rafmacalaba/data-use-mentions-extended huggingface dataset:
the effects of heat on test scores we find are primarily associated with conditions within the classroom environment.

Further, we also tested the differences in results by dividing the data into two subsamples: urban and rural (Table 4). We find that the effect sizes for urban areas are larger than those for rural areas (although they are not statistically different), helping us rule out the hypothesis that hot days during the agricultural season are driving our results.

## **6.2 Do temperature shocks reduce nutrition?**

To answer this question, we utilized data from rounds 2 and 3 of the LSMS-ISA surveys for Ethiopia. We identified household food shocks at the month-year level and aggregated the number of negative food shocks experienced by a household in a calendar year.<sup>19</sup> We

> 17 _Meher_ is the main growing season and lasts from May to September, while the minor growing season, _belg_ , starts in February and ends in April. Around 90% of crop production occurs during the main growing season ( _meher_ ).

> 18See Figure 4 for results for all temperature bins in our data.

> 19The LSMS-ISA surveys collect detailed information on food security status of households. Households were asked if they faced a situation where they did not have enough food to feed all members of the household in the past 12 months, and in which months they faced this food insecurity. We added the number of months where the household faced food insecurity to construct an aggregate measure of the number of food shocks

11
with respect to non-food expenditures. We evaluate intra-household inequalities to account for 14% of total inequalities in Senegal. This number is shown to be 42% of the maximum that could be reached given the very large share of common expenditures (including food) in this context. This might be an underestimation, given the difficulty to observe inequality in food consumption. The overall consumption inequality is much higher than what is commonly thought, with a Gini index estimated to reach 47.1%, before we even factor in intra-household inequality.<sup>2</sup> Further, holding account of the intra-household inequality we can observe leads to revise upward the overall level of consumption inequality, up to nearly 50% for the Gini coefficient.

We find poverty measures very comparable to those produced with a more standard household survey conducted only a few months earlier. On the one hand, the mode of data collection (more than one respondent per household in PSF) seems to allow for a more complete recording of consumption leading to a 15% higher level of average consumption. On the other hand, in this context, taking into account intra-household inequalities increases the share of individuals found below the poverty line. Remarkably, in the case of Senegal, these two differences compensate each other so that the overall evaluation of poverty level doesn’t depend on the survey used. The poverty rate reaches about 43% at the time of our survey when using a basic needs poverty threshold. We further discuss how ignoring inter-personal inequality leads to flawed poverty diagnostics. We find that nearly 14% of non-poor households contain at least one poor cell. There are also non-poor cells in poor households.

We conduct a number of robustness checks. Worries that the inequality results could be in part driven by measurement errors are tackled with corrections of the inequality estimates as well as with simulation techniques. Qualitatively, all results stand for plausible levels of measurement errors. Further, poverty estimates are obviously dependent on the poverty line and 5
keep the samples with different set of explanatory variables similar, we opt to impute the missing values of Trust and Individualistic Culture as the mean of the continent-income-level cell of countries.<sup>7</sup> In addition, we add their missing indicators to allow for sample selection associated with missing incidences. We do not find the missing indicators to ever be statistically significant, indicating non-selectivity of their missing incidences.

To capture preexisting vulnerabilities to the pandemic, we rely on several variables: the share of elderly in the population (Elderly) (from WDI),<sup>8</sup> the share of urban residents in total population (Urban, from WDI), obesity prevalence (Obesity, from World Health Organization), and atmospheric SO2 concentration (SO2, from European Space Agency). Obesity captures the prevalence of obesity among adults (i.e., those with BMI greater than 30), measured in 2016 and from the World Health Organization.<sup>9</sup> We use data on atmospheric sulfur dioxide (SO2) concentrations as a measure of air pollution. The SO2 data set is the Sentinel-5P near real-time high-resolution imagery, provided by the European Space Agency (ESA).<sup>10</sup> A complete list of variables and data sources is shown in Table 1. The summary statistics for the variables are in Table 2.

