rafmacalaba/datause-encoder-data huggingface dataset:
main results is provided in section 4. Section 5 offers policy recommendations and conclusions.

# **2. Data**

Our analysis is based on 250 articles published in Russian scientific journals over the last 15 years by 188 Russian scientists<sup>1</sup> . While this collection of papers may not be exhaustive, it is representative of the majority of publications related to poverty in Russia. Since 1992, the number of papers focusing on poverty has been increasing and reached a plateau of about 21 publications per year in 2000-2006 (Figure 1). More than 80 percent of all studies on poverty were conducted in the fields of economics and economic sociology, with the rest attributed to sociology and social policy.

Moscow-based scientists authored most publications on poverty in Russia (70 percent of publications), followed by their colleagues from Novosibirsk and SaintPetersburg (4 percent each), and Saratov (3 percent), see Table 2. The journal _Sociologicheskie Issledovaniya_ (Sociological Research) published 42 papers, or 17 percent of the total number of publications, the journal _Narodonaselinie_ (Population) published 32 papers (13 percent), and the journal _Monitoring Obshestvennogo Mneniya_ (Monitor of Public Opinion), 26 papers (11 percent). Each of Obshestvo i Economica (Society and Economics), _Economist_ (Economist), _Problemu Prognozirovaniay_ (Problems of Economic Forecasting), _Voprosy Economiki_ (Economic Matters), _Obshestvennue Nauki i Sovremennost_ (Social Sciences and Modern Times), and _Mir Rossii_ (Russian World) published about 15 papers on poverty (see Table 1 in Appendix).

The distribution of articles by authors is typical of scientific publications: while seven researchers (less than 4 percent of all authors) published more than a quarter of all articles, 135 researchers produced only one publication each (Table 1). Among the most productive, our database contains 13 publications by Rimashevskaya, 11 publications by Tapilina, and 9 publications each by Ovcharova and Taksanbaeva. Forty-seven percent of the authors in our database are PhD’s, 23 percent hold a second level (Habilitation)

> 1 See Appendix for a complete list of publications used for the analysis in this paper.

4
(Mandell 2006). The ‘consider the opposite’ method has been implemented by asking participants to list alternative choices before making a financial decision. Theory predicts that participants place increasing weight on these alternatives as they become more mentally available. However, the few studies that have tested the effectiveness of this method have found little or no results (Trout 2005). The third method of ‘counter-biasing’ builds on the premise that cognitive biases may be used to induce desired behavior (Jolls and Sunstein 2006). One example is the well-known ‘save-more-tomorrow plan’ that is built on the premise that people do not change saving rates they initially chose as a default (Thaler and Benartzi 2004).

The experiential learning approach contrasts to traditional forms of financial education based on cognitive learning theories that ‘ _emphasize cognition over effect_ ’ (Boyatzis et al. 1999). Kolb (1984: 194) argues that ‘ _learning is not just the result of cognition,_ [it] _involves the integrated functioning of the total person - thinking, feeling, perceiving, behaving._ ” This insight is captured in the concept of the four-stage learning cycle which posits that learning is most effective if it involves four aspects: an (i) immediate experience forms the basis for (ii) observations and reflections which are then (iii) distilled into abstract concepts (Boyatzis et al. 1999). These concepts are subsequently (iv) actively tested which leads to new experiences.<sup>5</sup> Lecture-based learning is less likely to activate all stages in the learning cycle. Traditional forms of financial education interventions may only convey abstract concept that are not actively tested and thus do not lead to new experiences. A reflection on the lesson learned is unlikely and the content may be quickly forgotten amidst the daily challenges and obligations people face. In contrast, the starting point of our intervention is the active experience of rolling dice and the game incorporates elements from each of the learning methods discussed above. Rolling dice provides the participants with immediate feedback on probabilities of winning in a lottery (presented by getting a certain number of sixes) and the intervention serves as a memorable event that can form the basis of a new decision heuristic. Participants are able to reflect on the difficulty of obtaining sixes and the facilitator explains the link between dice rolling and the abstract concept of lottery winning probabilities. Likewise, playing a game with dice allows the complex notion of probability to be conveyed in a low-key setting

> 5Neuroscience research shows that the different stages of the cycle of experiential learning corresponds to the structure and process of brain functioning. Zull (2002: 18-19) explains that “... _concrete experiences come through the sensory cortex, reflective observations involves the integrative cortex at the back, creating new abstract concepts occurs in the frontal integrative cortex, and active testing involves the motor brain. In other words, the learning cycle arises from the structure of the brain_ .”

