rafmacalaba/datause-encoder-data huggingface dataset:
It is generally the case that verifiable information on program applicants’ level of consumption or income will not be available due to the difficulty and cost of collecting such information. Careful data collection and statistical modeling are required to ensure an accurate prediction of household welfare. Nevertheless, due to these inaccuracies, it is important for programs to include a grievance or appeals process available to excluded program applicants who may like to provide additional information in support of their application or have their case reviewed for potential errors (Sebastian et al. 2018).

Even with careful data collection and statistical modeling, the PMT score is still a prediction of a households’ actual welfare and subject to inaccuracies. The magnitude of inclusion and exclusion errors depends on the program eligibility threshold (the PMT score cutoff) and the true level of poverty rate in a country. Considering a fixed poverty rate in a country, at one extreme, a program that does not include any beneficiaries will have zero inclusion error, but an exclusion error equal to the poverty rate. Likewise, at the other extreme, a program that includes everyone will have zero exclusion error, but an inclusion error equal to percentage of the population not living in poverty. In practice, the PMT score cutoff can be selected to balance these errors based on policy and program objectives.

# 3. Data and Methods

The development of the Sudan PMT proceeds as follows: First, we identify a set of candidate variables from household survey data that we believe could serve as a good proxy for a household’s welfare—in the case of Sudan this is household consumption expenditure. Second, we perform statistical analyses to determine a subset of variables to be used in the final PMT. In this step, we also explore different model specifications. Third, we evaluate candidate models’ out-of-sample predictions across different PMT cutoff scores using several evaluation metrics. Finally, we explore the performance of hypothetical targeting methods and transfer schemes using the final PMT.

## _Data and Variables_

That data used to develop the Sudan PMT comes from the National Household Budget and Poverty Survey (NHBPS 2014/2015) conducted by the Sudan Central Bureau of Statistics.<sup>9</sup> The survey includes modules on consumption, household characteristics, education, employment, housing, and asset ownership. The survey, representative at the national level, as well as states and rural and urban areas, includes 11,953 households.

> 9 This is the most recent household survey data available at the time of this study.

7
poverty implications of tariff reductions on the purchasing power of households with different expenditure patterns.

More importantly, there are other context-specific transmission channels (see Winters et al. 2004 for discussion) which cannot be captured in a cross-country exercise seeking to extract common elements that are likely to hold across a range of countries. For example, as shown by Nicita (2004) in his detailed case study of tariff liberalization in Mexico during the 90s, price pass-through effects were substantially different across commodities, and the poverty effects of trade liberalization varied substantially across regions.

2.2 Framework and Data Using panel data, the literature has usually estimated a relation of the form:

> where _INQit_ is the measure of inequality, _Yit_ is average income per capita (either from the national accounts or from household surveys), _OPENit_ is a measure (eventually lagged to control for endogeneity) that proxies for the country’s outward-orientation<sup>6</sup> , and _Zit_ is a vector of control variables. In the discussion above, there is no role for income as an explanatory variable. Its inclusion rests on some variant of a Kuznets-type relationship, but also for structural changes (other than endowments but including increased financial integration) that are associated with rising GDP per capita and could affect the transmission of globalization-related effects to households.

In all specifications we include a set of country dummies, _Di_ , that eliminate all cross-country variation.

> 6 Greater outward-orientation goes beyond integration in goods markets. It includes integration in capital markets, as well as behind-the-border measures. Insofar as a reduction in transaction costs affect countries equally, these can be ignored. See further discussion below.

