rafmacalaba/data-use-mentions-extended huggingface dataset:
Using data collected for this study, I find that potential migrants are indeed misinformed about potential earnings and mortality risk, but not always in a way that policymakers expect. Consistent with the widely held notions described above, inexperienced potential migrants, meaning those who have never before migrated abroad for work, overestimate their earning potential. Compared to experienced migrants – those who are better informed as they have migrated abroad for work before – they expect to earn 26 percent more. I argue that this estimate is a lower bound on the extent of misinformation as the pool of experienced migrants in my sample is likely to be selected from the higher end of the actual earnings distribution. This suggests that, even in a context where 15 percent of the households have a current migrant in one of these destinations, potential migrants can still be misinformed about

> 1An extreme example of the opinion of many policymakers is the following quote from an expert on Nepali migration: “They go without asking questions. They are not ready to listen. They just want to go. They never even bother to ask how much they will earn.” (Pattisson, 2013a). Though this statement may be an exaggeration, the view that potential migrants lack information or are misinformed is widely held.

> 2Migrants working at high-risk jobs for low wages has been dubbed a form of modern-day slavery. Several newspaper articles and commissioned research reports express this view (see Deen, 2013; The Asia Foundation, 2013, and other news articles quoted elsewhere in the paper).

2
. Zingales (1994 and 1995) studies the relationship between the premium attributed to voting rights and ownership structures for firms at the Milan stock exchange and in the U.S.. Morck, Shleifer and Vishny (1988) provide evidence of an inverse U-shaped relationship between ownership by managers and a firm's valuation and its profitability. McConnell and Servaes (1990) confirm this and provide in addition evidence of a positive relationship between institutional ownership and a firm's valuation and its profitability. For the Czech and Slovak Republics, Claessens (1997) finds a positive relationship between concentrated ownership and voucher and secondary market prices.

We pursue both approaches in this paper as we consider them complementary. Using performance and other accounting data alone to test effect of changes in corporate governance is difficult in formerly centrally planned economies. Data are of poorer quality as new accounting standards are introduced. Furthermore, since the demise of central planning, relative prices have 5. For the first round, prices (points per share) were set identically for all firms. If demand matched the supply of shares of particular firm at that price, all shares of that firm were sold in that round. If a firm's shares were undersubscribed, those that bid received shares at that price and the remaining shares were offered in the next round. When there was modest excess demand (less than 25% excess supply), citizens were given priority over investment funds and the demand of investment funds was scaled down to clear the market. With more than 25% excess demand, however, no shares were sold and all shares were to offered again in the next round at a new price which was set at (approximately) the previous price times the ratio of demand to supply. In some individual cases, prices were adjusted differently by the authorities (see Shafik 1995 for further details and some examples).
# Scarcity Nationalism during COVID-19: Identifying the Impact on Trade Costs

Peter H. Egger,<sup>¶</sup> Gerard Masllorens<sup>‖</sup> , Nadia Rocha<sup>∗∗</sup> , Michele Ruta<sup>††</sup>

**Keywords:** Covid-19; protectionism; trade; Global Trade Alert **JEL codes:** F13; F14

## **1 Introduction**

During the COVID-19 pandemic, we witnessed a reemergence of trade protectionism. However, while traditionally protectionism is something that is pursued on the part of importers to shield domestic producers from what they deem to be excessive competition from foreign suppliers, the protectionism applied during the current pandemic aimed at maximizing the availability of sensitive, foremost medical, products in the domestic market through a mix of trade facilitation and export controls (Evenett et al., 2022). We refer to this mix of export and import policy as ”scarcity nationalism” to distinguish it from traditional trade protectionist measures. This study empirically investigates the impact of these trade policies during COVID-19 on trade costs of critical medical goods.

The analysis relies on the Global Trade Alert (GTA) data on policy measures that have been adopted by countries at the HS6-traded-product level for medical products that have been critical

> ¶ETH Zurich, CEPR, CESifo, WIFO; Email: pegger@ethz.ch; Address: Leonhardstrasse 21, 8092 Zurich, Switzerland.

