rafmacalaba/data-use-mentions-extended huggingface dataset:
number of “knowledge-based” layers in each firm. If a firm has employees belonging to only one of these categories (e.g. blue collars), this firm will have no layers of knowledge. If there are employees in two of these categories (e.g. blue collars and senior staff), this firm will have one layer of knowledge-based hierarchy. We provide further details on the proxy for hierarchy in the appendix (section 6.2).

|**Classifcation**|**CBO**<sup>(</sup><sup>_a_)</sup>|**Occupation**|**Level**<sup>(</sup><sup>_b_)</sup>|
|---|---|---|---|
|CEOs, Directors, Managers|CG 1|CEOs, Directors and Managers|-|
|Professionals|CG 2|Art and Sciences - high level of experience|4|
|Technicians (middle-level)|CG 3|Quality control, technical, accounting|3|
|Clerks|CG 4|White color - internal administration services|2|
||CG 5|White color -external administration services|2|
|Blue collars|CG 7 and CG 8|Welders, assemblers, machine operators|2|
||CG 9|Maintanance workers|2|

Table 1: CBO classification Note: (a) CG 6 refers to agriculture and it was excluded; (b) Level of competency according to CBO.

> 7RAIS is a register of all formal firms in Brazil. It provides information about the size of the establishment, geographic distribution and workers, such as wage, education, age and gender.

> 8 _Secretaria de Com ́ercio Exterior_ (SECEX) from the Ministry of Development, Industry and Trade (MIDC). A list of exporting firms by year is publicly available on www.mdic.gov
effects of the program on the composition or assumed composition of borrowers. If, as posited by Demont, the lowest-risk borrowers are able to obtain loans through Jeevika, informal lenders are left with a higher-risk pool of clients, pushing up average informal lending rates. The interest rate paid on informal loans at baseline is uncorrelated with take-up of SHG credit by endline among landless households in our data, after controlling for the same set of baseline controls used in the estimation of treatment effects (coefficient = -0.003, p-value = 0.960). However, we cannot rule out the possibility that lenders’ assumptions about prospective borrowers could be affected by Jeevika’s entry. Indeed, splitting the sample by ‘repeat borrowers’ – those who had taken out a loan in the year prior to the baseline survey, versus new borrowers, we see that the negative impact on rates was entirely due to the impact on repeat borrowers (Table 3, column 5). This finding is consistent with lenders treating unknown borrowers with greater suspicion when joint-liability loans are widely available.

Village-level focus group discussions were used to elicit the publicly known ‘going rate’ for informal credit, and are thus expected to be less affected by the composition of borrowers. The point estimate of the intervention’s impact on interest rates using these village-level data is indeed larger than that obtained using household reports, and is significant at the 10 percent level despite reduced power to detect impacts at the village level (Table 3, column 7, Panel A).<sup>13</sup> The effect on rates is concentrated among those charged by professional moneylenders (as opposed to friends and relatives), though restricting loans to this group reduces number of observations and the effect is no longer statistically significant (column 8). The number of informal credit sources listed in the village-level survey shows a decline in Jeevika areas (Panel B, column 7). This is driven by fewer friends and relatives listed as sources of credit (column
property and included wives’ names on the land certificates. Married women in treated areas were also more likely to be able to lease, mortgage, sell or bequeath land (Ali et al., 2015). We use the baseline data collected in early 2011 before the beginning of the program and the first follow-up collected in 2012.

# **3.2 Sample Characteristics**

For each of the six datasets, we restrict our sample to female potential beneficiaries of reproductive age (18-50) at baseline.<sup>10</sup> Since across our datasets, less than 5% of women who report being single have children, we further restrict our sample to women that are married or in a relationship at baseline.<sup>11</sup> Table 1 shows descriptive statistics for the resulting sample of entrepreneurs, while Table 2 depicts our land titling sampling frame. The statistics are computed using baseline data for each sample, or the first follow-up if no baseline was conducted (this was the case in Benin and Ghana). The women in our dataset are on average in their thirties, with the youngest average age in Ethiopia DOT (31.6) and the oldest average age in the Togo sample (38.3).<sup>12</sup> Education levels vary substantially across datasets.

> 10While our main analysis restricts our sample to female potential beneficiaries, we do use the Togo study’s broader sample (including potential male beneficiaries) as a robustness check to analyze whether there are effects on fertility when the woman is not the direct beneficiary of the treatment.

