rafmacalaba/data-use-mentions-extended huggingface dataset:
that the effect of tone of the news is significant only in FMPs located in areas in which it is more likely that migrants have access to the Internet. Second, we conduct two different placebo tests showing that our results are not spurious. Finally, we provide suggestive evidence that our results are unlikely to be driven by reverse causality.

**Internet access** Our finding that migrants’ movement choices in Libya are influenced by the tone of news published in their preferred destination countries rests on the idea that migrants are able to access and read the news. While we do not have information on mobile ownership nor on individual-level Internet usage, Table 2 provides suggestive evidence that the tone of the news affects only migrants who are more likely to be able to access them. Column 1 shows that the effect of the tone of the news in the destination countries increases with the level of economic activity around the FMP (as proxied by night lights). As long as economic activity positively correlates with Internet access, these results indicate that the effect is stronger in areas where migrants are more likely to read the news and to use it to take their decisions. Columns 2 and 3 offer additional support to this interpretation by using information on internet coverage in Libya. Results show that the effect of the tone of the news is significant only where there is Internet coverage and there is a high-speed connection. The fact that the changes in the tone of the news have an effect only in the locations where migrants are more likely to actually access

> 30Aksoy et al. (2023) show that attitudes towards migrants (proxied by a “sentiment index” built using Twitter data) are as important as local unemployment rates in shaping refugees’ integration outcomes in Germany.

> 31Note that for our argument to be valid, migrants do not need to be able to read the news articles (e.g., to know the language of the destination country) nor to actually read them. It is enough to assume that someone in
elicitation exercise measures _wii_ and _wij_ , while the belief update interventions identify the impact of _p_<sup>_l_</sup> _ij_<sup>and</sup><sup>_pt_</sup> _ij_<sup>on</sup><sup>_wij_.</sup> Finally, it is important to notice that (9) embeds a selfish assumption: agents are only willing to pay for others to acquire the technology as long as it benefits them. I interpret a participants’ willingness-to-pay for another farmer as the total value to the participant of the externalities produced by the other farmer; as a result, the selfish assumption is crucial to identify spillovers. If participants had altruistic preferences, their willingnessto-pay would also include the value a participant attaches to providing the technology to others. I will further discuss the selfish assumption in the results section to provide evidence in its favor.

# **4 Data and Sample**

The sample includes 780 farmers from 103 villages located in the districts of Manafa, Mbale, and Tororo in Eastern Uganda. This sample had been selected for a field experiment carried out in previous years by a research team that included the author (Burchardi et al., 2022). The three contiguous districts were selected for their high suitability to maize cultivation according to the FAO GAEZ data.<sup>10</sup> The 103 villages<sup>11</sup> were selected by a randomization algorithm from the pool of villages in the 2010 national census with a population density lower than 100 households per square kilometer (to ensure they were in rural areas), stratifying by population density. In each village, we enlisted all residing households through a researcher-run census survey. We selected six to nine research participants (maximum one per enlisted household, always the head of the household) according to the following criteria: _i)_ engaging in commercial farming; _ii)_ having cultivated or planning to cultivate maize; _iii)_ reporting land
privatization) than those privatized later or not all. Likewise, late privatizers appeared to have been performing less well in 1992 than those not privatized at all.

**Table 2: Median 1992 Performance Indicators for Early, Late and Non-Privatized Provincial Banks**

|**Group**|No.**of**<br>**Banks**|Net**Worth**<br>**divided by Net**<br>**Liabilities****|**"Normal"`**<br>**Loans as Share**<br>**of**Total**Loans****|**Loans to Public**<br>**Sector as Share**<br>**of Total Loans****|
|---|---|---|---|---|
|_Privatized Prior to Dec.1994***_|4|0.082|0.474|0.205|
|_Prnatized After Dec. 1994_|13|0.191|0.625|0.174|
|_Not Privatized By Dec. 1996._|7|0.217|0.702|0.135|

* "Normal" Loans are Loans that are not overdue. (See Table 3)

7 ** All data is for June 1992 (prior to any privatizations).'

*** Omits Corrientes since this bank was privatized in 1991, and we want pre-privatization data. Including Corrientes makes all performance measures even worse for early privatizers.

# 4. ECONOMETRIC RESULTS.

Using Cox's proportional hazard model, the timing of privatization is estimated as a function of the quality of the provincial bank, the political affiliation of the key decision-makers, and the fiscal situation of the province.I" The hazard rate in this model - - which, loosely, is the
having the same status regardless of the group of assessors and the protocol, and with almost 90 percent of them being classified in the same way in at least five of the seven rounds. We also show that the characteristics of the assessors and the protocol followed affect judgment results, but their overall influence is very small. Given this high level of consistency, we pool all seven rounds of assessments which results in 50 or more human judgments for each place in the sample, and then classify every place as urban or rural based on a majority rule. We treat this pooled classification as the outcome of our human judgment exercise.

