rafmacalaba/data-use-mentions-extended huggingface dataset:
periods of global crises (Column (2)). On the contrary, injections increase in response to the lagged returns of the fund (Column (3)), which are observable by the underlying investors, and in response to increases in the contemporaneous returns in the country of origin of the fund (Column (4)), which capture local conditions. Interestingly, among both equity and bond funds, the coefficient on lagged

> 23 In the regressions, we normalize the injections to a fund (given by Equation (2)) by the average assets instead of the initial assets to isolate fluctuations in injections from fluctuations in initial assets. Results using injections normalized by initial assets (available upon request) are qualitatively and quantitatively similar to those reported here, but estimators are less precise because of the additional volatility of the denominator in the expression.

> 24 The weighted country crisis dummy is constructed using yearly country crisis data, weighted by the fund’s country portfolio weights at the beginning of the year. The crisis variable comes from Broner et al. (2010) and dates a crisis the years when a country suffers at least a banking, debt, or currency crisis, according to indicators widely used by the literature. The periods of global turmoil are: July 1997-December 1997 (the Asian crisis), August 1998-December 1998 (the Russian crisis), March 2001-December 2001 (the dotcom bust, September 11, and the Enron scandal), and September 2008-June 2009 (the global financial crisis). Fund returns are computed from fund-price data. Returns of the fund’s country of origin are measured using a broad equity price index from the country where the fund is located. Funds that are domiciled in Luxembourg are matched with country returns from Belgium because there are no available indexes for bonds and equity from Luxembourg.

> 25 Sirri and Tufano (1998) include a longer set of lags of injections and fund returns in their specification. We also estimated a version including up to three lags of both
> The data report the value of imports (defined in FOB terms) and imported quantities (in kg.).<sup>15</sup> 13Taylor (2021) uses these data to study the impact of large vessels on the reproduction rates of Southern Right Killer Wales.

> 14The variables we use to control for export supply and the trade elasticities we include to generate structural trade cost estimates are only available at the HS6-digit level. As a result, we aggregate our trade data to the HS6 level.

15The data do not report port of destination in PR; for the purpose of calculating shipping distances we assume that all seaborne freight traffic goes through San Juan. Our port-of-call data show this to 9
. Governance and political stability are important criteria here but, unfortunately, no direct indicator is available at the sub-national level. However, three good proxies for institutional strength are available in our data and all contribute to adaptive capacity:</mark>

- <mark>Social capital and trust is measured in the form of generalized trust. The question "Generally speaking, would you say that most people can be trusted, or that you can’t be too careful in dealing with people?" has been used in many settings to assess general interpersonal trust as a dimension of social capital. It is available for Tajikistan from the Life in Transition (LiTS) survey. We use the</mark>

> 1 Access to information and communications infrastructure is also arguably important in <mark>influencing vulnerability (Blake et al, 1994). In the past, authors have used telephone access measures as proxies for information sharing, early warning, and general connectivity. However, now that most households have cell phones, the value of telephone access as a proxy variable has arguably diminished. This leaves us with no usable data source on access to information.</mark>

<mark>6</mark>
PPI sample period is also slightly shorter, running from 1994-2006, and the life of goods is longer, with a mean life of around 4 years. As with the import data, non-market transactions are excluded. I drop all prices specifically for export. There are no fixed effects or clustering of standard errors by country (as all goods are produced by a single country). As a robustness test, in the online appendix I consider a specification with fixed effects and clustered standard errors at the NAICS3d level. The materials cost variables are also deflated by the CPI and reflect total materials costs (to which imported materials costs contribute).

B.3. **Measuring competitors’ exchange rates.** The primary measure of competitors’ exchange rates is the import-weighted average of the change in the log of exchange rates within the NAICS 6 digit sector. This is the measure suggested by the multi-country AB model in Section 4, and in a three-country model (for import prices) is equal to the third-country RER in Section 2. For import prices, I first remove the bilateral exchange rate of the country who manufactured the good (with weights rescaled so they add to 1),<sup>28</sup> though for US producer prices this is clearly not necessary. Ideally, I would measure competition at a lower level — AB argue their model is best applied at the “more disaggregated than ten-digit NAICS” level — but

> 27The countries/regions are: China, Canada, Mexico, Japan, Germany, France, United Kingdom, Republic of Korea, Brazil and Hong Kong SAR China. I replace Taiwan, China with Hong Kong SAR, China due to a lack of available data from Taiwan, China, and because many Chinese exports pass through Hong Kong SAR, China.

