rafmacalaba/datause-encoder-data huggingface dataset:
# **TABLE 7.2: Public Agency Level - OLS Regressions**

Source of Data: Public Officials' Survey Dependent variable: Accessibility of Public Services

|**Governance Indices**|Overall<br>corruption|Perceived<br>Corruption|Corruption<br>in<br>personnel|<br>Corruption<br>in budget|<br>Corruption<br>in public<br>contracts|<br>Administrative<br>Corruption|<br>Corruption<br>in laws|
|---|---|---|---|---|---|---|---|
|Corruption (*)|-0.219|-0.024|-0.095|-0.164|-0.132|-0.1|-0.121|
||[1.71]*|[0.13]|[0.85]|[1.98]*|[1.58]|[1.17]|[1.46]|
|Quality of the rules (**)|-0.39|-0.374|-0.391|-0.267|-0.37|-0.381|-0.388|
||[1.55]|[1.49]|[1.84]*|[1.50]|[1.47]|[1.52]|[1.54]|
|Effectiveness of rules (**)|0.121|0.175|0.404|0.047|0.193|0.143|0.138|
||[0.66]|[0.92]|[2.96]***|[0.30]|[1.05]|[0.78]|[0.77]|
|Audit (**)|0.063|0.126|0.058|-0.035|0.013|0.102|0.089|
||[0.38]|[0.74]|[0.44]|[0.29]|[0.08]|[0.62]|[0.54]|
|Meritocracy|0.044|0.099|0.017|0.099|0.066|0.073|0.066|
||[0.34]|[0.74]|[0.13]|[0.72]|[0.53]|[0.55]|[0.50]|
|Wage satisfaction|-0.058|-0.027|-0.034|-0.042|-0.033|-0.044|-0.049|
||[0.65]|[0.29]|[0.44]|[0.48]|[0.39]|[0.48]|[0.55]|
|Citizen voice|0.363|0.374|0.269|0.435|0.381|0.365|0.374|
||[2.08]**|[2.15]**|[1.54]|[2.62]***|[2.11]**|[2.09]**|[2.16]**|
|Mission|0.494|0.52|0.505|0.496|0.541|0.505|0.517|
||[1.70]*|[1.86]*|[1.85]*|[1.60]|[1.76]*|[1.76]*|[1.81]*|
|Resources|0.027|-0.002|-0.046|0.007|-0.029|0.016|0.022|
||[0.20]|[0.02]|[0.47]|[0.06]|[0.30]|[0.12]|[0.16]|
|Municipal (0,1)|1.933|1.217|0.963|2.862|1.676|1.261|1.128|
||[0.54]|[0.34]|[0.29]|[0.77]|[0.50]|[0.36]|[0.32]|
|Justice (0,1)|-16.685|-19.168|-19.363|-16.401|-18.255|-17.818|-17.191|
||[3.43]***|[4.21]***|[5.20]***|[3.66]***|[3.97]***|[3.78]***|[3.63]***|
|**Regional Variables**||||||||
|Language stratification|-0.19|-0.14|-0.155|-0.153|-0.187|-0.158|-0.173|
||[1.37]|[1.00]|[1.15]|[1.08]|[1.35]|[1.12]|[1.24]|
|Regional average income|-0.001|0|0|-0.001|-0.001|-0.001|-0.001|
||[0.84]|[0.28]|[0.28]|[0.44]|[0.82]|[0.54]|[0.65]|
|Lima (0,1)|4.458|-2.506|-3.191|1.284|3.516|0.87|2.231|
||[0.34]|[0.19]|[0.25]|[0.10]|[0.28]|[0.06]|[0.17]|
|Selva (0,1)|-4.482|-4.662|-5.203|-2.79|-4.93|-4.236|-4.507|
||[0.97]|[0.93]|[1.13]|[0.60]|[1.08]|[0.89]|[0.95]|
|Costa (0,1)|-3.267|-3.738|-2.827|-3.729|-3.359|-3.027|-3.101|
||[0.76]|[0.82]|[0.67]|[0.87]|[0.80]|[0.68]|[0.71]|
|Constant|43.127|14.273|23.142|28.931|33.28|27.264|30.519|
||[1.50]|[0.50]|[0.83]|[1.11]|[1.26]|[0.97]|[1.10]|
|Observations|133|133|136|137|136|133|133|
|R-squared|0.42|0.41|0.43|0.41|0.43|0.41|0.41|

