rafmacalaba/data-use-mentions-extended huggingface dataset:
expand access to both knowledge and input and output markets, via indicators of whether the firm established a new foreign joint venture and whether it entered into a new technology licensing agreement in the past three years.<sup>17</sup> The vector includes variables indicating the firm‘s ownership (foreign, government), level of organization/legal status (whether the firm is incorporated), size class and age group.<sup>18</sup> The vector contains a measure of the progressivity of the firm‘s sub-sector in terms of the US R&D intensity of that sub-sector, as calculated in Sharma et al. (2010). The vector is comprised of country fixed effects.

The second equations are enterprise product and process innovation equations, estimated using a probit estimator: Here, indicates whether the firm introduced a new product or process innovation in the last three years. The vector now includes a broader set of variables describing the firm‘s relationships to its business environment including the firm‘s R&D intensity as a correlate of its ability to innovate,<sup>19</sup> access to capital, access to implementation skills (proxied by whether the firm has a formal training program), access to ideas (use of the internet and whether the firm is part of a business association), as well as opportunities for expansion if the innovation is successful. The vector contains the firm-level controls as in specification (1), contains a vector of two-digit industry fixed effects, and is the same as in specification (1).

The third equation seeks to explain variation among levels of the enterprises‘ TFP<sup>20</sup> , and is estimated via OLS: 17 As summarized in Table 1c, these indicators are constructed based on responses to the following questions asked in the Enterprise Surveys: ―Has your company undertaken any of the following initiative in the last three years: Agreed to a new joint venture with foreign partner?
to conduct model and design-based evaluation of different unit-level SAE methods. Model-based methods assume a data generating process for the population that follows the model’s assumptions, and the target parameters are assumed to be random (Tzavidis et al., 2018). In this paper, model-based evaluation is done considering two-stage sampling strategies and thus assumes a model that includes variation at the domain level as well as at the cluster level, where clusters are the primary sampling units, nested within domains (see Marhuenda et al., 2017). Design-based simulation entails applying the chosen methods under realistic conditions (Tzavidis et al., 2018). Here, a design-based evaluation of several estimators is performed using the 2015 Mexican Intra Censal Survey, which is used here as a census, and from which we draw repeated samples. The estimation methods considered are those of Elbers, Lanjouw and Lanjouw (2003 - ELL henceforth), the empirical best predictor of Molina and Rao (2010 - MR henceforth),<sup>1</sup> the twofold nested error extension of MR (2010) presented by Marhuenda et al. (2017), and finally an adaptation, presented by Nguyen (2012), that combines unit and area level information, and which has been proposed as an alternative when the available census data is too outdated to be considered for use.

The paper’s contribution is twofold: i) it presents a thorough evaluation of the considered methods as well as their pros and cons, and ii) it employs the sampling strategies most often encountered

> 1In reality, we apply the Census EB variant introduced by Guadarrama, Molina and Rao (2016)

2
# **Methodology**

First, we regress an indicator for household income loss on gender, age, and education of the household head, rural/urban location, and the presence of children (Table 3, column 1).<sup>18</sup> Second, we run the same regression but also including indicators for whether the household received income from farming, nonfarm enterprises, wages, or remittances, in the year before the pandemic (Table 3, column 2).<sup>19</sup> By comparing results from these two analyses, we can assess the extent to which differences in income sources account for heterogeneity in income loss by household demographics. Third, we estimate how household characteristics are related to the probability of income loss for each income source (family farming, non-farm enterprises, wages, remittances from within the country, and remittances from abroad), within the subsample of households receiving income from the respective source (Table 3, columns 3-7). These analyses all focus on early-phase income loss based on the pooled data from the four countries’ first HPFS rounds. We then run the same models for one-year income loss, pooling the data from Nigeria’s Round 9 and Uganda’s Round 5 (as discussed in section 3). The results are reported in Table 4.

# **Results: Early-phase impacts**

In the early phase, urban households and households with children were more likely to lose income, while age and tertiary education of the household head are associated with a significantly lower probability of income loss (Table 3, column 1). These results are similar (in some cases a bit smaller in magnitude) when we condition on income source indicators (column 2), suggesting they are not primarily driven by differences in the types of income sources households relied on.

