rafmacalaba/data-use-mentions-extended huggingface dataset:
characteristics between any two arms of each type of intervention. The results show that randomization balances these characteristics across treatment cells.<sup>18</sup> Since most of my analysis focuses on subgroups of inexperienced and experienced migrants, I also check for balance within these subgroups. Within each subgroups, the joint test across all outcomes fails to reject equality across the treatment arms at conventional levels.<sup>19</sup>

## **3.4 Empirical specification**

The randomized nature of the intervention implies that the basic empirical specification to estimate the effect of the programs is quite straightforward. I estimate where _yi_ is the outcome for individual _i_ , _DeathLoi_ , _DeathHii_ , _WageLoi_ and _WageHii_ are indicators of whether individual _i_ receives any of these treatments. _Xi_ are a set of controls which includes full set of interactions between education categories, age categories and location, indicators for the chosen destination, and enumerator fixed effects. _εi_ represents the error term, and I allow arbitrary correlation across individuals at the date of initial survey _×_ enumerators level. The standard errors remain quantitatively similar with alternative clustering specifications.

# **4 Does providing information affect perceptions?**

Using data from the control group (which does not receive any information on wages or deaths), the first part of this section establishes that potential migrants are indeed misinformed about earnings and mortality risks of migration. To do so, I only use the data on the subjects that did not receive any informational intervention. In the second part of this section, I estimate the impact of the informational treatment on perceptions about mortality and earnings.

## **4.1 Descriptive evidence on the extent of misinformation**

### **Misinformation in expected earnings**

Misinformation about earnings abroad may persist even in cases where a large share of the population is a migrant. As discussed earlier, local agents and recruitment companies have an incentive to exaggerate earnings information to induce potential migrants to go. Moreover, previous
for other infectious diseases such as inuenza, Ebola, Zika or Coronavirus. As cell phone usage has become prevalent throughout the developing world and cell phone providers are beginning to understand how the data they collect can be used by policymakers to implement better policies, measuring short term movement becomes easier. The collection and integration of high frequency data on infectious diseases then makes it possible to study these models that help policymakers better target interventions, and then document their impact. This could help countries lower the disease burden from existing infectious diseases and prevent epidemics of new diseases, leading to benecial economic and social consequences.

# References

- Adda, Jérôme (2016). Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data. The Quarterly Journal of Economics 131.2, pp. 891941.

- Adhvaryu, Achyuta (2014). Learning, misallocation, and technology adoption: evidence from new malaria therapy in Tanzania. The Review of economic studies 81.4, pp. 13311365.

- Adriansen, Hanne Kirstine (2008). Understanding pastoral mobility: the case of Senegalese Fulani. The Geographical Journal 174.3, pp. 207222.

- Agence Nationale de la Statistique et de la Démographie - Ministére de l'Economie, des Finances et du Plan (2014). Enquete a l'ecoute du Senegal 2014.

- Aker, Jenny C and Christopher Ksoll (2019). Call Me Educated: Evidence from a Mobile Phone Experiment in Niger. Economics of Education Review.

- Alegana, Victor A et al. (2013). Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatialtemporal models. Spatial and spatiotemporal epidemiology 7, pp. 2536.

- ANSD and ICF International (2015). Senegal DHS, 2014 - Final Report Continuous 2012-14.

- Apouey, Bénédicte, Gabriel Picone, and Joshua Wilde (2018). The Economics of Malaria Prevention
electricity, water and etc., education of household head, sex of household head, age of household head and employment status of household head. _Xjz_ includes municipal level characteristics, including log of natural number of the total number of Venezuelans living in the municipality. The coefficient, _↵p_ 3, identifies the coverage gap between Venezuelan and Brazilian. This gap is then divided into the explained and the unexplained component as discussed in (Oaxaca and Ransom, 1994).

# **4 Data**

## **4.1 Data Sources**

The data for this analysis comes from five sources. The education data comes from the 2019 and 2020 School Census; the labor market data comes from the 2019 Annual Report on Social Information (RAIS); the social assistance data comes from the _Cadastro Unico_ ; and the population data comes from National Migration Registry System (SISMIGRA) and International Traffic System (STI-MAR) for Venezuelans and from Brazilian Institute of Geography and Statistics Foundation’s (IBGE) population estimation counts for Brazilians (Summary of Social Indicators).

