rafmacalaba/datause-encoder-data huggingface dataset:
PAR –population attributive risk for ICD-10 (proportion of tobacco-associated diseases).

The calculation of the costs of high-tech medical care related to tobacco-associated diseases was carried out in accordance with the following algorithm: CostHTМC = NHTМC * PAR * NFZ HTМCav, where: CostHTMC – the cost for high-tech medical care related to tobacco-associated diseases; NHTМC – number of cases of high-tech medical care according to ICD-10 codes; NFZHTMCav – Average standard of financial expenses per high-tech medical care case; PAR – population attributive risk for ICD-10 (proportion of tobacco-associated diseases).

The calculation of the cost of outpatient treatment of diseases caused by smoking was carried out in accordance with the following algorithm:

- extraction of the data from the form of statistical observation No 12 "Data on the number of diseases registered in patients living in the area of service of the medical organization" on the total number of cases of diseases for the considered ICD-10 codes;

- calculation of PAR – shares of diseases associated with smoking in the context of diseases (population attribute risk of smoking);

- determination of the number of cases of treatment related to smoking: the product of the number of cases of treatment according to the ICD-10 code and the PAR;

- determination of the correction factor to the cost of an outpatient visit taking into account the specialty, in accordance with the Letter of the Ministry of Health of Russia of 23.12.2016 N 117/10/2-8304 “On the formation and economic justification of the territorial program of state guarantees of free provision of medical care to citizens for 2017 and the planning period of 2018 and 2019”;

- determination of costs for the outpatient treatment of diseases associated with tobacco as a product of the number of cases of diseases related with tobacco, fee per cases in the outpatient setting, including drug coverage, the values of the population attribute risk.

The calculation of the costs of drugs for treatment of outpatient cases was carried out according to the data on the volume of costs of the pharmaceutical market in the commercial segment (p. 15) and within the preferential drug provision (p. 41, 43), presented in the analytical review of the pharmacological market of Russia for 2017, prepared by the DSM Group, for each of the considered classes of diseases, except tuberculosis, and the number of cases of diseases.

In total, the costs/losses associated with outpatient treatment of tobacco-related diseases are determined by formula 3: Outpatient costs = N cases * (KP * T visit + T drugs)* PAR, (3) where – Outpatient costs costs of outpatient treatment of tobacco-related diseases; N cases – number of diseases of the specified class of diseases; KP – correction factor to the cost of a visit taking into account the specialty corresponding to the disease; T visit – average norms of costs of outpatient treatment; Kr – ratio of outpatient visits in the completed outpatient case; – T drugs average cost of drug provision at the outpatient stage; PAR – population attributive risk for ICD-10 (proportion of tobacco-associated diseases).

23
**Figure 4. Tajikistan: Direction of Exports, 1992-2010**

<!-- Start of picture text --> 1% 1992 1% 1% 1997<br>1%<br>4%<br>4%<br>9% 21%<br>34%<br>14%<br>50%<br>4%<br>3%<br>10% 5% 36%<br>2%<br>1% 1% 0% 2010 CIS<br>EU-15+<br>8%<br>Southeast Europe<br>5%<br>EU-8<br>Middle East and North Africa<br>14%<br>Central Asia<br>East Asia<br>Rest of the World<br>70%<br>South Caucasus<br>North America<br>South Asia<br><!-- End of picture text --> Source: COMTRADE data and author’s calculation.

A closer look at 2010 data indicates that the top-10 countries receiving Tajik exports were respectively Russia, EU15, Bulgaria, Slovenia, Poland, China, Ukraine, Turkey, Norway and Turkmenistan. In 2007, before the global crisis hit, the top-ten importers of Tajik goods were Russia, Romania, Poland, EU15, Bulgaria, China, Norway, Mongolia, Slovenia and Belarus. The landlocked situation of Tajikistan seems to be favoring trade with traditional Soviet partners and regional powers such as the EU and China. This geographical dimension should be kept in mind when designing an export-led growth strategy for Tajikistan.

