rafmacalaba/datause-encoder-data huggingface dataset:
pronounced when household fixed effects are included, since in this case one only exploits the variation in exposure and outcomes across siblings.

As a way to control for this additional source of bias, we present IV estimates where children's school attendance is instrumented by the average school attendance in the child's cohort in his locality of residence. To compute these averages we use retrospective information on preschool exposure for all children born in the same cohort and living in the same locality independent of the year (2001-2005) in which they are observed.<sup>1011</sup> Identification of the IV estimates is warranted by the interaction of cohort and locality, which is excluded from the main equation. By exploiting the area specific variation in pre-school enrollment across cohorts we effectively control for children's unobserved traits that might be correlated with the outcome and treatment variable.<sup>12</sup> We revert to the basic specification with homogenous effects across groups and again we concentrate only on the effect at the extensive margin. We do so since the IV estimator is inevitably leading to a loss in precision and we are unable to estimate precisely a large number of cross effects.

We report the first stage estimates in Table A1 in the appendix. For brevity we only report results with the entire set of additional controls and household fixed effects (additional results are very similar and available upon request). Each column refers to the probability of having attended pre-school at a given age (e.g. age 7 in column 1, age 8 in column 2, etc.) on the average pre-school attendance by cohort and locality interacted with age. The first stage estimates illustrate that locality-cohort enrollment is a very good predictor of the individual probability of attending.

In Table 8, which has the same structure of Table 5, we present the instrumental variable estimates. The effect of the treatment on stay-on rates, columns (1) to (4), does not show such a clear pattern as the one reported in Table 5. However, we still find large differences at ages 14 and

> 10 Recall that there are 55 localities and 13 cohorts in the sample, with an average of 32 children by cohort and locality. 11 We also tried to compute these means excluding the child of interest. Results are unchanged.

> 12 Potentially a better instrument would only exploit differences in the local supply of preschool places across cohorts. Unfortunately we do not have detailed information on pre-school construction at such a detailed geographical level.

22
downside of this approach is that, because the underlying sources were not compiled by a single linguist, there may be measurement error at the language level. Specifically, while many historical sources explicitly state that languages either do or do not use a system of grammatical gender, we cannot always be certain that the same precise definition of grammatical gender is being used across sources.<sup>12</sup> The strength of our approach is that we are able to characterize the grammatical structure of thousands of languages accounting for almost all of the world’s population.

# **3.1 Building a Grammatical Gender Data Set**

## **3.1.1 Data on Native Languages**

Data on the world’s native languages comes from the _Ethnologue_ , a comprehensive database of over 7,000 languages (Lewis, Simons, and Fennig, eds., 2016). For every known language, the _Ethnologue_ provides an estimate of the number of native speakers (if any) in every country. Data are drawn from a range of sources including national censuses and surveys compiled by linguists. Combining the _Ethnologue_ data with information on the grammatical gender structure of the world’s languages allows us to construct an estimate of the fraction of each country’s population that speaks a gender language as their native language.

Of the 7,457 languages included in the _Ethnologue_ database, we drop languages that are extinct or have no native speakers, sign languages, and dying languages that had fewer than 100 native speakers when last assessed by _Ethnologue_ researchers. This leaves 6,190 languages. Together, these languages account for an estimated 6.50 billion native speakers. Of these, we successfully identify academic or historical sources characterizing the gender structure of native languages accounting for 6.44 billion speakers (or more than 99 percent of the total), as described below.

> 12Indeed, even recent work by linguists does not always agree on the definition of grammatical gender — see Corbett (1991) and Aikhenvald (2003) for discussion.

13
these responses are used to construct various food insecurity indicators. An advantage of FIES is that it generates a direct composite estimate of food insecurity summarized from a set of easy-tounderstand questions, convenient to include in high-frequency surveys. It, thus, allows comparability across time and space. This standard module was included in many of the highfrequency phone surveys, with reference period for the last 30 days preceding the survey date.

Based on the above FIES questions, we adopt two approaches to measure food insecurity. First, we use the raw values of the responses and associated “raw score” which we generate by summing the responses to the eight questions. The raw value of these responses assumes binary nature while the raw score assumes a value between zero and eight. By this definition, those households reporting experience of food insecurity across one or more of the eight dimensions are assumed to be facing food insecurity.

