rafmacalaba/data-use-mentions-extended huggingface dataset:
students from 5<sup>th</sup> to 8<sup>th</sup> grade had witnessed sexual violence within the school environment (Abramovay and Franco, 2004).<sup>37</sup> Data on sexual violence, however, remain spotty for Latin America and the Caribbean.

> 35  An unsafe environment in school may dissuade parents from enrolling girls in school or may lead to increased rates of school abandonment (WCRW, n/d).  While this has been documented for the African context, it may or may not be important in Latin America and the Caribbean, where girls’ enrollment rates typically exceed boys’ rates.

36 According to recent studies in six African countries , between 16% and 47% of girls in primary and secondary schools report sexual abuse or harassment, with both male fellow students and male teachers responsible for the abuse (Leach et al., 2003). In Botswana, 20% of female students reported having being asked by teachers for sexual relations (Rosetti, 2001, cited in Leach, 2003). In Cameroon, 8% of sexual abuse towards girls was accounted for by teachers (Mbassa Menick, 2001, reported in Leach, 2003). The DHS survey in South Africa, surveying women between 15 and 49 years of age, found that 37.7% of all rape victims identified a teacher or principal as the rapist (Medical Research Council, 2000). At the same time, girls in South Africa are more likely to be sexually assaulted by their male classmates than by teachers or school personnel (Human Rights Watch, 2001).

37 The study covered middle schools in 14 Brazilian state capitals. The percentage of students who had witnessed sexual violence ranged from a low of 5% in Vitoria (Espirito Santo) and Fortaleza (Ceara) to a high of 12% in Cuiaba (Mato Grosso). The way in which the question was formulated does not permit identifying what percentage of girls were victimized by
When it comes to estimating business outcomes, a key issue is how to handle businesses which are closed. Our approach is to code the outcomes for these firms as zero.<sup>19</sup> That is, a business which is closed is assumed no longer to have a formal license, a business bank account, or other such outcomes. For several savings outcomes for which it is possible that individuals are saving even without operating a business, we use the sample of firms still in business since we lack data on these outcomes for those whose businesses have closed. Appendix Table 2 shows there is no impact of any of the different treatments on business closure rates.

In addition, we follow the methodology of Kling et al. (2007) to test the significance of families of outcomes in a single aggregate. For each family of outcomes, we convert all outcomes so that the signs of all the variables in a family go in the same direction; calculate the z-score of each variable by subtracting the control group mean and dividing by the control group standard deviation; and take an average of the z-scores in the family. When considering the heterogeneity of outcomes, we follow Fink et al. (2014) and use the Benjamini and Hochberg (1995) method for limiting the false discovery rate (FDR).

# **4.2 Impacts on Formalization**

Table 4 reports the impacts of our different treatments on the three key dimensions of formality. These measures are self-reported by business owners from the four follow-up surveys.<sup>20</sup> Although we asked the business owners to show the certificates for each of the dimensions of formality, a significant number of them – including of those that we have delivered business registration certificates – said they had them in a secure place like at home. Hence, reporting only on certificates shown to enumerators would underestimate the impacts on these measures.

We see that obtaining a business registration certificate is rare in the absence of our treatment – only
# **2. Data**

# **Sampling design**

The overall evaluation design includes a series of household and child surveys, beginning with a baseline survey in 2001. Three surveys of the same sample of about 5,000 households from two of the program regions (Region 6 and 7) and a non-program region (Region 8) have been completed.<sup>7</sup> In each round, data were collected also from local officials and ECD service providers. The sampling strategy involved stratifying the barangays in each province into (1) pilot barangays that were supposed to have participated in a pilot phase launched in 1998,<sup>8</sup> (2) program or target barangays in Phase 1 of the project, and (3) non-program barangays or non-targeted barangays in the (pilot and Phase 1) program and non-program municipalities in the region.<sup>9</sup> In each program region, five pilot and five non-program barangays were randomly chosen, while the remaining barangays were drawn at random from the program barangays. In Region 8, sample barangays were randomly chosen from all barangays, with the number of barangays chosen proportionate to the total number of barangays in each province. In each sample barangay, an average of 24 eligible households (i.e., households with 0-6 year-old children or households with pregnant women) in Regions 6 and 7 and 14 households in Region 8 were selected. The overall response rate was high at 96 percent. During the baseline survey, 7,922 children aged 0-4 were selected to be followed up in the succeeding rounds; children aged 5-6 years old were not included for the evaluation sample because they were too old to be exposed much to the program. All estimates are weighted to account for the sampling scheme.

