rafmacalaba/datause-encoder-data huggingface dataset:
one) banking relationship ( _Multiple Banks Dummy_ ) and a separate variable that records the actual number of banking relationships up to 26 ( _Number of Banking Relationships_ ). About 60% of firms have multiple banking relationships, and the average firm has 2.74 relationships. Finally, we also include a dummy ( _Diversification_ ) for firms that diversify their relationships across banks of different ownership types, i.e., have relationships with at least two different ownership types. Almost half of the firms, 49%, diversify across bank ownership types.

Balance sheet information on the main banks is from the Accounts of Scheduled Commercial Banks, published by the RBI. We include main bank size ( _Log Assets_ ) and three measures of main bank fragility: the ratio of equity capital to total assets ( _Capital Asset Ratio_ ), the ratio of nonperforming loans to total net loans ( _Nonperforming Loans_ ), and the ratio of liquid assets (cash, balance with other banks, and money market instruments) to total assets ( _Liquidity_ ).<sup>21</sup> The average size of main banks is Rs. 351,512.3 **(** US$ 7 billion) in assets.<sup>22</sup> The average main bank fragility indicators are 0.05 for the capital ratio, 0.06 for the nonperforming loans ratio, and 0.19 for the ratio of liquid to total assets.

When we measure main bank fragility in the regressions, we always include the bank’s capital ratio, the most standard measure of bank financial condition. However, we report the regressions both with and without the nonperforming loans and liquidity ratios, since we find these to be more questionable indicators of fragility. Nonperforming loan numbers can be manipulated by banks through “evergreening,” the practice of rolling over overdue loans and/or providing additional credit to cover the required repayments on existing loans. Liquidity ratios may in some circumstances not adequately reflect the lending capacity of banks. For example, apparently illiquid but solvent banks might be able to borrow (from banks or the government) to continue to extend loans to clients with whom they have a relationship. Banks that appear to be highly liquid – for example because they have high balances with other banks – might find those resources to be unavailable if the other banks run into trouble.

We also create dummies for the ownership type of the main bank – foreign, SBI, nationalized, or private. Most firms, 62%, have a nationalized bank as its main bank, 16% have an SBI bank, 12% have a

> 21 Nonperforming loans in the sample have interest or principal past due more than 180 days.  This was changed to 90 days past due in 2004.

> 22 These averages are for all firm-bank observations in our sample and reflect the frequency with which the banks are reported as the main bank by firms in the sample. The average size of all the commercial banks in our sample (independent of how often they serve as a main bank) is considerably lower, approximately US$2 billion.

15
statistics, which are the sum of the three items, namely workers’ remittances, compensation of employees, and migrants transfers. The number of migrants in OECD countries and the ratio of those with tertiary education among migrants and among the working population in home country come from Docquier and Marfoulk (2005). The percentage of passport cost to GDP per capita is obtained from McKenzie (2005). Our financial sector development variables, namely the ratio of bank deposit and credit to GDP, are from the IMS’s _International Financial Statistics_ . The rest of the variables mostly come from the _World Development Indicators_ . Appendix I provides a description of the variables and their sources in more details.

Table 1 summarizes the mean values of some of the key variables by regions in order to provide an overview of the variations across countries. The ratio of remittances to GDP is found to be the highest in the Middle Eastern and North African countries, followed by the Sub-Saharan countries. As for the level of migration, in terms of its ratio to the population, Western Europe has the highest level and the Americas are the second largest source of migrants. The positive figures for the difference between the ratio of people with tertiary education among migrants and the ratio among the working population in home country indicate the higher level of education among migrants for all the regions. The difference is found to be the greatest for Sub-Saharan Africa and South Asia. In addition, we observe the expected variations in GDP per capita, the ratio of bank deposit/credit to GDP across the regions. If we were to examine the variation across countries, they would be even greater.

Our empirical analysis will thus examine whether the following factors explain some of the observed variation in remittance flows.