7
**Table 4: Impact of Ratifying the CRC on Immunization, IV Regressions**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
||DPT rate<br>(log)<br>All|DPT rate<br>(log)<br>Low<br>Income|DPT rate<br>(log)<br>Lower<br>Middle<br>Income|DPT rate<br>(log)<br>Upper<br>Middle<br>Income|DPT rate<br>(log)<br>High<br>Income|Measles<br>rate (log)<br>All|Measles<br>rate (log)<br>Low<br>Income|Measles<br>rate (log)<br>Lower<br>Middle<br>Income|Measles<br>rate (log)<br>Upper<br>Middle<br>Income|Measles<br>rate (log)<br>High<br>Income|
|Convention on the<br>Rights of the Child-<br>yes/no (CRC)|-0.028<br>(1.53)|-0.132<br>(1.19)|-0.077<br>(1.78)|0.056<br>(2.90)**|0.048<br>(2.41)*|-0.001<br>(0.07)|-0.15<br>(1.46)|-0.033<br>(0.82)|0.091<br>(4.18)**|0.067<br>(3.19)**|
|GDP per capita (in<br>tt 2000 US|0.087|0.362|0.123|-0.121|0.289|-0.013|0.193|-0.065|-0.082|-0.041|
|consan<br>dollars, log)|(2.83)**|(2.52)*|(1.58)|(3.00)**|(6.84)**|(0.45)|(1.44)|(0.91)|(1.78)|(0.9)|
|ICRG-Bureaucratic<br>Quality|0.009<br>(1.48)|-0.02<br>(0.6)|-0.015<br>(1.09)|0.019<br>(2.80)**|0.083<br>(7.60)**|0.006<br>(1.02)|-0.023<br>(0.75)|-0.009<br>(0.74)|0.035<br>(4.68)**|-0.025<br>(2.12)*|
|Democracy Score|0.002<br>(1.73)|0.002<br>(0.38)|-0.001<br>(0.31)|-0.004<br>(2.12)*|-0.021<br>(3.29)**|-0.001<br>(0.53)|-0.001<br>(0.18)|-0.002<br>(1)|-0.007<br>(3.11)**|-0.027<br>(4.05)**|
|Population Density|4.314|7.646|-2.778|0.741|0.801|4.931|8.805|-1.29|0.688|-0.509|
|(log)|(6.64)**|(5.16)**|(1.5)|(0.27)|(0.76)|(8.04)**|(6.37)**|(0.75)|(0.22)|(0.45)|
|Total Population|-3.855|-5.309|3.046|-0.386|-0.915|-4.676|-6.377|0.953|-0.412|0.357|
|(log)|(5.92)**|(3.68)**|(1.63)|(0.14)|(0.87)|(7.61)**|(4.74)**|(0.55)|(0.13)|(0.32)|
|YEAR|0.001<br>(0.51)|-0.043<br>(1.76)|0.007<br>(1.24)|0.004<br>(2.62)**|-0.003<br>(2.08)*|0.006<br>(4.27)**|-0.044<br>(1.95)|0.02<br>(3.96)**|0.005<br>(2.51)*|0.006<br>(3.75)**|
||48.278|145.125|-50.125|0.134|19.356|49.562|161.713|-46.731|-0.411|-10.955|
|Constant|(5.68)**|(3.22)**|(1.99)*|0|(1.45)|(6.18)**|(3.85)**|(2.01)*|(0.01)|(0.77)|
|Observations|2054|209|570|614|635|2054|209|570|614|635|
|Number of<br>group(countryc)|125|19|43|52|48|125|19|43|52|48|

Absolute value of t statistics in parentheses: * significant at 5%; ** significant at 1%. Country fixed effects included but not reported.

20
**Table 4. OLS regression results: Do differences in reported time use between time diary and stylized estimates vary** **<u>according to the activity?</u>**

||Bangladesh|Uganda|
|---|---|---|
|Variable|Model 1A|Model 1B|
|_Omitted category: Work as employed_|||
|Own business work|17.766**|-12.909|
||(8.501)|(8.490)|
|Farming/livestock/fishing|18.362*|-81.389***|
||(10.129)|(12.404)|
|Shopping/getting service|-1.541|-17.907**|
||(6.258)|(7.475)|
|Weaving/sewing/textile care|13.754*|-1.300|
||(7.137)|(4.537)|
|Cooking|10.516*|-5.063|
||(6.061)|(6.394)|
|Domestic work|18.609**|-62.973***|
||(8.747)|(8.398)|
|Care for children/adults/elderly|0.000|-44.288***|
||(6.655)|(7.365)|
|Travelling/commuting|21.278***|13.556*|
||(6.899)|(7.074)|
|Constant|-8.765|-1.555|
||(5.846)|(4.253)|
|Adjusted R-squared|0.006|0.065|
|Test of joint significance|8.217|24.336|
|Observations|3,591|2,952|

