rafmacalaba/datause-encoder-data huggingface dataset:
which can also be represented as elasticity We can use this framework to identify how fragmentation will affect the total unit cost of production, test if specific fragmentation measures will affect the total cost of production differently, and explore if higher levels of fragmentation increase all cost items equally or alter the relative cost structure.<sup>7</sup> Moreover, while inclusion of total farm size or cultivated area allows us to retrieve the aggregate elasticity of cost with respect to farm size, we can estimate the cost function for individual farm size groups and thus test if fragmentation differentially affects small, medium, and large farmers’ total cost or cost structure.

# **3. Data and descriptive evidence**

Detailed fragment-level data on agricultural production from 17 Indian states that cover a wide range of conditions in terms of production structure and levels of fragmentation allow us to estimate impacts of fragmentation on production cost for inherited fragments only. The data illustrate that fragment or farm size, the Simpson index, and mean fragment-fragment or homestead distances are different measures that are only weakly correlated.

# **3.1 Data and variable construction**

To explore impacts of fragmentation on cost of production, we use the Rural Economic and Demographic Survey (REDS) conducted by India’s National Council for Applied Economic Research (NCAER) in 2007/8 on a sample of 240 villages in 17 states with production information on 4,790 farm households who cultivate a total of 17,821 parcels. Beyond its national coverage and detail on parcel and household characteristics, this survey, which forms part of a long-running panel, has three desirable features.

First, the interview included a sketch depicting all parcels cultivated by a household, cross-checked with official cadastral maps maintained by the village administration. This allowed elaboration and crosschecking of a matrix that identified distances and travel times between each fragment and all the other ones cultivated by a household<sup>8</sup> as well as the cultivator’s homestead which we use to compute a measure of proximity and the travel time between fragments and/or fragments and the homestead.

> 7As prices are controlled for, such changes will mean that certain inputs are used more intensively than others.

> 8 As no GPS measurements were available, it is impossible to use a digital elevation model to cross-check self-reported distances and travel times as done elsewhere (Ali and Deininger 2014). As most of our sample is located in flat high-potential irrigable terrain, this may be less relevant.

8
**Table S3.2:  Missing data, subsamples and giving each study equal weight**

||(1)<br>No<br>imputations|(2)<br>US only|(3)<br>Classroom<br>only|(4)<br>Synthetic ES<br>OLS|(5)<br>Equal study<br>weights|
|---|---|---|---|---|---|
|RCT|-0.052*<br>(0.027)|-0.097**<br>(0.042)|-0.080***<br>(0.028)|-0.052<br>(0.033)|-0.042<br>(0.031)|
|TOT|0.057<br>(0.041)|0.114***<br>(0.040)|0.065**<br>(0.028)|0.107***<br>(0.028)|0.105***<br>(0.035)|
|Delay|-0.000*|0.000|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|1/SE|0.001|0.000|-0.000|0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Intensity / week|0.005***<br>(0.001)|0.006*<br>(0.003)|0.004***<br>(0.001)|0.001<br>(0.002)|0.001<br>(0.002)|
|Duration|<br>-0.000|<br>-0.001|<br>-0.000|<br>-0.001|<br>-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Low income clients|-0.047**|-0.003|-0.054***|-0.043|-0.049**|
||(0.020)|(0.025)|(0.017)|(0.027)|(0.022)|
|Years of schooling|<br>-0.022***<br>(0.007)||<br>-0.021***<br>(0.006)|<br>-0.022**<br>(0.009)|<br>-0.020**<br>(0.009)|
|Low/lower-mid .econ|<br>-0.113**||<br>-0.108**|<br>-0.113*|<br>-0.108*|
||(0.044)||(0.041)|(0.061)|(0.059)|
|Mandatory|<br>-0.086*|-0.097***|<br>-0.043|<br>-0.097**|<br>-0.095***|
||(0.049)|(0.033)|(0.028)|(0.038)|(0.029)|
|Teachable moment|<br>0.058|<br>0.129***|<br>0.075**|<br>0.058**|<br>0.058**|
||(0.052)|(0.035)|(0.033)|(0.028)|(0.026)|
|Constant|<br>0.359***<br>(0.097)|<br>0.042<br>(0.031)|<br>0.364***<br>(0.095)|<br>0.364***<br>(0.118)|<br>0.344***<br>(0.119)|
|R<sup>2</sup>|0.125|0.340|0.177|0.297|0.206|
|n (Studies)|35|55|70|90|90|
|n(Effectsizes)|24|135|317|90|349|

Notes: Column (1) reports results for complete cases only. Columns (2) present results for the sample split of USA studies only. These splits include only variables for which differential information from at least two studies are available. Column (3) presents results using one synthetic effect size (weighted within-study average effect size across all outcomes) per study. Column (4) shows results by weighting each observation by the inverse number of observations of the study the observation is nested in. Standard errors (clustered at the study-level for all Columns but (4)) in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level.

