rafmacalaba/data-use-mentions-extended huggingface dataset:
).

However, the difficulties in identifying urban areas have undermined empirical efforts to connect with the theoretical literature and even to establish the stylized facts with confidence. For example, it is widely accepted that a country’s urban population share is related to its income per capita. And it is also said that India’s urban population share is too low relative to its income level. However, some studies suggest that many areas of India are misclassified as rural by the official definition, and so the level of urbanization is underestimated (Denis and Marius‐Gnanou 2011; World Bank 2013).

This disconnect is unlikely to occur only in India. At the global level, the diversity of urban metrics could challenge regularities taken for granted. For example, it could well be that urbanization rates are more accurate in agrarian societies and in advanced economies but underestimated in between these two extremes. If so, the relationship between the urban population share and income per capita would have a different gradient relative to what is commonly accepted.

Recognition of this measurement problem has led to alternative approaches to defining the urban extent in ways that could ensure comparability across countries and over time. One approach is to rely on information from the ground, such as highly disaggregated data from population and economic censuses. An example of this more systematic approach is the paper by Rozenfeld and others (2011) on the Great Britain and the United States.

Another promising approach is the use of remote sensing technology to identify urban areas from outer space. A number of methods that rely on images taken by sensors aboard satellites have been proposed in this respect. Methods to identify cities from outer space can be broadly classified into three groups, 2
question, we introduce a new method for measuring data use in research articles based on 1 million English-language articles spanning 216 countries and various academic fields. These articles are made available by Semantic Scholar Open Research Corpus (s2orc), which has digitized millions of research papers worldwide and made its raw text accessible via APIs (Lo et al. 2020). With the aid of Amazon Mturk workers, we manually code 900 of these articles as using data or not, upon which we train a natural language model to predict the coding of the Mturk workers (Devlin et al. 2018). The model achieved an 87% out-of-sample accuracy rate, and when the articles are aggregated to the country level, the model had a correlation of 0.99 with the number of articles classified by Mturk workers. The model was then applied to 1 million academic articles from the s2orc database from 2000-2020. The model estimates the amount of data-driven research on a country regardless of where the researchers may be located, _not_ the amount of data-driven research by the citizens of a country. We argue that the former is the relevant quantity to understand the evidence base available to national decision makers.

We find that data-driven research is strongly correlated with GDP per capita and population, which together account for around 75% of the variation across countries. High-income countries are the subject of nearly 50% of all papers using data despite representing only around 15% of the world's population, while low-income countries, comprising approximately 10% of the world population, account for only about 5% of articles using data.

2
|Urbanization (%)|2.141***|1.306*|1.400*|1.189|
|---|---|---|---|---|
||(0.492)|(0.755)|(0.762)|(0.808)|
|WBL Index|12.840|5.369|6.806|9.124|
||(8.897)|(11.969)|(11.956)|(12.614)|
|Women can work the same night<br>|3.770|-3.051|-2.936|-3.790|
|hours as men Y:1 N:0|||||
||(3.457)|(4.147)|(4.105)|(4.175)|
|Women can work in the same|-0.319|-4.090|-4.243|-3.966|
|industries as men Y:1 N:0|||||
||(2.323)|(2.741)|(2.785)|(3.024)|
|Ratio of Female to Male Population|2.739***|9.206|10.477|9.140|
||(0.712)|(6.852)|(7.043)|(7.425)|
|Fertility Rate|-16.438***|2.041|3.243|4.626|
||(4.520)|(4.315)|(4.408)|(4.740)|
|Starting a Business (DTF)|0.108**|0.056|0.071
and consider their export destination markets.<sup>27</sup>

# **_6.1 Destination Entry, Exit and Survival Rates_**

Panels A and B of Figure 8 shows the destination entry rates for incumbent exporters during 2005-2007 and 2008-2009.<sup>28</sup> Incumbent exporters in LAC engaged in a high degree of experimentation along the destination dimension during the steady growth period: on average a

> 26 The decomposition of the contribution of new products during the crisis period is not presented due to the lack of data for 2010 for most LAC countries which would be necessary to calculate the survival of new products into the year following 2009. 27 Brazil, New Zealand, and Spain are not used in this section given that we do not have access to the raw exporter-level customs data for those countries necessary to compute the measures used here.

