rafmacalaba/datause-encoder-data huggingface dataset:
**b. Characteristics at the high school level**

|**Variables**<br>**Other professionals**|**Teachers**|**Difference between**<br>|
|---|---|---|
|||**groups**|
|**High school characteristics(%), as measured by Saber 11 unless otherwise not**|**ed**||
|School administration (public)<br>48.00<br>(0.23)|74.80<br>(0.67)|***|
|School type (mixed gender)<br>79.1<br>(0.19)|87.7<br>(0.51)|***|
|School day|||
|Complete<br>32.30|23.90|***|
|(0.22)|(0.66)||
|Morning<br>51.70|51.80||
|(0.23)|(0.78)||
|Afternoon<br>14.50|21.10|***|
|(0.06)|(0.26)||
|Night<br>1.40|2.90|***|
|(0.02)|(0.08)||
|Weekend<br>0.10|0.30||
|(0.16)|(0.63)||
|School Calendar (A calendar)†<br>82.10|87.30|***|
|(0.18)|(0.52)||
|Degree type (as measured by Saber Pro)|||
|Academic<br>78.40|69.40|***|
|(0.15)|(0.45)||
|Technical<br>19.10|21.40|***|
|(0.19)|(0.6)||
|Normal school††<br>2.60|9.20|***|
|(0.18)|(0.64)||

# **c. Characteristics at the university level**

|**Variables**|**Other professionals**|**Teachers**|**Difference between**<br>|
|---|---|---|---|
||||**groups**|
|<br>**Universtiy characteristics(%), as measured by S**|**aber Pro**|||
|University administration (public)|34.60|73.80|***|
||(0.18)|(0.56)||
|University fee (academic semester)†††||||
|None|1.00|1.40|*|
||(0.05)|(0.19)||
|Less than 1.000.000 COP|27.30|72.90|***|
||(0.21)|(0.69)||
|Between 1.000.000 and 3.000.000 COP|30.90|23.20|***|
||(0.21)|(0.66)||
|Between 3.000.000 and 5.000.000 COP|20.80|2.30|***|
||(0.19)|(0.23)||
|More than 5.000.000 COP|20.00|0.20|***|
||(0.19)|(0.08)||

Standard errors in parentheses. * Significant at ten percent; ** significant at five percent; *** significant at one percent.

† Colombia has two regular school calendars: The “ <mark>A calendar”, which goes from February to November; and the “B calendar”, which goes from August to June.</mark> †† A “Normal school” is a school that trains high school students to become teachers. ††† COP: Colombian Peso. The average exchange rate for the second semester of 2011 was 1856 COP/USD. Source: Authors’ calculations based on ICFES data.

8
other studies that exploit bilateral data, our paper analyzes a greater number of countries, particularly in the South. In addition, the longer sample period allows us to examine developments before and after the GFC.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 documents the rise of the South in the value of total investments. Section 4 focuses on the growth of new links and the relative importance of old and new links. Section 5 examines three extensions to assess the robustness of our findings. Section 6 concludes. The appendix describes in detail the global data sets we use and the treatments we apply to align the sources with each other for the analysis.

# **2. Constructing Bilateral Investment Positions**

In their work on the “External Wealth of Nations,” Lane and Milesi-Ferretti (2001, 2007, 2018) use external assets and liabilities to measure international financial integration for a large cross-section of countries vis-à-vis the rest of the world. They provide the most comprehensive database on each country’s IIP, covering 212 countries for 1970–2015 in the latest public version. In our paper, we focus instead on bilateral investment positions to study the sources and destinations of international investment, allowing us to analyze the network structure of international financial integration.

This section describes the four data sets we use and addresses common concerns related to data on bilateral investment. Throughout the paper, “international” means cross-border investment between any pair of countries or jurisdictions.<sup>2</sup> International investments are reported on a residency basis, in line with statistics on national accounts and the balance of payments. The resulting notion of financial integration thus focuses on the geography of investment, not on the nationality of lenders (McGuire and von Peter 2012) or ultimate borrowers (Coppola et al. 2020).

> 2 In this paper, we mostly use the term “countries” to denote sovereign countries as well as territories (or jurisdictions) that are usually reported as separate economies. These are not necessarily independent sovereign states recognized by the United Nations.

