rafmacalaba/datause-encoder-data huggingface dataset:
a high correlation between income and fiscal capacity, a transfer system like the one suggested above should also have strong redistributive effects on per capita income.

T he following regression is conducted to test the hypothesis that a transfer system equalizes per capita income across provinces: PFCTi = ao + a, PCGDPi where PC'Ti is the per capita transfer to province i, and PCGDPi is the per capita GDP of province i. If a, is negative and statistically significant, it means that the system has a significant equalization effect.

When per capita transfers are calculated using need figures unadjusted for cost differentials, a, is significantly negative. At the same time, the R-square is 0.55, implying that 55 percent of the variations of the transfers across provinces serves the purpose of "equalization." From Figure 1, one can see a strong negative correlation between the two indices, indicating a significant redistributive effect of the proposed transfer slystem. The regression results are as follows: Regression lll: PCTi = 544.26 - 91.26 PCGDP, (2.55) (-14.51) R.-square = 0.59, No. of observations = 30, Degrees of freedom =28.

When per capita transfers are calculated using needs figures adjusted for cost differentials, a, is also significantly negative. The R-square is 0.42. The two variables are plotted in Figure 2, which shows a slightly weaker correlation between the two indices than that in Figure 1. The regression results are as follows:

# Regression IV: PCTi = 518.77 - 83.93 PCGDP (1.93) (-4.54)

R-square = 0.42, No. of observations = 30, Degrees of freedom =28.

For comparison purposes, I used the actual transfer figures in 1994 to run the same regression. The resulting a, is statistically insignificant and the R-square is only 0.0002, showing not even a slight correlation between per capita transfers and per capita GDP levels. This suggests that the current transfer system has not effectively achieved any redistributive goal. The regression results are as follows: Regression V: PCTi = 206.46 - 0.97 PCGDPi (1.10) (-0.075) R-square = 0.0002, No. of observations = 30, Degrees of freedom =28.

The same regression is also conducted using the per capita transfer figures generated by the "partial equalization" transfer schemes proposed in the previous section. Table 2 compares the regression results of four systems: actual transfers in 1994, 20% equalization, 50% equalization, and full equalization. The results show that the full equalization and _50%_ equalization schemes have statistically significant equalization effect (the slope coefficients are significantly negative). The 20% equalization schemes do 57
In Ethiopia, Northeast Nigeria, South Sudan, and Sudan, the dependency ratio is somewhat higher among the forcibly displaced population, whereas in Somalia, the reverse is true.

_Table 4: Simple t test of means for household characteristics_

||**Ethiopi**|**a**|**N.E. Ni**|**geria**|**Somalia**||**South S**|**udan**|**Sudan**||
|---|---|---|---|---|---|---|---|---|---|---|
||_Host_|_Refugee_|_Host_|_IDP_|_Non-IDP_|_IDP_|_Host_|_IDP_|_Host_|_IDP_|
|_Household Composition_|||||||||||
|Female-Headed HH|0.26|0.52***|0.3|0.31|0.5|0.47***|0.39|0.47***|0.29|0.44***|
|Age of the HH Head|42.94|36.24***|44.17|43.27***|39.39|39.99***|42.49|41.87|45.16|43.70***|
|Household Size|5.7|6.60***|7.47|7.03***|6.48|6.13***|7.28|7.1|7.71|7.12***|
|Number of Adults|2.16|2.17|2.9|2.47***|2.54|2.55|3.91|3.7|3.64|3.17***|
|One Adult Household|0.02|0.01***|0|0|0|0|0.02|0.02|0|0.01***|
|One Adult with Children|0.16|0.23***|0.08|0.11***|0.08|0.06***|0.06|0.08|0.02|0.04***|
|Two or More Adults|0.76|0.69***|0.87|0.85***|0.86|0.85***|0.73|0.73|0.88|0.87*|
|with Children|||||||||||
|Dependency Ratio|1.91|2.57***|2.02|2.16***|1.93|1.75***|1.16|1.37***|1.48|1.61***|
|Access to Land|0.61|0.08***|0.67|0.64***|0.85|0.87**|0.43|0.65***|0.36|0.31***|
|_Individual_|||||||||||
|Literate|0.58|0.54***|0.66|0.53***|0.53|0.39***|0.59|0.54|0.67|0.69**|
|Married|0.86|0.75***|0.89|0.87***|0.91|0.9|0.82|0.81|0.88|0.89|
|Divorced or Separated|0.07|0.11***|0.02|0.03***|0.01|0.01|0.05|0.06|0.02|0.02|
|Widowed|0.06|0.05|0.06|0.08***|0.03|0.03|0.1|0.11|0.05|0.06|
|Single|0.01|0.09***|0.04|0.02***|0.06|0.06*|0.03|0.02|0.04|0.04*|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

