rafmacalaba/datause-encoder-data huggingface dataset:
Figure 12. The Share in Global Death Toll: Changes over the Last Three Weeks <!-- Start of picture text --> 80 86.0<br>78.9<br>April 30<br>May 23<br>60<br>40<br>21.1<br>20 17.8<br>14.0<br>11.9<br>1.9 3.1 0.1 0.2<br>0<br>High Upper−middle Lower−middle Low Entire<br>income income income income developing<br>countries countries countries countries world<br>Share in global death toll (percent)<br><!-- End of picture text --> Source: Johns Hopkins University Figure 13. Change in Cumulative Severity: Share of Countries where Severity Is Weakening versus Intensifying over Last Three Weeks <!-- Start of picture text --> Weakening severity Intensifying severity<br>100%<br>17%<br>75% 40% 38%<br>52%<br>73%<br>50%<br>83%<br>25% 60% 62%<br>48%<br>27%<br>0%<br>High Upper−middle Lower−middle Low Entire<br>income income income income developing<br>countries countries countries countries world<br>Share of countries according to changing severity<br><!-- End of picture text --> Source: Johns Hopkins University.

Note: Comparison is between April 30 and May 23. If the cumulative severity ratio has risen over that period, pandemic is described as intensifying (and vice versa). Sample of 145 countries. This excludes 15 countries which reported 0 fatalities throughout the two periods used to cumulate severity; for these countries the severity ratio thus remained unchanged at 0.

23
playing an important role in the decline of human resources, such as budgetary constraints, migration to richer countries or more frequent car accidents. We therefore proceed with the presentation of the study findings, recognizing that we are not in a position to strictly attribute all the negative effects of institutional capacity erosion to the pandemic.

# <u>6.2. Evidence of declining organizational capacity</u>

# _6.2.1. Staff Attrition_

There has been an increasing trend of attrition in the six organizations between 1995 and 2005. In the four organizations where quantitative data was available, the proportion of staff leaving the organization annually has ranged between 10% and 17.9% over the study period. The highest attrition rate was reported in NASFAM with 26.9%, followed by the Department of Poverty and Disaster Management (20.8%), Ministry of Irrigation and Water Development (18.7%) and Department of Meteorological Services (8.1%). When examining the data, an interesting pattern emerges regarding the fraction of staff attrition explained by mortality: It is much higher in public sector organizations than in NGOs, where resignation followed by redundancy were the main causes of attrition. One explanation given is that the public sector organizations do not have enforceable exit policies when an employee is chronically sick.

**Table 2:** Observed and expected deaths (based on Malawi’s average death rate) deaths among M.Agr. personnel, 1995-2005

|**Staff Category/Cadre**|**Expected Deaths**|**Observed Deaths**|
|---|---|---|
|Agriculture Officers|19.3|39|
|Research Scientist|8.8|21|
|DAHI Professionals|4.6|21|
|Technical Officers|41.9|68|
|Technical Assistants|474.6|707|
|Others|258|443|

All target institutions reported high staff mortality. Assuming that the conclusion of the UNAIDS (2001) report quoted at the beginning of section 2 is valid for the Malawi context (which should be the case, 19
The flipside of such high and prolonged current-account pattern was the resistance to exchange rate appreciation by surplus countries, compounded by the fact that many surplus countries also became poles attracting foreign capital. Some countries resorted to stringent capital controls and other barriers to capital entry, whereas most piled up huge foreign reserves. These were in turn put back as liquid assets acquired from deficit countries, which became one of the factors sustaining persistent current-account imbalances.

The global financial crisis has essentially been the unfolding of the discovery of the unsoundness of balance sheets once the widespread asset price overvaluation came to a halt (as we saw in item 1). Debtdeleveraging dynamics and macroeconomic slowdown in deficit countries explain the shrinkage of imbalances in the wake of the crisis (Figure 3). It is still to be seen whether surplus countries will increase their domestic absorption with intensity and speed enough to compensate for the retrenchment of absorption in heretofore deficit countries, and thus settle the forecast of unwinding global imbalances on a global growth path higher than the current one.

One may guess that “fear of floating (upward)” tends to rise in the next few years, one environment much less benign than that prevailing before the crisis. Some exchange-rate “floaters” will become more like “fixers”, which will affect not only monetary policy in those countries, but also the dynamics of crossborder movement of liquidity and asset trade.<sup>**15**</sup> For instance, in combination with the unconventional monetary policies pursued in several large advanced economies, more frequent tinkering with exchange rates will set the stage for potential “currency wars”.

