rafmacalaba/datause-encoder-data huggingface dataset:
_zijt_ is the error term. Like equation (1), this focuses on the variables‘ indirect effects, but on magnitude instead of frequency.

As with equation (1), we consider variants of equations (2)-(4) that include private credit, the Gini coefficient, and quadratic values of income and governance.<sup>5</sup>

# **3. Data**

We compiled an unbalanced panel with observations on the number of people killed in flood events, as well as variables capturing the physical magnitude of floods and the affected population‘s exposure and vulnerability, for 2,194 floods in 108 countries during 1985-2008.

# _3.1 Flood data_

We coded the dependent variable in equation (1), _Fjt_ , as zero if the DFO reported no floods in a country in a given year. Otherwise, we set it equal to the sum of reported events for that year. On average, there was just over one flood per country-year in the sample (Table 3), but the number varied considerably, as the standard deviation was more than twice the mean. More than half (57%) of the observations equaled zero.

Flood deaths recorded in disaster databases, including the DFO‘s, are typically from drowning and severe injuries. Deaths from unsafe or unhealthy conditions following a flood are also a health consequence, but disaster statistics typically include only the deaths

> 5 Some previous studies on disaster fatalities have also included a variable representing the frequency of disasters, typically calculated as the sum of previous events. Keefer et al. (2010) found that earthquake frequency reduces fatalities, and they argued that more frequent earthquakes increase the payoffs to fatality prevention measures. This may be so, but the results presented in this paper refer to models that exclude such a variable because the country fixed effects in those models should capture cross-country differences in flood frequency. As a test of this, we added the cumulative number of floods to the fatality models, and we found that it was insignificant and had little impact on other coefficient estimates.

9
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1.00 South America Central America and Mexico<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00 rd LI =<br>-0.20 |<br>-0.40<br>p10 p50 p90 p10 p50 p90<br>(1995-2002 m 2002-2015<br><!-- End of picture text -->
**Figure 2: LFP is lower in the Northern and Eastern provinces than in much of the rest of Sri Lanka**

<!-- Start of picture text --> 15% 14% 11% 16% 15%<br>22% 27% 31% 23% 20%<br>2%9% 12% 13% 2%9% 3%9%<br>2% 2%<br>51% 50% 54%<br>44% 42%<br>Sri Lanka Northern Eastern Western Other<br>Province Province Province Provinces<br>Employed Unemployed Student Housewife Others not working<br><!-- End of picture text --> Source: Labor Force Survey 2015 (Sample: 15+ year‐olds) Labor markets in the North and East are characterized by low rates of employment and labor force participation. Labor force participation is much lower in the Northern and Eastern provinces, at 46% and 44%, respectively, than the national average of 53% (Figure 2). These low rates of participation are driven by low employment rates in the Northern and Eastern provinces, rather than differences in unemployment. Furthermore, the low employment rates in the Northern and Eastern provinces have only slightly improved in recent years (Figure 3). The gradual upward trend in employment rate of the Northern province is smoother than the trend for the Eastern province.

**Figure 3: Employment rate in the Northern and Eastern provinces is below the national average, but may be gradually catching up**

<!-- Start of picture text --> 56%<br>52%<br>48%<br>44%<br>40%<br>2011 2012 2013 2014 2015<br>Northern Province Eastern Province Western Province<br>Other Provinces Sri Lanka<br>Employment rate<br><!-- End of picture text --> Source: Labor Force Survey 2011‐14 (Sample: 15+ year olds) 5
# **The Political Economy of Bank Lending: Evidence from an Emerging Market**<sup>*****</sup>

Sumit Agarwal<sup>†</sup> , Bernardo Morais<sup>‡</sup> , Claudia Ruiz<sup>§</sup> , Jian Zhang<sup>**</sup> JEL classification: D72, DG21, L33, P16 Keywords: Bank Lending, Political Economy, Privatization, Rent Seeking

> * We benefited from the comments of Gene Amromin, Bo Becker, Zahi Ben-David, Allen Berger, Souphala Chomsisengphet, Yongqiang Chu, Lou Dong, John Driscoll, Omrane Guedhami, Tatiana Kostova, Chuck Kwok, Sali Li, Gustavo Manso, Gerald McDermott, Jose-Luis Peydro, Tomek Piskorski, Eric Powers, Wenlan Qian, Paola Sapienza, Amit Seru, Sergey Tsyplakov, Bernard Yeung, Yu Yuan and seminar participants at Australian National University, Goethe University, National University of Singapore, University of South Carolina and World Bank. Contact author: Jian Zhang.