53
10

-

# III. ROEURAL TARGETS

3.1 In most developing countries, property tax reform cannot be limited to policy. Because the quality of tax administration is poor, the burden of the tax falls haphazardly on those unable to exploit its weaknesses. Raising rates alone would exaggerate these inequities. Reform efforts must therefore address problems in administration.

3.2 Figure 1 outlines the steps involved in the administration of the property tax. Two characteristics of these sequences deserve emphasis.

3.3 First, the amount of revenue raised depends upon the cumulative performance of all five steps--the proportion of properties discovered, the accuracy of valuations or. those properties, the tax rate, the proportion of bills effectively delivered, and the proportion of those bills that are paid. Failure at one stage in administration of the tax can counteract successes at others. This has important implications for the design of tax reform interventions. To ensure that reform ultimately results in an increase in revenues, the scope of intervention--at least at the diagnostic stage--must be comprehensive-confronting all five steps in administr'9ti: - thi **-.x.** 3.4 Second, the sequence is an ongoing, find **fD1COVERY all** taxabl prperties not a one-time event. Sustained increases in property tax revenues depend upon the, process's being repeated VALUATION **calaulate relatve value** annually: new properties L J have to be discovered and valued, tax rates i _ have to be adjusted, and i__ the billing anA A88E88MENT calculate amount of tax collection cycle has t. be followed throug.i. Intervention therefore has to be directed at , permanent change in the BILLING | Inform taxpayer of liability | way t a x e s a r e administered, if it is to have a sustained impact on revenues. COLLECTION collect tax payment Appropriate Standards FLgure 1: Sequence of Property Tax Administration

3.5 The environment of property taxation is more difficult in developing nations than in the industrial countries. The basic data on which the property tax is based is inaccessible or unreliable. Base maps on which property discovery and identification would be based are nonexistent. The market data
<!-- Start of picture text --> 600<br>Large Economies<br>Singapore<br>High-Perfomers<br>Low-Performers<br>Vietnam Cuba Poland<br>500<br>Bulgaria<br>China United Arab Emirates<br>Mexico<br>Brazil<br>400<br>Qatar<br>India Kuwait<br>South Africa<br>300<br>Ghana<br>Nigeria<br>200<br>6 8 10 12<br>log GDP per capita<br>SUPPLEMENT FIGURE VII<br>HLO<br><!-- End of picture text --> Average Learning (2000-2017) versus 2015 GDP per capita _Notes:_ Average learning is calculated across subjects and schooling levels over the given time period. _Source:_ GDP per capita estimates are from World Bank national accounts data; learning outcomes are from our database.

# III. DATA DESCRIPTION SUPPLEMENT

# _A. International Standardized Achievement Tests (ISATs)_

In the mid-1990s, there was an emergence of standardized, psychometrically robust and relatively consistent ISATs. Below we describe the major ISATs we use in this database.

_TIMSS_ **.** The Trends in International Mathematics and Science Study (TIMSS) is conducted by the IEA. Five TIMSS rounds have been held to date in Math and Science subjects covering grades 4 and 8. The first, conducted in 1995, covered 45 national educational systems and three groups of students.<sup>11</sup> The second round covered 38 educational systems in 1999, examining pupils from secondary education (grade 8). The third round covered 50 educational systems in 2003, focusing

> 11 IEA assessments define populations relative to specific grades, while PISA assessments focus on the age of pupils. In IEA studies, three different groups of pupils were generally assessed: pupils from grade 4, grade 8 and from the last grade of secondary education. In 1995, two adjacent grades were tested in both primary (3-4) and secondary schools (7-8). To obtain comparable trends, we restricted the sample to grades 4 and 8. Some Canadian provinces and states in the United States of America have occasionally taken part in the IEA surveys.