Second, data from this module is analyzed further, following procedures detailed by the FAO, to generate a food insecurity metric based on responses provided to each of the FIES questions (FAO, 2014b).<sup>16</sup> The analysis involves parameter estimation, statistical validation against global standards, and calculation of individual and population-level food insecurity prevalence rates.<sup>17</sup> For this purpose, we follow the following steps: First, we compile all binary responses for each of the eight FIES questions above (this requires specific ordering and naming of variables) along with appropriate weights. We retain this file and export it to .csv format. Second, we upload the .csv file to the FIES Shiny App: https://fies.shinyapps.io/ExtendedApp/, which is developed and managed by the FAO. Third, we follow the analyses, exclusion and inclusion steps described by FAO for generating respondent-level model-based food insecurity indicators. In particular, this analysis generates several important indicators of food insecurity, including: (i) raw score that is simply generated by adding the raw values of responses to the eight FIES questions, (ii) severity of food insecurity, an interval score ranging between zero and eight which is used to classify households’ severity of food insecurity (into moderate or severe food insecurity) (FAO, 2014b).<sup>18</sup> (iii) probability of moderate or severe food insecurity, and (iv) probability of severe food insecurity. We finally downloaded and merged these indicators with our main sample.

> 16 See detailed procedures and definitions at: https://www.fao.org/3/i7835e/i7835e.pdf

> 17 See detailed procedures at: https://www.fao.org/3/ca9318en/ca9318en.pdf

> 18 Details of the implementation procedures of this analysis are given in FAO, 2014a, 2014b and Josephson et al. (2020).

12
Policy Research Working Paper 7507

# **Abstract**

This paper presents estimates of the effects that terms of trade volatility has on growth of real gross domestic product per capita. Based on five-year non-overlapping panel data comprising 175 countries during 1980–2010, the paper finds that: (i) in model specifications that do not include country fixed effects, terms of trade volatility has a significant negative average effect on economic growth; (ii) once country fixed effects are included in the model, the average effect of terms of trade volatility on economic growth is not significantly different from zero; (iii) robust to the inclusion of country fixed effects, terms of trade volatility has significantly adverse effects on economic growth in countries with pro-cyclical fiscal policy; and (iv) in model specifications that do not include country fixed effects, financial development is a significant mediating factor with regard to the effect that terms of trade volatility has on economic growth, however, the significance of this effect vanishes once country fixed effects are included in the model. The paper also explores these relationships for the Organization of Eastern Caribbean States region. A key conclusion from the research is that countercyclical fiscal policy and deeper financial markets will have particularly high payoffs in reducing the adverse growth effects of terms of trade volatility in the Organization of Eastern Caribbean States region.

This paper is a product of the Caribbean Country Management Unit and the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fcarneiro@worldbank.org.

_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 necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Comparison between Bank-wide averages<br>100.0%<br>90.0%<br>80.0% —<br>Difference: 5.21%*** 74.6%<br>70.0% 69.4%4<br>60.0%<br>50.0% —<br>40.0%<br>30.0%<br>20.0% —<br>10.0%<br>0.0% T ee 1<br>PSM projects NON-PSM projects<br>PSM projects © NON-PSM projects<br><!-- End of picture text -->
that OPSW refers to migrants of all ages, while DLM instead only refers to those in the labour force. The difference are those aged 24 and below, which for the sake of simplicity we term _youth_ . To surmount this final issue, we run estimate Equation (4) twice more, for male youth and female youth.<sup>13</sup> Finally, putting together all of our estimates, for those destination countries for which raw migration data are unavailable we lastly use the following restriction to split the total gender-specific migrant stock provided by OPSW into the gender-skill specific migrant stock: Our final matrices are comprised of the original gender-education specific data for those destination countries for which we have original raw data in together with our predicted migrants stocks, _M_<sup> ̃</sup> _g,s,t_<sup>_jk_.</sup>

# **3.2 Econometric Issues**

The presence of a large number of zero or undefined observations in the dependent variable (gender and education specific bilateral migrant stocks) gives rise to econometric concerns that would yield inconsistent OLS estimates. Zero observations appear in large numbers in many bilateral contexts such as international trade, official aid, military conflict and political alliances. This phenomenon is especially prevalent in migration data sets, since there is no

> 13Youth (for each gender group) is defined as the difference between total migrant stock from OPSW minus low-skilled and high-skilled migrant stock.

20
22 Figure 1: International prices for rice and wheat, 2004 to September 2010 <!-- Start of picture text --> (current US dollars)<br><!-- End of picture text --> <!-- Start of picture text --> 900<br>800<br>700<br>600<br>500<br>400 Rice<br>300 Wheat<br>200<br>100<br>0<br>2004M1 2004M6 2004M11 2005M4 2005M9 2006M2 2006M7 2006M12 2007M5 2007M10 2008M3 2008M8 2009M1 2009M6 2009M11 2010M4 2010M9<br><!-- End of picture text --> Source: World Bank, _Pink Sheets,_ http:// econ.worldbank.org, accessed 25 October 2010
- 13 -

Figure 1. Real Exchange Rate Indices for Selected Country Groupings, 1978-88 (unweighted averages) <!-- Start of picture text --> Index 1tS<br>110<br>-<br>103 21 t O'<br>§ a 1w- : ~~~~~~~~~~~4a NTAL -<br>90 .5 \<br>so<br>75 -40 TALSJ<br>1676 107 1360 16 1O 1¶13 14 1U 16o6 lO 166<br>21 INDs - twenty-one industrialized countries.<br>40 TALs - forty trade adjustment loan recipient countries.<br>48 NTALs = forty-eight nonrecipients of trade adjustment loans.<br><!-- End of picture text -->

- Note: Increase in index indicates real appreciation. This figure does not indicate initial currency misalignments (for a discussion of 'proper" levels of exchange rates, see Williamson 1985).

