rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Simulated import responses to a reduction in trade barriers (tariffs only)**

|||Applied<br>MFN||50|%|100|%|
|---|---|---|---|---|---|---|---|
|Green goods lists by<br>country group|Elasticities|Tariff<br>(s.a.)|Initial<br>imports*|Add.<br>Imports*|Var.|Add.<br>Imports*|Var.|
|Australia|-4.9|2.6|5 894|95|1.6 %|197|3.4 %|
|Canada|-6.9|0.4|11 376|53|0.5 %|108|1.0 %|
|China|-3.0|5.0|91 115|3 045|3.3 %|6 421|7.0 %|
|Costa Rica|-1.6|0.5|254|0.0|0.0 %|0.1|0.0 %|
|European Union|-5.7|1.9|69 006|483|0.7 %|982|1.4 %|
|Hong Kong SAR,|-4.3|0.0|24 209|0.0|0.0 %|0.0|0.0 %|
|**t**<br>Japan|-13.4|0.0|13 205|1|0.0 %|3|0.0 %|
|**C lis**<br>Korea, Rep,|-5.9|5.4|26 138|1 221|4.7 %|2 561|9.8 %|
|**APE**<br>New Zealand|-2.8|2.9|608|16|2.6 %|33|5.5 %|
|<br>Norway|-3.2|0.0|2 358|0.0|0.0 %|0.0|0.0 %|
|Singapore|-1.6|0.0|12 636|0.0|0.0 %|0.0|0.0 %|
|Switzerland|-1.8|0.0|3 435|1|0.0 %|2|0.1 %|
|United States|-6.1|1.5|50 999|449|0.9 %|923|1.8 %|
|**Average**||||||||
|**Davos country (13)**|-4.7|1.6|23 941|413|1.1 %|864|2.3 %|
|<br>HIC (18)|-4.4|2.1|12 704|149|2.1%|310|4.5 %|
|**list**<br>UMIC (29)|-3.7|4.3|5 148|179|4.1%|391|9.2 %|
|**PEC**<br>LMIC (27)|-3.6|4.2|691|32|3.2%|69|7.1 %|
|**A**<br>LIC (21)|-2.7|5.7|67|3|4.3%|6|9.3 %|
|**t**<br>HIC (18)|-4.1|2.9|74 223|1 335|2.4%|2 822|5.2 %|
|**O lis**<br>UMIC (29)|-2.9|6.9|19 333|1 476|7.8%|3 886|20.9 %|
|**WT**<br>LMIC (27)|-2.6|6.8|5 036|342|5.8%|864|13.3 %|
|<br>LIC(21)|-1.8|8.9|688|57|7.9%|130|17.9 %|

Source: Authors’ calculations. BACI (2013), TRAINS (2013), Kee et al. (2008), WDI (2013) data.

Notes:* Million US$. For income groups, means calculated over countries within each group. (s.a.) is simple average applied MFN tariffs and initial imports (in million US$ ) are mean values for 20102011. To reduce the weight of extreme elasticity estimates when calculating import responses, for each income group, estimates outside the 1st and 9<sup>th</sup> deciles were fixed at the respective decile cutoffs. Estimates are from equation (1) in Melo and Vijil (2014).

4. Barriers to Market Access and National Treatment in Environmental Services: How Important Are They?

Environmental projects have a great degree of ‘jointness’ or complementarity between the services provided by EGs and those provided by ESs. This is especially the case in developing 16
**_Hypothesis 2: Credit to new borrowers._** Entrepreneurs who were rejected under the traditional method but accepted based on their EFL score do not display worse loan repayment behavior than entrepreneurs who were accepted under the traditional model.<sup>12</sup> In terms of Table 1, this hypothesis implies that entrepreneurs in quadrant 2 have no worse repayment patterns than do entrepreneurs in quadrants 1 and 3. If this hypothesis is true, banks can rely on the EFL tool to help them extend credit to applicants they would otherwise have rejected, without increasing the risk of their SME portfolio.

