rafmacalaba/datause-encoder-data huggingface dataset:
|that have been estimated from open-source data.<br>Our estimation algorithm<br>is designed to support<br>expansion of GBIF species maps with the continuing increase in occurrence reports. We test the maps<br>sets to compute species density counts for a high-resolution global grid and compare the resulting global<br>distributions. We find close similarity in the global comparisons for mammals, ants and vascular plants.<br>Where there are regional differences, we trace them to technical differences in estimation methods or<br>cases where the boundaries of existing expert maps could be revised to reflect species-level patterns in<br>We use our occurrence maps database to explore two important issues for priority-setting in the Global<br>Biodiversity Framework.<br>First, we estimate the incidence of endemism across broad species groups<br>because it identifies countries’ stewardship responsibilities for species that reside entirely or almost<br>entirely within their boundaries. We find a heavy incidence of endemism for 610,694 species in our<br>(55.6%) for90% habitat. We also find thatendemism varies widely by species group. In a high-resolution<br>spatial analysis, we identify candidate “hotspot” areas for protection within countries where endemic<br>Our second exploration focuses on species whose small habitat areas increase their extinction risk. Our<br>algorithm produces area estimates for all mapped species, offering a low-cost complement to traditional<br>risk indicators whose species coverage<br>is limited by their resource intensity.<br>Using a conservative<br>standard for minimum critical habitat size, we find 117,934 vulnerable species. Our global assessment<br>finds a highly-skewed spatial distribution 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|
|---|
# **Chart 2 – Evolution of exchange rate regimes over time**

<!-- Start of picture text --> Exchange Rate Regime in Commodity Exporters<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>Peg Crawling Peg Dirty Floating Floating<br>Source: Ilzetzki, Ethan, Carmen M. Reinhart and Kenneth S. Rogoff (2010) "Exchange Rate Arrangements Entering the<br>21st Century: Which Anchor Will Hold?"<br><!-- End of picture text --> Besides peg, many countries operate “managed floats”. That arrangement allows them to choose how much of the adjustment to a TOT shock should come from exchange rate adjustment vs. domestic price adjustment. TOT shock amounts to a negative wealth shock, suggesting the real exchange rate has to adjust toward a new equilibrium level. A resource windfall is also typically associated with the so-called Dutch “Disease”. In other words, a positive oil price shock leads to a relative price change between non-oil tradables and nontradables.

In the case of a pure float, a TOT shock leads to immediate adjustment of the real exchange rate through the nominal exchange rate when good prices and wages are rigid. Empirical evidence 5
- 1.38 -

Table LC.4: Cot rlson of Sbuated and Actual Cocoa Production

|Yer|Cooo<br>(000.<br>Simulated|a Prod<br>Metric<br>Actua|ucion<br>Tons<br>Simulated<br>/Actual|Cocoa B<br>(Ced<br>Currnt<br>Cedis|uying Price<br>is/kg)*<br>1987<br>Cedis|
|---|---|---|---|---|---|
|1964-85|227|175|52|30.0|54.2|
|1968.6|228|219|9|56.6|93.9|
|1968467|229|228|1|85.5|116.8|
|1967488|230|183|47|140.0|140.0|

Source: The WoMd Bank, Ghan Country Economic Meorandum, Report No. 751564 (Green Cover), November3, 1968, Statistical Appendix: Tabes 7.01, 9.01.

