rafmacalaba/datause-encoder-data huggingface dataset:
AO and K both also observe that different data sources might be influenced by the same media reports about a country, and argue that this justifies their claim that individual data sources make correlated errors. Logically this does not follow, as it depends on whether the media reports are accurate or not. If the media reports are accurate, then all the individual data sources that rely on this common media report will both be more accurate, and also more correlated with each other, as a result -- and this surely would be a desirable outcome. Of course, some media reports are more accurate than others, but AO and K do not enter into this crucial part of the argument.

While AO and K both provide some anecdotal evidence of data sources making correlated errors, only K attempts to provide some empirical evidence, for one measure of corruption.<sup>12</sup> K first documents convincing evidence of a methodological break in one widely-used expert assessment of corruption, the International Country Risk Guide (ICRG), in October of 2001, noting that in this particular month an extraordinarily large number of countries in the sample have their scores change when compared with typical other months. He then goes on to point out that, compared with earlier dates, the ICRG corruption ratings become more correlated with the Transparency International (TI) corruption ratings, with the correlation increasing from 0.71 to 0.92. He concludes that this provides evidence that the ICRG corruption ratings are not independent of the other data sources embodied in the TI ratings, and in particular suggests that the reason for the methodological break was to camouflage a move to greater consistency with the TI ratings. While interesting, we do not find this tidbit of evidence to be compelling, for two main reasons.

12 We note in passing that two of the four examples offered by AO are either incorrect or exaggerated. They incorrectly state that we use the Cingranelli and Richards Human Rights database and the Political Terror Scale, which both rely on numerical coding of information in the US State Department's Human Rights Report, as separate data sources in the same indicator. We do indeed use data from both these sources, but recognizing their common origin in the State Department reports, we average them together and use them as a single indicator in the aggregate indicators. Unfortunately our documentation of this detail in our data appendices was not completely clear. They also suggest that we use three different data sources from Freedom House in the same aggregate indicators. This is in fact the case only for Voice and Accountability. For Rule of Law and Control of Corruption we do rely on two data sources from Freedom House, Nations in Transit, and Countries at the Crossroads. However, in 2005 these two data sources, covering 28 and 30 countries respectively, have just two countries in common, Russia and Tajikistan. This means that there is practically no opportunity for correlated errors between these two sources to have any effect.

18
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## 5. Validation:

- a. Households cross-checked with pension and land cadaster data to confirm they are not wealthy.

6. Entry or waitlisting:

- a. Households enter program if sufficient budget, otherwise go on the waitlist.

7. Reverification:

- a. By law, households file an annual report on the MOLSA website;

- b. Social workers also revisit the households each year to maintain rapport and support case management.

The key to MoLSA eligibility is the PMT model. This model uses welfare proxies to predict consumption. Such proxies need to be generally easy to verify and correlated with consumption. Common categories include: (i) demographic; (ii) education and employment; (iii) housing; (iv) assets; and (v) displacement.

Statistical models are developed giving scoring weights to each proxy using national household surveys. The initial models were based on the 2012 IHSES survey. New models have been developed using the more recent 2017/18 Rapid Welfare Monitoring Survey, also known as the Survey of Well-being via Instant and Frequent Tracking (SWIFT) and are currently being implemented. Each household has an estimated consumption based on these weights.

# 3.2 MPCA Eligibility

The CLCI used a similar but distinct approach to develop a tool for MPCA vulnerability assessment and scoring, endorsed by the CWG and widely used by other MPCA actors including I/NGOs as well as UN agencies. However, the 2016 Vulnerability Model (VM) differs in a number of ways:

1. Both MoLSA PMT and VM use statistical regressions to derive weights for each indicator, but vary in what they are trying to estimate:

- a. PMT estimates household per capita consumption;

- b. VM estimates income to earnings ratio (initially estimated $2 a day poverty status).

2. They vary in how they do this:

- a. PMT uses a continuous regression with variable selection (stepwise in 2012, Lasso in 2017);

- b. VM uses a binary regression with variable selection (stepwise).

3. They vary in the number of geographical models:

- a. PMT produces regional models;

- b. VM produces a single model for all regions.

