rafmacalaba/data-use-mentions-extended huggingface dataset:
on poverty taken from the panel model specification on the _y_ -axis, and the effects of temperature on agriculture in Table A11 on the _x_ -axis in Figure A6 (Appendix A). Since the unit of analysis is different across two samples, we aggregate the data at the country level for better comparison. For all the panels, we find a negative and strongly statistically significant correlation between crop yield and poverty. Consistent with our previous findings, African countries are found to be most vulnerable to the effects of global warming. In Figure A7 (Appendix A), we further plot the effects of temperature on poverty against a country’s share of agriculture in GDP and also find the effects to be stronger among countries which rely on agriculture as the main source of income. Overall, these findings suggest that by reducing crop yield, warmer temperature may directly contribute to more poverty.<sup>16</sup> Another potential mechanism that may explain the effects of temperature is migration. Since poverty is a major driver of people’s vulnerability to climate-related shocks, it is reasonable to expect that the flow of people escaping poverty is also affected by climate change. In fact, an emerging body of literature has shown that higher temperature increases both internal and international immigration rates (e.g., <mark>Cattaneo and Peri, 2016; Missirian and Schlenker, 2017)</mark> . We reaffirm findings from the literature by using migration data available at the subnational level. The data is provided by WorldPop Open Data Repository which captures internal migration flows between 2005 and 2010.<sup>17</sup> Using a simple OLS regression, we find suggestive evidence that hotter temperature results in higher migration flow (see Figure A8, Appendix A).

# **_D2. Projected impacts under future climate change_**

We next provide projections of the effects of future temperature on poverty to better understand potential effects under different scenarios. To do this, we combine the model estimates in
institutional variables, this paper also includes the World Bank’s Governance Indicators (forthwith referred to as Governance Indicators) which rank political stability and absence from violence, corruption control, voice and accountability, regulatory quality, rule of law and government. An aggregate World Bank Governance Ranking, based on a simple average of the component indicators, is also included in some regressions. Different combinations of the Doing Business Rankings/components and the Governance Indicators are used to ensure robustness of results and counter possible multicollinearity. For example, arguably the Governance Indicators of government effectiveness, regulatory quality and rule of law are captured within the Doing Business components of starting a business and enforcing contracts – any correlation should be addressed in regression analysis.

Literature has extensively used the Doing Business Rankings and corresponding components in analysis (the World Bank Doing Business website lists more than 100 academic papers). Eifert (2009) focuses on individual components of the Doing Business Rankings and find improvements in the time taken to enforce contracts stimulates growth. However, Eifert (2009) fails to consider the aggregate impacts of all the Doing Business components. Conversely, Djankov, McLiesh and Ramalho (2006) do focus on aggregate impacts using cross-sectional analysis with fixed country effects. They show countries with a higher Doing Business Ranking in 2004 significantly influences growth. Surprisingly, Djankov _et al._ (2006) use as their dependent variable, the average GDP growth from 1993 to 2002 and estimate Doing Business Rankings using 2004 data (official rankings were not available until 2006). This approach could bias results as country rankings change over time and different results would be recorded when using data from alternative years.

Busse and Groizard (2008) use a separate set of fixed country effects when undertaking their crosssectional analysis on the influences of selected components of the World Bank’s Ease of Doing Business measures on GDP growth. Like Djankov _et al._ (2006), they also artificially create a rankings system but do so by creating a dummy variable of the 20 most regulated
resource intensive products over labor force, constructed from the UN Statistics Department’s Commodity Trade Statistics (COMTRADE).

_Innovation Effort:_ Consistent with much of the microeconomic literature we employ real R&D expenditures as a measure of innovation effort. The data are derived ultimately from national surveys that use as a common definition of expenditures that include “fundamental and applied research as well as experimental development.”<sup>11</sup> The data thus include not only the basic science expected in the more advanced countries, but also investments in the adoption and adaptation of existing technologies often thought more germane to developing countries. The series are constructed based on underlying data published by UNESCO, the OECD, the Ibero American Science and Technology Indicators Network (RICYT) and the Taiwan Statistical Data Book.

Though it would be desirable to study the evolution and efficiency of both private and public R&D, we work with aggregate R&D for several reasons. First, the data sources divide R&D not into private and public R&D, but into productive and nonproductive sectors, the latter accounting for roughly 20% of the total.<sup>12</sup> The definition of “productive sector” includes both public and private for profit and not-for profit firms while “non-productive sector” includes R&D financed or undertaken by the executive branch of government.

