rafmacalaba/data-use-mentions-extended huggingface dataset:
in market power, demand and quality (Cusolito and Maloney, 2018). Its attraction, however it its simplicity and direct interpretation compared to other relatively complicated productivity measures. We also use other measures of firm productivity, such as value added per worker and TFP. However, the latter two measures have a large number of missing observations, which we suspect are systematic. Another key firm performance indicator we examined, which is not reported here, is employment growth, which also proxies for firm growth or lack thereof. We dropped observations from the sample with outliers in sales per worker, which represented a very small proportion of the total sample size.<sup>9</sup>

> 7The WBES can be accessed at: http://www.enterprisesurveys.org We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department for making the data available.

> 8Some firms appear more than once in countries with repeat surveys.

> 9The criterion used to identify outliers is _Outlier_ = _|_<sup>_x_</sup> _SDi_<sup>_−x_ˆ</sup> _>_ 3 _|_ , where _x_ ˆ is the median value and _SD_ is the standard deviation.
importance of natural resource exports— or growth volatility, the coefficient estimate of trade openness continues to be significant but becomes smaller.

Export market concentration (as proxied by the Herfindahl index of exports from a certain country to its trading partners) has a positive impact on growth. The sign of this coefficient estimate is different in nature to what we expected; however, it is not robust across the different specifications. Diversifying export destinations may not necessarily have a robust impact on economic growth. In contrast, export product concentration (measured by the Herfindahl index of export products) has an expected negative and significant effect on growth. The coefficient estimate of export product concentration remains slightly invariant to the inclusion of additional control variables in Table 2 (around -0.27 and -0.29). This finding implies that product diversification of exports would foster economic growth.

**Table 2. Trade openness, diversification and growth: Baseline Specification** _Dependent Variable: Growth of real GDP per worker (5-year averages) Estimation method: GMM-IV System Estimator (Arellano and Bover, 1995; Blundell and Bond 1998)_

||[1]|[2]|[3]|[4]|[5]|
|---|---|---|---|---|---|
|_Convergence_||||||
|Initial GDP per worker|-0.022<sup>***</sup>|-0.021<sup>***</sup>|-0.029<sup>***</sup>|-0.022<sup>***</sup>|-0.030<sup>***</sup>|
|_(in logs)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.
Figure 4: Change in Actual and Intended Participation Over Survey Months <!-- Start of picture text --> Health Pension<br>Increase in health ins. Increase in pension<br>linear fit of the increase linear fit of the increase<br>Intention to participate Intention to participate<br>linear fit of intention linear fit of intention<br>3 4 5 6 7 8 9 10 3 4 5 6 7 8 9 10<br>Survey Month Survey Month<br>.1 .15<br>.08<br>.1<br>.06<br>Percent Percent<br>.04<br>.05<br>.02<br>0 0<br><!-- End of picture text -->

**Source** : 2015 and 2016 waves of the Rural-Urban Migration in China (RUMiC) Survey, and RUMiC Information Intervention Survey, 2016. **Notes** :The figures above show the change in actual and intended participation in health insurance and pension programs, respectively.

