rafmacalaba/data-use-mentions-extended huggingface dataset:
United States, the United Kingdom, Switzerland, and the European Union countries. These initiatives emphasized the need for banks and other financial intermediaries to collect better information on their clients and promote tax sharing across different jurisdictions (Sharman 2017). The intergovernmental efforts were supported by the work of transnational NGOs such as the aforementioned Tax Justice Network, the Washington DC-based thinktank Global Financial Integrity founded in 2006 to measure and study illegal financial flows, and other organizations such as the OCCRP and Global Witness committed to global corruption investigation and exposure. After a concerted effort by the OECD and the G20, most states today have agreed to cooperate with foreign information requests on tax-related matters.

The public debate about financial secrecy has been significantly catalyzed by data leaks from such offshore financial zones as Panama, The Bahamas, and other secrecy jurisdictions. The infamous Panama papers involving millions of documents exposed in 2016 (with an additional leak in 2018) implicated political and economic elites, including heads of states around the world. Other leaks, such as the Paradise papers, also involved millions of documents connecting specific names to offshore companies. These data leaks enabled a scholarly exploration of the effects of such public exposure on firm value quantifying (if indirectly) the economic value of secrecy. Donovan et al. (2019) identified 338 publicly listed firms with links to corporate vehicles in secrecy jurisdictions. Their findings suggest that such connections to secrecy jurisdictions are used both to create firm value and to expropriate corporate funds. When firms rely on secret corporate vehicles to avoid taxes, and as a means to bribe state officials to win business, they add value. This conclusion is also supported by an earlier study of companies involved in Luxembourg Leaks (Huesecken et al., 2016). However, this study suggested that secret corporate vehicles aid insiders to divert corporate funds (Ibid., 4142-4143). The public exposure of secret accounts and entities in offshore zones has affected the shareholder value of these firms. Following the data leaks, firms connected to the leak have
data from the second (2016) and the fourth (2023) rounds of the Life in Transition Survey (LITS). The LITS is a survey run by the European Bank of Reconstruction and Development and the World Bank covering the so-called “transition countries” of Europe and Central Asia and several comparator countries of Western Europe, the Middle East, and North Africa (EBRD 2023). The survey included a nationally representative sample of around 1,000 households in Azerbaijan in the second and fourth rounds. Azerbaijan experienced rapid growth in per capita GDP between 2016 and 2023, which makes the example of this country helpful in illustrating the properties of the methods. We construct the multidimensional inequality index for three dimensions captured by the monthly per capita income (in 2017 PPP terms), years of education, and respondent’s health GGiinnii MM assessment. The measure is calibrated for 2016 using normative weights of 1⁄2 for the income dimension and 1⁄4 for the education and health dimensions. Table 3 presents the specific and multidimensional inequality levels for Azerbaijan in 2016 and 2023. The mean monthly per capita income increased by almost 59 percent from about 852 PPP dollars in 2016 to 1350 PPP dollars in 2023. The average years of education and health self-assessment remained relatively stable. Income growth was accompanied by an increase in income inequality, from a Gini of 0.253 in 2016 to 0.339 in 2023. The inequality in years of education grew while the inequality in health assessment slightly declined.

26
<mark>Malawi.</mark> _<mark>Journal of Demographic Economics</mark>_ <mark>,</mark> _<mark>87</mark>_ <mark>(3), 437-477.</mark> **<mark>[</mark> 1]** <mark>Beegle, K., De Weerdt, J., & Dercon, S. (2011). Migration and economic mobility in Tanzania: Evidence from a tracking survey.</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>,</mark> _<mark>93</mark>_ <mark>(3), 1010-1033.</mark> **<mark>[</mark> 1]** <mark>Beine, M. A., & Jeusette, L. (2021). A meta-analysis of the literature on climate change and migration.</mark> _<mark>Journal of Demographic Economics</mark>_ <mark>, 1-52.</mark> **<mark>[</mark> 1]** <mark>Beine, M., & Parsons, C. (2015). Climatic factors as determinants of international migration.</mark> _<mark>The Scandinavian Journal of Economics</mark>_ <mark>,</mark> _<mark>117</mark>_ <mark>(2), 723-767.</mark> **<mark>[</mark> 1]** <mark>Bekaert, E., Ruyssen, I., & Salomone, S. (2021). Domestic and international migration intentions in response to environmental stress: A global cross-country analysis.</mark> _<mark>Journal of Demographic Economics</mark>_ <mark>,
|

**Note:** Based on annual data from 1990 to 2017. Cyclical components are calculated as the deviation of the actual data from a trend computed using the Hedrick-Prescott filter with the standard smoothing parameter for annual data (6.25). ***, **, * denote significance at the 1, 5 and 10 percent levels respectively.