> † National University of Singapore, 15 Kent Ridge Drive, Singapore 119245,  E-mail: bizagarw@nus.edu.sg

> ‡ Federal Reserve Board,  E-mail: bernardo.c.morais@frb.gov

> § The World Bank, Email: cruizortega@worldbank.org

> ** National University of Singapore, 15 Kent Ridge Drive, Singapore 119245, Email: jian_zhang@nus.edu.sg
industry (which received two-thirds of energy subsidies in 1994) have increased to world market levels, although gas prices still remain below border price levels (IFC, 2008).

The market-friendly reforms had a far-reaching impact on the transformation of the energy economies. When the sharp initial decline gave way to gradual economic recovery, energy consumption continued to decline and then stabilized at around 250 million tons of oil equivalent (mtoe) per year. Meanwhile, manufacturing output has increased by more than 50 percent during 2002- 2008, as shown in Figure 1. This has occurred because the average manufacturing energy intensity declined by 35 percent from 0.4 kilogram of oil equivalent (koe) per dollar (ppp) in 1998 to 0.26 koe per dollar (ppp) in 2008.<sup>5</sup> If energy intensity had remained at its 2002 level, energy demand would have been 776 mtoe higher, the equivalent of 10 years’ total energy consumption in Ukraine today.

While manufacturing energy intensity at the regional level had sharply declined, sub-regions show disparate behavior. Figure 2-a presents energy intensity trend for six sub-regions/countries. Central Asia is notably lagging behind in terms of the reduction in energy intensity. In fact, Kazakhstan, the Kyrgyz Republic, and Tajikistan had all experienced an increase in energy intensity since 2000 as shown in Figure 2-b. In the rest of the paper, we explore why countries were not doing equally well and identify the driving factors behind the trend of energy productivity.

# **III. Data**

Data used in this paper come from three sources. The energy consumption data are obtained from the International Energy Agency (IEA) World Energy Statistics and Balances database (2012). The database reports final energy consumption of manufacturing in 28 ECA countries. The manufacturing sector is further broken out by 11 subsectors at the two-digit level of the international standard industrial classification Revision 4 (ISIC Rev. 4).

The aggregate value added of manufacturing is reported in constant 2000 US dollars in the World Development Indicators (WDI) (2012). To compare energy intensity levels across countries, we apply purchasing power parity (PPP) exchange rates to convert all values into constant 2005 international dollars. The theoretically ideal PPP exchange rate to use at the sector level would be based on the output prices of the sector, rather than on aggregate expenditure prices. However, due to the limited availability of producer price-based PPPs, we follow the standard practice of using expenditure PPPs for cross-country convergence analysis (such as in Dollar and Wolff, 1993 and Mulder and Groot, 2007).

> 5 Energy intensity is measured by energy use per unit of manufacturing value added. Energy use is defined as final energy consumption excluding energy losses in the generation, transmission and distribution of purchased electric power. Value added presents the net economic output, or gross economic output less the value of purchased inputs. It is derived by subtracting the cost of materials, supplies, fuel and purchased electricity from the value of final outputs.

