rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 34. Effects of change in NZS floor on balanced budget tax revenue versus constant tax rate tax revenue**

with respect to NZS floor of 65% and 52.5% of after-tax average earnings (from the top) <!-- Start of picture text --> 40<br>39<br>38<br>37<br>36<br>35<br>34<br>33<br>% of GDP<br>32<br>31. . . . . .<br>30<br>29 - -<br>28 . ::<br>96197 00/01 10/11 20/21 30/31 40/41 50/51<br><!-- End of picture text -->

# **_3.5.4. Conclusions from sensitivity analysis_**

What conclusions can be drawn about the sensitivity of the future fiscal position of the New Zealand government?

_1. Sensitivity to a gap between productivity growth and real growth per capita in health spending_ is very strong. A long-term economic slow down without a proportionate reduction in health and other social spending would cause a significant 24 aggravation in the fiscal position. Analysis of the historical and international data on trends in social spending may indicate the expected urgency of this matter.

_2. Sensitivity to net migration_ is strong, suggesting that adoption of a sensible migration policy with respect to the age, skills, fertility, and mortality profiles of potential immigrants could partly offset future demographic pressures.

24 Optimally, the linkage between social spending and productivity growth would be developed across business cycles. A year-by-year indexation of social spending to productivity growth would be sub optimal with respect to the demand for social spending, which rises during recession.

38
# **S4 VALIDITY CHECKS**

In order for an instrument to be valid it must be exogenous and relevant. I discuss both assumptions in this section.

# **S4.1 Instrument’s validity**

Variations in unanticipated mining revenues do not lead directly to an increase in economic activity in the areas where the resource is located. I test that changes in unanticipated mining revenues do not impact changes in consumer price indexes (CPI) for commodities that could affect child well-being (such as food, medicines, and health care) nor do they modify changes in net per capita income. I do so by running panel data models of changes in unanticipated mining revenues on changes in CPI for all these commodities as well as for changes in net per capita income. I use region-year data and I control only for changes in public spending and a year fixed effect. No statistically significant impact is found for any of the specifications ( **Table 2** ).

Secondly, unanticipated mining revenues could be thought to affect health status directly. In order to test this hypothesis I use region-year data and run a panel model. The dependent variable is a change in the probability of a person suffering a chronic illness and the independent regressor is the change in the level of mining revenues. I only control for changes in public spending, regional debt and a year fixed effect. Changes in unanticipated mining revenues do not have a statistically significant impact on changes in the probability of suffering a chronic illness. These results are shown in the last column of **Table 2** . In addition, I used individual data from the Young Lives Study sample of children to verify that changes in unanticipated mining revenues do not modify the predicted probability of suffering a self-reported life threatening illness. Again I found that unanticipated mining revenues do not have a statistically significant effect on changes in this probability as shown in **Table 3** .

16
PROVISION OF WATER TO THE POOR IN AFRICA

**Table 1.1   Slum and unconnected population sorted by conflict incidence**

|Conflictindex|Country|(% oftotalurbanpop|
|---|---|---|
||DRC|50|
|High|Sudan|86|
||Uganda|93|
||Angola|83|
||Ethiopia|99|
||Mozambique|94|
|Medim|Rwanda|88|
|u|Côte d'Ivoire|68|
||Namibia|38|
||Chad|99|
||Kenya|71|
||Lesotho|57|
||Nigeria|79|
||Ghana|70|
||Benin|84|
||Burkina Faso|77|
||Niger|96|
|L|Senegal|76|
|ow|South Africa|33|
||Malawi|91|
||Zambia|74|
||Cameroon|67|
||Cape Verde|70|
||Madagascar|93|
||Tanzania|92|
||Mauritania|94|
|Average||77.73|
|Highconflictindex||78|
|Mediumconflictindex||81|
|Lowconflictindex||76|
|Source: UN Habitat 2005; AICD|DHS/MICS Database, 2007.||

