rafmacalaba/data-use-mentions-extended huggingface dataset:
of this path. An example is Mali, where poverty increased by more than 25 percent from 1989 to 2006 instead of falling. The segment BC measures its gap to become on target at the point of latest data.

Within the off-target group, countries are further classified in relation to the group’s average distance to be on target.<sup>7</sup> Mean gaps are convenient cut-off points, dividing the lagging countries into two subgroups: off target and above average; and off target and below average. We argue and show that lagging countries in the top half, off target and above average, are indeed ―close to the target,‖ whereas lagging countries in the bottom half, off target and below average, are therefore ―far from the target.‖ The computed mean gaps are generally more conservative or stringent than the cut-off points used in Leo and Barmeier (2010), which defines lagging countries as close to target if their trajectory is within 50 percent of the required progress to reach the goals, earning half a full score. In our methodology, we do not use an arbitrary cutoff point of 50 percent. Moreover, the mean gaps are all less than 50 percent across the MDGs; and they provide data-specific cutoff points to split the off-target countries. In addition to the classification of countries according to progress, the actual gaps are also retained to measure the mean gaps of each group, and to identify countries that are within 10 percent of becoming on target.

The three groups – countries on target, close to the target, and far from the target, roughly divides the developing countries into three thirds.

Detailed historical data on MDG performance are required to calculate the achievement path for each country to meet each of the MDGs. Unfortunately, such data are not available in many countries for 1990, although estimates for recent years tend to be more complete. If no country data are available for 1990, we used the closest available information in the late 1980s or early 1990s
Given that the CAR is a fragile state trying to catch up with other low-income countries (LICs) in the region, both fragile-state and LIC African benchmarks will be used to evaluate the CAR’s situation. Detailed comparisons will also be made with immediate regional neighbors in Central Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2009. Most technical data presented are for 2008 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, indicators were standardized so that analyses were done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Between 2002 and 2009 the CAR’s GDP grew at a rate of 2.2 percent per year. The effect of the global economic crisis was notable: the economy grew 3.7 percent in 2007, 2.2 percent in 2008, and 1.7 2
where contractual obligations are respected by both the state and private agents, where legal disputes are effectively resolved in the judicial system, and where legal rules for investment, employment, and taxation are not arbitrarily modified by the state.

The assessment of progress in these aspects of governance differs somewhat among risk evaluators. Whereas BERI drawsa picture of limited progress except for a handful of countries -- Argentina, Chile, Ecuador and Peru --, ICRG provides an optimistic appraisal of progress throughout the region. Although both suggest that there has been general improvement, a closer examination of the underlying indicators of the ICRG governance index reveals that by 1995 Latin America’s standards of governance remained well below that of the Asian NICs and the OECD countries. This gap is particularly large in the area of the rule of law (judicial and police systems) and in the quality of the public administration (technical competence and honesty of the bureaucracy, as well as procedural clarity). The difference between Latin America and the Asian NICs and OECD countries is smaller regarding respect of property rights, including the risk of nationalization of enterprise equity.

(5) | The area where the least progress has been made is labor market reform. Except for Chile and the English-speaking Caribbean countries, most countries in the region had rather rigid and distorted labor markets in the 1980s and early 1990s; excluding a few notable cases of labor market reform, the situation in the region by 1995 remained basically the same. The main factors

.

* The VAT revenue productivity ratio is defined as VAT revenue over GDP divided by average VAT rate.

> 100 percent would imply a VAT without exceptions and full enforcement.

iv
coverage rate of 48.7 percent. In contrast, the coverage rate of the benchmarked estimates from the household model is 68 percent, which is moderately below the coverage rate for all of Tanzania using the HBS sample reported in Table 8 (74.8%). The coverage rate of the household model, despite being much more precisely estimated, is only modestly below the HorvitzThompson direct estimates. This is because the household model estimates are more accurate, as seen in the higher correlation and lower root mean squared error.

To sum up, when using repeated samples in three poor Tanzanian regions, incorporating remote sensing data in a household-level model dramatically improves precision compared to the Horvitz-Thompson direct estimates. Adding the remote sensing data reduces mean squared error by about 75 percent and CV by about half, which is more or less comparable to quadrupling the sample size. Because the small area estimates are much more accurate than the direct estimates, the increased precision comes at a modest cost in terms of coverage rate. Compared with the standard direct estimates assuming independence across enumeration areas, the small area estimates are not only slightly more precise, but also significantly boost accuracy and coverage. Overall, the main results derived for the full country based on budget survey the specific sample drawn for the budget survey are maintained when focusing on three Tanzanian regions and generating estimates of the area poverty rates and their mean squared errors using 250 different samples.