> ‖ETH Zurich. Email: gmasllorens@ethz.ch; Address: Leonhardstrasse 21, 8092 Zurich, Switzerland.

> ∗∗World Bank; Email: nrocha@worldbank.org; Address: World Bank, 1818 H Street, Washington DC, USA.

> ††International Monetary Fund; Email: mruta@imf.org ; Address: International Monetary Fund, 700 19th st NW, Washington, DC 20431, USA
- Giles, J. and I. Murtazashvili (2013). A control function approach to estimating dynamic probit models with endogenous regressors. _Journal of Econometric Methods 2_ (1), 69–87.

- Gourlay, S., T. Kilic, A. Martuscelli, P. Wollburg, and A. Zezza (2021). High-frequency phone surveys on COVID-19: Good practices, open questions. _Food Policy 105_ , 102153.

- Gulesci, S., M. Puente-Beccar, and D. Ubfal (2021). Can youth empowerment programs reduce violence against girls during the COVID-19 pandemic? _Journal of Development Economics 153_ , 102716.

- Gupta, D., H. Fischer, S. Shrestha, S. Shoaib Ali, A. Chhatre, and K. Devkota (2021). Dark and bright spots in the shadow of the pandemic: Rural livelihoods, social vulnerability, and local governance in India and Nepal. _World Development 141_ , 105370.

- Gutierrez, E., A. Rubli, and T. Tavares (2022). Information and behavioral responses during a pandemic: Evidence from delays in COVID-19 death reports. _Journal of Development Economics 155_ , 102774.

- Harttgen, K., S. Klasen, and R. Rischke (2016). Analyzing nutritional impacts of price and income related shocks in Malawi: Simulating household entitlements to food. _Food Policy 60_ , 31–43.

- Hirvonen, K., A. de Brauw, and G. T. Abate (2022). Food consumption and food security during the COVID-19 pandemic in Addis Ababa. _American Journal of Agricultural Economics 103_ (2), 772–789.

- Hoenig, J. M. and D. M. Heisey (2001). The abuse of power: The pervasive fallacy of power calculations for data analysis. _American Statistician 55_ (1), 19–24.

- Humphreys, M., R. Sanchez de la Sierra, and P. van der Windt (2013).
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Policy Research Working Paper 6321

# **Abstract**

The proliferation of mobile phones in developing countries has generated a wave of interest in collecting high-frequency socioeconomic surveys using this technology. This paper considers lessons from one such survey effort in a difficult environment—the South Sudan Experimental Phone Survey, which gathered data on living conditions, access to services, and citizen attitudes via monthly interviews by phones provided to respondents. Non-response, particularly in later rounds of the survey, was a substantial problem, largely due to erratic functioning of the mobile network. However, selection due to non-response does not appear to have markedly affected survey results. Response rates were much higher for respondents who owned their own phones. Both compensation provided to respondents in the form of airtime and the type of phone (solarcharged or traditional) were varied experimentally. The type of phone was uncorrelated with response rates and, contrary to expectation, attrition was slightly higher for those receiving the higher level of compensation. The South Sudan Experimental Phone Survey experience suggests that mobile phones can be a viable means of data collection for some purposes, that calling people on their own phones is preferred to handing out phones, and that careful attention should be given to the potential for selective non-response.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gdemombynes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in
Devarajan et al., 2015).

The first step in the microsimulation exercise is to implement a set of changes in the household surveys’ demographic structure. The population growth adjustment is particularly important in countries with high fertility rates, such as those in Sub-Saharan African. In practical terms, the adjustment for population growth allows the analysis to explicitly take into account changes in the size of the working-age population. We perform population and education projections during the first stage of the micro-simulation model and in creating the Business as Usual scenario for the CGE model. For each country, we construct the demographic profile in two steps. First, the age and gender composition is exogenously determined following medium variant estimates from the World Population Prospects (United Department of Economic and Social Affairs Population Division, 2015). In a second step, following Bourguignon and Bussolo (2013), country-specific educational profiles are constructed using initial educational achievement levels observed in the household surveys with some conservative yet simple assumptions about educational progress.

