rafmacalaba/datause-encoder-data huggingface dataset:
throughout the day prior to the survey day. We develop our own questions around selfworth rather than employing the more standard Rosenberg Self-Esteem Scale, which we found inappropriate given the Rohingya’s recent experiences. Specifically, we construct an index of self-worth from two questions designed to elicit respondents’ beliefs about how they contribute to their family and community. Finally, we adapt the Cantril Self-Anchoring Striving Scale (Cantril, 1965) to measure how stable respondents feel in their present lives and in the future.

We additionally examine the impacts of each treatment on physical health, cognitive function, economic decision making, time-use, and consumption. We capture respondents’ sense of physical health by asking how many days they have fallen sick in the past thirty days and cognitive function by employing a digit-span memory test and a series of basic arithmetic problems. We explore economic decision making along two dimensions: incentivized time preferences (Andreoni and Sprenger, 2012; Gin ́e et al., 2018) and incentivized risk preferences (Holt and Laury, 2002). We measure time-use through the number of hours in the previous day a respondent reports spending idle, as well as the amount of time spent on a variety of other common activities one might do in the camps (including bathing, market, chores, collection of rations, eating, child-rearing, sitting at tea stalls, praying, sleeping, visiting friends/relatives, playing games, playing sport, sitting idle). Finally, we ask respondents how much they consume, borrow and save over the past week.

We further consider changes in perceptions on gender and power in two ways. First, we generate a Household Power Index, composed of a set of questions on perceptions of gendered decision-making and intimate partner violence. The questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block.

Each outcome is described in greater detail in Appendix C. The frequency with which each outcome is collected is also presented in Appendix C.

**Multiple hypothesis testing** We utilize two approaches to address the issue of multiple hypothesis testing. First, we present our primary outcome, psychosocial well-being, as an inverse-covariance weighted index variable following Anderson (2008). We also generate index variables for other outcomes in which this is possible, such as the cognitive index, the household power index, and the work rights index. Our second strategy is to report the sharpened False Discovery Rate (FDR) q-values for all outcomes within a particular table, which control for the expected proportion of rejections that are type I errors, likewise

12
(2015) examine the impact of electronic billing machines on tax compliance in Ethiopia and Rwanda respectively. While much of the existing literature across sectors indicates positive effects, this paper highlights the fact that technology may have heterogenous effects on users based on their compliance behavior prior to the introduction of the technology. Further, when adoption is voluntary, firms and individuals on whom the technology may have the greatest monitoring impact may be the least likely to adopt.

Second, this paper contributes to the rapidly growing literature on taxation in developing countries. To date, this literature has focused largely on taxpayer responses to different types of messaging, third-party reporting and tax policy changes (See Slemrod, 2017 for a review), devoting less attention to interactions between tax officials and taxpayers. Notable exceptions are Khan et al. (2016) and Amodio et al. (2017), who study the impact of performance pay for tax collectors on revenue and bribe payments. Whereas those interventions sought to change the incentives of tax collectors, this paper focuses on changing the opportunities for collusion and extortion between tax officials and taxpayers. Third, and more broadly, this paper contributes to our understanding of corruption and of the effectiveness of strategies to reduce it (see Olken and Pande, 2012 for a review). Similar to Sequeira and Djankov (2014), it differentiates between collusive and coercive corruption and the differential response of firms to both.

Lastly, this study provides some of the first evidence on the causal impact of e-filing. The only other study we are aware of is Kochanova et al. (2016) which uses variation in when different countries introduced e-filing and firm-level outcome data from different waves of the World Bank Enterprise Surveys to determine that, on average, e-filing reduces compliance costs and bribe solicitation, but provides no results on the impact on tax revenue. By using firm-level variation in e-filing adoption that arises from a randomized experiment, this paper avoids endogeneity bias that may be otherwise present.

6
**Table 1: Means Tests on Small Business Lending Shares**

||**Argentina**<br>Mean<br>(in%)|**Chile**<br>Mean<br>(in°/O)|**Colombia**<br>Mean<br>(in%)|**Peru**<br>Mean<br>(in**°/O)**|
|---|---|---|---|---|
|All Domestic Banks|25.7|29.8|1.9|23.8|
|AllForeign Banks|19.7|24.1|1.8|17.9|
||(6.293)***|(5.574)***|(1.087)|(4.608)***|
|LargeDomesticBanks|16.9|19.3|1.0|15.8|
|LargeForeignBanks|9.9|24.3|1.3|10.2|
||(6.451)***|-(1.577)|-(3.783)**|(3.774)***|
|SmallDomesticBanks|26.4|30.5|2.0|24.6|
|SmallForeign Banks|21.8|24.1|1.8|18.2|
||(4.324)***|(6.132)***|(1.065)|(4.905)***|

