rafmacalaba/data-use-mentions-extended huggingface dataset:
1.173|0|8|
|Crime Index|71044|0.096|0.359|0|10|
|SecurityIndex|71011|3.473|1.769|0|5|
|Environmental Index|74448|0.015|0.155|0|4|
|Financial literacy|74448|0.225|0.418|0|1|
|Occupation rate|74448|0.545|0.051|0.279|0.75|
|Unemployment rate|74448|0.043|0.027|0|0.27|
|Share of self-employed|74448|0.274|0.066|0.133|0.789|
|Share of entrepreneurs|74448|0.043|0.025|0|0.498|
|Share of public sector<br>employees|74448|0.071|0.037|0|0.333|
|Share of children attending<br>school|74448|0.66|0.071|0.143|1|
|Share of people with<br>secondaryeducation|74448|0.214|0.118|0|0.62|

_Notes: The table shows summary statistics at the individual level in El Salvador for survey data collected in 2019. The number of observations varies with the number of people eligible to the respective survey questions. We define informality as all employed people who do not have any health insurance or have no signed work contract. The number of observations is low in this case, given that only 17,884 people responded to the question on if they have a signed work contract or not. The crime index reflects the number of crimes a person reports to have been subject to and ranges from 0 to 10. Similarly, the security index reflects the personal restrictions, such as not going out at night, a person is subject to due to perceived insecurities
27.0|
|Girls (%)|25.3|28.8|26.1|25.8|26.0|25.9|
|Adults (%)|47.4|40.8|45.8|47.3|46.9|47.1|
|Men (%)|26.6|7.8|22.0|25.7|12.8|21.6|
|Women (%)|20.8|33.0|23.8|21.6|34.0|25.6|
|Dependency ratios|||||||
|Children|1.09|1.76|1.28|1.19|1.43|128.|
|Children and elderly|1.12|1.82|1.31|1.23|1.53|1.34|
|Children, elderly &|||||||
|disabled|1.14|1.84|1.34|1.27|1.58|1.38|

_Source_ : Own calculations based on ProGress and JD-HV database. _Note_ : PA = principal applicant. PA is determined at the time of registration. Dependency ratios are defined as the proportion of the groups in the rows to adults.

On average, family size has remained the same over time for female PAs but has increased slightly – by about 0.6 to 4.7 – in male PA households. In other words, about half of male-PA households have an additional family member in 2017-18. Households with a female PA have fewer male adults than households with male PAs in both waves. In the average household of four people, in male PA households’ adult males account for one of the household members, in contrast most female PA households have no male adults. In 2013 only 8 percent of female PA households had
# **2 Literature review**

A common approach to testing the convergence hypothesis is the _β_ -convergence that relies on whether poorer countries grow faster. It can be tested by using country-level cross-sectional or panel data: where _y_ represents per capita income in country _i_ and time _t_ ,<sup>2</sup> _τ >_ 0, and _ε_ is an error term. Empirically, testing for convergence entails conducting a test of the null hypothesis _β_ = 0 against the alternative _β <_ 0 (Johnson and Papageorgiou 2020). This test presumes that initial per capita income contains sufficient information to capture countries’ initial conditions. If the null hypothesis is rejected, this provides support to the _β_ -convergence hypothesis. Specifically, it implies the occurrence of unconditional convergence in that there is no effect of initial conditions in the long run.

However, several studies show that initial per capita income is not a sufficient statistic to determine convergence, and therefore, other factors such as geography, human capital, and institutions need to be accounted for in characterizing income growth.<sup>3</sup> This is called conditional convergence and can be tested as in equation 1 after introducing controls ( _Ci,t−τ_ ) and country fixed effects ( _λi_ ): Another approach to testing for convergence is the _σ_ -convergence depending on whether crosscountry dispersion in per capita income falls over time. That is, between two different points in time _t_ and _t_ + _τ_ where _τ >_ 0, the presence of _σ_ -convergence yields _σt_<sup>2</sup><sup>_>σ_</sup> _t_<sup>2</sup> + _τ_<sup>,where</sup><sup>_σ_2isthecross-</sup> country dispersion in per capita income. It is important to note that _β_ -convergence is a necessary but not sufficient condition for _σ_ -convergence.<sup>4</sup> Empirically, testing for
.86|0.35|0.88|0.32|0.83|0.38|0.0635***|
|Ashanti region|0.56|0.5|0.6|0.49|0.52|0.5|0.0788***|
|Observations|2,478||1375||1103|2478||