> 28 The average number of destinations served per incumbent exporter in LAC countries ranges from 2.6 in Mexico and Nicaragua to 4.7 in Chile, as shown in Appendix C.

27
_V_ ), equation (1) can be modified as follows:

# _aSLR_ =[( _α_ + _βΔt −V_ )( _T −_ 2000)]

where _aSLR_ is the _adjusted_ sea level rise. Data on VLM by country have been retrieved from the SONEL database (www.sonel.org).

For example, the adjusted SLR associated with an increase in temperature of +1°C and VLM of +0.001 m/yr (rising) at the year 2050 is:

# 0.16878=[(0.000954281+0.003421296 _x_ 1 _−_ 0.001)(2050 _−_ 2000)]

that is, about 0.17 meters.

Using the DIVA v2.04 model, Arnell et al. (2014) provide estimates of the percentage loss in the coastal wetland for 16 macro-regions and 3 single countries. These estimates, reported in Table 1, are associated with a future global mean SLR of 0.16 m, predicted by the HadCM3 climate model under the A1b SRES scenario.

**Table 1** . % change in coastal wetland at 0.16 m of SLR by macro-region (from Arnell et al. 2014)

|**Region/country**|**% change in coastal**|**Region/country**|**% change in coastal**|
|---|---|---|---|

4
models to look up to, appear to be important.

As mentioned earlier, evidence on the importance of socio-emotional skills in determining an individual’s life outcomes in a developing country context is scarce. Recent work in psychology has lamented the lack of rigorous evidence on what kind of psychological interventions are effective (Wilson, 2011), while Haushofer and Fehr (2014) point out the great need for such evaluations, especially in the context of developing countries. To the best of our knowledge, only two papers have recently made a foray in this regard: Ghosal _et al_ (2015) that studies the impact of mitigating psychological constraints on investment behavior of sex workers in India using a randomized field experiment, and Blattman _et al_ . (2015) that combines psychological and cash-based interventions in Liberia. Our paper fits into this nascent literature by providing further evidence on the importance of psychological factors for a specific group of individuals, i.e. adolescent girls and young women. Unlike the other two studies, however, the findings of our paper do not lay any claims to causality.

The remainder of the paper is organized as follows. Section 2 describes the survey design and data collection of the World Bank Adolescent Survey in Jharkhand. Section 3 presents the descriptive statistics from the survey, while Section 4 outlines the empirical methodology used in this paper. Section 5 presents the main findings and discusses their implication. Section 6 concludes.

# **2. Survey Design and Data**

3
uncertainty. Exceptions include PASEC and MLA which only have overlap with SACMEQ. To this end, scores are first made comparable to SACMEQ scores, before they are converted to our final HLO score.

Hierarchy of Tests. We construct a hierarchy of tests to facilitate year-level-subject matching between anchored and reference tests. For math and science, scores are converted to TIMSS-comparable scores at primary and secondary level. For reading, scores are converted to PIRLS-comparable scores at primary level and PISA-comparable scores at secondary level.

This hierarchy of tests can be extended to adjudicate which HLO score to use when multiple scores are available across sources tests. (e.g. a country that took part in EGRA and SACMEQ). The first HLO choice is: PIRLS, PISA or TIMSS depending on the subject and schooling level. If a country took part in PISA and not in TIMSS for math and reading, we next convert PISA scores into subject-specific secondary HLO scores. If a country took part in LLECE, we convert these scores into a subject-specific primary HLO score. Next, we take SACMEQ HLO scores, followed by PASEC HLO scores, then EGRA HLO scores, and finally by MLA HLO scores.

