rafmacalaba/data-use-mentions-extended huggingface dataset:
for estimating the probability of personal, household and community factors to have a significant association on the aspirations of youth aspirations for higher education (complete university degree or post-graduate studies), working on STEM-related jobs and for migrating to seek better life opportunities. The core specification is estimated for the entire sample of youth aged 15-25 years old _i_ interviewed at time _t_ =2021. The specification includes covariates captured in the LSMS-ISA pre-COVID-19 surveys denoted by the sub-index _t_ - _1_ .

A similar model is replicated for three different outcomes: educational aspirations (m=1), career aspirations (m=2), and migration aspirations (m=3). The main specification is presented as Equation 1.

> 4 The sampling frame for these phone surveys was the most recent nationally representative LSMS-supported national longitudinal household survey. This enables a methodologically sound comparison with the pre-COVID-19 survey data. This paper uses data from the latest rounds of LSMS surveys from three countries: Ethiopia (September/ December 2018 and June/August 2019), Malawi (April 2019 to March 2020), and Nigeria (July/August 2018 and January/February 2019).

> 5 For details on reweighting techniques, please consult the World Bank Guidelines on Sampling Design available at <u>https://documents1.worldbank.org/curated/en/742581588695955271/pdf/Guidelines-on-Sampling-Design.pdf</u>

7
**Table 1:** Median Sample Periods

|Regions|**Labor Prod.**|**TFP**|**Spectral**|
|---|---|---|---|
|AEs|1962-2018|1951-2018|1973-2018|
|EMDEs|1972-2018|1971-2018|1981-2018|
|LICs|1981-2018|1981-2018|1981-2018|

(TED). Data on capital services and human capital are taken from the Penn World Table 9.1, and they are used to estimate TFP (Dieppe et al., 2020b). This results in an unbalanced panel of 30 advanced economies and 96 EMDEs. The average sample length is just under 40 years for EMDEs and 45 years for advanced economies (Table 1).

Both individual and panel VAR estimations are performed. The panel VAR estimation is performed separately for both advanced economies and EMDEs to illustrate the typical effects of technology and primary business-cycle shocks on representative economies in both groups.

In the standard specification, two lags of the endogenous variables are included in the VAR estimations. This is the minimum number of lags required to account for cyclical processes (which can be described as an AR(2) process). Results are robust to including four lags (accounting for four years of data).

# **5 Technology Shocks: Dominant Long-Run Drivers of Productivity**

For our sample of countries and over the time period studied, the impulse responses to technology shocks exhibit many similarities but invariably differ in magnitude and persistence across advanced economies and EMDEs.

Despite our methodology placing no restriction on the direction of the impact of technology shocks on the variables contained in the VAR, the impulse response functions (IRFs) for both advanced economies and EMDEs are generally consistent with theory and previous findings for technology shocks in advanced economies (Ramey, 2016). A technology shock raises the level of labor productivity persistently in advanced economies and EMDEs (Figure 3). We find it prudent to scale IRFs by
- **Muralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar** , “Building State Capacity: Evidence from Biometric Smartcards in India,” _American Economic Review_ , October 2016, _106_ (10), 2895–2929.

- **Riley, Emma** , “Resisting Social Pressure in the Household Using Mobile Money: Experimental Evidence on Microenterprise Investment in Uganda,” _University of Oxford, May_ , 2020, _25._

- **Schaner, Simone** , “The Persistent Power of Behavioral Change: Long-run Impacts of Temporary Savings Subsidies for the Poor,” _American Economic Journal: Applied Economics_ , 2018, _10_ (3), 67–100.

- **Somville, Vincent and Lore Vandewalle** , “Saving by Default: Evidence from a Field Experiment in Rural India,” _American Economic Journal: Applied Economics_ , 2018, _10_ (3), 39–66.

- **Suri, Tavneet** , “Mobile Money,” _Annual Review of Economics_ , 2017, _9_ (1), 497–520.