* p<0.10, ** p<0.05, *** p<0.01

25
question excludes telescoping as the cause, it is likely that the rules of thumb used by respondents underlie the misreports. We provide further evidence of this below by showing that the overestimates are worse for infrequently purchased items. Finally, the consistent shortfall in incidence in the two household diaries with respect to the personal diary points to the importance of personal leave out error because this is the main driver of divergence between modules 6 and 7 and the benchmark.

Key household characteristics may exacerbate (or moderate) the module-specific reporting error in consumption incidence. This can be explored by interacting the module indicator with the select characteristics mentioned earlier Table 4 summarizes these results by reporting the food groups on which significant interaction effects have been estimated.<sup>13</sup> The effect of household characteristics on the incidence reported not only depends on the characteristics, but also on the particular food subgroup. For recall modules in general, the tendency to underreport incidence is mediated by urban location, the education of the household head, and household wealth, at least for numerous key food groups such as Cereals, Sugars, and Meat and Fish. (This is because, while the module effect is negative, most of the interaction terms are positive.) Thus, the underreporting of any consumption in these food groups is greatest among rural, less well educated, and low-wealth households. For some food groups, the number of household members, especially the number of children, also tends to exacerbate underreporting. This implies that more disadvantaged households, those that are rural, have less education, have more children, and have fewer assets are more likely to omit consumption during the survey experience. This will exaggerate their monetary poverty status. These households may especially benefit from increased enumerator attention and explicit prompting for consumption incidence on a good-by-good basis.

In contrast, the household diaries seldom have significant interactions with household characteristics, suggesting that the downward bias in consumption incidence in these modules is fairly constant across all households. Exceptions to this include the consumption incidence
_|_(0.00)_|
|ISIC Sector Fixed Effects|YES|YES|YES|
|City Fixed Effects|YES|YES|YES|
|Flood-threatened|28.583|54.883|0.150|
|_Dummy_|_(70.41)_|_(262.29)_|_(0.14)_|
|N<br>|2217|828|2600|
|R<sup>2</sup>|0.185|0.115|0.124|

Results from weighted OLS regression, city-level normalized survey stratification weights applied. Standard errors in italics, clustered on cities. Significance levels: *** 0.01, ** 0.05, * 0.10

# 3. What are the business costs of infrastructure disruptions?

Reliable and efficient infrastructure systems are crucial to enable firms to follow long-term planning, and to maximize their capacity, without the need to invest excessively in expensive back-up or contingency technology. For instance, the ownership of costly diesel-based electricity generators is far less common in countries with reliable electricity supply. In addition, reliable and efficient infrastructure is key to facilitating firms’ access to the markets and information that drive their ability to provide goods, services, and jobs. For as long as infrastructure systems remain disruption-prone, this will have substantial adverse effects on the continuity of production and service delivery, affecting the smooth functioning of supply chains, diminishing aggregate productivity, and restricting the ability of firms to trade and compete internationally.

This section assesses and quantifies the business cost of unreliable and vulnerable infrastructure. In the case of electricity outages, self-reported sales of firms are available through the World Bank’s Enterprise Surveys (ES). This makes it possible to estimate directly the rate at which an additional hour of electricity outages reduces the sales of firms (Section 4.1). Similarly, self-reported electricity generation rates are used to assess the upfront and operating costs of self-generation
250 per 1000 births, rates of infant mortality are also extremely high. Demographic and Health Surveys indicate high rates of anemia. On the whole, women are likely to suffer from lower physical resilience to shocks for a substantial part of their adult lives.