We then to develop an approach to predict urban status, as assessed through human judgment, based on observable characteristics of the corresponding places. We emphasize four characteristics for each place. Total population and population density, both drawn from the population census, are key indicators in the urban economics literature. Built-up cover and nighttime lights, both from satellite imageries, have been at the core of recent attempts to estimate a country’s “true” urbanization rate. We also consider other indicators, but the four key characteristics just listed remain the most important predictors of subjective urban status.

Several approaches, drawn from classical data modeling and from machine learning, are applied for the prediction exercise as well. We first use a standard Logit model linking the urban status of a place with the values of its characteristics. We then apply more sophisticated machine learning methods, including LASSO and random forests. We find that all three approaches yield a similar prediction accuracy, with random forests performing slightly better. These approaches are then used to predict whether each of the other places in the country is urban or rural in practice.

The urbanization rate emerging from this exercise for India’s is 29.9 percent. Unlike the remarkably high rates reported by some studies, this figure is quite close to the corresponding official estimate. However, we also find important gaps with the official estimates at more disaggregated levels. Consistent
them. Select summary statistics derived from the database are shown in Table 2. (Note that for the year 2023, the database covers the period January through June.) In total, the database counts about 90 million tweets.

# **3.3 Identifying Gender of Users**

Twitter does not indicate whether a user account belongs to an individual or an organization, and in the case of individuals, whether the user is female or male.<sup>3</sup> Accordingly. we have to determine these user attributes on our end. There are a variety of existing methods that have been developed for this task. We proceed in two steps. First we classify users as either individuals or organizations. Second, for users identified as individuals, we impute their gender. These two steps involve separate imputation methods that have been optimized for these individual tasks.

For the first step, we adopt the approach put forward by (Cetinkaya et al., 2023), which classifies accounts as either organizations or individuals. The predictors include the user account’s profile metadata, such as name, bio description, location, whether the bio includes an URL, time on Twitter, as well as Twitter account statistics, such as the number of tweets, retweets, following count, followers count, media count, and the number of likes.

The publicly available “Demographer” dataset is used by the authors to train the model and evaluate out of sample performance. These data contain both Twitter user identifiers and a variable indicating whether the account belongs to an individual or an organization for a total of 214,236 user accounts. The study conducts a 7-fold cross validation test, where 6 _/_ 7 _th_ of the data is used for training and the remaining 1 _/_ 7 _th_ of observations are used for out of sample prediction. The reported accuracy is about 97% (i.e., only 3 percent of user accounts are incorrectly classified). We also applied

>
baseline regressions we consider famine incidence. But famines differ in severity, and more severe famines might reflect more serious governance failure. Do our key results still hold if famine severity is considered? Because the data set does not provide a precise scale of famines, we construct our new famine measure relying on qualitative descriptions, as described in Su, Fang and Yin (2014). We create an ordinal variable that is 2 if the record mentions human casualties, 1 if the record mentions hunger or malnutrition, and 0 otherwise. Among the 167 famines, 78 are classified as severe (i.e., with a scale of 2), and 89 as mild (i.e., with a scale 17
four pieces of protective equipment were present together in only 66% of facilities. Figure 7 shows that the average availability of these four PPE items varies based on facility level, most notably for boots. Notably, the Sierra Leone survey occurred in 2018, following the West African Ebola epidemic in 2013-2016. The high rates of PPE ownership may be partly attributable to the epidemic response there and PPE availability may be lower in other African countries that have not experienced a recent similar event.

15
(7)|SF (2)|LA (1)|BOS (4)|SF (1)|
|City’s Patents|NAS (82)|AUS (6)|STL (19)|WPB (6)|PRT (6)|LA (2)|DC (1)|HRT (4)|LA (1)|
|||B. Im|migrant Scientis|t and Engineer|Shares Estim|ated from 1990|US Census R|ecords (%)||
|Bachelor's Share|87.6|2.7|2.3|2.4|2.3|0.6|0.5|0.4|1.2|
|Master's Share|78.9|6.7|3.4|2.2|5.4|0.9|0.7|0.8|1|
|Doctorate Share|71.2|13.2|4|1.7|6.5|0.9|1.5|0.5|0.4|

