rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3. Small Area Estimation Methodology**

Woreda level estimates of undernutrition are estimated by SAE. In essence, the methodology constructs a prediction model between the z-score and observable household and location characteristics in EMDHS. This model is then used to predict a z-score for every child using the census. This paper relies on the SAE method developed in Elbers, Lanjouw, and Lanjouw [26] , known as the ELL methodology. This method, in particular, is attractive, as it is both the most applied method and it can be implemented in the freeware program PovMap.

First, a set of variables deemed to be similarly defined and distributed in the survey and the census are identified. Utilizing only these variables ensures that the estimated z-scores from the model pertain to 2014 (the year of the survey) and not 2007 (the year of the census).

Second, a z-score model is constructed in the survey data:  where _X ih_<sup></sup> is the vector of explanatory variables for child _i_ in household _h_ ,  is the vector of coefficients, _Z_ is the vector of location specific variables,  is the vector of coefficients, and _uih_ is the error term due to the _X_<sup></sup> discrepancy between the imputed z-score and the actual value. _ih_<sup>is child and household level variables that</sup>  have similar definitions and distributions in survey and census, while _Z_ includes location-specific averages or other transformations of variables found in the census that cannot be found in the survey, and external variables, such as geo-spatial variables that can be added to both the survey and the census.

Third, undernutrition estimates and their standard errors are computed via simulations using the estimation model. There are two sources of errors involved in the estimation process: errors in the estimated regression 5
# **Figure 4: Share of workers by region of origin and risk type**

**Panel A. Telework jobs Panel B. Essential jobs**

<!-- Start of picture text --> 50% 50%<br>45%<br>40% 45%<br>35%<br>30% 40%<br>25%<br>20% 35%<br>15%<br>10% 30%<br>5%<br>0% 25%<br>Natives Foreign EU15 NMS13 Non-EU28 Natives Foreign EU15 NMS13 Non-EU28<br>born migrant migrant migrant born migrant migrant migrant<br><!-- End of picture text -->

**Panel C. Income risk jobs**

**Panel D. Health risk jobs**

<!-- Start of picture text --> 50% 50%<br>48% 45%<br>46% 40%<br>44% 35%<br>42% 30%<br>40% 25%<br>38% 20%<br>36% 15%<br>34% 10%<br>32% 5%<br>30% 0%<br>Natives Foreign EU15 NMS13 Non-EU28 Natives Foreign EU15 NMS13 Non-EU28<br>born migrant migrant migrant born migrant migrant migrant<br><!-- End of picture text --> Source: Own calculation based on EU-LFS 2018 data, following EC directive (2020) and Fasani and Mazza (2020).

Figure 4 highlights the heterogeneity in exposure to labor market risks between immigrants from different regions of the world, especially between migrants from high-income EU15 countries relative to the rest of immigrants. For telework jobs, the share of immigrant workers from EU15 countries is even higher than natives’ share, whereas it is significantly lower among migrants from 17
generational Mobility in Education? Evidence from Jordan. World Bank Economic Review, 32(3):633655.

- Azam, M. (2016). Intergenerational Educational Persistence among Daughters: Evidence from India. The B.E. Journal of Economic Analysis & Policy, 16(4):116.

- Azam, M. and Bhatt, V. (2015). Like Father, Like Son? Intergenerational Educational Mobility in India. Demography, 52(6):19291959.

- Azomahou, T. T. and Yitbarek, E. (2021). Intergenerational Mobility in Education: Is Africa Dierent? Contemporary Economic Policy, Forthcoming.

- Bank, W. (2011). Perspectives on poverty in India: Stylized facts from survey data. The World Bank.

- Becker, G., Kominers, S. D., Murphy, K., and Spenkuch, J. (2015). A Theory of Intergenerational Mobility. MPRA Paper 66334, University Library of Munich, Germany.

- Becker, G. and Tomes, N. (1986). Human Capital and the Rise and Fall of Families. Journal of Labor Economics, 4(3):139.

- Becker, G. S., Kominers, S. D., Murphy, K. M., and Spenkuch, J. L. (2018). A Theory of Intergenerational Mobility. Journal of Political Economy, 126(S1):725.