> 28That is, they are multiplied by 1/(1 − sBIIM<sup>).Theimportsharesusedtocalculatecompetitors’exchangeratesarethoseat</sup> the start of the life of the good in the BLS data.
but that diversity itself a decade ago would not change current economic activity. This methodology is particularly useful in mitigating potential bias due to the possibility that the economic differences between IDP and non-IDP communes actually attract IDPs, rather than the differences being caused by IDP presence. This method is only valid where we have a strong first-stage relationship between ethnic diversity and IDP presence and numbers, and we only report it for those cases.

The third approach is Propensity Score Matching (PSM) estimation, which seeks to match communes or households between IDP hosting communes and non-hosting communes. This method allows us to construct counterfactuals of communes and households that are very similar in characteristics to the actual IDP communes. Rather than using the full analysis sample, we construct two groups of IDP and non-IDP communes whose only difference is in the presence of IDPs, and compare them. Given the panel nature of the communelevel data, we implement an approach suggested by Imai, Kim, and Wang (2020). The PSM technique works well for the commune level estimates where we have good successes matching observable household characteristics between IDP and non-IDP communes (Figure 14), but does not work well at the household level since the same households are not sampled every round of the survey. In this exercise, we identify commune observations similar in characteristics to the actual IDP communes in the year of the event and the year before.

# **5 Results**

We now present the results from our regression analysis in three groups. First, we show the estimated results of IDP presence on economic outcomes in host communes using the binary indicator of IDP presence as the main explanatory variable. Second, we present the effects of IDPs at the intensive margin with the IDP population in communes as the main explanatory variable. Third, we investigate the possible existence of heterogeneous effects of IDP presence at the household level for different types of households. We use all three 22
6 At least 10% of surveys were monitored in person, and most surveys were checked for inconsistencies by field monitors. Surveys with significant inconsistencies were either readministered or a different replacement household was selected to complete a new survey.

## **2.1.2 Rwanga Sample**

A second data source we draw upon is from a sample of recently or currently active female job seekers, in partnership with a local non-profit, Rwanga Foundation– we refer to this as the “Rwanga sample” throughout the paper. Rwanga maintains a jobs portal, where students and other job seekers are recruited from universities and job fairs to register, and employers are invited to post new opportunities each month. Rwanga has offices and outreach programs in the three urban areas of Erbil, Dohuk, and Sulaymaniyah. We discuss findings from both a longer diagnostic exercise with a smaller sample of 100 job seekers, as well as a short survey with a larger sample of 1,780 job seekers (all of whom were registered with Rwanga at the time of the study) that we conducted. This sample of female job seekers is different from the social norms sample; in particular, women in the sample were younger and better educated, and most had completed a college degree.

# **2.2 Measuring Social Norms and Cultural Beliefs**

A review of the social norms literature concludes that it is important to distinguish between measuring empirical and normative beliefs when measuring social norms (Mackie et al, 2015). These have been characterized as “social empirical” and “social normative” expectations (Biccheiri et al, 2014) or often as “descriptive” and “injunctive” norms (Cialdini, Kallgren, & Reno, 1991). Another way of stating these dual aspects is to say that social norms consist of a rule for behavior and common knowledge of that rule (Brennan, Eriksson, Goodin, & Southwood, 2013) or between following a “typical” or “appropriate” action in a group (Paluck
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Angola’s road indicators benchmarked against Africa’s low- and middle-income countries**