**World Bank** , “Global Findex Database,” 2017. `https://globalfindex.worldbank.org` .

32
Policy Research Working Paper 10758

# **Abstract**

High-frequency monitoring of food commodity prices is important for assessing and responding to shocks, especially in fragile contexts where timely and targeted interventions for food security are critical. However, national price surveys are typically limited in temporal and spatial granularity. It is cost prohibitive to implement traditional data collection at frequent timescales to unravel spatiotemporal price evolution across market segments and at subnational geographic levels. Recent advancements in data innovation offer promising solutions to address the paucity of commodity price data and guide market intelligence for diverse development stakeholders. The use of artificial intelligence to estimate missing price data and a parallel effort to crowdsource commodity price data are both unlocking cost-effective opportunities to generate actionable price data. Yet, little is known about how the data from these alternative methods relate to independent ground truth data. To evaluate if these data strategies can meet the long-standing demand for realtime intelligence on food affordability, this paper analyzes open-source daily crowdsourced data (104,931 datapoints) from a recently published data set in _Nature Journal,_ relative to complementary ground truth sample. The paper subsequently compares these data to open-source monthly artificial intelligence–generated price data for identical commodities over a 36-month period in northern Nigeria, from 2019 to 2022. The results show that all the data sources share a high degree of comparability, with variation across commodity and market segments. Overall, the findings provide important support for leveraging these new and innovative data approaches to enable data-driven decision-making in near real time.

This paper is a product of the Development Data Group, Development Economics and the Agriculture and Food 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 jadewopo@worldbank.org and bandree@worldbank.org. A verified reproducibility package for this paper is available
_“They believed that women who weren’t wearing face masks were not only at high risk but also insensible. So we had to wear face masks not only to protect ourselves but also to attract clients.”_

# **Shifts in work landscape**

# **_Place of work_**

Interviewees shared that they were required to seek work in new locations during the initial phase of the pandemic. With bars closed and gatherings prohibited in March 2020, many resorted to connecting and arranging to meet with clients via phone.

_“We were often chased away from our working places because the government had prohibited gatherings and the nature of our work had us standing in groups beside the road to wait for clients. So it became difficult for us to continue working in this environment. Not only that, but also sitting in bars was prohibited too. We therefore had to work through the phone, as in waiting for the client to contact us through the phone which made work become difficult.”_ _“At first, we used to go to bars to find clients, but during the pandemic it was really challenging because gatherings were prohibited. There were few people at the bars. Some bars were closed. So, if you met a client you exchanged numbers.”_ Survey data reveal significant changes in the place of work from 2018 to 2021 and suggest that the ways of meeting clients may have shifted despite many venues having reopened. Seeking work in bars decreased from 40.9% in 2018 to 34.7% in 2021 and in brothels from 33.1% to 28.0% (Table 2). Participants were more likely to meet clients on the street (6.7% to 12.4%) and in “other locations” (19.3% to 24.9%).

7
.00)|(0.00)|(0.00)|
|Number of dependents|0.00|-0.03|0.02|0.00|-0.02|0.01*|-0.01**|-0.00|0.01*|
||(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Observations|4705|1402|1402|1402|1402|1420|1420|1420|1420|

_Note:_ Robust standard errors in parentheses. Sample is restricted to males aged 20-59. Marginal effects of Logit/Mlogit model are reported. Data * ** *** source: Bangladesh RMS 2018-19; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. p _>_ 0.1, p _>_ 0.05, p _>_ 0.01.
If the theoretical framework and analysis of data suggests the possible presence of bias due to sample selection, heteroscedasticity/nonlinearity or missing values, then the extent of the resulting bias will likely depend on parameters of the type considered in the design of the Monte Carlo analysis in this paper. If the dataset involves very different shares of zeros and/or distributions of predicted trade and model residuals from those in the dataset examined here, then Monte Carlo analysis based on the parameters of the DGP is desirable before final choice of estimator.

Several papers have argued for the PPML estimator of the gravity model on the ancillary grounds that it results in predicted trade flows that sum to actual imports and exports (Arvis and Shepherd 2014; Fally 2015). This creates an advantage when gravity models estimated using fixed effects are used to infer the multilateral resistance terms central to structural gravity. But if this advantage of PPML comes with substantially biased coefficient estimates, the price for analytical convenience seems too high. In this situation, the alternative of using fitted export and import flows when inferring the multilateral resistance terms (Fally 2015, p78) seems a better option.

