rafmacalaba/datause-encoder-data huggingface dataset:
_10_ _WATER WHEN IT COUNTS_ between August 2015 and July 2017.<sup>6</sup>

# _Precipitation and water requirements_

Finally, weekly precipitation data (mm/week) were extracted from the NOAAs Climate Prediction Centre (CPC) CMORPH product (Climate Prediction Center, 2015).<sup>7</sup> Crop specific water requirements are from the FAO Cropwat 8.0 tool (Doorenbos and Kassam, 1979; Allen et al., 1998; FAO, 201). These data allow us to estimate the quantity of rain that falls on these schemes in every day, so that we can assess total water available to a plot on a given day as the depth of rain that falls on a plot plus the depth of water that could be applied from irrigation.

## _C. Descriptive Statistics_

The first step in our study is to use our novel high-frequency irrigation monitoring system to assess irrigation and cultivation strategies in the absence of pricing or feedback interventions for one full year with the goal of uncovering whether farmers in these schemes are using water efficiently. If we find that farmers are using water inefficiently, the next step is to identify ways in which water could be more effectively allocated. These strategies underlie the content for the extension tools we use to test whether individual feedback is necessary to improve the efficiency of allocations. This sub-section summarizes findings from one full year of water monitoring to uncover what we believe to be a driving source of inefficiency: a particular type of dynamic inflexibility of allocations to time-specific requirements that we call rules of thumb.

_Cultivation Strategies_

> 6A detailed time-line of data collection activities can be found in the appendix in Figure A1.

> 7Rainfall data were extracted for the smallest geographical unit available in these data. Since the schemes are near to each other, this unit covers all three schemes, so precipitation data do not vary across schemes within a time period.
||ila|PRE Working Panr Seris<br>Athr|2la<br>e|Contact|
|---|---|---|---|---|
|WPS416|World Bank Efforts at Poverty<br>Measurement in the Third World: Th<br>Living Standards Measurement Stud|Paul Glewwe<br>e<br>y|||
|WPS417|Modeling the Macroeconomic<br>Requirements of Policy Reform|William Easterly<br>E. C. Hwa<br>Piyabha Kongsamut<br>Jan Zixik|May 1990|R. Luz<br>34303|
|WPS418|Does Devaluation Hurt Private<br>Investment? The Indonesian Case|Ajay Chhibbor<br>Nemat Shafik|May 1990|M. Colinet<br>33490|
|WPS419|The Design and Sequencing of<br>Trade and Investment Policy Reform<br>An Institutional Analysis|Brian Levy<br>:|||
|WPS420|Making Bank Irrigation Investments<br>More Sustainable|Gerald T. O7Aara|May 1990|C. Spooner<br>30464|
|WPS421|Taxation of Financial Intermediation<br>Measurement Principles and<br>Application to Five African Countries|: Christophe Chamley<br>Patrick Honohan<br>|||
|WPS422|Civil Service Reform and the World<br>Bank|Barbara Nunberg<br>John Nellis|||
|WPS423|Relative Price Changes and the<br>Growth of the Public Sector|M. Shahbaz Khan|||
|WPS424|Mexico's External Debt<br>Restructuring in 1989-1990: An<br>Economic Analysis|Sweder van Wiinbergen|||
|WPS425|The Earmarking of Government<br>Revenues in Colombia|William A. McCleary<br>Evamaria Uribe Tobon|||
|WPS426|Growth-Oriented Adjustment<br>Programs: A Statistical Analysis|Riccardo Faini<br>Jaime de Melo<br>Abdol Senhadji-Semlali<br>Julie Stanton|||
**Table 1: The impacts of Covid-19 confirmed cases on demand for services**