- Source: Trade-weighted multilateral index of the real exchange rate for the various countries based on IMP data.

for the different sectors would be needed or measures of effective exchange rates for exporting activities versus import-substituiting activities. Individual country studies of effective protection and antiexport bias exist (for example, for Chile, Colombia, Kenya. Korea, Mexico, Morocco, Pakistan, Philippines, and Turkey), but the results are not comparable across countries. Comparisons of even nominal protection
# **1 Introduction**

Lower income countries, on average, collect less taxes than higher income countries, limiting their ability to invest in infrastructure and public services. This tax gap occurs despite comparable statutory rates and accounting for different levels of economic activity, suggesting the presence of deficits in lower income states’ tax administration capacity (Besley and Persson, 2014). This paper distinguishes three key components of tax capacity that states must invest in to collect taxes effectively: identifying taxpayers (identification capacity), determining how much they owe (detection capacity), and taking in these liabilities to the state coffers (collection capacity). Whereas traditional tax evasion models (e.g. Allingham and Sandmo (1972)) and a resulting large literature on third-party reporting emphasize detection capacity, this paper focuses on the two other dimensions of tax capacity, identification and collection, which are particularly relevant in lower income countries.

Identifying taxpayers, a presumption in most developed economies, is a first order concern for tax collection in many lower income countries characterized by a large informal sector and the lack of proper identification systems for individuals, businesses, property and other taxable entities/ tax bases. For example, 45 percent of people in Sub Saharan Africa do not have an official proof of identity (World Bank, 2022), and majority of lower income countries have sparse and inaccurate taxpayer databases (Nyanga, 2021).<sup>1</sup> Many contexts are reminiscent of Scott’s (1999) description of the premodern state as “in many crucial respects, partially blind; it knew precious little about its subjects, their wealth, their landholdings and yields, their location, their very identity.” In order to tax, the premodern state sought to make society “legible” through the creation of permanent last names, the establishment of cadastral surveys and population registers, etc. Similarly, modern governments seeking to understand their potential tax base must increase coverage of foundational identification systems, business registries and property registries.

Detection capacity is closely related to identification and builds on it. After the presence of a taxpayer or tax base is known to the state, it must then verify the income, assets, and

> 1 Of the 61 Tax Administration Diagnostic Assessment Tool (TADAT) assessments conducted in lower income countries from 2013 to 2020, only 20 percent of tax authorities had a score of good or very good for knowledge of taxpayer database and 5 percent did so for accuracy of information in taxpayer registry.

2
# **Innovations in Globalized Regulation: Opportunities and Challenges**

Brian Levy<sup>1</sup> World Bank

> 1 I am grateful to Debbie Wetzel for her encouragement,  to Motoko Aizawa, Adrian Fozzard, Stephen Gelb, Michael Jarvis, Kai Kaiser, Mike Morris, Steven Rochlin, Michael Stanley and Graham Teskey for helpful comments, and to Paul Alois for his excellent research contribution, and shared commitment to better understand emerging trends in globalized regulation.
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_-_ Two features are immediately _apparent_ the models give quite different results for certain fuel pairs e.g. the tranolog shows normal substitution between electricity and gas, while the logistic shows complementarity (interestingly Pindyck's partial elasticity between electricity and gas was also positive). The translog model also gives very variable - elasticities for certain fuel pairs coal and all other fuels are notable here, while the logistic yields very stable values. For neither model has symmetry been imposed. For ease of comparison Pindyck's translog partial elasticities for the U.S. are repeated in table 4.9.

Table 4.9: Pindyck's Partial Price Elasticities for U S. Industry

||P|G|C|B|
|---|---|---|---|---|
|P|-1.10|-0.72|0.97|0.85|
|0|-0.32|-0.52|0.72|0.12|
|C|0.99|1.66|-2.17|-0.48|
|3|0.11|-0.03|-0.06|-0.08|

These are completely different from Considine's results for both 1970 and 1985 and suggest that the effect of pooling on the U.S. values may be very considerable. Considine does show that imposing an allowance for regulatory constraints does reduce the elasticities and this, plus the difference in the definition of the sector, must also be a contributory factor in explaining the variation in reoults.