The CLCI revised the models using the 2018 Multi Cluster Needs Assessment (MCNA) survey, which resulted in the 2019 Socio-Economic Vulnerability Assessment Tool (SEVAT).<sup>7</sup> The new models are closer

> 7 https://www.humanitarianresponse.info/en/operations/iraq/document/iraq-mpca-vulnerability-model-review

6
# **S.2 Additional Robustness Tests**

We undertook a number of further robustness tests. First, we also estimate the models with costs per square meter instead of costs per km and find that the results are substantively the same. We prefer to use costs per km as we loose 500 observations when using the cost per square meter. Second, instead of the three-year lagged average of the WGI measures, we also use the variable in the year before the road work activity, as well as taking 5-year averages; the results are not affected. Third, one concern with the ACD conflict measure is that it has a cut-off of at least 25 battle-related deaths so that we might be picking up conflicts in remote areas in large countries which do not actually affect the whole country. To test whether this is driving the results, we interact the conflict and the post conflict dummy variables with the size of the country. We perform a joint significance test on these two interaction terms and find that we fail to reject the null hypothesis that they are jointly equal to zero, suggesting that this is not driving the results.

Fourth, we test whether our results are robust to alternative ways of dealing with missing data. In the analysis so far we employed three different types of controls: (i) geographic controls and GDP; (ii) project specific controls; and (iii) measures of conflict and corruption. We discuss missing values in turn for these four categories. Geographic controls and GDP data<sup>35</sup> are from various other data sets and with some exceptions completely available for all countries. The main reason for GDP in 1985 being missing for 17 countries is that many of them did not exist in 1985, such as Bosnia and Herzegovina, Kazakhstan, Kyrgyz Republic and Russia. The project specific controls<sup>36</sup> with the largest number of missing values are terrain and the length of the road work activity. Our coverage of the conflict and corruption<sup>37</sup> indicators is extensive in terms of countries covered. Our main approach

> 35Ruggedness, population, land area, distance to the nearest ice-free coast and region are available for all countries and years; precipitation is available for all countries except the West Bank and Gaza from 20002008; road projects before this date are assigned the 2000 value; contemporaneous GDP is available for all countries except Afghanistan; GDP in 1985 is not available for 17 countries or 337 road projects.

> 36The type of costs (estimate, contract or actual) and work type is available for all road work activities; work activity (one classification below work activity) is available for 2,926 work activities; terrain is available for 1,587 work activities; length of the project is available for 2,452 work activities.

> 37The ACD conflict and post-conflict variables cover all countries and time periods; WGI indicators for political instability and corruption are available for all countries; the Transparency International measure of corruption is missing only for Fiji (1 road work activity) and the West Bank and Gaza (3 road work activities)

15
inessential depends on the elasticity of substitution between the resources and other inputs. If this is low enough it is essential.

One extreme case is the risk of catastrophe. An environmental catastrophe may mean that all economic production becomes impossible: In the absence of market failures—i.e. if all environmental externalities were internalized— environmental assets would have a price and a “Green GDP” could be calculated by introducing the environment into national accounts.<sup>4</sup> Unfortunately, environmental externalities, market failures and technical and ethical issues in calculating the “price” of environmental assets, complicate the calculation of this Green GDP.

One question is whether investing in the environment increases only the _level_ of production, or also its growth. The answer is likely to be context specific. As explained by Rodrik and Subramanian (2008), an increase in production level increases income and savings, and thus accelerates growth where production growth is limited by credit constraints (i.e. desirable and profitable investment opportunities exist but cannot be realized due to a lack of savings and inadequate access to capital markets). Not so where output growth is limited by investment opportunities (because institutions are not in place to allow investors to benefit from their investment revenues): where people are engaged in low-return activities an increase in production level will not translate into an increase in economic growth because economic activity does not generate sufficient returns to allow households to save and accumulate assets.

# **Green policies as a source of growth**

Up to now, the positive impact of environmental policies on economic output has operated through an improvement in environmental conditions: better forestry management reduces soil erosion leading to more productive agriculture. Environmental policies are not specifically included in the production function—only their impact on E; and their only impact is, in fact, on the environment. However, environmental policies may themselves have externalities and impact growth through channels other than an improvement in the quantity or quality of environmental inputs.

A broader framework, closer to reality, accounts for the fact that in practice real-world economies are far from their optimal, or first-best state due to market failure and other sub-optimalities such as:

- Knowledge spill-overs and economies of scale that lead to under-investments in research and development (e.g., Aghion and Howitt, 1992);

> 4 For a review of the literature on the integration of the environment in national accounts, see Heal and Kriström (2005). See also an application of these ideas in World Bank (2006).

