rafmacalaba/data-use-mentions-extended huggingface dataset:
In all regressions with female data, unless noted otherwise, the individual characteristics included in _Xict_ are age, highest grade completed, and binary indicators for the frequency of communication with mothers about SRH topics and whether the female’s household (i.e., parents) owns the house in which she lives. We included these controls because they are strongly correlated with sexual activity and relationship status and improve the precision of the estimates (Bruhn and McKenzie, 2009); however, the results are qualitatively similar if we do not include them. We estimate equation 1 for the whole analysis sample and for sub-populations of interest, such as females who had ever had sex at baseline and females who had partners in the last two years at baseline.

# **4.1 Baseline Balance and Follow-up**

The underlying identification assumption needed to interpret the results from an RCT as causal is that sample characteristics are balanced at baseline. We show balance for our baseline survey sample of 3,178 females for our primary outcomes in Table 1. In columns 1–2, we show balance for the cluster randomization, and we show balance for the individually-randomized _Goal_ intervention in columns 3–4. Overall, the RCT appears to be balanced across observed outcomes at baseline. Appendix Table B2 replicates Table 1, including the _Supply_ arm differences. Of the 3,178 females in our baseline sample, 2,591 were successfully tracked to the endline survey, an overall tracking rate of 81.5%. This tracking rate is similar across survey treatments (81% in the control arm, 85% in _Boys_ , and 80% of females invited to _Goal_ ). Table B3 presents baseline balance for this sub-sample. The treatment balance is maintained within this sub-sample. We discuss attrition in more detail in section 7.

In addition, we show balance for the IPV outcomes for the sub-sample of females who reported having ever had sex at baseline (25.7% of the sample) in Table B4, and we report
supplement these data with 12 additional public opinion surveys. The second dataset is on refugee populations at the sub-national level. We obtain preliminary estimates from the United Nations High Commissioner for Refugees (UNHCR), develop a methodology to impute missing totals, and aggregate at the level of sub-national regions as reported in GWP. The third dataset covers policies on camps, which we also obtain from UNHCR, and on _de jure_ access to the labor market, which we obtain from the authors of the Developing World Refugee and Asylum-seeker Policy dataset (Blair et al. 2021a) and supplement by coding the additional LMICs in our sample.

In order to causally identify the effect of refugees’ presence on attitudes, we use large, sudden arrivals of refugees that create clear before and after time windows at the sub-national level. We examine how attitudes changed within affected regions in our first set of specifications using event study designs. We extend the framework to include regions within the same countries that did not experience a similarly large increase for a difference-in-differences design.

Our preliminary findings suggest that the large arrivals of refugees do not have a negative effect on attitudes in the period we study (up to four years after arrival). The coefficients are positive, small, and statistically insignificant but, importantly, can rule out meaningful negative effects. We then find little evidence that the pattern differs across camp and non-camp settings or across liberal and restrictive right-to-work environments, although the analysis is still at a preliminary stage.

Our strategy allows us to estimate the average effect across contexts, which is arguably the parameter of interest to both policymakers and academics.<sup>4</sup> Studies within a single context have important advantages, but the external validity is always a concern. Meta-analyses may also not produce the true average effect if the set of contexts is systematically biased, perhaps due to data availability or potential publication bias. However, Pottie-Sherman and Wilkes (2017) conduct a metaanalysis of immigration attitudes and group size across 55 studies. Among 487

