rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9143

# **Abstract**

The fallout of nitrogen pollution is considered one of the largest global externalities facing the world, impacting air, water, soil, and human health. This paper combines data from the Demographic and Health Survey data set across India, Vietnam, and 33 African countries to analyze the causal links between pollution exposure experienced during the very earliest stages of life and later-life health. The results show that pollution exposure experienced in the critical years of development—from birth until age three—is associated with decreased height as an adult, a well-known indicator of overall health and productivity, and is robust to several statistical checks. Because adult height is related to education, labor productivity, and income, this also implies a loss of earning potential. The analysis begins within an assessment in India, where the data are more available, and is then extended to geographic settings including Vietnam and 33 countries in Africa. The results are consistent and show that early-life exposure to nitrogen pollution in water can lower height-for-age scores during childhood in Vietnam and during infancy in Africa. These findings add to the evidence on the enduring consequences of water pollution and identify a critical area for policy intervention.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at ezaveri@worldbank.org, jruss@worldbank.org, rdamania@worldbank.org, sebdburo@gmail.com, and arodella@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 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
**Table 5:** The effects of AIA on the adoption of big data and AI

|Regressions|(1)|(2)|(3)|
|---|---|---|---|
|Variables|Big Data|Big data|AI|
||2007/15|2007/19|2007/19|
|AIA Dummy|-0.185***|-0.182***|-0.027***|
||(0.030)|(0.034)|(0.011)|
|Observations|2,262|1,746|1,746|

Note: Regression 1 uses data for 2007 and 2015; regression 2 and 3 use data for 2007 and 2019. The dependent variables big data and AI adoption are dummy variables. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. All regressions use a binary AIA eligibility and are estimated via difference-in-differences. All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age, not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.

analytics use of 7 percentage points and AI use by 1 percentage point. While seemingly small numbers, these should be considered in the context that only 3% of UK firms used AI by 2019<sup>38</sup> . Moreover, big data analytics use would have been 14% higher and AI 30% higher in 2019 in the absence of the AIA policy. Or put another way, the policy slowed down big data analytics by 1.4 years and AI diffusion by
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 15. Expansion of water and sanitation technologies at the national, urban, rural levels**

Population gaining access per year between 2000–2008 <!-- Start of picture text --> b. Sanitation<br><!-- End of picture text --> <!-- Start of picture text --> a. Water b. Sanitation<br>5<br>5<br>4 4.0 4 4.0<br>3.3<br>3.3<br>3 3<br>2.8<br>2 2<br>1 1<br>0 0<br>National Urban Rural Nationa l Urban Rural<br>-1 -1<br>-2 -2<br>Piped Water Standposts Septic tanks Improved Latrines<br>Wells/Boreholes Surface Water Traditional Latrines Open Defecation<br>Annualized Growth Annualized Growth<br>Source: Access figures calculated by AICD using data from the 2000 Multiple Indicators Cluster Survey as published by the JMP in March 2010<br>and the 2008 Demographic and Health Survey.<br>Rapid population growth (particularly in urban areas) has been an important contributor to declining<br>access (figure 16). Between 2000 and 2008 Sierra Leone‘s population grew at a pace of 3.4 percent per<br>year, one of the highest among Sub-Saharan African countries. In urban areas, where the average<br>population growth has been 4 percent per year, experienced important inflows of refugees who fled<br>during the civil conflict in the 1990s.<br>Figure 16. Urban versus rural access to water supply and sanitation, 2008<br>a. Water supply b. Sanitation<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br
3 and 4 is determined by the distance from camps (the refugee crisis).</mark> <mark>Fourth, we made use of the 1990/1991 pre-refugee arrival DHS survey to study whether parents/children living further away from refugee camps were already better or worse off prior to refugees’ arrival in late 1993. Again, we find no statistically significant differences between health anthropometrics of under five years old children from communities of refugees hosting areas and non-refugees hosting areas (Test 4, Table 5).</mark> <mark>Finally, the extent to which other events in Tanzania such as natural disasters (namely the 1998</mark> _<mark>el nino</mark>_ <mark>flooding and the 1999 drought) might have undermined socio-economic conditions of mothers during refugee influx and subsequently their children’s health outcomes in 2015/2016 is dismissed. The</mark> _<mark>el nino</mark>_ <mark>flooding of 1998 followed by the severe drought in 1999 mainly hit the central and northeastern part of the country (Arusha, Manyara, Shinyanga, Simiyu, and Dodoma regions), all areas that did not experience the massive inflow of refugees in the country.</mark>

> 19 Recall that for households declaring to have moved to the current location after 2001 we cannot identify their location during the refugee crisis. We can consider this group as a random sample in terms of distance from refugee areas. 20 Biharamulo, Muleba, Bukoka Rural and Bukoka Urban.

