rafmacalaba/datause-encoder-data huggingface dataset:
can be explained by teaching practices (Burchinal, 2018; Leyva et al., 2015), there are several studies that highlight its importance for low- and middle-income countries, especially at the kindergarten level. For example, a study in Ecuador found a one SD increase in teaching practices is associated with a 0.18 SD increase in learning outcomes (Araujo et al., 2016). In Ghana, teaching practices account for a 0.07 to 0.17 SD increase in student learning outcomes (Wolf et al., 2018) – similar results are found in Chile (Leyva et al., 2015). Further, there is evidence that improvements in teaching practices lead to positive effects on student learning. For instance, a meta-analysis of over 60 coaching programs found those designed to improve teaching practices (0.58 SD), also resulted in increased learning outcomes (0.15 SD) (Kraft et al., 2018).

Even when education systems attempt to capture teaching practices, most tools used in low- and middle-income countries fall short on several accounts, as they: (i) measure either the quantity or quality of teaching practices; (ii) do not explicitly focus on teachers’ efforts to develop students’ socioemotional skills; (iii) use tools designed for other contexts, which may include irrelevant items or fail to include important ones; and (iv) use tools that are neither evidence-based nor meet basic reliability criteria.

The _Teach_ classroom observational tool was developed in response to these concerns and to foster the measurement of teaching practices in low- and middle-income countries. _Teach_ measures teacher practices at a primary school level and is intended to be used as a system diagnostic and monitoring tool and for professional development. As a diagnostic and monitoring tool at the system level, _Teach_ helps governments identify bottlenecks in service delivery, monitor the effectiveness of their policies, and focus efforts to improve teacher practices. As a professional development tool, _<mark>Teach</mark>_ <mark>can be used to identify individual teachers’ strengths and weaknesses and coach teachers to improve their practice.</mark> In this paper, we made two contributions to the literature. The first contribution is primarily a methodological one. To the best of our knowledge, this is the first paper to use item response theory (IRT) for a classroom observation tool to go beyond reliability and factor analysis by assessing the information each element provides as it relates to teachers’ latent ability. The second contribution is empirical. This study validates the use of _Teach_ scores for system diagnostics and monitoring with data from Punjab, Pakistan.

This paper is organized as follows. Section 2 presents the framework to validate the _Teach_ scores for system diagnosis. Section 3 provides evidence on the tool’s content and cognition. Section 4 provides evidence of coherence and correlation. Section 5 concludes with a brief discussion of our findings.

3
# DETERMINANTS OF VALUE ADDED TAX REVENUE: A CROSS-SECTION ANALYSIS

## **2eUko Bogetid and Fareed Hassan'**

# I. Introduction

Value-Added Tax (VAT) has become a major tax instrument in about 50 countries worldwide contributing to their budgets, on average, 5.1 percent of Gross Domestic Product (GDP) in 1988 (see Table 1 in the Appendix). The global trend to introduce VAT in more countries is continuing. Since 1988, another 8 countries, including Canada and Japan, have introduced VAT, and an additional 15 countries, including the U.S., are seriously considering its adoption (Tait, 1991). VAT has also . The become an indispensable component of the tax advice and tax reforms in developing countries<sup>2</sup> growing practice of VAT is reflected in the extensive literature on technical, economic, and distributional dimensions of VAT, and there is a growing consensus on the "best practice" and desirable features required of a good VAT system<sup>3</sup> .

However, very few studies have empirically tested some basic hypotheses about key features and the performance of VAT as a revenue-raising instrument. This is surprising since there now exist relatively rich data on the VAT revenue performance (e.g., Government Finance Statistics, IMF), rates and other important characteristics of this tax. In particular, revenue performance and its I/ The authors are a Country Economist and a Consultant at the World Bank, Washington D.C., Department for South-East Europe. Helpful and thought-provoking comments and criticisms from Alan A. Tait, Carlos A. Silvani, Milka Casanegra de J.intscher, Dan Hewitt and R. Kyle Peters are gratefully acknowledged. The authors are solely responsitle for any remaining errors.

