rafmacalaba/datause-encoder-data huggingface dataset:
Table A32. Total CO2 emissions from combustion of fossil fuels in 2001, Gigatons _(Gt)_ .

|**Abbreviation**|**Type of fossil fuel**|**WTO model**|**Rosgidromet data**|
|---|---|---|---|
|Oil|Liquid fuel|0,35|0,33|
|Gaz|Fluid fossil fuel|0,75|0,74|
|Col|Solid fossil fuels|0,31|0,31|

_Source: Authors’ estimations and Rosgidromet (2013Table A)._ Table A33. CO2 emission intensities from combustion of fossil fuels in 2001, Gt/bln RUB 2001

|**Abbreviation**|**Type of fossil fuel**|**WTO model**|
|---|---|---|
|Oil|Liquid fossil fuel|8,20E-04|
|Gaz|Fluid fossil fuel|9,98E-03|
|Col|Solid fossil fuels|4,46E-03|

_Source: Authors’ estimations_ 148
socioeconomic resilience to disasters 23

||**Annual**<br>**So**<br>**asset risk**|**cioeconomic**<br>**resilience **|**Ann**<br> **wellbei**|**ual**<br>**ng risk**|
|---|---|---|---|---|
|**District**<br>|[,000US$] [% of total]|[%]|[,000US$]|[% total]|
|Colombo|2,891<br>7|25|11,574|31|
|Rathnapura|701<br>9|8|8,587|34|
|Kalutara|451<br>6|12|3,627|31|
|Gampaha|519<br>7|19|2,751|30|
|Puttalam|279<br>8|13|2,211|31|
|Trincomalee|97<br>8|6|1,568|37|
|Kurunegala|176<br>7|12|1,430|34|
|Polonnaruwa|162<br>8|13|1,231|29|
|Kegalle|81<br>8|10|846|35|
|Batticaloa|50<br>8|7|767|35|
|Hambantota|118<br>8|16|722|29|
|Galle|50<br>7|13|396|31|
|Mannar|48<br>9|16|304|22|
|Kandy|29<br>7|10|303|35|
|Badulla|22<br>8|7|303|32|
|Anuradhapura|36<br>8|14|258|31|
|Ampara|28<br>9|14|200|28|
|Matale|13<br>7|10|128|34|
|Moneragala|4<br>10|8|49|36|
|Mullaitivu|2<br>8|7|32|31|
|Vavuniya|3<br>8|15|23|29|
|Nuwara Eliya|0<br>9|9|1|34|
|Matara|–<br>–|9|–|–|
|Jaffna|–<br>–|8|–|–|
|Kilinochchi|–<br>–|5|–|–|
|Total|5,761<br>7|15|37,313|32|

**Table 3:** Risk to assets, socioeconomic resilience, and risk to wellbeing for the poorest 20% (by expenditures) of the population of each district.

# 3.2 Poorest quintile risk summary

Because our results are based on information at the household level, we are able to assess asset and wellbeing losses not just at the district level, but also for different income groups.<sup>7</sup> Table 3 lists annual asset and wellbeing losses for the poorest quintile in each district. Across all districts, the asset losses of the poorest 20% are US$5.7 million, or just 7% of total asset losses. On the other hand, the wellbeing losses of the

> 7 It is also possible to look at different subgroups in the country or at the district scale (e.g., per occupation, head of household gender, household size, ethnic background or religion, social transfer enrollees), within the limits of the representativeness of the household survey.
periods. Because of limitations in the data on human capital related to education (HS), the intangible capital residual (HR) is used as a proxy measure of human capital in this study. Henceforth, HR would be referred in this paper as human and intangible capital. The different types of capital are determined by a number of factors, such as household income, community infrastructures and strength of institutions (see Appendix 1). In addition, we include the magnitude of natural disasters as a determinant in order to examine its impacts on capital. The number of occurrences of natural disasters and the extent of damages are described below.

