rafmacalaba/datause-encoder-data huggingface dataset:
References on Expert Opinion (*Cited in the paper) Bedford T. and Cooke, R.T. (2001), “Probabilistic Risk Analysis: Foundations and Methods,” Cambridge University Press.* Bier, V.M. and Yi, W. (1995), “A Bayesian Method for Analyzing Dependencies in Precursor Data,” _International Journal of Forecasting_ , Volume 11, Issue 1, pp.25-41.* Bordley, R.F. (1982), “A Multiplicative Formula for Aggregating Probability Assessments,” _Management Science_ , Volume 28, Issue 10, pp.1137-1148.* Campodonico, S. and Singpurwalla, N.D. (1995), “Inference and Predictions from Poisson Point Processes Incorporating Expert Knowledge,” _Journal of the American Statistical Association_ , Volume 90, Issue 429, pp.220-226.* Chande, P.K. and Tokekar, S. V. (1998), “Expert-Based Maintenance: A Study of Its Effectiveness,” _IEEE Transactions on Reliability_ , Volume 46, No. 1, pp.53-58.

Chhibber, S. and Apostolakis, G. (1993), “Some Approximations Useful to the Use of Dependent Information Sources,” _Reliability Engineering and System Safety_ , Volume 42, Issue 1, pp.6786.* Clemen, R.T. (1986), “Calibration and the Aggregation of Probabilities,” _Management Science_ , Volume 32, Issue 3, pp.312-314.

Clemen, R.T., Fischer, G.W., and Winkler, R.L. (2000), “Assessing Dependence: Some Experimental Results,” _Management Science_ , Volume 46, Issue 8, pp.1100-1115.* Clemen, R.T. and Winkler, R.L. (1999), “Combining Probability Distributions from Experts in Risk Analysis,” _Risk Analysis_ , Volume 19, Issue 2, pp.187-203.* Clemen, R.T. and Reily, T. (1999), “Correlations and Copulas for Decision and Risk Analysis,” _Management Science_ , Volume 45, Issue 2, pp. 208-224.* Clemen, R.T. and Winker, R.L. (1993), “Aggregating Point Estimates: A Flexible Modeling Approach,” _Management Science,_ Issue 39, No. 4, pp. 501-515.* Clemen, R.T. and Winker, R.L. (1985), “Limits for the Precision and Values of Information from Dependent Sources,” _Operations Research,_ Volume 33, pp. 427-442.* Cooke, R.M. and Slijkhuis, K.A. (2003), “Expert Judgment in the Uncertain Analysis of Dike Ring Failure Frequency,” Appearing in Case Studies in Reliability and Maintenance, pp.331-350.

Cooke, R.M. (2001), “EXCALIBUR – Windows version of EXCALIBR: Software for performance based combination of expert judgments,” Department of Mathematics, Delft University of Technology.* 12
1 s113 maNYTICL aWJY OF TNRIl

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# emerging economies like India.<sup>12</sup>

About a third of the unbanked also reported a lack of trust in financial institutions. In a separate series of questions on trust in national institutions (e.g. government, military, etc.), on average, 90 percent of adults reported that they have confidence in the country's financial institutions. Yet 40 percent of these adults cited lack of trust as reason for not opening an account. Difference across states in people’s perception of the safety of financial institutions (and the trustworthiness of bankers and agents) might reflect overall lawfulness and crime rates. For example, lack of trust in financial institutions was the biggest reason cited by 52 percent and 63 percent, respectively. in the states of Uttar Pradesh (cited by 52 percent) and Bihar (cited by 63 percent). While there were no significant gender gaps, rural residents (41 percent) and the poor (43 percent) were significantly more likely to cite this as a barrier than their urban (29 percent) and rich (33 percent) counterparts. This barrier can stem from cultural norms, past policy failures, and uncertainty about the future.

Documentation requirements are another important barrier to account ownership, cited by around 30 percent of adults without an account across all states in the survey. These requirements may especially affect those living in rural areas or employed in the informal sector. In fact, rural adults without an account were 11 percentage points more likely to report this as a barrier than urban adults. Since more than 90 percent of Indian workforce are a part of the informal economy,<sup>13</sup> producing a formal proof of domicile or an authentic wage slip may be challenging.

Documentation requirements should be less of a barrier in the future given that almost 93 percent of adults in India have an Aadhaar identification card. Moreover, in 2016 the Reserve Bank of India launched eKYC, a biometric Aadhaar authentication system, eliminating the need for paper documentation. eKYC is a step in the right direction towards simplifying the process of opening an account.

While none of the factors are strong enough on their own to dissuade adults from opening an account, the confluence of these factors makes account opening a tedious task.