To compare farm Income estimates (Concept 2) from model solutions with estimates from the natlonal accounts, some adjustments to national accounts data are necessary. These are shown In l.C.5. The national accounts sectoral GDP estimates separate cocoa production and marketing from other agriculture and livestock, as shown (in current cedis) In rows 1 and 2 of Table l.C.8. To calculate the amount of cocoa GDP that accrues as Income to farmers, cocoa GDP (row 2) Is multiplied by the farmer's share of cocoa export revenue (row 3). yielding the desired amount (row 4). Estimated farm Income (row 5) Is then the sum of rows 1 and 4. The estimates In current cedis are then deflated to constant 1987 cedis, using deflators based on the average rural CPI for each year (row 6) to obtain farm Income (in 1987 cedis) In row 7. The GDP based estimates are then computed as percentages of the model estimates (row 9). Note that the exclusIon of Ilvestock and some high vaiued minor crops from the model Implies that the GDP based estimates should bo greater than the model estimates by perhaps 10 to 20 percent. Thus, the model estinates for 1984 are clearly too high by at least 10 percent; and the model estimate for 1987 Is low by perhaps 10 percent. However, given the weakness of the data base, and hence the strong assumptions that had to be made to
while present consumption has declined by more than in the perfect mobility case. Hence, the exchange rate must depreciated by more in the short run, and this greater depreciation would cause output to expand even more in the short run. In sum, financial integration would exacerbate debt levels and mitigate the volatility of nominal and real exchange rates in response to a goods demand shock.

# **3. Data and Methodology**

In the present section we describe the data used for our empirical evaluation of RER volatility, fundamental volatility and openness, and we explain a detailed outline of the econometric technique used.

# **3.1 The Data**

We have collected annual data on real effective exchange rates and its fundamentals (labor productivity, fiscal policy and monetary policy) for a sample of 82 countries over the period 1975-2005 (see list of countries in Table A.1). We have ignored the Bretton Woods period for two reasons (Levy-Yeyati and Sturzenegger, 2001): (a) to focus on the recent period of increasing integration to the world markets of goods and assets, and (b) the predominance of fixed exchange rate regimes implemented for political reasons.

Our dependent variable, the _volatility of RER fluctuations,_ is the standard deviation of 12month variation in the real effective exchange rate over a 5-year window computed using a monthly RER database. We chose the real effective exchange rate (instead of the bilateral real exchange rate of country _i_ vis-à-vis the United States) due to its relevance for countries with more important macroeconomic and trade linkages with other countries rather than the United States and due to its equivalence to the exchange rate in models —as the one stated in section 2 —that summarizes foreign countries as a single foreign trading partner. The real effective exchange rate index for country _i_ at period _t_ , _Qit_ , is defined as, where _sit_ is the nominal exchange rate for country _i_ observed in period _t_ expressed in units of local currency vis-à-vis the US dollar, _Pit_ is the consumer price level of country _i_ in period t, _skt_ is the nominal exchange rate of the _k-th_ trading partner in period _t_ (with _k_  _i_ ), 10
# Is investment in preprimary education too low? Lessons from more than 50 (quasi) experimental studies around the world<sup>*</sup>

Alaka Holla<sup>†</sup> Magdalena Bendini<sup>‡</sup> Lelys Dinarte-Diaz<sup>§</sup> Iva Trako<sup>**</sup> Originally published in the Policy Research Working Paper Series on _June 2021._ This version is updated on _March 2022._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Preprimary education, preschool, meta-regression, cognitive and non-cognitive skills **JEL Classification:** I20, I26, I28

> * We would like to thank Kathleen Beegle, David Evans, Emanuela Galasso, Amer Hasan, Marcus Holmlund, Katherine King, Arianna Legovini, Florencia Lopez Boo, Dana McCoy, David McKenzie, Emma Näslund-Hadley, Berk Ozler, Sharon Wolf, and Hirokazu Yoshikawa for their valuable comments and suggestions. We are also grateful to Steffanny Romero Esteban, Yilin Pan, Luiza Cardoso de Andrade for their assistance in the meta-regression and benefit-cost calculations. This research benefitted from funding from the World Bank’s Strategic Impact Evaluation Fund. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> † Senior Economist. Human Development Vice Presidency. The World Bank. email: aholla@worldbank.org

> ‡ Senior Economist. Education Global Practice. The World Bank. email: mbendini@worldbank.org

> § Economist. Development Research Group. The World Bank. email: ldinartediaz@worldbank.org

