rafmacalaba/datause-encoder-data huggingface dataset:
Table A5: Estimation of propensity score for schools that joined Mais Educação in 2010, using pretreament data (2009)

||Panel A:|Fifth-grade schools|Panel B: N|inth-grade schools|
|---|---|---|---|---|
||Coefficient|_P_value|Coefficient|_P_value|
||(1)|(2)|(3)|(4)|
|∆ Portuguese grade|0.00|0.63|-0.01|0.00|
|∆ Mathematics grade|0.01|0.00|0.01|0.00|
|∆ Dropout rate|0.01|0.31|0.02|0.00|
|Portuguese grade|0.03|0.00|0.03|0.00|
|Mathematics grade|-0.05|0.00|-0.02|0.00|
|Dropout rate|0.04|0.00|-0.02|0.00|
|IDEB Index|-0.94|0.00|-1.18|0.00|
|∆ IDEB|0.56|0.00|0.80|0.00|
|Municipal population|0.00|0.00|0.00|0.00|
|Students' enrollments|0.00|0.00|0.00|0.00|
|IDHM|11.26|0.00|10.51|0.00|
|Urban|1.13|0.00|1.10|0.00|
|Basic sanitation|0.18|0.44|0.13|0.66|
|Library|0.15|0.00|0.01|0.81|
|Sports court|-0.08|0.01|-0.06|0.13|
|Number of classrooms|-0.01|0.06|-0.01|0.16|
|Number of computers|0.04|0.50|0.38|0.01|
|Science lab|0.16|0.00|0.08|0.02|
|Computer lab|-0.14|0.00|-0.09|0.03|
|Teachers room|0.08|0.07|0.15|0.04|
|DVD|0.21|0.01|0.02|0.87|
|Overhead projector|-0.12|0.00|-0.12|0.01|
|Number of employees|0.00|0.00|0.00|0.00|
|School meals|-0.11|0.50|0.24|0.21|
|Number of classrooms used|-0.01|0.14|-0.02|0.00|
|Morning lessons|0.28|0.00|-0.06|0.48|
|Child care|-0.17|0.03|0.06|0.64|
|Kindergarten|0.03|0.39|0.04|0.41|
|State schools|-0.02|0.67|-0.15|0.00|
|High school|-0.08|0.12|-0.18|0.00|
|Share classes in school with fu|1.40|0.00|1.07|0.00|
|Total duration of school day (|-0.37|0.00|-0.18|0.00|
|Intercept|-5.80|0.00|-6.14|0.00|
|Number of observations||25374||20013|
|Pseudo-_R_ <sup>2</sup>||0.24||0.28|

_Sources:_ MEC/FNDE, _Prova Brasil,_ School Census, and INEP. _Note:_ Propensity score was estimated using a Probit model and 2009 data. Treated schools are the schools that joined _Mais Educação_ in 2010 and remained until 2011. Our control group is the schools that, by 2011, had not yet joined _Mais Educação._ Schools that joined the program in 2009, 2010, and 2011 were excluded from the sample to define the control group. Table reports the estimated coefficients (columns (1) and (3)) and _p_ values (columns (2) and (4)). Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
instrumenting for the pandemic variable (column 4). Higher daily death rates have a significant adverse effect on electricity consumption, as usual. Including daily death rates and instrumenting for it leaves the sign and significance of the speed variable unchanged, however the magnitude is slightly attenuated at about 4 percentage points. According to that specification, an increase in reopening speed by one unit results in a 3.8 percent reduction in electricity consumption.

The results of incorporating the trust variable and estimating equation (5) are presented in Table 4. The first column presents the benchmark regression of equation (3) as a reference. The second column looks at the mediating effect of trust in government institutions, which is measured using the share of individuals having high trust in parliament, as reported in the 2018 round of the European Social Survey or the 2016 Life in Transition Survey.<sup>11</sup> The coefficients of the interaction between the lockdown sequencing dummies and the level of trust are statistically significant. In the case of the dummy indicating a direct transition from full reopening to no restrictions, controlling for trust makes the baseline coefficient significant and larger at 3.9 percentage points. However, the interaction with trust variable results in a negative coefficient, and given that the average share of people having high trust in parliament in countries that opted for a fast reopening is 5.9 percent, the combined effect would imply that the average country that reopened in that way saw a zero or negative change in relative electricity consumption.<sup>12</sup> Hence, once we incorporate trust, for the average country a fast reopening process is associated with a negligible change in relative electricity consumption – qualitatively equivalent to the benchmark results. For the gradual reopening process, the coefficients suggest that higher levels of trust are clearly associated with better outcomes, since the interaction coefficient is now positive. The indicator of trust in parliament is, on average, 3.6 percent in countries that reopened gradually. For the average country in this group, the increase in relative electricity consumption when moving from full to partial lockdown is about 8.7 percent. When moving from partial lockdown to no restrictions, the increase is of 7.6 percent, bringing the total increase in electricity consumption associated with a gradual reopening process to close to 16 percent. For this group of countries, an additional 1 percent in individuals having high trust in parliament is associated with around 2 percent higher relative electricity consumption over the course of the whole reopening process.