> 12 It should be noted, however, that under government-led schemes, no fee is imposed on the account holder and any condition of a minimum balance is also waived.

> 13 “Report of the Committee on Unorganized Sector Statistics”, National Statistical Commission, Government of India, February 2012.

9
**Table 2. Mean Plot-Level Maize Yield (Kg/Ha) and Production (Kg) by Round, Survey Method & Cultivation Pattern & Results from Tests of Mean & Distributional Differences**

|||**MAPS I**<br>|||**MAPS II**<br>||
|---|---|---|---|---|---|---|
|**Yields (kg/ha)**|All|Pure Stand|Intercropped|All|Pure Stand|Intercropped|
|Self-Reported (SR)|2,634|3,035|2,296|1,838|1,822|1,845|
|Sub-Plot Crop Cutting (CC)|1,064|1,220|932|731|835|693|
|Remote Sensing (RS)|1,425|1,191|1,622|N/A|N/A|N/A|
|Full Plot Crop Cutting (FP)|N/A|N/A|N/A|665|854|608|
|Difference in Means:|||||||
|_SR vs CC_|***|***|***|***|***|**|
|_SR vs RS_|***|***|-|N/A|N/A|N/A|
|_CC vs FP_|N/A|N/A|N/A|**|-|**|
|_CC vs RS_|***|-|***|N/A|N/A|N/A|
|Difference in Distributions:|||||||
|_SR vs CC_|***|***|***|***|***|***|
|_SR vs RS_|***|***|***|N/A|N/A|N/A|
|_CC vs FP_|N/A|N/A|N/A|***|***|***|
|_CC vs RS_|***|***|***|N/A|N/A|N/A|
|**Quantity Harvestedper Plot(k**|**g)**||||||
|Self-Reported (SR)|242|319|177|212|293|178|
|Sub-Plot Crop Cutting (CC)|173|234|122|145|205|123|
|Remote Sensing (RS)|191|186|195|N/A|N/A|N/A|
|Full Plot Crop Cutting (FP)|N/A|N/A|N/A|115|211|85|
|Difference in Means:|||||||
|_SR vs CC_|***|***|***|***|***|**|
|_SR vs RS_|***|***|-|N/A|N/A|N/A|
|_CC vs FP_|N/A|N/A|N/A|***|-|***|
|_CC vs RS_|-|**|***|N/A|N/A|N/A|
|Difference in Distributions:|||||||
|_SR vs CC_|***|***|***|***|***|***|
|_SR vs RS_|***|***|***|N/A|N/A|N/A|
|_CC vs FP_|N/A|N/A|N/A|***|***|***|
|_CC vs RS_|***|***|***|N/A|N/A|N/A|

**Notes:** The sample is limited to households with crop cutting in both MAPS I and MAPS II. MAPS II self-reported figures further exclude observations subject to full-plot harvest. ***/**/* denote statistical significance at the 1/5/10 percent level, respectively. The distributional differences are assessed based on the Kolmogorov–Smirnov tests of the equality of distributions.

32
Policy ReseaRch WoRking PaPeR 4576

# **Abstract**

Using panel data for the fourteen major states of India over the 1980-2000 period, the authors estimate the effect of human capital endowment on the performance of the state economies. They find that greater availability of skilled workers had a positive and significant impact on output in the service sectors. They do not find any such effect for the manufacturing sectors. The paper shows that the differential effect on services and manufacturing arises because service sectors are more skill intensive.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand the determinants of services trade and output. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mamin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1. Introduction**

The provision of piped water and sanitation services is often cited as the typical textbook illustration of a natural monopoly. This "natural monopoly" concept reflects technological and associated cost attributes that imply that a single firm can produce at a lower cost than multiple firms (Joskow, 2005). Such natural monopolies arise where the largest supplier in an industry, or the first supplier in a local area, has an overwhelming cost advantage. This tends to be the case in industries where capital costs are large and as a result create barriers to entry. The existence of a natural monopoly is one of the main reasons for regulating the sector. Yet, so far there have been few analyses of the cost structure of water utilities in particular in developing countries.

