rafmacalaba/datause-encoder-data huggingface dataset:
Table 9: Comparison of survey and qualitative validation means at endline

|||Potenti|ally sensiti|ve behaviors||Ex|penditure|s|All|
|---|---|---|---|---|---|---|---|---|---|
||All (0-4)|Steal|Marijuan|a<br>Gamble|Homeless|All (0-2)|Video|Phone|(0-6)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|||||a. Full samp|le|||||
|Survey mean (_y_<sup>_s_</sup>)|1.12|0.22|0.48|0.18|0.23|0.82|0.42|0.39|1.93|
||(1.14)|(0.42)|(0.50)|(0.39)|(0.42)|(0.73)|(0.50)|(0.49)|(1.31)|
|Validation mean (_y_<sup>_v_</sup>)|1.21|0.20|0.51|0.29|0.21|1.09|0.61|0.48|2.30|
||(1.18)|(0.40)|(0.50)|(0.45)|(0.41)|(0.74)|(0.49)|(0.50)|(1.21)|
|% in agreement||79%|85%|72%|82%||62%|82%||
||||b.|Control gr|oup|||||
|Survey mean (_y_<sup>_s_</sup>)|1.25|0.27|0.48|0.23|0.27|0.68|0.37|0.32|1.93|
||(1.31)|(0.45)|(0.50)|(0.43)|(0.45)|(0.70)|(0.49)|(0.47)|(1.44)|
|Validation mean (_y_<sup>_v_</sup>)|1.30|0.23|0.49|0.34|0.23|1.18|0.65|0.54|2.48|
||(1.23)|(0.42)|(0.50)|(0.48)|(0.42)|(0.70)|(0.48)|(0.50)|(1.21)|
|% in agreement||80%|88%|72%|77%||47%|75%||
||||c|. Therapy on|ly|||||
|Survey mean (_y_<sup>_s_</sup>)|1.06|0.19|0.48|0.17|0.22|0.81|0.41|0.41|1.87|
||(1.11)|(0.39)|(0.50)|(0.38)|(0.42)|(0.75)|(0.50)|(0.50)|(1.35)|
|Validation mean (_y_<sup>_v_</sup>)|1.09|0.17|0.48|0.24|0.20|0.98|0.54|0.44|2.07|
||(1.14)|(0.38)|(0.50)|(0.43)|(0.41)|(0.76)|(0.50)|(0.50)|(1.24)|
|% in agreement||80%|89%|74%|80%||72%|81%||
|||||d. Cash onl|y|||||
|Survey mean (_y_<sup>_s_</sup>)|1.03|0.21|0.49|0.13|0.21|0.77|0.37|0.40|1.81|
||(1.16)|(0.41)|(0.50)|(0.34)|(0.41)|(0.71)|(0.49)|(0.49)|(1.35)|
|Validation mean (_y_<sup>_v_</sup>)|1.32|0.23|0.53|0.33|0.24|1.00|0.55|0.45|2.32|
||(1.26)|(0.42)|(0.50)|(0.47)|(0.43)|(0.81)|(0.50)|(0.50)|(1.33)|
|% in agreement||76%|82%|74%|90%||56%|85%||
||||e.|Therapy + c|ash|||||
|Survey mean (_y_<sup>_s_</sup>)|1.13|0.22|0.48|0.21|0.22|0.98|0.54|0.44|2.11|
||(0.98)|(0.42)|(0.50)|(0.41)|(0.42)|(0.73)|(0.50)|(0.50)|(1.11)|
|Validation mean (_y_<sup>_v_</sup>)|1.11|0.19|0.52|0.24|0.16|1.17|0.70|0.48|2.29|
||(1.11)|(0.40)|(0.50)|(0.43)|(0.37)|(0.68)|(0.46)|(0.50)|(1.05)|
|% in agreement||81%|83%|68%|81%||71%|87%||
|Observations|239|238|238|238|239|239|238|239|239|

_Notes:_ The table reports the means (standard deviations) of the survey and the qualitatively validated measures for the full sample and by treatment arm. “% in agreement” is the percentage of respondents for whom the survey indicator equals the qualitatively validated indicator.

35
- Auctions and tenders for land privatization should be further encouraged, but need to be monitored closely for transparency and fairness.

