rafmacalaba/data-use-mentions-extended huggingface dataset:
_<sup>(</sup><sup>_k_)</sup> ( _r_ )<sup>)2</sup> from ( ̃ _στ,_ ( _r_ ))<sup>2</sup> , _A_<sup>(</sup> _∗_<sup>_k_</sup> _,_ (<sup>)</sup> _r_ )<sup>,</sup><sup>_B_</sup> _∗_<sup>(</sup><sup>_k_</sup> _,_ (<sup>)</sup> _r_ )<sup>fromEq(3).</sup>

For each census record, we draw the standardized cluster, household and individual effects. Each of these disturbance components is drawn jointly for multiple indicators to capture the intra-personal effect. Letting the standardized components of disturbance terms drawn in the _r_ -th simulation be _η_ ̃ _c,_<sup>(</sup><sup>_k_</sup> (<sup>)</sup> _r_ )<sup>,</sup><sup>_ε_ ̃(</sup> _ch,_<sup>_k_)</sup> ( _r_ )<sup>,and</sup><sup>_δ_ ̃</sup> _chi_<sup>(</sup><sup>_k_)respectively,the</sup><sup>_k_-thimputedanthropometric</sup> indicator for the individual in the census in _r_ -th simulation is<sup>7</sup> :

> 6Another possible implementation is to draw _α_  ̃ and _β_  ̃ from the bootstrapping sample.

7 To eliminate extreme values, we drop census observations for which the point estimate **x**<sup>(</sup> _chi_<sup>_k_)</sup><sup>
based on information present in the OECD's Local Governments in the CEE and CIS: an anthology of descriptive Dapers as well as that present in the IMF Report No. SMW95/194 of August 11, 1995, and relates to 1992. Data on employment in state-owned enterprises are taken from the Statistical Handbook 1995: States of the Former USSR and refer to 1994. Data on military exclude 1,200 Ministry of Defense staff as well as personnel of paramilitary units, i.e., Border Guards (8,000). GDP and wages and salaries estimates are from WB's Statistical Handbook 1995: States of the Former USSR, and relate to 1992. Average Government wage is also for the same year, from the same source. Data on wages in manufacturing (monthly basis) are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1992.

Estonia Population, labor force and total employment estimates are from Vera Wilhelm (EC4BS) after consultation with the statistical office and relate to 1995. Unemployment rate is estimated on the basis of the above data and relates to 1995. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Central Government, Non Central Government, Education and Health employment is data is from Vera Wilhelm (EC4BS) and relates to 1995. Non Central Government includes municipalities, lower municipalities. Data on employment in state-owned enterprises are taken from the Statistical Handbook 1995: States of the former USSR and refer to 1991.

Data on military employment includes conscripts (2,650), but excludes personnel in paramilitary units, e.g., Border Guards, who are under the authority of the Ministry of Interior.

GDP and wages and salaries estimates are from WB's Statistical Handbook 1995: States of the Former USSR, and relate to 1993. Average Government wages are taken from IMF Report SM/94/81 of March 29, 1994 and relate to 1993. Data
Bosnia and Herzegovina in the late 1990s, Equatorial Guinea in the late 1990s, Iraq in 1990 and 2004, and Libya in 2012. Source: based on World Bank national accounts data.

Figure A.3. The effect of growth during the Golden Decade offsets the divergence trend between 1960-2003 Source: based on Penn World Table version 10.0 data and Patel et al (2021) methodology. Note: The horizontal axis shows the natural log of real per capita GDP, and the vertical axis shows average annual real growth over the period listed. Shaded area represents a 95% confidence interval around the regression line. The sample excludes oil exporters and countries with populations under 1 million.

Figure A.3 compares beta convergence coefficients for growth periods that include the Old Normal, the Golden Decade and the New Normal. A negative beta convergence coefficient indicates that growth rates in poor countries are faster than in rich countries. A positive beta convergence coefficient indicates the opposite. Before the Golden Decade, for every 1 percent increase in world economies’ income, we would have expected an increase of 0.34 percent of annual growth, statistically significant at 95 percent level. This was a signal of a diverging world economy, until the Golden Decade halted this trend of a positive long-term historical beta convergence coefficient since 1960, reducing it to about one third. However, this impetus was short-lived lasting only a few years, particularly between the 2008 financial crisis and the 2014 oil price crash. These five years are the only time since 1960 where we were able to see real statistically significant convergence between the rich and the poor. During this time the beta convergence coefficient reached a value of -0.6, which contrasts with the value of +0.1 during the years that preceded and followed the Golden Decade. Figure A.4 divides the post-Old 36
that this work is an extension of Robertson, Vergara, Kokas and Acevedo (2021) who use the Bartik (1991) estimates to analyze if higher exports generated better local labor market outcomes in Egypt between 2009 and 2017. Their results suggest that that trade does impact labor market outcomes in the short run, but the effects quickly dissipate throughout the country and remain statistically insignificant for most types of workers. In this paper, we use the same methodological framework but now account for indirect effects through supply chain linkages.