A number of studies in various countries have also suggested that men and women may have different preferences (Croson and Gneezy 2009; Tertilt and Doepke 2011) although it is difficult to disentangle preferences from constraints.<sup>7</sup> For example, men are often found to spend more of their incomes on themselves, while women tend to spend more of theirs on their children and on household nutrition and health care more generally (for example, see Schultz 1990, Thomas 1990 and Duflo 2003; Tertilt and Doepke 2011 review the literature). They may also deprive themselves more in order to protect their children‘s nutrition and health following a

> 7 Note that whether gender differences in preferences are biological or due to nurture, social norms, roles and responsibilities which can be altered does not detract from their relevance in the short to medium term.

5
product nomenclatures of 2007, 2012, and 2017 to the HS combined version. Moreover, we exclude all firm observations from the mining sector (HS chapter 25–27). That helps to avoid potential bias from Latin America’s commodity price cycles during 2007–2017. Thus, our sample frame contains all Colombian exporting firms between 2007 and 2017, except those from the mining sector.

# **2.2 NTM Data**

We use NTM data from UNCTAD and the Latin American Integration Association (LAIA) for its 18 core members in LAC between 2007 and 2017.<sup>4</sup> The raw data is recorded at the reporter-national tariff line leveldestination-NTM, 4-digit, and year level. We apply two steps to process the data, resulting in a novel and unexploited dataset comprising an exhaustive and updated set of trade regulations in Latin America between 2007 and 2017.

First, given a change in the classification of NTMs in 2012 we reconcile two NTM classifications pertaining to the periods 2007–2011 and 2012–2017. To that end, we derive a correspondence table between the pre-2012 and post-2012 classification at the 1-digit NTM chapter level using LAIA’s data collection in both classifications in 2011 and 2012. Using the new classification as the reference, we reclassified NTMs between 2007 and 2011 at the chapter level, consistent with UNCTAD’s (2019) Mast Classification M5. As a result, we observe the number of NTMs of each type (e.g. SPS, TBT, PSI, quantity control) for all Latin American reporter-destinationproducts continuously in each year between 2007 and 2017.

Second, we aggregate NTM data from the national tariff line level to the HS 6-digit product level. The background is that NTM data are collected at the national tariff line at 10 digits. However, since we take into account exports to various destination countries that do not harmonize national tariff line classifications, our two datasets cannot be satisfactorily merged 4Argentina, Bolivia, Brazil, Chile, Costa Rica, Colombia
of GDP in the wake of the global crisis.

This performance is partly due to the low level of private inflows in the period up to 2006 when HIPC countries, being under IMF-supported programs, had to abide by stringent limits on non-concessional borrowing. However, Figure 1a shows that the increase in long-term borrowing from private lenders in relief-recipient countries was not much faster than in other non-HIPC IDA countries. Despite the latter group has an unweighted average debt-to-GDP ratio of 51%, higher than the 32% ratio of post-MDRI countries, in 2007 it also experienced a sustained increase in new borrowing; from 1.2 to 1.9% of GDP.

# —Insert Figure 1a—

Further evidence on private foreign lending to low-income countries can be obtained from data on the amount of international claims of the commercial banks of the 30 industrial countries reporting to the Bank of International Settlement (BIS). Although BIS data exclude loans from emerging lenders, they cover short-term loans that are not reported in the GDF statistics of the World Bank. Figure 1b shows banks’total financial claims in foreign currencies, on the same groups of countries considered above, on a consolidated basis,<sup>5</sup> and tells a similar story: while, after the full implementation of MDRI relief in 2006, foreign claims (relative to GDP) on post-MDRI countries have increased faster than in non-HIPC IDA-only countries, since then the two groups behaved quite similarly.

> 4 The MDRI group also includes Bolivia which is an IDA-blend country.

> 5 That is, net of accounts between head offi ces and their branches and subsidiaries.

4
survey data with potential yield estimates from the FAO’s GAEZ database allows for an estimate of production gains if the technology set, or intensity of input use, is dramatically improved.