# Null hypothesis: Mean(domestic)-Mean(foreign)=0

t-stats are in parenthesis

*, **, *** denote significance at the 10%, 5%, and 1% level, respectively

**Table 2:** Means Tests **on** Small **Business Lending Growth Rates**

||**Argentina**<br>Mean<br>(m%)|**Chile**<br>Mean<br>(in°/O)|**Colombia**<br>Mean<br>(in%/O)|**Peru**<br>Mean<br>(in%)|
|---|---|---|---|---|
|All DomesticBanks|8.1|7.2|-12.9|-2.8|
|AllForeign Banks|-3.8|0.0|-22.9|19.5|
||(3.326)***|(2.798)***|(1.604)|-(4.281)**|
|LargeDomesticBanks|14.9|13.8|-24.7||
|Large ForeignBanks|27.0<br>-(2.142)**|-0.2<br>(1.399)|-2.1<br>-(1.603)||
|SmallDomestic Banks|7.6|6.8|-12.1|-1.9|
|SmallForeign Banks|-6.5|0.0|-23.8|19.5|
||(3.659)*|(2.569)**|(1.794)*|-(3.886)*|

Null hypothesis: Mean(domestic)-Mean(foreign)=0 t-stats in parenthesis

*, *, *** denote significance at the 10%, 5%, and 1% level, respectively

24
Table 9. SIMULATION RESULTS - EFFECTS ON REVENUES

|INCREASE|IN<br>BY<br>|I<br>(1|)<br>|1<br>(2|)<br>|1<br>|(3)<br>|1<br>(|4)<br>|1<br>(|5)<br>|1<br>|(6<br>|1<br>|17<br>l|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Years of|Schooling||2||||||||||||2|
|Monthly<br>H|ours||||100||||||||100||100|
|Expenses|S||||||1000||||500||500||500|
|Total Cap|ital<br>$||||.I||||1000||500||500||500|
|_||<br>INobs|-I<br>S|<br>Nobs|S|Nobs|S|.<br>Nobs|S|Nobs|S|Nobs|S|Nobs|_S_|
|**Glven to**|AllFLrmsb<br>**Y**|||||||||||||||
|All En|terprises<br>|11098|103|1098|187|1098|1079|1098|<br>3141|1098|843|1098|1100|1098|1245|
|Female|Enterprises|576|97|576|182|<br>576|988|576|<br>323|576|828|576|1101|576|<br>1250|
|Male|Enterprises|230|112|230|237|<br>230|1136|230|<br>399|230|907|<br>230|1255|230|1377|
|Mixed|Enterprises|292|109|292|157|<br>292|1213|292|<br>231|292|820|292|1001|292|<br>1130|
|GLven to|Bottom 50 Z of Firms|||||||||||||||
|All E|nterprises|528|157|546|239|<br>549|1279|549|<br>578|755|872|845|1001|926|1039|
|Female|Enterprises|343|126|366|218|<br>361|1102|358|<br>495|471|810|514|977|538|1057|
|Male|Enterprises|100|175|127|299|<br>111|1394|125|<br>690|165|908|195|1030|209|1074|
|Mixed|<br>Enterprises|85|2601|53|243|<br>77|1944|66|<br>816|119|10701|136|1052|179|943|
|Given to|Bottom 251of Flrms|||I||l||||||||||
|All En|terprises|242|1441|274|284|<br>274|1409|274|641|446|6691|541|737|637|695|
|Female|Enterprises|180|119||197|257|<br>163|1176|182|588|281|5671|347|650|411|6151|
|Male|Enterprises|40|199|67|363|<br>71|1449|72|671|109|7291|131|843|145|8321|
|Mixed|Enterprises|22|248|10|267|<br>40|2285|20|1011|56|1068|63|998|81|853|
|Givento|Top<br>25 2 of Firms||||||||||I||||I|
|All En|terprises|146|42|274|115|274|978|274|58|421|3341|522|330|577|309||
|Female|Enterprises|47|32|75|104|81|973|82|49|138|296|181|270|210|239|
|Male|Enterprises|36|40|27|114|61|988|59|58|96|328|108|321|121|2991|
|Mixed|Enterprises|63|<br>50|172|120|132|<br>975[|133|<br>641|187|3641|233|3821|246|<br>3741|

Notes: The bottom 50X refers to those firms that have less than the median value of the variables) of interest for all firms. For example, if $1000 of expenses is provided, then only those firms with less than the median value of expenses ($806) receive the $1000. The same applies to the bottom 25X and the top 252. The median and quartile values are:

||Median|Bnttom 25X|Top 252|
|---|---|---|---|
|Years of Schooling|<br>7.0|4.0|10.0|
|Monthly Hours|234.0|113.6|386.3|
|Expenses|$ 805.6|247.2|2478.3|
|Total Capital|$ 967.7|170.5|4337.8|

cooperative child care and facilities for meals. This experiment suggests that revenues will increase by a respectable 200 to 300 intis a month, especially among firms in the bottom 50 and 25 percent (in terms of labor).