Of the original cohort of 7,922 children age 0-4, 86 percent (or 6,774 children) were successfully followed up _in the same barangays_ in the first follow-up survey in
and Lederman, 2020), external borrowing costs (Kubota and Zeufack, 2020) as well as a positive relationship with more democracy (Janus, 2022). More data dissemination, as measured by the amount of data that countries reported to the World Development Indicators (WDI) database maintained by the World Bank, could also help raise economic growth (Hodelin, 2022). The beneficial impacts of improved data transparency are also well-documented in other disciplines such as political science (e.g., Kelly and Simmons (2014)) and natural sciences (e.g., Nagaraj _et al._ (2020)). As a recent example, Canergie _et al._ (2021) observe that populist governments report less data to the WDI compared to non-populist governments.

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# <u>Figure 3: Budget shares and progressivity indices for out-of-pocket health expenditures</u>

Household Budget Share on Out−of−Pocket Health Expenditures <!-- Start of picture text --> Kakwani Progressivity Index of Out−of−Pocket Health Expenditures<br><!-- End of picture text --> <!-- Start of picture text --> 5.1–8.3 0.20–0.38<br>3.9–5.1 0.10–0.20<br>2.9–3.9 0.00–0.10<br>2.3–2.9 -0.10–0.00<br>1.5–2.3 -0.20–-0.10<br>0.0–1.5 -0.57–-0.20<br>no data no data<br><!-- End of picture text --> Kakwani Index for Out−of−Pocket Health Expenditures, Consumption−Based <!-- Start of picture text --> Kakwani Index for Out−of−Pocket Health Expenditures, Income−Based<br><!-- End of picture text --> <!-- Start of picture text --> 0.20–0.20 0.20–0.20<br>0.10–0.20 0.10–0.20<br>0.00–0.10 0.00–0.10<br>-0.10–0.00 -0.10–0.00<br>-0.20–-0.10 -0.20–-0.10<br>-0.20–-0.20 -0.41–-0.20<br>no data no data
more than two years after the cessation of the cash transfer experiment in December 2009, tracking the adolescents as many of them moved on to establish their own families. These longitudinal data paint a very rich picture of the transition from adolescence into adulthood in this context. By 2012, the study stratum that had dropped out of school at baseline had effectively completed their schooling with an average of a seventh-grade education; 81% were married, 92% had been pregnant, and only about a third had done any wage work in the past three months. More than one in eight (13.5%) had been infected with HIV. The stratum of baseline schoolgirls are better-off and younger, and therefore had not proceeded as far in their transition to adulthood: in 2012, their average years of schooling was 10.4 and increasing, with only 40% ever married, 50% ever pregnant, and 5.5% HIV-positive.

In the latest follow-up survey of the study sample, which was more than two years after the cessation of cash transfers, we attempted to trace the pathways through which experimentally induced changes in human capital may translate into longer-term outcomes. Zomba is an almost exclusively agricultural economy characterized by low educational attainment and few opportunities for formal employment. As of 2009, this district was the third poorest in Malawi (in our sample, real monthly per-capita exchange rate comparable consumption in 2008 was USD 20.6). Secondary school completion rates are low – in our sample, among baseline schoolgirls, half of whom had completed primary school at baseline, only 17.0% had completed secondary school as of 2012. Although most adults 15 and over participate in some form of employment, the majority do not receive a formal income. In 2008, only 6% of the adult population received a formal income (Zomba City Assembly 2009), a number that is reflected in our data with 6% of baseline dropouts and 3% of baseline schoolgirls participating in any formal work. This context is typical
]|[0.095]|[0.005]|[0.001]||
|Observations|826|826|827|827|827|827|
|Av. Dep. Var. (Control group)|0.793|0.558|0.000|0.000|0.000|0.000|
|Diff. p-value|0.004|0.842|0.272|0.020|0.000|0.001|
|YWYjoint test|0.007|0.183|0.302|0.002|0.000||