> 4Our
, however, there are no FDI data sets that are not based on media coverage while simultaneously covering an equally large set of countries and allowing for disaggregation at the sector level. Nevertheless, it is unlikely that our results on sector heterogeneity are driven by sample selection bias. It could be expected that particularly resource-related FDI is sensitive to public outcry because this type of investment is generally believed to have negative environmental, economic and institutional development effects (Collier & Hoeffler, 1998; Fearon and Laitin, 2003; Hodler, 2006; Ross, 2004; Sachs and Warner, 1995). Non-resourcerelated greenfield FDI, however, is widely considered a vehicle for economic development and 41
|Share 35-49|0.20|0.20|0.21|0.22|
|Share 50-64|0.10|0.11|0.11|0.12|
|District avg HH age structure|||||
|Share 0-14|0.33|0.30|0.29|0.28|
|Share 15-24|0.17|0.17|0.17|0.17|
|Share 25-34|0.16|0.17|0.17|0.17|
|Share 35-49|0.19|0.20|0.20|0.21|
|Share 50-64|0.10|0.11|0.11|0.12|
|Religion and social group|||||
|Hindu|0.78|0.78|0.77|0.76|
|Share Hindu in district|0.81|0.81|0.81|0.79|
|Share sched caste in district|0.63|0.65|0.67|0.68|
|Household type|||||
|Self-employed|0.43|0.42|0.41|0.37|
|Share self-employed in district|0.48|0.45|0.46|0.44|
|Casual laborer|0.12|0.14|0.13|0.15|
|Share casual labor in district|0.04|0.06|0.05|0.06|
|Regular wage worker|0.39|0.37|0.40|0.41|
|Share reg wage worker in distric|0.20|0.19|0.21|0.23|
|Principal industry|||||
|Agriculture|0.06|0.06|0.06
second strand indicates that in some contexts, descriptive mean differences in agricultural productivity across plots owned/managed by males vs. females continue to be large and statistically significant in multivariate analyses that control for differences in input use (Saito et al., 1994 for Nigeria; Udry, 1996 for Burkina Faso; Quisumbing et al., 2001 for Ghana; Peterman et al., 2011 for Uganda), while in other contexts, the gender gap ceases to be statistically significant once the researcher controls for differential utilization of productive inputs (Saito et al., 1994 for Kenya; Gilbert et al., 2002 for Malawi; Akresh, 2005 for Burkina Faso; Goldstein and Udry, 2008 for Ghana).<sup>12</sup> A common limitation of these studies is the reliance on data that are at best regionallyrepresentative in terms of population dynamics (with the exception of Akresh, 2005), whereby results have limited external validity beyond the study area, within or across countries. For instance, Akresh (2005) uses nationally-representative data from Burkina Faso, and while he is

> 12 See Peterman et al. (2011) for a succinct review of the main findings of the studies cited here. Only Udry (1996), Quisumbing et al. (2001), Akresh (2005), Goldstein and Udry (2008), and Peterman et al. (2011) conduct withinhousehold analysis.

6
unemployment.

> 17 Sinha (2017) uses the emerging market/new EU member state average efficiency as a reference point for public investment efficiency in Bangladesh.

> 18 Length of road network, electricity production, access to water, number of secondary teachers and number of hospital beds.

> 19 Based on the World Economic Forum Global Competitiveness Report survey responses on the quality of key infrastructure services.

> 20 With GDP per capita as an auxiliary input variable.

15
# **<<B>>** _Control variables_

In the empirical specification, we include additional controls that are likely to affect agricultural and food policies, as suggested by many previous studies (e.g., Anderson 1995; Beghin and Kherallah 1994; Swinnen et al. 2000; Olper 2007). Specifically, our basic D-in-D specification always includes the following structural controls: the level of development, measured by the log of real per capita GDP; the share of agricultural employment in total employment; the log of agricultural land per capita; and the log of total population. All of these variables are computed from World Bank ( _WDI_ ), FAO, or national statistics.

We also test the robustness of our findings by controlling for several other (macro) covariates, such as different indicators of (aggregate) openness, government expenditures, and economic and political crises (wars and conflicts). Openness indicators (the trade to GDP ratio and the Sachs and Warner (1995) index) and government expenditures are obtained from Wacziarg and Welch (2008)<sup>15</sup> and the Penn World Table, respectively. War and conflict year dummies are based on the UCDP/PRIO Armed Conflict Dataset Version 4-2008 (see Gleditsch _et al._ 2002).

For our matching strategy, we use a limited number of covariates that are likely to influence both regime change and agricultural and food policies. As discussed previously, a shift in agricultural policy may require political reforms of sufficient size (Swinnen _et al._ 2001). Therefore, in our model, we include a variable, _initial polity2_ , that takes the value of our democracy index at the beginning of the sample. This variable is included to take into account that countries with Polity2 values close to zero are more likely to have a political regime change.

To control for the fact that the sample period varies in length across countries and that the length of the sample may be correlated with the probability of changes in the
24 (WIDOW x YEAR05) was even lower (by a factor exp(-0.41) = 0.66) compared to widows before the genocide. In 1992, sex ratios (SEXRATIO) do not significantly influence fertility rates among middle age women. This is very different after the genocide: more balanced sex ratios significantly increase fertility rates in the early post-war period (SEXRATIO x YEAR00). Given that most of the women of middle age are in union, this finding again suggests that fertility and partnership are linked. Considering the number of living children in 2005, women of middle age who lost a child during the genocide do not have significantly fewer living children than women who were not exposed to conflict (holding other characteristics constant). In short, it seems that women who were exposed to the genocide aged between 14 and 28 fully adjusted their fertility in the post-war period.