||a) H<br>|otel and restau<br>|rant; transport<br>|, tourism<br>|||
|---|---|---|---|---|---|---|
||Hotels an<br>|d restaurant|Tran<br>|sport<br>|Tour<br>|ism|
||hotel|restaurant|flight|airport|tourist|museum|
|IHS (cases)|-0.09<sup>***</sup>|-0.09<sup>***</sup>|-0.04<sup>***</sup>|-0.07<sup>***</sup>|-0.01<sup>**</sup>|-0.04<sup>***</sup>|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.00)|(0.01)|
|R-squared|0.92|0.92|0.91|0.87|0.84|0.9|
|Observations|8,160|8,160|8,160|8,160|7,344|7,344|
||I|b) ICT, delive<br>CT|ry, and retail t<br>Delivery|rade<br>Retai|l trade||
||Zoom|Skype|delivery|mall|shopping||
|IHS (cases)|0.07<sup>***</sup>|0.05<sup>***</sup>|-0.01|-0.09<sup>***</sup>|-0.03<sup>**</sup>||
||(0.02)|(0.01)|(0.02)|(0.01)|(0.01)||
|R-squared|0.87|0.75|0.88|0.92|0.91||
|Observations|8,208|8,208|7,872|7,776|7,968||

Note: the data used cover January-April of 2018-2020. For every country where Google trends data is available, the search index reflects searches in English and the most dominant local language. Each regression includes weekly dummies to capture aggregate temporal trends. IHS stands for inverse hyperbolic sine transformation. Standard errors, clustered at country level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

The magnitude of the impact we find is quite large. And it is important to put our results in perspective. Unlike previous pandemics, COVID-19 has been spreading quickly, increasing by over 100 percent weekly, on average. The sheer magnitude of the pandemic and the speed at which it transmitted translates into substantially large reductions (increases) in demand for services. Figure 3 presents the weekly marginal (Panel (a)) and cumulative (Panel (b)) impact of COVID19 on demand for selected services. At the margin, the pandemic has led to a 4.2 and 3.5 percent weekly decline in demand for hotels and travel. The corresponding impacts on demand for Zoom and Skype are 4 and 2.3 percent, respectively. The impact is particularly greater in the earlier stages of the pandemic. The sharp drops (rises) in demand for services stabilize after week five of the pandemic.

As shown in Panel (b) of Figure 3, the cumulative impacts for the three months (January 25 to April 30, 2020) amount to 63 and 52 percent drops in demand for hotel and travel, and 59 and 34 percent increases in demand for Zoom and Skype, respectively. These rates are comparable to evolving estimates in other studies on the impacts of the pandemic on consumer spending (e.g., Andersen et al., 2020) and U.S. Commerce Department monthly retail sales reports (U.S. Census Bureau, 2020).

18
# **Table 7**

# **Preference Erosion with Adjustment for Compliance Costs**

||Change<br>Effects of<br>EU<br>liberalization,<br>unadjusted|in annual natio<br> <br>Effects of<br>EU<br>liberalization,<br>adjusted for<br>compliance<br>costs|nal income, $ Total<br>preference<br>loss,<br>unadjusted|US million<br>Total<br>preference loss,<br>adjusted|
|---|---|---|---|---|
|_African LDCs_|_-458.3_|_-341.5_|_-110.5_|<br>_6.3_|
|Botswana|0.2|-0.3|16.4|<br>16.0|
|Madagascar|-7.1|0.1|9.8|<br>17.0|
|Malawi|-22.6|-18.1|-7.0|<br>-2.5|
|Mozambique|-27.3|-27.3|-14.3|<br>-14.3|
|Tanzania|4.6|24.0|1.5|<br>20.9|
|Uganda|-5.9|0.0|-4.3|<br>1.6|
|<br>Zambia|-18.9|-18.6|-21.3|<br>-20.9|
|Other Sub-Saharan Africa LDCs|-381.2|-301.3|-91.3|<br>-11.4|
|_Asia/other LDCs_|_93.4_|_155.8_|_-87.4_|<br>-25.1|
|Bangladesh|-101.0|-40.0|-138.2|<br>-77.2|
|<br>Other Central/South Asia LDCs|194.4|195.8|50.8|<br>52.2|
|_Other Low-income_|_587.4_|_507.3_|_2050.5_|<br>1970.5|
|India|174.0|166.1|275.8|<br>267.9|
|Vietnam|413.4|341.3|1774.8|<br>1702.6|
|TOTAL|222.5|321.6|1852.6|<br>1951.7|

LDC and low-income classification is based on World Bank designations. note: adjustments relate to rules of origin and other compliance costs for EU preferences.