# **Tajikistan, an economy that may benefit from an export-led growth strategy**

As long as remittance inflows are stable, Tajikistan can continue enjoying higher growth rates on the account of strong domestic consumption. But the experience of developing countries that have grown sustainably over decades teaches us that higher investments and export expansion to tap global demand are essential ingredients (The Growth Commission, 2008). However, Tajikistan’s geographical disadvantage and poor logistics and business environment call for a parsimonious export diversification 6
risk as negligible (Oberholzer-Gee, 1998). It is also not clear how much knowledge farmers or communities have regarding the likelihood and consequences of an untoward event.

Even experts in risk disregard some hazards. After the first terrorist attack against the World Trade Center in 1993, terrorism risk continued to be included as an unnamed peril in most commercial insurance policies in the United States. Insurers were thus liable for losses from a terrorist attack without their ever receiving a penny for this coverage. (Kunreuther and MichelKerjan, 2004). Following the attacks of September 11, 2001, insurers and their reinsurers had to pay over $35 billion in claims due to losses from the terrorist attacks, at that time the most costly event in the history of insurance worldwide, now second only to Hurricane Katrina.

# **4. The Importance of** **_Undertaking Benefit-Cost Analyses of Risk Reducing Measures_**

As discussed in the previous section, decision makers may not understand the risk their country faces and actions that should be taken to reduce it. To evaluate the relative merits of alternative measures, key decision makers can undertake a systematic benefit-cost analysis. In this section we describe the elements of this evaluation process in the context of investing in measures to reduce the impact of flood losses to property in Jakarta, Indonesia as part of a study undertaken for the World Bank by our research team at the Wharton Risk Center, in collaboration with the International Institute for Applied Systems Analysis (IIASA) and Risk Management Solutions (RMS) (Hochrainer-Stigler et al., 2010). A similar process could be utilized to evaluate the costeffectiveness of measures to increase agricultural productivity.

Jakarta is the capital of Indonesia with around 8.5 million inhabitants. Severe flooding is frequent and closely linked to extreme rainfall events. This case study focuses on the region around the Ciliwung River in Central East Jakarta, a densely populated and economically important area of the city where flooding occurs most frequently. Jakarta has a wide variety of buildings, from very modern skyscrapers to informal settlements erected on wooden stilts. We focus our study on residential properties in East Jakarta, which make up about 60 percent of the city’s structures. Research using Google Earth suggests that the vast majority of the buildings located outside the commercial center are two- or three-story masonry residential homes typically occupied by persons of high and medium wealth.

10
A second inference is that if counter-cyclical policies are effective in reducing consumption volatility, the volatility of private consumption can be expected to be higher than that of total consumption. Graph 1.2 plots the volatility of per capita consumption against the volatility of per capita private consumption. Countries typically lie below the 45-degree line where the volatility of total consumption is lower than that of private consumption. Applicable to all regions and almost all countries, this observation indicates that governments indeed play some positive role in reducing total consumption volatility or in smoothing total consumption by providing more public goods during periods of low private consumption. In the Caribbean region, the volatility of total consumption is 8.6 percent, while that of private consumption is 10.6 percent. However, the countercyclical role of public consumption varies across countries. For example, public consumption is very effective in reducing private consumption volatility in Guyana and Trinidad and Tobago, while in Barbados and the Dominican Republic public consumption does not exhibit the same degree of smoothing.

<!-- Start of picture text --> Graph 1.2: Consumption Volatility and Private Consumption Volatility<br>25 -__ _ _ _ __ _____________________ ________<br>0 Caribbean<br>+ Others<br>20<br>45 d,pde h.e<br>o 0<br>o 5 lo0 1 5 20 25<br>Voladlty of Per Capita Private Consumptlon<br>tSitdi df i + x +s)<br><!-- End of picture text -->

