rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix B. Correlations between Domestic Variables**

In the main body of the text we include only subsets of our country characteristics and policy variables in the regressions on the grounds that a number of these variables are highly correlated with one another. It is also interesting that some of these correlations seem to have changed significantly over time. In the first half of the period correlation is stronger between capital flows and current account deficit and weaker between capital flows and reserves—suggestive of that the capital flows were instrumental in financing current account deficit than in the accumulation of reserves. The domestic banking sector seems to have played a less prominent role in mediating the capital flows in the first half of the period. In comparison, in the last decade capital flows correlate more strongly with reserves than in the past; and larger capital inflows go hand in hand with larger banking sector and rapid credit growth. These patterns suggest that the concerns related to financial sector stability matter more in recent sudden stops.

**Table B1.  Correlation Coefficients between Selective Domestic Factors, 1991-2002**

||Capital<br>flows/GDP|Current account<br>deficit/GDP|Reserves<br>/GDP|Credit/GDP|Credit<br>Growth|% Change in Real<br>Exchange Rate|
|---|---|---|---|---|---|---|
|Capital flows/GDP|1||||||
|Current account deficit/GDP|0.62|1|||||
||(0.0)||||||
|Reserves/GDP|0.017|-0.05|1||||
||(0.62)|(0.26)|||||
|Credit/GDP|0.066|-0.12|0.36|1|||
||(0.05)|(0.01)|(0.0)||||
|Creditgrowth|0.28|0.25|0.004|-0.03|1||
||(0.0)|(0.0)|(0.92)|(0.50)|||
|% change in real exchange rate|-0.19|0.003|-0.03|0.009|-0.071|1|
||(0.0)|(0.95)|(0.32)|(0.79)|(0.08)||
||**Correlation Coeffi**<br>|**cients between Sele**<br>|**ctive Domest**<br>|**ic Factors, 2003-2**<br>|**014**<br>||
||Capital<br>flows/GDP|Current account<br>deficit/GDP|Reserves<br>/GDP|Credit/GDP|Credit<br>Growth|% Change in Real<br>Exchange Rate|
|Capital flows/GDP|1||||||
|Current account deficit/GDP|0.56|1|||||
||(0.0)||||||
|Reserves/GDP|0.08|-0.15|1||||
||(0.00)|(0.00)|||||
|Credit/GDP|0.13|-0.10|0.51|1|||
||(0.05)|(0.00)|(0.00)||||
|Creditgrowth|0.54|0.27|-0.12|-0.22|1||
||(0.0)|(0.00)|(0.00)|(0.00)|||
|% change in real exchange rate|-0.29|-0.06|-0.03|0.04|-0.35|1|
||(0.0)|(0.04)|(0.24)|(0.16)|(0.00)||

In parentheses are the p values to accept the null hypothesis that the correlation coefficients are equal to zero.

31
# **Table 4: MDG indicators used in current study**

|MDG goal|Indicator|Definition|
|---|---|---|
|Goal 1: Eradicate extreme<br>poverty and hunger|Underweight|Percentage of children with a weight-for-age z-score <-2 standard<br>deviations from the reference median<sup>§</sup>|
||Stunting|Percentage of children with a height-for-age z-score <-2 standard<br>deviationsfromthereferencemedian<sup>§</sup>|
|Goal 4: Reduce child<br>mortality|Infant mortality rate<br>(IMR)|Number of deaths among children under 12 months of age per 1,000<br>live births<sup>$</sup>|
||Under-five mortality<br>rate (U5MR)|Number of deaths among children under 5 years of age per 1,000 live<br>births<sup>$</sup>|
||Measles immunization|Percentage of children aged 12-23 months who received measles<br>either verified by card or by recall of respondent|
||Full immunization|Percentage of children aged 12-23 months who received BCG,<br>measles, and three doses of polio and DPT, either verified by card or<br>byrecallof respondent|
||Malaria nets (children)|Percentage of children under 5 sleeping under an (ever) insecticide-<br>treated bednet the previous night|
|Goal 5: Improve maternal<br>health|Skilled birth attendant<br>(SBA)|Percentage of births of births to mothers aged 15-49 that were<br>attended by skilled health attendant (SBA)|
||Antenatal care visits – 4<br>or more (ANC4+)|Percentage of mothers aged 15 to 49 who received at least four<br>antenatal care (ANC4+) visits from any skilled personnel (as defined<br>in the country’s DHS or MICS)|
||Contraceptive<br>prevalence|Percentage of women aged 15 to 49 who currently use a modern<br>method of contraception|
|Goal 6: Combat HIV/AIDS,|HIV prevalence|Percentage of adults aged 15 to 49 testing positive for HIV 1 or 2|
|malaria and other diseases|Condom use in risky<br>intercourse|Percentage of women aged 15 to 49 who had more than one partner<br>inthe past yearand used a condomduringlast sexual intercourse|
||Malaria nets (children)|Percentage of children under 5 sleeping under an (ever) insecticide-<br>treated bednet the previousnight|

