rafmacalaba/data-use-mentions-extended huggingface dataset:
The social accounting data come from the GTAP database (http://www.gtap.org) and are benchmarked to 2001. These data include national production and international trade flows.

The import protection data are based on a thorough and careful effort to include use of preferences in a matrix of global import protection (Bouet et al. 2004). These data are the product of a joint effort between the UN International Trade Centre, the United Nations Conference on Trade And Development (UNCTAD), the World Trade Organization

> 21 See Ozden and Sharma (2004) for a similar analysis of the CBI program. Tangermann (2002) also notes that exporters often do not capture all the rents.

> 22 See e.g., IMF (2003), Alexandraki and Lankes (2004), Brenton and Ikezuki (2004). Earlier efforts at quantifying the value of preferential access for LDCs such as Ianchovichina at al. (2003) did not have access to accurate information on preferences for these countries.

17
five countries studied here – Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. As the MPI is a household-level measure of multidimensional poverty, strictly reporting the MPI headline results obscures the divergent experience of individuals within the household. This paper builds on the work of Alkire, Ul Haq, and Alim (2019) with its focus on individual deprivations and intrahousehold analysis using the gender-sensitive refugee

> [†] World Bank, Washington, D.C, USA

> [‡] Oxford Poverty & Human Development Initiative, University of Oxford, Oxford, UK

2
efficiency and productivity analysis (2nd ed.). Springer.

Damijan, J. P, Knell , M, Majcen, B., and Rojec, M. (2003): The role of FDI, R&D accumulation and trade in transferring technology to transition countries: evidence from firm panel data for eight transition countries, _Economic Systems_ , (27) 189–204.

Das, S. (1987): ―Externalities and technology transfer through multinational corporations‖, _Journal of International Economics_ , 22(1–2), 171–182.

Demsetz, H. (1973): ―Industry structure, market rivalry, and public policy‖, _Journal of Law and Economics_ , 16(1), 1–9.

Dunning, J.H., (1993): Multinational Enterprises and the Global Economy. Addison-Wesley, Reading.

Farrel, M.J. (1957), ―The Measurement of Productivity Efficiency‖, _Journal of the Royal Statistical Society_ , Series A, CXX, Part 3. 253-290.

Findlay, R. (1978): ―Relative backwardness, direct foreign investment, and the transfer of technology: A simple dynamic model‖, _Quarterly Journal of Economics_ , 92(1), 1–16.

13
012|
|p-val|0.789|0.598|0.228|0.307|0,754|

Notes: The table shows the results of regressing the variables regarding intention to use parental leave on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,242 individuals in experiment 2 (304 men and 938 women). Source: Postintervention survey.

# _4.3. Robustness checks_

Finally, we estimate some alternative model specifications in order to provide robustness to the main results.

We first re-estimate the main specification excluding the set of control variables. Then we estimate the same

> 15 As shown in

> Table B. 8, the specification with standard errors clustered at strata level shows that the message about couple planning (M3) helped increase the effective use of parental leave among fathers compared to the information message. The magnitude of this effect (0.057) is quite important given the low take-up among fathers in the control group (0.011).

> 16 This effect holds when estimating the results using a multinomial logit for a 4-category variable instead of separate binary outcomes. Results available upon request.

20
# **<mark>Foreword</mark>**

The work presented in this document grew out of the experiences of the AIDS Strategy and Action Plan program (ASAP) in Latin America. ASAP’s mission is to support countries in creating effective strategies in response to the HIV epidemic. It is now recognized that the most effective way to develop high-impact HIV policies and programs is through results-based management, a decision-making process that focuses on the use of evidence to make informed decisions. Therefore, an important aspect of strategic planning based on evidence is to provide such evidence—data that highlights the evolution and trends of the epidemic and its determinants—to decision-makers. The World Bank has worked in collaboration with the Ministries of Health from several countries to compile 15 years of locally-produced, primary-source data. The data for each country has been integrated into a document called “ _Database and References for an Evidence-Based Strategy”._ When using information from multiple and diverse sources, it is important: (i) to be able to understand the scope and limitations of the reported findings; (ii) to assess whether the studies can be accurately compared; and (iii) to take an in-depth look at the methodology used. Such an analysis can reveal, for example, the true representativeness of a sample, or the ability to generalize results to the study’s priority population. To this end, ASAP proposes an analysis tool in a questionnaire or verification list format to assess the methodological quality of data as reported,. The ASAP tool offers a practical approach that is especially useful for the kind of studies that are frequently used within the HIV field. The assessment tool can help to identify biases or methodological problems that may have emerged in design or implementation, providing a better understanding of the quality of information that the studies contain. Such a critical analysis will also help to identify the difficulties and constraints faced by researchers when working in the HIV field—especially when dealing with most-at-risk and hard to reach populations.