7
The IRR of 1.62 on the change in domestic unemployment rate in the second row of column (1) is greater than 1 and indicates that import protection increases when the domestic economy is weakening through rising unemployment. The IRR of 0.88 on foreign real GDP growth is evidence that additional import protection is used against trading partners that are going through their own periods of weak economic growth. Furthermore, an estimated IRR of 1.02 indicates that a real appreciation of the domestic currency is associated with increased import protection through TTBs. This evidence is consistent with related results from a 1980-1998 sample of data on antidumping policy use reported by Knetter and Prusa (2003). Finally, the time trend estimate of 0.99 indicates that, on average, import protection through these policies has been declining over the sample period.

Specification (2) provides our first sensitivity analysis by replacing the variable capturing the domestic macroeconomic shock – the change in the domestic unemployment rate – with a variable defined as the growth rate of domestic real GDP in _t-1_ . Theory predicts an IRR that is less than 1; i.e., weak domestic growth is associated with a higher incidence of TTBs. The estimated IRR is 0.93 and statistically significant. The IRR estimates for the other determinants of interest are qualitatively unaffected by this alternative indicator of the health of the domestic macroeconomy.

Before proceeding with additional robustness checks, consider next the economic magnitude of the estimated IRRs; Figure 2 provides a graphical interpretation. We present the percent increase in the count of imported products per trading partner per quarter subject to new TTBs that are associated with a one standard deviation change in each macroeconomic determinant. Specifically, we compute the median of the model’s predicted estimates of import protection evaluated using the sample data; and we then introduce (one at a time) a one standard deviation shock to each of the macroeconomic determinants of interest, holding everything else constant, and regenerate the model’s predictions of import protection.

The grey bars of Figure 2 present estimates based on specification (1) of Table 2, whereas the black bars illustrate impacts based on specification (2). First note from Table 1 that a one standard deviation increase in the percent change of the bilateral real exchange rate is roughly a 15 percent appreciation. Figure 2 indicates this 15 percent appreciation is associated with a 33 (34) percent increase in imported products subject to TTB protection per trading partner per quarter according to specification (1) (alternatively, specification (2)). Next, the figure illustrates a sizeable estimated impact of domestic macroeconomic shocks. A one standard deviation increase to the change in the domestic unemployment rate – i.e., 0.86 or nearly one full percentage point (see again Table 1) - is associated with, per trading partner, a 52 percent increase in import protection. In specification (2), 12
Particularly when litigation is part of a broader social mobilization process, social movements or NGOs may use the process itself (―the shadow of litigation‖) as leverage in a bargaining process with political authorities or commercial companies – sometimes settling cases out of court, and sometimes ―winning even when losing‖ (McCann 2006). More generally, any consideration of the potential of social and economic rights litigation to bring about social change should see litigation as part of a broader mobilization process. Not only may the litigation process strengthen social mobilization in different ways – by providing visibility, structure and a focal point for mobilization efforts – out of court mobilization also increases possibilities for winning in court and, even more important, the chances of getting a favorable judgment implemented.

