rafmacalaba/datause-encoder-data huggingface dataset:
Mortality cost is estimated based on the number of HAIs annually multiplied by the case fatality rate of HAIs multiplied by the value of a human life. The value of a human life is based on the value of statistical life from the latest meta-analysis from the USA (US$8 million) converted to SSA using the difference in GDP per capita and an income elasticity of 1.5.

The outputs generated include the annual cost of HAIs, including associated AMR, consisting of the direct healthcare costs, productivity losses, and premature mortality. The annual health care cost is presented as a percent of total health spending, and the annual total cost of HAIs is presented as a percent of GDP. Healthcare and total costs are also estimated per population (in entire country) and per admission.

A one-way sensitivity analysis was conducted by varying input values for each variable shown in Table 2. Where indicated, a lower value was not used because the baseline value is already considered conservative. Justification and references for these values are provided in Annex 1.

**Table 2. Baseline and low/high values for selected variables in sensitivity analysis**

|**Variable**|**Low(conservative)**|**Baseline**|**High **|
|---|---|---|---|
|HAI rate|Half(6.4%)|12.8%|Double(25.6%)|
|AMR rate|Half(25%)|50%|50% higher(75%)|
|Cost per hospital bed<br>day|Primary hospital|Secondary hospital|Tertiary hospital|
|Costs<br>of<br>drugs,<br>supplies,laboratory|-|Mozambique<br>study<br>(district hospital)|Ethiopia study (tertiary<br>hospital)|
|HAI excess length of<br>stay (impacts AMR and<br>productivitycost)|3 days|5 days|10 days|
|Value of productive<br>time|-|Value<br>added<br>per<br>worker/year<br>in<br>agriculture|Value<br>added<br>per<br>worker/year in industry|
|Case fatalityrate|4.0%|6.9%|15.0%|
|Value of life|-|Extrapolated from USA<br>based on difference in<br>GDP<br>using<br>income<br>elasticityof 1.5|Extrapolated from USA<br>based on difference in<br>GDP<br>using<br>income<br>elasticityof 1.2|

Several costs associated with HAIs as identified by Morel et al (2020) are excluded, either because they are unlikely to be saved in the short- to medium-term (e.g. surveillance and containment programs, education programs and public health or information campaigns) or because they are difficult to quantify and few or no estimates exist in the literature (e.g., cost of unoccupied beds due to isolation, funeral costs and psychological impacts). Treatment of HAIs in healthcare workers are excluded due to lack of estimates. Overall, estimated costs are conservative, drawing on average unit costs that would be typical at secondary hospital level and not considering the additional costs of complications, long-term ICU stay, long-term care and rehabilitation, disability benefits, and expensive pharmaceuticals which might be required in some cases. For AMR, the costs of second-line drugs are used.

10
least one country from the set of countries that did not have a crisis. As a further criterion, we use World Bank income categories to ensure comparing countries with similar levels of economic development.

# [TABLE 8: The role of initial banking market conditions]

Each cell in Table 8 represents a single regression, and we only show the interaction term of the intervention variables with the corresponding initial condition. Since these regressions include countryfixed effects the initial condition itself is dropped from the regression.

We first discuss the results for the Lerner index. The increase in competition is significantly greater in magnitude in concentrated markets. Figure 1 reinforces this point. Concentration in Japan is low with a HHI of 0.097. In this market, we observe an increase in the Lerner index relative to the control group. Likewise, the concentration indices for Malaysia (0.101) and Ireland (0.289) are also below the mean, suggesting that the effect blanket guarantees is mitigated there. Regarding the presence of foreign banks, we find a positively significant coefficient, except for blanket guarantees. The negative relation between interventions and competition is mitigated in countries with higher foreign bank penetration because those institutions are not intervened. Figure 1 illustrates this phenomenon. Ireland and Latvia exhibit high foreign bank presence (0.63 and 0.66% of banks are foreign owned; sample mean 0.36%). Upon announcement of liquidity support, Lerner indices increase relative to the control groups. Activity restrictions have a weaker interaction with interventions – they are only significant for recapitalizations and nationalizations. Increases in competition from recapitalizations and nationalizations are reaped in less contestable markets. Reductions in the Lerner index induced by interventions tend to be larger in systems with more entry restrictions, except for liquidity support. Deposit insurance only plays a role for the effects of nationalizations. Explicit deposit insurance mitigates the improvement in competition.

The initial conditions play little role for net interest margins in Panel B, reflecting less cross-country variation. The exception is deposit insurance. The interaction term (except for blanket guarantees) implies that the deposit insurance offsets (at least partly) the former effect.

# **4. Extensions: Transparency, charter values, and the presence of zombie banks**

To further understand mitigating or amplifying factors for the effects of bailouts on competition, we offer several extensions that focus on other characteristics of the banking systems in this section.