# _3.4 Waning Rule-Based Policy Making and Political Economy Pressures on Central Banks_

As we have argued above, the acknowledgement that asset price cycles and cross-border spillovers lead to weakening of the belief that monetary policy making and prudential regulation could eventually become entirely based on rules. Without denying the benefits accrued by rules and clear communication, we remarked the inevitability of some discretionary policy choices even under normal conditions.

We have added additional reasons why discretion and off-the-rule central banking decisions in emerging countries may become more frequent. In this case, gains derived from central bank credibility will inevitably risk erosion, which will substantially increase the requirement in terms of communication and justification of measures taken. In addition to the analytical and implementation challenges not fully realized by the “flexible-inflation-targeting- _cum-_ isolated-prudential-regulation” framework, discretionary policy decisions may open a venue for political economy pressures against central bank autonomy.

> **15** “(...) there is an element of externality in capital controls in that one country’s success in evading capital inflows only increases the difficulty of other countries doing the same. This is certainly a problem at the level of emerging markets as a group.” (CIEPR, 2011, p.27).

25
|<br>**Table 28. High costs and low income groups ac**|<br>**cess to the banking**|<br>**system**|**Children's**|
|---|---|---|---|
|**Cheking Account**<br>**with interest**|**Cheking account**<br>**without interest**|**Savings**<br>**Accounts**|<br>**Savings**<br>**Accounts**|
|**Stage 1. Minimum Deposit is matched by incom**<br>|**e: yes or no**|||
|**Minimum deposit required**<br>**$8,704.6**|**$3,076.9**|**$800.0**|**$387.6**|
|**Income percentile 25 th:**<br>**no**<br>**$ 1, 547**|**no**|**yes**|**yes**|
|**Income percentile 50 th:**<br>**no**|**no**|**yes**|**yes**|
|**$ 2, 380**||||
|**Income percentile 75 th:**||||
|**$ 3, 703**<br>**no**|**yes**|**yes**|**yes**|
|**Income percentile 100 th:**||||
|**$ 11, 619**<br>**yes**|**yes**|**yes**|**yes**|
|**Stage 2. Minimum balance is matched by incom**|**e: yes or no**|||
|**Minimum balance required**<br>**$7,391.3**|**$2,666.7**|**$900.0**|**$287.5**|
|**Income percentile 25 th:**||||
|**$ 1, 547**<br>**no**|**no**|**yes**|**yes**|
|**Income percentile 50 th:**||||
|**$ 2, 380**<br>**no**|**yes**|**yes**|**yes**|
|**Income percentile 75 th:**||||
|**$ 3, 703**<br>**no**<br>**Income percentile 100 th:**|**yes**|**yes**|**yes**|
|**$ 11, 619**<br>**yes**|**yes**|**yes**|**yes**|
|**Stage 3. Montly charges should not exced 0.01**<br>|**% of monthly incom**<br>|**e: yes or no**<br>||
|**Maintenance fee**<br>**$18.6**|**$16.6**|**$6.0**|**$2.6**|
|**Income percentile 25 th:**||||
|**$ 1, 547**<br>**no**|**yes**|**yes**|**yes**|
|**Income percentile 50 th:**||||
|**$ 2, 380**<br>**yes**<br>**Income percentile 75 th:**|**yes**|**yes**|**yes**|
|**$ 3, 703**<br>**yes**|**yes**|**yes**|**yes**|
|**Income percentile 100 th:**||||
|**$ 11, 619**<br>**yes**|**yes**|**yes**|**yes**|
|**Source: Own estimatios with datum of Bansefi.**||||

The Mexican government recognizes that a lack of competition among banks likely contributes to high bank fees and the high percentages of people outside of the banking system. It has embarked on a three-pronged policy to address the problem. First, it has recently begun to apply verbal pressure to encourage large banks to make basic deposit accounts financially accessible to moderate-income households ( _New York Times_ , April 7, 2004, p. W1). Second, the government has allowed a new bank ( _Banco Azteca_ ) that targets middle- and moderate-income households to open. Its success could demonstrate to other banks or investors that this can be a profitable market. Third, under BANSEFI, the government is working to expand the popular banking sector and to improve its financial stability.

