rafmacalaba/datause-encoder-data huggingface dataset:
Table 6 reports then the results of the causality tests. We see from this table that there is no statistical evidence that Provision does not cause Participation when using both the MENA and OECD data. The same is true for Protection. These results suggest that a regression of (Participation) on (Provision) and (Protection) is warranted using both the MENA and PP3 OECD datasets, though the latter will only serve the purpose of a benchmark. We now turn to PP2 PP1 the regression analysis of our data.

|**Table 6.**Causality tests<sup>+</sup><br>H0<br>MENA<br> <br>|OECD|
|---|---|
|P2does not Granger-causeP3<br>7.22<sup>∗∗∗</sup><br><br>|6.59<sup>∗∗∗</sup><br>|
|<br>P1does not Granger-causeP3<br>3.29<sup>∗∗∗</sup><br><br>|2.39<sup>∗∗</sup><br>|
|<br><br>+StandardizedZstatistics reported; Optimal lag length= 1in all cases (choice bas<br>criterion);<sup>*</sup>:p< 0.10,<sup>**</sup>:p< 0.05,<sup>***</sup>:p< 0.01.<br>|ed on the Bayesian information<br>|

**_Probit analysis_** Our variable of main interest, , is "fractional," that is, it may take all possible values in the unit interval. Hence, a linear regression is not appropriate to analyze the effects of and PP3 on as it does not ensure that the predicted values of lie in the unit interval (Papke and PP2 PP1 Wooldridge, 1996). Instead, we should estimate a fractional outcome model (Papke and PP3 PP3 Wooldridge, 1996, 2008; Wooldridge, 2019). Given the panel nature of our datasets, we run the fractional Probit regression proposed by Papke and Wooldridge (2008).<sup>36</sup> This model guarantees that the predicted values of lie in the unit interval through a link function, that is, a cumulative distribution function (CDF). PP3 Let denotes a vector of explanatory variables. The fractional Probit model is as follows: xx 1 × KK i i i ) ii ii (4) EE(PP3 |xx , mm ) = ΦΦ(xx ββ+ mm where i ≤1, and refer respectively to the country and the year, is the standard normal CDF, is a vector of parameters to estimate. denotes the unobserved effects. 0 ≤PP3 ii ff ΦΦ(. ) As stressed by Papke and Wooldridge (2008), the use of a Probit link function, i.e., a standard ii ββ KK× 1 mm normal CDF instead of a logistic one, does not cause any "conceptual or theoretical" problem. Instead, in the presence of endogenous covariates, using the Probit link function is

> 36 For cross-sectional data, interested readers may refer to Papke and Wooldridge (1996). Wooldridge (2019) discusses recent theoretical developments on the fractional Probit within the framework of the correlated random effects modeling with unbalanced panels.

22
decreasing score variability. Torgler, Schaffner, and Macintyre (2010), e.g., have used an index that covers the following questions to be able to measure tax morale:

1.  Given present tax burdens, one can hardly blame tax evaders.

2. Given the easy availability of opportunities to evade taxes, one can hardly blame tax evaders.

3. If in doubt about whether or not to report a certain source of income, I would not report it.

4. Since the government gets enough taxes, it does not matter that some people evade taxes.

5. Taxes are so heavy that tax evasion is an economic necessity for many to survive.

6. If I receive $2000 in cash for services rendered, I would not report it.

7. Cheating on taxes is justifiable in light of the unfairness of the tax system.

8. Taxes are something which is taken away from me.

9. Since everybody evades taxes, one can hardly be blamed for doing it.

10. There is nothing bad about under-reporting taxable income on one‘s tax return (1=strongly disagree; 2=disagree; 3=neutral; 4=agree, 5=strongly agree).

In general, single-item measures in cross-cultural comparisons should be treated with some caution. For example, in countries where tax revenues are collected to finance a ―dictator‘s war machine‖, tax evasion might be justifiable, and there could even be a ―moral duty‖ not to pay taxes. Similarly, in authoritarian political systems people will search for ―voice‖ or ―exit‖ mechanisms via tax resistance to express their preferences (Torgler, 2001). Moreover, there is still the potential problem in the survey studies that some individuals may excuse their non-cooperative behavior in the past by declaring relatively high tax morale values.