# **2. Background and Methodology**

# **Project Background and Objectives**

In 2005, FIAS received a request from the Ministry of Economic Development and Trade and the Federal Antimonopoly Service to conduct a survey on land and real estate accessibility for enterprises in 15 regions of the Russian Federation, and provide recommendations for improvement.<sup>8</sup> This paper aims to carry out a detailed statistical analysis of one set of the results of the recent survey of administrative procedures for land and real estate transactions in 15 regions of the Russian Federation conducted by FIAS.

Between the survey, past research and a mass of anecdotal evidence about businesses’ access to land in the Russian Federation, we developed a number of “stylized facts” as follows:

- Land privatization in the Russian Federation has a checkered history, with a clear policy in favor of land privatization not established until the enactment of the 2001 Land Code. Even after the enactment of the Land Code, many vital parameters of land privatization, including pricing parameters, are still not settled.

- The new Land Code of the Russian Federation explicitly calls for land to be privatized as follows:

- Land under buildings that were privatized earlier is supposed to be sold (or leased) to the owner of the building at an administered price within parameters set by federal legislation (this category accounts for the great majority of land transactions involving businesses in the Russian Federation).

- Vacant land intended for new construction is supposed to be privatized by transparent auction or tender procedures (although there have been relatively few transactions of this type to date).

- While the large variations (both in pricing and in formulas for distribution of revenues between Federal, Regional and Local levels of government) were certainly problematic from the standpoint of policy stability, the responses of the relevant stakeholders showed clearly that the basics of economic behavior were following predictable patterns (e.g., when normative prices for land were low, businesses responded by increasing applications for land privatization, while public bodies, as sellers, were reluctant to conclude such transactions; conversely during periods when normative prices were relatively high, there were fewer applications from businesses for land privatization even as many public bodies were encouraging it).

> _8 See FIAS/EU Report on Access of Enterprises to Land (prepared for MEDT) and FIAS/EU Report on Land and Real Estate Transactions (prepared for FIAS) at_ <u>www.worldbank.org/russia/fias</u>

5
females between 2004-05 and 2011-12 was in the secondary and above levels. The share of females in the working age population with secondary or above education increased by more than 10 percentage points during this period. A steeper, upward sloping curve implies that the income effect is weaker than what the conventional wisdom would imply.

Figure 10: Controlling for local employment attenuates the impact of asset ownership, implying a weaker income effect <!-- Start of picture text --> 0.6 0.6<br>Model A<br>Model B 0.5<br>0.5<br>Model C<br>0.4<br>Model D<br>0.4<br>Model A<br>0.3<br>Model B<br>0.3 Model C<br>0.2<br>Model D<br>0.2 0.1<br>0 2 5 8 10 12 15 17 -0.5 -0.1 0.3 0.7 1.1 1.5 1.9<br>Schooling Years Log Land Owned<br>Pr obability LFPR Pr obability LFPR<br><!-- End of picture text --> Land ownership becomes less relevant too. The corresponding marginal effect is close to zero when the local employment structure is taken into account. One interpretation of this change is that land ownership was capturing the availability of jobs considered suitable for women. The share of farming jobs at the local level may be a more precise indicator of such availability, thus reducing the explanatory power of the land ownership variable. But land ownership can also be seen as an indicator of income generation potential. If so, the absence of an effect on LFPR can be seen as rejecting the income effect hypothesis.

Similarly, the effect of household size changes substantially. In Models A, B and C, female labor force participation was lower among bigger households, but the household size has no bearing on labor force participation of females in Model D. When employment and urbanization are factored in the participation decisions, the effects of the individual’s social group or religion are attenuated. On the other hand, the effects of having children are enhanced.

The _second hypothesis_ , which is that the misclassification of urban areas as rural matters, is supported in the model without the location fixed effects, but is rejected when district fixed 24
TABLE 9: logit, probit and ivprobit regressions on binomial outcome and borrower