§ WHO 2006 child growth standards used to calculate z-score $ Mortality rate calculated using the true cohort life table approach. The DHS reports use the synthetic cohort life table approach 23
these effects have asymmetric impacts across the various classes of customers. These aspects deserve further elaboration because they have important bearings on how to interpret the credit crunch and, especially, on which policy actions are best suited to mitigate its undesirable consequences.

**The balance sheet channel** in principle has a symmetric impact on the economy. It raises the risk premium and thus the cost of borrowing for all firms, irrespective of their financial structure. In practice, however, even the balance sheet channel will likely penalize the small and medium-sized enterprises (SMEs) more since they typically do not have access to the commercial paper market.

**The lending channel** and **credit rationing** specifically affect **bank-dependent borrowers,** i.e. those firms that cannot directly place liabilities on the open market and the equity market. This should particularly be the case for SMEs.<sup>6</sup> In the first place, they are too small to justify the fixed costs entailed by listing securities. In addition, even when they have the intention of issuing debt on the market, they would most likely refrain from doing so because given the low liquidity of their debt, investors would ask for very high yields, thus making issuance unattractive to SMEs. SMEs would also be specifically penalized by the **flight to quality.** Lenders perceive them to be more risky since they generally have a shorter track record and typically release less --and less structured-- information.<sup>7</sup>

Furthermore, when the credit crunch ensues, there may be an additional channel negatively affecting SMEs in terms of availability and cost of external finance, that is **flight to quality (safety) by depositors.** Envisaging increased fragility of the intermediaries, depositors may shift their savings towards institutions that are perceived to be less likely to go bankrupt. For instance, **foreign banks** could be deemed safer than domestic ones; smaller banks may be viewed **less likely to be bailed out by the**

- 6 Gertler and Gilchrist (1994) show evidence consistent with this hypothesis.

- 7 Bemanke, Gertler and Gilchrist (1996) report evidence consistent with this hypothesis. A negative bias similar to that regarding SMEs might apply to fast-growing firms, since they have a higher ratio of expected future profits to the current value of physical assets and thus can provide lower collateral.

12
mode effects thus do not appear to be driving the observed difference between survey and administrative data.

**<mark>-</mark>** **<u><mark>Table 5: Hypothesis 3 Survey Mode</mark></u>**

|**Panel A:**Interviewing phone survey main respondents in-person||
|---|---|
|Phone Survey Main Respondents, Apr - Jun 2022|46.0<br>(41.0 to 51.1)|
|Admin data (adults 15+), June 5|33.4|
|_N (Phone Survey Main Respondents)_|2,403|
|t-Test,Main respondents(survey)vs. Admin(adults 15+)|p< 0.0001 ***|
|**Panel B:**Collecting vaccination status of all phone survey household|members in-person|
|All Phone Survey Household Members, Apr - Jun 2022|30.5<br>(27.2 to 33.7)|
|Admin data (general population), June 5|20.1|
|_N (All Phone Survey Household Members)_|10,786|
|t-Test,All HH members(survey)vs. Admin(generalpopulation)<br>Note: Weighted estimates of vaccine uptake in face-to-face data from the Ethiopia Socio|0.0001 ***<br>economic Survey, Wave 5|

Note: Weighted estimates of vaccine uptake in face-to-face data from the Ethiopia Socioeconomic Survey, Wave 5 (ESS 5) (Apr-June 2022). Administrative vaccine coverage rate for adults conservatively assumes that no children below the age of 15 were vaccinated at the time of reporting. All values in percent. 95%-Confidence Intervals in parentheses.

# **Panel conditioning**

Respondents may change their vaccination behavior in response to being repeatedly surveyed on the topic of COVID-19 vaccination. We therefore predicted that respondents previously interviewed or those in households that were part of the phone survey panel would report higher rates of vaccination than a sample interviewed for the first time.

