rafmacalaba/data-use-mentions-extended huggingface dataset:
techniques. To assess the performance of reweighting procedures and the SWIFT-based poverty projections, we conduct experiments by drawing subsamples of phone owners from reference surveys in Rwanda, St Lucia, Uganda (refugees), and Ethiopia. In the experiment in Ethiopia, we excluded households who did not own a phone from the ESS4 survey and further excluded those who did not respond to the Ethiopia High-Frequency Phone Survey, which leads to both sampling bias and nonresponse bias by construction. Using these artificially biased subsamples, we test whether reweighting procedures and the SWIFT poverty projections combined can reduce the sampling and nonresponse bias in poverty estimation.

Interestingly, the contribution of reweighting procedures and the SWIFT-based poverty projection technique in reducing the bias of the poverty estimates differ by context. In the experiments of Rwanda EICV5 and Uganda Refugee Household Survey, the SWIFT poverty projections are the main contributor to reducing the bias in poverty estimation. In the experiments with Ethiopia ESS4 and St Lucia 2016 HBS surveys, reweighting procedures are the main contributor to reducing the bias in poverty estimation.

How does the SWIFT approach to poverty projection play a role in minimizing sampling bias? Existing literature indicates that while reweighting can mitigate sampling bias, it cannot completely remove it. This paper's experiments also confirm that poverty rates derived from actual consumption data are still subject to bias. In contrast, the poverty rates imputed by SWIFT, post-reweighting, have almost no sampling bias in all four examined cases. What accounts for this difference?

A plausible explanation lies in the limitations of reweighting. Even though it balances many observed variables between a biased subsample and the full sample, reweighting may not balance certain unobserved variables correlated with household expenditures and poverty. Consequently, poverty rates based on actual consumption data, which are correlated with the unobserved but unbalanced variables, could retain the sampling bias. In contrast, SWIFT's poverty projections are not influenced by these unobserved household characteristics in imputing household-level consumption. Thus, if all regression variables in the SWIFT models are effectively balanced through reweighting, the resulting poverty
Educational Achievement (IEA) in 35 developed and emerging countries.<sup>5</sup> The aim was to obtain an internationally comparable database for the reading literacy of students in what is the fourth grade of primary school in most education systems. The assessment was designed to capture the skills and features that are necessary “to understand and use those written language forms required by society and/or valued by the individual” (Campbell et al. 2001, p. 3). This definition leads to a real-life focus of the study. Because most national curricula require that students are able to use reading to learn, the definition also ensures that the international comparability of the measured reading performance should not be severely affected by differing national curricula (cf. Gonzalez and Kennedy 2003).

The Latin American countries that participated in PIRLS are Argentina and Colombia. To be able to compare and benchmark the Latin American results, we also analyze an additional six countries. These are Turkey and Macedonia, which show average performance levels similar to Argentina and Colombia. Together, these four countries form our group of “low performing countries” (LPC). We also look at benchmark countries from the industrialized world. Germany and England have been chosen as two large developed countries representing a Continental-European and Anglo-Saxon background, respectively.<sup>6</sup> In addition, two further Western European countries that might constitute interesting benchmarks for the Latin American countries are Greece and Italy. The two countries are presumably the European countries participating in PIRLS that are most comparable to Argentina and Colombia. Many Argentines are of Italian origin, while Greece is perhaps closest among the European countries to the Latin American countries in terms of its level of economic development. Germany, Greece, Italy and England together form our group of “high performing countries” (HPC).