Robust t statistics in brackets * significant at 10%; ** significant at 5%; *** significant at 1% (*) Corruption indicator for each regression appears in the heading of each regression (**) Corresponding indicator in each regression (Overall in 1st, 2nd, 6th and 7th columns, Personnel in 3th, Budget in 4th, Contracts in 5th) 36
**HOW DOES BRIBERY AFFECT PUBLIC SERVICE DELIVERY?**

**MICRO-EVIDENCE FROM SERVICE USERS AND PUBLIC OFFICIALS IN PERU**

Daniel Kaufmann, Judit Montoriol-Garriga and Francesca Recanatini<sup>1</sup>

> 1 World Bank Institute, Universitat Pompeu Fabra and World Bank Institute, respectively. We thank the participants to the LACEA-NIP Workshop, LACEA 2005 and the 2005 Quality of Government Conference for useful comments on an initial version of this work, and to Sergiy Biletsky for excellent research assistance. This paper builds on preliminary results that benefited from inputs by Marta Menendez and Juanita Riano. The views expressed in this paper are those of the authors and should not be attributed to the World Bank, their Executive Board, or their managements. Authors’ email address: dkaufmann@worldbank.org, judit.montoriol@upf.edu, frecanatini@worldbank.org
majority of firms in 34 percent of the 278 six-digit sectors in manufacturing, in 62 percent of the 142 six-digit sectors in commerce, and in 66 percent of the 252 six-digit sectors in services. By 2013 these percentages had increased to 51 in manufacturing, 81 in commerce and 88 in services.

# **The Schooling Composition of the Demand for Labor Differs Between Formal and**

# **Informal Firms**

Transportation services in Mexico may be provided by 100 self-employed workers driving their own trucks or by a single firm with 100 salaried employees. In both cases, there will be 100 trucks and 100 drivers, but, in the latter, there will be a need for a logistics engineer doing dispatches and a sales manager. Tortillas can be produced with simple technologies in small establishments with unskilled labor or in large plants that require engineers; the same holds for apparel and food processing, among many manufacturing activities. This is also true for retail commerce, which can take place in small stores employing workers with basic literacy and numeracy or through large supermarket chains employing industrial designers. In general, the complexity of tasks and the division of labor increase with firm size and generate the need for more educated workers. But complexity is not only an issue of size: a small informal firm producing jeans to be sold in a street market will be less likely to need an accountant than a formal firm of the same size selling jeans to a large retailer.

Table 4 uses data from the employment survey to show the distribution of employment by schooling level and firm size and type.<sup>8</sup> In each cell, the upper number is the share of workers of a given schooling level in the total number of workers in firms of that size; thus, columns add to 100

> 8 If there were no mixed firms, all informal (formal) workers would be employed by informal (formal) firms. Mixed firms complicate this picture. Unfortunately, we cannot use ENOE (Encuesta Nacional de Ocupación y Empleo) data to correct for this problem, and table 4 is the best approximation to differences in the schooling composition of the workforce between formal and informal firms.

12
decompose significant interactions, following the same procedure as for main effects but only including observations of the relevant subgroup.

### **_Study-Level Moderator Analysis_**

Study-level moderator analysis is conducted for framings where significant study-level variation is observed according to the _I_<sup>_2_</sup> index. Specifically, random-effects meta-regressions where study-level moderators are regressed on the effect sizes of a given framing across different studies are performed, with a meta-regression estimated for each framing. In this model, regression coefficients indicate the relationship between a covariate and the impact of a given framing. For example, we can interpret a regression coefficient of 0.03 for intervention start date as indicating that an increase in one month of the start date of a study corresponds to an increase of 0.03 units in log odds ratio in the impact of the experts framing on vaccination intentions.

Given the number of explored moderators (7) and the small number of studies testing certain framings (i.e., endemic, variants), we first examined the bivariate correlation between moderators and effect sizes and included only those with a significant relationship in our final meta-regression model for a given framing. While other IPD MA studies also had small numbers of studies (i.e., 6-7) when conducting moderator analysis (e.g., Jenum et al., 2019; Louise et al., 2021; Smelt et al., 2018), we note that our results should be interpreted with caution as we have a modest number of 6-16 studies included in our analyses to explain heterogeneity across studies. For efficacy and pro-social ( _k_ = 14), endemic ( _k_ = 7) and variant ( _k_ = 6) framings, none of the tested moderators explained a significant amount of variation in effect sizes.