Since not all applicants who were offered a loan accepted it, and because some obtained loans from other banks, we also examined the fraction of clients obtaining loans as an additional outcome of interest for each hypothesis. Comparing loan take-up across different groups provides information about how the size of the portfolio might change using different screening techniques.

To what extent can using the EFL tool provide access to loans for unbanked entrepreneurs without a credit history? To address this question, we tested a third hypothesis.

**_Hypothesis 3: Banking the unbanked_ .** Unbanked entrepreneurs who were accepted based on their EFL score were more likely to get a loan than unbanked entrepreneurs who were rejected based on their EFL score. Looking back at Table 1, this hypothesis implies that unbanked clients in quadrants 1 and 2 are more likely to be offered a loan after being screened by the EFL tool than unbanked clients in quadrants 3 and 4. Since clients in these two groups are likely to have very different characteristics, we also restrict the sample here to the unbanked entrepreneurs who scored near the EFL threshold defined by the implementing institution, so as to compare clients with similar characteristics.

# **3. Data, Descriptive Statistics, and Methodology**

# **_3.1 Data Sources_**

We obtained data collected by an EFL questionnaire that the implementing bank administered to 1,993 loan applicants between March 2012 and August 2013. These data include the EFL score

> 12 To test this hypothesis for banked entrepreneurs we can use 1,309 observations, which allows us to detect a difference of 8.8 percentage points between groups; for unbanked entrepreneurs we can only use 368 observations, which allows us to detect a difference of 29.5 percentage points between groups. In both cases the calculations were conducted at an 80 percent power and 95 percent confidence level for the binary indicator with the incidence closest to 50 percent.

9
Table 13a: Impacts of PROSPERA on ENLACE Tests 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 State Fixed Effects

|Variables|7th-6th score<br>9th-6th score|12th-6th level|
|---|---|---|
||_Panel A: Spanish_||
|PROSPERA beneficiary 2008|9.321*<br>12.644**|-0.023|
||(5.211)<br>(6.142)|(0.037)|
|Female beneficiary|-3.54<br>-0.779|0.056|
||(7.085)<br>(8.346)|(0.049)|
|Female|10.664*<br>-0.023|-0.052|
||(6.477)<br>(7.631)|(0.045)|
|Constant|-101.052***<br>-9.61|0.661***|
||(26.207)<br>(30.968)|(0.193)|
|Observations|4,439<br>4,616|2,402|
|R-squared|0.078<br>0.15|0.053|
||_Panel B: Mathematics_||
|PROSPERA beneficiary 2008|9.314<br>10.673|0.009|
||(5.756)<br>(7.819)|(0.038)|
|Female beneficiary|-5.422<br>-2.401|-0.004|
||(7.826)<br>(10.626)|(0.05)|
|Female|7.077<br>7.663|-0.108**|
||(7.154)<br>(9.715)|(0.046)|
|Constant|-83.487***<br>-15.03|0.914***|
||(28.947)<br>(39.426)|(0.197)|
|Observations|4,439<br>4,616|2,370|
|R-squared|0.094<br>0.147|0.07|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

With respect to twelfth grade achievement tests there are no significant effects on having a higher classification on achievement in twelfth grade with respect to the classification in sixth grade. Potential selection issues in enrollment of course become worse at the high school level than at the secondary level and so this may explain in part a lack of significant results.

33
increasing the family’s lifetime resources and the expected returns to such investments. In contrast, measures that promote education directly, for example, school-attendance – subsidies, have no demographic effects in our model an assumption that demands great care when making comparisons of policies across these two domains. Improvements in the general conduct of economic policy manifest themselves in increases in _α_ and thence in families’ full income. They have no effect on the long-run rate of growth in this framework, and are not considered here.

