rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5: Data Transparency and Sovereign Bond Spreads (IV), Baseline with External Public Debt**

|Freedom of information, lag of ICRG, lag of interaction<br>b d ICRG l f l db d|||||||
|---|---|---|---|---|---|---|
|IV variables<br>etween transparency an , ag o externa ets an<br>lag of interaction between transparency and external<br>|||dependent va|riable: spread|||
|debts|||||||
||||1995|-2018|||
|Variables|[1]|[2]|[3]|[4]|[5]|[6]|
|GDP per capita (constant 2010 US$)|-0.524**|-0.448*|-0.562**|-0.493**|-0.241|-0.536***|
||(0.233)<br>|(0.231)<br>|(0.237)<br>|(0.227)<br>|(0.181)<br>|(0.204)<br>|
|Growth (annual %)|-0.0470***<br>(0.00736)|-0.0475***<br>(0.00735)|-0.0469***<br>(0.00737)|-0.0470***<br>(0.00739)|-0.0399***<br>(0.00627)|-0.0438***<br>(0.00723)|
|CPI Inflation (average consumer prices)|0.0836**|0.0784**|0.0840**|0.0813**|0.111***|0.0877***|
||(0.0334)<br>|(0.0335)<br>*|(0.0332)<br>*|(0.0336)<br>|(0.0299)<br>|(0.0339)<br>***|
|Primary Balance to GDP (%)|0.0143|0.0155|0.0144|0.0142|0.0128|0.0297|
||(0.00877)<br>|(0.00878)<br>|(0.00872)<br>|(0.00885)<br>|(0.00881)<br>**|(0.00964)<br>|
|Current Account Balance to GDP (%)|-0.00374|-0.00411|-0.00357|-0.00368|-0.0102|-0.00800|
|VIX Id|(0.00505)<br>0475***|(0.00504)<br>0482***|(0.00508)<br>0474***|(0.00509)<br>0471***|(0.00517)<br>0574***|(0.00568)<br>0565***|
|nex|.<br>(0.0637)|.<br>(0.0638)|.<br>(0.0634)|.<br>(0.0643)|.<br>(0.0685)|.<br>(0.0816)|
|US 10-year Treasury Bond Yield|-0.394***|-0.384***|-0.389***|-0.402***|-0.272***|-0.135|
|Transparency Index (WB)|(0.0887)<br>3.112***<br>|(0.0891)|(0.0891)|(0.0891)|(0.0922)|(0.105)|
|Methodology Assessment|(0.609)|2.976***|||||
|||(0.557)|||||
|Periodicity and Timeliness Assessment|||3.475***||||
||||(0.821)||||
|Date Source Assessment||||2.900***|||
|||||(0.535)|||
|Transparency Dummy (IMF) Subscription|||||7.675***||
||||||(1.986)||
|Transparency Dummy (IMF) Standard compliance||||||25.87***<br>(4536)|
|Transparency (WB) x ICRG|-0.713***<br>(0.122)|||||.|
|Methodology x ICRG||-0.747***<br>(0.132)|||||
|Periodicity and timeliness x ICRG|||-0.744***||||
||||(0.134)|***|||
|Data source x ICRG||||-0.696<br>(0.127)|||
|Transparency (IMF) Subscription x ICRG|||||-2.148***<br>(0.485)||
|Transparency (IMF) Standard compliance x ICRG||||||-6.452***<br>|
|Transparency (WB) x external debt|000224***|||||(1.096)|
||.<br>(0.000744)||||||
|Methodology x external debt||0.00233***|||||
|||(0.000769)|||||
|Periodicity and timeliness x external debt|||0.00213***<br>||||
|Data source x external debt|||(0.000711)|0.00233***<br>0000762|||
|Transparency (IMF) Subscription x external debt||||(.)|0.0209***||
||||||(000234)||
|Transparency (IMF) Standard compliance x external debt|||||.|0.0146***|
|||||||(0.00378)|
|Constant|8.143***|8.688***|7.385***|8.539***|6.970***|9.091***|
||(2.066)|(1.982)|(2.245)|(2.003)|(1.607)|(1.811)|
|Observations|438|438|438|436|570|570|
|No. Countries<br>|47|47|47|47|47|47|
|Standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||||