- Bhalotra, S. and Rawlings, S. (2013). Gradients of the Intergenerational Transmission of Health in Developing Countries. The Review of Economics and Statistics, 95(2):660672.

- Bingley, P. and Cappellari, L. (2019). Correlation of Brothers' Earnings and Intergenerational Transmission. The Review of Economics and Statistics, 101(2):370383.

- Bjorklund, A. and Jantti, M. (
|I<br>Periodaverage<br>1991-1998<br>|99-2004<br>2005-08<br>|
|---|---|
|||
|144.7<br> <br>|63.3<br>44.5<br><br>|
|PrivateConsumption<br>127.4|54.7<br>40.5|
|PublicConsumption<br>17.3|8.6<br>3.9|
|<br><br>Investment<br>-52.8|20.2<br>49.7|
|PrivateInvestment<br>50.6<br> <br>|-13.6<br>11.4<br><br>|
|PublicInvestment<br>-67.8|29.9<br>40.9|
|Exports<br>33.8|36.8<br>147.1|
|Imports<br>25.8|20.3<br>141.3|
|SounociAuthorscalculationsromt|eMinsnyoF|
|<br>EconomicDevelopmentdata||

<!-- Start of picture text --> 100%80% 26 A fal a30 12<br>fs Ms “ 174<br>“n%<br>103 Pr<br>20% . 468 48.5<br>2 84<br>0%<br>2004/05 2005/05 = 200607 2007/08 2008/09<br>Newestablishments and expansions Sales of assets to non residents<br>Inflows to petroleum sector H Real estate sales tonon residents<br><!-- End of picture text -->
# WPS 3872

## **Diversification, Innovation, and Imitation inside the Global Technological Frontier***

## Bailey Klinger Daniel Lederman

**Abstract:** Recent research highlights the relationship between economic development and productive diversification, which may be hindered by market failures. After identifying stages of diversification in disaggregated export data, the authors develop a metric for the flows of export “discoveries,” or inside-the-frontier innovations in developing countries. The authors then explore the empirical relationship between economic development and (1) inside-the-frontier-innovation as reflected by the introduction of new export products, (2) export diversification measured by an index of export-revenue concentration, and (3) on-the-frontier innovation as reflected in patents. The data suggest that inside-the-frontier innovation is more common among poor countries for whom export growth entails diversification, whereas on-the-frontier innovation and specialization characterize the export growth of highly developed economies. The data also suggest that the relationship between the frequency of export discoveries and economic development is not due to changes in the industrial composition of exports. The authors utilize a simple model of innovation and imitation to test the hypothesis that the threat of imitation inhibits the discovery of new exports. Econometric evidence suggests that the frequency of export discoveries across countries rises with the returns of export activities (proxied by exogenous export growth during the sample period), but the magnitude of this effect increases with barriers to entry. The count-data estimations deal with unobserved international heterogeneity, and the results are robust to various changes in the specification of the empirical model. This finding supports the hypothesis that market failures inhibit inside-the-frontier innovation.

World Bank Policy Research Working Paper 3872, April 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of
., A. Garcia, R. Lensink, B. Wydick. 2022. “Aspirational hope, dairy farming practices, and milk production: Evidence from a randomized controlled trial in Bolivia”. _World Development_ 160: 106087.

- Chiapa, C., J. L. Garrido, and S. Prina. 2012. “The effect of social programs and exposure to professionals on the educational aspirations of the poor.” _Economics of Education Review_ 31(5): 778–798.

- Chong, A., I. Cohen, E. Field, E. Nakasone, and M. Torero. 2016. “Iron Deficiency and Schooling Attainment in Peru”. _American Economic Journal: Applied Economics_ 8(4): 222–255.

- Cools, A., R. Fernández, and E. Patacchini. 2022. “The Asymmetric Gender Effects of High Flyers.” _Labour Economics_ 79: 102287.

- Contreras Suarez, D., and L. Cameron. 2020. “Conditional Cash Transfers: Do They Result in More Patient Choices and Increased Educational Aspirations?” _Economic Development and Cultural Change_ 68(3): 729–761.

- Dalton, P. S., S. Ghosal, and A. Mani. 2016. “Poverty and aspirations failure.” _The Economic Journal_ 126(596): 165–188.

