rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2: Account Penetration in Africa by Demographic Covariates**

<!-- Start of picture text --> Female<br>Male<br>Rural<br>Urban<br>15-24<br>25-64<br>65+<br>Elementary<br>Secondary<br>Tertiary<br>Lowest<br>Q2<br>Q3<br>Q4<br>Highest<br>0% 20% 40% 60% 80% 100%<br>Percent of Adults<br><!-- End of picture text --> Source: Demirguc-Kunt and Klapper, 2012.

Indicators of financial use show a positive but imperfect correlation with indicators of financial depth such as credit to the private sector/GDP (Figure 3). This correlation shows that access really is a distinct dimension. Financial systems can become deep without delivering access to all. The positive but imperfect correlations of financial services usage with financial depth raise questions regarding the drivers of cross-country differences in financial use and access. The correlations also suggest that there might be room for policy reforms to increase the level of financial inclusion (World Bank, 2008).

6
. This result is meaningful for two reasons. First, the LPI index covers the timeliness of the whole import process, including transportation from the port of origin to the port of destination as well as road transportation to deliver the imported

> 32Full regressions are available upon request.

> 33See for instance Li and Wilson (2009); Djankov, Freund, and Pham (2010) and Gamberoni, Lanz, and Piermartini (2010) for studies relying on the Doing Business Indicators to show the adverse impact of delays on trade outcomes.

24
1 percent per month (Tanzania NPS 2012/13: 2.4 at the plot-level, 2.5 percent at the household-level; Malawi IHS4: 5.1 percent at the plotlevel, 3.0 percent at the household-level; Table A.19; Table A.20; Figure 16).

# 5. Conclusions, policy implications, and recommendations

In this paper, we set out to evaluate the impact of the recall length and related design choices on the quality of key agricultural input and output data – land, labor, fertilizer, and production. Our results demonstrate, consistently across the three data sets used in the analysis, that the survey-based estimates of these variables depend on the length of the recall period, which indicates the presence of nonrandom measurement error of economically significant size. With longer recall periods, farmers report higher quantities of harvest, labor and fertilizer inputs, all three of which are recorded at the plot-level in our data sets. At the same time, farmers list fewer plots as the recall period increases. We have argued that it is plausible that farmers overestimate plot-level outcomes – harvest, labor and fertilizer inputs – while it is also plausible that they forget some of their more marginal plots, as their memory decays due to longer recall periods. We also show that the recall length has a meaningful impact on agricultural productivity measurement.

<mark>The reliability of agricultural data matters for policy effectiveness</mark> . Policy makers and the international community recognize agricultural input, output, and productivity outcomes as integral to agricultural growth and food security, and a large body of empirical evidence supports this view. These outcomes have therefore been made priorities within several development frameworks. Under the Sustainable Development Goals (SDGs), target 2.3 is to double the agricultural productivity and incomes of small-scale food producers by 2030. The target is monitored through indicator 2.3.1, volume of production per labor unit – that is, one of the productivity measures we found were subject to measurement
on public funding, 60 percent of TPOs in high-income countries get 75 to 100 percent of their funding from public sources. In low-income countries it is close to 80 percent of TPOs that get 75 to 100 percent of their funding from public sources. It is 67 and 80 percent of TPOs in lower and upper middle income countries that get 75 to 100 percent of their funding from public sources.

# **5 Adaptation of TPOs during the COVID pandemic**

The evolution of TPOs’ budgets around the COVID pandemic shows some interesting patterns. The top panel of Figure 8 shows the distribution of TPOs’ budgets during the period 2018-2021, and the bottom panel decomposes the distribution by income group.

In the top panel the distribution seems to be quite stable across years, but the median budget declined in 2020 as the pandemic emerged. This is only marginally visible from the top panel of Figure 8 because of the scale of the y-axis, but the median budget is around USD 3.2 million before and after the global pandemic lockdowns (2018, 2019, and 2021) and only USD 2.7 million in 2020. The bottom panel shows that this pattern is mainly driven by a drop in the median as well as the entire distribution of TPOs’ budgets in low-income countries. Interestingly, the bottom panel also shows that there was a significant increase in the median TPO budget of high-income countries in 2021 (more than 50 percent), suggesting an intensified effort by TPOs in high-income countries as lockdown measures eased. Upper-middle-income countries also followed with a 35 percent increase in the median budget. However, the same was not observed in low-income and lower-middle-income countries where the median TPO budget remained at levels similar to those observed in 2020. A potential explanation for these trends is differences in fiscal space. Low-income countries with little fiscal space may have had no choice but to reduce TPO budgets. To explore this, we correlated changes in total government expenditure (using IMF data) with changes in TPO budgets and found a small positive
reduce the role of EU-specific foreign savings. If this is not the case, then other EU-specific factors may be more instrumental.