> 11The one exception is for Ethiopia DOT, where we keep the entire sample because 61.8% of single women report having children at baseline.

> 12In our four study countries, women bear approximately 22% of their children at age 35 or above. This figure is calculated using the most recently available round of DHS data for each of the four countries, accessed via USAID’
# **A Prevalence of Prejudice Toward Migrants by Level of Education**

Figure A.1. Prejudice Prevalence by Level of Education Panel A. Colombia <!-- Start of picture text --> At Least One Variable Equals 1 Index Average Index of Variables<br>1 0 .9 4 0 .9 6 0 .9 5 1<br>.9 0.89 .9<br>.8 .8 0 .7 6<br>0 .7 2<br>.7 .7 0.66 0 .6 6<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Education Level Education Level<br>No Studies Primary Secondary Universitary or more No Studies Primary Secondary Universitary or more<br>Panel B. Latin America (18 Countries)<br>At Least One Variable Equals 1 Index Average Index of Variables<br>1 1<br>.9 0 .8 7 0 .9 0 0.89 0 .8 6 .9<br>.8 .8<br>.7 .7<br>.6 .6 0 .5 8 0 .5 9 0.59 0 .5 9<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Education Level Education Level<br>No Studies Primary Secondary Universitary or more No Studies Primary Secondary Universitary or more<br>Prejudice Prevalence Prejudice Prevalence<br>Prejudice Prevalence Prejudice Prevalence<br><!-- End of picture text --> _Notes:_ The figure depicts the average level of prejudice by level of education for both the Colombian population and
<!-- Start of picture text --> Bulgaria 2020<br>Child poverty (AROP)<br>30.7 30.4<br>30<br>24.2<br>20<br>10<br>0<br>TM Market Income plus pensions TM Disposable Income mm Consumable Income<br>Source: Author estimations based on EU-SILC 2021 and HBS 2021 using CEQ methodology<br><!-- End of picture text -->
enrollment and the available micro-level evidence on dowry payments suggest that such incentives are neither strong nor generalized. Dasgupta, Maitra, and Mukherjee (2008) argue that marital arrangements may be responsible for these patterns. In South Asia, married sons typically live with their parents in a subordinate capacity. Thus, when parents seek wives for their sons, they may value characteristics that facilitate the continuation of parental control over their sons after marriage. A lack of education on the part of the bride may constitute such a characteristic. Hence, parents may prefer uneducated brides unless educated brides have significantly larger dowries. This situation reduces parental incentives to educate daughters. Therefore, the success of the human capital investment policies identified by previous studies crucially depends on the living arrangements and cultural norms of each society.

In the developed world, women have caught up with men in terms of human capital accumulation, and the gender gap is shrinking (Heathcote et al. 2010). This substantial economic empowerment of women has been accompanied by a decreasing trend in female domestic violence. Aizer (2010) proposes an economic theory of household bargaining that incorporates domestic violence, suggesting that an increase in a woman’s relative wage increases her bargaining power and reduces levels of violence by improving her outside options. Using new sources of administrative data for the United States, the author finds that the decline in the wage gap witnessed over the past 13 years can explain 9 percent of the reduction in violence and the costs associated with it. Although Aizer’s model seems appropriate to describe the situation in developed countries, it does not consider the role of culture, which is likely to have a crucial effect on domestic violence in developing countries. Female economic empowerment may threaten the image of men as breadwinners, and men may respond by increasing violence against their wives (Angelucci 2008).

Information and media exposure have proved useful in affecting individuals’ attitudes toward a variety of issues. Several papers by Fernandez et al. show that the increase in female labor force participation
|**Super-area variables**||**Super-area variables**||
|---|---|---|---|
|Colombo|2.47***|Morogoro|0.18**|
|Kalutara|1.06***|Simiyu|-0.21**|
|Hambantota|-0.49|||
|Jaffna|-0.46|||
|Mannar|-0.83***|||
|Mullaitivu|-1.74***|||
|Kilinochchi|-0.69**|||
|Batticaloa|1.00***|||
|Trincomalee|-0.14|||
|Puttalam|7.33***|||
|Anuradhapura|2.47***|||
|Badulla|1.06***|||
|Moneragala|-0.49|||
|Rathnapura|-0.46|||
|Sector||Sector|-0.34***|
|Urban|2.65***|Rural||
|Estate|-2.02***|||
||||-1.29***|
|Constant|-4.49***|Constant||
|R<sup>2</sup>|0.251|R<sup>2</sup>|0.321|
|Of which: sub-area variables|0.060|Of which: sub-area variables|0.134|
|area variables|0.058|area variables|0.059|
|Super-area and sector dummies|0.129|Super-area and sector dummies|0.104|
|Number of observations|19,570|Number of observations|9,393|