# **IV. Empirical specifications and results**

To explain the pandemic outcomes across countries, we estimate the following regression at the country level: + α α θ (1) Here, _i_ refers to a country. ∂∂ ∂∂ ∂∂ _Y_ is the pandemic outcomes: the number of cumulative confirmed cases ∂∂ ∂∂ ∂∂ YY = ββXX ααyy + δδ V ααVVααVVααV ααVVyy + θθα θθVVααθθyy+ η VVααV VVαα + εε of COVID-19 per 1,000 residents, and the mortality rate from confirmed COVID-19 cases. _X_ is basic country characteristics, including log GDP per capita, log
mentioned in Alkire and Foster (2011b), AF weights do not correspond to marginal rates of substitution. The intuition is illustrated for the two dimensions case, where i is identified as poor when w1si1 + w2si2 ≥ k (Eq. (2)). Assume without loss of generality that w1 > w2 = 1 − w1. Figure 7 illustrates the resulting identification contour (in red) for two different cases. This contour corresponds to the union approach when w2 ≥ k while it corresponds to the intersection approach when w1 < k. Crucially, there is no achievement vector on the identification contour at which the slope of this

> 62The selection of dimension could also be guided by SWB data, as argued by Kingdon and Knight (2006).

> 63This is less of an issue when assuming that preference u is the same for all individuals. In any case, weights should not take equal values if preference data reliably shows that some variables should receive a larger / smaller weight.

> 64 First, subjective well-being questions do not provide unbiased information on well-being. For instance, people make trade-offs between their income and their level of happiness, which is the component of happiness they tend to prefer (Adler et al., 2017). Also, choices need not align with anticipated subjective well-being (Benjamin et al., 2014a). The link between different aspects of SWB questions and different aspects of preferences is still imperfectly understood (Benjamin et al., 2017, 2023). This matters because inter-personal comparisons may change when we change the aspects of SWB from which preference u is elicited (Defloor et al., 2017). See also Ravallion (2014) for additional concerns with the use of SWB data in poverty measurement. However, subjective well-being data are easy to collect. Second, preference elicitation surveys may work relatively well for two or three variables at a time, but multidimensional poverty indicators often rely on more than 10 variables. Also, the ranking that subjects provide on achievement vectors that they
-

- Milesi-Ferretti, Gian Maria and Cédric Tille, 2011. "The Great Retrenchment: International Capital Flows during the Global Financial Crisis," Economic Policy, CEPR, CES, MSH, 26(66): 285-342.

- Morgan, Donald P., Bertrand Rime, and Philip E. Strahan, 2004. ―Bank Integration and State Business Cycles.‖ _Quarterly Journal of Economics,_ 119(4): 1555-1584.

- Myers, Stewart C. and Majluf, Nicholas S., 1984. ―Corporate Financing and Investment Decisions When Firms Have Information that Investors do not Have.‖ _Journal of Financial Economics,_ 13(2): 187-221.

- Peek, Joe, and Eric S. Rosengren. 2000. "Implications of the Globalization of the Banking Sector: The Latin American Experience _." Federal Reserve Bank of Boston New England Economic Review_ (Sept-Oct):45–62.

- Ramalho, Rita, Jorge Rodriguez-Meza, and Judy Yang. 2010. "How are Firms in Eastern and Central Europe Reacting to the Financial Crisis?" Enterprise Note 8. World Bank. Available on line at http://www.enterprisesurveys.org/ResearchPapers/Reaction-to- <u>Financial-Crisis-in-Europe.aspx.</u>

- Svensson, Jakob. 2003. "Who Must Pay Bribes and How Much? Evidence From a Cross Section of Firms." _Quarterly Journal of Economics_ 118(1):207–230.

- Vogel, Ursula and Adalbert Winkler, 2011. ―Cross-border Bank Flows and Foreign Banks in the Global Financial Crisis: is Eastern Europe Different?‖ Frankfurt School of Finance and Management, mimeo.