_Notes:_ This table presents OLS estimates derived from Equation 1. The dependent variables are (i) Migrants Put Effort: indicator equals 1 for respondents who attribute the poverty of Venezuelan migrants in Colombia primarily to circumstances beyond their control. (ii) Same Job Opportunity: indicator equals 1 for respondents agreeing that Venezuelan immigrants should have equal job opportunities in Colombia. (iii) Acceptance: Z-scored variable from a scale ranging from 1 (strongly agree) to 4 (strongly disagree) on the statement, “Colombians have the right to exclude Venezuelan immigrants from their neighborhoods.” (iv) Government Must Help: Z-scored variable from a scale ranging from 1 (strongly disagree) to 4 (strongly agree) on the statement, “The Colombian government is obligated to help Venezuelan immigrants.” (v) Increase Gov. Spending: Z-scored variable from a scale ranging from 1 (strongly disagree) to 4 (strongly agree) on the statement, “I would vote for a policy to increase government spending to assist Venezuelan immigrants.” (vi) Attitudes Index is constructed using dependent variables of columns (1) to (5) using the methodology of Kling, Liebman and Katz (2007). It implies standardizing each variable within the index, calculating the average, and standardizing this average using the mean of the control group. All regressions control
Mexico, which has been observed since the early 2000s. The fall in returns to college education may be the outcome of a process in a particular age group, for example, lower quality of tertiary education in younger workers—the “degraded tertiary education effect”. Another explanation is that older workers in recent cohorts receive lower wages than their counterparts in previous cohorts due to a skill mismatch with the demands of the economy or to a technical change that makes skilled-labor redundant—the “skills-obsolescence effect”.

Using data from Mexican labor surveys for the period between 2000 and 2014, we do not find evidence to support the hypothesis that the decline in the relative wages of skilled workers is explained by a degraded tertiary effect or a demand-supply mismatch among younger workers. If these effects have taken place, they have been more than offset by other forces. There is evidence, however, of a process that is taking a negative toll on workers ages 50 years and older. Rather than a continuously increasing concave wage function, these workers present a sharper decline in their wage.

In addition, there is some support of an accelerated skills-obsolescence effect, which is suggested by the fact that the age at which the slope becomes negative occurs at younger ages with each cohort. Nevertheless, this finding must be interpreted with caution, as the year at which the wage becomes negative for the college-educated of every cohort (2008) is the same year in which the country entered into recession.

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indicator is populationneutral, and unaffected by the overall level of a country’s population. The dispersion-adjusted measure of population density is defined as being proportional to the country’s population divided by the square of the dispersion indicator (with the square operation applied to transform the distance-based dispersion measure to an area-equivalent). It’s inverse captures population sparsity. This indicator is again highly sensitive to population settlement patterns (in contrast to calculations of the country’s average population density).

In this paper, we calculate the local-area remoteness measures, and country-level dispersion and sparsity indicators for eight case-study PICs, relying on census data collected by country National Statistics Offices and made available through the Pacific Community (SPC) PopGIS platform. Our estimates of community remoteness are correlated with local area socio-economic characteristics (the link between remoteness and socio-economic characteristics is explored more comprehensively in a companion piece<sup>2</sup> to this work). At the country level, our indicators emphasize the enormity of the geographical scale of dispersion in Kiribati and the Federated States of Micronesia (FSM); residents live an average of around 650-700km from the rest of the population. The populations of Solomon Islands, Vanuatu and Republic of Marshall Islands (RMI) and are less widely dispersed, with people living an average of around 250km from the rest of the population. The populations of Fiji, Tonga and Tuvalu are more concentrated still, with people living on average around 100-150km from the rest of the population. This dispersion indicator is a population-

> 2 World Bank, 2019. ‘Socio-economic analysis: A spatial analysis of population and housing census data’, a background paper prepared for _A Review of Spatial Development Issues in the Pacific Islands Countries_ (P166346).