_Notes: Stars indicate statistical significance at 5, 1
In this sense, it is necessary to bear in mind that all indicators of prevalence and profile of non-standard employment will be limited to a subset of this kind of workers.

We study a sample of 7 developing countries from the ECA region, particularly from Eastern Europe and Central Asia. Specifically, the analysis was conducted for the Russian Federation, Georgia, Kyrgyzstan, Turkey, Armenia, Albania, and Moldova.

In the 7 cases mentioned, it was possible to identify part-time employees; while only in 4 cases temporary workers were also identified (it was not possible to identify temporary employment in the databases of Russia, Armenia, and Albania).

## 4.2.1 Variation of the NSE as a percentage of total employment

The trends of non-standard employment in Eastern Europe and Central Asia do not show a homogeneous pattern in the last 10/15 years (Figure 16). Indeed, there is an increase of the NSE share in the total wage-employees in the Kyrgyzstan Republic, Turkey and Moldova, while Russia Georgia, Armenia, and Albania, present the inverse trend.

Analyzing the prevalence of NSE by types of occupations, we find a quite similar pattern across countries which is also similar to the findings for Latin American and the Caribbean countries. In most of the countries, the “Elementary occupations” is the category where the prevalence of NSE is

> 7 Regarding the depth of changes, we identify some differences between NSE and SE but without a common pattern across countries.

Page 24 of 51
Response Mechanism), to respond to the needs of governments by setting up a minimum institutional</mark> infrastructure to coordinate state-wide responses to investor grievances.

<mark>The most common issues identified by the SIRM pilots are sudden/arbitrary regulatory changes (60%), followed by contract breaches (22%) and expropriation (18%), which confirms the findings of MIGA/EIU 2009-2013 surveys and the 2017 GIC Survey. Regarding which types of agencies generate grievances, the SIRM pilots confirm the critical role that specialized and subnational regulatory agencies play in generating most of the conflicts. Among the 39 contentious cases, specialized agencies were involved in 64% (25) of cases and subnational agencies for 15% (6) of them. The pilots also show that grievances arise in all sectors, with service sector cases counting for 46% of the total, followed by primary/extractive industries (36%) and manufacturing</mark> (18%).

<mark>Source: Echandi and Sauve (2019)</mark> 7
. Premand, F. Rosati and R. Vakis (2015) “Women's Economic Empowerment and Children’s Human Capital Accumulation”. UCW Working Paper

- Del Carpio, X., N. Loayza and W. Tomoko, (2016). “The Impact of Conditional Cash Transfers on the Amount and Type of Child Labor,” _World Development_ , 80(C): 33-47.

- Dessy, S. and S. Pallage (2005) “A theory of the worst forms of child labour”, _Economic Journal_ 115: 68-87.

- Dillon, A., Bardasi, E., Beegle, K., and Serneels, P. (2012). “Explaining variation in child labor statistics”. _Journal of Development Economics_ . 98(1):136-147

- Duflo, E., R. Glennerster, and M. Kremer. (2008). “Using Randomization in Development Economics: A Toolkit.” In _Handbook of Development Economics_ , 4, Amsterdam, NorthHolland.

- <mark>Duryea, S., D. Lam, and D. Levison. (2007). “Effects of Economic Shocks on Children’s Employment and Schooling.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 84 (1): 188-214.</mark>

- <mark>Edmonds, E. (2005). “Does Child Labor Decline with Improving Economic Status?”</mark> _<mark>Journal of Human Resources</mark>_ <mark>, 40(1)</mark>

- <mark>Edmonds, E. (2008) “Child Labour.” in T</mark> . P. Schultz and J. Strauss, eds., _Handbook of Development Economics_ . Volume 4, Elsevier Science, Amsterdam, North-Holland.