- World Bank. 2009. "Enterprise Survey and Indicator Surveys: Sampling Methodology." World Bank: Washington DC. Available on line at <u>http://www.enterprisesurveys.org/documents/Sampling_Note.pdf.</u>

- World Bank. 2009. ―BEEPS At-a-Glance 2008-9 Cross Country Analysis.‖ World Bank
To estimate the median voter price, we specify the reduced form probability that a household votes yes on the referendum by where yi j = 1 denotes a yes vote and yi j = 0 indicates a no, and αj is a community fixed effect. We use this relationship to implicitly define the median voter price for community j according to To recover values for the median voter prices we first estimate φ in equation 9 using a fixed effects binary logit model. The estimator recovers a consistent estimate for φ by concentrating the fixed effects out of the likelihood function. To recover values for the fixed effects we implement a contraction mapping that calibrates each αj so that the observed fraction of yes votes in community j matches the average predicted probability of a yes vote for sampled community members. With values for φ and αj in hand, we then implement a numerical routine based on equation 10 to recover B<sup>50</sup> j<sup>for each village in our sample.Details on these steps are included in Appendix A.1.</sup>

# 4.4 Summary statistics

The left panel in Figure 1 shows the distribution of per hectare payments actually received by beneficiary communities, or calculations of what would have been received by non-beneficiaries. The mean value of these payments is 412 pesos per hectare. The second histogram shows the distribution of the random offers given in the referendum question. In 65% of the cases, the value offered was less than what the community was or would have been receiving. These distributions are similar for the household survey.

Table 3 shows summary statistics for the actual payments and offered prices, as well as the proportion of ‘yes’ votes. The acceptance rates for the contract offers overall were high – 72% for leaders and 79% for households. We note that the lower rate of acceptance among the leaders implies that estimates of WTA made from their responses will be higher than those from the household survey, and thus more conservative (i.e.
lower aggregate totals of frequent food expenditures. Gibson and Kim (2007) hypothesize that when respondents are asked to recall larger amounts of information, from a greater number of transactions due to larger household size or longer recall period, they will shift from summing the total of individual remembered events to estimating the overall total. In a survey experiment in Papua New Guinea, Gibson (2002) found that the average food expenditure was 26 percent higher with a consumption diary (asking for consumption for each of the 7 days covered) than asking for one reported amount for the entire 7 days. In a survey experiment in Ghana, reported expenditure on frequently purchased items fell by nearly 3 percent for each day added to the recall period, although it leveled off at about 20 percent after 2 weeks (Scott and Amenuvegbe, 1991).<sup>1</sup> There are also examples of recall decay for analysis of other topics. Bound et al. (2001) provide a general review of this literature, with examples taken from labor, health care, crime and motor vehicle accident statistics. The authors summarize the overall findings as “the greater the length of the recall period, the greater the expected bias due to respondent retrieval and reporting error” (p. 3743).

Relating these findings to agricultural data and the concept of salience, we would expect that large scale, unusual or expensive events for farm households are less likely to suffer from recall decay than smaller, less-salient events. We would expect to find less recall decay in cash crops than staple crops. Cash crops are sold, generally by weight at a fixed price, while at least a portion of staple crops are kept for home consumption. Moreover, cash crops are critical sources of cash income to subsistence farmers. With respect to inputs, we would expect to find more evidence of decay in labor usage as opposed to fertilizer use, as labor can be used sporadically throughout the growing season while fertilizer purchase and application are generally singular events.
Specifically, we estimate the following regression: where yh,t is the natural logarithm of annual spatially adjusted household consumption expressed in local currency units for household _h_ in time _t_ . Xi,h,t is a vector of household head’s characteristics, Zonet are geographical zone-areas dummy variables. The error term is drawn from a normal distribution. The results of this estimation are available in Table _5_ .

In the third step, using the parameters estimated from this consumption model, we can impute consumption into the target survey and calculate the relevant poverty rates. The imputed consumption vector is estimated using 100 imputations. Table _6_ shows the distribution of the imputed consumption vector and compares it to the distribution of the NLSS-based consumption aggregate.<sup>16</sup> The imputed consumption vector is then converted to US$ 2011 PPP to estimate poverty rates at different international poverty lines. We conduct the same exercise separately using data on the same non-monetary indicators from each wave of the GHS to impute consumption in 2010/11, 2012/13, 2015/16, and 2018/19.