**Sources:** ADB, IMF, World Bank, and own calculations.

Like for public expenditure, the cyclical components of tax revenue and GDP move closely together in most countries in the region. In India, for example, the correlation between the two variables is 0.66 and in Nepal it is 0.48. In Bangladesh, on the other hand, there is no correlation between the two series (-0.07). The results are similar when analyzing the coefficient between growth rates of the tax revenue and GDP (0.65, 0.43, and 0.06 respectively). The correlation between these two variables is statistically significant in India and Nepal. It is also positive in Bhutan, Sri Lanka, and Pakistan, but not statistically significant.

# _4.2. Regression analysis_

In analyzing fiscal cyclicality, we additionally rely on regression-based results. As pointed out by Lane (2003), the reduced-form relation may be the most appropriate concept, since there is no strong reason to exclude any equilibrium feedback from fiscal policy to the level of output. Short-term tax buoyancy 6
18 networks are more ethnically homogeneous than the population at large, however, this implies that politics could look “ethnic”, even though the strategy of political mobilization is clientelist. Reliance on such narrow promises is more likely when institutionalized political parties, in which like-minded citizens can act collectively to nominate and support political challengers, do not exist. Conflict is more likely in these settings. In the Democratic Republic of Congo, for example, voting in the 2006 elections clearly followed ethnic lines. Lingala speakers voted for Jean-Pierre Bemba (e.g., Bemba received 97 percent of the second round votes from his home province of Equateur) and Swahili speakers voted for Joseph Kabila (e.g., Kabila received 98 percent of the second round votes in his home province of South Kivu) (Weiss 2008).

In principle, ethnic voting could simply be a reflection of citizens‟ confidence that co-ethnic candidates will support the interests of their ethnic group. However, ethnic groups are not politically organized: parties are fragmented (even those appealing to a single ethnic group) and organized around personalities rather than the pursuit of ethnic interests. This is more generally true across Sub-Saharan Africa, where conflict and ethnicity are most often linked. Keefer (2010) uses Afrobarometer survey data from 16 Sub-Saharan African countries and compares the partisan behavior of two kinds of respondents: one belongs to ethnic groups that cluster their political support on only some parties; the other kind is no more likely to support any particular party than the population at large. Respondents from the first group should be less likely than those in the second group to express partisan indifference if clustered partisan support exhibited by the first group is the product of credible appeals by ethnic parties to serve the interests of the entire ethnic group (since support for the party benefits them more than support for no party). In most countries, this is not the case: respondents from ethnic groups that do not cluster their support are no more likely to express partisan indifference than those from
<!-- Start of picture text --> ae<br>Sf KN<br>ee ————esesesl<br><!-- End of picture text --> <!-- Start of picture text --> Shift in sectors of employment of migrants from ..has led to decoupling of tight relationship<br>construction to other sectors... between US housing and remittances to Mexico<br>31Millions Year-on-year growth (9%) Yeor-on-yeor growth (%)<br>50x 40%<br>29 Manufacturing Wholesale and<br>retail trade 1S% 20%<br>ad ‘ a — = /| New housing started<br><3 20% a (4scale months earlier), right 4 o%<br>23 Restaurants and ho 5% 7 wn ! 20%<br>4 sty J<br>é<br>al -10% \ 40%<br>Remittances to Mexico<br>14 -25% -60%<br>Sep-O8 Aupe-D9 News Jun-10 Jan-11 ee sh os ge ia & i & Ooty ay<br>ye FF Pe re ee<br><!-- End of picture text -->

Source: US Current Population Survey, US Census Bureau, Banxico and Migration and Remittances Unit calculations
dynamics and other policies not considered in this paper, such as investments in public charging infrastructure.