In what follows, the model is not ‘closed’ where public finance is concerned; for the budgetary outlays entailed by the interventions analysed below are financed not by domestic taxes, but rather by outright grants from abroad. Kenya has been quite – heavily dependent on budgetary support of this kind in the past though it should be added that its present prospects are clouded by the fact that corruption continues unabated under the latest government. Be that as it may, we adopt this simplifying assumption. Closing the model by introducing the government’s budget constraint and associated tax instruments would involve a substantial increase in complexity.<sup>5</sup>

# **6.1 Public spending and mortality**

Interventions aimed at altering the course of mortality in the presence of the epidemic produce variants of the benchmark _D_ = 1. In order to formulate them precisely, it is necessary to establish the relationship between the level of spending on measures to contain the epidemic and treat those infected and the profile of age-specific mortality. For this purpose, we draw heavily on both the procedure and estimates in Bell, Devarajan and Gersbach (2006). In essence, this involves choosing a functional form for the relationship between the probability of premature death among adults and the level of aggregate expenditures on combating the disease. Bell, Devarajan and Gersbach argue

> 5Some idea of what is involved can be obtained from the corresponding sections of Bell, Devarajan and Gersbach (2006), whose two-generation OLG model for South Africa has a much simpler structure.

36
we can calculate a TE. That value is: 915%, based on the estimate for Cambodia.

Note that in equation 37 the price without restrictions is in the denominator, so the TE can exceed 100 percent.

So to calculate the TE, we use equations (37a) and (37b). Start with (35a) and (35b),we need data on Q (which varies by country), the estimated value of b from Warren (5.26 for fixed line and 3.05 for mobile) and our value of TRI, which is estimated in Section 2.3. This gives us QI. Note that following Idrisov (2010), we used the number of quantity of fixed/mobile lines per 100 inhabitants in each country as a proxy for Q. We took this data from an <mark>International telecommunication Union</mark> website at: <u><mark>http://www.itu.int/ITU-D/ict/statistics/ for the year 2010.</mark></u> Then, with QI and the elasticity of substitution value -1.2 (from Dee and others), we get the value of PI from (36a) and (36b). Then with PI, we get the TE% in (37a) and (37b).

To illustrate an example, consider the detailed calculation of AVE for fixed line and mobile line services sectors in Albania.

71
Croke et al. (2016) TMSDG (p.11).<sup>19</sup> Only trials for which a proper intention-to-treat estimate can be obtained are included. Therefore, we require that the study (or trial authors, through personal communication) report outcomes for the population assigned to treatment and comparison groups, independent of whether they received treatment or not.

When estimating the mean effect of MDA on weight, we report results both in the set of trials that take place in settings where the WHO recommends deworming (i.e., those where prevalence of either hookworm, whipworm, or roundworm is over 20%, which is the threshold for annual MDA, or 50%, which is the threshold for multiple annual MDA), and in the full sample (for completeness and comparability to TMSDG).

# **3.2 Search procedure**

We start with the sample of studies included in TMSDG, as a well-known and oft-cited systematic review, for their analysis of the impact of multiple-dose deworming treatment of “all children living in an endemic area” (i.e. mass drug administration, or MDA) at longest follow-up on child weight. We supplement this sample with additional studies we could identify that meet the trial inclusion criteria above. The Campbell Collaboration deworming team generously shared information on additional trials which they identified for use in their own forthcoming systematic review on the impacts of mass deworming. All studies we include were identified by the Campbell Collaboration and any study that they

> hookworm; hookworm infection is the third leading contributor to the global burden of anemia, whereas neither of the other two STH species rank among the top causes of anemia (Kassebaum et al., 2013; Smith and Brooker, 2010). Of the 7 trials in the TMSDG meta-analysis of hemoglobin (which produce 9 treatment estimates, since two are factorial), only one appears to have any significant hookworm prevalence (Dossa and Ategbo, 2001); the other six either do not report hookworm prevalence or report very low values, between 1% — 11%.

> 19 The other two are hemoglobin and cognition.

