rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> No Yes No Yes<br>Outages but Power is not the biggest obstacle Outages and Power as the biggest obstacle<br>AFR EAP ECA LAC MNA SAR AFR EAP ECA LAC MNA SAR<br>2.7<br>2.7<br>2.6 2.6<br>2.5<br>2.5<br>2.4<br>Total factor productivity 2.4 Total factor productivity<br>2.3<br>2.3 2.2<br><!-- End of picture text --> Figure 3: The combined effects of outages and other power constraints ized differences in means between these two groups with the corresponding level of significance in each case.

Table 4: Descriptive Statistics (T=Power Outages)

||[1]|[2]|[1]_−_[2]|
|---|---|---|---|
||Outages|No Outages|Diff.|
|Firm size|1.802|1.831|-0.038***|
|Firm’s longevity|29.690|29.737|-0.003**|
|Own website|0.386|0.471|-0.173***|
|Foreign private participation|8.082|7.222|0.034**|
|GDP growth (annual %)|3.321|4.995|-0.511***|
|Infation (annual %)|7.726|7.480|0.043***|
|log(Bank concentration in %)|4.047|4.084|-0.108***|
|log(FD index)|-1.496|-1.243|-0.436***|
|log(GDP per capita)|7.771|8.231|-0.542***|
|log(Net ODA in % of GNI)|-0.185|-0.883|0.392***|
|log(Regulatory Quality)|3.601|3.784|-0.370***|
|Observations|19086|12317||

Notes: This Table shows the sample means of all matching covariates, divided into two groups: observations of firms facing outages or the treatment group (column 1) and observations of firms not facing outages or the potential control group (column 2). The last column shows the standardized differences in means between these two groups with the corresponding level of significance in each case. The analysis of the results for all the relevant observable characteristics reveals that firms facing outages differ drastically compared to firms not facing outages.

The analysis of the results for all relevant observable characteristics reveals that firms facing power constraints differ drastically and operate in very different environments compared to firms not facing power constraints. Indeed, we find that firms facing power constraints are on average smaller than those not facing power constraints. In the same vein, these firms facing the constraints are on average younger than those not facing the constraints. We also note that firms not facing constraints have on average a better marketing management (approximated by the possession of a website). Finally, we also notice that the firms that face 15
|**Tabl**<br>Dependent variable: Female<br>|**e 6: Falsifi**<br>**(1)**|**cation test u**<br>**(2)**|**sing female o**<br>**(3)**|**wnership**<br>**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|owner present Y:1 N:0|||||||
|||**All firms**||**Sole pro**|**prietorshi**|**p firms**|
||||**Panel A: Log**|**odds ratios**|||
|Maternity leave (logs)|**-0.017**|**-0.114****|**-0.099**|**0.127**|**0.094**|**0.107**|
||**(0.044)**|**(0.049)**|**(0.068)**|**(0.082)**|**(0.074)**|**(0.098)**|
|GDP per capita (logs)||0.114**|-0.123||0.117|0.074|
|||(0.057)|(0.129)||(0.083)|(0.207)|
|Growth rate of GDP per<br>capita (%, annual)||0.005|-0.005||0.021|0.027|
|||(0.017)|(0.018)||(0.037)|(0.037)|
|Females in adult population<br>(%)||-0.007|-0.014||0.105|0.127*|
|||(0.035)|(0.037)||(0.064)|(0.069)|
|Number of workers (logs)||0.062**|0.074***||0.087|0.078|
|||(0.027)|(0.026)||(0.067)|(0.063)|
|Top manager is female Y:1<br>N:0||2.647***|<br>2.639***||3.544***|3.563***|
|||(0.091)|(0.092)||(0.145)|(0.145)|
|Age of firm (logs)||0.230***|<br>0.224***||-0.095|-0.105|
|||(0.040)|(0.038)||(0.081)|(0.080)|
|Labor productivity (logs)||0.013|0.017||-0.047|-0.054|
|||(0.018)|(0.017)||(0.037)|(0.036)|
|Exports (% of sales)||0.001|0.000||0.002|0.002|
|||(0.001)|(0.001)||(0.003)|(0.003)|
|Foreign ownership (%)||-0.005***|-0.005***||-0.007*|-0.007*|
|||(0.001)|(0.001)||(0.004)|(0.004)|
|Firm offers formal training<br>to workers Y:1 N:0||0.249***|<br>0.226***||0.205|0.197|
|||(0.058)|(0.057)||(0.141)|(0.131)|
|Inadequately educated<br>workers: Obstacle severity<br>(0-4 scale)||0.024|0.027||0.085**|0.078*|
|||(0.020)|(0.020)||(0.043)|(0.041)|
|Firm registered when started<br>operations Y:1 N:0||0.053|0.024||0.070|0.080|
|||(0.086)|(0.081)||(0.116)|(0.111)|
|Firm experienced losses due||0.062|0.064||-0.009|-0.030|
|to crime Y:1 N:0|||||||
|||(0.070)|(0.071)||(0.184)|(0.174)|
C10_hours1 How many hours (on average) do you work during a normal WEEK in this MAIN job?

