB DRS produce statistics on public and publicly guaranteed (PPG) debt. In this case, public refers to general government, central banks, and the public sector portions of banks and corporates. Non-publicly

> 42It should be noted that the BOP category “other sectors” is broader than what is captured by the term “corporates”. Nevertheless, in most cases, there is fairly broad overlap between the two categories. That is why, in the rest of this paper, we use the two terms interchangeably for presentational convenience.

> 43See Chapter 4 Section D of the 6th Edition Balance of Payments Manual for an overview of Systems of National Accounts sectoral breakdowns, and the sectoral breakdowns used in the BOP (and often other) data sources.

48
spending falling by about one-third (INSEE, 2020b; Bounie et al., 2020b,a). In the COVID-19 era in France, the first cases of COVID-19 were identified at the end of January 2020. As a result, France has put in place a plan to respond to the coronavirus epidemic. On March 12, the French President announced in his first speech on public television that all schools and universities would close, and then ordered the closure of non-essential stores (exceptions for stores such as supermarkets, grocery stores, bakeries, pharmacies, banks, grocery stores, gas stations and tobacconists). This first speech was followed by a second one on March 16, when more than 4,500 cases of infection were confirmed. The French authorities announced a mandatory home lockdown for 15 days starting at noon on March 17. This period was extended twice and ended on May 10.During this period, all travel was reduced to what was deemed strictly necessary, companies were required to organize themselves to facilitate remote work, and meetings with family or friends were prohibited. Besides, the borders of the Schengen area were closed and travel between non-European countries and the European Union was suspended. Meanwhile, the number of deaths due to coronavirus increased dramatically, with more than 10,000 people dying. Between May 11 and June 1, the first phase of a gradual deconfinement and lifting of restrictions took place. During this period, all gatherings, meetings, activities, travel and public transport users were required to respect the rules of social distancing. Public access to parks, gardens and green spaces in urban areas was prohibited in French areas classified as ”red zones”. Most businesses reopened, but restaurants, caf ́es and bars remained closed. A travel declaration is no longer required for travel outside of one’s place of residence. As of June 2, the second, third and final phase of deconfinition began, with an easing of restrictions. All regions have been classified as ”green zones”. Caf ́es, restaurants, pubs, cinemas, vacation centers, sports halls for group sports, all day care centers, schools and colleges (junior high schools) were allowed to reopen. France reopened its borders with non-EU countries. From August to October, cases of infection and death due to coronavirus began to increase again. On September 12, France recorded for the first time more than 10,000 new cases in a single 24-hour period. From October 17, French authorities imposed a curfew in the large city where the virus was circulating heavily. This measure was followed by a second national containment from October 30 to December 15, followed by a national curfew extended to every day between 6 p.m. and 6 a.m.

# **2.2 Resilience to Shocks: Theoretical Framework and Hypothesis**

In this section, I propose a very simple framework to examine the resilience of merchants using the latest digital payment technology to different types of staggered shocks. To do so, I study the effects of these shocks on merchant sales and substitution between payment methods. I compare 6
64 TABLE 5: Proportion of Land not In Production by Tenure Category, 1981.

||Private<br>'000 has|%|Ejldo<br>'000 has|X|Mixed<br>'000 has|X|
|---|---|---|---|---|---|---|
|ALL ENTERPRIS|ES||||||
|In Production|54,199|73.4|12,975|85.2|2,115|73.1|
|Not In Pro-<br>duction|19,662|26.6|2,260|14.8|778|26.9|
|Total|73,861|100.0|15,235|100.0|2,893|100.0|
|ENTERPRISES<br>UP TO FIVE<br>HECTARES|||||||
|In Production|919|93.6|2,778|94.1|259|98.1|
|Not in Pro-<br>duction|63|6.4|174|5.9|5|1.9|
|Total|982|100.0|2,952|100.0|264|100.0|

Note "Not In production" means that the land was not put to agricultural or silvicultural use in the perlod April-September 1981; this category Includes enterprises that have never used the land for these purposes.

SOURCE: Same as Table 4
> <sup>32</sup>

The more interesting finding is in the second stage regressions. Among the 12 regressions, in only five is even one of the three legal origin variables significant and in none is more than one significant; in general, they are highly insignificant. In eight of the twelve regressions, the political variables are significant. In all cases, their estimated magnitudes are only slightly lower relative to the large magnitudes reported in Tables 1b and 2b. Moreover, in all of these cases, the _J_ -statistic supports the validity of the instruments.