# 5. Main results

The results of the backcasting and survey-to-survey imputation yield very similar results. Both show a decline in poverty in the first half of the decade followed by a period of stagnation – and even a slight increase – between the 2016 economic recession and 2019. The backcasted poverty series shows a decrease of at most 7 percentage points between 2009 and 2018/19, when using a pass-through rate of one. The survey-to-survey imputations show that poverty fell by around 3 percentage points between 2010/11 and 2015/16, but that progress towards poverty reduction has halted since then.

# _<u>5.1. Backcasting</u>_

Figure 1 shows the backcasted trend in poverty headcount rates (Panel a) and number of poor (Panel b) at the US$1.90 poverty line for the period 2009-2019, assuming a pass-through rate of one, that is, the growth rate
situation, seem to be creating an overcrowding e↵ect constraining integration.

This paper is divided into six sections. Section 2 starts with a description of the migration trend in Brazil and key demographic characteristics of the Venezuelan refugees and migrants and then moves on to give a brief literature review on forced displacement and economic integration, focusing on the work on Venezuelan refugees and migrants in Latin American countries. Section 3 provides details on the variables and the methodology employed. Section 4 describes the data and presents the summary statistics with an emphasis on the e↵ect of the COVID-19 pandemic.<sup>8</sup> Section 5 presents the results, while Section 6 concludes with some policy implications and discusses venue for further research.

# **2 Refugee and Migration Trends and Literature Review**

The refugees and migrants population in Brazil has been increasing rapidly from 2016 and the blue bar in Figure 1 shows that the total refugees and migrants population increased from about 0.7 million in 2016 to about 1.4 million in July 2020. From being one of the lowest asylum seeking and migrants population in 2016, among the Latin American countries, Venezuelans in Brazil quickly became the largest refugee and migrant population by 2019. Brazil also has significant number of nationals<sup>9</sup> from Bolivia and Haiti, followed by Colombia, Argentina and China. During the period of July 2017 to October 2020, Brazil received about 126,256 migrants and 30,000 asylum seekers

> 6Venezuelans could also be compared with the other migrants, but this study concentrates on the Brazilians by birth as this paper is more focused on the extent of local integration in Brazil.

> 7The Unified Registry of Social Programs (CadUnico) is a database that collects details about low-income families in Brazil, which can be used to identify vulnerable people in the society and develop appropriate benefits for them. The details are given in the Methodology section.

> 8Impact analysis of the COVID-19 pandemic is not possible due to unavailability of data.
# **3. Data**

## _Multidimensional poverty index_

Our analysis uses the Multidimensional Poverty Index (MPI) and its indicators as dependent variables. The MPI, developed by the Oxford Poverty and Human Development Initiative (OPHI), provides a nuanced understanding of global poverty beyond income-based metrics. It incorporates a range of indicators reflecting various deprivations that individuals face across different aspects of life. This multidimensional approach aligns with the idea that poverty is not only about income, but also the inability to access essential services and enjoy a standard quality of life (Alkire et al., 2021). The MPI includes ten indicators grouped under three significant dimensions, as depicted in Table 1.

Table 1: Dimensions, indicators, and weights of the MPI

|**Dimensions**<br>**(Weight)**|**Indicator(Weight)**|**Aperson is deprived of living in a household where...**|
|---|---|---|
|Health (1/3)|Nutrition (1/6)|Any person under 70 years of age for whom nutritional<br>information is available is undernourished.|
||Child mortality (1/6)|A child under 18 has died in the household in the five<br>years preceding the survey.|
|Education (1/3)|Years of schooling<br>(1/6)|No eligible household member has completed six years<br>of schooling.|
||School attendance (1/6)|Any school-aged child is not attending school up to the<br>age at which he/she would complete class 8.|
||Cooking fuel (1/18)|A household cooks using solid fuel, such as dung, crops,<br>shrubs, wood, charcoal, or coal.|
||Sanitation (1/18)|The household has unimproved
wage inequality in Brazil. The first develops a Roy model in which heterogeneous workers self-select into a commodity and a non-commodity sector based on their productivity in each of these sectors. An increase in commodity prices increases the wages of both skilled and unskilled workers, benefiting more the latter due to the high intensity of unskilled labor in the commodity sector. As a result, workers reallocate across sectors, and regions specialized in producing commodities observe a convergence in wages with more industrialized regions. Using data from the Brazilian Census, Ad ̃ao (2015) estimates that this process can account for 5-10 percent of the reduction in the variance of log wages between 1991 and 2010. Benguria et al. (2018), using matched data of workers in the formal sector and firms, consider a similar cost mechanism by which the increase in commodity prices rises the relative price of unskilled workers, thus reallocating workers across industries. However, they also consider a second mechanism, a wealth channel. With the increase in commodity prices there is a surge in demand in the whole economy, benefiting firms that serve the domestic market. Hence, there should be a reallocation of