# **III. Natural disasters**

Four types of natural disasters are considered in the study: droughts, earthquakes, floods, and hurricanes/storms. Data on the number of natural disasters are obtained for 196 countries from the Emergency Events Database (EM-DAT)<sup>1</sup> . _Drought_ is characterized by a shortage in a region's water supply as a result of constantly below average precipitation. _Earthquake_ is characterized by the shaking and displacement of ground due to seismic waves. This variable refers to the occurrences of earthquakes only without secondary effects. _Flood_ is defined by a significant rise of water level in a stream, lake, reservoir or coastal region. _Storm_ is represented by wind with a speed between 48 and 55 knots.

Table 1 shows a maximum of 196 countries for which data are available, with a total of 55 events of drought, 82 events of earthquake, 447 events of flood and 303 events of storm that started during the three given periods. A natural disaster, such as drought, may last for several years but the data presented here refer explicitly on the tally of events in a single year, i.e., the year that a natural disaster started. Table 2 presents the countries with more than 3 reported events of a natural disaster in a given year. The highest number of drought events is recorded in China in 2000 (3 incidences). Over the given time periods, China was hit with the most incidences of natural disasters: a total of 5 drought events<sup>2</sup> , 8 earthquakes, 23 events of flood and 30 events of storm.

_<u>Table 1: Summary statistics of events of natural disasters, by type, 1995-2005.</u>_

|Natural|Year|No. of||No. o|fevents||
|---|---|---|---|---|---|---|
|disaster||observations|Total|Minimum|Maximum|Standard<br>deviation|
|Drought|1995|196|6|0|1|0.17|
||2000|196|27|0|3|0.40|
||2005|196|22|0|2|0.33|
|Earthquake|1995|196|26|0|3|0.52|
||2000|196|31|0|5|0.62|
||2005|196|25|0|4|0.51|
|Flood|1995|196|95|0|6|1.05|
||2000|196|158|0|9|1.54|
||2005|196|194|0|17|1.95|
|Storm|1995|196|76|0|10|1.19|
||2000|196|101|0|12|1.50|
||2005|196|126|0|14|1.51|

> 1 http://www.emdat.be/

> 2 China had three events of drought in 1995 and two events in 2005.

3
funding than was available. Rather, we asked about their general agreement with statements on deviant behaviors in their institutional environment. Finally, given the differences in some HRM processes across the two states, the wording of some questions was dissimilar in the PS questionnaires applied in each state.<sup>8</sup> The same groups of physicians in each state completed both the PS and UCT surveys. The surveys were applied to physicians working in Primary Health Centers (PHCs), Community Health Centers (CHCs), and to a lesser extent, block (subdistrict) medical officers in three districts in each state.<sup>9</sup> The surveys were field tested in one district in each state. 273 and 266 physicians responded in States A and B respectively, with a response rate of 65 percent.<sup>10</sup> All instruments were applied individually and confidentiality was ensured.<sup>11</sup> The districts were selected as representative of the universe of districts in each state, and include areas which are considered desirable (e.g., near urban areas) and undesirable (distant rural and tribal areas) to physicians. Sampling remote rural and near-urban districts also allowed for a more representative sample of physicians in terms of years of tenure. Typical of most districts in each state, the sampled districts had a large number of PHCs without assigned physicians due to a generalized situation of physician shortages in India. However, these vacant PHCs tend to be located in rural areas lacking social infrastructure and distant from urban centers. State A performs better than State B in female literacy, is more urbanized, and has higher per capita income. State A also has significantly lower mortality rates, and unlike State B, has undergone rapid improvements in many health outcomes in the last couple of decades. Progress in state B has been muted in comparison.

# 3. INSTITUTIONAL SETTINGS

In order to interpret the perceptions of medical officers on de facto HRM practices, we briefly review the institutional context of each of these states as it relates to HRM. Although nomenclature varies somewhat, the Department of Health, Medical and Family Welfare (DH) is responsible for health services in both states. It is headed by a minister, an elected politician. Most high level administrative officials are career civil servants and belong to the centrally managed Indian Administrative Service (IAS). The remainder belongs to state civil service cadres. Many procedures and rules are similar for both states for the HRM functions under study here in part because they follow civil service rules that vary little across states. This 6
the system is expressed with sensitivity analysis. Output uncertainty, e.g. uncertainty that derives from the modeling and simulation, is expressed using confidence intervals.