Measures of Uncertainty. We include measures of uncertainty to quantify the degree of confidence around our estimates. We capture two sources of uncertainty: scores on the original test and uncertainty in the calculation of the conversion factor across tests. Simplifying equations (2) and (3), the HLO for a given country on test X is: We calculate the variance of the HLO by bootstrapping. We assume country-level average scores are asymptotically normally distributed . We take 1000 draws from the distribution of subject-level-grade average test scores for each testing regime. We create doubloon indices and HLOs from each bootstrapped sample. We take the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the distribution and use this to construct lower and upper bounds of uncertainty for our HLO scores.

We find small
# **6 Conclusion**

In this paper, we estimate heterogeneous economic returns to SC programs in Colombia. We account for the self-selection of students across multiple higher education alternatives (bachelor’s programs, SC programs, and not enrolling in higher education). In line with the literature, we show that the effects of SC programs largely differ depending on the fallback alternative (next-best option) of students. We use an Instrumental Variables (IV) approach, where identification comes from the supply of SC programs at the municipal level. More specifically, we define a binary instrument to indicate whether a municipality has an institution specialized in offering SC programs in a 10-kilometer radius.

We present three main findings. First, we document 3.7-4.5 pp increase in SC program enrollment as the local supply of these programs rises. Students who respond to the supply increase (compliers) are concentrated in municipalities where bachelor’s programs were already available. Second, at least 70% of compliers would react along the diversion margin, switching from bachelor’s programs into SC programs. The remaining 30% would react along the expansion margin, entering the higher education system. These patterns are similar for males and females. Third, females experience gains from SC programs in terms of formal employment while males experience losses in formal employment and wages. Based on the high diversion share and the fact that males are more advantaged and better prepared academically than females, we argue that SC programs might constitute an “undermatch” for males, who might have otherwise attended good bachelor’s programs, but a better match for females, who might have otherwise attended low-quality bachelor’s programs.

While different from findings for the U.S., our findings can be reconciled with those. Using data from Texas, Mountjoy (2022) finds that the expansion margin share is about two-thirds, much higher than our 30%. One possible explanation for this discrepancy is that SC programs (typically taught at community colleges in the U
Low-skilled 0.92 0.70 0.76 0.67 0.58 Note: Real weekly wages in USD and prices of 2011. Author's estimates using labor force survey data from India.

We complement these results by computing district and industry premiums for Bangladesh. First, state and industry premiums can be indicators of segmented labor markets. A lack of labor mobility across sectors and districts would result in premiums. If labor is perfectly mobile, wage premiums after any shock should decline over time since workers with the same characteristics would move to districts and industries offering higher wages. Furthermore, the correlation of the premiums over time should then be small and decline.

This is, however, not the case. Figure 9 compares Bangladesh and India in terms of regional wages over time. On the one hand, this figure clearly shows that the wages are not equalized across states in India. If anything, the states with the highest wages early in the sample have experienced the largest wage increases. For instance, weekly average real wages in Mizoram are more than three times larger than those in Tripura and Chhattisgarh. On the other hand, earnings dispersion across districts is not only much lower in Bangladesh but also seems to have been decreasing during the last decade. These findings suggest that Bangladeshi faces less labor mobility restrictions than India, spreading more rapidly the benefits form trade shocks over time.

**Figure 9. Average Real Wages Across Regions a) India**

<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000<br>500<br>0<br>States<br>1999 2004 2007 2009 2011<br>Average weekly real wage (rupees)<br>Mizoram Nagaland Delhi Chandigarh Lakshadweep Sikkim Manipur Haryana GOA Uttaranchal Meghalaya Assam Jharkhand Kerala Maharashtra Daman & Diu Pondicherry Punjab Rajasthan West Bengal Karnataka Dadra and Nagar... Uttar Pradesh Andhra Pradesh Bihar Tamil Nadu Gujarat Orissa Madhya Pradesh Tripura Chhattisgarh<br>Arunachal
and D. Winkler. 2014. “The Role of Mediating Factors for FDI Spillovers in Developing</mark>

- <mark>Countries: Evidence from a Global Dataset,” in Farole, T. and D. Winkler (eds.),</mark> _<mark>Making Foreign Direct Investment Work for Sub-Saharan Africa: Local Spillovers and Competitiveness in Global Value Chains</mark>_ <mark>, Washington, DC: The World Bank, 59-86.</mark>