* 2001 data ** latest <u>available data.</u>

2
Table D.2: Program impacts on self control skills, 12–13 month survey only

||||I|TT regres|sion (N=94|3)||
|---|---|---|---|---|---|---|---|
||Control|Ther|apy only|Cas|h only||Both|
|Outcome|mean|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Summary index of self-control skills|-0.070|0.159|[.090]*|-0.025|[.095]|0.244|[.095]**|
|Impulsiveness|0.082|-0.178|[.096]*|0.006|[.098]|-0.212|[.099]**|
|I buy things quick without thinking|1.246|-0.097|[.089]|0.012|[.091]|-0.082|[.092]|
|I can take action before thinking (-)|0.931|-0.025|[.091]|0.140|[.092]|-0.082|[.090]|
|I can just talk without thinking|0.744|-0.072|[.082]|0.021|[.086]|-0.208|[.082]**|
|I am not set or relax at lectures|1.153|-0.124|[.095]|-0.035|[.097]|-0.129|[.099]|
|I can catch hard time thinking|1.271|-0.074|[.093]|0.014|[.096]|-0.275|[.095]***|
|I believe in the present rather than the<br>future|1.650|-0.150|[.105]|-0.190|[.106]*|-0.063|[.112]|
|I able to control myself (-)|0.596|-0.051|[.071]|0.066|[.078]|0.020|[.078]|
|I spend money on things and regret it|1.848|-0.045|[.073]|0.017|[.074]|0.047|[.075]|
|later||||||||
|Conscientiousness|0.018|-0.065|[.097]|-0.028|[.100]|0.044|[.097]|
|I am ready anytime|2.148|-0.267|[.067]***|-0.146|[.066]**|-0.215|[.067]***|
|I pay attention to things good good|2.305|-0.046|[.050]|-0.054|[.051]|-0.038|[.051]|
|I get everyday work done right away|2.044|0.006|[.062]|0.037|[.063]|0.108|[.065]*|
|I make plans and go by them|2.177|0.120|[.057]**|-0.011|[.057]|0.054|[.059]|
|I catch hard time to do my work (-)|1.704|-0.076|[.070]|-0.059|[.071]|-0.030|[.073]|
|I do unasked additional work after|1.468|-0.002|[.069]|0.075|[.072]|0.046|[.073]|
|fnishing my work.||||||||
|I can’t complete/fnish things (-)|1.970|0.033|[.056]|0.026|[.060]|0.054|[.057]|
|I run away from work (-)|1.788|0.079|[.063]|0.066|[.062]|0.125|[.063]**|
|Perseverance/GRIT|-0.037|0.116|[.099]|0.057|[.099]|0.105|[.103]|
|I have overcome hard times to subdue an|2.020|-0.047|[.063]|-0.009|[.063]|0.060|[.068]|
|important challenge||||||||
|I can think big about my future|2.300|0.079|[.053]|0.094|[.053]*|0.114|[.054]**|
|Difcult conditions don’t discourage me|1.980|0.026|[.065]|-0.090|[.068]|0.007|[.067]|
|My greatest prayer is to be successful in|2.468|0.054|[.051]|0.100|[.052]*|0.026|[.055]|
|life||||||||
|I’m trying hard to make it|2.305|0.009|[.053]|-0.008|[.053]|0.015|[.051]|
|I sometime make a plan but later on|1.271|0.085|[.064]|-0.058|[.063]|-0.068|[.066]|
|change my plan to a diferent one (-)||||||||
|I do not think too much about big<br>things/success (-)|1.340|0.025|[.075]|0.086|[.075]|0.055|[.075]|
|Reward responsiveness|0.072|-0.165|[.102]|0.084|[.100]|-0.242|[.102]**|
|When I want something I can go to any<br>corner to make sure I get it.|1.473|-0.149|[.075]**|-0.039|[.076]|-0.170|[.076]**|
|When I go after something, even the devil<br>in hell can’t stop me.|1.404|-0.072|[.078]|0.050|[.076]|-0.072|[.080]|

Continued on following page.

xxiv
Policy Research Working Paper 5307

# **Abstract**

This paper assesses the impact of water supply variability on treaty cooperation between international bilateral river basin riparian states. Climate change is anticipated to change the variability of water supply, as well as its expected magnitude. Previous studies have focused mainly on water scarcity, measured in terms of mean precipitation or per capita water availability in the country, as a trigger for conflict or cooperation. The water variability measure used here captures both annual runoff variability and precipitation variability over periods of 30 and 100 years. The analysis used economic and international relations data to identify incentives for international cooperation in addressing water supply variability. The authors find that small-to-moderate increases in variability create an impetus for cooperation, although large increases in variability would reduce incentives for treaty cooperation. Stronger diplomatic and trade relations support cooperation, while uneven economic power inhibits cooperation. Various measures of democracy/governance suggest different impacts on cooperation across the basin riparians. The findings have policy implications in the context of preparedness for impacts of climate change on the water sector.

This paper—a product of the Environment and Energy Team, Development Research Group—is part of a larger effort in the department to mainstream research on climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at bblankespoor@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
Given MENA’s apparent underperformance in Figures 1-3, Table 5 reports the results for the preferred specifications for each dependent variable plus the MENA-country dummy variable. For stages 1 and 2 of payment technology adoption, the goodness of fit is slightly improved by the addition of the MENA variable, while it remains the same for the Stage 3 estimate. In addition, the values and significance levels of the other coefficients are not much affected by the new dummy variable, signaling that the results are consistent with those reported in Tables 3 and 4. These results suggest that MENA’s population is on average underserved in terms of access to bank accounts and the use of digital payments, but not necessarily for mobile money accounts, given the region’s level of development.