> 11 We get qualitatively similar results when using trust in the legal system or interpersonal trust variables instead of "trust in parliament" indicator. These estimations are available from the authors.

> 12 This is the result of 0.039-0.007*5.9=-0.0023.

18
Finally, we investigate if the depth of trade agreements concluded by countries influences the marginal effect of trade preferences on third countries. Specifically, we identify the effect of deep PTAs on tariff preferences by estimating equation (3’’). Results are reported in Table 7. We interact the _RPM_ variable with the importer’s average depth of trade agreements with the rest of the world and with the importer's maximum value of depth (i.e. its deepest trade agreement) in a given year. Therefore, we test if commitments, for instance, to improve customs efficiency or to reduce subsidies, soften the consequences of trade preferences for excluded countries.

|Table 7. PPML|Regression<br>Depth PPM<br>|: The Influ<br>L Diversion<br>|ence of Dep<br>Revisited<br>|th on the I<br>|mpact of T<br>|rade Prefer<br>|ences<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VARIABLES|Trade|Trade|Trade|Trade|Trade|Trade|Trade|Trade|
|Depth LE|0.189***|0.184***|0.234***|0.231***|0.191***|0.185***|0.236***|0.229***|
||(0.064)|(0.060)|(0.080)|(0.079)|(0.064)|(0.063)|(0.080)|(0.079)|
|RPM|-0.254|-0.546|-0.242|-0.532|-2.169|-3.659*|-2.131|-3.609*|
||(0.864)|(0.909)|(0.865)|(0.910)|(1.543)|(1.871)|(1.550)|(1.882)|
|(RPM * Others|1.618***||1.608***||||||
|Depth LE)|(0.617)||(0.619)||||||
|(RPM * Others||1.903***||1.893***|||||
|Depth Core LE)||(0.676)||(0.678)|||||
|(RPM * Max|||||3.776**||3.726**||
|Depth LE)|||||(1.790)||(1.799)||
|(RPM * Max||||||4.174***||4.127***|
|Depth Core LE)||||||(1.554)||(1.564)|
|ln(1+TTRI)|-0.009|0.057|-0.013|0.052|-0.052|-0.063|-0.057|-0.068|
||(0.671)|(0.668)|(0.671)|(0.668)|(0.685)|(0.687)|(0.685)|(0.687)|
|Others Depth LE|0.278*||0.274*||0.292*|0.285*|0.286*|0.281*|
||(0.151)||(0.152)||(0.153)|(0.153)|(0.154)|(0.154)|
|old PTAs|||0.039|0.044|||0.044|0.042|
||||(0.060)|(0.062)|||(0.060)|(0.059)|
|Others old PTAs|||-0.049*|-0.050**|||-0.047*|-0.048*|
||||(0.027)|(0.025)|||(0.027)|(0.027)|
|Others Depth Core LE||0.167**||0.165**|||||
|||(0.076)||(0.077)|||||
|N|94,057|94,057|94,057|94,057|94,057|94,057|94,057|94,057|
|Exp.-Year FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Imp.-Year FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Exp.-Imp. FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Period|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|

Note: LE stands for legally enforceable. Robust standard errors, clustered at the country-pair level, are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
It may be enlightening to explain the change in the inequality of total income in terms of its income or expenditure components. This may easily be done by means of equation (3) which can also be written as where G is the Gini index of the total income, C; is the concentration index of the jth income or expenditure component and wj = gfIt is the share of the jth income or expenditure component.