**<mark>-</mark>** **<u><mark>Table 6: Hypothesis 4 Panel Conditioning</mark></u>**

||(1)|(2)|
|---|---|---|
|VARIABLES|Panel Household|Panel Individual|
|Household Previously Interviewed|-0.0177<br>(0.0172)||
|Respondent Previously Interviewed||0.00847<br>(0.0172)|
|Constant|0.324***|0.310***|
||(0.0129)|(0.0117)|
|Observations|2,942|2,942|
|R-squared<br>Note: Bivariate OLS regression of vaccination st|0.000<br>atus on a dummy for respondent|0.000<br>living in a|

Note: Bivariate OLS regression of vaccination status on a dummy for respondent living in a household interviewed before (Column 1) or having been interviewed before themselves (Column 2). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 22
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# **INTRODUCTION**

Among the trans-boundary environmental problems that can be empirically analyzed, freshwater is unique in that like problems occur throughout the world. Similarly, there are a large number of river basins that can be studied. To be sure, the details are unique to each river basin problem yet similarities are sufficient to make comparisons. When rivers and other bodies of water transverse or divide countries, transboundary externalities often culminate in inter-state conflict. However, conflict almost always provides the impetus for cooperation and cooperation is most regularly codified in international treaties (Wolf and Hamner 2000:66; Deudney 1999:207). Therefore, commons regimes, corresponding to the many freshwater problems, can be analyzed and compared empirically. The aim of this paper is to answer one fundamental question: Why are agreements negotiated between some states,<sup>i</sup> or river riparians, and not others, and what affects the level of cooperation measured by these agreements?

The motivation for the above query, and the empirical investigation, stem from the common claim that water scarcity is likely to lead to inter-state conflict, and possibly violence.<sup>ii</sup> More importantly, building on a theory that considers the relationship between scarcity and cooperation, this investigation strives to show that it is the ‘critical need’ for a given transboundary resource, and the dispute that may ensue, that provides the impetus for inter-state cooperation codified in international water agreements.

The history of hydro-political cooperation is rich in documented international water agreements. The empirical investigation proposed here considers 271 treaties negotiated between riparian states between the years 1850 and 2002 (Dinar S. 2007). The agreement texts are obtained from various depositories.<sup>iii</sup> In total 226 rivers shared by two states are investigated<sup>iv</sup> Thus, the available sample pertaining to an extensive number of international rivers, some governed by treaties while others are not, makes the inference of various hypotheses, across a large number of observations, possible. The bilateral focus of this paper facilitates a methodologically simpler analysis, compared to, say, a multilateral focus, at least as a first attempt in understanding this complex issue. Future research will build on this model and investigate rivers shared by more than two riparians.

Several past works are relevant for this particular study. Espey and Towfique (2004) and Song and Whittington (2004), for example, are also interested in the emergence of water agreements
# _Table 1: Representative Components of the CPI (2000)_

|**Source**|**Who Was**<br>**surveyed/asked?**|**Question/**<br>**Assessment**<br>**(Bureaucratic/Both/Political)**|**Availability**<br>**of data**|**Sample size**<br>**(year)?**|
|---|---|---|---|---|
|Political &<br>Economic Risk<br>Consultancy|Expatriate<br>business<br>executives|‘Extent of corruption in a way<br>that detracts from the business<br>environment for<br>Foreign companies’|1998, 1999,<br>2000 in 12-<br>14 Asian<br>countries|280 (1998)<br>700 (1999- app)<br>1027 (2000)|
|Institute for<br>Management<br>Development|Executives in<br>top- and middle-<br>management;<br>domestic and<br>international<br>companies|‘Bribing and corruption exists<br>In the public sphere’|1998, 1999,<br>2000<br>In 46-47<br>countries|2515 (1998)<br>4314 (1999)<br>4160 (2000)|
|The Economist<br>Intelligence Unit|Expert staff<br>assessment|‘Assessment of the<br>pervasiveness of Corruption<br>among politicians and civil<br>servants’|2000 in 115<br>countries|NA (expert<br>assessment)|
|International Crime<br>Victim Survey|General public|‘During 1999, has any<br>government official<br>In your own country, asked<br>you to pay a bribe for his<br>service?’|1999, 2000<br>in 11<br>countries|20,000 (1999)<br>20,000 (2000)|
|The World Bank &<br>EBRD|Senior business-<br>people|‘State capture and frequency<br>of irregular, additional<br>payments to public officials’|1999 in 20<br>countries|3000 (1999)|
|Freedom House|US academics<br>and Freedom<br>House Staff|‘Levels of corruption’|1998 in 28<br>countries|NA (expert<br>assessment)|
|The World<br>Economic Forum<br>(Global<br>Competitiveness<br>Report)|Senior business<br>leaders; domestic<br>and international<br>companies|‘Irregular, additional<br>payments connected with<br>import and export permits,<br>business licenses, exchange<br>controls, tax assessments,<br>police protection or loan<br>application’|1998, 1999,<br>2000 in 53-<br>59 countries|3167 (1998)<br>3934 (1999)<br>4022 (2000)|