Overall, while statistically significant differences do exist in the household composition of forcibly displaced and non-displaced households, the magnitude of these differences is mostly minor. The fact that the household composition is similar across the populations suggests that any relationships we observe regarding gender and displacement at the individual level are not confounded by individuals living in different household types.

# **_4.3 Hypotheses_**

Several hypotheses interrogate the intersection of gender, displacement, and multidimensional poverty to extend our household-level analysis to the individual arena.

The first set of hypotheses are among women only: 13
<!-- Start of picture text --> Selected Advanced Economies<br>180<br>160<br>140 —<br>120 —<br>100<br>80 +<br>60 -<br>°C———<————<br>20<br>o0-<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>——France ——=Germany -——Sweden ——United Kingdom —-——~United States<br><!-- End of picture text -->
Using historical data on physician densities, GDP per capita, OOP spending per capita, and the size of the population aged 65 or over, we estimated the relationship between the economic and population indicators and physicians per 1,000 population using a generalized linear model (GLM). Missing data points for physicians per 1,000 between any two real data points were linearly interpolated. No adjustments were made where data was missing in projected physician densities.

For countries where historical data for total per capita health expenditures and OOP health spending was completely missing, mean yearly values for the specific World Bank region and income group combination to which the country belonged were substituted (see Annex C, below, _Projecting out‐of‐pocket expenditures_ ). No adjustments for missing data were made where data was not available for population or GDP per capita; this data tended to be missing for a handful of very small countries, where the projections would minimally affect overall shortage estimates. Thus, countries for which population or GDP per capita data were not available are excluded from the demand projections.

All variables were transformed into logs. To avoid endogeneity, GDP per capita, OOP spending per capita (OOPPC), and the size of the population aged 65 or over (Pop65) were all lagged up to five years to allow time for such factors to work through the economy and affect the labor market, as other authors have done in previous projection exercises (Getzen 1990; Scheffer et la. 2008). A stepwise approach was used to select the specific combination of year lags that maximized the predictive power of each variable. Lagged variables that achieved a minimum 1% level of significance after repeated iteration were kept within the model, resulting in the following optimal model: (Eq 1) ln( _physicians per 1000 populationit_ ) = β0 + β1*ln( _GDP per capitait‐1_ ) + β2*ln( _GDP per capitait‐4_ ) + β3*ln( _GDP per capitait‐5_ ) +β4*ln( _OOPPCit‐2_ ) + β5*ln( _Pop65it‐3_ ) + _μi_ + _ξit_ where _μc_ represents a vector of country fixed effects, _ξct_ is the disturbance terms, and β coefficients are unknown parameters to be estimated from the model. Country fixed effects are included to account for time‐invariant unobservable heterogeneity across countries (i.e. differences in baseline characteristics between countries). Quadratic terms for income and health spending indicators to additionally account for nonlinearities were investigated but ultimately excluded due to multicollinearity.

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# **Table 1. Variable Description**

