rafmacalaba/datause-encoder-data huggingface dataset:
In this paper we summarize and respond to some recent critiques of our Worldwide Governance Indicators project (as described in our series of papers Kaufmann, Kraay and Zoido-Lobatón (1999a,b) and (2001), and Kaufmann, Kraay and Mastruzzi (2004, 2005, and 2006)). The latest round of these governance indicators reports on six dimensions of governance every two years since 1996, and annually between 2002 and 2005, for over 200 countries. For brevity we will refer to them here as the WGI. The WGI are based on the aggregation of perceptions of governance from 31 different data sources provided by 25 different organizations, and so provide a synthesis of the views of a very large and diverse group of stakeholders regarding the quality of governance across countries. We report the aggregate governance indicators, the underlying individual indicators from all but three of the sources, together with accompanying descriptive papers and a Web-based interactive data tool, at www.govindicators.org.

While this paper is primarily devoted to responding to critiques of the WGI, we think it useful to begin by noting that the WGI have in recent years become among the most widely-used indicators of governance by policymakers and academics.<sup>2</sup> The usefulness of the aggregate indicators in the WGI stems from the fact that (a) they provide very broad country coverage, greater than that provided by any individual data source on governance; (b) by averaging information from many different data sources they are able to conveniently summarize the wealth of existing information on governance; (c) by averaging they are also able to smooth out some of the inevitable idiosyncracies of individual measures of governance and so be more informative about the broad notions of governance they are intended to measure than any individual data source; and (d) the estimates of governance are (unusually in this field) accompanied by explicit margins of error that transparently indicate the unavoidable degree of uncertainty associated with measuring governance by any means. Indeed, we think that it is in part because of the widespread use of the WGI that they are increasingly also beginning to attract critiques. We think that this process of discussion and debate of these critiques is very useful in identifying -- but also often discarding -- potential problems that arise in efforts to measure governance.

> 2 For example, the United States Millennium Challenge Account aid program prominently relies on five of the WGI in its procedures for determining country eligibility, see www.mcc.gov for details.

1
**Figure 1. Kernel density distribution of wages by year of age**

<!-- Start of picture text --> 15 16 17 18<br>19 20 21 22<br>23 24 25 26<br>2 4 6 8<br>27 28 29<br>2 4 6 8 2 4 6 8 2 4 6 8<br>wage<br>Density kdensity wage normal wage<br>Graphs by Age of respondent<br>4<br>3<br>2<br>1<br>0<br>4<br>3<br>2<br>1<br>0<br>4<br>Density 3<br>2<br>1<br>0<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

**Figure 2. Age at first marriage**

(a) All <!-- Start of picture text --> Men Women<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>Age at first marriage (Q15)<br>Density kdensity age_m normal age_m<br>Graphs by women<br>(a) Non-employed<br>Men Women<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>Age at first marriage (Q15)<br>Density kdensity age_m normal age_m<br>Graphs by women<br>.4<br>.2<br>Density<br>0<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text -->

# (a) Non-employed

# (b) Employed

<!-- Start of picture text --> Men Women<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>Age at first marriage (Q15)<br>Density kdensity age_m normal age_m<br>Graphs by women<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 33
# **Table 7: Girls' Enrollment - Grade 8**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||OLS|FE|FE + Controls|Excluding mosque<br>andprimaryschools|Excluding mosque<br>and primary schools<br>+ Multan +<br>Khanewal|
|Number of Basic Facilities = 1|||0.413|0.412|0.452|
||||(0.542)|(0.543)|(0.582)|
|Number of Basic Facilities = 2|||0.460|0.459|0.488|
||||(0.548)|(0.550)|(0.589)|
|Number of Basic Facilities = 3|||0.585|0.584|0.573|
||||(0.557)|(0.559)|(0.600)|
|Constant|21.29***|16.13***|15.63***|16.05***|16.09***|
||(0.605)|(0.118)|(0.546)|(0.549)|(0.588)|
|45<br>Observations|71600|71600|71600|70058|65463|
|Number of Schools||13674|13674|12165|11377|
|Adjusted R-squared|0.007|0.032|0.032|0.032|0.032|
|R-squarewithin||0.0320|0.0320|0.0320|0.0327|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. The dependent variable is girls’ enrollment in grade 8. The average school in a treatment district school at baseline had 12 girls enrolled in grade 8. Column 1 reports estimates from an OLS specification. Column 2 reports estimates from a fixed effects specification at the school level. Column 3 adds to column 2 a series of indicators for how many of the following three facilities a school has: toilets, drinking water and electricity. The excluded group is no facilities. The excluded year is the baseline year 2003. Column 4 excludes mosque schools and primary schools from the sample. Column 5 removes two districts whose treatment status was interchanged once the program began: Khanewal (changed from treatment to control) and Multan (changed from control to treatment).
# **7. Appendix**

