rafmacalaba/data-use-mentions-extended huggingface dataset:
Chile, Mexico, and Peru. We define large-scale natural disasters as the most economically damaging climatological, hydrological, or meteorological natural disasters occurred in our country-province sample during the past ten years. Based on EM-DAT, this identification strategy provides us with the 2018 Argentine drought that affected the north-eastern provinces during January 2018 (economic costs of US$ 3.5 billion);<sup>20</sup> the Brazilian drought that initiated in early 2014 in the northeast region (economic costs of US$ 5.4 billion); the March 2015 flood that hit northern Chile (economic costs of US$ 1.6 billion); the Hurricane Manuel that affected the southeastern coast of Mexico in September 2013 (economic costs of US$ 4.6 billion); and the March 2017 flood triggered by El Niño affecting coastal Peru (economic costs of US$ 3.2 billion).<sup>21</sup> All in all, the disasters’ time span ranges between 2011q3 and 2020q1, allowing us to observe outcomes in a window of two years before and after each event.

Our baseline empirical methodology consists of a difference-in-difference (DiD) approach that allows identifying the causal effect of natural disasters on banks’ asset quality. Specifically, we analyze the difference between affected and non-affected provinces in the change in NPLs. We

> 18 For additional details, see: https://gfdrr.github.io/thinkhazardmethods/#classification-process. 19 As in Bos et al. (2018), we focus on large-scale natural disasters only. The authors argue that only relatively large events can reasonably be assumed to cause significant loan defaults, as (i) firms and banks are to some extent prepared for common disasters, and (ii) only large-scale disasters generate a significant and regionally correlated shock that affects nearly all local firms and residents at the same time.

> 20 According to the Central Bank of Argentina, the GDP growth rate decline of 4 percent during the second quarter of 2018 was
# **Employment Outcomes**

The first outcome variable to analyze is whether the person is employed or not, including those in wage and self-employment. Estimates from the five models are in table 3.2. All models, except for model 5, provide the ITT estimates using probit regressions. Model 1 takes only treatment status as an explanatory variable, while model 2 includes other explanatory variables such as gender, age, education level, and the division of residence. Further, model 3 looks at an interaction term between gender and treatment status. Previous employment status is the obvious predictor of current employment status. Therefore, model 4 adds the baseline survey’s employment status as an additional variable. Finally, model 5 uses the IV approach using an IV probit regression to estimate the _treatment on the treated_ effect.

**<u>Table 3.2 Impact of RPL on Whether the Person Is Employed or Not</u>**

||||Models<br>|||
|---|---|---|---|---|---|
|Variables|(1)<br>Treatment<br>only<br>|(2)<br>Standard<br>|(3)<br>With gender<br>interaction|(4)<br>With baseline<br>employment<br>|(5)<br>Instrumental<br>variable<br>|
|**In treatment**|0.182<sup>**</sup><br>(2.24)|0.236<sup>***</sup><br>(2.58)|0.038<br>(0.35)<br>|0.333<sup>***</sup><br>(1.48)|0.410<sup
expenditures for pregnant women. However, wealthier women are also significantly more likely to be counseled on family planning, prescribed iron, and asked about their blood type. Of the six non‐clinical quality indicators, only one is statistically significant at the 95 percent level. A standard deviation increase in wealth is associated with a 2.6 percentage points increase in the likelihood of being encouraged to ask questions during the consultation.

Analysis of the household survey data reveals a strong relationship between education level and reported content of care during the sequence of consultations. Relative to women who never went to school, women with at least some primary or secondary education reported receiving an additional 3.3 and 9 percent of the content of antenatal care (table 2, column 2). As can be seen in table 2, column 3, holding wealth level fixed, women with higher education levels report receiving better care relative to less educated women. Conversely, holding schooling level fixed, wealthier women receive higher quality care. Interestingly, the education‐quality relationship does not show in the direct observation data. It could be that the relationship presented in columns 2 and 3 is due to different recall by women of different education levels. Alternatively, it could be that this relationship is because, conditional on wealth, education is significantly correlated to the number of consultations. However, controlling for the number of consultations does not change the quality‐education relationship.