1. Fewer than 15 hours per week

2. 15-30 hours per week 3. 30-45 hours per week 4. 45-60 hours per week 5. 60-75 hours per week 6. More than 75 hours per week 888. Don’t know <u>999. Refused</u> C11_hourspref Provided that you could make a free choice regarding your working hours and taking into account the need to earn a living: how many hours would you prefer to work during a normal WEEK in this MAIN job?

1. Fewer than 15 hours per week

2. 15-30 hours per week 3. 30-45 hours per week 4. 45-60 hours per week 5. 60-75 hours per week 6. More than 75 hours per week 888. Don’t know 999. Refused C12a_earnings_cash1 How much do you usually earn PER MONTH in cash from this MAIN job (in Taka)?

**[FOR SELF-EMPLOYED, EARNINGS REFER TO PROFITS, NOT REVENUES. FOR FAMILY MEMBERS WORKING IN A HOUSEHOLD BUSINESS, RESPONDENT SHOULD SAY HOW MUCH HE OR SHE EARNS, NOT HOW MUCH THE BUSINESS EARNS.]**

**[If answered then go to C12c]**

**[If don’t know then put “Don’t Know” and go to C12b] [If refused then put “Refused” and go to C12b]**

C12b_earnings_cash_range1 In what range does the amount you usually earn PER MONTH from this MAIN job fall?

1. Less than 5,000 Taka 2. 5,000-10,000 Taka 3. 10,000-20,000 Taka 4. 20,000-30,000 Taka 5. 30,000 Taka or more 888. Don’t know <u>999. Refused</u>

C12c_earnings_kind1 How much do you usually earn per month **_in kind_** (for example, free meals or clothing) from this MAIN job (estimated Taka equivalent)?

**[If no in-kind earnings, then put “0”, do not leave blank] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

4
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other variable for which we test balance at baseline. The interventions were not means-tested, and more generally there was no room to manipulate the eligibility criteria (gender and grade). In all schools, the treatments were announced after the official enrollment period and, in three quarters of schools (corresponding to 7 out of 11 districts), well after the start of the new school year, so these announcements were unlikely to affect enrollment decisions, especially considering the likely delay in spreading information to the parents of marginal enrollees.<sup>18</sup> Table 1 presents the allocation of schools and girls across the four study groups as well as the attrition rate for girls sampled for the household survey. While attrition of girls taking part in the household sample was limited at 5.3% overall, it was slightly larger in the control group than in the treated groups. Robustness checks reported in Section IV show that this differential attrition is unlikely to be driving our conclusions. Our main outcome of interest (independently verified attendance rate at school), however, is available for all schools between one (for 3 schools) and 6 times (for 132 schools), and on average 5.6 times during the school year - corresponding to 5.6 spot checks on average, and the number of times each school was surveyed is independent of experimental arm (the p-value of a joint FF-test is 0.55).