The School Census on basic education is carried out annually by INEP (Anisio Teixeira National Institute for Educational Research and Studies). It collects information on early childhood education, elementary education, high school education and professional education, irrespective of whether the organization is public or private. It contains information on school amenities, infrastructures and management, as well as, detailed information on students and teachers. Information on the teachers include their level of training, teaching activities, places of origin along with sex, gender and race, while the information on the students include demographics data along with places of origin. One caveat in this data is that it does not include any data on student’s socio-economic or family background. In 2019, it included data on about 176,000 schools, 2.3 million teachers and 50 million students, with 20,272 (0.05% of all students) Venezuelan students in regular traditional school, all
studies to rely on recall data, sometimes dating back several years, along with several aggregations, which are prone to recall bias (e.g., Gibson and Kim, 2007; Beegle et al., 2012) and aggregation bias (Sharma and Gibson, 2019; Rockmore, 2017; Rockmore et al., 2020) that may significantly affect statistical estimates of the impacts of violent conflicts. Besides limiting our understandings of the nature and consequences of violent conflicts, such data-related limitations are, among others, likely to hinder the speed and capacity of humanitarian organizations to target and deliver lifesaving humanitarian assistance to affected populations (Baker et al., 2020).

We make several key contributions to this literature, including to addressing some of the above data-related limitations. First, to our knowledge, we provide the first quantitative _ex durante_ study of the microeconomic consequences of an active large-scale conflict, giving insights into the immediate effects of war. Second, in addition to examining food security – a natural focus of war’s impacts on immediate welfare outcomes – we also document disruptions to household participation in livelihood pursuits and food markets, giving insights into sectoral and geographical patterns of resilience. We then discuss how such _ex durante_ monitoring and analysis may inform post-conflict recovery efforts. Finally, we discuss how similar high-frequency phone surveys and related remote data collection efforts could best be mobilized for monitoring of similar conflict contexts in other settings in the future.

Our analysis is enabled by combining High-Frequency Phone Surveys (HFPS) with conflict events data to identify the impact of the conflict on welfare outcomes.<sup>7</sup> The HFPS are monthly phone surveys that cover all regions of Ethiopia and span April 2020-May 2021, with multiple waves before and after the outbreak of the civil war. The HFPS sample is drawn from a nationally representative face-to-face survey fielded in 2019 (the 4<sup>th</sup> round of the Ethiopian LSMSISA). These combined data offer several advantages and a unique opportunity to link households
# **LEGISLATIVE MALAPPORTIONMENT AND INSTITUTIONAL PERSISTENCE**<sup></sup>

Miriam Bruhn<sup>1</sup> Francisco Gallego<sup>2</sup> Massimiliano Onorato<sup>3</sup>

**Keywords:** democracy, dictatorship, institutions, Latin America, persistence, political economy.

**JEL Classification:** H1, N46, N10, P16, P48.

1 Miriam Bruhn, The World Bank, Washington DC, USA. email: mbruhn@worldbank.org 2Francisco Gallego, Pontificia Universidad Católica de Chile, Department of Economics and Economic History and Cliometrics Laboratory, Santiago, Chile. email: <u>fgallego@uc.cl</u> 3Massimiliano Onorato, Post-Doctoral Associate, Leitner Program in International and Comparative Political Economy, Yale University, New Haven CT, USA. email: <u>massimiliano.onorato@yale.edu</u> We thank Daron Acemoglu, Pedro Dal Bó, Leopoldo Fergusson, Claudio Ferraz, John Londregan, Matthias Messner, Nathan Nunn, Pablo Querubin, James Robinson, Pablo Selaya, Guido Tabellini, and participants in seminars at Bocconi, MIT, Brown, Yale, the Catholic University of Chile, the 13<sup>th</sup> Annual Conference of the International Society for New Institutional Economics, the 2009 North American Summer Meetings of the Econometric Society, the 2009 SECHI meetings, the 2009 UNU WIDER Conference on “The Role of Elite in Economic Development”, the 2008 LACEA meetings and IMT Lucca for comments. We also thank Felipe González, Felipe Joannon, Piero Montebruno, Francisco Muñoz, and Diego Verdugo for excellent research assistance; David Samuels and Richard Snyder for sharing with us their data on malapportionment in Latin America; Jasmin Chakeri, Christian Gonzalez, Delia Lanz, Julio Loayza, Lars Moller, Rossana Polastri, Rashmi Shankar, Emily Sinnott, Rajeev Swami, and Manuel Vargas for their help with obtaining sub-national data on transfers from the central government for
Table 5: Having an Impact Evaluation from the Same Country is not Seen as a Substitute for Local Expertise

||Pooled<br>(1)|Policymaker<br>(2)|Policy<br>Practitioner<br>(3)|
|---|---|---|---|
|Impact|1.07***|1.05***|1.09***|
||(0.01)|(0.02)|(0.02)|
|Same country|1.61**<br>(0.32)|1.51<br>(0.40)|1.78*<br>(0.53)|
|Recommended by local expert|1.31**|1.44**|1.19|
||(0.17)|(0.27)|(0.23)|
|Same country * Recommended|0.80|0.75|0.84|
|by local expert|(0.28)|(0.35)|(0.46)|
|Observations|818|407|411|

This table reports the results of conditional logit regressions on which program was selected. Odds ratios are reported. The significance of the “Recommended by local expert” indicator and the insignificant interaction between “Same country” and “Recommended by local expert” in Column 2 suggests that policymakers do not put more or less weight on advice from a local expert depending on whether another form of local evidence - an impact evaluation done in the same country - is available. The number of observations represents the total number of choices made across individuals. Standard errors are provided in parentheses. * pă0.10, ** pă0.05, *** pă0.01.