> ** Economist. Development Impact Evaluation Group (DIME). The World Bank. email: itrako@worldbank.org
Table of contents

# 1. Introduction

2. The duopoly period (1984-1991)

- 2.1 Rebalancing of call charges

- 2.2 The 1985 determination

3. Transition to competition (1991-1997)

- 3.1 Access deficit contributions

- 3.2 Unbundling and accounting separation

- 3.3 Determination of Long Run Incremental Costs

4. Normalisation (1997-2001)

- 4.1 The network price cap

- 4.2 Universal service

- 4.3 Assessment of recent changes and controversial issues

5. Conclusions 6. References

# Annexes

- Al. Summary statistics

- A2. The 1985 determination

- A3. The 1993 determination and ADC A4. Recent charges

2
low of 77.2% for finished socks to a high of 100% for men’s dress and sports coats (in volume terms) (U.S. Census Bureau, 2009a; 2009b).

Table 2 charts trends over time in the top 15 countries that supply U.S. apparel imports. Most striking is the dramatic increase in China’s import share, which climbed from 13.3% of all U.S. apparel imports in 2000 to 26.4% in 2005 and 34.7% in 2008. The big losers during this period were Mexico, whose apparel import share fell from 13.1% in 2000 to just 5.2% in 2008, and the DR-CAFTA (Dominican Republic and the five countries in the Central American Free Trade Agreement), whose import share dropped from 13.9% in 2000 to 9.6% in 2008. A more graphic illustration of the shifts in the regional structure of U.S. apparel imports is found in the Appendix, Figure A-1.

**Table 2: U.S. Top 15 Apparel Import Shifts: 1995, 2000, 2005, & 2007-2009**

[Value in $US Million; % Represents Country/Region’s % of Year’s World Value]

|**Country/**|**1995**||**2000**||**2005**||**2007**|**2008**|**2009**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Region**|Value|%|Value|%|Value|%|Value|Value|Value|%|
|**World**|**41,367**||**67,115**||**80,071**||**84,853**|**82,466**|||
|China|6,170|14.9|8,924|13.3|21,138|26.4|28,530|28,575|28,201|39.1|
|DR-CAFTA|4,920|11.9|9,341|13.9|9,413|11.8|8,199|7,903|6,405|8.9|
|Vietnam|--||--||2,911|3.6|4,619|5,527|5,332|7.4|
|Indonesia|1,376|3.3|2,333|3.5|3,163|4.0|4,306|4,358|4,154|5.8|
|Mexico|2,904|7.0|8,809|13.1|6,374|8.0|4,743|4,250|3,580|5.0|
|Bangladesh|1,142|2.8|2,279|3.4|2,537|3.2|3,286|3,657|3,580|5.0|
|India|1,379|3.3|2,157|3.2|3,376|4.2|3,505|3,412|3,126|4.3|
|Cambodia|--||--||1,818|2.3|2,559|2,508|1,950|2.7|
|Thailand|1,209|2.9|2,276|3.4|2,351|2.9|2,311|2,238|1,765|2.4|
|EU-15|2,003|4.8|2,644|3.9|2,535|3.2|2,602|2,412|1,646|2.3|
|Pakistan|--||--||1,447|1.8|1,696|1,691|1,467|2.0|
|Sri Lanka|1,029|2.5|1,609|2.4|1,796|2.2|1,711|1,620|1,319|1.8|
|Malaysia|1,253|3.0|1,380|2.1|--||1,422|1,505|1,300|1.8|
|Philippines|1,685|4.1|2,037|3.0|1,949|2.4|1,821|1,443|1,071|1.5|
|Jordan|--||--||--||--|--|791|1.1|
|HongKong|4,566|11.0|4,808|7.2|3,738|4.7|2,162|1,645|--||
|Korea|1,923|4.6|2,591|3.9|1,319|1.6|--|--|--||
|Taiwan|2,261|5.5|2,285|3.4|--||--|--|--||
|Canada|896|2.2|1,933|2.9|--||--|--|--||
|**Top 15 Total**|**s and % o**|**f World**|**Total**||||||||
||**34,715**|**83.9**|**55,407**|**82.6**|**65,866**|**82.3**|**73,470**|**72,744**|**72,064**|**91.2**|