**Source:** Authors’ calculations based on WEAI 2.0 pilot data. **Notes:** Dependent variable is the difference between time diary and stylized estimates. Household-level cluster-robust standard errors in parentheses. ***, **, and * denote statistical significance levels at 1%, 5%, and 10%, respectively.

28
The main contribution of our paper is proposing a tool that displays these changes but also identifies and quantifies the underlying drivers. We focus our attention on polarization defined as the combination of divergence from global and convergence on mean local incomes. The method developed blends two different frameworks of distributional analysis: relative distribution (Handcock and Morris, 1998 1999) and unconditional quantile regression (Firpo et al., 2009). The advantage over other methodologies is that it allows to single out the different drivers of polarization at different points of the consumption distribution.

Ghana, almost unique among SSA countries, offers the opportunity to analyze the last two decades’ distributional changes, since four comparable household surveys are available. The country also presents interesting specificities. Since 1991, poverty had declined very fast, inequality has not increased dramatically and yet the country has seen a rapid surge in polarization. The results of our analysis suggest that the distributional changes hollowed out the middle of the Ghanaian household consumption distribution and increased the concentration of households around the highest and lowest deciles.

Results on drivers of polarization indicate that although there is some heterogeneity across the various sub-periods in particular in terms of magnitude, household characteristics, educational attainment and access to basic infrastructures all tended to increase over time the size of the upper and lower tails of the consumption distribution and as a consequence the degree of polarization. Urban rural and regional variables started to have a strong impact on polarization only in the last decade; households residing in Greater Accra and the urban areas of Ashanti region performed well and increased their relative economic advantage over the rest of the country.

From a policy perspective, the pro-polarization impact of variables that tend to change slowly over time is of particular concern. It is very unlikely that policy makers can find a quick fix to the problem and any intervention will produce results only in the long run. This implies that the country needs to start now to develop a strategy that, if not able to immediately reverse polarization, at least can mitigate its impact. The creation of a modern social protection system, the expansion in the access to basic services, the continued effort to expand primary and secondary education are all interventions that can pay off and help the country to maintain its social cohesion.

20
Building on Allcott and Ortega’s (2009) work, the data set and methodology that we use in this paper enables us to test in a more robust way for the performance of Fe y Alegría schools than previous work for three main reasons. First, while Allcott and Ortega (2009) use data on test scores for one year only, we have five years of good data at our disposal, which enables us to test whether differences in performance are robust over time. Second, we have a larger number of subjects on which students are tested. Beyond test scores for mathematics and Spanish, we also look at data on test scores for scientific topics. Third, we have an especially good set of controls regarding the socio-economic characteristics of the families to which the children belong. Finally, we use alternative matching methods (propensity score matching as well as coarse exact matching) to ensure that the results obtained are robust to the choice of method.

Our results confirm previous work suggesting that faith-based schools perform relatively well. Simple statistics on test scores suggest that Fe y Alegría schools perform worse than other schools for all years in the sample. However, Fe y Alegría schools also cater to poorer students. Once controls are included for student background, Fe y Alegría schools actually perform at least as well as, and in several cases better than other schools. Section 2 of the paper describes our data and methodology. Section 3 presents our empirical results. A conclusion follows.

# **2. Data and methodology**

The ICFES (Colombian Institute for the Promotion of Higher Education) test is a standardized test administered by the Ministry of Education that every student has to take during the last year of high school<sup>4</sup> . The test score is used mainly by colleges for admission purposes, and had no implications for high school graduation for the years used in this study<sup>5</sup> . The database provided by ICFES has test scores for every single student taking the test from 1998 to 2006.

> 4 The test can be retaken after graduation from high school.