- <mark>Feenstra, R. C. and G. H. Hanson. 1996. “Foreign Investment, Outsourcing and Relative Wages,” in Feenstra, R. C., G. M. Grossman, and D. A. Irwin (eds.),</mark> _<mark>Political Economy of Trade Policy: Essays in Honor of Jagdish Bhagwati</mark>_ <mark>, Cambridge, MA: MIT Press, 89-128.</mark>

- <mark>Francesconi, G. and R. Ruben. 2014. “Fair Trade’s theory of change: an evaluation based on the cooperative life cycle framework and mixed methods,”</mark> _<mark>Journal of Development Effectiveness</mark>_ <mark>, 6:3, 268-83.</mark>

- <mark>Görg, H. and A. Hanley. 2003. “International Outsourcing and Productivity: Evidence from Plant Level Data,” Research Paper Series: Globalisation, Productivity and Technology, Research Paper No. 20/2003 (University of Nottingham).</mark>

- <mark>Görg, H. and E. Strobl. 2001. “Multinational Companies and Productivity Spillovers: A Metaanalysis,”</mark> _<mark>Economic Journal</mark>_ <mark>, 111(475), F723–F739.</mark>

- <mark>Görg, H.,
|***|
|Time dummy τt+1|−0.079|0.066||1.551|0.041|***|
|Sample size||1750|||7786||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# **CONCLUSION**

To explain the differences in earnings between the public and private sectors, the two usual candidates are the differences in the workers' skills and the differences in the returns to the workers' skills. The two factors have very different interpretations, and a considerable amount of research has been devoted to disentangling the former, which implies sorting by abilities, from the latter, which is an indicator of labor market segmentation. A plausible explanation for the rising public-private wage gap between 1993 and 2006 is that the selection into the public sector increased, with a greater flexibility in the hiring and the firing of workers. Using a model of comparative advantage and the differential returns between the two sectors, we find evidence that workers in the public sector have a comparative advantage in working there. Even if 33
woodworking sector trains the least; 35 percent of the firms train externally and 23 percent provide internal training.

## **5.2 Ethnic groups**

This paper considers three ethnic groups. The 1982 census reports that 97.5 percent of the Zimbabweans were African. The second largest ethnic group, Europeans, accounts for 2 percent of the total population. The RPED data sample consists of 95 percent Africans, 3 percent Europeans, and 2 percent of Asian origin. Hence, the sample reflects the actual composition of the population.

Prior to 1980 skilled jobs were rmore or less reserved for Europeans or other white people and education was intended to benefit this group in particular. Hence, Africans are less well equipped in terms of education than Europeans to engage in formal labor market activities. Therefore, the immigration of Europeans commencing in the middle of the 1970s led to a skill shortage in Zimbabwe. Africans were not only discriminated against in terms of education but also in apprenticeships controlled by trade unions'.

# **6. Findings**

This section presents evidence amassed from the data. Appendix A encloses a list of variables and describes how each variable is constructed. The findings of the regression analysis are presented in Appendix B.

## **6.1 Regression findings**

This sub-section reports findings of the joint estimation of wages and productivity. The tables present a subsection of the findings of presented in the tables in Appendix B.

> 9The number of apprentices has not increased since Independence, rather it has decreased by 25 percent: there were 2000 in 1981 and 1500 in 1993 (Kanyenze; 1995).

12
MONGOLIA'S INFORMAL SECTOR Overview 2

# **What Does It Mean to be "Informal" in Mongolia?**

_5._ There are at least as many ways of defining the informal sector as there are countries where it has been studied - every country has unique features and circumstances that make a universally accepted definition impossible? And as the informal sector, _per se,_ has never been studied in Mongolia, it would have been a mistake to impose an objective definition _a priori_ for the Mongolian situation. Indeed, one purpose of this study was to shed light on the specific character of Mongolia's informal sector. Thus the working definition of the informal sector used at the outset of this research was chosen broadly: _The informal sector consists of small scale, usuallyfamily-based, economic activities that may be undercounted by official statistics, and may not be subject, in practice, to the same set of regulations and taxation as formal enterprises._ As it is extremely difficult to distinguish this concept of the informal sector from general micro-enterprise, the two were considered to be nearly interchangeable.