# **3. Database and Measurement**

The aim of the paper is to assess the total impact of export expansion on local labor market outcomes in Egypt accounting for supply chain linkages, exploiting variation in export expansion across kizms and markaz, between 2007 and 2018. To this effect, we combine export data from UNCOMTRADE data, input-output coefficient matrix, and information on local labor market outcomes using the databases and techniques described below.

Labor Force Data and Descriptive Statistics Our main source of labor market data is the labor force survey (LFS) provided by Economic Research Forum, and available for years between 2007 and 2018, and implemented every 1-2 years. The LFS surveys are detailed individual level surveys and collect information in a host of areas including key labor market characteristics, household characteristics, and individual
# **References and resources**

Akinci, Ozge, Gianluca Benigno, Ruth Cesar Heymann, Julian di Giovanni, Jan J.J. Groen, Lawrence Lin, and Adam I. Noble. 2022. “The Global Supply Side of Inflationary Pressures.” Liberty Street Economics, FRB New York, https://libertystreeteconomics.newyorkfed.org/2022/01/the-global-supply-side-of-inflationary-pressures/ Arslanalp, S.; R. Koepke ; J. Verschuur. 2021. “Tracking Trade from Space: An Application to Pacific Island Countries”, IMF Working Paper No. 2021/225 (August 2021) https://www.imf.org/en/Publications/WP/Issues/2021/08/20/TrackingTrade-from-Space-An-Application-to-Pacific-Island-Countries-464345 Baltic Exchange. 2024. "BDI Baltic Exchange Dry Index"; London, United Kingdom Retrieved from Trading Economics: https://tradingeconomics.com/commodity/baltic Accessed: May 2, 2024 Flexport Research. 2024. "Ocean Timeliness Indicator", January 8, 2024 https://www.flexport.com/research/oceantimeliness-indicator/ Accessed: April 25, 2024 KPMG LLP and Association for Supply Chain Management (ASCM). 2022. "The KPMG Supply Chain Stability Index" 2022 https://kpmg.com/us/en/articles/2023/supply-chain-stability-index.html Accessed: April 25, 2024 Marine Traffic. 2023. MarineTraffic: Global Ship Tracking Intelligence. https://www.marinetraffic.com/en/ais/home/ Notteboom, T., A. Pallis and J-P Rodrigue (2021) “Disruptions and Resilience in Global Container Shipping and Ports: The COVID-19 Pandemic vs the 2008-2009 Financial Crisis”, Maritime Economics and Logistics. https://doi.org/10.1057/s41278-020-00180-5.

Rodrigue, J-P. 2020. “The Geography of Transport Systems”, Fifth Edition, London: Routledge. 456 pages. ISBN: 978-0367-36463-2. https://doi.org/10.4324
# **1. Introduction**

This paper presents findings on the adoption and use of digital technologies (DTs) by microenterprises across Sub-Saharan African (SSA) countries. The paper explores associations between enterprise and entrepreneur (owner) characteristics, use of basic 2G-2.5G feature phones versus computers and internet-enabled 3G-4G smartphones and more specialized DTs enabled by internet, and business outcomes linked to productivity, sales, export status and the generation of more and better jobs. It explicitly focuses on informality and the extent to which top-performing firms include both informal and formal firms. The findings are based on a sample of over 3,300 firms across seven SSA countries, namely Ghana, Kenya, Mozambique, Nigeria, Senegal, South Africa, and Tanzania. Over 70 percent of enterprises are informal, with over half of the sample being selfemployed household enterprises with no full-time paid employees.

The paper provides new evidence, both with respect to associations between adoption of various DTs and firm performance and with respect to correlates of adoption. With respect to adoption, the paper explores the correlates of adoption of smartphones and computers while controlling for firm level characteristics such as the degree of informality, being in an urban versus rural location, access to finance, the level of education of the owner, and whether the owner has any vocational training.

As indicators of output-related performance outcomes, the paper uses labor productivity, sales, and whether the firm has customers abroad (a proxy for exports). The paper also measures the performance of enterprises against the objective of creating more and better jobs: as a measure of more jobs, the number of full-time employees plus owners generated by each firm, and as earningsrelated measures of better jobs, wages per full time workers and profits per owner. The latter variable is likely to be especially important to assess whether entrepreneurial firms generate sufficient profits to support the well-being of their owners.

