rafmacalaba/data-use-mentions-extended huggingface dataset:
timeliness, price, efficacy, and satisfaction. For the sake of simplicity, service delivery will be operationalized in two dimensions: (i) provision of services and (ii) outcomes of services. The rationale for this is that provision (e.g. number of teachers in a district, number of books purchased) may be more easily impacted by improvements in OGD as opposed to outcomes (e.g. primary school completion, literacy, etc.). This index will likewise draw upon the IIAG, which rates education and health at the aggregate level. Based on a review of the sub-indicators, the IIAG aggregate indicators were remixed in order to better parse out provision and outcomes in both the health and education sectors. Annex 2 provides a listing of all the sub-indicators used.

119 See https://opendatabarometer.org/barometer/ 120 For instance, these indicators measure the existence and extent of OGD based on the following questions, for which countries receive a score between 1–100: Do the data exist? Are data available online from government in any form? Is the data set provided in machine-readable formats? Are the machine-readable data available in bulk? Is the data set available free of charge? Are the data openly licensed? Is the data set up to date?

121 In the regression specification related to the relationship between OGD on government accountability, the Open Data Barometer Raw Scores were used for the Overall OGD Index, which measure the extent to which data are open on a de facto basis across more than a dozen categories. This figure averages the performance across all data categories on a yearly basis in order to provide an aggregate snapshot of the openness of the data environment. These broad scores were used since they best reflect the actual situation on the ground, and have the most direct effect on accountability, irrespective of the different dimensions of OGD that they may cover. These include the aggregate score for data on maps, land ownership, census, government budget, government spending,
.38***<br>(2.89)|-2.63***<br>(-4.50)|-4.05***<br>(-7.93)|
|Political stability|0.0605***|0.467***|0.414***|
|(0 to 100)|(4.60)|(20.77)|(17.66)|
|n|1,378|1,392|1,378|
|R-squared|0.143|0.299|0.224|
|RMSE|4.72|8.04|8.02|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996-2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

18
The empirics that follow below explore the role of the _I − METR_ , and its two interacting components, the rate differential term ∆ _h_ and the _KF_ - _METRh_ , as the key channels through which taxes affect investment. It also considers one further implication of (4) (and reasons why it might fail): for countries in which some investment occurs, there is, in the absence of any capital constraint on the multinational, no spillover effect on investment in _h_ from the _KF_ - _METR_ in any other country.

# **3 Foreign Affiliate Investment**

This section describes the FAI data used in the empirics, comparing it both conceptually and empirically with the more commonly employed FDI data.

## **3.1 Understanding Foreign Affiliate Investment (and FDI) data**

The two crucial features of FAI data are that they report cross-border investment in physical capital and that in doing so it looks through conduit structures to identify the ultimate parent country. In contrast, as stressed at the outset, much of the empirical work on taxation and cross-border investment has used data not on FAI but on foreign direct investment (FDI),<sup>24</sup> which differ from FAI data in both respects.

First, whereas FAI data capture real investment, FDI data capture funds received by a foreign affiliate that may or may not correspond to tangible investment. These funds comprise both direct net transfers from the immediate parent company (not necessarily the ultimate parent), whether through equity or debt, and earnings retained by the foreign affiliate. And these amounts appear as FDI regardless of their end use. Retentions that are recorded as FDI may, for example, be kept within the foreign affiliate as cash reserves, with

> 24Some studies have used foreign affiliate statistics for other purposes, including Tørsløv, Wier and Zucman (2018) to assess the extent of profit shifting by multinationals (for 2017), and by Fukui and Lakatos (2012) and Ramondo, Rodr ́ıguez-Clare and Tintelnot (
-55-

# Comparison with ICP III Results

It is not possible to make a direct or detailed confrontation of our results with those of ICP. This is partly because its expenditure approach breaks down economic activity in a different way from our value added approach as demonstrated in the input-output table 1.1 in our chapter I. There is atlso the problem that the ICP national price data for consuimption items are confidential and could not be consulted at the UN Headquarters, or retrieved from the archives when we visited Brazil and Mexico. UNSO was able to let us have a copy of their own estimates of prices for capital goods, which enabled us to make some rather partial cross-checks.