Implementation, though, is often the Achilles heel of legally based strategies (Shankar and Mehta 2008). Critics have expressed doubts regarding the potential of litigation to bring about significant social change (Baxi 1988; Hirschl 2004; Rosenberg 1991). It is argued that the ̳haves‘ are most likely to come out ahead in court, and that, even cases that do succeed in court are unlikely to lead to significant change unless they are supported by strong social actors, who are able to follow up on implementation and make the judgment bear on policy. It is also argued that human rights based mobilization is constraining political contestation within a liberal framework, and diverting the attention and resources of social movements away from other, potentially more productive strategies. This should be taken seriously. On the other hand, it is important to consider what the alternatives to legal mobilization are in different societies. Much of the criticism of (and support for) litigation as a transformative strategy is also primarily based on normative arguments, and rest on relatively weak empirical grounds, typically from the US (McCann 2006). Studies of social rights litigation in developing countries include (Abramovich 18
the likelihood of mothers reporting a diarrhea episode for the child also appears significantly larger in Sub-Saharan Africa and rural areas.

The second variable of interest we analyze in this paper is child mortality. We restrict the mortality data to the five-year time window prior to the interview because we aim to link child mortality to water and sanitation infrastructure observed at the time of the interview. Given the censored nature of our data, we hence estimate the determinants of under-5 mortality using a survival model.

We do not include children who were born more than 5 years before the respective DHS survey. Including older children would have the advantage of (i) increasing statistical power and (ii) reducing the problem of right censoring. The main shortcoming of extending the time period under consideration is, however, that the link between the information on current household characteristics and child mortality becomes weaker. Even within a five year period, water and sanitation infrastructure is likely to change for a fraction of households, which would be an argument for restricting the analysis to an even shorter time period (e.g. 12 months prior to the survey interviews). However, this would yield a very small number of child deaths and would therefore make the empirical identification of the relevant effects difficult. The five year horizon chosen in this paper was deemed long enough to achieve sensible mortality estimates, while still being short enough to avoid major shifts in water and sanitation infrastructure at the household level.

As Figure 2 shows, child death under age 5 is mostly concentrated in the first 12 months of children’s life. The cumulative survival functions fall sharply between birth and the age of 12 months and then flatten out, with relatively few deaths occurring between the 3<sup>rd</sup> and the 5<sup>th</sup> year of children’s life. Similar to the plotted diarrhea rates in Figure 1, and as expected, large differences in levels can be found between Sub-Saharan Africa and other regions of the world as well as between urban and rural households.

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<!-- Start of picture text --> .<br>.6<br>.4<br>.2<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Income decile<br>Consumption poverty Energy poverty based on total energy<br>Energy poverty based on end-use energy<br>Poverty headcount<br><!-- End of picture text --> Source: BIDS Survey (2004)

**Figure 7: Household energy poverty and consumption poverty measures**

**by income decile**

Why are the two measures of energy poverty almost same even when the end use energy is about one-seventh of the total energy? As we have seen, the end-use energy the households consume depends on two things: the total energy they use and the relative composition of different energy sources in total energy. For the rural households in Bangladesh, this composition does not vary much (except for the wealthy households who use more modern energy, including electricity). That is why the end-use energy for these households has almost a one-to-one relationship to their total energy, and switching between the end-use and total energy does not change the relative energy consumption pattern of one household compared to others. As a result, both measures of energy poverty yield very similar poverty headcounts. However, for a population where relative composition of the energy sources varies among the households, the same total energy may yield 25
processing exports are greater for destinations that are more populated and have a higher GDP per capita. Furthermore, we find a general trend for processing exports to become more sensitive to internal and import distances over time.

The results in columns 5 and 6 of Table 7 provide supporting evidence for Hypothesis 4. In column 5, the coefficient on ln ���� �ln ���� is negative, which suggests that a rise in oil prices reduces the share of China’s processing exports in total exports especially for far away destinations. Conversely, the coefficient on ln ���� �ln ��� is not significant.

The coefficients on the other control variables in columns 4, 5 and 6 suggest that the share of processing exports in total exports is larger for more populated provinces, closer to the coast with lower wages. The share is also larger when destined for richer, less populated countries that are further away. Finally, we find an upward trend for the share of processing exports in total exports, but this trend is smaller for internal provinces and faraway destinations.

In conclusion, we find evidence that China’s processing exports become more sensitive to both import and export distance in times of rising oil prices, but not more sensitive to internal distance. We also find supporting evidence that processing exports are more sensitive to changes in trade costs related to oil price movements than non-processing exports. Specifically, an increase in oil prices tends to reduce the share of processing exports in total exports, and especially when destined for far away countries.

