rafmacalaba/data-use-mentions-extended huggingface dataset:
included a specific battery of questions that provided respondents the opportunity to report their perceptions of whether IDPs or refugees were present in their communities and, if so, what impact hosting displaced persons had on social cohesion their communities. Additionally, respondents reported whether their communities hosted IDPs (displaced persons from within DRC) or refugees (displaced persons from Burundi, Rwanda, Uganda, South Sudan, or other countries).

Given the lack of reliable census data and frequent population movements in eastern DRCongo, sampling and weighting procedures are necessarily conservative. All of the surveys randomly select _groupements_ (or _quartiers_ in cities) in each _territoire_ . Within selected _groupements_ , select villages are drawn (or _avenues_ in cities), which are clusters per _territoire_ . Enumerators carry out 8 interviews per cluster using a random walk procedure. Responses are weighted to adjust for differences in probability of selection at the _territoire_ level and all samples are gender-balanced. Additional details on the survey design are provided in the Appendix, Section D.

# **4.1 Measuring Displacement Context**

Because this paper is interested in a context of frequent, unregistered, and informal displacement, it relies on self-reported measures for all variables included in the analysis.

The aggregate analysis calculates the percentage of respondents in each _groupement_ that report being displaced in response to either of the following two questions: “Are you currently displaced?” and “In the past 12 months, have you been involuntarily moved?” The aggregate analysis is thus a representation of the proportion of respondents within each _groupement_ that self-reports being displaced currently or having recently been displaced (but not necessarily displaced any longer).

In the individual level analysis, the independent variable measures whether respondents’ communities host displaced persons. The 2018 survey asks “Are there any displaced persons or refugees here in the city or the territory?” At the individual level, respondents who respond yes are coded as hosting displaced persons. Additionally, if respondents reported that they did host 23
**Figure 1.** Trends in the categories of body mass index in adult persons between 20 to 24 years of age by gender. Mexico 2000, 2006 and 2012.

<!-- Start of picture text --> Females Males<br>100% 100%<br>0.08<br>90% 0.14 0.14 0.22 90% 0.14 0.17<br>80% 80%<br>0.29<br>70% 0.29 0.32 70% 0.3 0.27<br>60% 0.32 60%<br>50% 50%<br>40% 40%<br>30% 0.57 0.53 30% 0.63 0.56 0.56<br>0.46<br>20% 20%<br>10% 10%<br>0% 0%<br>2000 2006 2012 2000 2006 2012<br>Normal Sobrepeso Obesidad Normal Sobrepeso Obesidad<br><!-- End of picture text --> <!-- Start of picture text --> Normal Sobrepeso Obesidad<br><!-- End of picture text --> <!-- Start of picture text --> Normal Sobrepeso Obesidad<br><!-- End of picture text --> Source: Authors’ estimates based on data from the National Health Survey 2000 (ENSA 2000), National Health and Nutrition Survey 2006 (ENSANUT 2006) and the National Health and Nutrition Survey 2012 (ENSANUT, 2012).

When performing the same analysis on the population group relevant to PROSPERA, the lowest income quintile, the increase in the prevalence of obesity was even more pronounced. As shown in Figure 2, between 2000 and 2012, the percentage obese of those in the lowest income quantile increased from 8% to 17% for females and 3% to 19
Coleman, 2001; Laitner and Stolyarov, 2003; Comin and Hobijn, 2004; Buchak et al., 2018).<sup>3</sup> It is one of several recent studies that investigate the effects of the COVID-19 pandemic (see for instance Alon et al., 2020; Baker et al., 2020; Chiou and Tucker, 2020; Alfaro et al., 2020; Jones et al., 2020; Alber and Dabour, 2020; Jonker et al., 2020; Bounie et al., 2020b; Guerrieri et al., 2020; Piguillem and Shi, 2020; Bounie et al., 2020a).

The rest of the paper flows as follows. Section 2 introduces the institutional background of card payment and the chronology of COVID-19 crisis in France and describes the data and methodology. Results are presented in section 3. Section 4 checks the robustness of the results, and section 5 concludes.