The
# 1 Introduction

Millennium Development Goal 2 calls for increased primary school enrollment and reductions in the gender gap in school enrollment. Many policy solutions have been proposed to achieve this goal, from teacher quality initiatives to transfer programs. School feeding programs, a form of conditional transfer, have been an important and prominent part of this policy portfolio in recent decades, and have been implemented widely in developing countries and for low-income populations in developed countries. In exchange for regular attendance, children are provided a meal or snack during the school day and/or food rations to take home. This transfer is hypothesized to shift parental preferences for children’s educational participation by reducing the costs and increasing the benefits of time spent at school. If successful, the transfers associated with school feeding can improve educational participation (enrollment, attendance, and age at school entry), achievement and cognition (test scores, grade progression), and nutritional status (height and weight-for-age and micronutrient status).

Despite the attention and resources devoted to school feedings programs, little rigorous evidence exists to support these investments. Prior research on school feeding has been hindered by school-based rather than household-based samples, cross-sectional data, and non-randomized designs (Adelman, Gilligan & Lehrer, 2008). In 2005, the World Food Programme initiated a three-country study of school feeding programs jointly with the World Bank that was designed to address these shortcomings. Impact evaluations were launched in Burkina Faso, Uganda, and Lao PDR. This paper reports the results from the study in Lao PDR, which took place in two northern provinces of the country from 2006-2008. The intervention region is notable for its ethnic diversity, low school participation, poor household food security, and lack of 2
of medical services.

11 This is still the case with many countries that refuse to release micro data to any institution or individual. Such countries are quite diverse, ranging from Japan to Algeria. There is thus a paradoxical situation that a number of expensive instruments like household surveys exist in the world, yet they cannot be used because of misplaced policies of some countries’ statistical offices. The issue of confidentiality with which they sometimes defend their practice is clearly bogus since no researcher can ever identify the participating households. Are we to believe that a researcher at (say) Cornell University in 2005 is going to identify the Japanese households that took part in a survey conducted in 1973?

> 12 It has not always been like that. Consider the problems of converting  Communist countries’ national accounts methodology into NAS system, and of course the issue of deliberate falsification of national accounts.

8
measures a country‘s jump from a low _PRODY_ product to a high _PRODY_ product (that is the share of high _PRODY_ good in the export basket increased). However, in the case of _Service EXPY_ we will use what we call _dynamic PRODY_ s, i.e. the _PRODY_ values of a certain service export may vary year to year. Hence, an increase i _n dynamic EXPY_ can be due to (i) an increase in the _PRODY_ of a service and/or (ii) an increase in the share of high _PRODY_ products in the export basket. This is important for two reasons. First, it gives us an opportunity to capture the effect of changes in PRODY on EXPY. We do not only want to measure the fact that countries are getting more and more engaged in higher value service export, but also that the service exports themselves are increasing in sophistication due to improvements in ICT and 3T‘s. In fact many richer and high-skilled countries are engaged in service exports boosting the _PRODY_ s of some service export categories.<sup>7</sup> Secondly, since the data is much less disaggregated for services, using _static PRODY_ fails to capture the movements to higher _PRODY_ service exports that are only sub-categories of the broader categories captured by the Balance of Payments data.

A second characteristic, and just mentioned, is that service export data are not collected in the same detailed manner as goods export data. This means we will end up with only ten broad categories of service exports. They are different enough to make the analysis interesting, but it of course limits the extent to which we can understand the mechanisms in play. For example, if a country – within the same service export category - move from a sub-category of services with presumed low _PRODY_ , to a sub-category of services with presumed high _PRODY_ that would not show up at all in the _static EXPY_ . As mentioned, the _dynamic EXPY_ would however capture this as it allows for the _PRODY_ s to change
individual or household level means-testing or the incorporation of self-selection targeting mechanisms within communities.

Our simulations are carried out using recently produced poverty maps for Ecuador, Madagascar and Cambodia. These countries are obviously highly heterogeneous in terms of their geographic location, but have also very different social and political structures and are at different stages of overall development. We are interested to examine whether there exist commonalities across these countries in terms of the degree to which the availability of poverty data at the local level can contribute to improvements in the targeting of public resources.

