rafmacalaba/data-use-mentions-extended huggingface dataset:
Migration data from the population census demonstrates the following: a) relative to other migrant and non-migrant women, women who migrate between ages 10 and 18 are significantly more likely to be teenage parents; b) migrants tend to leave municipalities with low school expansion in favor of municipalities with high school expansion; c) the difference in school expansion between the destination and origin municipality is higher for teenage mothers than for non-teenage mothers. These stylized facts all work against finding a negative relationship between school density and teenage birthrates. As a result, migration may lead to an underestimate of the true magnitude. Therefore, the results reported in Table 3 should be considered a lower bound on the parameter . **7. Concluding Remarks** αα

# **7. Concluding Remarks**

In this article, we investigate whether an increase in secondary education access, through an increase in the municipal density of secondary schools, impacts the fertility decisions of teenagers in the middle-income context of Brazil. Our estimates are consistent with the hypothesis that the school expansion played an important role for teenage fertility in Brazil. A one-unit increase in secondary school density in municipalities leads to an average decrease in a cohort’s teenage birth rate of between 0.250 and 0.563 births per 100. These estimates suggest, under some assumptions, a reduction of one birth for roughly every 50 to 100 students who enroll in secondary education due to the expansion.

The data we use in this study have the advantage of near-universal coverage of births, and significant variation from a large school expansion across 4,884 municipalities. However, a limitation of these data is that we cannot link birth outcomes to schools at an individual level. Therefore, our results should not be interpreted as capturing the effect of school enrolment on an 21
draw resources from and compete with the formalized business sector (this has been categorized as the ‘parasitic view’)<sup>4</sup> ; by contrast, a competing view is that widespread informality represents a ‘segmentation’ between a formal labor market, the business environment they experience, and their informal analogs (Perry et al 2007; Maloney 2007; Kanbur, 2017; Ulyssea, 2020). Understanding the estimates and nature of informal sector businesses informs policy, particularly considering the absence of consistently effective policies to address informality, e.g., the limited success of formalization efforts (see Bruhn and McKenzie, 2014; Floridi et al 2020).

Existing methodologies, such as household-based sampling, are not designed to measure informal sector businesses directly. Such methods often do not capture informal sector businesses at their point of business or operation; when they do, businesses are reached via the places of employment of household members and, consequently, they are unable to estimate business density in a geographical area. Likewise, informal sector businesses are, in all but a few cases, excluded from economic censuses, which tend to be infrequent and expensive. Informal sector businesses, virtually by definition, are also not reached by official surveys proceeding from administrative sampling frames. By contrast, the proposed ACS method reaches units at their point of operation and is designed to measure informal sector businesses _per se_ ; this paper builds upon existing ACS literature and shows that this method not only provides a precise, unbiased population estimate but can also provide practitioners with large fieldwork efficiency gains when compared to other methods.

However, the ACS method does bring trade-offs and challenges. As noted above, to operationalize such a survey, a clearly delimited population of inference is needed. In this case, this consists of un-registered business activity, within a delineated geographic area (e.g., a set urban area). This implies that informality in non-covered locations, rural or excluded cities, will not fall within the purview of these
to more than 10 percent of GDP since 2010, hence it is on this group of countries that the paper focusses.<sup>1</sup> The other four FSU countries in CASC – Azerbaijan, Kazakhstan, Turkmenistan and Uzbekistan – are major hydrocarbon producers and exporters.<sup>2</sup> The paper is organized as follows. Section 2 sets out the context for the subsequent analysis by providing data on net migration flows, the stock of migrants and the countries in which they reside and the implied impact on the labor forces of the eight FSU countries in CASC. This section also provides data on the remittance receipts of these countries. Section 3 explores the

> 1 The fifth FSU country which receives remittances of more than 10 percent of its GDP is Moldova, which is not covered in this paper.

> 2 Uzbekistan has also been a major recipient of remittances, but since 2010, remittances have only exceeded 10 percent of its GDP in two years, 2012 and 2013, and they have since 2013 fallen sharply to 3 percent of GDP in 2016, according to the WDI data. The remittance data for Uzbekistan are somewhat of an enigma in that they are very volatile, which is difficult to explain, and that the higher numbers for remittance receipts, such as 11.6 percent of GDP in 2013, do not appear consistent with the migration data shown in table 1.

2
provide countries with a way to benchmark their existing surveillance systems and result in a substantial increase in data for tracking road traffic injuries globally.

