rafmacalaba/data-use-mentions-extended huggingface dataset:
/sup> Results remain robust if slightly weaker.

When additionally controlling for the number of informal workers we find that the higher the number of informal employees in a firm pre-COVID, the greater the increase in the number of ‘male’ workers compared to females post-COVID (Tables 5-7 in Annex 3, regression 2), leading to a very slight deterioration of the gender gap (Tables 6-7 in Annex 3, regression 3). Nevertheless, for firms with informal workers that have additionally experienced a shock (i.e. the interaction term) this effect more or less cancels this employment effect out, even though that the overall estimated effects of informality are not substantially large. There is no contradiction here with the fact that the informal sector, which is made up strictly of informal firms, is a ‘survival sector’ that has provided ‘helping hand employment’ to the type of employment harder hit by the pandemic.

# _Propensity Score Matching_

The propensity score matching results (Table 5) basically confirm the baseline results in tables.<sup>13</sup> The COVID shock, be it a demand one, a supply one, or combined has led to a decline in overall employment and in the percentage change of employment. This is true of both sexes. Nevertheless, the reduction in ‘male’ employment has been on average over three times greater than that for women. This relative weaker direct employment impact on women is primarily due to the very limited number of women in employment in manufacturing to start

> 12 In the EIFBS data these are leather, clothing, furniture and wood products, textiles, other manufacturing and food.

> 13 The balance tests for the covariates are provided in Tables 8-9 in Annex 3. In all cases, the standardized mean differences (i.e., median bias) are less than the recommended threshold of 5%.

13
, 54% of the migrants still have a longer migration duration.

The returns to investment for temporary migration are not restricted to the migration episode. Indeed, when temporary migration enables workers to engage in self-employment and entrepreneurship after return, their lifetime incomes at home significantly increase compared to a counterfactual scenario where they could not have migrated. To calculate the long-term returns to migrating, we estimate a migrant’s income gains after return until retirement which can be attributed to the transition into self-employment, and compare it with the cost of migration. We approximate these long-term returns to migration as follows: first, we use the nationally representative HIES data to estimate the income trajectories for self-employed and dependently employed individuals. That is, we run an auxiliary regression of individual _j_ ’s income on a full set of age indicators **1** [ _agej_ = _a_ ] interacted with a dummy _SEj_ that takes value 1 if a respondent is self-employed, and 0 otherwise. Controls _Xj_ include 16
Policy Research Working Paper 10981

# **Abstract**

During the COVID-19 pandemic, low- and middle-income countries struggled with lower vaccination rates compared to wealthier countries, posing challenges to reducing virus transmission, mitigating healthcare system pressures, and promoting economic recovery. Communications campaigns offer low-cost opportunities to overcome such challenges by strengthening vaccine confidence and intentions to get vaccinated, but empirical testing is needed to identify which messages will be most effective in different contexts. To support policy-making efforts to design effective communication rapidly during the pandemic, a global research program of 28 online experiments was conducted by recruiting respondents (123,270 individuals) through social media between January 2021 and June 2022 across 23 mostly low- and middle-income countries and territories. An individual participant data (IPD) meta-analysis of these data summarizes the results of this research program testing the impact of behaviorally informed messaging on vaccine intentions. Results from the meta-analysis show that among unvaccinated survey respondents, behaviorally informed messages significantly increased the odds of vaccination intention by 1.28 times overall and up to 1.93 times in individual studies (safety messages in Papua New Guinea). Significant pooled effects of specific framings ranged from increasing the odds of vaccination intention by 1.16 times (variant framing) to 1.45 times (experts and religious leaders framing). This research underscores the importance of communication tailored to address different drivers of vaccine hesitancy and offers insights for handling future health crises with behavioral communication strategies leveraging rapid insights afforded by social media.

This paper is a product of the Development Impact 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 dpinzonhernandez@worldbank.org, rhyslim@worldbank.org, and mdugas@worldbank.org. A verified reproducibility package for this paper is available at
**Figure 3: Merchandise exports as a share of Gross Domestic Product, 2000 GDP weighted**

<!-- Start of picture text --> GDP weighted Exports/GDP ratio<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>SACU World Rest of SADC<br>Rest of SSA High-income Other developing<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Notes: Own calculations using World Bank Development Indicators. Region aggregates are constructed using 2000 GDP values as weights. Zimbabwe is excluded.