Second, the productive/non productive split seems to occasionally lead to some critical issues in categorization. For instance, if a public company finances its R&D from retained earnings, this will count as productive sector R&D. If instead the same R&D is financed by a transfer from the treasury to the firm, it counts as “non-productive” R&D. For several countries in our sample, there were striking shifts in composition from one year to the next suggesting sensitivity to these accounting conventions. By contrast, the total R&D series were reasonably stable. The final consideration is more prosaic
Our findings so far have provided strong evidence of the effects of poor mental health on refugees’ labor outcomes. While the BNLA survey does not allow us to examine whether the mental health impacts are more pronounced for refugees than other groups, we explore a quick comparison between migrants and the general population using longitudinal data from the HILDA survey over the period 2001-2019. Migrants in this survey are identified based on their country of birth. We create an interaction term between migrant status and mental health score with the latter being measured by the Mental Health Inventory (MHI-5) scale. Employing the non-IV individual FE model, we find a negative correlation between mental illness and labor outcomes for both population groups, but no evidence of differential impacts between the groups (Appendix A, Table A17). This finding, however, should be interpreted with caution given the endogeneity of mental health.

# **5. Conclusions**

The wide-ranging labor market consequences associated with mental illness have been well documented in the literature. However, hardly any economic study currently exists on the causal effects of mental health on refugee labor market outcomes. Understanding this relationship is important given that many richer countries are facing a large influx of refugees. We provide an early study that identifies the causal effects of mental illness on refugees by instrumenting for it with past exposure to traumatic events interacted with time after resettlement, which is further strengthened with individual fixed effects.

Our findings indicate that worse mental health lowers the probability of having paid employment and labor income. Worse mental health also reduces LFP, job quality, and life satisfaction and exerts negative externalities on refugee partners and children. Yet, these effects appear more pronounced for refugees who recently arrived or are without a social network. At the same time, the effects are weaker for those who receive benefits from the government. This 20
- [22] Davoodi, Hamid, Paul Elger, Alexandra Fotiou, Daniel Garcia-Macia, Andresa Lagerborg, Raphael Lam, and Sharanya Pillai, “Fiscal rules dataset: 1985-2021,” IMF (2022).

- [23] Dolan, Julie, “The budget-minimizing bureaucrat? Empirical evidence from the senior executive service,”Public Administration Review, Vol. 62 (2002), pp. 42-50.

- [24] Dzhambova, Krastina, “When it rains, it pours: Fiscal policy, credit constraints and business cycle in emerging and developed economies,” Journal of Macroeconomics, Vol. 69 (2021), pp. 1-19.

- [25] Fasano, Ugo, “Review of the experience with oil stabilizations and savings funds in selected countries,”IMF Working Paper No. 00/112 (2000).

- [26] Fernandez, Andres, Daniel Guzman, Ruy Lama, and Carlos A. Vegh, “Procyclical fiscal policy and asset market incompleteness,”NBER Working Paper No. 29149 (2021).

- [27] Frankel, Jeffrey A., “A solution to fiscal procyclicality: The structural budget institutions pioneered by Chile,”NBER Working Paper No. 16945 (2011).

- [28] Frankel, Jeffrey, Carlos A. Vegh, and Guillermo Vuletin, “On graduation from fiscal procyclicality,” Journal of Development Economics, Vol. 100 (2013), pp. 32-47.

- [29] Ghassibe, Mishel, and Franceso Zanetti, “State dependence of fiscal multipliers: The source of fluctuations matters,” Journal of Monetary Economics, Vol. 132 (2022), pp. 1-23.

- [30] Gruss, Bertrand, and Suhaib Kebhaj, “Commodity terms of trade: A new database,”IMF Working Paper 19/21 (2019).

- [31] Guay, Alain, and Pierre St.-Amant, “Do the Hodrick-Prescott and Baxter-King filters provide a good approximation of business cycles? Annales d’
- V. Experienced P4P creates effort incentives which contribute to higher (or lower) teacher performance, as measured by the learning outcomes of their students;

- VI. These selection and incentive effects are apparent in the composite performance metric.

The model predicts that the set of ( _τ, θ_ ) types preferring a teaching job advertised under P4P to a job in the outside sector is different from the set of types preferring a teaching job advertised under FW to a job in the outside sector. This gives Hypothesis I. Since the model abstracts from labor demand effects (by assuming employers hire at random from the set of ( _τ, θ_ ) types that apply), this prediction simply maps through to placed recruits; i.e. to Hypothesis II via _θ_ , Hypothesis III via _τ_ , and Hypotheses IV to VI via the effect of _θ_ and _τ_ on performance.<sup>14</sup> The model also predicts that any given ( _τ, θ_ ) type who applies to, and is placed in, a teaching job will exert more effort under experienced P4P than experienced FW. This gives Hypotheses V and VI via the effect of _e_ on performance.