More specifically, starting with the household surveys, the country-specific demographic profiles are constructed by partitioning each country’s total population into: (1) 16 age-groups (0-4, 5-9, 10-14, ..., 6569, 70-74, 75; (2) two gender groups; and (3) three different levels of educational attainment: (i) Noeducation or primary; (ii) secondary; and (iii) tertiary education. As mentioned earlier, age and gender totals are based on data from the United Nations’ (2015) medium variant population projections. In terms of education, we assume that as the population ages, the average educational attainment in a country increases through a pure pipeline effect, as younger and more educated cohorts replace older cohorts. For example, if at time _t_ half of the population in the cohort formed by individuals between 25 and 30 years of age have post-secondary education, then after 10 years (at _t_ +10), half of the population between 35 and 40 will have post-secondary
one point in their lives.

The enumerator began fielding the module with a statement that guaranteed the anonymity of the respondent and that nobody else in the household would be informed of the responses. The extensive module asked whether the respondent’s husband or partner engaged in three different types of emotional abuse (e.g., humiliate or threaten, etc.) and ten different types of physical abuse (e.g., slap or punch, etc.) directed at the respondent.<sup>21</sup> For each type of abuse that the respondent experienced, they were then asked if the abuse happened ”often,” ”sometimes,” or ”not in the past 12 months.” The module further asked additional relevant questions about IPV. Specifically, the module asks whether the respondent is often afraid of her husband or partner, sometimes afraid, or never afraid; and the module further asks the respondent if she had physically harmed her husband or partner at times when he was not harming her.

The respondent in the women’s survey is explicitly told that they are allowed to not answer specific questions if they so choose. However, given the very unique circumstances of PNG and the careful fieldwork ensuring the safety of respondents, the rates of non-response are very low. In each

> 21An exhaustive list of types of abuse covered can be provided upon request.

10
. However, a reliable sampling frame is often missing in such conflict situations where population census rarely takes place, and registration data may also be obsolete, thereby complicating the implementation of sample household survey (Aguilera et al. 2020). Furthermore, collecting data in the fragile context also raises a concern for the safety of enumerators, inhibiting survey implementation in the unsafe regions (Corral et al. 2020). Furthermore, FCS countries also suffer from limited statistical capacity and a lack of resources to implement data collection (Cas et al. 2022).

In terms of the regional representation of publicly available FDP datasets, data is relatively rich in SubSaharan Africa in contrast to other regions.<sup>28</sup> In fact, Sub-Saharan Africa accounts for 53 percent of all FDP micro-level datasets (excluding project-specific datasets and those datasets collected before 2010). These patterns hold even after accounting for the above-mentioned baseline country characteristics such as GDP per capita, conflict intensity, and number of FDP (see Figure 3 Panel B). The average marginal effects of regional dummies are all negative and significant (except for South Asia) using Sub-Saharan Africa as a benchmark.

Data is particularly rich in East Africa. This skew is primarily driven by three countries that have a higher number of datasets than expected given the number of FDPs they host: Uganda, Kenya, and Tanzania. This pattern is reflective of a broader data and evidence landscape in Africa. Indeed, these three countries are also among the top in the region with the largest number of general micro-level datasets in the WB MDL. Additionally, Kenya and Uganda are among the most extensively studied countries in impact 27 For this country-level analysis, we exclude those micro-level datasets that are collected prior to 2010 or projectspecific datasets. Furthermore, some survey datasets are stored as separate entries in the WB and UNHCR MDL even though they are indeed part of the same survey (e.g., entries by camp or by wave). Those independent entries are collapsed as one
are read the following: “Antonio and Lilibeth are married. Antonio makes all the financial decisions for the family and does not seek Lilibeth’s opinion because he makes all the decisions for the household.”<sup>6</sup> We create a dummy variable which is equal to 1 if a respondent identifies with a specific decision-making style and evaluate the impact of CCTs on increasing the likelihood of identification with a specific decisionmaking style, relative to all other styles.