The contributions of the paper are twofold. This paper provides a unique review of transportrelated questions in LSMS surveys and discusses possible improvements to facilitate the harmonization of questions in future rounds and the inclusion of questions useful for climate change and road safety topics. This paper covers many countries and goes beyond affordability issues by collecting questions on accessibility, ownership, and transport behaviors in LSMS and harmonized datasets.

The first part of the paper reviews transport-related questions included in LSMS surveys. The second part reviews transport-related questions in non-LSMS surveys. The third part focuses on two harmonized surveys to show the potentials for more harmonization efforts. The last part concludes by providing a module complementary to the existing LSMS household surveys covering multiple aspects linked to transportation issues.

# **2. Transport-Related Questions in LSMS Surveys**

Multiple sources could be used to collect household-level information to learn about transport. Table 1 lists a variety of instruments for collecting households’ information. For the sake of this 3
Drawing upon 78 participatory poverty assessments (PPAs) in different countries, the 2000 report _Voices of the Poor_ remarked that “poor people’s interactions with representatives of the state leave them feeling powerless, unheard, and silenced” (Narayan et al. 2000, 65). The report found that “the poor experience many barriers in trying to access government-provided services. These include bureaucratic hurdles, rules and regulations, the need for documents, and difficulties in accessing information” (Narayan et al. 2000, 65). When bureaucracy imposes excessively complicated procedures to access public services or state entitlements, the poor are more likely to be affected as more affluent and more educated citizens can better manage their way around the rules or more easily bear the costs of compliance. Simplifying the procedures and reducing the costs associated with citizen-government interactions would therefore empower the poor to take better advantage of the services and rights to which they are entitled.

This paper presents pilot results from a new World Bank project, _Living Life_ . The project developed and tested a methodology to collect data on the bureaucratic complexity in citizen-government interactions that is essential for accessing basic services and for exercising fundamental rights and civic duties.

First, the paper discusses the current knowledge gaps and the project’s contribution to the existing literature and evidence. Second, the paper presents the results from pilot data collected in four countries: Chile, Ghana, India, and Vietnam. Finally, the paper discusses lessons learned and next steps for improving its methodology and expanding data coverage to a larger number of countries.

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Stock, James H., and Mark W. Watson. 2010. _Introduction to Econometrics_ , 3rd ed. Boston: Addison-Wesley.

Tolonen, A. (2015). "Local Industrial Shocks, Female Empowerment and Infant Health: Evidence from Africa's Gold Mining Industry." Job Market Paper, Gothenburg University, Sweden World Bank (2014). World Development Indicators. Washington, DC: World Bank.

# **APPENDIX I:** _Figures_

# Figure 1: Mineral price trends: pre- and post- China’s WTO accession

<!-- Start of picture text --> 140.00<br>120.00<br>100.00<br>80.00<br>60.00<br>40.00<br>20.00<br>Year<br>Energy Metals & minerals<br>Base metals (ex. iron ore) Precious metals<br>Source: Authors' construction using World Bank Commodity Price Data<br>Real 2005 US dollars<br>Annual indices (2010=100)<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> 25
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data Panel C shows the results for the measurement equation using the estimated sales from the survey as indicator.<sup>31</sup> The coefficient of the log of true sales is 0.66 and significantly different from zero and one at the 5% significance level. Therefore survey sales are significantly and positively correlated with true sales, but with a correlation smaller than one. This does not imply underreporting, however, as the constant term is also positive and significant at the 5% level, and for small values of true sales this would suggest overreporting. However, based on the estimated conditional predictions of the firms’ true sales (see next subsection), most of the firms (74%) are estimated to underreport sales in the survey.

The above estimation of the MIMIC model assumes a multivariate normal distribution for the disturbances. When this assumption is violated, the estimated parameter values from truncated maximum likelihood become inconsistent (Cameron and Trivedi 2005). We test the multivariate normality assumption on the reduced-form MIMIC model residuals with the results shown in panel D. Both the multivariate and asymptotic multivariate normality tests do not reject the null hypothesis of multivariate normality. We have also estimated the MIMIC model with different choices for the capital return and depreciation rates used in the calculation of the capital variable (equation 6), and we find that our results are robust with respect to plausible alternative parameter choices.

> 31 We’ve tried specifications including various firm characteristics in panel C similar as Panel B, but the coefficients all appear to be very small and insignificant.