# **_Stock of migrants abroad_**

As Freund and Spatafora (2005) find in their work, the level of migration is likely to be the most important determinant of the size of remittances. This variable will be expressed as the logarithm of migrants abroad in our estimation. The key issue that needs to be properly addressed is the endegeneity bias since the desire to send remittances is among 6
|**Country**|**Absolute QFD under**<br>**benchmark performance**<br>**(current US$ million)**|**Relative QFD unde**<br>**(% current GDP)**|**r benchmark perfor**<br>**(% revenues)**|**mance**<br>**(% cash collected)**|
|---|---|---|---|---|
|Mauritius|-$3|0.0|-1|-1|
|Mozambique|$19|0.1|5|6|
|Niger|-$8|-0.1|-6|-7|
|Nigeria|$696|0.1|41|63|
|Rwanda|$34|0.4|35|37|
|São Tomé and Príncipe|$3|0.8|22|37|
|Senegal|$149|1.0|25|27|
|Seychelles|-$12|-0.9|-12|-12|
|Sierra Leone|$4|0.1|11|14|
|South Africa|$9,193|2.8|73|75|
|Sudan|$953|1.3|217|217|
|Swaziland|$30|0.7|23|24|
|Tanzania|-$38|-0.1|-3|-4|
|Togo|-$17|-0.4|-8|-9|
|Uganda|-$86|-0.3|-22|-22|
|Zambia|$203|0.8|30|31|
|Zimbabwe|$321|2.6|41|47|
|Maximum|$9,193|3.04|223|258|
|Minimum|-$86|-0.85|-22|-27|
|Median|$14|0.42|22|24|

_Source_ : World Bank staff calculations based on utility financial statements, annual reports, and other documents.

The QFD is reduced under benchmark performance operations as cash collected increases and costs fall (Figure 9). On average, the QFD is reduced from 1.5 to 0.9 percent of GDP (from 0.9 to 0.3 percent of GDP excluding South Africa). The QFD is reduced to below zero in an additional eleven countries, bringing the total to 13. The main mechanism for eliminating the QFD under benchmark performance would be to raise revenue by a combination of tariff increases and loss reduction. That said, the actual reduction in QFDs would be smaller at least over the short term, because the calculations in Figure 9 assumes that benchmark performance can be achieved at no additional cost, whereas reducing technical losses and implementing revenue protection measures (such as by means of advanced metering infrastructure and metering control centers) all incur additional expenditures.

40
and Ψ( _<u>x, ν</u>_ <u>(</u> _<u>e</u>_ <u>)).</u> This yields _ν_ ( _e_ ) + _C_ ( _πmax, e_ ) = _ν_ <u>(</u> _<u>e</u>_ <u>)</u> + _C_ ( _πmax, e_ ) where _ν_ <u>(</u> _<u>e</u>_ <u>)</u> is provided by the limit when _e →_ _<u>e</u>_ +<sup>ofthefunction</sup><sup>_ν_over</sup><sup><u>[</u></sup><sup>_<u>e</u>_</sup> _,_ _<u>e</u>_ ]. Plugging _ν_ <u>(</u> _<u>e</u>_ ) + _C_ ( _πmax, e_ <u>)</u> back into Equation (20) and using Equation (18) to express _ν_ <u>(</u> _<u>e</u>_ ), we obtain Equation (21) with _K_ 3 = _C_ ( _πmax, e_ <u>) +</u> � _<u>ee ∂C∂e</u>_<sup>[</sup><sup>_π∗_(</sup><sup>_x_(</sup><sup>_e_)</sup><sup>_, e_)</sup><sup>_, e_] +</sup><sup>_K_2.</sup> _R_ ( _x_ ) = _πmax_ ( _yu−xt_ )+(1 _−πmax_ )( _yr−Ra_ ) _−K_ 3 and _ν_ ( _e_ ) = _−C_ ( _πmax, e_ )+ _K_ 3 (21) The land market equilibrium is represented in Figure 6 below.<sup>19</sup> Other aggregates from the model such as the utilities, the land administration’s revenue and the total income of landowners are presented in Appendix C.

<!-- Start of picture text --> R�x�<br>x<br>Secure x Precarious x Insecure x f<br><!-- End of picture text -->

**Figure 6:** The equilibrium land rent _R_ ( _x_ ) and the threshold values _<u>x</u>_ <u>,</u> _<u>x</u>_ <u>,</u> and _xf_

> 19The predictions of our model that more secure forms of tenure are located closer to the city center and that the price gradient is steeper for secure forms than for insecure forms of tenure are confirmed by an empirical study of land transfers in Bamako, Mali (Durand-Lasserve et al., 2015). To our knowledge, it is the only study so far which has documented the spatial patterns of the continuum of tenure rights at the scale of a single city.