18 There is no statistically significant difference in the timing of the treatment announcements across treatment arms relative to the start of the academic year. More precisely, when regressing the number of days between treatment announcement and the start of the academic year on two treatment indicators (where the third treatment is the omitted category) and a set of district fixed effects, the p-value of a joint F-test of significance of the two treatment coefficients is 0.72.

21
16

**Table 6. Alternative Specifications (logit)**

|**Estimates for graduate (1= graduate,**|**0= non-grad**|**uate)**||||
|---|---|---|---|---|---|
||**FY89–**<br>**FY18**|**FY89–**<br>**FY18**|**FY89–**<br>**FY18**|**FY89–**<br>**FY18**|**FY00–**<br>**FY18**|
|GNIper capita/operational cutoff|0.089***|0.091***|0.095***|0.093***|0.086***|
||(4.67)|(4.89)|(5.15)|(5.30)|(4.34)|
|Logofyears over cutoff - capped at 9|0.132***|0.121***|0.122***|0.121***|0.061***|
||(5.37)|(5.07)|(5.53)|(5.20)|(2.15)|
|Log of years above cutoff in excess of<br>9|−0.033**|−0.033**|−0.033**|−0.031**|−0.027**|
||(−2.25)|(−2.47)|(−2.39)|(−2.36)|(−2.04)|
|Creditworthiness|0.007***|0.006***|0.005***|0.006***|0.002**|
||(4.91)|(4.70)|(4.75)|(4.79)|(2.32)|
|Povertyheadcount|−0.361|−0.332|−0.294||−0.547**|
||(−1.60)|(−1.55)|(−1.56)||(−2.11)|
|Poverty gap||||−0.894||
|||||(−1.44)||
|Logofpopulation|0.054***|0.057***|0.056***|0.055***|0.102***|
||(3.70)|(4.90)|(4.80)|(4.51)|(5.07)|
|Logof life expectancy|0.780***|0.685***|0.665***|0.704***|1.617***|
||(3.06)|(3.11)|(2.92)|(3.07)|(2.66)|
|Urbanpopulation share|0.429**|0.420**|0.404**|0.430**|0.929**|
||(2.36)|(2.48)|(2.58)|(2.53)|(3.15)|
|Institutional development|0.571**|0.466*|0.470*|0.429*|0.583***|
||(1.97)|(1.81)|(1.89)|(1.67)|(3.04)|
|Resource rents/GDP|0.025|−0.022|0.000|0.005|−0.268*|
||(−0.10)|(−0.10)|(−0.00)|(−0.02)|(−1.73)|
|Logof OECD GNIper capita/cutoff|1.447***|1.375***|1.414***|1.395***|1.065***|
||(7.27)|(7.30)|(8.37)|(7.56)|(5.84)|
|Angola dummy|0.173|0.125|0.11|0.109|0.333**|
||(1.51)|(1.19)|(1.13)|(1.11)|(2.03)|
|Exports/GDP|−0.100|||||
||(−0.90)|||||
|Battle dummy||−0.010||||
|||(−0.72)||||
|Conflict ongoing|||0.11|||
||||(0.36)|||
|_Number of observations_|_674_|_705_|_705_|_705_|_581_|
|_Number of countries_|_64_|_65_|_65_|_65_|_65_|
|_pseudo-R_<sup>_2_</sup>|_0.78_|_0.78_|_0.78_|_0.79_|_0.85_|
|_Mean dependent variable_|_0.28_|_0.27_|_0.27_|_0.27_|_0.23_|
|_Correctly predicted(%)_|_94.5_|_94.5_|_94.8_|_94.6_|_96.4_|

_Note:_ t-statistics (in parentheses, beneath marginal effects) are adjusted for clustering by country; * indicates significance at the 10 percent level, ** at the 5 percent level, and *** at the 1 percent level for two-tailed tests.
**Figure 12: Wage premium to college education, 1995-2012**

<!-- Start of picture text --> 2.00<br>1.50<br>1.00<br>College to High School College to Technical and Vocational<br>1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> _Source:_ own elaboration based on RLMS (Rounds VI to XXI) _Notes:_ all indicators have been calculated based on the subsample of currently employed wage workers in the 18-60age group, reporting more than 140 worked hours in the last 30 days. The distribution of hourly wages was trimmed at the bottom and top 0.25 percent in each round, to remove outliers. Wages for rounds VI and VII were divided by 1,000 to account for the nominal revaluation of the ruble in January 1998 (1,000 old rubles = 1 new ruble). The RLMS was not administered in 1997 and 1999.

