rafmacalaba/datause-encoder-data huggingface dataset:
several indices of bank internationalization for parent banks. These internationalization indices pertain to foreign subsidiaries and not to foreign branches, as information on foreign branches is not available from Bankscope. Furthermore, we only use information on directly owned foreign subsidiaries. Thus, our internationalization measures to some extent undermeasure the size and complexity of a bank’s foreign operations.<sup>7</sup> First, we consider internationalization as indicated by the extent to which a bank’s liabilities are foreign. Banks with large foreign liabilities potentially receive lower bailout benefits. We focus on bank liabilities, as these are commensurate to the potential size of bank bailouts. _Foreign liability share_ , specifically, is constructed as the ratio of the sum of all foreign subsidiaries’ liabilities, weighted by the parent bank’s ownership share, to the consolidated liabilities of the parent firm. The mean value of the foreign liabilities share is 1.9% for the samples of all banks as seen in Table 1, and it is around 5.4% for the sample of only international banks.<sup>8</sup> Figure 1 plots the distribution of the foreign liabilities share for only banks with at least some foreign liabilities, which constitute 34.8% of the overall sample. Among these banks, 55.5% are seen to have a foreign liabilities share of less than 2.5%.

As an alternative internationalization measure, we consider the level of concentration of the overall bank’s liabilities among the countries where the parent bank has some liabilities. A

> 7 For the EU, the European Central Bank (2010, Tables 11 and 13) reports that the total assets of foreign intra-EU branches of EU banks amounted to 3.3 trillion euros in 2008, while total assets of foreign intra-EU subsidiaries of EU banks were 4.6 trillion euros in that year. Foreign subsidiaries thus represent 58% of the assets of intra-EU foreign bank establishments. Our data do not appear to undermeasure the overall importance of foreign subsidiaries in the EU. The ECB data for the EU, in particular, allow us to calculate a foreign subsidiary assets share relative to the total assets of credit institutions of 14.1%, while our Bankscope data yield a comparable foreign subsidiary assets share relative to the consolidated assets of banks of 13.1%. Our focus on banks also implies that we ignore the non-bank subsidiaries that banks tend to have (see Herring and Carmassi, 2010, p. 209).

> 8 The calculated value of the foreign liabilities share potentially exceeds 1, as some foreign subsidiaries may have internal debt to the parent bank. Indeed, there are very few observation of the foreign liabilities share exceeding 1, and this variable reaches a maximum of 1.64 in our sample.

8
areas.<sup>25</sup> This said, 24.4 kilometers per hour is much higher than the sometimes apocalyptic descriptions found in the popular press.

We note considerable differences in mean speed across cities. The standard deviation across cities is 3.8 kilometers per hour, more than half the standard deviation of 7.2 across trips in table 1. Mean speed for the slowest city is 16.2 kilometers per hour whereas it is more than twice as high for the fastest city at 34.9. We show below that these wide raw speed differences remain once we adequately control for features of our sampling strategy.

The second to the fifth rows of table 3 report mean speed for each type of trip separately. Circumferential trips are slower whereas amenity trips are faster. As we show below, these differences are mostly caused by differences in length and location.

The sixth row of table 3 reports a measure of mean speed by city, which, unlike the other rows, is not weighted by trip length. Because this increases the influence of shorter trips that are also slower, this unweighted mean of 21.8 kilometers per hour is slightly lower than the length-weighted mean of 24.4 reported in the first row.

The seventh row of table 3 exploits the information provided by Google Maps regarding trip duration in the absence of traffic. As expected, mean speed in the absence of traffic is higher but the difference is small. At 26.8 kilometers per hour, mean speed in the absence of traffic is only about 10% above the mean of actual speed reported in the first row. Interestingly, the variation across cities is not smaller for mean speed in the absence of traffic than for actual mean speed. If anything, it becomes slightly larger. We return to this intriguing finding below.

Finally, the last row of table 3 reports a measure of mean effective speed. Rather than trip length, we use the haversine distance between the origin and destination. Since the ratio between mean trip length and effective trip length is about 1.5 in table 1, we unsurprisingly find a roughly similar ratio between actual and effective trip speed.

# **_4.2 Trip regressions_**

Before an in-depth analysis of mobility indices and their correlates, we first estimate a number of variants of the generic regression described by equation (2).

25If anything, 38.5 kilometers per hour understates true travel speed since it is measured from a travel survey where respondents view trip duration as much more than just the time spent driving in traffic.

15
Brewer, Thomas 2005 “U.S. Public Opinion on Climate Change Issues: Implications for Consensus building and Policymaking” Climate Policy, 4: 359-376.

