rafmacalaba/data-use-mentions-extended huggingface dataset:
a CV of 55.76, 96.05, and -23.10 in Tables 2A, 2B, and 2C, respectively. By contrast, the corresponding CV for Profits2 is 0.85, 1.77, and 1.12, respectively; and for Profits3 is 1.13, 2.68, and 1.20, respectively. Not surprisingly, the correlation between Profits1 and either Profits2 or Profits3 is low, likely due to the substantial difference and adjustments between the measures. These findings on precision demonstrate that the AAA approach can meaningfully reduce noise in traditional profit measurement.

Finally, we compare the autocorrelation of AAA performance measures to traditional performance measures over time. Previous studies have found that traditional microenterprise sales and profit measures are quite noisy not only in the cross-section but also over time. To examine how our tool compares in this regard, we leverage two panel data sets of small firms in Rwanda and Uganda.<sup>7</sup> Table 3 presents the autocorrelation coefficients of the AAA and traditional sales and profits measures. Across both settings, we find that the AAA measures have higher autocorrelation over time. For sales, we observe small improvements in autocorrelation coefficients from 0.55 to 0.62 in Uganda, and from 0.53 to 059 in Rwanda. The improvements are particularly large for profits, from -0.05 (Profits1) to 0.34 (Profits2) to 0.41 (Profits3) in Uganda, and from 0.05 (Profits1) to 0.28 (Profits2) to 0.37 (Profits3) in Rwanda. Hence, for both sales and profits, we find consistently higher autocorrelation in measurement using the AAA tool over traditional measures.

# **3.3. Comparison with Self-Reported Profits**

As outlined in Section 2.4, the AAA tool does not record unaided self-reported profits. The first recording of profits is _after_ the respondent has gone through the detailed _Money In_ section of the survey.
# Appendix 2. Definitions and sources of variables

|Variable|Definition|Source|
|---|---|---|
|Economic activity|||
|Output|GDP in constant 2010 U.S. dollars (market weighted), taken from the World Bank. Prior to 1960, the<br>series is extended using data from the Penn World Tables 9.1 (PWT 9.1). Sample includes 180<br>countries, including 36 advanced economies and 144 EMDEs. For PPP-weighted series, GDP is in<br>constant 2010 international dollars.|Feenstra, Inklaar, and<br>Timmer (2015); World<br>Bank.|
|Output per<br>capita|GDP per capita, in constant 2010 U.S. dollars (market weighted), taken from the World Bank. Prior<br>to 1960, both GDP and population series are extended using data from PWT 9.1. Sample includes 180<br>countries. For PPP-weighted series,GDPper capita is in constant 2010 international dollars.|Feenstra, Inklaar, and<br>Timmer (2015); World<br>Bank.|
|Industrial<br>production|Industrial production index (if not available, manufacturing production index is used). Data are<br>obtained at a quarterly frequency and used as annual averages of year-on-year growth rates. For<br>aggregated groups, market-weighted output is used as a weight. The main source of the series is the<br>World Bank and, for countries and quarters without data, the series is extended using growth rates of<br>data from the Organisation for Economic Co-operation and Development (OECD) and Haver<br>Analytics.|Haver Analytics, OECD,<br>World Bank.|
|Trade|Exports plus imports of goods and services, in constant 2010 U.S. dollars, taken from the World Bank.<br>
and ultimately converged with those for regression. Therefore, the number of poverty classes selected entails a tradeoff between the accuracy and the granularity of the prediction.

**Result #2: In Guatemala CDRs predict urban and total poverty more accurately than rural poverty**

In both the regression and classification models, rural poverty rates were consistently more difficult to predict than either total or urban poverty rates. R<sup>2</sup> values fell to around 0.25 for rural poverty, implying that CDR data could explain just 25 percent of the variation in rural poverty rates. The accuracy of the classification models dropped to between 0.3 and 0.65 depending on the specification. Two hypotheses could explain this phenomenon. First, cellular penetration rates in urban areas tend to be higher, and thus CDR-based analysis provides more robust modeling signals in urban areas, where they represent the behavior of a larger share of the population. In rural areas, fewer phones and fewer calls weaken the signal that can be extracted from CDR data, and their predictive power decreases. Second, urban areas tend to have more cellular antennae per square kilometer, which results in smaller polygons. The much larger polygons in rural areas may tend to reduce the granularity of the data by aggregating behaviors, weakening the predictive power of the algorithm. Testing these hypotheses will require further research.

