rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Global Competitiveness Index 2008-09 Competitiveness Index 2008-09 Index 2008-09 2008-09<br>Labor market efficiency market efficiency efficiency<br>Institutians ——<br>Financial market sophistication market sophistication sophistication ——<br>Goods market efficiency market efficiency efficiency<br>Innovation ——<br>Business sophistication<br>Higher education and education and and training ———<br>Infrastructure<br>Overall;; Ranking:: See||<br>Health and primary education SS<br>Maes.<br>Technological readiness<br>Macroeconomic stability —— tf<br>go 20 40 60 80 100 120 140<br><!-- End of picture text -->

<!-- Start of picture text --> Corruption Perception Index - 2008 2008<br>Botswana<br>South Africa Africa<br>Namibia<br>Ghana<br>Lesotho<br>Benin<br>Tanzania<br>Rwanda<br>canbe<br>Uganda<br>Mozambique<br>Kenya<br>Burundi<br>a 50 100 150 200<br><!-- End of picture text --> Corruption Perception Index - 2008 2008 Global Competitiveness Index 2008-09 Competitiveness Index 2008-09 Index 2008-09 2008-09 Botswana Labor market efficiency market efficiency efficiency South Africa Africa Institutians —— Namibia Financial market sophistication market sophistication sophistication —— Ghana Goods market efficiency market efficiency efficiency Lesotho Innovation —— Benin Business sophistication Tanzania Higher education and education and and training ——— Rwanda Infrastructure canbe Overall;; Ranking:: See|| Uganda Health and primary education SS Mozambique Maes. Kenya Technological readiness Burundi Macroeconomic stability —— tf a 50 100 150 200 go 20 40 60 80 100 120 140
To check for biases in the matched sample, we compared the average consumption for both the matched and unmatched sample using a simple t‐test, and found no significant difference between the two samples.<sup>11</sup>

# **_Tenant households_**

It is common in Pakistan for the name on the electricity bill to be that of the owner of the house, even if the property is rented by another family. If this is the case, it is likely that the better‐off landlord’s PMT score is matched against the poorer tenant’s actual electricity consumption, perhaps contributing to the low recorded consumption among wealthy households.

To determine whether this affects the results of the study, we look at the extent of rentership in the study area. The PSLM 2013‐14 data indicate that in rural areas, 93 percent of families own their house, while in urban households, home ownership was around 72 percent. However, this cannot explain a large part of the findings since the service areas of the three DISCOs are predominantly semi‐urban and rural. A district‐wise representation of the billing data sample can also be found in the Appendix.

# **_Representativeness of the billing data_**

The PSLM data examined in Part I are nationally representative. The billing data sample is fully random, but comes from only three non‐randomly selected DISCOs. How might the analysis differ if the other DISCOs were included? First, we note that the three DISCOs covered in this analysis comprise 38% of all households. Being better‐off districts, we would expect to see higher electricity consumption among this sample overall. This would imply that nationwide there would be proportionately less households above the 300 kWh/month level, reinforcing the above analysis. There is no reason to expect the consumption patterns across quintiles to differ significantly given the large sample.

# **_Multiple connections_**

The final potential concern is that households may have multiple connections, spreading their consumption across meters and thereby understating their total consumption. Approximately 83 percent of the matched sample has a single meter connection (that is, the bill payer’s CNIC appeared only once in the database), 6 percent have 2 or 3 meters, and 11 percent have more than 3 meters (see Appendix, part A). For the analysis we removed all households with duplicate observations, since it is not clear whether this reflects an effort on the part of households to evade higher tariffs or is simply an error in the CNIC records. However, even if wealthier households are using multiple meters, they qualify (illegally) for subsidies despite having higher overall electricity consumption. Thus this would not affect our finding that the bulk of subsidies go to non‐poor households.

> 11 Results are available upon request.

9
profits. This type of contract more closely resembles a debt contract between the parties. A fully integrated solution may not draw the same distinction between bank and mobile service provider. In this case, the distribution of surplus depends on the relative bargaining power of the bank and mobile service provider. This type of contract more closely resembles an equity contract between the parties. Equity-like contracts are likely to be more complex and therefore more difficult to negotiate than debt-like contracts, thereby presenting a potential hurdle towards the goal of increasing access.