However, the point estimates of the MENA dummy variable, regardless of their statistical significance, suggest that the magnitudes are large, even for the case of mobile money accounts. For bank accounts, the average under-performance of MENA is about 8.9 percentage points (p.p.) of the adult population. For digital payments it falls to roughly 7.3 percentage points, and to 2.7 percentage points for the case of mobile money accounts. These estimates are large considering the common-sample averages reported in Table 2 above. For the case of bank accounts, MENA’s 8.9 p.p. gap is 22.9 percent of the common-sample average of 38.9 p.p.; the gap of 7.3 p.p. for digital payments is 27.3 percent of the 26.7 p.p. common-sample average; and the gap of 2.7 p.p. equals 25.7 percent of the common-sample average of 10.5 p.p.

**Table 5. Regression results: Best-fit specifications with the MENA dummy variable**

|VARIABLES|STAGE 1<br>bankaccount<br>(quadratic)|STAGE 2<br>digitalpayment<br>(quadratic)|STAGE 3<br>mobilemoney<br>(log-linear)|
|---|---|---|---|
|log_gdppc|-0.243<br>(0.177)|-0.662***<br>(0.166)|-0.0255**<br>(0.00999)|
|log_gdppc_2|0.0240**<br>(0.0101)|0.0436***<br>(0.00944)||
|year2017|0.0350<br>(0.0252)|0.0805***<br>(0.0237)|0.0889***<br>(0.0198)|
|**MENA**|**-0.0886***<br>(0.0466)|**-0.0731***<br>(0.0438)|**-0.0265**<br>(0.0366)|
|Constant|0.649<br>(0.774)|2.638***<br>(0.727)|0.285***<br>(0.0872)|
|Observations<br>|149|149|149|
|R<sup>2</sup>|0.594|0.440|0.161|
|AdjustedR<sup>2</sup>|0.583|0.424|0.144|
|Min. of log_gdppc||6.70||
|Predicted value at minimum|0.105**|0.197***|0.157***|
|20<sup>th</sup>percentile of log_gdppc<br>||7.64||
|Predicted value at 20<sup>th</sup>perc.|0.200***|0.163***|0.133***|

14
in São Paulo, the proportion of those receiving remittances who refuse to report how much they receive is much higher (82 percent) in the intercept survey than in the household survey (31 percent). However, in Parana, the refusal rate for the amount received is lower in the intercept survey. This difference may arise from the fear of crime being higher in São Paulo, leading to more reticence in public places there. However, it should be noted that the number of households receiving remittances is small in each sample, so these differences are based on subsamples of 10 to 50 households.

# **5.2 Do the different sample methods give different results in regressions**

Although one goal of representative surveys is to estimate population means, such as the proportion of Nikkei households which have migrant members or which receive remittances, a second goal is to use the individual level data to estimate regression models. The limited number of questions contained in the intercept point survey, and the fact that data is collected for a single member rather than a full household roster, limits the use of the intercept point method for this approach. Nevertheless, we can compare the three methods in terms of how they characterize return migrants. Given that most of the migration from Brazil to Japan is temporary, this can be viewed as also a proxy for regressions intended to examine the selectivity of migrants.

Table 13 presents the results of probit regressions intended to determine which characteristics are associated with being a return migrant. We estimate two models using each method. The first uses age, sex, marital status, education, and Nikkei generation as controls, while the second specification also adds a Nikkei media engagement index, constructed as the first principal component of the six questions on whether household members read Japanese newspapers, watch Japanese television, etc. The education distribution shows three main education levels, corresponding to natural stopping points in schooling: individuals with high school or less, those with undergraduate education, and those with postgraduate education. We therefore include dummy variables for 33

-  -
however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10% of the territory and one where the population is concentrated in two clusters of 5% each, but with a considerable geographical distance between them.

Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red/grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC.

**Proposition 6** **_Concentration and Dispersion:_** _The risk of civil war events at a location increases more strongly in local population concentrations in locations distant from the capital of countries._

# **_2.5 Residual State-Level Mechanisms_**

We have pointed out a set of location-specific factors, each of which imply a positive relationship between country size and national-level risk of armed conflict. But it is not certain that such location-specific factors are the only relevant ones. The size of a country itself may affect risk over and beyond what is implied by sheer population size, distance, and population distributions. If the economies of scale with respect to defense are sufficiently large, the risk of conflict events at a location at a given distance from the capital may be lower the larger is the country (Collier & Hoeffler, 2002). Moreover, large countries may rather be _more_ conflictual than small ones for several reasons. Fearon & Laitin (2003: 81), for instance, note that insurgency will be favored when potential rebels 11
accounts (part of commodity demands) and in the government budget (part of current government spending) is a single cell entry. Such consistency is not automatic if, let’s say, national accounts and government budget data are produced separately. As shown in the second section of Table 3.2 (21‐24), additional data needed for calculations are also assembled in this step.

