rafmacalaba/data-use-mentions-extended huggingface dataset:
child care policy in Quebec, Canada. They find an increase in child care use and maternal employment yet negative effects on child outcomes. Havnes and Mogstad (2011b) find that universal child care in Norway crowds out informal care modes but does not increase maternal employment. The impact on long-run children’s outcomes is heterogeneous, with positive effects at the bottom of the income distribution and negative at the top. Our model captures such effects by introducing rich heterogeneity in demand and supply while allowing some structural parameters to vary with mothers’ education.

The third strand of the demand-side literature studies the effect of maternal time as an input for child development. The earlier papers estimate hybrid equations, where parameters embody both technological properties of the production function and the effect of mediating variables. For example, Ruhm (2004) uses data on the children of individuals from the National Longitudinal Survey of Youth (NLSY), and finds that maternal employment during the first three years of life is negatively associated with children outcomes at ages three to five. Bernal and Keane (2011) evaluate the effect of child care versus maternal time using single mothers from the NLSY79. Exploiting exogenous variation in welfare policy rules, they find a negative effect of child care relative to maternal care on child development. Attanasio et al. (2020) estimate dynamic factor models of child cognitive and socio-emotional skills as a function of endogenous material and time investments, and find that time inputs are an important determinant of child outcomes.<sup>8</sup> Our model includes both mother’s and father’s time as inputs in child development production as well as time with non-parental providers, whose impact varies across providers.

The fourth strand of the demand-side literature employs a fully structural approach. Bernal (2008) estimates a dynamic model of employment, child care use, and production of child cognitive ability, which allows her to handle endogeneity in maternal employment and perform counterfactuals. She estimates the model on a sample of single mothers from the NLSY79,
An employment guarantee as risk insurance? Assessing the effects of the NREGS on agricultural production decisions Esther Gehrke<sup>_∗_</sup>

**Keywords:** Uncertainty; Employment Guarantee; Crop choice **JEL:** I38; O12; Q16

> _∗_ Esther Gehrke is post-doctoral researcher at the University of G ̈ottingen, e-mail: _esther.gehrke@unigoettingen.de_ . The author is grateful to the editor, Andrew Foster, as well as three anonymous referees for their valuable comments and suggestions. Moreover, she would like to thank Michael Grimm for continuous guidance and advice and Denis Cogneau, Clement Imbert, Markus Loewe, Abhiroop Mukhopadhyay, Dominique Van de Walle and the participants of the German Economic Association AEL Conference (Munich), the AEL PhD seminar (Z ̈urich), the ECINEQ Conference (Bari), the IZA/World Bank Conference on Employment and Development (Bonn), the PEGNet Conference (Copenhagen) and the Brown University Applied Microeconomics lunch for many helpful comments. This work was written while the author was affiliated with German Development Institute / Deutsches Institut f ̈ur Entwicklungspolitik (DIE) and the University of Passau, and supported by the German Federal Ministry for Economic Cooperation and Development [grant number 2011 95 809]. The data used in this publication comes from Young Lives, a 15-year study of the changing nature of childhood poverty in Ethiopia, India (Andhra Pradesh), Peru and Vietnam (www.younglives.org.uk). The views expressed here are those of the author. They are not necessarily those of Young Lives, the University of Oxford, DFID or other funders.
Policy Research Working Paper 1024

# **Abstract**

Starting in 2011, the Syrian civil war has resulted in the displacement of over 80% of the Syrian population. This paper analyzes how the widespread use of social media has recorded migration considerations for Syrian refugees using social media text and image data from three popular platforms (Twitter, Telegram, and Facebook). Leveraging survey data as a source of ground truth on the presence of IDPs and returnees, it uses topic modeling and image analysis to find that areas without return have a higher prevalence of violence-related discourse and images while areas with return feature content related to services and the economy. Building on these findings, the paper first uses mixed effects models to show that these results hold preand post- return as well as when migration is quantified as monthly population flows. Second, it leverages mediation analysis to find that discussion on social media mediates the relationship between violence and return in months where there are fewer violent events. Monitoring refugee return in war prone areas is a complex task and social media may provide researchers, aid groups, and policymakers with tools for assessing return in areas where survey or other data is unavailable or difficult to obtain.