In our view, the government’s priorities are correct. Considering the large market power of a small number of big banks, pressure should be applied to these banks to do more to serve 56
as offering circulars, prospectus, surveys of investment banks, brokers, and other financial advisors, news sources, trade publications, and wires. While data for issues in the U.S. start in the 1970s, coverage of other markets starts later, with most regional databases starting in 1991.<sup>11</sup> Therefore, we restrict our sample to the period 1991-2008.

In additional, unreported analyses, we considered several subsets of these data.<sup>12</sup> First, we were concerned that including data for the onset of the recent global financial crisis might affect the results. Consequently, we re-did all the analyses reported throughout the paper using data for only the period 1991-2006 and obtained similar conclusions. Second, our sample includes bond issues by both financial and non-financial firms. We include all firms in our analyses because we want to provide a comprehensive view of bond markets around the world. Although a priori financial and non-financial firms might differ in their use of domestic and international bond markets, we obtained results similar to those reported throughout the paper when restricting the sample to non-financial firms. Third, there are some firms that are very active in debt markets, conducting many issues and capturing a significant fraction of the overall debt issuance activity. Therefore, as an additional robustness check, we re-estimated all our regressions excluding the top five percent of the firms in terms of the number of debt issues and obtained similar results.

> 11 The SDC database is divided into twelve regional sub-databases covering different markets: Asian Pacific Domestic (Hong Kong SAR, China; Indonesia; Malaysia; Philippines; Singapore; Taiwan, China; and Thailand); Australian/New Zealand Domestic (Australia, New Zealand, and Papua New Guinea); Canadian Domestic (Canada); Continental European Domestic (Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia, Spain, Sweden, and Switzerland); Indian and Subcontinent (Bangladesh, India, Pakistan, and Sri Lanka); International (Eurobonds and other cross-border issues); Japanese Domestic (Japan); Korean Domestic (South Korea); Latin American Domestic (Argentina, Bolivia, Brazil, Colombia, Costa Rica, Ecuador, Guatemala, Mexico, Panama, Peru, Uruguay, and Venezuela); United States (United States); United Kingdom Domestic (United Kingdom); and Rest of the World (countries not included in other SDC regional sub-databases, such as China). The academic version of SDC to which we have access does not include the Canadian and Korean Domestic sub-databases. Therefore, we exclude all Canadian and South Korean firms from our analysis.

> 12 The results are available upon request.

10
marginal costs, as expected. However, in most industry/country panels we studied, past international activities do not help much to predict current performance, once past realizations on quality and marginal cost are controlled for. That is, activities do not typically Granger cause performance. Interestingly, in the minority of cases where significant associations emerge, international activities appear to move costs and product quality in the same direction. Thus, the net effect on profits in these cases is not immediately apparent.

Concerning the determinants of international activities, several basic patterns emerge. Most fundamentally, activities are highly persistent, _even_ after one controls for unobserved heterogeneity. This suggests that firms incur sunk threshold costs when they initiate or cease activities, so temporary policy or macro shocks may have long run effects on the patterns of activities observed in a particular country or industry.

Second activities tend to go together. Thus, studies that relate firms' performance to one international activity and ignore the others may generate very misleading conclusions. On the other hand, the bundling of activities together seems to mainly reflect unobserved plant characteristics, like managerial philosophy, contacts, product niche and location. Once these are controlled for, there is little evidence that engaging in one international activity increases the probability that others will occur in the future.

Finally, we find weak evidence from Colombia that firms that are already strong performers seek out foreign markets as a way to enhance their profits. But these results do not generalize to Morocco, where plastics firms that have been performing poorly are relatively likely to become exporters and receive foreign direct investment.

33
- 40 -

# 1985-88 Average Yields: (MT/IIA, MT/COW)

|**Country**|**Barley**|**Milk**|Rye|**Sugar**Beet|Wheat|
|---|---|---|---|---|---|
|Bulgaria|3.544|3.386|10.473|17.636|3.638|
|Czechoslovakia|4.336|3.843|18.942|35.854|4.936|
|GDR|4.700|4.312|25.241|31.196|5.282|
|Hungary|3.866|4.803|18.221|37.435|4.765|
|Poland|3.252|3.098|18.555|33.632|3.584|
|Romania*|n.a.|n.a.|n.a.|n.a.|n.a.|
|Soviet Union|1.700|2.395|11.799|25.029|1.747|
|Yugoslavia|2.611|1.750|8.294|38.813|3.726|
|Austria|4.289|3.804|27.954|54.471|4.780|
|Canada|2.722|5.444|25.8u3|39.027|1.782|
|Denmark|4.911|4.793|35.339|50.824|6.293|
|France|5.104|3.603|34.187|61.123|5.790|
|Greece|2.296|1.890|17.465|61.320|2.387|
|USA|2.596|6.159|33.020|46.539|2.415|