_Table A 1_ : Loss-to-harvest ratio using only planted area losses.

|||Lo<br>|ss-to-harvest Ratio<br>|<br>||
|---|---|---|---|---|---|
|**_Ethiopia_**|**Crop**|**Loss /**<br>**Production**|**Loss p. ha /**<br>**Yield**|**Loss / Production**<br>**(any loss)**|**Loss p. ha / Yield**<br>**(any loss)**|
|ESS13|Maize|0.06|0.06|1.00|1.01|
||Sorghum|0.21|0.14|1.69|0.99|
|ESS15|Maize|0.14|0.11|1.31|0.91|
||Sorghum|0.33|0.36|1.58|1.80|
|ESS18|Maize|0.10|0.09|0.91|1.02|
||Sorghum|0.22|0.16|1.53|0.96|
|**_Malawi_**||||||
|IHPS19|Maize|0.79|0.74|0.71|0.70|
||Harvest Value|1.57|1.43|1.36|1.26|

_Table A 2_ : Yield regressions with different loss proxies.

## **MALAWI: Yield regressions with planted area losses proxies -- excl. full losses**

|VARIABLES|(1)<br>logValue/Ha|(2)<br>logMaize Yield|(3)<br>logValue/Ha|(4)<br>logMaize Yield|
|---|---|---|---|---|
|Planted area harvested (%)|0.0243***<br>(0.00286)||||
|Planted maize area harvested (%)||0.00965***<br>(0.00139)|||
|log Area-based loss (MWK/ha)|||-0.0903***<br>(0.0131)||
|log Area-based loss (kg/ha)||||-0.0342***<br>(0.0116)|
|Constant|9.898***|3.890***|12.14***|4.531***|
||(0.559)|(0.301)|(0.665)|(0.287)|
|Observations|2,087|1,507|2,087|1,507|
|Adjusted R-squared|0.263|0.450|0.232|0.407|
|District FE|YES|YES|YES|YES|
|Controls|YES|YES|YES|YES|

Regression of log harvest value per hectare and log maize yield on loss proxies and a range of household, plot, and plot manager characteristics as well as inputs. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 51
As discussed, the sample is unbalanced in that the same triads of commodity and location pairings do not appear in each survey round. This is potentially due to location-specific stock-outs, but is also reasonably attributed to sampling outcomes. The 1,988 observations are based on 939 unique triads. Of these, 299 are observed in all survey rounds.<sup>27</sup> Implied trade reversals are common. Among the 299 repeating triads, the location at which a higher price was observed switch in 124 markets between 1995 and 1997 and in 115 markets between 1997 and 2001, with some markets switching between both periods. Implied unidirectional trade, as indicated by a constant inequality relationship, was a feature of only 124 triads. All of this suggests the potential for shifting trade patterns between the survey periods.

Predicted values for the triad-specific slack variables were recovered from the estimated model. They are calculated as the expected value of the asymmetric component of the composite ˆ ˆ error, conditional on the jointly estimated symmetric error term, that is  _st_  _E_ [  _dt_ | _est_ ] . Two sample statistics from these predictions are reported in table 5 by survey year for the full sample and also for a sub-sample of the 299 repeating triads. The first is the average value of the slack variable. The second is the average squared value of the slack variable, which corresponds to the average squared distance from the trading frontier.

The predicted slack variables indicate that, once the fixed market effects and the timedifference in transaction costs are taken into account, differences among the survey years in the average value of the slack variables are small. This is true of the overall sample and also of the subsample of repeatedly observed markets. The distribution of the slack variables does appear to be more concentrated with time however, as the average squared distance measure falls. This indicates that with time, the distribution of slack variables became more concentrated and more closely bunched near zero. This can also be seen in figure 1, where the density distributions of the estimated slack variables of the repeating triads are graphed by survey year. The results are notionally consistent with improved market integration, as the tail of the distribution attenuates with time.