27
**Table 12: Regression of log(earnings) on human/physical capital and possible segmentation**<sup>**variables**</sup>

||need mo|re credit|stay|/leave|migr|ant status<br>|volun<br>|tariness<br>|
|---|---|---|---|---|---|---|---|---|
||_i_|ii|_iii_|_iv_|v|vi|vii|_viii_|
|Education|0.04<br>(4.29)|0.04<br>(3.76)|0.04<br>(4.27)|0.04<br>(3.70)|0.04<br>(4.39)|0.04<br>(3.20)|0.05<br>(4.74)|0.05<br>_(5.05)_|
|Experience|0.03<br>(6.1)|0.03<br>(5.87)|0.03<br>(5.92)|0.03<br>(5.90)|0.03<br>(6.17)|0.06<br>(4.20)|0.03<br>(6.55)|0.04<br>(6.61)|
|Exp<sup>2 </sup>(104)|-5.14<br>(-7.82)|-5.35<br>(-7.69)|-5.06<br>(-7.73)|-5.52<br>(-7.94)|-5.19<br>(-7.89)|-4.29<br>(-5.49)|-5.23<br>(-8.02)|-5.45<br>(-7.72)|
|Ed*Exp (104)|-6.20<br>(-2.13)|-6.25<br>(-2.02)|-5.77<br>(-1.99)|-4.69<br>(-1.54)|-0.06<br>(-2.16)|-3.16<br>(-0.89)|-7.58<br>(-2.62)|-0.001<br>(-3.23)|
|Capital(10-5)|1.80<br>(15.23)|1.98<br>(13.84)|1.86<br>(16.13)|1.83<br>(15.53)|1.88<br>(16.26)|1.86<br>(13.28)|1.83<br>(15.91)|1.73<br>(14.55)|
|Log(hours)|0.39<br>(19.47)|0.41<br>(18.97)|0.38<br>(19.02)|0.36<br>(16.84)|0.39<br>(19.49)|0.40<br>(16.88)|0.38<br>(19.29)|0.38<br>(17.35)|
|Intercept|4.58<br>(36.49)|4.53<br>(34.16)|4.66<br>(37.14)|4.72<br>(34.82)|4.57<br>(36.38)|4.62<br>(31.97)|4.59<br>(36.91)|4.55<br>(33.91)|
|Dummy'<br>|0.10<br>(3.42)|0.29<br>(0.68)|-0.21<br>(-7.11)|-0.58<br>(-1.63)|0.001<br>(0.07)|-0.34<br>(-1.09)|-0.24<br>(-9.16)|0.22<br>(0.61)|
|Dummy interactive|||||||||
|<br>Education||0.04||0.03||0.03||_-0.05_|
|||(1.33)||(0.89)||(1.13)||(-1.73)|
|Experience||0.004||-0.02||0.03||-0.02|
|||(0.21)||(-1.02)||(2.16)||(-1.61)|
|Exp<sup>2 </sup>(104)||1.27||4.50||-3.63||2.35|
|||(0.58)||(2.09)||(-2.27)||(1.27)|
|Ed*Exp (104)||-5.85||-9.58||-10.05||16.56|
|||(-0.61)||(-0.96)||_(-1.50)_||(2.00)|
|Capital(10-5)||-0.58||0.03||0.06||1.61|
|||(-2.29)||(0.62)||(0.26)||(3.46)|
|Log(hours)||-0.15||0.11||-0.04||0.01|
|||(-0.25)||(1.84)||(-0.87)||(0.14)|
|R<sup>2 </sup>adjusted<br>Fvalue"<sup>2</sup>|0.19<br>11.70|0.19<br>4.50|0.19<br>50.59|0.20<br>10.19|0.19<br>0.016|0.19<br>0.93|0.20<br>83.88|0.20<br>14.60|

Numbers in parentheses are t-statistics. Number of observations = 4426 for each regression. lThe dummy corresponds to the segmentation variable being tested in each column. It takes a value of I if credit is a constraint (regressions , _ii),_ if the firm owner wants to shutdown his business (regressions _iii, iv),_ if the firm owner migrated as an adult (regressions v, vi), and if the firm owner involuntarily entered the small firm sector (regressions _vii, viii),_ 0 otherwise.<sup>2</sup> where the degrees of freedom for the regressions with only a dummy term _(i, i, v, vi)_ are (1, 4418) and the degrees of freedom for the regressions with all the dummy interactive terms _(ii, iv, vi, viii)_ are (7, 4412).