16
**Moldova Lei-R.Ruble exchange rate**

<!-- Start of picture text --> l _ ___ _ - 160<br>09 _ISO__ 150<br>0 8 _ \ \__-140<br>0713<br>0.6 120_ )<br>0 5 _ __1 =0_ l<br>0 4 100<br>0.3 l l l l l l l l l90<br>Source Moldova Econonnc Trends (2001)<br><!-- End of picture text --> IFigure 2 Event though the nominal and real Lei/Ruble rates were slowly falling since 1996, during the third and fourth quarter of 1998 both fell more significantly. The nominal rate (continuous line) fell 0.78 Lei per Ruble in the preceding months to 0.40 during the fourth quarter of 1998 and even to 0.37 in the first quarter of 1999 (almost a 50 percent reduction). It would later reach an equilibrium level at 0.44 afterwards representing a 44 percent decrease. The real rate (dashed line) followed the same pattern, although less pronounced because of the lagged inflationary effect on non-food items and services. It decreased more than 21 percent when comparing the fourth quarter with the second quarter of 1998. In the short run Moldova experience also the negative income effect in Russia (recession) that outweighed the competitiveness gains.

A more important implication was the surge in the Lei/Dollar exchange rate. Even though the Lei lost value against the US dollar during most of the decade, this depreciation was very gradual: during 1997 it only increased 0.7 percent. Between August and November 1998, however, the Lei/USD exchange rate increased by 60 percent, and continued increasing during the fourth quarter (almost 80 percent compared to August 1998). The inflationary consequences of the Lei/Dollar increase were reflected in the subsequent months as seen in Figure 3.

In summary, even though the economy of Moldova was still suffering the effects of the recession during the late nineties, the Russian financial crisis negatively affected the economy through price increases due to the Lei/Dollar devaluation, temporary loss of Russian external markets due to recession, and other intermediate effects (such as decreased remittances from Russia).

5
# **The Interplay of Policy, Institutions, and Culture During COVID-19**<sup>**1**</sup>

Sheng Fang Sun Yat-sen University Mike W. Peng University of Texas at Dallas L. Colin Xu World Bank Yuanyuan Yi Peking University Originally published in the <u>Policy Research Working Paper Series on</u> _November 2020._ This version is updated on _March 2021._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** coronavirus, pandemic spread, mortality, lockdown, institutions, culture.

**JEL classification:** H0, I00, Q50, Z1.

> 1 <mark>We thank Bob Cull and Sebastian Galiani for comments. Fang, Xu and Yi have not received funding for this study. Peng thanks the Jindal Chair at UT Dallas. The views are the authors’ own and do not implicate the World Bank or its member countries. Sheng Fang: Sun Yat-sen University, 2 Daxue Road, Zhuhai City, Guangdong, China 19082; fangs13@126.com.</mark> Mike W. Peng: Jindal School of Management, University of Texas at Dallas, 800 W. Campbell, SM 43, Richardson, TX 75080; mikepeng@utdallas.edu. <mark>L. Colin Xu: The World Bank, 1818 H Street, N.W., Washington, DC 20433; lxu1@worldbank.org.</mark> Yuanyuan Yi: National School of Development, Peking University, Beijing, China 100871; <u>yuanyuan9yi@gmail.com.</u>
10 One way to gauge the impact of the credit tax is to look at the growth of the sum of onbalance sheet bank lending, leases granted, and foreign borrowing by Croatian agents. Figure 1 plots the growth of this broader aggregate as well as the growth of on-balance sheet bank lending. We see that the broader aggregate appears more stable and, both in 2003 and during the 2007– 2009 period, shows less of an impact than the conventional bank loan aggregate.

**Figure 3. Growth of debt of households and enterprises, 2001–2010**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>2001. 2002. 2003. 2004. 2005. 2006. 2007. 2008. 2009. IX.<br>2010.<br>Debt to banks Debt to NBFIs<br>Foreign debt Total debt<br>% change, y-y<br><!-- End of picture text --> Nonetheless, one can see an impact of the imposition of the credit growth reserve in 2003 and in 2007, in that the broad aggregate does slow somewhat. Not all banks were able to use the evasion techniques effectively, and there very likely were customers who were unable to get loans because of the tax.