Suppose that over a period, income or expenditure shares change from wj to w; which may be accompanied by changes in their concentration indices from C3 to C;. These changes would change the inequality of total income fiom G to G, which from (15) can be written as **_A_** _-_ G-~~~~ **_J-y_** _wiC) (16)_ 1 which shows that (wjCj - wj will be the contribution of the jth income or expenditure component to the change in the inequality of total income. This contribution may be further decomposed into two components; one, due to a change in the share of the jth income compone and second, due to a change in the distiuton of the jth income component These individual components may be wntten as in which tie first term on the right side measures the effect of a change in the share of the jth income component and te seoond term measures the effect of a change in the distrbution of the jth incoe amponent.

Subsfituting (17) into (16) gives

**The** calcdlai using **(18)** are presented in Tables 12 and 13. The conclusions emerging fom them ae ummarized below.

Th Gini index of total income declined by 4.7 points between 1989 and 1991. The major cause of this reduction was the salary income of blue and white collar workers which contributed a reducdon of 3.9 points. The reduction in the **sbare** of **salary** income caused a **reduction** in total inequality by 1.6 points and the remainig **reduction** of 2.3 points in total income inequality was caused by the redistribufion of **salary** income **in** favor of the poorer families.

27
**Table 7: Explaining social norms**

||(1)<br>SN ind<br>|(2)<br>ex after factor a<br>|(3)<br>nalysis<br>|
|---|---|---|---|
||Standardized<br>SN index|Standardized<br>SN index|Standardized<br>SN index|
||All|Female|Male|
|female|0.113**|||
||[0.0486]|||
|age|‐0.000618|‐0.0326|‐0.00810|
||[0.0105]|[0.0251]|[0.0162]|
|age_squared|0.000144|0.000659*|0.000176|
||[0.000117]|[0.000340]|[0.000166]|
|years of education|0.0612***|0.0766***|0.0500***|
||[0.00690]|[0.00985]|[0.00997]|
|whether married|‐0.171**|‐0.178*|‐0.129|
||[0.0783]|[0.0940]|[0.138]|
|Has young children|0.0524|0.167**|‐0.00546|
||[0.0557]|[0.0762]|[0.0857]|
|Amman|‐0.117***|‐0.231***|0.00499|
||[0.0437]|[0.0563]|[0.0674]|
|mother worked|0.00757|0.0631|‐0.111|
||[0.0672]|[0.0789]|[0.119]|
|ever worked|0.0380|0.0779|‐0.0436|
||[0.0490]|[0.0609]|[0.0868]|
|log household income|0.275***|0.237***|0.310***|
||[0.0393]|[0.0509]|[0.0610]|
|Observations|1,919|1,010|909|
|Adjusted R‐squared|0.108|0.143|0.087|

Note: *** denotes significance at 1%, ** at 5%, and * at 10% level. Standard errors are in parentheses. All columns report results from OLS regressions. The dependent variable is the standardized index of items asked for the social empirical and normative components after factor analysis, as specified in Annex 2, Table 2.8.

# **A rational model of female labor force participation?**

We present OLS regressions for a framework that incorporates “rational” incentives for women to join the labor market. In Table 8, the left-hand side variable is whether women are working, or want to work, and the explanatory variables are women’s education, household income, the reservation wage, and the revised social empirical, social normative, and personal belief indices after factor analysis.<sup>10</sup> The specification attempts to ascertain whether social norms and personal beliefs contribute to a woman’s decision to work, controlling for “rational” determinants such as earning power (proxied by years of education), the laborleisure trade-off (proxied by responses to the reservation wage question in the survey), and substitution effects (proxied by household income without women’s earnings). The main finding is that social norms (in particular, social empirical expectations) add explanatory power to a simple “rational” model of FLFP for women currently working. As seen before, the variable “want to work” is better predicted by personal

> 10 Results are similar when using the social empirical and normative index with all original items.

22
to their job than high-skilled expecting mothers. Thus, by ensuring job continuity of low-skilled women workers who would otherwise lose their job due to childbirth, maternity leave is expected to impact low-skilled women workers more than high-skilled workers. Our emprical results below provide suggestive and preliminary evidence to support this view.

