rafmacalaba/data-use-mentions-extended huggingface dataset:
estimates of β 1 demonstrate that political organization increases agricultural protection to import competing sectors and subsidies to exporting sectors according to their _z_ / _e_ ratio. For commodities whose producers are politically organized, the greater the stakes from protection (high output) and the lower the deadweight loss from protection (low imports, low _e_ ), the greater is the difference between domestic and world prices.

A change in _z_ / _e_ in an organized industry changes the level of protection/subsidy by β 1>0. For example, the middle two rows of Table 2 indicate that an increase in the output-to-import or output-to-export ratio by 100 would increase the dependent variable in (4) by anywhere between 18% and 24%.<sup>44</sup> This translates into an increase in the price received by the domestic producer by approximately 19% to 26% at the sample mean (of 0.082).<sup>45</sup> Since it is highly likely that between 10% and 80% of the commodities (more precisely, observations in the sample) are actually organized those estimates capture reality.

Quantitatively, the results echo a puzzling finding from previous examinations of the GrossmanHelpman model that used manufacturing data. Inverting the coefficient estimate yields estimates of the government preference parameter _a_ . These implied estimates are skewed towards welfare. The last two columns of Table 2 indicate that, across the models, the implied estimates of _a_ lies between 47.6 and 62.5 when the dependent variable is the {Core NTM, Export subsidy/Total subsidy} combination, between 41.7 and 55.6 with the {Overall Protection, Export subsidy/Total subsidy} combination, and between 83.3 and 100 with the {Tariff, Export subsidy/Total subsidy} combination. They show that government places far greater weight on a dollar of welfare, at least 40 times as much, as on a dollar contributed politically.

The first robustness test we undertake is with respect to the validity
social security regulations and labor market tests in importing countries (which may be among the most burdensome regulation that importing countries resort to; see Section 3 for details).

These costs are accentuated by limitations on duration of stay associated with a visa, which again differ by importing country, as they necessitate multiple re-applications especially in the case of frequent service suppliers. In fact, a well-recognized challenge for MNP is the absence of any linkage between the duration for which a service supplier requires a visa to deliver the service and the actual duration for which a visa may be granted. This problem is compounded when importing countries do not entertain visa extensions or do not grant multiple-entry visas, which is especially true in the case of aircrew, seamen and truck drivers (see Section 4 for details). In fact, the Russian Federation does not grant multiple-entry visas even to business visitors (see Section 4 for details).

All these attributes generate costs, including in terms of time spent, even if they are applied on a nondiscriminatory basis but the costs get compounded in the event of “(i) (asymmetric) information on the applicable rules and requirements; (ii) the associated certification/conformity assessment processes; and (iii) uncertainty/variability in administration of (i) and (ii).” (Hoekman, 2016, op.cit.) Given the above, there are three potential channels for lowering trade costs pertaining to MNP and facilitating Mode 4 trade. One, to completely eliminate or sufficiently reduce unnecessarily restrictive formal (explicit) barriers to Mode 4 trade (these are captured in the OECD’s STRI data on MNP and discussed in detail in Section 3). Two, work to attenuate the prevalence of regulatory heterogeneity across countries for given sectors or activities (some suggestions on this are discussed in detail in Section 4 on the basis of the OECD’s STRI data on regulatory transparency for MNP). Three, take actions to lower compliance and administrative costs for service suppliers (some suggestions
In the intermediate scenario, we expect schools to be closed for 5 months. In the last, and most pessimistic scenario, we expect schools to be closed for 7 months, or 70% of the school year.

These scenarios are aligned with what we have been observing with the existing data on school closures from both UNESCO and the World Bank. As of June 8<sup>h</sup> , school systems were closed on average 79 days, or 2.6 months<sup>49</sup> (Figure 3). If we include in this school closure estimate the announcement of several countries that they will only reopen their schools by August or September, the average expected school closure will increase to 110 days, or 3.6 months, and those are mostly northern hemisphere countries (Figure 4).<sup>50</sup> In the optimistic scenario, we are not assuming that schools might close again, nor that the summer learnings loss will be significantly larger than usual. Our intermediate scenario, with an average 5 months of school closure, and our pessimistic scenario with 7 months of school closure extends the length of the expected school closure.

> 48 PISA defines minimum reading proficiency as a score below level 2 which is 407.47 points.