- Del Carpio, L., and M. Guadalupe. 2022. “More women in tech? Evidence from a field experiment addressing social identity.” _Management Science_ 68(5):3196-3218.

- Dinkelman, T., and C. Martínez. 2014. “Investing in schooling in Chile: The role of information about financial aid for higher education. _Review of Economics and Statistics_ 96(2): 244—57.

28
Ferreira, and Menéndez (2007), using Brazilian household data, find that individuals’ place of residence, parents education, occupation, and race are the most significant variables linked to opportunity. Their analysis additionally reveals a sizable inequality of opportunities in Brazil and shows that urban residents have potentially more opportunities than rural residents.

As cities plan for an uncertain future, current practices illustrate the positive outcomes that can result from the empowerment of citizens during the planning and financing of urban renewal projects. The Asian Coalition for Community Action (ACCA), for example, uses small grants (less than US$58,000) funded by the Bill and Melinda Gates Foundation both to leverage government and private capital, and to promote community involvement in planning urban projects. The program has enabled the construction of street lights, community latrines, transport infrastructure and other public goods in cities of all sizes in Cambodia, Vietnam, Indonesia,

> 12 On opportunities and empowerment, see Stern, Dethier and Rogers (2005).

15
adjustments. Although some expansion of the width of the confidence intervals can be observed, in all cases the difference is minor or imperceptible. Therefore, it appears the bias corrections implemented in the four countries has not come at the cost of substantially larger standard errors.

Overall, the results from this analysis have demonstrated the effectiveness of the weight adjustment techniques employed in the Ethiopia, Malawi, Nigeria, and Uganda HFPS for reducing bias in the interviewed samples. Absent these adjustments, the phone survey samples would suffer from substantial bias that would compromise the representativeness of the results obtained from the phone survey sample. The reduction in the bias from these methods highlights the distinct advantage of phone surveys taken from representative F2F household survey samples. Although difficult to effectively simulate here, the adjustments possible for RDD and telecom list-based phone surveys would likely be less effective at reducing bias than adjustments made using the rich information from the F2F representative surveys used in these four countries. Therefore, it might be expected that effectiveness of bias adjustments in RDD and telecom list-based surveys to fall between the unadjusted and adjusted corrections considered here. The effectiveness of adjustments under these other phone survey sample approaches will vary depending on the availability of external information on demographic and economic characteristics of the general population and the extent to which corresponding information can be captured in the phone survey itself.

33
time series data are notoriously difficult to obtain. The weakness in the openness measures that confound the literature on trade and growth are equally problematic here. More fundamentally, trade policy is endogenous—among other things, labor market concerns are one determinant of trade policy, and the factors affecting the latter may affect the formation of wages. Moreover, it is

> 1 Surveys include Baldwin (1995), Cline (1997), Slaughter (1998), Johnson and Stafford (1999), Gaston and Nelson (2000), Greenaway and Nelson (2001), Acemoglu (2002), Feenstra and Hanson (2004), and Goldberg and Pavcnik (2004).

> 2 Income distributional effects extend of course beyond wages/employment to include the prices of produced outputs, the non-wage income, transfers, income from assets and consumption prices—see, for example, Winters, McCulloch and McKay (2004).

1
The blue line shows percent of students who drop out in a given college year. The red line shows, among all dropouts, percent of those who drop out in a given college year.

**Figure 3: College Outcomes by Ability.**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Ability quintile<br>Dropout 1st year Dropout 2nd year<br>On−time graduate Late graduate<br>Dropout after 2nd year<br>Percent<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES. Students belong to the 2006 entry cohort (first semester). _Notes_ : For each quintile, the graph shows the percent of students who attained each outcome. Percents add to 100 by quintile.

# _Fact 3: Cumulative performance varies more within than across abilities._

Figure 4’s panel a shows average number of classes completed by ability quintile in year 1. The thick black line depicts average over all students, whereas the color lines depict averages among the students with the following final outcomes: on-time graduation, late graduation, dropout in year 1, and dropout later. On average, high-ability students complete more classes than lowability students in year 1. However, the figure also shows a pattern repeated every year: average classes completed varies little _across_ abilities—overall and conditional on outcomes—but varies greatly _within_ abilities. For a given ability, on-time graduates complete more classes than late graduates, who complete more than dropouts. In other words, number of classes completed—as

> 6For comparison, in the U.S. 59.2 percent of students from the 2006 cohort graduated within six years—39 percent on time (in four years), and 20.2 percent late  ̇Source: Digest of Education Statistics.