# **3. Data and methods**

# 3.1 _Data description_

In this section, we discuss the data and estimation method used in the empirical analysis. The main data sources are Enterprise Surveys (ES) and Women, Business and the Law (WBL) project. Both these data are collected by the World Bank. The ES is firm-level survey data on formal or registered private sector firms with five or more full-time permanent workers. The surveys cover all of manufacturing but exclude agriculture and certain services industries such as finance & insurance, health care, and education. Informal or unregistered firms are also excluded and so are fully state-owned enterprises. The surveys use a common sampling methodology, stratified random sampling, as well as a common questionnaire across all countries.<sup>3</sup> Stratification is done by firm size, industry, and location within the country. Sampling weights are provided in the ES and used in all our regressions so that the sample is representative of the targeted private sector in the country.<sup>4</sup> The WBL provides information on legal institutions that discriminate by gender across the world as well as on the length and cost of paid maternity leave. This information is collected through several rounds of interactions with practitioners with expertise in the different areas covered by the database. Inputs collected from practitioners are verified against codified

> 3 Details of the sampling methodology and other survey related information are available at <u>www.enterprisesurveys.org.</u>

> 4 The median sampling weights provided by ES are used in the regressions below.

18
data.<sup>15</sup> The firms are, on average, very large by Ethiopian and African standards. The mean number of employees per firm is 171.5 workers, but this masks considerable heterogeneity, particularly in the ‘Tours & Hospitality’ sector, which is dominated by relatively small hotels and restaurants. Average firm size, when this sector is excluded, is 326 workers per firm. These firms size numbers are given in Table 1 below. Note that these numbers exclude casual daily labourers: on average, firms report employing 34 casual labourers per day.

## _<_ **Table 1 here.** _>_

The firms in our sample are growing in size and looking to hire new workers. At the median, the number of workers that firms expected to hire in the next 12 months amounts to 12% of their current workforce. The median rate of hiring was highest (16%) among service sector firms, which were also the most likely to come to the job fairs. The most common types of workers which firms expected to hire were white collar workers, usually requiring university degrees. These results are shown in Table A.3 in the Online Appendix.

# **2.3 Randomization of job-seekers**

We assign treatment at the level of geographic cluster, after blocking on cluster characteristics (see Abebe et al. (2016) for further details). Our sample is balanced across all treatment and control groups, and across a wide range of outcomes (including outcomes that were not used in the randomization procedure). We present extensive balance tests in Table A.1. For each baseline outcome of interest, we report the _p_ -values for a test of the null hypothesis that we have balance between the experiment and control groups. We cannot reject this null for any of variables that we study.

# **2.4 Randomisation of**

We assigned firms to either a treatment group or a control group using block level randomization techniques suggested by Bruhn and McKenzie (2009). Firms in the treatment group were invited to attend the job fairs, while firms in the control group did not receive an invitation. The following method was used to group firms together: firstly, firms were partitioned by five main groups of industry (defined in Table A.5, in the Online Appendix). Then firms were partitioned into nearest neighbour groups of four firms on the basis of Mahalanobis

> 15 Table A.5 in the Online Appendix shows the number of firms surveyed in our sample, divided into five main categories. Column (2) provides weighted percentages obtained by applying the inverse of the weights used to sample the firms. For instance we surveyed NGOs (“Education, Health, Aid”) relatively infrequently because of the large number of NGOs in the data.

8
economies. Section 4 analyzes household characteristics that predict mortgage holdings across countries, while section 5 explores to which extent mortgage holdings can turn into financial burden. Section 6 concludes

# **2. Data**

We utilize both aggregate and household-level survey data in our empirical analysis. Specifically, we use information from the European Credit Research Institute (ECRI) to illustrate aggregate trends in household and mortgage lending across the new EU member states over time and in comparison between them and the old EU member states. While data are available for 1995 to 2007 for many Western European countries, they are available for most transition economies only starting in the late 1990s. We complement these aggregate data with data on the share of foreign exchange loans and the share of variable interest rate loans in total mortgage lending, hand-collected from countryspecific sources. While such aggregate data allow us to make cross-country comparisons, they do not allow us to make in-depth assessment of the benefits and costs of mortgage holdings. We therefore use these data largely for illustrative and motivational purposes, before proceeding to the use of micro-data for a deeper analysis.