This paper is a product of the Social Sustainability and Inclusion 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 ewalk@mit.edu.

_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 the authors.
.012*<br>_0.091_|-0.004<br>_0.617_|0.0382**<br>_0.027_|-0.019<br>_0.192_|
|S&P Returns*US Absolute Flows|||||||||
|US Absolute Flows|||||||||
|||||0.8549***|0.8549***|1.080***|0.7516***|1.201***|
|S&P returns||||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|R-squared|0.558|0.682|0.467|0.347|0.347|0.554|0.274|0.504|
|Observations|1539|651|802|1628|1628|682|858|273|
|Time dummies|Yes|Yes|Yes|No|No|No|No|No|
|Countries|74|31|39|74|74|31|39|21|

This table presents stress tests to estimations in Didier et al., 2010. First three columns presents data from Didier et al., and their methodology. Returns are normal local returns, ar outliers out, and US Holdings are normalized by substracting its sample average and dividing it by its sample standard deviation. Additionally their crisis period are defined as 6/2 opposed to the 03/2008-03/2009 crisis period used in this paper. In turn, columns 4-10 are presented for the same data but with our methodology. Returns are dlog(stock_index) a any local stock market returns. *, **, *** Denote significance at the 1%, 5%, and 10% levels, respectively. In italics appears the correspondent the p-value. Source
Appendix Table A1 presents the basic summary statistics of the U.S.-based _FL_ measure, breaking up the manufacturing and the non-manufacturing sectors.

While the U.S.-based alternative _FL_ measure has the advantage of extending the set of sectors to agriculture and mining, it has two important drawbacks. First, the data are compiled based on individual-level surveys rather than firm- or plant-level data, and thus relies on workers selfreporting their industry of occupation. Thus, if the number of individuals in the survey who report working in a particular sector is small, or if workers make mistakes in reporting their industry of employment, the data will be measured with error. And second, the U.S. is only one, very special country, and thus its values of of FL may not be representative of the average country’s experience.<sup>9</sup> Both of these considerations will raise the amount of measurement error on the right-hand side, leading to attenuation bias in the coefficients.

With these caveats in mind, Appendix Tables A3 through A5 replicate all of the regression estimates in the paper using the U.S.-based _FL_ indicator instead. We can see that by and large, the results using our main measure of gender gap – fertility – are robust. However, the other two outcome variables, that are marginally significant in our main results, are less so here.

> 8For these two outcome variables, adding country effects but no time effects produces marginally significant coefficients with the right sign, implying that the time effects are responsible for changing the sign of the coefficient.

> 9For our UNIDO-based measure, averaging of the share of female workers across a couple of dozen countries helps alleviate both of these problems.

21
17 are listed in Appendix Table A1. The median change among points with coding corrections was 20 percent.<sup>18</sup>

# 3.3. <u>New data points</u>

The 2019 version of the HEFPI database includes 535 newly added data points. Most new points come from surveys which were not included in the 2018 HEFPI database, but there are also a number of new points from previously included surveys. For instance, we added mammography data points from the previously included Jordan 2002 and 2007 DHS, and the child stunting and underweight data points from the previously included Myanmar 2000 and Albania 2005 MICS.