22
greater freedom of the press; and countries with parliamentary systems (see, for example, Ades and Di Tella, 1999; Treisman, 2000; Lederman _et al.,_ 2001; and Wei, 2000).4 Most of these earlier studies have used cross-country subjective indices (e.g., from the International Country Risk Guide) and have focused on how macroeconomic, cultural and institutional factors affect the overall level of corruption.

Although this paper fits squarely into the existing literature on the determinants of corruption, it complements it in several ways. First, rather than using subjective survey data on the level of corruption, this paper uses data on the actual bribes that enterprise managers report paying (as percent of revenues) - a measure that does not suffer from some of the problems associated with subjective measures.<sup>5</sup> More importantly, rather than focusing on the overall level of corruption in a country, the paper looks at firm-level data on bribes paid to a single sector of the economy - infrastructure. This allows us to focus on characteristics of the enterprises that pay and receive bribes in addition to characteristics of the institutional and macroeconomic environment. For enterprises paying bribes, we look at whether willingness-topay and ownership of the enterprise offering the bribe and the nature of the enterprises' relationship with the utility affects the equilibrium level of the bribe payment. On the other side of the equation, we look at whether the equilibrium bribe payment is affected by capacity, competition and privatization in infrastructure - factors that might affect either the internal incentives of the utility companies or their ability to demand bribes.

The empirical results are largely consistent with the conceptual framework presented in the next section of the paper. We find that the bribe payments are lower in countries where infrastructure is better developed, suggesting that excess demand is an important determinant of corruption. The extent of competition in the telecommunications sector, measured by the number of cellular operators in the country, also appears to reduce the equilibrium level of

> 4 However, some results vary between studies. For example, Lederman _et al._ (2001) find that corruption is lower in democracies, while Treisman (2000) finds no evidence of this. In addition, whereas Fisman and Gatti (2001) find that corruption is lower in countries with greater decentralization, Treisman (2000) finds corruption is higher in federal states. Finally, Knack and Azfar (2000) find that the association between corruption and trade intensity is not robust when they use measures of corruption that are available for larger samples of countries. They argue that this is because larger samples are less subject to selection bias.

2
and negatively impact poverty and income inequality.<sup>8</sup>

# **5.1.3 Impact on trade**

Table 4 below summarizes the regional impact of increases in barriers to trade on both exports and imports. Such increases in global protectionism would translate into a decline of global imports by 2.1 percent amounting to more than US$614 billion relative to the baseline by 2020. In relative terms, imports of regions such as LAC and ECA would decline the most, by 5.7 percent and 4.5 percent, respectively, relative to the baseline after three years. Latin America and the Caribbean and Sub-Saharan Africa which benefit are from important bilateral and unilateral preferences granted by advanced economies, expected to be the most affected by the loss in external competitiveness with a decline in exports of 3.6 percent and 3.4 percent relative to the baseline by 2020. In LAC, exporters of textiles and clothing and transport equipment are likely to be most affected by a decline in exports of 33 percent and 27 percent, respectively.

Table 4: Impact on regional trade

||**Ex**|**ports**|**Im**|**ports**|
|---|---|---|---|---|
||Percent|US$ billion|Percent|US$ billion|
|Latin America and the Caribbean|-3.6|-56.8|-5.7|-97.2|
|Sub-Saharan Africa|-3.4|-24.6|-3.8|-32.2|
|Europe and Central Asia|-3.3|-46.9|-4.5|-65.0|
|European Union|-3.3|-103.4|-1.8|-88.2|
|United States|-2.6|-84.9|-1.8|-58.8|
|Other Advanced Economies|-2.5|-104.6|-2.3|-96.4|
|Middle East and North Africa|-2.4|-41.7|-2.4|-45.6|
|East Asia and Pacifc|-2.2|-58.7|-3.8|-117.4|
|South Asia|-2.1|-20.2|-0.8|-11.6|
|China|-1.6|-72.4|-1.0|-28.7|
|**Total**|**-2.1**|**-598.1**|**-2.1**|**-614.6**|

Source: Authors’ simulations.

