rafmacalaba/data-use-mentions-extended huggingface dataset:
which are not nationally representative.<sup>8</sup> Without comprehensive global data that cover all possible destinations, it is difficult for empirical and analytical papers to explore beyond static models.

The data in this paper come from two different sources. The global bilateral skilled migration database, as described in great detail in Artuc et al (2015), provides migrant stocks by gender and education level (tertiary and non-tertiary) for 1990 and 2000 for each pair of countries in the world. Part of the data comes from original statistical sources and comprises around 80% of the migrant stock in the world. The rest are imputed using gravity based models. Artuc et al (2015) describe the empirical methods behind the imputation based on a gravity model. Since the imputed data might bias our results, we use only the raw data for 2000. The parallel data for 2010 for OECD destinations come from the DIOC database which is described in Arslan et al (2014). The DIOC 2010 database also has information on the time of arrival of migrants by skill level and country of origin for all of the OECD destinations. This new disaggregation, unavailable for earlier decades, allows us to construct the number of new arrivals by skill and origin country for 2000-2010. We discuss in the next section how we use this new data in our empirical analysis. Finally, we have collected additional bilateral migrant stock data by skill level for around 50 non-OECD destinations in 2010. We use this data to complement the DIOC data.

The second data source is the annual American Community Survey (ACS) of the United States for 2001-2011. ACS provides detailed information on a large sample of the population, including migrants who are defined as people born outside the United States, regardless of their citizenship or residency status. The survey asks the country of birth, the year of migration to the US and where people were living a year earlier. Using these questions, we take the people who declared that they had moved to the US within
Policy Research Working Paper 10359

# **Abstract**

Online surveys are convenient, cost-effective, speedy, and increasingly popular instruments for data collection. This study investigates whether online surveys that used Random Domain Intercept Technology to recruit respondents were accurately measured labor market outcomes in six middle-income countries in the aftermath of the COVID-19 pandemic. Compared with the national average, online surveys oversampled males, youth, those with higher levels of education, and those in smaller households. Reweighting using propensity score estimates fails to equalize the means of variables excluded from the model. When comparing the employment-to-population ratio from the internet surveys to the most recent relevant nationally representative surveys, the average deviation is 30 percent. Reweighting using propensity scores in that case worsened the bias. Internet survey estimates of informal and self-employment rates also tend to be inconsistent with benchmark data, although the latter are available for fewer countries. Overall, the results suggest that despite the advantages and convenience of recruiting internet survey participants through Random Domain Intercept Technology, the resulting sample is not representative and even after propensity score reweighting, it can yield estimates that are at odds with nationally representative surveys.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at dnewhouse@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 this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data also suffers from underreporting in the survey (Table 4). The direct approach gives comparable but somewhat lower estimates than the MIMIC approach for mean underreporting at the firm-level, while for aggregate underreporting the estimate is clearly higher. The relative high estimate for aggregate underreporting by the direct approach

# **TABLE 5 Comparison of Three Approaches to Measuring Underreporting**

|Firm-level<br>(mean)|Aggregate|
|---|---|
|**Direct Approach**<br>35.1%|43.9%|
|**Indirect Approach**<br>15.2%|17.4%|
|**MIMIC Approach**<br>38.0%|36.9%|
|_Note: allfigures are weighted by sampling weights_||

follows from the fact that the predicted true sales in the direct approach are calculated by dividing the reported tax office sales of a firm by the percentage of sales the “typical establishment” reports to the tax office according to the same firm (multiplied by 100). Consequently a higher reported percentage of underreporting gives a higher estimate of true sales for the same firm and therefore is weighted more in the calculation of aggregate underreporting.

In sum, the MIMIC approach appears to give the more accurate estimate of underreporting. The indirect approach performs poorly and underestimates underreporting because of being sensitive to measurement errors and underreporting in the survey data. The direct approach gives an estimate of the firm-level average percentage of underreporting which is somewhat lower than the MIMIC approach, confirming the conjecture of Schneider and Enste (2000, p.92) that the direct approach provides a lower bound for true underreporting. However, the direct approach gives a too high estimate for aggregate 34
(see section 2.9.5). This impact evaluation sample is balanced between treatment and control on key variables ( **Table 2** ).<sup>23</sup>

> 17 Like the intervention sampling, the MLSS sampling uses a population frame of all Malawi public primary schools. However, the MLSS sampling does not exclude junior primary schools.

> 18 Divisions are sub-regional administration bodies established for management of education.