Most newly added surveys come from the MICS family, and there mainly from the addition of all currently available fifth and sixth round surveys. We also added a number MICS surveys from earlier rounds that were missing from the 2018 HEFPI version, namely the round two surveys from Bosnia and Herzegovina (2000), Equatorial Guinea (2000), Indonesia (2000), Iraq (2000), Madagascar (2000), South Sudan (2000), Trinidad and Tobago (2000), Uzbekistan (2000) and Zambia (1999); the round three survey from Jamaica (2005), and the round four surveys from Bangladesh (2012), Saint Lucia (2012), South Sudan (2010), and Trinidad and Tobago (2011). With these additions, we have included all MICS surveys from the second to the fifth round which are nationally representative and where the micro-data are publicly available.<sup>19</sup> We also added points from newly available surveys of the seventh round of the DHS, namely Albania (2017), Benin (2017), Burundi (2016), Haiti (2016), Jordan (2017), Malawi (2016), Maldives

> 18 Appendix Table A1 and our calculation of the median change exclude the pap smear indicator, as all values of the indicator changed between the 2018 and 2019 database versions
# **Table 10: Conditional estimates of trade and infrastructure constraints to trade in SADC** **<u>countries</u>**

||World Bank<br>(Trading on<br>to worst outc<br>|Doing Busin<br>Time data) (<br>ome,7 to best<br> <br>WB Trading<br>|ess indicators<br>1 corresponds<br>outcome)<br> <br> <br>WB Trading<br>|<br> <br>World Ec<br>correspond<br> <br> <br>|onomic Fo<br>s to worst o|rum Enab<br>utcome,7 to<br>|ling Trade<br>best outcome<br>Transport<br>communicat|Indices (1<br> )<br>&<br>i<br>|
|---|---|---|---|---|---|---|---|---|
||WB Trading|<br>on<br>Time|<br>on<br>Time|<br>WEF||Border|ons|Business|
||on<br>Time<br>index|<br>index,<br>imports|index,<br>exports|Enabling<br>trade index|<br>Market<br>access|administr<br>ation|infrastructur<br>e|environm<br>ent|
|ln(GDP/Capita)|-0.073|-0.004|-0.143|-1.336**|0.631|-1.607**|-1.706**|-2.651***|
|ln(GDP/Capita
Restuccia, D., and R. Rogerson. 2008. “Policy Distortions and Aggregate Productivity with Heterogeneous Establishments.” _Review of Economic Dynamics_ 11: 707–20.

Ross, Michael L. 2015. “What Have We Learned About the Resource Curse?” _Annual Review of Political Science_ 18. Annual Reviews: 239–59.

Ross, M.L. 2012. _The Oil Curse: How Petroleum Wealth Shapes the Development of Nations._ Princeton University Press.

Ross, M. L. 2017. “What Do We Know About Economic Diversification in Oil Producing Countries?” _Social Science Research Network_ .

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3048585.

Sachs, J.D., and A.M. Warner. 1997. “Sources of Slow Growth in African Economies.” _Journal of African Economies_ 3 (6): 335–76.

Stiglitz, Joseph. 1999. “On Liberty, Their Right to Know, and Public Discourse: The Role of Transparency in Public Life.” Oxford Amnesty Lecture.

Toews, Gerhard, and Alexander Naumov. 2015. “The Relationship Between Oil Price and Costs in the Oil Industry.” _The Energy Journal_ 36 (Adelman Special Issue). International Association for Energy Economics.

United Nations University World Institute for Development. 2017. “World income inequality database.” https://www.wider.unu.edu/project/wiid-world-income-inequalitydatabase.

Van der Ploeg, Frederick. 2011. “Natural Resources: Curse or Blessing?” _Journal of Economic Literature_ 49 (2): 366–420.