## 2/ See Goode (1993).

3/ See, for example, the collection of napers in a World Bank book edited by Gillis, Shoup and Sicat (1990). A comprehensive treatment of practical problems and options in implementing a VAT is given in Tait (1988). Also, see chapter one by Bird and Casanegra de Jantscher (on tax administration), chapter five by Due and Greany (on the introduction of VAT in Trinidad and Tobago), and chapter eight by Silvani (on compliance), in the recent IMF volume oii tax administration edited by Bird and Casanegra de Jantscher (1992).
Although geographical remoteness plays a role in shaping connectivity, it is not the sole determinant: policy also plays an important role in areas such as air transport, maritime shipping, and trade facilitation and, more broadly, logistics. Concretely, policy choices regarding the degree of liberalization of air transport and shipping markets, as well as the level of investment in key infrastructure such as ports and airports, can affect the number of countries to which a country is directly connected, and the regularity of service between them. Both of these factors affect a country’s connectivity, in the sense of its position in global networks relative to other countries (Arvis, Duval, Shepherd, & Utoktham, 2013).

The measure used most commonly to evaluate maritime connectivity is the UNCTAD Liner Shipping Connectivity Index.<sup>20</sup> The higher the index, the easier it is to access a high capacity and high frequency global maritime freight transport system, and thus effectively participate to international trade. The definition of connectivity, therefore, involves time–bound capacity.

For the Caribbean countries the liner shipping connectivity index shows that these countries remain very much disconnected from the shipping lines, despite their close location to mayor transshipment ports and major shipping routes (Figure 35). The connectivity is lowest in small island states, order of magnitudes below well-performing countries. This problem of lack of connectivity is common to small islands, though, since the Pacific Islands also have low levels. According to literature on the topic this has an impact on trading all along global value chains (Arvis & Shepherd, 2011).

**Figure 35: Connectivity comparison between small island states’ ports and other ports (2012)**

<!-- Start of picture text --> Liner Shipping Connectivty Index<br>Hong Kong<br>Egypt<br>Panama<br>Colombia<br>Bahamas<br>Mauritius<br>Dominican Republic<br>Jamaica<br>Fiji<br>Belize<br>Cuba<br>Haiti<br>Barbados<br>St. Lucia<br>Cape Verde<br>Suriname<br>Guyana<br>Grenada<br>St. Vincent and the Grenadines<br>St. Kitts and Nevis<br>Antigua and Barbuda<br>Dominica<br>0 20 40 60 80 100 120<br>Index (base 2004=100)<br><!-- End of picture text --> _Source: UNCTAD, 2012._ The largest ports in the region, such as Kingston, Freeport, Caucedo, and, to a smaller degree, Port of Spain, serve as the leading transshipment hubs for east-west and north-south traffic in the Caribbean basin, and are therefore served by frequent direct shipping services, including some of the major shipping lines. These major ports are serviced by shipping lines such as CMA-CGM and ZIM that use Kingston as their hub, and MSC operating through the hub of Freeport.

> 20 The index indicates a country's integration level into global liner shipping networks. The index base year is 2004, and the base value is on a country showing a maximum figure for 2004. It takes into account containership deployment, container carrying capacity, number of shipping companies, liner services, vessels per company, and average and maximum vessel size.

82
|Substitute for collected fuelwood|-0.395***||-0.354***||-0.614***||
|---|---|---|---|---|---|---|
||(0.072)||(0.080)||(0.176)||
|Constant|-1.454***|-1.408|-1.400***|-0.766|-1.487***|-3.020|
||(0.250)|(1.837)|(0.314)|(2.577)|(0.452)|(1.966)|
|Observations|11169|11169|7336|7336|3833|3833|
|Inverse Mills Ratio||1.041||0.693||1.972**|
|||(0.801)||(1.110)||(0.813)|

Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 49
Table 2: Agro-ecological and food production strategy

|Statistic|N|Mean|St. Dev.|
|---|---|---|---|
|herding|578,558|19.192|15.277|
|fshing|578,558|10.133|9.199|
|gathering|578,558|4.082|3.448|
|hunting|578,558|10.177|6.328|
|agriculture|578,558|59.110|14.565|
|husbandry|578,558|22.143|15.299|

Notes: This table reports the distribution of sample by historical mode of subsistence and food production strategy production. These modes include agriculture, herding, fishing, gathering, husbandry, and hunting.