> 49 School closure days were only counted until June 8th, 2020, using the UNESCO school closure monitoring database, available at https://en.unesco.org/sites/default/files/covid_impact_education.csv (as of June 8th 2020). 50 3.6 months of school closures includes a subset of 62 economies which have already reopened their schools, or announced a date in which their educational system will be re-open; the 2.6 months average school closures is the measure of actual number of days systems have been closed until June 8<sup>th</sup> , 2020. In this later case, the 150 economies in which schools are still closed, were
# **2.4 Methods and assumptions**

Our analysis is based on several key assumptions. First, we assume that any urbanization that occurs on current land used for crop and livestock (i.e., as of 2010) will result in a significant impact on the agricultural production there. Specifically, such an overlap could culminate in either a loss of crop or livestock previously produced on the piece of land, or it could lead to a proximity of urban and agricultural functions, which may alter agricultural production cycles and scales.

Second, for the baseline condition, since the urban expansion data use 2015 as the starting year, any crop or livestock that overlap with the 2015 urban extent are _not_ counted toward the initial agricultural production (see Table 2). Consequently, only crop and livestock outside of the 2015 urban extent are aggregated for the baseline quantity of crop and livestock production. This would lead to eliminating about 2- 6 percent of crop or livestock production in the data (see Table A1.6).<sup>9</sup> Third, we assume no further expansion of land used for crop and livestock beyond its footprint in 2010. To clarify, the potential impact on crop and livestock production is calculated based on how much future urban expansion is projected to extend into current land used for crop and livestock production. This assumption is made despite acknowledging that a growing global population would entail increasing crop and livestock demands in the future. While such intricate dynamics would affect impacts on crop and livestock production, it is beyond the scope of this paper to devise robust models for future land uses.

Fourth, we assume that receding urban frontiers does _not_ replenish land used for crop and livestock production. To elaborate, while the data from Chen et al. (2020) shows that across different SSP scenarios, urban extents could in fact decrease in some parts of the world, in part due to declining urban populations, we assume that these urban contractions will not result in land
**_Respondents_** : Half of the respondents are a head/manager of a human resource department. About 18% are a manager of engineering graduates‘ department.  Approximately 15% are a business owner or partner.  The large share of head/manager of human resource department could have been a potential problem if the survey evaluated individual skill sets.  However, the survey rated a group of new hires across the company.  Therefore, the human resources department would be best placed to assess skills of newly hired engineers as a group.

Consequently, the sample covered a wide range of employers across sectors, regions, size of companies, etc. This suggests that the results are relatively representative. However, the sample may not fully represent the true population, i.e., the total number of the employers that hire engineering graduates in India. Since we do not have detailed enterprise level data on who hires engineers, we cannot compare our sample with the true population.

# **5. Findings**

This section presents the major findings. First the section presents the results of a factor analysis of the 25 skills rated by the employers.<sup>6</sup> Based on the skill groups, detail analysis is further conducted to respond to the three research questions raised in the introduction: (i) How satisfied are employers with the skills of engineering graduates? (ii) Which skills do employers consider important when hiring new engineering graduates? and (iii) In which important skills are the engineers falling short?

# **5.1 Grouping Skills**

We conduct a factor analysis of the 25 individual skills to group the individual skills into a small number of skill groups (factors).

We group the skills because it is plausible that a common latent factor (skill/ability) partially drives the importance and satisfaction ratings of a group of individual abilities. For instance, employers and HR-staff often talk about the importance of ―soft skills‖. There is hence a
7 hydraulics, port, airports, and modal integration. Moreover, they assume that investments are used efficiently. Ignoring the important fact that in the telecom sector progress continues to cut costs dramatically, these estimates are thus probably lower bounds of the needs. Moreover, the assumption that investments are used efficiently is a strong assumption for most countries, industrial or developing.<sup>6</sup>

**Table 1: Expected annual investment and operations and maintenance needs, 2005-2010** (percentage of GDP)

|**Country group**|**Investment needs**|**O&M needs**|**Total**|
|---|---|---|---|
|**Low income**|3.5 - 4.5|4.0 - 4.5|7.5 - 9.0|
|**Middle income**|2.8 - 3.8|2.7- 3.2|5.5 – 7.0|
|**Developing**<br>**county average**|3.2 – 4.2|3.3 - 3.5|6.5 – 7.7|