# _4.1 Transparency_

The idea that transparency plays a role for the effectiveness of policy responses to crises has been advocated repeatedly (e.g., Hakenes and Schnabel (2010); Gropp, Hakenes, and Schnabel (2011)). The basic premise is that transparency mitigates the effects of bailouts which undermine market discipline. In a transparent system, depositors can easily observe risk. Therefore, they will discipline banks if they 19
- _Labor regulations as a constraint_ .  The responses of firms on a four-point scale (1=“major obstacle” to 4=“no obstacle”) to the question: How problematic are “labor regulations” to the operation and growth of your business? (Question 63).

# _Labor Quality_

The _labor quality indicator_ measures the skill level and educational attainment of workers. Underlying variables are rescaled as explained below so that higher values of the indicator signify higher levels of labor quality. The indicator is based on a PCA of the following three BEEPS variables:

- _Skilled workers/Total employees_ .  The percentage of the firm’s current permanent, full-time workers that are managers, professionals, or skilled production workers (Question 68).

- _Time to fill vacancy_ .  The average number of weeks it took to fill the most recent vacancy for a manager, professional, or skilled production worker (multiplied by −1) (Question 70).

- _Labor quality as a constraint_ .  The responses of firms on a four-point scale (1=“major obstacle” to 4=“no obstacle”) to the question: How problematic are the “skills and education of available workers’” to the operation and growth of your business? (Question 63).

Table 8 presents descriptive statistics of the five synthetic indicators, constructed using PCA on the BEEPS manufacturing dataset for the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. For years 2001 and 2003, means are provided for the whole sample, by country, and also by groups defined by country, sub-national location, and firm size upon which the AMADEUS data is merged. On average, countries from 2001 to 2003 improved in the areas of infrastructure quality, governance, and labor quality, but faced worsening financial development and decreasing labor market flexibility. All countries improved infrastructure quality over this period, but results were mixed across countries in the other four areas. Labor market flexibility worsened in the largest number of countries, only improving in Poland and Romania.

21
2015 etc.). When years of education are not available, a typical estimation strategy has also been to infer that from the number of years required to attain a particular level. (Black and Devereux 2013) But since the LITS surveys do not collect information on years of schooling and converting attainment levels into years of schooling would require us to make very strong assumptions, we take an alternative approach that entails working directly with the joint distribution. Specifically, we convert the cross-tabulation of educational attainments across generations into a transition matrix (such that every entry represents a conditional probability of being in that particular state), and examine the extent to which the transition matrices for each of the countries we analyze deviates from an alternative, hypothetical matrix that would characterize “perfect mobility”. Given any marginal distribution, i.e., say, the distribution of parental education, the transition matrix that characterizes perfect mobility would be one in which for any state of parental educational attainment, the child has equal probability of being in each of the (four in this case) possible states. In other words, irrespective of whether the parent has tertiary education or no education at all, a world of perfect mobility – which is also a world of “origin independence” – is one in which the child can land up in any of the possible states and is certainly not bound in any way to the parental level of attainment.

This idea is encapsulated in a statistic known as the Altham statistic which essentially summarizes the degree of association between the rows and columns of any given matrix, relative to the degree of association between the rows and columns of the matrix of perfect mobility. (See Box 1.) This statistic, originally proposed by Altham (1970) has been used to calculate and report on occupational mobility differences between the US and the UK by Long and Ferrie (2007), occupational mobility in India by Azam (2015), World Bank (2015).

# **Box 1: Altham statistic as “distance” from a perfectly mobile society**

When the transition matrix is a simple, 2x2 matrix such that there are only two educational attainment levels, let’s say high and low, the concept of mobility utilized here essentially reduces to the cross-product ratio of the matrix. This cross-product ratio is the ratio of the odds that children of parents in the low level of education will remain in the low level as opposed to the higher level and the odds that children of parents in high level of education will remain at the high level and not the low level. When the knowledge of the father’s educational attainment level yields no information on the attainment level of the child, this cross-product ratio will be exactly 1. In higher-order matrices, the number of cross-products increases as well. The Altham statistic is simply a way of summarizing all possible crossproduct ratios and it can be understood. Annex 4 has details on the calculation of the Altham statistic.

29
We use panel data from the Ethiopian Rural Household Survey covering four rounds from the period 1994 to 1999 to assess whether transfer rights land tenure insecurity has affected investment in trees in this period. Using household panel data allows us to control for a number of standard problems in analyzing this issue including the endogeneity related to the reverse causality of explaining tree planting to obtain land rights, measurement error in our property rights data, household-level heterogeneity affecting perceptions of security and transferability, as well as allowing us to identify any effects on investment from changes over time rather than cross-sectional variation (only).