We find that there are potentially large gains to targeting performance from disaggregating to the local level. The benefits become increasingly evident as one makes use of more and more disaggregated data on poverty. In all three countries examined, we show that relative to a uniform transfer the same impact on poverty can be achieved at considerably less expense when targeting is based on the highly disaggregated poverty estimates that are available in poverty maps. The gains are generally more muted when the targeting scheme makes only crude use of the local level poverty estimates. They are also lower if the poverty line in a given country is particularly high, or if the budget available for transfer is particularly large. In all countries we find as well that despite the gains from geographic targeting, our inability to target households directly implies that overall targeting performance remains far from perfect. This implies that there may be scope for combining geographic targeting with other targeting methods in order to reduce errors of inclusion and exclusion even further.

The results in this paper are likely to be of use to policy makers interested in the design of transfer schemes aimed at reducing poverty. It is important to emphasize, however, that there are important caveats that attach to these results. First, we assume that the willingness of 7
climate and the future climate. However, in the future, more powerful storms become slightly more frequent. The bulk of these more powerful storms strike Florida and the Gulf Coast states.

The storms in each climate scenario have different properties. As temperatures warm, hurricane wind speeds increase and minimum pressure decreases, indicating more powerful storms. However, the average change is quite small. Except for the CNRM scenario, average wind speed increases from 4 to 7 percent across the climate models. Average minimum pressure falls 0.01 to 0.33 percent. The overall frequency of hurricanes changes only slightly, except in the GFDL model where it almost doubles. Although the average features of storms do not change significantly, the distribution of storms changes quite a lot. There is a noticeable increase in the intensity of the largest storms. So although all hurricanes do not change, the most powerful storms appear to become even more powerful.

The amount of damage caused by a storm depends on the intensity of the storm and what is in harm’s way. Two damage functions are estimated using aggregate damages per storm and storm characteristics at landfall from US storms since 1960 (NOAA 2009). Although earlier storm data is available, there is some question about its validity. Aggregate damages include insured and uninsured private property losses as well as infrastructure losses. Fatalities are not included. Partly, this is because fatalities are low in the US because of sophisticated warning and evacuation plans (Anbarci et al 2005; Dash and Gladwin (2007); Kahn 2005; Lindell et al 2005) and partly because the residual US fatalities appear to be random events. Whether or not losses are insured is beyond the scope of this paper (see Gares 2002; Kriesel and Landry 2004; Kunreuther and Pauly 2004). The paper also does not measure long term effects on real estate (Beracha and Prati 2008) or labor markets (Belasen and Polachek 2008) as these effects would be double counting the damages included.

We estimate two measures of
The evaluation of the voucher program’s efficacy adopted a retrospective quasi-experimental design using the second of two rounds of data collection, which included respondents drawn from a group exposed to the voucher and a comparison group. The design was informed by the fact that the intervention sites were not randomly assigned. The original 2008 design incorporated a prospective cluster randomized study that called for the systematic placement of voucher distributors—with a random start at one cluster—in half of the 22 clusters of local administrative units (parishes) to ensure an even distribution of voucher- and nonvoucher-exposed communities. Each of the 22 clusters centered on a voucher-contracted facility. The target population, drawn from Bushenyi, Ibanda, Isingiro, Kiruhura, Kamwenge, and Mbarara districts, consisted of women who were between 15 and 49 years of age and had had a pregnancy during the previous 12 months. Respondents were selected from 22 clusters within five to ten kilometers of contracted facilities and three kilometers from a main road. It was assumed that individuals located within those ranges had similar levels of access to the contracted facilities. A two-stage design was used to identify potential study participants. The first stage was a simple random sample of villages within each cluster, and the second stage was a census of households with recent pregnancies within selected villages. The sampling frame used maps and population figures from the Ugandan Bureau of Statistics (UBOS) 2002 census.