In the second step, shown in Table 3.3, the remaining SAM cells are defined. To make the calculations shorter and more transparent, initially data for a set of intermediate variables are specified (GDP at market prices, net indirect taxes, and total current government revenue) drawing on data covered in Table 3.2 (1‐3).<sup>9</sup> After this, the logic behind the different calculations is as follows: Labor and capital (non‐labor) value‐added is defined as GDP at factor cost (the difference between GDP at market prices and net indirect taxes; computed using data already in the SAM) times the labor and non‐labor value‐added shares, respectively, using country‐ or region‐specific GTAP data for the latter (4‐5).<sup>10</sup> Intermediate consumption is defined as the product of total (labor and capital) value‐added times the ratio between intermediate demand and total value‐added according to GTAP data (6). Sales of domestic output is simply the sum of the activity column, i.e. its payments for factors of production, intermediates, and activity taxes (7). Household labor incomes are defined as total labor value‐added plus net labor incomes from the rest of the world minus social contributions (8). The definition of household capital incomes follows the same logic (9). Household transfers to the government are computed as the difference between total non‐tax revenues (in effect different transfers from different sources, foreign and domestic) and transfers to the government from the rest of the world (10). Government savings is computed as the difference between total government current revenue (defined as the sum of tax and non‐tax revenue) and total current expenditures (11). Transfers to households from the government are a residual, balancing the government current account (12). It is defined as the difference between the total of the row of the current government account

> 9 Reference is made to net indirect taxes to reflect that any activity or commodity subsidies should be deducted. However, in the cross‐country databases that are used, such subsidies are not singled out. The interpretation is that they are part of transfers from the government to the household or treated as negative tax payments.

> 10 A cell payment is referred to via a bracketed listing of its row and column accounts. For example, [f‐lab,act] refers to the payment in the intersection between the row of the account f‐lab and the column of the account act, i.e., a payment to the labor account from the activity account.

‐10‐
# **Section 2: Ownership**

Section 2 of the 2011–12 survey includes 12 questions and focuses on regulations of bank ownership. We observe that public disclosure of the ultimate owner of banks is required in over 50 percent of the jurisdictions (Table A2). Furthermore, while the criminal and bankruptcy records of potential shareholders are scrutinized by regulators in most countries, educational background and banking-related experience are assessed only in 40 to 60 percent of the countries. While not all questions in Section 2 can be compared to the previous round of the survey, generally, we observe small changes for those questions that are comparable. There is, however, one notable exception, related to nonfinancial firms’ ability to own voting shares in commercial banks. Specifically, a significant share of EMDEs shifted from not having the right to own voting shares to being able to own 100 percent equity in commercial banks.

**Table A2.** **<u>Ownership: Selected Responses</u>**

||**ALL**|**ADV**|**EMDEs**|
|---|---|---|---|
|**A. Overview**|**% Yes**|**% Yes**|**% Yes**|
|Does the regulator have the legal authority to oppose the ultimate (beneficial) owner<br>when assessingbank ownership?|89|90|89|
|Do laws or regulations require the ultimate (beneficial) owner and controller of a bank<br>to bepubliclydisclosed?|52|47|54|
|Can related parties own capital in a bank?|96<br>(97)|100<br>(95)|95<br>(98)|
|**B.**<br>**Requirements for Evaluation/Approval of Significant Bank**<br>**Shareholders**|**% Yes**|**% Yes**|**% Yes**|
|Minimum level of education|42|37|44|
|Minimum level of financial and/or banking related experience|61|54|63|
|Financial capacity to support bank capital|93|91|94|
|No criminal record|94|89|96|
|No bankruptcy record|92|91|93|
|Lack of conflict of interest|81|80|81|
|**C. Nonfinancial Firms Own Voting Shares in Commercial Banks**|**% Yes**|**% Yes**|**% Yes**|
|Nonfinancial firm may own 100 of the equity in a commercial bank.|15<br>(7)|11<br>(14)|17<br>(5)|
|Nonfinancial firm may own 100 of the equity in a commercial bank, but prior<br>authorization or approval is required.|56<br>(52)|69<br>(62)|51<br>(48)|
|Limits are placed on ownership of banks by nonfinancial firms, such as maximum<br>percentage of a commercial bank's capital or shares.|29<br>(30)|22<br>(24)|32<br>(32)|
|Nonfinancial firms cannot own any equity investment in a commercial bank|1<br>(11)|0<br>(0)|1<br>(15)|
|**D. Maximum Percentage of a Bank's Equity That Can be Owned by a**<br>**Single Owner**|**Median**|**Median**|**Median**|
|2010 (%)<br>2005 (%)|100<br>(100)|100<br>(100)|100<br>(100)|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

26