35
|**Table A10: Heterogeneity in Impacts for Women**|Tot|al Profits i|n Long-Ter|m Follow-|up|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Assigned to Personal Initiative Training|69.9*|25.2|38.4|50.1|2.22|
||(38.7)|(45.8)|(53.4)|(62.8)|(46.0)|
|Assigned to Traditional Training|22.9|-17.5|-4.58|-8.80|-15.8|
||(34.7)|(46.7)|(54.4)|(48.3)|(44.6)|
|Assigned to PI * Not Sole Decision Maker on Revenues|-296.4***<br>(107.9)|||||
|Assigned to Trad * Not Sole Decision Maker on Revenues|-163.7<br>(111.9)|||||
|Assigned to PI* Not Sole Decision Maker on HH Expenses||-0.58<br>(75.5)||||
|Assigned to Trad * Not Sole Decision Maker on HH Expenses||25.2<br>(67.6)||||
|Assigned to PI*Looks after Kids or Elderly|||-19.4|||
||||(74.6)|||
|Assigned to Trad*Looks After Kids or Elderly|||6.45<br>(70.8)|||
|Assigned to PI*Below 9 Years Education||||-42.8<br>(76.2)||
|Assigned to Trad*Below 9 Years Education||||15.4||
|||||(67.2)||
|Assigned to PI*Not Married|||||105.5|
||||||(72.0)|
|Assigned to Trad*Not Married|||||45.6<br>(49.7)|
|Sample Size|699|699|699|699|699|
|Proportion with Interaction=1|0.15|0.49|0.55|0.57|0.24|
|Control Mean for Interaction=1|314|164|155|158|70|
|Control Mean for Interaction=0|127|151|159|156|187|
|Notes:<br>Sample restricted to female entrepreneurs. All regressions incl<br>variable, and the interacting variable interacted with baseline<br>indicated. Robust standard errors in parentheses. *, **, *** d|ude controls<br>profit. Intera<br>enote signific|for basel<br>cting vari<br>ance at th|ine profits<br>able varies<br>e 10, 5, an|, the intera<br>across col<br>d 1 percen|cting<br>umns as<br>t levels.|

38
The third indicator is the adult total fertility rate (TFR), defined as the total number of children a woman would have by the end of her reproductive period if she experienced the currently prevailing age-specific fertility rates throughout her childbearing life. All three indicators feature in the international development targets (International Monetary Fund and others 2000), and there are specific targets for the first two.<sup>8</sup> There is, however, a concern (Gwatkin 2000) that progress toward population-based targets could mask uneven progress across socio-economic groups. Indeed, there is evidence that in some countries progress in reducing child mortality and malnutrition has been slower amongst the poor (Victora and others 2000; Stecklov, Bommier, and Boerma 1999; Vega and others 2001; Wagstaff, van Doorslaer, and Watanabe 2001).

Households were ranked in the production of the tables in Gwatkin and others (2000) using an index of wealth obtained from a principal components analysis (PCA) of questions on housing characteristics (e.g., the material from which the floor is made of) and ownership of household durables (e.g., bicycle, refrigerator, etc.) (Filmer and Pritchett 1999). These methods along with the factor score matrices are reported elsewhere (Gwatkin and others 2000). The data are in grouped form, based on quintiles of households. The denominators relevant for computation of the concentration indices are the sample at risk (e.g., children under the age of five in the case of child malnutrition) so that the groups are not necessarily quintiles of the sample at risk. In the case where grouped data are used to compute the extended concentration indices, certain modifications need to be made to the equations in the previous section. These and other computational issues are discussed in the Appendix.

# _3.2. Poor-nonpoor inequalities_

Inequalities to the disadvantage of the poor are evident in all three health indicators (see Tables 1-3). They are especially pronounced for malnutrition, where the average value of C(2) is equal to -0.1475. The extent of prorich inequalities varies across countries, the values of C(2) ranging from -0.2590 (Brazil) to 0.0020 (Kazakhstan) in the

**8** The targets are to reduce the under-five mortality rate by two-thirds between 1990 and 2015, to halve the percentage of children suffering from malnutrition between 1995 and 2015, and to reduce child