29
district level, how implementation of India’s National Rural Employment Guarantee Scheme (NREGS) affected official reports of violent crime from 2001 to 2011. The results indicate that the program reduced reports of kidnapping, but most of the relationships between crime and program implementation were not robust to the inclusion of district‐ specific time trends. Other studies have examined the effect of NREGS on the prevalence on Maoist violence, and found mixed results. Dasgupta et al. (2014) find that NREGS substantially reduced the prevalence of armed conflict as proxied by local news reports. This is consistent with Feltzer (2014) who found that NREGS mediated the effect of adverse rainfall shocks on conflict. However, Khanna and Zimmerman (2015) study the same program using a different methodology, and find that the program increased insurgent activity, which they ascribe to increased cooperation between villagers and the police outweighing the increase in the opportunity cost of conflict.

A second set of studies evaluates the social effects of training programs in Latin America. Calearo and Rozo (2016)) use experimental data from the Galpao training program in Brazil. The results fail to show a discernible average effect on risky behaviors such as tobacco and alcohol use, though positive effects were apparent for a subset of participants with stronger self‐reported socio‐emotional skills. In the Dominican Republic, meanwhile, a youth training program reduced the probability of teenage pregnancy by about 8 percentage points, or 20 percent (Novella and Ripani, 2014), though no evidence is available on the effects on crime.

Finally, Blattman and Annan (2015) examine the results of a randomized evaluation of a program to rehabilitate high‐risk men in Liberia, and also find mixed results. The program provided agricultural training and capital inputs to former soldiers in Liberia. Program participants reduced hours devoted to illicit resource extraction by 20 percent, and reduced interest in fighting in a short‐lived conflict in 5
|
|Eld|LR chi2|1.99|5.17|6.69|0.90|5.79|0.49|
|mpoye|p>chi2|1.00|0.98|0.95|1.00|0.97|1.00|

_Source:_ Authors’ tabulations using SEDLAC (CEDLAS and the World Bank).

# Findings

Remittances can impact different dimensions of labor supply: 1) the extensive margin – the extent to which remittances impact who is working, and 2) the intensive margin – the extent to which labor intensity is affected by remittances. In particular, for the extensive margin we look at the impact of remittances on entry into the labor force and unemployment among all adults, and _nini_ status among youth. The intensive margin is assessed looking at informality, type of employment, and hours worked. The results are organized into two sections: the first considers the extensive margin of labor supply – participation and unemployment, while the second considers the intensive margin – employment outcomes.

## _Labor participation and unemployment_

Among working age adults, remittances are associated with lower levels of labor force participation in all countries and for both years analyzed (Table 3). In line with other results in the literature, the reduction in labor force participation associated with remittances is more pronounced among women than men, though evidence of drops in male participation rates is evident for both years in Guatemala and El Salvador and in 2006 in Honduras. As suggested from Table 1, participation seems particularly affected by remittances in El Salvador. The difference in participation rates between recipients and non-recipients is largest among Salvadoran women (17.5 percentage points in 2006) and smallest among Honduran men in 2014 (1 percentage point, a difference not statistically different from 0). The results also suggest a change between 2006 and 2014 in how remittances affect participation in Honduras: while the negative effect on participation for men fell and lost statistical significance, the effect on
the general population globally in 2019, and at 45 percent in Sub-Saharan Africa (GSMA, 2020), though penetration continues to increase at a significant rate. Moreover, agricultural households are less likely to own phones than the general population (Ambel et al., 2021; Himelein et al., 2015; Leo et al., 2015) and adoption of mobile phones in low- and middle-income countries has been found to be associated with wealth, gender, remoteness, and education (Henderson and Rosenbaum, 2020; Himelein et al., 2020). Drawing on household survey evidence from 28 countries, **Error! Reference source not found.** Figure 2 illustrates the discrepancy in access to mobile phones, with access defined as mobile phone ownership within the household, for the population with no household income from agriculture and those with over 30% of household income coming from agriculture.<sup>5</sup> ￼ The majority of countries exhibit a pattern in which households that are reliant on agriculture, i.e., those with over 30% of their total income coming from agriculture, have lower rates of mobile phone access<sup>6</sup> ￼ This is especially pronounced in Ethiopia, Malawi, and Sierra Leone where the difference between the share of

> 5 Households earning between 0% and 30% of total income from agriculture are excluded for presentation purposes. The rates of phone access for this group of households generally falls between the rate of those with no income from agriculture and those earning more than 30% of income from agriculture.