||Logit(1)|Logit FE(2)|Probit(1)|Probit FE(2)|Pro|bit 2SLS (1)|
|---|---|---|---|---|---|---|
||Outcome|Outcome|Outcome|Outcome|Outcome|Borrower|
|Borrower|4.079***<br>(0.181)|4.766***<br>(0.246)|2.321***<br>(0.092)|2.653***<br>(0.119)|4.621***<br>(0.249)||
|Size<br>GDP p.c.|-0.101<br>(0.074)<br>-0.00006<br>(0.0005)|-0.145<br>(0.093)<br>-0.00002<br>(0.0003)|-0.051<br>(0.038)<br>-0.00003<br>(0.0002)|-0.072<br>(0.049)<br>-0.0001<br>(0.0001)|-0.066<br>(0.082)<br>-0.0001<br>(0.0002)|0.005<br>(0.010)<br>-0.00002<br>(0.0003)|
|Volatility<br>Growth|-0.0003<br>(0.0002)<br>1.479***<br>(0.536)|-0.0001<br>(0.0004)<br>0.771<br>(1.078)|-0.00001<br>(0.0001)<br>0.781***<br>(0.280)|-0.00003<br>(0.0002)<br>0.464<br>(0.562)|-0.0003<br>(0.0003)<br>1.499<br>(0.936)|0.00004<br>(0.00004)<br>0.009<br>(0.105)|
|Infrastructure|1.893<br>(2.907)|3.231<br>(8.501)|1.077<br>(1.501)|1.138<br>(4.545)|-3.782<br>(7.132)|0.558<br>(0.796)|
|Bureaucracy|-0.085<br>(0.135)|-0.618**<br>(0.309)|-0.034<br>(0.071)|-0.306*<br>(0.159)|0.153<br>(0.269)|0.0004<br>(0.035)|
|Corruption|-0.030<br>(0.127)|-0.197<br>(0.255)|-0.014<br>(0.066)|-0.102<br>(0.135)|-0.248<br>(0.226)|0.0008<br>(0.028)|
|Accountability|-0.146**<br>(0.072)|-0.263<br>(0.172)|-0.081**<br>(0.038)|-0.131<br>(0.092)|0.224*<br>(0.116)|-0.026*<br>(0.016)|
|Stability<br>Ethnic|<br>-0.016<br>(0.061)<br>0.051<br>(0.082)|<br>-0.067<br>(0.100)<br>0.240<br>(0.228)|<br>-0.003<br>(0.032)<br>0.026<br>(0.043)|<br>-0.036<br>(0.052)<br>0.094<br>(0.120)|<br>-0.086<br>(0.072)<br>0.095<br>(0.225)|<br>-0.001<br>(0.019)<br>0.009<br>(0.026)|
|Ext. conflict<br>Instruments<br>Bank<br>Bank1|0.033<br>(0.062)|-0.122<br>(0.117)|0.015<br>(0.033)|-0.063<br>(0.063)|-0.117<br>(0.077)|0.019<br>(0.011)<br>0.665***(0.02)<br>2.61***(0.82)|
|Bank2||||||12.6***(3.51)|
|Borr1<br>Borr2||||||-3.88***(1.10)<br>-19.5***(5.17)|
|Country FE||Yes||Yes|Yes|Yes|
|Year FE||Yes||Yes|Yes|Yes|

# Other Statistics

|Observ.|1710|1576<sup>-</sup>|1710|1576<sup>-</sup>|1582<sup>-</sup>|
|---|---|---|---|---|---|
|L.Likelihood|-477.50|-396.97|-477.53|-395.97||
|Pseudo R<sup>2</sup>|0.489|0.553|0.489|0.554|0.467|
|Prob(χ<sup>2</sup>)<br>ALN test<sup>+</sup>|0.000|0.000|0.000|0.000|0.000<br>0.293|

Note:<sup><u>–</u></sup> some observations removed because of collinearity between the country and year effects.<sup><u>+</u></sup> ALN stands for the AmemiyaLee-Newey minimum chi-sq statistic, it is an overidentification test performed on the ivprobit using the two-step estimation method.

26
# **SUMMARY**

This report summarizes the methods and findings of the hydrological assessment component of the project studying likely impacts of climate change on water resources and agriculture in Africa. The study employed a version of a conceptual rainfall-runoff model called WatBal (Water Balance), applied to gridded data to simulate changes in soil moisture and runoff across the whole continent of Africa rather than to any particular catchment or water resource system. The model inputs were the climate variables of the 1961–1990 climatology and physiological parameters (e.g. soil properties and land use) derived from global datasets for each of the 0.5<sup>o</sup> latitude/longitude cells across the continent. The primary model output comprised a time series (monthly time step) of simulated runoff for all the grid cells for each of the districts in the countries of interest.