# **2.3. Gender and urbanization**

Higher female labor force participation—particularly of more educated women— is one of the main factors explaining the decrease in wage inequality in the U.S. and in Latin America (Mulligan and Rubinstein, 2008; World Bank 2012). However, the case of the Russian Federation is different, as female labor force participation was already high during the soviet period, with female workers accounting for half of the labor force since 2002. Moreover, despite the increase in educational attainment, the gender wage gap remained almost constant between 2002 and 2012 (Figure 13), except for the widening of the gap in 2000-2001 and 2003-2004 (Atencio and Posadas, 2013).<sup>13</sup> Thus, women’s role in the labor market seems to be a less relevant factor in the Russian Federation, except for the effect that could come from changes in occupational segregation and discrimination.

> 13 According to Kosakova (2007), the temporary widening of the gender gap during the economic recovery of 20002001 responds to the fact that low-wage female workers became more likely to receive their full wages than lowwage male workers, during a period where wage arrears were a typical feature of the Russian labor market.

15
# Appendix A. Additional Figures

## **Distribution of Main Outcome Groups**

## **Figure A1. Most Interventions Focus on Health and Water Residuals**

(Classification of interventions and outcomes analyzed) <!-- Start of picture text --> water quality<br>combined<br>hygiene<br>water supply<br>sanitation<br>water supply; sanitation<br>sanitation; hygiene<br>0% 25% 50% 75% 100%<br>Education Socio‐economical/expend Access Behavioral Water residuals Health<br><!-- End of picture text --> _Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database.

## **Figure A2. Water Quality Interventions Dominate**

(Groups of outcomes distinguished by type of intervention) <!-- Start of picture text --> Health<br>Water residuals<br>Behavioral<br>Access<br>Socio‐economical/expend<br>Education<br>0% 25% 50% 75% 100%<br>hygiene sanitation water quality<br>water supply sanitation; hygiene water supply; sanitation<br>combined<br><!-- End of picture text --> _Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database.

20
1

|**Contents**||
|---|---|
|**I. Introduction**|**2**|
|**II. Conceptual Origins of Risk-Based Supervision - Basel II and Solvency II**|**3**|
|**III. Introduction of Risk-based Supervision for Pensions**|**5**|
|1. Overview of the Four Pension Systems|5|
|2. Factors Motivating the Adoption of Risk-Based Supervision in Pension Systems|7|
|**IV. The Main Elements of Risk-Based Supervision for Pensions**|**9**|
|1. Common Objectives and Elements of Design|9|
|2. Overview of the Main Components of Pension Supervision in the Four Countries|11|
|3. Regulatory Requirements for Risk Management Architecture|13|
|4. Risk-Based Solvency Standards|14|
|5. Supervisory Risk Scoring Systems|18|
|6. Market Based Discipline and Third Parties|21|
|7. Internal Structure of the Supervision Agency|22|
|**V. Preliminary Assessment of the Impact of RBS**|**23**|
|1. The Netherlands|23|
|2. Denmark|25|
|3. Australia|27|
|4. Mexico|29|
|**VI. Concluding Observations**|**31**|
|**Tables**||
|Table 1: Main Characteristics of the Four Private Pension Systems, Dec. 2005|6|
|Table 2: Factors Motivating the Adoption of Risk-Based Supervision|8|
|Table 3: Main Components of Risk-Based Supervision in the Four Countries|12|
|Table 4: Regulatory Requirements on the Risk Management Architecture|14|
|Table 5: Risk-Based Solvency Requirements|16|
|Table 6: Risk Scoring Methods|20|
|Table 7: Role of Market Discipline, Third Parties, Disclosure|22|
|Table 8: Asset Allocation of Dutch Pension Funds 2001 – 2005 (in % of total)|24|
|Table 9: Changes in the Asset Allocation of Life Companies and Pension Funds|26|
|Table 10: Simulation Results of Change in Interest Rates|27|
|Table 11: Average Composition of the Aggressive Pension Portfolio in Mexico (Siefores 2)|30|
|**Figures**||
|Figure 1: The Three Pillars of Basel II|4|
|Figure 2|10|
|Figure 3: Distribution of the Duration of Dutch Pension Fund Fixed Income Investments|25|
|Figure 4|26|
|Figure 5: Shifts in the Efficient Frontier and Actual Risk-Return Combinations|30|
|Figure 6: Maturity of the Public Debt Stock and Government Securities in the Riskier Portfoli|o 31|
|Figure 7: Basic and Aggressive Portfolios: VaR Limits and Actual VaRs, 2005-2006|31|
# **The Drivers of Non-Revenue Water:**