14
, which in turn informed the design of the EDGEsupported country pilots that were implemented by the national statistical offices across Georgia, Maldives, Mexico, Mongolia, the Philippines and South Africa. These activities ultimately

> 3 <mark>These include the SDG 1.4.2 indicator, namely the proportion of total adult population with secure tenure rights to land, with legally recognized documentation and who perceive their rights to land as secure, by sex and by type of tenure, and the SDG 5.a.1 indicators, namely (a) the proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) the share of women among owners or rights-bearers of agricultural land, by type of tenure.</mark>

> 4 These households have very different socioeconomic and demographic compositions, with women also being more likely to be living in male-headed households than men living in female-headed counterparts (Deere and Doss, 2006; Beegle and van de Walle, 2019). In the context of a household survey that solicits information on “headship”, this information is gathered when a sampled household is first approached for an interview, and often through the question: “Who is the head of this household?” The simplicity of the question is, however, deceiving. First, headship definitions vary across countries. The head of household could be equated to the eldest member of the household, the primary breadwinner and/or the primary decision maker. Second, headship definitions typically refer to the head of household as the individual whose “authority” is recognized by the household members, but this definition overlooks the potential intra-household variation in authority in different realms of decision making. Relatedly, headship has rarely been extended to capture “dual-headed” households. Finally, there may be a disconnect between the headship definition and the interpretation of the survey question, with the latter exhibiting idiosyncrasy potentially at the household-level. 5 Following the 2008 System of National Accounts, the reported owner of assets is
,154|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Fixed effects model by year and municipality for the years 2005, 2010, and 2014. Gini refers to the Gini coefficient.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 31
among poorer households. In order to mitigate differential educational decisions by socioeconomic status, it is likely important to understand parental perceptions of their child’s abilities before they start school. Future research should examine how caregiver perceptions of child intelligence and development vary across socioeconomic status and how these differential beliefs may affect investments in education and home environment in order to target behavior change messaging to caregivers with inaccurate perceptions and encourage educational investments for children early in life to prepare them for school.

Some limitations of the current study warrant discussion. First, this study used crosssectional data to examine determinants of the discordance between caregiver-perceived child intelligence and child developmental abilities assessed with the ASQ-I; therefore, we do not claim causality. Second, we captured caregiver-perceived child intelligence through one rank-based question with an open interpretation of intelligence. We acknowledge that caregiver perceptions of child intelligence and assessments of children’s developmental abilities are context-dependent. Indeed, Harkness et al emphasize the important link between culture and early child development, noting that settings, customs, and caretaker ethno theories, or parents’ culturally constructed beliefs about child behaviors and development, operate together in a system that continually change and ultimately influence early child development (Harkness, et al., 2013). Future work should incorporate qualitative methods to identify beliefs of child development and consider experimenting with vignettes to anchor the perceived ladder scale (Ravallion, Himelein, & Beegle, 2016). Third, we were unable to assess early child development through direct observation due to cost and time constraints and we acknowledge the potential for recall and social desirability bias in caregiver reports; however, when we limited our analysis to the interviewer-observed items of the ASQ-I, our results did not substantially change.

22
0.026)|(0.024)|(0.031)|
|ln(Electricity)||||0.103***||||0.081***|0.060***|
|||||(0.025)||||(0.022)|(0.016)|
|ln(Survey)|||||0.328***<br>(0.069)||||0.349***<br>(0.051)|
|Observations|2,752|2,752|2,472|2,548|2,303|2,752|2,472|2,371|2,071|
|Countries|178|178|174|170|154|178|174|167|146|
|Years|05-20|05-20|05-19|05-19|05-19|05-20|05-20|05-19|05-19|
|R-Squared|0.805|0.803|0.816|0.811|0.866|0.806|0.821|0.824|0.882|

Robust standard errors in parentheses

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

_Notes._ All specifications include country and year fixed effects, and a country-specific time trend. When nights lights are used, fixed effects are included for either DMSP-OLS (pre-2014) or VIIRS (2014-2020) sensor. Both NO2 and lights are per area. Robust standard errors, clustered by country are reported in parentheses. R-Squared is adjusted and within all fixed effects.