# _3. Correlation between Tax Compliance/Tax Evasion and Tax Morale_

A number of previous studies have investigated the simple correlation between tax morale and the size of the shadow economy in Western societies, transition countries or Latin America (Alm and Torgler, 2006; Alm, Martinez-Vazquez and Torgler, 2006, Torgler, 2001, 2005a). These studies report a negative correlation with Pearson _r_ values between -0.51 and -0.66. _Figures 1_ and _2_ show the correlation between tax morale in a 10
cover thoroughly in one paper, and there is every reason to expect such research to continue for a long time to come. The Crash of 1929 and ensuing Great Depression resulted in over 50 years of great research. For example, it took about 30 years to discover the full role of the shrinkage of the money supply in causing and perpetuating the Great Depression (Friedman and Schwartz, 1963), and it required about 50 years to understand how the loss of bank credit supply resulting from bank failures during the crisis made the Depression much worse (Bernanke, 1983). The research on the GFC and the Great Recession it generated has been ongoing for more than a decade and is still filling the journals as of this writing. The COVID-19 crisis, with all its unique characteristics, is likely to create a tidal wave of research papers for many years to come, and we hope that this paper may help perpetuate and improve this research.

It is also notable that a number of valuable research datasets are now available to research the COVID-19 crisis that were created for the purpose of monitoring the banks in the wake of the GFC. As examples, the Federal Reserve’s Y14 A, Q, and M datasets provide detailed information on large banking organizations’ projections of their conditions under a variety of macroeconomic scenarios; their asset classes and capital positions; and their loan portfolios; respectively. Some of these data have already been put to work in the research we review here, and we expect much more will be forthcoming in the next years.

Our main goals are to help understand the COVID-19 crisis and the resultant economic and financial research efforts as they relate to banking. We review, to the extent feasible, the extant research from the first year or so of past research. We focus most intently on how future banking research on this crisis can be improved, broadened, and deepened. We leverage considerably on the much larger banking research agenda from the GFC, which provides some useful directions where the COVID-19 research might fruitfully proceed.

5
# Annex 1: Data Sources

Data appraisal and availability represent the very key issues for this type of analyses. This paper is the outcome of a heroic effort in identifying pieces of information and data coming from the public sector and the private sector, both at the macro and micro level, undertaken through empirical research (exchange of information and meetings) involving several stakeholders in Mauritania.

Data on final consumption, exchange rate (MRO/USD), population, GDP deflator, and Gross Fixed Capital Formation is sourced from the World Development Indicators (WDI) of the World Bank<sup>48</sup> . Data on the projections for iron and gold prices are based on the estimates of monthly world prices of commodities and indices by the Development Economics Department (DEC) of the World Bank<sup>49</sup> .

## **_Iron and Gold_**

Data on annual production, exports, profits, average price (EUR/ton), CAPEX and cost estimates for the iron sector are sourced directly from SNIM. Data on annual production, CAPEX and unit cost of production have been sourced from KINROSS Tasiast, during meetings in persons with World Bank staff in Nouakchott, as well as from company’s documentation. In the case of gold, upfront capital costs have been estimated by World Bank staff basing on discussions with company representatives and local stakeholders. The team also met with the Office Mauritanien des Recherches Géologiques (OMRG) which provided precious background information and useful data on the overall mining sector.