Brewer compiles and analyzes over 40 U.S. public opinion surveys from 1989 through 2004. He concludes that a substantial majority want the government to take stronger steps on climate change and welcome U.S. participation in the Kyoto protocol, and do not support “key elements of the Bush administration policies.” Mandatory emissions reductions on industry and economic assistance to developing countries are both supported.

Dunlap, Riley E. 1998. "Lay Perceptions of Global Risk: Public Views of Global Warming in Cross-National Context." International Sociology 13:473-498.

Dunlap reports results from a 1992 Gallup survey conducted in Canada, the U.S., Mexico, Brazil, Portugal, and Russia on public perceptions of global warming. Most respondents saw global warming as a problem, although it was not generally ranked as serious as ozone depletion or rain forest destruction. Most respondents did not understand climate science well. In particular the study, as have others, found confusion between climate change and ozone depletion. Despite limitations in knowledge however, citizens in all nations except Russia believed that climate change would have consequences in their lifetimes. As predicted by the concept of a risk society suggest, public perceptions of global warming do not vary consistently across differing social strata within nations. Dunlap makes the important consideration, however, whether detailed public understanding of highly complex issues such as global warming is feasible or even necessary for effective policy making.

Dunlap, Riley E.; Xiao, Chenyang McCright, Aaron 2001. Politics and Environment in America: Partisan and Ideological Cleavages in Public Support for Environmentalism Environmental <u>Politics</u> 10(4): 23-48.

Surveys U.S. trends in concern and level of information regarding climate change. Notes especially a new and growing divide between perceptions of Democrats and Republicans. The authors note that historically support for environmental protection in the United States has been relatively nonpartisan, but that especially since 1994 a partisan divide has been noticeable, especially among members of Congress (who tend to be more ideologically polarized than the general public). Data from the League of Conservation Voters on voting scores indicated a chasm in both the House and Senate over the past decade. Also, “nowhere is the partisan gap on environmental issues more apparent than on climate change.” Currently a very large gap exists between self-identified Republicans and Democrats in terms of perceptions of global warming. See below for more updated and detailed information on this trend.

Dunlap, Riley and Aaron M. McCright. 2008. "A Widening Gap: Republican and Democratic 50
Table <u>7: Impacts of hosting refugees on alternative measures of household poverty status</u>

||Consu|mption pove|rty|W|ealth pov|erty|
|---|---|---|---|---|---|---|
||||<br>Househ|<br>olds|||
||All|Rural|Urban|All|Rural|Urban|
|Refugee Intensity|0.179***|0.211***|0.001|-0.007|0.004|-0.052|
||(0.051)|(0.055)|(0.077)|(0.045)|(0.049)|(0.033)|
|Constant|-0.896***|-1.108***|0.144|0.651**|0.678*|0.351*|
||(0.336)|(0.369)|(0.445)|(0.328)|(0.374)|(0.187)|
|Round FEs|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|
|R-squared|0.043|0.051|0.018|0.115|0.128|0.020|
|# of EAs|433|290|143|433|290|143|
|N|12937|9337|3600|13522|9734|3788|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on status of household consumption poverty (first three columns) and wealth poverty (last three columns). Household sample weights from ESS2 applied. Columns labeled “All,” “Rural,” and “Urban” report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

45
short-term, there could be a very substantial role for increased “sharing,” as has been the case in a number of countries historically – as discussed in the next section.

**Table 1:  Long-run growth with increased “sharing”—an example**

||**1950**|**2000**|**End/Start**<br>**Ratio**|**%**<br>**Change**|**Annualized**<br>**Compound**<br>**% Change**|
|---|---|---|---|---|---|
|_s_|0.1|0.15|1.50|50|0.8|
|Total Income (_Y_)|2000|8800|4.40|340|3.0|
|Population (_P_)|50|85|1.70|70|1.1|
|Income per Capita<br>(_y = Y/P_)|40|104|2.59|159|1.9|
|_SP_Indicator:<br>_s*Y/(.4*P)_|10|38.8|3.88|288|2.8|

Note: The End/Start Ratio for SP = 1.5*2.6 – the product of the ratios of income per capita and the share of total income, as noted in the equations above. Also note that the approximation by summing percentage change is much more precise when comparing compound annualized growth rates than 50 year cumulative growth rates, which is to be expected.

Another simulation can illustrate a phenomenon that concerns many in the development community: “enclave” patterns of growth that do not benefit the bottom end of the income distribution. Often these might be associated with a brief growth spurt of a decade or so. Consider, for example, a hypothetical case of a country that is stagnated but experiences a natural resource boom that results in about 5 percent annual growth. As a poor country, perhaps a reasonable population growth would be about 1.8 percent. Now suppose that much of the new growth attracts resources from traditional sectors that employ much of the bottom 40 percent, so that there is a fairly dramatic drop in the share of total income accruing to that group: from 15 percent to 10 percent in a decade. In such a scenario (Table 2), the _SP_ indicator could actually decline despite the growth in income per capita of the total population.