Source: **FAO Production Yearbook, 1989.**

* **Revised Romanian data for 198' -88 are not yet available.**
follow-up surveys, which captures inflow into apprenticeships. We can also analyze impacts on participation in apprenticeship at the time of the follow-up survey. In Table 1, the right part of the lower panel presents ITT estimates of impacts on youth participation in apprenticeship at the time of the follow-up survey (i.e. approximately 20 months after the start of the program). The data is obtained from a survey module on youth occupations, which includes apprenticeship as an option. The information covers all types of apprenticeships, including traditional and formal apprenticeships. The left part of the lower panel of Table 1 presents ITT estimates for the share of youth who started apprenticeship but dropped-out by the time of the follow-up survey. Those estimates (labeled as _d_ in the table) are simply obtained as the difference between estimates of entry in apprenticeship since the start of the experiment (labeled as _e_ ) and estimates of youth still in apprenticeship at the moment of the survey (labeled as _c_ ).

Drop-out is a common issue in many employment or training programs. It has been shown to be important in apprenticeship as well (e.g. Cho et al. (2013)). We can compute drop-out rates from Table 1. Drop-out is estimated as 0.222/0.712=31.2% in formal apprenticeships and 0.060/0.185=32.5% in traditional apprenticeships.<sup>25</sup> These results show that drop-out is important, but also that it is not an issue specific to formal apprenticeships as the drop-out rate in traditional apprenticeship is close.<sup>26</sup> Although it is reassuring that drop-out is not higher in formal apprenticeship, these results show that the program did not reduce the prevalence of drop-out compared to traditional apprenticeships.

Results on participation in apprenticeship at the time of the follow-up survey confirm previous findings on increased participation but also significant windfall effects. The share of youth in formal apprenticeship increases by 49 percentage points. However, the share of youth in any type of apprenticeship increases by only 36.5 percentage points. The difference between the two estimates, 12.5 percentage points, provides another estimate of the windfall effect, namely the share of youth who substituted out of traditional apprenticeships to be in formal apprenticeships at the time of the follow-up survey. Expressed relatively to the share of formal apprentices placed, the windfall effect is again 26 percent (12.5/49), which is the

> 25We could also obtain these estimates by running a regression of dropout status on entry into formal or traditional apprenticeship using the treatment assignment variable as an instrument.

> 26These numbers overestimate drop-out in the treatment group because a small share of formal apprenticeships only lasted 12 months and these contracts were over at the moment of the follow-up survey. See footnote 17.