One can, however, get a rough idea of the ICP results for manufacturing by grouping the PPPs for the 82 ICP items with a manufacturing content, using a similar technique to researchers who have mined the ICP results as proxies for the kind of study we have made (see table 1.2). The confrontation of our results with the ICP can be seen in table 3.17. Whilst our estimate of the ICP result for manufacturing is rather crude and is not presented in this way in the ICP itself, nevertheless it is an acceptable and indeed the only way of comparing the two sets of results. In the case of Brazil our results and those of ICP are strikingly similar. In the case of Mexico, the results differ substantially.

It should be recalled that one of our original reasons for including Mexico in the pilot study was that there was the same type of discrepancy between the results of an earlier industry-of-origin study and ICP III (see Maddison 1970, and 1983), and it also seemed most unlikely that Mexico would be in such a favourable PPP position after 22 years of a fixed rate for the dollar and on the eve of a major devaluation.

------------------------------
for the program may not have participated, or some of the control beneficiaries may have participated. To ensure robustness, estimates have been produced of the effect of the treatment on those households with a treatment status affected by the random assignment, that is, the compliers; this effect is known as the local average treatment effect (LATE).<sup>12</sup> These estimates are the instrumental variable (IV estimates) of _β_ in the equation above, using the dummy for random assignment as an instrument for treatment. However, the results are similar to the ITT estimates because of the low levels of noncompliance: the household surveys indicate that only around 1.8 percent of the treatment households reported they did not participate, and 7.4 percent of the control households reported they participated. Therefore, only a subset of the LATE estimates—the overall estimates—are presented in this paper.

Outlier corrections and log transformations have been conducted for variables in leone values. To reduce the influence of outliers, variables in leone values that were identified with outliers have been winsorized at the 1st and 99th percentiles.<sup>13</sup> The outlier‐corrected estimates and the log‐transformed estimates are presented in Annex 2.

> 12 See Angrist and Imbens (1995); Angrist, Imbens, and Rubin (1996).

> 13 Outliers were identified using the blocked adaptive computationally efficient outlier nominators algorithm proposed by Billor, Hadi, and Velleman (2000), using the 15th percentile of the chi‐squared distribution as a threshold to separate outliers from

6
Because it is the most complex situation, we focus our analysis on the specific case where both (on an individual basis) who is affected from a shock and the value of the loss are unknown (or known without precision) by policy makers. The analysis focuses on critical design choices and their relation to the distributional characteristics of the shock that is affecting households, which will ultimately define the impact of the response. Rather than providing a formal, theoretical characterization between design choices and impacts, we use microsimulations based on household survey data for Albania, Moldova, and North Macedonia to illustrate important relations and trade-offs. Simulations also present the advantage to allow assessing the effectiveness of actual instruments available to policy makers (such as increasing the benefit paid through existing social programs or expanding coverage to different populations, as selected through varying targeting methods), something that would not be feasible with a theoretical model. In this case, we focus on different design options associated with one social protection instrument: cash transfers. We do not take into account behavioral responses or general equilibrium effects, so that we consider first-order effects only.

The policy relevance of bringing forward practical solutions for the design of social protection responses to shocks is high. Analyses of the social protection responses to the COVID-19 pandemic suggest that in many cases policy makers attempted to cover the informal sector using a variety of design “tweaks” to existing programs and their delivery systems. For instance, some countries topped-up benefits to existing beneficiaries (Kosovo), others expedited/automatized enrollment processes into existing programs (Moldova) or provided a one-off transfer to former beneficiaries or people in the program beneficiary registry (not current beneficiaries; Albania), while others provided a flat, universal transfer to ensure that informal sector workers are covered (Serbia).<sup>12</sup> Alas, the variety of designs is not the result of optimizing responses to a given type of shock (these were all responses to the same shock, hitting countries in similar ways),
Moreover, cells receive a black border if the identity of the export-destination for a particular position would change if small exporters are included. For mining this was never the case and for agriculture it only changed the destination country in four cells. In the manufacturing sector, almost half of the top-5 destination identities are different if the contribution of small exporters were excluded. In most cases this would only change the ordering of countries, but in a few cases it even introduces a new country in the table. The most important pattern is that without small exporters the ranking of Uganda would often be lower and in a few years also for Kenya. Ignoring smaller exporters would underestimate the importance of regional trade in manufacturing products. A pattern that is explored further in the next section.