It is often attractive in applied work to use curated trade datasets such as the CEPII BACI database (Gaulier and Zignago 2010), the IMF Direction of Trade database (Marini, Dippelsman and Stanger 2018) used by Head and Mayer (2014) or the GTAP trade database (Gehlhar, Zhi and Yao 2010) used by Fally (2015) rather than raw data from UN COMTRADE. The curators of these databases add value, sometimes by choosing between the two estimates available for many trade flows—one from the reporter and one from its partner—on the basis of data quality, and sometimes by replacing missing values for intermittent reporters with trade patterns from nearby years or with real data from other than their primary sources. Alternatively, researchers 24
- Deaton, A. (1997). _The analysis of household surveys: a microeconometric approach to development policy_ . World Bank Publications.

- Deaton, A. (2016, June). Measuring and understanding behavior, welfare, and poverty. _American Economic Review_ , _106_ (6), 1221-43. Retrieved from `https://www.aeaweb.org/ articles?id=10.1257/aer.106.6.1221` doi: 10.1257/aer.106.6.1221

- de Montjoye, Y., Gambs, S., Blondel, V., Canright, G., De Cordes, N., Deletaille, S., . . . others (2018). On the privacy-conscientious use of mobile phone data. _Scientific data_ , _5_ (1), 1–6.

- de Montjoye, Y., Rocher, L., & Pentland, A. (2016). bandicoot: a python toolbox for mobile phone metadata. _Journal of Machine Learning Research_ , _17_ , 1-5.

- Engstrom, R., Hersh, J. S., & Newhouse, D. L. (2017). Poverty from space: using highresolution satellite imagery for estimating economic well-being. _World Bank Policy Research Working Paper_ (8284).

- Fatehkia, M., Tingzon, I., Orden, A., Sy, S., Sekara, V., Garcia-Herranz, M., & Weber, I. (2020). Mapping socioeconomic indicators using social media advertising data. _EPJ Data Science_ , _9_ (1), 22.

- Filmer, D., & Pritchett, L. (2001). Wealth effects without expenditure data—or tears: An application to educational enrollments in states of India. _Demography_ , _39_ , 115-132.

- Fortin, S., Kameli, Y., Kone, K., Belem, B., Sangho, H., & Savy, M. (2018)
decline in the PIP of the volatility of the black market premium to 14%. Finally, the inclusion probability of private credit increases marginally to 9%.

We also limit the period under consideration to 1960–1990 and examine whether the effect of financial development is stronger for this time period, as suggested by Rousseau and Wachtel (2011). We find that none of these modifications substantially affects our primary results concerning the relationship between private credit and economic growth. The PIP of private credit estimated on the subsample before 1990 does not appear to differ from that obtained for the full period up to 2011. As a robustness check of our results, we also use the values of private credit from the beginning of the observed period instead of the averages, but we find that the coding change has a negligible effect. These results are available upon request.

# _New Financial Development Indicators_

We examine the effect of new financial indicators on long-term growth in this subsection. Specifically, we additionally include the following variables in our estimation: bank Z-score, net interest margin, stock market turnover, and stock market capitalization. Cihak et al. (2013) identify these as proxies for different aspects of the financial sector. Specifically, they propose using bank Z-score to assess the stability of the banking sector, the net interest margin to proxy for the efficiency of the banking sector, stock market turnover as a proxy for the efficiency of the stock market, and stock market capitalization to measure the depth of stock markets. These measures, particularly the first two, are rarely used in growth regressions (Berger et al. [2004] and Hasan et al. [2009] being the exceptions), despite the fact that they might better depict the relationships outlined by theory than traditionally employed variables. As we discuss in section II, the main issue lies in their availability. However, the GFDD provides a significant improvement in this regard, and many series are available since the late 1980s. In 30
wished to settle in and integrate with their host community, mainly because of security concerns in their place of origin and better opportunities at their destination (IOM, 2013).