The results clearly illustrate the production penalties for cultivating maize on land that is not highly suitable for maize production, particularly when using MAPS plot-specific soil samples. The use of AFSIS geospatially-derived soil data provided a close approximation to the results of the MAPS-based results on the overall sample but failed to distinguish between soil suitability classes to the same degree as, and in a consistent manner with, the MAPS plot-level soil data. The MAPSbased analysis reveals that farmers cultivating only marginally suitable land are operating with higher technical efficiency and, thus, have less room for improvement than farmers cultivating more agronomically suitable land, given the condition of their soil. This result has implications for agriculture-based poverty reduction and food security policies. Effectively, by cultivating maize on land that is only marginally suitable rather than highly suitable, farmers limit their production potential by as much as 1,694 kg/ha, or 129 percent. Extrapolating the potential yields to the household level, based on multiply-imputed total maize area per household, suggests that given the current set of inputs and soil constraints households only have the potential to increase the value of production by USD 90 per bi-annual season. Assuming equal production in both agricultural seasons, and given the average household size of 6.12 persons, this translates into a gain of USD 0.08 per capita per day on average, not considering additional expenditures that may be required to reach that production frontier. For those cultivating marginally-suitable soils, they can hope to earn only an additional USD 0.05 per capita per day. If soil constraints were addressed such that all households operated on highly-suitable soils, potential gains would increase to USD 0.21 per capita per day on average. Enhancing the technology set and achieving the GAEZ highinput use potential yield on highly-suitable soil would increase gains to USD 0.79 per capita per day. Although
., Hamre, B. K., and Pianta, R. C. (2013). Effect of observation mode on measures of secondary mathematics teaching. _Educational and Psychological Measurement_ , 73(5), 757-783.

Casabianca, J. M., Lockwood, J. R., and McCaffrey, D. F. (2015). Trends in classroom observation scores. _Educational and Psychological Measurement_ , 75(2), 311-337.

Castillo Castro, C. (2017). Teacher Practices in Primary Schools with High Value-Added Scores and Engaging Lessons in Disadvantaged Communities in Rural Mexico. A dissertation submitted to the University of Cambridge for the degree of Doctor of Philosophy in the Faculty of Education.

Carver, S. M., and Klahr, D. (Eds.). (2001). Cognition and instruction: Twenty-five years of progress. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.

Chetty, R., Friedman, J. N., Hilger, N., Saez, E., Whitmore Schanzenbach, D., and Yagan, D. (2011). How does your kindergarten classroom affect your earnings? Evidence from Project Star. _Quarterly Journal of Economics_ , 126(4).

Chetty, R., Friedman, J. N., and Rockoff, J. E. (2014a). Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates. _American Economic Review_ , 104(9): 25932632.

29
counterparts (46.9% vs. 52.7%, and 31 vs. 42 years old). As mentioned above, Venezuelans are more educated than Peruvians: only 17.2% had less than secondary education and 38.8% had a university education compared to 47.8% and 13.4% only, respectively, among Peruvians. Despite the differences in human capital, immigrants had worst labor market outcomes: 13% were unemployed, 8% had a formal job and the average income was of S/ 941. On the other hand, Peruvians had a slightly higher unemployment rate (16.1%), but were almost three times as likely to have a formal employment (21.4%) and earned 50% more than immigrants (S/ 1,482). Despite being more educated, Venezuelans work in less skilled jobs than Peruvians, especially in sales and services and elementary occupations. The big exception is agriculture and fishing which is the occupation for 16.2% of Peruvians but almost no Venezuelans in Peru.

# **4.4 Empirical Model and Identification**

We first examine the impact of local labor market conditions on immigrants’ reports of experiencing discrimination. More specifically, we estimate the following regression model: where _yij_ equals one if individual _i_ in centro poblado _j_ reports having experienced discrimination in the 2018 in the ENPOVE (in general, or in a particular location) and zero otherwise. _lnEmpj_ is the log informal employment rate in the same centro poblado measured in the 2017 census. We control for a variety of individual ( _X_ ) and centro poblado ( _Z_ ) level controls.<sup>7</sup> We also include origin municipality (in Venezuela) fixed effects ( _αo_ ) to control for any origin-specific factors that could affect perceptions of discrimination (e.g. skin color, accent). _εij_ is an error term clustered at the centro poblado level as this is the level of aggregation of our main explanatory
highlight that countries need to be more aware of these features to maintain sustainable development paths.