These desired effects of the reserve should be set against its costs, which included decreased transparency as banks scrambled to satisfy customers through more opaque channels, and distortion of competition as smaller banks were discouraged from growing rapidly to increase their market share. Smaller banks were also less likely to be able to use the evasion techniques described above.

Furthermore, it could be argued that the rapid growth of small banks intending to increase market share would likely have led to deteriorating credit quality. If so, the restrictions would have served a prudential purpose to the extent they were effective in limiting credit growth.

Because of concerns about the impact of the credit growth reserve on small banks, there was discussion of limiting the measure to large banks, or of creating a dual system of lower percentage limits for large banks than for small banks. However, it was feared that such an overtly differentiated approach would have been subject to legal challenge as discriminatory.
<!-- Start of picture text --> Figure 1<br>Market Shares Premium Gasoline (Country level)<br>60<br>50 _<br>40<br>%30-<br>20 -<br>10 -- - - -- -<br>0 -<br>1995 1996 1997 1998 1999 2000 2001 2002<br>I-EG3(PETROBRAS)+--ESSO -- REPSOLYPF -v-SHELL -*-OTHERS<br>Source: Secretariat of Energy, Argentina<br><!-- End of picture text --> TABLE 1: Evolution of retail outlets

||1990|%Total|1994|%Total|1998|%Total|2001|%Total|
|---|---|---|---|---|---|---|---|---|
|RepsolYpf|2909|54.78|2753|51.70|2537|40.49|1955|31.08|
|Esso|947|17.83|1003|18.84|976|15.58|1145|18.20|
|Shell|952|17.93|933|17.52|1068|17.04|849|13.50|
|Eg3|502|9.45|565|10.61|622|9.93|668|10.62|
|Sol|0|0|23|0.43|146|2.33|254|4.04|
|Rhasa|0|0|N.D|N.D|54|0.86|146|2.32|
|Refinor|0|0|N.D|N.D|42|0.67|57|0.91|
|SanLorenzo|0|0|N.D|N.D|46|0.73|62|0.99|
|Unbranded|0|0|48|0.90|775|12.37|1155|18.36|
|TOTAL|5310||5325||6266||6291||

Source: Secretariat of Energy, Argentina ND: No data available 7
Table 7: Temporary Trade Barriers and Value Added Content

|Panel A: Homogeneous Coefci|ents||
|---|---|---|
||(1)|(2)|
|Log DVA-Ratio: ln(_DV A_<sup>_j_</sup><br>_xi,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup>|-0.40***<br>|-0.19***<br>|
||(0.079)|(0.065)|
|Log FVA-Ratio: ln(_FV A_<sup>_i_</sup><br>_x,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup>|-5.96***<br>(1.29)||
|Log Inv. IP-Ratio: ln(_FG_<sup>_i_</sup><br>_x,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup>|6.29***<br>(1.31)||
|Log IP-Ratio + Log FVA-Ratio (_γ_<sup>_IP_ </sup>+_γ_<sup>_FV A_</sup>)||0.17***<br>(0.063)|
|Reciprocal Trade Agreement: _RTAijt_|0.12<br>(0.13)|-0.056<br>(0.080)|
|Observations|5,912|5,912|
|R-Squared|0.371|0.761|
|Panel B: Heterogeneous Coefcients for Chin|a as an Ex|porter|
||(3)|(4)|
|ln(_DV A_<sup>_j_</sup><br>_xi,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup><sup>_×_ exporter = China</sup>|-1.27***<br>(0.41)|-0.62*<br>(0.33)|
|ln(_DV A_<sup>_j_</sup><br>_xi,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup><sup>_×_ exporter = China</sup>|-0.27***<br>(0.073)|-0.16**<br>(0.062)|
|ln(_FV A_<sup>_i_</sup><br>_x,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup><sup>_×_ exporter = China</sup>|-5.16***<br>(1.37)||
|ln(_FV A_<sup>_i_</sup><br>_x,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup><sup>_×_ exporter = China</sup>|-6.03***<br>(1.30)||
|Log Inv. IP-Ratio: ln(_FG_<sup>_i_</sup><br>_x,t−_5<sup>_/IM i_</sup><br>_xj,t−_5<sup>)</sup>|6.24***<br>(1.31)||
|Log IP-Ratio + Log FVA-Ratio (_γ_<sup>_IP_ </sup>+_γ_<sup>_FV A_</sup>)||0.14**<br>(0.057)|
|Reciprocal Trade Agreement: _RTAijt_|0.070<br>(0.14)|-0.053<br>(0.079)|
|Observations|5,912|5,912|
|R-Squared|0.376|0.762|
|Column Fixed Efects (both panels)|||
|Importer-Year|Y|N|
|Industry-Year|Y|N|
|Importer-Industry|Y|N|
|Importer-Industry-Year|N|Y|
|Exporter-Industry-Year|Y|Y|

