rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 1:**<br>Dependent variable|**Marginal**<br>:_Computer_|**effects from**<br>_s_|**Probit re**|**gressions**||
|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|
|_Perception_index|.179<sup>**</sup>|.189<sup>***</sup>|.346<sup>***</sup>|.412<sup>***</sup>|.369<sup>**</sup>|
||(.021)|(.004)|(.001)|(.000)|(.045)|
|_Land_index|-.304<sup>***</sup>|-.208<sup>***</sup>||||
||(.000)|(.000)||||
|_Sex ratio_||<br>.709<sup>***</sup>|.487<sup>***</sup>|.272<sup>**</sup>|.217|
|||(.000)|(.001)|(.031)|(.128)|
|_Regulation_|||-.408<sup>***</sup>|-.411<sup>***</sup>|-.335<sup>***</sup>|
||||(.000)|(.000)|(.008)|
|_BI_index||||<br>.042<sup>**</sup>|<br>.037|
|||||(.035)|(.267)|
|_Literacy_||||.002|.0001|
|||||(.106)|(.997)|
|Labor force||||-.001|.0004|
|Participation rate||||(.556)|(.887)|
|<br>Population of the||||-.005|-.004|
|<br>city||||(.223)|(.552)|
|Services share in||||.001|.001|
|total employment||||(.589)|(.400)|
|<br>Floor area of the|||||<br>.062<sup>*</sup>|
|shop|||||(.087)|
|Age of the store|||||-.002<sup>***</sup><br>(.001)|
|_Outage_|||||-.003|
||||||(.212)|
|_Generator_|||||.199<sup>***</sup>|
||||||(.000)|
|_Checking_|||||.097<sup>***</sup>|
||||||(.000)|
|_Liquid_|||||<br>.015|
||||||(.426)|
|_Overdraft_|||||<br>.133<sup>***</sup>|
||||||(.000)|
|Line of Credit|||||<br>.040<br>(.137)|
|Pseudo R<sup>2</sup>|.015|.029|.030|.032|<br>.323|
|Sample Size|1948|1948|1948|1948|1893|

p-values in brackets; significance level is denoted by ***(1% or less), ** (5% or less) and * (10% or less). All standard errors are clustered on the state. Sample size smaller in column 5 due to missing observations and one outlier dropped.

8
The example below for electricity subsidies in Jordan illustrates a simple table for presenting results. Estimates of elasticities for the demand of electricity in countries similar to Jordan vary around values of - 0.4/-0.5 while our own estimate of elasticity provided a value of -0.55. To make sure of including these estimates and also to allow for a certain margin of error we modeled a lower bound of -0.3 and an upper bound of -0.6. Following discussions with the client about possible reforms in prices, it was finally decided to model a range of price increases in between 30% and 60% which we took as lower and upper bounds. We also agreed with the client on the parameters of interest that were identified in changes in the poverty rate and changes in government revenues. We then presented results on changes in poverty and government revenues by quintiles of electricity consumers using lower and upper bounds of elasticities and two different price increases. It is noticeable how the increase in elasticity can actually outweigh the effect of price increases in terms of budget revenues.

**Table 3 – Electricity subsidies reform simulation in Jordan**

|||**Poverty in**|**crease (%)**|**Revenues**|**Increase**|
|---|---|---|---|---|---|
||**Quintiles**|**+30% tariffs**|**+60% tariffs**|**+30% tariffs**|**+60% tariffs**|
|**e=-0.3**|1|0.0000|0.0000|297,352|506,961|
||2|0.0001|0.0003|476,101|811,713|
||3|0.0008|0.0022|684,948|1,167,779|
||4|0.0006|0.0019|970,770|1,655,084|
||5|0.0000|0.0021|1,806,516|3,079,962|
||**Total **|**0.0003**|**0.0014**|**4,235,687 **|**7,221,499**|
|**e=-0.6**|1|0.0000|0.0017|107,242|38,997|
||2|0.0003|0.0028|171,709|62,439|
||3|0.0022|0.0054|247,030|89,829|
||4|0.0019|0.0058|350,114|127,314|
||5|0.0021|0.0031|651,530|236,920|
||**Total **|**0.0014**|**0.0038**|**1,527,625**|**555,500**|

Source: Verme (2011). (*) Quintiles of electricity consumption.

