rafmacalaba/datause-encoder-data huggingface dataset:
|||||USA<br>JPN<br>GER<br>IND|KOR|MYS||
|---|---|---|---|---|---|---|---|
|**Accum**|**ulated Impulse**|**Responses**|0.000||||0.00|
||6 months|12 months|-0.002||||-0.06<br>-0.03|
|USA<br>JPN|-0.002<br>-0.0042|-0.0021<br>-0.0047|-0.004||||-012<br>-0.09|
|GER|-0.0044|-0.0047|-0.006||||-015<br>.|
|IND|-0.0042|-0.0038|||||018<br>.|
|KOR|-0.0054|-0.0057|-0.008|Accumulated Impulse Responses|||021<br>-.|
|MYS|-0.0077|-0.0082|-0.010|VAR elasticities|||-0.24<br>-.|

**Table 6.** Six and twelve months accumulated impulse response functions of IP to a RV shock; **Figure 6.** Accumulated 12 months Impulse Responses (left axis) in comparison with estimated VAR elasticities (right axis). Both estimation methods suggest similar levels of sensitivity across sample countries.

# **3.6. Variance Decomposition**

Enders (2010) advocates forecast error variance decomposition to confirm the results from the above impulse response analysis. Variance decomposition allows distinguishing between respective shocks to the elements of a VAR, in order to explain variation in an endogenous variable. Hence, it investigates the relative importance of each random shock in affecting variables of a VAR. For the purposes of this study it is essential to investigate to which extent shocks to realized volatility explain the τ-step-ahead IP forecast error variance σIP(τ)<sup>2</sup> . The τ- steps-ahead conditional mean forecast of the infinite vector moving average process from Section 3.6. is Accordingly, the τ-period forecast error em+τ is given by As Xm = [IPm RVm]′, the τ-period forecast error eIP,m+τ for the IPm sequence alone is The τ-step-ahead forecast error variance of IPm+τ is then denoted as σIP(τ)<sup>2</sup> : Note that σIP(τ)<sup>2</sup> increases in the forecast horizon τ, since A1,1(i)<sup>2</sup> and A1,2(i)<sup>2</sup> are nonnegative. Furthermore, σIP(τ)<sup>2</sup> can now be decomposed into the proportions which are due to shocks in the �εIP,m� and �εRV,m� sequences respectively, This decomposition states the extent to which movements in industrial production are due to its own shocks, as opposed to shocks to realized volatility. The results confirm that, as expected, shocks in the �εIP,m� sequence explain most of the forecast error variance for the industrial production sequence – however εRV,m shocks are also found to explain between 2% and 5.6% of the variation in the first one to five periods. On the contrary, εIP,m shocks explain none of the forecast error variance in the realized volatility sequence. These results are supportive of the findings from the analysis in previous sections, particularly the impulse response functions (Section 3.6).

11
# **Appendix B:  Estimated collected tariff rates**

**Table B1.   Summary statistics of estimated collected import tariff rates in Russia in 2001-2005**<sup>**_(a)_**</sup>

|Year|Tariff|Observations|M|ean<br>|Standard|Deviation<br>|imum<br>ate|imum<br>ate|
|---|---|---|---|---|---|---|---|---|
||||Simple|Trade<br>weighted|Simple|Trade<br>weighted|Min<br>r|Max<br>r|
|2001|_Actual MFN tariff rate_|11,076|10.5|10.6|10.4|8.9|0|518|
|2002|_Actual MFN tariff rate_|11,148|11.0|12.7|12.7|14.6|0|388|
|2003|_Actual MFN tariff rate_|11,161|11.5|13.5|16.3|17.5|0|939|
|2004|_Actual MFN tariff rate_|11,218|11.2|13.4|12.8|16.5|0|293|
|2005|_Actual MFN tariff rate_|11,365|11.0|13.3|12.1|14.7|0|384|

Notes: _(a) Table 1 presents summary statistics at the ten digit level_ 42
of people they had to mobilize, and the number of groups that they had to create. They also achieved these targets more quickly than their local counterparts (Table 3, Panel C).