Evidence shows IDPs live in precarious conditions. The majority of settlements are self-built overcrowded houses or illegally occupied houses, with limited access to basic services and poor infrastructure, which may lead to poor health outcomes (IOM, 2013). Besides, IDPs might have difficulties finding a formal job due to lack of an identity document, language or cultural barriers. Additionally, forced displacement may have damaging consequences on IDPs’ family structure. For instance, family members may be separated, with working-age adults, and especially males, unable to return to their place of origin. Displacement may also disrupt children’s schooling. This suggests IDPs may be more likely to be deprived in multiple dimensions of well-being; which calls for an in-depth analysis of the deprivation of IDPs to identify the dimensions that demand urgent intervention and to help design policies that are best-suited to the needs of IDPs.

The variation of exposure to violence and insecurity between IDPs and non-IDPs provides a unique quasi-experimental setting, with the war representing an exogenous shock to multiple components of household well-being. The dimensions may be analyzed separately, or combined into a single index of multidimensional poverty, enabling a consolidated analysis of multiple dimensions of deprivation. In the following analysis, we apply the Multidimensional Poverty Index (MPI) methodology developed by Alkire and Foster (2011)<sup>1</sup> to the 2012-2013 Iraqi Household Socio-Economic Survey (IHSES-II)<sup>2</sup> to investigate the effect of forced displacement on multidimensional poverty.

Although there have been numerous works on the macro-level implications of wars and conflicts (e.g. Collier, 1999; Barron et al., 2004; Collier and Hoeffler, 2004; Miguel et al., 2004), 1 See also Alkire and Santos (2010).

2This survey is an outcome from the cooperation between the Central Organization for
> 70 8<br>CAP MAS SOU<br>60 SOU 7 DJI CHATOG MAL<br>50 SEN 6 11.708 MAU<br>MAR<br>40 ANG<br>GUB 5<br>30 NAM LIB<br>BOT 4<br>20 SUD SWA GHA<br>ANG 3<br>10 COMETH SIE GAM MAD 2 BTS CMRUGA<br>0 MAINGRKENCAR ZAM<br>-10 0 10 20 30 40 50 1 14.5BENGABBUR BDICDRCONRWA GUI MAD<br>0<br>-20<br>Mortgage rate (%) 0 10 20 30 40 50<br>Mortgage policy (%)<br>Regression Mortgage access = f( Mortgage policy) Mortgage penetration=f(mortgage policy)<br>Linear (Regression Mortgage access = f( Mortgage policy)) Linear (Mortgage penetration=f(mortgage policy))<br>mortgage market (%) Mortgage penetration (%)<br>Proportion of population with access to<br><!-- End of picture text --> _Source_ : Authors’ calculation using the World Bank database on housing finance launched by Badev et al., 2014: “Housing finance across countries: New data and analysis,” WPS6756.

_Source_ : Authors’ calculation using the Global Financial Inclusion database (FINDEX) launched by Demirguc-Kunt, Asli and Leora Klapper (2012), “Measuring financial inclusion: the global Findex Database,” World Bank Policy Research Working Paper 6025.

8
cities tend more to serve local sewerage access better (water sewerage, electricity).

A preliminary analysis of city level data from EOS 2004 corroborates the main findings in our paper (summarized in Annexes A and B). These results were corroborated when replacing 2003 data with 2004 data. Finally, 2004 data confirms the strong positive correlation between city performance and globalization as well as the strong positive correlation between city performance and city governance that we highlighted in Sections 4 and 5 . This is indicative of the robustness of our results.

> 18 We have presented synthesis results and have not shown the results of multiple correlations.  We also have chosen to use data plots and not the actual econometric results.  We thought the synthesis results were a better way to present the results.  The actual regressions were tested for sign switches, and except for the access to electricity variable, the regressions did not show sign switches for the key variables.  This is why we thought it best to show the range of coefficient estimates and the statistical significance.  All the synthesis tables we have developed present controlled statistical results, as opposed to bivariate regressions, even if shown only in synthesis form.