## **6.2 The Measurement of Savings**

In the 2010 FSD-Kenya survey, respondents were asked ―Do you save any portion of your income?‖ Those who responded affirmatively were asked ―Where do you save your money?‖ The options (not mutually exclusive) were M-PESA, MKESHO/PESA PAP/ KCB Connect, Bank account, SACCO account, ASCA, ROSCA, Microfinance Institution and Other. These responses reflect each respondent‘s subjective understanding of what it means to ―save your money‖ and whether the respondent‘s use of each service constituted savings.

In the analysis presented in the following section, we define an individual to have ―M-PESA savings‖ if he or she reports saving a portion of income and lists M-PESA as one of the places for savings. We also examine the use of bank-integrated mobile savings. Respondents are defined to have bank-integrated mobile savings if they report having used M-KESHO.

# **7 Analysis**

## **7.1 Who Uses Mobile Savings?**

Both savings with M-PESA and savings with M-KESHO require the individual to be registered for M-PESA. Table 3 presents a comparison of those registered with M-PESA to the overall population. Forty five percent of individuals in the sample report being registered with M- PESA.<sup>5</sup> In terms of gender, age, and geographic distribution, there are not large differences

> 5 This figure drawn from the survey conducted October-November 2010 is substantially lower than a similar figure presented earlier in the paper: the number of mobile money subscribers in June 2011 was equivalent to roughly 75 percent of the adult population. The difference between the two figures largely reflects the large growth in mobile money subscriptions during that period. It also stems in part from differences inherent in the two sources of the data:

10
# **3 Data**

## **3.1 Texas Department of Public Safety (TDPS) Data**

We use confidential data on arrests reported to the Texas Department of Public Safety (TDPS) by arresting agencies every 7 to 30 days, as required by the Texas Code of Criminal Procedures, Chapter 66.252. Our data span 2010 through 2017 and cover every Texas county. Texas state law requires that counties maintain at least a 90% data completeness rate over a rolling five year period in order to be eligible for certain state funds. Completeness means that the data reflect the most up-to-date status or disposition of each case. We received our data in 2019, so at least 90% of the cases through 2017 have been deemed to accurately reflect their most up-to-date status in our data (Department of Public Safety, 2019).

The TDPS arrest disposition data come in several parts. We combine files providing data on the individual arrested, the circumstances of the arrest, details of any prosecution, details of any court trial, and details of the subsequent sentencing or appeal. The individual data provide a unique ID for each individual arrested in our data, as well as the sex, race, ethnicity, and date of birth. The arrest data include the date of arrest, the date of offense, the arresting agency, the level of the arrested offence (for instance, misdemeanor A), the arrest charge (for instance, manslaughter), and the address of the defendant at the time of the arrest.<sup>7</sup> Each arrest charge is given a unique entry in the data. For example, if an individual is arrested on October 1, 2010 and charged with assault and resisting arrest, we will have two records for that individual, one for each charge. If they are then arrested again in 2011 for another charge, we will have a third entry for them. Each of these incidents can be linked to the same individual with their unique ID and each incident has a unique incident ID.

We drop all arrests and charges for which we do not have court outcome data (i.e., the

> 7About 25% of the arrests do not have a date of offense recorded, so we conduct our analysis using the date of arrest. For the arrests for which we have both date of arrest and date of offense, these dates are the same 84% of the time. We run robustness checks using the date of offense and find broadly similar results.

10
## **1.4 Data**

# _Data Sources_

We use plant level data for 1998-99 from the Annual Survey of Industries (ASI), conducted by the Central Statistical Organization of the Government of India.<sup>4</sup> The "factory' or plant is the unit of observation in the survey and data are based on returns provided by factories.<sup>5</sup> Data on various firm level production parameters such as output, sales, value added, labor cost, employees, capital, materials and energy are used in the analysis. In summary, factory level output is defined as the ex-factory value of products manufactured during the accounting year for sale. Capital is often measured by perpetual inventory techniques. However, this requires tracking the sample plant over time. This is a major task for micro-level research due to changes in sampling design and incomplete tracking of factories over time. Instead, in our study (and in the ASI dataset) capital is defined as the gross value of plant and machinery. It includes not only the book value of installed plant and machinery, but also the approximate value of rented-in plant and machinery. Doms (1992) demonstrates that defining capital as a gross stock is a reasonable approximation for capital. Labor is defined as the total number of employee person-days worked and paid for by the factory during the accounting year.