Taken together, the estimation results indicate that food market performance improved on average as policies implemented in 1998 as average transfer costs fell and as a larger number of markets appeared to integrate.<sup>28</sup> Even so, the results also indicate shifting trade patterns and a significant remaining heterogeneity in outcomes.

> 27 Of the remaining unique triads in the sample, 189 are observed once and 451 are observed twice.

> 28 The finding complements results from a study by Feinberg and Meurs (2005). Using industry-level data, the authors provide evidence that market reforms in Bulgaria were associated with greater integration between domestic and international markets.

16
commitments, and the “offer gaps” of these countries are similar to those of the OECD and LAC regions.

**<u>Annex Table 7:Restrictiveness index of Uruguay Round Commitments</u>**

||**Financial**|**Telecommunications**|**Retail**|**Maritime**|**Professional**|**Aggregate**|
|---|---|---|---|---|---|---|
|SAR(3)|82|67|100|100|100|91|
|EAP(7)|53|51|61|24|70|58|
|MENA(5)|49|36|71|47|71|59|
|LAC (9)|34|50|38|87|72|49|
|AFR(6)|56|57|26|96|77|55|
|ECA(4)|43|29|0|63|53|32|
|OECD(20)|14|12|8|95|65|29|
|WORLD(54)|23|20|16|89|67|35|

Note: Numbers in parentheses indicate the number of countries surveyed (2007) per region _Source_ : Gootiiz and Mattoo (2008)

**<u>Annex Table 8:Restrictiveness index of Doha Offers</u>**

||**Financial**|**Telecommunications**|**Retail**|**Maritime**|**Professional**|**Aggregate**|
|---|---|---|---|---|---|---|
|SAR(3)|55|43|94|54|88|73|
|EAP(7)|45|51|60|21|67|54|
|MENA(5)|46|25|67|42|71|55|
|LAC (9)|33|50|38|87|66|48|
|AFR(6)|48|25|17|65|75|44|
|ECA(4)|43|29|0|10|53|29|
|OECD(20)|10|7|8|50|52|21|
|WORLD(54)|17|13|15|48|56|27|

Note: Numbers in parentheses indicate the number of countries surveyed (2007) per region _Source_ : Gootiiz and Mattoo (2008)

**<u>Annex Table 9: Restrictiveness Index of Actual Policies in Services Trade</u>**

||**Financial**|**Telecommunications**|**Retail**|**Maritime**|**Professional**|**Aggregate**|
|---|---|---|---|---|---|---|
|SAR(3)|39|43|65|39|82|57|
|EAP(7)|41|47|26|19|67|42|
|MENA(5)|38|21|24|27|65|38|
|LAC (9)|5|27|1|8|44|16|
|AFR(6)|23|11|7|16|54|24|
|ECA(4)|7|0|0|9|42|12|
|OECD(20)|2|3|4|8|52|15|
|WORLD(54)|8|9|7|11|54|19|

Note: Numbers in parentheses indicate the number of countries surveyed (2007) per region. Note: Completely open = 0; completely closed = 100. _Source_ : Gootiiz and Mattoo (2008) At the sector level:

- Financial and telecommunications services are relatively open, and the Doha offers have significantly improved on the Uruguay Round commitments but still do not reflect actual policy.

- Retail is also quite open, but offers do not improve significantly on the UR commitments.

28
Where _Yijrt_ is, in turn, the primary schooling completion dummy, an indicator for enrollment status, and the number of completed years of education. _aget_ is a set of dummies for the age of the index individual at the time of the survey, _dhs2006_ is a dummy equal to one for 2006 DHS observations and zero for 2001 DHS observations, and _conflict_by_surveyjt_ is the total number of conflict-related deaths at the district level, per 1000 inhabitants, up to the (Nepali) year in which the bulk of the survey data was collected. The inclusion of region and age-specific dummies for the change in outcome between the two surveys controls comprehensively for potential regionspecific, time-varying confounding factors, allowing these to vary by age group. **_Xijrt_** is a vector of household characteristics (binary indicator for rural location, and dummies for the educational attainment of the household head<sup>3</sup> ). In general, the inclusion of covariates that explain a sizeable share of the variation in the outcome variable tends to increase the precision of the estimate of the treatment effect (Duflo et al., 2008). However, the inclusion of socioeconomic household regressors may cloud the interpretation of the effect of conflict intensity because the latter may have an effect on the former. Therefore, I focus my attention on largely predetermined variables.