The specific target population of the PIRLS study was defined as the upper of the two grades with the highest share of nine-year-olds of a country at the time of testing.
percentage points in urban areas between 2009/10 and 2011/12. Conversely, in the middle of Argentina’s debt crisis, the poverty headcount rate rose by nearly 10 percentage points in urban areas within a single year. Therefore, in line with the GDDS upper bound for survey periodicity, the World Bank Household Survey Strategy recommends that data on poverty and inequality be produced every 3 years at a minimum. Given the requirement of reporting poverty and shared prosperity statistics on an annual basis as part of global monitoring, increasing the frequency and availability of household survey data in IDA countries becomes even more critical to improving the reliability and precision of SDG monitoring by reducing reliance on projections.<sup>10</sup> <mark>Furthermore, the IDA status of a given country is important to understanding the underpinnings of the WBG’s commitment to alleviate data deprivation over the period of 2016-2030. Sixty-eight percent of the countries that are classified as data deprived by Serajuddin et al. (2015) have a lending status of either IDA (40 countries) or Blend (i.e. 12 IDA/IBRD countries). If a minimum of 3 poverty data points in a 10 year window is considered a necessary condition for avoiding data deprivation, we observe 82 percent of IDA-funding eligible countries to be data deprived. The remaining countries with IDA or Blend lending status could easily fall into deprivation if any funding problems were to arise. India, a special case which graduated from Blend to IBRD lending status in FY14 but which can also borrow under IDA terms through FY17, would also be classified as data deprived under the broadened definition of the concept.</mark><sup>11</sup> <mark>Since prior funding is no guarantee of future funding, all 77 countries with either IDA (59 countries) or Blend (18 countries) lending status as well as India constitute the focus of the WBG’s commitment to alleviate data deprivation over the period of 2016-2030.</mark
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[17] Kaminsky, Graciella, Carmen Reinhart and Carlos A. Végh (2004).<br>“When It Rains It Pours: Procyclical Capital Flows and Macroeconomic<br>Policies.” in NBER Macroeconomics Annual, edited by Mark Gertler and<br>Kenneth Rogoff, Cambridge, MA: MIT Press.<br>[18] Kraay, Aart (2010) “How Large is the Government Spending Multi-<br>plier? Evidence from World Bank Lending,” World Bank Policy Re-<br>search Working Paper No. 5500.<br>[19] Lopez, Humberto and Luis Serven (2006). “A normal relationship ?<br>Poverty, growth, and inequality,” World Bank Policy Research Work-<br>ing Paper Series 3814.<br>[20] Mendoza, Enrique G. and Jonathan D. Ostry (2008). “International<br>Evidence on Fiscal Solvency: Is Fiscal Policy Responsible?,” Journal of<br>Monetary Economics 55(6).<br>[21] Nakamura, Emi and Jén Steinsson (2010) “Fiscal Stimulus in a Mone-<br>tary Union: Evidence from U.S. Regions,” Mimeo: Columbia University.<br>[22] Perotti, Roberto (2004) “Estimating the effects of fiscal policy in OECD<br>countries,” Working Papers 276, IGIER Bocconi University.<br>[23] Perotti Roberto (2011) “The Effects of Tax Shocks on Output: Not So<br>Large, But Not Small Either” NBER Working Papers 16786.<br>[24] Ramey, Valerie A., 2009. “Identifying Government Spending Shocks: It’s<br>All in the Timing,” NBER Working Papers 15464.<br>[25] Ramey, Valerie A. and Matthew D. Shapiro (
jobs in the textile industry from India to Morocco were destroyed since their wages were no longer competitive when compared to the cost reduction associated with mechanization.

In this article, we examine the impacts of robotization in the US on exports from Mexico to the US. As articulated in the theoretical model (forthcoming), automation in the US may affect the demand for Mexican goods through different channels. First, automation could improve the efficiency of firms in the tradable sector, which may foster the demand for complementary goods from Mexico. This increase in efficiency could, at the same time, increase the demand for Mexican goods through an income effect. Second, automation may improve the comparative advantage of the US in certain sectors, which would reduce the demand for Mexican products. Third, automation may lower the prices of American intermediate inputs imported by Mexico, which may boost the production and export levels of the latter.

