rafmacalaba/data-use-mentions-extended huggingface dataset:
In a recent paper using U.S. Census data, Haltiwanger, Jarmin, and Miranda (2009) argue that there is no systematic relationship between firm size and growth once they control for firm age and find young businesses and startups to be critical for U.S. job creation. While this may be true of the U.S., we have little information on the importance of age versus size in other parts of the world, especially in developing economies, where there are greater barriers to entrepreneurship, and venture capital markets (very important for young firms in the U.S.) may not be so developed. Hence, below we investigate the access to finance statistics for young firms.

Table 8 presents data on access to finance for young establishments. In our sample of countries, the mean percentage of young firms below 2 years old that have access to a checking or saving account is 83.23% and median is 92.24%. If we were to use 5 years as the cut-off, the mean and median numbers are 85.06% and 90.21% respectively. Thus of the young firms in the population, a large proportion have access to bank accounts.

In the median country in our sample, 25.78% of firms less than two years old and 33.96% of firms less than five years old have access to an overdraft facility. The medians for access to lines of credit or loan from financial institutions are 23.58% for firms less than two years old and 33.97% for firms less than five years old. When we look across the low to high income groups in Figure 5, we do not find a monotonic increase for access to overdraft facility or line of credit. Instead, we find that the percentage of firms with access to overdraft facility and line of credit is significantly higher in the median high income countries (19.96% and 72.21% respectively) than in the median low income
p<0.01; *** p<0.001

# _Estimates for individual countries_

Under some assumptions, we can identify the learning loss for the EU student population as a whole. We take into account differences between countries that are stable over time using country fixed effects. We also control for time-variant effects of changes in the student sampling, namely for changes in individual student age and grade and country-average student age. We estimate the linear trend in student achievement, separately for each country. Assuming that these are valid controls for time variant and time invariant country characteristics, we identify the average departure from the achievement trend for the population of students in EU or ECA+ countries participating in PIRLS 2021. This departure can be interpreted as the learning loss caused by the COVID-19 pandemic as the pandemic was the only factor affecting achievement results in all countries. To separate the overall negative impact of the pandemic from the learning disruption caused by school closures, we also provide comparisons between schools and countries affected by shorter or longer closures.

Providing similar estimates for individual countries is possible with the model presented in the section discussing our empirical strategy. However, in this case, it is not possible to interpret achievement change in 2021 as a measure of learning loss as for individual countries other factors could change between 2016 and 2021 affecting student scores. Anyhow, we provide these results to allow comparisons between countries that are adjusted for the overall time trends and changes in the sampling (age and grade).

Table A1 in the annex compares results for ECA+ countries, using separate data for each country. The first three columns provide results for 2016, 2021, and the difference. These unadjusted results should not be interpreted as measures of the learning loss as they could reflect long-term trends in student achievement. Column 4 compares 2021 results to the 8
those using optical and SAR features. In the case of maize area mapping in Malawi, the potential benefits offered by SAR - providing signals unaffected by cloud cover - were offset by additional noise introduced with SAR imagery.

Many outstanding questions remain for future research. Crop classification accuracies of 0.9 or greater are not unusual in the literature, though small plot sizes in the African context may limit realistically attainable accuracy. Nevertheless, attempting to improve the accuracy of maize classification and gauging the sensitivity of our recommendations for alternative crops and countries should be among the foci of future research. Improvements may be gained by removing pixels with few cloud-free observations during the growing season. Experimenting with alternative machine learning approaches and an expanded set of geospatial covariates may increase performance as well. Further work is also needed to distinguish between intercropped and monocropped (“pure stand”) maize plots in order to a) improve classification performance, b) support the creation of intercrop maps and area estimates, and c) lead to continued refinements of downstream research related also to satellite-based crop yield estimation. Related to the latter, future research should similarly identify the minimum-required volume of and approach to survey data collection that would yield optimal data for training and validating remote sensing models for high-resolution crop yield estimation. Moreover, documenting the accuracy of out-of-season 34
policy that was ultimately adopted by the government.<sup>25</sup> The survey evidence in Section 5 shows that a sizeable share of the World Bank operational economists and NGO staff say they have changed their views of the effectiveness of particular interventions as a result of blog posts. Given that some of these individuals are in a position to indeed influence policy change, it seems likely that blogs are sometimes reaching the right readers to at the right time. These cases illustrate that it may be possible for blog posts to influence policies. However, given that we were able to gather only a few examples from discussions widely held on several economics blogs with thousands of readers, it appears that such cases may in fact be very rare.