**Result #3: Further analysis will be necessary to determine the extent to which CDR-based models based on past poverty data can be used to predict future poverty dynamics**

CDR-based models could potentially be used for temporal prediction or time extrapolation (see Boxes 2 and 3), but no research project has yet been able to prove that models trained on past poverty values can be used to predict future poverty levels. Such an analysis would require an extremely large and detailed dataset. For example, predicting future poverty values in Guatemala would require CDR data from 2006 and 2011, as well as corresponding poverty survey data for the same time periods.
and policy lessons for other CIS countries with similar institutional setup and legal frameworks for bank resolution. It can also prompt earlier reforms in this area in peer countries to ensure systemic risk events can be resolved according to best practice and to the benefit of the economies.

To gain new insights into the link between bank bailouts and fiscal costs, and to develop a tool for projecting fiscal contingent liabilities from bank bailouts, this paper draws on a rich history of various forms of staggered bailouts in Kazakhstan. It studies the link between bank bailouts and fiscal contingent liabilities using bank-level data for Kazakhstan. The paper first estimates the probability that a bank in distress is bailed out, conditioning on bank characteristics such as financial soundness (CAMEL indicators), ownership type (state, politically exposed persons, local, foreign), and systemic importance (size).<sup>e</sup> Second, it estimates the extent of bailout costs depending on the size of banks, their ownership type, financial soundness, and the type of bailout instrument used by the government. The latter aims to contrast the fiscal contingent liabilities when the government uses bailout instruments without taxpayer recourse on bank future profits—such as government purchases of bad loans at 100 percent of the nominal value—versus instruments that require banks to repay state financial support with appropriate compensation for the alternative fiscal costs—such as properly governed and priced senior debt or equity injections. Third, the paper illustrates how the estimations could be used for projecting the expected contingent liabilities from bank bailouts.

We find that larger banks are more likely to experience a bailout despite their business diversification and economies of scale. We surmise that moral hazard could be also one underlying explanation given the history of bailouts in Kazakhstan. Foreign-owned banks get bailed out much less than other domestic banks (keeping size constant). Similarly, banks owned by local politically exposed persons (PEPs) get resolved through private solutions much less than other domestic banks (keeping size constant)—perhaps owing to their better management
# **1. Introduction**

Prostitution is often described as the world's oldest profession yet economists have almost never studied it. The neglect of this important market is particularly glaring in the face of the AIDS epidemic and the consensus that commercially purchased sex is central to the spread of the disease in the developing world (World Bank 1997, Jain et. al., 1994). More than 90 per cent of all adult HIV infections are in developing countries and the vast majority of these cases were contracted from heterosexual sex with an infected partner. To date, AIDS has had the greatest impact in Africa where it is estimated that over 2.5 percent of adults have been infected with the disease. Now, however, the disease is thought to be widespread in South Asia where the first case was reported in 1986. Experts believe that without drastic measures South Asia may suffer effects as devastating as those observed in sub-Saharan Africa.