# 6. **Intra-GPN Trade and the Great Recession**

Finally, we can use the processing trade data to examine the impact of the Great Recession on intra-GPN trade flows. This analysis has become particularly relevant in light of the collapse in trade during the crisis, which was significantly larger than the drop in world GDP. A number of scholars have attributed the disproportionate trade collapse to vertical specialization. Barry Eichengreen, for example, stated that “the most important factor is probably the growth of global supply chains, which has magnified the impact of declining final demand on trade” (International Economy, 2009). Bems, Johnson and Yi (2009) argue that “international supply chains are a leading contender for explaining why the great collapse was so great.” 27
||**Tab**|**le 16. Mean**|**s of Paying Bills**|||
|---|---|---|---|---|---|
||**Banked**|**Unbanked**||**Banked**|**Unbanked**|
|**Communications**|||**Electricity**|||
|**Cash**|**92.3%**|**97.6%**|**Cash**|**93.8%**|**98.9%**|
|**Check**|**4.0%**|**0.6%**|**Check**|**3.3%**|**0.5%**|
|**Credit card**|**3.2%**|**1.3%**|**Credit card**|**2.6%**|**0.5%**|
|**Food**|||**Water supply**|||
|**Cash**|**96.1%**|**99.5%**|**Cash**|**94.1%**|**98.9%**|
|**Credit card**|**5.3%**|**0.8%**|**Check**|**3.0%**|**0.4%**|
|**Check**|**1.1%**||**Credit card**|**2.6%**|**0.7%**|
|**Health expenditure**|||**Furniture**|||
|**Cash**|**97.5%**|**98.9%**|**Cash**|**87.0%**|**99.3%**|
|**Check**|**1.5%**|**0.2%**|**Check**|**3.9%**||
|**Credit card**|**1.5%**|**0.9%**|**Credit card**<br>**Debit card**|**7.8%**<br>**2.6%**|**0.7%**|
|**Leisure articles**|||**Transportation service**|||
|**Cash**|**89.8%**|**98.9%**|**Cash**|**97.5%**|**99.0%**|
|**Credit card**|**6.8%**|**1.1%**|**Credit card**|**1.2%**|**0.8%**|
|**Debit card**|**3.4%**||**Debit card**|**1.2%**|**0.1%**|
|**Appliances**|||**Department stores**|||
|**Cash**|**87.5%**|**98.4%**|**Cash**|**82.1%**|**96.3%**|
|**Check**|**1.6%**|**0.8%**|**Check**|**2.6%**||
|**Credit card**|**9.4%**|**1.6%**|**Credit card**|**10.3%**|**2.5%**|
|**Debit card**|**3.1%**||**Debit card**|**5.1%**|**1.3%**|
|**Rent**|||**Education**|||
|**Cash**|**95.6%**|**97.2%**|**Cash**|**94.7%**|**98.1%**|
|**Check**|**2.2%**|**1.4%**|**Check**|**5.9%**|**2.1%**|
|**Debit card**|**2.2%**|**2.1%**||||
|**Source: ENDESFI**||||||

As shown in table 17, a large share of the individuals in the survey reported that they had requested credit from a department store. This credit was undoubtedly used to finance purchases. Among banked individuals, 38% requested a loan from a bank. By definition, none of the unbanked reported borrowing from a bank, however almost half of them reported requesting credit from a department store. Significant percentages of banked and unbanked individuals borrowed from the “other” category, which refers to a variety of informal sources. A striking result in table 17 is the low percentage (0.8%) of individuals who reported that they used a money lender. Even more striking is that only banked individuals reported using a money lender as a source of credit. We suspect that this pattern would not hold in a larger survey.