Van Ruijven, B.J., M.A. Levy, A. Agrawal, F. Biermann, J. Birkmann, T.R. Carter, K.L. Ebi, M. Garschagen, B. Jones, and R. Jones. 2014. “Enhancing the Relevance of Shared Socioeconomic Pathways for Climate Change Impacts, Adaptation and Vulnerability Research.”
from breaking relational contracts. This follows from the very nature of relation-based governance. Contractor’s freedom of action is not restricted by any legal requirement, but by concern for subsequent loss of business, reputation, or trust. For future benefits to be large enough to induce cooperative behavior, the discounted value of expected future profits must be larger than the gains that could be made from reneging on the deal. Two conditions in the SSA business environment negatively influence the value and predictability of future gains from such relationships and make it harder to establish and sustain cooperation.<sup>6</sup> First, all business relationships involve risks. Generally such risks are known and can be planned for or ways can be found to hedge them. But the shock-prone SSA environment adds an extra element of uncertainty to the equation. Uncertainty makes it more difficult to predict a business partner’s gains and undermines the effectiveness of repeated interaction incentives. When conditions are stable, contracts and business relationships have a predictable value. Firms offering credit, for example, know the loan value – it is predictable to the supplier and to the customer – and the amount of credit offered can be set to benefit the supplier when it is repaid. The value of continuing the relationship is thus predictable. Unforeseen shocks change all of this. The value of not making the required payment fluctuates because of the shocks. If the gains from breaking the promise to pay are large enough, then the customer will default. Relational contracts are thus much harder to sustain in shock-prone environments, because it is harder to predict the behavior of the business partner and to value the relationship. In addition, the costs of establishing relational contracts (i.e. costs of building trust) are much higher in shock-prone environments. Shocks induce unforeseen turnover and

> 6 One of the consequences of the difficult environment for sustaining cooperation in the SSA region is a very low level of subcontracting.  A comparison of data from firm-level surveys conducted under the World Bank’s
of the total tax base. Second, the standard and average reduced VAT rates tend to be highly and positively correlated over time. In 80 percent of the countries, this correlation is larger than 0.5 and statistically signi...cant at the one percent level. Third, the share of transactions covered by di¤erent statutory tax rates does not vary much over time in any given country. As a result, the standard VAT rate explains about 85 percent of the observed variability of the e¤ective VAT rate (computed as the average of the di¤erent VAT rates weighted by their share in transactions as a percentage of the taxable base).<sup>11</sup>

# 3.2 Sources

The VAT rates were obtained from various primary sources, including countries’ revenue agencies, national libraries, books, newspapers, tax law experts, and research and policy papers. In most cases,

> 9 Vegh and Vuletin (2015) build a novel annual dataset of tax rates for 62 countries for the period 1960-2013 that comprises corporate income, highest personal income, and standard VAT rates. Riera-Crichton, Vegh, and Vuletin (2016) build a new quarterly standard VAT rate series for 14 industrial countries for the period 1980-2009.

> 10 Indirect (value-added) taxes represent, on average, 43 (28) percent of total tax revenues in industrial countries and 47 (32) percent in the developing world.

> 11 While the use of standard VAT rates is a good proxy for overall VAT tax policy, our narrative analysis will take into account the fact that, in some cases, changes in the standard VAT rate are intended to compensate for changes in the VAT base and/or VAT reduced rates.

9
6 the respective roles of natural resources, industrialization, and de-industrialization in urbanization. Sections 4, 5, and 6 study the role of these factors for urban employment, construction, and urban primacy, respectively. Section 7 focuses on urban human capital.

# **1. The Global Sectoral Composition of Cities**

It is not obvious how cities of the same population size and located in countries with similar levels of economic development differ globally in their employment composition. GJV16 find examples of countries where urban areas have high shares of _urban tradables_ , which include manufacturing (MFG) and FIRE (finance, insurance, real estate, and business services). They also find examples of countries where urban areas have high shares of _urban non-tradables_ , which include the (non-tradable) wholesale and retail sector.

