rafmacalaba/datause-encoder-data huggingface dataset:
Most questions could be answered “Yes/No” though in many cases we requested that in case of doubt the authorities attach governing regulations or laws. Some of the questions in the latest version explicitly or implicitly refer to Basel II, such as those enquiring as to the plans for the implementation of Basel II, and if so then the variant of the first pillar to be adopted. Similarly, some of the questions relating to capital, provisioning, and supervision have been modified to keep abreast of current thinking and emerging practice in these areas.

Before turning to the data, an obvious question concerns the accuracy of the responses. The survey was sent to the principal contacts in each country of the Basel Committee on Bank Supervision. Even though these contacts should know the regulatory environment, the survey’s scope is such that for any country a number of people usually are involved in its completion, and some or all of the members of this group might change over time, raising the issue of differences in the interpretation of questions over time (in addition to changes in the wording noted above). In order to attain the greatest possible consistency over time, we adopted several approaches: going back to authorities for clarification, where there were notable changes, as well as posting the survey responses on the web, so that the data could be challenged and inconsistencies resolved.

We also searched for instances in which there was a reversal in a country’s response to a question across the different surveys, e.g., if a component of an index rose from Surveys I to II and then declined from Survey II to Survey III, or vice versa. Such a change could, though need not, be due to an error in reporting, or possibly a difference in interpretation due to a change in the person or persons replying on behalf of the regulatory agency. With the exception of some of the components of the index on the overall restrictiveness of bank activities, where mostly small reversals occurred in about 20% of the cases, few reversals were seen in the other components. The full results of this analysis are reported in the working paper version of this paper. Again, these reversals are not necessarily an indication of errors, particularly for those questions that require a simple yes or no answer. However, these cases might merit further checking.

Another way to insure accuracy is through the publication of the results. Surveys I and II have been posted since 2000 and 2004, respectively, so one would assume that authorities, especially after prompting from the Bank, would have reported errors by now. Yet each survey had only a handful of countries questioning an individual response, notwithstanding that each survey has been posted for a number of years. Survey III was just posted in July 2007.

4
# **Table A.9. The district fixed-effect RDD-DD regressions of individual’s health and children’s education using the sample** **<u>without the program 135 communes</u>**

||||D<br>|ependent variab<br>|les<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Having<br>health<br>insurance<br>(yes=1,<br>no=0)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|The annual<br>number of<br>inpatient<br>healthcare<br>visits|The number<br>of completed<br>schooling<br>years<br>(children<br>aged 6-17)|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students<br>(children<br>aged 6-17)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program * Post-program period|0.1206|0.4203**|0.4249**|-0.0047|0.4343|0.0071|-0.0142|
||(0.1006)|(0.2041)|(0.1831)|(0.0530)|(0.3342)|(0.0497)|(0.0283)|
|(Poverty rate – 50) * Post-program|0.0285**|0.0723**|0.0684**|0.0038|-0.0134|-0.0097|0.0007|
|period|(0.0111)|(0.0312)|(0.0300)|(0.0064)|(0.0377)|(0.0062)|(0.0027)|
|Program * (Poverty rate – 50) *|-0.0188*|-0.0743**|-0.0709**|-0.0033|0.0150|0.0070|0.0009|
|Post-program period|(0.0104)|(0.0309)|(0.0298)|(0.0063)|(0.0359)|(0.0060)|(0.0022)|
|Age|-0.0029***|-0.0049|-0.0046|-0.0002|1.1898***|0.1488***|-0.0095***|
||(0.0009)|(0.0044)|(0.0044)|(0.0007)|(0.0484)|(0.0114)|(0.0008)|
|Age squared|0.0000***|0.0003***|0.0002***|0.0000***|-0.0203***|-0.0078***|0.0001***|
||(0.0000)|(0.0001)|(0.0001)|(0.0000)|(0.0023)|(0.0005)|(0.0000)|
|Male|0.0158***|-0.1239***|-0.1213***|-0.0025|0.1608**|0.0421***|0.0081**|
||(0.0032)|(0.0234)|(0.0209)|(0.0068)|(0.0622)|(0.0120)|(0.0034)|
|Ethnic minorities (yes=1, Kinh=0)|0.0365|-0.2617***|-0.2596***|-0.0021|-0.8599***|-0.0915***|0.0484***|
||(0.0263)|(0.0905)|(0.0926)|(0.0109)|(0.1712)|(0.0296)|(0.0128)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2514***|0.6532***|0.5861***|0.0670***|-5.7179***|0.2867***|0.2305***|
||(0.0245)|(0.0717)|(0.0701)|(0.0124)|(0.3146)|(0.0685)|(0.0187)|
|Observations|134,049|61,118|61,118|61,118|35,930|35,930|134,049|
|R-squared|0.532|0.097|0.092|0.030|0.520|0.196|0.182|