-- Indicates country not in the Top 15 apparel suppliers that year. Source: UN Comtrade; Apparel represented by SITC 84

**European Union-15** .  In 2008, Europe accounted for 41% of global apparel retail sales of $1,026 billion (Datamonitor, 2009). In the EU-15, the apparel import penetration varies significantly among countries. In 2006, the estimated import shares for the main consuming countries were: the United Kingdom and Germany 95%, France 85%, Italy 65%, and Spain 55% (Clothesource, 2008).

5
8 _Tourism Statistical Report_ . The CTO has adopted the international definition of “tourist” advocated by the World Tourism Organization of “Any person visiting a country other than that in which they normally reside, for not more than one year, and whose purpose of visit is leisure, business (including meetings, study and research) health treatments, religious pilgrimages, and aircraft and ship crew stayover.” Most statistics are obtained by analysis of embarkation / disembarkation cards completed for the national immigration authorities. In cases where a tourist card is not required because travel is consider domestic (e.g. U.S. to Puerto Rico of U.S. Virgin Islands), greater reliance is put on the analysis of hotel registrations. We focus on stayover tourists which is the larger and more lucrative market.

Data on lobbying in tourism by foreign principals residing in the Caribbean destinations for the period 1990-99 was compiled from FARA reports for those years. In each year there were about 1,300 entries containing data on the calendar year of the activity, the country of the principal, the name of the principal, and the amount of money the agent and principal transacted. On the basis of the description of the representation activity by the U.S. agent in the FARA report, each entry was assigned a primary 3-digit SIC industry. Over 25 percent of the FARA entries were in the tourism sector (SIC 961). Those entries from the Caribbean destinations are used in this study.<sup>11</sup> They are aggregated up to the country level for each year.

In the FARA reports, dollar figures for the three years 1992-94 are completely missing. However, foreign principals who lobbied are quite completely identified. In order to obtain a complete sample with no discontinuities, we imputed lobbying expenditures for each identified principal for the missing years, based on how much they spend on lobbying in other years. The imputed value, in 1992 for example, for a specific principal is simply the predicted lobbying spending from a trend equation for that principal for 1992. In general the trend equations fit the data adequately. These imputed values are then aggregated up to the country level.

We restrict the sample to countries that participated in foreign lobbying at least once during this period, because we want to exploit the within-country variation in lobbying across time, and only if countries participated would we expect to see their impact on tourism. If in any year one of these countries did not participate in U.S. lobbying, lobbying spending for that

> 11 Only data on lobbying devoted to tourism (SIC 961) are used in this study. Data on lobbying devoted to advertising (SIC 731) and to media and media relations (SIC 874) are excluded from the study. Thus, the analysis is focused exclusively on lobbying for tourism.
applied to a limited subset of public expenditure programs, mainly in education, health and infrastructure.

# **Method**

The construction of the distribution of benefits associated with a particular public expenditure program involves the following three basic steps: (1) using data from public expenditure accounts and the operation of the program under study, estimate the unit cost or subsidy associated with the public service in question; (2) from household survey data, estimate the rate of use<sup>23</sup> of the service by each household and impute the benefits on the basis of these estimates; (3) report the results by policy-relevant socioeconomic groups (e.g. by quantiles of the distribution of some welfare indicator, or by region, gender or ethnic groups).

