rafmacalaba/datause-encoder-data huggingface dataset:
**<mark>Figure 5.3. Strictly Formal Employment Share comparisons</mark>**

<!-- Start of picture text --> 35<br>30<br>25<br>20 45 degree<br>Brazil<br>15 Kenya<br>10<br>Average relative deviation of RIWI: 39.12%<br>5<br>0<br>0 5 10 15 20 25 30 35<br>Comparison data<br>RIWI<br><!-- End of picture text --> <mark>Note: Each point represents the combination of formal employment share (comparative data, Internet survey data). Average relative absolute deviation is the average of relative values of absolute deviation of the Internet survey from the comparative data. Specifically, we calculate the average of abs(Internet-Comparative)/comparative.</mark>

**<mark>Figure 5.4. Self-employment share comparisons</mark>**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>45 degree<br>40<br>Brazil<br>30<br>Indonesia<br>20 Kenya<br>10 Avereage relative deviation of RIWI: 166.90%<br>0<br>0 10 20 30 40 50<br>Comparison data<br>RIWI<br><!-- End of picture text --> <mark>Note: Each point represents the combination of self-employment share (comparative data, Internet survey data). Average relative absolute deviation is the average of relative values of absolute deviation of the Internet survey from the comparative data. Specifically, we calculate the average of abs(Internet-Comparative)/comparative.</mark> <mark>Since we do not have comparative literature focusing on employment status or related indicators such as self-employment and formality rate, we are unable to compare the bias itself directly with the other estimates in the literature. However, we can compare our bias-reduction from propensity score weights-based adjustments with others’. For example, using propensity score</mark> 21
Policy Research Working Paper 5181

# **Abstract**

Governance indicators are now widely used as tools for conducting development dialogue, allocating external assistance, and influencing foreign direct investment. This paper argues that available governance indicators are not suitable for these purposes as they do not conceptualize governance and fail to capture how citizens perceive the governance environment and outcomes in their countries. The paper attempts to fill this void by conceptualizing governance and implementing a uniform and consistent framework for measuring governance quality across countries and over time based on citizens' evaluations. Using data from the World Values Survey (and other sources) we implement this framework into practice and build citizen-centric governance indicators for 120 countries over the period 1994 to 2005.

This paper—a product of the Governance Division, World Bank Institute—is part of a larger effort in the department to develop analytical methodologies for governance assessments to guide public sector reform efforts in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at shah.anwar@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Weights have signs consistent with interpretation of the first component as an assetpoverty index. Contribution of having no education appears to be particularly high (17.7%). Having no toilet also contributes in a large extent to inertia (19.7%). Having access to surface water contributes to 21.5% of inertia. Other items contribute to less than 10% of inertia.

# **3.2.Other Welfare Indices**

# **_Income Determination_**

Other indices than the asset index can be used in order to approximate well-being. Firstly, economists generally consider that total expenditure or income should be favoured. However, in developing countries, national surveys sometimes do not provide such information on households. It is even more difficult to get it on a regular basis.

Let us start with a log linear model of income determination:

> where<sup>_y_</sup> _i_ ( _t_ ) _t_ is the income of household _i_ (t) at time _t_ ,<sup>_x_</sup> _i_ ( _t_ ) _t_ is a vector of explanatory

> variables and<sup>_e_</sup> _i_ ( _t_ ) _t_ is an error term that is supposed to be independent and identically distributed. As proposed for instance by Stifel and Christiaensen (2007), it is possible to ' calculate ln _yi_ ( _t_  _k_ ) _t_  _k_  _x i_ ( _t_  _k_ ) _t_  _k_  _t_  _k_  _ei_ ( _t_  _k_ ) _t_  _k_ , for all integers _k_ , using estimates of<sup>_e_</sup> _i_ ( _t_  _k_ ) _t_  _k_ and  _t_  _k_ drawn from the estimated distributions of<sup>_e_</sup> _i_ ( _t_ ) _t_ and<sup></sup> _t_<sup>obtained from the previous</sup>