32
# **Table 3. (...), continued**

## **Large banks**

||Stock return %|RWRt|RWRt1|RWRt2|LRt|LRt1|LRt2|Common<br>equity/RWA|Common<br>equity/TA|Other<br>capita/RWA|Other capital<br>/TA|<br>Market to<br>book value<br>of equity<br>(PB)|<br>Price-<br>earnings<br>ratio (PE)|Beta|Loan Loss<br>Provisions<br>/TA|<br>Liquid<br>Assets/<br>TA<br>|Total<br>Deposits<br>TA|/<br>Net<br>Loans/TA|log(TA)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Stock return %|1|||||||||||||||||||
|RWRt|0.0442|1||||||||||||||||||
|RWRt1|0.0483|0.6588*|1|||||||||||||||||
|RWRt2|-0.027|0.2927*|-0.5265*|1||||||||||||||||
|LRt|-0.0117|0.3123*|0.0434|0.3085*|1|||||||||||||||
|LRt1|-0.005|0.4011*|0.3336*|0.0268|0.9109*|1||||||||||||||
|LRt2|-0.0384|0.0927*|-0.4593*|0.6647*|0.6985*|0.3410*|1|||||||||||||
|Common equity/RWA|0.0623|0.1316*|0.3210*|-0.2533*|0.3251*|0.4591*|-0.0246|1||||||||||||
|Common equity/TA|-0.0133|0.1165*|0.1883*|-0.0606|0.6145*|0.6493*|0.2957*|0.9017*|1|||||||||||
|Other capita/RWA|-0.0417|0.2206*|0.027|0.2068*|-0.0913*|-0.1134*|-0.0287|-0.7976*|-0.7138*|1||||||||||
|Other capital /TA|-0.0475|0.2508*|0.0753*|0.1800*|-0.0302|-0.0299|-0.029|-0.7723*|-0.7029*|0.9708*|1|||||||||
|Market to book value of equity (PB)|-0.0056|0.043|0.0023|0.0563|0.0394|0.0204|0.058|0.1015*|0.0955*|-0.2516*|-0.2309*|1||||||||
|Price-earnings ratio (PE)|-0.0358|-0.0066|-0.0244|0.0216|-0.0213|-0.0275|0.0057|-0.0371|-0.0339|0.0268|0.0244|0.0013|1|||||||
|Beta|-0.1058*|0.1504*|-0.1211*|0.3455*|-0.1780*|-0.2731*|0.0675|-0.4276*|-0.1655*|0.3881*|0.3689*|-0.0776*|0.0608|1||||||
|Loan Loss Provisions/TA|-0.1585*|0.2044*|-0.0739*|0.1611*|0.2807*|0.2369*|0.2779*|-0.0255|0.2755*|0.0725*|0.0754*|0.1139*|-0.0104|0.0679*|1|||||
|Liquid Assets/TA|0.1070*|-0.1292*|-0.1352*|0.0218|-0.3938*|-0.4003*|-0.2712*|-0.2894*|-0.3957*|0.0889*|0.0693|0.03|-0.019|0.0468|-0.1800*|1||||
|Total Deposits/TA|0.0799*|0.0291|0.1210*|-0.1992*|-0.0348|0.0895*|-0.1277*|0.2365*|0.1097*|-0.1043*|-0.1027*|-0.2143*|0.0257|-0.2423*|-0.0786*|-0.4410*|1|||
|Net Loans/TA|-0.0717*|-0.0955*|-0.1968*|0.1115*|0.4807*|0.4764*|0.3292*|0.2174*|0.3680*|-0.07|-0.0487|-0.1001*|-0.0092|-0.2303*|0.2681*|-0.5965*|0.5380*|1||
|log(TA)|-0.0511|-0.3073*|-0.0629|-0.0085|-0.2613*|-0.3192*|-0.0812*|-0.0745*|-0.0956*|-0.0256|-0.0996*|-0.0096|0.0227|0.1644*|-0.1264*|0.3786*<br>|-0.3830*|-0.3750*|1|

(*) denotes statistical significance at the 5 % level and above.