**Source: SIMA data'oase (98 countnes included).**

A third testable inference is that on average consumption volatility can be expected to be higher in countries with less developed financial and insurance risk management mechanisms. Financial depth (monetary aggregate M2 divided by GDP) divided by the volatility of output is used as a proxy for the presence of financial risk coping mechanisms. This variable -- which increases as financial depth increases or production volatility decreases -- is used as a proxy for the availability of financial insoPuments in the presence of volatile output. Insurance depth (insurance premiums divided by GDP) divided by the volatility of output is similarly used as a proxy for the presence of insurance risk coping mechanisms. Graphs 1.3 and 1.4 illustrate the fact that financial and insurance instruments are important instruments of consumption smoothing, allowing individuals to transfer outputs across space and time. Each of these two variables by
_Figure 6.10: Urban household consumption (poorest quintile Q1)_ <!-- Start of picture text --> Change in Urban-HH consumption (as % change from base case)<br>for poorest quintile (Q1)<br>2018 2022 2026 2030<br>(5.0 USD/ton) (13.3 USD/ton) (21.7 USD/ton) (30.0 USD/ton)<br>0.0 %<br>-0.5%<br>-1.0%<br>-1.5%<br>-2.0%<br>-2.5%<br>-3.0%<br>-3.5%<br>-4.0%<br>DTAX STAX SAVING CORPORATE TRANS<br>-0.5% -0.6%<br>-0.7% -0.7%<br>-0.8%<br>-1.5%<br>-1.6%<br>-1.7%<br>-1.9% -1.9%<br>-2.0%<br>-2.1%<br>% change from base -2.2% -2.2%<br>-2.5% -2.6%<br>-2.7%<br>-2.9%<br>-3.1%<br>-3.4%<br><!-- End of picture text --> 22
**Page**

# **Contents**

|I.|Introduction ......................................|4|
|---|---|---|
|II.|The Theoretical Literature .<br>......................................|5|
|III.|Indicators and Crises: The Empirical Literature ............|................<br>8|
|IV.|Methodological Issues .................<br>...................|..<br>_15_|
|V.<br>Tables|Concluding Remarks .23<br>||
|1.|Indicators of Crises: A Review of the Literature .24||
|2.|Indicators by Category .31||
|3.|Indicators of Crises: What Worked Best?<sup>.</sup>|33|
|4.|Performance of Indicators .37||
|_5._|"Signals" Approach. Performance of Indicators .39||
|6.|Average Lead Time .40||
|7.|<br>Persistence of Signals .41||
|Refere|nces .....|42|
26 longer time series though for a more limited set of policy instruments. The results from this augmented specification for the world, the United States and China are presented in Table 7, which shows only the coefficients of interest. Once the ECM is augmented to include TTBs, the estimate of the long-run elasticity of world trade with respect to income is essentially unaltered and continues to be significant, while the variable capturing protection at the world level is not significant. The same result holds true when we use the augmented framework to estimate the responsiveness of imports to GDP for China. The US results appear to tell a slightly different story, as the magnitude of the estimated coefficient for the long-run trade elasticity in the 2000s is marginally larger. This suggests that an increase in contingent protection may have had a role, even if minor, in the decline of the responsiveness of imports in the United States.

These findings suggest that protectionist trade policies are playing a negligible (if any) role in explaining the reduction in the world trade elasticity and, hence, in the current trade slowdown. However, a word of caution may be warranted. First, changes in still-opaque nontariff measures are not captured by standard measures of protection and they may be playing a role that our analysis cannot account for.<sup>20</sup> Second, it is still well possible that the slower pace of trade liberalization in the 2000s relative to the long 1990s, rather than a surge in protection, may contribute to explain the declining responsiveness of world trade to GDP.

# **VI.   CONCLUSION**

To assess whether the global trade slowdown of recent years is cyclical or structural, we analyzed the relationship between trade and income in the past four decades. We demonstrated the rise of the long-run world trade elasticity in the long 1990s and its decline in the 2000s, which set in before the Great Recession. Furthermore, we used the estimates of

> 20 The Global Trade Alert initiative considers a broader range of policy instruments relative to the WTO and has documented a larger number of protectionist measures since the crisis (Evenett, 2014). This count, however, includes both trade restrictive and trade promoting measures, particularly export subsidies and various forms of fiscal incentives to exporting firms, with potentially contrasting effects on the volume of trade.
service quality, we show that the correlation holds not only for the aggregate score but also for a majority of individual sub-components of the index. The results become stronger if we use the first principal component of individual health indicators.