> equation. In doing so, we suppose that  _t_  _k_ and<sup></sup> _t_<sup>have the same distribution. This method</sup> is directly inspired from poverty mapping methodology ( _cf._ Elbers et al., 2003). It is then possible to compare several predicted distributions of income over time even if these distributions are not observed in each time period. This is actually the case when using, on the one hand, the Household Living Conditions Survey (HLCS), which is the most recent national household survey, conducted in 2001 by the Haitian Statistical Office (IHSI), and which includes modules on income, health, education, and other household’s assets; and, on the other hand, the Demographic Health Survey (DHS), a nationally representative household survey conducted every 5 years (1995, 2000, 2005) that provides data for a wide range of indicators in the areas of population, health, nutrition and other individual and household variables like assets and education. Finally, the combination of<sup>_e_ˆ</sup> _i_ ( _t_  _k_ ) _t_  _k_ and <sup>ˆ</sup> _t_  _k_ , along with the available variables<sup>_x_</sup> _i_ ( _t_  _k_ ) _t_  _k_ , yields :

7
A final approach estimates the Human Opportunity Index (HOI) developed by World Bank authors Barros, Ferreira, Molinas and Saavedra (2009) and Molinas et al. (2010). The HOI has the clear analytical objective of measuring how equitably a society progresses towards universal access of basic opportunities. The index synthesizes in a single indicator how close a society is to universal coverage in a given opportunity (as with standard coverage measurements), along with how equitably coverage of that opportunity is distributed. Opportunities are goods and services that constitute investments in children, thus increasing their human capital, such as primary education and adequate housing infrastructure. An equitable policy<sup>3</sup> ensures that a child‘s chance of accessing these key goods and services is not correlated with circumstances that are beyond their control, such as gender, parental background or ethnicity. The HOI ̳penalizes‘ the extent to which different circumstance groups ( _types_ in Roemer‘s terminology) have different coverage rates: the penalty is zero if coverage rates among multiple circumstance groups are equal and is positive and increasing as differences in coverage among circumstance groups increase. The HOI is equality-sensitive and Pareto-consistent. When access to an opportunity increases equally for all circumstance groups, the index increases proportionally. Changes in the HOI over time can be decomposed into changes in the distribution of circumstances in the population (composition effect), changes in coverage rates for all groups (scale effect), and changes in the degree of inequality of opportunity (equalization effect).

> 3 Molinas et al. (2010) note that increasing opportunities requires not only increasing access to goods and services but, sometimes, may also come from civil rights or migration rights, for instance.

7
<!-- Start of picture text --> 0 .2 .4 .6 .8<br>Monetary Poverty 0 .2 .4 .6 .8<br>Monetary Poverty<br>1 1<br>.8 .8<br>resilience .6 .6<br>.4 .4<br>process legitimacy<br>.2<br>.2<br>0<br>0<br><!-- End of picture text --> Note: Fitted values and 95% confidence interval shown Source: Social Sustainability Global Database 2022.

Social sustainability scatterplots also provide insights for redressing inequality. Certain aspects of social sustainability are associated with lower income inequality in countries, as shown in Figure 5. Higher levels of process legitimacy and, to a lesser extent, stronger social cohesion are associated with greater income equality. Put another way, rule of law, control of corruption, a fair judicial system and interpersonal and institutional trust are related with increased equality. By contrast, resilience and inclusion show a weaker, almost neutral, relationship with inequality. While we cannot claim causality, our results suggest that a highly targeted focus on improving access to services and markets and social protection (which are all captured by inclusion and resilience components) is unlikely to boost equality in a marked way. This might reflect the wide inequalities that such policies try to address in the first place, and/or the need for long-term investment in equal opportunities in education or health before results are rendered visible. All in all, while more cohesive and legitimate societies are more equal, inclusive and resilient societies can still coexist with high levels of income inequality.