The first phase of the hydrology component generated the following data at district level: runoff (in mm); relative soil moisture storage - z (0–1): 1 is fully saturated; potential evapotranspiration (in mm); actual evapotranspiration (in mm); temperature (in degrees Celsius); precipitation (in mm) and streamflow (in m<sup>3</sup> ). This data was generated for the 11 countries in the study on a monthly time step from 1961 to 1990. Additional results included a river density index (indicator of stream frequency and hence surface water availability within each district) and the area irrigated (an estimate of the percentage area irrigated within each district.) The second phase of the study extended the hydrology analyses to update the above hydroclimatic series to the year 2000 using updated input data. To ascertain the possible impacts of climate change within the districts being investigated this study used synthetic or GCM-based climate change scenarios as input to the WatBal model. A subset of the 20 scenarios produced by the Climate Research Unit (CRU) for which data are available at 0.5o x 0.5o for the globe was employed to represent a range of equally plausible future climates with differences attributable to the different climate models used and to different emission scenarios that the world may follow. This study derived 16 scenarios using four different models (i.e. CSIRO2, HadCM3, CGCM2, ECHAM and PCM) based on four different emission scenarios (i.e. A2 & B2). The WatBal model was used to determine the impact of these different scenarios on runoff and actual evaporation and hence flow in the districts under study. The generated hydroclimatic series and scenario analyses were used as inputs into various Ricardian regressions measuring likely impacts of climate change on the agricultural economies of Africa.

2
7 access to quality ECE is associated with delayed school entry, grade repetition and school dropout and lower educational achievement (Naudeau et al., 2011; UNICEF, 2019). In their simulations of the economic costs of preschool closures in 140 countries, Lopez Boo and colleagues (2020) estimate the median losses of a 6-month gap in pre-primary program participation to be about 3 percent of GDP in high-income and upper-middle income countries and closer to 1-2 percent of GDP in lower-income countries, where initial enrollments were lower (Lopez Boo, et al., 2020). Given that school closures in some countries have continued for almost a year, further losses in learning and earnings are expected.

Unequal access to technology and limited distance learning options have magnified the impacts of this crisis for the most vulnerable young children. While almost all countries around the world responded to the education crisis by offering digital and broadcast distance learning for primary and secondary education, only two-thirds included pre-primary students. Pre-primary teachers also are less likely to receive training and support in offering distance learning compared to their primary school counterparts (Nugroho et al., 2020).

In a 2020 UNESCO, UNICEF, World Bank survey of 122 ministries of education, 73 countries reported that they provided one or more remote learning modalities for pre-primary students (Nugroho et al., 2020).<sup>3</sup> Countries were less likely to provide distance learning options, across different media, for ECE than for other levels of education (see Table 1). There is limited information on how many young children are regularly accessing these options. As with older children, challenges in reaching preschoolers in more rural and disadvantaged communities are expected. About 30 percent of pre-primary students globally are estimated to be reachable by

> 3 These modalities include online learning platforms, television, radio, take-home materials, or mobile phone. Schools remained open in another 10 countries (Nugroho et al., 2020).
# 3. **Field experiment**

# _3.1. Design_

The field experiment involved distributing the Mirt stove under different terms to a sample of households in 2013. In our study area and throughout much of Ethiopia, households with no refrigeration typically bake injera at least twice per week (Bizzarri, 2010; Tesfay, 2014; DFID, undated). This is the definition of regular usage.

We identify cooking events by measuring the surface temperature of the Mirt stoves using electronic Stove Use Monitors (SUMs).<sup>2</sup> The SUMs we used are approximately the size of a watch battery (see Figure 1). We set the SUM device to record surface temperature every 10 minutes and the memory can record for approximately 60 days and can tolerate temperatures up to 120 degrees Celsius. Temperature, time and date data are then downloaded after the monitoring period is completed. Respondents were informed that the purpose of the SUM device was to record the surface temperature of the stove and that enumerators would come back periodically to download the data and reinstall the SUM devices.

We have three levels of monetary incentives: (i) users receive the stove for free; (ii) users pay 25 birr for the Mirt stove (about 13% of the real cost),<sup>3</sup> and (iii) users pay nothing for their stoves and receive a 50 birr<sup>4</sup> incentive payment if the stove use monitors (SUM) that were affixed to all

> 2 The Mirt stoves are monitored over five periods of up to about 8 weeks per period over approximately three-and-a-half years. A subset of 108 households participated in three controlled cooking tests that are not part of this study but are discussed in Gebreegziabher et al. (2018).  In our regression models we control for participation in this activity.