The existence of a wealth‐quality gradient could theoretically be driven by several mechanisms. First, wealthier women may simply live in the catchment areas of facilities with higher structural quality. Table C.4, in appendix C, shows that wealth is indeed positively correlated with the availability of equipment and consumables in the health centers.<sup>13</sup> Wealthier women are also more likely to live in the catchment areas of referral health centers that offer a wider package of services, including laboratory services, and have a doctor on staff. These correlations are significant at the
**URL:** _https://www.crisisgroup.org/asia/south-east-asia/myanmar-bangladesh/rohingyarefugees-bangladesh-limiting-damage-protracted_

- Li, X, D Li, H Xu and C Wu. 2017. “Intercalibration between DMSP/OLS and VIIRS Nighttime Light Images to Evaluate City Light Dynamics of Syria’s Major Human Settlement during Syrian Civil War.” International <u>Journal</u> of Remote Sensing 38(21):5934–5951.

- Li, X., Y Zhou, M. Zhao and X. Zhao. 2020. “A Harmonized Global Nighttime Light Dataset 1992–2018.” Sci Data 7(168).

- Loschmann, Craig, Ozge Bilgili and Melissa Siegel. 2019. “Considering the Benefits of Hosting Refugees: Evidence of Refugee Camps Influencing Local Labor Market Activity and Economic Welfare in Rwanda.” IZA <u>Journal</u> of Development and Migration Working Paper 9.

- MacKinnon, James G. and Lonnie Magee. 1990. “Transforming the Dependent Variable in Regression Models.” International Economic Review 31(2):315–339.

- Maystadt, Jean-Franc ̧ois, Valerie Mueller, Jamon Van Den Hoek and Stijn van Weezel. 2020. “Vegetation Changes Attributable to Refugees in Africa Coincide with Agricultural Deforestation.” Environmental Research Letters 15(4):044008.

- Maystadt, Jean-Franc ̧ois and Philip Verwimp. 2014. “Winners and Losers among a Refugee-Hosting Population.” Economic Development and Cultural Change 62(4):769– 809.

- Montalvo, Jose G and Marta Reynal-Querol. 2007. “Fighting Against Malaria: Prevent Wars while Waiting for the “Miraculous” Vaccine.” The Review of Economics and Statistics 89(1):165–177.

- Ozden, Caglar et al. 2022. “The Effects of Refugee Camps on Children of Host Communities: Workin Pa er . Evidence from Ethiopia.” <u>g p</u>

- Reid, Kathryn. 2023
says that the majority of the MENA countries, with still relatively low levels of Digitalization, are expected to gain much in terms of Participation by intensifying the digitalization of their economies. However, although Digitalization has a number of benefits, notably in terms of improving citizens' political awareness, reaching a significant level of Participation in MENA takes more than simply intensively digitalizing the economy. Indeed, beyond the threshold level of Digitalization (0.57), its investment return in terms of improving Participation diminishes. In contrast, in OECD countries with more mature economies and democratic regimes that have already enjoyed most of the beneficial digitalization-driven effects on participation, the results rather show a -shaped relationship. This could have been expected as this organization mostly includes developed countries with such high levels of Digitalization that UU for it to significantly improve Participation it has to go beyond the threshold level of 0.78.<sup>57</sup> In the OECD data we found that, over the 2007-2020 period, a significant fraction of 47% of the countries included in the sample has reached a three-year lagged level of Digitalization less than this threshold.<sup>58</sup> These results suggest that 18 out of 38 OECD countries need to step up their effort to further digitalize their economies so that digitalization becomes participationbeneficial.