19
**Table 6: Labor market assortative mating and labor market transitions**

|**Observed**|**Assortativ**<br>**labo**<br>W|**e mating h**<br>**r marker s**<br>ives' Labor|**usbands and**<br>**tate**<br>Mkt. Status:|**wives'**|**Simulated**|**Assortativ**<br>**labo**<br>W|**e mating h**<br>**r marker s**<br>ives' Labor|**usbands a**<br>**tate**<br>Mkt. Statu|**nd wives'**<br>s:|
|---|---|---|---|---|---|---|---|---|---|
||NP|UE|PT|FT||NP|UE|PT|FT|
|Husbands:|||||Husbands:|||||
|NP|39.3|4.2|1.1|2.3|NP|29.9|5.0|2.9|6.9|
|UE|18.6|2.7|0.7|0.8|UE|18.9|0.0|0.0|0.0|
|PT|9.3|1.2|0.1|0.4|PT|11.1|0.0|0.0|0.0|
|FT|15.8|2.0|0.5|0.9|FT|25.2|0.0|0.0|0.0|

|**Observed**|**Labor ma**<br>|**rket transitio**<br>**the present.**<br>Males|**ns from a**|**year to**|**Simulated La**|**bor mark**|**et transition**<br>**present.**<br>Males|**s from a ye**|**ar to the**|
|---|---|---|---|---|---|---|---|---|---|
|From\To|NP|UE|PT|FT|From\To|NP|UE|PT|FT|
|NP|41.81|-|-|-|NP|44.19|-|-|-|
|UE|-|16.50|7.26|13.77|UE|0.24|6.60|4.48|10.26|
|PT|-|-|3.82|-|PT|-|-|5.86|-|
|FT|-|-|-|5.42|FT|-|-|-|13.41|
|EMP|5.17|6.27|-|-|EMP|0.35|12.31|0.77|1.53|
|||Females|||||Females|||
|From\To|NP|UE|PT|FT|From\To|NP|UE|PT|FT|
|NP|81.31|-|-|-|NP|85.11|-|-|-|
|UE|-|9.17|1.45|3.29|UE|-|1.68|1.26|2.69|
|PT|-|-|1.03|-|PT|-|-|1.51|-|
|FT|-|-|-|1.13|FT|-|-|-|3.72|
|EMP|1.63|0.99|-|-|EMP|-|3.36|0.16|0.51|

Notes: Author’s calculations based on 2017 UNDP-WB-EC Roma Regional survey. Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= non-participant; EMP= Past Employment. Each matrix sum up to 100. Transitions are found from selfreported transitions. Past job schedule is not available in the survey, for this reason past jobs can only be characterized as EMP.

**From the self-reported changes in the labor market, we build male and female transitions.** All the matrices in the middle and bottom panel of table 6 sum up to 100. In the survey, only the time from the last job for the nonworkers and job tenure for the workers were collected. Consequently, only changes from previous employment to the nonwork states can be identified. Moreover, as we assumed that the current worker moved from unemployment, we only have transitions from unemployment to work. Due to this data limitation, other extensions such as on-the-job search cannot be implemented. Even when the model does not have job-to-job transitions, there are some workers who found a job within the 12 months considered, and we have simulated transitions from employment to PT or FT, but they have frequencies of less than 1 percent. Table 6 shows that the model does not predict many transitions from the labor market to nonparticipation. Besides that, the model is capturing the labor market dynamics decently for the Roma population.