|Indicator|Unit|Low-<br>income,<br>nonfragile<br>countries|Resource-<br>rich<br>countries|Angola|Middle-<br>income<br>countries|
|---|---|---|---|---|---|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|88|98|29|278|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|132|128|41|318|
|GIS Rural accessibility|% of rural pop. within 2 km of all-season road|25|20|31|31|
|Main road network condition [2]|% in good or fair condition|72|68|58|86|
|Rural road network condition [3]|% in good or fair condition|53|61|40|65|
|Classified paved road traffic|AADT|1,131|1,408|884|2,451|
|Classified unpaved road traffic|AADT|57|54|10|107|
|Primary network overengineering|% of primary network paved with 300 AADT<br>or less|30|15|42|18|
|Perceived transport quality [4]|% firms identifying transport as major<br>business constraint|13|27|**—**|20|

_Source:_ AICD Road Sector Database on 40 Sub-Saharan African countries.

[1] Total network includes the classified and estimates of unclassified and urban networks.

[2] Main network for most countries is defined as result of adding the primary and secondary
, vehicle tax and social security contributions paid by both employers and employees as direct taxes. The property and betterment levies tax and the vehicle tax had the same treatment as in the previous CEQ, as the values reported in the survey were used. The main difference lies in the treatment of personal income tax and in the inclusion of social security contributions that were calculated with the rates in law and the income of the people who report they are contributing.

Concerning income tax, for this version of the CEQ, there was the advantage of having aggregate information from the DIAN tax returns. This information allowed for a better simulation of what households pay. Information from DIAN was used to calculate, for each of the income types, the proportion of revenue that does not constitute taxable income, that is exemptions and deductions, to finally deduce the net taxable income. It is also important to recall the significant changes in the tax code between 2010 and 2017, where the form of a statement of personal income and rates have changed at least twice. The tax law applicable to 2017 classifies income according to their origin and imposed upper bounds on the values that could be deducted in each income type and tax dividends.

24
analyzed in the following are inventory size, supply network size, generator ownership, water tank ownership, and investment volumes. If is continuous—for example, in the case of firms’ inventory size—ordinary least square regressions are carried out, where yy . Here, is the vector of explanatory variables; is a set of controls; and is the error term. All regressions yy= XX<sup>′</sup> ββ+ ZZ<sup>′</sup> θθ+ εε take the survey design, stratification, and respective sampling weights into account.<sup>1</sup> Estimates are hence XX ZZ εε representative for the overall population of firms in Dar es Salaam, Dodoma, and Tanga. As the sample size is relatively small compared to the overall population size (about 1.4 percent of the NBS registry), no finite population correction is employed. Standard errors are clustered at the ward level. If is binary—for example, in the case of firm generator ownership—a logit model is used, where εε yy All variables are defined as before and is the logit link function, taking the form PP(yy= 1) = EE[yy] = gg(XX′ββ+ ZZ′θθ). gg ~~.~~ exp(ηη) As before, appropriate sampling weights and stratification are considered. gg(ηη) =<sup>2</sup> If data on dependent or exp(ηη) + 1 As before, appropriate sampling weights and stratification are considered.<sup>2</sup> If data on dependent or independent variables for specific firms was missing—that is, there was no response to the survey—these firms were excluded from the respective regressions. Some parts of the questionnaire were only posed to relevant firms— for example, questions on water tank ownership were only posed to firms that require water to run their business. As a result, some regressions rely
such as children and<br>disabled people (including through water-borne diseases)<br>A significant share of the affected population is expected to face risk<br>to life, especially if flood waters have a current; major disruptions to<br>livelihoods<br>Most affected people could face substantial risk to life and severe and<br>prolonged disruptions to livelihoods|

# 4. Results

## 4.1. Substantial flood hazard exposure in all regions

Estimates from this study show that in 2015, at least 11.3 percent of all built-up areas globally face high or very high flood hazards; i.e. inundation depths of at least 50 cm during 1-in-100 year flood events (figure 3). Exposure is lowest in Sub-Saharan Africa (4.6 percent) and North America (4.5 percent), and highest in the East Asia and Pacific region (18.4 percent). These figures confirm that flood exposure levels are substantial across all global regions and income groups. While the normalized shares of exposed settlements (Figure 3) illustrate the relative proportions of safe and at-risk settlements over time, they hide that total settlement extents have expanded significantly in the considered period. This is assessed further in the following.