Income sources do matter (column 2): there is a strong negative association between income from wages and income loss, suggesting that wage employment was an important
This model confirms the intuition a parent that places higher value on education is more likely to send their child to public schools, which are assumed to be of higher average quality. Meanwhile, holding other factors constant, the likelihood of attending private school increases as the household’s wealth increases. In the U.S., where private schools are generally considered to be higher quality, wealthier and more motivated students select into private schools. In Indonesia, however, where in general public schools are considered to be of higher quality, the two sources of selection bias are of opposite sign. Thus, the direction and magnitude of bias in the OLS models is unknown, and depends on the relative strength of unmeasured wealth and unmeasured motivation, as well as the importance of these factors in determining students’ test score.

# **DATA**

The primary data source for this study is the three full rounds of the Indonesia Family Life Survey (IFLS1, IFLS2, and IFLS3) (see Frankenberg and Karoly, 1995; Frankenberg and Thomas, 2000; Strauss _et al._ , 2004). The first round of the survey sampled 7,200 households in 1993; subsequent surveys attempted to re-interview these households and households to which previous household members had relocated since the original interview (“split-off” households). The 1993 sample was drawn from 321 randomly selected villages, spread among 13 Indonesian provinces containing 83 percent of the country’s two hundred million people. The 321 villages were selected from the sample frame of the 1993 SUSENAS, the national economic survey, and are located in 9
Table 5. Marginal effects of the ordered probit and probit models: the probability of psychological distress (anxiety and depression) over the last 2 weeks of survey <u>(based on PHQ scale)</u>

||<br>|Ordered Probit Est<br>|imation(full scale<br>|)<br>|Probit<br>|Estimation<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||No<br>psychological<br>distress|Mild<br>psychological<br>distress|Moderate<br>psychological<br>distress|Severe<br>psychological<br>distress|Has anxiety<br>(Subscale)|Has depression<br>(Subscale)|
|_Ref: Cismen_|||||||
|Ciswomen|-0.095***|-0.013|0.031***|0.077**|0.204***|0.041|
||(0.034)|(0.010)|(0.010)|(0.033)|(0.052)|(0.051)|
|Transgender|-0.044|-0.003|0.015|0.032|0.082|0.036|
||(0.041)|(0.005)|(0.014)|(0.033)|(0.057)|(0.057)|
|Non-binary|-0.064***|-0.006|0.022***|0.048***|0.094*
will not occur without major policy incentives. A large number of governments have decided to increase solar energy development, using a range of fiscal, regulatory, market and other instruments<sup>16</sup> . In fact, the strong growth in solar energy markets, notably those for gridconnected solar PV and solar thermal water heating, has been driven by the sustained implementation of policy instruments in Europe, the United States and some developing countries to induce or require increased use of solar power.

This section briefly presents key characteristics of policy instruments that support solar energy for both electric and direct heating applications. A large number of policy instruments have been implemented to increase power supplies from solar PV and CSP. The key instruments we highlight here include feed-in-tariffs, investment tax credits, direct subsidies, favorable financing, mandatory access and purchase, renewable energy portfolio standards and public investment. Three rationales are commonly offered for utilizing these policies. One is to encourage the use of low-carbon technology in the absence of a more comprehensive policy for greenhouse gas mitigation, like a carbon tax. The disadvantage of this approach for greenhouse gas mitigation is that it does not create incentives for cost-effective mitigation choices. The second rationale is that expanded investments will ultimately help drive down the costs of those technologies through economies of scale and learning-by-doing. There is clear evidence that scaling-up has driven down unit costs for PV, though not yet to the point that it is cost-effective with conventional alternatives in most cases. CSP is still relatively a pioneer technology with only a few medium-scale investments and no larger-scale investments, though some are planned. It remains to be seen how scale economies and learning-by-doing will lower its costs. The third and most unambiguous rationale is that subsidization of small-scale, off-grid PV (and other renewable energy sources) to bring electricity to remote and poor areas lacking access is a powerful force for stimulating economic development.

> 16 A number of recent studies, such as ESMAP 2011a, 2011b and EPIA 2011 present in-depth analysis of various policy instruments designed to
Educational Profiles by Gender **and** Residence Table 2 presents the educational profiles of the sample by gender and type of residence. There are clearly large differences in levels of educational attainment between males and females, and between those residing in Lima, other urban and rural areas. Females, on average, have completed only 5.5 years of schooling compared to 7 years for males. While only 8 percent of the males never attended school, 25 percent of the females never enrolled; and, only 28 and 12 percent of the females had some secondary and higher education, respectively, compared to 35 and 18 percent of the males. These results are very similar to the 1981 census results (Appendix Table A.1). In comparison to the census data, average years of schooling for males and females have risen slightly since 1981; a smaller proportion report primary schooling as their terminal level since a slightly larger proportion have continued on to higher education.