15
Table 2: Share of Exporting Firms, by Country and Manufacturing Sector

||Food &<br>Agroindustry|Textiles|Garments &<br>Leather|Metal &<br>Machinery|Electronics|Chemicals and<br>Plastics|<br>Wood & Paper|All<br>Manufacturing|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Cambodia|-|0.50|0.93|-|-|-|-|0.92|
|China|0.04|0.63|0.39|0.16|0.30|0.05|-|0.25|
|Indonesia|0.17|0.42|0.43|0.45|0.85|0.24|0.49|0.39|
|Malaysia|0.74|0.88|0.92|0.79|0.93|0.81|0.87|0.83|
|Philippines|0.11|0.40|0.47|-|0.71|-|-|0.37|
|SouthKorea|0.13|0.65|0.41|0.37|0.50|0.36|0.26|0.38|
|Thailand|0.91|0.43|0.74|0.43|0.66|0.42|0.50|0.56|
|Vietnam|0.47|0.79|0.88|0.23|0.45|0.28|0.43|0.42|
|All Countries|0.38|0.51|0.56|0.26|0.44|0.40|0.48|0.42|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: Table reports the share of exporting firms in the sample, by country and manufacturing sector. Exporting firms are defined as those exporting at least 10% of their sales. The last row reports the mean share of exporting firms for all the countries. The latter is a weighted average of the mean share of exporting firms in each country weighted by each country's sample size in the total sample. The last column reports the mean share of exporting firms for all manufacturing sectors. Again, this is the weighted average of the mean shares in each sector, weighted by sector's size in the total country sample.
percent of their agricultural exports are to other developing countries, showing the continuing importance of industrial country markets for their exports. In 1990/01, these ratios were 42 and 22 percent respectively. This is a very large swing, where developing countries are trading much more among themselves than was the case two decades ago.

For low-income countries, other developing countries accounted for 51 percent of their exports and 69 percent of imports in 2006/07; up from 27 percent and 57 percent respectively in 1990/91. Thus, other developing countries are now a bigger market for the exports of low income countries than the industrial ones. Shares for middle-income countries were similar, with other developing countries accounting for 47 percent of their exports and 50 percent of their imports in 2006/07. Developing countries have become major players in the world agricultural trade, especially if intra-EU and intra-NAFTA trade are excluded.

Table 6 shows the net trade flows derived from table 5. They show that, while low income countries have continued to increase their agricultural trade surpluses, middle income countries have had a more mixed performance. They still have an export surplus but a relatively much smaller one. This is contrary to the common belief that low income countries have not benefited from the global trading system, they not only have increased their exports, and their exports have increased more then their imports.

For industrial countries, there are few important developments. Japan continues to be the biggest net agricultural importer in the world. NAFTA has a decrease in its export surplus. It is gradually becoming less of a net exporter. Other industrial countries are becoming bigger net exporters.