2
comparable surveys (Beegle et al., 2016). Perhaps these studies can provide supportive evidence to help understand why Botswana sees its SPI ranking go up significantly to number 52, while the corresponding figure for Madagascar falls to number 133, and that of Zimbabwe remains almost unchanged.

# **6. Conclusion**

In this paper we offer a systematic discussion of the construction of the Statistical Performance Index. Our theoretical and empirical methodology builds on the existing strengths of the widelyused Statistical Capacity Indicator of the World Bank. We present the conceptual and mathematical foundation behind this index, and significantly expand the number of indicators and countries covered by this index. We also provide some brief empirical illustrations of the updated index with global data in 2016.

This new index could be seen as the first step before more resource-intensive country-specific assessments to inform multi-year improvement plans. Our proposed framework is also flexible enough to allow for future revisions as the global data landscape evolves. For example, we can incorporate new indicators such as whether an NSO uses cloud computing to store their data or implements household panel surveys in the relevant dimensions without creating major changes to the total scores. Our framework may also be relevant to the construction of other indexes in related areas, such as tracking the global SDGs or child development. Since the SPI is currently available only for 2016, another promising direction is to collect time series data for the new index, both going forward and for several years past, and expand the current framework to allow for dynamic changes over time.

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## **2.5 Auxiliary Data**

To construct control variables and measures of grocery industry concentration in addition to other measures of interest, we use a variety of auxiliary data sources, which we document in detail in Appendix Section F.

# **3 Empirical Strategy**

## **3.1 Empirical Specifications**

The discussion in Section 2 makes it clear that, despite the maximum benefit levels being set at the federal level, state-level idiosyncrasies can be introduced to the benefit series. To estimate the impact of SNAP benefits on prices, we first lay out typical panel fixed-effects approaches before combining it with our IV. Our research design exploits variation in magnitudes of benefit increases across states and events, because benefit increases mostly occur at the same time across states due to federal implementation of the Farm Bill and the ARRA. While both price indices at the state and store level can be constructed, we report store-level regressions because more information is available on store type and geographic location, although results are robust to using indices aggregated up to the state level. In addition, we report results using monthly price indices to take advantage of the sharp timing of the events. In our preferred specification, the log of outcome _Yit_ for store _i_ in state _s_ is regressed on state-level log benefits per population _Bst_ for the store-year-month panel with store and period fixed effects to control for unobserved store characteristics and common time trends that affect prices, as shown in equation (3): _Yit_ are outcomes such as the store price index or store real sales. Because the level of the price index is not interpretable, only relative changes are relevant. The log-log specification gives the interpretation of _β_ as the elasticity of prices (or real sales) with respect to SNAP benefits per population.<sup>27</sup> We also include control variables _Xit_ matched to the store’s location, such as log state housing price, log county unemployment rate, log county average

> 27Note that since we define
which is what these assistance sessions were designed to alleviate.

# **III Data**

Firm-level data were collected at baseline, mid-line (6 months after the intervention), and end-line (18 months after the intervention). The baseline assessed business owner background characteristics, business characteristics, and the use of business practices.<sup>18</sup>

## **A Summary Statistics**

Summary statistics from the baseline survey are presented in Table 1. Columns (1)-(2) provide mean and standard-deviation values for the total sample of 1301 business, while columns (3)-(7) present the mean separately for businesses assigned to control and each of the four treatment groups. The last four columns of Table 1 present the p-values for tests of differences in means between each of the treatment groups and control group. Most (66 out of 68) p-values are large (well above 0.10). We also run a Multinomial Logit specification regressing assignment to treatment on baseline characteristics and test for joint orthogonality with a Chi-Square test. The p-value of this test is 0.857, which also suggests that the randomization was effective.