Note: This table reports district fixed-effect RDD-DD regressions of individual’s health and children’s education using individual-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

59
# Acknowledgement and Disclaimer

The authors’ benefitted from comments from Martin Raiser, Carolina Sanchez-Paramo, Marina Wes, Kamer Ozdemir, William Wiseman, Jose Guilherme Reis, Stephen Karam and Çevdet Cagdas. The author’s would like to thank the comments received from the participants of the ECA-Poverty Seminar in October 2013, the Turkey Country Team seminar in October 2013, and the Turkey Flagship Workshop held in November 2013, and participants from discussions with TUIK, the Ministry of Development and Treasury. Finally we thank Kimberly Bolch for editing the paper.

The numbers presented in this brief are based on a regional data harmonization effort known as ECAPOV that increases cross-country comparability of selected findings from official household surveys. For that reason, the numbers discussed here may be different from official statistics reported by governments and national offices of statistics. Such differences should not be interpreted in any way as a claim of methodological superiority, as both sets of numbers serve the complementary objectives, namely, regional comparability and the best possible representation of the facts of individual countries. The usual disclaimer applies.

2
# **_3.2  Competition for land_**

Historical land-use changes since 1987 were estimated in GEO4 (2007), and these are shown in Table 6 in relation to the change in area of forests, woodlands and grasslands, farmland including cropland and intensive pasture, and urban areas. Over the last 20 years, large forest conversions have occurred in the Amazon Basin, South East Asia, and Central and West Africa, while forest area has increased in the Eurasian boreal forest and parts of Asia, North America, and Latin America and the Caribbean (GEO4, 2007). Cropland has expanded significantly in South East Asia and in parts of West and Central Asia, the Great Lakes region of Eastern Africa, the southern Amazon Basin, and the Great Plains of the United States. Some cropland has been converted to other uses, including urban development around many major cities. Agriculture now occupies about 40% of the global land surface. At the same time, land-use intensity has increased substantially in many places. Figure 5 shows cereal yield increases since 1961. There is substantial regional variation: cereal yields have trebled in East Asia over this time, while yields have increased not at all in Sub-Saharan Africa, for example. Land-use change is complex and driven by a range of drivers, although it is possible to see some strong historical associations between land abundance, application of science and technology, and land-use change in some regions (Rosegrant et al., 2009) -- in Latin America, for instance, land abundance has slowed the introduction of new technologies that can raise productivity.

Table 6. Global land use change between 1987 and 2006 (1000 km<sup>2</sup> per year) (GEO4, 2007).

|**To**<br>**From**|**Forest**|**Woodland/**<br>**Grassland**|**Farmland**|**Urban**<br>**Areas**|**Losses**|**Gains**|**Net**<br>**Change**|
|---|---|---|---|---|---|---|---|
|**Forest**|39,699|30|98|2|-130|57|-73|
|**Woodland/**<br>**Grassland**|14|34,355|10|2|-26|50|24|
|**Farmland**|43|20|15,138|16|-79|108|29|
|**Urban Areas**|~0|~0|~0|380|0|20|20|
|**Total**|||||-235|235||