The third simulation lends 1,000 intis to selected firms for operating expenses. The results are impressive. If all firms receive the loan, on average 68
a sequence of four proposals. The order in which the the four proposals were presented was randomized at the PSU level by producing multiple versions of the same video. This procedure aimed to avoid any systematic anchoring effect due to a particular proposal order. The facilitator’s role was to distribute and collect decision cards and questionnaires, answer questions according to a pre-developed answer script, and lead the focus group discussion that followed the experiment.

# 2.4 The transparency-enhancing treatment

Treatment status was assigned at the PSU level and the sample of 20 individuals in each PSU was randomly divided into two groups of equal size: treatment and control. After the random assignment, the experiment was started simultaneously in two separate rooms, one room with individuals in the control group and another room with individuals in the treatment group. In each PSU we implemented the experiment only once. Hence, the total sample contained 420 individuals in 21 PSUs: 210 individuals in the control group and 210 individuals in the treatment group.

The video for the treatment groups contained all the features of the video for the control groups, but it included additional information that would make the transfer delivery to the poor and content more transparent for participants. In particular, individuals in the treatment group were offered the option to accompany the facilitator after the experiment to witness the actual implementation of the proposal among poor families, if the randomlyselected decision was an acceptance of the proposal. To reinforce this message, right before participants were asked to make their decisions on each proposal, they were told that the facilitator would wait after the conclusion of the experiment for anyone who wanted to follow and witness the implementation of the proposal.<sup>16</sup> In addition, participants in the treatment groups were shown in the video a basket of essential supplies worth JD 20, as in proposal 2 (unconditional in-kind transfer). Thus, the treatment increased transparency of the redistributive proposals by alleviating participants’uncertainty about the delivery of the transfer to the intended beneficiaries and about the value of the JD 20 in the case of an unconditional food transfer. The treatment was chosen as a result of a focus group on the perceived barriers to redistribution as well as consultations with Jordanian experts to offer concrete recommendations on measures to implement as part of a fuel subsidy reform.

# 2.5 Data

The data used in this paper were collected between late May and June 2012. The quantitative data from Jordan Gives includes decisions made by each individual participant

> 16 Indeed, in some cases, participants in the treatment group did decide to follow the facilitator and, as highlighted by participants in the focus group discussion, the mere availability of this option sent a credible commitment signal of trustworthiness.

9
Policy Research Working Paper 10881

# **Abstract**

Current investment trends in emerging market and developing economies are not enough to meet the needs of their growing populations and will fall short of achieving the Sustainable Development Goals related to human and physical capital development. Public investment can play a critical role in addressing this shortfall, especially if it can crowd-in private investment. Using theory and panel data for 109 developing countries from 1980–2019, this paper investigates whether public investment crowds in or crowds out private investment. The paper also explores how the relationship changes across different groups of countries and under different institutional settings. The analysis uses changes in predicted disbursements on loans from official creditors to developing country governments as an instrument for changes in public investment. The findings show that public investment is a complement to private investment, raising the marginal productivity of the latter. As a result, an extra dollar of public investment raises private investment by 1.6 dollars. The findings also reveal stronger evidence of crowding-in of private investment in low-income countries and Sub-Saharan Africa, where investment needs are greatest. Finally, the findings are embedded in a model with imperfect capital markets, which shows that public investment can be used as an effective vehicle to address underinvestment issues induced by capital market distortions.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jfrancois1@worldbank.org, mkonte@ifc.org, or fruch@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 10334

# **Abstract**

Analyzing data from four waves of the Nigerian General Household Survey and the Nigerian Multiple Indicator Cluster Survey, covering the period from 2010 through 2019, this study provides evidence that poverty levels of children exceed those of adults. Overall, rural children throughout the country and children in the North face higher poverty and chronic poverty rates than urban children and those living in the South without clear trends of a closing of those gaps. These findings hold for monetary poverty as well as, for severe health, education, food, shelter, water, information deprivation and improved sanitation deprivation across Nigeria’s six regions. One exception is severe sanitation deprivation, for which especially rural areas in the Southwest stand out with higher levels of severe sanitation deprivation than in rural areas in the north and any other region. Large inter-state heterogeneity of estimates within regions, ranging up to 50 percentage points, for all except severe food deprivation however highlight the importance of looking beyond regional poverty estimates and regional differences. Only state specific, but no systematic evidence has been found for a gender difference in severe educational deprivation and school enrollment rates. Existing gender gaps though seem negligible compared to the overall level of deprivation and urban-rural and northsouth gaps. Moreover, the parents’ literacy and more so the educational level is highly correlated with the probability of being poor or deprived in any dimension, in particular in rural and northern areas. Interestingly, up to about half of the monetary non-poor children at the top of the consumption distribution still face at least one severe deprivation.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at at mazad@worldbank.org and cschimanski@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _26_

- Reinsdorf, M., Triplett, J. E., Reinsdorf, M., and Triplett, J. E. (2009). A Review of Reviews: Ninety Years of Professional Thinking About the Consumer Price Index. In _Price Index Concepts and Measurement_ , pages 17–83. National Bureau of Economic Research, Inc.