6. Seven hundred and seventy micro-entrepreneurs that seemed to fit this working definition were surveyed by the author in the first half of 1997. The sample included taxicab drivers, kiosks proprietors, boot repairmen, newspaper vendors, and many others. Table 1 illustrates how a selection of these occupations measure up to some popular criteria for judging informality, such as the degree to which they are subject to, and comply with, official registrations, laws, and taxes; the effective incomes of participants; the division between capital and labor; the sophistication of record-keeping; the relative ease with which the occupation could be incorporated into national accounts statistics; the "permnanence" of the place of business; and participation in social and health insurance schemes.<sup>4</sup> 7. Table 1 highlights the diversity of experience of Mongolia's micro-entrepreneurs. By most of the criteria, all of the activities listed have the characteristics
MINIMUM WAGES IN CAMBODIA 9 <!-- Start of picture text --> All<br>Oct 2010 May 2013 Feb 2014 Jan 2015<br>10 12 14 16 10 12 14 16 10 12 14 16 10 12 14 16<br>Female<br>Oct 2010 May 2013 Feb 2014 Jan 2015<br>10 12 14 16 10 12 14 16 10 12 14 16 10 12 14 16<br>Private Min wage before change<br>Garments Min wage after change<br>3<br>2<br>1<br>0<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Figure 3. Wage densities 12 months before minimum wage change _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : Densities of logarithm of real monthly wages of 12 months preceding each minimum wage hike. Sample restricted to working age population, whose main employment is wagework in the private sector, and have worked at least 20 hours in the past week.

only the bottom quarter of the monthly earnings distribution in the garment and footwear sector. These shares are similar across genders and locality as well.

In all of these cases, the monthly earnings distribution is more widespread in the other private sectors. Particularly for female workers, the bottom tail of the earnings distribution is particularly large. During the 8-12 months preceding these minimum wage changes, a quarter to a third of the wage workers in the private sector were getting paid lower than the prevailing minimum wage. The shares of private sector wage-workers getting paid below the minimum wage are higher for women and for rural workers. If minimum wages in garment and footwear pulled the private sector wages, then one would expect a lower proportion of workers getting paid below the minimum wage levels in the private sector. However, it could be possible that the garment and footwear sector attracts high productivity workers among individuals with similar characteristics.
Note: Bivariate OLS regression of vaccination status on a dummy for different respondent selection options. Panel 1 tests the effect of randomly selecting the respondent (vs. purposive selection). Panel 2 tests the effect of eliciting the vaccination status of all household members aged 15+ via proxy reporting (vs. purposive selection). Panel 3 tests the effect of randomly selecting the respondent (vs. proxy reporting for all members aged 15+). Vaccination rate estimated using survey weights and as reported in administrative data reported at bottom. Burkina Faso - Survey dates: August 30 - September 25; Administrative data: October 2. Malawi - Survey dates: July 26 - September 7; Administrative data: September 4. Uganda - Survey dates: August 5 - September 1; Administrative data: September 4. Administrative data for adult coverage assumes no children were vaccinated at the time of data reporting. This is assumption likely overstates the true vaccination rate among adults where countries started vaccinating children already. This is the case for children under 16 years of age in Malawi (starting Dec 4, 2021) and Uganda (starting Jul 26, 2021) Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Further, there is a statistically significant difference between the estimated vaccination rate based on random respondent selection and based on proxy reports for all household members across all countries (Panel C), which goes against our prediction that both approaches should lead to statistically indistinguishable estimates.

There are several possible explanations for this difference, relating both to errors of representation and errors of measurement. These include errors in proxy reporting (see section 4.2) and enumerator demand (see section 4.3).

After weighting our random respondent estimates using the re-calibrated phone survey weights (see Section 3), the gap between survey data and administrative records remains substantial in all cases (11 percentage points or 46% of administrative coverage in Burkina Faso, 19