In our fieldwork, we found that external facilitators used their status as “outsiders” to create new systems of working. A salient aspect of this is that they often chose to work late at night, sometimes holding meetings until 2AM, almost unaware of the dangers of doing so.<sup>26</sup> We observed that the Rajasthani culture of hospitality towards outsiders often served to benefit the external facilitators. There was a great deal of curiosity about them, and their distinctive language, appearance and diet, made them highly visible in a way that local women were not. In our qualitative work, we found mixed opinions about whether this was an advantage or disadvantage, but most agreed that external facilitators found it easier to get the attention of villagers and develop an initial interest in the SHG program.

# Differences in Impact

Self-Reported Measures of Impact In our facilitator survey, we asked respondents to provide examples of the impact of their mobilization. Specifically, we asked them to describe an example of collective action the women had taken that made them most proud (as mobilizers).<sup>27</sup> The response was left open-ended and we post-coded the responses into the following groups: woman-specific issues (dowry, domestic violence, women’s credit groups, child-care issues, etc.), political issues and issues related to public service delivery. To test the hypothesis that external facilitators induce collective action differently than internal facilitators, we ran the following regression: where cfbis a measure of collective-action outcomes reported by facilitator _f_ in block _b_ , EXTERNALb is a dummy variable that takes value 1 if the SHGs in the block are being facilitated by external facilitators, Xf is a vector of controls for facilitator _f_ (this includes her age, education, experience, job-description and salary), and εibis the error term. We used the logit model to estimate this question, with local facilitators as the base (excluded) group.

Given the results of our qualitative fieldwork, we expected external facilitators to report _more_ examples of female empowerment as defined by individual stories about women using the SHG platform to secure financial independence, gain bargaining power in their households and 26 Their supervisors told them that this was ‘free’ time, and that village women would understand that these meetings were for their benefit and so would be willing to temporary relax prevailing social norms. A Rajasthan government official told us that he once had a 2AM call from a SERP facilitator asking for transportation because she was standing alone at a bus stop and needed to get back to her lodging site.

> 27 The question was worded as follows: “Provide any example of an SHG woman engaging in collective action?”

14
between 2006 and 2011, but corresponds to only one point in time, which does not allow for examination of city height evolution.

As mentioned before, for each city, the data from the DLR provides average height of every pixel inside the defined urban extent, with a resolution of 90x90 meters. Table 3 provides summary statistics on building heights across cities at different income levels. These are average heights, covering the entire urban extent.

**Table 3: City Heights Measures - Summary Statistics by Income Group**

||**Low**<br>**Income**|**Lower-**<br>**Middle**<br>**Income**|**Upper-**<br>**Middle**<br>**Income**|**High**<br>**Income**|
|---|---|---|---|---|
|Ave Height (in m)|2.37|2.34|3.58|3.34|
||(0.53)|(0.77)|(1.32)|(1.14)|
|Ave q90 (in m)|3.63|3.72|6.24|5.93|
||(1.21)|(1.56)|(2.73)|(2.62)|
|Ave q95 (in m)|4.26|4.35|7.56|7.69|
||(1.58)|(2.00)|(3.43)|(3.88)|
|Ave max (in m)|10.52|13.26|25.12|31.78|
||(6.65)|(12.26)|(23.58)|(16.83)|
|Ave TFA (in m2)|78.65|140.75|354.58|707.37|
||(111.24)|(325.67)|(1,118.25)|(1,209.62)|
|Ave TFA/City Area|0.79|0.78|1.19|1.11|
||(0.18)|(0.26)|(0.44)|(0.38)|

Source: Analysis based on DLR WSF 3D data set Note: These are average heights for 90m x 90 m blocks, further averaged across the city’s extent

# 3. Stylized facts

In this section, we provide stylized facts on the magnitude and spatial distribution of built-up area growth and vertical layering in cities. As we have data on heights and can compute floor area, we provide a new measure of floor space adjusted density.