**Table 2: “Enclave” growth spurt simulation**

||**2000**|**2010**|**End/Start**<br>**Ratio**|**%**<br>**Change**|**Annualized**<br>**Compound**<br>**% Change**|
|---|---|---|---|---|---|
|_s_|0.15|0.1|0.67|-33|-4.0|
|Total Income(_Y_)|2000|3300|1.65|65|5.1|
|Population(_P_)|50|60|1.20|20|1.8|
|Income per<br>Capita(_y = Y/P_)|40|55|1.38|38|3.2|
|_SP_Indicator:<br>_s*Y/(.4*P)_|15|14|0.92|-8|-0.9|

10
- J. S. Lerner and D Keltner. Fear, anger, and risk. _Journal of Personality and Social Psychology_ , 81(1):146–159, 2001.

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19
# **Table 1. Data sources**

||**Firm demogra**|**phics an**|**d survival**|**Labo**|**r productivit**<br>**Pid**|**y**<br>**Thhld**||
|---|---|---|---|---|---|---|---|
|**Country**|**Source**|**Period**|**Threshold**|**Source**|**ero**|**reso **|**Sectors**|
|Canada|Business register|84-98|Emp ≥1||||All Economy|
|Denmark|Business register|81-94|Emp ≥1||||All|
|Finland|Business register|88-98|Emp ≥1|Census|95-00 to 97-02||All|
||||Turnover:<br>Man: Euro 3.8m||85-90 to 90-95|Turnover:<br>Man: Euro||
|France|Fiscal database|89-97|Serv: Euro 1.1m|Fiscal database||3.8m|All|
|Germany (West)|Social security|77-99|Emp ≥1|Survey, pop.<br>weighted|95-00 to 97-02||All but civil<br>service,<br>self employed|
||||||82-87 to 93-98|Turnover:<br>Euro 5m||
|Italy|Social security|86-94|Emp ≥1|Survey|||All|
|Netherlands|Business register|87-97|None|Survey, pop.<br>weighted|94-99 to 1996-0|1|Private Business|
||Employment-|||Employment-|86-91 & 89-94||All but public|
|Portugal|based register|83-98|Emp ≥1|based register|||administration|
|||||Survey, pop.|95-00 & 96-01|||
|United Kingdom|Business register|80-98|Emp ≥1|weighted|||Manufacturing|
|USA|Business register|88-97|Emp ≥1|Census|87-92 to 92-97|Emp>1|Private businesses|
||||E 1|Al Idi|<br>9095 9601|E 9 &|<br>Fi|
||Register, based on<br>||mp ≥|nnua nustra<br>Survey. INDEC|<br>- to -|mp ≥<br>$2m<br>|<br>rm<br>demographics =<br>|
||Integrated System|||||threshold|all; productivity =|
|Argentina|<br>of Pensions|95-02|||||<br>manufacturing|
|||||Annual Industrial<br>|1997-2001|Emp ≥30 +||
|||||Survey||sample of||
|Brazil|Census|96-01||||10-29|Manufacturing|
||Annual Industry|||Annual Industry|80-85 to 94-99|Emp. ≥ 10||
|Chile|Survey (ENIA)|79-99|Emp.≥10|Survey (ENIA)|||Manufacturing|
||Annual<br>Manufacturing|||Annual<br>Manufacturing|82-86 to 94-98|Emp. ≥ 10||
|Colombia|survey (EAM)|82-98|Emp.≥10|survey (EAM)|||Manufacturing|
||||Emp ≥ 1|Business register|95-00 to 96-01|Emp ≥ 1|All|
|Estonia|Business Register|95-01||||||
||Fiscal register|||Fiscal register|92-96 to 97-01|Emp>1|All|
|Hungary|<br>(APEH)|92-01|Emp ≥1|<br>(APEH)||||
||Manufacturing|||Manufacturing|90-95|Emp. ≥ 10||
|Indonesia|survey|90-95|Emp.≥10|survey|||Manufacturing|
|||83-93 (3|||88 & 93|Emp ≥ 5|Manufacturing|
|Korea(Rep.)|Census|<br>years)|Emp ≥5|Census||||
|Latvia|Business register|96-02|Emp ≥ 1|Business register|96-01 97-02|Emp ≥ 1|All|
|Mexico|Social security|85-01|Emp ≥1||||All|
|Romania|Business register|92-01|Emp ≥ 1|Business register|95-98 to 96-99|Emp ≥ 1|All|
||||||92-97 to 97-01|Emp>1|All|
|Slovenia|Business register|92-01|Emp ≥1|Business register||||
|||86-91 (2|||86-91 to 91-96|Emp ≥ 1|Manufacturing|
|Taiwan(China)|Census|<br>years)|Emp ≥1|Census||||
||Annual Industrial||Emp ≥ 15, sample|<br>Annual Industrial|<br>95-99 to 96-00|Emp ≥ 5||
|Venezuela|Survey|95-00|of smaller|Survey|||Manufacturing|