Promoting the use of condoms and other safe sex practices among sex workers is known to be perhaps the most effective method of preventing the spread of the epidemic in the developing world. In India as in most other developing countries, however, men have a strong preference against using condoms. One indicator of this is that despite many decades of extensive government sponsored "family planning" campaigns promoting their use, condoms have not become a commonly used method of contraception. Recent data from a nationally representative sample show, for instance, that only 7% of married couples use condoms, with female sterilization being the most widely used method of contraception (27 per cent), followed by abstinence and other traditional methods (20 per centV. This inherent resistance to using condoms is compounded by a lack of awareness about HIV/AlDS and safe sex practices in the general population<sup>2</sup> . Consequently sex workers who want to practice safe sex potentially face losing a considerable amount of income by doing so. Anecdotal evidence suggests that sex workers could face at least a
# **Table A7: Impacts of mental health on labor outcomes – Weighted regression**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health scores|-0.058***<br>(0.009)|-0.188***<br>(0.057)|
|_First stage of 2SLS (dependent varia_|_ble is standardized m_|_ental health scores)_|
|Trauma experience*Year after<br>arrival|-0.043***<br>(0.011)|-0.058***<br>(0.017)|
|Kleibergen-Paap F stat.|21.383|21.383|
|AR 95-CIs|[-0.111, -0.031]|[-0.400, -0.124]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,687|
|Number of individuals|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a
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by hindering asset accumulation and return on assets. Indirectly, climate change affects poverty through output prices, labor productivity and the availability of offfarm employment opportunities. This paper focuses primarily on the direct livelihood effects. A <mark>s in other mainly agrarian SSA countries, the incidence of climate shocks such as droughts,</mark> seasonal and flash <mark>floods, and e</mark> xtreme temperatures and dry spells is increasing in Zambia. <mark>These weather extremes</mark> have significant carryover side effects on rural farmers who depend on rainfed agriculture for their livelihoods.

Climate shocks are likely to become more intense and more frequent. Ahmed et al. (2009) predict that by 2080, climate change will increase the intensity of dry spells and subsequently worsen the incidence of poverty among smallholder farmers by 4.6 percentage points in all agro-ecological regions in Zambia. Average temperatures are predicted to rise by between

> 5 From which the national poverty rate is derived.

> 6 At the time of writing, the 2015 LCMS is the data set that provides the most recent estimate of national, urban/rural, and provincial poverty rates in Zambia.

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this second set of questions, we establish a first, direct empirical link between contagion and trading strategies.

The methodological contribution of the paper is our approach to attributing actions to fund managers versus underlying investors. Despite a vast literature on the behavior of domestic (i.e., **U.S.)** funds, to our knowledge we are the first to disentangle the two. In effect, the trades of mutual funds reflect both institutional and individual decisions. To understand those trades, particularly in the international context, ensuring that the decisions are not commingled is an important step.

Our results show that emerging-market funds do indeed engage in momentum trading. Their strategies exhibit positive momentum-they systematically buy winners and sell losers. This is due to momentum trading at both the fund-manager level and the investor level (through redemptions/inflows). We further distinguish between

> 2 Though a lovely data set, the Froot el al. **(1998)** data do not include transactions settled in foreign currencies, e.g., ADR trades in New York and Brady bonds. These trades can be especially important in times of crisis when local-market liquidity is at a minimum. Our fund-portfolio data include these trades.

2
# **6 Effect Heterogeneity**

In this section we extend our analysis by examining the above identified effects’ heterogeneous nature. This allows us to narrow down the range of possible mechanisms and identify world regions where the effect of drought on urbanization appears most evidently. We begin by interrogating effect heterogeneity in the global sample before turning to the developing world. Existing literature leads us to expect particularly strong positive effects of drought on urbanization in less developed countries both because much of the developing world is geographically located in the Global South and because poverty has typically been associated with fewer coping mechanisms that are at vulnerable populations’ disposal.

## **6.1 Country Income Categories**

Figure 3 displays the interactions between country-level income categories and our spatially and temporally lagged measure of drought. Note that each of the displayed models uses our preferred specification with year and grid cell fixed effects (model (3) from Table 1). Including grid cell fixed effects allows us to account for time-invariant features of individual grid cells, thus significantly lowering the threat of unobserved confounding. By the same token, this means that while the interactions between country-income categories and the impact of drought can be estimated, the main effects themselves become inestimable. Thus, the direction of the interactions is generally more informative than their magnitude.

To capture country-level income, we use the World Bank’s income classification (grouping lower- and upper-middle income into “middle-income” countries for simplicity). In addition, we distinguish between countries that were industrialized at the beginning of our panel dataset in 1985 (defined by a share of agriculture in national income below 30 percent) from countries that reached this milestone later or countries that have not reached it at the time of our analyses. Overall, we find a clear indication that the effect of drought on urban growth is uniquely pronounced in the world’s poorer countries. Whereas low- and middle-income countries exhibit positive and statistically significant 18
. We decided to limit the analysis of the EGSF anthropometric data to height-for-age for two reasons: (1) since weight-for-age is a composite of weight-for-height and heightfor-age, deficits in weight-for-age in Guatemalan children almost entirely reflect deficits in height-forage;<sup>4</sup> and (2) since the prevalence of low weight-for-height is rare in Guatemala, much larger samples would be needed to explore the correlates of this condition.