Using export data at the local labor market (LLM) level in Mexico, this article studies if higher exposure to US automation through trade lowered local exports to the US from 2004 to 2014. Exposure of Mexican areas to US automation is defined as the increase in the number of robots per thousand workers by sector in the US, weighted by the initial exports of each sector at the LLM level in Mexico. The larger the increase in the number of robots and the higher the share of exports from that sector, the higher the exposure to US automation. The results indicate a strong and robust negative relationship between exposure to US automation and exports from Mexico to the US. This suggests that the substitution effect of automation seems to be stronger than the complementary and efficiency channels. These findings are consistent with automation increasing the pace of reshoring and/or lowering the pace offshoring.

Given that both economies are highly integrated, there are valid concerns about the endogeneity of the exposure to automation in the US. For instance, industries that face higher import competition from Mexico may be more likely to adopt robots to become more competitive
|-0.0006|
|Account(pub requirement)|||||0.0622<sup>∗∗</sup>|0.0580<sup>∗∗</sup>|
|Account(least ambitious)|||||0.0034|-0.0001|
|EU Pair|0.2475<sup>∗</sup>|0.3119|0.2479<sup>∗</sup>|0.3123|0.2474<sup>∗</sup>|0.3121|
|Observations|235,876|203,732|235,876|203,732|235,876|203,732|
|Exporter-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Source_ : Authors’ estimations using WB-DTA 2.0 and ITPD-E data. Dependent variable: bilateral services trade _Xijt_ . PPML estimation with three-way clustered standard errors. Other policy patterns controlled for but coefficients not reported. Columns 1, 3, 5 include all observations whereas columns 2, 4, 6 restrict the sample to PTA pairs.

35
information treatments would ensure that at least one of them would serve as new information to the potential migrants. Since deliberate misinformation was already a concern in this context, I chose not to deceive them. For the wage information, the only source of information available was the wage reports made by previous cohorts of migrants to the DoFE in their application to receive the permit for employment abroad. Therefore, two different years were chosen to generate the ‘high’ and the ‘low’ variant of the wage information, and the year the information was pertinent to was stated clearly when providing the information. For the death information treatment arms, I provided information on the death toll from a reference district. I varied the reference district to generate the ‘high’ and ‘low’ variants. Death toll was provided instead of death rates to emulate the kind of information they would see in reality. Further, providing respondents with numbers prevents them 8
the math test score autocorrelation is 0.47 for the treatment group and 0.55 for the control group. Therefore in these samples this assumption seems reasonable.

Table 2 provides autocorrelations from other data sets for a range of different economic outcomes. The purpose of this is twofold. First, it demonstrates that for many economic outcomes, the autocorrelations are typically lower than 0.5, with many around 0.3. An exception is test scores, which have higher autocorrelations. Second, it provides some parameters that researchers can use when conducting their own power calculations, since it is rare to have

> 6 The Sri Lankan microenterprise surveys only measured household expenditure annually, which is why household expenditure is not shown for this sample.

11
In terms of the hazard, the flood maps for HCMC show flood depth and extent from the river and from sea (when looking at the sea level rise scenario). Pluvial flooding and possible ‘sink’‐areas in the city are not taken into account. Moreover, the lowest return period we have flood maps for is not low, compared to what is experienced in the city. Some areas of HCMC are flooded every year. Since this analysis used a flood with a 10‐year return period as the flooding scenario with the highest recurrence interval we were not able to capture the relative differences in exposure to these yearly/bi‐annual flooding events (and we hypothesize that poor people are relatively more exposed to these types of flooding than the general population).

# **7. Policy Recommendations**

Despite the limitations, this analysis presents some initial findings on what exposure to floods looks like in Vietnam, how it may change under a changing climate, and the exposure of poor people. The results from this paper have implications regarding infrastructure development, land use planning, and strategies to manage flood risk.

First, the results of this paper suggest that climate change is likely to increase the number of people exposed to floods, especially in the Mekong and Red River Deltas. Climate change impacts can make frequent events as important as rare ones in terms of exposure: for instance, a 25‐year flood under future conditions of high sea level rise exposes more people than a 200‐year flood under current conditions. In addition to showing the benefits of investing in flood risk management today, these results also suggest that new investments in flood protection (whether natural protection through mangroves or physical protection through dikes and drainage systems) should be planned with climate change considerations (e.g. where will flood be the worst in the future and how can new infrastructure withstand future levels of flood).