# **3.2 Insurance pricing and preliminary analysis based on historical data**

The 2005-6 Malawi pilot project offered a bundled loan and insurance product in four pilot areas in central Malawi, where rainfall patterns compared to other areas in Malawi are relatively favorable for agriculture. The following analysis considers only the insurance (and not the full loan and insurance package) and assumes uniform conditions in these four areas based on information in only one (Chitedze). The premium pricing for this site follows Osgood (2006) and, in keeping with standard practice, is based on the expected payout (expected value). This can be expressed as follows: Premium = expected payout + 6.5% (Value of payout at 98<sup>th</sup> percentile – expected payout) The value of payout at the 98<sup>th</sup> percentile is set as the highest losses in the past given specified triggering events. Furthermore, table 2 shows important input parameters and calculated variables (in bold) used for pricing the insurance scheme.

Table 2: Important parameter settings and computed values (in bold) for the groundnut <u>insurance contract in Chitedze.</u>

|Variable|Value|
|---|---|
|Seed price (MWK/kg)|100|
|Seed amount (kg per acre)|32|
|**Ground input price (MKW per acre)**|**3200**|
|Typical yield (kg per acre)|420|
|Harvest price (MWK/kg)|75|
|**Typical groundnut value (MKW per acre)**|**31500**|
|Loan size (MKW per acre)|4667|
|Groundnut only insurance premium (%value)|8.4|
|Insurance tax (%)|17.5|
|**Groundnut only insurance rate with tax (%)**|**9.9**|
|**Premium with tax (MKW per acre)**|**461**|

Note: MKW: Malawi Kwacha. Source: Osgood (2006).

The ground input price (seed price times seed amount) per acre amounts to 3200 Kwachas, the typical groundnut value (as the product of typical yield and expected harvest price) was calculated at 31,500 Kwachas. Adding a premium of 8.4% and an insurance tax on the premium of 17.5% on the typical loan value of 4667 Kwachas leads to a premium of 461 Kwachas (or 9.9% of the loan size).

Since the primary risk to groundnut in Malawi is drought during the critical growth periods, the contract specifies levels of rainfall that trigger a claim payment. There are 8
# 2. TRENDS IN OCCUPATIONAL TRAINING

Since the Second World War occupational training has received increasing attention in policymaking of both national and international agencies. In the 1950s and 1960s the dominant motivation in most countries was the improvement of labor productivity and the promotion of economic growth. While these objectives remain important, they were joined in the 1970s by a concern to minimize the problem of youth unemployment by equipping young people with employable skills.

# 2.1 The imDact of manpower Dlanning

The greatest single impetus to the growth of interest in training undoubtedly came from the manpower planning movement which gathered strength in the late 1950s. Originally this took the form of forecasts of the incremental need for high level manpower derived from detailed employer surveys, the efforts to forecast the demand for scientific and technological manpower in the U.K. in the 1950s and 1960s by this method being a typical example. But it was the version embraced by the OECD in the early 1960s that had the greatest impact (Parnes, 1962). In linking the demand for labor, by occupation, to growth in output and productivity, by se tor, it integrated macroeconomic and manpower forecasting and thus provided a basis for manpower planning which was both seemingly scientific and comprehensive. A third variation, promoted by Horowitz, Zymelman and Herrnstadt (1966), used international comparisons to predict the evolution of the occupational structures of different industries.

Irrespective of the methodology used, each of these variations of what has become known as the manpower requirements approach imposes order on the unruly problems involved in manpower planning by treating labor as a commodity, albeit it one with special features. The object of the manpower planner, in this approach, is to predict potential imbalances between supply and demand and eliminate them by intervention on the supply side, "producing" more of the commodity through training. Data were supposedly the key to the quality of the analysis, and the more data the better.