> 19 MLSS baseline data (Asim and Casley Gera, 2024) shows that variability in inputs and outcomes is substantial at sub-district level (the lowest unit) and shows limited differences between three regions and six divisions (overall).

> 20 Of these, 258 are from the first cohort, of which 133 are treatment schools and 125 are control schools; and 128 are from the second cohort, of which 69 are treatment schools and 59 are control schools.

- 21 Owing to an error in implementation planning, the initial batch of training included 51 schools which were not part of the treatment group. Eight of these additional schools are in the control group. However, none of these are in the MLSS overlap and therefore not in the IE sample.

- 22 Owing to financial constraints, 337 of these were visited for the baseline round. As we employ a longitudinal sample of students, only this subset of schools is used for analyses including student test scores. There are no significant differences between this subset of schools and the rest of the impact evaluation sample (see Appendix B).

> 23 The impact evaluation sample is also balanced within each cohort (see Appendix B).

18
...rms to an economic downturn in Chile, do not ...nd multinationals to react to the economic crisis di¤erently than domestic ...rms. Desai, Foley and Forbes (2008), evaluating the response of multinational and local ...rms to sharp currency depreciations, ...nd sales, assets, and investments to increase signi...cantly more for U.S. multinational a¢ liates than for local ...rms. Kalemli-Ozcan et al. (2011) utilize a ...rm-level database from six Latin American countries between 1990 to 2005 to compare investment undertaken by domestic exporters to that of foreign-owned exporters in the aftermath of currency crises. They ...nd that, conditional on ...rm exposure to short term foreign currency debt, foreign-owned exporters increase investment signi...cantly more than domestic exporters.

Di¤erent from the above studies’ focus on regional economic slowdowns and currency depreciations, our analysis draws from the recent Global Financial Crisis. We look to the global coverage and the considerable heterogeneity of the recent crisis to examine the di¤erent roles of foreign ownership in establishments’ reactions to economic shocks. Furthermore, in contrast to previous studies’focus on evaluating the average e¤ect of foreign ownership, we seek to explore the channels through which foreign ownership can a¤ect establishment

> 5 Another notable literature on the host-country e¤ects of FDI focuses on the productivity spillovers of multinational ...rms. See, for example, Haddad and Harrison (1993), Aitken and Harrison (1999), Blomström and Sjöholm (1999), and Javorcik (2004) for studies in this area.

6
|88|79|80|79|76|64|65|
|Kebeles|(%)|0.105|0.119|0.122|0.144|0.373|0.390|0.405|0.408|0.389|0.409|0.399|

_Source:_ Own computation from 2003/4-2013/14 CSA smallholder farm surveys.

_Note:_ Area is in 100 ha. Statistics are calculated using the sub-sample of kebeles that are included in the analysis for each of the crops. The criteria for inclusion in the sub-sample are that the kebele is within 150 km of a commercial farm growing the specified crop in 2014 and that more than 10% of households in the kebele grow the crop.

20
of 2019, and 7 percent by the end of 2020. Firms assigned to treatment are more likely to survive by 3 percentage points in 2019, and 5 percentage points in 2020. Firms may not appear in the PILA because they have closed, or because of reporting issues. For example, a couple of the firms appear to be registered only under the owner’s name as a natural person, rather than as companies. We therefore consider a second definition of survival, which involves cross-checking data from formal employment reported in the PILA with data on the firm from the export database, from the RUES (Mercantile Registry), and from IPA calling and visiting firms to verify their status. We believe this is the most accurate measure of firm survival as a result. Column 3 looks at impacts on this measure of survival for the full 200 firms, and again finds treated firms are 5.1 percentage points more likely to survive, with this effect significant at the 10 percent level.

The firms that closed were considerably smaller to begin with on average than those that survived. For the control group, mean (median) January 2018 employment was 87 (48) for the survivors, compared to 51 (21) for firms that closed; for the treatment group it was 73 (43) for the survivors, compared to 23 (11) for firms that closed. Moreover, the firms that died were much less likely to be engaged in exporting: 0 out of the control firms that died had exported in the past three years prior to applying for the program compared to 64 percent of the control firms that survived, and only 1 of the treated firms that died had exported in the three years prior to applying for the program. Since we code exports as zero for firms that are closed, closure effectively results in replacing 0 exports for an open firm with 0 exports for a closed firm.

