rafmacalaba/data-use-mentions-extended huggingface dataset:
and other countries in the periphery experienced major withdrawals of foreign-owned funds in the course of the crisis. The crisis exposed Ireland's vulnerabilities to financial contagion.<sup>13</sup> <!-- Start of picture text --> Figure 13: Foreign-owned banking sector liabilities<br>30000%<br>25000%<br>20000% Ireland<br>Spain<br>15000% Iceland<br>Switzerland<br>10000%<br>5000%<br>0%<br>Percent GDP<br><!-- End of picture text --> Sources: BIS; and IMF, World Economic Outlook database.

35. The housing boom was also the crucial factor obfuscating the soundness of Ireland‘s fiscal position. On the surface, Ireland had pursued conservative fiscal policies and in fact had been running fiscal surpluses since the mid-90s, except in 2002. However, these surpluses were increasingly driven by tax revenue boosted from the property bubble. In fact, during 2000-2007, driven largely by the revenue boom, revenue from capital taxes and stamp duties increased from 2 percent in 2000 to 4 percent of GDP, while VAT increased by one percentage point of GDP to 7 1⁄2 percent.<sup>14</sup> At their peak in 2007, these revenues represented over a third of total current revenue, as opposed to one-fifth of current revenue in Spain and the UK, two other countries with large asset booms. This asset-related revenue encouraged increases in structural expenditure and masked a rising structural deficit. When adjusting for the impact of asset prices, the Irish structural deficit reached 8 percent in 2007 (as opposed to a measured surplus of 0.9 percent of GDP), spiking to 12 percent of GDP in 2008.

> 13 Lenihan and Honohan (2010).  Also, Lane (2011), which discusses how Ireland was able to refinance their debt through the ECB-IMF.  Iceland, on the other hand, was
**Appendix C : Estimates of the Dixit-Stiglitz Elasticities of Substitution for Goods**

It was necessary for us to obtain estimates of the Dixit-Stiglitz product variety elasticities of substitution for the imperfectly competitive sectors in our model. Christian Broda, Joshua Greenfield and David Weinstein (2006) estimated Dixit-Stiglitz product variety elasticities of substitution at the 3 digit level in 73 countries. Among the 73 countries, there were four in sub-Saharan Africa: the Central African Republic, Madagascar, Malawi and Mauritius. We judged that Madagascar was the country closest in characteristics to Tanzania, so we took the values of the elasticities estimated for Madagascar as a proxy for the elasticities for Tanzania.

Broda et al., estimate 3 digit elasticities for 130 goods sectors, but there are 34 goods sectors in our model, It was necessary to map the sectors estimated by Broda et al. into the sectors of our model. In table C1 of this appendix, we show the mapping for the imperfectly competitive sectors. (These elasticities are not relevant in our model for perfectly competitive sectors.) Next, since there are often multiple sectors from Broda et al. mapped into a single sector in our model, it was necessary to determine a method of weighting the Broda et al. elasticities. There are reasons to use both export shares as well as import shares. A larger share of a subcategory in imports reflects more imports, and more likely there are more varieties of imports. So weighting by the import share of a subcategory is better than an unweighted measure. Domestic varieties are also important. Since we do not have production data for the subcategories, we use export shares as a proxy for domestic production by subcategory. Analogously, weighting subcategories by export shares is better than unweighted categories. Since both import shares and export shares are useful in the weighting, we take one half the shares of both exports and imports as the weights. We obtained the data for the import and export shares from the COMTRADE database. The resulting elasticities are reported in table C1
_Estimation results._ In the regression analysis, all possible combinations of the abovementioned 13 regressors are tried as the right-hand side variables and, after excluding nonsensical combinations of regressors, the regression results that are considered to be the best according to explanatory power, as judged by W<sup>2</sup> -adjusted, are chosen. Table 3 summarizes these results using direct trade-based and currency area-based trade shares for a sample of 146 developing economies. Generally speaking, use of currency-area based trade shares seems to explain G-5 currency weights better than does the use of direct-trade based variables: the more a developing economy trades with one of the G-5 currency areas (rather than the G-5 countries), the larger the weight of this anchor currency in the economy's exchange rate stabilization policy. One notable exception is the case of the DM weight, where use of direct-trade based shares produces better results, although there is no qualitative difference in the estimation results. Another exception is the case of the UKP weight where use of currency-area based trade shares does not yield an expected positive coefficient.