# **3. Results**

## **3.1. Characteristics of Participants and the Study Sample**

Table 1 presents the demographic breakdown of the study sample, including the total sample that completed the surveys—both vaccinated participants who did not participate in the survey experiment and unvaccinated participants who were included in the survey experiments. Reflecting the typical social media user in LMICs, the sample skewed toward being male (52.4%), highly educated (43.6% tertiary education), and somewhat young (29.2% 18-29 years old).

14
# **5   Acting on goals and values**

## **5.1 How has this been measured?**

### **Intra-household decision-making questions**

Women’s ability to act and make decisions over important aspects of their lives is the third key dimension of agency. Most survey questions on this dimension of agency have focused on capturing decision-making roles within the household over different domains such as family planning, employment, agriculture, health, consumption and education. Questions on decision-making roles within the household were first employed in developed countries starting in the late 1950s and early 1960s. The first well-known decision-making module was introduced by Blood and Wolfe (1960) with their Decision Power Index. In this index, the respondent is asked to indicate “who has the final say” in respect to eight family decisions, and response alternatives are weighted from 5 (husband always) to 1 (wife always). A sample question is included in Box 7 below.

#### **Box 7 Decision Power Index, Blood and Wolfe (1960)**

##### **Sample question**

“In every family someone has to decide such things as where the family will live and so on. Many couples talk such things over first, but the final decision often has to be made by the husband or the wife. For instance, who usually makes the final decision about whether or not the wife should go to work or quit work?” _Response Options: husband always, husband more than wife, husband and wife exactly the same, wife more than husband, wife always._ This approach to measuring decision-making roles has not been changed or adapted substantially over time, with its use increasing, especially in large-scale surveys in developing countries. This focus on the main decision-maker stems from the notion that the greater number of decisions an individual is involved in, the greater control they have over their own life (Kishor 2005). The nationally-representative Demographic and Health Surveys (DHS), conducted in over 90 countries, form the largest source of data on women’s participation in decision-making. Initially, the surveys used slightly different questionwordings depending on the topic at hand. For example, questions regarding decision-making over health care, household purchases, consumption, and visiting friends or relatives would ask “who in your family usually has the final say on [X]”, while questions regarding control over use of earnings would ask “who mainly decides how the money you earn will be used?” More recently, the DHS decision-making questions have been standardized across countries and decision-making areas (‘domains’), asking the respondent “who usually makes decisions about [X]” (see Box 8 below).

30
We use the predetermined nature of the branch locations – which were opened in all stores of its parent company, Grupo Elektra - to identify the casual impact of Azteca opening on economic activity through a difference-in-difference strategy. Specifically, we compare the changes in outcome variables before and after Azteca opening across municipalities with pre-existing Grupo Elektra branches and those without branches at a time of the bank opening. Our analysis controls for the possibility that time trends in the outcome variables may be different in municipalities that had Grupo Elektra branches and from time trends in municipalities that did not have Grupo Elektra branches. Using the Mexican Labor Market Survey, ENE, we study the impact of this event on individuals’ employment choices and income levels.

Our results show that the new bank opening led to an increase in the proportion of informal businesses by 7.6 percent, but to no change in formal businesses. This is consistent with anecdotal evidence suggesting that Azteca targeted lower-income individuals and also with Azteca’s low documentation requirements. In contrast, formal business owners have easier access to commercial bank credit, and likely prefer it because of higher interest rates charged by Azteca<sup>3</sup> .

Second, the increase in informal businesses is only significant for men. However, we find a higher proportion of women working as wage-earners in municipalities with Azteca branches after its opening. This possibly signifies an expansion of labor market opportunities for women, from house makers to paid staff, perhaps in the informal businesses owned by their husbands or other male family members. Overall, total employment, including informal business owners and wage earners, rose by 1.4 percent for the complete sample.

Third, the new bank opening led to higher income levels for both men and women by about 7 percent. Similarly to this result, Burgess and Pande (2005) find that the expansion of bank branches in rural India had a significant impact on alleviating poverty, although Kochar (2005) and Panagariya (2006) cast doubt on their findings.

While Burgess and Pande do not examine the channel through which increased bank presence alleviates poverty, we are able to investigate the effect of Banco Azteca opening in more detail, by considering how the impact varies with pre-event occupation. We find that

> 3 Azteca charged interest rates of about 50 percent per annum, while commercial banks at the time charged rates of 20 to 40 percent. However, commercial banks rejected all but the most creditworthy customers (The Dallas Morning News, 31 October 2002.)