> 5 It became a requirement for high school graduation in 2009.
food insecurity have? Third, do basic cross-country patterns in this indicator align with expectations? Because the GWP is conducted by a private organization and its collaborators, much of the description of the formal survey characteristics relies on Gallup materials. We explore correlations between the GWP indicator and non-GWP welfare indicators by conducting a correlation analysis of a cross-section of countries and, in the next section, a multivariate analysis of the full panel dataset.<sup>5</sup>

# **<<B>>** _General characteristics of the Gallup World Poll_

Since 2005–06, the GWP has interviewed households in approximately 150 countries, although not always annually. Most questions are constructed to have yes or no answers to minimize translation errors. In developing countries, all but one of the GWP surveys are conducted face to face (China 2009 is the exception), and most take approximately one hour to complete. The surveys follow a complex design and employ probability-based samples intended to be nationally representative of the entire resident civilian noninstitutionalized population aged 15 years and older. In the first stage of sampling, primary sampling units consisting of clusters of households are stratified by population size, geography, or both, with clustering achieved through one or more stages of sampling. When population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Gallup typically surveys 1,000 individuals in each country, except in larger countries such as India (roughly 6,000), China (4,000), and Russia (3,000). In the second stage, random route procedures are used to select sampled households within a primary sampling unit, and Kish grids are used to select respondents within households. Finally, the data are internally assessed for consistency and validity and then centrally aggregated and cleaned. Data weighting

> 5 Much of what follows is drawn directly from the Gallup _Worldwide Research Methodology_ (Gallup 2010a). The present author purchased country-level data directly from Gallup and corresponded with senior Gallup staff about specific questions.

6
# TABLE OF CONTENTS

||||Page|
|---|---|---|---|
|**SUM**|**MARY**|.......|I|
|NAT|IONA|L**INDEX**FUNDS: EMPIRICAL PERSPECTIVES.|**.**<br>3|
|1.|THE|DATA AND SAMPLE ................................|4|
|11.|GAI|NS FROM DIVERSIFCATION INTO EMERGING MARKETS ...|<br>5|
||I. 1|**Pairwise Correlations**.<br>.<br>.<br>..............|7|
||II.2|Components of Risk...........|9|
||11.3|Mean-Variance Efficient Frontiers ........................|I l|
|||11.3.1<br>Idealized Diversification Gains ....................|I 1|
|||11.3.2<br>U.S. Based Diversification .......................|12|
|ilI.|PRIC|ING OF NIFs .....................................|13|
||III.I|Imperfect Substitution**.14**||
||111.2|Methodology .14||
||111.3|Results .........................................|16|
|IV.|IPOs|OF NIFs<br>..|18|
||IV.I|Initial Returns .19||
||IV.2|After Market Returns .19||
||IV.3|The Seasoning Effect .20||
|V.|PRE|MIUMS/DISCOUNTS ON NIFs<br>..|20|
||V.1.|Determinants**ol**Premium/Discount .21||
||V.2.|Variable Definilions .21||
||V.3.|The Test Procedure .................................|23|
||V.4.|Test Results .....................|25|
|VI.|CON|CLUDING REMARKS ....................|26|
|REF|ERENC|ES .............|28|
|ANN|EX T|ABLES .............|29|
|li-JR|ES ......|.........................<br>I ................|47|
# **Table 6: Relationship between firm innovation and the sources of funding of fixed assets for the most performing firms**

|**Variables**|**OLS**|**Logit**|
|---|---|---|
|**% Fixed assets funded by banks**|0.0133|0.0114|
||(0.0359)|(0.0373)|
|**% Fixed assets funded by NBFI**|0.2036*|0.1962*|
||(0.1194)|(0.1172)|
|**% Fixed assets funded by other**|0.0291|0.0202|
||(0.0423)|(0.0409)|
||**_Adoption_**||
|**% Fixed assets funded by banks**|0.0560|0.0629|
||(0.0385)|(0.0441)|
|**% Fixed assets funded by NBFI**|0.2255*|0.2331*|
||(0.1196)|(0.1318)|
|**% Fixed assets funded by other**|0.0571|0.0521|
||(0.0455)|(0.0471)|
||**_Invention_**||
|**% Fixed assets funded by banks**||0.0952*|
|||(0.0553)|
|**% Fixed assets funded by NBFI**||0.0845|
|||(0.1116)|
|**% Fixed assets funded by other**||0.0299|
|||(0.0635)|
|**Additional controls**|Asper Eq (1)|Asper Eq (2)|
|**Country FE**|Yes|Yes|
|**Year FE**|Yes|Yes|
|**Observations**|9,126|9,112|
|**Number of countries**|104|104|