6
# **1. Introduction**

During the recent global financial crisis, several countries were forced to nationalize failing private banks. Abn Amro in the Netherlands, for instance, is now fully owned by the Dutch government. As a result, the average share of government ownership of banks by bank assets has increased in high-income countries form 7.3% in 2007 to 10.8% in 2009, to fall back slightly to 9.9% in 2010. The increased prevalence of state banks is providing renewed impetus to the debate on the economic costs and benefits of state banking. While previous research has shown that state banks tend to perform badly, misallocate resources and lead to lower economic growth, relatively little is known about how state banks react to business cycle fluctuations. To fill this gap, this paper examines the lending behavior of state banks over the business cycle, and also fluctuations in the main types of bank funding that make this lending possible. In addition, this paper considers the relative accounting for non-performing loans by state banks, as differences in the reporting of bad loans over the business cycle by state and private banks are a potential mechanism to explain different capacities to provide new loans. Our analysis is based on a sample of 1,633 banks from 111 countries over the 1999-2010 period.

We find that lending by state banks is less procyclical than the lending by private banks, especially if the bank is located in a country with good governance. We capture good governance by an index of government effectiveness, which increases with perceptions of the quality of public services, the degree of independence from political pressures and the credibility of a government‟s commitment to its own effectiveness. Moreover, lending by state banks located in high-income countries is even countercyclical. State banks also expand their credit relatively more during banking crises, which points at a stabilizing influence of state banks at a time of financial instability. Among private banks, we find that foreign-owned banks‟ lending is especially procyclical, perhaps because these banks have ready access to funding from their international parent firms to take advantage of local lending opportunities during economic upswings.

On the liability side, state banks increase their non-deposit liabilities relatively little during booms, especially if these banks are located in countries with good governance. Since non-deposit liabilities tend to be less stable than deposits, private banks‟ increased reliance on 2
- [24] Savage, L.J., 1954. _The Foundations of Statistics._ New York: Wiley.

- [25] Schmeidler, D., 1989. Subjective probability and expected utility without additivity. _Econometrica_ 57(3), pp. 571-587.

- [26] Snow, A. and R.S. Warren Jr., 2005. Ambiguity about audit probability, tax compliance, and taxpayer welfare. _Economic Inquiry_ 43(4), pp. 865-871.

- [27] Teitelbaum, J.C., 2007. A unilateral accident model under ambiguity. _Journal of Legal Studies_ 36(2), pp. 431–477.

- [28] Widdowson, D. and S. Holloway, 2011. Chapter 6: Core border management disciplines: risk based compliance management. In: Gerard McLinden, Enrique Fanta, David Widdowson and Tom Doyle (eds.), _Border Management Modernization_ . The World Bank, Washington, D.C., pp. 95-113.

43
For sampling purposes, only households with children were included. The listing data were stratified into two categories—households with children below 5 years and households with children aged 6 to14 years. Each of these was further stratified into households with toilets and without toilets. So, altogether four strata were created, and six households were randomly drawn from each of the four strata.

# 4.4 Data Collection

Data on the selected sample were collected using household, community, and school surveys. The following is a brief description of the various survey instruments designed for the study.

(i) _Household surveys._ These collected information on the number and demographic profiles of household members, their occupations, the ownership of assets (including toilets), access to water supply and sanitation, usage of toilets by household members, hygiene awareness and hygiene behavior, and incidence of water-related diseases (including diarrhea). Within the survey, a separate module for children aged 6 to 14 collected data on hygiene awareness and behavior. In addition, anthropometric measurements of children under age 5 were also carried out.

(ii) _Community survey._ The _sarpanch_ (head of the GP) was interviewed. The survey collected information on GP characteristics like the presence of schools, primary health care centers, electricity connections, and implementation of water and sanitation programs. The survey also collected personal information on the _sarpanch_ .

Baseline data were collected from September 25, 2016, to October 28, 2016. A follow-up midline survey of the same households was undertaken from November 2, 2017, to January 15, 2018. A total of 6,067 households from 260 GPs were surveyed at baseline, and 5,275 of these households, from 251 GPs, were followed up with during the midline survey. The project interventions were implemented from November 2016 to the first week of November 2017, when data collection for the midline survey started. So, none of the health- and nutrition-related impacts would have materialized by then. As has been noted, a follow-up survey has been planned for 2021 to measure these impacts.