35
Table A.12: Parental health investments, NFHS data

||Months breastfed|Received _≥_1 vaccine|No. of antenatal checks|
|---|---|---|---|
||(1)|(2)|(3)|
|_Firstborn girl_ * _Female_|-0.373**|-0.028*|-0.076|
||(0.172)|(0.014)|(0.061)|
|_Firstborn girl_ * _Female_ * _Post2_|0.056|0.022|0.061|
||(0.291)|(0.015)|(0.071)|
|N|59,654|56,430|67,001|
|_Xijt_|x|x|x|
|_1st Girl_ x Birth year FE|x|x|x|
|_Female_ x Birth year FE|x|x|x|
|_Female_ x State FE|x|x|x|
|_Female_ x Birth order FE|x|x|x|
|Birth order x State FE|x|x|x|
|State-specifc time trends|x|x|x|
|_Firstborn girl_ x State FE|x|x|x|
|_Firstborn girl_ x Birth order FE|x|x|x|

NOTES: NFHS data. Sample include second and higher order births. Investments are only queried for cohorts born in a few years before each survey so there are no pre-ultrasound cohorts. The comparison here is therefore across the two post-ultrasound periods. The estimates are from the following specification for child _i_ of birth order _b_ born to mother _j_ in year _t_ : _Iibjt_ = _α_ + _βGj ∗ Fi ∗ Post_<sup>2</sup> _t_<sup>+</sup><sup>_γGj∗Fi_+</sup><sup>_ωtGj_+</sup><sup>_σtFi_+</sup> _ψbFi_ + _Xijt′_<sup>_τ_+</sup><sup>_δsFi_+</sup><sup>_ρbt_+</sup><sup>_ηbs_+</sup><sup>_φst_+</sup><sup>_εibjt_.Amongchildrenwhowereatleast12monthsoldatthetimeof</sup> survey, we define a child to be fully immunized if he or she had received the eight most common vaccines by that time. The sample in column (1) is restricted to children below age three and in column (2) to children of age _≥_ 1. Standard errors in parentheses are clustered by state. *** 1%, ** 5%, * 10%.

59
that younger students and girls are more negatively affected by increased classroom age heterogeneity. Classroom age variance also appears to impede student achievement as they progress into the eighth grade. Although classroom age variance hurts academic achievement, it does not significantly increase the incidence of behavioral problems or make schooling experience less enjoyable for students.

The robust negative effect of classroom age variance and the insignificant effect of classroom average age on student achievement suggest that grouping students by age can lead to test score improvements. A simulation shows that by switching from age mixing students into age grouping students, schools can reduce classroom standard deviation of age by 0.26 years on average. The corresponding average achievement gain is 0.09 standard deviations in math score and 0.10 standard deviations in science score. According to Sander’s (1999) estimates using U.S data, such effect sizes are similar to that of raising expenditures per student by 26 percent. Furthermore, gains are experienced by students of all age groups, but more so for students at the median age or younger, leading to smaller achievement differences between older and younger students. Countries that have larger within-grade-level variance of student age and lower average achievement are the ones that tend to gain the most from age grouping.

Given the low administrative cost, age grouping shows promise as a method to improve learning outcomes. Nevertheless, since the estimates are based on observational data and it is not possible to completely rule out the presence of unobserved influences which are correlated with classroom age distribution, readers should be cautious in attaching a causal interpretation to the estimates. It will certainly improve the confidence in recommending age grouping to policy makers in countries with large classroom age variances if randomized controlled experiments can be conducted to ascertain whether findings reported here stand up to scrutiny.

25
Table 2: Mask manufacturing exposure increases purchases of masks from manufacturers, decreases mask prices, and reduces COVID-19 infections