**Table 6 Agricultural Trade Flows (excluding Intra-EU and Intra-NAFTA Trade), 1990/91–2006/07**

|||Tr<br>|ade Value ($|billion)<br>|
|---|---|---|---|---|
|CountryGroup|Period Avg.|Exports|Imports|Net Imports|
|Low income countries|1990-91|14.2|4.2|-10.1|
||2000-01|27.3|16.9|-10.5|
||2006-07|48.9|32.2|-16.8|
|Middle income countries|1990-91|106.6|58.5|-48.0|
||2000-01|186.4|174.2|-12.2|
||2006-07|370.3|335.7|-34.7|
|EU-15, excl. intra-eu15 trade|1990-91|29.1|76.0|46.9|
||2000-01|60.8|82.8|22.0|
||2006-07|110.4|152.8|42.4|
|NAFTA, excl. intra-nafta trade|1990-91|53.5|30.6|-22.9|
||2000-01|75.6|47.7|-27.9|
||2006-07|98.4|82.0|-16.4|
|Japan|1990-91|2.6|51.7|49.1|
||2000-01|4.7|59.6|54.9|
||2006-07|7.2|67.3|60.1|
|Other industrial countries|1990-91|23.4|11.6|-11.9|
||2000-01|36.3|14.1|-22.3|
||2006-07|54.7|26.8|-27.8|

Source: Based on mirror data from UN COMTRADE Statistics.

9
|**Table 3. Value Added in 2001 by Sector**|**and Russian**|**Federal Ok**|**rug**|(billions of|2001 rubles)<sup>a/</sup>||||
|---|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|Sector Total|
|Railway transportation|25.3|67.4|21.2|47.1|36.6|32.4|10.8|240.7|
|Truck transportation|11.7|39.0|12.0|23.0|22.4|14.8|5.5|128.5|
|Pipelines transportation|1.9|3.8|2.1|7.1|7.9|2.7|0.8|26.2|
|Maritime transportation|4.0|9.0|3.8|9.1|10.9|6.9|1.7|45.4|
|Air transportation|3.4|17.0|4.5|6.1|6.6|5.3|1.7|44.6|
|Other transportation|5.4|18.7|5.7|11.2|10.4|6.6|2.4|60.4|
|Telecommunications|8.4|32.1|8.5|16.1|14.2|10.6|4.1|93.9|
|Science & science servicing|9.9|31.5|8.4|21.7|18.8|11.4|5.4|107.3|
|Financial services|52.9|197.3|47.2|97.5|90.4|66.6|27.0|578.9|
|Ferrous metallurgy|19.6|30.5|5.4|11.0|37.4|12.7|0.7|117.3|
|Non-ferrous metallurgy|15.6|11.3|5.4|13.5|37.9|98.8|42.1|224.7|
|Chemical & oil-chemical industry|11.0|34.8|10.0|48.8|3.6|14.2|0.8|123.4|
|Mechanical engineering & metal-working|38.8|131.4|20.8|129.8|32.3|24.6|16.9|394.7|
|Timber & woodworking & pulp & paper industry|33.7|21.1|3.2|15.8|3.3|15.0|4.6|96.6|
|Construction materials industry|5.5|28.0|7.8|13.4|6.9|6.5|1.8|70.0|
|Food industry|47.2|105.7|35.4|42.9|13.0|24.1|21.1|289.4|
|Other industries|5.7|32.2|4.6|12.2|1.6|3.4|0.9|60.6|
|Public services, culture and arts|41.1|153.2|36.1|74.3|59.0|48.0|21.3|433.1|
|Agriculture & forestry|33.5|125.1|110.5|144.4|33.9|82.7|19.4|549.3|
|Coalmining|2.9|0.1|1.5|0.0|1.0|29.7|4.3|39.5|
|Housing and communal services|20.3|81.6|20.1|36.6|24.3|24.2|10.1|217.2|
|Construction|44.3|202.6|89.4|127.0|48.4|48.2|10.1|570.0|
|Electric industry|21.6|64.2|18.5|46.0|32.3|28.8|10.4|221.8|
|Gas|0.3||1.0|2.0|36.4|0.2|0.2|40.1|
|Crude oil extraction|14.8||29.5|82.5|234.2|7.7|4.1|372.7|
|Oil refining and processing|7.0|13.2|7.4|31.8|3.1|13.8|3.8|80.1|
|Other goods-producing sectors|7.1|13.6|3.9|13.9|11.9|7.3|3.1|60.9|
|Post|2.4|8.8|2.4|4.7|4.0|2.9|1.2|26.5|
|Wholesale and retail trade|211.3|646.3|196.1|417.0|505.7|266.8|103.1|2346.3|
|Textiles and apparel|3.3|14.1|3.4|5.7|3.5|3.8|1.6|35.6|
|Okrug Total|710.1|2133.7|725.6|1512.1|1352.0|920.8|341.5||