# **Marriage**

Given the short time elapsed between the start of the conflict and the latest data available (10 years), it is not possible to estimate the impact of conflict during primary school age on long term marital status. For instance, an individual aged 5 at the beginning of the conflict is only 14 or 15 years old at the time of the 2006 DHS survey. It is however possible to estimate the impact of exposure to conflict during primary schooling age on early marriage, e.g., on the probability of being married by the age of 15 years old.

It is also relevant to analyze the effect of exposure to conflict during core marriageable age (15 to 20 years old for Nepalese women) on long-term marital status. I therefore carry out a separate analysis considering the impact of exposure to conflict during marriageable age on marriage by age 21, at which over 80 percent of women are married (Figure 1).

I start by estimating Equation (1), where _Yijrt_ is now a dummy variable equal to one if the woman was married at age 15 or younger, and zero otherwise, and where _casualtiesj_ is the number of casualties (per 1000 inhabitants) during 1996-2004<sup>4</sup> . The only difference with the treated and control cohorts used in the primary education analysis is that individuals aged 5 at

> 3 More household characteristics such as female headship, asset ownership, and dwelling materials are available, but these are excluded as they are more likely to be caused by conflict.

> 4 The Western Calendar is used here for the convenience of the reader. In the analysis, the cut-off point is the end of Nepalese year 2060 (April 2004), which coincides with the bulk of data collection for the 2003/2004 NLSS.

17
> city and commuting technology. For a concentric city, we adopt the value of 0 ≤γ ≤ 2 from Henderson and Venables (2004, pp. 4-5).

Assuming no growth and elasticity of substitution, e, being 1,<sup>7</sup> the Euler’s equations with respect to The above prices represent household’s willingness to pay or demand for respective components in order to maintain their housing quality.

# **_2.2. Developers_**<sup>**_8_**</sup>

The developer is assumed to supply building structure and developed land in the form of housing (Ht). We consider that developers use the available technology to transform land and building materials into residential units by incurring certain cost. We assume the market for housing to be perfectly competitive and the developer’s marginal cost is exactly equal to price of each housing unit. The production function of housing is assumed to have constant return to scale of the following form:

> 7 Elasticity of substitution is considered to be 1 for computational simplicity. It does not alter the basic results.  However, the model can be calibrated for any value of e between [0,1]. 8 Developers can be either in the public or private sector

9
premium affecting all skilled workers in the labor market. With departures from that model, including imperfect factor mobility of skilled labor (but also of unskilled labor), wage equalization does not follow, and skill premiums at the industry level can result in equilibrium. To investigate this scenario, we expand our previous model to estimate skill premiums by sector. Specifically, we multiply the skill categories, using the different definitions described above, by dummy variables for each industry code at the 2-digit International Standard Industry Classification (ISIC) Revision 3.<sup>7</sup> The coefficient on this interaction provides an estimate (relative to the industry of reference) of industry-specific skill premiums.

At the 2-digit level, there are 60 sectors in the ISIC Revision 3 classification. With a sample of 16 countries, we estimate approximately 960 industry-skill premiums (which are listed in Table A2 of the on-line Appendix). There are significant differences in the skill premiums, both across sectors for a given country and across countries for a given sector. Table 5 presents the distribution of industry-skill premiums within countries. Consistent with the estimates of the aggregate skill premiums (Table 3), there are wide differences in the average (and median) skill premium across countries that unsurprisingly mimic the patterns observed in Table 3. Figure 1 also illustrates the notable dispersion in the estimated skill premiums across industries within countries.

In addition, there is considerable dispersion in the average skill premium across countries (for a given industry). Table 6 reports the top-10 industries with the highest cross-country average skill premium (average computed across countries for a given industry) and the bottom-10 industries with the lowest cross-country average premiums. The cross-country averages in the skill premium range from 1.12 in sector 99 (“Extra-territorial organization and bodies”) to 0.13 in sector 95 (“Private households with employed persons”). Additionally, we construct industry rankings for each country. Columns (3), (4) and (5) report the fraction of countries for which a given industry ranks in the top 50 percent, top 25 percent, and bottom 25 percent. Heterogeneity in the rankings of the skill premiums even within the highestand lowest-ranked sectors is apparent. For instance, the skill premium in sector 99 (with the highest average) is above the median only in 88 percent of the countries, while for 13

> 7For those surveys that do not use ISIC Rev.3 to classify industries, concordance tables were utilized.