> The factory or plant level data from the Indian ASI allows us to compute input costs. With respect to input costs and input prices, capital cost is defined as the sum of rent paid for land, building, plant, and machinery, repair and maintenance cost for fixed capital, and interest on capital. Labor cost is calculated as the total wage paid for employees. Energy cost is the sum

> 4The ASI covers factories registered under sections 2m(i) and 2m(ii) of the Factories Act 1948, employing 10 or more workers and using power, and those employing 20 or more workers but not using power on any day of the preceding 12 months.

> 5Goldar (1997) notes that factories are classified into industries according to their principal products. In some cases

> this causes reclassification of factories from one class to another in successive surveys, making inter-temporal comparisons difficult.

19
# Table 6 (contd)

Relative Importance of Selected Factors in Facilitating Initial Access of Small and Medium Leather Goods Producers to Export Markets, by Size of Firm at Start-up and by Year of First Export

||||Fi rms|||
|---|---|---|---|---|---|
|Factor. Start-up|Avera|ge Score|Assinina a Score|of|Count|
|<br>Size of Firm. and<br>Year of First Export|<br>Am<br>Resp|<br>ong<br>All<br>ondants||||
||||(Pe<br> <br>|rcent)||
||||3<br>4<br>5|4or 5||
|Fit Et Bt<br>1984|d 1989|||||
|rs xpor eween<br>an<br>|<br>|||||
|Direct Efforts by the|All|4.57|2<br>2<br>10|100.0|14|
|Firs to Contact Foreign|2-4|3.67|2<br>0<br>1|100.0|3|
|Buyers or Export Agents|5-9|5.00|0<br>0<br>3|100.0|3|
||10+|4.83|0<br>1<br>5|100.0|6|
||na|4.50|0<br>1<br>L|100.0|2|
|Identification of the|All|3.47|3<br>6<br>2|57.1|14|
|Firm by Foreign Buyers<br>|2-4|3.33|1<br>0<br>1|33.3|3|
|of Export agents|5-9|4.67|0<br>2<br>1|100.0|3|
||10+|3.16|2<br>3<br>0|83.3|6|
||na|2.50|0<br>1<br>0|50.0|2|
|Support by Public or|All|3.00|4<br>4<br>2|42.9|14|
|Non-ProfitAgencies|2-4|3.00|0<br>2<br>0|66.7|3|
||5-9|4.33|1<br>2<br>0|66.7|3|
||10+|3.33|3<br>0<br>2|33.3|6|
||na|1.00|0<br>0<br>0|0.0|2|
|Subcontracting|All|1.92|0<br>1<br>2|23.0|13|
|<br>Relationship with|2-4|3.33|0<br>1<br>1|33.3|3|
|Larger Exporters|5-9|1.50|0<br>0<br>0|0.0|2|
||10+|<br>1.67|a<br>0<br>1|16.7|6|
||na|1.00|0<br>0<br>0|0.0|2|
|First Export 1990 or Later||||||
|Direct Efforts by the|All|3.46|2<br>2<br>10|100.0|13|
|<br>Firm to Contact Foreign<br>|2-4<br>|2.00<br>|0<br>1<br>0<br> <br> <br>|33.3<br>|3<br>|
|Buyers or Export Ageints|5-9<br>|4.00<br>|0<br>0<br>3<br> <br> <br>|75.0<br>|4<br>|
||10+|5.00|0<br>0<br>2|100.0|2|
||na|3.25|3<br>1<br>0|25.0|4|
|Identification of the|All|3.67|3<br>3<br>4|53.8|12|
|Firm by Foreign Buyers|2-4|3.33|0<br>0<br>2|56.7|3|
|<br>of Export agents|5-9.|4.00|0<br>1-<br>1|66.7|3|
||10+|<br>4.50|0<br>1<br>1|66.7|2|
||na|3.25|3<br>1<br>0|25.0|4|
|Support by Public or|All|1.83|3<br>1<br>0|7.7|12|
|Non-Profit Agencies|2-4|2.67|2<br>0<br>0|0.0|3|

38
the number of CFLs in a house at the follow-up survey, controlling for the number of CFLs distributed to the households at baseline. We utilize the spatial variation in exposure of control households to close treated neighbors induced by the two-stage randomization to account for potential peer effects in technology adoption as follows: where the new term _C_ is an indicator for whether the household _i_ is a control household _ig_ with at least one treated household within 100 meters from the house; _Nd_ is the total number of households within 100 meters from the house;<sup>58</sup> and _Xig_ is a vector of controls, including the number of CFLs the household received from our project at baseline. Standard errors are clustered at the transformer level.