8
Table 1: Total schools enrolled in _Mais Educação_ between 2008 and 2011

||2008|2009|2010|2011|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|North|163|879|1,301|1791|
||0.6%|3.1%|4.5%|6.1%|
|Northeast|791|1,893|3,511|4,770|
||0.9%|2.1%|3.9%|5.2%|
|Southeast|207|1,105|3,066|3,544|
||0.4%|2.1%|5.7%|6.6%|
|South|99|326|1,034|1,792|
||0.4%|1.3%|4.1%|7.1%|
|Midwest|120|437|745|1,027|
||1.4%|4.9%|8.2%|11.2%|
|Ttl|1,380|4,640|9,657|12,924|
|oa|0.7%|2.3%|4.7%|6.2%|

_Source:_ MEC/FNDE and School Census (INEP), 2008–2011.

_Note:_ Table reports the total number of schools enrolled in _Mais Educação_ between 2008 and 2011 in different regions and nationwide. Percentages show the share _Mais Educação_ 's schools represent on the region's public schools and nationwide. We calculate the percentages by dividing the number of schools enrolled in _Mais Educação_ by total public schools by regions and nationwide, using school census. Column (1) presents data for
(remember that we use the same age cohort in estimating the synthetic panel, so that mean birthyear should remain the same if the composition of households does not change). A possible explanation is that there are more deaths among the older part of the sample. We also investigated the sensitivity of round 1 coefficients to the age cohort on which the model is estimated. Fortunately, our estimators of the coefficients are not much affected by the specification of the age cohort. This implies that the round 1 predictions for round 2 households are robust to the difference in population characteristics.

We use a vulnerability line to study income mobility in more detail. The line is set based on the synthetic panel for the first interval in the dataset (2004–2007) and kept constant afterwards. It is derived from a vulnerability index of 0.4, implying that all non-poor household in 2004 that face a probability of 0.4 or more of falling into poverty in 2007 are considered vulnerable.<sup>21</sup> In our data, this conditional probability is attained at an income level of MYR 703 per capita per month (about MYR 23 per day, or USD 14 at 2016 PPP levels) at the upper bound of mobility. This means that in any round all households with per capita income between MYR 527 and MYR 703 are considered vulnerable. Households with incomes above MYR 703 are labeled economically secure.

# 5. Population-level results

This section presents findings from applying the synthetic panel methodology to Malaysia’s official household survey data. In interpreting these results, it is important to recall that, although the poverty line is defined in relative terms in 2004, it is kept constant in real terms after that. That means we do not adjust it for greater general affluence in Malaysian society over time. This also holds for the vulnerability line. Moreover, all comparisons are based on households of heads aged 25–55 in round 1 and adjusted accordingly for round 2.<sup>22</sup
At the time of this writing (in 2012), the available data indicate that 1.2 billion people in the world live in poverty, as judged by the frugal standards used for defining poverty in the world’s poorest countries—giving an international poverty line of $1.25 a day (at 2005 purchasing power parity). This paper draws on recent research on global poverty to assess how long it might take to lift one billion people out of such extreme poverty. The paper identifies both “pessimistic” and “optimistic” paths to that goal.

International development institutions and almost all developing countries track poverty measures, and the results are keenly watched. The development literature has devoted less analytic attention to the important task of benchmarking performance.<sup>2</sup> When can we say that performance against poverty is “good” or “bad”?

Setting poverty-reduction goals can help motivate their achievement. The Millennium Development Goals (MDGs) were clearly conceived with that intention. As Hume (2009, p.4) states, the MDGs aimed to “stretch ambitions and mobilize political commitment and public support.”<sup>3</sup> The first MDG was to halve the developing world’s 1990 “$1 a day” poverty rate by 2015. Using the $1.25-a-day poverty line in 2005 prices, the first MDG was attained in 2010, a full five years ahead of the goal (Chen and Ravallion 2012). Even so, that important achievement leaves over one billion people living in extreme poverty.