10
incomes more equal”, which is a more neutral statement than “bridging the gap between the rich and the poor.”<sup>8</sup> In section 4, we examine inequality aversion as measured by the two questions.

The importance of framing the question for eliciting the respondents’ preferences for redistribution is also confirmed by observing the weak correlation of (i) and (ii) with the belief that assisting the poor should be among the top two priorities for extra government spending. Preferences for stronger government involvement in helping the poor appear to be slightly better, but still weakly aligned with the respondents’ own willingness to help either by giving part of their income or to pay higher taxes, and with the belief that the (working) poor deserve government support.

# **_Trends in inequality aversion_**

Comparing levels of inequality aversion across the two rounds of the survey, these preferences are not stable, which does not suggest a model of preferences determined by stable social norms. While the preference for reducing the gap between the rich and the poor remains widespread in the region, it decreased markedly during the 2006-2010 period. Overall, in 2010, 70 percent of adults agreed that the gap between the rich and the poor should be reduced, versus 79 percent in 2006 for the same sample of countries<sup>9</sup> . The decline in the preference for redistribution can be observed in the majority of the countries in the LiTS sample (see Figure 1).

> 8 This would be in line with the findings of Pirttila and Uusitalo (2010) who find based on household survey data from Finland that two scenarios, one based on comparisons of income distributions, and one based on costly transfers from the rich to the poor, generate rather different estimates of the degree of inequality aversion. 9 Five Western European countries (France, Germany, Italy, UK, and Sweden), and, additionally, Kosovo, are included in the 2010 data, but not in the 2006 data.

8
<!-- Start of picture text --> 75<br>g 5.0 | 24<br>2 men an<br>§ 2.5 a Z<br>5 [A 188<br>3S —— AA 7<br>5 0.0 I= | 3<br>2-2.5<br>in} 12<br>-5.0<br>1980 1990 2000 2010<br>7.5<br>3 5.0 |<br>§ 2.5 a<br>5 44-7<br>= 0.0 aaa |<br>-5.0<br>1980 1990 2000<br><!-- End of picture text -->
Ozler 1995, Standing 1999). Nationally representative surveys of manufacturing firms in Mexico between 1992 and 2001, show that foreign and export-oriented firms employ significantly more women at every occupational level than nationally-owned firms producing goods for sale in the domestic market (Villarreal and Yu 2007).

|**Table 3**|
|---|
|**Mexico-Social Network Effects Among Indigenous Males 20-65years of age **|

|**Dependent variable**||**Rural areas**|**Semi-Urban**<br>**areas**|**Urban areas**|
|---|---|---|---|---|
|**Empleado/Obrero**||-0.005|0.022|0.017|
||t-value|(0.24)|(0.53)|(0.58)|
||Adjusted R-squared|0.2892|0.3227|0.2729|
||Number of observations|145,464|41,119|25,792|
|**Jornalero/peon**||**0.042**|-0.008|**0.0504**|
||t-value|**(1.96)****|(0.24)|**(2.50)**|
||Adjusted R-squared|0.2022|0.1983|0.1567|
||Number of observations|145,464|41,119|25,792|
|**Self employed**||**0.048**|0.050|0.038|
||t-value|**(2.58)*****|(1.45)|(1.44)|
||Adjusted R-squared|0.2086|0.2237|0.1884|
||Number of observations|145,464|41,119|25,792|
|**Sector of Employment is**|**Agriculture (code=11)**|**0.032**|0.028|0.003|
||t-value|**(2.42)*****|(1.17)|(0.24)|
||Adjusted R-squared|0.3134|0.3535|0.2511|
||Number of observations|145,464|41,119|25,792|
|**Sector of Employment is**|**Manufacturing (code 31-33)**|0.028|-0.041|0.004|
||t-value|(1.39)|(-0.79)|(0.11)|
||Adjusted R-squared|0.1486|0.1824|0.0911|
||Numberofobservations|145,464|41,119|25,792|

Source: Authors’ estimates of the social network effect based on the 2000 Population and Housing Census (10% sample) Notes:

- t-values based on robust standard errors corrected unknown forms of heteroskedasticity

- * p-value <0.10

- ** p-value<0.05

- *** p-value<0.01

15