The paper explores the associations between these performance variables and the use of access technologies as well as additional more specialized DTs enabled by
# **Bring a Friend: Strengthening Women’s Social Networks and Reproductive Autonomy in India**<sup>∗</sup>

S Anukriti Catalina Herrera-Almanza Mahesh Karra The World Bank<sup>†</sup> University of Illinois at UrbanaBoston University<sup>§</sup> Champaign<sup>‡</sup> _Keywords:_ Women’s Social Networks; Peers; Vouchers; India; Family Planning; Contraception; Female Autonomy; Mother-in-law _JEL codes:_ J13, J16, O15, O33, I15, Z13.

> ∗This study was registered in the AEA RCT Registry (AEARCTR-0003283). We are grateful to Praveen K. Pathak for overseeing the fieldwork in his capacity as the local Principal Investigator. We thank Rocio Valdebenito and Esther Lee for research assistance, and Sonam Bhadouria and Pratibha Tomar for fieldwork, management, and oversight of the study in Jaunpur. We also thank Kathleen Beegle, Tanya Byker, David Canning, Peter Christensen, Andrea Flores, Andrew Foster, Matt Jackson, Seema Jayachandran, Annemie Maertens, Hope Michelson, Mushfiq Mobarak, Emily Oster, Berk Özler, Silvia Prina, Carolina SanchezParamo, Anja Sautmann, Rebecca Thornton, and various seminar and conference participants at ASSA Meetings, Ashoka University, Boston University, BREAD Conference at Stanford, Delhi School of Economics, IFPRI, IIM Bangalore, ISI Delhi Conference, Midwest Development Day, NEUDC, OARES-Minnesota, PAA Meetings, Population Health Science Research Workshop, Universidad del Rosario, Advances with Field Experiments Conference at University of Chicago, and World Bank for helpful comments. This project was supported by a Northeastern University Tier-1 Grant and funding from the Human Capital Initiative’s Program for Women’s Empowerment Research at the Boston University Global Development Policy Center. Ethical approval for the trial was granted by the Northeastern University Institutional Review Board and in India by the University of Delhi Research Council. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent
|Pan-Arab Free Trade Area<br>(PAFTA)|1998|Bahrain, Kingdom of; Egypt, Arab Rep.;<br>Iraq; Jordan; Kuwait, the State of;<br>Lebanese Republic; Libya; Morocco;<br>Oman; Qatar; Saudi Arabia, Kingdom of;<br>Sudan; Syrian Arab Republic; Tunisia;<br>United Arab Emirates; Yemen, Rep.|
|---|---|---|
|Pan-Arab Free Trade Area<br>(PAFTA)|1998|Bahrain, Kingdom of; Iraq; Jordan;<br>Kuwait, the State of; Lebanese Republic;<br>Libya; Morocco; Oman; Qatar; Saudi<br>Arabia, Kingdom of; Sudan; Syrian Arab<br>Republic; Tunisia; United Arab Emirates;<br>Yemen, Rep.|
|Protocol on Trade<br>Negotiations (PTN)|1973|Bangladesh; Brazil; Chile; Egypt, Arab<br>Rep.; Israel; Korea, Rep.; Mexico;<br>Pakistan; Paraguay; Peru; Philippines;<br>Serbia; Tunisia;Türkiye; Uruguay|
|Southern Common Market<br>(MERCOSUR)-Egypt|2017|Argentina; Brazil; Paraguay; and Uruguay|
|United Kingdom-Egypt|2021|United Kingdom|

Source: World Trade Organization Regional Trade Agreements Database

**Table 2. Mean values of main variables**

||2013|2016|2020|
|---|---|---|---|
|Employment (Number)|50.69|34.28|30.87|
|Export sales (EGP)|579,873|599,057|4,843,432|
|Exporter (yes=1)|0.05|0.07|0.08|
|Value Machinery (EGP)|1,066,
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implications of exports for the economy, our data allow us to evaluate the extent to which traditional versus nontraditional exports indeed support incomes and jobs.

The paper has three key findings. First, the sectoral composition as well as the labor intensity of exports in South Africa provides a nuanced understanding of the observed export-led ‘jobless growth’. The LACEX data combined with South Africa’s Labor Force Survey data suggest that much of the labor content of exports is shaped by the minerals sector. Between 2001 and 2011, minerals exports grew 22.2% annually, wages supported by minerals exports grew 24.6% annually, but jobs supported by minerals exports grew by only 3.9% annually.<sup>3</sup> South Africa is one of the middle-income countries in the sample with the lowest number of jobs associated with exports, and the quantity of jobs and the labor value added per unit of exports has diminished overall. Decompositions show that the observed decline in the labor intensity of exports is driven by exports becoming less labor intensive (labor-intensity effect), only slightly amplified by South Africa shifting to less labor-intensive exports (composition effect). While this is generally consistent with unbundling of production globally, where exports contain more foreign inputs, the data also show that exports in South Africa are becoming more capital intensive and less labor intensive: a decline in the share of total labor value added in gross exports has been accompanied by an increase in the share of capital value added in gross exports.<sup>4</sup>

> 3 Direct wages supported by minerals exports grew 33.4% annually, but direct jobs supported by minerals exports contracted by 16.8% annually.