# **7. Conclusions**

Economic blogs are doing more than providing a new source of procrastination for writers and readers. Using a variety of empirical approaches, we have provided the first quantitative evidence that they are having impacts. There are large impacts on dissemination of research – a link on a popular blog results in a substantial increase in abstract views and downloads, while a majority of economics blog readers say they have read a new paper in the past month as a result of a blog. There also appear to be benefits in terms of the bloggers becoming better known and more respected within the profession – bloggers are over-represented relative to their academic publication records in a poll of favorite economists, and readers of a new blog have become more aware of the researchers writing it. Finally, we find some evidence from our experiment that blogs influence attitudes and knowledge: readers of the new Development Impact blog think more highly of World Bank research and are more interested in

> 25 See the comments in http://blogs.worldbank.org/impactevaluations/the-impact-of-blogs-part-iiblogging-enhances-the-blogger-s-reputation-but-does-it-influence-policy

29
as well as data about specific tasks carried out at work. For Georgia, we use the 2019 Labor Force Survey (LFS) by the National Statistics Office of Georgia (GeoStat). The LFS also provides information on employment for different socio-demographic groups defined by gender, age, education level, sector, occupations and earnings. Our final sample for all datasets includes “employed” individuals in the private sector.<sup>5</sup> In addition, we use the STEP (Skills Towards Employability and Productivity) survey for Georgia to understand workers’ amenability to working from home.

To construct variables as proxies for labor demand and supply shocks, our paper also draws from three additional sources of data. First, we use the list of essential industries developed by the Italian government in the absence of published country-specific lists. As Italy was one of the countries affected earliest, the government had made a significant effort to determine essential industries. Their list uses NACE industrial classification codes, which can be mapped to the ISIC industry classification used in the WBES. We also use occupation-level data from the Occupational Information Network (O*NET) to estimate the infection risks of workers. O*NET has information on work activities data for 775 occupations on the level of 4-digit NAICS (North American Industry Classification System). We follow WEF<sup>6</sup> and calculate scores on the extent to which workers in given occupation face infection risks based on their responses to three questions regarding exposure to disease and infection, contact with others and physical proximity to others . Finally, we proxy changes in global demand by sector using the data from UN Comtrade and FlightRadar24. For the detailed information about the construction of variable used, please refer to Appendix 1 in the <u>longer version of this</u> paper.

To complement our findings and assess the crisis impact on workers who are outside our model (i.e., not formally employed in the private sector), we utilize real-time
# III. Data

# A. Sample Selection

The data for this paper were collected from a sample of women engaging in transactional sex in Busia, a semi-urban town in Western Province, Kenya. Busia is a town of 44,196 (Central Bureau of Statistics 2001) which is located on the Ugandan border, along one of two major trucking routes connecting the port city of Mombasa to Kampala, Uganda (via Nairobi). Due to its location along this trucking corridor, Busia is a known “hotspot” for transactional sex. HIV prevalence is also very high: in 2003 (just before the data for this paper was collected), prevalence was estimated at 9.8 percent in the Kenya Demographic and Health Survey, substantially higher than the national average of 6.7 percent (Central Bureau of Statistics 2004). Since sex workers are difficult to identify, we worked closely with the Strengthening STD/HIV Control Project in Kenya (SHCP), a Kenyan organization that worked with thousands of formal and informal sex workers across Kenya. SHCP identified women and organized them into peer groups to provide HIV education and promote behavioral change and condom use. By 2005, when this study began, SHCP had recruited approximately 400 women into 30 peer groups. In order to collect reliable data, we secured the cooperation of each peer group leader, and employed SHCP’s district coordinator and one of the peer educators as enumerators.

As discussed earlier, the line between commercial sex and other, more standard types of sexual relationships can be blurry in Kenya, as in much of Africa (since many types of relationships have a transactional sex-for-money/services component). For this reason, SHCP employed a broad definition of a sex worker: any single, widowed, divorced, or separated woman, aged 18 or older, who had multiple concurrent sex partners. Because SHCP was already using this definition, we continued to do so in this project. Though such a definition would be 12
a toilet or latrine.<sup>3</sup> _Height_ is the average height of children under 5 or children under 3, used in separate regressions. As robustness checks, results are replicated with country fixed effects _αc_ , year fixed effects _γy_ , and time-varying controls _Xcy_ , including the log of GDP per capita, all of which are added separately in stages. Standard errors are clustered by 65 countries.