Table 2 Educational Attainumet of Males and Females, Residents in Lima, Other Urban, and Rural Areas

|Educational attainment|Females|<br>Males|Lima|Other<br>Urban|Rural|
|---|---|---|---|---|---|
|Average years of schooling|5.5|7.1|8.8|7.5|3.2|
|(standard deviation)|(4.6)|(4.4)|(3.9)|<br>(4.3)|(3.5)|
|Highest level of schooling<br>attended (percent of sample)||||||
|Never attended|24.3|7.7|3.2|7.3|33.2|
|Primary|35.9|39.4|24.0|34.9|50.2|
|Secondary|
<br>(0.96)|||0.30<br>(0.71)|
|Observations|1,087|1,087|1,089|1,068|1,068|1,068|
|R-squared|0.99|0.99|0.99|0.98|0.98|0.95|
|Kleibergen-Paap rk LM statistic|17.35***|18.59***|14.08***|4.37**|5.26**|0.20|
|Cragg-Donald Wald F statistic|19.85|22.94|12.91|4.80|5.39|0.18|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). “High (low) capital account openness” are countries where Chinn-Ito index is above (below) median. Commercial bank branches are per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. See sections 2 and 3 for detailed data descriptions and empirical methods. Data are between 2004 and 2018.

25
In addition to these data, there are three sources which estimate poverty rates for at least two points in time:

- BPS, which estimates poverty in February 1996 and February 1999 using

Susenas and December 1998 and August 1999 using Mini Susenas. Their estimates which are presented in table 7 are those which consistently use the February 1996 poverty bundle."<sup>5</sup>

- Gardiner (1 999), who has used the core (not consumption module) expenditures of the Susenas to create poverty estimates for February 1996, February 1997, and February 1998.

- The Indonesia Family Life Survey (IFLS), carried out jointly by Lembaga Demografi - Universitas Indonesia (LDUI) and RAND, produced estimates for August-October 1997 and September-December 1998 (the extended periods for each round are due to the tracking of individuals in the panel survey).'<sup>6</sup>

The reconciliation of all the above estimates is presented in table 7, with column A starts from our estimate of 9.75 percent headcount poverty in February 1996, while column B uses the official BPS figure of 11.34 percent as the initial point. To see the pattern of the poverty evolution from these series more easily, figure 2 shows the column A series after transformed into a poverty index, where the lowest point is set to equal 100. The December 1998 point is not connected because the temporary drop in poverty during this period is difficult to explain. After this point we present two series, where the one with continuous line is connecting

> 5 See BPSIUNDP (1999).

> **16** See Frankenberg _et al_ (1999).

25
to treatment status.

If we look at stealing, marijuana use, and homelessness, however, none of the survey-validation differences are statistically significant. There is possibly some slight underreporting of drug use and slight over-reporting of stealing, but the magnitudes are generally small in the sense that they are less than 10% of the survey means reported in Table 9. The sample size is small, however, and so many of these differences are not precisely estimated.

We see much stronger evidence of underreporting of expenditures in the survey. The difference for both expenditures is -0.27 in the full sample (Table F.3, Column 6). This difference is large—about a third of the survey mean reported in Table 9. Expenditure underreporting is largest for the video club measure, but both expenditures appear to be underreported. Interestingly, the mean differences appear to be smaller and less statistically significant if the men received one of the treatments. We return to these across treatment arms below.

# **F.9 Further results: Adjusted treatment effects**

We estimate the effect of each treatment on survey over-reporting, in Table F.4. These estimates effectively take the simple survey-validation differences in Panel A of Table 10 and estimate the difference across treatment arms, adjusting for baseline covariates as well as block fixed effects. We use these to calculate an adjusted treatment effect.

> 30Both figures were driven by the fact that it typically took one to two days of searching to find each respondent for surveying, plus the time to survey itself. Both surveying and validating in Liberia were expensive by the standards of household surveys, largely because of the cost of operating in a fragile, post-conflict state and the great difficulties in tracking such an unstable population.