# **2 Background, Setting, Data, and Methodology**

This section provides background information on the card markets, the COVID-19 crisis in France, and my various data sets and estimation strategies. I explain how I use the unique features of card sales transaction data for the study of the response of merchants with the latest payment technology to the COVID-19 pandemic.

## **2.1 Contactless Payments and the COVID-19 Crisis in France: A Chronology**

_French card market and new digital payment_ . France has a well-developed bankcard market, including Cartes Bancaires CB, the leading payment system, created by French banks in 1984.<sup>4</sup> In 2020, the latter had more than 100 members (including payment service providers, banks and electronic money institutions) and counted 73 million CB cards (including 64 million contactless cards) and 2 million CB-affiliated merchants (Cartes Bancaires CB, 2020). In order to make bank cards more attractive and easier to use, French banks have decided to launch massive quantities of cards with contactless technology
, MCD charge penalty from the vendors for setting up illegal markets. Thus, vendors are exploited by both, Mafias and MCD.”<sup>1</sup> Along with lower levels of income, lower levels of productivity among enterprises in the informal sector are well documented (Jutting and de Laglesia, 2009). Most recently, careful work by Busso, Fazio and Levy (2012) for Mexico argues that Total Factor Productivity (TFP) is much higher in the formal sector than in the informal sector—so much so that “one peso of capital and labor allocated to formal and legal firms is worth 28% more than if allocated to illegal and informal firms, and 50% more than if allocated to legal and informal firms.”. However, other studies have pointed out strong evidence for manufacturing convergence. In a cross-country study of 100 countries, using industry data for formal sectors, Rodrik (2012) finds that industries that start at lower levels of labor productivity grow faster.

Figure 5a, plots labor productivity levels in the unorganized industry at the district level in India. Each dot in the scatter plot reflects labor productivity in the unorganized industry in a district. The horizontal axis is the initial labor productivity in 1989. The vertical axis shows the growth of labor productivity in unorganized industry for that district for the period 1989-2205. A downward sloping line suggests that districts that started with a lower level of labor productivity in the initial period experienced a faster productivity growth rate in subsequent periods. But there is a lot of dispersion. No doubt, the pace of convergence will accelerate with better infrastructure, improved policies, and stronger institutions, as discussed earlier. The convergence in the unorganized manufacturing sector can have a huge impact on aggregate manufacturing convergence, given that more than 80% of manufacturing employment in India is in the unorganized sector. The impact of formal manufacturing on aggregate manufacturing convergence is reduced by its small share of employment.

> 1 SEWA Newsletter No. 39, http://
.00428]***|[0.00208]***|[0.00416]***|[0.00439]***|
|Household head|0.0992|0.105|0.104|0.105|0.1|
||[0.00381]***|[0.00655]***|[0.00397]***|[0.00663]***|[0.00629]***|
|Spouse works|-0.00907|-0.00684|-0.0089|-0.00725|-0.0079|
||[0.00366]**|[0.00961]|[0.00369]**|[0.00965]|[0.00959]|
|Income stability|0.155|0.156|0.156|0.156|0.155|
||[0.00441]***|[0.0109]***|[0.00445]***|[0.0110]***|[0.0107]***|
|At least one person receives remittances regularly|-0.0664<br>***|-0.0661<br>***|-0.066<br>***|-0.0663<br>***|-0.0666<br>***|
||[0.00250]|[0.00368]|[0.00251]|[0.00370]|[0.00365]|
|Household Home & WASH index|0.00812|0.00827|0.00817|0.00861|0.00896|
||[0.00164]***|[0.00175]***|
<!-- Start of picture text --> b. Concentration shares: taxes & contributions<br>30<br>20<br>10<br>0<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Decile of Market income<br>Direct taxes Contributions Indirect taxes Market income<br>Source: Authors' estimates based on PECS 2016/2017; LFS 2017.<br>Notes:<br>[1] Concentration shares show the proportion of each tax/transfer paid by / benefitting each decile. They sum to 100%.<br>[2] Households are ranked over Market income<br>Concentration shares (%)<br><!-- End of picture text -->

# **7.2. Transfers**

**The incidence of the social protection direct transfers is higher in poorer deciles; and the largest direct transfer is the CTP.** The incidence of the CTP is largest in the first decile and decreases steeply with income (Figure 7-2a) from 56.3 percent of Market Income in decile 1 to 0.04 in decile 10; this is consistent with the fact that the CTP is targeted to the poor through a PMT. The incidence of the social protection nearcash transfers is much smaller than the CTP in all but the top decile, ranging from 4.4 in decile 1 to 0.06 in decile 10.