> 3 This is a typical subsidized cost level if stoves are not provided free as in Rosa et al. (2014).  For example, in Ethiopia, World Food Program (undated) reports offering an 80% subsidy.  Though the technology analyzed is LPG stoves rather than ICS, Bailis (2018) also reported an 80% subsidy in their RCT in India.

4The exchange rate in June 2013 when the Mirt stoves were distributed was approximately ETB 19/$US and the daily unskilled wage rate was approximately ETB 40. Half the sample was randomized into village level groups that received one-time group training. These groups were made up of households in the same village sites who received the Mirt stove. These groups were found to have no effect on usage and are treated as a control in all models, but not analyzed.

9
WPS3818

# **The Use of Willingness to Pay Experiments: Estimating demand for piped water connections in Sri Lanka**

**_Subhrendu K. Pattanayak, Caroline van den Berg, Jui‐Chen Yang, and George Van Houtven_**<sup>**_1_**</sup>

## Abstract

_This paper shows how Willingness to Pay surveys can be used to gauge household demand for improved network water and sanitation services when a private sector transaction is considered. We do this by presenting a case‐ study from Sri Lanka, where we surveyed approximately 1,800 households in 2003. Using multivariate regression, we show that a complex combination of factors drives demand for service improvements. While poverty and costs are found to be key determinants of demand, we also find that location, self‐provision, and perceptions matter as well, and that sub‐sets of these factors matter differently for sub‐samples of the population. To evaluate the policy implications of the demand analysis, we use the model to estimate uptake rates of improved service under various scenarios – demand in sub‐groups, the institutional decision to rely on private sector provision, and various financial incentives targeted to the poor. The simulations show that in this particular environment in Sri Lanka, demand for piped water services is low, and that it is unlikely that under the present circumstances the goal of nearly universal piped water coverage is going to be achieved. Policy instruments, such as subsidization of connection fees, could be used to increase demand for piped water, but it is unclear whether the benefits of the use of such policies would outweigh the costs._

**JEL classification** : **H4** Publicly Provided Goods; **R2** Household Analysis; **O1** Economic Development; **O2** Development Planning and Policy

## World Bank Policy Research Working Paper 3818, January 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

- 1 Subhrendu K. Pattanayak is a Fellow, Environment, Health and Development Economics at RTI International (subrendu@rti.org) and Research Associate Professor at NC State University. Caroline van den Berg is a Senior Economist in the Energy and Water Department at the World Bank (cvandenberg@worldbank.org). Jui‐Chen Yang (yang@rti.org) and George Van Houtven (gvh@rti.org) are Research Economist and Program Director in the Environment and Natural Resource Economics Program at RTI International. We have benefited from helpful discussions with Christine Poulos.

1
budget line in figure 2 shifts from AB to ACD, where at least OE (=AC) of the assisted public good will be acquired.

# **Figure 2 Effect of Conditional Nonmatching Grant**

<!-- Start of picture text --> Spending<br>on other<br>public<br>goods<br>A C<br>F<br>O E B D<br>Spending on assisted public<br>goods<br><!-- End of picture text --> _Source_ : Shah (1994) Conditional nonmatching grants are best suited for subsidizing activities considered high priority by a higher-level government but low priority by local governments. This may be the case if a program generates a high degree of spillovers up to a given level of provision (OE), after which the external benefits terminate abruptly.

For a given level of available assistance, grant recipients prefer unconditional nonmatching transfers, which provide them with maximum flexibility to pursue their own objectives. Because such grants augment resources without influencing spending patterns, they allow recipients to maximize their own welfare. Grantors, however, may be prepared to sacrifice some recipient satisfaction to ensure that the funds are directed toward expenditures on which they place a priority. This is particularly so when federal objectives are implemented by line agencies or departments rather than through a central agency, such as the Ministry of Finance, with a broader mandate. Federal departments do not want local governments to shift their program funds toward other areas. In this situation, conditional (selective) nonmatching (bloc) grants can ensure that the funds are spent in a department’s area of interest (for example, health care) without distorting local priorities among alternative activities or inducing inefficient allocations in the targeted expenditure area.