## **_Fishery_**

Data appraisal for the fishery sector was based on empirical research and direct surveying and meetings with: Société Mauritanienne de Commercialisation de Poissons (Mauritanian Company for Fish Exports, SMCP), Federation National des Pêches (National Federation of Fishery, FNP), the Institut Mauritanien de Recherches Océanographiques et des Pêches (Mauritanian Institute for Oceanographic Research and Fishery, IMROP), Direction de la Marine Marchande du Ministere des Peches (Directorate of Merchant Shipping at the Ministry of Fishery). In addition, World Bank staff visited the artisanal port of Nouakchott and the artisanal and commercial port of Nouadhibou, conducting direct interviews to local fishermen and insiders to obtain empirical data. This research utilized data on the number of boats, on the average catch (in tons) per year, on the average number of trips (per boat) per year, on the average number of sailors per boat, on labor costs (docking costs, crew costs, fuel costs and equipment costs), and on the price of various type of catches. The team provided assumptions for a

> 48 Online database available at: data.worldbank.org/data-catalog/world-development-indicators

> 49 Data available at http://go.worldbank.org/QNYZRM5540

22
the general perception among investors that these the states have the “best” business environment in India. Responses to the World Bank Investment Climate surveys seem to confirm that this reputation holds among domestic small and medium sized firms as well. What we have grouped as moderate FDI states are also ranked higher by respondents of the World Bank survey than the remaining six states. If self-selectivity of businesses to or away from better business environment cities takes place on unobservable factors, we should observe it at the level of mobility between high FDI states and low FDI states as well.

The first panel of the table reports OLS estimates of equation (7) with location dummies aggregated into the three regions of FDI flows with moderate FDI states as the base group. The second panel presents random effects (GLS) estimates of the same specifications. We report t-values based on robust standard errors in both cases. Within each panel, specifications differ only to the extent of exclusion restrictions intended to highlight correlation among exogenous variables. The specifications that we should focus on in each panel are the first two. The difference between the two within each panel is that we adjust for differences in product market power only in one of them. The most obvious significance of the omission is that a halving of the goodness of fit of both the OLS and the GLS regressions.

Comparison of the GLS estimates with their OLS counterparts suggests the presence of heterogeneity bias: our estimate of rho is statistically different from zero in all alternative specifications, and the GLS estimates of the effects of economic geography are as a rule significantly different from their OLS counterparts. This is setting aside the problem of selectivity bias to which we now turn, and of which there are signs already in Table 5. One sign is that, although labor regulation comes out as a statistically significant influence on productivity in the GLS panel, this is not the case with power shortages. The GLS estimates suggest that a doubling of the percentage of managers complaining about labor regulation would be associated with a four-percentage point fall in labor productivity. This seems to be a plausible figure in the light of estimates provided in other studies. On the other hand, the estimate of the coefficient of the power shortage 25
Figure 3: Joint Distribution of WTP for Self and Other <!-- Start of picture text --> WTP For Self<br>0 10,000 20,000 30,000 40,000<br>WTP For Other<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br><!-- End of picture text --> _Notes:_ The figures depicts a scatterplot of the joint distribution of the willingness-to-pay for self against willingness-to-pay for another farmer to receive the training, for each of the 21 possible training prices.

total value to the participant of the externalities produced by the other farmer. In line with this assumption, the willingness-to-pay data suggest that participants do not internalize the social benefit of training. If they did, we would expect to observe two patterns in the data. First, valuations would be uniform within villages - both within and across participants. The previous paragraph disproves that valuations are uniform within participant, as it shows that willingness-to-pay for self is substantially higher than willingness-to-pay for others; in addition, the average coefficient of variation of willingness-to-pay for others at the participant level is 35%. Having uniform valuations across participants means that different participants have a similar willingness to pay for the same farmer; in the data, the average coefficient of variation of willingness-to-pay for the same farmer is equal to 74%, pointing to high dispersion. Second, if participants internalized the social benefit of the training, their willingness-to-pay would increase with village size or with the number of maize farmers in a village. I find that willingness-to-pay for others is not positively correlated with village size and number of maize farmers in a village (the correlation is negative, -11% and -7% respectively), and willingness to pay for self is not meaningfully correlated with either (0 and 2% respectively). Figure A.7 plots willingness to pay (for self or others) against number of households engaged in maize farming in the village at baseline, and bubble size measures frequency. Participants appear to have similar valuations irrespective of the number of households engaged in maize farming in their village. My experiment is not designed to identify or rule out altruism, but I can quantify warm 20
share of workers (aged 21-60) who work in agriculture, and who work as manual laborers. New roads cause a 10.1 percentage point reduction in workers in agriculture (representing a 21% decrease from the control group mean) and an 8.0 percentage point increase in workers in (non-agricultural) manual labor.<sup>15</sup> Columns 3 and 4 report estimates on the share of households deriving their primary source of income from cultivation and manual labor. In contrast to worker-level estimates, these regressions demonstrate that household income source does not change significantly, suggesting that many of the workers exiting agriculture are not the primary earners in the household. This result is also consistent with the finding that consumption and income do not change dramatically in response to new roads.