**Table 6a:  Continuous years of elections, newspaper circulation and lending to the** **<u>private sector</u>**

||Con<br>competi<br>|tinuous ye<br>tive electio<br>|ars of<br>ns(1975)<br>|Average<br>|Newspaper Ci<br>(1975-2000)<br>|rculation<br>|
|---|---|---|---|---|---|---|
|Lending to the|Growth|Level|Level|Growth|Level 2000|Level|
|private sector by<br>banks and non-<br>bank financial<br>institutions|75 – 00<br>(_OLS_)|2000<br>(_OLS_)|2000<br>(_OLS_)|75 – 00<br>(_OLS_)|(_OLS_)|2000<br>(_OLS_)|
|Political variable|.0006|.003|.011|.0001|.001|.002|
||(_.009_)|(_.32_)|(_0.00_)|(_0.00_)|(_0.00_)|(_0.00_)|
|British legal|0.014|.22|.15|.014|.16|.18|
|origin|(_.18_)|(_.05_)|(._15_)|(_.10_)|(_.05_)|(_.01_)|
|Scandinavian|-.01|-.016|-.0004|-.04|-.28|-.21|
|legal origin|(_.42_)|(_.91_)|(_0.998_)|(_.03_)|(_.08_)|(_0.18_)|
|German legal|.02|.62|.75|-.006|.34|.37|
|origin|(_.16_)|(_0.001_)|(_0.00_)|(_.67_)|(_0.09_)|(_0.02_)|
|_R_<sup>_2_</sup>|.14|.50|.45|.21|.52|.51|
|_N_|66|73|88|78|94|115|

N.B. _p_ -values are reported in parentheses, based on robust standard errors. Constants not reported. Specifications based on Table 1a, with the addition of legal origin variables.

From the evidence in Tables 5 and 6 one can conclude that, at a minimum, the effects of political checks and balances, political credibility (continuous years of competitive elections) and voter information (newspaper circulation) are significant determinants of financial sector development even controlling for legal origin. The effect of competitive elections is more fragile, but this was already established in the earlier regressions.
- 29 -

In the case of the BE countries, the distinct pattern of their exports to the EC might not have been influenced significantly by the MFA. Given income levels similar to Western European countries prior to World War II, we can presume that, historically, EE started with a much more diversified product mix compareL to the East Asians. During the last four decades, there never was a notable export boom to the West, neither in general nor in selective itemg.3 This would be consistent with a rather uniform pattern of quota coverage and restrictiveness. We speculate that the distortive impact of the quotas, in terms of product composition, came not so much from changes in EE itself -- which were limited over the last two decades, except probably in Hungary -- but from the relative decline in this region's income level in the face of the rising East Asians and others. As well, both the quotas in place and the weak adjustment mechanisms inherent in a centralized economic system likely inhibited EE's expansion of relatively labor-intensive products.

As to EE's product specialization, taste similarities between the two halves of Europe certainly come into play besides relative factor abundance. In fact the Linder-type of trade, based on taste similarities, might explain in part the relatively low level of specialization in SE's exports to the EC.

one important distinction has to be made concerning the analysis of EE's tride patterns. We exclusively dealt with the composition of textile and clothing exports identified by MFA categories and their relative capital intensity. In fact, most of these products correspond to a relatively narrow band on the capital-intensity spectrum, and hence the error margins involved in such intra-industry analyses are considerable. On the other hand, since textiles and clothing items make up a large part of labor-intensive manufactures, where the comparative advantage of EE lies in the immediate future, we expect that exports of these industries from SE to the West will expand considerably.

It might be possible to make more specific inferences from other countries' experiences concerning EE's prospects. The main methodologies here n As the World Bank's computerized files on textiles and clothing start with 1981, we could not readily check on these presumptions.
<!-- Start of picture text --> 2014 2017<br>division |as division |<br>equipment_basic |] equipment_basic] ii!<br>ready_del] ready_del] Hii<br>wealth | staff_del] Hi<br>labtests | amenities_basic | Hl<br>staff_del] wealth 7 Hil<br>amenities_basic |] HE age]<br>education | labtests 7<br>age; Hi education] Hl<br>skilledBirth | Hil employmentStatus 7 Hl<br>fe) 10 20 fe) 10 20 30<br>Relative importance Relative importance<br><!-- End of picture text -->
- For housework or own-enterprise (typically own-farm) work, forgone income was assumed to be zero, on the plausible assumption that such work can be readily reallocated over time to ensure little or no forgone income.

- Forgone work and incomes were asked for each spell of BREGS work, for each individual. The gender-specific median was then used as the household value.

- Missing values of forgone income were replaced with the median for the household’s stratum in the village or the village median (across all strata) if it was still missing.