Finally, we explore the impact on time-use. We check whether exposure to CCTs has affected the fraction of time spent on chores, paid work and unpaid work. The impacts for men and women may be in different directions.

# _Changes in exposure to gender-based violence_

Third, benefitting from CCTs in adolescence may have long-lasting impacts on extreme form of disempowerment, or exposure to GBV through change in behavior/relationship of parents. Buller et al. (2019) demonstrate in a review of literature on the relationship between cash transfers and GBV that CCTs are generally associated with reduction of GBV. Exposure to violence in childhood (either direct exposure or by witnessing violence perpetrated or suffered by parents) is an important predictor of exposure to and/or perpetration of GBV in adulthood (Garcia-Moreno et al. 2006). Thus, the 4P program could reduce incidence of GBV among our sample through lowering GBV their mothers were exposed to a decade ago.

We collect data on intimate partner violence (IPV), domestic violence perpetrated by other household members,<sup>7</sup> and violence outside home.<sup>8,9</sup> For each type of violence, we construct indicators of physical, emotional, sexual and economic violence, with questions largely borrowed from Demographic and Health Surveys (DHS). For IPV, we collect data on prevalence and incidence during the 12 months preceding the survey. For domestic violence and violence outside home, we only administer questions about life-time prevalence.

> 6 The full list of vignettes
definition of the 2011 Census. Land available for formal private development corresponds to all land that is not constrained for construction.<sup>34</sup> The amenities that we consider include natural amenities (such as slope and proximity to the ocean) as well as historical amenities (such as the proximity to the historical center). The data sets used are listed in Appendix C.4.

For the estimation of the model’s parameters (see below), we also use property price data extracted from the City of Cape Town’s geocoded data set on property transactions for 2011, as well as data on dwelling sizes made available to us by the City of Cape Town.

# _4.2. Estimation of the parameters of the model_

The estimation of the model is done in three steps. First, we choose a first set of parameters using available information, without solving the model. These include the minimum dwelling size _qmin_ , the size of subsidized plots _qFS_ and of backyards _Y_ , the construction lag τ, the physical and financial depreciation of housing θ and ρ, the interest rate δ, and the agricultural rent _RA_ . The minimum dwelling-size for formal housing is set at _qmin_ = 31 _._ 6 _m_<sup>2</sup> , which is the minimum dwelling size observed in formal neighborhoods. Backyards of subsidized houses have a size _Y_ = 70 _m_<sup>2</sup> . We choose the time lag of housing investment τ to be 3 years and the physical depreciation time of building stock θ to be 100 years, as in Viguié et al. (2014). The financial depreciation rate of the built capital is ρ=.05 (i.e., 5%). We allow the interest rate δ and the agricultural land rent _PA_ to vary with time. For δ, we use the annual values for South Africa in the World Development Indicator database (World Bank, 2016). We set the agricultural price at
11]|[0.26]|
||_{_16%_}_|_{_8%_}_|_{_8%_}_||||
||_⟨_0.0547_⟩_|_⟨_0.0256_⟩_|_⟨_0.0291_⟩_||||
|Tertiary education jobs|0.0013<sup>_∗∗∗_</sup>|0.0011<sup>_∗∗∗_</sup>|0.0001|-0.54|-0.69|-0.15|
||[0.0003]|[0.0002]|[0.0003]|[0.12]|[0.13]|[0.44]|
||_{_14%_}_|_{_7%_}_|_{_7%_}_||||
||_⟨_0.0528_⟩_|_⟨_0.0258_⟩_|_⟨_0.0270_⟩_||||
|_∗p <_0_._1, _∗∗p <_0_._05, _∗∗∗p <_0_._01|||||||