# **2.2 Regression results**

Figure 1 depicts the main result of this section: a negative association between open defecation and child height is visible across country years both for children under 3 and children under 5. Regression lines are plotted with and without weighting by country population. The three largest circles are India’s National Family and Health Surveys (only one large circle appears in panel (b) because only the 2005 survey measured height of children up to age 5). Average height in India is, indeed, low. However, the fact that the Indian observations are on the regression line – and not special outliers – is an initial suggestion that sanitation might help resolve the “Asian enigma” of Indian height.

Table 1 reports estimates of regression 1 and will be referenced throughout this section of the paper. The main estimate of a linear decrease in height of 1.24 standard deviations associated with changing the fraction openly defecating from 0 to 1 is qualitatively similar to Spears’s (2012a) estimates of 1.15 to 1.59, where effects of sanitation are identified using heterogeneity in the implementation of an Indian government program. In column 1, sanitation alone linearly explains 54 percent of the country-year variation of children’s height in DHS surveys. Because sanitation and height are both improving over time, column 2 adds year fixed effects; the point estimates slightly increase and standard errors decrease, suggesting that the result is not an artifact of time trends.

Does the significance of open defecation merely reflect general economic development? Column 3 adds a control for GDP
2011). Multinationals can require strict productivity and production standards of their suppliers, and may share new technologies or encourage the adoption of new production methods to achieve this (Criscuolo and Timmis, 2017). To ensure the robustness of our instrument, we use ex-ante trade networks at the start of the sample period for the year 2000. We also only consider trade networks with other world regions (i.e. excluding trade with the home country’s region) – to exclude common regional shocks that may contaminate the analysis. In addition, the relevance of the instrument withstands a barrage of fixed effects including exporter*HS 10-digit product and exporter*year fixed effects, in order to control for potential trends across countries overtime, as well as potential time-varying multilateral resistance terms highlighted by the trade literature (Anderson and van Wincoop, 2003).<sup>6</sup> The existing literature on robotics, and technology more generally, often pertains to developed economies (Acemoglu and Restrepo, 2020; Presidente, 2017; Graetz and Michaels, 2018; Cardona et al 2013). A priori it is unclear how quality upgrading gains relate to country development levels. On the one hand, developing countries typically produce lower-quality exports and so have larger potential to catch-up through automation (Hallak and Schott, 2011; Henn et al., 2020). On the other hand, developed countries are more likely to have access to the complementary factors needed to realize the potential quality gains from robotics, such as the management skills to reorganize production (e.g. Brynjolfsson and Hitt, 2000, Bresnahan et al., 2002). Our study uses data that spans 59 countries allowing us to identify important heterogeneity in quality gains from robotics across developed and developing countries.<sup>7</sup> We also leverage new data on robots types – e.g. laser guided cutting vs mechanical cutting – to examine whether certain robots are more crucial for quality upgrading than others.

Export quality has attracted a
transportation mode is car, whereas, when they travel to restaurants or retail shops, the most preferred transportation mode is walking. The last two rows report the average iceberg commuting and trade costs across locations in Mexico City before and after the transit shock. On average, after Line B of the subway opens, commuting costs drop by 4.7%, and trade costs by 4.5%.<sup>44</sup>

# **5.2 Commuting and Trade Elasticities**

**Commuting Elasticities:** To estimate the commuting elasticities, I use the 2015 Intercensal Survey. In this survey, workers report the municipality of their residence and workplace, and I am also able to define formal and informal workers by using employment and social security information. From the model, it is easy to derive the following gravity equation relating commuting flows across municipalities and iceberg costs:

> 43I also estimate a heterogeneous _δ_ between the formal and informal sector for the consumption trips. However, I do not find significant differences between the two coefficients, _δ_ I = _−_ 0 _._ 0128, and _δF_ = _−_ 0 _._ 0114. These results are available upon request. However, I cannot distinguish between the formal and informal sectors for the working trips since I construct the commuting flows using the 2015 Intercensal survey.

> 44I restrict the sample to trips that only use one transportation mode or two transportation modes + walking.

26
0.260<br>(0.247)|1.107***<br>(0.275)|18.33<br>[0.000]|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. The _p_ - values are in brackets in the third column. Controls: teacher characteristics (gender, age, years of experience, if she/he has postgraduate certificates). The dependent variables are four-point scale, where 1 means strongly disagree, 2 disagree, 3 agree, and 4 strongly agree.

7
sector to another. As one would expect, the probability of moving out of a larger sector is lower than the probability of moving out of a smaller sector, and the probability of moving into a larger sector is higher than the probability of moving into a smaller sector.

