rafmacalaba/datause-encoder-data huggingface dataset:
Panel B of Table I presents data on cross-listing by country of origin. Following LLSV (1998), the countries are further classified on the basis of their legal tradition into English Common Law, French civil law, German civil law, Scandinavian civil law and Socialist Law countries. I exclude cross-listing firms from China since it does not belong to any of the legal traditions identified above. I also exclude cross-listing firms from Canada since none of them issue ADRs and all are direct cross-listings. The sample confirms the findings of Reese and Weisbach (2002) that more firms list from French civil law countries than from English common law countries and they do so disproportionately on the NYSE than on the NASDAQ. While 81% of firms from French civil law countries list on the NYSE, only 53% from the English common law countries do so.

# _3.2. Ownership Characteristics—Prior to Cross-listing_

For each of the companies in the sample, I identify who the ultimate owner of the company is, what his cash flow to voting rights are and whether there has been a change in ultimate owner from two years prior to issuing an ADR to five years, post-listing.

As a starting point for the data collection, I use Worldscope, which provides annual data on the names and holdings of all owners that hold more than 5% of a company’s stock. This information is supplemented with data from a variety of sources to include all owners who have more than 5% of outstanding stock and are missing in Worldscope. All the sources used are listed country-wise in Appendix B.

For the years prior to ADR listing, the main sources of data are Worldscope and company annual reports filed either with the domestic stock exchange or published on the company web pages. Other data sources include country specific company handbooks like the _Brazil Company Handbook and Venezuelan Company Handbook_ and international databases such as _Mergent International._ The ownership information for the years after ADR listing is more easily available because a large fraction of the ADRs file Form 20-F with the SEC. Section 7 of this form titled 13
Table 7. Impacts of Extremism on Income and Consumption

||Income and<br>Consumption|Monthly<br>House Income|Monthly<br>Consumption|
|---|---|---|---|
||Index<br>(1)|(log)<br>(2)|(log)<br>(3)|
|_Panel A. Taliban Exposure_||||
|_ETi_|-0.176|-0.026|-0.178|
||(0.165)|(0.108)|(0.110)|
|FDR q-values|[0.460]|[0.756]|[0.460]|
|R-squared|0.091|0.095|0.073|
|_Panel B. Contested Territories Exposure_||||
|_ECi_|-0.051|-0.035|-0.011|
||(0.064)|(0.041)|(0.043)|
|FDR q-values|[1.00]|[1.00]|[1.00]|
|R-squared|0.090|0.095|0.073|
|_Panel C. U.S.- Allies Exposure_||||
|_EAi_|0.050|0.025|0.025|
||(0.049)|(0.032)|(0.033)|
|FDR q-values|[0.780]|[0.780]|[0.780]|
|R-squared|0.091|0.095|0.073|
|Mean Dependent Variable|0.011|8.385|8.326|
|Observations|5,592|5,453|5,558|
|_Controls in All Panels_||||
|Individual Covariates|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|

_Notes:_ The Income and Consumption Index is constructed using the outcome variables of columns (ii) to (iv) using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again. See Appendix B for details of variable’s constructions. The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

46
share. Such a situation implies that the negative effect of that particular channel is more than offset by the extension caused by the others.<sup>7</sup> Let me first look at the regional averages reported in the last row. I find that changes in the patterns of final consumption and trade created grounds for an industrial expansion. Shifts in trade shares have supported an extension of the industrial employment allocation and imply an average increase of 2pp. However, the amplifying force generated by the two channels is more than offset by that affected by shifts in labor market wedges. The exercise shows that the overtime changes in wedges, on average, shaved off 4pp from the industrial employment share.

Now, let's see how the inference drawn at the regional level compares with the experience of the constituent economies. Consistent with the regional finding, wedges generate contractionary pressure in each country. Still, its impact varies substantially. Implied shrinkage is comparatively much higher in Panama (8.7pp) and is also quantitatively above the regional average in El Salvador (5.2pp). On the other hand, the channel's impact is milder in Costa Rica and Honduras. Like for the region, the channel accounts for more than the total contraction in industrial share in three of the five economies that underwent deindustrialization and can explain 85 percent of the decline in Honduras. In contrast, though aiding the fall, it accounts for less than 15 percent of the 6.2pp decline in Costa Rica.