3
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Table 3. Skills Measures by Aggregate Industrial Groups. 2005-2010 and 2011-2015 Panel A: 2005-2010

||Agriculture,|Manufactures|Cotton fur|Ot|<br>her manufactu|Mineral metals &<br>res||||
|---|---|---|---|---|---|---|---|---|---|
||forestry and<br>fishing|of food<br>products|, ,<br>articles of<br>leather|Wood and<br>paper|Chemicals<br>|,<br>electrical<br>equipment|<br>Vehicles|Mining|Textiles|
|Non-routine analytical|-0.62|-0.85|-0.86|-0.98|-0.97|-0.76|-0.85|-0.78|-0.95|
|Non-routine interpersonal|-0.70|-0.72|-0.73|-0.80|-0.79|-0.62|-0.69|-0.64|-0.79|
|Routine manual|0.96|0.18|0.16|0.18|0.19|0.08|0.14|0.00|0.24|
|Routine cognitive|0.61|0.43|0.42|0.37|0.36|0.30|0.31|0.28|0.41|
|Non-routine manual|0.86|0.58|0.59|0.62|0.62|0.48|0.53|0.49|0.63|

Panel B: 2011-2015

||Agriculture,<br>forestry and|Manufactures<br>of food|Cotton, fur,<br>|Wood and<br>Ot|her manufact|Mineral, metals &<br> <br>ures|<br>|Mining|Textiles|
|---|---|---|---|---|---|---|---|---|---|
||<br>fishing|products|articles of<br>leather|paper|Chemicals|electrical<br>equipment|Vehicles|||
|Non-routine analytical|-0.62|-0.80|-0.84|-1.01|-0.92|-0.70|-0.85|-0.75|-0.92|
|Non-routine interpersonal|-0.70|-0.68|-0.71|-0.82|-0.75|-0.56|-0.69|-0.63|-0.76|
|Routine manual|0.96|0.16|0.20|0.20|0.18|0.06|0.17|0.04|0.23|
|Routine cognitive|0.60|0.38|0.42|0.34|0.32|0.22|0.30|0.32|0.39|
|Non-routine manual|0.86|0.54|0.57|0.63|0.57|0.41|0.53|0.49|0.60|

Source: Authors’ calculations based on Sakernas 2005-2015 and 2015-2016 Philippines STEP survey. Notes: The skills measures are constructed based on Acemoglu and Autor (2011) methodology described in the Appendix.

24
# **Table 10. Guatemala: Additional Explanatory Variables to Growth**

||Witht lit|D<br>With lit|ependent variable: Perc|ent change of GDP/|worker||
|---|---|---|---|---|---|---|
||ou quay<br>adjustment for<br>capital<sup>b/</sup>|quay<br>adjustment for<br>capital<sup>b/</sup>|Terms of trade and<br>trade openness<sup>b/</sup>|Capital imports/<br>investment<sup>c/</sup>|<br>Life expectancy<br>instead of schooling<sup>d/</sup>|Military spending/<br>GDP|
||OLS 1971-02|OLS 1971-02|OLS 1951-02|IV 1951-02|IV 1961-00|IV 1951-02|
|Explanatory variables|(1)|(2)|(3)|(4)|(5)|(6)|
|Constant|-0.080**|-0.120**|-0.073**|-0.070**|-1.266**|-0.215**|
||(-4.62)|(-3.66)|(-5.49)|(-3.69)|(-5.16)|(-3.85)|
|Percent change of capital/worker|0.818**|0.793**|0.891**|0.929**|0.784**|0.846**|
||(28.1)|(27.6)|(35.7)|(8.84)|(9.96)|(8.31)|
|Percent change of capital/worker [-1]|0.170**|0.171**|0.115**|0.118*|0.134**|0.140**|
||(3.92)|(3.61)|(3.56)|(2.20)|(2.95)|(2.81)|
|log GDP/worker [-1]<sup>a/</sup>|-0.230**|-0.408**|-0.227**|-0.316**|-0.307**|-0.316**|
||(-5.40)|(-5.83)|(-6.76)|(-5.96)|(-5.34)|(-6.38)|
|log capital/worker [-1]|0.070*<br>(2.13)|0.167**<br>(3.77)|0.095**<br>(4.10)|0.206**<br>(4.24)|0.135**<br>(2.81)|0.159**<br>(4.35)|
|log average years of schooling [-1]|0.105**|0.149**|0.086**|0.092**|...|0.102**|
||(4.94)|(4.46)|(6.00)|(5.19)||(6.20)|
|log life expectancy [-1]|...|...|...|...|0.316**<br>(5.09)|...|
|log military expenditure/GDP [-1]|...|...|...|...|...|-0.024*<br>(-2.42)|
|Imported capital goods/investment [-1]|...|...|...|0.032**<br>(2.73)|...|...|
|Percent change of trade volume/GDP|...|...|0.089**<br>(3.57)|...|...|...|
|Percent change of terms of trade|...|...|0.037*<br>(2.39)|...|...|...|
|Long-run elasticity of capital|0.306|0.409|0.420|0.653|0.439|0.501|
|Long-run elasticity of schooling|0.458|0.365|0.378|0.289|N.A.|0.323|
|Adjusted R<sup>2</sup>|0.976|0.974|0.977|0.965|0.969|0.972|
|F-statistic|158.2|140.3|191.7|45.82|61.45|57.49|
|Durbin Watson<sup>e/</sup>|1.785|2.013|2.303|2.308|2.208|2.365|
|S.E. of regression|0.010|0.011|0.009|0.012|0.011|0.010|
|N|31|31|51|50|39|50|

