rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3**

||MomentumTradingRe|sults**by**Crisis||
|---|---|---|---|
||**MexicanCrisis**|**Asian**Crisis|RussianCrisis|
|**Whole-FundMomentum**||||
|UOM|12.11***|**1.69*****|**8.26*****|
|T-statistic|3.45|**2.97**|4.24|
|Observations|**268**|**920**|417|
|LIM|**1.00***|**-0.25**|0.22|
|T-statistic|**1.82**|**-0.69**|**0.57**|
|Observations|**297**|**898**|413|
|**LOM**|**_6.56**_**|**0.99****|1.04|
|T-statistic|**2.16**|**2-32**|**0.90**|
|Observations|**279**|**920**|412|
|LIM|**1.00*****|**-0.17**|-0.04|
|T-statistic|**2.71**|-0.74|-0.21|
|Observations|**297**|**898**|414|
|LOM|**_7.56**_**|**0.71****|**6.86*****|
|T-statistic|**2.38**|**2.30**|5.84|
|Observations|284|**921**|426|
|L1M|0.12|**0.00**|0.64|
|T-statistic|0.34|-0.02|**_130_**|
|Observations|294|**910**|**398**|

LOM is the point estimate for the mean lag-0 momentum trading measure. LIM is the point estimate for the mean lag-I momentum trading measure (measured from return over the previous month). Whole-Fund momentum tests whether the mean _of_ **_(AQV /q )A,._** is zero, per equation **(1).** Manager-Only momentum controls for investor redemption effects as in equation (2). Investor-Only momentum reflects only investor redemption effects as in equation **(3). All** t-statistics are corrected for heteroskedasticity across funds. The Mexican Crisis portion of the sample is December 1994-June **1995.** The Asian Crisis portion of the sample is July 1997-March **1998.** The Russian Crisis portion of the sample is August 1998-October **1998.** For robustness, results in each cell are based only on observations within three standard deviations of the mean.

- Statistically Significant at the 10-percent level

** Statistically Significant at the 5-percent level Statistically Significant at the 1-percent level

**28**
the failure of Lehman. Tong and Wei (2008) follow a similar approach to this paper, and test whether the crisis spills over to the real economy through reducing consumer demand or tightening liquidity. They look at the stock price response of non-financial firms in the US, between July 31, 2007 and March 31, 2008, and test whether a firm’s sensitivity to consumer demand or financial constraints better explains the cross-section of returns, finding that a firm’s degree of financial constraints explains a larger fraction of the stock price declines observed in the US. This paper contributes to this literature by providing systematic evidence of the role of the sources of bank financing in the international transmission of the crisis. In contrast to Eichengreen et al. (2009), this paper looks at a broad set of banks across a large number of developed and developing countries, and for the most part focuses on differences in returns across individual banks to identify a specific transmission mechanism rather than focusing on common factors and their determinants. This paper shares with Tong and Wei (2008) the use of stock market returns and ex-ante characteristics to identify the role of a transmission mechanism, but it looks at the international transmission of the crisis to the banking sector instead of the transmission to non-financial US industries, and focuses on the stock market responses to a clearly defined episode to identify the mechanism, instead of looking at the longrun performance of stock price returns.

Another recent literature closely related to this paper has focused on the role of interbank markets in the allocation of liquidity during the crisis, and on the appropriate policy interventions in these markets. Motivated by the drying up of interbank markets during the recent crisis and the lack of theoretical foundations for central bank intervention, Allen Carletti and Gale (2008) and Freixas, Martin, and Skeie (2009) present theoretical models of the functioning of interbank markets and derive optimal policies that provide and allocate liquidity among banks. On the empirical side, Brunetti, di Filippo, and Harris (2009) study the impact of central bank intervention on European interbank markets during the crisis using high frequency and find that these interventions seem to have increased the level and volatility of spreads, which they interpret as evidence of a crowding out of private liquidity. Cassola, Hortacsu, and Kastl (2009) use bidding data from the European Central Bank (ECB) auctions for one-week loans to study how the bidding schedule of banks changes during the crisis and how these changes relate to ex-post bank performance. They document that banks bid more aggressively for ECB funds after the beginning of the crisis in August 2007, and that the stock price of banks that were more willing to pay for these funds, those facing higher liquidity costs, performed relatively worse. This paper complements this literature by providing further evidence of the consequences of a drying up of interbank lending markets on banks’ performance and lending 5
**Table 5: Infant and neonatal death and drinking water salinity, Restricted Splines Model at** **<u>GWEC = 0.7, 2, 10 mS/cm</u>**