According to the statistics in Table 1, shop owners in our sample are mostly female (71%) and are 45.27 years old on average. Educational backgrounds are mixed, with the mean educational attainment of 9 years of schooling. The average entrepreneur is risk averse, and is neither patient nor impatient.<sup>19</sup> The average firm has a size of 13.22 sq. mts, 13.6 years of age, employs two workers, receives about 50 customers per day and has monthly sales of 4786.16 USD PPP and monthly profits of 907.73 USD PPP.<sup>20</sup> Only 20% of the businesses report having a tax ID and 18% has applied for a loan in the last 12 months. Finally, the average adoption rate of
There are a number of particular concerns with Turkey’s use of antidumping and safeguards. First, policymakers are frequently _extending_ the duration of imposed antidumping and safeguards barriers well _beyond_ the period that WTO rules indicate they are supposed to be removed. Second, there is evidence that imposed new trade barriers cover upstream (petrochemicals, fibers and yarns) and downstream (made-up textiles and apparel) segments of the textiles industry. These particular barriers affect the sector’s competitiveness locally and in global markets, and their piecemeal application across the industry complicates the difficulty facing policymakers regarding how to coordinate policy removal. Third, the application of discriminatory new import restrictions reflects a special concern with China’s exports, and there is also evidence of additional implicit discrimination in favor of countries already receiving sizeable tariff preferences through existing trade agreements; such additional discrimination could impose new trade diversion costs on the Turkish economy.

A final goal of this paper has been to highlight trends, patterns and puzzles arising from scrutiny of the detailed, micro-level data regarding Turkey’s access to trade policy flexibility in order to stimulate additional theoretical and empirical research on a number of policy-relevant questions. For example, why does a country access flexibilities for some products but not others? Furthermore, why does it choose one policy instrument over another? What affects the decision in one instance to raise applied MFN tariffs and hold PTA tariffs unchanged and in another instance to raise both sets of applied tariffs? Alternatively, why do policymakers sometimes avoid raising applied tariffs altogether but instead turn to more formal TTBs? When accessing TTBs, what affects the choice between using a global safeguard versus antidumping? What is the impact of WTO dispute settlement and the evolving DSU jurisprudence regarding trade policy flexibilities on how policymakers access these policies? Finally, what do answers to these and other questions regarding use of trade policy flexibilities imply for the overall design of trade agreements and the commitments that countries can be expected to take on under cooperative trade policy?

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but did not eat, and (viii) went without eating for a whole day.</mark> <mark>The approach to process and analyze FIES data comes from Item Response Theory</mark><sup>_31_</sup> <mark>, also known as the Rasch model</mark><sup>_32_</sup> <mark>, which accounts for the measurement of unobservable traits through the analysis of responses to survey. Analysis of FIES data involves parameter estimation, statistical validation, and calculation of individual and population prevalence estimates, as appropriate, for food insecurity. In this analysis, a respondent’s raw score (an integer number between zero and eight) is determined. Based on this raw score, an interval measure of the severity of food security, based on global standards, is determined, by equating (calibrating the score on a common metric)</mark><sup>_33_</sup> <mark>. Equating is completed for each household, which allows us to estimate the probability of (i) moderate or severe and (ii) severe food insecurity among adult household members. Following procedures detailed by the Food and Agriculture Organization of the United Nations and the associated program, Voices of the Hungry, we calculate FIES for households in our data as follows:</mark> <mark>1. Beginning with Stata constructed data files, we construct binary variables for each of the eight FIES components that take a value of 1 for “Yes”, and 0 for “No” answers (and leave untouched any missing answers). We reorder the variables and recode the variable names to ensure the following order and naming convention: Worried, Healthy, FewFood, Skipped, AteLess, RunOut, Hungry, and WhlDay.</mark> <mark>2. We construct adult population weights (</mark> wa <mark>), by multiplying household sampling weights</mark> (wi,