**Finally, the simulated wages fit the data closely (table 7).**

24
**<u>Table 2: Evolution of the small and medium farm sector</u>**

|||**Small a**|**nd Medi**|**um Farm**|**s**|
|---|---|---|---|---|---|
||**2002 **|**2005**|**2008**|**2012**|**2014**|
|Total area cultivated|2.40|2.49|2.23|1.77|1.71|
|... of which with annuals|0.88|0.91|0.92|0.92|0.92|
|... of which with perennials|0.08|0.03|0.02|0.01|0.01|
|... of which in partial fallow|0.04|0.06|0.05|0.05|0.06|
|Received extension advice|0.15|0.18|0.09|0.07|0.11|
|Used tractor|0.04|0.04|0.03||0.04|
|Used animal traction|0.20|0.21|0.20|0.18|0.21|
|Used improved seed||0.09|0.12|0.10|0.11|
|Used fertilizer|0.05|0.05|0.04|0.03|0.05|
|Used pesticide|0.07|0.07|0.03|0.04|0.04|
|Used manure|0.09|0.05|0.03|0.03|0.03|
|Practiced crop rotation||0.35|0.26|0.28|0.24|
|Practiced inter-cropping||0.88|0.83|0.79|0.81|
|Practiced line sowing||0.49|0.45|0.45|0.45|
|Sold crops|0.59|0.53|0.53|0.50|0.49|
|<br>Any off-farm employment||0.19|0.17|0.12|0.17|
|<br>Received credit||0.04|0.03|0.02|0.02|
|Better off than 3 years ago||0.20|0.29|0.34|0.45|
|Worse off than 3 years ago||0.49|0.35|0.31|0.21|
|**Yields**||||||
|Maize (kg/ha)|973|781|717|949|1,024|
|Cassava (kg/ha)|2,961|4,098|2,554|3,566|2,921|
|Sorghum (kg/ha)|725|511|473|720|714|
|Peanuts (kg/ha)|414|324|287|422|470|
|Rice (kg/ha)|558|463|382|492|494|
|<br>Sugarcane (MT/ha)|NA|8,638|9,549|13,783|13,379|
|**Rainfall**||||||
|Daily rainfall (mm)|1.58|1.42|1.77|1.89|2.23|
||Z| of daily rainfall compared to 1990-2001|0.87|0.68|0.56|0.84|0.84|
|Share of negative z score|0.70|0.91|0.58|0.51|0.11|
|Share of z-score < -1|0.23|0.19|0.05|0.05|0.00|
|No. of observations|4,840|6,000|5,783|6,430|5,772|

_Source:_ Small and medium farmer data from various TIA survey rounds _Note:_ Output of sugarcane is in 2014 MT using the World Bank’s consumer price index as a deflator (http://data.worldbank.org/indicator/FP.CPI.TOTL.ZG/countries/MZ?display=default).

17
# **Table 8:** **<mark>Proportion of undernourished individuals who fall into the poorest 20% and 40% of the household wealth distribution</mark>**