||(1)<br>_model 1_|(2)<br>**Infant**<br>_model 2_|(3)<br>_model 3_|(4)<br>_model 1_|(5)<br>**Neonatal**<br>_model 2_|(6)<br>_model 3_|
|---|---|---|---|---|---|---|
|rcgwec1|-0.0109***|-0.00694**|-0.00611**|-0.0104***|-0.00625**|-0.00582**|
||[0.00225]|[0.00292]|[0.00310]|[0.00205]|[0.00265]|[0.00280]|
|rcgwec2|0.0448***|0.0317***|0.0273**|0.0404***|0.0265***|0.0241**|
||[0.00949]|[0.0110]|[0.0116]|[0.00872]|[0.0101]|[0.0106]|
|child male||0.00462|0.00464||0.00534|0.00530|
|||[0.00395]|[0.00394]||[0.00362]|[0.00361]|
|child birth order||0.00207|-0.000792||-0.0000268|-0.00203|
|||[0.00186]|[0.00197]||[0.00167]|[0.00178]|
|maternal age||-0.00144***|-0.00103*||-0.00119**|-0.000921*|
|||[0.000545]|[0.000552]||[0.000509]|[0.000518]|
|mother's years of education|||-0.00132**|||-0.00121*|
||||[0.000673]|||[0.000623]|
|partner's years of education|||-0.000770|||-0.000306|
||||[0.000542]|||[0.000515]|
|mother currently married or living|||-0.00881|||-0.0128|
|in union|||[0.0151]|||[0.0148]|
|urban residence|||-0.000587|||-0.00230|
||||[0.00444]|||[0.00404]|
|standardized asset index|||-0.0135|||-0.0101|
||||[0.0130]|||[0.0121]|
|arsenic|||-0.0000147|||-0.00000553|
||||[0.0000245]|||[0.0000232]|
|groundwater depth|||-0.000138|||0.000263|
||||[0.000655]|||[0.000634]|
|improved household sanitation|||-0.00102|||-0.000956|
||||[0.00448]|||[0.00426]|
|basin: Ganges-Padma|||-0.00505|||-0.000849|
||||[0.00907]|||[0.00843]|
|basin: Meghna|||-0.0145*|||-0.0145**|
||||[0.00782]|||[0.00681]|
|Constant|0.0620***|0.103***|0.127***|0.0539***|0.0922***|0.112***|
||[0.00306]|[0.0153]|[0.0236]|[0.00285]|[0.0141]|[0.0224]|
|Birth Year Dummies|No|Yes|Yes|No|Yes|Yes|
|Division Fixed Effects|No|Yes|Yes|No|Yes|Yes|
|Wald-test P-value|0.00000246|0.00391|0.0187|0.00000388|0.00843|0.0224|
|R-squared|0.00133|0.00982|0.0114|0.00146|0.00959|0.0107|
|Obs.|22158|22158|22158|22158|22158|22158|

Notes: omitted group of basins is basin: Brahmaputra-Jamuna; model 1: unadjusted (no controls); model 2: adjusted for maternal age, birth order and sex of the child; model 3: additionally adjusted for both parents years of education, rural or urban residence, household wealth score , improved sanitation, maternal marital status, groundwater arsenic level, depth of tubewell, riven basin children's birth year and geographical division fixed effects 27
TABLE 6: Impact of leaders’ exposure by distance to the closest leader’s dwelling

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|**_Panel A: Leader treatment interacted with distance from closest leader_**||||
|ITT Leader|-0.168<sup>_∗∗∗_</sup><br>(0.05)|-0.126<sup>_∗_</sup><br>(0.07)|-0.195<sup>_∗∗∗_</sup><br>(0.07)|
|Distance from closest leader|-0.009<sup>_∗∗∗_</sup>|-0.017<sup>_∗∗∗_</sup>|-0.006|
||(0.00)|(0.00)|(0.00)|
|ITT leader X distance from closest leader|0.012<sup>_∗∗∗_</sup><br>(0.00)|0.022<sup>_∗∗∗_</sup><br>(0.01)|0.010<sup>_∗_</sup><br>(0.01)|
|**_Panel B: Leader treatment interacted with household treatment status_**||||
|ITT|-0.046|0.036|-0.112<sup>_∗_</sup>|
||(0.06)|(0.13)|(0.06)|
|ITT Leader|-0.151<sup>_∗∗_</sup>|0.029|-0.251<sup>_∗∗∗_</sup>|
||(0.06)|(0.13)|(0.07)|
|ITT X ITT Leader|0.060|-0.071|0.150<sup>_∗_</sup>|
||(0.07)|(0.15)|(0.08)|
|Observations|2051|624|1427|
|**_P-vales joint signifcance test_**||||
|ITT Leader + ITT Leader X ITT|0,04|0,51|0,07|