To motivate extra effort, the goals must not be either too easy or too hard. Past experience can provide useful data for assessing proposed benchmarks for future performance. At the time that the first MDG was set, there were very few time-series observations to draw on. Fortunately, the situation has changed dramatically. Although the data are still far from ideal, we now have a firmer empirical basis for considering alternative goals that are within the
19</sup> on host community’s livelihood strategy choice, we use the following basic econometric model:<sup>_(1)_</sup> Where, indexes a household, ii ii<sup>+ γγXX</sup> is an outcome variable of interest (livelihood diversification ii<sup>+ νν+ εε</sup> ii YY = αα+ ββR or commercialization of agriculture), R i ii ii YY population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of<sup>is the measure of refugee inflow, i.e., the refugee</sup> distance of the household to the refugee camps, is a set of household controls, is kebele fixed effects, and is the error term. ii XX νν Several variables, from the DRDIP data set, were used as controls in our model. These include εεii total livestock values, size of land owned, distance to all weather roads, membership in

> 14 We focus on livestock products as we do not have data on the number of live animals sold.

> 15 https://data.humdata.org/dataset/ethiopia-refugee-camp-locations.

> 16 https://gadm.org/data.html.

> 17 https://acleddata.com/curated-data-files/.

> 18 https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11.

> 19 One could argue that our estimate commingled with refugee camp and international aid. We believe that refugee inflow and refugee camps are the same if most of the refugees live in the camps as the camps and refugee attributes are the same and could be used interchangeably as it is in the literature. We, however, prefer refugee inflow to refugee camp as our main explanatory variable is constructed as the number of refugees weighted by inverted distance. Drawing experiences from Ethiopia and other refugee host communities, we believe that international aid and other benefits coming to our
<sup>_f(t+T)_</sup> _f(t+T)_ loan level in year _t+T_ of _Y_ instead of the actual level y _f(t+T)._ The difference _y_ _9_<sup>_f(t+T)_</sup> _f(t+T)_ between y _f(t+T)_ and Y 9: can be classified as the amount of excessive loans. We will 1 _~~~~Oft y_ thus define excessive loan growth as Taking the year 1992 as the base year and the 1992 efficiency figures as the benchmark and assuming a constant efficiency over 5 years we define the following as a measure of bank risk-taking for the purpose of our application to East Asian banking data for the period 1992-96 in the following section 9
discussion with SSC experts, it was determined that the dramatic fall in the number of births between 1999 and 2003 was in part an ‘echo effect’ of the sharp drop in fertility and high death rate during the Second World War. The low fertility and high death rates during WW2 resulted in a much smaller birth cohort during this period. The population pyramids for Azerbaijan have since been marked by a regular drop in the number of births from this cohort (and their offspring) in a 25 years cycle. The period between 1999 and 2002-3 coincided with the second 25 years cycle. As we can see from Figure 1, there has been a sharp decline in the Total Fertility Rate from 1999 to 2001.Comparison of household size and age structure between the census and the HBS revealed a systematic change in both size and age structure, with a significant drop in the number of young children in the household.

One of the key assumptions inherent in the poverty mapping procedure outlined by Elbers et al. (2002) is that the models estimated from the survey data apply to census observations. Given the sharp drop in fertility between the implementation of the 1999 Census and HBS 2002, it was decided to modify the dependent variable and to use adult equivalent monthly household consumption as the welfare indicator as opposed to the commonly used average per capita consumption (Elbers et al. 2003; Mistiaen et al. 2002). The equivalence scale used gives less weight to young children than other household members and so is less sensitive to changes in the numbers of young children compared to a more straightforward per capita measure<sup>9</sup> . This minimizes the impact of the drop in the number of young children in the household at the time of the 2002 HBS.

> 9 Adult Equivalent: children aged below the age of six have been assigned a weight of 0.2, children aged between 7 to 12 have been assigned a weight of 0.3, children age 13 to 17 have been assigned a weight