||Poores|t 20% of househ|olds|Poore|st 40% of house|holds|
|---|---|---|---|---|---|---|
||Underweight<br>women|Stunted<br>children|Wasted<br>children|Underweight<br>women|Stunted<br>children|Wasted<br>children|
|Benin|0.248|0.233|0.223|0.444|0.446|0.464|
|Burkina Faso|0.307|0.242|0.224|0.551|0.458|0.433|
|Burundi|0.276|0.249|0.281|0.464|0.451|0.506|
|Cameroon|0.396|0.326|0.364|0.637|0.594|0.630|
|Congo, Rep.|0.221|0.310|0.232|0.460|0.534|0.465|
|Côte d'Ivoire|0.226|0.289|0.240|0.414|0.516|0.447|
|Congo, Dem. Rep.|0.252|0.247|0.209|0.521|0.482|0.442|
|Ethiopia|0.235|0.218|0.259|0.461|0.445|0.534|
|Gabon|0.246|0.434|0.206|0.422|0.634|0.388|
|Gambia|0.212|0.262|0.178|0.474|0.486|0.385|
|Ghana|0.355|0.317|0.256|0.614|0.597|0.448|
|Guinea|0.295|0.200|0.283|0.499|0.491|0.496|
|Kenya|0.329|0.262|0.396|0.599|0.497|0.614|
|Lesotho|0.304|0.238|0.374|0.595|0.447|0.475|
|Liberia|0.285|0.229|0.253|0.481|0.443|0.475|
|Malawi|0.230|0.237|0.259|0.448|0.462|0.495|
|Mali|0.218|0.252|0.252|0.434|0.486|0.492|
|Mozambique|0.283|0.242|0.312|0.548|0.476|0.554|
|Namibia|0.324|0.248|0.279|0.537|0.529|0.475|
|Niger|0.260|0.215|0.236|0.498|0.445|0.418|
|Nigeria|0.294|0.307|0.225|0.531|0.565|0.440|
|Rwanda|0.259|0.252|0.256|0.492|0.494|0.499|
|Senegal|0.241|0.249|0.165|0.462|0.484|0.339|
|Sierra Leone|0.241|0.235|0.147|0.460|0.458|0.365|
|Swaziland|0.285|0.256|0.118|0.494|0.502|0.443|
|Tanzania|0.316|0.243|0.277|0.539|0.459|0.441|
|Togo|0.339|0.252|0.234|0.607|0.521|0.410|
|Uganda|0.377|0.211|0.264|0.634|0.419|0.534|
|Zambia|0.296|0.241|0.230|0.528|0.456|0.454|
|Zimbabwe|0.315|0.219|0.260|0.564|0.430|0.433|
|Mean|0.275|0.255|0.240|0.508|0.487|0.461|

Note: Data are drawn from DHSs. Means are population weighted. The table lists the proportion of underweight women, stunted children and wasted children who fall below the bottom 20th and 40th percentiles of the wealth index distribution. For example, 24.8 percent of underweight women fall below the bottom 20th percentile of wealth in Benin.

45
-31-

- (a) A property valuation and tax role update exercise is ongoing in Dar es Salaam and in the eight project towns, computerized data base systems are in place in most towns, and the property tax administration has been strengthened (Table 4.2).

- (b) A new activity-based budget format, which improves the budgetary control, is being used, and a new accounting system contained in the Local Authorities Accounting Manual (LAAM; para 4.17) has been successfully tested and is being implemented in all eight project towns.

**Table 4.2: Property Tax Collection in the Eight Project Towns and Dar es Salaam*** (T Sh Million)

|. . . . .<br>.<br>-.<br>. . .<br>.<br>., ...,-...|**1992**|**1993**|1994|1995**|
|---|---|---|---|---|
|Dar es Salaam*|57|128|110|180|
|Percent Increase||124|-14|64|
|Eight Project Towns, Combined|115|266|337|380|
|Percent Increase||132|26|13|
|Eight Project Towns, Average|14|33|42|48|

* _The Dar es Salaam tax roll was gazetted in October 1994._

* _*Estimates._

Source: PMO 4.11 Similarly, the autonomy of the UWSDs in the project towns and Dodoma would be supported under the project (para. 1.26). The scheme which had been successfully piloted in Arusha, Moshi and Tanga will be replicated in other towns. The results from the three towns indicate that the water supply sector operations can become financially sustainable through user charges in the short to medium term, possibly in 3 to 5 years (O&M cost only). Assurances have been obtained from the Government during negotiations that it will ensure that the UWSDs in the eight project towns and Dodoma will reach a billing and collection efficiency of at least 80 percent of all consumers by the time of the Mid-Term Review. To achieve this, the UWBSs in the eight project towns and Dodoma would at least annually review the water and sewerage tariffs, starting not later than six months after the Board members of the respective UWSB have been appointed (para 3.6), and adjust them as required to ensure that O&M costs are recovered by the time of the Mid-Term Review. The production and financial data from the beneficiary towns, including the pilot schemes, formed the basis of the projections and analysis. The project production figures for each town and projected average tariff have been used to estimate the revenues. The Net Present Value (NPV) of the cash flow streams of this component during the implementation and 15 years thereafter, at a twelve percent discount rate, is estimated at US$1.7 million (Financial Internal Rate of Return of 13%). This requires stiff tariff increases in some towns, annual increases of 30-35% in
Table 7. Tobit model estimates of the impact of interventions on reading domains (in standard deviations). Includes schools in both baseline and end‐line surveys only