10
Table 7: Heterogeneity by gender

||Psycho|social Index|P|HQ|Value to|Family|
|---|---|---|---|---|---|---|
||Female|Male|Female|Male|Female|Male|
|Work|0.165|0.233|0.120|0.276|0.091|0.121|
||(0.068)|(0.051)|(0.124)|(0.089)|(0.114)|(0.089)|
|Cash|0.086|0.045|0.089|-0.031|0.080|-0.084|
||(0.088)|(0.057)|(0.146)|(0.101)|(0.142)|(0.109)|
|Test: Cash = Work|0.268|0.000|0.799|0.001|0.921|0.021|
|Shrp. q-val Cash=Work|1.000|0.001|1.000|0.008|1.000|0.026|
|Test: Male = Female|0.247|0.247|0.047|0.047|0.223|0.223|
|Shrp. q-val Male = Female|0.396|0.396|0.396|0.396|0.396|0.396|
|Observations|223|503|223|503|223|503|
||Stab|ility|Household P|ower Index|Work Rig|hts Index|
||Female|Male|Female|Male|Female|Male|
|Work|0.241|0.284|0.323|0.019|0.349|0.086|
||(0.140)|(0.087)|(0.129)|(0.093)|(0.104)|(0.094)|
|Cash|0.196|0.045|0.279|0.072|0.110|0.040|
||(0.163)|(0.111)|(0.142)|(0.097)|(0.113)|(0.111)|
|Test: Cash = Work|0.724|0.018|0.745|0.532|0.011|0.600|
|Shrp. q-val Cash=Work|1.000|0.026|1.000|0.271|0.146|0.280|
|Test: Male = Female|0.174|0.174|0.674|0.674|0.195|0.195|
|Shrp. q-val Male = Female|0.396|0.396|0.491|0.491|0.396|0.396|
|Observations|223|503|223|503|223|503|

**Notes:** This table reports the treatment effect of the employment and cash arms on psychosocial outcomes separately for male and female participants. Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and controlling for the baseline measure of the dependent variable (ANCOVA), camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014), run separately by respondent gender. Each row presents the coefficients of the specified treatment relative to the control group (which received a small weekly cash payment).

All outcomes are standardized. The ‘Household Power Index’ is an index of a series of questions around who in the household makes key decisions, with a higher household power index signaling greater decision-making power for the female head of household. The ‘Work Rights Index’ is an index of a series of questions around whether women should be permitted to work outside of the home.

Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” reflects p-values, referred to as ‘sharpened q-values,’ adjusted for multiple hypothesis testing using Anderson (2008). The adjustments are made within gender for all outcomes in this table. The row labeled “Test Male = Female” reflects the unadjusted p-value for the test of equality between the Male and Female work treatment effects based on a pooled regression (not shown) where the treatment indicators were interacted with the gender of the respondent. The row labeled “Shrp. q-val Male = Female” reflects the adjusted p-value for this test of equality across all five outcomes.

39
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|---|---|
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|EmergencyPost-Conflict Assistance Project|Nicolas Perrin|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|Access to Finance for SMEs|Samuel Munzele Maimbo|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|3A-EAC Power Master Plan|Kwawu Mensan Gaba|
|CO: Programmatic Labor Reform and Social Structural Adjustment|L WendyCunningham|
|Programmatic Decentralization and Competitiveness SAL|Rossana Polastri|
|Programmatic Decentralization and Competitiveness SAL|Rossana Polastri|
|Youth Inclusion Learning& Innovation Credit|Carine Clert|
|Youth Inclusion Learning& Innovation Credit|Carine Clert|
|Youth Inclusion Learning& Innovation Credit|Carine Clert|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|Health Sector Modernization Support Technical Assistance Project|Owen K. Smith|
|NICARAGUA PRSC I|Amparo Ballivian|
Table 1. Antidumping Duties in Force against Brazilian Exporters <u>(by Importing Country) (1997–2000)</u>

||Number of|
|---|---|
|Country|cases|
|Argentina|14|
|European Union|4|
|Mexico|3|
|Canada|2|
|United States of America|1|
|South Africa|1|

_Source_ : Bown(2010) 18
# **Table 6: Impacts on Corruption**

## **Robust Standard Errors**

||**(1) Breaking**<br>**Rules**|**(2) Corruption**<br>**Pressure**|**(3) Change**<br>**Location**|**(4) Change**<br>**Specifications**|**(5) Select**<br>**Contractors**|**(6) Divert Funds**|
|---|---|---|---|---|---|---|
|**Relevant Committee Membership [Member=1]**|1.36**|1.70***|0.59|1.18**|3.97***|1.12*|
||(0.59)|(0.60)|(0.46)|(0.60)|(0.86)|(0.65)|
||[0.02]|[0.00]|[0.20]|[0.05]|[0.00]|[0.08]|
|**Constituency controls**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Mean of dependent variable**|38|20|19|20|23|17|
|**Adjusted R-squared**|0.24|0.14|0.13|0.20|0.12|0.08|
|**First Stage F-Statistic**||||503|||
|**Observations**|3,008|3,008|3,008|3,008|3,008|3,008|