Table 4 reports the estimated impacts on CFL adoption at the follow-up. Column 1 uses the basic specification from Eq. 1. As before, these results are expected to be biased because the omitted group comprises all control households and some of them are potentially contaminated by spillovers. Column 2 shows results from Eq. 4, which accounts for spillovers by controlling for whether a control household is within 100 meters from any treated household; and Column 3 mirrors the alternative specification given by Eq. 2, which corrects for contamination by including a dummy variable for control households in treated transformers. Their corresponding omitted groups are detailed at the bottom of each column.

Results from Columns 2 and 3 are similar, as they should be, given that households within the same transformer are in close proximity spatially.<sup>59</sup> Both treated and control households have an average of half an additional CFL at follow-up, controlling for the CFLs distributed by our intervention. This result contrasts with the basic specification’s much smaller and insignificant estimated impact, making clear that spillovers also bias the basic estimates of the effect on later take-up. Interestingly, the correct estimated impacts suggest that treatment households likely purchased CFLs themselves between baseline and follow-up, in addition to those they had received from our project. The correct estimates also indicate that, indeed, there is a positive and statistically significant relationship between being close

> 58We consider a control household to be “close” if the entrance is within 100 meters of a treated household. We choose 100 meters as the cutoff, as some prior analyses of peer effects on a number of outcomes variables had suggested that this distance was an important cutoff, past which the effect dissipates.

> 59This reassures that the impacts on household electricity consumption reported in Table 1, Columns 2 and 3, are well estimated.

24
# **TALL CLAIMS:**

# **MORTALITY SELECTION AND THE HEIGHT OF CHILDREN**

_Harold Alderman, Michael Lokshin and Sergiy Radyakin_<sup>_*_</sup>

**Keywords** : Mortality, nutrition, children, India

**JEL** : J12, I32, O12

> * Harold Alderman, World Bank, 1818 H Street, NW, Washington DC, 20433, MSN MC3-306; <u>halderman@worldbank.org. Michael Lokshin, World Bank, 1818 H Street, NW, Washington DC, 20433,</u> MSN MC3-306; Sergiy Radyakin, World Bank, 1818 H Street, NW, Washington DC, 20433, MSN MC3306. The authors are grateful to Jishnu Das, Monica Das Gupta, and Adam Wagstaff for comments on an earlier draft. The findings, interpretations and conclusions of this paper are those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
Table 10: Impacts on overall MFI borrowing (credit bureau data)

||(1)<br>Amount from any MFI|(2)<br>Amount from partner MFI|(3)<br>Amount from other MFIs|
|---|---|---|---|
|SL - Subsidy eligible|10665.6<sup>_∗_</sup>|7106.4<sup>_∗_</sup>|3559.2|
||(6040.8)|(4254.5)|(3622.8)|
|SL - Subsidy ineligible|-767.6|109.3|-876.9|
||(6583.9)|(4975.2)|(4600.7)|
|Subsidy eligible|503.6|1657.8|-1154.2|
||(5846.6)|(3666.7)|(4040.4)|
|Strata FE|Yes|Yes|Yes|
|~~F~~<br>~~i~~nteraction|0.210|0.249|0.450|
|controlmea~~n~~<br>~~n~~otel|82820.6|47361.7|35458.9|
|controlmea~~n~~<br>~~e~~l|83322.1|50388.5|32933.6|
|N|865|865|865|