40
1683-1725.</mark> <mark>Brazil (2020a). Despesas por Função do Governo Central. Classificação COFOG – 2019. Available at:</mark> <u><mark>https://www.tesourotransparente.gov.br/publicacoes/cofog-despesas-por-funcao-do-governocentral/2019/114</mark></u> Brazil (2020b). Censo da Educação Básica 2019: resumo técnico. Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (Inep). Available at: <u>https://download.inep.gov.br/publicacoes/institucionais/estatisticas_e_indicadores/resumo_tecnico_ce nso_da_educacao_basica_2019.pdf</u> <mark>Brazil (2022a). Contas de saúde na perspectiva da contabilidade internacional: conta SHA para o Brasil, 2015 a 2019. Ministry of Health, IPEA and Oswaldo Cruz Foundation joint publication. Available at: https://repositorio.ipea.gov.br/bitstream/11058/11014/4/Contas_de_saude.pdf</mark> <mark>Brazil (2022b). Estatísticas Fiscais do Governo Geral. Available at:</mark> <u><mark>https://www.tesourotransparente.gov.br/publicacoes/estatisticas-fiscais-do-governo-geral/2021/22</mark></u> Brazil (2022c). VIS Data 3: Secretaria de Avaliação e Gestão , MInistério da Cidadania. Available at: - <u>https://aplicacoes.cidadania.gov.br/vis/data3/data explorer.php</u> <mark>Brummund, P., Mann, C., & Rodriguez-Castelan, C. (2018). Job quality and poverty in Latin America.</mark> _<mark>Review of Development Economics</mark>_ <mark>, 22(4), 1682-1708.</mark> <mark>Bucheli, M., Rossi, M., & Amábile, F. (2018). Inequality and
</sup> was used to “match” villages that received KDP with those that would have been likely to receive it but did not, further verified by qualitative interviews to

> 33 See Barron, et al (2004) for a more detailed description of the sampling methodology.

> 34 For more detailed information on how this sampling framework applied across three distinct stages of research, see Barron et al (2004).

> 35 The propensity score is a statistical measure designed to calculate the probability of a given household or village being selected for inclusion in a program (for an introduction, see Baker 2000).  The score was estimated using the PODES (1996) dataset.  (PODES, which stands for Pontesi Desa (Village Potential), is a key informant survey administered every two years by BPS that contains information on each of 60,000 plus villages in Indonesia.)  The actual propensity score was derived using explanatory variables that could serve as proxies for the economic level of the kecamatan.  Among the PODES variables used were: population, access to urban facilities such as markets, hospitals, department stores, health and education resources, main source of income, perception of poverty level, etc. These are all ‘observable’ factors, however, so to control for ‘unobservable’ factors (e.g., motivation, cohesion, leadership) we used the propensity score to select _three_ statistically comparable non-KDP (‘control’) villages and then asked our field research team to identify which of these was, in their view, the most appropriate match for the KDP (‘treatment’) village.

8
questions that are included in the full sample period. Moreover, given the large number of questions, the assessment does not consider compliance question‐by‐question. Rather, it focuses on in what clusters / compliance points are firms more likely to adopt compliance requirements, overall and within the 26 critical issues. Table 4 gives the number of compliance questions per cluster, the number of questions that appear consistently throughout the sample period, and the number of public disclosure questions. It shows that the compliance questions are a minority of the total questions in the sample.

**Table 4: Number of compliance questions per cluster**

|**Compliance cluster**|**Total questions**|**Public disclosure**<br>**questions**|**Questions asked**<br>**consistently**|
|---|---|---|---|
|Child Labor|7|2|6|
|Compensation|40|4|24|
|Contracts and Human Resources|31|2|15|
|Discrimination|56|3|35|
|Forced Labor|18|4|9|
|Freedom of Association and Collective Bargaining|33|5|18|
|Occupational Safety and Health|83|7|43|
|Working Time|26|0|11|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Data from the Compliance Synthesis Report are then merged with firm‐level information collected from Better Work Vietnam’s Registration Document. These data are used as control variables in the assessment, including firm size, firm age, and information on buyer relationships. Table A.4 lists the variables names and their definitions available in the Registration Document.<sup>7</sup>

# Descriptive statistics

Table 5 shows the mean compliance across all questions and factories without controlling for any other potentially relevant variables. The mean compliance in
for the current workers, and 55% comes from decreased earnings for the next generation. It suggests that the cost estimates using only the earnings of the current generation may only capture less than half of the overall cost.

There is, however, some potential drawbacks of this method. First, this procedure relies on the assumption that cohort effects on the earnings profile are minimal. Second, this simple calculation ignores individual characteristics that can affect children’s earnings.