# _<u>Economic Development and Economic Structure</u>_

An increase in the initial income level (relative to the G7 GDP) decreases the probability of being in either of the higher growth clusters and increases the probability of being in one of the lower growth clusters. This is consistent with income convergence—growth rates are expected to be lower at higher levels of initial income. Interestingly, vulnerability is also a factor—cluster V is more likely than cluster L. This is a control for level of development, which can be related to the income convergence path. Also, we controlled for a country’s progress with its transition path was not statistically significant, but this could reflect the particular transition measure used. Specifically, we used the real GDP deviation from the pre-transition peak, but other measures, such as EBRD transition indicators, may be worth exploring.

A larger oil trade balance (as a percent of exports and imports) reduces the likelihood of being in a lower growth cluster. Specifically, it reduces the probability of being in lower growth and higher vulnerability (V) cluster and the lower growth and lower vulnerability (L) cluster. Conversely, it increases the probability of being in either of the higher growth clusters—and much more so in cluster G than in cluster H.

# _<u>Economic Policies</u>_

A key challenge for emerging markets going forward is to what extent economic policies serve to avoid the worst of all possible outcomes (that is, cluster V); in other words, a high risk (increased vulnerability) and low reward (lower growth) scenario is clearly suboptimal. The main findings are,

- Counter-cyclical fiscal policies seem to lead to an increase in the probability of being in the lower growth and lower vulnerability (L) cluster at the cost of reducing the likelihood of all the other three clusters. This result could be
toddlers (age 2-4) for 6 hours a week, children in kindergartens (age 4-6) are to attend 3 hours a day for 5 to 6 days per week, and those in playgroups or daycare (age 4-6) are meant to attend 3 hours a day for 3 days per week. Although these teacher and structural policies have been in place since 2009, little is known about whether and how strictly they are adhered to and whether they actually help promote child development outcomes.

As the government of Indonesia moves to expand and improve early childhood education programs across the country, there is immense interest among policy makers for evidence on whether classroom quality, teacher qualifications, and other structural characteristics (i.e., class size and duration)—all of which are outlined in the national ECED standard—do in fact support positive developmental outcomes for children. Thus, this study focuses on the following three research questions in the context of 303 poor villages in Indonesia:

- i. Does preschool quality—as measured by direct observation of preschool centers in session—predict children’s development outcomes?

- ii. Do teacher characteristics (averaged at the center level) predict children’s development? and

- iii. Do structural characteristics of preschool centers—such as student-to-staff ratios and hours of operation—predict children’s developmental outcomes?

# **4. Data and Measures**

# _Data_

Data for this analysis was collected in 2013 as part of an impact evaluation of the Indonesia ECED Project (see Pradhan et al., 2013 for detailed study protocol). The Indonesia ECED Project aimed to improve poor children’s access to ECED services and enhance their school readiness. Through the project, participating villages created playgroups (hereafter referred to as _project playgroups_ ) and received training for teachers in these playgroups. As part of the project evaluation, data on children enrolled in project playgroups as well as their peers living in the same village attending other types of early childhood programs (i.e.,
blacksmiths, electricians, barbers, tailors, real estate agents, social workers, counseling, banking, doctors, restaurant and hotel business, and so on, and the miscellaneous sector includes other small activities. Source: HIES 2000, 2005 and 2010

**Table 4: Salient characteristics of rural microenterprises: 2000-2010**

|**Enterprise characteristics**|**2000**|**2005**|**2010**|
|---|---|---|---|
||**(N=1,427)**|**(N=1,426)**|**(N=1,909)**|
|Years of operation|9.5|9.2|10.0|
|If the enterprise is registered (%)|9.9|14.7|18.2|
|If the enterprise is home-based (%)|11.9|14.2|16.7|
|Months of operation per year|10.3|10.7|10.9|
|Number of workers|1.6|1.7|1.4|
|Share of hired labor in total workforce (%)|11.2|10.3|8.7|
|Owner’s sex (1-Male, 0=Female)|96.4|94.8|95.5|
|Owner’s age (years)|44.5|46.1|46.7|
|Owner’s education (years)|3.2|3.5|3.5|

Note: Owner’s characteristics are that of the head of the household that owns the enterprise. Source: HIES 2000, 2005 and 2010

**Table 5: Distribution of the sources of startup capital of the rural microenterprises:**

|**200**|*