**Table 2: Actual and implied change in industrial shares**

||||**Actual**||**Implied c**|**hange (pp)**<br>**Final**|**Labor**|
|---|---|---|---|---|---|---|---|
|**Country**|**Initial**<br>**year**|**Final**<br>**year**|<br>**change**<br>**(pp)**|**Final**<br>**cons.**|**Trade**|<br>**cons. &**<br>**trade**|<br>**market**<br>**wedge**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|CRI|1995|2016|-6.52|-8.26|4.46|-6.17|-0.91|
|GTM|2001|2016|-3.18|0.48|1.79|0.41|-3.74|
|HND|1995|2016|-1.57|-2.69|1.17|-0.80|-1.34|
|NIC|2003|2012|-1.81|1.90|-0.51|2.79|-4.14|
|PAN|1999|2016|1.14|13.13|5.58|13.40|-8.73|
|SLV|1995|2016|-3.30|0.51|-0.18|2.05|-5.24|
|Average|||-2.54|0.85|2.05|1.95|-4.02|

Figures in columns (4)-(7) indicate the change in industrial employment shares when the variables pertaining to a factor listed in the column is counterfactually changed to the level observed in the final year, keeping the value of all the other factors fixed at the initial year. All figures in percentage points (pp).

> 7 For example, changes in wedges caused considerable downward pressure on industrial shares in Panama. Yet, the tailwinds provided by other channels ensured that the country saw a modest net expansion. In other words, if there were no shifts in consumption and trade shares, Panama would have experienced significant deindustrialization.

13
# ANNEX 1 – Additional Figures

# Figure A1. Most IE Methods used are Comparable

<!-- Start of picture text --> Funnel plot with pseudo 95% confidence limits<br>-1 -.5 0 .5 1<br>pcorr<br>Structural Quasiexperimental<br>Simulation Lower CI<br>Lower CI Pooled<br>0<br>.1<br>.2<br>.3<br>Standard Error<br>.4<br>.5<br><!-- End of picture text --> Source: Own estimations

# Figure A2. Meta-regression Shows Some Publication Bias

<!-- Start of picture text --> 0 .5 1 1.5<br>sqrt(V)<br>Study regression line<br>95% CI for intercept<br>2<br>0<br>Z / sqrt(V)<br>-2<br><!-- End of picture text --> Source: Own estimations 33
Rocholl and Steffen (2010) match information from loan applications and loan contracts to disentangle whether reduced credit availability is the result of demand or supply determinants. Jimenez, Ongena, Peydro and Saurina (2012) find for their sample of Spanish firms that bank balance-sheet strength determines loan approval in crisis times. Firm balance-sheet strength determines loan granting in good as well as in crisis times but matters more during the latter. Puri, Rocholl and Steffen (2010) study the effects of the financial crisis on retail lending at German savings banks and find a general decrease of loan demand after the beginning of the crisis. On the supply side, it turns out that affected banks are less likely to grant loans but that bank relationships help to mitigate this effect. Finally, Popov and Udell (2011) find crosscountry evidence for Eastern Europe that firms located in areas served by foreign banks whose parent banks experience financial distress have a higher probability to be credit constrained.

In contrast to these papers, our study focuses on two distinct shocks occurring during the crisis and can thereby disentangle how different shocks impact on different borrower groups.<sup>6</sup> Our results imply that a funding shock may also show up in a reduced number of loan applications (i.e. on the demand side), while a real economy shock may impact on loan approval (i.e. on the supply side). Undoubtedly, having information on loan applications is an important feature in better disentangling demand and supply effects in bank lending during crisis times. Our results, however, indicate that information from loan applications may not be the remedy per se and future research in this area may need to more explicitly account for the different mechanisms banks have at hand to react to certain shocks. Our paper sheds some first light on these mechanisms.

Finally, our study is related to the literature on the performance of MSME banks and their portfolios during the East Asian financial crisis of 1997-1998. By analyzing the effects of the crisis on Bank Rakyat Indonesia (BRI), one of the largest MSME banks in the world, Patten, Rosengard and Johnston (2001) find nearly no effects on repayment behavior in the microfinance portfolio, while non-performance rates increased considerably in the SME portfolio suggesting that micro loans are less risky in times of crises compared to SME loans. Since higher default rates and reduced credit availability may both be outcomes of higher credit risk, we complement these results by showing that credit availability is similarly less affected for micro loans than for SME loans during the 2007-2009 financial and economic crisis.

> 6 In such, it is also related to the studies on specific liquidity or supply shocks to banks (e.g. Khwaja and Mian (2008) and Paravisini (2008)).

6
31 Columns (3) and (4) shed light on the issue of intrasectoral fungibility, and specifically between the provinces targeted by the two Bank projects and Vietnam’s remaining provinces. If domestic and external resources are fungible between the project and nonproject provinces, an increase in the province’s total resources ought to have the same impact on government health spending in project provinces as in nonproject provinces. The equations in columns (3) and (4) regress the same dependent variable— provincial government health expenditures (gross of overseas development assistance (ODA) to the health sector—on total provincial resources gross of ODA (either revenues or expenditures, as before) and this variable interacted with a dummy indicating whether the province is a project province. In neither column is the coefficient on the interaction term anywhere near significant; this suggests that domestic and external resources in Vietnam are fungible within the health sector across provinces. This is consistent with the opinions expressed in the Bank’s population and family health project documents and with the government behavior depicted in Figure 1.