31
and homogeneous states by using a dummy variable.

> 11 Ethnic fractionalization may have plausibly increased during the 1990s as a result of significant external migration and various shocks. However, we did not have access to the data from the latest (2002) Russian census. 12 Electoral rules vary across regions. In some regions a simple majority is not sufficient for victory and unless one candidate gets more than 50 percent of votes a second round of elections is called between the two candidates who obtained most votes in the first round. Hence, only the first round spreads are comparable across regions.

> 13 Authors’ calculations based on the World Bank Database of Political Indicators. Higher values of Gastil Index correspond to lower levels of democracy and political freedom. Unfortunately the data on the runner-up votes are not readily available for a broad sample of countries, leaving the above comparison approximate.
schooling for which the biases are somewhat larger.

## (OB.2.2) Inverse Probability Weighting for Correction of Coresidency Bias

Given the evidence that the estimates of the mobility models using census are in general close to the IFLS estimates, it seems unlikely that our conclusions about the causal eects of Inpres schools can be driven by coresidency bias. But one can argue that we do not know the direction of bias for the causal eects from a priori considerations, and additional evidence would be helpful. To address any remaining concerns, we use inverse probability weighting to correct for possible biases in our estimates of the causal eects of Inpres. Nicoletti and Francesconi (2006) provides evidence in favor of IPW for correcting the coresidency bias in intergenerational mobility analysis relative to Heckman selection correction.

> 1For son's primary completion, the estimated slope parameter is identical across census and IFLS data.

60
|34.8|
|Vulnerable to extreme poverty|27.5|7.0|100.0|100.0|100.0|100.0|85.7|87.0|100.0|71.7|
|Highly vulnerable to poverty|51.3|10.7|98.6|98.4|100.0|100.0|53.1|53.4|53.6|38.4|
|Highlyvulnerable to extremepoverty|19.7|5.8|100.0|100.0|100.0|100.0|94.1|95.3|100.0|100.0|

Source: Own computations using DHS 19
RCT studies have their own methodological weaknesses and thus are not always necessarily better than non-RCT studies (Deaton 2010; Rodrik 2008; Ravallion 2012).  Because microcredit operations are small in scale, the timeframe for realizing their impacts may exceed 1 or 2 years, which is the typical timeframe for most RCT studies in order to minimize spillover effects and contamination (Hermes and Lensink 2007).  In fact, the seminal study of Pitt and Khandker (1998), which uses an innovative quasi-experimental design, relies on data collected on borrowing over a 5- year period from areas where microcredit had been in operation for at least 3 years.

> 4 Roodman and Morduch have been critical of the underlying methodology of Pitt and Khandker (1998), the most cited study in the literature on microcredit impacts.  Roodman and Morduch (2011) is the most recent in a series of papers and postings that seek to refute the findings of Pitt and Khandker.  In 2012, Pitt and Khandker revisited the claims of Roodman and Morduch, observing a lack of due diligence in their replication of the Pitt and Khandker study and confirming that none of their statistical claims invalidate Pitt and Khandker’s findings on the substantial positive effects of microcredit, especially among women borrowers (Pitt and Khandker 2012).

3
Policy Research Working Paper 10757

# **Abstract**

This paper examines spillovers in the use of digital technologies from formal to informal businesses by exploring differences in geographic proximity. Using a unique set of geocoded data from the 2019 World Bank Enterprise Surveys in Zambia, the findings indicate that closer geographic proximity to formal firms is associated with a significantly higher likelihood of digital adoption by informal businesses. The finding holds for various types of digital technologies, ranging from computers, tablets, and cell phones to mobile money transactions, and is robust to various measures of geographic proximity and model modifications. The results vary by the owner’s level of education and business age. The results also suggest that the spillovers in information and communications technology use can be explained by competition in the local market and learning through enhanced interactions.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group and the Global Indicators Group, Development Economics. 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 at fjolevski@worldbank.org, gnayyar@worldbank.org, rpleninger@worldbank.org, and syu2@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

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