> 42 As shown in the bottom of Table 17, the kurtosis of the residuals is 2.5 for the area effect and 3.7 for the household errors.

39
**_Figure 4. Annual TFP growth rate for developing countries, average weighted by real GDP by region and decade_**

<!-- Start of picture text --> 3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04<br>-94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14<br>East Asia Europe Latin America Middle East South Sub-Saharan<br>and and and and Asia Africa<br>Pacific Central Asia Caribbean North Africa<br>Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data.<br>Note: The former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s.<br>4.2 Main determinant indexes<br>Figure 5 shows the median of the subcomponent indexes representing the main categories of TFP determinants, as<br>well as the median of the overall index, for all, 21 OECD, and 115 developing countries by decade. All the<br>median indexes are lower for the developing countries as compared to the OECD countries. A noticeable<br>difference is that the innovation index stays at the lowest level for the developing countries, whereas it increases<br>in the OECD group over time. For both groups, the subcomponent indexes of education, market efficiency, and<br>infrastructure increase over decades, whereas that of institutions stays at the same level.<br>Figure 5. Median of subcomponent and overall determinant indexes for all, OECD, and developing countries by decade<br>World<br>85 95 05 85 95 05 85 95 05
incorporating both year and location-specific fixed effects and conclude that fractional assessment is associated with political factors, such as higher levels of Republican legislative representation and divided government, suggesting that the fractional assessment of local property taxation may be the path of least resistance for enacting effective property tax relief.

Norregaard (2013) discusses policy and administrative issues to be considered for successful reform. Based on panel data for 64 countries between 1990−2010, he uses GDP per capita, urbanization, openness of the economy, and a dummy if the country's legal heritage (is of Anglo−Saxon origin) to estimate recurrent immovable property revenue. He also estimates a Fixed Effects model to account for the influence the variations of income per capita have on property tax collection over time. He finds that a country's development over time has positive exponential effects over time.

# **III. Data**

5
history of the multilateral trading system by focusing on major trade policy developments taking place over the period spanning 1947-1994, covering the full GATT era.

We begin with the level of tariffs for the major economies at the outset of the GATT negotiations in 1947 that established the new multilateral trading system.<sup>65</sup> After reporting on the subsequent evolution of applied tariffs, we introduce the major exceptions to the GATT rule that countries should limit the form of their import protection to their applied MFN tariffs and we assess recently released data from the GATT Archives on their use. One key observation is that protectionist forces have been pushing back against trade liberalization since the inception of the multilateral system. Amidst a general decline in tariffs, new trade restrictions emerged. Thus still open questions for researchers to pursue include quantifying the importance of this push-back and further clarifying how it relates to the policy substitution that we documented in Section 3 as having arisen today.

The GATT members introduced a number of contingency clauses regarding the use of trade restrictions when they drafted the agreement because they understood that changing economic

> 65The historical context matters, as the GATT system arose and was shaped by a number of major geo-political events. These include the catastrophic economic policies of the 1930s Great Depression era, the devastation of Western Europe and Japan during World War II, and the rise of the Cold War between the United States and Soviet Union. See, for example, Irwin, Mavroidis and Sykes (2008) for the negotiating origins of the GATT.

37
|

Notes: Column (1) uses predicted income equations to measure the situation excluding remittances for all 7,276 households. Column (2) measures the situation for all households when only internal remittances (from Guatemala) are included in predicted household expenditure. Column (3) measures the situation for all households when only international remittances (from USA) are included in predicted household expenditure. Column (4) measures the situation for all households when both internal and international remittances are included in predicted household expenditure. Poverty calculations made using 2000 World Bank poverty line of 4,319 quetzals/ person/ year. For predicted income equation, see equation (2) and text. All values are weighted.

In 2000, 1 Guatemalan quetzal = US $0.128.

Source: Calculated from Guatemala 2000 ENCOVI Survey
2007**|**2008**|**2018**|
|North|North|88.9%|87.4%|88.9%|78.3%|
|North|South|6.6%|6.5%|5.5%|11.5%|
|South|North|3.4%|4.5%|4.2%|6.9%|
|South|South|1.2%|1.5%|1.3%|3.4%|

Note: This table shows, for each block, the year-end value of international portfolio investment positions distinguishing between equity and debt. Panel A shows the value of international investment positions scaled by world gross domestic product (GDP). For each investment type and year, world GDP includes the GDP of destination countries for which we have information for that year (all source countries with information for a given year also have information as destination countries). Within each investment type and year, world GDP is the same across blocks. Panel B shows the share of international investment positions. For each investment type, the sum of North-to-North, North-to-South, South-to-North, and South-to-South shares in a given year is 100 percent. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
than their male employed peers ( **Figure 3** ). Similarly, female students aged 19-25 also demonstrate higher career aspirations than their male peers in Malawi ( **Figure 4** ).