# **5.2.2 Effect on Education Spending**

So far, I have looked at the educational attainment measured by the enrollment rates and years of education. Now, I examine the effect of the sanctions on investment in children’s education measured by household spending on education.<sup>50</sup> The education spending is the explicit costs associated with payments in cash such as (primary/secondary) school tuition, university tuition, books, private tutoring, donations, and other education expenditures (for instance extra classes). The average percentage of family educational spending was about 2% over the pre-sanction period (Source: Calculation by the author using HIES).<sup>51</sup> The primary/secondary school tuition fee constituted a significant proportion of total education costs (21%).

Table 3 presents the effect of the sanctions on education spending by items (they include zero for non-enrolled

> 48I also decompose the total effect of the sanctions on the children’s lifetime income into the sole effect of the reduction in education levels and the sole effect of the reduction in the wage rates. My calculation shows that under different scenarios the effect of reduction in education on the lifetime earnings is relatively smaller than that of the wage reduction.

> 49This effect is larger than previous studies. Oreopoulos et al. (2008) using Canadian data find that a one-dollar reduction in father’s permanent earnings due to a job loss leads to a subsequent reduction in his son’s earnings of 66 cents
and/or special bank taxation as a device to counterbalance implicit bailouts, to achieve optimal managerial discipline, and to balance large-scale maturity mismatch. These conclusions are in line with the work of Kashyap, Rajan, and Stein (2008), who propose the replacement of capital requirements with a mandatory holding of government bonds. On an analogous line, Calomiris and Gorton (1991), Calomiris (1999), and Calomiris and Mason (2003) highlight the trade-off between the public finance costs of safety nets and the gains from panic elimination. Although we do not introduce a formal macroeconomic model, we also offer an argument to reconcile the radically partitioned literature on the relation between financial regulation and the macroeconomy. Roubini and Sala-i-Martin (1992, 1995), Pagano (1993), and King and Levine (1993) support a limited regulation of financial intermediation, because this would lower equilibrium growth through a lower saving rate, fewer resources for investment, less risk diversification, and screening of entrepreneurs. On a critical stance, after a cross-country empirical assessment, Dornbusch and Reynoso (1989) argue that “the beneficial effects of removing financial regulation remains open to challenge”. While, on the entire opposite side of the spectrum, Stiglitz (1994) defends financial regulation as a growth-enhancing force, because by artificially lowering interest rates, it improves borrowers’ incentives, reinforces firms’ equities, and creates capital scarcity. In our work, we find that both views can be right and indeed financial regulation can be either welfare-enhancing or detrimental, depending on the magnitude through which it is implemented.

In Section 1, we present the essential theoretical framework, describing first the economic environment and then investigating the bank decision problem. In Section 2, we discuss empirical evidence 6
# **1. Introduction**

This paper empirically analyzes decisions by Mexican youth regarding whether or not to attend school and participate in the labor market, as well as the factors driving these choices. In Latin America, the prevalence of youth “neither in work nor in school” (Ninis) has increasingly attracted the attention of policy makers and the general public (see, for example, Cardenas, De Hoyos, and Székely, 2015; Bassi, Busso, Urzúa, and Vargas, 2012).<sup>1</sup> Analyzing the phenomenon is important for public policy as it is perceived to be associated with other social problems in the region, such as shortfalls in the quality and relevance of education, which may lead to early dropout from school, increased youth unemployment, and heightened propensity for crime, violence, insecurity, and political instability (Bárcena, López, Hopenhayn, and Frishman, 2008; Grogger, 1997; and Jacob and Lefgren, 2003).<sup>2</sup> Other socioeconomic factors such as teenage pregnancy are also commonly associated with the number of Ninis, with the region exhibiting the second highest adolescent fertility rate in the world (69 births per 1,000 women 15-19) after Sub-Saharan Africa (World Development Indicators, 2014).