In the Rwandan firms surveyed in the WBES, the median exporter reports to be exporting 160,000 USD a year, or 10% of its total output. The average export value is nine times as large, which underscores the very skewed distribution. This value has decreased slightly from the 2006 to the 2011 survey year. Across all other African countries, the median export value across all exporters is notably higher at 244,000 USD and the mean is far higher still. Omitting the 1% largest export flows because they might be misreported, the average export value over all non-Rwandan firms is 30.1 million USD or more than 100 times the median value. The low values for Rwanda are not inconsistent with the results in the administrative data and they are also low relative to other African countries.

# **4.4 Export destinations: local and concentrated**

It is well-known, see for example Bastos and Silva (2010), that product quality varies across export destinations. Richer consumers in more developed countries have stronger preferences for higher quality products, and firms naturally can charge higher prices in highquality segments. There is evidence suggesting that such variation is not only the result of firms specializing by market, but it is also driven by
||
|Agriculture|42|3|30|1|24|0|96|4|100|
|Mining & Construction|8|0|8|0|21|4|37|4|41|
|Food Processing|19|2|5|0|13|0|37|2|39|
|Other Manufacturing|25|10|6|0|24|13|55|34|89|
|Retail-wholesales|47|2|56|0|55|3|158|5|163|
|Other services|5|0|1|0|0|0|6|0|6|
|Total|146|17|106|1|137|20|389|38|427|

_Source:_ 2010 Yemen RIC Survey The rural investment climate (RIC) is assessed based on major obstacles perceived by entrepreneurs, major constraints assessed by percentile ranking of RIC indicators, entry and performance regressions, and quantitative survey data.<sup>6</sup> Major obstacles perceived by entrepreneurs are defined as the obstacles that entrepreneurs perceived as major or very severe obstacles for their businesses. The objective of this assessment is to determine which RIC components are critical to influence entry and performance of rural enterprises.

# **3.1 Major Obstacles Perceived by Entrepreneurs**

Major obstacles perceived by rural entrepreneurs are compared with urban small enterprises to identify what investment climate components are different from each other. Farm and nonfarm comparison and gender comparison are included.

> 6 The assessment using RIC indicators is newly developed to assess the RIC more objectively using quantitative data, whereas major obstacles perceived by rural entrepreneurs may be biased due to limited knowledge and experience. The percentile ranking methodology of “DOING BUSINESS” was applied for assessment by RIC indicators.

5
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women’s political representation significantly increases modern contraceptive use and decreases the use of traditional contraception, with stronger effects on rural women, potentially due to improvements in the supply of family planning services that we document above. Rural women’s likelihood of using any contraceptive method also increases significantly. Our estimates imply that increasing the share of seats held by women by one s.d. results in an increase in modern contraceptive use among rural women by 4 percentage points (an 8 percent increase relative to the mean).<sup>19</sup> Consistent with the contraception results, exposure to female leaders increases birth spacing in rural areas (Panel D of Table 3). These estimates are based on the woman– year panel dataset and imply that increasing the share of seats held by women at the district level by one s.d. increases birth spacing among rural women by 0.15 years, corresponding to 2 percent of the mean.<sup>20</sup>

**Intrahousehold misalignment of fertility preferences.** Previous studies show that household decision-making about childbearing and contraceptive use may be characterized by inefficiency and noncooperative behavior (Ashraf et al. 2014; Anukriti 2020; Ashraf et al. 2020). As women’s use of contraceptives may not be perfectly observable to the male partner, moral hazard can arise due to hidden action and asymmetric information. Ashraf et al. (2014) show that an increase in the supply of family planning services can make the influence of moral hazard more salient in couples with misaligned preferences over the number of children, leading to spousal conflict.

Spousal discordance in fertility preferences is prevalent in our sample—44 percent of couples disagree on their ideal number of sons and, in 23 percent of couples, the husband’s ideal number of sons exceeds the wife’s.<sup>21</sup> Moreover, 30 percent of husbands express that they want to have more sons than they currently have. In such a setting
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33 Fig. 2.13. Ratio of moves to new and previously occupied units in Moscow (% of mover households, by year) <!-- Start of picture text --> Newly constructed Previously occupied<br>4 -4-<br>3.5 3.5 3.2<br>3 3 -<br>2.5 2.5<br>1.9 ~ ~ ~ ~ ~ .5 1<br>12 2 -1.5<br>1 ~~~~~~~~~~~~~1.6<br>2 179 1.84 29519 94 1s<br>05 0.5<br>0 0<br>1993 1994 1996 1993 1994 1995<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, January 1996.