# 6. References

- Alberini, Anna, Markus Bareit, Massimo Filippini, and Adan L. Martinez-Cruz. 2018. “The Impact of Emissions-Based Taxes on the Retirement of Used and Inefficient Vehicles: The Case of Switzerland.” _Journal of Environmental Economics and Management_ 88: 234–58.

- Auerbach, Alan J. 2019. “Tax Equivalences and Their Implications.” _Tax Policy and the Economy_ 33: 81–107.

- Aznar, Alexandra et al. 2021. _Building Blocks of Electric Vehicle Deployment: A Guide for Developing Countries_ . https://www.osti.gov/servlets/purl/1785089/ (October 14, 2021).

- Bansal, Prateek et al. 2021. “Willingness to Pay and Attitudinal Preferences of Indian Consumers for Electric Vehicles.” _Energy Economics_ 100: 105340.

- Barwick, Panle Jia, Shengmao Cao, and Shanjun Li. 2021. “Local Protectionism, Market Structure, and Social Welfare: China’s Automobile Market.” _American Economic Journal: Economic Policy_ , forthcoming.

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- Berry, Steven, James Levinsohn, and Ariel Pakes. 1995. “Automobile Prices in Market Equilibrium.” _Econometrica_ 63(4): 841.

- ———. 2004. “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market.” _Journal of Political Economy_ 112(1): 68–105.

- Brambilla, Irene. 2005. _A Customs Union with Multinational Firms: The Automobile Market in Argentina and Brazil_ . Cambridge, MA: National Bureau of Economic Research. http://www.nber.org/papers/w11745.pdf (September 22, 2021).

- Clerides, Sofronis
- Adj<br>Note: Night-lights data series shows in orange adjusted for intensity variations emanating from gas and oil flares.<br>Source: World Bank estimates based on Chen et al. (2021)<br>Night time light intensity<br><!-- End of picture text --> 35 In order to consistently analyze variation in night time light emissions over the period 2000-2020, we use the combined VIIRS, DMSP series. See Chen et al. 2021.
be lower in high conflict-intensity districts before the conflict, and this rate has decreased faster in the low-conflict intensity district group.

Table A-3 tests for differences in pre-conflict marriage and early marriage trends. Preconflict early marriage trends do not differ systematically across districts more or less affected by conflict. However, the rate of marriage by 21 years old tends to increase somewhat faster before the conflict in districts which experienced more conflict-related casualties. This would tend to lead estimates of Equation (1) to bias upwards the effect of conflict intensity on marriage by 21 years old. There is a significant correlation between pre-conflict, year to year variation in the rate of marriage by 21 years old and future Maoist abductions, but the direction of the differential “trend” is unclear.

Having established that pre-conflict trends are unlikely to drive my estimates of the impact of conflict on primary education and early marriage, but that the long-term marriage analysis may suffer from some bias, I now turn to the difference-in-difference estimation results.

# 5.2. Education

The inspection of Figures 5 and 6 suggested that inequality in primary schooling achievement between districts which experienced different degrees of conflict intensity has tended to decrease with the onset of the insurgency, and especially so for girls. In Tables 4 to 7, I present estimates of the impact of exposure to conflict on educational outcomes to check whether the conclusion of the graphical analysis is confirmed when controlling for unobserved heterogeneity between individual districts and between regions over time, and when using different identification strategies.

# Table 4 goes about here

Table 4 reports findings on the impact of conflict exposure on primary education attainment. The first two columns of Table 4 present estimates of the long-term effect of conflict intensity on primary schooling completion, using the baseline difference-in-difference specification (Equation 1). The last two columns of Table 4 test the robustness of these findings by comparing the change in primary completion rates at any given age between 10 and 18 years old, for districts with varying degrees of conflict intensification between the 2001 and 2006 DHS surveys (
food insecurity, because adequate diets and nutritious foods are crucial for child development. In our review, we found twelve studies that reported the effects of humanitarian assistance programs on household-level food security outcomes, nine of which compared the intervention to a pure control group. These studies used various ways to measure food security, ranging from its simplest form (e.g., number of meals eaten per day) to standard indicators (e.g., Household Dietary Diversity Score, Food Consumption Score, Food Insecurity Experience Scale).<sup>11</sup> Most of the studies in our review find positive and statistically significant effects of humanitarian assistance programs on food security outcomes (Gilligan and Hoddinott 2007; Tusiime et al. 2013; Hidrobo et al. 2014; Bonilla et al. 2017; Kurdi et al. 2019; Tranchant et al. 2019; Chaaban et al. 2020; Quattrochi et al. 2020). Four of these studies find improvements in Household Dietary Diversity Score (HDDS), although some of the coefficients are not statistically significant. The only exception is Lombardini and Mager (2019), who find that a cash-for-work intervention in a refugee camp in Jordan did not have any positive effect on food security.