Note : These are preliminary estimates, currently being updated. Source: Briceno and others 2004 The most interesting fact emerging from these rough orders of magnitude is that there are large differences across country income groups, with needs ranging from as much as 9 percent of GDP for low-income countries to 5.5 percent for middle-income countries, with an average of about 7.1 percent for all developing countries.<sup>7</sup> Combining this fact with the lessons from the growth convergence literature, it is not difficult to see why some academics, some policymakers, and many NGOs make a case for an extra co-financing effort by the international community in favor of the lowest-income countries to ease their catching
To deal with these issues, a panel data approach including changes over time in the variables in question would be preferable. Unfortunately, our regulations indicator is limited to information in 2003 only, which does allow us to explore changes in a dynamic setting.<sup>8</sup> Nevertheless, since changes in government regulations are relatively slow, it is reasonable to assume that our regulation index is roughly constant over a limited period of time, for instance, the last 10 years. Based on this assumption, we can analyze changes in the other key variables, that is, FDI flows, GDP growth rates and the control variables, over time in dynamic regressions. In effect, we follow the approach of Caselli, Esquivel and Lefort (1996) and Dollar and Kraay (2002) and transform (1) by taking into account that there are country effects η _i_ included in the error term that are likely to be correlated to the explanatory variables producing biased coefficients in a pure OLS estimation. Thus, the model can be rewritten as:

> where α is 1 + θ .

To avoid the country effect bias we estimate (2) in differences: In essence, we regress growth in the most recent period of 10 years, that is, 1994-2003, on growth in the previous period (1984-1993) and on changes from the previous to the current period in FDI and the other explanatory variables.<sup>9</sup> As the independent variables, we always use FDI inflows, measured as annual average of net FDI inflows as a share of GDP, and an indicator for the rule of law. The latter indicator measures various components of the institutional quality of a country that might not be captured by our regulation index, such as the incidence of crime or the effectiveness and predictability of the judiciary. Overall, countries with well-designed institutions are less likely to have bad regulations.

> 8 The World Bank’s Doing Business dataset will be updated and extended every year, which will
market research are estimated as twice revenues of the market and consumer research industry as reported in SAS, and purchased ―organizational‖ capital is estimated using SAS data on the revenues of the management consulting industry.

Finally, due to the absence of direct data, some intangible expenditures are ―crudely‖ derived from the closest available proxy. For example, new product development costs in the financial services industry are assumed to be 20 percent of intermediate purchases in that industry. Spending on organizational change is also difficult to measure, because there is no broad consensus on the scope of these assets and little hard data on spending. A substantial portion of these activities are handled in-house and there are no available data on these activities; CHS propose a rudimentary estimate based on the value of executive time, estimated from BLS data on employment and wages in executive occupations.

In general, economic competencies, which is the largest category in the CHS framework, presents the most conceptual and measurement challenges (Aizcorbe et al. 2009). For instance, there are questions about whether brand equity should be treated as investment in a national account. Some argue that advertising and marketing expenditures are in some sense unproductive, because they are thought to affect the demand function instead of the production function. In contrast, spending on other intangibles directly affects the production function by either creating a better output or the same output using fewer inputs or better inputs.

Appropriate price deflators and depreciation rates are other outstanding questions in the measurement of intangible capital. CHS and some other recent studies use the goods price deflator associated with the final industry (that is, the industry purchasing the intangible asset) or the economy as a whole (CHS 2009, Marrano et al. 2007). The problem here is that this is not the price index for the intangible per se, but rather the price index for the good that used the intangible asset as an input. An alternative solution has been to develop a price index for the costs involved in producing the intangible asset.
Policy Research Working Paper 7796

# **Abstract**

This is the first study to investigate the short- and long-term causal effects of a child-labor ban. The study explores the law that increased the minimum employment age from 14 to 16 in Brazil in 1998, and uncovers its impact on time allocated to schooling and work in the short term and on school attainment and labor market outcomes in the long term. The analysis uses cross-sectional data from 1998 to 2014, and applies a fuzzy regression discontinuity design to estimate the impact of the ban at different points of individuals’ lifecycles. The estimates show that the ban reduced the incidence of boys in paid work activities by 4 percentage points or 27 percent. The study finds that the fall in child labor is mostly explained by the change in the proportions of boys working for pay and studying, and observes an increase in the proportion of boys only studying as a consequence. The results suggest that the ban reduced boys’ participation in the labor force. The study follows the same cohort affected by the ban over the years, and finds that the short-term effects persisted until 2003 when the boys turned 18. The study pooled data from 2007 to 2014 to check whether the ban affected individuals’ stock of human capital and labor market outcomes. The estimates suggest that the ban did not have long-term effects for the whole cohort, but found some indication that it did negatively affect the log earnings of individuals at the lower tail of the earnings distribution.

This paper is a product of the Impact Evaluation Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at caiopiza@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An
reasons after the initial trial. As such, the paper is not based on a specific trial, but instead uses secondary, observational data as part of a larger natural experiment.