Finally, we provide evidence consistent with the mechanism that higher political integrity influences bureaucratic productivity through improved oversight and facilitation by district councilors who are elected at the frontlines of health service delivery. Among the three types of politicians surveyed - local councillors, District Chairperson and nonelected Contenders -, it is the integrity of councillors, who are supposed to monitor and facilitate delivery, that is robustly correlated with several indicators of health service delivery.

# **4.1 Overall correlations**

To investigate whether characteristics of politicians and bureaucrats in Uganda’s districts are robustly correlated with the quality of service delivery, we start by presenting simple graphs showing how the DLT Health Scores and average traits of local policy actors covary in the sample of 75 districts in Figures 6 and 7. To account for broad differences in income across districts, we regress the DLT scores on district poverty rates, and present the scatter plot of estimated residuals against selection traits.

The first finding is that, among politicians, integrity is the only robust predictor of variations in DLT between districts: a 1 s.d. increase in politicians’ average Integrity Index is correlated with a 0.3 s.d. increase in the residual of health score. The only other trait of politicians that is marginally correlated with health scores is Altruism. Importantly, personality traits like those measured by the Big Five Index and Public Sector Motivation are not correlated with quality of service delivery.

Among bureaucrats, the scatter plots indicate no correlation between average integrity and quality of service delivery. Three traits of bureaucrats are significantly correlated with DLT scores: districts where bureaucrats are more risk-averse and more altruistic, and Districts where bureaucrats’ PSM is lower, also have higher scores.<sup>26</sup> .

> 26The correlation between Risk-Aversion and health scores, however, disappears if we drop the outlier

19
potentially involve long recall periods, which could result in recall bias. This would affect accuracy of information on some job characteristics. Evidence suggests that we can draw fairly accurate information on labor market dynamics from retrospective data. More elaborate information about job characteristics such as fine distinctions between different employment statuses are less reliable (Assaad et al., 2018). In our analysis, we focus on transitions in and out of different jobs, so we have reasons to believe that the data is reliable for this purpose. Recall of job start and end dates might be trickier. This is evident in the important number of respondents who could not recall the exact month or year when they started or ended a job. Since information on starting and ending dates of a job are essential for constructing the synthetic panel, we excluded from our dataset 19 individuals who could not recall the year where they started or ended any of their jobs. It is, however, easier for respondents to recall the year they started or ended a job rather than the specific month. For example, one third of the individuals who reported the starting year of their first job had a missing starting month. To avoid losing those who did not recall the specific month, we assigned a random number between 1 to 12 when the month was missing. In doing so, we considered the following: a) Respondents who ended and started two different jobs in the same year and reported the month of ending the former job are assigned a starting month for the latter job that is equal to or greater than the month when they ended their former job.

b) Respondents who ended and started two different jobs in the same year and reported the month of starting the latter job are assigned an ending month for their former job that is equal to or less than the month where they started their latter job c) Since the survey ended before the end of 2020, we make sure that in 2020 the month assigned is less than or equal to the month where the individual took the survey.

13
excessive delays resulted from lengthy processes of clearance and inspection, the number of documents and signatures needed to process a trade transaction, and the frequency of problems with customs and other government authorities. Based on selected evidence, the incidence of NTMs in the MENA region would seem to be higher or at least similarly comparable to that of other regions at similar income levels Figure 5. Also, the nature of NTMs has tended to shift away from old-style command-and-control to product standards and technical requirements (“technical barriers”), as shown in Figure 6.

**_Figure 5.  Simple and trade-weighted NTM coverage ratios_**

<!-- Start of picture text --> Share of Product Lines Share of Imports<br>Philippines<br>Indonesia<br>Lebanon<br>Tunisia<br>Morocco<br>Egypt<br>Mexico<br>Brazil<br>Argentina<br>100 80 60 40 20 0 20 40 60 80 100<br>Asia<br>MENA<br>LAC<br><!-- End of picture text --> Source: Gourdon 2011.