**How Effective Are Non-Revenue Water Reduction Programs?**

_Caroline van den Berg_<sup>_1_</sup>

**Key Words** Water supply, utilities, non-revenue water, benchmarking, developing countries **JEL Classification** :  H4, L95, O57, Q25

> 1 The author would like to thank Celine Nauges and Abel Mejia for helpful suggestions.
The implications of the model are supported by panel data from both developed and developing countries. Our main finding is that better credit information (as proxied by the existence and coverage of private and public credit registries as well as by improvements in accounting standards) is associated with a higher share of long-term debt as a proportion of total corporate debt. This does not imply that, in and of itself, better credit information is a sufficient condition for developing long-term corporate debt markets. Our results do suggest, however, that improving credit reporting systems and accounting standards around the world may increase the maturity horizon at which financial institutions feel safe to lend. This would contribute to lengthening the maturity structure of corporate debt thus placing firms on a more solid footing to avoid illiquidity risks and exploit their full growth potential.

# **References:**

- Admati, A. and M. Perry, 1987, Strategic delay in bargaining, _Review of Economic Studies_ 54 (3), 345-364

> Allen, F., 2001, Financial structure and financial crises, _International Review of Finance_ 2, 1- 19

- Altman, E., 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, _Journal of Finance_ 23, 589-609

- Altman, E. and H. Suggitt, 2000, Default rates in the syndicated bank loan market: 1991– 1997, _Journal of Banking and Finance_ 24, 229-253

- Barclay, M. and C. Smith, 1995, The maturity of corporate debt, _Journal of Finance_ 50, 609631

- Barnea, A., R. Haugen and L. Senbet, 1980, A rationale for debt maturity structure and call provisions in the agency theoretic framework, _Journal of Finance_ 35, 1223-1234

- Barron, J.M. and Staten, M., ‘The Value of Comprehensive Credit Reports: Lessons from the US Experience’ in M. Miller (2003), _Credit Reporting Systems and the International Economy_ , Cambridge, Massachusetts: MIT Press.

Beck, T., A. Demirguc-Kunt and R. Levine, 2000, A new database on financial development and structure, _World Bank Economic Review_ 14, 597-605 Berger, A., M. Espinosa-Vega, W. Frame and N. Miller, 2005, Debt maturity, risk, and asymmetric information, _Journal of Finance_ 60(6), 2895-2923 24
percent and 49 percent of the salaried and self-employed workers, respectively. This evidence is in line with findings reported in Perry et al. (2007), who argue that informality in Latin America is not a dominant exclusion phenomenon but, rather, one in which exclusion factors and voluntary selection of self-employment compete. This is also the case in Colombia, although Perry et al. (2007, p. 7) note that a larger-than-average proportion of self-employed workers in Colombia (this paper estimates 51 percent; Rentería 2007 estimates 60 percent) would prefer formal occupations vis-à-vis the Latin American region as a whole (about a third).