Gravity model based estimates, which control for geographical and other observed and unobserved country characteristics, corroborate these findings (see Appendix 1 for a brief description of the gravity model specifications and the full results in Table 9). Exports from the average country in the sample were 18.5 log points in higher in the 2001-2005 period than the 1991-1995 period, controlling for GDP growth (and unobserved time invariant characteristics) (column 1). The trend is no different for SADC countries, as revealed by the insignificant coefficients on the SADC-period interactions terms.

In addition, SADC countries are found to be no more or less prone to trade relative to the rest of the world, controlling for GDP, distance and a number of other geographical features (see the coefficients on the SADC exporter and importer dummies in column 2).

6
these concerns, we add indicators of information acquisition and financial literacy in the main model. Our main results remain robust, indicating that neither information acquisition nor financial literacy drives the link between credit constraints and fraud victimization.

Further analyses on potential mechanisms suggest that the personal discount rate (impatience) and the need to expand the social network are important pathways through which credit constraints affect fraud victimization. Borrowing constraints can shape people’s preferences on current versus future consumption (Harrison et al., 2002; Ventura, 2003; Nakata and Sawada, 2015). Credit constraints may lead to a higher discount rate over the future, and thus make people more prone to believe well-disguised fraud schemes that promise an egregious return within a short period. In addition, to obtain social collateral, households with severe credit constraints would engage in activities to expand their social networks; more social interaction itself, along with the associated behavior changes such as emphasis on cooperative behavior, could make them more susceptible to become victims.

We contribute to the literature in several ways. First, we propose a new perspective to understand fraud victimization. To the best of our knowledge, while there is an economic literature on credit constraints and crime (Garmaise and Maskowitz, 2006; Cortés et al., 2016; Avenancio-León, 2019); Palmer et al., 2019), this is the first paper using nationally representative data on fraud to examine the impact of credit constraints on fraud victimization. Thanks to the richness of our data, we look at both people being approached for fraud and victimization. Second, we explore the mechanisms through which credit constraints affect fraud victimization. Third, we assess the effects of alternative policies to combat fraud (such as improving information acquisition and financial literacy) and find that improving financial access is more effective in reducing fraud victimization. Finally, we contribute to the literature on the impact of credit constraints. It is well established in the literature that credit constraints can shape household behaviors such as consumption (Garcia et al.
the actual reduction in the incidence of poverty between the first half of 2012 and 2014 was about 2 percentage points higher. Inequality micro‐simulation predictions were more aligned to what Iraq experienced between 2007 and 2012 rather than what has happened in the first half of 2012 and 2014. In other words, actual movements in inequality indicators such as Gini and Theil were in the opposite direction as predicted.

The simulation model preformed much better when the incidence of poverty was predicted at the regional level. Two‐thirds of the regional poverty headcount levels were not significantly different from actual levels in 2014 (i.e. Kurdistan, South, Rest of North and Centre) (Figure 9). Baghdad and IS‐affected governorates’ headcounts were overestimated by the micro‐simulation method in the BaU scenario. The simulation also overestimated the poverty gap in most regions except for Rest of North and Centre whose point estimates are within the confidence interval of indices for the first half of 2014 (Figure 10).

# _Measuring the impact of the twin crises in host governorates_

As mentioned above, the household survey was implemented only in 13 out of 18 governorates throughout the year. This allows us to measure the impact of the twin crisis by implementing a before and after approach on these geographical areas. This method measures the difference on welfare or any other outcome like poverty or inequality between the first and second half of 2014. However, it is possible

> 25 Further details on the methodology used see Krishnan, Olivieri and Germiniasi (2015)

22
at the time of reporting. All values in percent. 95%-Confidence Intervals in parentheses.

# **Panel conditioning**

Respondents may change their vaccination behavior in response to being repeatedly surveyed on the topic of COVID-19 vaccination. We therefore predicted that respondents previously interviewed or those in households that were part of the phone survey panel would report higher rates of vaccination than a sample interviewed for the first time.

**<mark>-</mark>** **<u><mark>Table 6: Hypothesis 4 Panel Conditioning</mark></u>**

||(1)|(2)|
|---|---|---|
|VARIABLES|Panel Household|Panel Individual|
|Household Previously Interviewed|-0.0177<br>(0.0172)||
|Respondent Previously Interviewed||0.00847<br>(0.0172)|
|Constant|0.324***|0.310***|
||(0.0129)|(0.0117)|
|Observations|2,942|2,942|
|R-squared<br>Note: Bivariate OLS regression of vaccination st|0.000<br>atus on a dummy for respondent|0.000<br>living in a|

Note: Bivariate OLS regression of vaccination status on a dummy for respondent living in a household interviewed before (Column 1) or having been interviewed before themselves (Column 2). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 22
the ‘informal economy’ focuses on the nature of employment in addition to the characteristics of enterprises.” (Chen 2006, p. 76).