> 4 These findings are consistent with the labor-saving technology changing across countries (see, for example, Kumar and Russell (2002) and Berman (2000)). Behar (2010a) shows that the elasticity of substitution between capital and labor has been negative in South Africa, also supporting our finding of
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51
<mark>more $500 million bonds, providing further evidence that firms have responded to a tradeoff when deciding to issue large bonds in amounts that exceed their funding needs.</mark> <mark>We present suggestive evidence that the channel driving these results is the investor demand for emerging market debt that is skewed towards index-eligible bonds. We show that an increase in the demand for emerging market debt, as proxied by investor flows to emerging market debt funds, is highly positively correlated with the percentage of bond issuances with face value of $500 million. Additionally, we show that funds that are less familiar with emerging market corporate debt tend to have the highest demand for indexeligible bonds. For instance, funds that specialize in developed market securities increased their share of investments in emerging markets after the GFC. Also, using portfolio-level data, we show that these funds tend to invest significantly more of their portfolio in bonds with face value equal to or greater than $500 million, relative to funds that specialize in emerging market securities.</mark> <mark>Although the literature has emphasized the role of the monetary policy environment in shifting the demand for emerging market securities, it is conceivable that factors in emerging markets could also be contributing to aggregate changes in issuance behavior. For example, changes in the willingness of emerging market firms to issue bonds could reflect higher commodity prices that increase the profitability of investment opportunities. The fact that we observe emerging market firms clustering their issuances at exactly $500 million after 2008, however, strongly suggests the importance of bond investor demand-side influences on the change in issuance behavior. It is highly unlikely that new investment opportunities leading to greater needs for funds are clustered exactly at issuance amounts of $500 million. Moreover, th</mark> e fact that yield reductions are discontinuous at the $500 million threshold is highly suggestive of bond investor demandside influences. Exogenous increases in firms’ desires for more funds in each capital raising activity should lead to higher yields, not the
the _SAH_ as a smooth but unknown function of real expenditure with the control variables entering linearly in parameters.<sup>7</sup> We use the STATA program for estimating the PLM written by Lokshin (2006). While this method allows a relatively flexible representation of the income effect, it has the disadvantage that it ignores the fact that our dependent variables come in the form of ordinal qualitative variables for which it cannot be assumed that the difference between (say) “very good” and “good” is the same in terms of health status as the difference between “good” and “average”. To address this concern, we follow past work in the literature in interpreting the answers to the subjective health question as an ordinal, categorical, summary of unobserved true health status, which is assumed to be a continuous variable that is determined by a vector of specific health attributes and an i.i.d. error term. In modeling the income effect on health we include a cubic function of real expenditure. Assuming level comparability of _SAH_ (conditional on covariates) across persons and that the error term is normally distributed we can use an Ordered Probit (OP) to model the responses. We estimate similar models for _SAW_ , where the latent continuous variable is interpreted as individual utility. We will allow for level noncomparability of subjective scales by adding controls for likely socio-economic covariates of reporting bias, following Thomas and Frankenberg (2000). We also allow for a latent individual effect using panel data, though this requires modifications to the econometric specification, for which we use the method of Ravallion and Lokshin (2001).

Our use of objective indicators of economic welfare, based on household expenditures and personal incomes, as regressors raises concerns about their possible endogeneity. The most likely way this could happen appears to be through work effort, based on the argument that latent

> 7 The parameters on the control variables are estimated by first ordering the data in terms of real expenditures and then taking differences to
.

- Balakrishnan, Pulapre, K. Pushpangadan and M. Suresh Babu, “Trade Liberalisation and Productivity Growth in Manufacturing: Evidence from Firm-level Panel Data,” _Economic and Political Weekly_ , 35(41), (2000): 3679-3682.

> 13 Evaluating the impact of infrastructure investment on Russia’s peripheral regions, Brown et al. (2008) find that the firm-level industrial data for 1989 and 2004 highlights a ‘death of distance.’ Particularly, regional infrastructure is found to be a critical driver of productivity for new and for privately-owned firms. Further, simulations of their model show that the benefits of infrastructure improvements are highest in regions where economic activity is already concentrated.

20