Note: Results capture changes in the volume of trade relative to the baseline in 2020.

8In the model underlying the simulations, there is one representative household and as a result we cannot explicitly capture the poverty and inequality impacts of the simulated increases in tariffs.

19
# Table A7: ECUADOR: MERCHANDISE TRADE

|**Merchandise exports (FOB):***<br>|**1990**<br>**2724**|**1992**<br>**3102**|**1994**<br>**3843**|**1996**<br>**4900**|**1997**<br>**5264**|**1998**<br>**4203**|
|---|---|---|---|---|---|---|
|**Primary and semi-processed**<br>|**2617**|**2924**|**3476**|**4373**|**4708**|**3663**|
|goods:<br>|||||||
|**Oil and oil derivatives:**|**1418**|**1345**|**1305**|**1776**|**1557**|**923**|
|Crude oil|1268|1260|1185|1521|1412|789|
|Oil derivatives|150|86|120|255|146|134|
|**Bananas and plantains**|**471**|**683**|**708**|**973**|**1327**|**1070**|
|Coffee and coffee products:|130|82|414|160|121|105|
|Coffee|104|61|366|129|92|72|
|Processed coffee|26|21|48|30|30|33|
|**Shrimp**|**340**|**542**|**551**|**631**|**886**|**872**|
|**Cacao and cacao products:**|**131**|**75**|**102**|**164**|**132**|47|
|Cacao|75|36|66|91|60|19|
|Processed cacao<br>|56|39|35|73|72|28|
|**Fish and sea products:**|88|107|187|**291**|**307**|**351**|
|Tuna|13|30|21|59|69|61|
|Fish|34|26|52|26|30|22|
|Fishmeal|9|7|10|54|23|13|
|Other processed sea products|32|44|105|152|185|255|
|Hemp<br>|8.<br>|7<br>|11<br>|15|15|13|
|Wood<br>|0<br>|8|20|29|38|23|
|**Natural flowers**|**14**|**30**|**59**|**105**|**131**|**162**|
|**Other primary products**<br>|17<br>|**44**<br>|**119**<br>|**230**<br>|**194**<br>|**97**|
|**Manufactured products:**<br>|107|177|**366**|**527**|**556**|**540**|
|Chemicals and pharrnaceuticals|12|17|32|46|51|57|
|Metal manufactures<br>|14<br>|34<br>|119|109|142|130|
|Hats<br>|8<br>|6<br>|8|5|5|4|
|Textile manufactures|6|19|41|52|61|52|
|Other manufactured products|68|101|166|315|296|298|
|**Merchandise imports**(FOB):|**1647**|**1977**|**3209**|**3571**|**4520**|**5110**|
|**Consumption goods:**|160|**321**|**715**|779|948|1080|
|Consumer durables|97|138|304|459|563|660|
|Consumer non-durables|63|183|411|319|385|420|
|**Fuels and lubricants**|**69**|**75**|**78**|**122**|**379**|**273**|
|Intermediate goods:|860|817|1157|1586|1796|1991|
|<br>Agricultural|73|97|114|219|246|247|
|Industrial|707|652|957|1221|1393|1572|
|Construction materials|80|68|86|145|157|171|
|**Capital goods:**|**554**|**761**|**1259**|**1083**|**1396**|**1766**|
|Agricultural|24|20|31|34|43|51|
|Industrial|341|440|596|698|918|1108|
|Transport|189|301|632|351|435|607|
|**Other imports**|**4**|**2**|0|1|1|1|

> _Source:_ Informacion Estadistica Mensual, Septiembre 30 de 1999, Banco de Ecuador. *: In thousands of dollars.