19
Logistics performance of the source-host pair is also found to be matter: a one-percent increase in the logistics performance index leads to 0.1 to 0.2 percent increase in FDI flows.

Most other country characteristics including market size, contiguity, common language, and colonial ties are also found to exert expected effects. Countries with a larger market size not only attract more FDI but also invest more abroad. Country pairs with contiguity, common languages, or colonial ties are also shown to have greater investment flows. However, the PTA depth is found in Table 9 to have only a weakly positive effect on FDI.

In Table 10, we repeat the analysis using a Poisson Pseudo Maximum Likelihood (PPML) Estimator following Santos Siliva and Tenreyro (2006) to address the zero FDI values in the data sample. The results are qualitatively similar. Both air transportation and land transportation costs, proxied by either distance or driving time, have a significant and negative effect on FDI flows. The effect of sea distance is largely insignificant. The PTA depth in this table is found to be positively and significantly associated with FDI flows, suggesting that deeper trade agreements that cover more provisions have a stronger effect on FDI flows.

We also separately examine FDI flows from OECD source countries using a longer time series of OECD FDI statistics. The results on transportation costs shown in Table 11 are qualitatively similar to Table 9. The elasticities of FDI with respect to air distance, driving distance, and driving time are found to be all around -1 for OECD source countries. The elasticity of FDI with respect to sea distance is also found negative and significant, albeit much smaller in magnitude and around -0.1. OECD countries’ FDI outflows are also responsive to host countries’ airway and port qualities, but insignificantly related to railway. The PTA depth exerts a significant and positive effect on OECD countries’ FDI flows.

<mark>Next, we explore how the effects of distances have evolved over time given the technological improvements in transportation
the sample as “looking more like formal businesses before the program”.<sup>13</sup> Formalization rates were 3-10 percentage points higher among informal businesses that were similar to formal businesses before program implementation. Finally, businesses that received more than one visit from a

> 12 Results not shown but available upon request.

> 13 Looking like a formal business owner is based on the predicted probability of being formal from a logit of formality status on baseline characteristics. This logit uses the data collected during the listing/baseline survey on 7,829 businesses who accepted the survey. Among them, 608 (7.8%) were formal at the time of the survey. We used the following baseline characteristics in the logit: operating in Dantokpa market, gender, age, only primary education, only JHS or SHS level, higher level of education, operating in services, craftsman, business created less than 1 year ago, firm connected to electricity network, total number of employees, firm is doing some accounting, have done any advertising in the last 6 months, log of total amount of sales in an average week, log amount of last month profit, firm owner owns a bank account, the firm pays taxes, have done any advertising in the last 6 months (and controls for missing levels of these variables). Using the “predict” command in STATA, we end up classifying as “looking more like formal” 654 (18.2%) businesses out of the 3,596 in the study sample. This classification was done before we got access to any follow-up data and was mentioned in the pre-analysis plan on the AEA social science registry website.

20
does not have migration or geographic identifiers. The SILC contains geographic identifying variables, (at the NUTS1 level) but still lacks migration variables.

The data set this study uses is the Labor Force Survey (LFS) since there is an adequate availability of both migration and geographic variables. The LFS is representative at the NUTS2 level which corresponds to 26 regions in Turkey. One caveat is that income in the LFS refers to only wage income from employment,<sup>9</sup> and is an insufficient measure of income that should be used for welfare measurement. For example, important sources of income such as social assistance, asset liquidation, or remittances are missing. Therefore, income in the LFS is imputed with a few assumptions using information from the SILC. The NUTS1 spatial effects of the SILC are a good proxy for NUTS2 welfare dynamics in the LFS which increases the accuracy of the imputation model. However, since the original sample frame of the LFS does not account for the recent influx of foreign migrants in Turkey, the labor market characteristics of recent migrants might not be representative of the actual SUTP population. Therefore, results of the imputation could be interpreted as upper bound estimates for recent migrants. More details of survey techniques used to complete this exercise are available in the Annex. As a result, imputed poverty is measured as income poverty. Another advantage of using the LFS is the availability of CPI at the NUTS2 level in Turkey which allows for spatial deflation of different price levels across the country.

Table 1. Survey Comparison and Data Availability

||Years<br>Available|Migration<br>Variables|Income or Consumption|Geographic Identifier|Spatial<br>Deflation|
|---|---|---|---|---|---|
|**HICES**|2003-2012|No|Consumption, income|National, urban/rural|No|
|**SILC**|2009-2012|No|Income
). The development push of refugees: Evidence from Tanzania. _Journal of Economic Geography, 19_ (2), 299-334.