**Poor households receive a larger share of direct transfers.** Almost 60 percent of the direct transfers are received by decile 1 and only 1.2 percent in decile 10.

**The incidence of the indirect subsidies is small.** The electricity subsidies are visible in deciles 1 to 5 only, they start at 1.8 percent of Market Income in decile 1, decrease to 0.6 percent in decile 5, and further to 0.2 percent in decile 10. The fuel subsidies are not visible
measurement errors are not uncommon. As the vulnerability rate draws from the idiosyncratic errors, measurement errors could inflate our vulnerability rate (measurement errors form part of the idiosyncratic error). Therefore, we assume that 25-50 percent of the overall volatility, which we observe across households when looking at their income, stems from measurement errors. We apply this exercise to our tool using the 2019 crosssection of the household data for El Salvador.

**When assuming that half of the total variance is due to measurement errors of income, the vulnerability rate decreases to 18.4 percent.** Therefore, it is lower than when we assume no measurement error. This means that the vulnerability rate is lower than the poverty rate, with a 50 percent measurement error. Still, several of our main findings hold. First of all, rural vulnerability is much higher than the urban one. The vulnerability rate of the rural population is 0.336 compared to 0.095 in urban areas (see Figure

17
and relief organizations are more prevalent in areas which people have returned to. An important difference in return and non return seems to be access to services as well as markets for goods in places of return. Thus, it is important to think of policies that stabilize the provision of such services and provide for the flow of goods. Those interested in the economic needs of Syrians can look to groups online, including online markets and job postings, for information about what goods are in high demand as well as current inflation levels. Images in these groups also indicate a clear trend that mobility is important for returnees: there is a high prevalence of bike and car sales in areas with return.

Ultimately, social media data provides a relatively comprehensive picture of the situation on the ground in Syria, especially when a variety of sources are used. By identifying groups and pages in different regions, program managers can both monitor ongoing events and witness the impact of interventions on discussion and attitudes in real time. Such monitoring is less costly than survey and other methods, and feedback is more immediate.

**Next steps** . Most of the analysis in this paper focused on descriptive analysis of trends that can be gleaned from publicly available social media data. Even though the descriptive analysis presented in this paper are valuable (Munger et al. (2021)), the value of such insights for policy makers might be even more relevant in a _predictive_ setting. This is one of the areas which future research may focus on. The clear trends in various types of analysis indicating the presence of refugee return and IDPs gives us hope that such trends can be used to predict whether future return will occur in a location just by looking at trends in social media discussion. The implication from causal mediation that information sharing on social media plays a role in return outcomes provides additional support for this goal.

Given the longitudinal nature of the data, we have the opportunity to clearly identify and quantify changes in tone of the conversation, particularly around
(Pooled waves)**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes a|s % in househ|old income|)|
|placebo|0_._31|0_._85|_−_0_._47|0_._56|0_._60|
||(1_._93)|(0_._71)|(0_._55)|(0_._51)|(1_._62)|
|N|296|116|296|129|295|
|Nominated mean|8_._13|2_._29|4_._46|2_._00|9_._25|
|Nominated SD|14_._83|3_._00|5_._14|2_._28|12_._91|

Notes: Table only includes women selected for the promotion program, where the upper half of the ranked list in each factory is assigned to a placebo treatment which is zero for the lower half. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso (ivlasso for Panel A, Column 4) from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. The sample in Panel C, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

**Table C.21: Lee (2009) bounds**

|**Outcome**||**Pooled waves**|||**Wave 1**|||**Wave 2**||
|---|---|---|---|---|---|---|-
across the decade (see Figure A.2 in Annex 4). Women face particularly challenging labor markets in urban areas, where their unemployment rate is close to 50 percent against about 30 percent for men. In rural areas, overall unemployment rates are much lower, at 27 percent, but the gender gap is as high as in urban areas

> 6 All unemployment rates for LAC and other countries are from the World Development Indicators (WDI).