Overall, studies of the performance of water utilities in developed countries provide contrasting findings regarding scale economies in this sector. Using data on 190 public and 31 private urban water utilities from the United States, Bhattachryya et al. (1995) estimate returns to density at the mean (the short-run equivalent of returns to scale) at 1.25 for privately owned utilities and at 0.93 for publicly owned utilities; they find economies of scale for only private water utilities. Kim and Lee (1998), using data for 42 Korean municipal water supply companies for the period between 1989 and 1995, find evidence of diseconomies of scale in four cities, constant returns to scale in 12 cities, and economies of scale in 12 cities. Fabbri and Fraquelli (2000), using data on Italian water utilities, cannot reject constant returns to scale at the mean. Saal and Parker (2000), using data on water and sewerage companies from England and Wales, report diseconomies of scale at the mean (see also Hunt and Lynk, 1995, for a study of the UK water industry). Garcia and Thomas (2001), using a panel of French local communities, cannot reject the hypothesis of constant returns to scale (for the delivery of water supply services). They even find evidence of diseconomies of scale for some utilities, in particular utilities which deliver a high volume of water per customer. Mizutani and Urakami (2001) also find evidence of slight diseconomies of scale at the mean for Japanese water utilities.

As far as water utilities in developing and transition economies are concerned, the main focus has been on efficiency measures computed through the estimation of a stochastic cost frontier or using the Data Envelopment Analysis (DEA) technique (see Estache, Perelman and
Results from regressions in table 8 allow us to explore if reservation increased women’s demand for work as well as their income and bargaining power. As one would expect if reservation relaxes constraints to female labor supply, it triggers significant lagged increases of women’s individual income, estimated to have increased by some 78% and females’ (but not males’) demand for work by some 16 percentage points. Although not available for the entire sample, data on intra-household bargaining power support the notion of a role-model effect of reservation having, with a lag, led to higher levels of female autonomy: The share of women who participate in decision-making on food, health, and education is estimated to have increased by 16, 14, and 7 percentage points, respectively.

We conclude that, beyond improving supply of jobs that are suitable and attractive for females, reservation enhanced female decision-making autonomy and their potential and actual participation in the labor force. A possible interpretation of the above evidence is that the role model effect provided by past female leaders enhanced women’s ability to take advantage of changes in the availability of jobs, including those made available via NREGs, available to everybody.

# **5. Conclusion and policy implications**

Motivated by the recent decline of female labor force participation in India, this paper explores if random reservation of political leadership positions for women affects women’s labor force participation as well as supply- and demand-related factors. While there is no contemporaneous effect, past leadership reservation for women significantly increased females labor supply by allowing individuals to join the labor force and increasing the amount of time spent working by those already in work.

While large part of the observed effects is attributable to females’ improved ability to take advantage of public workfare under NREGS, female participation in non-NREGS labor markets (especially nonagricultural casual and self-employment) expands as well. Estimated effects are stronger for married than for unmarried women. Labor force participation allows women to obtain higher levels of individual income, increases their demand for work, and affects bargaining power by enhancing their participation in intrahousehold decision making on spending for consumption, health, and education. Avenues to enhance these effects by combining them with targeted provision of information and training to change not only norms regarding women’s labor force participation but also equip them with the skills to adapt to changing labor market conditions are a priority area for further research.

15
in the electricity sector of Mali, a poor country in West Africa (Boccanfuso et al., 2009b). The increase in electricity prices did not affect poverty directly as very few poor households are connected to the electricity grid while households also decrease their electricity consumption when price rises. Unlike in Senegal, compensating measures such as cash transfer after price rise did not help the low-income households losing from pricing reform. Based on the broad trends of energy reforms across the African countries, Clark et al. (2005) show that the impacts of reforms on the poor are neither direct nor inevitable.

**Table 9: Reforms and Poverty Reduction**

|**_Study_**|**_Approach_**|**_Region_**|**_Data_**|**_Method_**|**_Policy impacts and_**<br>**_relevance_**|
|---|---|---|---|---|---|
|Victor<br>(2005)|case study|global|energy sector<br>indicators|multi-<br>country|energy access and<br>development correlated;<br>link between reforms and<br>poverty reduction<br>complex and non-<br>inherent|
|Khandker,<br>Barnes and<br>Samad<br>(2012a)|econometric|India|cross-sectional<br>survey data for<br>households in<br>2005|probit<br>estimates|energy poverty and<br>income poverty are<br>directly linked to each<br>other|
|Khandker,<br>Barnes and<br>Samad<br>(2012b)|econometric|Bangladesh|cross-sectional<br>survey data for<br>households in<br>2005|propensit<br>y score<br>matching|electrification led to<br>household gains in<br>income and poverty<br>reduction|
|Khandker et<br>al. (2012)|econometric|India|cross-sectional<br>survey data for<br>households in<br>2005|maximum<br>likelihood<br>probit<br>model|rural electrification also<br>helped reduce poverty;<br>larger share of benefits<br>accrued to wealthier rural<br>households|
|Boccanfuso,<br>Estache and<br>Savard<br>(2009a)|macro study|Senegal|macro –micro<br>variables<br>between 1995<br>to 2001|CGE<br>model|direct price effects are<br>weaker than general<br>equilibrium effects on<br>poverty and inequality|
|Boccanfuso,<br>Estache and<br>Savard<br>(2009b)|macro study|Mali|macro –micro<br>variables|CGE<br>model|direct price increases<br>have a minimal effect on<br>poverty and inequality,<br>whereas the general<br>equilibrium effects of|

37
5 choice.lQ/ In countries where education systems are still expanding, as in many developing countries, economic factors predominate in advocacy for coeducation.