Theoretically, we should expect those who exit agriculture in favor of nonfarm labor market opportunities will be those for whom the losses of agricultural income are smallest and the labor market gains are largest. By using individual-level census data, we can examine the distribution of treatment effects across subgroups with different factor endowments. As land is the major input into agricultural production, land endowments may play a major role in determining which workers respond most to a rural road. We first examine the impact of road construction on the landholding distribution in Table A4. We find that a new road does not significantly change the share of households that are landless, own less than 2 acres, or have between 2 and 4 acres of agricultural land. However, we do find a 3.4 percentage point increase in the share of households with over four acres of land (significant at the 10 percent level). We are hesitant to over-interpret one marginally significant result out of four tests, but it is possible that there is some land consolidation following the construction of a road. Regardless, we do not find major changes in the landholding distribution and thus treat ex post observed landholdings as a baseline variable upon which to conduct heterogeneity

> 15The SECC does not report manual labor occupations in more detail. Table A3 breaks down the sectoral distribution of non-agricultural manual laborers using the 68th round of the National Sample Survey (201112). By far the most common category of manual labor in India is construction, making it a likely sector for many of these former agricultural workers.

17
|Andean|7|5|0|3||60.0|
|---|---|---|---|---|---|---|
|AEC 24|1|0|0|0|||
|**TOTAL**|12|59|3|20|25.0|33.9|
|**Mechanism**|**Percen**|**tage breakdo**|**wn of total investig**|**ations**|||
||**Condu**|**cted**|**No duty or mea**|**sures imposed**|||
||**Agricultural**|**Industrial**|**Agricultural**|**Industrial**|||
|Dumping|25.0|57.6|100.0|55.0|||
|Safeguard|75.0|42.4|0.0|45.0|||
|WTO|8.3|16.9|0.0|5.0|||
|_Special_|0.0|16.9|0.0|25.0|||
|Andean|58.3|8.5|0.0|15.0|||
|AEC 24|8.3|0.0|0.0|0.0|||
|**TOTAL**|100.0|100.0|100.0|100.0|||
|**Mechanism**|**Per**|**centage break**|**downper mechan**|**ism**|||
||**Condu**|**cted**|**No duty or mea**|**sures imposed**|||
||**Agricultural**|**Industrial**|**Agricultural**|**Industrial**|||
|Dumping|8.1|91.9|21.4|78.6|||
|Safeguard|26.5|73.5|0.0|100.0|||
|WTO|9.1|90.9|0.0|100.0|||
|_Special_|0.0|100.0|0.0|100.0|||
|Andean|58.3|41.7|0.0|100.0|||
|AEC 24|100.0|0.0|||||
|**TOTAL**|16.9|83.1|13.0|87.0|||

Source: Calculations by the authors based on data from the Ministry of Trade.

Another interesting result is that all the investigations for dumping of agricultural products have ended with no duties being imposed, while in the case of industrial products, duties were denied in 32% of the cases. In the case of safeguards, it may be noted that all the investigations conducted for agricultural products have ended with the levying of measures, while 36% of the investigations for industrial products have resulted in no measures being imposed.

A more detailed breakdown of the sectors that have requested the application of anti-dumping duties shows that chemicals, iron and steel products and petrochemicals represent almost 70% of the investigations (Chart III.3).<sup>19</sup>

> 19  In most sectors, the investigations have concentrated on a few products. The cases of dumping in the chemical sector focus on orthophosphoric acid, ethyl acetate and fertilizers; in the petrochemical sector, polypropylene and suspension type polyvinyl chloride. The agricultural products are corn byproducts, poultry and rice. In textiles the investigated products are denim, polyester fiber and texturized yarns. In iron

24
8.84 percentage point if method I is employed and 11.39 percentage point by method II.