In theory, there is a general consensus that making land rights more secure and transferable would promote investment incentives and efficient use of resources. This conventional view has three major justifications. First, it is believed that secure rights provide a guarantee to farmers that the fruits of their investments will not be appropriated by government or other agents. This encourages them to make long-term investments on their land (Atwood, 1990; Feder and Feeny, 1991; Besley, 1995)<sup>1</sup> .

The second effect works through the credit market. As pointed out by Feder and Onchan (1987), security of ownership improves chances of obtaining loans to finance agricultural investments. This is because ownership rights facilitate the development of an efficient land market. This reduces information costs for the lender and provides the basis for using land as a collateral asset. Finally, secure tenure rights would allow a relaxation of the impediments to factor mobility and hence enables the allocation of land from the less to the most productive farmers, including via lease markets. Moreover, it allows farmers to make immobile investments since they will be sure to recuperate the present value of the future income that would be generated by the investment.

Several recent studies, however, argue that causality may also run the other way round: investments on land, particularly planting trees, enhance tenure security (Atwood, 1990; Besley, 1995; Otsuka et al., 1997; Brasselle et al., 2002). There have been several empirical investigations

> 1 Theoretical justifications for this relationship are derived in Besley (1995). A dynamic stochastic programming model, allowing for gestation lags in benefiting from the investment as well accounting for the irreversibility of these investments can be found in Daniel Ayalew (2003). Deininger et al. (2003) helpfully clarify in a simple model

4
error of 3.4) per 1,000 people versus 15.9 (standard error of 0.9) for the non-poor. The birth rate differential is 2.6. However, there is a marked clustering of low birth rates and low poverty rates evident in Figure 3, which on closer inspection turns out to be mainly due to the countries of Eastern Europe and Central Asia (EECA). Excluding EECA the birth rate for the poor is 55.5 (3.1) versus 20.4 (1.0) for the non-poor and the _BRD_ =2.0, which can be considered more representative of low and middle-income developing countries. However, the BR method is clearly giving a higher differential than suggested by the DHS.

Here too the demographic differential implied by the BR method is lower for Africa. Focusing only on the 18 observations for SSA, the birth rate for the poor is 46.9 (standard error of 4.1) versus 31.4 (2.7) for the non-poor, implying _BRD_ =1.2 (the same as from the DHS). As for the death rate differential, the higher poverty rate in SSA is a plausible explanation for the lower birth rate differential, though attenuation bias cannot be ruled out.

# **4. Impacts on poverty measures**

Given the data problems noted in the last section, it would be unwise to insist on a single point estimate of either the _DRD_ or _BRD_ . Values of both the demographic differentials in the range 1.2 to 2 are suggested by the evidence presented above. Since the CDR and CBR are calculated for one year, it is convenient to set τ = 1.<sup>17</sup> The following calculations will focus on 2001, the latest year for the Chen-Ravallion estimates of global poverty, for which the poverty rate in developing countries as a whole is 21.1%.

Starting again with mortality, Table 1 gives the implied impacts ( _H_ − _H_ 1 _D_ ) on the ChenRavallion estimates of the headcount index of poverty for $1 a day over this range of values for

> 17 In principle one can use τ > 1 though this requires a more complicated calculation involving the past time series of death rates and population growth rates at country level; it is easier to use τ = 1 but take proper account of this when interpreting the results.

15
Among those in high-income OECD economies who reported receiving wages, 86 percent said that they received the payments into an account (figure 2.1). About 6 percent reported receiving their wages in cash only. And the other 8 percent reported receiving their wages neither into an account nor in cash, a result driven by the 17 percent of wage recipients in the United States who likely received their wages by check.