12
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Table 7: Accounting for tariff binding, tobit random effects

|Dep. variable|ln simpl|e tarifs|ln weight|ed tarifs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ln rain_jt_|0.030|0.034|0.091**|0.105**|
||[0.026]|[0.026]|[0.043]|[0.043]|
|ln rain_jt ∗_agric_i_|0.220***<br>[0.018]|0.220***<br>[0.017]|0.273***<br>[0.029]|0.267***<br>[0.028]|
|Observations|33,454|33,454|33,114|33,114|
|Number of countries|69|69|69|69|
|Number of products|65|65|65|65|
|Country efects|Random|Fixed|Random|Fixed|
|Product efects|Fixed|Random|Fixed|Random|
|Log likelihood|-38777.222|-38718.094|-52882.104|-52832.222|

The estimation method is Tobit random effects. Due to availability of data for bound tariffs, the period of analysis is restricted to 1995-2006. Each model includes year fixed-effects. Marginal effects resported. Standard errors in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 Table 8: Non-linear specification of rainfall

|Dep. variable|ln|simple ta|rifs|ln w|eighted ta|rifs|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|low rain_jt_|0.137||0.131|0.081||0.073|
||[0.086]||[0.086]|[0.098]||[0.098]|
|low rain_jt ∗_agric_i_|-0.340**||-0.296**|-0.407**||-0.343*|
||[0.143]||[0.143]|[0.182]||[0.186]|
|high rain_jt_||-0.062|-0.060||-0.044|-0.042|
|||[0.068]|[0.068]||[0.094]|[0.094]|
|high rain_jt ∗_agric_i_||0.265*|0.253*||0.379**|0.366**|
|||[0.143]|[0.144]||[0.181]|[0.183]|
|Observations|41,043|41,043|41,043|40,606|40,606|40,606|
|Number of countries|70|70|70|70|70|70|
|Number of products|65|65|65|65|65|65|
|R-squared|0.572|0.572|0.572|0.401|0.401|0.402|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 5
<!-- Start of picture text --> Mission Days per year - IDA project<br>s<br>o<br>3<br>S<br>5<br>co]<br>g<br>oO<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br>ME NeverFCS [ME Aways+Partial FCS<br>Source: BI Travel RM Reports<br><!-- End of picture text --> <!-- Start of picture text --> Consultant time - IDA project<br>3<br>Bo<br>et<br>a<br>3<br>=<br>oO ll<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>MES NeverFCS ME AwaystPartial FCS<br>Source: Business Warehouse<br><!-- End of picture text --> <!-- Start of picture text --> GH+ Mission Days per year - IDA project<br>&<br>&<br>s<br>a<br>ze<br>x<br>6R<br>oO<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br>ME NeverFCS [ME Aways+Partial FCS<br>Source: BI Travel RM Reports<br><!-- End of picture text --> <!-- Start of picture text --> Total time (days) - IDA project<br>sN<br>a<br>><br>a<br>a<br>=82<br>=<br>a<br>; oO<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>MES NeverFCS ME AwaystPartial FCS<br>Sources: TRS Reports, BW, BI<br><!-- End of picture text -->
# **IV. Final remarks**

Weak economic growth and deterioration of social outcomes since the plunge in oil prices combined with a high influx of Venezuelans demanded a rapid response from Ecuador’s government. The survey (i.e., EPEC) was designed and implemented to produce comparable findings of the lives and livelihoods of host communities and Venezuelan migrants and refugees for policy design. Even though Ecuador has a reliable and updated sampling frame, the lack of information on how many Venezuelan migrants are settled in Ecuador and where they reside in the territory posed challenges for the design and implementation of the survey. This difficulty shows in many developing and developed countries that are affected by a rapid influx of people. The exclusion of these types of populations from national sampling frames provides a biased picture of the situation the country is experiencing (World Bank, 2018). Moreover, as the number of migrants and refugees continues to increase, it becomes more relevant to develop strategies to include them in frequent representative socio-economic surveys.