_Note_ : Table shows difference-in-differences estimates of the impact of the payroll tax cut on private sector employment based on estimating equation (9) separately for various subgroups. Panel A considers employment in jobs paying above vs. below 150% of the minimum wage. Panel B considers employment in occupations with above- vs. below-median average wages in 2012. Panel C considers employment in occupations requiring different levels of education, defined as the modal level of education in the Labor Force Survey in 2012. In each case we estimate the regression equation (9) using employment in a given subgroup (job or occupation) and firm type (all firms in column 1, below-median TFP firms in column 2,
and resources. The simulations by Choquette-Levy et al. (2021), who parameterize an agent-based model on household survey data from Nepal, support this perspective, but also highlight that cash transfers and risk transfer mechanisms may prevent climateinduced immobility of farmers.

In short, the final outcome is ultimately household- and context-specific. In such a complex picture, migration and immobility are only two of the possible outcomes, and the decision to leave can be interpreted as one of many potential adaptation strategies that an individual or household can adopt. As such, there is a need to improve our understanding of the causes and consequences of the immobility of populations ‘trapped’ by environmental disasters, because too often the policy focus is on ‘those who leave’ rather than to ‘those who cannot’ (Findlay, 2012).

# _E. Migration as adaptation_

One of the most interesting advances in the scientific literature is the progressive integration of the issues 10
productivity convergence,” in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, https://doi.org/10.1787/63318cf5-en.

- Szarzec, Katarzyna, Ákos Dombi, and Piotr Matuszak. 2021. “State-owned enterprises and economic growth: Evidence from the post-Lehman period,” _Economic Modelling_ 99(2021): 1-20. <u>https://www.sciencedirect.com/science/article/pii/S0264999321000730</u>

- Vitoria, Rafaela; Bressan, Aureliano and Iquiapaza, Robert. 2020. “Do State-owned Enterprises in Brazil Require a Risk Premium Factor?” _Brazilian Business Review_ , 17(5): 488-505.

- World Bank. 2018. “From Uneven Growth to Inclusive Development: Romania’s Path to Shared Prosperity.” Systematic Country Diagnostics. Washington, DC: World Bank.

- World Bank. 2020. “Markets and People: Romania Country Economic Memorandum.” International Development in Focus. Washington, DC: World Bank.

- World Bank. 2022. “Who’s the BOSs: Shedding New Light on Businesses of the State.” Equitable Growth, Finance, and Institutions Notes. Washington, D.C.: World Bank

32
Figure 1: Establishment distribution: Economy-wide <!-- Start of picture text --> Burkina Faso (2015) Cameroon (2008) Ghana (2013)<br>Rwanda (2013) USA (2013)<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>100 100<br>80 80<br>60 60<br>40 40<br>Share of establishments (%) 20 20<br>0 0<br>100<br>80<br>60<br>40<br>Share of establishments (%) 20<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. For the U.S., the data comes from the 2019 Business Dynamics Statistics (BDS) dataset. _Note:_ The figure is constructed based on data for establishments in all sectors irrespective of their state or foreign ownership status. Establishments with missing employment data are excluded.

11
sex totals from external demographic data. It is then perhaps unsurprising that unit nonresponse is found to be the cause of the differences in the top 1%.** Burkhauser et al. (2017) compare UK household surveys and admin data based on tax records. They show that the surveys diverge from tax data around the 95<sup>th</sup> percentile of the income distribution and argue that under-reporting rather than unit non-response is responsible for this divergence, because the 95<sup>th</sup> percentiles and below are similar in both data sources. Burkhauser et al. (2023) provide further evidence in support of under-reporting being more important than unit non-response in the UK, in the context of investigating the share of women in the top 1%. They show that the top 1% in surveys and tax data have very similar observable characteristics, and that these are distinctly different to the 9 percentiles below the top 1%.

We noted above that Flachaire et al. (2022) found that under-reporting at the top of the distribution was important in Uruguay using linked survey and tax data, but the authors did not investigate unit 26