A quite different approach to modelling energy price elasticities is exhibited in the work of Mountain and Haaio (19893. They disaggregate industry data to a regional level within Canada and by industry type (2 digit level). They also argue that, given the need to find a good approximation to the true cost furntion, it is not necossary to impose tie same functional form on separate industries. They try different flexible functional forms in order to see which fits best in each case. In addition they introduce a wider range of
8 estimate is calculated from macroeconomic data, assessing the growth in real GDP attributable to investment in physical and human capital. There are a number of problems with his study. His method (table 3i) shows a negative return to higher education. If a longer lag is put on higher education, then the term in primary/secondary education loses its significance. He calculates the return to primary and secondary education from the first regression in table 3i and the return to higher education from the second regression, adding the current and lagged terms on higher education together. This is somewhat lacking in coherence. Thus his claim that his method shows a return very similar to the figures presented by Psacharopoulos for Africa should be regarded with skepticism. If his claim were true, it would imply that there is no benefit from education except that reflected in those earnings differentials which are used to calculate the figures surveyed by Psacharopoulos (1985), or that the external benefits offset the biases referred to above.

The main virtue of McMahon's study is that he attempts to measure investment in human capital by means of expenditure on education (including earnings foregone), and this allows him to interpret his regression coefficients as rates of return. However, these expenditure data must be subject to a large inargin of error, and this probably explains why, in the other studies in tables 2 and 3, enrolment data were used instead.

Baumol, Blackman and Wolff (1989) find growth in real _GDP/GNPper capita_ to be positively influenced by education at all levels. They explain the growth in _per capita_ GDP/GNP by a catch-up effect (expressed by the use of initial GDP/GNP), the rate of population growth and the fraction of the appropriate age group enrolled in primary or secondary school or in higher education in 1965.

Five regressions are presented, using different measures of GDP/GNP over slightly different periods, but only one, looking at real GNP in constant price dollars begins in 1965 and so avoids the risk of simultaneity bias with the education data; this is presented in table 3ii. An increase of 10% in the fraction at primary school raises the level per _capita_ GNP in 1984 by 4.2% for a given value of 1965 _per capita_ GNP. _This_ suggests an effect on the growth rate of 0.2'% p.a. Secondary education is found to be twice as good, with a figure of 9.1% over 20 years or 0.46% p.a. Tertiary education is even better. The figure is 11.3% over 20 years or 0.57% p.a. These are the results of three separate regressions: in those for secondary and higher education the population growth variables are not significant, although they become so with measures of GDP based on purchasing power parity.
<!-- Start of picture text --> CO2 Emissions eq. (Mn T) 95 Global Anomalies Temp. °C<br>80000 2<br>60000 Ls ——- — — —<br>40000<br>1<br>20000<br>0 0,5<br><!-- End of picture text --> <!-- Start of picture text --> ST<br><!-- End of picture text --> <!-- Start of picture text --> WH<br>ER<br><!-- End of picture text --> <!-- Start of picture text --> XN<br><!-- End of picture text --> —HM2.0 ——HM1.5 ——HM1.7
because this algorithm depends on variables endogenous to my outcome of interest, such as the percentage juvenile values in the cluster of sets reported to the regulator.

Specifically, I use the single-linkage clustering algorithm to group sets that occur within 5 nautical miles of each other on the same day between midnight and 3 PM (R Core Team, 2019). I choose this time period because closures that begin at midnight (91% of closures during my study period) must be announced by 3 PM (9 hours in advance). The remaining 9% of closures begin at 6 AM and must be announced by 6 PM of the previous day. I use a 5 nautical mile threshold in the single-linkage clustering algorithm because the regulator’s second-stage algorithm rounds the boundaries of rectangular closures to the nearest 5 nautical mile interval (Instituto Humboldt and SNP, 2017). Then for each cluster containing more than three sets, I draw a rectangle to cover its convex hull (the smallest convex polygon that encloses the cluster), rounded up to the nearest 5 nautical mile interval. I drop all potential closures that are smaller than the smallest closure declared by the regulator that season.

As an illustrative example, Figure 3 displays sets from the electronic logbook data in one region of the fishery between midnight and 3 pm on April 28, 2019. The single-linkage clustering algorithm creates two clusters from these sets (Figure 3a). Figure 3b displays the potential closures that result from these clusters.

I assume all potential closures last for three days, which is the modal length of closures declared by the regulator (the regulator can also declare closures that last four or five days). Since a closure cannot be declared in the same place and time as an already-existing closure, I loop forward in time and subtract areas of potential closures that overlap with alreadyexisting potential closures. I drop potential closures that have become non-convex or smaller than that season’s smallest closure after this procedure.

My potential closures algorithm generates 973 potential closures in total, compared to 410 actual closures declared by the regulator during my study period. 88% of actual closures have positive overlap with a potential closure (intersect in space at the same time). The average potential closure is smaller than the average actual closure (950 km<sup>2</sup> compared to 1,328 km<sup>2</sup> ).

18