Third, Table 14 shows that the effect of Provision on Participation in MENA remains negative and significant even when one controls for digitalization. Similarly, the effect of Protection is still positive and significant. Against this backdrop, a point that is worth examining is whether, when we control for digitalization, the negative influence of Provision on Participation in MENA is lower than that obtained when digitalization is not controlled for. Table 16 helps to answer this question by comparing the estimations without and with as a control variable. For a better interpretation of the results, this table reports elasticities. DDiiDDiiffvvrr We see that when digitalization is not controlled for, a 1% increase in Provision in MENA reduces Participation by 0.
(CEM) algorithm described in Iacus, King, and Porro (2012). This technique minimizes imbalances between groups and provides a larger comparison group for each of our SOEs than we would get with exact matching techniques, which tend to produce only a few matches. The underlying idea of the CEM algorithm is to yield coarsened groups, or strata, to match the treated and the control subjects, which bounds the imbalance between the two groups. The CEM has several benefits, as Blackwell et al. (2009) note. First, it meets the congruence principle, which states that the data space and analysis space should be the same, thereby reducing the possibility of obtaining strange or counterintuitive results. Second, the CEM bounds the matched data to areas of common empirical support, thereby reducing the possibility

> 8 There is still the slim possibility that we may have left firms in the sample that are owned by entities that are ultimately controlled by governments. A more specialized database that tracks ultimate ownership, such as Bureau van Dyk’s Orbis, can be used to resolve this issue in future work.

> 9 We run the same matching exercise but with partially privatized firms, using the sample of Lazzarini, and Musacchio (2018), and find no significant differences with fully owned SOEs.

15
length<br>(months) to harvest<br>end|
||Organic fertilizer use|||Recall length|
|Inputs –|Inorganic fertilizer use|binary|Plot-level|(months) to planting<br>ofplot*|
|other|Inorganic fertilizer application quantity|kg||Recall length<br>(months) to first<br>fertilizer application*|

* In the Tanzania NPS 2012/13 survey, these variables are not available at the plot-level. Regional sowing season timing information is used instead.

20
over 2010‐2017. International tourism expenditures as a share of total imports, as well as receipts as a fraction of total exports, fell during the period leading to the global financial crisis, but have both recovered over the past decade, each at around 6.5 percent in 2017; see Figure 2 (a).

<!-- Start of picture text --> Figure 2: Tourism trends, 1995‐2017<br>Fig. 2(a): World<br><!-- End of picture text -->

**Fig. 2(b): MENA** (excl. high income)

<!-- Start of picture text --> MENA Tourism Trends, 1995-2017<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>year year year<br>Source: World Bank, World Development Indicators<br>60 10 25<br>50<br>8 20<br>40<br>30<br>6 15<br>20<br>International tourism, number of arrivals (millions) International tourism, receipts (% of total exports)<br>International tourism, expenditures (% of total imports)<br>10 4 10<br><!-- End of picture text --> Growth in the MENA region has been spotty, however. Excluding high‐income countries, tourist arrivals to the MENA region outpaced growth in other regions from 1995 to 2010, increasing at a rate of 5.5% p.a. However, since 2010, arrivals have fallen by 2.1% year by year, reaching 47.7 million in 2017 (Fig. 2(b) and Table 1). The region as a whole, including high‐income countries, fared slightly better, as arrivals have remained slightly below 90 million.

Page **6** of **34**
R&D, estimated at the firm level, and those at the national level suggesting substantial spillovers.<sup>6</sup> Jaffe (1986) also finds that firms whose research is in areas where there is much research activity by other firms generate, on average, more patents per dollar of R&D and he finds the magnitudes of the spillovers to be substantial.

To date, there are very few investigations at the aggregate level that would more completely capture such spillovers and none are comparable to the micro studies. Bottazi and Peri (2003) have estimated the R&D/patent relationship at the aggregate level using a cross section of European regions. As in the other cross-sectional firm literature, they find an elasticity close to 0.9 and in addition, significant, although small, regional spillover effects within 300 kilometers. Using an OECD panel on 17 countries, Furman, Porter and Stern (2002) more generally explore the relationship between a range of innovation inputs and institutions and patents but also do not offer parameters estimates comparable to the panel studies discussed above.

In this paper we construct a large panel of advanced and developing countries and generate estimates for the R&D-patent relationship at the global level that are comparable to those at the micro level discussed above. Including a large sample of emerging countries puts us somewhere between the count and continuous dependent variable literatures. Only 6.5% of the sample has observations of zero value, just under half the number found in Blundell, Griffith and Windmeijer (2002), so clearly the issue of integers and zero values is reduced, yet not so small as to be dismissed altogether. We, therefore employ the estimators recently developed in both literatures. This allows tests for robustness to the assumptions underlying the various estimators. We find that regardless of the technique employed, there is a strong relationship between R&D effort at the global level and innovation with a long run elasticity surprisingly close to 1.