9
likely to fall into or remain in poverty when connected by all-weather roads in drought areas, indicating the mitigating role of rural roads for drought shocks. The study also finds the effects of rural road developments for agriculture and non-agriculture work in communities farther away from towns. More farmers have come to sell their crops once connected to rural roads. In addition, more households—particularly women and the youth—have engaged in wage jobs in remote communities. By connecting to markets, rural roads have widened the economic base in otherwise physically and economically isolated communities.

This study contributes to the literature on the impacts of rural road development. There is a small number of previous studies that evaluate the impacts of rural road development in the developing world.<sup>4</sup> Khandker, Bakht, and Koolwal (2009) examined the impact of road investments on poverty in Bangladesh, finding the overall effect of road improvement on household consumption to be about 8–10 percent in the project areas. By contrast, a recent study of India’s PMGSY program by Asher and Novosad (2018) finds no clear impact on household consumption and agricultural outcomes in connected villages.<sup>5</sup> Several studies investigate rural roads in Ethiopia. Dercon et al. (2009), using panel household surveys collected in selected villages between 1994 and 2004, found that access to all-weather roads reduced poverty by 6.9 percent and increased consumption growth

> 4 There are some other studies investigating the impacts of rural road access in the developing world. Jalan and Ravallion (2002) find positive impacts of rural roads on consumption growth in China. A study of Vietnam by Mu and van de Walle (2011) found positive impacts of rural road rehabilitation on local market development. Damania et al. (2016) found that transport cost reduction encourages farmers to adopt modern technologies in Nigeria.

> 5 Aggarwal (2018) finds lower prices, increased availability of non-local goods, increased use of agricultural technologies, and increased
are particularly important in critical biodiversity areas that span national borders and fragile/conflict regions.

The estimates derived from GBIF data presented in this paper can serve as a valuable baseline for initiatives to address such problems. As thousands of new reports are added daily to the GBIF repository, a steadily-expanding inventory of up-to-date species occurrence maps can provide a critical, objective information resource for tracking biodiversity trends, evaluating conservation progress, and developing effective policies.

This critical information can also provide more general benefits, because initiatives that focus on biodiversity can serve as valuable entry points for general dialogue and confidence-building among divided groups, fostering collaboration towards shared goals with multiple benefits (such as peace dividends). Threats to natural resources, including biodiversity loss, may be less politically sensitive and overlapping than other issues, and can encourage stakeholders to consider long-term perspectives on issues that transcend political boundaries.

Finally, we should re-emphasize the importance of new species knowledge in this context. This exercise has shown that the GBIF data can support a large expansion of information about species (particularly invertebrates and plants) that have critical importance for biodiversity conservation because they are endemic, inhabit small areas, or face serious extinction threats. While past transboundary and fragile/conflict state initiatives have made important contributions to environmental protection, they can be enhanced by moving beyond the traditional focus on vertebrate species to a much broader set of species that warrant protection.

# **Data Availability**

The data can be downloaded from <u>https://datacatalog.worldbank.org/search/dataset/0066034/global_biodiversity_data</u> For a related blog, see Bridging Conflicts and Protecting Global Biodiversity 23
- Guadarrama M, Molina I, Rao J (2018). “Small area estimation of general parameters under complex sampling designs.” Computational Statistics & Data Analysis, 121, 20–40.

- Hossain MJ, Das S, Chandra H, Islam MA (2020) Disaggregate level estimates and spatial mapping of food insecurity in Bangladesh by linking survey and census data. PLoS ONE 15(4): e0230906. <u>https://doi.org/10.1371/journal.pone.0230906</u>

- Hurles et al. (2005). “The dual origin of the Malagasy in Island Southeast Asia and East Africa: evidence from maternal and paternal lineages”. American Journal of Human Genetics 76(5);

- Institut National de la Statistique (INSTAT) et ICF. 2022. Enquête Démographique et de Santé à Madagascar, 2021. Antananarivo, Madagascar et Rockville, Maryland, USA : INSTAT et ICF

- Kreutzmann, A. K., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M., & Tzavidis, N. (2019). The R package emdi for estimating and mapping regionally disaggregated indicators. Journal of Statistical Software, 91.