To explore the relationship between household characteristics, mortgage holdings and financial vulnerability, we utilize information from the databases of the EU-Statistics on Income and Living Conditions (EU-SILC), an annual, EU-wide household survey anchored in the European Statistical System. This survey is conducted by national statistical offices and has a cross-sectional as well as a longitudinal dimension, i.e., sub-samples of households are surveyed over several years. While the EUSILC was initiated in 2003, the new EU member countries undertook their first surveys only in 2005. As data are made available to the general public two years after the survey, we currently have data for 7
# **1. Introduction**

One of the most salient and pervasive forms of gender discrimination is the unequal treatment of women and men by the law. Until recently, women did not have the right to vote in most countries. In many parts of the world, women are still not allowed to participate in certain professions. In England, until 2003, the law of rape postulated, in effect, that a man who had sex with a woman believing that it was consensual had to be acquitted, even if there were no reasonable grounds for this belief. While examples of gendered laws abound, what has been missing is a complete picture of the relative severity, evolution, and impact of legal gender discrimination around the world. Most of the existing evidence on gendered laws and reforms comes from a limited number of (usually, developed) economies, but it is largely unknown to what extent the findings hold when measured on a much wider scale. The World Bank’s newly compiled _Women, Business and the Law_ (WBL) database aims to fill this knowledge gap. The database measures equality of economic opportunity under the law between men and women in 190 economies, for 50 years, from 1970 until today.

The purpose of this paper is twofold. First, we provide a description of the newly constructed WBL database, which will be updated annually and made available to the public, and use it to document a series of stylized facts regarding legal gender discrimination across the world. Second, we examine whether improvements in the legal treatment of women have contributed to more equal labor market outcomes, specifically to higher female labor force participation, closing of the wage gap between men and women, and lower occupational segregation. To establish a causal effect of legal reforms, we exploit a novel methodology suggested by a recent paper by Freyaldenhoven, Hansen and Shapiro (AER, 2019) – FHS henceforth.

We document the existence of large gaps between the legal treatment of men and women despite the tremendous progress that has been made in the past five decades. The gaps are most pronounced in the areas of _Getting Paid_ and _Having Children_ – that is, we find that women are most disadvantaged by the law when it comes to the compensation they receive and their treatment once they have had children. We find positive associations between improvements in the law and higher female labor force participation, a lower wage gap between men and women, and less occupational segregation. We establish that the first of these correlations allows for a causal interpretation. Specifically, application of the FHS methodology suggests that more equal laws have led to higher female labor force participation. The effect is small, suggesting that factors other than laws are also responsible for women’s higher labor 2
# **APPENDIX A: SURVEY EXPERIMENT DETAILS**

**Table (A.1)** Comparison between survey respondents and Bogot ́a inhabitants

|**Variable**||Sample||DANE|
|---|---|---|---|---|
||Average|Std. Deviation|Average|Std. Deviation|
|**Male[=1]**|0.53|0.50|0.47|0.50|
|**Age (Age groups)**|||||
|18 years - 24 years [=1]|0.18|0.007|0.13|0.000|
|25 years - 34 years [=1]|0.21|0.007|0.24|0.000|
|35 years - 44 years [=1]|0.21|0.007|0.20|0.000|
|45 years - 54 years [=1]|0.20|0.007|0.17|0.000|
|_≥_55 years [=1]|0.20|0.007|0.25|0.000|
|**Education (Education L**|**evel)**||||
|Nothing [=1]|0.00|0.001|0.04|0.000|
|Primary [=1]|0.03|0.003|0.15|0.000|
|Secondary [=1]|0.28|0.008|0.38|0.000|
|Technical [=1]|0.33|0.008|0.13|0.000|
|University [=1]|0.24|0.007|0.22|0.000|
|Postgraduate [=1]|0.12|0.006|0.08|0.000|
|**Economic Strata**|||||
|Strata 1 [=1]|0.09|0.005|0.09|0.000|
|Strata 2 [=1]|0.35|0.008|0.41|0.000|
|Strata 3 [=1]|0.39|0.008|0.36|0.000|
|Strata 4 [=1]|0.12|0.006|0.09|0.000|
|Strata 5 [=1]|0.04|0.003|0.03|0.000|
|Strata 6[=1]|0.01|0.002|0.02|0.000|

_Notes:_ We use the CNVP 2018 _(National Population and Housing Census 2018)_ to construct the following variables: male, age and education levels for Bogot ́a, Colombia. To construct the economic strata data, we use the EM 2017 _(Multipurpose survey 2017)_ . Both, CNVP 2018 and EM 2017 were designed and implemented by the National Administrative Department of Statistics (DANE) from Colombia.

**Table (A.2)** Order of survey modules in treatment and control groups

|**Treatment**|**Control**|
|---|---|
|1) Sociodemographics|1) Sociodemographics|
|2) COVID-19|2) Attitudes towards migrants|
|3) Crisis questions|3) Crisis questions|
|4) Attitudes towards migrants|4) COVID-19|
|5)Social Desirability|5)Social Desirability|

23