- FAO. (2020). _Impact of COVID-19 on informal workers._ Food and Agriculture Organization of the United Nations.

- KNBS. (2016). _Basic Report: 2015/16 Kenya Integrated Household Budget Survey (KIHBS)._ Nairobi: Kenya National Bureau of Statistics.

- Hounsell, B., & Owuor, J. (2018). _Innovating mobile solutions for refugees in East Africa. Opportunities and barrier to using mobile technology and the internet in Kakuma refugee camp and Nakivale refugee settlement._ Nairobi: Samuel Hall Consulting.

- Kumar, N., & Quisumbing, A. R. (2014). Gender, shocks, and resilience. _2020 conference briefs 11._ International Food Policy Research Institute (IFPRI).

22
characteristics.<sup>12</sup> Many studies discussed in section 2 indicate that the burden of inflation is not equally distributed across households from different parts of the income distribution and that it differs based on household characteristics. This literature motivates our empirical specification: where _Li_ is the inflation rate experienced by household _i_ , **_X_** _i_ is a vector of household characteristics (including household size, household size squared, the share of children and elderly in the household, whether female heads the household, and whether a household member is unemployed); **_D_** _i_ is a set of dummy variables indicating the decile of consumption expenditure distribution a household belongs to; **_R_** _i_ are regional variables, including urban/rural and, for some countries, regional dummies; **_β_** , **_π_** , and **_γ_** are the estimated coefficients, and _ε_ i is an error term. For this analysis, we rely on microdata on expenditure consumption in 19 EMDE ECA countries.

<u>Figure 7</u> displays the magnitudes and significance levels of the regression coefficients estimated separately for each country. The estimations of (4) reveal that household size is a statistically significant determinant of inflation for most countries in our sample. In 16 of 19 countries, larger households appear to face lower inflation than smaller households, holding total consumption expenditure constant. In most countries, the magnitude of this effect appears to be non-linear and to decrease with household size, indicating that the more substantial effect may be evident when comparing single-person households with two- or three-person households. This effect should not be interpreted as evidence that larger households pay lower prices for the same good than smaller households do; in our analysis, prices for a given good or service are the same for all households. Rather, this effect indicates that larger households allocate proportionally more spending to goods and services for which inflation is lower than do smaller households.

The location
**Table 6: Proportion of labor productivity FEVD explained at horizon t in each**

|Identifcation|**Max-Share**|**Long-Run**|**Spectral**|**Limited Spectral**|**NAMS**|
|---|---|---|---|---|---|
|**Levels: t=1**|0.45|NA|0.31|0.44|0.29|
|**Levels: t=20**|0.89|NA|0.81|0.89|0.69|
|**Levels: t=40**|0.93|NA|0.84|0.92|0.83|
|**Diferences: t=1**|0.99|0.16|0.36|0.35|0.13|
|**Diferences: t=20**|0.40|0.56|0.92|0.92|0.11|
|**Diferences: t=40**|0.32|0.64|0.94|0.94|0.13|

The growth rate of US productivity exhibits a wide range of frequencies driving its spectral density, unlike the level, suggesting an estimation of the VAR with productivity in log-differences will also be informative.

# **_Spectral identifications best capture technology shocks with persistent effects on productivity growth_**

When estimating the VARs in differences (Figure 12), the Spectral approaches show a more persistent IRF than when estimated on the level of productivity, and a similar share of forecast error variance explained of productivity. This demonstrates similar circumstances to the model-based scenario which simulated data in the case of technology taking the form of a unit-root plus stochastic trend growth specification (Section (3.4)
the demand for inputs; by raising the demand for trade related services (e.g., transport); and through a multiplier effect if workers employed by exporting firms increase consumption in the area.

The data on the number of workers engaged in exports are not available directly at each province level. I construct a province-specific export index ( _Exportpt_ ), using the exports data at the national level as well as the employment and output data at the firm level. The Export Index is the sum of the workers over each industry _j_ aggregated at the province level _p_ , multiplied by the export intensity of each industry, relative to the total economically active population in each province _p_ at time _t_ . Specifically, where _XSjt_ is the export intensity computed as the proportion of exports in gross output for industry _j_ at time _t_ at the national level;<sup>31</sup> _Employmentjpt_ is the number of workers employed by industry _j_ in province _p_ at time _t_ ; and _Employmentpt_ is the size of economically active population in province _p_ at time _t_ ; _j_ represents Vietnam’s industries at the two-digit VSIC level (VSIC 1041), _p_ represents Vietnam’s 61 provinces,<sup>32</sup> and _t_ = 2002, 2004 in my regressions. Thus, the impact of exports on labor demand is likely to be larger, the higher is the export-intensity in the composition of industries, and the larger is the share of workers employed by industries relative to total employment in province _p_ .