- Edmonds, E. and N. Schady. (2012). “Poverty Alleviation and Child Labor.
privatization.<sup>33</sup> The coefficient is negative but statistically insignificant -- the large negative coefficient might indicate that only PJ governors are affected by provincial deficits (i.e. we are unable to reject the null hypothesis that the sum of the deficits and interaction coefficients is zero). However, we are also unable to reject the null hypothesis that all governors are affected equally.

**Table 5: Proportional Hazard Model for Bank Privatization (Buenos Aires Included)**

||(1)<br>**1**<br>**Pr**<br>|<br>(2)<br>1<br>**oportional Ha**<br>|<br>(3)<br> <br>**zard Model**<br>|1<br>(4)<br> <br>**(Monthly Dat**<br>|1<br>_(5)_<br>**a)**<br>|
|---|---|---|---|---|---|
|**_Prov. de Buenos Aires Included_**|Yes|Yes|Yes|Yes|Yes|
|_# of_**_Banks_**|23|23|23|23|17|
|_# of Privatizations_|16|16|16|16|12|
|_# of_**_Months at Risk_**_of_**_Privatization_**|1010|1010|1010|1010|825|
|**_Does the PJ control the_**_assembly_|-0.732|-0.625|-0.598|-0.235|-1.935|
|**_and the executive?_**_(l=yes,O=no)_|(-
Survey. However, we did not find similar studies across developing countries in general and the LAC region in particular.<sup>2</sup> Hence, it is not possible to draw any conclusions about whether the above theories are borne out in the region from existing literature. At the global level, the main relationship investigated is between citizens’ trust in government and their willingness to obey laws and pay taxes. There is evidence, though, that weak trust in government can (including in developing-economy settings) erode the social contract and lead to citizens disengaging from the state (that is, citizens evade taxes, stop participating in elections, disobey laws, take justice on their own, among others; see Arizti et al. 2010; Kumagai and Iorio, 2020).

We contribute to the literature studying the relationship between trust in government—or political trust—and citizen participation in two ways. First, we develop a quantitative strategy that relies on the use of machine learning methods on micro-level data. Second, we focus the analysis across multiple countries in the Latin America and Caribbean region simultaneously using comparable data.<sup>3</sup> To be clear, we seek neither to construct a formal hypothesis testing nor develop a general theory of political participation. Rather, our study builds on the work of Lee and Schachter (2019) in

> 2 One exception is Wagle (2006), who uses a structural equation model to study political participation and civic engagement in Kathmandu, Nepal.

> 3 This comparative analysis does not undermine the need for case studies that provide further insights into specific contexts such as, for example, the sudden and dramatic increase in political trust in El Salvador or the effects on trust that Constitutional referendums have (as recently held in Chile), to mention a few examples. Nonetheless, the use of country fixed effects in our empirical analysis aims at controlling for those idiosyncratic factors in specific countries.

4
I use job tenure as a measure of job specific skills, i.e. I assume that individuals who have been at their job for longer have greater job specific skills. The ENOE collects information on job tenure through the amplified questionnaire. Unfortunately, this questionnaire is only available once a year for most years and for 2009 it was applied in the first quarter, when the wage subsidy program was recently launched. Figure 9a plots the number of individuals working in eligible industry in a job with access to IMSS, separately for those with two or less years of job tenure and those with more than two years of tenure. Figure 9b displays the corresponding numbers for ineligible industries. Both figures show that the number of employees with high job tenure remained stable from 2008 to 2009. In fact, the drop in employment during the crisis seems to be entirely driven by individuals with low job tenure, who presumably have the lowest levels of job specific skills, both in eligible and ineligible industries. That is, even in industries that were not eligible for the wage subsidy program firms decided to lay off employees with the lowest job tenure, thereby preserving job specific skills.<sup>13</sup> Evidence from the ENOE data does thus not support the hypothesis that the wage subsidy program caused firms to keep employees with job specific skills to a greater extent than they would have in the absence of the program. This evidence needs to be interpreted with caution due to the issues with matching ENOE data to eligible and ineligible IMSS industries, as mentioned above, and also due to the fact that tenure data only exists for the first quarter of the program. However, it does suggest that the program may have worked through a different channel than preserving job‐specific skills.