29
# **<u>Table 1:  Indicators Used to Construct the Indices</u>**

|**Category**|**Indicator**|**In**<br>**I**|**dex for whic**<br>**ndicator is U**|**h the**<br>**sed**|**Year**|
|---|---|---|---|---|---|
|||**Youth**<br>**WI**|**Child-**<br>**Youth WI**|**General**<br>**WI**||
|Health|Infant mortality rate (0-1)||X|X|2002|
||Post-Neonatal mortality rate (28-365 days)||X|X|2002|
||Share of0-10 yearolds with AIDS||X|X|2002|
||Share of 11-17 year olds with AIDS|X|X|X|2002|
||Share of 18-24yearolds with AIDS|X|X|X|2002|
|Behavior|Pregnancy rate–live births (age 15-19)|X|X|X|2002|
||Suiciderate (age15-24)|X|X|X|2002|
||Homicide rate (age 15-24)|X|X|X|2002|
||Morbidity rate due to“external causes”(females age 15-19)|X|X|X|2002|
||Share of 12-17 yearolds whohave used alcohol|X|X|X|2001|
||Share of 12-17 year olds who have used tobacco|X|X|X|2001|
||Share of 12-17 year olds who have used marijuana|X|X|X|2001|
||<br>Share of 12-17 yearolds whohave used cocaine|X|X|X|2001|
|School|Share of 4<sup>th</sup>graders who complete the grade level<br>||X|X|2002|
|Performance|Share of 8<sup>th</sup>graders who complete the grade level|X|X|X|2002|
||Share of 11<sup>th</sup> graders (_ensino medio_) who complete the gradelevel|X|X|X|2002|
||Literacy rate (15-24)|X|X|X|2002|
||Score on Portuguese language test, 8<sup>th</sup>graders<br>|X|X|X|2001|
||Score on mathtest, 8<sup>th</sup> graders<br>|X|X|X|2001|
||Score on Portuguese language test, 11<sup>th</sup>graders|X|X|X|2001|
||<br>Score on math test, 11<sup>th</sup>graders|X|X|X|2001|
||Average years of education of the 14 year old population|X|X|X|2002|
|Institutional|<br>Schoolattendancerates of0-3 yearolds||X|X|2002|
|Connectedness|School attendance rates of 4-6 year olds||X|X|2002|
||<br>Schoolattendancerates of7-14yearolds|X|X|X|2002|

15
We also look at the relationship between firms’ worker turnover and the view of their managers about the reasons for avoiding wage cuts. Firms were asked to report the percentage of employees joining and leaving the firm during the last year. Using this information and the total number of employees reported by the firm we calculate worker turnover as the sum of the workers joining and leaving the firm during the reference year as a percentage of total employment.<sup>18</sup> The results for the effect of employee turnover on the reported answers are presented in Panel C of Table 7. Firms featuring higher turnover rates show more support to practically all the reasons for avoiding wage cuts. The estimated effects are of particular importance with the fear of best employees leaving the firm, reputational hazards, and the difficulty of hiring employees in the future. Hence firms operating in more unstable environments appear to be more conscious of the negative consequences of cutting wages on maintaining a high quality workforce.

Our results are based on data collected prior to the economic downturn experienced by European countries in recent years. However, research using data covering periods of recessions also shows that wages are very rarely cut (Agell and Lundborg, 2003). Messina and Rõõm (2012) use data from a survey that covers the recent downturn for a sub-sample of the firms surveyed here and show that wage cuts remained rare. They find that broadly the same ranking of theoretical reasons for wage rigidity continued to hold, which suggests that the managers’ views of the reasons for avoiding wage cuts are not strongly affected by the business cycle.

> 18For the entire sample, we find an average turnover rate of 33%.  The variation across countries covers a range from 25% in the Netherlands to 42% in the Czech Republic, with overall higher average worker turnover in the CEE countries (36%) than in the EU-15 (31%). The correlation between labour turnover across firms and percentage of temporary workers is 0.27.

29
# **VI. Conclusions**

In this paper we have used a unique set of surveys measuring cognitive and noncognitive skills in low- and middle-income countries to examine differences in earnings between men and women. Focusing on seven low- and middle-income countries (Armenia, Bolivia, Colombia, Georgia, Kenya, Ukraine, and Vietnam), our objective was to determine if these newly available skills measures could explain the gender wage gap beyond what is explained by traditional measures of schooling and experience. While gender wage discrimination has been extensively studied in various contexts, our use of measures capturing different dimensions of human capital extends what we know about the gender wage gap in developing contexts and contributes to our understanding of the role of cognitive and noncognitive skills in research related to labor market outcomes. The findings from this paper are summarized next.