4
Table 1: (continued)

|Name|Type|Defnition|N|Distribution|
|---|---|---|---|---|
|LG vertical gap|Continuous:<br>0-100|Grants from other govt’s (same- or upper-tier, also<br>from other countries) as % of LG revenues<br>1 - LG regulates fully (sets base and rate) at least<br>one major tax (property, income, or sales tax); 0.5|123|3<br>13<br>7<br>15<br>15<br>17<br>5<br>16<br>12<br>19<br>1|
|LG taxation au-<br>tonomy|Discrete: 0,<br>0.25,<br>0.5,<br>0.75, 1|- LG partly regulates (sets rate or base in CG de-<br>fned boundaries, or only after CG approval) at least<br>one major tax, or fully regulates some fees and minor<br>taxes; 0 - no administration of major taxes, partial|158|71<br>5<br>~~57~~<br>25|
|||administration of minor taxes; 0.25 or 0.75 - LG are<br>treated asymmetrically<br>1 - at least half of transfers (to LG budgets from|||
|LG unconditional<br>transfers|Discrete: 0,<br>0.25,<br>0.5,<br>0.75, 1|same- or upper-tier governments) are unconditional<br>and formula-based; 0.5 - quarter to half of transfers<br>are unconditional and formula-based; 0 - all transfers<br>are either conditional or discretionary; 0.25 or 0.75 -|159|40<br>25<br>1<br>17|
|||LG are treated asymmetrically|||
|LG<br>borrowing<br>freedom<br>|Discrete: 0,<br>0.25,<br>0.5,<br>0.75, 1<br>|1 - borrowing is not regulated by CG; 0.5 - borrowing<br>only from CG or under CG approval or regulation; 0 -<br>borrowing is not allowed; 0.25 or 0.75 - LG are treated<br>asymmetrically<br>|160<br>|89<br>49<br>22<br>|

<u>Note: Defnitions and characteristics of decentralization variables are shown. Year of the data - 2005. Datasource: Ivanyna</u> and Shah (2010). Abbreviations used: LG - local government, CG - central government, GG - general government.
 Telecommunications: `o` Main telephone lines.

`o` Number of mobile phones.

- Energy:

`o` Electricity generating capacity (in million kilowatt).

`o` Electric power transmission and distribution losses (in % of output)

- Transport:

`o` Road total network (in km).

`o` Paved roads (in % of total network).

`o` Rail route length (in km).

- Water:

`o` improved water source (% of population with access) Notice that when available, we use “quality proxies”: electric power transmission and distribution losses as a % of output for the quality of electric services, and paved roads as a % of total network for roads. Additional variables used include GDP per capita, gross fixed capital formation, primary and secondary school enrolment, life expectancy, and inflation (all from WDI).

# **3.3. Sample**

We rely on a sample of 102 developing or emerging countries, of which 20 (Algeria, Bahrain, Djibouti, Egypt, Arab Republic, Iran Islamic Republic, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates, Yemen Republic, West Bank and Gaza) belong to the MENA geographical area.

14
-26-

Mankiw, N. G., D. Romer, and D. N. Weil (1992), "A Contribution to the Empirics of Economic Growth," _The Quarterly Journal of Economics,_ May.

Mundlak, Y. (1978), "On the Pooling of Time Series and Cross Section Data," _E:conometrica,_ 46, no. 1.

Ramsey, F.P. (1928), "A Mathematical Theory of Saving," _Economic Journal, 38,_ December.

Rebelo, S. (1991), 'Long-Run Policy Analysis and Long-Run Growth," _Journal of Political Economy,_ vol. 99, no.3.

Romer, P. (1989), "Human Capital and Growth: Theory and Evidence,' mimeo, The University of Chicago, April.

Sala-i-Martin, X. (1990), "Lecture Notes on Economic Growth (I)," NBER w.p. no. 3563, December.

Summers, R. and A. Heston (1991), "The Penn Worid Table (Mark 5): An Ezxpanded Set of international Comparisons, 1950-1988," 7he _Quarterly Journal of Economics, May._ Solow, R. (1956), "A Contribution to the Theory of Economic Growth," _The Quarterly Jounal of Economics,_ February.

White, H. (1980), "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," _Economzetrica,_ 50, 483-499.
# Toilet Construction and Use

ANCOVA estimates for outcomes related to the construction and use of toilets were generally smaller in magnitude than the DID estimates. For the full sample, unlike DID estimates, no significant impact was observed on the coverage of basic facilities across treatment arms or access to toilets in arm 1. In arms 2 and 3, access improved significantly relative to the control group, by around 2 percentage points, compared with 4 percentage points estimated under DID. Estimated effects on the coverage of safely managed facilities, toilets under construction, and open defecation also had significance levels similar to DID estimates but smaller in magnitude. As per ANCOVA estimates, open defecation declined in the treatment arms relative to the control group by around 7 to 8 percentage points, and households without initial access but reporting construction of toilets increased by 5.9 percentage points in arm 1, 7 percentage points in arm 2, and 12 percentage points in arm 3.