> 31 Since the export intensity of each industry is not available at the province level, that at the national level is used. For some industries for some years, the export values from the Comtrade data exceed the production values from the _Enterprise Survey_ data. As a potential source of this incompatibility, Jenkins (2004) points out that the gross output reported in the _Enterprise Survey_ data may refer not to the value of the products produced, but to the processing cost. This is particularly the case
Policy Research Working Paper 8019

# **Abstract**

Two long-established stylized facts in the urban and development economics literatures are that: (a) a country’s level of economic development is strongly positively correlated with its level of urbanization; and (b) a country’s level of urbanization is strongly negatively correlated with the size of its agricultural sector. However, countries in the Latin America and Caribbean region appear to depart significantly from the rest of the world in these two basic relationships. Although Latin American countries appear to be significantly more urbanized than predicted based on these global relationships, Caribbean countries appear significantly less urbanized. However, analyses involving cross-country comparisons of urbanization levels are undermined by systematic measurement errors arising from differences in how countries define their urban areas. This paper reexamines whether Latin America and Caribbean countries differ from the rest of the world in the basic stylized facts of urbanization, development, and structural transformation. The analysis makes use of two alternative methodologies for the consistent definition of urban areas across countries: the Agglomeration Index methodology and a methodology based on the identification of dense spatially contiguous clusters of population. Both methodologies rely on globally gridded population data sets as input. There exist several such data sets, and so the paper also assesses the robustness of the findings to the choice of input population layer.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region and the Social, Urban, Rural and Resilience Global Practice 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 mroberts1@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 objective of the series is to get the findings out quickly, even if
In order to measure the impact of the intervention, we focus on two sets of outcomes: socioemotional skills, and academic outcomes including math test scores, probability of enrollment in 11<sup>th</sup> grade, and “student engagement” as defined below. Through the CBT-based sessions, PODER aimed at influencing a large array of socioemotional skills, including: a) intrapersonal skills (such as assertiveness, perseverance, emotional regulation), b) stress management (self-control), and c) problem solving.

In June 2017, among those students who had enrolled in the second semester, we administered a multiple-choice questionnaire that students had to complete autonomously under the supervision of two enumerators, and a math standardized test and. The questionnaire included two instruments to elicit socioemotional skills: the PLANEA instrument designed by INEE in collaboration with SEP and the BarOn Emotional Quotient Inventory: Youth Version. Questions on socioemotional outcomes are asked as part of the PLANEA standardized test applied at the end of upper secondary. The instrument has the advantage that is possible to isolate some of the skills targeted by the program: perseverance, feelings’ management and decision making. The BarOn aims at capturing five broad domains: (intrapersonal and interpersonal skills, stress management, adaptability, overall feeling) and 15 specific skills in total. We focus on intrapersonal skills, management, and adaptability, since PODER aimed at influencing a subset of skills within these three broad categories. The math standardized test was developed by taking items from previous editions of the national assessment “ENLACE” for 9<sup>th</sup> grade.

Nationwide, about 25 percent of the students who enrolled in 10<sup>th</sup> grade in the academic year 2015—the year prior to the implementation of PODER—did not enroll in 11<sup>th</sup> grade in the following academic year and 28 percent re-sat the COMIPEMS examination. We measure whether PODER affected the probability of enrolling in the third semester of upper
-|-|-|
|Bio informatics<br>and others|2|2||||
|**Total**|**86**|**67**|**11**|**3**|**5**|

Source: DBT SBIRI database.

Table 3 describes the main source of technology for SBIRI funding recipients. Overall 78% of the funded projects relied on their own technologies for further advancement, validation and commercialization. Although in-house technology is by far the largest source of technology supported by SBIR to-date, this figure appears to be an overstatement, as evidenced by one of our detailed case studies (see Box 4), where the recipient company, SPAN Diagnostics, acquired and leveraged foreign intellectual assets through its prior purchase of a French company. Other companies may well have reported in-house technologies as the main source based on prior further development of existing technologies acquired from outside the firm, as well as access to technologies and research tools that are no longer protected due to expiration of the underlying patents. Of the 22% of projects reporting absorption of external technologies, the area of agriculture has experienced a far greater transfer of technology for further advancement (12 or 43% of agriculture projects) than in healthcare (18%) or industrial processes (15%). This could have been due to a relatively lesser engagement in translational research and technology transfer of the public research bodies in the area of bio-medical sciences. The agriculture universities and international agriculture research institutions as well as private collaborations appear to have supported technology transfer to private enterprises relatively more, by way of transfer of

> 19 A classification of all approved projects as either product development-oriented (D) or process-oriented (P) based on the hypotheses stated in the project titles is available from the authors upon request.

24