Panel B of Table E2 provides treatment estimates on monthly firm employment using the PILA. Column 1 and 2
# 1. Introduction

The most immediate consequences of natural hazards are the fatalities and casualties, and the first priority of disaster risk management is to save lives. But natural disasters also have economic consequences, which affect wellbeing and need to be accounted for and managed (Cavallo and Noy, 2011; Rose, 2009; Skoufias, 2003). These economic consequences and wellbeing losses depend on the value of what is lost or damaged, and on many other factors, including how long it takes to rebuild, how asset losses translate into income losses, and how coping mechanisms and ex‐post support (from insurance to social protection) protect the victims and help smooth consumption losses (Carter et al., 2007; Le De et al., 2013). In addition, wellbeing losses are larger when losses are concentrated, especially when concentrated on poor people – as is often the case (Hallegatte et al., 2016a). Disasters can also have irreversible and long‐term health consequences, particularly on children (Dercon, 2004; Maccini and Yang, 2009).

Many policies can minimize wellbeing losses and protect the population: from building dikes and restoring mangroves to better land‐use planning and early warning, to evacuation, insurance and social safety nets. Risk management policies are best designed as holistic strategies that combine many of these levers (World Bank, 2013).

Designing such a consistent policy package is challenging; here we build on Hallegatte et al. (2016b) and propose an approach and a model to support this process. In this work‐in‐progress, we combine data on natural hazards, population and asset location, asset vulnerability, and socioeconomic characteristics and combine insights from natural and social sciences to assess how natural disasters affect wellbeing, measured using a _social welfare function_ ( _welfare_ is the metric used by economists to measure wellbeing). We first present the model, and the global data used to calibrate it. We then use the model to define and assess the socioeconomic resilience of
Pathfinder survey<sup>13</sup> , the final sample consists of 109 communities (91 rural and 18 urban) and 2,700 women, of which just over 2,000 remain after excluding never married and never partnered women.

# **3.1 Estimation Strategy**

Our approach is to first estimate the determinants of the decision on whether to place a program _P_ in area _k_ and then to estimate the effect of the program on the individual outcome _yi_ (fertility). The equations are: where _X k_ is a vector of exogenous variables that are area specific, and _Zk_ is a vector of area specific exogenous variables that affect program placement but do not affect the individual fertility decision. Individual characteristics are captured by _X i_ . Whether a program is available in the area, _Pk_ , is the main variable of interest and _β_ 3 measures the program’s impact on the outcome of interest. The main outcome of interest is the number of children ever born. In addition, to probe into the channels through which family planning affects fertility and to distinguish its effect from the presence of the health facilities through which the family planning services are provided, we estimate the effects of family planning on various measures of child mortality, recent birth or pregnancy, and whether last birth or pregnancy was wanted. Unfortunately, lack of birth histories in the data means that we cannot examine how the timing of births responds to family planning.

Using a modified two-stage method, _β_ 3 can be estimated under relatively relaxed conditions (Wooldridge 2002, Chapter 18). The first stage estimates the determinants of the placement decision using a Probit model and the fitted probabilities of having a program are calculated. In the second stage, the individual decision equation is estimated by IV, using the fitted probabilities from the first stage for _Pk_ , **Xk** and **Xi** as instruments. Identification comes from _Zk_ in the first stage. An attractive feature of this approach is that the results
# **_c. First Differences_**

We now examine the relationship between change in employment and change in export sales, controlling for changes in the value of machinery and labor costs per worker. Here, we find that the positive relationship between export sales and employment holds, significant at the 1 percent level. Table 9 reports the estimated results from equation (5.3).

# **_5.4. Robustness Checks_**

Our main analysis relies on the 2013-2016-2020 Enterprise Survey panel data for Egypt. It is important to recognize that these represent a subset of the universe of firms collected in each survey year. When reporting descriptive statistics for panel firms, we used panel rather than the cross-section weights. Nonetheless, one might worry that our results are driven by selection patterns. That is, surviving firms observed in three consecutive rounds are likely to be different from those that did not. To address this concern, we report descriptive statistics for firms that survived between 2016 and 2020 with those that did not (see Appendix A, Table A1). Then, we report the cross-section estimates to compare with our main panel results (Tables A2 and A3).

The descriptive statistics comparing 2016 firms that survived to be eligible in 2020 with those that did not suggests that the two groups of firms are broadly comparable. The exceptions are that surviving firms are more likely to be exporters and hold a larger value in machinery (difference is significant at the 10 percent level). While surviving firms are larger, the difference is not statistically significant.