# 3.1 The growth of urban built-up area is slower than has been thought

Analysis of the satellite data show that between 1990 and 2015 the world’s total urban built-up area increased by 66,000 km<sup>2</sup> , or the size of Sri Lanka. Urban built-up area increased from 226,768 km<sup>2</sup> to 294,550 km<sup>2</sup> , a growth of 30 percent. In developing countries, urban built-up area increased from 117,977 km<sup>2</sup> to 166,231 km<sup>2</sup> over the same time period. That is a 34 percent growth in built-up expansion, an annual growth of 1.36%. While this is significant, it is not quite the explosive spread estimated by many recent studies. For example, the _Atlas of Urban Expansion_ (Angel et al, 2016) argued that during this period the area occupied by cities in less developed countries increased by a factor of 3.5—and that if this rate were to continue, the total amount of land taken over by urban land use would be equivalent to the entire country of India by 2050 (Angel et al 2016). Huang et al (2019) project urban land areas to expand 7
2025. Transition losses are driven primarily by utilities (EUR 54 million), wholesale and retail trade (EUR 29 million), and transport (EUR 19 million). The physical risk scenario is expected to lead to gradually increasing losses culminating in an increase in expected annual loss of 6.3 percent by 2050. Physical risk losses are driven primarily by wholesale and retail trade (EUR 28 million), agriculture (EUR 23 billion), and accommodation and food (EUR 14 million). Results are presented in Table 3.

# 5. Conclusions and recommendations

**This paper presents an assessment of the vulnerability to climate-relate financial risks for a sample of European CGSs.** Based on unique sectoral and spatial data collected through a survey of AECM members, to the best of our knowledge this paper represents the first attempt to measure climate-related financial risks for CGSs, both transition risks and physical risks. In particular, this paper links CGSs’ guarantee portfolio data to a set of sectoral and spatially disaggregated transition and physical risk vulnerability indicators, which allows us to identify the portfolio shares at risk per indicator. We also identify sectors that are most relevant for CGSs to focus on by creating a transition risk score which combines exposure with GHG intensity. Finally, we conduct a stylized stress test to assess potential financial losses that CGSs could incur under adverse climate-related scenarios.

**CGSs in Europe are exposed to transition risks in a broad range of sectors as well as to physical risks in vulnerable sectors and regions.** Our results show that about one-third of CGSs’ guarantee portfolios is towards sectors that have high exposure to a disorderly energy transition. The most relevant sectors for CGSs from a transition perspective include crop and animal production, land transport, and wholesale trade. European CGSs are also exposed to a broad range of climaterelated physical risks, especially wildfire, coastal flood, and river flood, with 24-31 percent of outstanding guarantees towards sectors that have elevated exposure to climate change and weather variability. The annual expected losses on the aggregate guarantee portfolios could increase by EUR 181 million and EUR 128 million for transition and physical risk scenarios, respectively. These figures represent specific future scenarios as investigated by the ECB and we note that other, more severe, scenarios are possible. This is especially the case for physical risks, for which there are large and deep uncertainties regarding the underlying climate science.

Table 3 – Expected annual credit losses on CGS portfolios per sector, under baseline and stressed scenarios (NACE 1 sample, 2020, EUR million)

|||**(A) Orderly**<br>**(20**|**transition**<br>**25)**|**(B) Hot ho**<br>**(20**|**use world**<br>**50)**|
|---|---|---|---|---|---|
|**Sector**|**Baseline**|**Stressed**|**Difference**|**Stressed**|**Difference**|
|A - Agriculture, forestry and fishing|208|213|5|231|23|
|B - Mining and quarrying|3|15|11|3|0|
|C - Manufacturing|220|235|15|233|13|
|D - Electricity, gas, steam, and A/C|19|73|54|21|2|