**Analysis of blinded data** Combining several previously-known insights, we used blinded data to maximize statistical power for our main hypothesis tests.

The first insights, pertaining to simulation, are due to Humphreys et al. (2013) and Olken (2015). Researchers can use actual outcome data with the treatment variable scrambled or removed to estimate specifications in ‘mock’ data. This permits navigation of an otherwise intractable ‘analysis tree’. They can also improve statistical power by simulating treatment effects and choosing the specification that minimizes the standard error. Without true treatment assignments, the influence of any decision over eventual treatment effect estimates is unknown; thus, these benefits are garnered without risk of _p_ -hacking.<sup>15</sup>

> 14When mapping the theory to our empirical context, we distinguish between these hypotheses
# **Figure 6**

**Co-movement of Brazil C Bond Prices and the Dow Jones Index Evidence of Global Factors’ Influence on Brazilian Asset Prices**

<!-- Start of picture text --> 90 12000<br>DJIA BR C Bond Price<br>(right scale)<br>80 11000<br>70 10000<br>60 9000<br>50 8000<br>40 7000<br>1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: World Bank staff calculations 21. Faced with mounting difficulties in rolling over the domestic debt, the central bank redeemed a fraction of debt falling due by printing money. Consequently, the monetary base expansion exceeded nominal GDP growth (Figure 7). The monetary effect of public domestic debt redemptions during the second semester of 2002 reached the tenor of 30 percent of base money (Figure 8). It is crucial to point out, however, that the positive monetary expansion due to the treasury’s operation began in the second semester of 2001 and could have been interpreted as a leading indicator of the more turbulent episodes that were to unravel in mid 2002.

**Figure 7 Figure 8 Money Base as a Share of GDP Monetary Impact of Treasury’s (seasonally adjusted data Operations 1999- 2003 (ratio to the monetary base**

<!-- Start of picture text --> 5.0 .4<br>.3<br>4.5<br>.2<br>4.0<br>.1<br>3.5<br>.0<br>3.0 -.1<br>2.5 -.2<br>-.3<br>2.0 1999 2000 2001 2002 2003<br>95 96 97 98 99 00 01 02 03<br>Source: World Bank staff calculations based on Bacen Source: World
# **Chapter 2**

# **Direct estimation**

The earliest estimates for subpopulations based on sample surveys were “direct”, in the sense that they used only the survey data from the subpopulation of interest without “borrowing strength” from other subpopulations. These estimates are based on the sampling design; that is, they exhibit good properties across all possible samples of units drawn from the target population using the specified sampling design (random mechanism used to draw the samples). For a detailed account of the sampling theory, refer to the well-known books by Cochran (1977), S ̈arndal, Swensson and Wretman (1992), Thompson (1997), or Lohr (1999). The guidelines by Corral et al. (2022) briefly introduce the design-based setup for inference in the context of SAE for poverty mapping in Section 2.1.

This section introduces common direct estimators based on sampling theory, as they serve as the benchmark for any comparison. These estimators make no model assumptions and exhibit good properties for areas or domains with sufficiently large sample sizes. However, they are inefficient (with large variances) in areas with small sample sizes. SAE methods aim to improve the efficiency (reduce the variance) of direct estimators, typically at the cost of increased bias. For example, refer to Figure 2.1 in Corral et al. (2022), which illustrates the design bias of model-based 5
Policy Research Working Paper 10165

# **Abstract**

This study investigates whether Twitter data can be used to infer attitudes towards COVID-19 vaccination with an application to the Arabic speaking world. At first glance, anti-vaccine sentiment estimated from Twitter data is surprisingly low in comparison to estimates obtained from survey data. Only about 3 percent of Twitter accounts in our database are identified as anti-COVID-vaccination (compared to 20 to 30 percent of survey respondents). This bias is resolved when: (1) filtering out accounts belonging to organizations that make up a significant share of the discourse on Twitter, and (2) adjusting for the fact that the population of Twitter users is biased towards more educated individuals. The most effective messages on the anti-vaccine side highlight claims that the vaccine causes serious life-threatening side effects. In the pro-vaccine camp, tweets containing content showing public figures receiving the vaccine are found to have the largest reach by far.

This paper is a product of the Development Research Group, Development Economics. 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 author may be contacted at rvanderweide@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 Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
22 covers utility patents<sup>35</sup> and other types of U.S. documents, such as design patents<sup>36</sup> , plant patents<sup>37</sup> , reissues, defensive publications, and statutory inventions registrations granted between 1977 and 2001 and is aggregated by year and country of the first named inventor.