7
Table 5: Village characteristics based on the Census of India 1991, by mandate during which a first SHG was created in the village

||First SHG crea|ted in the village i|n|
|---|---|---|---|
||1997-2001|2002-2006|Difference|
||(Std. Dev.)|(Std. Dev.)|(Std. Err.)|
|Number of households|129.3|112.4|16.9|
||(83.2)|(84.5)|(16.5)|
|Literacy rate of women|12.3|15.5|-3.2|
||(9.5)|(10.9)|(2.0)|
|Literacy rate of men|33.1|37.6|-4.5|
||(16.0)|(17.2)|(3.3)|
|Employment rate of women|18.8|16.3|2.5|
||(15.8)|(16.9)|(3.2)|
|Employment rate of men|53.6<br>|51.9|1.7<br>|
||(5.2)|(9.3)|(1.5)|
|EDUCATION FACILITIES||||
|Number of primary schools|0.87|0.78|0.09|
||(0.40)|(0.46)|(0.09)|
|Number of middle schools|0.24|0.14|0.10|
||(0.43)|(0.39)|(0.08)|
|Number of high schools|0.13|0.07|0.06|
||(0.34)|(0.31)|(0.06)|
|MEDICAL FACILITIES||||
|A medical facility is available (% of villages)|15.2|6.8|8.4|
||(36.3)|(25.4)|(6.0)|
|DRINKING WATER FACILITIES||||
|Well water (% of villages)|73.9|83.1|-9.2|
||(44.4)|(37.8)|(8.0)|
|Tank water (% of villages)|45.7|54.2|-8.5|
||(50.4)|(50.2)|(9.9)|
|Tube well (% of villages)|30.4|32.2|-1.8|
||(46.5)|(47.1)|(9.2)|
|River water (% of villages)|28.3|20.3|8.0|
||(45.5)|(40.6)|(8.4)|
|OTHER INFRASTRUCTURE||||
|Bus stop (% of villages)|17.4|13.6|3.8|
||(38.3)|(34.5)|(7.1)|
|Village accessibility by paved road (% of villages)|28.3|25.4|2.9|
||(45.5)|(43.9)|(8.8)|
|Market facility available (% of villages)|13.0|6.8|6.2|
||(34.1)|(25.4)|(5.8)|
|Power supply (% of villages)|54.3|71.2|-16.9<sup>_∗_</sup>|
||(50.4)|(45.7)|(9.4)|
|Irrigated land (% of land)|5.2|6.1|-0.9|
||(11.4)|(17.6)|(3.0)|
|Number of villages|46|59||

> _∗∗∗_ significant difference at 1 percent, _∗∗_ significant difference at 5 percent, _∗_ significant difference at 10 percent. _Source_ : Authors’ analysis based on data from the Census of India, 1991. 37
<mark>scores. This will increase the scope of the credit bureaus and contribute to reducing the cost of credit</mark> by improving financial institutions’ and other non-financial companies’ ability to determine the creditworthiness of clients. That reform is meant to lower non-performing loans and therefore lower interest charges.

In addition, the central bank took measures to strengthen the regulatory framework for banks. Specifically, the country adopted reform of “duplicata eletrônica,” to improve the efficiency of registration and trading of trade acceptances (duplicatas). The “duplicata eletrônica” reform will eliminate paper-based documents, thereby reducing forgery and fraud and improving the legal and transactional certainty (transparency) through the usage and registration of electronic documents. Hence, it is expected to lead to increased efficiency and transparency and reducing transaction costs.

<u>Reduction of credit market segmentation and long-term finance: Brazil is also reforming the lending</u> practices of the BNDES in order to reduce credit market segmentation. In 2017 Congress approved a Bill replacing the Long-term Interest Rate (TJLP) with a new rate for contracts signed with BNDES, called the Long-term Rate (TLP). The TLP, which is pegged to inflation and to the National Treasury’s cost of funds, became effective in January 2018.<sup>7</sup> The aim of the newly introduced rate is to allow BNDES to fill gaps in the market for long-term finance without imposing a cost to the government. It also mitigates the impact on monetary transmission.

<u>Reduction of opportunity costs and implicit taxes</u> **:** In 2018 the central bank reduced reserve requirements on savings and checking accounts. The central bank also issued a new set of rules allowing smaller financial companies to access services provided by banks, such as automatic debit and transfers between institutions.