||Domains t<br>the inter|argeted by<br>vention|||Other readi|ngdomains||Oral|
|---|---|---|---|---|---|---|---|---|
||Initial<br>sounds|Familiar<br>words|Letter<br>names|Unfamiliar<br>words|Reading<br>comp.|Oral<br>comp.|Dictation|read.<br>fluency|
|time|0.027|0.562***|0.529***|0.42**|0.412*|‐0.178|0.335|0.695***|
||(0.178)|(0.199)|(0.153)|(0.177)|(0.22)|(0.161)|(0.204)|(0.215)|
|treatment|0.103|0.044|0.072|0.113|0.091|‐0.164|0.068|0.1|
||(0.129)|(0.127)|(0.115)|(0.16)|(0.224)|(0.146)|(0.146)|(0.137)|
|time x treatment|0.627***|0.304|‐0.03|0.121|0.245|0.485*|0.063|0.12|
|(**impact on males**)|(0.186)|(0.273)|(0.175)|(0.199)|(0.285)|(0.266)|(0.225)|(0.253)|
|female|‐0.092|0.025|‐0.068|‐0.07|‐0.035|‐0.068|‐0.078|0.171|
||(0.07)|(0.108)|(0.096)|(0.125)|(0.107)|(0.112)|(0.081)|(0.107)|
|time x female|0.039|‐0.158|‐0.061|‐0.114|0.033|‐0.062|‐0.113|‐0.258|
||(0.123)|(0.174)|(0.126)|(0.174)|(0.132)|(0.196)|(0.155)|(0.184)|
|treatment x female|0.003|‐0.126|0.046|‐0.318*|‐0.151|0.007|‐0.086|‐0.266**|
||(0.132)|(0.134)|(0.133)|(0.161)|(0.147)|(0.159)|(0.12)|(0.12)|
|time x treatment x|0.115|0.64**|0.205|0.518**|0.454**|0.303|0.322|0.668***|
|female|(0.171)|(0.255)|(0.164)|(0.221)|(0.214)|(0.279)|(0.192)|(0.217)|
|constant|0.242***|0.872***|0.162***|0.603***|0.685***|0.262***|0.788***|0.979***|
||(0.175)|(0.154)|(0.16)|(0.138)|(0.144)|(0.127)|(0.145)|(0.148)|
|other control<br>variables|yes|yes|yes|yes|yes|yes|yes|yes|
|**impact on females**|0.742***|0.944***|0.175|0.639***|0.698***|0.788***|0.385**|0.788***|
||(0.184)|(0.228)|(0.171)|(0.189)|(0.236)|(0.238)|(0.172)|(0.244)|
|**average impact**|0.678***|0.612***|0.069|0.358**|0.456*|0.63***|0.214|0.445*|
||(0.163)|(0.219)|(0.153)|(0.158)|(0.239)|(0.21)|(0.177)|(0.225)|

Table presents estimates of nine Tobit regression models. Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted by ***, ** and *, respectively. Impact on females is the estimated sum of time x treatment + time x treatment x female. Number of observations is 893 students. Average impact (for both genders) is estimated using a separate Tobit model including time, treatment and time, treatment (average impact) and other control variables as regressors.

An average impact is also estimated and presented in Table 7. This average impact is estimated using a Tobit model but excluding the gender variables. Overall, the program had a strong positive impact in the three reading domains targeted by the intervention and four of the six other reading domains.

# _C. Internal validity and robustness checks_

The effect size was estimated using five other methods to test the robustness of the model’s estimates given the imbalance and school attrition described above; these results are not presented 13