The main conclusion is that what matters for growth are institutional factors and that these are likely to be EU-specific. Specifically, two thresholds are used; a threshold defined when financial depth belongs to the top two quartiles (Table 6, column 2) and a threshold for the top quartile only (Table 6, column 3). In either case, the importance and magnitude of the EUspecific foreign savings interaction remain unchanged when financial development is used to construct the threshold variable (i.e., it barely changes from -0.164 without thresholds effects to a coefficient of -0.169 when using the quartile 4 threshold definition). This suggests that financial development is not the driver of the foreign savings and growth link. Slightly different is the conclusion reached using measures of institutional development. We use the first principal component of six ICRG indicators on institutional development to construct the threshold variable. Again, two thresholds are used; a threshold defined when institutional development belongs to the top two quartiles (Table 6, column 2) and a threshold for the top quartile only (Table 6, column 3). The impact of the EU-specific foreign savings interactions weakens for the highest quartile (column 6 compared to the benchmark in column 4); the relevant coefficient changes from -0.674 to -0.505, but remains statistically significant). This suggests that EU membership might reflect institutional development factors. But the observed weakening is also limited, suggesting that EU-specific factors are still at play. The fact that institutional indicators do not eliminate the effect of EU-specific regressors might reflect that EU membership (actual or prospective) acts as an anchor for expectations of improved institutional quality even if actual change on the ground improves more slowly.

# _<u>Unsustainable Pre-Crisis External Imbalances</u>_

The next step is to investigate whether the estimation results are driven by outlier observations
2</sup> .<sup>2</sup> Other upgrading costs totaled US$2018 per household, or US$404 per person for land and infrastructure. These costs are not dissimilar from the estimates for Novos Alagados II.

The 2004 study with comparative data noted that the estimation of costs can depend greatly on several factors.<sup>3</sup> Among them, i) upgrading of densely populated informal settlements can be considerably more expensive than building new units in available plots. This is due to several factors -- the land is of poor quality, access is difficult, the operations have to be done in an area already inhabited, and some removals are always necessary, implying high resettlement costs; ii) upgrading costs depend on the nature of the site. Informal settlements are generally built on steep hillsides, sloping or rocky ground, heavy or unstable soils, flood-prone sites, or areas with poor drainage where it is particularly expensive to upgrade; and iii) house construction costs depend on the materials and technology used, the size of house, and the quality of fittings. These findings seem to be consistent with the cost data from Novos Alagados II.

> 1 See Flood, Joe 2004, “Cost Estimate for Millennium Development Goal 7, Target 11 on Slums.” Background Report for UN Millennium Project Task Force on Improving the Lives of Slum Dwellers and UN-HABITAT, Urban resources, Elsternwick, Australia, also in, Millennium Project, 2005, Improving the Lives of Slum Dwellers, http://www.unmillenniumproject.org/documents/Slumdwellers-chapters5-8.pdf 2 See, Millennium Project, 2005, Improving the Lives of Slum Dwellers, <u>http://www.unmillenniumproject.org/documents/Slumdwellers-chapters5-8.pdf; Adapted from FUNDEVI,</u> 2001, (Fundacion para el Desarrollo de la Vivienda Social Urbana y Rural); Angel, et.al., 2004, “Rapid Urbanization in Tegucigalpa, Honduras:  Preparing for
by an unskilled operative at a non-exporting firm.

From the detailed employment records of the ENIA, we define the following production activities or tasks: management (directors), administrative services (accountants, lawyers), production services (engineers, blue-collar workers), and general maintenance services (unskilled non-production workers). We define skills according to formal education. We assume that directors, accountants, lawyers, engineers comprise skilled labor (because these workers need to obtain a college degree). To enrich the analysis, we also define a highly-skilled group, which includes managers and engineers. Unskilled workers are blue-collar, non-specialized and general maintenance workers (assuming these type of workers typically have lower formal educational levels). Finally, we let production workers include engineers and blue-collar operatives, while non-production workers include managers, administrative workers and maintenance workers.

Table 1 presents some key summary statistics. We report the unconditional averages as well as averages for exporting firms and non-exporting firms. On average, 39% of workers in Chilean firms are skilled workers and 61% are unskilled workers. As expected, exporters utilize a higher share of skilled workers (41%) than non-exporters (39%). Exporters are much larger and they hire, on average, more workers in all skilled and unskilled categories than non-exporters. Production workers account for 73% of employment of all Chilean firms, but 70% of the employment of exporters. In addition, exporters employ more managers, engineers and administrative services workers than non-exporters. Employment of unskilled blue-collar workers is less, while maintenance employees are slightly more, among exporters. Finally, the average exporter ships around 32% of its sales abroad. Among all firms, exports accounts for only 5% of total firm sales.