**Figure 3. Share of youths aged 15-18 who would like to work in STEM-related jobs, by sex and current activity.**

**Figure 4.  Share of youths aged 19-25 who would like to work in STEM-related jobs, by sex and current activity.**

<!-- Start of picture text --> 100%<br>100% Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>90%<br>90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 37% 40%<br>30% 26% 30%<br>20% 18% 16% 15% 11% 20% 21% 15%<br>11% 11% 10%<br>10% 8%<br>10%<br>0%<br>Male Female Male Female Male Female 0%<br>Male Female Male Female Male Female<br>NEETs Currently at school Working<br>NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024 11
and understanding, or a preference not to participate?

32. Parents appear seriously under-informed about accountability structures in their children‟s schools, and in many places these structures are not operating as they should. The findings summarized previously, highlight both a departure in _de facto_ accountability structures from how the MoES intends them to function _de jure_ , as well as a severe lack of information among parents about these structures. Contrary to MoES directives, in about 30 percent of schools, the school director participated and voted in school board meetings. In 81 percent of the schools, parent class representatives were elected by parents as intended, rather than appointed by teachers; and in only 43 percent of the schools were teacher school board members elected by teachers rather than appointed by the school directors. Only half of parents report participating in electing parent class representatives, and about 70 percent of those who did not participate were unaware either of the existence of parent class representatives or of the fact that they should be elected by parents. Moreover the survey identified a worrying proportion of parents who were unaware of the existence of the school board (43%), including a large proportion of elected parent class representatives (23%) and parents who had voted for parent class representatives (31%). This indicates that information problems are severe, even among parents who are participating in school governance in some way.

33. But if they had more information, would parents participate? The survey findings about the functioning of participatory accountability structures and parents‟ information, begs the question: _If parents were informed, would they participate in holding schools and teachers to account?_ Part III presents the conclusions of an analytical attempt to directly measure parents‟ preferences for participation through specially designed lab-type experiments (LTEs) that were conducted with a random sample of teachers and parents who responded to the school stakeholder survey.

34. Experimental techniques allow a controlled simulation of the parent-teacher accountability relationship. Lab-type experiments (LTEs) facilitate the measurement of individuals‟ values,
by students near the minimum proficiency threshold and those driven by students at the very bottom of the learning distribution. Students who are further away from the minimum proficiency threshold likely have different learning needs than those closer to the threshold.

**Learning Poverty Severity** indicates inequality of learning among those below the threshold. It brings together the concepts of Learning Deprivation Severity and Schooling Deprivation Severity. Learning Deprivation Severity measures learning inequality among learning deprived children in school. Compared to the gap measure, the severity measure is more sensitive to changes in learning levels of learning deprived children who are further away from the minimum proficiency threshold and changes in children who are out of school.

For example, as shown in Figure 3, Vietnam and Myanmar are expected to have similar increases in the Learning Poverty Gap under an intermediate scenario, but the two countries have very different expected changes in Learning Poverty Severity. This implies that both countries may need different levels of policy focus on identifying the diverse learning needs among children below minimum proficiency and on providing flexible and tailored learning opportunities. As school systems reopen, it will be critical to meet students at their point of need and to monitor changes in the learning distribution among the learning poor; for that, Learning Poverty Severity is the appropriate measure.

**Figure 3. Relative Changes in Learning Poverty Gap and Learning Poverty Severity**

<!-- Start of picture text --> 400%<br>350%<br>300%<br>250%<br>HIC<br>200%<br>UMC<br>150% LMC<br>LIC<br>100%<br>VNM<br>MMR<br>50%<br>0%<br>0% 100% 200% 300% 400% 500% 600% 700%<br>Change in Learning Poverty Severity (%)<br>Change in Learning Poverty Gap (%)<br><!-- End of picture
and WEO (Constant 2005 prices)<br>80 PriceStats Congress CPI Official CPI 45000<br>CPI - INDEC CPI - PriceStats GDP deflator<br>70 40000<br>60 35000<br>50 30000<br>40 25000<br>30 20000<br>20 15000<br>10 10000<br>0 5000<br>-10 0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> inflation has been systematically lower for CPI than PPI. Over the period 2008-15, CPI inflation was on average 3.3% lower than PPI inflation.