> 6 Only countries with data on access to mobile phones in 2010 or later are included. The year of data collection by country is as follows: Armenia - 2013; Bangladesh - 2010; Burkina Faso - 2014; Cameroon - 2014; Ecuador - 2014; Ethiopia - 2016; Georgia - 2015; Ghana - 2013; Guatemala - 2014; India - 2012; Iraq - 2012; Kyrgyzstan - 2013; Malawi -
# **Table A10: Impacts of mental health on labor outcomes: Impacts of mental health on labor outcomes – Dummy indicator**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Serious mental illness|-0.064***<br>(0.014)|-0.423***<br>(0.159)|
|_First stage of 2SLS (dependent va_<br>Trauma experience*Year after<br>arrival|_riable is serious me_<br>-0.023***<br>(0.005)|_ntal illness)_<br>-0.026***<br>(0.010)|
|Kleibergen-Paap F stat.|17.312|16.988|
|AR 95-CIs|[-0.304, -0.039]|[-1.414, -0.244]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,687|
|Numberof individuals|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and
A year later, therapy participants also described applying skills of self-regulation in their lives. To avoid fights, they used new tactics: removing themselves from emotionally-charged situations, allowing space to process their feelings, and ignoring negative automatic thoughts in the favor of more controlled thinking. Related were improved social and communication skills. Interviewees described how such skills allowed them to engage with community members or in disputes and express themselves without anger or violence.

Not only did the community regard them differently, many said, but troubled young men began coming to them for advice and lessons learned from the therapy once they saw the sudden and sustained change–another important source of reinforcement, and perhaps one reason we do not see a change in peer quality in the data.

# **7 Can we believe our self-reported data?**

Self-reported data raise several worries, the most serious being measurement error correlated with treatment. For instance, men who receive an anti-violence intervention might be more likely to tell us they are non-violent, overestimating the estimated treatment effect of therapy.

This kind of bias is hard to square with the patterns of effects we observed. Therapy followed by cash would have to induce systematic errors where therapy or cash alone did not. Nonetheless, this is possible. Thus, concerned that our survey measure, _y_<sup>_s_</sup> , may be biased, we set out to intensively validate some measures, _y_<sup>_v_</sup> . If _y_<sup>_v_</sup> is closer to the true behavior, _y_<sup>_∗_</sup> , this allows us to estimate the degree and direction of bias. We summarize the approach, empirical strategy, and results here, with details in Appendix F and Blattman et al. (2015).

## **7.1 Approach to validation**

Of more than 4,000 endline surveys, we randomly selected 7.3% and validated answers to six survey-based measures with two-week
period**|**No. of children (6-59 months)**|
|---|---|---|
|2013/14|September 2013-March 2014|2,880|
|2015/16|September 2015-April 2016|2,993|

We study individual nutritional outcomes of children and follow previous studies and compute indicators of undernutrition for children between 6 and 59 months.<sup>8</sup> To achieve this we compute z-scores for height for age (stunting).<sup>9</sup> A child is stunted if the height-for-age z-score is below -2 (that is two standard deviations below the mean of the reference group). According to WHO

> 7 There was also a first wave of the LSMS in Ethiopia in 2011/12.

> 8 Infants aged under 6 months are often excluded from nutrition surveys.

> 9 The z-scores are calculated using the 2006 World Health Organization (WHO) child growth guidelines. The analysis is done using zscore06 Stata module.

6
best in those cases when economies have similar structures and are at similar levels of development. A study on Egypt (ILO 2010) provides information from the most recent Egyptian IO table which includes 22 sectors based on ILO data for 2007/08 and the calculation of multipliers for all sectors. This information is relevant to the oil importing MENA countries whose IO structures are similar to Egypt’s. However, the GCC and developing oil exporters were judged to be too different in terms of economic structure and GDP per capita levels to make extrapolation from the Egyptian case reliable. For this reason we follow Schwartz et al. (2009) and employ an alternative method for assessing the employment effects of investment spending in MENA.

Schwartz et al. (2009) investigated the employment effects of a stimulus package of spending in various infrastructure sectors in the Latin America and Caribbean (LAC). They used project data from a number of World Bank studies to derive the share of investment expenditure on labor, and the share going to imports, in the main infrastructure sectors. Combining the expenditure share on labor with data on region-wide average wages (plus benefits) allowed them to compute the direct employment per US$ billion. The authors used data from the US highway sector to derive type I and type II multipliers and calculate indirect and induced employment. Taking a weighted average portfolio of infrastructure sectors allowed them to estimate the employment created by a representative basket of infrastructure investments.

> 6 Studies include Tregenna (2007), Bekhet (2011), DIT (2007), SEME (2010), Vesin (2011), Ishihara and Bennett (2010), and Schwartz et al. (2009).

10