|**Variables**|**Source**|**Long description**|
|---|---|---|
|**_Dependent variables_**|||
|||The percentage of respondents who report using mobile money, a debit or credit card, or a<br>mobile phone to make a payment from an account -or report using the internet to pay bills<br>|
|Made or received a digital payment<br>(% age 15+)|Global<br>Findex|or to buy something online or in a store--in the past year. This includes respondents who<br>report paying bills, sending or receiving remittances, receiving payments for agricultural<br>products, receiving government transfers, receiving wages, or receiving a public sector<br>pension directly from or into a financial institution account or through a mobile money<br>account in the past year.|
|**_Independent variables – banking_**|||
|Overall Restrictions on Banking<br>Activities (2016)|BRSS|Level of regulatory restrictions for bank participation in securities activities, insurance, real<br>estate financial activities and nonfinancial businesses. The question on nonfinancial<br>businesses became available in the 4th round of the BRSS.|
|Banks' assets (% GDP) (2019)|GFDD|Total assets held by deposit money banks as a share of GDP.|
|**_Macro controls_**|||
|GDP per capita growth (mean over<br>2015-2019)|WDI|Mean of the annual percentage growth rate of GDP per capita over past five years (including<br>the current one), based on constant local currency.|
|log(population) (2019)|WDI|Natural logarithm of the total population in a country.|
|Inflation, consumer prices (annual<br>%) (2019)|WDI|Inflation, consumer prices (annual %).|
|COVID growth differential (2020)|Global<br>Economic<br>Prospects|Difference between the two growth forecasts for 2020. The first forecast was produced in<br>January 2020 (World Bank 2020a) and the second forecast is from June 2020 World Bank<br>2020b).|
|**_Enablers_**|||
|Mean years of schooling (2019)|UNDP|Mean of years of schooling for adults aged 25 years and more.|
|Access<br>to<br>electricity<br>(%<br>of<br>population) (2019)|WDI|The percentage of population with access to electricity.|
|Individuals using the Internet (% of<br>population) (2019)|WDI|Individuals who have used the Internet (from any location) in the last 3 months.|
|Mobile cellular subscriptions (per<br>100 people) (2019)|WDI|Subscriptions to a public mobile telephone service that provide access to the PSTN using<br>cellular technology.|
|Enablers data economy|Chen<br>(2021)|Score capturing the presence of enablers of the data economy. The higher the score, the<br>more the regulatory environment is fit for the development of a data economy. Data as of<br>2020.|
|Safeguards data economy|Chen<br>(2021)|Score capturing the presence of safeguards of the data economy. The higher the score, the<br>more the regulatory environment is fit for the development of a data economy. Data as of<br>2020.|

32
shipping costs by more than 12 percent.<sup>4</sup> Fink et al. (2002) argue that both public policies, like restrictions on the provision of port services and private practices— collusive carrier arrangements—exercise a significant influence on maritime transport costs. A policy implication derived from their estimation is the need to pursue attempts to break up international cartels in that market.<sup>5</sup> Because their data do not include a change in the intensity of competition in the market (i.e., from cartel to non-collusive behavior), they cannot estimate the effects on transport costs of a change in the competition regime.

The aim of this paper is to address this gap in the literature. In particular, we estimate the effects of infrastructure, quality of regulation and changes in the competition regime on air transport costs. We focus exclusively on the costs of air transport due to its increasing importance as a transport mode, the availability of detailed micro data for US imports and the recent change in competition regimes introduced by Open Skies Agreements.

The advent of wide-body aircrafts in the 1970s made available large volumes of belly space. Being able to accept palletized or containerized freight, airlines began addressing the air cargo market more aggressively. As the evolution and design of aircrafts made it possible to carry more cargo in an efficient manner, dedicated cargo airlines entered this market.<sup>6</sup> The size of the airfreight and express market worldwide is approximately USD 75 billion, and during the 1990s this market grew at an average rate of 6% per year.<sup>7</sup> The geographic distribution of the revenue generated in the air cargo and air passenger markets are similar, explaining the US almost 40 % of total revenue. In the United States, as indicated by Figure 1, the value of air shipments relative to the aggregate value of air and vessel shipments increased from 24% in 1990 to 35% in 2000. The drastic drop in air

> 3 Deardorff (1984) surveys the early work on this subject.

> 4<sup>Clark et al (2004) show that reductions in country inefficiencies associated to transport costs from the 25th to 75th percentiles imply</sup> an increase in bilateral trade of around 25 percent.

> 5 Clark et al. (2004) show that their results are not robust to the inclusion of additional control variables (e.g., unit value of the shipped merchandise).

> 6 As Walker (1999) explains, until recently, the majority of airlines considered cargo a by-product of their passenger activities. The consequence was a pricing strategy that regarded cargo as a low additional cost product. In recent times, airlines significantly improved the way common costs are allocated and implemented a yield management strategy, which involves the creation of a standalone company with full profit responsibility.

> 7 Data obtained from Air Cargo Management Group (www.cargofacts.com).

3
# **The importance of measuring reading skills in the early grades:**

**_How can reading tests help guide policymaking and strengthen accountability?_**

## **<mark>1. INTRODUCTION</mark>**

In the past two decades, Peru has focused on improving access to education and has made impressive gains in coverage, meeting the second Millennium Development Goal (MDG) of universal access to primary education and staying well above regional averages in secondary and tertiary coverage. However, learning outcomes are still quite low by international standards and show little tendency to improve (Banco Mundial, 2006).