Note: This table reports the results of the estimation of Equation (1) and (2). The sample comprehends firms above the median value of value of real annual sales scaled by total number of employees. Under the column (OLS), the dependent variables are the innovation score and adoption score, as described in Table 1. Under the column (Logit), the dependent variables are the innovation dummy, the adoption dummy and invention, as described in Table 1. For logit regressions, we report the marginal effects at the means for the explanatory variables. *, **, and *** represent statistical significance at 10%, 5%, and 1% twotailed level, respectively. Robust standard errors in parentheses, clustered at strata level.

The degree of a country's financial development may also play a role in explaining the relationship between firm innovation and the sourcing of funding of fixed assets. In detail, the association between financing obtained through the formal financial system may be stronger in countries with more developed financial markets and institutions because of the variety of instruments, investors and ancillary services that may spur investment in innovation. Using the financial development index computed by the International Monetary Fund (IMF),<sup>8</sup> we split the sample into countries with low financial development (i.e., in the first quartile of the distribution of the IMF financial development index), and high financial

> 8 The explanation and data for the index are available at the following link: <u>https://data.imf.org/?sk=F8032E80-B36C-43B1AC26-493C5B1CD33B.</u>

18
a WBES country from the size ranking in the US of the same ISIC3 industries.<sup>5</sup> The first stage relation, that is the conditional expectation of a WBES country’s industry size rank across ranks of US industry size, is positive and linear. When repeated across subsamples of countries at different income levels, the slope remains constant: the US distribution has the same power to predict industry size ranking in Ethiopia, Indonesia or Brazil.<sup>6</sup> This suggests that the identification strategy captures technological differences in labor demand, which vary across industries but not across countries.

The US Census industry size distribution is a valid source of exogenous variation under the assumption that US firms determine their size orthogonally to tax enforcement. This assumption might be reasonable in the US, where the Internal Revenue Service has access to comprehensive sources of third-party information and might not need to rely on imprecise size-proxies for tax enforcement. In contrast, tax authorities in developing countries with low fiscal capacity may be constrained to use size-proxies. We take the following steps to alleviate concerns with the identifying assumption. First, the estimates are robust when using the industry ranking based on European firms from Amadeus data,<sup>7</sup> instead of the US Census. These firms are subject to comprehensive financial reporting, and hence size-proxies might no longer be determinants of tax inspection. Second, since it has been documented that tax evasion is important among the self-employed in the U.S. (Blumenthal et al. 2001), we only consider firms with more than five employees in the US Census.<sup>8</sup> Third, for the few countries in the WBES with similar GDP as the US and thus similar tax enforcement capacity, we find no size gradient in tax inspection.<sup>9</sup> Finally we support the exclusion restriction by showing that the predicted WBES size-ranking is

> 5Since our identification relies on industry comparisons of firm size, we do not use the within-industry size variation. In effect, this reduces our sample to 12,152 ISIC3-country-year observations.

> 6This supports the following type of statement: “If the average car manufacturer requires more workers than the average retail firm in the US, then this ranking of industries by size also holds in Ethiopia, Indonesia, and Brazil.”

> 7We use Amadeus data for German and British firms. Germany data derive from financial statements filed with the business registry. British data come from audited annual reports presented to shareholders. 8The selection of firms with five or more employees also matches the sampling strategy of the WBES. 9We cannot test this directly on the US since it does not have a World Bank Enterprise Survey.

3