9
<mark>Fleurbaey, M., & Peragine, V. (2013). Ex Ante Versus Ex Post Equality of Opportunity.</mark> _<mark>Economica, 80</mark>_ <mark>(317), 118-130.</mark> Freund, Caroline (2016). Rich People Poor Countries: The Rise of Emerging-market Tycoons and Their Mega Firms. Peterson Institute for International Economics, Washington DC.

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James (2015) establishes a negative relationship between resource and non resource revenues as the expression of a crowding out effect between these sources of revenue.

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12
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" percent of the households in Cote d'Ivoire used vendors, more than in any of the other three countries. Vendors were the primary source for 1 percent of households in Ghana and less than I percent in Pakistan and Nicaragua. In all four countries, a greater percentage of rich households than poor households used vendors. Less than 1 percent of households using vendors were in the poorest decile of their countries, whereas 20 percent were in the richest decile.

In three of the four countries, households using water vendors spent on average more per month than households with in-house water taps or those using other improved sources (Table 6). But only in Pakistan (where households with in-house service were spending very little per month on water) were the median expenditures of those using vendors significantly higher than those with inhouse taps. It is striking that average monthly expenditures on vended water are not higher than the likely full cost of in-house piped water service. Although the per-unit price of vended water is certainly higher than the per-unit price of water from in-house service, total household expenditures on water were smaller than what one might expect from the water vending literature (e.g., Crane, 1994; Fass, 1988; Whittington et al., 1989, 1990, 1991; Zaroff and Okun, 1984).34 6.3. Sanitation Some of the LSMS country data sets have information on two aspects of a household's sanitation situation: (1) whether a household had a toilet or latrine, and (2) whether a household had a means of removing wastewater from the house -- either a sewer connection or a septic tank. Information on septic tank usage is available in six of the fifteen countries (Bulgaria, Ecuador, Kazakhstan, Nepal, Nicaragua, and Pakistan). In these six countries more households had sewer connections than septic tanks, but septic tanks nonetheless made a significant contribution to sanitation infrastructure. More than half of all households in Bulgaria, Ecuador, and Kazakhstan had either a sewer connection or a septic tank. By contrast, most households in Nepal (84 percent),

> 34 These findings should not, however, be considered definitive because such a small percentage of sample households in these countries used water vendors.

17
of the impact of refugee intensity on the measures of household welfare and the status of poverty considered in the main analyses. The main findings remain robust quantitatively and qualitatively to this test.

# **7.2. Representativeness of the Sample**

The national representativeness of the sample is affected by attrition of observations (e.g., households) across rounds. The household-level attrition rate is 5% and 6% between the first and the last two rounds of surveys, respectively. In the main analyses, I run weighted regressions whereby each sample observation is weighted by its sample weight, the inverse of its probability of inclusion, in the ESS2. This implies that the main results account for attrition across the first two rounds, as the sample weights in ESS2 are adjusted weights of ESS1 taking into account relisting, nonresponse and attrition of households across the two rounds (CSA and World Bank, 2015). However, these estimates may not be fully nationally representative, since attrition across the last two rounds is not taken into consideration. To assess if the national representativeness of the main results are significantly affected by attrition across the last two rounds, I replaced the sample weights used in the main analyses by the weights from the ESS3, which are adjusted weights of the ESS2 taking into account relisting, nonresponse and attrition of households across these rounds of surveys (CSA and World Bank, 2017). Table A2 presents estimates of the impact of refugee intensity on the measures of household welfare and the status of poverty used in the main analyses. The main findings remain valid. Additionally, I excluded a small number of households (67) from the ESS3. These are households that moved from their location in the ESS2 to outside their woreda after the ESS2. I assume that these households are small to significantly change the national representativeness of the main results.

# **7.3. Specification of the Main Variables**

In the main analyses, household consumption expenditure and its components are specified in per capita terms. In this robustness exercise, I test whether the findings are robust to specifying these outcome variables in per adult equivalent terms. I present the results of this test in Tables A3 and A4. The main findings remain robust.

Additionally, I use the “started log” transformation of the wealth score, which takes values of all signs, in the main analyses. And I applied the standard logarithmic transformation to variables that take only positive values: household consumption expenditure per capita and the main explanatory variable of interest, i.e., refugee intensity. In this robustness test, I check whether 27