Mean decompositions with and without occupation controls can be indicative of the role of sorting in earnings differences between men and women. Controlling for occupation, we find that in Vietnam, openness to experience and emotional stability are important factors in explaining gender wage gaps. Our decomposition results indicate that men receive a reward for scoring higher, on average, on openness and emotional stability traits. The sorting argument would suggest that men sort into occupations that require the appearance of more openness and emotional stability. Controlling for occupation, however, we can infer that sorting perhaps does not explain the entire difference between men’s and women’s returns to noncognitive skills in Vietnam. Mueller and Plug (2006) found similar results for men with lower agreeableness scores than women in the United States, and Nyhus and Pons (2005) found that emotional stability scores positively affect wage setting for women. Our mean decompositions also show some evidence for the influence of cognitive skills in Georgia and Kenya.

Findings from the quantile decompositions indicate that the wage gap follows a “sticky floor” pattern in the Latin American countries as well as Kenya and Vietnam but follows a “glass ceiling” effect in the ECA countries. Previous research has also found evidence for the glass ceiling effect in transitional economies, and this pattern has been observed in Vietnam (Pham and Reilly, 2007). Further, our results indicate that schooling matters, across countries, in explaining differences in earnings between men and women. In some countries, schooling explains a larger share of the wage gap at the top end of the wage distribution (Bolivia and Vietnam) while in others education and skills are found to matter more at the lower end of the wage distribution (Kenya and Armenia).

30
# Table 7: Aggregate Net Resource Flows to the CIS Members,<sup>1992-94</sup> (million US dollars)

||Total CIS|Russia|Ukraine,<br>Belarus, and<br>Moldova|Transcaucasus|Central<br>Asia|
|---|---|---|---|---|---|
|Official development finance|16,871.0|12,146.3|2,360.4|590.9|1,773.4|
|Official development assistance|11,252.0|9,214.8|1,468.9|192.8|375.5|
|Official grants|10,125.8|8,700.0|1,187.1|154.9|83.8|
|Official concessionary loans (net)|1,126.2|514.8|281.8|37.9|291.7|
|Bilateral|944.4|454.0|252.1|5.1|233.2|
|Multilateral|181.8|60.8|29.7|32.8|58.5|
|Official non-concessionary loans (net)|5,619.0|2,931.5|891.5|398.1|1,397.9|
|Bilateral|3,389.3|1,810.9|368.9|179.8|1,029.7|
|Multilateral|2,229.7|1,120.6|522.6|218.3|368.2|
|Private flows|18,609.6|14,186.3|2,647.4|34.3|1,741.6|
|Private loans (net)|13,387.3|11,584.0|1,207.4|34.3|561.6|
|Foreign direct investment|5,020.0|2,400.0|1,440.0|0.0|1,180.0|
|Portfolio equity investment|202.3|202.3|0.0|0.0|0.0|
|Memorandum item:||||||
|Net use of IMF credit|5,096.3|4,062.7|613.7|63.9|356.0|
|Interstate known financing|10,547.0|..|6,969.8|947.4|2,629.9|

Sources: World Bank debtor reporting system and Table 6
factor leading to the increase in the wage bill is the 2 percent relativity imposed on the single spine pay distribution.

What factors drive the estimated costs of the public sector wage bill? Table 3 provides estimates of the effect of changes in key variable on the overall wage bill. The results in Table 3 compare the base case with four key changes: (i) an increase in the number of steps in each grade on the spine (25 additional steps altogether); (ii) changes in the difference between grade on the single pay spine – either a 0.02 percent decrease or a 0.02 percent increase (iii) an increase in the minimum wage; and (iv) an across‐ the‐board increase in the number of staff. The results indicate that the changes in the differences between grades (‘the relativity’) have the greatest impact on the wage bill, followed by changes in the minimum wages and increases in the number of staff. For instance, an increase in the difference between the grades on the single pay spine to 2.2 percent would lead to a wage increase of just under one‐third when compared to the base case estimate of GHC2.8 billion. Meanwhile, an increase in the number of steps on the single pay spine would have a relatively small effect.