Overall, these results suggest that most types of humanitarian assistance interventions have large impacts on food security outcomes in fragile and conflict-affected settings, not only through increased consumption but also through improved quality of diets and less severe experiences of food security. One limitation of the current evidence is that food security measures are mainly measured at the household level, which leaves a gap of knowledge regarding the intrahousehold distribution of food consumed and especially the food security situation among children.

# **Food expenditure**

While food security is typically measured by looking at consumption and dietary patterns in food groups, one can look at the dollar expenditure on food purchases as a complementary measure of food security. Food expenditure can be defined as the amount of money spent on food in any given period. This
# **Table A9: Impacts of overall health and physical health**

|Dependent variable:|Employme|nt status|Weeklylabor inco|me ($1,000AUD)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Overall health issue|-0.020**||-0.002***||
||(0.008)||(0.001)||
|Physical health issue||-0.010||-0.030*|
|||(0.165)||(0.017)|
|_First stage of 2SLS_|||||
|Trauma experience*Year<br>after arrival|-0.001***<br>(0.0002)|-0.0001***<br>(0.00003)|-0.001***<br>(0.0002)|-0.0001***<br>(0.00003)|
|Kleibergen-Paap F stat.|24.517|7.652|24.517|7.652|
|AR 95-CIs|[-0.040, -0.005]|[-0.386, 0.545]|[-0.005, -0.001]|[-0.106, -0.002]|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|1,863|3,687|1,863|
|Number
in the models producing the data will contaminate our analysis which is based on the data. This might lead to large errors from the regression if, for example, a crop in SPAM is in large quantity in a given pixel, but in reality it is not in the pixel at all, or only in small quantity.

# Results and Discussion

## Spatial disaggregation

The regression results from using crop production from SPAM ‐‐ along with livestock, forest, and fisheries data – are found in Table 1.

For crops, the units are in metric tons, and the dependent variable is in thousands of reais. So, if we were to try to interpret the parameters as prices, the value for the parameter estimate for wheat is 0.456, or R$456 per metric ton (MT), which at the end of 2010 would be equal to USD274 per MT. This is within the price range it could have sold for. Maize price from the regression, on the other hand, is low at R$101 per metric ton. Given the explanatory variables are all modeled values, it is not entirely surprising that the parameters do not reflect prices as well as we might hope. They do, however, allow us to use these parameters to project agricultural GDP to each pixel, which was the goal.

Table 1 shows that most of the agricultural outputs that are important for the Brazilian agricultural sector have statistically significant parameters. In addition to key cereals, cassava, beans, soybeans, and a number of cash crops can be found, along with chickens, cattle, and pigs, and non‐timber forest products.

_Table 1. Regression using pixel‐level production data_

|**Variable**|**Param**|**Std**<br>**error**|**t‐stat**|**Prob**|
|---|---|---|---|--
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that isonly modestly correlated with country size. We find many<br>skewed distributions within countries as well, with 90% ofsmall-occurrence region species concentrated<br>in less than 10% of WB level-2 administrative units for 50 of 167 entities analyzed. This creates a<br>significant opportunity for species protection that incorporates both extinction risk and cost-effective<br>administration within limited areas. We illustrate the potential with a Brazilian case that identifies 5 of<br>of species maps estimated from the GBIF’s open-source occurrence records database.<br>Our estimation<br>algorithm is designed for rapid production of new maps as more records become available. We find that<br>existing expert mapping exercises, as well as highlighting cases where existing expert maps could be<br>revised to incorporate new GBIF information.<br>In pilot applications, we use our newly-estimated maps to<br>shed new light on the global distributions ofendemicand small-occurrence region species. We hopethat<br>manymoreapplicationswillaccompanythecontinuedrapidexpansionofopen-sourceGBIFoccurrence|
|---|
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