This is not the case for women in the oldest age group of 35-49 years during the survey collection (Table 9). In this age group, the negative effects of conflict exposure on fertility prevail – through widowhood (WIDOW x YEAR00; WIDOW x YEAR05), the loss of a brother in the short term (BROTHERDEATH), and the loss of a child (CHILDDEATH) and a sister in the long term (SISTERDEATH). In fact, women in the oldest age group who lost a child or their husband during the genocide have 0.91 and 0.72 fewer living children in 2005, respectively compared to women not affected by the genocide. Considering the fact that the dependent variables of interest only capture births given during the past five and ten years – and that many of the women in the old age group may have reached menopause – this result is not surprising. Women who were exposed to conflict between age 29-43 (those surveyed in 2000) and 24-38 (those surveyed in 2005) have fewer chances to replace close kin who died in 1994. To conclude, it is the oldest group
**|

Source: Analysis using TIMSS 8<sup>th</sup> grade data, 2007 and 2011.

**Table 6: Pairwise correlations of the PISA disciplinary climate by country and by wave**

|2000|2003|2009|2012|2015|
|---|---|---|---|---|
|2003|0.7884|1|||
|2009|0.7767|0.813|1||
|2012|0.7772|0.7916|0.7874|1|
|2015|0.5533|0.6377|0.7689|0.781|

Source: Analysis from PISA data 2000-2015

> 11 Eighth grade data were used consistently for the 2011 and 2015 analysis.

14
all aparments in the realty companies' database that matched the MLHS, 1994 apartments by qualitative parameters: number of rooms, area of geneal and hving space, category of dwelling wall material, etc., as well as by location, which was detenmined by small Moscow districts. The mrnket estimates of the value ofthe surveyed apaitments calculated in this way were icluded in the savey database and were later analyzed as regular questions included in the questionnaires. This made it possible to correlate the value estimates with the social status, incomes, and demographic composition of Moscow households, as well as with the other survey questions.
Caeyers, B. and S. Dercon. 2012. “Political Connections and Social Networks in Targeted Transfer Programs: Evidence from Rural Ethiopia.” _Economic Development and Cultural Change_ , 60 (4): 639-675.

Carlin, W. 2012. “A Progressive Economic Strategy: Innovation, redistribution and labourabsorbing services.” Policy Network Working Paper, London. Retrieved on 4 August 2015 at: <u>http://www.policy-network.net/publications_detail.aspx?ID=4269</u> Carlson, E. 2015. “Ethnic Voting and Accountability in Africa: A Choice Experiment in Uganda.” _World Politics_ , 67 (2): 353-385.

Carreras, Y.I. 2015. “Federalism, Governance and Inequality: A Comparative Study in Europe and the Case of Germany.” EUSA 14<sup>th</sup> Biennial Conference Paper, Boston, MA, USA. Central Statistical Agency [Ethiopia]. 2014 _. Ethiopia Mini Demographic and Health Survey 2014._ Addis Ababa, Ethiopia.

Central Statistical Agency [Ethiopia] and ICF International. 2012 _. Ethiopia Demographic and Health Survey 2011_ . Addis Ababa, Ethiopia and Calverton, Maryland, USA: Central Statistical Agency and ICF International.

Central Statistical Agency [Ethiopia] and ORC Macro. 2006. _Ethiopia Demographic and Health Survey 2005._ Addis Ababa, Ethiopia and Calverton, Maryland, USA: Central Statistical Agency and ORC Macro.

Chanie, P. 2007. “Clientelism and Ethiopia's post-1991 decentralisation.” Journal of Modern African Studies, 45 (3): 355-384.

Clark, D. 2009. “The Performance and Competitive Effects of School Autonomy.” _Journal of Political Economy_ 117 (4): 745–82.

Collier, P. and D. Dollar. 2002. “Aid allocation and poverty reduction.” _European Economic Review,_ 46: 1475–1500.