11
priori theoretical grounds, this is likely to result in depressed wages with obvious distributional consequences, as the income of landless poor households su¤ers. The land market restrictions may also lead to higher dispersion in equilibrium wages across sectors and geographic areas, higher than what one would expect to observe because of standard transactions and search costs in the labor market. Large dispersion in equilibrium wages can create allocational ine¢ ciency, and adversely a¤ect productivity by distorting technological choice and creating technological dualism. Evidence from recent studies shows that misallocation of labor –re‡ected in sustained di¤erences in wages across areas and sectors– can explain a large fraction of international di¤erences in per capita income and overall total factor productivity (Vollrath, 2008; Temple and Wobmann, 2006). In a recent paper, Hayashi and Prescott (2008) argue that the informal restrictions on land inheritance in pre-war Japan had signi...cant negative e¤ects on per capita income growth and employment structure.

We analyze the general equilibrium interactions between land and labor markets, with a focus on the e¤ects of land market restrictions on equilibrium wages and its spatial distribution. There has been a growing interest in recent literature on the e¤ects of land market restrictions on household choices and outcomes (for evidence on labor supply and savings, see, among others, Field (2007), Iyer et al (2009); for evidence on productivity see Deininger et al (2008, 2009), and for evidence on migration see Yang (1997), among others). However, to the best of our knowledge, there is no empirical analysis of the causal e¤ects of land market restrictions on the equilibrium wage in the existing literature. Taking advantage of a quasi-experiment in historical land settlements and restrictions in Sri Lanka, we provide evidence on the e¤ects of land market restrictions on the equilibrium wage and its spatial pattern. We use household survey data for the year 2002 (Sri Lanka HIES 2002
Layet, J.-L. Just and G. Ninot (2011). "Psychometric properties of the Center for Epidemiologic Studies Depression Scale (CES-D) in French clinical and nonclinical adults." <u>Revue d'epidemiologie et de sante publique</u> **59** (5): 327-340.

Murthy, R. S. and R. Lakshminarayana (2006). "Mental health consequences of war: a brief review of research findings." World Psychiatry **5** (1): 25.

Naoi, M. and I. Kume (2011). "Explaining mass support for agricultural protectionism: Evidence from a survey experiment during the global recession." International Organization **65** (4): 771-795.

Payne, C. F., I. V. Kohler, C. Bandawe, K. A. Lawler and H.-P. Kohler (2016). "Cognitive Health among Older Adults: Evidence from Rural Sub-Saharan Africa." Quiñones, A. R., S. M. Thielke, M. E. Clark, K. M. Phillips, C. Elnitsky and E. M. Andresen (2016). "Validity of Center for Epidemiologic Studies Depression (CES-D) scale in a sample of Iraq and Afghanistan Veterans." SAGE open medicine **4** : 2050312116643906.

Radloff, L. S. (1977). "The CES-D scale: A self-report depression scale for research in the general population." Applied psychological measurement **1** (3): 385-401.

Rasinski, K. A., P. S. Visser, M. Zagatsky and E. M. Rickett (2005). "Using implicit goal priming to improve the quality of self-report data." Journal of Experimental Social Psychology **41** (3): 321-327. Richards, A., J. Ospina-Duque, M. Barrera-Valencia, J. Escobar-Rincón, M.
For attitude indicators, there was no consistent evidence of spillovers across classrooms.<sup>18</sup>

# Sexual Activity and Condom Demand

Table 5 reports results on sexual activity and condom voucher redemptions. Specifically, columns 1–3 report effects of the course on self-reported sexual activity six months after the end of the course.<sup>19</sup> Columns 1–3 uniformly show that the course did not increase or decrease self-reported sexual activity among adolescents. Column 1 shows that students were not more likely to be sexually active in the past six months compared to the control group. Similarly, the frequency of sex did not change in the previous six months compared to the control group (column 2) nor in terms of number of partners (column 3). The finding that sexual education does not result in increases in sexual activity is a robust finding in this literature (cf. Kirby, Laris, and Rolleri 2007).