15
Table A4: Regressions on transfer

||Amount transferred (std)|Transferre|d nothing|
|---|---|---|---|
||(1)<br>(2)|(3)|(4)|
|Cooperation|0.232<sup>_∗∗∗_</sup><br>0.234<sup>_∗∗∗_</sup>|-0.122<sup>_∗∗∗_</sup>|-0.122<sup>_∗∗∗_</sup>|
||(0.089)<br>(0.090)|(0.044)|(0.044)|
|Between|0.186<sup>_∗∗_</sup><br>0.183<sup>_∗_</sup>|-0.035|-0.032|
||(0.094)<br>(0.094)|(0.044)|(0.044)|
|Cooperation*Between|-0.242<sup>_∗_</sup><br>-0.251<sup>_∗_</sup>|0.104<sup>_∗_</sup>|0.109<sup>_∗_</sup>|
||(0.132)<br>(0.133)|(0.063)|(0.062)|
|Republican|-0.057||0.040|
||(0.075)||(0.035)|
|High age|-0.156<sup>_∗∗_</sup>||0.135<sup>_∗∗∗_</sup>|
||(0.067)||(0.032)|
|Female|-0.030||-0.001|
||(0.066)||(0.031)|
|High income|-0.042||0.024|
||(0.069)||(0.033)|
|Constant|0.796<sup>_∗∗∗_</sup><br>0.924<sup>_∗∗∗_</sup>|0.524<sup>_∗∗∗_</sup>|0.434<sup>_∗∗∗_</sup>|
||(0.063)<br>(0.079)|(0.031)|(0.040)|
|Observations|1015<br>1015|1015|1015|
|_R_<sup>2</sup>|0.007<br>0.015|0.008|0.030|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 _Note:_ The table reports OLS regressions on transfers. The dependent variable in columns (1) and (2) is the standardized amount transferred to the unlucky worker. The dependent variable in columns (3) and (4) is an indicator for the spectator transferring nothing to the unlucky worker. “Cooperation” is an indicator for the spectator being assigned to a treatment where the workers cooperate within group and “Between” is a dummy for the two workers belonging to different groups. “Republican” is an indicator for the spectator self-reporting voting Republican. “High age” is an indicator for the spectator being above median age in the sample. “Female” is an indicator variable for the spectator being female. “High income” is an indicator for the spectator having an income above the median income in the sample. Standard errors in parentheses, where * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

18
are constructed. If credit growth in asset class _i_ of bank _j_ is less than the average credit growth, then the probability of default in asset class _i_ of bank _j_ is equal to the aggregate PD, that is, _PDi_<sup>S</sup> , _j_<sup>=</sup><sup>_PD_</sup> _i_<sup>S.28If, however, credit growth for bank</sup><sup>_j_is greater than the average</sup> credit growth, then the probability of default under scenario S is scaled up by an amount that depends upon how much larger credit growth was for this bank in a given asset class, relative to the median credit growth in the entire system.

Due to the restriction of a zero weight when _CGi_ , _j <_ median( _CGi_ ), the scaling term [ _CGi_ , _j −_ median( _CGi_ )]/[max ( _CGi_ ) _−_ median( _CGi_ ] is bounded between 0 and 1. This implies that the maximum amount by which the PD for bank _j_ in asset class _i_ can increase over the aggregate TTC PD due to the penalty is given by the size of the scaling parameter _κ_ . The effective bounds on _PDi_<sup>S</sup> , _j_<sup>are thus given by [</sup><sup>_PD_</sup> _i_<sup>S,</sup><sup>_PD_</sup> _i_<sup>S+</sup><sup>_κ_].Should it be the case</sup> that no annual credit growth data at the individual bank level are available, then the _κ_ parameter can be set to 0. The consequence of this is that each _PDi_<sup>S</sup> , _j_<sup>then collapses to</sup><sup>_PD_</sup> _i_<sup>S.</sup> This effectively results in each bank having the same PD for asset class _i_ . Finally, notice that the scaling of the PDs is linear. Therefore, a bank that experienced credit growth in a particular asset class at twice the rate of another bank in that asset class will have a twice as large penalty increase in the probability of default compared to the other bank.

LGDs for the individual banks and asset classes under the different scenarios are constructed as follows. Let _LGDi_<sup>S</sup> , _j_<sup>be the Loss Given Default for bank</sup><sup>_j_in asset class</sup><sup>_i_under</sup> scenario S. _LGDi_<sup>S</sup> , _j_<sup>is then computed as</sup> efforts should be directed to making sure that more empirically motivated calibrations could be adopted. The section with empirical example will illustrate sensitivity of the stress test outcomes to changes in the calibrations in the context of the particular example.