Source: Author's analysis based on data described in text. Notes: Panel A presents descriptive statistics for inventors residing in the US at the time of patent application. Inventor ethnicities are estimated through inventors' names using techniques described in the text. Patents are grouped by application years and major technology fields. Cities, defined through Metropolitan Statistical Areas, include AUS (Austin), BAL (Baltimore), BOS (Boston), DC (Washington), HRT (Hartford), LA (Los Angeles), MIA (Miami), MIL (Milwaukee), NAS (Nashville), NOR (New Orleans), NYC (New York City), PRT (Portland), SA (San Antonio), SD (San Diego), SF (San Francisco), SLC (Salt Lake City), STL
be less than one.<sup>8</sup> A non-trivial fraction of the variation in intermediate shares is accounted for by nothing but the variation in prices across countries.<sup>9</sup> A ramification is that models of sectoral linkages must allow for an effective price channel to operate so that shares respond to changes in relative prices. The aggregate implications of changes in productivity and distortions derived in absence of the price channel will be biased.<sup>10</sup>

> 8 Peter and Ruane (2018) (PR) examine input substitutability using data on Indian firms. Their point estimate of elasticity for inputs aggregated at a broad level is less than one. However, they cannot rule out the CD association. The estimates in figure 1 and table 1 lie in the CI of the estimates in PR. Their analysis also highlights the relevance of aggregation as they conclusively find the elasticity to be greater than one when inputs are disaggregated to withinsector levels.

> 9 From a development accounting perspective, the price channel owing to complementarity in inputs implies that sectoral linkages constrict income differences as richer countries allocate more resources into the sector with a higher relative price. Fadinger et al. (2018) also report the same effect, though they take variations in linkages to be exogenous.

> 10 Jones (2011) discusses the relationship between aggregate output and input substitutability. Peter and Ruane (2018) quantitatively investigate the issue using data on Indian firms.

5
|5.5|6.6|6.3|6.5|7.1|7.1|6.4|
|Resting|3.3|3.4|3.8|3.7|2.4|2.7|2.8|2.9|3.0|3.1|
|Other reasons|2.9|2.6|4.2|5.0|5.2|5.0|4.0|4.0|4.2|4.3|

Source: raw data from the Thai LFS, authors' tabulation.
2014. Since 2011, the decline has been fairly steep. However, note that the decline has been not as pronounced when removing outlier-recipient observations from our donor aid allocation regressions using our mechanism explained earlier. The large discrepancy between the two lines, particularly from 2012 onward, is mainly driven by the US. In the full sample analysis, the US is classified as policy-selective only in the years 2010 and 2011, whereas in the analysis where we remove outliers the US is classified as being policy-selective from 2008 onward. Consistent outliers are for example Chile, Iraq and Zimbabwe, which all bias the CPIA estimate downward. Iraq and Zimbabwe because of too much aid and Chile because of too little aid given their CPIA. Note that from 2003 onward, Chile has been identified as an outlier for about 14 donors in average in our sample of 72 donors. We believe that these examples illustrate that our outlier adjusted policy-selectivity measure is probably the better measure for policy-selective aid as it seems hardly plausible to us to see recipient countries such as Chile and Iraq as serious participants in

> 1GDP and population data are obtained from the Penn World Tables (pwt90).

6
Unfortunately, however, growth in HFCE tends to substantially exceed mean consumption growth in household surveys.<sup>3</sup> One study, for example, concludes that on average only about half of the growth in HFCE was reflected in growth in survey consumption.<sup>4</sup> This is partly because official surveys face severe challenges obtaining accurate measures of welfare for the very wealthy. Because there are few very wealthy households, they may not be included in the sample. Wealthy households are more likely than poor households to refuse to participate in the survey, and tend to underreport their income or consumption. However, there are also good reasons why national accounts data may overestimate growth in mean income or consumption, including the use of outdated ‘rates and ratios’ based on old survey data.<sup>5</sup> Because of the systematic discrepancy between growth in national accounts data and household surveys, using HFCE to adjust survey data is prone to overstating poverty reduction for countries that lack a recent survey such as India. In fact, past analysis from India demonstrates how the typical line up adjustment procedure can greatly overestimate poverty decline, using data from 2004-05 and 2009-10.<sup>6</sup> Estimates were generated for 2009-10 by lining up the 2004-05 data to 2009-10, applying the HFCE growth rate during that period. These line-up estimates for 2009-10 were then compared with measurements from actual survey data from that year. The line-up procedure generated an estimated reduction in poverty from 41.6 to 13.4 percent, whereas the actual data from 2009-10 yielded a poverty rate of 32.7%.<sup>7</sup> In other words, the line-up method overstated the decline in poverty by 19 percentage points. However, virtually all of the discrepancy between the lineup estimate and the actual data resulted from consumption expenditure growth in the national accounts exceeding mean welfare growth in the survey. In contrast, assuming that the 2004-05 welfare distribution remained constant except
significant increase in the trend of large magnitude floods, with this in the face of a lack of effective flood management systems (UNDP 2006).