- About 10% of respondents reported forgone earnings greater than their earnings from public works. This is implausible, and most likely reflects a misunderstanding of the survey question. We assumed that this was an error and so we truncated the data such that forgone income in any period cannot exceed earnings from public works.

In estimating poverty measures, we use a comprehensive consumption aggregate (using a survey module based on the NSS Employment-Unemployment Schedule). The poverty line is the median per capita consumption level in R1 and we update this over time using the Consumer Price Index for Agricultural Laborers to get the R2 line. This gives poverty lines of Rs. 6,988 per person per year in R1 and Rs. 7,836 in R2. However, recognizing that any poverty line is bound to be somewhat arbitrary, we also provide estimates of the poverty impacts over a wide range of potential lines.

In setting the cost of the scheme for the counterfactual analysis, we include all public expenditures attributed to BREGS in the central administrative data. These include materials and supervision as well as BREGS wages; these non-wage costs represented 36% and 39% of total spending on the program in Bihar in R1 and R2, respectively (Dutta et al., 2013). The precise budgets we use are Rs. 858.42 per household in R1 and Rs.1,194.92 per household in R2.<sup>14</sup> The calculation of counterfactual poverty measures is then a simple accounting exercise. The actual (observed) post-BREGS poverty measure is based on the observed distribution of consumption per person **_y_** = ( _y_ 1,...., _yn_ ) (where _yi_ is consumption per person for household _i_ ). The counterfactual in the absence of BREGS is based instead on the distribution **_y_ –** **_w_ +** **_f_** where **_w_** is the _n-_ vector of actual wages received from BREGS and **_f_** is the _n-_ vector of forgone incomes due

> 14 The administrative data indicate total expenditures of Rs 13,058 million in FY 2008-09 and Rs 18,177 million for FY 2009-10 (Dutta et al., 2013, Chapter 1). These were divided by our count of 152 million households in rural Bihar, as implied by our survey weights. Recent Census projections gave slightly higher counts but it is better to use our survey-based numbers for internal consistency.

10
19 highest wage distortion and the smallest proportion of wage employment. Eastern Europe and Central Asia, for obvious historical reasons, have the largest proportion of formal employment.

Figure 2 Measures of labor market distortions (1990) <!-- Start of picture text --> 4".<br>Sm<br>30.<br>East AMica East Asia Lan Arnuc a Mdc East & NAU-= South AdA Wmt AftC<br>amrn *Pw manm I l<br><!-- End of picture text --> Source: World Bank data.

We now consider the explanatory variables relating to economic structure. We assume that the three elasticities-n, y, X-will vary across countries depending on their levels of development. Therefore, we proxy them by per capita GDP and the level of educationmeasured below by the secondary school enrollment rate."5 The international price level is taken to be the same for all countries and hence not included as a separate explanatory variable.

15 Except for Africa, the primary school enrollment rates show very little variability across countries. That is why we preferred to use secondary enrollment. We also ran our regressions using the primazy enrollment rate, as a test of robustness, and our conclusions did not change.
and 0.39 respectively. Indeed, many of the households with the highest consumption levels reside in urban areas, with better access to markets and opportunities.

**Figure 7: Cumulative consumption distribution by state.**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>WRP<br>70%<br>NBG<br>60%<br>WBG<br>50%<br>LKS<br>40%<br>WEQ<br>30%<br>CEQ<br>20%<br>EEQ<br>10%<br>US$1.90 PPP<br>0%<br>0 20 40 60 80 100 120 140 160 180 200<br>Total cons. per capita per day (Oct. 2016 SSP)<br><!-- End of picture text -->

**Source: Authors’ own calculations based on HFS 2016 data.**

Poverty in 2016 is generally high but it is higher in former states that were more exposed to the conflict. The incidence of poverty reached extremely high levels in the former states of Eastern Equatoria, Northern Bahr el Ghazal, and Western Bahr el Ghazal, where about 9 in 10 people live under the international poverty line (95, 90, and 90 percent, respectively). In the former states of Lakes and Central Equatoria, the poverty headcount is slightly lower at about 8 in 10 people, though still extremely high by international standards (84 and 80 percent, respectively). One notable exception is the former state of Western Equatoria, as it was less affected by the conflict compared to the other states and has benefitted from high fertility and favorable weather conditions. Indeed, Western Equatoria, in the “green belt” of South Sudan, was the only state to record a consistent cereal production surplus in the years from 2014 to 2016 (FAO and WFP, 2017). Accordingly, the residents of Western Equatoria were much more likely to be able to sustain their livelihoods through own production compared to those in other states and thus maintain better standards of living (World Bank, 2018).