Note: The regressions include a step dummy for 1977 and impulse dummies for 1963 and 1982 significant at 1%. a/ Asymptotic critical values of the t-ratio are from Banerjee et al. (1998). b/ Includes a 1976 impulse dummy significant at 1%. c/ Includes a time trend starting in 1999 significant at 5%. d/ Data is interpolated. e/ A Breusch-Godfrey test finds no evidence for the presence of serial correlation in the residuals.

t-statistics in parenthesis. ** Significant at 1%, * significant at 5%.

_Source:_ Author’s calculations.

-51-
43 Table 1. Regression results by region to account for variations in national RRAs, 1955–2007

||Asia|Africa|Latin|High-|All focus|
|---|---|---|---|---|---|
||||America|income|countries|
|||||countries||
|Log (real GDP per capita)|-1.85***|-0.48***|-1.63***|-1.87*|-0.713***|
||(0.16)|(0.19)|(0.37)|(1.10)|(0.07)|
|Log (real GDP/cap.)squared|0.16***|0.045***|0.112***|0.122**|0.063***|
||(0.011)|(0.014)|(0.024)|(0.058)|(0.004)|
|Log (arable land per capita)|-0.10***|-0.017|-0.22***|-0.31***|-0.23***|
||(0.024)|(0.017)|(0.018)|(0.018)|(0.009)|
|Constant|4.89***|0.948|5.46***|6.90|1.38***|
||(0.558)|(0.597)|(1.407)|(5.245)|(0.250)|
|Number of observations|405|619|295|872|2336|
|Adjusted R-squared|0.72|0.07|0.33|0.42|0.59|

Source: Anderson, Cockburn, and Martin 2010, Table 2.12, based on data in Anderson and Valenzuela 2008.

Note: Results are calculated using ordinary least squares. Numbers in parentheses are the standard errors; *** p<0.01, ** p<0.05, * p<0.10.
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

Indicators reflect or represent the state of a phenomenon in the form of information. At the same time, indicators do not exist in isolation. Rather, they are often part of larger systems within which they assume their meaning. This system or framework may be presented in the form of more or less formal models. The following definition of an indicator in OECD (1993) is relevant here: _“A parameter, or a value derived from parameters, which points to/provides information about/describes the state of a phenomenon/environment/area with a significance extending beyond that directly associated with a parameter value”_ .

Indicators, as simple measures or parts of a formal model, help in understanding complex issues and systems that otherwise could be constrained by bounded rationality. Although indicators have an important role in forming our understanding, simplification inevitably involves a degree of abstraction and loss of information. In formal models where the indicators become variables and interact with other variables to help determine the outcomes, this issue is of great importance. It is only recently that the indicators themselves have become the subject of inquiry.

The few studies that have attempted to develop indicators for electricity reform have tended to focus on developed countries. For example, DTI (2000) defines a set of indicators of electricity and gas liberalisation for European countries, and CAEM (2002) develops an index for retail competition in the US electricity sector. A notable exception is Bacon (1999) who defines and surveys some key steps in electricity reform for a large number of developing countries.

There are some key differences between developed and developing countries that have to be taken into account, namely:

- i. The expected direction of price changes in developed and developing countries are often different. In many developing countries, residential customers are subsidised by industrial users while the reverse holds in some developed countries. Consequently, the expected direction of price changes from reform depends on the starting point.

2