**_Figure 6.  Shifting nature of non-tariff measures in the MENA region_**

<!-- Start of picture text --> 6000<br>5000 Licences<br>Prohibitions-Quotas<br>4000<br>Technical Barriers<br>3000<br>2000<br>1000<br>0<br>2001 2010 2001 2010 2001 2010 2001 2010<br>Egypt Lebanon Tunisia Morocco<br><!-- End of picture text --> Source: Gourdon 2011 14
likelihood function for Ghana was in the range of -0.7 to -0.8 in 1987 and -0.38 in 1992, while in Uganda it assumed values of-2.6 to -2.7 in 1992 and -1.08 in 1996. All of these were significant at one percent. This negative and significant p indicates that on the whole disturbances in the farm/non-farm participation decisions are affected in opposite directions and are not statistically independent. This clearly indicates that parameter estimates obtained from a uimvariate probit will be inefficient and require _A_ joint estimation as in a bivarate probit. Similarly the likelihood ratio test for joint significance of the independent variables is significant at one porcenFl level. The McFadden fV shows' an indication of goodness of fit of all equations.

# _6.1 Ghana_

The results for Ghana are presented in Table 15 for the 1987/88 survey and in Table 16 for the 1991/92 survey. The descriptive statistics for all of the varables used in the estimation are shown in Table 14. For the most part, thb estimation results show the factors to have fairly distinguishabl **e** ffects on participation in one sector or the other. The estimations for the two periods are mostly similar, though there are interesting differences.

Looking at all versions of the models for both years, a clear commonality is that women are significantly less likely than men to be in agriculture and more likely than men to be in non-farm activities. As anticipated, female household heads behave slightly different from women as a whole in that they participate more in agiculture (though not significant in 1992). They also participate significantly more than others in non-farm, as is the case for women on the whole. These effects are found in both yearm with the 22
gender. Table **3** presents the results. As a reference, the table also reports figures in the case of Bulgaria and Poland, for which estimates are available. These numbers take into account that, in the pre-reform system, women were allowed to retire **5** years earlier. When we use them in the analysis, we make the assumption that women will not change their behavior. Thus far, this seems to be correct.

||**Table**|**3.AverageLeng**<br>**(beforepensi**|**thofService**<br>**on)**||
|---|---|---|---|---|
|**Country**|**Year**|**Men**|**Women**|**GenderGap**|
|**Kazakhstan**|**1996**|**40**|**_35_**|**5.0**|
|**KyrgyzRepublic**|**1997**|**_38.5_**|**30.2**|**6.7**|
|**Latvia**|**1997**|**39.3**|**32.3**|**7.0**|
|**Moldova**|**1997**|**44**|**36.9**|**7.1**|
|**Bulgaria**|**1995**|34.8|**28.7**|**6.1**|
|**Poland**|**1998**|**36.9**|**32.0**|4.9|

_Source:_ Country data (Bulgaria and Poland) and authors' calculations

**3.10** Women live significantly longer than men do in the **FSU.** Life expectancy and the expected length of the retirement period are central in valuing the pension. When unisex annuities are used (as they are in a PAYGO system, and in most defined contribution systems), women get a higher rate of return on contributions than men. Table 4 shows the gender gap difference in life expectancy in the four countries under consideration. The largest gap is observed in Kazakhstan.

**Table 4. Life Expectancy**

|**Country**|**Kazakhstan**|**Kyrgyz Republic**|**Latvia**|**Moldova**|
|---|---|---|---|---|
|**Year**|**1996**|**1997**|**1998**|**1997**|
|**AtBirth**|||||
|Female|70.4|71.4|74.9|**70.3**|
|Male|**59.7**|**62.6**|**63.8**|**62.9**|
|**Age60**|||||
|Female|20.4|**19.1**|**19.9**|**18.8**|
|Male|**13.7**|14.9|**_15.5_**|**_15.8_**|
|**Age65**|||||
|Female|17.4|**15.6**|**16.0**|**15.3**|
|Male|**11.5**|12.2|**12.5**|**13.3**|

_Source:_ World Bank data and Kyrgyz Republic estimates.

**15**