Note: Dependent variable in all columns is the temporary trade barrier coverage ratio for importer _i_ against partner _j_ for final goods imports in industry _x_ : _TTBxjt_<sup>_i_.LogDVA-Ratio,FVA-Ratio,andInv.IP-Ratios</sup> are lagged, one period back (five years), to reflect information available when TTBs were adopted. In Panel B, DVA and FVA are interacted with indicators for whether China is the exporting country. Standard errors * ** *** (in parentheses) are clustered by importer-exporter-industry. Significance levels: _p < ._ 1 , _p < ._ 05, _p < ._ 01.

43
_17_ _Price Support at Any Price?_ The FAST PATH/OUTPUT REDUCTION experiment leads to much lower costs (within the GATT commitments), as the production surpluses remain low and high prices are applied only to limited production quotas. In the LAST MOMENT INTEGRATION experiment, policy costs do not appear before 2005. However, as the domestic demand is relatively high, and the price level lower than in the other experiments, policy costs are also much smaller.

Figure 6 presents a comparison of policy costs in the three experiments. The FAST PATH experiment appears infeasible, as costs are exploding. Therefore, only the FAST PATH/OUTPUT REDUCTION experiment may be treated as a feasible scenario for a fast path of price level adjustment (the cost stabilizes on an acceptable level). The last experiment does not cause, by definition, any costs <!-- Start of picture text --> Targeting EU prices<br>Policy cost ($ billions)<br>wwn VyWwru<br>7 Ofld.aodhiec ¶sWflIoPns iiw(901p 1 s<br>6<br>5 /<br>4 Fast patl/output reduct.<br>3 Last moment integration<br>2 -<br>1 .<br>1 190 191 20106~i-'<br>I_I<br>Figure 6<br><!-- End of picture text --> before Poland's accession to the EU (by that time, a big part of costs is likely to be covered by the common CAP budget). The costs in 2010 are lower than in the other scenarios, mainly because of a lower level of EU prices. However, a part of cost reduction comes also from a slightly higher level of the personal income, that leads to bigger domestic consumption of food and reduces production surpluses.

The next figure shows a comparison of the policy results with respect to the income relation (one of the primary policy targets). The best improvement is obtained in the FAST PATH experiment (the income relation 50% above the base run level). However, such a result requires unbearable costs. A feasible variant (FAST PATH/OUTPUT REDUCTION) stabilizes the income relation on a level 20% above the base run. Exactly the same result is obtained by the year 2010 in the LAST MOMENT INTEGRATION experiment.

<!-- Start of picture text --> Targeting EU prioes<br>Income relation<br>70 VW se V bw %<br>70<br>60<br>Fast path<br>F a te<br>30Fr<br>20 Last moment Integration<br>10<br>0 ,<br>-10 20<br>Figure 7<br><!-- End of picture text -->