For the subsamples of households without access to toilets at baseline, we observed a significant impact of the project on the coverage of basic and safely managed facilities. The estimated coefficients were very similar to those of DID estimates. Impacts on open defecation across treatment arms were also similar. Open defecation declined by 1.2 percentage points in arm 1, 3.8 percentage points in arm 2, and 6.5 percentage points in arm 3. However, unlike DID estimates, in which all the coefficients were non-significant, the impact on arm 3 was found to be significant at the 10 percent level. For the subsample of households with access to toilets at the baseline, we found no significant impact on basic coverage. The estimated impacts on safely managed facilities were also similar to DID, except that the impact on arm 1 was significant at the 10 percent level. We also found large reductions in open defecation across treatment arms for this subgroup, which is in line with the findings from the DID analysis.

Hygiene Awareness and Hygienic Behaviors

# Adults

Based on the ANCOVA estimates, awareness of the importance of handwashing, both before eating and after defecation, improved significantly among adults across all treatment arms compared with the control group. All the coefficients were found to be significant at least at the 5 percent level. Awareness of the importance of handwashing after defecation increased by 4 to 5 percentage points, which is less than that shown by the DID estimates. Moreover, unlike the DID estimates, which found a statistically significant impact on the awareness of handwashing before eating only in arm 3, the ANCOVA results suggest an improvement of 5 and 7 percentage points across all treatment arms.

# **Table** **<u>6. Impact of Project Interventions: ANCOVA Unadjusted Model</u>**

**N No. of Clusters Adj. R^2 Treatment 1 Treatment 2 Treatment 3**

31
# **III.1     Trends in International Air Passenger Travel**

In order to understand the context of our work on the 2010 data, we provide some initial summary statistics about the levels and changes of air passenger traffic in the years 2000 and 2010.<sup>15</sup> Table 1 reports passenger traffic for select origin and destination countries using the on-flight origin and destination data from the ICAO. These figures report total scheduled air passenger traffic. These figures do not include traffic on non-scheduled flights, such as charter flights, but include scheduled traffic accounts for most international air passenger travel. The table also reports the number of scheduled flights.

The table shows that Turkey has seen rapid growth, both inbound and outbound, within the ACAC and with the rest-of the world. Official decisions to lift visa requirements for many Arab countries and sign free trade agreements with Morocco, Tunisia, Libya and Jordan have increased the flow of people and goods between Turkey and the Middle East and North Africa.<sup>16</sup> Growth within the ACAC has also been rapid, although varying over origins and destinations. The spectacular levels of growth in traffic involving the UAE is notable, and observed across most countries in the region.<sup>17</sup> The figures in Table 1 are consistent with Turkey’s aspirations to become a regional hub for international air travel. Given the underlying fundamentals this seems possible, but there is not, as yet, a firm international policy setting that can facilitate such a development. We turn to an empirical model to help us understand the likely effects of policy change.

# **III.2     Patterns of Policy Governing Air Passenger Traffic**

As noted above, most air traffic is governed by bilateral air service agreements between pairs of countries, and some by plurilateral agreements between groups of countries. We are interested primarily

> 15 2010 is the most recent year for which the ICAO data is complete.

> 16 “Regional push resumes following Arab Spring,” by Borzou Daragahi, Financial Times Special Report on “New Trade Routes:  Turkey,” Thursday May 9, 2013.

> 17 Often, these growth rates are off a relatively small base.

9
22 Table 6: Summary statistics for pass-through coefficients for kerosene (percentage of countries in each category except for the median)

|**Parameter**(number of countries)|**Median**|**<0%**|**0–50%**|**50–75%**|**75–100%**|**100–150%**|**>150%**|
|---|---|---|---|---|---|---|---|
|**Income category**||||||||
|Low income (11)|49|18|36|18|9|18|0|
|Lower-middle income (19)|44|16|37|21|16|11|0|
|Upper-middle income (14)|81|14|14|14|21|36|0|
|**Developing countries only (excluding h**|**igh-income**|**countries**|**)**|||||
|Net oil importer in 2009 (32)|63|6|31|19|19|25|0|
|Net oil exporter in 2009 (12)|9|42|17|25|8|8|0|
|East Asia & Pacific (5)|63|0|40|20|20|20|0|
|Latin America & the Caribbean (11)|84|0|9|18|27|45|0|
|Middle East & North Africa (5)|14|20|60|20|0|0|0|
|South Asia (5)|44|0|80|0|20|0|0|
|Sub-Saharan Africa(17)|38|35|18|24|6|18|0|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations. _Notes:_ The median is that for pass-through coefficients. The income category does not include high-income countries because there was only one. Europe and Central Asia is similarly excluded because there was only one country.