We contribute to the understanding of
new repeat-sales house price index that describes house values at the contract date when the price is determined rather than the closing date when the property is transferred. Other big data price collection efforts include, for instance, scraping of online listings in Great Britain (e.g., Rae 2015), the US (e.g., Boeing et al. 2021), the Netherlands (ten Bosch & Windmeijer 2014), <mark>Türkiye</mark> (Keskin & Watkins 2017), Japan (Sadayuki 2018), or China (Hu et al. 2019; He et al. 2019; Wang et al. 2020).

In their data collection efforts, most authors focus exclusively on a localized housing market (i.e., a particular region, city, or neighborhood) in developed countries for which well-structured property listing websites with a plethora of private properties listed for sale are available. Additionally, while very comprehensive in scope, most efforts of web scraping of private property prices are centered on developed markets. Similar approaches in EMDEs, particularly in low-income economies, are scarce. Notable exceptions are, for instance, Gnagey and Tans (2018), who collate a data set of over 64,000 properties in 2016 from listing websites to estimate house prices in Indonesia. The authors find that desirable housing attributes, structural quality, advantageous location on major thoroughfares, and secure land tenure increase property asking prices.

Almost all studies that collect price data from online listing websites focus on only one or few markets within a particular region. One notable exception is a recent _HouseLev_ database project that assembled house prices for 40 countries, mainly European and advanced economies, including some emerging economies such as Türkiye or the Russian Federation (Bricongne et al. 2019). The authors do not solely rely on web scraping for all 40 countries. They instead relate to national accounts data and implement web scraping as a “fallback methodology” in case of missing data. As the authors use both methods,
9For more details, see Shrestha (2017b,a).

> 10Though the earnings in Malaysia seem to be lower than the earnings in the Gulf countries, it is believed that the wages are actually higher in Malaysia. Workers in the Gulf countries tend to work more hours.

> 11Author’s calculations from Nepal Migration Survey 2009 and The World Bank (2011).

> 12Author’s calculations from Nepal Migration Survey 2009 and The World Bank (2011).

9
.

# **Table 2. Poverty rates** **<u>by Governorate</u>**

|**Governorate**|**E**<br>**Poverty**<br>**rate**|**BCNV 2015**<br>**Standard**<br>**error**|**cv**|**Poverty m**<br>**Poverty**<br>**rate**|**apping - Cen**<br>**Standard**<br>**error**|**sus 2014**<br>**cv**|
|---|---|---|---|---|---|---|
|**TUNIS**|3.5|0.575|0.164|4.6|0.360|0.078|
|**ARIANA**|5.4|0.698|0.129|7.0|1.024|0.146|
|**BEN AROUS**|4.3|0.595|0.138|5.6|0.288|0.051|
|**MANOUBA**|12.1|1.379|0.114|9.8|0.697|0.071|

12
had a small contribution to the decline in inequality.

The results in table 2, panel b, suggest that the changes in the skill and gender composition in the sector-occupation-formality status cells did not play a large role in the observed changes in inequality between 2002 and 2015. This is somewhat puzzling, because in a period of rapid formalization, those who exit informality and enter the formal sector are likely to be very different from those who do not change formal/informal status (McCaig and Pavcnik 2015). To provide a better understanding of who became formal, this paper analyzes this issue in more detail for Argentina, Brazil, and Mexico, whose labor force surveys contain a rotating panel that tracks individuals over a period of up to 12 months. Using these rotating panels, this paper builds annual transitions from informality and self-employment to the formal sector during the 2000s (see supplementary online appendix S1.2 for details on the construction of the data and years covered) and examined differences in workers’ observable characteristics. As documented by McCaig and Pavcnik (2015) for Vietnam, workers who switch to the formal sector from informal employment or self-employment are more likely to be male, younger, and more educated than stayers (supplementary online appendix S4, table S4.1). In other words, they tend to have observable characteristics that are more similar to those observed among workers who are already formal.