To see some of the computations involved, we consider the case of public education as explained by Demery (2003). Let **j=1, 2, ..., m** represent policy-relevant socioeconomic groups. Let **i=1,2,3** stand for the usual three levels of education, primary, secondary and tertiary. Furthermore, let **Ri** represent the total public school enrollment at level **i** , and **Rij** the enrollment from group **j** . Finally, let **S** and **Si** stand respectively for total government subsidy and the amount of subsidies going to level **i** . The total amount of the public education subsidy imputed to group **j** , **Bj** , can be formally written as: Expression (3.1) is written in a way that clearly shows that the benefit incidence of education spending allocated to group **j** for each level of education is equal to the enrollment of the group at that level of education times the unit cost. The expression can also be normalized by **S** to yield the share of the total education spending imputed to group **j** , **bj** . The result can be written as follows:

> 23 In the case of education and health, this information relates to enrollment rates or clinic visits reported by members of a household.

26
# **Appendix 1. Background on Colombian Bankruptcy Law Reform.**<sup>**23**</sup>

In 1995, the Law 222 was enacted in an attempt to reduce the judiciary burden by allowing disputes among creditors and debtors to be resolved under the Superintendence of Companies. In addition, the superintendence, under the Ministry of Industry and Commerce, is in charge of supervising firms to prevent insolvency and fraud. The law established the procedures for both _mandatory_ liquidation and restructuring under the _Concordato_ proceedings. _Voluntary_ liquidation was and is still regulated by the Commercial Code.

> Before Law 222, _mandatory_ liquidations were civil bankruptcy proceedings that lasted for many years because civil courts lacked capacity and specific business knowledge. Under Law 222, however, mandatory liquidation proceedings are still lengthy, usually taking more than three years to resolve. The length of the proceedings in practice implies that a substantial part of the assets of the debtor are lost either because they loose value over time (indirect cost) or are spent towards paying the fees and expenses of the liquidation (direct costs). As a result, the perception is that mandatory liquidation is very inefficient.

> Although more authority was given to the superintendence, the _Concordato_ proceedings under Law 222 still suffered from being excessively long. In essence, too much leverage was given to the debtor in the negotiations with creditors. Delays were favorable to debtors as they allowed them to suspend the debt service, and also granted protection by the stay against execution actions commenced by creditors (Tamayo et al., 2002).

This situation resulted in many private agreements (workouts), mainly between the financial institutions and the debtor, outside the scope of the _Concordato_ . These agreements were typically used to restructure and reschedule the debtor’s obligations but are considered onerous by both parties as it is difficult to reach agreements. In Colombia, they are regulated by the general Civil Law under the principle of freedom of will of all the parties involved.

> 23 This Section draws from Urrutia (2004).

26
Brazil and the Northeast region, respectively. But Paraíba managed to reduce the P0 by 17.5 percentage points, more than twice the reduction in the national rate. Since 1996, poverty has remained largely unchanged, at around 39 percent (Figure 2.3).

The fall in Paraíba’s P0 during 1981–99 was nearly twice the average for the Northeast and five times that of Brazil as a whole (Figure 2.3). Paraíba experienced an 18.9percentage-point reduction in the P0, while the reduction in the region and the nation was 5.5 and 2.0 percentage points, respectively.

The fall in Paraíba’s social indicators such as infant mortality and adult illiteracy during 1980–99 corroborates the improvement in measured income poverty. The infant mortality rate dropped dramatically from 170.6 per 1,000 live births in 1980 to 60.3 per 1,000 live births in 1999, a far greater decline than in the Northeast and Brazil as a whole (Table 2.1). Adult illiteracy took the same path as the P0 and infant mortality. During 1980–99 the illiteracy rate for people over age 15 fell 23 percentage points, to 25.9 in 1999. The downward trends in the poverty rate, infant mortality, and adult illiteracy testify to government success in areas including health and education.

**Figure 2.3:   Indigent Poverty Rate (P0), selected years, 1981-99**

<!-- Start of picture text --> 70<br>Brazil<br>65<br>Northeast<br>Ceará<br>Paraíba<br>60<br>Pernambuco<br>Bahia<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Calculations based on selected years of PNADs in 1981-99.

7