Table 6: Changes in Poverty Rates Using Various Food Shares and Prices (%)

|Share of food in inflation|Base<br>Case|Usin|g CPI|Using Su<br>Prices|senas Unit<br>for Food|
|---|---|---|---|---|---|
||1999|1996|Change|1996|Change|
|Method I: Actual share of<br>food in consumption<br>expenditures, at povertyline|27.13|19.95|7.18|18.29|8.84|
|Method II: Using the share<br>of food in the poverty basket|27.13|17.55|9.58|15.74|11.39|

Notes: Working backward - start with poverty rate in February 1999 resulted from the iterative method.

Since the iterative method in 1999 produces a much higher level of poverty, these absolute changes are smaller percentage changes. Take for example the method II with CPI prices. Using the 1996 base case, poverty rate increased from 9.75 percent to 16.27 percent, which is 6.52 percentage points or 66.9 percent.

Meanwhile, using the 1999 base case, poverty rate rose from 17.55 percent to 27.13 percent, which is 9.58 percentage points but only 54.6 percent.

# IV) A **consistent set of estimates of poverty over the course of the crisis**

Over the course of the crisis there have been a number of estimates of poverty rates, using different large scale (but not necessarily nationally representative) household surveys. Unfortunately, each of those used a different and noncomparable _base_ for the "pre-crisis" poverty rate and a different method of deflation for the changes in the poverty line, so that these estimates of the headcount poverty 22
Figure 4: U.S. trade flows: Composition and partners 7

- (A) U.S. share of global goods and services trade

- (B) Composition of U.S. exports and imports

<!-- Start of picture text --> Percent of total United States China<br>Germany Japan Percent of total<br>40 100<br>30 80<br>60<br>20<br>40<br>10 20<br>0<br>0<br>Exports Imports Exports Imports<br>Energy Electronics Goods<br>Transport Chemicals Services<br>Machinery Other<br>Exports Imports<br>(C) Main sources of U.S. imports (D) Exports destinations of EMDE<br>regions<br>Percent of total China United Kingdom Percent of total<br>Japan Euro Area United States China Other AE<br>70<br>Mexico+Canada Other<br>100 60<br>80<br>50<br>60<br>40<br>40<br>20 30<br>0 20<br>10<br>0<br>EAP ECA LAC MNA SAR SSA<br>(E) Selected EMDEs: Exports to the (F) Share of EMDEs for which United<br>United States States is a major export destination<br>Percent Percent Percent of EMDEs<br>Percent of GDP<br>30 80 Largest export share with the United States<br>60<br>70<br>25 Percent of total (RHS)<br>30% or more of exports with the United<br>60 50<br>20 States<br>50<br>40<br>15 40<br>30 30<br>10<br>20<br>5 20<br>10<br>0 0 10<br>0<br>EAP ECA LAC MNA SAR SSA<br>Mexico Vietnam Malaysia Thailand Colombia China Peru Sri Lanka Indonesia<br>Total Services Goods Total Services Goods<br>Electrical Equipment Machinery Transport Equipment Energy<br>India<br><!-- End of picture text --> Sources: World Trade Organization, World Integrated Trade Statistics, Bureau of Economic Analysis, IMF, World Bank.

Note: Averages for 2010-15 unless otherwise specified.

A. U.S. imports of goods and services in percent of global goods and services imports.

B. U.S. imports of goods or services in percent of total U.S. imports of goods and services (purple bars); U.S. imports in each sector in percent of total U.S. goods imports (other bars). Averages for 2010-2014. D. Exports to the United States, other advanced economies, and China in percent of total exports of each EMDE region. "AE" stands for advanced economies.

E. Exports to the United States in percent of total exports or in percent of GDP of each EMDE economy. F. Share of EMDE economies in each region for which exports to the United States account for the single largest share of total exports or for which exports to the United States account for at least 30 percent of total exports.