For the USD weight equation, use of currency-area data produces reasonable results with an expected positive coefficient on the USD area. While the coefficient on the French colony dummy is negative as expected, the model for the USD weight may not be completely satisfactory because of the negative coefficients of East Asia regional dummies. For the DM weight equation, the choice of direct-trade based or currency-area based trade share is not important: a country's share of trade with Germany or with the DM area has the expected positive sign in explaining the DM weight in a currency basket. For the JY weight equation, the results are relatively weak: the coefficient on the JY area or Japan variable is statistically insignificant, and explanatory power is the lowest among the 5 equations. Trading with the JY 12
or slightly below - in both states three-quarters of deviations are in the range [-0.5%, 5%], but with more mass for larger absolute deviations in some municipality-years. We also present the same deviations but considering total committed amounts at the municipality-year-function level, where one observation is, for example, the total amount committed by one city for the Health function in 2020. We present these results in Figure A.1, again documenting that our measures of aggregate commitments closely follow those available at SICONFI.

We also provide the distribution of deviations from SICONFI in verification and payment amounts in Figure 4 and Figure 5, respectively. Here our concordance with SICONFI is less precise – while PB and SP show very small deviations, in the remaining states we systematically _overestimate_ total amounts of verification and payments. The distributions of deviations are somewhat similar across states, centered at around 5%-8% and with some mass up to 15%20% in some municipality-years – meaning that the aggregate amount in our data exceeds that in SICONFI by that amount. As reported in Figure A.4 for the states of MG and PR, these deviations do not seem to be driven by specific municipalities or to be systematically different across years, suggesting that there are consistent differences between our measures of verification and payments and those of SICONFI. While we are unable to precisely explain those differences, we expect they are partly driven by amounts carried over across fiscal years, the

> 20We note that SICONFI contains self-reported information and as a result can also contain errors. Moreover, some differences may arise due to different aggregation methods and the inclusion of government entities besides the municipal executive branch, such as state-owned companies.

> 21We use the SICONFI dataset available on the Data Basis ( _Base dos Dados_ ) platform at https://basedosdados.org/dataset/5a3dec52-8740-460e-b31d-0e0347979da0.

> 22We are only able to compute these indicators for municipality-year observations available both in our dataset and SICONFI.

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areas. For these areas, either the prediction from the auxiliary data must be ignored, the sample must be ignored, or the area must be dropped from the estimation. Finally, the Fay-Herriot model does not account for uncertainty in the survey-based estimates of variance, which tends to underestimate the variance of the prediction.

Despite these limitations, the Fay-Herriot model is much simpler to apply and explain than a unit level model. It generally provides credible estimates and is a standard workhorse of small area estimation. We estimate a Fay-Herriot model by aggregating all indicators to the area level, weighting by population. The Fay-Herriot models are estimated using the Stata FHSAE command (Corral, et al, 2018). We utilize the “Chandra method”, which estimates the prediction model using GLS (Chandra et al, 2013).

# C. Household-level model

In contrast to area level models, unit level models are specified at the level of the individual unit, which in this case is the household.<sup>11</sup> Although the auxiliary data only varies at the sub-area level, predicting welfare at the household level fully utilizes the information in the sample survey on the level and variability of household welfare. In addition, unlike sub-area models, well-established software packages are available that utilize empirical best methods to estimate unit-level models with area-level mixed effects.

The EBP model can be written as follows:<sup>12</sup> (3) G(Y 1 sa 2 a 3 Xr a Where: ii ii ) = ββ XX + ββ XX + ββ + ηη + εε The EBP model can be written as follows:<sup>12</sup> Yi is the non-monetary welfare for household i, which in each country is the principal component score constructed using the loading factors in Table 1.

G(Yi) is a transformed version of welfare. Traditionally, ELL uses the log function to transform welfare, but any monotonic transformation can be used. As described in greater detail below,
POLICY RESEARCH WORKING PAPER 2334

# Summary findings

The practice of safe sex by commercial sex workers is considered central to preventing the transmission of AIDS in developing countries. Rao, Gupta, and Jana estimate the compensating differential for condom use among sex workers in Calcutta, based on results from a survey conducted in 1993.

If, as suggested by anecdotal evidence, this loss in income is large, it would indicate the existence of strong disincentives for practicing safe sex.

To identify the relationship between condom use and the average price per sex act, they follow an instrumental variable approach, exploiting an intervention program focused on providing information about the AIDS virus and about safe sex practices. The program, instituted in 1992, was not systematically administered.