# **B. Illustrative econometric analysis of common correlates of lending rates and spreads**

In a first exploration of a battery of commonly used variables in the empirical literature, we calculate descriptive statistics to identify the macro-fiscal, banking, and business environment factors which appear to be strongly associated with nominal interest rates and spreads. Table 2 shows the descriptive statistics of key drivers of spreads by tercile of the variable in column 1 (i.e., nominal interest rate, lending-deposit spread, net interest margin, and real interest rate). The following patterns emerge:<sup>8</sup>

> 7 BNDES 2017 annual report.

> 8 We first sorted countries by the 2003-2017 average value of the variables in column 1, and then calculated the median value of each factor by tercile.

14
|Diarrheal morbidity|105|103|208|
|---|---|---|---|
|Typhoid morbidity<sup>~~12~~</sup>|||3.3|
|**TOTAL ANNUAL COST**|155|330|489.1|

_Source:_ Staff estimates.

# **_4.2 Averting Expenditures_**

In the presence of perceived health risks, individuals often take measures to avoid these risks. These are usually considered as a cost of the health risks of environmental burdens. If consumers perceive that the municipal water supply or the other sources of water supply they rely on are unsafe, they are likely to purchase bottled water for drinking purposes, or boil their water, or install water purification filters. The estimated costs of these options are given in Table 4.4. The assumed hypothetical level of expenditure here is zero (i.e. no avertive expenses would be incurred if the water supplied was safe). The total amount of avertive expenditures for India amount to about Rs. 55 Bn. a year.

**Table 4.4: Estimated Total Annual Household** **<u>Cost of Averting Expenditures</u>**

||**Total An**<br>**(Billio**|**nual Cost**<br>**n Rs.)**|
|---|---|---|
||**Urban **|**Rural **|
|Cost of bottled water consumption|20|7|
|Cost of household boilingdrinkingwater|4|3|
|Cost of household filteringdrinkingwater|14|7|
|**Total annual cost**|38|17|

_Source:_ Staff estimates.

In summary the estimated annual cost associated with inadequate water supply, sanitation and hygiene is presented in Figure 4.1, totaling 470-610 billion Rs. per year, with a mean of 540 billion Rs. The cost of health impacts represents an estimated 90 percent of total mean cost, with averting expenditures accounting for about 10 percent. Health impacts include both mortality and morbidity, and averting expenditures include bottled water consumption, and household boiling of drinking water. Annual costs by major category are presented on Figure 4.1.

> 12 About 25 percent of estimated COI is from hospitalization and doctor visits, 70 percent is from time losses for the ill individuals and their caregivers during illness

18
**Table 2: Convergence across Provinces in real per capita sectoral GVA and GDP**

||**Manufacturing**|**Construction**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|
||Dep. Var:%Ch<br>|ange between 2<br>|004 and 2006<br>|<br>||
|Ln Value in 2004|-1.55**|-4.55**|-2.97|-1.77**|-0.05|
||[-2.19]|[-2.13]|[-0.55]|[-2.07]|[-0.04]|
|Constant|27.23***|55.39***|28.55|29.48***|12.82|
||[6.17]|[4.23]|[0.73]|[4.07]|[1.07]|
|R-squared|0.04|0.06|0.01|0.05|0.00|
||Dep. Var:%Ch|ange between 2|007 and 2009|||
|Ln Value in 2007|-3.10***|-0.94|-0.30|-3.51***|-5.55***|
||[-4.09]|[-0.69]|[-0.08]|[-3.21]|[-5.08]|
|Constant|10.49*|-18.85**|7.62|28.27***|47.03***|
||[1.95]|[-2.17]|[0.27]|[3.01]|[4.62]|
|R-squared|0.15|0.00|0.00|0.14|0.22|
||Dep. Var:%Ch|ange between 2|009 and 2011|||
|Ln Value in 2009|-1.59|-27.87***|-0.36|-4.19***|-8.29***|
||[-1.24]|[-6.59]|[-0.16]|[-2.95]|[-5.32]|
|Constant|37.85***|218.66***|12.00|45.12***|94.46***|
||[4.32]|[8.30]|[0.72]|[3.74]|[6.50]|
|R-squared|0.01|0.33|0.00|0.10|0.25|
||Dep. Var:%Ch|ange between 2|013 and 2017|||
|Ln Value in 2013|-2.22|-27.85***|0.32|-8.87***|-4.86**|
||[-0.80]|[-3.91]|[0.13]|[-3.96]|[-2.16]|
|Constant|44.09**|211.40***|5.50|93.67***|61.68***|
||[2.07]|[4.31]|[0.31]|[4.79]|[2.87]|
|R-squared|0.01|0.18|0.00|0.20|0.05|
||Dep. Var:%Ch|ange between 2|017 and 2019|||
|Ln Value in 2017|3.31|3.02|0.27|-0.04|-1.36|
||[1.38]|[0.36]|[0.17]|[-0.02]|[-0.79]|
|Constant|-26.49|-68.18|0.35|5.75|14.38|
||[-1.39]|[-1.14]|[0.03]|[0.31]|[0.86]|
|R-squared|005|000|000|000|001|
||.<br>Dep. Var:%Ch|.<br>ange between 2|.<br>004 and 2019|.<br>|.|
|Ln Value in 2004|-9.27***|-40.22***|4.62|-22.77***|-18.70***|
||[-2.77]|[-4.99]|[0.55]|[-5.54]|[-5.75]|
|Constant|138.08***|300.64***|-1.66|250.57***|225.37***|
||[5.71]|[6.13]|[-0.03]|[7.27]|[7.46]|
|R-squared|0.11|0.30|0.01|0.39|0.32|