Farmland includes cropland and intensive pasture 29
21

**Table 3. Baseline Results - The Effects of Signing Tax Treaties with Mauritius**

|SUR Estimation with GLS<br>Dependent variable||Log|(FDI)|||Log(CIT|revenue)||
|---|---|---|---|---|---|---|---|---|
|Explanatory|Model 1|Model 2|Model 3|Model 4|Model 1|Model 2|Model 3|Model 4|
|Mauritius treaty|‐0.025|‐0.034|‐0.026|‐0.035|‐0.248**|‐0.184 *|‐0.155 *|‐0.168*|
|Non‐Mauritius treaty|[0.031]|[0.032]|[0.032]|[0.038]|[0.098]|[0.101]<br>‐|[0.092]<br>‐|[0.096]<br>‐|
|||0.006|0.007|0.007||0.048***|0.055***|0.043***|
|||[0.005]|[0.005]|[0.005]||[0.013]|[0.014]|[0.013]|
|CIT rate|‐0.004 *|‐0.003 *|‐0.003||0.024***|0.019***|0.016***|0.019***|
||[0.002]|[0.002]|[0.002]||[0.005]|[0.005]|[0.005]|[0.005]|
|Lag Log FDI|0.935***|0.933***|0.936***|0.936***|||||
||[0.029]|[0.030]|[0.030]|[0.026]|||||
|Log Imports|0.129***|0.125***|0.129***|0.149***|‐0.027|0.03|0.056||
||[0.040]|[0.040]|[0.039]|[0.045]|[0.151]|[0.154]|[0.145]||
|Log Exports|0.005|0.005|‐0.003||0.664***|0.631***|0.581***|0.628***|
|Lo Gd|[0.053]|[0.053]|[0.057]|‐|[0.087]|[0.086]|[0.082]|[0.074]|
|g p|‐0.134**|‐0.131 *|‐0.127 *|0.134***|0.797***|0.782***|0.901***|0.894***|
||[0.067]|[0.068]|[0.071]|[0.043]|[0.128]|[0.126]|[0.136]|[0.131]|
|Gdp Growth|0.148**|0.151**|0.161**|0.147**|0.034|0.045|0.048||
||[0.060]|[0.060]|[0.073]|[0.069]|[0.158]|[0.157]|[0.180]||
|Log Population|0.236**|0.231**|0.287||0.653 *|0.812**|0.748||
||[0.102]|[0.101]|[0.195]||[0.348]|[0.324]|[0.490]||
|Inflation|0.001|0.001|0.002||0.001|0.002|0.005**||
|Eh|[0.001]|[0.001]|[0.001]||[0.002]|[0.002]|[0.002]||
|xcange rate|0.008|0.009|0.011||‐<br>0.171***|‐<br>0.170***|‐<br>0.227***|‐<br>0.241***|
||[0.014]|[0.014]|[0.013]||[0.059]|[0.058]|[0.055]|[0.054]|
|Control of corruption|0.087|0.089|0.1||0.138|0.165|0.18||
|Government|[0.065]|[0.065]|[0.065]||[0.123]|[0.123]|[0.119]||
|<br>effectiveness|0.006|0.006|‐0.012||‐0.202|‐0.262 *|‐0.305**|‐0.289**|
||[0.054]|[0.054]|[0.053]||[0.141]|[0.136]|[0.130]|[0.117]|
|Oil price index|‐0.002 *|‐0.002 *|||0|0|||
||[0.001]|[0.001]|||[0.001]|[0.001]|||
|Producer price index|‐0.003|‐0.003|||‐<br>0.013***|‐0.012**|||
||[0.002]|[0.002]|||[0.005]|[0.005]|||
|Metal price index|0.003**|0.003**|||0.007**|0.007**|||
||[0.002]|[0.002]|||[0.003]|[0.003]|||
|Observations|1156|1156|1156|1190|614|614|614|617|
|Time FE|No|No|Yes|Yes|No|No|Yes|Yes|

*, **, and *** indicate significance at the 10%, 5%, and 1% level. Heteroscedasticity and autocorrelation robust standard errors in square brackets.
equals 3 percent); short-term debt as a share of GDP (threshold equals 4 percent); public debt held by non-residents as a share of gross debt (threshold equals 45 percent); and EMBI global diversified yield spread (threshold equals 600 basis points). The debt sustainability framework (DSF) uses indicative thresholds, linked by country classification, to analyze the risk of external debt distress. Thresholds are (statistically determined) bounds above which the risk of debt distress is considered elevated. Note that these thresholds were derived to maximize their joint correlation with a very specific definition of a debt distress event conditional on a very specific set of control variables. In this methodology we use the data on actual defaults, which may not correlated perfectly with the debt distress indicators used for deriving these thresholds.<sup>8</sup> Figure 11 compares the unweighted average debt to GDP ratios for different shares of indices exceeding their thresholds over time. In the majority of cases very high debt is associated with a higher share of indicators breaching their thresholds.