37
25 over the road pavement (variable administrative and routine maintenance costs) and by the passage of heavy axles (variable resurfacing costs). Being marginal, such costs should clearly be recovered from users: uie former in terms of veh km (since there is no other straight-forward way of attributing them to users), the latter according to ESA km (to reflect the damage done to the road pavement by the passage of heavy axles).

**Table** 4. **Analysis** of **Costs in Tarn** of Thdr **Avoidability:** Tunisia, 1982 (a) **(Dollar,** milGlon)

|Item|Immediately<br>Avoidable<br>Costs<br>(Marginal)<br>(1)|Comm<br>_Grou_<br>Heavy<br>Vehicles<br>(2)|on_to Spec_<br>_ps of User_<br>Urban<br>Roads<br>(3)|_ufc_<br>_s_<br>Rural<br>Roads<br>(4)|Common<br>to all<br>Users<br>(Fixed)<br>_(5)_|Total<br>User<br>Costs<br>_(6)_|
|---|---|---|---|---|---|---|
|Administration (b)|0.49|-|-|-|1.95|2.44|
|Routine Maintenance (c)|5.62|2.81||-|19.68|28.11|
|Resurfacing (d)|20.68|-|-|-|5.17|_2S.85_|
|Extensions/lmprovements|(e)<br>-|4.42|_5.55_|11.10|8.33|29.40|
|Financing Charges (f|-|-|-|-|_5.83_|5.83|
|Total|26.79|7.23|_5.55_|11.10|40.96|91.63|
|Percent of Grand Total|29.20||26.10|-|44.70|100.0|

Notes: (a) lduding shortfal of regular road maintenance.

- (b) Fixed cost include ai expenditures on buDdings and 70 percent of salaries.

- (c) 70 percet of these costs are fimed and 10 percent are attributable to heavy vehicles.

- (d) In dry non-freeme conditions, 20 percent of resurfacing costs are attributable to **esfronmental** factors.

(e) Assuming one-third is new construction and the remainder is to expand capacity (of tbis, **one4lUr@** i in urban areas and two-thirds in rural areas) and that 15 percent of these **cost** are incurred to ensure **roads** are strong enougb to carry heavy vehicles. **(fM These** costs **can only** be avoided by defaulting, or cancelling the loans.
constrain women’s (and not men’s) labor. Computing the female shares at the sector- rather than at the plant-level allows to reduce the endogeneity concerns of this exposure measure.

The second dimension is the exposure to different degrees of preschool expansion: as Figure 2 demonstrates, there is a substantial variation in access to preschools across time and districts. For any given initial share of female workers, the plants located in districts with a greater increase in preschool expansion are more likely to increase their performance due to the related labor supply shock.

More formally, we estimate a triple difference regression of the type: where Yidst is the outcome variable of manufacturing plant _i_ in district _d_ and sector _s_ in year _t._ Our key outcome variables include TFPR and TFPQ, which measure plant-level productivity in revenue and quantity terms, respectively, and markup. TFPQ captures the pure technical efficiency of plants as it carves markup out of the TFPR (see Appendix I). αi , γdt , and δst are plant fixed effect, district-time and sector-time fixed effects, respectively. Xidst is a vector of timevarying plant level controls (raw materials and share of foreign ownership). The variable Preschooldt captures preschool access in district _d_ at time _t,_ measured as the number of preschools per 1,000 children of eligible age, as described above. We expect preschool availability to affect plants differently, depending on how strongly they rely on female labor. Thus, we interact preschool density with FemSharest0 - the share of female workers in sector _s_ at t0 (2002 or in the first year the plant entered the manufacturing survey, whatever comes later).<sup>4</sup> The inclusion of plant, district-time, and sector-time fixed effects addresses two primary threats to the causal identification of the effects of preschool availability on plant productivity. First, preschool expansion may be correlated with other governmental programs which may affect firms’ performance. For example, if the Ministry of Education targets a subset of districts for all its programs, from preschool to tertiary, preschool access may be correlated with the

> 4 To minimize possible remaining endogeneity concerns, we exclude the plant itself from the computation of the sector-specific shares applied to each plant.