Notes: t-statistics based on robust standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.1. All regressions use data on all 81 provinces.

19
Table 1: Mean Difference between Opportunity and Necessity Entrepreneurs (full sample)

||Opportunity groupmean|Necessity groupmean|Difference|
|---|---|---|---|
|**Measures ofperformance**||||
|Weekly profts (self reported)|1937.635|1349.177|588.4576<sup>_∗∗∗_</sup>|
|Weekly sales (self reported)|4507.258|3540.586|966.6726<sup>_∗∗∗_</sup>|
|Weekly sales/workers|4395.353|4302.254|93.09963|
|Composite Business Practice score(standarized)|.3060325|-.0842768|.3903093<sup>_∗∗∗_</sup>|
|**Business characteristics**||||
|Age of entrepreneur|41.81658|45.92807|-4.111494<sup>_∗∗∗_</sup>|
|Age of business in months|90.59086|103.6411|-13.05026<sup>_∗∗∗_</sup>|
|<br>Proportion with one worker or more|.3185|.2278|.0907<sup>_∗∗∗_</sup>|
|Costs:monthlysalaryexpenses|1025.357|498.751|526.6064<sup>_∗∗∗_</sup>|
|**Cognitive skills**||||
|Total score of raven test (standarized)|.0827833|-.0211648|.1039482<sup>_∗∗∗_</sup>|
|<br>Total score of digit span test (standarized)|.1459293|-.0328984|.1788277<sup>_∗∗∗_</sup>|
|Years of schooling|9.789416|8.251668|1.537748<sup>_∗∗∗_</sup>|
|**Non-cognitive skills**||||
|Extraversion (standarized)|-.0365413|.0155396|-.0520809<sup>_∗∗_</sup>|
|Agreeableness (standarized)|-.0093652|.0075027|-.0168679|
|Conscientousness (standarized)|-.069074|.0121551|-.0812292<sup>_∗∗∗_</sup>|
|Neuroticism (standarized)|-.0246642|.0130341|-.0376982|
|Intellect/imagination (standarized)|.0074094|-.0046432|.0120526|
|<br>Self effcacy (standarized)|-.0957561|.0110521|-.1068082<sup>_∗∗∗_</sup>|
|<br>Locus of control (standarized)|.0954217|-.0202922|.1157139<sup>_∗∗∗_</sup>|
|Impulsiveness (standarized)|.0480202|-.0170598|.06508<sup>_∗∗_</sup>|
|Self confdence (standarized)|.1074716|-.0390423|.1465139<sup>_∗∗∗_</sup>|
|Attitude towards risk (standarized)|.0975181|-.0298163|.1273345<sup>_∗∗∗_</sup>|
|Self satisfaction (standarized)|.1163573|-.0443902|.1607475<sup>_∗∗∗_</sup>|
|<br>Optimism (standarized)|.138387|-.0400507|.1784377<sup>_∗∗∗_</sup>|
|Attitude towards trust (standarized)|.0137271|-.0011845|.0149116|
|Attitude towardsgrowth(standarized)|.0621355|-.0119533|.0740888<sup>_∗∗∗_</sup>|
|Observations|8949|||

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01