The same household and community questionnaires were used for both the baseline and midline surveys, though the midline survey included some additional questions to assess the implementation of the project interventions. A school survey was included at midline. The community survey and the school survey were used mostly to assess implementation.

# 4.5 Measurement of Outcomes

The main indicators of interest for this study are hygiene awareness and hygienic behaviors, toilet construction, and toilet use. All these outcomes of interest were measured using data collected from the household survey. A brief description of the indicators used to measure these outcomes follows.

15
another group, also without connections, but which does not view such connections as helping to secure important opportunities in life.

Both hypotheses are similarly rejected in the estimates for the group of non-EU countries in column (3), whereas inside the EU (column 2), it appears that when access to opportunities is perceived to be unequal, expectations of future mobility appear to be similar among those with connections and those without. I return to this below.

What do the magnitudes of the estimated coefficients imply? When there is inequality in access to opportunities, lack of connections is associated with a 0.11 points reduction in expected future mobility. This should be viewed against the fact that the overall level of expected mobility, as measured by the difference in the future and – current ladders, is quite small in the pooled sample the mean difference between the future and the current ladders is 0.27, such that the reduction in expected future mobility is notable. In the non-EU countries, average expected mobility is higher, 0.69, such that the higher estimated coefficient in absolute terms is actually smaller in relative terms, but still not trivial, at more than 25 percent.

Returning to the estimates in Table 4, several other factors appear to be salient for expectations of future mobility, as suggested by the heuristic model of mobility described above. Upward mobility in the past is associated with expectations of further upward mobility in the future. The same is true of past downward mobility in non-EU countries, but not in the EU countries, where the estimated coefficient on the downward past mobility is negative. One interpretation that is consistent with this difference is that perceptions of downward mobility in the EU are perceived to be of a less transitory nature. It could be that a more stable economic environment in the EU may leave less scope for those who perceive to have moved downward in the past to expect a reversal of their fortunes in the immediate future. It is not possible, however, to confirm this with the LiTS data. Willingness to take risks is also positively associated with future mobility expectations outside of the EU, whereas this is not the case in EU countries. On the other hand, the belief that need is the product of laziness is positively associated with future mobility expectations only in the EU.

17
|||**Very S**|**.**||**Satisfied**||**In**|**differe**|**nt**|**U**|**nsatisfi**|**ed**||**Very U**|**.**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Public<br>DMS|In-<br>house|<br>Comm.|Public<br>DMS|In-<br>house|Comm.|Public<br>DMS|In-<br>house|<br>Comm<br>.|Public<br>DMS|In-<br>house|<br>Comm.|<br>Public<br>DMS|<br>In-<br>hous|e<br>Comm<br>.|
|**How satisfied are you**<br>**with your DMS?**|4|2|0|7|5|6|2|1|1|1|0|0|0|1|0|
|**How satisfied are you**<br>**with the system**<br>**technical support?**|4|2|1|6|4|5|4|1|1|1|1|0|0|1|0|
|**User-friendliness**|5|0|2|6|5|3|3|1|1|1|3|1|0|0|0|
|**Security of data**|6|3|2|7|5|3|2|0|2|0|0|0|0|1|0|
|**Reporting features**|3|1|2|10|3|3|0|4|1|2|0|1|0|1|0|
|**Speed**|2|0|0|9|5|6|2|1|0|1|3|1|1|0|0|
|**Connection with other**<br>**platforms (e.g.**<br>**Treasury, Excel,**<br>**Bloomberg)**|0|1|0|2|4|4|7|2|3|5|2|0|1|0|0|
|**Integration among**<br>**modules**<br>**(front/middle/back**<br>**offices)**|3|1|0|4|5|6|4|0|1|3|3|0|1|0|0|
|**Straight-Through-**<br>**Processing (e.g.**|3|4|0|2|3|6|8|0|1|2|0|0|0|1|0|
|**settlement, repayment)**||||||||||||||||
|**Customization (e.g.**<br>**ability to create new**<br>**instruments/transactio**<br>**ns)**|3|2|0|7|5|2|3|0|4|2|0|0|0|1|1|
|**Maintenance and**<br>**upgrade cost**|5|1|2|6|4|2|4|1|2|0|2|0|0|1|0|
|**Access to historical**<br>**information**|5|3|1|9|5|4|1|0|1|0|0|1|0|1|0|
|**Does your current**<br>**hardware satisfactorily**<br>**support the DMS?**|14|8|6|0|0|0|0|0|0|0|0|0|1|1|1|