||||_Dependen_|_t variable:_|||
|---|---|---|---|---|---|---|
||log buy|er price|Ef mask Q fro<br>Ef m|m manufacturers<br>ask Q|# paraceta|mol receipts|
||(1)|(2)|(3)|(4)|(5)|(6)|
||0.445|0.263|-0.016|0.155|0.588|0.029|
|Mask manufacturing exposure_d ×_Nov_t_|(0.680)|(0.666)|(0.123)|(0.202)|(1.040)|(0.859)|
||[0.514]|[0.694]|[0.897]|[0.444]|[0.572]|[0.973]|
||0.173|0.199|0.035|0.135|0.267|-0.194|
|Mask manufacturing exposure_d ×_Dec_t_|(0.732)|(0.717)|(0.114)|(0.214)|(0.734)|(0.618)|
||[0.814]|[0.782]|[0.760]|[0.531]|[0.716]|[0.753]|
||0.296|-0.101|0.057|0.224|0.254|0.415|
|Mask manufacturing exposure_d ×_Jan_t_|(0.383)|(0.343)|(0.115)|(0.211)|(0.685)|(0.685)|
||[0.441]|[0.769]|[0.620]|[0.290]|[0.711]|[0.545]|
||0.367|0.393|0.046|0.130|-0.080|0.524|
|Mask manufacturing exposure_d ×_Feb_t_|(0.374)|(0.318)|(0.089)|(0.181)|(0.723)|(0.752)|
||[0.328]|[0.219]|[0.608]|[0.475]|[0.912]|[0.485]|
|Mask manufacturing exposure_d ×_Mar_t_|-|-|-|-|-|-|
||-0.521|-0.600|1.095|0.973|0.208|-0.393|
|Mask manufacturing exposure_d ×_Apr_t_|(0.311)|(0.300)|(0.252)|(0.413)|(0.832)|(0.953)|
||[0.098]|[0.049]|[0.000]|[0.021]|[0.803]|[0.680]|
||-0.613|-0.673|0.533|0.464|-1.178|-1.056|
|Mask manufacturing exposure_d ×_May_t_|(0.327)|(0.290)|(0.179)|(0.303)|(0.730)|(0.565)|
||[0.064]|[0.023]|[0.004]|[0.130]|[0.107]|[0.062]|
||-0.671|-0.792|0.196|0.513|-1.968|-1.669|
|Mask manufacturing exposure_d ×_Jun_t_|(0.391)|(0.423)|(0.194)|(0.293)|(0.728)|(0.571)|
||[0.089]|[0.065]|[0.316]|[0.083]|[0.007]|[0.003]|
||-0.210|-0.316|0.438|0.398|-1.973|-1.619|
|Mask manufacturing exposure_d ×_Jul_t_|(0.309)|(0.314)|(0.136)|(0.262)|(0.825)|(0.753)|
||[0.499]|[0.318]|[0.002]|[0.132]|[0.017]|[0.032]|
||-0.460|-0.606|-0.138|0.031|-1.734|-1.564|
|Mask manufacturing exposure_d ×_Aug_t_|(0.272)|(0.251)|(0.197)|(0.311)|(0.918)|(0.862)|
||[0.095]|[0.018]|[0.485]|[0.920]|[0.059]|[0.070]|
|Estimation method|OLS|OLS|OLS|OLS|Poisson|Poisson|
|Firm-product FE|X|X|||||
|Destination sub-District FE|X|X|X|X|X|X|
|Month FE|X|X|X|X|X|X|
|Residualized shocks||X||X||X|
|Controls _×_ Month FE||X||X||X|
|# observations|3,937|3,937|506|506|780|780|
|# clusters (sub-Districts)|86|86|86|86|78|78|

_Notes:_ Columns 1 through 6 report coefficients on mask manufacturing exposure interacted with month fixed effects from estimates of Equation 2. Standard errors are reported in parentheses, and p- values are reported in brackets. All columns include destination sub-District fixed effects and month fixed effects. Columns 1 and 2 include firm-product fixed effects, while Columns 5 and 6 estimate coefficients using Poisson pseudo maximum likelihood. Columns 2, 4, and 6 include month fixed effects interacted with controls for Province fixed effects, log population density, and log number of textile manufacturers, and residualize leave-out mask manufacturing intensity by regression on those same controls before constructing mask manufacturing exposure.

22
- Hummels, D. and V. Lugovsky (2005), ―Trade in Ideal Varieties: Theory and Evidence,‖ National Bureau of Economic Research Working Paper No. 11828.

- Ianchovichina, Elena and Will Martin (2004), ―Impacts of China‘s Accession to the World Trade Organization,‖ _World Bank Economic Review,_ Vol. 18, 3-27.

- Inklaar, Robert, Marcel P. Timmer and Bart van Ark (2008), ―Market Services Productivity Across Europe and the U.S.,‖ _Economic Policy_ , Vol. 53, 139-171, 178-194.