However, current planning approaches in Vietnam have not yet adequately taken these existing and future floods into account
be developed at a cost of $3,000 per hectare, while schemes based on localized water collection could be developed at a cost of $2,000 per hectare. Should these costs be significantly exceeded, the number of viable hectares falls sharply. Source: You and others 2009.

# Information and communication technologies

## **Achievements**

As of 2005, when the AICD baseline survey was conducted, Ghana’s ICT sector was a strong performer by regional standards (table 12). About 60 percent of Ghana’s population lived within access of a GSM voice signal. This was above the average for the peer group, although it still fell short of exceptional performers such as Kenya, Malawi, and Uganda, which had already reached about 95 percent of their population with the GSM signal at that time (figure 17). Ghana also achieved strikingly high rates of service penetration. Some 32 percent of the population already subscribed to a mobile telephone service, twice as the level in the peer group. With respect to Internet penetration, however, Ghana did not outperform the rest of the peer group.

Since 2005, an increasingly competitive environment has resulted in further impressive growth in the ICT sector. There are now six mobile operators (two of which were licensed after 2007), and the international gateway has been liberalized. As a result, the mobile footprint expanded to 82 percent in 2009—still below the performance of Kenya, Malawi and Uganda, but impressive nonetheless. Furthermore, the mobile penetration rate more than doubled, from 32 percent in 2005 to 67 percent in 2009. International bandwidth has grown substantially, and Internet subscriptions have also doubled, albeit from a very low base.

**Table 12. Benchmarking ICT indicators**

|Unit|Low-income<br>**Ghana**|
|---|---|

Middle-income 30
- **MONGOLIA'S INFORMAL SEC(TOR**

Size **and** Trends

**11**

conjecture that many have simply left the labor force, leading to a precipitous decline in the labor force participation rate."<sup>8</sup> However, there is overwhelming corroborating evidence from the informal sector surveys conducted during this study, and from the household survey conducted by the SSO, that this is not the case.

23. Seven hundred and seventy informals were surveyed during the course of this study. The details of **"Unemployment" and Informal Income** the surveys will be presented later, but a certain combination of questions Percentage of households in Ulaanbaatar with informal income are pertinent for the issue of how when the household head is ... informals are reflected in official employment statistics. As reported in ... employed 44% the literature on informal sector . . . unemployed 73% employment statistics (ILO, 1993) one reason that informais are often one~~~~~~~ thtifra. ~ reso r fe Percentage of households in Ulaanbaatar with informal incomewhen the household has .. . omitted from statistics is that they do not consider themselves to be ... no unemployed people 42% "employed." Thus, a question was at least one unemployed person 54% put on each of our informal sector ... at least one unemployed person, surveys to closely mirror the and no employed people 68% employment questions asked during the annual employment census - the **Box 1** responses indicate how informals _Source:_ Author's calculations based on SSO household survey data. perceive their own employment status, and how they are reflected in official statistics as a result!<sup>9</sup> The responses provided by Ulaanbaatar's kiosks, taxicabs, boot repairmen, petty traders, and others suggest that Mongolian informals, generally, do not consider themselves to be employed, and are therefore not
the relation between GDP and corporate sales for just the private firms and find a similar pattern as before – corporate sales begin to recover one period after GDP recovery. All our results hold when we restrict the sample to firms with fiscal year ends in December.