Dalgaard, C.J. and H. Hansen. 2001. “On Aid, Growth
main purpose of the IHS is to train a model that predicts welfare based on publicly available satellite data, which is one of the candidate prediction methods that is evaluated.

## Satellite data

We obtained the satellite data from three different sources: Google Earth Engine, WorldPop, and Facebook. Table 1 contains a summary of the indicators used for the analysis.<sup>13</sup> For indicators for which annual data are available, we collected information for 2017 and 2018 that correspond to the years of the UBR and census, respectively.

This data is collected at the grid level, and then is merged with the village centroids obtained from the UBR.

**Table 1.** Satellite indicators used in the analysis

|**Source**|**Indicators**|
|---|---|
|Google Earth Engine|Land cover type, weather, vegetation, nightlights, year of change to<br>impervious surface. 7 km by 7 km resolution|
|Worldpop|Population density, build-settlement growth, OSM distance to roads<br>(2016), and building patterns data (2020). Resolution is 0.1 km|
|Meta|Relative Wealth Index. The resolution is 2.4 km.|

Note: Data is for 2017-2018 unless otherwise indicated.

# 2.2 Matching the census and the UBR data by village

Matching villages between the UBR and the census data is a critical step that enables the linking of census villages with remote sensing indicators. The matching is based on names using an algorithm that matches two text variables and assigns a similarity score. The matching starts at the biggest administrative unit, Traditional Authorities (TA), followed by Group Village Names (GVN), and finally at the village level. This process resulted in 32% (4,727<sup>14</sup> ) of the UBR villages being matched with the census villages. Six UBR TAs and 24% of the GVNs are
# **1. Introduction**

E-commerce is not a phenomenon that Colombians or Argentines only came to know during the pandemic. In 2019, 136 million annual transactions related to e-commerce were recorded in Colombia (Blacksip 2022), or an average of 2.7 transactions per person. The growth of e-commerce in Colombia was very high in the years prior to the pandemic, rising from 17 million transactions in 2014 to 136 million in 2019. The growth rate was higher than in the rest of the large countries in the Latin America region. In the case of Argentina, 89 million e- commerce purchase orders were made in 2019 (CACE 2020), or around 2 purchase orders per person. The growth of e-commerce in Argentina before the pandemic went through three phases: (i) an eruption in 20002005, with growth rates in the number of users and sales exceeding 50 percent; (ii) a consolidation in 20052015, during which the share of Argentines connected to the Internet who had tried e-commerce rose from 15 to 75 percent and year-on-year growth rates in sales adjusted for inflation remained above 25 percent; and (iii) a phase of relative stagnation in the years prior to the pandemic, probably due to limited economic growth.

Bogota and Buenos Aires are the most active e-commerce demand centers in Colombia and Argentina, respectively. In the case of Bogota, data reported by marketplaces such as Rappi show a share close to 60 percent in 2019 (Blacksip 2020), and Colombia's Quality of Life Survey (LCS) suggests that Bogota’s e-commerce adoption is double the national average. The highest volume of online orders in 2019 was concentrated in the northeastern localities of the city (i.e., Usaquén, Chapinero, and Teusaquillo), coinciding with a greater adoption of e-commerce among households of higher socioeconomic status. In comparison, while in 2010 almost 70 percent of Argentina’s online purchases were made in Buenos Aires and its metropolitan area (AMBA for its Spanish acronym), this share
An Evaluation Using the 1999 Brazilian Exchange Rate Devaluation,” _Journal od Development Economics_ , 111(C), 1–16.

- Bastos, P., and N. P. Monteiro (2011): “Managers and Wage Policies,” _Journal of Economics and Management Strategy_ , 20(4), 957–984.

- Bastos, P., N. P. Monteiro, and O. R. Straume (2009): “Firm Heterogeneity and Wages in Unionised Labour Markets: Theory and Evidence,” _Labour Economics_ , 16(4), 440–450.

- Bastos, P., J. Silva, and E. Verhoogen (2018): “Export Destinations and Input Prices,” _American Economic Review_ , 108(2), 353–392.

- Baumgarten, D. (2013): “Exporters and the Rise in Wage Inequality Evidence from German Linked Employer-Employee Data,” _Journal of International Economics_ , 90(1), 201–217.

- Bender, S., N. Bloom, D. Card, J. Van-Reenen, and S. Wolter (2018): “Management Practices, Workforce Selection, and Productivity,” _Journal of Labor Economics_ , 36(S1), S372–S409.