**Table 1: Occupational Statistics**

||**2008**|**2009**|
|---|---|---|
|**Gender (%Total Population)**|||
|Male|45.4|45.4|
|Female|54.6|54.6|
|**Education Level (% Total Sample)**|||
|Until Primary Complete|26.6|26.2|
|Until Secondary Complete|48.7|47.4|
|Tertiaryor More|24.7|26.4|
|**Age Group (% Total Sample)**|||
|12 to 18|15.3|12.3|
|19 – 35|31.0|28.4|
|36 – 64|41.5|40.4|
|**SISBEN Level (% Total Sample)**|||
|1 or 2|27.3|29.8|
|3+|72.7|70.2|
|**Working Age Population, WAP, (% Total Sample)**|82.1|82.9|
|Economically Active Population, EAP, (% WAP)|55.4|51.9|
|Occupied People (% EAP)|89.1|88.3|
|Unemployed People (% EAP)|10.9|11.7|
|Inactive Population(% WAP)|44.6|48.1|
|**Economic Sector (% Occupied People)**|||
|Agriculture/Mining|1.2|1.3|
|Manufacturing|13.5|14.5|
|Construction|6.6|5.6|
|Retail|17.4|18.9|
|Services<br>**Type of Occupation (% Occupied People)**|51.8|48.5|

9
- 22 -

The average vehicle in 1988 traveled a trunk road that was shared by another 1,600 vehicles that day.' Information on the distribution of traffic flows over the hours of the day is scarce. Available data (SWKP, 1988b) indicate that interurban traffic in daytime (6 a.m. to 6 p.m.) is almost evenly distributed over all hours, constituting about 80 percent of daily traffic (about 20 percent travel by night). According to this estimate, the average hourly traffic is about 110 vehicles in the daytirne and negligible (28 vehicles per hour) at night.

The major component of congestion is travel time. The value of travel time in Ghana is naturally low, given the low income per capita (about $500). The hourly crew costs (assuming 2,000 annual working hours) are ¢25 for cars (i.e., taxis), C50 for minibuses and pickup trucks, c85 for buses, $1.00 for medium trucks and $1. 10 for heavy trucks (SWKP, 1988b, Appendix 4). Given the distribution of traffic, average crew costs are about ¢60 per hour. Data on the passenger load factor of different types of cFrs are unavailable. A crude estimate places the average number of passengers pe. vehicle at 10. It is customary to assume that the value of time of passengers is 25-33 percent of their hourly earnings. Assuming hourly earnings of ¢25 ($500/2,000), the value of passenger time is ¢62.5-83.3 per vehicle hour.

# Given the lInear speed function,

_S_ = a-, the marginal congestion costs (MCC) are: MCC = v,8X/S<sup>2</sup> , where v is the value of time per vehicle-hour.

Given the interurban speed function (i.e., ,B = 0.02) and X _=_ 110, then MCC = ¢0.03 per vehicle/km if one allows only for the travel time of the crew, and MCC = ¢0.06 if one also takes into account the travel time of passengers (assigning it a value of 1/3 of hourly earnings, v = 143.3C(/hour). Since congestion conditions exist only in the daytime (i.e., apply to only 80 percent of traffic), the average MCC is in the range of ¢'0.02-0.05 per vehicle. Had we used the 2-lane suburban speed function, these estimates would halved.

Our estimate depends heavily on the number of vehicles and on the estimate of hourly earnings (the large difference between Newbery's estimates and ours is explained mainly by these two factors: hourly earnings in Tunisia are six times higher than in Ghana and average traffic volume is twice as large). The estimate is, however, also sensitive to the assumptions about the parameters of the speed function. The speed function used in our calculation was estimated for a road in relatively good condition **(IRI** = 3.35), with an average speed of 65 kph (traffic volume being 350). In view of the poor condition of Ghanaian roads, it is clear that the estimate described above may relate to the long run, once the highway system is restructured. In the short run, average speed in Ghana is much lower and constrained by the

> 7 See footnote _5_ for the calct'lation of this average. Our method of calculating the congestion costs is equivalent to a procedure where these costs are computed separately for each **AADT** class and then weighed by the veh.--km 'belonging' to that class.