27
|er fxed-efects<br>_yO,sit−_1 included<br>(6a)<br>(6b)|parameter<br>se<br>0.149*<br>0.090<br>.<br>.|.<br>.<br>.<br>.|.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>0.328***<br>0.077|37.2<br>0.000<br>186<br>28392|∆_yE,sit−_1<br>_xt−_1<br>_sit_|tudent test scores are||
|---|---|---|---|---|---|---|---|
|Student-teach<br>_yE,sit−_1 included<br>(5a)<br>(5b)|parameter<br>se<br>0.201**<br>0.092<br>.<br>.|.<br>.<br>.<br>.|.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>0.282***<br>0.075|45.2<br>0.000<br>186<br>28392|∆_yO,sit−_1<br>_xt−_1<br>_sit_|%. In the analysis, s||
|cluded<br>(4b)|se<br>0.082<br>0.050|0.004<br>0.082|0.097<br>0.005<br>0.007<br>0.180<br>0.040|5|_−_1|ant at 1||
|ed-efects<br>_yO,sit−_1 in<br>(4a)|parameter<br>0.195**<br>0.052|0.007*<br>-0.015|-0.091<br>-0.003<br>0.038***<br>0.123<br>0.512***|20.5<br>0.000<br>186<br>2842|∆_yE,sit_<br>_xt−_1<br>_sit_|; *** signifc|ubject only.|
|School fx<br>cluded<br>(3b)|se<br>0.078<br>0.050|0.005<br>0.082|0.104<br>0.005<br>0.008<br>0.168<br>0.049||_−_1|nt at 5%|ized by s|
|_yE,sit−_1 in<br>(3a)|parameter<br>0.222***<br>0.021|0.008*<br>-0.022|-0.077<br>-0.004<br>0.038***<br>0.023<br>0.424***|20.4<br>0.000<br>186<br>28425|∆_yO,sit_<br>_xt−_1<br>_sit_|; ** signifca|are standard|
|luded<br>(2b)|se<br>0.058<br>0.048|0.003<br>0.061|0.083<br>0.002<br>0.012<br>0.227<br>0.040||1|t at 10%|t scores|
|vel<br>_yO,sit−_1 inc<br>(2a)|parameter<br>0.256***<br>0.025|0.012***<br>-0.042|-0.015<br>0.000<br>0.052***<br>0.251<br>0.565***|79.9<br>0.000<br>186<br>28425|∆_yE,sit−_<br>_xt−_1<br>_sit_|el. * signifcan|nd teacher tes|
|Le<br>_yE,sit−_1 included<br>(1a)<br>(1b)|parameter<br>se<br>0.291***<br>0.062<br>0.003<br>0.050|0.014***<br>0.004<br>-0.039<br>0.062|-0.031<br>0.090<br>0.001<br>0.002<br>0.051***<br>0.014<br>0.165<br>0.213<br>0.507***<br>0.049|77.7<br>0.000<br>186<br>28425|∆_yO,sit−_1<br>_xt−_1<br>_sit_|ring at the school lev|e level and subject, a|
||e (_xt_<br>_sit_)||(even items)<br> (odd items)|||bust to cluste|(12)) by grad|
||’s subject-matter knowledg<br> has a bachelor’s degree|’s age<br> is certifed|is civil servant<br>ze<br> asset index<br> asset index is missing<br> student test score _yE,sit−_1 <br> student test score _yO,sit−_1|nderidentifcation test<br>t stage regressions<br> underidentifcation test<br>t stage regressions<br> of clusters (schools)<br> of obs. (student-subject)|d instruments|. Standard errors are ro|ardized (through formula|
||Teacher<br>Teacher|Teacher<br>Teacher|Teacher <br>Class si<br>Student <br>Student <br>Lagged <br>Lagged|_F_-stat u<br>of frs<br>_p_-value <br>of frs<br>Number<br>Number|exclude|Note|stand|

25
and self-assessed exposure to past violence. Many studies use self-reported measures of traumatization, as measured by the Harvard Trauma Questionnaire, for instance. While the association between accounts of past traumatic events and symptoms of anxiety and depression is of independent interest, the potential recall bias affects the ability to make a causal inference of conflict on mental status. In particular, individuals suffering from depression or anxiety might be less prone to “move on” and thus may be more likely to recall past traumatic events. Such a problem might also affect other studies of the impact of conflict on mental attitudes and trust following the conflict. One instance is Bellows and Miguel (2009), who average household selfreported answers to four conflict-related questions to form a chiefdom-level measure of conflict intensity in Sierra Leone.