Using this method, they found that sex workers who always use condoms face a loss of 79 percent in the average earnings per sex act.

- - This paper a product of Poverty and Human Resources, Development Research Group is part of a larger effort in the group to understand the behavior underlying HIV/AIDS transmission. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-4733902, fax 202-522-1153, email address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Vijayendra Rao may be contacted at vrao@worldbank.org. May 2000. (18 pages)

_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 necessanily represent the view of the World Bank, its Executive Directors, or the countries they represent.
country dummy variables used to control for

# **4.2 Product innovation and spillovers between**

Our second set of estimations builds on previous research on spillovers by Jaffe et al. (1993), Branstetter (2001) and Griffith et al. (2006). This literature argues that knowledge spillovers between two firms should be proportional to the similarity and intensity of their research programs and related to geographical distance between the source and the receiver of the spillover. To capture these effects, these papers construct a measure of technological proximity between firms or split sources of spillovers between local, national and international. For technological similarity they use the uncentered correlation coefficient between firms’ vectors of R&D expenditures. Additionally, all these papers use patent citation data to identify the direction of knowledge flows.

Unfortunately, we are not able to follow completely Branstetter (2001) and Griffith et al. (2006) as we do not have information about the way firms allocate R&D expenditures across technological categories. To overcome this drawback, we work under the assumption that if two firms operating in the same country and industry are equally productive, they are involved in similar research projects. Therefore, we measure technological proximity between two firms by comparing their productivities, although we do not compute productivity per se but work with a set of productivity correlates. Many studies show that more than one factor determines the productivity of a firm. Clerides et al. (1998) and Bernard and Bradford (1999) provide evidence that exporters are more productive. Feenstra and Hanson (1997) show that affiliates of multinational corporations outperform domestic firms in recipient countries; Almeida and Fernandes (2008) report that majority foreign-owned firms are less likely to engage in technological innovations than minority foreign-owned ones, and Brambilla (2006) finds that firms with more than 50 percent of foreign ownership introduce on average more than twice as many more new products as private domestic companies. Finally, Cohen and Kelpper (1996) report that the likelihood of a firm reporting
also confirmed when we model the duration of tenure at the individual level to account for changes in the labor market that might affect the composition of characteristics within the cohorts. We estimate the probability of having a job of three lengths of tenure—short, medium, and long—conditional on a set of individual characteristics (such as age, gender, and education) and employment-related characteristics (such as occupation and the size of the firm). After controlling for these characteristics, the probability of having a medium- or long-term job declined and having a short-term job increased over the survey period. The probability of having a short-term job

> 4 We replicated the analysis presented in this paper on a sample that excludes data from the 2020 EU-LFS. Our concern was that the COVID-19 pandemic and the non-pharmaceutical interventions implemented by EU governments had such a profound impact on the EU economies and labor markets that 2020 could be an outlier in tenure trends and other dimensions we study in this paper (see <u>Demirguc-Kunt et al. 2021). However, the results of these estimations are</u> qualitatively very similar to the results based on the 1995 – 2020 sample. (These results are available from the authors on request.)

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with less than half a percent of young children having access to all three adequacies. Between 6% (Ethiopia) and 39% (Bolivia) of children are adequate in two of the three adequacies. When only one adequacy is met, in most of our study countries it is care. The only exception is Indonesia where a child who only meets one of the adequacy categories is more likely to be adequate in food than in care or in environment and health. However, in Indonesia the food adequacy measure is based on household level information on food consumption and not child specific information on what kinds of foods the child consumed.

35
, finishing high school is a critical achievement for lowincome individuals and may lead to significant positive externalities in various dimensions. The first obvious channel is eligibility to apply for college or formal

> 35 Reference about work from Sanchez

> 36 If households classified in SISBEN 1 have on average 1.37 children between 7 and 18 years and the number of families covered by the program is about 2.8 million, _FA_ supports nearly 3.8 million children. The effect of the program in terms of the number of students is proxied by the increment in the high school completion rates induced by the program (from 36 percent to 40-44 percent) with respect to the 3.8 million that are currently participating in FA. Estimates based on the high school completion rates observed for the control group from the PSM analysis (children between grades 4 and 10 at baseline) suggests that approximately 50 percent of these children would graduate from high school. Applying the effects of the program to this completion rate implies that the aggregate effect would be in the order of 140,000-280,000 additional high school graduates.