**Figure 11:** Share of indicators exceeding thresholds

<!-- Start of picture text --> Debt and judgment<br>2011 2012 2013 2014 2015 2016 2017 2019 2020<br>150<br>100<br>50<br>0<br>0.4 0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.0 0.4<br>Share of indices<br>Debt (%GDP)<br><!-- End of picture text --> Note that DSA’s are subject to revisions. This makes sense as new types of information and analytical tools help to make analysis more concise. As an example, the new MAC-DSA framework includes several new features and is being rolled out towards the end of 2021.<sup>9</sup>

# **3.4 The Theory Index**

In this section we derive an angle of vulnerability by constructing an index related to a theoretical model of default. This theoretical model derives the economic conditions for when it is optimal for a country to default, and when it is strategic to avoid defaults. The derived quantities of the model produces time varying threshold debt to GDP ratios, which are codified into an index used in the DVA. The Theory Index relies on the models developed by Cole and Kehoe (1996, 2000) and Conesa and Kehoe (2017) which we extend to study the debt-thresholds of multiple economies. The underlying model is a dynamic stochastic general equilibrium model that characterizes the values of government debt and

> 8For more detailed discussion of why the DSF employs thresholds, and on the probit methodology, please see IMF (2017), and Kraay and Nehru (2006). 9SeeSee

> 9SeeSee `https://www.imf.org/en/Publications/Policy-Papers/Issues/2021/02/03/ Review-of-The-Debt-Sustainability-Framework-For-Market-Access-Countries-50060`

16
In short, based on the existing evidence, we do not consider the relative poverty hypothesis further, since we do not expect the cost of eliminating poverty to be correlated with mean income.

A further hypothesis relates to access to technology for supporting SP programs:

**_Technology: The ICT hypothesis._** _The governments of poorer countries have less access to ICT, which makes SP programs more costly options for public spending._ The literature on SP has not often discussed this issue, but it is a seemingly plausible assumption that SP programs <mark>tend to be ICT-intensive, such as in creating and maintaining social registries of participants and</mark> payment and monitoring systems. There is evidence that such ICT capabilities enhance the quality and coverage of SP programs in poor countries.<sup>16</sup> Better communication infrastructure in a country can facilitate new options for using mobile money technologies that have proven to be <mark>helpful for cash transfer delivery in some low-income settings.</mark><sup>17</sup> <mark>However, with limited access to</mark> ICT at the national level due to poor infrastructure, the cost of implementing SP programs can be expected to be higher in poorer countries, leading policy makers to substitute SP in favor of other types of public spending. We will test this hypothesis using ICT usage indicators at the national level over time.

The next hypothesis concerns population aging:

**_<mark>Demographics: The aging hypothesis.</mark>_** _<mark>Richer countries tend to have older populations,</mark> which increases the demand for SP._ The fact that richer countries tend to have more elderly populations follows almost immediately from the well-known Preston (1975) curve, whereby life expectancy at birth tends to be an increasing (concave) function of average income across countries.<sup>18</sup> <mark>With an older population in richer countries we can expect to see demands for extra</mark> public spending to help support the elderly, who have diminished capabilities for supporting themselves; Shelton (2007) finds cross-country evidence to support this claim. Since pensions are one component of SP spending, countries with an older population will tend to have higher SP spending, though non-pension components may also be higher. A preliminary look at the evolution of SPECs excluding pension spending (Addendum Figure A3 and Figure A4) does not suggest that pension spending explains the pattern in Figure 1. We will test the aging hypothesis.

> 16 <mark>For examples of the use of ICT in registration, payments and other functions, see World Bank (2022c).</mark> 17 See, for example, Jack et al. (2013). 18 For more recent descriptions and analysis see (inter alia) Gómez and Hernández de Cos (2008) and Ritchie and <u>Roser (2019).</u>