_Note:_ Distance from leader is measured in terms of the number of houses between the household and the closest household with an opinion leader. Sample includes only non-leader households in 92 villages with opinion leaders. Five villages without leaders were excluded from the estimation. All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months old and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, memory and self-control) of children aged 36 to 83 months. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender, and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1, _∗∗_ p<0.05, _∗∗∗_ p<0.01 40
**Policy Research Working Paper Series**

|**Title**<br>|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2403 Can the World Cut Poverty in Half?<br>How Policy Reform and Effective<br>Aid Can Meet International<br>Development Goals|Paul Collier<br>David Dollar|July 2000|E. Khine<br>37471|
|WPS2404 The Distribution of Mexico's Public<br>Spending on Education|Gladys Lopez-Acevedo<br>Angel Salinas|July 2000|M. Geller<br>85155|
|WPS2405 Marginal Willingness to Pay for<br>Education and the Determinants of<br>Enrollment in Mexico|Gladys Lopez-Acevedo<br>Angel Salinas|July 2000|M. Geller<br>85155|
|WPS2406 How Mexico's Financial Crisis<br>Affected Income Distribution|Gladys Lopez-Acevedo<br>Angel Salinas|July 2000|M. Geller<br>85155|
|WPS2407 Utility Privatization and the Needs of<br>the Poor in Latin America: Have We<br>Learned Enough to Get It Right?|Antonio<sup>Estache</sup><br>Andres Gomez-Lobo<br>Danny Leipziger|August 2000|G. Chenet-Smith<br>36370|
|WPS2408 What Makes Banks Special? A Study<br>of Banking, Finance, and Economic<br>Development|Biagio Bossone|August 2000|E. Mekhova<br>85984|
|WPS2409 How Did the World's Poorest Fare<br>in the 1990s?|Shaohua Chen<br>Martin Ravallion|August 2000|P. Sader<br>MC3-556|
|WPS2410 Is Functional Literacy a Prerequisite<br>for Entering the Labor Market?<br>An Analysis of Determinants of Adult<br>Literacy and Earnings in Ghana|Niels-Hugo<sup>Blunch</sup><br>Dorte Verner|August 2000|H. Vargas<br>37871|
|WPS2411 Natural Openness and Good<br>Government|Shang-Jin Wei|August 2000|H. Sladovich<br>37698|
|WPS2412 Urbanization without Growth:<br>A Not-So-Uncommon<br>Phenomenon|Marianne Fay<br>Charlotte Opal|August 2000|A. Abuzeid<br>33348|
|WPS2413 Foreign Direct Investment in Services<br>and the Domestic Market for Expertis|James Markusen<br>e Thomas F. Rutherford<br>David Tarr|August 2000|L. Tabada<br>36896|
|WPS2414 Pension Reform and Capital Market<br>Development: "Feasibility" and<br>'Impact" Preconditions|Dimitri Vittas|August 2000|A. Yaptenco<br>31823|
|WPS2415 Infrastructure Restructuring and<br>Regulation: Building a Base for<br>Sustainable Growth|Ian Alexander<br>Antonio Estache|August 2000|M. Salehi<br>37157|
# Effective Tax Rates and Firm Size

Pierre Bachas, Anne Brockmeyer, Roel Dom & Camille Semelet<sup>_∗_</sup> JEL Classification: H25, H87, O23 Keywords: Corporate Effective Tax Rate, Global Minimum Tax, Firm Size, Tax Incentives