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Lebanon in the emigration.” This sentence from the study ordered by the Turkish governor of Mount Lebanon in 1918, ( _Lubnan, mabaheth ilmiah wa ijtimaya – Lebanon, scientific and social studies – 1918_ ) remains true more than a century later.

This part of the Middle East was prematurely exposed to the international flows of people, goods, and capital during the first globalization. Statistics from 1912 show that 25 to 30 percent of the population of Mount Lebanon was living and working abroad. The reasons for this emigration, according to contemporaneous sources, are the repeated crisis in an economy that was heavily relying on the production and export of raw natural silk. Another reason was the high concentration of the population in Mount Lebanon with a density of 110 individuals per square kilometer, which was high compared to the international standards of that period.

# **_1. The Silk Curse: Monoculture, Deindustrialization and Emigration_**

Since the first quarter of the 19th century and the implementation of the first factories for the treatment of raw silk, Lebanese agriculture has gradually been monopolized by mulberry culture to provide mulberry leaves to silkworm breeding plants. The low cost of a large labor force concentrated in regions close to the coast and ports was the main reason that motivated foreign investors to develop silk industry in Mount Lebanon.<sup>2</sup> This exporting activity raised the revenues of Mount Lebanon and triggered an enhancement of the standard of living together with substantial

> 2 Mount-Lebanon, or the Mount Lebanon Mutessarifat (1861-1915) covered one-third of the area of modern Lebanon and was a semi-autonomous district of the Ottoman Empire created in 1861 as a consequence of the civil war of 1860 between Christian Maronites and Muslim Druses.

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- iii) Children aged 17 and younger engaged in activities that are designated as “ _hazardous”_ (affecting the child’s safety, physical and mental development) or the “ _worst forms of child labor”_ (e.g. children in bondage or forced labor, commercial sexual exploitation, illicit activities and armed conflict, among others).

Based on these definitions, ILO (2013) estimates that there were 264 million children ages 5 to 17 engaged in an economic activity in the world (16.7 percent), of which 168 million children were involved in child labor. About 51 percent of child laborers (85.3 million) were engaged in hazardous work.

Academic researchers, however, rarely measure child labor following the ICLS resolution outlined above. The child labor literature often uses the terms “ _child labor”_ and “ _children engaged in productive activities”_ interchangeably. Moreover, there is considerable heterogeneity in the types of productive activities considered in this literature: some papers define children in wage work as child laborers while others include children engaged in any economic activity for the market (paid or unpaid) or even household chores. The challenge of using wage work as an indicator for child labor is that many children working outside the household are not paid. However, not all forms of unpaid activities are considered detrimental to children’s development. For that reason, some studies apply an hour limit to the number of domestic hours a child works to consider activities where children regularly participate (Edmonds 2008).

There is also variation in the reference period applied in these studies. Some studies look at work in the 7 days prior to the household survey while other studies focus on work in the past one or twelve months. This heterogeneity complicates the comparison of program effects across studies. We report the definition and reference time used in each paper (when available) in our review; we also use child labor and children engaged in an economic activity interchangeably throughout the paper.

It is important
data collection in 2 visits that were 6 months apart (R1); and (4) 12-month recall-based data collection in a single visit (R2) – the current approach in Malawi and arguably the most cost effective practice for household and farm surveys collecting information on cassava production in low- and middle-income countries. Beyond the socio-economic and agricultural data collected on the sampled households, we leverage (i) the GPS-based areas of the cassava plots cultivated by the households, and (ii) the crop cutting data tied to a 5x5m sub-plot that was placed at random within a randomly-selected cassava plot of each household (regardless of the survey treatment) and that was harvested and weighed at the time of each household’s preference during the 12-month fieldwork.

The contributions of this work are fourfold. First, the results from this study provide another point of empirical evidence regarding the relative accuracy and cost-effectiveness of recall-based methods vis-à-vis their diary-based counterparts for household-level annual cassava production estimation. The findings feed into the guidance we provide on survey design for data collection on

> 5 The body of work investigating the accuracy of recall vis-a-vis diary extends beyond agricultural data. See, for instance, Beegle et al. (2012); Backiny-Yetna et al. (2017); Brzozowski et al. (2017); Friedman et al. (2017); and Troubat and Grunberger (2017) for the work on consumption data. Though less relevant for extended-harvest crops, Desiere and Jolliffe (2018) and Gourlay et al. (2017) document, in Uganda and Ethiopia, respectively, large (upward) biases in recall-based data on seasonal crop production and yield in comparison to crop cutting.

3