U.S. patents and patents in general have long been recognized as a very rich and potentially fruitful source of data for the study of innovation and technical change<sup>38</sup> . One key reason for this recognition is because each patent contains highly detailed information on the innovation itself, the technological area to which it belongs, the inventors (e.g., their country of residence), etc. Also, the number of U.S. patents granted to foreign inventors has consistently been increasing during the last three decades. More specifically, the percentage of U.S. patents awarded to foreign inventors has risen from about 20 percent in the early 1960s to about 46 percent in 2001. In addition, inventors from 106 foreign countries were granted USPTO patents in 2001. Hence, U.S. patents increasingly reflect not only inventive activity in the U.S. itself, but also around the world. Lastly, the number of patents potentially available for research is enormous. The stock of patents is currently in excess of 6 million, and the flow comprises over 150,000 patents per year (as of 1999-2000). In addition, patents have been granted in the U.S. continuously since the late 18<sup>th</sup> century. The current numbering and reporting system dates to the 1870s, which means that there are (in principle) over 100 years of consistently reported data (Hall, Jaffe and Trajtenberg (2002)).

However, the use of patent data to measure the level innovation activity is not without problems. The most obvious limitation
**|0.90***|0.48***|0.48***|
|(βP P I)|||[7.3]|[7.1]|[3.5]|[3.5]|
|Other Compatriot’s|0.50***|0.48***|||0.25***|0.25***|
|Prices (βOCP)|[6.7]|[6.7]|||[2.8]|[2.9]|
|Observations|4030|4028|3231|3228|2491|2489|

Notes: Robust t-statistics are in brackets. ***,**,* indicate significance at the 1, 5 and 10 per cent levels. 1% outliers dropped. All specification include BLS primary lower stratum × country fixed effects, with standard errors clustered at this level. Regressions include controls for US GDP growth, date and life-length.

As the different measures of competitors’ prices are correlated, including only one measure will bias upward the estimated coefficient. Columns 5-6 includes all three measures of competitors’ prices in the regression, which approximately halves the estimated coefficients βF CP , βOCP and βP P I relative to regressions with only one of these variables. It also makes βC small and insignificant, which is consistent with competitors’ RERs working through competitors’ prices.

How does this relate to the model’s predictions? In the quantitative model (Section 5), changes in exchange rates are the only shocks, which lead to overfitting when similar measures of competitors’ prices and exchange rates are included concurrently in the model.<sup>26</sup> When a measure of the sectoral PPI is added to a standard

> 26In order to separately identify the role of competitors’ prices and competitors’ exchange rates, the model likely needs sector-specific cost or demand shocks
|(0.131)|(0.164)|(0.169)|
|Student-level covariates|YES|YES|YES|YES|
|School-level covariates|YES|YES|YES|YES|
|Average mathematics score|||||
|for control|38.78|20.18|33.37|20.74|
|Standard deviation of|||||
|mathematics score for control|21.91|15.60|18.51|15.15|
|Observations|875|873|1,044|1,037|
|R-squared|0.265|0.334|0.226|0.235|

_Note:_ The results are based on equation (2). The outcome variables are the standardized mathematics score from the researcher designed exam and the standardized WASSCE mathematics exam score. Robust standard errors are in parentheses. PSI-PMI = Progressive Science Initiative and Progressive Mathematics Initiative; WASSCE = West African Secondary School Certificate Examination. *** p < 0.01, ** p < 0.05, * p < 0.1. _School covariates_ variables include: presence of electricity in school, good desks in school, school has a library, a computer lab, running water in the school, student enrollment, student-teacher ratio, percentage of qualified teachers, percentage of female teachers, experience of the school principal, age of school principal, administration experience of school principal, level of education of school principal, school principal is female, share of students with high performance in mathematics, English, and science on the WASSCE. _Student-level covariates_ variables include the following: percentage of students not repeating any grade, percentage of students earned credit and above on GABECE mathematics, percentage of students earned credit and above on GABECE English, percent of students earned credit and above on general science, all at baseline. And socioeconomic characteristics of students—percent of students in households that has a quality roof, a
Liberalization of the services sector in Ukraine, first and foremost is linked to the WTO accession negotiations. Ukraine applied for accession on 30 November, 1993. The major obstacle on the way to the WTO accession was to bring the national legislation in compliance with the WTO rules and regulations. However, not much had been done till 2001, when

> 1The WTO database on services reports the profile of the EU services sector in 2007.

6