- <mark>Martorell R. 2017. Improved nutrition in the first 1000 days and adult human capital and health. Am J Hum Biol. 2017 Mar;29(2):10.1002/ajhb.22952. doi: 10.1002/ajhb.22952. Epub 2017 Jan 24. PMID: 28117514; PMCID: PMC5761352.</mark>

- Molina, I., & Rao, J. N. K. (2010). Small area estimation of poverty indicators. Canadian Journal of statistics, 38(3), 369-385.

- Pfeffermann D, Skinner CJ, Holmes DJ, Goldstein H, Rasbash J (1998). “Weighting for unequal selection probabilities in multilevel models.” Journal of the Royal Statistical Society
|PB index (male)|1.617***|1.535***|1.729***|1.576***|1.465|0.777|
|---|---|---|---|---|---|---|
||(0.192)|(0.193)|(0.257)|(0.215)|(0.324)|(0.345)|
|IH index (male)|0.893|1.079|0.965|1.115|0.692|1.906|
||(0.104)|(0.122)|(0.145)|(0.137)|(0.150)|(0.937)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|# of households|825|573|621|468|199|103|

Note: *** denotes significance at 1%, ** at 5%, and * at 10% level. Standard errors are in parentheses. All columns report results from Logistic regressions. Control variables include age, years of education, marital status, number of children, dummy variables for Amman and Zarqa and if male counterpart works. The standardized indices include all items asked for each component after factor analysis, as specified in Annex 2, Table 2.8, and separated for men and women from the same household.

The overall finding for married women is that the expectations of their husband’s views, and the husbands’ actual views, seem to be correlated with decision making about women’s work, and especially by perceptions of acceptability of woman’s work and traditional gender roles. Husbands are also uncomfortable with women working with other men (the publicness and mixing index).
Figure 1: SCP Enrollment Growth in Colombia

# (a) Total SCP Enrollment

<!-- Start of picture text --> 1 600 000<br>1 400 000<br>1 200 000<br>1 000 000<br>800 000<br>600 000<br>400 000<br>200 000<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>National Enrollment Metropolitan Areas<br>(b) Enrollment in Metropolitan Areas<br>1 200 000<br>1 000 000<br>800 000<br>600 000<br>400 000<br>200 000<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Private HEIs Public HEIs SENA<br>Total SCP Enrollment<br>SCP Enrollment in Metropolitan Areas<br><!-- End of picture text --> Note: Panel (a) shows total number of students enrolled in SCP programs by year for Colombia (“national enrollment”) and metropolitan areas (“metropolitan areas”). Panel (b) focuses exclusively on SCP enrollment in metropolitan areas and shows the fraction of those students enrolled in private HEIs, public HEIs, and SENA by year.

8
7%|6%|4%|12%|21%|20%|14%|7%|7%|6%|15%|
|Portugal|42%|46%|44%|44%|46%|46%|43%|50%|56%|55%|53%|57%|56%|51%|
|Romania|43%|47%|49%|51%|54%|58%|58%|56%|63%|69%|71%|72%|81%|82%|
|Serbia|||||||59%|||||||102%|
|Slovak Republic|52%|60%|59%|65%|65%|59%|58%|54%|71%|68%|68%|66%|64%|61%|
|Slovenia|64%|65%|69%|69%|68%|66%|67%|70%|76%|78%|79%|78%|76%|74%|
|Spain|42%|44%|43%|44%|45%|45%|42%|50%|52%|52%|53%|53%|54%|49%|

Note: Blanks correspond to country-year pairs for which the Eurostat’s SBS Database does not report information. Montenegro and Ukraine are excluded from this table as the SBS Database does not include information for any of the years in our sample.
|Female teachers|0.381|(30.445)|133|29.834|136.656|
|---|---|---|---|---|---|
|Total teachers|10.831|(48.1)|133|58.837|215.94|

Notes: the values in column 3 “PODER Schools” are the outcome of running a regression of each of the variables in column 2 against a dummy variable for PODER schools and sub-systems fixed effects. Using data from Formato 911-EMS 2016-2017.

25