- Rubin, D. B. (1976). Inference and missing data. _Biometrika_ , 63(3):581–592.

- Rubin, D. B. (1996). Multiple Imputation After 18+ Years. _Journal of the American Statistical Association_ , 91(434):473.

- Sbahi, S., Ouazzani, N., Hejjaj, A., and Mandi, L. (2021). Neural network and cubist algorithms to predict fecal coliform content in treated wastewater by multi-soil-layering system for potential reuse. _Journal of Environmental Quality_ , 50(1):144–157.

- Seabold, S. and Coppola, A. (2015). Nowcasting prices using Google trends : an application to Central America. _Policy Research Working Paper Series_ .

- Seaman, J. and Holt, J. (1980). Markets and Famines in the Third World. _Disasters_ , 4(3):283–297.

- Shephard, N. (1994). Partial non-Gaussian state space. _Biometrika_ , 81(1):115– 131.

- The World Bank (2019). _Inflation in Emerging and Developing Economies: Evolution, Drivers, and Policies_ . The World Bank.

- van Buuren, S. (2012). Flexible Imputation of Missing Data. _Flexible Imputation of Missing Data_ .

- van Buuren, S. and Groothuis-Oudshoorn, K. (2011). Multivariate Imputation by Chained Equations in R. _Journal of Statistical Software_ , 45(3):1–67.

- Wan, E. A. and Van Der Merwe, R. (2000). The unscented Kalman filter for nonlinear estimation. In _IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, AS-SPCC 2000_ , pages 153–158. Institute of Electrical and Electronics Engineers Inc.

- Wang, D., Andr ́ee, B. P. J., Chamorro, A. F., and Spencer, P. G. (2020). Stochastic modeling of food insecurity. _World Bank Policy Research Working Papers_ .

- Waterlander, W. E., Jiang, Y., Nghiem, N., Eyles, H., Wilson, N., Cleghorn, C., Gen ̧c, M., Swinburn, B., Mhurchu, C. N., and Blakely, T. (2019). The effect of food price changes on consumer purchases: a randomised experiment. _The Lancet Public Health_ , 4(8):e394–e405.

- Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). _Data Mining: Practical Machine Learning Tools and Techniques_ . Elsevier Inc.

- World Bank (2021). South Sudan Economic Update, June 2021 : Pathways to Sustainable Food Security. Technical report, World Bank, Washington, DC.
are unrelated to access to safe water. Moreover, the probability of dying from diarrhea disease is the most responsive, at least in relative terms, to interventions that improve access to safe water. These findings lend support to our causal interpretation of the impact of access to safe water on reducing child mortality risk.

# **4  Conclusions**

In this paper, we have analyzed the impact of environmental factors on child mortality in rural China using a competing risk model that takes into account the cause of death. We have argued that, given the ultimate objective of predicting the total number of child deaths that would be averted by a specific policy change, information on cause of death may, in principal, be superfluous. However, we have also identified two reasons why such information might be useful in practice: first, to obtain more accurate estimates of policy impacts and, second, to validate that these estimated impacts are indeed causal.

What have we learned by estimating a competing risk mode versus a simpler _allcause_ model of child mortality? First, we find that taking into account cause of death information does not affect conclusions about the effectiveness of policy interventions on reducing child mortality risks. Second, knowledge on cause of death is particularly useful for validating causal interpretation of the effect of access to safe water on child mortality, hence increasing our confidence that we are not picking up spurious effects when modeling using _all-cause_ mortality framework. Third, the policy simulations show that policy interventions targeted at poor localities or poor households avert more U5 deaths than untargeted interventions. Fourth, the findings from this study suggest that modifying future DHS survey instruments to incorporate the collection of simple cause of death information, especially for high mortality countries, may be worthwhile.

If one accepts the analytical benefits of knowing cause of death, then it is proper to ask about the costs of accurately collecting such information. In the China survey that we analyze, cause of death is based on the mother’s report alone, prompted by a questionnaire listing the main causes and symptoms of fatal events. Verbal autopsies, though undoubtedly more accurate, are also much more expensive to implement, and 13