In the analysis of the impact of conflict on welfare, many social scientists have instead used secondary sources of data on conflict (see e.g. Akresh and de Walque, 2008 or Shemyakina, 2009). The number of casualties or assessments of physical destruction are often reported by either non-governmental organizations or government institutions. In Nepal, the Informal Sector Service Center (INSEC) reports the number of casualties of the Maoist insurgency in various Human Rights Yearbooks (Do and Iyer, 2009). In the study of Bosnia and Herzegovina presented in this chapter, data on war casualties have been made available by the Sarajevo Research and Documentation Center (http://www.idc.org.ba/). An important initiative worth highlighting is the Correlates of War Project that was initiated in 1963 by scholars at the University of Michigan (http://www.correlatesofwar.org/). This project has allowed an impressive wealth of information on inter, extra and intra-State wars and militarized disputes spanning the time period 1816-2001 to be made publicly available for the benefit of the scientific community as a whole.

While these measures are certainly more objective than self-reported conflict exposure, they do have some limitations. First, administrative records or official information might differ substantially from what is actually experienced on the ground. Such classical measurement error is likely to affect the precision of estimates. Second, local surveys of war casualties are not immune to non-classical measurement errors. For example, it might well be possible that the accuracy of reports of war damages depends on several factors (such as local governance, levels of economic development, and more broadly social capital) that in turn have an effect on mental health of the population. Thus, analyses of the association between reported war damages and subsequent mental health status must take the possibility of biased reports into account when interpreting empirical findings.

7
overweight and obesity, diabetes and hypertension as the most commonly assessed in LMICs but excluded the overarching category of CVD and cancers.

Because this was a scoping review, we did not assess the quality of the studies included, which increases the risk of bias in the results reported, although most of the SRM had stringent quality review criteria for studies they included. The characteristics of the included studies and level of detail describing how diet exposures and outcomes were defined were also based on the quality of information provided in the SRM articles. There was high study variability in terms of methods and how independent and dependent variables were defined and also the extent to which confounding factors such as unhealthy lifestyle behaviours were considered in the multivariable models. For some diet exposures, the same studies were included in multiple SRM; however, we assessed the availability of this evidence qualitatively and did not calculate any summary estimate. The effect size was not often estimated for HICs vs. LMICs in subgroup analysis, preventing us from comparing this difference.

# **Conclusion**

Our results highlight the need to develop theoretical frameworks and conduct research to better quantify the contribution of diet to the coexisting burden of multiple forms of malnutrition and NCDs. This will require increased investment in nationally representative dietary surveys at the individual level in LMICs that support monitoring of the nutrition and health effects of the nutrition transition in these contexts and inform program and policy decision making.

15
- 29 -

# Table 3.3. Average Cost of Electric Power Generation by Size of Own Electricity Production

# (per kWh)

|OwnELeetricity<br>Production|Niger|ia|Indone|sia|
|---|---|---|---|---|
|(1000<br>kWh)|Nair|**USS**|RLpiah|**USS**|
|1- 4|32.76_a/_|8.190|8091.11|4.046|
|5 - 9|17.54<br>a/|4.385|6339.37|3.170|
|10- 19|4.20|1.050|4560.73|2.280|
|20- 49|2.22|0.555|1464.47|0.732|
|50- 99|2.10|0.525|4377.85|2.189|
|100 - 199|1.04|0.260|998.83|0.499|
|200 - 499|0.73|0.183|1088.16<br>a/|0.544|
|500- 999|0.69|0.173|138.69|0.069|
|1000 - 1999|0.29**a/**|0.073|114.54<br>**a/**|0.057|
|2000&over|0.32a/|0.080|165.15<br>a/|0.083|
|ALL|2.75|0.688|4282.57|2.141|
|Numberof<br>Observations|164||182||

NOTE: (1) The exchange rates were 4 baira/S in 1987 **and** 2000 Rupiah/S in 1992.

C2) The *amuatized values of generators were catculated **at an** Interest rate of 10 percent per **year.** 3) From Appendix Tables EO a*nd F1O. Pi The cell mean my not be representativb **e** cause of the small nuober of observations in the cell.

Source: World Bank Nigeria infrastructure Project Estsblishment Survey 1988. World **Sank** Research Project CRPO 676-71) Establishment Survey for Indonesia **and Thailtnd** 1992.