Given that the objective population is teens at high risk of sexual initiation, the setting is not designed to capture other sexual behavior outcomes such as STIs and pregnancy, given their extremely low prevalence (the control group means for these self-reported outcomes are 1 percent and 2 percent; thus there is little room for improvement). The study nevertheless estimated treatment effects for these outcomes and found no statistically significant changes (columns 4 and 5 of table 5).

Of course, the validity of studies using self-reported sexual behavior among adolescents has long been a criticism in this literature (Brener, Billy, and Grady 2003). This challenge was addressed by measuring the percentage of students who redeem vouchers for condoms. This strategy makes it possible to address the possible lack of reliability in self-reported outcomes via an objective safe-sex behavior metric. Condom availability is important for adolescent health given the sporadic nature of adolescent sexual activity. Column 6 in table 5 reports the results of the voucher experiment.

The administrative data from voucher redemption shows statistically significant and important effects. The table shows that
||2000|2007|2014||
|---|---|---|---|---|
|A. Males|||||
|% less than mean in 2000|0.5088|0.5375|0.6655||
|||2000-2007|2007-2014|2000-2014|
|Difference||-0.0286|-0.1279|-0.1567|
|||[0.003]|[0.004]|[0.004]|
|Growth Effect||-0.022|-0.136|-0.1486|
|||[0.002]|[0.003]|[0.003]|
|Redistribution||-0.0065|0.008|-0.008|
|||[0.002]|[0.002]|[0.002]|
|B. Females|||||
|% less than mean in 2000|0.5115|0.5049|0.6094||
|||2000-2007|2007-2014|2000-2014|
|Difference||0.0066|-0.1045|-0.0979|
|||[0.005]|[0.005]|[0.005]|
|Growth Effect||0.02|-0.116|-0.1073|
|||[0.003]|[0.003]|[0.003]|
|Redistribution||-0.014|0.012|-0.009|
|||[0.003]|[0.003]|[0.003]|

Notes: Authors’ calculations using quarterly Labor Force Surveys. Each year uses information from all available quarters: 2000 uses quarters 2-4, 2007 uses quarters 1-4, 2014 uses quarters 1-2. Sample restricted to
# **Annex 3: Data Sources and Definitions**

This paper makes use of two main sources of data: the Rwanda Development Board’s (RDB) company registration database and the Rwanda Revenue Authority’s (RRA) tax records. This section provides a brief description of each of the sources of data, data cleaning procedures, as well as the definition of basic indicators.

## **RDB business registration database**

The RDB records the data of every new business, business line, or branch at the time of registration. It contains information on 32,250 companies, with a total of 140,146 registered business lines. Registrations date from as early as 1900; however data before 2006 was not considered. Most of the analysis presented here is focused on the 2008‐2012 period, both for purposes of alignment with tax records, and also to coincide with the establishment of the one‐stop shop and the business registration drives that took place in 2008.

Upon registration, companies are assigned a Taxpayer Identification Number (TIN), generated from the RRA, which is then used to match registrations and tax records.

Companies are requested to specify (to Industrial Standard International Classification [ISIC] level 4) all the activities that they will be undertaking (referred to here as business lines). However, the RDB has omitted to inquire what the main business focus of the company will be. Instead, this is imputed as the broad activity that appears most often among the registered activities.<sup>16</sup> In absence of a clear activity, companies are classified as operating in multiple sectors. An example of how this is reported is illustrated in Table 1 below. The same process is followed for the subsectors.

**Table 1: Example of Sector Classification**

|**Company Code**|**Business Lines**|**Main Business (imputed)**|
|---|---|-