**Notes** : *** denotes significance at 1%, ** at 5%, and * at 10% level.  Robust standard errors are in round brackets and associated p-values are in square brackets.  The dependant variables are the average proportion of projects on which staff in the implementing organization corresponding to the project report: observing others breaking the Public Service Rules in Column 1; pressure to engage in corruption, which is an average of the variables presented in the next four columns, in Column 2; pressure to change project location in Column 3; pressure to change project specifications in Column 4; pressure to change project contractors in Column 5; and, pressure to divert project resources in Column 6.  Relevant Committee Membership is a binary variable that indicates whether the project is in a constituency and sector in which the corresponding House representative sits on the relevant sectoral committee, taking the value 1 if so and 0 otherwise. Constituency characteristics are comprised of the means and standard deviations of the following indices: the proportion of poor in the constituency, measured by a national poverty index, the average years of education of the household head, the proportion of constituents with access to potable water, the average time in minutes to the nearest primary school, and the average time in minutes to the nearest secondary school.  Means and standard deviations of the following indices are also included to reflect the frequency with which constituents benefit from a public project of the named type in the five years preceding 2006: construction of electrification infrastructure, rehabilitation of electrification infrastructure, well/borehole, construction of piped water infrastructure, rehabilitation of piped water infrastructure, sanitation, school construction project, school rehabilitation, health facility construction, health facility rehabilitation, road construction, tarring/grading of roads, transportation services, and agricultural-inputs schemes.  Finally, constituency characteristics include a set of indicators of the economic dynamics of the constituency, comprised of indicators of improvements in opportunities for employment, the availability of agricultural inputs, number of buyers of agriculture produce, the availability of extension services, the availability of credit facilities, and the availability of consumer goods.  Figures are rounded to two decimal places.
i) Information on fee schedules is only accessible by meeting with a public official, ii) Information on fee schedules can be accessed via the website of the relevant or a related agency and iii) Information is available through a public notice or through brochures that can be easily consulted or picked up without prior appointment at the relevant agency.

The last option was specifically added to capture the transparency efforts made by agencies that might not be able to rely on online solutions for connectivity or capital reasons as is the case in many developing economies.

The answers to these questions were scored as follows: in cases where a majority of respondents chose option ii) or iii) in their response, a value of one was assigned, indicating that information is accessible without the need for an appointment with a government official. In addition, where respondents reported that the information could be obtained online, a fact check of the website was undertaken by the team. On the other hand, if more than half of the respondents chose option i) and an Internet search brought up no website, a value of zero was assigned, indicating that the particular type of regulatory information can only be accessed by meeting with an official. Table 1a in Annex 1 provides detailed descriptions of all four new variables as well as the scoring methodology applied.

# **III. Description of the Data**

Tables 5, 6 and 7 summarize the information that we have collected for the four new variables from 185 economies. The sample includes 50 high income, 49 upper-middle income, 53 lowermiddle income, and 33 low-income economies. Following the World Bank regional classifications, the data cover 46 of the economies in Sub-Saharan Africa, 18 in the Middle East and North Africa region, 24 in East Asia and the Pacific, 8 in South Asia, 33 in Latin America and the Caribbean, 26 in Europe and Central Asia and 30 in OECD high income economies.<sup>7</sup> According to the data, worldwide company registries are the public agencies that are most likely to make information easily accessible without the need for meeting a government official. Table 5a presents results by region and income revealing that businesses can access fee schedules for incorporating a new company via a website or through brochures or billboards available at company registries in 79% of all economies in the selected sample. Company registries are least transparent in Sub-Saharan Africa where only 47% of economies make incorporation fees available through brochures and 41% also publish the fees on the web. By contrast, in more than 90% of OECD high-income economies incorporation fees can be accessed electronically.

The greater relative transparency of company registries can most likely be explained by the great number of reforms that have happened around the world in this particular area of business

> 7 The regional classification is of June 2013.

5