> _∗_ Pierre Bachas: ESSEC Business School and World Bank Research, pbachas@worldbank.org. Anne Brockmeyer: IFS, UCL, World Bank and CEPR, anne.brockmeyer@ifs.org.uk. Roel Dom, roeldom@gmail.com. Camille Semelet: University of Munich, ifo Institute and World Bank, semelet@ifo.de. We gratefully acknowledge funding from the Global Tax Program and the Knowledge for Change Trust Fund. Brockmeyer’s time was partly funded by UKAID from the UK government through the Centre for Tax Analysis in Developing Countries (TaxDev) and a UKRI Future Leaders Fellowship (grant reference MR/V025058/1). The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments that they represent. All errors are our own. We thank the revenue authorities in all 13 countries for an excellent collaboration, as well as colleagues from the World Bank. In particular, we thank Antonio Giraldi, Christoph Ungerer, David Fernando Pineda Pinto, Denis Mukama, Dora A. Vivas Perez de Gonzalez, Enamorado Irias, Fernando Pelaez Longinotti, Innocente Murasi, John Karangwa, Jose C. Bermudez Sanchez, Jose F. Suriano Buezo, Marc Schiffbauer, Maria Rodriguez Quezada, Milan Lakicevic, Naomi Alexander, Pedro Rafael Zuniga Figueroa, Roldan Manuel, Sol Mascarenhas, and Theonille Mukamana. We are grateful to Alipio Ferreira, Adrienne Lees, Giulia Mascagni, Kyle McNabb, Vedanth Nair, Edris Seid, Ben Waltmann for their support in producing the analysis in selected countries. We thank Flurim Aliu, Annette Alstadsaeter, Katy Bergstrom, Michael Best, Michael Devereux, Petr Jansk ́y, Claus Thustrup Kreiner, John Loeser, Helen Miller, Vedanth Nair, Oyebola Okunogbe, Steven Pennings, Imran Rasul, Mahvish Shaukat, Joel Slemrod, Gabriel Zucman and Davide Zufacchi as well as seminar/conference participants at the ifo Institute, IIPF, PSE Summer School, TaxDev, University of Munich and the World Bank for helpful comments.
( _≈_ 1 _−_ (2 _._ 4 _/_ 2 _._ 9)<sup>2</sup> ) reduction in variance. Without further information, however, it is hard to determine how much of this cost-precision trade-off can be attributed to the fact that their double sampling estimator substituted predicted data for real data.<sup>15</sup> It is unlikely that the gains are due to a favorably low level of _τ_ as _τ_ is estimated to be around 0 _._ 6 in their case, which is in the mid range. If the gains can indeed be attributed to the use of predicted data, then it is more likely that this is due to favorable values of _wh_ and _wc_ .

# **5 Discussion**

The primary motivation for the use of prediction in economics, health sciences, and other disciplines has been to deal with various forms of missing data problems. One could also make a case for adopting prediction estimators to obtain more cost-efficient estimates of the population mean when it is expensive to observe the outcome of interest in comparison with its covariates. For example, consider the estimation of poverty and malnutrition rates. The conventional sample direct estimators in this case require household- and individual-level data on expenditures and health outcomes. Collecting this data is generally costly. It is not uncommon that in developing countries, where poverty and poor health outcomes are most pressing, statistical agencies do not have the budget that is needed to collect these data frequently. As a result, official estimates of poverty and malnutrition are often outdated. This then makes it difficult to monitor progress (or lack thereof) in times when circumstances might be subject to considerable change, such as shocks to international staple prices, domestic climate shocks, etc. Using predicted data as a substitute for real data may then offer a valuable alternative.

In recent years, a number of studies have explored the option of predicting household expenditure data into existing secondary surveys in an effort to supplement existing poverty estimates and increase their frequency (Stifel and Christiaensen, 2007; Douidich et al., 2015). Douidich et al. (2015) for example considers the Labour Force Survey as their secondary survey, which is often available at a higher frequency than household expenditure surveys.

There is also a large literature that predicts household expenditure data into the population census, see for example Elbers et al. (2003) and the references therein. The objective here is to

> 15Also, the study does not report the total cost of data collection, but selected cost components instead.

29
Figure A.12: The Impact of Reductions in Commission Fees and Tax Withholding Rates Difference-in-Difference Estimation on Monthly Data

# A. Number of Card Transactions

<!-- Start of picture text -->
-10 -5 0 5 10<br>Months to Reform<br>Retail Wholesale<br>OLS DD (Log): 0.036 (0.014). N Retailers=4352, N Wholesalers=821<br>B. Volume of Card Transactions<br>-10 -5 0 5 10<br>Months to Reform<br>Retail Wholesale<br>OLS DD (Log): 0.031 (0.016). N Retailers=4352, N Wholesalers=821<br>C. Having a POS<br>-10 -5 0 5 10<br>Months to Reform<br>Retail Wholesale<br>OLS DD (Log): 0.011 (0.002). N Retailers=8662, N Wholesalers=5552<br>1.1<br>1.05<br>1<br>Normalized Log Outcome<br>(Relative to Pre-Reform Average) .95<br>1.06<br>1.04<br>1.02<br>1<br>Normalized Log Outcome<br>(Relative to Pre-Reform Average) .98<br>1.1<br>1.05<br>1<br>Normalized Log Outcome<br>(Relative to Pre-Reform Average) .95<br><!-- End of picture text -->