35
capacity has to be available and ready to provide power almost instantaneously to make up for unexpected changes in the supply of renewables. Baseload plants, like coal and nuclear, have limited ability to provide spinning reserves. On the other hand, open cycle gas turbines can ramp up and down power output more quickly and can provide spinning reserves. Combined‐cycle gas turbines are increasingly able to provide ancillary services. Ramp‐up rates of the latest technologies can achieve up to 4‐5% of nameplate capacity per minute. When the steam cycle is decoupled the increase in generation can be even faster.

**Table 1. Spinning Ramp Rate Assumptions (% of capacity/min)**<sup>**12**</sup>

|Plant and fuel type|Existing|New Build|Existing<br>(MW/Min)|
|---|---|---|---|
|Reciprocating Engines||||
|Gas|10%|10%|151|
|Fuel Oil|5%|5%|128|
|Diesel|5%|5%|13|
|Combustion Turbines (Gas)|10%|10%||
|Combined Cycle Gas Turbine (Gas)|5%|5%|140|
|Steam Turbines||||
|Gas|2%|5%|64|
|Fuel Oil|2%|5%|1|
|Diesel|2%|5%|0|
|Coal|2%|5%|4|
|Total|||501|

The key question on variation in solar output concerns the changes in solar radiation. The impact of weather can change radiation significantly. As of now there has been no study done on solar irradiance in Bangladesh on a sub‐hour timeframe but studies from other regions indicate that variations in output can be significant within minutes even seconds, which requires the minute‐by‐minute and second‐by‐second control mechanism from generators or other new technologies such as battery energy storage system (BESS). Minute‐by‐minute data on GHI from other countries including USA shows that during peak solar hours GHI can drop by up to 80%, due to changes in cloud cover. We note that some studies show that within minute variations could mean ramp rate needs of up to 50% of a solar plant’s output. However, as spinning reserves need only to cover unexpected and unforeseen changes in solar, which means spinning reserve requirements can be reduced with improved accuracy in forecasting.

In general, many power markets have spinning reserve assumptions that go beyond renewable generation. The California Independent System operator requires that spinning reserves cover 5% of demand and all power imports at any given time. In the Bangladesh context, this would require covering up to 9.5 GW of peak demand and 600 MW of imports (in 2017). The current generation, however, has an estimated 501 MW of spinning reserve so implementing a system‐wide requirement similar to the one California remains infeasible in the current system without significant changes to the grid operation practices.

> 12 The assumptions here reflect a review of existing literature on spinning ramp‐up rates. We note that these rates differ significantly depending on technology of the plant and operational practice.

13
ArcGIS to delineate the catchment. For example, Map 2 shows the basin shared by Turkey and Iran. In a few cases, the publicly available data on river mouth locations were insufficient and experts from the region were consulted to verify the locations (e.g., AL-Jabbari, 2009).

# **_Runoff data by basin and country-basin_**

The Global Runoff Data Center (GRDC) provided flow data for stations within international river basins ( <u>http://www.bafg.de/GRDC/EN/Home/homepage__node.html)</u> . The distribution of the GRDC data availability is not uniform across the world. Also, the temporal distribution varies widely. With additional data requirements such as 12 monthly observations per year and at least 5 years of observations, we ended up with 98 basin observations only (compared with the 224 basins in our dataset). Therefore, we could not use the GRDC data.

We turned to another alternative. Monthly runoff data over a thirty year period (19611990) was taken from a stand-alone hydrologic model CLIRUN-II (Strzepek et al., 2008) that is designed for application in water resource projects and generates global output at a 0.5 x 0.5 degree grid scale. The basin runoff is the sum of the area-weighted runoff from the grids within the basin. The flows are calculated for three values per basin: for the entire basin and for the area of the basin in each riparian country (country-basin). For the country-basin level, international boundaries from the World Bank (2009) are used and intersected with the river basin boundaries. Then, similar to the country-basin level runoff, the basin runoff is the sum of the area-weighted runoff from the grids with the treaty basin Runoff values expressed in units of m3/s. The annual Coefficient of Variation (CV) is calculated to measure runoff variance.