**Table 3: Changes in key parameters for the simulations ‐ sensitivity results**

||Scenario 1|Scenario 2|Scenario 3|Scenario 4|Scenario 5|Scenario 6|
|---|---|---|---|---|---|---|
||Base case|Base case<br>plus one<br>additional<br>level|Base case +<br>reduce<br>relativity to<br>1.8%|Base case +<br>increase<br>relativity to<br>2.2|Base case<br>+GHC1,100<br>minimum<br>wage|Base case<br>+10%<br>increase in<br>staff|
|Wage Bill (GHC<br>million)|2.8|2.9|2.5|3.7|3.0|3.0|
|Change (GHC Million)||0.1|‐0.3|0.9|0.2|0.2|
|Change (%)||3.6%|‐10.7%|32.1%|7.1%|7.1%|

_Source_ : Author’s calculations.

How are the benefits of the single spine pay reform distributed? The resulting estimation provide an approximation of the expected changes in average remuneration for each category due to either the introduction of the single spine pay reform or to changes in remuneration for each grade level (Figures 4 and 5). Figure 4 shows that certain categories within the health sector (especially medical staff assigned to the Ghana Armed Forces) we see their salaries increase at a slower pace with the implementation of the single spine pay reform.<sup>10</sup> Meanwhile, all the other categories are expected to see a faster increase in their salaries, although the exact distribution of gains among the different grades is far from uniform. Most of the gains are concentrated between grades five and twenty‐four, with more pronounced increases for grades twelve to nineteen. Other grades, especially the lowest grades, will see a much slower rise in their salaries as a result of these reforms.

‐12‐
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The second step determines the share of health expenditure attributable to pollution. For this step data on the disability adjusted life years (DALY),<sup>17</sup> for each disease “d” associated with air pollution (chronic obstructive pulmonary disease, lower respiratory infections, stroke, diabetes mellitus type 2, ischemic heart disease, tracheal, bronchus, and lung cancer), is divided by total disability adjusted life years in each year and then multiplied by the proportion of the population with disease d. � dd,cc,2017 DDAAWWYYss cc,2017 In the model nominal spending on health care is assumed to rise linearly in line with GDP and the dd TTCCS llDDAAWWYYss In the model nominal spending on health care is assumed to rise linearly in line with GDP and the increase in the concentration of 2.5mm particles in excess of the previously discussed threshold level.

## Thus, health expenditure due to pollution in year t is given by

The 2017 HEAP value is calculated as <u>, ,2017</u> . ∗� II EE DDAAWWYYss PPVVKK,2017 PPVVKK,2017 PPVVKK,2017 II,2017 DDXXAAPP = GGDDPP ∗TTDDXX EE,2017 ∗PPAAFF are disability adjusted life years, for disease d, country c, year t and II TTCCS llDDAAWWYYss = . (population attributable fraction) represents the share of all people suffering II,EE,tt EE,2017 DDAAWWYYss TTCCS llDDAAWWYYss from the disease in year , that contracted it because of pollution.<sup>18</sup> Data for the PAF are sourced dd II,EE,2017 II,tt ∑DDAAWWYYss PPAAFF from the CPAT model (World Bank 2020b). dd SS

# 3.8.3.2 Mapping working days lost to economic outcomes

Pollution impacts the economy on the one hand by reducing the labor force due to pollution-related deaths, and reducing total labor supply (measured in hours worked) by the hours lost by those workers who suffer from pollution related disease but do not die.

> 17 The disability-adjusted life year, , is a summary measure which combines time lost through premature death and time lived in states of less than optimal health or “disability”. DALY, for a specific cause , sex , age and year is defined as: DDAAWWYY cc ss SS ,SS EE,ff,bb,tt EE,ff,bb,tt EE,ff,bb,tt where YLL are years of life lost and YLD are years lived with disease. The values for DDAAWWYY = YYW + YYWWDD , and are obtained from the GBD results tool (IHME 2018). DDAAWWYYss YYW YYWWDD

> 18 The population attributable fraction ( ), is the reduction in incidence that would be observed if the population were entirely unexposed, compared with its current exposure pattern. for each mortality type and age range is calculated as PPAAFF PPAAFF = ∑ ∑CCii=1 ii,bb∗�R∗cc <u>�R</u> EE,bb<sup>−1</sup> + 1<sup><u>�</u></sup> ~~,~~ SS EE,bb CC PP PPAAFF ii=1 ii,bb EE,bb<sup>−1�</sup> where R PP EE,bb<sup>is the relative risk for causeccand age groupSSandPP</sup> ii<sup>is the percentage of the population exposed to level of pollutionii.</sup>

45