The results in columns (3) and (4), together with the conclusions of section III, suggest that a simple diffs-in-diffs comparing the change in infant mortality between the project and nonproject provinces is likely to misestimate the benefits of the Bank projects. Column (5) reports the results of this diffs-in-diffs calculation, where the infant mortality dummy (at the child level) is regressed on a 2002 dummy, a project province dummy, and the interaction between the two; the coefficient on the latter indicates the diffs-in-diffs estimate of the project’s impact on the infant mortality rate (cf. e.g. Ravallion 2008). The coefficient is negative and implies a reduction in the infant
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 19. Illustrative investment targets for infrastructure in Senegal**

||**Economic target**|**Social target**|
|---|---|---|
|ICT|Strengthen connectivity with main economic partners in<br>the region through the installation of fiber-optic links to<br>neighboring capitals and submarine cables.|Provide universal access to the GSM signal and<br>modern communication services with public<br>broadband facilities.|
|Power|Increase electricity available to the economy—258 MW<br>of new generation capacity and 487 MW<br>interconnectors.|Expand electrification to 51 percent (100 percent<br>urban and 10 percent rural).|
|Transport|Achieve regional (national) connectivity with good-<br>quality 2-lane (1-lane) paved road.|Provide rural road access to 30 percent of the<br>highest-value agricultural land, and urban road<br>access within 500 meters.|
|WSS|n.a.|Achieve Millennium Development Goals and clear<br>sector rehabilitation backlog.|

_Source:_ Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology; GSM = global system for mobile communications. n.a. = Not applicable.

Meeting these illustrative infrastructure targets for Senegal would cost $1.8 billion per year over a decade, with capital expenditure accounting for 57 percent of this requirement. The highest spending needs are associated with the power sector, which will require an estimated $1.1 billion per year to install 258 MW of new generation capacity and expand electrification to meet the growing demand. The water and sanitation sector has the second-highest spending needs: almost $0.3 billion will be needed each year to meet the MDGs, with capital expenditure accounting for 60 percent of the total. While less than the amounts needed for power and water and sanitation, requirements for the ICT and transport sectors are also high in absolute terms, amounting to $0.25 billion and $0.14 billion, respectively (table 20).

**Table 20. Indicative infrastructure spending needs in Senegal, 2006–15**

<u>$ million per year</u>

||**Sector**|**Capital expenditure**|**Operations and maintenance**|**Total needs**|
|---|---|---|---|---|
|ICT||229|20|248|
|Power||507|555|1,062|
|Transport||87|54|141|
|WSS||204|138|342|
|Total||1,026|766|1,792|

_Source_ : Mayer and others 2009 **;** Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models.

_Note:_ WSS = water supply and sanitation; ICT = information and communication technology.

35
# Appendix D: **Sensitivity Analysis of the Simulation**

The model simulation relies on the assumption of the values of parameters. While the empirical estimates provide the values of the parameters in the model, the lack of data often induce modelers to use estimated values in other countries. Sensitivity analysis is undertaken to observe the difference of the simulation outcome by changing the values of parameters. The magnitude of the difference in the outcome provides modelers with confidence of the reliability of the simulation, which may affect judgments on the reliability of policy implications to be drawn from the simulation.

The simulation results of the GTAP model, particularly in regard to the impact on changes in trade depend on the assumed values of the Armington elasticities. They represent the elasticity of substitution between the share of an exporter county in the total imports of the importing country and the relative import prices of the country<sup>25</sup> . Sensitivity analyses are conducted on the values of the Armington elasticities in many cases to test the robustness of the simulation results.

The Gempack software, used for the simulation, provides a tool for conducting Systematic Sensitivity Analysis (SSA). Selecting one or two different sets of parameter values and solving the model for each set is a simple form of sensitivity analysis, sometimes referred to as _ad hoc_ sensitivity analysis. However, this approach sometimes requires unrealistically many runs and reruns of simulations to obtain a result. The SSA approach can considerably reduce the required number of the simulations. Table D-1 below summarizes the results of SSA on our simulation on the impacts to the nominal imports, by changing the values of the Armington elasticities: (i) between the imports of a country and (ii) between the imports and domestically produced goods. The elasticities are changed to 125 percent (i.e. 25 percent increase) and 75 percent (i.e. 25 percent reduction). The two elasticities are assumed to change together, and the

> 25 Assuming that the Armington elasticiy of some goods imported by the United States is six, Japanese share in the total imports of the United States is 10% for the goods, and the relative price of the imported goods from Japan compared to the average import prices of goods is one. If the relative price rises by one percent, then the share of the goods imported from Japan in the United States will become 9.4%, six percent decline (i.e. 0.01 x 6 x 10%). The same idea also applies to the substitution between the domestically produced goods and imported goods.