# **2.1 Regression Analysis**

We begin our empirical analysis with the following regression model where _i_ indices firms, _t_ , time, and _j_ , an industry. Outcomes are denoted by _yijt_ and export intensity (the ratio of exports to total sales) is
Several recent papers make significant progress in addressing these issues. In terms of classification, most papers had simply used an indicator variable measuring the presence of labor clauses, regardless of the content of the clauses. Using such an indicator, Kamata (2014) finds no statistically significant effects of RTAs with or without labor clauses. Using the “Labor Provisions in Trade Agreements” (LABPTA) dataset created by Raess and Sari (2018), Carrere et al. (2017) extend this work by differentiating labor clauses as those with and without enforcement and labor clauses designed to foster deep cooperation. Using a sample covering the 1995-2004 period, restricted to countries with positive trade flows, and by differentiating trade flows from low-income to high-income countries, they find that enforcement has no effect on trade flows, but clauses with “deep” cooperation are associated with more trade.

Extending the analysis to include a legal-based classification of labor clauses, including different forms of enforcement, LeClercq et al. (2020) find that labor provisions, defined in their paper as a combination of standards and enforcement clauses, are associated with lower trade flows but they are accompanied by clauses the promote trade, so that the overall effect of labor clauses on trade is positive.

This paper offers three extensions to the current literature. The main question asked by this paper is whether the application of new techniques and incorporating other key characteristics of trade agreements affects the estimate of the relationship between labor clauses and trade flows. The first contribution is to apply the Pseudo-Poisson Maximum Likelihood (PPML) estimation with country-pair and importer and exporter time effects to the estimate the relationship between trade flows and labor clauses. This approach is widely accepted in the gravity model literature, but has not been applied in most papers assessing the labor clause-trade flow relationship.

The second contribution is to use the depth of the Handbook of Deep Trade Agreements (HDTA) (Mattoo et al. 2020) to estimate the correlation between labor clauses and other clauses, including tariff preference margins. The effects of
in their new locations, facing living conditions less favorable than those of the urban poor (V ́elez, 2002; Ib ́a ̃nez and Moya, 2006). Ib ́a ̃nez and Moya (2006), more particularly, estimate that the consumption of displaced households falls by 35.7 percentage points upon migration and drops even further during the year following displacement.

# **III Data**

## **III.1 Firm data**

Our main source of information is the _Encuesta Anual Manufacturera_ (Annual Manufacturing Survey), collected by the _Departamento Nacional de Estad ́ıstica_ (DANE), the Colombian statistics agency. These data set is a census of all manufacturing plants with ten or more workers or with a total output value larger than 65 million in 1992 Colombian pesos (approximately USD$95,000). Once a plant is included in the survey, it is followed over time until it goes out of business. The survey includes information on all production-related variables, including employment and wages. In conjunction with the standard plant information, the census contains information on all physical quantities and prices (valued at factory-gate prices) of each output and input used or produced by each plant. In this article, firms’ prices are defined as the plant-product-year observation estimated by dividing the value of revenues or expenditures by physical quantities. These data is regarded as one of the best and most complete sources of information for studying firm behavior in developing countries (Kugler and Verhoogen, 2011). The data set covers the years 1995 to 2010, though its data distinguishing employment and wages by white- and blue-collar workers is only available after the year 1999.<sup>9</sup> Our sample consists of all municipalities where more than two formal firms with ten or more workers are observed.<sup>10</sup> These municipalities are typically more populated and have higher eco-

> 9The data also contains information on firms’ average output and input prices.
# **Big Data for Sampling Design: The Venezuelan Migration Crisis in Ecuador**<sup>∗</sup>

Juan Muñoz<sup>1</sup> , José Muñoz<sup>2</sup> and Sergio Olivieri<sup>3</sup> JEL: C8, C83, F22 Keywords : Sampling design, stratification, Big Data, migration, Venezuela, weight calibration, maximum entropy.