11
Table A3: Robustness check using the subsample with tenure being 3 years and less— Observable abilities

||S|chooling (years)||Age at hi|re|
|---|---|---|---|---|---|
||(1)|(2)<br>(3)|(4)|(5)|(6)|
|Social networks|-3.444<sup>_∗∗∗_</sup>|-2.846<sup>_∗∗∗_</sup><br>-1.521<sup>_∗∗_</sup>|<sup>_∗_</sup><br>-1.553<sup>_∗∗_</sup>|-1.695<sup>_∗_</sup>|-0.339|
||(0.349)|(0.416)<br>(0.406)|(0.738)|(0.928)|(1.491)|
|Firm fxed efect||X||X||
|Firm-occ fxed efect||X|||X|
|R-squared|0.237|0.151<br>0.079|0.034|0.029|0.004|
|N|1745|1745<br>1745|1745|1745|1745|
||Pri.|school: father|Pri. s|chool: mo|ther|
||(7)|(8)<br>(9)|(10)|(11)|(12)|
|Social networks|-0.047|-0.030<br>-0.041<br>|-0.184<sup>_∗∗∗_</sup>|-0.124<sup>_∗∗_</sup>|-0.096<sup>_∗∗_</sup>|
||(0.039)|(0.037)<br>(0.041)|(0.047)|(0.050)|(0.044)|
|Schooing in years|0.035<sup>_∗∗∗_</sup>|0.027<sup>_∗∗∗_</sup><br>0.025<sup>_∗∗∗_</sup>|0.032<sup>_∗∗∗_</sup>|0.023<sup>_∗∗_</sup>|0.017|
||(0.005)|(0.006)<br>(0.007)|(0.007)|(0.009)|(0.011)|
|Age at hire|-0.006|-0.006<br>-0.005|-0.001|0.003|-0.001|
||(0.005)|(0.006)<br>(0.008)|(0.004)|(0.005)|(0.006)|
|Firm fxed efect||X||X||
|Firm-occ fxed efect||X|||X|
|R-squared|0.163|0.058<br>0.033|0.184|0.089|0.053|
|N|1745|1745<br>1745|1745|1745|1745|

_Note_ . The dependent variables in columns (7)–(9) are the dummy indicating that the father completed primary education while those in columns (10)–(12) are the dummy for mother’s primary education. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

40
number of non-cholera diarrheal cases per week is 5.6 percent for a 1 degree Celsius increase in temperature (Hashizume et al., 2007).

# 6.2  Socioeconomic variables

This study also confirms findings in the existing literature that better access to health services, improving female education and access to sanitation facilities all bring significant health benefits. Greater access to health services significantly reduces the incidence of diarrhea by a large proportion, although the size of the coefficient is not directly comparable to the other coefficients as it is an index variable. We also attempt to identify the interaction effect between extreme weather events and adaptive capacity as measured by access to basic services, including health, water and sanitation facilities. The estimated coefficients of the interactive terms are statistically insignificant.

Table 4 shows that variables that have been found to affect diarrhea in other studies, such as access to piped water and connection to a toilet, are also found to be significant in the random effects model, but not in the full model presented in Table A in which we control for country fixed effects, country per capita GDP, climate zone and year. We interpret this as a problem of correlation between the variables for access to piped water and sanitation and the region fixed effects. The existing literature finds contradictory evidence of the impact of access to piped water (Esrey et al., 1991), but the incidence of diarrhea is generally considered to be affected by access to both piped water and sanitation.

Access to safe water is also found to be important for growth and nutrition. Esrey et al. (1992) found that children living in homes with contaminated drinking water were significantly shorter. Our results are consistent with this finding in that the impact of piped water on the prevalence of height for age malnutrition significantly reduces the prevalence of height for age malnutrition in our random effects specification with country, climatic zone, time and country per capita GDP as is seen in Table A.

The importance of maternal education for children’s health and nutrition outcomes is well established and reviewed in surveys provided by Behrman and Deolalikar (1988) and Strauss and Thomas (1998). We find that maternal education is important in lowering the incidence of short-term and long-term malnutrition, and the effects are significant.

The large and significant effect of maternal education on the under five mortality rate is in line with a large body of other research, for example in Caldwell (1979), Hobcraft et al. (1984), Subbarao and Raney (1995), Cleland and Van Ginneken (1988) and Deaton and Paxson (2001). We also find that access to health care has a significant impact in safeguarding children’s survival. These results on the determinants of the under-five mortality rate are in line with findings from a large body of research and provide validation for the other disease models that we study.

In the full specification models the region’s average per capita income is controlled, which is found to be significant both for diarrhea and wasting (Table A). A one-unit 13
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