# 5 Regression Results

We have two data sets, CPS and NLSY, for the estimation. CPS is missing the sectoral experience and work history variables, while NLSY has enough observations for only limited 18
said, endogeneity may still be an issue if, for instance, the expectation of further product concentration in a given sector (e.g. a long term decline in a major sub-sector) leads to policy decisions (e.g. tariff changes) or a decline of the sector as a whole.

Our key policy variable on the right hand side is the statutory tariff, computed as the average tariff in each 2-digit category.<sup>22</sup> Variables that capture more the structural changes of the economy include trade volume—which serves as our proxy for sectorspecific output effects<sup>23</sup> —along with measures identified in Section 2 as related to diversification potential.

The partial correlation between export diversification and two of the explanatory variables we use is significantly different from zero across all regressions. Intuitively, one may expect further diversification to be less likely in a sector that has already achieved a revealed comparative advantage, because the product space might already be very dense. However, the density of the product space may also produce synergies that lead to further diversification. Interestingly, the regressions show that an increase in RCA is associated with a reduction in the number of products exported (the coefficient entering with a negative sign). Yet, when using the Herfindahl index and the share indicator as dependent variables, the sign of the coefficient continues to be negative.

Although seemingly contradictory at first glance, tis finding may have interesting implications. The results may in fact suggest that, while revealed comparative advantage in a sector actually hinders the discovery of new products (presumably because the product space is already well populated), it nevertheless may prove conducive to diversification overall (potentially by strengthening the export of existing products previously exported at inefficiently low volumes). Thus, as a country’s relative specialization in a sector rises, diversification in that sector will occur more through exporting existing products to new markets. The finding by others (Amurgo-Pacheco & Pierola 2008; Brenton & Newfarmer 2009) that
also have an identical reference period and are able to illustrate a more precise worsening of the food security situation in Figure 3.

And third, it is possible that only a subset of women who experience IPV accurately describe those experiences to enumerators, and that the underreporting is worse than for men who are asked about the acceptability of a hypothetical man committing acts of IPV. Not admitting to abuse suffered in the pre-disaster period would result in no change for underreporting households even if there was a reduction in IPV. This could result in an estimate downward biased to zero and in an estimate that is less precisely estimated.

Figure 9b investigates this hypothesis and estimates the baseline empirical specification separately for women that are potentially more and less likely to report IPV experiences. Specifically, one of the many reasons that women might not report IPV experiences is the fear of additional abuse if other household members suspected that they might have admitted to the abuse that was occurring. It has been shown that women who have more resources and productive capacity can result in a more credible recourse of being able to leave an abusive household, and thus might fear this repercussion less than women who are less able to leave the household (e.g., Agarwal 1997; Hesse 2011; etc.). And reporting patterns in PNG are consistent with the least educated women underreporting IPV incidents. Women who never attended school report fewer instances of IPV despite the fact that the least educated women and men are _more_ likely to view instances of IPV being acceptable.<sup>35</sup> Thus, using a school attainment indicator to proxy for productive capacity, which was fixed long before the earthquake happened for women eligible for the women’s survey, we estimate the baseline empirical specification separately for women that never attended school and women that attended at least some primary school or above.<sup>36</sup> The results illustrate a substantial difference in the experiences of the 921 women who had never any school and the 3,024 women
Figure 2. Evolution of Potential Growth A. Potential growth <!-- Start of picture text --> Percent<br>8 Potential growth<br>Actual growth<br>6<br>1998-2017 potential growth<br>4<br>2<br>0<br>World AEs EMDEs<br>C. Contribution of potential growth and<br>business cycle to actual growth<br>Percent<br>8 Growth Potential Cycle<br>6<br>4<br>2<br>0<br>World EMDE<br>E. Per capita potential growth<br>Percent<br>8 Potential growth<br>6 Actual growth<br>1998-2017 potential growth<br>4<br>2<br>0<br>-2<br>World AEs EMDEs<br>1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27<br>2003-07 Contribution 2013-17 2003-07 Contribution 2013-17<br>1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27<br><!-- End of picture text --> <!-- Start of picture text --> B. Potential growth<br>Percent Potential growth<br>10 Actual growth<br>1998-2017 potential growth<br>8<br>6<br>4<br>2<br>0<br>G7 EM7 LICs<br>D. Share of economies and GDP with potential<br>growth below 1998-2017 average<br>Percent<br>Share of countries<br>100<br>Share of GDP<br>80<br>60<br>40<br>20<br>0<br>World AEs EMDEs<br>F. Per capita potential growth<br>Percent<br>8 Potential growth<br>Actual growth<br>6 1998-2017 potential growth<br>4