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**Table 12c: Predicted change in number of each animal per farm from AOGCM climate scenarios for large farms**

|||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|---|
|<br>Baseline|number|77.58|35.82|23.81|49.40|1907.25|
|2020|||||||
||CCC|1.49|1.04|5.00|0.98|-57.80|
||CCSR|-2.16|0.91|6.01|1.82|-123.32|
||PCM|-1.18|-0.92|0.00|-1.24|-96.66|
|2060|||||||
||CCC|-0.51|-0.56|2.44|-1.43|-139.20|
||CCSR|-3.41|1.53|6.64|-0.13|-184.73|
||PCM|-1.56|1.42|11.59|-1.99|-141.62|
|2100|||||||
||CCC|-14.18|1.17|25.53|-2.69|-289.22|
||CCSR|-4.75|-1.98|5.78|41.22|-8.72|
||PCM|0.21|-0.64|3.57|5.24|-180.19|

**Table 12d. Predicted change in expected income from AOGCM climate scenarios for large farms (US$)**

|||**Mean**<br>**(US$/farm)**|**Total**<br>**(billions US$)**|**% change**|**Bootstrap**<br>**lower 95 %**|**Bootstrap**<br>**upper 95%**|
|---|---|---|---|---|---|---|
|<br>Expected|income||||||
|2020|||||||
||CCC|-779|-6|-10.0%|-932|-442|
||CCSR|-1000|-8|-12.8%|-1068|-703|
||PCM|-2817|-22|-36.0%|-4624|-2481|
|2060|||||||
||CCC|-3171|-24|-40.5%|-5358|-2926|
||CCSR|-1232|-9|-15.7%|-2084|-345|
||PCM|-2362|-18|-30.2%|-3431|-2040|
|2100|||||||
||CCC|-6023|-46|-77.0%|-8567|-5732|
||CCSR|-2685|-21|-34.3%|-6003|-2184|
||PCM|-3382|-26|-43.2%|-5156|-2896|

39
Georgia and Iran will cease completely. Therefore, we have applied a correction factor of 0.5 to cost economy estimates. The final estimates range from US$0.5 to 1.2 million for imports and from US$0.2 to 0.6 million for exports (based on 1999 data). Total savings are in the range of 0.7-1.8 million dollars, or 1-3 percent of the 1999 debit in freight services in the BOP.

# Georgia

As mentioned above, Georgia is likely to lose a part of its transit traffic to/from Arnenia if the Turkish border with Armenia opens. Mostly container traffic would be affected. Much of long-haul general cargo would probably continue to travel through Georgia by rail and sea, since the Turkish railways are not likely to be used for mass transit of goods due to their low technical capacities.

Using the data provided by the authorities of the port of Poti, we have estimated current container traffic between Arnenia and the rest of the world through Georgia at 5,000 units per year. Interviewed forwarders have estimated that, if Turkish ports were used instead of Poti, the road portion of transportation would cost 25 percent less. At the current cost of $1,400 per 20 foot container and $1,800 per 40 foot, and the estimated 80 percent share of 20 foot containers, this amounts to a total potential savings of US$1.9 million. If a quarter of the traffic still continued to use Poti, the estimate would stand at US$1.4 million. The resulting loss of transit revenues to Georgia would range between US$5.6 and 7.4 million dollars, or 16-21 percent of the surplus in cargo services in the BOP.

# **4. Trade in Energy**

# **4.1. Current Flows**

Energy flows have immense importance in the region. Armenia and Georgia are the importers of petroleum and natural gas. The economies of these countries are very dependent on fuel imports. The disruption of fuel supplies in the early 1990s brought these economies to nearly total collapse. Armenia has large surplus electric power capacities. Azerbaijan has relatively large proven resources of oil and gas but is a net importer of power. (This happens not because of a lack of generating capacities but due to disruptions in the fuel supply to power stations caused by payment problem.) It should be noted that all countries in the region export and import electric power on a swap basis in order to smoothen seasonal and peak differences.

The major energy flows in the region are as follows.

Armenia imports natural gas and nuclear fuel from Russia. Petroleum products are imported from a variety of destinations, including Iran, Georgia, East and West Europe, and, recently, Turkey. It exports electricity to Georgia and ran.

31