The manpower requirements approach has been discredited in the eyes of most experts, partly because its predictions are invariably too erratic and too aggregated to be useful for decisionmaking (Hollister, 1967; Ahamad and Blaug, 1973), but mostly because it embodies a naive conception of how skilled manpower is created and utilized. Put simply, its central assumption is that productive relationships are fixed and that the labor force must adapt to them. The reality is almost tt.e opposite, it now being well-documented that productive relationships are highly capable of being adapted to a given labor force: tasks may be re-arranged, subdivided and executed more or less rapidly to allow the substitution of one type of labor for another, more or less capital-intensive techniques may be chosen, and product design may be varied, to accommodate the actual profile of labor supply.

-5S-
the inclusion of location indicator variables. This improvement leads to one additional correctly identified poor household and the exclusion of one additional correctly identified non-poor households per 100 program applicants at the national level when those in the bottom 20% are targeted. However, in practice, the PMT will likely not be used to administer funds to states, rather the states will administer a program given a pre-determined program budget. Therefore, within a state, the ranking of households based on the PMT score will not change with or without state indicator variables and may not be necessary to include in the PMT.

Table 9. Evaluation of the National PMT with Additional State and Area Variables (OLS Regression)

**Evaluation of Including a Complete Set of Location Indicators in the PMT**

||**OLS Model w**<br>**Poverty Line**<br>|**ith Location In**<br> **/ PMT Cutoff**<br>|**dicators**<br>|**OLS Model w**<br>**Poverty Line**<br>|**ithout Locatio**<br>**/ PMT Cutoff**<br>|**n Indicators**<br> <br>|
|---|---|---|---|---|---|---|
|**Evaluation Criteria**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|
|Poverty headcount (observed)|19.96%|39.95%|59.89%|19.96%|39.95%|59.89%|
|Householdpovertyrate(observed)|19.36%|37.59%|54.92%|19.36%|37.59%|54.92%|
|Household poverty rate (predicted)|12.71%|38.76%|58.73%|12.81%|38.76%|59.60%|
|Total accuracy|85.32%|79.63%|80.40%|83.01%|77.89%|79.26%|
|Poverty accuracy|44.91%|74.47%|85.63%|39.21%|72.15%|85.38%|
|Undercoverage|55.09%|25.53%|14.37%|60.79%|27.85%|14.62%|
|Leakage|4.98%|17.26%|25.96%|6.47%|18.65%|28.19%|

_Notes_ : This model includes all variables in Table 3, as well as location indicators for states and rural and urban areas where noted. The results are based on out-of-sample prediction on a random sample of 25% of households.

Table 10 presents the results of the Sudan national PMT with the inclusion of employment information in the form of the proportion of household members that have ever worked for income to total household members. Although this information is strongly and statistically significantly associated with per capita household consumption. Including this information does not impact the targeting performance of the PMT in a meaningful way. Employment information is generally difficult and time consuming to collect in household surveys, consisting of a series of questions to capture the employment status of each household member of working age and the type of employment, for example, salaried or non-paid, family workers.

20
Croke et al. (2016) While we have argued that deworming MDA is cost effective based on its impact on child weight alone, there is evidence that deworming also leads to gains in other outcomes (Ahuja et al. 2015). TMSDG aggregate data from a more limited number of studies for outcomes other than weight, and some of these are from low-prevalence environments where the WHO does not recommend deworming, further reducing statistical power. This means that for each outcome assessed individually, it is typically impossible to reject either the hypothesis of no average effect, or the hypothesis of effects large enough to make deworming cost effective. In such a setting, it may be appropriate to consider the joint hypothesis that there is no impact on any of the child outcomes considered. We hope to do this in future work.

We have begun to explore heterogeneity of impact with covariates suggested by the scientific literature, including prevalence and intensity, age, and whether the study design captures epidemiological spillovers. One could also examine heterogeneity by helminth type, drugs used, and co-morbidities. However, given the limited number of studies and hence limited degrees of freedom, scope for examining heterogeneity while maintaining statistical power is limited. In future work, we hope to systematically examine heterogeneity, but here we simply capture heterogeneity using random effects estimation.