> 9Premand and Schnitzer (2021) describe the household targeting methods in detail and analyze their performance.

> 10We worked with the national cash transfer project office to collect geo-coordinates for a subset of beneficiary villages that were not already geo-localized.

> 11Note that the agency that implements the cash transfer program is used to perform public lotteries, which have been ascertained to achieve a good balance in the smaller-scale trials in which household survey micro-data were collected (Bossuroy et al. 2022; Premand and Barry 2022). We rely on the locality registry as we could not obtain the list of localities that entered the public lotteries in each commune outside the samples from those smaller-scale RCTs.

> 12Merging the administrative data set and the locality registry presented two slight complications. First, the cash transfer program considered villages as the targeting unit, but this is not a formal administrative unit. Smaller “hamlets” may, at times, be considered part of a village and, at other times, a stand-alone community. Second, small villages were not considered eligible for the cash transfer program or were, at times, grouped with neighboring villages to facilitate implementation. Eligible villages were listed commune by commune, without strict rules that we could apply across the full locality database. When we use population data from the 2012 national census, we find that the pool of all the control localities in the locality registry is slightly smaller than the pool of treatment localities. This is consistent with hamlets or small villages being filtered out during the cash transfer program’s targeting process. In communes where we find that non-treatment localities are smaller on average, we trim the smallest localities until (i) we get balance in population size within the commune or (ii) the smallest remaining locality has no more than 250 individuals (approximately 25

6
Milesi-Ferretti dataset updated up to 2011.<sup>19</sup> In addition, we also consider trade openness (as measured by imports plus exports divided by GDP, taken from PWT 7.1) as a measure of real integration. We include a variable measuring real integration because, in the end, redistribution of endowments due to or any (implicit explicit) risk-sharing agreements should be materialized through flows of goods. Lastly, because domestic financial depth can also contribute to risk sharing among domestic agents (perhaps lessening the need for, or the value of, international risk sharing) we also use data on domestic financial depth, as captured by the ratio of domestic credit to GDP, taken from the World Development Indicators.

Figure 1 illustrates the trends in de jure international financial openness in the sample countries, as captured by the cross-country average of the Chinn-Ito openness measure. The figure shows both the overall mean as well as the means for advanced and developing countries separately. All three exhibit a rising trend, although with varying timing and magnitude.<sup>20</sup> The advanced-country mean rises steadily since the late 1970s, from under 0.50 to almost the maximum admissible value of 1, with little change over the final decade of the sample. In turn, the developing-country mean also displays an upward trend, but starting more than a decade later (in the early 1990s), and the extent of the rise – from under 0.30 at the beginning of the sample to just over 0.50 at the end – is also more modest than in advanced countries. The time path of the overall mean lies between those of the two group means.

# **4 Empirical results**

We proceed in two stages. First, we estimate the income process given by (19) and construct the quasi-innovations to the present value of income growth; then we use the latter to estimate the risk-sharing model (13).

## **4.1 Estimation of
# **3.2 Respondents of the survey**

Following the framework in Figure 1, data were gathered from the following types of respondents:

## 1. **Citizens:**

The citizen survey was aimed at respondents from 16 households residing in each GP area. The survey firm was provided with a list of respondents (with replacements) drawn randomly from the electoral rolls available of all voting-age adults in Bihar’s population. The target sample size is thus 4064 citizens (16 each from 254 GPs).

Within the category of citizens, the survey additionally targeted office-bearing members of women’s Self Help Groups (SHG) under a rural livelihoods program in Bihar known as Jeevika. However, we had no lists available with names of SHG leaders of the villagelevel organizations across GPs. In the absence of these lists, we relied on the survey firm to ensure that enumerator teams would identify SHG leaders during their field-work. The data from SHG leaders that have been provided to us is thus subject to a greater than usual caveat: the risk of whether the enumerator teams accurately identified and obtained interviews with the targeted SHG respondents. The instructions provided to the survey teams was to ask the GP Mukhiya and other GP-level respondents (such as the ANM, ASHA and AWW) about the GP-level federated organization of all the SHGs across the GP’s communities to identify its President, Secretary and Treasurer. That is, 3 SHG leaders were targeted for each GP, for a total sample of 762 (3 each from 254 GPs) SHG leaders.

## 2. **Politicians:**

- Village level:

- GP Mukhiya (or head of the elected village council; 1 per GP)

- Elected village councilors or Ward members: 3 per GP

- Candidates who contested the Mukhiya position in the last election of 2016,

12