A sample of 2,627 women was to be selected from 84 villages in the 22 clusters. Using the national rate of 5.2 deliveries per 100 total populations per year, the average number of pregnant and recently delivered women in all villages within each cluster was estimated to be 28 women per village. Assuming a baseline proportion of 30 percent facility deliveries, it was estimated that 120 pregnant and recently delivered women would be needed in each cluster to detect a change of 2 percent in the proportion of facility-based births between the baseline and a one-year follow-up among the treatment groups for a two-sided T-test at 5 percent significance and 90
|IRP|Ghana Center<br>for Democratc<br>Development|African Electoral<br>Integrity Index|54 economies in Africa.|Every two years.<br>Used in WGI<br>since 2000.|Expert/NGO|Expert assessment measuring various dimensions of electoral<br>integrity. Scored by network of respondents convened by<br>GCDD following a structure methodology subject to centralized<br>review. This data source is commissioned by theMo Ibrahim<br>Foundatonas an input to theIbrahim Index of African<br>Governance. Data and methodology are publicly available<br>here.|
|LBO|Latnobarometro|Latnobarometro|17 countries in Latn<br>America.|Every one to<br>three years.<br>Used in WGI<br>since 1996.|Survey/HH|Household survey of households in countries in Latn America<br>conducted by Latnobarometro. Full access to the record-level<br>data, questonnaires, and survey methodology are available<br>here.|
|MSI|Internatonal<br>Research and<br>Exchanges Board|Vibrant<br>Informaton<br>Barometer|18 countries in Europe<br>and Central Asia|Annual. Used in<br>WGI since 2000.|Expert/NGO|Expert assessment scored by local panels of experts convened<br>by the Internatonal Research and Exchanges Board in each<br>country. Scoring follows a structured methodology and is<br>subject to centralized review. Data are publicly availablehere.<br>Details on the methodology are available in the annual report<br>availablehere. Note: Between 2000 and 2019, the<br>Internatonal Research and Exchanges Board published the<br>Media Sustainability Index, which was substantally revised<br>into the Vibrant Informaton Barometer in 2020. We use data<br>from the Media SustainabilityIndex between 2000 and 2019.|
|OBI|Internatonal<br>Budget<
across small and large countries then the possibility of spurious correlation with our main results cannot be ruled out; hence, the need to control for income level.

We had mentioned above that trade openness is one of the few variables that shows a strong correlation with country size. Small countries tend to be more open to trade than large countries. There is also some hint in the literature that the more open economies tend to have a better business environment. If tax administration specifically or as part of the broader business climate is also correlated with trade openness then failure to control for trade openness can lead to an upward bias in our estimate of the tax administration and country-size relationship. We counter such possibility by controlling for trade openness defined as (log of) merchandise exports plus imports as a percentage of country’s GDP, average values over 2001-2005. Data source for the variable is World Development Indicators, World Bank.

Our next control is the overall quality of the business environment as measured by the Business Freedom sub-component of the Index of Economic Freedom, Heritage Foundation. As for the other country-level variables, we use average values of the sub-index taken over 20012005. Higher values of the Business Freedom index imply less burdensome regulation of the private businesses. Controlling for the index allows us to guard against the possibility that country size could be correlated with the overall quality of the business environment and this correlation is spuriously picked by tax administration.

Next, we control for the level of corporate tax rate (average values over 2001-2005). The data source for the variable is Doing Business, World Bank. One might expect that countries will low tax rates are conscious of imposing as little burden as possible on the tax payers. Hence, such countries are likely to have better tax administration too. Further, low tax rates could also be a proxy for less reliance on taxes and hence a less cumbersome tax administration. However, 9
- <mark>Deshpande, A., & Kabeer, N. (2021). Norms that matter: Exploring the distribution of women's work between income generation, expenditure-saving, and unpaid domestic responsibilities in India (No. wp-2021-130). World Institute for Development Economic Research (UNU-WIDER).</mark>

- <mark>Deshpande, A., & Singh, J. (2021). Dropping Out, Being Pushed out or Can't Get In? Decoding Declining Labour Force Participation of Indian Women. IZA Discussion Papers No. 14639</mark>

- <mark>Donald, A., Koolwal, G., Annan, J., Falb, K., & Goldstein, M. (2020). Measuring women’s agency. Feminist Economics, 26(3), 200-226.</mark>

- <mark>Durand, J. D. (1975): The Labor Force in Economic Development: A Comparison of International Census Data, 1946-1966, vol. 1237, Princeton University Press.</mark>

- <mark>Fernandez, R. (2013). Cultural change as learning: The evolution of female labor force participation over a century. American Economic Review, 103(1):472–500.</mark>

- <mark>Fernandez, R. and Fogli, A. (2009). Culture: An empirical investigation of beliefs, work, and fertility. American Economic Journal: Macroeconomics, 1(1):146–77.</mark>

- <mark>Field, Erica, and Attila Ambrus. (2008). Early marriage, age of menarche, and female schooling attainment in Bangladesh. Journal of Political Economy, 116(5): 881-930.</mark>

- <mark>Gaddis I., Klasen S. (2014). Economic Development, Structural Change, and Women's Labor Force Participation: A Re-Examination of the Feminization U Hypothesis.