According to the most recent estimates, almost one in five youth between the ages of 15 and 24 in Latin America are Ninis, accounting for approximately 18 million individuals (De Hoyos, Popova, and Rogers, 2014). Of all youth aged 15 to 24 in 2010, 34 percent were only studying, 33 percent were only working, 12 percent were both studying and working, and 19 percent were Ninis.<sup>3,4</sup> During the last two decades, the percentage of Ninis in the region has gradually declined (although the absolute number has increased), although there has been substantial heterogeneity in trends within this group (De Hoyos et al., 2014; Cardenas
Fig. 8 Susceptibility of roads and highways in the CHT and Sylhet divisions based on combinations of weights that result in (a-b) the lowest susceptibility and (c-d) the highest susceptibility. The frame colors of the maps (i.e., red and blue) correspond to the lines with similar colors in Fig. 6.

This study focused on one source of uncertainty: the weights used to overlay the factors contributing to landslide events. However, other potential uncertainty sources should be investigated. For instance, in the case study application, a scoring scheme for different factor classes has been adopted from a previous study conducted by CEGIS (see Table 2). However, CEGIS scores are based on expert judgment, which can result in biases. Another potential source of uncertainty in landslide susceptibility mapping is the susceptibility classes, which were obtained from CEGIS and can introduce uncertainty. Sensitivity analysis of landslide susceptibility mapping to factor scores and hazard classes should be conducted alongside factor weights to fully account for the uncertainties involved.

In the case study application, we used factor data resampled at a 50-meter spatial resolution. Using a finer spatial resolution could improve performance and reduce the uncertainty around the weights used in landslide susceptibility mapping. For instance, improving the spatial resolution of the digital elevation model layer would improve slope estimates and could influence the resulting weights of the slope layer. In this study, we relied on an inventory of landslide locations compiled by IMC Worldwide and CEGIS. While this inventory comprehensively accounts for many past landslide locations, other locations in remote areas are typically not recorded due to inaccessibility or the absence of infrastructure at risk. Any bias in the landslide inventory would propagate to a bias in the weight uncertainty analysis. For example, if most past landslide locations were recorded in urban areas, the weight of the land cover layer would increase, as areas with human interventions are assigned a high score in reclassifying the land cover layer. Improving the landslide inventory can improve weight estimates and reduce biases.

27
effect with the interaction term is -0.256, so the total reduction in forest cover lost for this type of land overall is 29.6% (0.256/0.865). For the cohorts that have been enrolled for longer in the program (2011-2012), this effect is 38.1% (0.491/1.294). For the combined and the 2011-2012 cohorts, the impact on avoided deforestation is statistically significant at the 1 and 5 percent level respectively. We also observe a smaller (but not significant) effect in the expected direction for program recipients that have only been in the program a short period of time (2013-2014). We believe that the larger and statistically significant impacts in high risk areas can be ascribed to the fact that there is more scope for behavioral change in an area with high risk of deforestation – in other words, it is easier to have an impact when there is greater possibility for change.

In addition, the program has differential impacts across regions (see **Appendix Figure A8)** . While effects on percent of avoided deforestation were not detected in the north, center and southwest regions, the program’s effectiveness was high in the Yucatan peninsula, which is an area of high risk of deforestation.

# _Robustness Checks_

The above results are consistent across a series of robustness checks. Results for the fuzzy design (i.e., the instrumental variables approach where the discontinuity is used as an instrument while controlling for the point score) are shown in **Appendix Table A3** . The point estimates for the different outcomes and for the different windows are very similar to our intent to treat estimates. We conducted two further robustness checks. First, we ran a placebo test using deforestation outcomes from the years 2007-2010. These estimates are shown in the **Appendix Table A4** . As expected, this effect shows no pattern of program impacts in the years prior to enrollment. Second, our
|

Notes: The units are cm of height. Standard errors in parentheses. *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1 indicate statistical significance comparing the mean of each group to the mean of the reference group, group 0. Source: estimations based on ENCEL 2017.