- Bertrand, M. (2004): “From the Invisible Handshake to the Invisible Hand? How Import Competition Changes the Employment Relationship,” _Journal of Labor Economics_ , 2(22), 723–765.

- Bertrand, M., and S. Mullainathan (2003): “Enjoying the Quiet Life? Corporate Governance and Managerial Preferences,” _Journal of Political Economy_ , 111(5), 1043–1075.

- Bertrand, M., and A. Schoar (2003): “Managing with Style: The Effect of Managers on Firm Policies,” _Quarterly Journal of Economics_ , 118(4), 1169–1208.

- Bloom, N., and J. V. Reenen (2007): “
turned on.

Economists increasingly use night lights detected by satellites as a proxy for economic indicators (Chen & Nordhaus, 2011; Gibson et al., 2021; Henderson et al., 2012; Ishizawa et al., 2017; Mellander et al., 2015). In developing countries with low-quality economic and social accounting systems, or when satellite images are more frequently available than such data, the satellite-based nightlight data may provide valuable information on social and economic development. In addition to the widely used night lights, other types of remotely sensed data have recently been explored in economics. For example, Castellanos & Boersma (2012) find that NO2 is correlated with economic activities and thus can be used to measure the magnitude of social and economic activities (Keola & Hayakawa, 2021). The Normalized Difference 3
Here the (informal) employment status dummy equals 1 when worker j in region i (or sector i) and year t is employed (informally employed), and 0 otherwise. Weights capture the extent of representativeness of worker j in the whole sample. Both simple and weighted measures are constructed with the latter giving more weight to more representative observations in the sample. As shown above, while BPS provides three definitions of informal employment, the simplified definition is preferred for the analyses as it is less linked with Indonesia’s sectoral composition. These data series are then used to show the levels, trends, and features of informality in Indonesia.

**3. Levels and trends of informality: EMDEs versus Indonesia**

# **3.1 Informality in EMDEs**<sup>**5**</sup>

19. **Informal economic activity is more pervasive in EMDEs than in advanced economies (AEs).** In AEs, it accounted for less than one-fifth of official GDP and total employment in 2019, whereas about one-third of economic activities and employment in EMDEs happens in the informal sector (Elgin et al. 2021). Informality is common in all EMDE regions but takes different forms. On average, the informal economy’s share of output is highest in Sub-Saharan Africa (SSA), Europe and Central Asia (ECA), and Latin America and the Caribbean (LAC). The share of self-employment, however, is highest in SSA, South Asia (SAR), and East Asia and the Pacific (EAP).

20. **The share of informal output and employment has contracted in EMDEs since the 1990s.** The downward trend is observed both in AEs and EMDEs (Ohnsorge and Yu 2022). In the case of EMDEs, the downward trend is more prominent in output informality, whereas employment informality fell more noticeably during the recent two decades than during the 1990s.

**Figure 1. Informality in EMDEs**

<!
# <u>West</u>

<!-- Start of picture text --> 250 Chongqing (province) 2008 400 Guangxi (province) 2008<br>200<br>300<br>150<br>Nanning (capital)<br>200<br>100<br>50 100<br>0 0<br>3000 30000 7500<br>Prefecture GDP per capita (RMB, log scale) Prefecture GDP per capita (RMB, log scale)<br>Public health spending per capita Public health spending per capita<br>Highest/ Standard Coefficient Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance M ean Highest Lowest lowest deviation of variance<br>162.40 236.65 75.95 3.12 32.17 0.20 179.24 347.26 4.80 72.34 88.62 0.49<br>Inner Mongolia (province) 2008 Inner Mongolia (province) 2000‐2007<br>700 500<br>600<br>400<br>500<br>400 300<br>2007<br>300<br>200<br>200<br>100 Houhot (capital) 100 2000 Houhot (capital)<br>‐ 0 Houhot (capital)<br>10000 100000 1500 15000<br>Prefecture GDP per capita (RMB, log scale) Prefecturde GDP per capita (RMB, log scale)<br>Public health spending per capita Public health spending per capita (2007)<br>Highest/ Standard Coefficient Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance M ean Highest Lowest lowest deviation of variance<br>266.90 601.37 145.14 4.14 129.45 0.49 221.43 453.20 87.69 5.17 122.77 0.55<br>Public health spending per capita (2000)