Since the work of GVJ16, which is cross-sectional and only focuses on the urban sector as a whole, many countries have added censuses to the IPUMS repository (Minnesota Population Center, 2020) and GIS files corresponding to the second level administrative units in which a household is located.<sup>6</sup> In addition, the Global Human Settlements Layer (GHSL) database provides geocoded polygons of urban extent boundaries for the world c. 2015 (Schiavina et al., 2019). More precisely, this database uses satellite data on built-up area to identify “Functional Urban Areas” (FUAs), i.e. commuting zones of at least 50,000 residents today. Combining the two data sets allows us to obtain the sectoral composition of most world cities. Using a simple methodology presented below, we then classify each city as being a _production city_ – a city with a disproportionately high share of employment in tradables; a _consumption city_ – a city with a disproportionately low share of employment in tradables; or a _neutral city_ – a city in which the shares of employment in tradables is neither disproportionately low nor disproportionately high.<sup>7</sup>

**Data.**
Policy Research Working Paper 9196

# **Abstract**

This paper assesses the impact of Internet job search on employment in the Arab Republic of Egypt, the most populous country in the Middle East and North Africa region. Using panel data from the 2012 and 2018 rounds of the Egypt Labor Market Panel Survey, the paper examines the impact of Internet job search by the unemployed on their employment prospects. It also examines the impact of Internet job search by employed job seekers on their wage growth, as well as the impact of digitalization at the workplace on earnings. Accounting for individual and geographical unobserved heterogeneity using panel data, the results suggest that Internet job search is an effective job search method, as it increases the probability that the unemployed—and in particular unemployed men—will find employment. Auxiliary placebo regressions confirm that preexisting trends in labor market outcomes are not driving the results. However, Internet job search by employed job seekers does not appear to have an impact on their wage growth, nor does digitalization at the workplace affect the wage growth of employed individuals.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 author may be contacted at nelly.elmallakh@psemail.eu.

_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 the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/
on malapportionment in Latin America; Jasmin Chakeri, Christian Gonzalez, Delia Lanz, Julio Loayza, Lars Moller, Rossana Polastri, Rashmi Shankar, Emily Sinnott, Rajeev Swami, and Manuel Vargas for their help with obtaining sub-national data on transfers from the central government for various countries; and Claudio Ferraz for providing us with electoral data for Brazil during dictatorships. We are also grateful to the Millennium Nuclei Research in Social Sciences, Planning Ministry (MIDEPLAN), Republic of Chile and Fondecyt (Grant # 11070181), for financial support. A previous version of the paper circulated with the title “Legislative Malapportionment and Economic Outcomes: A First Assessment”. The usual disclaimer applies.
changes happen at the industry level over time, they should be adequately captured by the inclusion of industry-year fixed effects in our baseline regressions. In our robustness exercises, therefore, we control for a set of industry-year interaction dummies, and obtain very similar trends as in our main specifications, regardless of whether sectors are identified at the two- or three-digit level.<sup>21</sup> A potential confounder to the interpretation of our analysis could be related to selection bias as firms enter and exit the market in response to unrelated changes in the business environment. For example, distortions may result spuriously high both in states that are growing rapidly and have many entrants who have not yet reached productive efficiency, _and_ in less well-off states that have a high share of struggling and exiting firms – but this would obviously have very different policy implications in the two cases. To deal with this concern, we first exploit information in the ASI data on the reasons why an establishment in the sample frame could not be included in the survey, in order to get some information on firm closures.<sup>22</sup> Annex Table A5.1 shows that in our sample period there was indeed quite a high turnover of firms, but that the difference between the firm exit rates of high- and low-growth states does not seem to be substantial, nor to follow a defined pattern over time. In Annex Table A5.2, we also document firm entry,<sup>23</sup> which turns out to be equally limited in our sample, as well as evenly distributed across states with different rates of growth. As a further robustness check, we also replicate our analysis excluding new entrants from the sample and find very similar results to our baseline specification.

Finally, one could be concerned that land as an input is quite different in scope from capital or labor, as firms do not often make year to year changes in their land holdings, instead undertaking lumpier land adjustments over longer periods of
Demirgüç-Kunt and Klapper (2012) for more information on the Global Findex Database 2011.

> 23 The Findex database is available at <u>https://www.worldbank.org/en/publication/globalfindex. We currently use the global</u> dataset but in future work the country level data could be used to generate quintile estimates and overlay in the same was as social protection.

21