# **2.2 Measurement and Trends**

With so many different conceptualizations of informality and formality, it is perhaps not surprising that getting nationally comparable estimates of informality is fraught with difficulties. Even if the basic concept is agreed--for example, registered enterprises--the detailed specification may vary across countries, since the requirement for registration will vary. Estimates produced by national authorities may differ from those produced by international agencies because of differing definitions and data sources.

Despite concerns about data and definitions, there is an agreement that the informal sector is large in most developing countries, and it is the overwhelming employer of labor in the non-agricultural sector. For developing countries as a group, more than half of all jobs—over 900 million workers—are in the informal sector (Jutting and de Laglesia, 2009). The sector is large not just in terms of employment, but also by the number of enterprises. Many more new establishments are created in the informal sector than in the formal sector.

Figure 1 plots the share of informal employment in total non-agricultural employment for 50 developing countries against their per capita income, using data from ILO KLIM, OECD, and World Development Indicators. For these 50 developing countries as a group, nearly half of the total nonagricultural jobs is generated in the informal sector. In India, it is much higher, at more than 80 percent of total non-agricultural jobs generated in the informal sector. It is also high in Sub Saharan Africa. The share of the informal sector is slightly lower in Latin America, with Brazil at 42% and Mexico at more than 50%. As a region, the lowest share of informal employment is in the Central & South-Eastern Europe, with the share of informal employment in Poland and Russia in single digits.

**Figure 1: Jobs are concentrated in the Informal Sectors in
between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C9.3. SOSD: Social Sustainability Index C9.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.96*** 0.96*** 0.78***<br>H<br>Kendall tau-b 0.85*** 0.87*** 0.66***<br>Spearman 0.94*** 0.83*** 0.82***<br>A<br>Kendall tau-b 0.82*** 0.65*** 0.7***<br>Spearman 0.97*** 0.96*** 0.78***<br>M0<br>Kendall tau-b 0.87*** 0.87*** 0.66***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.1<br>1<br>.8<br>.05<br>.6<br>CCDF<br>.4 Difference<br>0
diminishing returns to financial investment in the education production function plays a critical role. The evidence that peer and role model effects do not generate the convexity in the mobility equation suggests more efficient investment by educated parents as a likely source of convexity as emphasized by Becker et al. (2015, 2018).

The rest of the paper is organized as follows. Section (2) provides a brief discussion on the related literature with a focus on work on developing countries, and on Indonesia in particular. Section (3) develops the extension of the Becker et al. (2015) model with selffinance constraint on financing children’s education and derives the estimating equations for intergenerational persistence and optimal parental investment. Section (4) discusses the data with a focus on the issues of sample truncation due to coresidency and the construction of a cognitive ability index using three measures of ability in IFLS 2014. The next section presents the estimates for the mobility equation. Section (6) is devoted to understanding the economic forces at work behind the rural-urban divide in educational mobility. Section (7) explores the role of peer and role model effects in the convexity of mobility equation. The paper concludes with a summary of the results and highlights the portability of the model and methods developed here to other developing country analysis.

# **(2) Related Literature**

The literature on intergenerational economic mobility in developed countries is vast, with fundamental theoretical and empirical contributions, from Becker-Tomes (1979, 1986), to Solon (1992), to Chetty et al. (2014) are some of the seminal studies. Most of the studies focus on intergenerational persistence in permanent income between father and son. An important finding from this literature is that rich panel data on income for many years from appropriate phases of the life-cycle are required to get credible estimates of intergenerational persistence in permanent income (Solon (1992), Mazumder (2005)). For excellent surveys of this literature see, Solon (1999), and Black and
US than in Brazil or China where activity in top locations is concentrated in just one-half of the country (Figure 6). However, after adjusting for distance, it appears that there are many more pairs of locations with relatively high trade costs in Brazil compared to the US and China.