Table 7: Summary statistics for pass-through coefficients for LPG (percentage of countries in each category except for the median)

|**Parameter**(number of countries)|**Median**|**<0%**|**0–50%**<sup>**a**</sup>|**50–75%**|**75–100%**|**100–150%**|**>150%**|
|---|---|---|---|---|---|---|---|
|**Income category**||||||||
|Low income (7)|91|0|14|14|29|43|0|
|Lower-middle income (22)|58|18|32|5|14|18|14|
|Upper-middle income (20)|64|24|24|10|0|24|19|
|**Developing countries only (excluding h**|**igh-income**|**countri**|**es)**|||||
|Net oil importer in 2009 (33)|91|3|24|12|12|33|15|
|Net oil exporter in 2009 (17)|1|47|29|0|6|6|12|
|East Asia & Pacific (8)|58|0|50|13|13|25|0|
|Latin America & the Caribbean (16)|128|12|18|6|0|29|35|
|Middle East & North Africa (7)|1|43|43|0|14|0|0|
|South Asia (5)|88|0|40|0|40|20|0|
|Sub-Saharan Africa(12)|64|33|8|17|8|25|8|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations. _Notes:_ The median is that for pass-through coefficients. The income category does not include high-income countries because there were only two. Europe and Central Asia is similarly excluded because there was only one country. a. Includes pass-through coefficients equal to 0 percent.

The pricing policy of each government is briefly summarized in table A2.2 in appendix 2. Bolivia, Cameroon, Egypt, Indonesia, and República Bolivariana de Venezuela froze the prices of the four fuels in local currency between January 2009 and January 2012. Currency depreciation in Cameroon, Egypt, and Venezuela made pass-through coefficients based on U.S. dollars negative, while Bolivia’s currency appreciated slightly during the interval and Indonesia’s appreciably, but even in Indonesia not nearly enough to offset the price freeze. Gabon also effectively froze fuel prices in local currency despite slight currency depreciation—
|Economic RecoveryLoan Project||
|---|---|
|ECON. RECOVERY LN.TA||
|ECON. RECOVERY LN.TA||
|ECON. RECOVERY LN.TA||
|Bacon Manito Geothermal Development Project|John R. Irving|
|Bacon Manito Geothermal Development Project|John R. Irving|
|Bacon Manito Geothermal Development Project|John R. Irving|
|Bacon Manito Geothermal Development Project|John R. Irving|
|Bacon Manito Geothermal Development Project|John R. Irving|
|Bacon Manito Geothermal Development Project|John R. Irving|
|Bacon Manito Geothermal Development Project|John R. Irving|
|Bacon Manito Geothermal Development Project|John R. Irving|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Environment and Natural Resources Sector Adjustment Project|Carolina V. Figueroa-Geron|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Water Supply,Sewerage and Sanitation Project(01)|Wael Zakout|
|Manila Power Distribution Project|John R. Irving|
|Manila Power Distribution Project|John R. Irving|
|Manila Power Distribution Project|John R. Irving|
|Manila Power Distribution Project|John R. Irving|
|Manila Power Distribution Project|John R. Irving|
|Manila Power Distribution Project|John R. Irving|
|Manila Power Distribution Project|John R. Irving|
|Manila Power Distribution Project|John R. Irving|
|ElementaryEducation Project(02)|L. Richard Meyers|
|ElementaryEducation Project(02)|L. Richard Meyers|
|ElementaryEducation Project(02)|L. Richard Meyers|
|ElementaryEducation Project(02)|L. Richard Meyers|
|ElementaryEducation Project(02)|L. Richard Meyers|
|ElementaryEducation Project(02)|L. Richard Meyers|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|