Another potential explanation for the effects measured in this paper is that it was not the firing restrictions that allowed employment to recover faster after the crisis, but rather the payment of the subsidy funds itself. These funds may have provided liquidity for hiring back
The randomization was undertaken in two steps. First, among all eligible villages in the 6 targeted communes, a public lottery randomly selected treatment and control clusters. In total, 152 clusters (244 villages) were drawn, including 100 treatment clusters (159 villages) and 52 control clusters (85 villages). After cash transfers started, a second public lottery was undertaken to assign treatment villages to receive either the cash transfers only (CT, 50 clusters, 84 villages), or the cash transfers plus the behavioral change component (CT + BCC, 50 clusters, 75 villages).<sup>13</sup>

# 3.2 <u>Sampling, Data and Timeline</u>

After the first public lottery, a listing of households was undertaken in all villages drawn into the sample. The sample for the baseline survey was drawn from the listing by taking a random sample of 30 eligible households in each cluster.<sup>14</sup> A baseline survey was then collected between April and June 2012 among sample households. The baseline sample included 4,330 households and detailed information on 6,132 children aged 6-59 months.

The baseline survey included a household module and a module for children aged 6 to 59 months. The household survey instrument built on the 2011 Niger LSMS-ISA national household survey questionnaire. The survey for children aged 6-59 months built on the Niger DHS/MICS questionnaire. It contained modules on nutrition and food security, parenting practices, as well as anthropometric measures. The child instrument also included a cognitive test adapted by Zeitlin and Barry (2008) to measure cognitive development among young children in West Africa. The test is an adapted version of the Bayley<sup>TM</sup> cognitive scale, discussed further in Section 4.4 below.<sup>15</sup>

> 13 Two nomadic clusters (one control and one treatment cluster, including 6 villages in total) were originally drawn into the evaluation sample but had to be
base = permanent full_<br>_time)_|||||
|Temporary full time|-0.035|-0.060|-0.040|-0.050|
||(-0.50)|(-0.92)|(-0.58)|(-0.76)|
|Part time|-0.178<sup>**</sup>|-0.126<sup>*</sup>|-0.158<sup>**</sup>|-0.115<sup>*</sup>|
||(-2.48)|(-1.87)|(-2.18)|(-1.67)|
|Day work|-0.412<sup>**</sup>|-0.416<sup>**</sup>|-0.417<sup>**</sup>|-0.457<sup>**</sup>|
||(-2.05)|(-2.15)|(-2.08)|(-2.39)|
|R2 /Log likelihood|0.3472|-679.5752|0.3527|-666.8276|
|Observations|415|978|415|978|

_Source:_ Original table for this publication.

_Note:_ OLS = ordinary least square. Y is log value of wage-earning. t-statistics in parentheses. All the estimates are also controlled for geographical divisions of respondents and the economic sectors where the respondents are working. The sample selection for Heckman models takes the same set of variables as model 4 of the employment outcome regressions. *** p<0.01, ** p<0.05, * p<0.1.

Next, we added an interaction
The present paper aims to provide causal evidence of the effects of maternal mental health on children’s human capital accumulation in developing countries. We study the under-explored relationship between maternal depression and child cognition, a dimension of child development that has been extensively documented as a crucial determinant of life outcomes (Becker 1964; Currie and Thomas 1999; Feinstein 2003; Cunha et al 2005). We focus our analysis in the context of Peru, a developing country with a high prevalence of maternal depression.

To shed light on the issue, we conduct our analysis using information from the Young Lives (YL) survey in Peru, a rich longitudinal household survey that follows households with at least one child born between 2001 and 2002 (index child). For our analysis, we use YL’s first three rounds: a baseline round in 2002, when the index child was 6-20 months old, the first follow-up when the child was 4 - 6 years old, and the last round in 2009-2010, when the index child was 7 - 8 years of age. The YL also has the novelty that includes questions related to maternal mental health and child vocabulary, along with a wealth of information on child, family and community characteristics.

Inspired by the literature that links the exposure to shocks during pregnancy, maternal mental health and children’s outcomes, we employ an instrumental variables (IV) approach as estimation strategy. This approach helps us to address potential simultaneity bias in the estimation of the effect of maternal depression on child’s vocabulary. We exploit the richness and longitudinal nature of the data to better capture the dynamic nature of maternal mental health on child cognitive development at 5 and 8 years of age. In particular, we instrument maternal depression with experience of a shock (loss of crop or livestock) at baseline (when the child was in utero or recently born). Given the richness of the YL data, there are several potential exogenous shocks that can serve as instruments for maternal depression or additional controls that can