**Figure 5. Societies with legitimate governments are more equal**

<!-- Start of picture text --> .2 .3 .4 .5 .6 .2 .3 .4 .5 .6<br>Gini Gini<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>social cohesion<br>process legitimacy<br>.2 .2<br>0 0<br><!-- End of picture text --> 16
<!-- Start of picture text --> 2015 2016 2017 2018<br>[Month [5 6 7 89 10 11 12;123456789 10 11 1271123456789 10 11 1271123456789 1011127112345 67 89<br>Survey Baseline Endline<br>School year<br>Ebola Crisis<br>Payments T1 T2 T3 T4 TS Te 17 T8<br><!-- End of picture text --> <!-- Start of picture text --> School Enrollment<br>All children, aged 5 to 15 years old<br>22<br>NN<br>© ©<br>5 5<br>=i Ee<br>Ss Ss<br>2 ‘J<br>at at<br>33<br>22<br>DY BH?<br>nu ou<br>o oO<br>Control Treated Control Treated ControlO 70k 140k ControlO 70k 140k<br>Baseline Endline Baseline Endline<br><!-- End of picture text --> <!-- Start of picture text --> School enrollment by age<br>22<br>~ 2<br>3" J woes . NK. 1 . “<br>3 , ©<br>Lo<br>3°5g - re)<br>ow 1<br>5*<br>g .<br>ct<br>. 2<br>i}<br>° “0<br>t<br>i) oF<br>6 7 8 9 10 11 12 13 14 15 6 7 8 9 10 11 12 13 14 15<br>Age Age<br>—__— Baseline Treatment Baseline Control Base, Control B,O GNF rr B, 70k nn B, 140<br>_—" ~Endline Treatment Endline Control End, Control ~—————«£E, OGNF ns iE, 70k ns E, 140<br><!-- End of picture text -->
# **2. World Health Organisation, WHO, ‘Healthy Cities Project’**

During the first phase of the Healthy Cities Project, a set of 53 indicators was produced to assist cities in gathering appropriate data to describe city conditions. Between 1992 and 1994, data were collected from 47 cities for 53 indicators. After an analysis of these data by experts in each of the 53 fields, some indicators were excluded as the information provided was not reliable or appropriate. A more concise set of 32 indicators was then proposed. Cities such as Toronto, Sai Kung District (China), Seoul, Baltimore, Jakarta, Belfast, Kansas City and Glasgow have applied them.

|**Sector**|**Table 2– WHO List of City Indicators**<br>**Indicator**|
|---|---|
|Health|Mortality: allcauses|
||Cause ofdeath|
||Low birthweight|
|Health services|Existence ofa cityhealtheducationprogramme|
||Percentage ofchildren fullyimmunized|
||Numberof inhabitants perpractising primaryhealthcare practitioner|
||Numberof inhabitants per nurse|
||Percentage ofpopulationcovered byhealth insurance|
||Availability ofprimaryhealthcare servicesin foreign languages|
||Numberof health related questions examined by the city councilevery year|
|Environmental|Atmospheric pollution|
||Waterquality|
||Percentage ofwaterpollutantsremovedfrom totalsewage produced|
||Household waste collectionqualityindex|
||Household wastetreatmentqualityindex|
||Relative surface area ofgreenspacesin the city|
||Public accessto greenspace|
||Derelict industrialsites|
||Sport andleisure|
||Pedestrianstreets|
||Cyclingincity|
||Publictransport|
||Publictransport networkcover|
||Living space|
|Socio economic|Percentage ofpopulation livinginsubstandard accommodation|
||Estimatednumberof homeless people|
||Unemployment rate|
||Percentage ofpeople earninglessthan themeanpercapitaincome|
||Percentage ofchild care placesforpre-schoolchildren|
||Percentage ofall live birthstomothers-20;21-34;35+|
||Abortion ratein relation tototal numberof live births|
||Percentage of disabledpersons employed|

32
shown the positive impact of training on productivity (Nielsen and Rosholm 2002; Batra and Tan 1995; Dearden, Reed, and Van Reenen 2000).

Extensive literature has been compiled in the closely related field of the impact of human capital (training) on workers' wages. A consensus has nearly been made that earnings increase with more training. The increase is in the range of 20 percent in most developing countries (Middleton and others 1993; Dar and others 2000; Nielsen and Rosholm 2001).

Given the well-documented correlation between wage growth, on-the-job training, and productivity observed in many countries, the observed labor productivity growth rate difference between Mexico and elsewhere is consistent with the hypothesis that in Mexico post-school investment in human capital results in lower productivity growth. The observed low level of investment in human capital could also be explained by the incentive structure of labor regulations. In practice, as has been well-documented, firms appear to enjoy more flexibility than a strict interpretation of the law would suggest (World Bank 1999).