|Table 6. Mean of absolut|e intergenerationa<br>|l mobility in educ<br>|ation, by gende<br>|r and analysis<br>|group<br>|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|VARIABLES|All|Group 0|Group 1|Group 2|Group 3|
|Males|5.20|4.05|5.43***|5.26***|5.56***|
||(0.10)|(0.20)|(0.19)|(0.14)|(0.17)|
|Females|5.40|4.00|5.52***|5.69***|5.44***|
||(0.09)|(0.22)|(0.20)|(0.13)|(0.16)|
|Observations|3,816|432|685|1,819|880|

Notes: The units are years of schooling. Standard errors in parentheses. *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1 indicate statistical significance comparing the mean of each group to the mean of the reference group, group 0. Source: estimations based
# **List of acronyms**

|CAR|Central African Republic|
|---|---|
|DHS|Demographic and Health Surveys|
|DRC|Democratic Republic of Congo|
|DTM|Displacement Tracking Matrix|
|FCS|Fragile and Conflict-affected Situations|
|GIDD|Global Internal Displacement Database|
|GIS|Geographic Information Systems|
|IASC|Inter-Agency Standing Committee|
|ICRC|International Committee of the Red Cross|
|IDMC|Internal Displacement Monitoring Centre|
|IDPs|Internally Displaced Persons|
|ILO|International Labour Organization|
|IOM|International Organization for Migration|
|IRRS|International Recommendations for Refugee Statistics|
|JIPs|Joint IDP Profiling Service|
|LSMS|Living Standards Measurement Study|
|MICS|Multiple Indicator Cluster Surveys|
|NGOs|Non-Governmental Organizations|
|NRC|Norwegian Refugee Council|
|OCHA|Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat|
|OAU|Organization of African Unity|
|ODA|Official Development Assistance|
|OECD|Organisation for Economic Co-operation and Development|
|SDG|Sustainable Development Goal|
|SKOPE|Somalia Knowledge for Operations and Political Economy|
|SuTPs|Syrians under Temporary Protection|
|UAV|Unmanned Aerial Vehicle|
|UNDP|United Nations Development Programme|
|UNHCR|United Nations High Commissioner for Refugees|
|UNITAR|United Nations Institute for Training and Research|
|UNOSAT|UNITAR’s Operational Satellite Applications Programme|
|UNRWA|United Nations Relief and Works Agency for Palestine Refugees in the Near East|
|UNSD|United Nations Statistical Commission|
|WFP|World Food Programme|
. We have in fact introduced one further dimension of labor market segmentation, the wage/non-wage labor divide. Labor flows between these different states may contribute to explain the employment puzzle.

For this purpose, we turn to Moldova, a country that in many respects could be considered as the average scenario in our CIS-7 sample. Moldova is also the only country that disposes of a consistent longitudinal panel survey between 1997 and 2002 which can be used to assess labor market flows during the growth period and test some hypotheses on the evolution of the labor market. We use the whole longitudinal sample and the restricted panel sample to compute the statistics, the transitional probabilities and the probit regressions presented below.<sup>19</sup> In figure 4.1, we show the distribution of the population across working categories and between 1998 ad 2002. The employed population has marginally increased in percentage of the total population. A migration of workers has also occurred from wage labor to non wage labor and this migration has taken place mostly within agriculture. The most significant change in fact occurred among rural workers with farmers growing very significantly at the expenses of agricultural employees. Non agricultural labor has remained practically unchanged during the period while agricultural employment has increased marginally. This phenomenon occurred during the post-1998 recession (1998-1999) and during the subsequent growth period (2000-2002).

In table 4.4, we report the population structure by category<sup>20</sup> . It is visible the constant growth of private agriculture and the constant decline of employment in agricultural enterprises in both the public and private sectors. Among non-agricultural enterprises, there is a growth in the private sector and a decline in the pubic sector suggesting a migration of workers between the two sectors

> 19 See http://www.statistica.md/ for details on the survey. 20

> Categories are identified on the basis of the main source of income of respondents.

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