During the last 30 years, the total number of flood events which occurred in Asia was about 40% of all the events in the world as compared to 25% in the Americas, 17% in Africa, 14% in Europe and 4% in Oceania (IPCC AR3; CRED). The most affected country is China followed by India, Indonesia, Bangladesh, Vietnam, Thailand and Pakistan. In the last century, the number of major events occurring in Asia was the highest at 1,551, the Americas was 893, Africa was 739, Europe 473 and Oceania 117 (EM-DAT).

The question then arises as to whether the number of flood events is increasing, and whether in the future the frequency of flood events will increase causing even more devastation. In examining the trends in flood events an internationally recognized source of data is the above cited reported events database held by EM-DAT. Data from this source shows the number of reported flood events has in fact increased dramatically as shown in Figure 4 below.

**Figure 4: Number of reported flood events**

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>Events 10 year moving median<br>1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010<br><!-- End of picture text --> Medium-scale events are increasing the fastest. Recent events such as the Pakistan floods and to some extent the events in New Orleans may lead us to believe that only large scale disasters are becoming more frequent and devastating. Somewhat surprisingly, the growth in reported events is stronger in medium events characterized by between 5-50 deaths, 1,500150,000 affected or 8-200 million 2003 USD of
An alternative explanation is that customers may have felt more comfortable doing larger transactions at branches because they perceived a higher level of privacy there. Agents are more physically and socially proximate to clients than bank tellers, but that could raise privacy concerns because an agent may talk to friends and relatives of the client about the transaction, making it harder for the client to fend off cash demands from relatives and friends. In contrast, at the branch, the transaction and its details are known only to the teller, who is highly unlikely to have personal dealings with the client or her social network. This could provide a rationale for why individuals would choose the branch for large transactions and the agent for smaller ones. Focus group interviews and evidence from lab-in-the-field experiments with BSN clients provide support for this hypothesis (Buri et al., 2018b) .

Another lens on the importance of privacy for clients comes from an analysis of a large database of transactions from one of the largest microfinance institutions in DRC (Chamboko et al., 2020). A key finding is that clients were significantly more likely to transact with agents of their own gender. Women, in particular, were significantly more likely to transact with female agents, a tendency which increased both with the value of transactions and the customer’s balance. These patterns held when controlling for customer characteristics, agent experience, and market-level covariates and when the analysis was restricted to markets with substantial representation of female agents. That pattern was also detected in data from the experiment with BSN clients in Senegal: the probability that female clients used female agents increased with the size of the transaction (Buri et al., 2018a; Chamboko et al., 2020).

Trust and the desire for privacy are factors that could help account for women’s transactional behavior with female agents. Since the agents could see not only the amount transacted but also the customer’s account balance, women may have preferred to go to a female agent, especially when they had large balances or wanted
may not necessarily translate to improved access to financing if market concentration remains high. Hence, this study also examines if and how concentration changes as markets grow.

Understanding issuer composition is important to gauge how effective capital markets are in meeting the range of financing needs across the economy. Additionally, a more inclusive market can be beneficial in itself, providing further motivation to study the topic. For example, it can help to improve capital market resilience (Xing, 2004). Volatility in the market may increase if there are only a handful of dominant firms or if listed firms are concentrated in just a few sectors. Lack of a broad and diverse issuer base also makes capital markets less attractive to investors as it becomes more difficult to achieve low-cost diversification - one of the key advantages of capital markets. Moreover, it has implications for the real economy as research shows that equity markets are essential in providing financing to more innovative and high-growth firms as well as longerterm projects (Didier et al., 2020; Bae et al., 2021). When stock markets are restrictive, such firms and projects are unable to get funding, which in turn stifles innovation and productivity in the economy.

This paper studies the relationship between stock market growth and changes in issuer composition. Stock market growth is captured along two dimensions: market size (proxied by stock market capitalization) and market activity (proxied by trading activity and stock turnover ratio). We study the relationship of these market growth indicators with different aspects of issuer composition, which include the number of listed firms, issuer concentration, and sectoral diversity. Additionally, we examine the number and average size of initial and seasoned equity offerings to understand how equity issuances change as stock markets grow.

Our paper relies on a novel dataset assembled by the International Finance Corporation (IFC) that captures stock market data from over 150 countries for the period 2015-2020. We employ a crosscountry fixed effects panel to uncover relationships between stock market growth and issuer composition. Our results show that stock market