> 31In one extreme example, in the India NSS consumption survey, enumerators physically measure the volume of all food consumption Group (2003).

lvi
# **1 Introduction**

In the 1990s advances in transportation and communication technologies led many commentators to believe that geographic distance between countries would soon no longer encumber international transactions (e.g. Cairncross 1997). Despite some anecdotal evidence in support of the “death of distance” hypothesis (e.g. Friedman 2005), a large number of academic papers suggests that distance is “thriving”, not “dying”. Disdier and Head (2008), using a meta-analysis based on 1,000 gravity equations, found that the estimated coefficient on distance has been slightly on the rise since 1950. Chaney (2011) argues that the need for direct interactions between trading partners, resulting from information frictions first highlighted by Rauch (1999), explains why distance still matters for international trade today. Similarly, Allen (2011) suggests information frictions account for 93 percent of the distance effect. This would suggest that advances in technology in recent decades have failed to reduce information frictions. Is this the death knell for the “death of distance” hypothesis?

In this paper we breathe new life into the “death of distance” hypothesis. We argue that the right place to look is in online markets which, as opposed to “offline” markets, make full use of technologies that can reduce information frictions. Indeed, as argued by Horta ̧csu et al. (2009) and Goldmanis et al. (2010), the main benefit of the internet as a trade facilitator is to reduce search costs, and it is reasonable to think of online marketplaces as “frictionless” in this regard. Exporters no longer need to make multiple phone calls, send faxes, write emails, attend trade fairs and networking events. And while importers still incur some search costs, these are typically brought down to a simple internet search. In any event, online search costs are not necessarily correlated with how remote markets are.

The heart of our paper is a dataset on cross-border transactions conducted over eBay,
<!-- Start of picture text --> 25 24.4<br>20<br>15<br>10.6<br>10<br>5<br>0<br>Offline Online<br>Conditional on having each channel<br>P90 vs Median<br>Gap in sales per channel per worker<br><!-- End of picture text --> Figure 16: Ratio of sales value by channel P90 relative to median

# **6. What drives these results?**

Several factors may explain firms’ decisions to increase digital investments as a response to the pandemic, and the heterogeneous response that explains an increasing digital divide. This section tries to understand more formally what are the firm characteristics pre-pandemic that may explain these firm digital choices. For this exercise, The analysis is restricted to the sample to countries for which pre-pandemic information from WBES was available. Specifically, this analysis estimates Equation 1 using some variables extracted from the baseline WBES survey,<sup>15</sup> and check the correlation for two potential channels: i) The technological capabilities of firms before the shock; ii) Access to finance. Table 1 and Table 2 summarize the main results.

The first hypothesis is that firms with higher levels of technology capabilities pre-pandemic were able to adjust faster to digital adoption. Comin et al. (2022) combine data from the Firm-level Adoption of Technology survey with the BPS for Brazil, Senegal, and Vietnam and show that firms using a higher level of technology sophistication pre-pandemic were significantly more likely to increase the use to digital technology, which also contributed to a better sales performance at the initial stage of the shock. Using a different sample and simpler proxies for technological capabilities pre-pandemic - such as having own website and spending on R&D - the data also shows that these characteristics are significantly and positively associated with the likelihood that firms start or increase the use of digital technologies, increase the share of
less empirical evidence in favor of a causal effect of environmental shocks on migration (Beine and Jeusette, 2021). The use of migration flows as the dependent variable in the regressions increases the probability of finding an impact, and data on direct measures of migration should be collected accordingly. _Ex post_ counterfactual policy evaluation of development and social policy interventions aimed at either favoring or reducing weather-induced migration flows is also scant. There is a need for data observed on such programs to improve our understanding about the role of policy in mediating the causal relationship between climatic shocks and human mobility. As a complement to that, the collection of detailed community-level data to supplement household survey data on the roll-out and implementation of such programs is also a key element to be considered.

Last but not least, the growing availability of big data and citizen-generated information has spurred a debate on their potential integration or complementarity with more traditional data collection methods, such as census and surveys, to fill migration data gaps. In principle, these new data sources could massively improve the quantity of data available to study climate change and migration. Entities such as the European Commission and the International Organization for Migration have already started to assess the potential of sources of big data. Among the most prominent examples of these non-traditional data sources: mobile phone call detail records (CDR); Internet activity such as Google searches; online media content; geo-referenced social media activity, which can be obtained via advertising platforms offered by social media (IOM, 2018). There are also some first recent scientific works based directly on these sources. Lai et al. (2019) employed a massive data set of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, to explore how internal migration estimates can be derived and modeled from CDRs at subnational and annual scales. As for social networks, Spyratos et al. (2019) used anonymized and publicly available data provided by Facebook’s advertising platform to estimate the number of Facebook Network (FN) “migrants” in