# **5. Effects of reduced mobility frictions on spatial development in Latin America**

This section investigates how changes in mobility frictions could affect the evolution of Latin America’s spatial development over the next century. We rely again on the spatial dynamic growth model of Desmet et al. (2018) who evaluated the model by running a simulation backwards in time from 2000 to 1870 and comparing the model’s predictions on the distribution of population levels in the data. The correlations between the simulated population levels and the actual country-level population data were high: 0.96 until 1950 and around 0.70 before 1900. They also found highly significant correlations between the calculated amenities and commonly used measures of amenities such as temperature, proximity to water, and precipitation. Finally, one of the main predictions of their model – namely that the correlation between density and productivity is higher in areas with higher per capita incomes – was confirmed in the data.

We first establish a baseline in which the model predicts the spatial evolution of Latin America over the next century, assuming no change in mobility frictions. Starting from the calibrated model with data for 2000 and adjusted migration frictions, the equilibrium of the model is obtained by simultaneously solving equations (1) and (2) for a period _t_ as in lemma 3 of Desmet et al. (2018). Within this procedure, given the trade and adjusted migration frictions in 2000, the distribution of local amenities, net of congestion disutility, and productivities reflecting innovation choices and spatial diffusion are updated and used to solve for the equilibrium of the subsequent period, _t+1_ . This procedure for solving the dynamic problem as a sequence of static problems is repeated as many periods as
# B. Constructing synthetic household surveys

With a measure of non-monetary welfare and poverty for each census household in hand, we turn to drawing a synthetic household survey from each country’s census. The synthetic survey, along with the auxiliary geospatial data, are key inputs into the small area estimation procedures. To draw the synthetic survey, we utilize the actual two-stage sample conducted by the National Statistics Offices for two household budget surveys: The 2018 Tanzania Household Budget Survey, and the 2016 Sri Lanka Household income and Expenditure Survey. These surveys were merged with the census at the subarea level, which is the GN Division in Sri Lanka and the village in Tanzania. After retaining the GN Divisions and EAs present in the budget survey, we randomly select census households in each matching EA to match the number of households in each EA for each survey. Finally, we merged the sample weights from the household budget surveys for each subarea. Essentially, this procedure draws a survey that mimics as much as possible the sample drawn by the NSO for the budget surveys.

# C. Remote sensing data

The auxiliary data for the small area estimation exercise are drawn from a large candidate pool of satellite-based information, most of which is derived from publicly available layers and imagery. These include night-time lights from the Visible Infrared Imaging Remote Sensor (VIIRS), at a spatial resolution of 15 arc-seconds, precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), elevation and slope taken from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite, global forest cover change from Hansen (2013) and estimates of built-up area from the Global Human Settlement Layer (GHSL). From this last layer, we compute the percentage of total built-up area observed in 2014 that was constructed prior to 1975 or during 19751990, 1975-1990, and 2000-2014.

The Sri Lanka indicators were also supplemented by a variety of spatial “texture” features derived from a cloud-free mosaic of 2017-2018 Sentinel-2 imagery, which is collected every 5 days by
likely to be poor than non-displaced people and they also have unique vulnerabilities that are impediments to achieving higher incomes. Additionally, the burden of hosting displaced populations is overwhelmingly carried by developing countries, which can undermine their efforts to reduce poverty and promote development. In particular, sudden inflows of displaced people can create short-term disequilibria across a number of markets (including the labor market), and place additional strain on infrastructure and local services such as education, health, solid waste management, water services and urban transport. The presence of displaced populations can also increase the risks of conflict diffusion, instability, and further displacement.

Traditional responses that focus only on the immediate needs of the newly displaced or on durable solutions are often inadequate. Given the protracted nature of many forced displacement situations and the limited immediate prospects for durable solutions for most displaced people, short term humanitarian responses are insufficient. Even the achievement of one of the three durable solutions does not automatically eliminate the vulnerabilities associated with having been forcibly displacement. In particular, many returnees continue to face impediments to their sustainable reintegration in countries/places of origin. There are also significant, varied and long-term impacts of forced displacement (both positive and negative) on host countries and communities that need to be understood and managed. A development engagement, anchored in a medium to longer-term perspective, can therefore complement a humanitarian engagement throughout the entire period of a forced displacement situation.

Currently, there is a very weak link between the collection of forced displacement statistics and the analysis required to inform an effective development response.<sup>95</sup> For development actors, several analytical questions may be considered, such as:

> 95 A development response to forced displacement may encompass several elements including: (a) policy adjustments to encourage greater inclusion of displaced populations within mainstream economic and social development processes; (b) medium-term interventions to lessen the dependency of displaced populations on humanitarian assistance and mitigate tensions between displaced people and their host communities; (c) support to mitigate the