_Notes_ : Sample restricted to matched MF client households not having a toilet at baseline survey and for which credit bureau data is available; Dependent variables are total amounts borrowed (1) from any MFI, (2) from our partner MFI in particular, and (3) from all MFIs other than our partner MFI; Amounts are in Indian Rupees (1 USD = Rs. 71.2); SL stands for sanitation loan treatment arm; Robust standard errors clustered at the village level are shown in parentheses; *, **, *** indicates significance at the 10, 5 and 1 percent level; F interaction stats show p-value of F-test of equality of subsidy-eligible/ineligible interactions with SL; Covariates: Indicator for presence of a child aged 0-2 at baseline, ratio between number of sampled clients and village size, indicator for whether there is any uncertainty around the matching quality; Strata and interviewer fixed effects included; Table 11: Impacts on partner MFI borrowing for different loan types (MFI admin data)

||(1)<br>Sanitation|(2)<br>Business|(3)<br>Education|(4)<br>Emergency|(5)<br>Consumption|
|---|---|---|---|---|---|
|SL - Subsidy eligible|2464.8<sup>_∗∗∗_</sup>|2853.4|1375.6|323.6<sup>_∗∗_</sup>|161.0|
||(717.3)|(3210.4)|(1114.9)|(153.7)|(153.8)|
|SL - Subsidy ineligible|3198.5<sup>_∗∗∗_</sup>|600.5|-3581.3<sup>_∗∗∗_</sup>|-122.0|-51.89|
||(828.0)|(4011.0)|(1314.2)|(215.5)|(134.5)|
|Subsidy eligible|802.4<sup>_∗∗_</sup>|1192.8|-1353.8|-215.2|-13.24|
||(397.0)|(2956.0)|(1059.3)|(157.9)|(127.6)|
|Strata FE|Yes|Yes|Yes|Yes|Yes|
|~~F~~<br>~~i~~nteraction|0.336|0.608|0.00255|0.0308|0.254|
|controlmea~~n~~<br>~~n~~otel|0|36034.1|8024.4|775.6|775.6|
|controlmea~~n~~<br>~~e~~l|218.7|39317.8|7148.7|586.0|586.0|
|N|954|954|954|954|954|

_Notes_ : Sample restricted to matched MF client households not having a toilet at survey baseline; Data missing for 5 observations; Dependent variables are amounts borrowed for different purposes, coming from MFI administrative data; Amounts are in Indian Rupees (1 USD = Rs. 71.2); SL stands for sanitation loan treatment arm; Robust standard errors clustered at the village level are shown in parentheses; *, **, *** indicates; significance at the 10, 5 and 1 percent level; F-interaction stats show p-value of F-test of equality of subsidy eligible/ineligible interactions with SL; Covariates: Indicator for presence of a child aged 0-2 at baseline, ratio between number of sampled clients and village size, indicator for whether there is any uncertainty around the matching quality; Strata and interviewer included 37
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-51-

Accunting Procdure and the auditing provisions of the MC, provide for Contractor's cost recovery, which, as earlier explained, may be bid and negotiated within the acceptable ranges or levels of percentages of cost oil established in the Rep; (b) detail the manner in which operating, capital and pre-commercial production costs and expeses shall be allocated and recovered against cost oil; and (c) set out - in proportions to be bid and negotiated within the acceptable ranges established in the Regs - the shar of profit oil due to the State or CA and the Contractor, based on average daily production over a contract month from the Contract Area<sup>3</sup> '. Similar such provision will also be made for natural gas in a modern contractual framework"'.

(7) Taxes and Duties - Once again, under a sliding-scale PSC MC - where Contract-or's taxes are deemed-paid within its progressive (or regressive) production shares - the CA, on behalf of the State, will undertake to hold and keep Contractor harmless from all present and future host State taxes and duties otherwise generally applicable to petroleum operatione', except 3A. a provision wil **appear** In this arfcbtothe effctth prior to the commencementof cmmercliproductio, theCande CotC actorwillare on a Uingp pocedure fortheirrespective prmduction shares on a reular bab. Some MCwildebil the **sentW** pwixons ofsuch a lifing procedure white othes wIv go furthrand attach a hmf lNtig aWemennt n **an** appendbL 40As eabr nentioned In Sction A. above, In host States whch are known or expected to be paricularly gapons, bodh th PL and the MC wi contain a parate article providing for special inontve and priorile for Contractor to duvlop such retua gas. **Pleas** m footnots 21., above, for geter detail.

4t ib ntrng to note here that, aftr seerl year of uncertainty under an essentially unworkable licensing and tax systm, te Russian Federation, by a Psidential Decre of Dem ber 24, IMfi, **Intibtd** do **optin oda** PSC, under whih fe producton shaing aangemo nt neotiabd beten the patis wi1N be In Ibu of all axe oherwls payabe on Prn op.