> 3A community was defined as rural if it contained less than 1800 households. Because the sample entailed selecting an average of 50 women of the specified ages within each community, communities with less than 100 households were replaced with communities from an alternate list.

> 4Keller (1983) used correlation analysis to show that weight-for-height and height-for-age are virtually independent of each other (that is they represent different processes of malnutrition). Regressing weight-for-age on weight-forheight and height-for-age (all expressed in term of standard deviations scores) in several populations with varying prevalence of malnutrition, Keller also found very high coefficients of determination (between 0.95 and 0.98). Combining both findings, he concluded that (1) deficits in weight-for-age are a composite of deficits in weight-

4
to the extent that the latest cohort reaching US exit age falls short only about 0.6 years of schooling with respect to the region –with an average of 10.9 years completed, the same as Costa Rica. Thus, for Costa Rica, Panama, and El Salvador, the US dropout phenomenon is clearly a central challenge.

Guatemala, Honduras, and Nicaragua also face this challenge, but additionally, they are still struggling with the issue of LS completion. As shown in Figure 3b, Guatemala and Honduras reached average primary school completion about 30 and 25 years after the Latin America average, respectively, and have just entered the second phase where the average individual of US exit age completes LS. Nicaragua reached the first benchmark more quickly (10 years later than the Latin America average), but also only recently reached the second benchmark of average LS completion.

For every country, national averages mask large within-country disparities, which will be explored in the subsequent sections. The picture that emerges is one of a sub-region facing new challenges of the 21<sup>st</sup> century, while still dealing with unresolved issues from the past.

# **1.4 Patterns of secondary dropout**

By following a similar cohort approach, Bentaouet-Kattan and Székely (2015) have shown that higher enrollment and completion rates at the primary and LS levels in Latin America have implied higher dropout rates at the US level. In Figures 4a and 4b, we use the same approach as those authors by following the enrollment trajectories of two cohorts, the first born 1980-1982 and the second born 1994-1996.<sup>8</sup>

> 8 See Appendix Table A.3 for a complementary measure of dropout, specifically looking at the educational attainment of 25-29 year olds in each country’s most recent household survey.

7
_Ingo Borchert and Mattia Di Ubaldo_ results with non-services depth included are available upon request.

We follow standard practice of estimating equation (4.1) with a non-linear Pseudo Poisson Maximum Likelihood (PPML) estimator as suggested by Silva and Tenreyro (2006). Apart from accounting for the presence of zeroes, which are rife in bilateral services trade data, this approach also handles the heteroscedasticity arising from the presence of several small countries (and flows) in our data. In order to be able to include the three sets of high-dimensional fixed effects described above, we employ the recent PPML estimator developed by Correia, Guimar ̃aes and Zylkin (2019).

Finally, we follow the recent innovation by Cameron et al. (2011) and Egger and Tarlea (2015) and base our inferences on three-way clustered standard errors, i.e. at the importer, exporter, and year levels. This strict multi-level clustering takes into account simultaneous correlations across the three main dimension of the panel (over time within countries, and across countries within time periods); these intra-cluster correlations are plausible for trade data.<sup>18</sup> This leads to dramatically more conservative results, compared to robust standard errors, and thus increases our confidence in the significance levels associated with estimated PTA<sup>19</sup>

# **4.2 Aggregation of services provisions**

A principal contribution of this paper is to quantify not only the overall impact of services PTAs but to elucidate specific trade effects that may emanate from the structure of the agreements or certain configurations of individual provisions therein. The design and

> 18We exploit two-way (importer and exporter) instead of three-way clustered standard errors in the models estimating the PTA effect on services VA. This is because the number of years is limited in the TiVA data, which would imply standard errors to be adjusted to a very small number of clusters. Furthermore, we believe that the correlation across-countries within time period is potentially less concerning than the correlation across-years