Notes: These graphs implement a DID estimation similar to the one from Section 5.1, equation 2, except that the post-reform period starts in January 2012, when withholding rates applied by credit/debit card companies on card purchases from non-CEDE firms were reduced (see Figure A.7) and commissions charged by credit/debit card companies were lowered (see Section 3.3). This figure is discussed in Section 6.

46
system as well as the expansion of specific banks (e.g., Equity Bank) and ownership characteristics of banks (e.g., private domestic banks, foreign banks and government banks).

As shown in Table 1, the Kenyan banking system expanded greatly in terms of the number of branches in recent years. Between 2006 and 2009, the total number of bank branches in Kenya increased from almost 600 to almost 1,000 (a 68 percent increase). The expansion involved all ownership categories of banks, especially domestic private banks, among which Equity Bank played an important role. The number of Equity Bank’s branches increased from 44 to over 110 in the period 2006-2009, representing an expansion of 155 percent. No other bank experienced such expansion, in terms of the number of branches, in the same period. The number of bank branches of Barclays and Kenya Commercial Bank (i.e., the banks with the largest branch networks together with Equity Bank) grew only 92 percent and 44 percent, respectively. As Table 2 shows, the strong branch expansion in Kenya occurred not only in urban districts but also in rural, arid and semi-arid districts.

Our data on bank branches across districts are augmented by the FinAccess’ households surveys. The 2006 surveys consist of 4,420 completed interviews, while the 2009 surveys consist of 6,598 completed interviews. The sample was constructed using a stratified three-stage design. In the first stage, the number of households to be allocated to each district was assigned. In the second stage, clusters were randomly selected from the district with each cluster comprising an equal sample of 10 households. Finally, in the third stage, individuals within the household were randomly selected based on a listing of all household members aged 16 years and above, using the Kish Grid-diagram.<sup>4</sup> We use the FinAccess surveys in our analysis for a number of reasons. _First_ , it captures important dimensions of financial inclusion as it was specifically designed to measure access to financial services in Kenya. _Second,_ it also provides information on individual and household characteristics, such as education level, gender, asset ownership, income source, age group, language, location and household size. _Third,_ it is a nationally representative household survey that covers the universe of districts in Kenya, alleviating potential sample selection bias problems. _Fourth,_ its implementation over different years allows us to explore the within-district time variation in financial inclusion.

> 4 The Kish Grid-diagram is a method that uses a pre-assigned table of random numbers for selecting individuals within a household to be interviewed.

7
where _YL_ , _j_ () _i_ is a binary variable taking the value 1 if the individual i speaking language L and residing in municipality j participates in the specific activity _Y_ , α 0 , α , β , and γ are parameters to be estimated, _SN L_ , _j_<sup>and</sup> _CAL_ , _j_ , respectively, are the social network and contact availability variables defined above, and _X_ () _i_ is a set of variables summarizing observed individual characteristics. The term δ _L_ denotes the fixed effects for (or set of binary variables used to identify) the language group of the individual, whereas θ _j_ denotes the fixed effects for the individual’s municipality of residence. The language-specific fixed effects absorb all the observed and unobserved characteristics of each language group, such as different levels of discrimination, cultural endowments, and other ethnic-specific attributes different than those related to the networks. The municipality-specific fixed effects absorb all the observed and unobserved characteristics of municipalities, such as access to infrastructure, distance from urban and commercial centers and job opportunities, among others. The last term, ε _L_ , _j_ () _i_ is an error term summarizing the role unobservable variables including individual participation in activity _Y_ .

Our empirical analysis of the role of social networks is based on the 2000 Census data from Mexico. One advantage offered by the Census data is that their large sample sizes allow one to construct reliable measures of the quantity and quality of the networks as defines above. This advantage, however, comes at a cost since Census data contain only a limited number of interesting economic activities. With these caveats in mind, the a set of variables summarizing observed individual and household characteristics, i.e., _X(i)_ , consists of a person’s age, years of schooling, marital status, whether the person speaks Spanish in addition to an indigenous language, characteristics of the residence, (family size, number of rooms, dirt floor, no access to 7