To verify the values produced by the global stand-alone hydrologic model CLIRUN-II (Strzepek et al., 2008), we calculated their correlation with the runoff data that is recorded by the Global Runoff Data Center for various (98) world rivers and runoff estimates provided by the GRDC-UNH Composite Runoff Fields V1.0 (Fekete et al., 2000). We found that the correlation between the GRDC-based flow data and the CLIRUN-II based data for the same 98 basins was R<sup>2</sup> =0.846. This correlation gives us confidence in the data we calculated from the stand alone hydrological model so that we can use the remaining 126 observations for which actual flow data is not available in the GRDC dataset. Then, comparing the two model results using the Pearson method, the correlation statistic between the mean annual runoff of CLIRUN-II and UNH12
the size distribution of an indicator of economic welfare. In the section, we review two types of approaches to modeling the size distribution of economic welfare across a population. The first includes purely statistical models such as _POVCAL_ (Chen, Datt, and Ravallion 1991) and _SimSIP Poverty_ (Wodon, Ramadas, and van der Mensbrugghe 2003). These two tools rely on a parameterization of the _Lorenz curve_ and, arguably, offer the simplest way of simulating the poverty effect of macroeconomic shocks and policies. The second approach relies on _unit record data_ and includes (1) _PovStat_ (Datt and Walker 2002), (2) the _maximum value or envelope model_<sup>2</sup> (Chen and Ravallion 2004; Ravallion and Lokshin 2004), and (3) the _household income and occupational choice model_ (Bourguignon and Ferreira 2005).

Section 3 discusses poverty and distributional analysis within a general equilibrium framework (Dervis, de Melo, and Robinson 1982; Decaluwé and others 1999).

Section 4 reviews _modular_ approaches to linking macroeconomic models to models of income distribution. The section focuses on the 123PRSP model – the name stands for one country, two sectors, and three commodities, and the model is built to feed into the macroeconomic framework for Poverty Reduction Strategy Papers – (Devarajan and Go 2003); the Poverty Analysis Macroeconomic Simulator I (PAMS I) (Pereira da Silva, Essama-Nssah, and Samaké 2003); PAMS II (Essama-Nssah 2004); a macro-micro simulation model for Brazil that uses the investment savings–liquidity money (IS-LM) framework for macroeconomic analysis (Ferreira and others 2004); and the integrated macroeconomic model for poverty analysis (IMMPA) framework, which links a dynamic computable general equilibrium (CGE) model to unit record data (Agénor, Izquierdo, and Jensen, forthcoming).

Concluding remarks are presented in Section 5. An annex provides a summary description of the tools reviewed in this paper.

Table 1 presents a synoptic view of the modeling approaches reviewed in this paper, with a focus on three elements: the treatment of the macroeconomic framework, the modeling of the size distribution, and the variables linking the macroeconomic framework to the model of distribution. The first category has no explicit macro-model. The second embeds distributional mechanisms in a general equilibrium model.

4
- 15 -

# Table 5: Cumulative Productive Investment In the Period 1983-1988 a/ (sample means In Yuan)

|Item|County|Gongzhullng<br>(N-200)|Jurong<br>(N-199)|Tal<br>(N-200)|Xlajiang<br>(N-200)|
|---|---|---|---|---|---|
|Crop-|related<br>Investment|||||
|(i)|Tractor|309|139|0|95|
|(11)|Other equlpment|173|82|42|127|
|(ill)|Draft anknal|200|76|12|357|
|Sub-t|otal|682|297|54|579|
|Other|Productive Investment|||||
|(i)|Truck/boat|120|0|123|0|
|(ni)|Livestock|104|214|150|556|
|Sub-t|otal|224|214|273|556|
|Total|Productive Investment|906|511|327|1135|
|Total|productive Investment|159%|127%|131%|107%|

as % of initlal capital a/ Figures for Gongzhullng are for the period 1983-1987, while the figures for other counties pertain to the perlod 1984-1988. The figures are undeflated. The price Index for agricultural equipment Increased by 15 percent between 1983/4 and 1987/8.