10
|EmergencyDemobilization and Reintegration Project|Ingo Wiederhofer|
|---|---|
|EmergencyDemobilization and Reintegration Project|Ingo Wiederhofer|
|EmergencyDemobilization and Reintegration Project|Ingo Wiederhofer|
|EmergencyDemobilization and Reintegration Project|Ingo Wiederhofer|
|EmergencyDemobilization and Reintegration Project|Ingo Wiederhofer|
|EmergencyDemobilization and Reintegration Project|Ingo Wiederhofer|
|EmergencyDemobilization and Reintegration Project|Ingo Wiederhofer|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|LAND ADMINISTRATION AND MANAGEMENT II PROJECT|Keith Clifford Bell|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Vilnius Heat Demand Management GEF Project|Peter Johansen|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
|Offgrid Rural Electrification(PERZA)|XiaopingWang|
can resonate with findings from such an intensive village study, would appear to give support to the notion that its application in other settings might also offer insights that are not being captured by the standard inequality decomposition approach. Furthermore these differences do not seem to be driven by characteristics other than caste such as education and land.

When the analysis is repeated in the Western Indian village of Sugao, Maharashtra, however, it is found that the ELMO statistic does not provide any obvious additional insight into the level and evolution of income inequality in this village. In this village, unlike Palanpur, the basic story of income generation appears to be largely driven by access to outside employment (in Mumbai) and remittance income, and in Sugao there does not appear to have been a particular caste or social group that has been systematically excluded from such income sources.

The differences between Palanpur and Sugao are reflective of larger differences in the caste-dynamics of North and South India. Palanpur is in a state – Uttar Pradesh - where land reform has largely been ineffective; where the “politics of identity”, in the period under consideration, dominated the “politics of development” (Varshney 2000). The deeply entrenched inequalities that we observe have resulted in an uphill battle for lower castes and lower caste emancipation has, consequently been more about voice and respect than economic well-being. Sugao, on the other hand, is in the state of Maharashtra where land reform has worked relatively well, and where the economic vitality of the city of Bombay (now Mumbai) resulted in the migration-driven empowerment of people from all castes, a process that continues to this day (Rosenzweig and Munshi, 2006). Sugao, as a village of migrants, has benefited from such processes for a long time and has consequently not experienced sticky caste-based inequality traps in the same way that Palanpur has.

28
The ratings information provided followed the standard format of showing a star rating out of 5, in whole number increments. Around one-third (34%) of providers received the top rating of 5 stars, 42% had a rating of 4 stars, and 23% had ratings of 3 or below (including five providers with no ratings at all, or 0 stars). The median rating was 4 out of 5 stars. This is similar to what is seen on many online platforms, for example Reimers and Waldfogel (forthcoming) report an interquartile range of 4.1 to 4.7 for star ratings of books on Amazon.com. Showing any negative comments was intended to provide context for any low ratings, as well as mimic a mechanism in which customers may leave complaints about a company (as is the case with organizations such as the Better Business Bureau). For example “client response time was slow” or “the action plan did not meet my needs”. Showing both positive and negative comments mimics a Yelp or Amazon ratings process, where positive feedback is also left (e.g. “It was very educational, and gave me answers to mistakes I have made with recruiting in the past” or “The service was very high quality. They do not just recruit and send you staff, but look at your whole business needs and analyze exactly what your business needs in terms of staffing”).