In order to improve educational quality and outcomes, the Peruvian Government has adopted a strategy focused on clarifying standards and establishing a stronger accountability system with greater local participation. The government sees universal testing as the best way to provide feedback on outcomes to policymakers, sector officials, teachers, parents and other stakeholders, in line with its policy of increasing local participation in monitoring service delivery. In 2006, the Quality Measurement Unit (Unidad de Medición de la Calidad, UMC) established a census assessment system to document the 2nd grade reading performance of each child and school. The ECE (for its initials in Spanish: “Evaluación Censal Escolar”) uses a pencil-and-paper reading comprehension test with a multiple-choice format and targets the entire second and third grade student population. It provides a critical starting point for benchmarking the performance of specific schools over the years, as well as against others in their region and nationwide.

In 2007, the World Bank’s Human Development Network –at the request of the Regional President of Junin– conducted a study on the provision of education services in this region. As part of this study, over 1000 third-grade students from public primary schools across the region were given the ECE 2006 test and some of them were also tested in oral reading fluency and comprehension (Banco Mundial, 2008). The study was implemented as part of the third phase of the RECURSO series, an analytical program focusing on accountability and the social sectors (education, health and social protection).

Results from this study revealed that academic performance in Junin is similar to national performance: most children read badly, with poor fluency and limited comprehension. Even those students attending urban, full grade schools –which have more resources and better quality education– finish their primary education with a two-year performance delay, as they read at a level expected for fourth grade students. Preliminary findings also indicate that written and oral measures of reading skills give similar results. In other words, children who read more fluently have better chances of understanding what they read. Inequality persists in this context of generalized low performance. The largest performance gaps are found between the Ashaninka indigenous students and the rest, even when controlling for other social factors such as gender, age, location and type of school, and access to preschool education.

3
Table 8.1 Fees for Service **at** Health Centers Bokoro Health Zone, 1989

|.............................................<br>SERVICE CATEGORY<br>IZAIRES<br>.....................................<br>...............................**.**|
|---|
|CONSLILTATIONS|
|Children,<br>registered<br>at NCH<br>120|
|Children,<br>not registered<br>150|
|Patient<br>above 14 years<br>180|
|Patient<br>from outside<br>zone<br>250|
|MATERNAL<br>and CHILC HEALTH|
|Pre-school<br>care|
|inct.immdization<br>(5 yrs.)<br>200|
|Ante**natal**care**(9 months***)<br>200|
|Planrnd parenthood (12 months)<br>300|

- F) for one disease episode up to 5 days duration. Extra charges apply for: antibiotics, drugs after the fifth day, fee for after hour consultations.

- ') Excluded: weekly malari prevention.

utilization substantially. From the survey, it was not clear whether insured respondents were actually reimbursed for all medical costs.

Services are fully available to uninsured persons upon payment of a flat fee for an episode of care. A minimum charge was set at 180 Z for an adult in 1988 by the BCZS. However, the health committee for a health center can decide to charge more, for example 500 Z. Services covered by the flat rate are limited. Extra charges arise for antibiotics, prenatal and child care.

To better characterize the insured and uninsured population and to assess interest in other forms of insurance, a small survey was conducted. The questionnaire was similar, though not identical, to the one used in Bwamanda, discussed above. The questionnaire was written in Lingala. Annex V contains a copy of a completed questionnaire in Lingala and an English translation. Of the 47 subjects interviewed, 21 were a systematic one-third sample of the 63 non-maternity inpatients in Bokoro Hospital, and 26 were heads of families (all farmers and fishermen) from the towns of Bokoro and Kempa, 12 km outside Bokoro. These 26 families were chosen systematically from family cards in the health centers of Bokoro and Kempa. Table B.2 characterizes the insured and uninsured respondents.

Table 5.2 Bokoro Survey: Population Characteristics.

|**Characteristic**|Insured<br>**(Nu21)**|Unirnsured<br>**WMZ6)**|Valid<br>**Responses**|
|---|---|---|---|
|.........<br>.......................................||||
|Gender:||||
|**Fm_le**|29X|<br>**46X**|47|
|**Male**|**11**|S4X||
|**Average**Age|35|32|**47**|
|**SDO**f**Age**|14|20||

29
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