consumption distributional data from more than 1900 household surveys spanning 1967-2019 and 168 economies.<sup>13</sup> As a caveat for this data, it is worth noting that not all these surveys are comparable
nowcasting year, _tn_ , has the advantage that it can yield estimates for countries without any previous poverty estimates at all. This is particularly a concern for countries that do not produce or share household survey data. Predicting changes in poverty from the past survey conducted at time _ts_ , in contrast, needs some assumptions about poverty levels in countries without data to arrive at global poverty rates. Yet, by utilizing the past survey, one can exploit that there is a past estimate to anchor the analysis around. When we predict changes in poverty, _c_ ˆ _poverty_ , _tn_ , _ts_ , the nowcasted poverty rates are given by When predicting changes from the past survey, we look at the annualized change in poverty rates in percentage points. This avoids having to predict extreme and undefined values, which often occur when predicting annualized growth in poverty rates, due to countries with poverty rates close to or at zero percent.

_(3) Mean welfare and (4) growth in mean welfare._ While predicting changes in poverty rates from the past survey exploits some past information available, it 6
to the EU.<sup>35</sup> This decline may be explained by the more stringent ROO in the EU which partially eroded the generous preferences it granted to African LDCs. Our finding of a negative effect of AGOA preferences on apparel exports to the EU stands in contrast with the finding of no trade redirection from the EU by Frazer and Van Biesebroeck (2010), possibly due to their use of a short period before and after AGOA (1999-2000 and 2002-2003).

We also consider potential trade redirection of African exports from non-EU destinations, relying on WITS data over the 1992-2016 period for imports by the US, EU and other countries designated as rest of the world (ROW).<sup>36</sup> Column (4) reports a negative and significant coefficient on the AGOA apparel term of imports by the EU plus ROW, indicating that the AGOA trade preference shock reduced African exports of apparel to all countries other than the US. This shows that US trade preferences did not immediately generate economies of scale that could spur growth of apparel exports also to other destinations, and that some of the increase in exports to the US was due to reorientation of installed capacity.<sup>37</sup> But column (5) shows that exports by African countries of AGOA apparel (as well as non-apparel) products and GSP LDC products to the combined US, EU plus ROW increase, suggesting that despite evidence of export diversion away from the EU and ROW towards the US, AGOA did lead to export creation reflected in growth in total exports of beneficiary countries.

# **_6.3 Robustness:  Omitted Variables_**

> 35 Column (1) replicates the estimates for the impact of GSP LDC and AGOA on US imports shown in Table 2 restricting the sample to that used for EU imports (i.e., dropping year 2017 and excluding all 28 EU countries from the control group). 36 Column (3) replicates the estimates for the impact of GSP
.<br><!-- End of picture text --> Growth drivers during this time were 1) investment-to-GDP ratio averaged 23% (private investment averaged 20.5% and public investment 2.5%), 2) average saving was 22.3% and Current Account Balance-to GDP ratio was, on average, -0.8%, 3) human capital growth averaged 0.6%, 4) TFP growth averaged 0.5%, 5) the growth of the working age-to-total population ratio (WATP) was around 0.35%, indicating a demographic dividend throughout this period and 6) total population growth declined from 1.65% to 1.05%.

# **3. Growth drivers in the business-as-usual baseline**

In this section we calibrate the LTGM-NR model to simulate Peru’s business-as-usual baseline over the 2023-50 period. To create a baseline for the Peruvian economy in the long run, we calibrate the future paths of the growth drivers such as investment, human capital, the growth of TFP in the non-resource and natural resource sectors,<sup>6</sup> as well as other key parameters. These assumptions are summarized in Table 1. We calibrate the LTGM-NR as the foundation of the analysis and add the LTGM-TFP extension and the LTGM-HC extension to simulate non-resource sector TFP growth and human capital growth, respectively. We use population growth projections (by age cohort) from the UN for demographic trends until 2050.

6 The resource sector is composed of the copper mining sector, which is around 8 percent of the total GDP and is expected to grow in the short run by around 2 percent due to the entrance of the Quellaveco mine. In the long run, the depletion in reserves will shrink the sector to 6 percent of GDP and grow below 1 percent on average.

7
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- Brookings. 2010. IASC Framework on durable solutions for internally displaced persons. In _Brookings Institution University of Bern Project on Internal Displacement_ . pp. 57–57.

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- Cassar, Alessandra, Pauline Grosjean & Sam Whitt. 2013. “Legacies of violence: trust and market development.” _Journal of Economic Growth_ 18(3):285–318.

- Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M Steven Fish et al. 2021. “VDem Dataset v11.