# 5. Imputing Poverty Using Geo‐Spatial Data

## Extending Poverty Estimates to Non‐Covered Areas

Despite initial intentions to expand the HFS across the entire country, continued insecurity made it impossible to extend the survey to the former North‐Eastern states of Jonglei, Unity, and Upper Nile. To account for this gap in coverage and obtain countrywide poverty rates, a statistical model was developed to impute poverty in non‐covered areas leveraging the growing availability of satellite imagery and geo‐spatial data. Recent advances in the processing and availability of satellite imagery and geo‐spatial data have led to a growing field of research on predicting a range of outcomes based on diverse such data sources.<sup>36</sup> Indeed, there is a growing body of evidence indicating that household‐survey derived indices of poverty correlate strongly with many geographic features that can be observed from space or derived from ground‐based data (Engstrom et al., 2017; Jean et al., 2016; Krizhevsky et al., 2012; Sedda et al., 2015).

> 36 An organization called Planet currently operates more satellites than even the U.S. and Russian governments. Planet recently launched 88 additional satellites, allowing almost daily coverage of the entire globe with a resolution of 3 to 5 meters per pixel (Engstrom et al., 2017a).

16
<!-- Start of picture text --> MDA: Growth Orientation of Markets RUS: Growth Orientation of Markets<br>Top 10 Markets Top 10 Markets<br>TUR<br>BLR<br>ITA ROM<br>POL<br>FRA DEU CHN<br>DEU BLR TUR UKR<br>UKR<br>RUS<br>ITA KOR<br>USA USA<br>JPN<br>-20 0 20 40<br>WLD: CAGR of Exports 05-10(%) 0 5 10 15<br>WLD: CAGR of Exports 05-10(%)<br>TKM: Growth Orientation of Markets TJK: Growth Orientation of Markets<br>Top 10 Markets Top 10 Markets<br>PAKTUR CHN TUR<br>RUS<br>IRN RUS<br>IND DZA<br>DEU ITA<br>USA NOR<br>BGR<br>NLD<br>GEO USA<br>-40 -20 0 20 -60 -40 -20 0 20 40<br>WLD: CAGR of Exports 05-10(%) WLD: CAGR of Exports 05-10(%)<br>UKR: Growth Orientation of Markets UZB: Growth Orientation of Markets<br>Top 10 Markets Top 10 Markets<br>POL BLR TUR CHN<br>RUS<br>DEU RUS JPN<br>IND UKR<br>TUR IRN<br>ITA USA<br>SYR<br>USA AUT<br>-5 0 5 10 15 20 -60 -40 -20 0 20<br>WLD: CAGR of Exports 05-10(%) WLD: CAGR of Exports 05-10(%)<br>Source: Authors’ calculations based on Comtrade data from WITS.<br>40 15<br>20 10<br>0<br>5<br>MDA: CAGR of Exports 05-10(%) RUS: CAGR of Exports 05-10(%)<br>-20<br>0<br>20 40<br>20<br>0<br>0<br>-20 -20<br>-40<br>TKM: CAGR of Exports 05-10(%) TJK: CAGR of Exports 05-10(%)<br>-40<br>-60<br>15<br>20<br>10<br>0<br>5 -20<br>0 -40<br>UKR: CAGR of Exports 05-10(%) UZB: CAGR of Exports 05-10(%)<br>-5 -60<br><!-- End of picture text -->

# **5. Conclusions**

This paper examined the export diversification performance of a subset of twelve ECA countries along the product and market dimensions, as well as export performance in the mentioned countries. It put substantial focus on the link between export diversification, resource abundance and the impact of the commodity boom, first, by examining where ECA countries stand in the cross-country long-run link between export diversification, natural resource abundance, development and size, and second, by 20
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106
Table 2

# Summary of Shortages and Ancillary Services Provision for Outpatients, Emergency and Inpatient Services

||Outpatients|Emergency|Inpatient|
|---|---|---|---|
|Number of Patients|1554|1616|61|
|**Lab Tests**||||
|Number Ordered|2,163|830<br>|152<br>|
|% Completed|9.1%|70.0%|71.1%|
|**X-Ray**Tests||||
|<br>Number Ordered|101|108<br>|46<br>|
|% Completed|27.7%|72.2%|60.9%|
|**Norinvasive**<br>**and**||||
|**Pathology**<br>**Tests**||||
|<br>Number Ordered|128|88<br>|35<br>|
|%Completed|69.5%|90.9%|57*'.%|
|Reported Shortages<br>of||||
|<br>Any Kind|12.6%|6.9%|6.3%|

Source: The Urban Institute