> 28 _PDi_ S<sup>is equal to the values obtained in (8), for the TTC, PIT and Stress scenarios.</sup>

35
We run the Heckman estimations focusing only on one chapter, namely 85. Chapter 85 records trade in electrical machinery, equipment, and parts as well as telecommunications equipment, sound recorders, and television recorders. It is thus a chapter that holds highly differentiated products, in a horizontal as well as vertical sense. The estimation results are reported in Table 11. A very similar picture emerges as in the estimations above. Facing high costs of starting a business, in terms of procedures and cost, indeed reduces the probability of trade links between countries in goods of product group 85. Trade costs and market size are again positively correlated with the unit values. The coefficients of both variables in columns (1) and (3) are nearly identical to the ones observed in Table 10, (27).

**Table 11:** **_Heckman Estimation for Quality Trade (Chapter 85)_**

||(1)Flow|(2) Select|(3)Flow|(4) Select|
|---|---|---|---|---|
|Wei. Dist.|0.235***|-0.952***|0.234***|-0.948***|
||(0.026)|(0.077)|(0.026)|(0.076)|
|GDP Ratio|0.078***|-0.490***|0.078***|-0.456***|
||(0.009)|(0.046)|(0.009)|(0.041)|
|Procedures||-1.000***<br>(0.347)|||
|Cost||||-0.143***|
|||||(0.039)|
|Observations|137130|137130|137130|137130|

Notes: All regressions are run with importing and exporting country fixed effects as well as product fixed effects, for which the coefficients are not reported; Wei. Dist. and GDP Ratio stand for the log of weighted distance and the ratio (in logs) of GDP between the importing and exporting country, respectively; Procedures and Cost present the log of the sum of both variables between the exporting and importing country; ***,**,* denotes significance at the 1 %, 5 %, and 10 % level, respectively; robust standard errors, which are in parentheses, are corrected for clustering by country pair.

# **4. Summary and Conclusion**

This paper constitutes a first attempt to fully model quality in a heterogeneous quality firms’ framework. In our model high quality firms enjoy a relatively stronger demand for their products, as a result, high quality products are more likely to be exported and to be traded over greater distances.

Our model also features a market size effect. The export cut-off level in the smaller market is always higher which means that more firms are able to enter the export market. The reason is 35
**Table 2. An Example:  changes in trend from pre to post crisis in Mexico**

||E|mploym<br>(per|ent rat<br>cent)|io|Year<br>(perc|-on-Ye<br>entage|ar Cha<br>point|nge<br>s, pp)|Average<br>pre-crisis<br>trend<br>(pp)|Average<br>post-<br>crisis<br>trend<br>(pp)|difference<br>(post-pre)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Year/Q**|**uarter**<br>1Q|2Q|3Q|4Q|1Q|2Q|3Q|4Q|2007-<br>2008|2009||
|A.|One cell: Low|educa|ted mal|e yout|h in ur|ban ar|eas|||||
|**2007**|57.3|57.4|58.2|60.8|-|-|-|-||||
|**2008**|57.8|58.3|57.8|57.0|0.5|0.9|-0.4|-2.2|-0.3|||
|**2009**|54.2|53.9|54.1|-|-3.6|-4.4|-2.7|-||-3.6|-3.3|
|B.|All: weighted|averag|e of 16|cells||||||||
|**2007**|61.3|61.7|61.6|63.0|-|-|-|-||||
|**2008**|61.8|62.4|62.0|61.4|0.5|0.7|0.4|-1.6|0.0|||
|**2009**|60.2|60.4|60.6|-|-1.6|-2.0|-1.4|-||-1.7|-1.7|

27
However, the percentage of countries offering residential electricity service at tariffs lower than operating and maintenance cost fell from 16% to 0% over the entire period.