This paper analyzes wage and productivity determination in Mexico. This paper differs from previous labor market studies in Mexico in five ways. First, by using linked employer-employee datasets, this paper adds a new dimension to traditional wage analysis. Most wage studies only have detailed information about individual workers; however, this paper has detailed information about both individual workers and the firms. Second, the impact of employer and employee characteristics on wages and productivity before and after NAFTA is analyzed. Third, to assess the marginal impact of human capital and other characteristics on both wages and productivity, joint equations of wage and production functions are estimated. This methodology allows us to compare the impact that each variable has on wages with the impact that it has on productivity. In other words, how the distribution of benefits (in terms of wages and productivity) are shared by workers and firms Fourth, training is treated as an endogenous variable following Nielsen and Rosholm (2002). And fifth, restricting the sample set to firms of a 3
**Table 5.  Access To and Use of Different Energy Sources** ( _in percent of all households, 2008_ )

||**Access**|||**Heat**|**ing Use**||
|---|---|---|---|---|---|---|
|_Quintile_|_Central Gas Supply_|_Electricity_|_Gas_|_Electricity_|_Fuelwood_|_Other_|
|**Armenia**|||||||
|_Poorest_|66|87|49|11|31|19|
|_2_|67|98|51|12|31|20|
|_3_|75|100|59|13|26|15|
|_4_|74|100|59|15|25|12|
|_Richest_|79|100|66|19|17|7|
|_All_|73|97|58|14|26|14|
|**Yerevan**|||||||
|_Poorest_|70|82|56|26|11|8|
|_2_|79|100|67|29|9|0|
|_3_|80|100|65|26|8|1|
|_4_|79|100|65|34|6|1|
|_Richest_|80|100|66|34|3|0|
|_All_|78|96|64|30|7|2|
|**Other Urban**|||||||
|_Poorest_|77|85|61|8|25|10|
|_2_|81|95|63|10|26|7|
|_3_|84|100|72|9|17|5|
|_4_|90|100|77|8|16|2|
|_Richest_|89|100|77|11|13|2|
|_All_|85|96|71|9|19|5|
|**Rural**|||||||
|_Poorest_|47|95|29|3|57|39|
|_2_|49|99|29|4|51|43|
|_3_|56|100|37|4|51|39|
|_4_|59|100|42|4|51|32|
|_Richest_|65|100|50|2|46|24|
|_All_|56|99|39|3|51|35|

_Source:_ Author’s calculations based on 2008 ILCS

# **_How much do households spend on energy consumption?_**

On average, Armenian households spend about 8 percent of their budget on energy, with slightly more than half of it on gas. According to the ILCS, which provides self-reported data on energy consumption expenditures, about 60 percent of the energy consumption is for heating purposes (Table 6). The poorest quintile spend 7 and 6.5 percent of their budget on energy overall and 11
<!-- Start of picture text --> Figure 8<br><!-- End of picture text --> <!-- Start of picture text --> Export Value, 2000 US$<br>Top 10 of Argentine Exports 2004<br>3,500,000<br>3,000,000<br>2,500,000<br>2,000,000<br>1,500,000<br>1,000,000<br>500,000<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Oil-cake and other solid residues, Soya-bean oil and its fractions<br>Petroleum oils and oils obtained fr Petroleum oils, etc, (excl. crude);<br>Soya beans Wheat and meslin<br>M aize (corn) Petroleum gases and other gaseous h<br>Copper ores and concentrates Leather of bovine or equine animals<br><!-- End of picture text -->

**Figure 9**

<!-- Start of picture text --> Product Income, PPP 2000 $<br>Top 10 of Argentine Exports 2004<br>18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Oil-cake and other solid residues, Soya-bean oil and its fractions<br>Petroleum oils and oils obtained fr Petroleum oils, etc, (excl. crude);<br>Soya beans Wheat and meslin<br>M aize (corn) Petroleum gases and other gaseous h<br>Copper ores and concentrates Leather of bovine or equine animals<br><!-- End of picture text --> In the following figures we show the performance of high-productivity goods among the top 125 Argentine exports, which account for 20.88 percent of total exports, in terms of their trade value, their export share, their product income and “distance”. The first graphs in figures 10 to 12, show high-productivity products from the top 25 Argentine exports. The second graphs show highproductivity products from the top 50 that were not already included in the previous graph.