Once we disaggregate across countries in Figure 4, we again find a great deal of heterogeneity. In Figure 4A, when we look at the set of countries where the macro data showed a Phoenix Miracle, we find that sales (dotted line) begins to recover simultaneously with GDP (bold line) as in the case of Thailand or one to three periods after GDP recovery as in the case of Argentina, Indonesia, Malaysia, Mexico, and Russia. However the sales and GDP patterns do not appear to be correlated at all in the case of the Non-Miracle countries – Korea and the two Turkey episodes (see Figure 4B). In the case of Korea there is no drop in sales during the crisis period and in the case of Turkey’s two crisis episodes, aggregate sales does not seem to recover even three periods after GDP recovery. While we previously classified Korea and the two Turkey episodes as Non-miracles at the aggregate level since GDP and credit recovered simultaneously, Figure 4B suggests that micro data also point towards them being non-miracles. We discuss this in greater detail in section 3.2.2.

In addition to the robustness checks in Figures 3B-3D, we undertake several other checks to understand whether statistical issues or sample selection issues are driving the discrepancy between recovery in economy-wide GDP versus corporate sales. First, we find that our results are robust to restricting the sample to firms with fiscal year ends in Dec 31<sup>st</sup> ( <15% of our sample of firms have fiscal year ends before Dec 31<sup>st</sup> ) suggesting that our results are not driven by timing issues related to the reporting of balance sheet information.<sup>16</sup> Second, our results
||1.226||||||
|Δ share of WAP*|1.073**|1.073**||**|1.219**||0.43|1.05||
|Schooling (years)|‐0.52|‐0.52||‐0.56|‐0.55||‐0.83|‐0.72||
|Δ share of WAP*|||‐3.91|||‐2.75|||‐9.19|
|Δ Schooling (years)|||‐4.04|||‐3.94|||‐7.74|
|Observations|1,796|1,776|1,307|1,796|1,776|1,427|1,796|1,776|1,307|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Author’s estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

17 Using a similar specifications of savings, an increase of 1 percentage point in the share of working-age population is found to be associated with an increase by 0.6 to
the Early Microcomputer Market.” _Journal of Industrial Economics_ . Vol. 47, pp. 97-116.

Griliches, Zvi (1990). “Patent Statistics as Economic Statistics.” _Journal of Economic Literature_ . Vol. 28. pp. 1661-1707.

Guellec, Dominique and Bruno van Pottelsberghe de la Potterie (2001). “R&D and Productivity Growth: Panel Data Analysis of 16 OECD Countries.” _STI Working Papers_ 2001/3. Organisation for Economic Co-operation and Development.
welfare cost of elevated premature mortality risk (Narain and Sall, 2016), as well as the welfare cost of threats to child health and development.

An additional important question, with philosophical as well as methodological dimensions, involves the extent to which threats to family health are internalized by those making choices about cooking approaches (including fuel, stove type, ventilation, and presence of children), and the extent to which those preferences should drive policy decisions. Rational choice theory would imply that users would balance costs and other factors against health threats when deciding how to cook. In practice, however, households may not be very well informed about the consequences of these choices; may discount the consequences of pollutant exposure, either because of their longer-term nature or for other reasons; may find cleaner stoves or alternative fuels less convenient to use; or for other reasons may value smoke reductions differently – and typically less – than estimates based on highly controlled cost-of-illness studies (Mobarak et al., 2012).

Consequently, greater awareness of the adverse effects of exposure to indoor air pollution may make cleaner fuels or stoves more attractive, but so would offering a package of measures for promoting cleaner and improved cooking alternatives, including greater stove durability and ease of use as well as financing options (Pattanayak et al., 2016). Aside from time discounting, individuals may associate a relatively lower cost with pollutant exposure than would be implied by cost-of-illness studies because they underestimate their own vulnerability, or because longestablished habits are not easily changed. Sunstein (2012-2013) provides a discussion of these and other possibilities, and advances the argument that public policy should contain an element of paternalism in such situations by not being entirely bound by revealed individual preferences if those preferences reflect incomplete or incorrect information.

Other health threats for women and girls include insects and snake bites and musculoskeletal injuries associated with collection of fuelwood for cooking. Especially disturbing is that fuelwood collection is one of the key sources of physical and psychological violence against women. Somali refugee women have been