Fig. 2.14. Methods of obtaining housing in the primary and secondary housing markets in Moscow (% of mover households in 1993-1995.) <!-- Start of picture text --> 7 -<br>6 _<br>5 _<br>4 - 3.4 / owaiting list<br>3/ _exchange<br>H purchase<br>2-<br>0<br>New housing Previously occupied<br>housing<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, January 1996 The means of solving the housing problem by income group are presented in Fig. 2.15. The poor tenants displayed low mobility. It is noteworthy that among them, relatively few households (2.3% as against an average of 4%/) received housing through the waiting list system. Clearly the poorest people on the waiting list do not have preferential access to free housing, which is indicative of a poor targeting program. A large share of middle-income families demonstrated
# B. Compliance under the US Data Privacy Regime

The United States data privacy regime lacks a comprehensive law that regulates the collection and processing of personal data of U.S. residents by private parties. While there are constraints against government information collection through both the Federal Constitution and an extensive statutory framework regulating government use of personal data, there is no similar broad federal regulatory privacy law regulating private parties. Instead, the current data privacy framework arises out of a patchwork of federal and state laws, many of which are focused on a particular sector of the economy. By focusing the law on particular areas of concern, the United States has effectively chosen business freedom as a key principle in the area. Outside specified areas, the focus is limited to enforcing the privacy promises that businesses make to users, rather than on specific mandates setting out what businesses can and cannot do with data. Sectoral laws include the Health Insurance Portability and Accountability Act (HIPAA), covering the health industry, and the Gramm-Leach-Bliley Act (GLBA), covering the financial sector. In addition, the Federal Trade Commission Act (FTCA) gives the Federal Trade Commission broad authority to regulate data practices if they constitute “unfair or deceptive acts or practices in or affecting commerce.”<sup>19</sup> Through the FTCA, the Federal Trade Commission serves as the nation’s de facto privacy regulator, and its settlements create a kind of common law of privacy.<sup>20</sup> HIPAA imposes an extensive set of privacy protections for personal health data gathered by covered entities, including hospitals, health care providers, and health insurers. Not only must health plans and health care providers give patients a written notice of their privacy practices, they must also “maintain reasonable and appropriate administrative, physical, and technical safeguards to ensure the integrity and confidentiality of the information, and to protect against any reasonably anticipated threats.”<sup>21</sup> These safeguards include designating a privacy official,
# **1 Introduction**

In most countries, social security benefits depend on an individual’s earnings history. This contribution-benefit link creates a trade-off between the incentive to report higher earnings to increase the amount of future benefits (Fitzpatrick, 2017; French et al., 2022; Dean et al., 2024) and the incentive to report lower earnings to decrease the amount of taxes owed (Mortenson and Whitten, 2020; Bjørneby et al., 2021; B ́ır ́o et al., 2022). This trade-off is important for both the design of social security systems and approaches to combating tax evasion and encouraging formalization.

This paper studies the relationship between tax evasion and the contribution-benefit link in the context of maternity benefits among private sector workers in Hungary. We analyze reported earnings and employment before and after childbirth in an environment where the amount of maternity benefits received for two years after giving birth depends on earnings over a narrow time window before childbirth. Benefits are 70% of average earnings calculated over a reference period and are uncapped for six months and capped at 140% of the monthly minimum wage thereafter. In 2015, the incentive to report higher earnings became much stronger as the contribution period used to determine benefits changed from 12 months in the most recent calendar year to the first six months of pregnancy. Using detailed administrative data on employment, earnings, benefits, and health, we show that in response to these incentives, reported earnings and formal employment increase during pregnancy and that this response is much stronger after 2015.

Before 2015, the earnings of expectant mothers working in firms with fewer than 50 employees increased by 1.8% between the second and sixth months of pregnancy. This increase was much larger, 5.3%, after reporting incentives became stronger in 2015. In firms with fewer than 10 employees, the increase was even more pronounced, 3.0% before and 7.6% after 2015, while pre-birth earnings declined during pregnancy both before and after 2015