- Ivanic, M., 2004. Reconciliation of the GTAP and Household Survey Data. GTAP Resource Memorandum No. 1408.  Available at:

- http://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1408

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2010) ―Modeling Services Liberalization: The Case of Tanzania,‖ _Journal of Economic Integration,_ Vol. 25(4), 644675.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2007), ―The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,‖ _Review of Development Economics_ , Vol. 11 (3), August, 482-506.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2006) "Telecommunications Reform within Russia's Accession to the WTO," _Eastern European Economics_ , Vol. 44 (1), January-February,  25-58.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2004) ―The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,‖ World Bank Policy and Research Working Paper No. 3391, September. Available at:

- http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372 &piPK=64165421&menuPK=64166093&entityID=000012009_20040909153130.

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87
that otherwise flies would carry particles of low-caste people’s feces into high-caste people’s food.

WaterAid (2008) documents “significant policy variations across the states”: “there are variations across states and districts in terms of approaches and strategies adopted and results achieved as well” (p. 7). This heterogeneity has been “at times, not really in line with the stated TSC strategy of the program being ‘community led”’ (p. 9). India is a federal democracy with powerful central and state bureaucracies and several levels of elected officials with autonomous power.<sup>6</sup> Because the TSC depended not only upon construction but also upon information and persuasion, its implementation was heterogeneous, in large part according to the commitment of state, mid-level, and especially local government officials. Alok (2010), an Indian Administrative Service officer involved in the design and central oversight of the TSC, details the importance of these differing levels of political commitment. As an official once explained to one of the authors, “it’s person driven here in [our state] – if the person in charge wants it _. . ._ ” Of course, a large political economy literature documents that policy outcomes can be correlated with many social and economic variables; however, in this case, section 4.3 will show that the within-district trends in TSC implementation that we exploit for identification are not correlated with within-district trends in a range of observed socioeconomic and educational variables.

# **4 Empirical Strategy**

Are children who live in districts in which more TSC latrines had been constructed by their first year of life more likely to recognize letters and numbers when they are six years old, compared with children born in the same district in different years, or in different districts

> 6WaterAid (2008) notes that in some states the TSC was implemented by Rural Development departments, while in others it is implemented by a Public Heath Engineering department. “In most places TSC [was] being implemented in a routine administrative fashion without any conscious and visible attempt to strategise the implementation of the program” (p. 22). States and districts have also varied in whether they have collaborated with NGOs to implement the rural information and education components of the program.

9
**<u>Figure 3. Shares of total expenditures on subsidized petroleum products by quintiles, %</u>**

<!-- Start of picture text --> 100<br>90<br>26<br>30<br>80 45 42<br>70<br>60<br>92<br>50<br>40<br>30<br>20<br>10<br>13 13<br>0 4 1 6<br>kerosene LPG gasoline diesel Total<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>%<br><!-- End of picture text --> Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Note: Population quintiles based on spatially adjusted consumption per capita before the reform.</u>

# **2.2 Electricity**

There are seven electricity tariff brackets in Jordan. According to the latest revision made in August 2013, electricity tariffs range from 0.033 JD/kWh for the lowest consumption bracket (1-160 kWh/month) to 0.259 JD/kWh for the highest consumption bracket (1,000 plus kWh/month), with households paying progressively higher amounts only on the incremental consumption of the higher brackets.

Table 4 contains a list of tariffs, mean annual expenditures on electricity and the number of households for each tariff bracket. As can be seen, more than half of all households in Jordan consume electricity between 301-500 kWh per month. These households spend an estimated JD 270 on electricity per year. Hardly any households consume in the lowest tariff bracket (of less than 160 kWh per month), and the same is true for the highest tariff bracket (of more than 1000 kWh per month).

**<u>Table 4. Parameters to calculate electricity consumption in Jordan</u>**

|Brackets:|2014|Upper bound|Mean annual|N of|% of|
|---|---|---|---|---|---|
|KWh/Month|tariff, JD|consumption, yearly<br>(JD)|consumption on electricity,<br>JD|households|HH|
|1-160|0.033|63|54|8,967|1|
|161-300|0.072|184|136|355,443|29|
|301-500|0.086|391|270|620,619|51|
|501-600|0.114|528|448|127,452|10|
|601-750|0.152|801|631|80,494|7|
|751-1000|0.181|1344|986|26,901|2|
|>1000|0.259||1828|4,673|0|

<u>Source:</u> Authors’ calculations <u>based on extrapolated HEIS 2010 data and official information.</u> 7