Comparing tables A2 and A3 with the corresponding panel results in Tables 8 and 9, column 1, we find that the results are broadly consistent across the three years. This similarity reassures us that the main results do not appear to be driven by selection patterns.

22
We therefore take an instrumental variables approach to address this problem by instrumenting for Δln(miht1-) with ln(miht-2 ) .<sup>22</sup>

# **3.3 Comparison to the literature**

With a number of important caveats, this estimation approach has similarities to the method that is employed by Prusa (1997, 2001), which examines the trade effects of US antidumping actions that were implemented between 1980 and 1994.<sup>23</sup> Prusa focuses only on AD cases for the 1980-1994 time period but does examine all (i.e., steel and non-steel) products that were targeted in those cases. In contrast, our focus is on steel products only, so that our lack of US industry-level covariates that would be needed to control for product-level import variation across industries is less of a concern. Furthermore, our sample covers the 1989-2003 time period for which we have a consistent time series of 10-digit HTS import data; and we also consider the impact of the imposition of SG and CVD cases in addition to AD, as well as the removal of such policies on product-level imports.

A second important contribution made by our approach is that we have constructed a dynamic panel of all steel products that were imported by the US over the time period - regardless of whether a particular product was targeted by a TTB. By contrast, Prusa's estimation sample considers only US imports in product categories that were hit with an AD measure, and he uses separate samples to examine the impact of TTBs on products that were exported by targeted and non-targeted suppliers. Our approach goes one step further. A dynamic panel of all US steel imports allows us to take advantage of the substantial variation in the trade data of the products that were targeted and not-targeted by US TTBs in addition to the exporters that were targeted and not-targeted by US TTBs within categories of targeted products.

# **3.4 Data**

# **3.4.1  Trade
the basis of the ―voices of the popular sector‖ (consumers, in this case), evidence of effective consumer activism is limited. This is understandable given the collective action problem expected in the nature of the electricity distribution service. The business sector, by virtue of being organized, may be a partial answer to this problem. Polls have shown that the unreliable and insufficient supply of power at reasonable cost is a major perceived obstacle to improving the investment climate in the Philippines. This sentiment was echoed by some of the business sector representatives we interviewed for this study. But here again, there are certain built-in obstacles which may be difficult to overcome, at least in some localities. First, in a large number of small ECs, the weight of the business sector (commercial and industrial users) among the EC clientele is limited, and some of the large businesses (e.g., a large mining company in Toledo City, in the catchment area of the ―best-managed‖ CEBECO III) supply their own electricity or rely on their own back-up generators (e.g., a large sugar mill operator in Bacolod). Second, given the nature of the Philippine political economy often characterized with local factionalism cutting across not only political-partisan but also economic-business lines, mobilization of the business sector could simply amplify the political divide that may be undermining the EC‘s governance and management. Nevertheless, increasing consumer voice through the creation of advisory and supervisory fora with participation of sub-groups of consumers with the greatest stakes in improved services, notably the business community, can provide some of the much-needed checks and balances and improve the governance of ECs. Palawan‘s power commission discussed earlier is a promising initiative that also has the support of the Philippine Chamber of Commerce and Industry who are viewing it as a pilot which can be replicated in other ECs. Our finding is that such an approach is likely to be promising in some ECs but not in most.

28
longitudinal administrative data over the 2007-2012 period from the national registry of low-income households (Cadastro Unico), linked to all the payment records of the Bolsa Familia cash transfer program (PBF) (Folhas de Pagamento) through the individual unique identifier as in RAIS. This administrative database is collected by the Brazilian Ministry of Social Development. The database covers the universe of PBF recipients along with all the benefits disbursed through the program. We use information on the amount and number of months each worker received cash from the PBF program.<sup>11</sup> To capture the potential role of worker training, we link our worker panel database to longitudinal administrative data over the 2009–2012 period on training provision collected by Brazil’s main training provider of training for the manufacturing sector—Servico Nacional de Aprendizagem Industrial (SENAI)—the training arm of the national confederation of Industry (CNI). This provider is financed by a tax charged to all Brazilian firms equal to 1% of their wage bill. SENAI is the fifth largest training provider in the world (Silva et al. (2015), Bastos et al. (2016)). From these data, we extracted information on the set of workers trained in

> 10Gerard & Gonzaga (2021) for details on unemployment insurance in Brazil.

> 11See Gerard, Naritomi, & Silva (2021) for further details on the PBF program and the interaction between the program and formal labor markets.

8