After rating their top three firms in each category, firms were given a printed pamphlet which listed all the service providers, and highlighted the top three they had selected in each category. They were then encouraged to contact any providers of interest, and research which one was of most interest to them by calling or visiting any providers, and to then respond to an SMS message by logging in and indicating their number one most preferred provider. They were told that all firms that indicated their most preferred provider would then go into a draw to receive one of ten labeled cash prizes of 70,000 Naira that they could use to spend on the services of their top-ranked provider. Only 92 firms responded with this final selection, and so we do not use this final provider choice as an outcome.<sup>6</sup>

> 6 Of these 92 firms, 30% chose an HR provider, 50% a marketing provider, and 20% an accounting provider as their most preferred. The prizes were given as labeled cash, with the suggestion they could be used to pay for the provider they chose, but this was not enforced in any way.

11
with the community driven schemes possibly explain the sense of satisfaction with the water tariffs even when they are considerably higher than the traditional supply driven schemes.

# **Table 5: Household Cost of Service Index**

|**Indicators**|**Jala**<br>**Sch**<br>**Mean**|**nidhi**<br>**emes**<br>**SD**|**KWA B**<br>**Managed**<br>**Mean**|**uilt &**<br>**Schemes**<br>**SD**|**Other Com**<br>**Managed S**<br>**Mean**|**munity**<br>**chemes**<br>**SD**|
|---|---|---|---|---|---|---|
|HH Opinion on Contribution<br>(High=1,Fair=2,Low=3)|2.08|0.26|2.53|0.72|2.25|0.53|
|HH Opinion on Tariffs<br>(High=1,Fair=2,Low=3)|2.02|0.28|2.19|0.57|1.95|0.2|
|Monthly Water Charges (₹)|62.88|24.11|41.25|13.15|65.00|24.18|

The O&M index was constructed completely based on data collected through technical audits and aims to capture the operational sustainability of the schemes. Unlike the community managed schemes, the majority of the KWA schemes carried out yearly water quality testing. 64 percent of KWA schemes reported carrying out yearly water quality testing compared to only 21 percent of Jalanidhi schemes and 13 percent of other community managed schemes. However, Jalanidhi schemes on average have fewer breakdowns and fewer days of water outages compared to either KWA schemes or the other community managed schemes. Also fewer percentage of community managed schemes (both Jalanidhi and other community schemes) reported facing water shortage anytime of the year compared to supply driven KWA schemes.

**Table** **<u>6: Operation and Maintenance Index</u>**

|**Indicators**|**Jalan**<br>**Sche**<br>**Mean**|**idhi**<br>**mes**<br>**SD**|**KWA**<br>**Managed**<br>**Mean**|**Built &**<br>**Schemes**<br>**SD**|**Other Co**<br>**Managed**<br>**Mean**|**mmunity**<br>**Schemes**<br>**SD**|
|---|---|---|---|---|---|---|
|Yearly Analysis of Water Quality|0.21|0.41|0.64|0.49|0.13|0.34|
|Number of Breakdowns Last Year|3.16|2.34|5.17|4.87|5.46|5.05|
|Longest Breakdown Last Year (Days)|10.64|14.78|11.94|12.92|12.99|15.02|
|Scheme Facing No Water Shortage<br>Anytime Last Year|0.54|0.5|0.36|0.49|0.47|0.5|

The financial audits indicate that full O&M cost recovery remains a challenge for many of the community driven schemes in spite of the fact that on average revenue from water sales exceeds the O&M costs for both Jalanidhi and other community managed schemes (Table 7). Around 50 percent of the community managed schemes reported to achieve full O&M cost recovery. Revenue as proportion to O&M costs is higher for other community managed schemes compared to Jalanidhi schemes. But these other community managed schemes also show large variations in performance as reflected by high standard deviation. Cost recovery indicators were not reported by most of the KWA schemes. So a comparative analysis with traditional supply driven schemes could not be undertaken.

14