This paper investigates the effects of single-sex and coeducational schooling on adolescents in the public sector in a developing country's scho-l system. Specifically, we have compared the effects of single-sex and coeducational school organization at the ninth grade in Nigerian public schools, examining whether these effects are different for males and females and whether the effects are sustained once statistical adjustments are introduced for differences in student background, school location, and teaching practices. The study compares the effects of these two school organizational types on students' academic achievement in mathematics and on stereotypic views of mathematics. The data for this study are drawn from the Second International Mathematics Study (SIMS) conducted by the International Association for the Evaluation of Educational Achievament (IEA) in Nigeria during the 1981-82 academic year. The analytic sample for this study consists of 1,012 students in 40 Nigerian 9th grade classes. The IEA design sampled a single class in each school, so that 40 schools are represented, half of them single-sex. The sample was 22.4% female, which is slightly underrepresentative of female secondary school participation in Nigeria.
# **3.4 Implications of changes in cost variables**

The discounted value of damages caused by energy consumption resulting from the initial investment at _t_ = 0 is given from (7) by Consider here first an upward shift in the path of damages, represented by a shift in Θ0. Such a shift will shift _D_ up for two reasons: first, directly as seen from (13); and secondly, as _t*_ is now reduced from (8).

Consider next an increase in the unit retrofit cost, _k_ . This affects the discounted damage from (13) in two separate, opposite, ways. First, the initial energy intensity of the infrastructure, _E_ * , is reduced, from (12). Secondly, _t_ *is increased, so that a retrofit occurs later (and energy consumption continues for longer), from (8). Using the latter expression and − ( _r_ −α ) * _t_ = α _t_ *(1 −γ 0 ) , discounted climate damages can be rewritten as a function of _k_ : whose derivative with respect to _k_ is a difference between positive terms: From the expression for d _E_ * / dk – namely, in particular, the φ′′ in the denominator – we have the following (negative) result:

**Proposition 1** : _There is no universal sign of the relation between the magnitude of (proportional) retrofit cost and expected discounted environmental damage._

Cases with more general retrofit cost and uncertainty will yield the same result.

# **4. Infrastructure investment and retrofit when environmental damage is stochastic**

We now extend the above model to the more realistic and interesting but complicated case of stochastic environmental damages. Unless otherwise stated, we assume that conditions 12
**O**

0 r 0 m x X Q , s R

|Table B7. Informal Working Arrangements<br>from Informal Sector Surveys conducted by the author|<25 hours per<br>week<br>Taxicabs|245hours<br>per week<br>Taxicabs|Entire<br>Sample<br>Taxicabs|Entire<br>Sample<br>Kiosks|Entire<br>Sample<br>Stre<br>Markets|Entire<br>Sample<br>et Services &<br>Vendors|
|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending on the<br>question)|99|101|308|245|137|80|
|Number of People Present (kiosks only)|||||||
|Mean|na|na|na|1.6|na|na|
|Hours open per week (kiosks only)|||||||
|Mean|na|na|na|86.1|na|na|
|Hours open on an average ...|||||||
|Weekday|na|na|na|12.9|na|na|
|Saturday|na|na|na|12.2|na|na|
|Sunday|na|na|na|11.8|na|na|
|Hours worked on an average day (markets and street informals)|||||||
|Mean|na|na|na|na|7.9|7.2|
|Days worked in an average week (markets and street informals)<br>Mean|na|na|na|na|5.4|6.4|
|Months of the year worked (markets and street informals)|||||||
|Mean|na|na|na|na|8.4|8.2|
|Person-hours per week occupying kiosk|||||||
|Mean|na|na|na|113.0|na|na|
|Person-hours per week procuring inventory|||||||
|Mean|na|na|na|16.6|na|na|
|Hours used as taxi in a week.|||||||
|Mean|14.0|62.8|37.1|na|na|na|
|Percentage personal use, not as a taxi.|||||||
|Mean|50.3|22.9|37.3|na|na|na|
|Only person using vehicle as a taxi<br>Yes|88.8|89.1|89.6|na|na|na|
|Hours used as taxi with anyone driving.|||||||
|Mean|34.3|40.7|42.1|na|na|na|

-