What are the implications of workers’ transitions for inequality? To shed some light on this question, this paper builds on the work of Maurizio (2015) and compute the average yearly transitions to the formal sector from informal wage employment and self-employment, by wage quartile of the origin and destination sectors (table 4). The first interesting fact this paper notes is that the majority of workers who formalized during the 2000s were previously informal employees, rather than out of labor force, unemployed or self-employed. Among those who became formal, 77 percent were previously informal employees in Argentina, 66 percent in Brazil, and 58 percent
of the standardized test scores in reading, math and science, in that order, for all 57 countries in the 2006 PISA surveys. The column immediately to the right of each S.D. column reports its bootstrapped standard error. Among the countries with higher inequality in math scores are Western European countries such as Austria, Belgium, France, Germany, and Italy; East European ones such as Czech Republic and Bulgaria, Latin American countries such as Argentina and Uruguay, but also Israel and Taiwan, China. Among the ones with lower inequality in achievements are other European countries such as Croatia, Denmark, Estonia, Finland, Ireland, and Latvia, but also Asian countries such as Indonesia, Thailand and Jordan. Countries such as the UK, Japan, and the United States take intermediate rankings.<sup>10</sup> Figure 1 portrays the S.D. (and its confidence interval) in the mathematics test scores for all countries in the sample.

> 9 The absolute Gini coefficient, of course, is the standard (relative) Gini index scaled up by the mean.

> 10 The inequality measures obtained for Azerbaijan seem particularly small and place the country as an outlier in all the analyses. It is unclear how much of this is due to the data collection procedures in this country, but such a different pattern is not likely due to real differences only.

11
- Menon, Carlo, and Gianluca Tarasconi. “Matching Crunchbase with patent data”. _OECD Science, Technology and Industry Working Papers_ , vol. 2017, no. 7, 2017, 0 1–21.

- Montobbio, Fabio, and Valerio Sterzi. “The globalization of technology in emerging markets: a gravity model on the determinants of international patent collaborations”. _World development_ , vol. 44, 2013, pp. 281–99.

- Rassenfosse, Ga ́etan de, and Florian Seliger. “Imputation of missing information in worldwide patent data”. _Data in Brief_ , vol. 34, 2021, p. 106615.

- Reuters. “Huawei unveils Harmony operating system, won’t ditch Android for smartphones”. _Reuters_ , 19 Aug. 2019. `www.reuters.com/article/technology/huawei-unveils-`

32
The price variables continue to have the expected signs. Inflation has a significantly negative effect. Looking at the country-specific environmental variables, water availability increases agricultural productivity. Potential dry matter is significantly negative.<sup>31</sup>

# **Estimates utilizing FAO data on tractors**

As discussed, in the absence of cross-country data sets on agricultural fixed capital, researchers often used FAO data on number of tractors as a proxy. We showed earlier that the data series on tractors are not a convincing proxy for agricultural fixed capital data. To demonstrate the implications for econometric analysis, we estimate agricultural productivity once again, with the FAO data on number of tractors in use replacing the data on agricultural fixed capital stocks. The results are presented in Table 6. The coefficient on tractors is not significant in the within estimations. Due to the omission of the fixed capital input, the other input elasticities change as well; the elasticities of capital of agricultural origin and land increase, while the elasticitiy of fertilizer falls slightly, and the labor elasticity becomes negative. Omitting this critical input for agricultural production from the econometric analysis leads to unrealistic results.

Interestingly, the input elasticities from the between-country estimations using the tractor data are similar to those using the agricultural fixed capital data (compare the last two columns of Tables 5 and 6). The coefficient on tractors is 0.11, while the coefficient on fixed capital is 0.13. The choice of capital series (tractors vs. fixed capital) has little effect on the between-country elasticities of capital of agricultural origin and labor. The reason for the similar results from the two regressions lies in the correlations of the transformed capital data. Examining the transformations of the capital data series, the correlations of the between-country transformations are much higher than those of the within transformations (0.79 vs. 0.19). This is because the differences in the composition of capital and the declining importance of tractors are lost as the time-dimension of the data is collapsed into country