In developing economies, by contrast, only 41 percent of wage recipients reported receiving their wage payments into an account. But there is much variation among regions. In Europe and Central Asia and Latin America and the Caribbean around 60 percent of wage earners said that they receive their wage payments into an account, and in East Asia and the Pacific and Sub-Saharan Africa around 45 percent reported doing so. But in the Middle East and South Asia less than 25 percent did.<sup>4</sup> Women in developing economies were about a third less likely than men to report having received any wage payments in the past 12 months (figure 2.2). But among those receiving wages, women were more likely than men to report receiving their wages into an account: 44 percent of female wage earners reported this, compared with 39 percent of male wage earners.<sup>5</sup> Not surprisingly, wage employment in developing economies also varies by income. On average, adults in the poorest 40 percent of households within economies were about a third (or 11 percentage points) less likely than those in the richest 60 percent to report having received any wage payments in the past 12 months (figure 2.3). And among those receiving wages, adults in the poorest 40 percent of households were half as likely as those in the richest 60 percent to report receiving their wages into an account.<sup>6</sup> <!-- Start of picture text --> 2.1<br>Wage earners and how they receive wage payments<br>Adults receiving wages in the past year, by method (as % of all adults), 2014<br>52<br>37<br>35<br>32<br>19<br>17<br>13 Using other method<br>In cash only<br>Into an account<br>East Asia Europe High-income Latin Middle South Sub-Saharan<br>& Pacific & Central OECD America & East Asia Africa<br>Asia economies Caribbean<br>Source: Global Findex database.<br> 2.2<br>Male and female wage earners and how<br>they receive wage payments<br>in developing economies<br>Adults receiving wages in the past year, by method<br>(as % of total), 2014<br>30<br>20<br>In cash only<br>10<br>Into an account<br>0<br>Male Female<br>Source: Global Findex database.<br> 2.3<br>Wage earners by household income<br>and how they receive wage payments<br>in developing economies<br>Adults receiving wages in the past year, by method<br>(as % of total household income group), 2014<br>30<br>20<br>10 In cash only<br>Into an account<br>0<br>Richest 60% Poorest 40%<br>of households of households<br>Note: Data for the poorest 40 percent and richest 60 percent of households<br>are based on household income quintiles within economies.<br>Source: Global Findex database.<br>THE GLOBAL FINDEX DATABASE 30<br><!-- End of picture text --> FIGURE 2.1

# FIGURE 2.2

# FIGURE 2.3
converted at 2005 PPPs.<sup>15</sup> The price levels in most developing countries relative to the US were found to be much lower in the ICP 2011 round compared to the 2005 round.<sup>16</sup> Figure 1 reveals that $1.44 falls significantly below the 2011 mean value of poverty lines of the 15 countries that were used to establish the $1.25 line. Therefore, a US-inflation based approach for updating the international poverty line is in contradiction to the principle that the global poverty line should reflect the national definitions of poverty of the poorest countries in the world.

While the methods have varied somewhat over the last 25 years, the common guiding principle has been that the global extreme poverty line should reflect how the world’s poorest countries estimate a minimum threshold of living which meets basic needs. National poverty lines – set in the country context and in local currencies – give a good indication of each country’s own definition of monetary poverty and have therefore been used as a guide in setting an international poverty line. Moreover, past studies have found that national poverty lines vary considerably and tend to increase with national per capita income. However, among the poorest countries, there appears to be little correlation between poverty lines and mean income levels (World Bank, 1990; Ravallion, Datt and van de Walle, 1991; Ravallion, Chen and Sangraula, 2009). This finding has been taken to suggest that for this set of countries, poverty lines may be reflective of some absolute cost of basic needs, a threshold which the international poverty line also should represent.

The method for quantifying the threshold that reflects the poverty lines of the world’s poorest countries has varied across past studies, as described in World Bank (2014b). The most recent approach that resulted in the $1.25 global poverty line is based on taking the average of

> 15 In other words, converting the $1.44 into national currencies using the 2011 PPPs results in a lower value of the poverty line in most local currency values than a 1.25 line at 2005 PPPs.

> 16 Price level in this case is defined as the ratio of the PPP exchange rate to the market exchange rate.

13
0.367** 0.361** Aggregate of all activities

**denotes that the figures are statistically significant at less than 5% level. Note: Marginal return is calculated from the findings of Table 11 and is defined as the return (profit) on each additional taka invested in working capital and the value of the enterprise.

Source: HIES 2000, 2005 and 2010

**Table 13: Household distribution by constraints in the operation of microenterprise**

|Constraint type|**2000**|**2005**|**2010**|
|---|---|---|---|
|No constraints|33.0|23.8|49.7|
|Inadequate capital or credit|27.1|25.5|22.1|
|Inadequate knowhow|3.8|3.9|2.2|
|High operating cost|0.1|0|3.6|
|Unreliable/inadequate power supply|0.5|1.1|2.9|
|Problems with equipment/spare parts|1.7|0.6|0.4|
|Government regulations|2.5|4.0|1.1|
|Lack of raw materials|10.5|18.2|2.9|
|Inadequate demand of products|8.3|6.2|7.1|
|Transport problems|2.5|7.1|5.0|
|Other miscellaneous problems|10.0|9.6|3.0|
|N|1424|1418|1904|

Source: HIES 2000, 2005, 2010

**Table 14: Extent of credit and non-credit constraints by microenterprise activity**

|**Enterprise activity**|**Enterprises are**|**Among those ent**|**erprises that are**|
|---|---|---|---|
|**and year**|**constrained (%)**|**constrained**|**(N=3002)**|
||**(N=4,761)**|**Enterprises are credit-**|**Enterprises are non-**|
|||**constrained (%)**|**constrained (%)**|
|**Service**||||
|2000|70.52|45.47|54.53|
|2005|77.87|35.73|64.27|
|2010|56.59|47.18|52.82|

45