∗This paper has benefited from comments by Ana Aguilera, Tara Vishwanath, Nandini Krishnan, Beatriz Godoy, Teresa Reinaga, Ana Rivadeneira, Tanja Goodwin, Carlos Vayas from Telefonica de Ecuador, Alexandra Escobar and Paul Guerrero from UNICEF - Ecuador. We are very grateful to Roberto Carrillo (ex-Director of NSO – INEC acronym in Spanish) and Christian Garces, Xavier Núñez and Francisco Céspedes from the sampling division of INEC for facilitating access to update information from the 2010 Census. Different versions of the paper benefitted with comments from participants to Conference on Inclusion of Refugees in National Surveys that Measure Poverty, October 2-3, 2019, Washington, DC; Research Conference on Forced Displacement, January 16-18, 2020, Copenhagen; and Urban Migration & Forced Displacement: Data Collection in Fragile States, February 27, 2020, Washington DC The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent

> 1 Sistemas Integrales, Santiago, Chile.

> 2 Telefónica de Ecuador.

> 3 World Bank, Poverty and Equity Global Practice.
past three months, labor income in the past five seasons, and an effective daily wage rate for work done in the past seven days. Per capita consumption is reported as a secondary outcome.

_Husbands._ Our analysis of husbands focuses on spousal quality and their attitudes towards women’s empowerment. The husband quality index, the first primary outcome, includes subcomponents that measure the husband’s highest grade completed and highest qualification obtained, his cognitive score on the Raven’s Colored Progressive Matrices, his employment status and wage, his HIV status, his marital fidelity (self-reported), and his mental health measured through the GHQ-12. The index of spousal attitudes towards women’s empowerment, also a primary outcome, includes sub-indices for attitudes towards their daughters’ schooling and marriage, their wives’ autonomy, domestic violence, as well as their divorce prospects and desired fertility levels. Components of these two indices are presented as secondary outcomes.

_Children._ The primary child outcomes fall under four domains: anthropometrics, health, parental practices and educational testing. For anthropometrics, we construct height-for-age z- score (HAZ) for living children younger than 60 months old. Our health outcomes include neonatal and post-neonatal mortality. For parental practices, we construct variables for exclusive breastfeeding in the past six months and an index of parenting practices. Finally, for educational testing we report MDAT and SDQ scores for all 36-59 month-olds.<sup>37</sup> The self-reported data on children come from complete birth and death histories collected at Rounds 3 and 4 from the mother or the primary caregiver.

> 37 The pre-analysis plan also indicates that we would report impacts for an indicator for child mortality, but there are only 22 child deaths in our entire sample during the study period, so we exclude this outcome. We also exclude weight for height, as the prevalence of wasting (weight-for-height z-score <-2) is negligible in Malawi. Finally, due to space considerations, we do not show impacts on secondary child outcomes
of informality status_|
|Formal Employment<br>47.5%|
|With access to health<br>41.5%|
|With a secondary job<br>4.2%|

Source: Own calculations based on ENOE 2005-2019 There are marked differences in current labor market outcomes between individuals who experience a plant closing and those who experience other types of job separation. We construct a transition matrix that relates the reason for job separation with the individual’s current labor market status. Through the matrix, we can identify both voluntary and involuntary separation. Involuntary separations are captured in the top two rows of table 2 —the “plant closing” and “fired” categories. The bottom two rows of table 2 captured voluntary separations—the “quit” and “closed own 8
expectations among jobseekers, causing them to raise their reservation wages and only search for jobs in the area in which they were trained (Acevedo et al., 2020).

In part due to the evidence from the first waves of rigorous training evaluations, policy efforts have aimed to improve the effectiveness of vocational training programs. The two approaches usually mentioned are to make training more demand-driven, and to link payments for the training program more clearly to results. Demand-driven programs aim to have private sector firms and providers, rather than the government, determine what courses are offered and how they are delivered, and to link on-the-job training explicitly to employer demand. An often-mentioned example is the _Jóvenes_ programs in Latin America, including the Colombian program studied by Attanasio et al. (2011), which had the largest impacts among the government programs summarized in Figure 1. Attanasio et al. (2017) link participants in this program to social security records and find, up to a decade later, a lasting effect of 3.8 percentage points on being employed in the formal sector, with trained individuals earning US$13 more per month in formal earnings. But simply having private sector providers offering the training may be insufficient: Hirschleifer et al. (2016) find privately run courses in Türkiye have larger short-term impacts than governmentrun courses, but that this difference disappears in the three years after training.

Results-based contracting aims to increase the incentives for providers to deliver employment impacts by linking some of the payments to targets such as the percentage of trainees in jobs. World Bank (2020) discusses some of the practical issues and experiences with such an approach. It sounds promising in theory, but in practice many governments lack the administrative capacity to measure results and manage such a process. In addition, the share of the total payment linked to performance may in practice end up being relatively small and incentivize only shortterm, and not long-term, employment outcomes.

A more optimistic view of the potential impact of job training programs has emerged from impact