25 Elypsis, a private consulting firm, estimates its coincident index of economic activity (ICAE) grew by an accumulated 21% between 2007 and 2013, compared to 36% according to official figures for the monthly economic activity index (EMAE). Prior to 2007, the accumulated discrepancy between the ICAE and EMAE was merely 2%.

> 26 Including China in the productivity differential results in an insignificant coefficient.

27 The agricultural sector consists of Agriculture, hunting, forestry and fishing, and Mining and quarrying. The other tradables sector consists of: Manufacturing. The nontradables sector consists of: Electricity, gas and water supply, Construction, Wholesale and retail trade, hotels and restaurants, Transport, storage, and communication, Finance, insurance, real estate and business services, Government services, and Community, social and personal services. Results are robust to alternative classifications of nontradables that, for example, exclude: (i) Electricity, gas and water supply, and (ii) also Transport, storage, and communication. Data for Germany was obtained from the OECD. Missing data for years 2011-2014 were obtained from the OECD, the World
Low-skilled labor migration from South Asia is temporary by design, as permanent stay in the main destinations of low-skilled migrants from the region is prohibited. The main host countries of migrants from South Asia in the GCC and Southeast Asia only grant temporary residence rights to low-skilled migrants. In the GCC countries, the acquisition of citizenship is effectively prohibited, irrespective of their duration of stay in the destination countries (Wahba 2015; Fargues 2011; Fargues and De Bel-Air 2015).<sup>3</sup> In addition, staying in the main destinations is strictly conditional on holding a valid employment contract, which is for a fixed time duration. Temporary migrants however have the possibility to renew their labor contract and work permit at destination, if there is demand for further work by the employer at destination (Bossavie, Gorlach, et al. 2021). An expiration of the employment contract without renewal, or a layoff by the employer automatically entails returning to the home country. In this institutional setting, a job loss or the expiration of the employment contract at destination is the main reason for returning.<sup>4</sup> In Bangladesh, Nepal and Pakistan, the average duration of stay overseas of return migrants estimated from surveys is 6.5, 2 and 1.8 years, respectively.

# **3 Data**

## **3.1 Household Survey Data**

Suitable microdata to carry out a detailed analysis of temporary and return migration is rare globally (Bossavie and Ozden 2022), including for South Asia. Administrative data at origin in Bangladesh, Nepal and Pakistan only records temporary migrants once they exit the origin country, but not when they return. The only microdata for South Asian countries that allow to identify return migrants are national household surveys. In those surveys, return migrants are captured at the time they have returned home after completing their most recent migration spell overseas. Such surveys in origin countries are suitable to carry out nationally-representative comparisons of the characteristics and labor market
the private sector ran a significant surplus—private saving plus transfers exceeded private investment—as seen in Tables 1 and 4b of IMF (2020).

The public sector DSA in IMF (2020) uses the 2017 LIC DSF to update the assessment in the 2019 Article <u>IV report done a few months earlier. It concludes that the risk of external and overall debt distress is</u> high; but public debt is “deemed sustainable”, albeit with higher downside risks on account of the thennascent COVID-19 pandemic. The high risk of debt distress is mainly on account of external sector vulnerabilities related to “an overvalued exchange rate and a small export base”.

Pre-COVID, Ethiopia was among the fastest growing economies in the world, with growth driven by large debt-financed large infrastructure projects such as Addis light rail, the Ethiopia-Djibouti railway line and ambitious power projects.<sup>11</sup> It was recognized that public sector-led growth would pave the way for private sector growth. For this to happen, financial repression (that has enabled the public sector to issue local currency debt at highly negative real interest rates) and the parallel market for foreign exchange (leading to a market rate more depreciated than the official rate) would have to be eliminated by adopting market-determined interest and exchange rates.

Table 5 outlines Ethiopia’s public debt dynamics for the three years 2017 to 2019 using data from IMF (2020) prior to the onset of COVID-19 and the 2020 civil war. The DSA’s conclusion that public debt is sustainable is borne out at first glance by the numbers in Table 5. But a useful counterfactual would be to ask what impact a move to market interest rates and exchange rates would have on debt levels and dynamics. Take 2019 as an example. The public debt-to-GDP ratio d was 56.9%. But r was -5.0% while g

> 11 NB: The analysis here is based on data before Ethiopia’s civil war which began in November 2020