> 12These labor markets are defined based on microregions, which are groups of municipalities developed by IBGE based on the integration of their local economies.

> 13The commodity sector includes agriculture, mining and fuels. Skilled labor is defined as collegeeducated labor force.

17
control for these factors, the survey asked respondents to focus the case study on a manufactured product that is of medium value; is transportable in a dry-cargo, 20-foot FCL container; requires no refrigeration or special environmental conditions; requires no special phytosanitary or environmental standards check; and includes no hazardous material or military equipment.

To produce indicators that are robustly comparable across 140 economies ranging from Australia, Norway, and the United States to Burundi, Malawi, and Nepal, three SITC (Standard International Trade Classification) codes were chosen to specify the type of product being traded:

- SITC 65: Textile yarn, fabrics, made-up articles.

- SITC 84: Articles of apparel and clothing accessories.

- SITC 07: Coffee, tea, cocoa, spices, and manufactures thereof.

An examination of world trade data confirmed that most economies in the sample trade in these products.<sup>7</sup>

# **_Assumptions about procedures_**

A procedure for which the time and cost were measured was defined as any interaction of the firm with external parties (government agencies or officials, inspection agencies or officials, port officials, customs, and the like). Intrafirm interactions among employees were excluded. All procedures that are legally or in practice required for trading or shipping a containerized product were recorded, even if they may be avoided in exceptional cases.

An important issue to control for was simultaneity of procedures. When two or more procedures are simultaneously performed, the time for the entire transaction (import or export) cannot be obtained by summing the time for individual procedures. A separate question in the questionnaire made it possible to identify and correct for simultaneously completed procedures.

# **_Missing or inconsistent responses_**

The questionnaire was carefully designed to provide consistency checks across responses. Where gaps or discrepancies were noted, follow-up telephone calls were made to the source to resolve the discrepancy or fill in the missing data. The cost data presented particular difficulties, requiring substantial efforts to follow up with the original source as
Figure 22: Absolute gains of four CCT scenarios aimed at households with young children <!-- Start of picture text --> A. Absolute gain per HH by market income decile B. Absolute gains per HH by demographic<br>structure<br>3,000 1,500<br>2,500<br>2,000 1,000<br>1,500<br>500<br>1,000<br>500<br>0<br>0 No children No children, 1 child 2 children 3+ children<br>1 2 3 4 5 6 7 8 9 10 or elderly with elderly<br>CCT Version 1 CCT Version 2 CCT Version 1 CCT Version 2<br>CCT Version 3 CCT Version 4 CCT Version 3 CCT Version 4<br>KHR/month<br>KHR/month<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Increasing the amount of targeted cash transfers to households regardless of whether they have young children has an even greater poverty-reducing effect. Simulations demonstrate that allocating cash transfers targeted to IDPoor households on a per-capita basis regardless of the age structure of the households at the value of US$ 200 per year would reduce the poverty rate by 1.4 percentage points.

Table 7: Poverty reduction and cost of two CCT scenarios aimed at IDPoor households

|**CCT scenario**|**Design**|**Poverty reduction (pp)**|**Cost (Millions US$/year)**|
|---|---|---|---|
|CCT version 5|IDPoor households: 150<br>US$ perperson|-0.996|236.8|
|CCT version 6|IDPoor households: 200<br>US$ perperson|-1.418|315.7