The contribution of this study relies on the innovative methodological approach of using Big Data for sampling design. The methodology brings a proper solution for approximating the total number of migrants -including regular and irregular migrants- and their location in a country. The strategy implemented in this survey can be useful in designing similar exercises in countries with limited information (i.e., lack of a recent Census or a migratory registry) and scarce resources to rapidly gather socio-economic data of migrants and host communities for policy design.<sup>15</sup> Even though access to Big Data is beneficial for sampling design, researchers and policy makers could face a limitation in the lack of knowledge and technical capacity from telecommunication companies to implement the method. Also, Big Data are particularly precise when both market share and coverage -i.e., urban areas- are high. Thus, information on geographic regions (e.g., rural areas) where coverage is low, or market shares are small might be taken with caution. Notice that this type of population is hugely mobile and difficult to locate if the information is not frequently updated. This poses significant trade-offs between having the most updated data and the cost of getting it. Lastly, although the confidentiality of users is not an issue in this type of exercise (the number of Venezuelan phones was requested as an aggregated PSU-level figure), our telecommunication partner still censored the precise number when it referred to fewer than five clients.

> 15 Notice that the Government of Ecuador approved Executive Decree 826 on July 25, 2019 for conducting a migratory registry to be finalized by end March 2020.

10
# Table 1: **2014 Summary Statistics by 2015 Firm Type**

Panel A variables were collected during the February 2014 census. Panel B variables were collected during the summer 2014 baseline survey. Panel C variables were collected retrospectively, during a June 2015 follow-up survey. The sample is split into “one-person” and “has workers” firms using information from the weekly panel data collected in March and April 2015, consistent with the remainder of the analysis. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||Full<br>Sample<br>_mean_|One-<br>Person<br>_mean_|Has<br>Workers<br>_mean_|_diff_<br>D|_p-val_<br>iff|
|---|---|---|---|---|---|
|**Panel A: Source- Census**||||||
|Male|0.26|0.26|0.26|0.00|0.98|
|Years of schooling|8.90|8.91|8.89|0.02|0.95|
|Owner age|35.67|35.95|35.31|0.64|0.52|
|Firm age|9.66|9.21|10.24|-1.03|0.26|
|Has any worker(s) besides owner|0.49|0.25|0.79|-0.54|0.00***|
|Firm size (including owner)|2.12|1.40|3.04|-1.64|0.00***|
|Revenues Last Month (GHC)|215|149.77|290.50|-140.74|0.00***|
|Profits Last Month (GHC)|151|106.92|203.22|-96.30|0.00***|
|Registered w/any govt agency|0.19|0.11|0.29|-0.18|0.00***|
|Trade association member|0.24|0.15|0.35|-0.19|0.00***|
|**Panel B: Source- Baseline**||||||
|Ewe ethnicity|0.77|0.79|0.75|0.03|0.44|
|Ravens|5.73|5.52|5.96|-0.44|0.14|
|Management practices (of 5)|2.57|2.34|2.84|-0.49|0.00***|
|Assets excl land/building (GHC)|1349|999.39|1756.38|-756.99|0.00***|
|**Panel C: Source- Retrospective Follow-up**||||||
|Has Manual Sewing Machine|0.86|0.86|0.86|-0.00|0.92|
|# Manual Sewing Machines|1.57|1.23|1.93|-0.69|0.00***|
|Has Electric Sewing Machine|0.43|0.40|0.45|-0.05|0.35|
|# Electric Sewing Machines|0.57|0.48|0.68|-0.20|0.04***|
|Has Any Electric Machine|0.82|0.75|0.88|-0.13|0.00***|
|# Any Electric Machine|1.74|1.40|2.10|-0.70|0.00***|
|Has Generator|0.03|0.01|0.04|-0.03|0.13|
|Number of Firms|342|184|158|||

Source: Author calculations from The Hohoe Garment Makers Project Data 16