38
Contrary to expectation, the study also shows that the cross-border electricity trade itself does not help much promote renewable energy expansion in the MENA region because the trade reduces the total capacity requirement through sharing of capacities and reserve margins across the countries. On the other hand, the removal of natural gas subsidy substantially promotes renewable energy as the latter becomes cost-competitive. Interestingly, the renewable energy expansion effect of subsidy removal policy would be more prominent in the presence of crossborder electricity trade as compared to the situation in the absence of such a trade. The expansion of solar and wind capacities due to natural gas subsidy removal in the presence of cross-border electricity trade would be 21% higher than that in the absence of the trade.

Subsidy removal and electricity trade affect the power sector CO2 emissions. An interesting finding is that these two policies together reduce 10% of the power sector emissions during the 2018-2035 period with net regional cost savings of US$111 billion. On the other hand, to achieve the same level of power sector emission reduction, the carbon constraining policy would cost US$97 billion. If the CO2 constraining policy is combined with the subsidy removal policy, they would together reduce 15.9% of power sector CO2 emissions at the net cost of US$77 billion in the absence of cross-border trade. The cross-border trade plays the biggest role in reducing CO2 mitigation costs. If all three policies are combined, they would reduce 16.6% of power sector CO2 emissions at net savings of US$58 billion during the 2010-2035 period.

# **References**

Camos, Daniel, Robert Bacon, Antonio Estache, Mohamad Mahgoub Hamid. (2019). Shedding Light on Electricity Utilities in the Middle East and North Africa. World Bank, Washington, DC.

- CESI. (2014). Feasibility Study of the Electrical Interconnection and Energy Trade between Arab Countries. Arab Fund for Economic and Social Development. http://www.arabfund.org/Data/site1/pdf/other/execsumen.pdf.

- CESI. (2016). Pan-Arab Regional Energy Trade Platform (PA-RETP). Power Market Study: Gas Comsumption Assessment for the Power Sector accross MENA Region. CESI.

- CIA Factbook. (last updated 6/26/2018). Jordan (https://www.cia.gov/library/publications/theworld-factbook/geos/jo.html)

- El-Katiri, L. (2011). Interlinking the Arab Gulf: opportunities and challenges of GCC electricity market cooperation. Oxford Institute for Energy Studies. Oxford, UK.

34
Figure S1. Residual Inequality and Inequality <!-- Start of picture text --> Net Gini, Solt (2015)<br>.8 1 1.2 1.4 1.6<br>Residual Inequality<br>Market Gini, Solt (2015)<br>1 1.5 2<br>Residual Inequality<br>.7<br>.6<br>.5<br>Inequality .4<br>.3<br>.2<br>.8<br>.6<br>Inequality<br>.4<br>.2<br><!-- End of picture text --> 44
We should conclude this section by noting that controls on capital inflows may be ineffective against runs by domestic residents, which were particularly important in the Mexican and Brazilian crisis episodes (Frenkel & Schmukler, 1996 and Goldfajn, 2000). Nevertheless, one has to note that by changing the maturity structure of external debt, the controls make runs more expensive and hence could play an indirect role in preventing them.

# **2 Evidence from Vector Autoregressive Models**

Our objective in this section is to obtain some stylized facts about the response of domestic macroeconomic variables to external shocks, measured by the spread paid by emerging markets over U.S. instruments of the same maturity. For this purpose, we will use the global Emerging Markets Bond Index (EMBI) spreads constructed by J.P. Morgan. This index tracks the traded market for U.S. dollar denominated Brady and other similar bonds and is widely regarded as an appropriate measure of foreign investors’ risk perceptions regarding emerging markets. We chose to use the EMBI rather than the commonly used EMBI+ spreads (which is a less restrictive index, including Eurobonds and other sovereign debt instruments) because of data availability as the latter series only starts in 1998<sup>iii</sup> .

We will implement reduced-form models of the transmission of international shocks to macroeconomic variables using the vector autoregressive (VAR) methodology. Other authors such as Edwards (2000) and Edison and Reinhart (2001) have previously used this framework for similar purposes.

When specifying a reduced-form VAR model it is crucial to assess whether the variables included in the system are stationary or not for inference to be valid, since it is well known that testing hypotheses on coefficients of integrated variables requires non-standard asymptotic theory<sup>iv</sup> (Canova, 1995). In our first model, we will use a foreign exchange market pressure index for Argentina, Chile, Colombia and Mexico as endogenous variables in the system.

We construct the pressure index following the empirical literature on currency crises in order to account for the fact that in periods of stress due to external shocks the burden of adjustment does not fall exclusively on interest rates, but it is also reflected in changes in international reserves and changes in the nominal exchange rate. Our index is a weighted average of changes in domestic interest rates; nominal 5