Industrial tariffs tend to be slightly lower than residential tariffs, but follow a similar pattern of evolution. Thus, average residential electricity tariffs in the 19 Latin American countries considered rose from US$0.07 to US$0.09 per kilowatt-hour between 1990/96, and back down to US$0.07 per kilowatt-hour by 2002. Correspondingly, the percentage of countries recovering some degree of capital costs rose from 74% to 89% between 1990/96, falling down to 68% by 2002. Furthermore, the percentage of countries offering industrial electricity service at tariffs lower than operating and maintenance cost rose from 5% to 16% over the entire period.

**Table 5: Real electricity tariff trends in 19 Latin American countries**

|US$/kWh|Residen|tial||Industria|l||
|---|---|---|---|---|---|---|
||1990|1996|2002|1990|1996|2002|
|**Average tariffs**<br>|||||||
|Argentina|0.09|0.12|0.04|0.09|0.09|0.02|
|Bolivia|0.07|0.08|0.06|0.07|0.09|0.04|
|Brazil|0.08|0.15|0.08|0.06|0.06|0.04|
|Chile|0.14|0.15|0.08|0.08|0.08|0.06|
|Colombia|0.04|0.05|0.07|0.07|0.10|0.06|
|Costa Rica|0.06|0.08|0.06|0.08|0.11|0.07|
|Dominican Republic|0.07|0.11|0.10|0.11|0.13|0.10|
|Ecuador|0.02|0.03|0.09|0.04|0.06|0.08|
|El Salvador|0.04|0.09|0.09|0.04|0.12|0.11|
|Guatemala|0.05|0.08|0.08|0.07|0.11|0.07|
|Honduras|0.07|0.07|0.07|0.05|0.10|0.06|
|Jamaica|0.18|0.15|0.16|0.12|0.12|0.11|
|México|0.06|0.05|0.08|0.06|0.04|0.06|
|Nicaragua|0.06|0.12|0.13|0.07|0.11|0.11|
|Panama|0.16|0.13|0.12|0.14|0.11|0.10|
|Paraguay|0.06|0.07|0.05|0.04|0.06|0.03|
|Peru|0.02|0.16|0.11|0.08|0.06|0.07|
|Uruguay|0.11|0.17|0.10|0.08|0.09|0.05|
|Venezuela|0.02|0.02|0.06|0.02|0.02|0.03|
|Average|0.07|0.10|0.09|0.07|0.09|0.07|
|Median|0.06|0.09|0.08|0.07|0.09|0.06|
|Minimum|0.02|0.02|0.04|0.02|0.02|0.02|
|Maximum|0.18|0.16|0.16|0.14|0.13|0.11|
|**Cost recovery (%)**|||||||
|Partial capital costs|32|68|63|74|89|68|
|Partial O&M costs|52|21|37|21|6|16|
|Nil|16|11|0|5|5|16|

Source: OLADE, 2004 Once again, it is interesting to see how current electricity tariffs in Latin America compare with those in other regions (Table 6). The comparisons are based on average national tariff data for a sample of 83 countries.

12
|Fraction Hindu|0.94|0.03725|0.01387|
|---|---|---|---|
|SD of black<br>market premium|0.88|0.00003|0.00002|
|**Private credit**|**0.53**|**-0.00003**|**0.00004**|
|Life expectancy|0.31|0.00011|0.00021|
|High school<br>enrolment|0.25|-0.00932|0.02257|
|**Bank Z-score**|**0.21**|**-0.00004**|**0.00010**|
|Rule of law|0.16|0.00106|0.00369|
|French colony<br>dummy|0.15|-0.00091|0.00311|
|Degree of<br>capitalism|0.14|0.00014|0.00055|
|Size of labor force|0.13|1.3e-08|5.0e-08|
|Black market<br>premium|0.12|0.00084|0.00375|
|Spanish colony<br>dummy|0.11|-0.00046|0.00250|
|Civil liberties|0.10|-0.00018|0.00096|
|Number of years<br>of open economy|0.09|0.00034|0.00206|
|Age|0.09|1.1e-06|0.00001|
|GDP level in 2000|0.09|0.00009|0.00076|
|British colony<br>dummy|0.08|0.00004|0.00079|
|Public education|0.08|0.00470|0.03815|

38