We follow TMSDG in assuming a normal distribution of the effect of MDA across study environments, but future work could relax the assumptions of symmetry and normality on the distribution of effects across studies and estimate these from the data. There is no reason, _a priori_ , to expect that the distribution of deworming effects follows such a distribution. In fact, the underlying science naturally suggests a non-symmetric 30
# **III. Evolution of the Gross Domestic Bonded Debt during the Real Plan: a**

# **Decomposition Exercise**

During the four years of President Fernando Henrique Cardoso's first term, the federal bonded debt increased to more than five times its original value; from R$ 60 billion to R$ 323 billion.'<sup>7</sup> This spectacular debt growth raises many questions about the sustainability of economic policy, especially if one considers the effects of the exchange-rate devaluation in January of 1999, which increased even more the service costs of federal bonded debt, due to the existence of dollar-linked indexation clauses on part of the debt.

This Section decomposes the federal bonded debt growth, searching for the macroeconomic causes of the huge growth that occurred in 1995-1998. We attempt to quantify the contraction and expansion sources of the federal bonded debt.

Consider the federal government and Central Bank aggregate balance sheets in 12/31/94 and 12/31/98, respectively. One of the accounts on the liability side is the federal bonded debt. The value we are interested in explaining is the difference between this account's balances on these two dates. Due to accounting identities, this value is the sum (with opposite sign) of the differences during the period of all the other accounts' balances. Consequently, by aggregating these other accounts' balances in a way amenable to our macroeconomic analysis, we measure the factors responsible for the growth of the federal bonded debt in this four-year period. The idea of this decomposition exercise can be better understood with the accounting framework provided in Table III.1. The table starts from

> '' These numbers obtained great repercussion in the press. The headline of the 2/13/1999 edition of the Sao Paulo's daily newspaper _0 Estado de Sdo Paulo_ was **The public debt increased 424% in FHC term.**

14
_COTE D’IVOIRE: Volatility, Shocks and Growth_ Figure 2 (above) depicts the terms of trade versus trend in logarithms. Early in the study period the real relative prices of Cote d’Ivoire’s agricultural commodity exports was high, so the terms of trade were favorable, and GDP grew rapidly. After the peak of the late 1970s, those commodity prices fell, and so did Cote d’Ivoire’s terms of trade. The terms of trade trough occurred during the extended depression of the late 1980s and early 1990s. The devaluation of the CFA in 1994 coincided with a surge in cocoa and coffee prices, and the terms of trade accordingly increased, only to slide below trend in about 2000.

**Figure 3 Real Exchange Rate Between Cote d’Ivoire Versus France, Versus Trend**

<!-- Start of picture text --> Real Exchange Rate and Trend, Cote d'Ivoire Versus France<br> Calculated Using CIV and France's Relative CPI<br>4.9<br>RER CIV FR<br>HP Trend<br>4.8<br>4.7<br>4.6<br>4.5<br>4.4<br>4.3<br>4.2<br>Year<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> While a single measure of terms of trade applies for each individual country, measures of real exchange rates are trade-partner dependent. We consider here the bi-lateral real exchange rate of Cote d’Ivoire with France, the major trade partner and reference currency.F<sup>3</sup> F (Figure 3). During the rapid growth of the 1970s, Cote d’Ivoire had a favorable RER, but in the peak of the late 1970s the RER fell below trend. In the period leading up to devaluation it was below trend, then shot up in 1994 and was above trend (favorable) until 1998, when it slipped beneath trend.

# **Commodity Time Series and Trends**

Figure 4 shows inflation-adjusted world cocoa prices in logarithms (which changes the scale of the prices.) These are spot prices on New York commodity exchange markets. The apparent resemblance of this commodity price times series to output per capita is very remarkable. The trend rises in the 1960s and early 1970s, with actual prices cycling

> 3 The CFA was pegged to the French Franc, then to euro.  Here, a high value indicates a large number of CFA per dollar and indicates “competitiveness”, while a low value indicates an overvalued currency.