18
_Index_ vi

|_WPS #_|<br>_Author_|_Working Paper Title_|_Page_|
|---|---|---|---|
|162|Miguel A. Kiguel|Inflation in Argentina: Stop and Go Since the<br>Austral Plan|56|
|185|Stephen A. O'Connell|Uniform Irade Taxes, Devaluation, and the Real<br>Exchange Rate: A Theoretical Analysis|65|
|188|Luis A. Riveros|Intemational Differences in Wage and Nonwage<br>Labor Costs|66|
|**Popula**|**tion, Health, and Nutrition**|||
|11|Norman Gratz and Bernhard Liese|The Selection and Use of Pesticides in Bank Financed<br>Public Health Projects|7|
|13|Trudy Harpham and Ian Timaeus|Objectives and Methods of a World Health Survey|7|
|23|A. Mead Over, Jr.|Cost-Effective Integration of Immunization and<br>Basic Health Services in Developing Countries:<br>The Problem of Joint Costs|11|
|65|Howard Barnum|Interaction of Infant Mortality and Fertility and<br>the Effectiveness of Health and Family Planning Programs|22|
|81|Richard Heaver|Reaching People at the Periphery: Can the World Bank's<br>Population, Health, and Nutrition Operations Do Better?|28|
|82|Dov Chernichovsky and Linda Zangwill|Microeconomic Theory of the Household and<br>Nutrition Programs|28|
|88|John A. Ross and Stephen L. Isaacs|Costs, Payments, and Incentives in Family<br>Planning Programs|30|
|93|Susan H. Cochrane|The Effects of Education, Health, and Social Security<br>on Fertility in Developing Countries|31|
|94|George Simmons and Rushikesh Maru|The World Bank's Population Lending and Sector Review|32|
|96|Maureen A. Lewis and Genevieve Kenney|The Private Sector and Family Planning in<br>Developing Countries|32|
|114|My T. Vu, Eduard Bos,<br>and Rodolfo A. Bulatao|Africa Region Population Projections: 1988-89 Edition|41|
|115|My T. Vu, Eduard Bos,<br>and Rodolfo A. Bulatao|Asia Region Population Projections: 1988-89 Edition|41|
|116|My T. Vu, Eduard Bos,<br>and Rodolfo A. Bulatao|Latin America and the Caribbean Region Population<br>Projections: 1988-89 Edition|41|
|117|My T. Vu, Eduard Bos,<br>and Rodolfo A. Bulatao|Europe, Middle East, and North Africa Region<br>Population Projections: 1988-89 Edition|41|
|155|Ian Timaeus and Wendy Graham|Measuring Adult Mortality in Developing Countries:<br>A Review and Assessment of Methods|54|
||(6)<br>Non-MNF|0.155<br>(0.236)<br>-1.761***<br>(0.357)<br>0.851***<br>(0.160)<br>1.232***<br>(0.113)<br>-0.017<br>(0.012)<br>Yes<br>Yes<br>Yes<br>8,830<br>0.622<br>he dependent variable<br>ariables defnitions are|
|---|---|---|
||Log export sales<br>(5)<br>MNF|-0.694**<br>(0.321)<br>-2.607***<br>(0.569)<br>1.070***<br>(0.185)<br>1.592***<br>(0.138)<br>-0.016<br>(0.018)<br>Yes<br>Yes<br>Yes<br>4,394<br>0.566<br>Philippines and Thailand. T<br>ns. Country level control v|
|countries|(4)<br>Full sample|-0.228<br>(0.191)<br>-1.767***<br>(0.304)<br>0.943***<br>(0.123)<br>1.415***<br>(0.088)<br>-0.018*<br>(0.010)<br>Yes<br>Yes<br>Yes<br>13,224<br>0.628<br>, Indonesia, Pakistan,<br>ded in all specifcatio|
|ng to LDC Asian|ducts<br>(3)<br>Non-MNF|-0.048<br>(0.163)<br>-0.762***<br>(0.293)<br>0.550***<br>(0.127)<br>0.927***<br>(0.093)<br>-0.059***<br>(0.010)<br>Yes<br>Yes<br>Yes<br>8,830<br>0.587<br>sian countries: China, India<br>ear and a constant are inclu|
|frms exporti|exported pro<br>(2)<br>MNF|-0.340<br>(0.239)<br>-1.372**<br>(0.543)<br>0.364***<br>(0.130)<br>0.920***<br>(0.098)<br>-0.072***<br>(0.014)<br>No<br>Yes<br>Yes<br>4,394<br>0.568<br>of least developed A<br>frm, country and y|
|ubsample of French|Log N_◦_<br>(1)<br>Full sample|-0.216<br>(0.134)<br>-0.782***<br>(0.262)<br>0.459***<br>(0.092)<br>0.945***<br>(0.067)<br>-0.065***<br>(0.008)<br>No<br>Yes<br>Yes<br>13,224<br>0.590<br>1999-2005 for the subsample<br>_f_ in year_t_. Fixed effects by<br>percent levels respectively.|
|Table 8: S|Dependent variable|Tariff_fj,t−_1<br>Tariff_fj,t−_1_×_China_j_<br>Extensive margin competition_fjt_(N_◦_countries)<br>Extensive margin competition_fjt_(N_◦_frms)<br>Intensive margin competition_fjt_<br>Firm fxed effects<br>Country fxed effects<br>Year fxed effects<br>Observations<br>_R_2<br>_Notes_: The regressions are OLS estimations of equation (3) for the period<br>is the logarithm of the number of exported products to country_j_ by frm <br>reported in table 4. _∗∗∗, ∗∗,_and_∗_indicate signifcance at the 1, 5 and 10|

31