14
the cards handed out. At that time, however, agents did not have the authority to open Sukaliku accounts on behalf of Microcred, and as a result, a Microcred representative was present with the agent during the account opening days. His or her role was to collect the paperwork needed to open the account, while any information about the account was provided by the agent who also answered all the clients’ questions. The different treatment conditions are summarized in Table 2.

Table 3 shows that experimental groups are balanced. Columns 2 – 6 report the mean for each of group while Column 7 reports the p-value of an F-test that all treatment dummies are jointly zero in a regression where each household or individual characteristic is the dependent variable. Panel A reports characteristics from the census data and Panel B from the baseline data. We do not observe systematic differences between the different treatment and control groups with respect to demographic characteristics, education level, monthly income or the proportion of respondents with financial products. Similarly, there are no significant differences in the distance to the closest Microcred agent and branch. The number of observations drops to 2,089 because GPS data were incomplete for 112 households. We find significant differences between the different treatment and control groups at the 10 percent level for the proportion of self-employed individuals and age. Following Bruhn and McKenzie (2009) we control for these variables in the analysis of Section 5. An F-test of the null hypothesis that all of these variables are jointly insignificant in predicting whether an individual is in a particular experimental group cannot be rejected (all p-values are higher than 0.128).

# _2.3 Account Opening, Neighborhood and Endline Survey_

Among individuals in the four treatment groups, a sample that opened the Sukaliku account were surveyed immediately afterwards and asked about their experience opening the account. Online Appendix Table OA1 shows that the 274 individuals surveyed are comparable to the 227 individuals not surveyed for 12 of 14 characteristics. There are statistical differences in a dummy for whether the individual saves informally and the Raven’s test and these are included in the analysis of Panel A, Table 4 which compares the total time in opening the account, including waiting time and face to face meeting time for individuals sent to an agent compared to those sent to the branch. Column 1 of Panel A shows that the account opening visit for individuals sent to the agent was 9.8 minutes shorter than that of individuals sent to the branch, which lasted around one hour. Most of this time reduction comes from shorter waiting time at the agent. The actual face to face meeting with 10
frequency of antenatal care visits, significantly lowers perinatal mortality, and pays dividends even among uneducated and low socioeconomic groups.

The previous evidence suggests that involving men in reproductive healthrelated issues has a positive effect on women’s welfare. However, the magnitude of this effect and its external validity are difficult to assess because research has been limited to a small number of country case studies.

# **<<B>>** _Reducing Domestic Violence_

In recent years, there has been a growing recognition that the role of men is crucial in changing unequal power relations. In particular, actions that involve men in movements to end violence against women focus on men’s roles and responsibilities and emphasize men as part of the solution to combat gender violence have received increased attention. Local, national, and international laws, conventions, and agreements define genderbased violence and legislate against those who use it. There has been more public education, awareness, and acceptance of the problem and a larger number of institutions acting in accordance with the laws.

The evidence in the previous section indicates that empowering women is not always a solution to eradicate gender violence. There is growing awareness that men can play a significant role in ending violence; thus, violence prevention programs should be addressed to men. Reviews of the literature suggest that sexual assault prevention programs for college men can effectively improve attitudes that may put men at risk for committing violence against women, although these attitudinal changes are often limited to periods of a few months. In contrast, programs that focus only on providing information have not been found to be effective. The evidence suggests that these programs are more effective in all-male groups. For example, Brecklin and Forde (2001) conducted a comprehensive meta-analysis of 43 college rape prevention program evaluations and concluded that both men and women experienced more beneficial changes in single-gender groups than in mixed-gender groups.

The only experimental evidence on the impact of women’s economic status on domestic violence comes from a randomized intervention combining microfinance with violence education in South Africa. Women randomized to receive the intervention experienced a 55 percent decrease in domestic violence relative to the control group (Pronyk et al. 2006). These results suggest that a combination of women’s economic 23