rafmacalaba/data-use-mentions-extended huggingface dataset:
30|2.65|3.20|3.20|3.40|3.15|

Note: The Index of Policy Z for CEE countries is based on the original IEF Index while the EU Average is based on the Modified IEF Index.

# Difference in Growth Paths

The goal in this sub-section is to identify how much higher is the growth rate of GDP per capita of CEE countries vis-a-vis that of the EU average country and, in particular, the behavior of the difference in growth rates under alternative scenarios. These "differential" growth paths are calculated solving iteratively equation [xv] in Appendix 4. As before, we assume in these exercises that the EU average country maintains the current EU Average growth determinants 19 (see Table 6)

**19** Our analysis is based on differences in growth rates of per capita GDP. To obtain the growth rate of the economy we must add the growth rate of the labor force (about half a percentage point a year) and the EU growth rate of GDP per capita (say, for example, 1.5 percentage points a year). The EU growth rate of GDP per capita can be viewed as reflecting the exogenously given technological progress and the growth that results from the current growth determinants of the EU average country. Given our current coefficient estimates, the latter explains growth rates in the EU average country of about one percentage point.
correction model (ECM) method, which estimates not only the long-run relationship between the cointegrated variables, but also the potential short-run deviations from this long-run relationship.

We use the pooled mean group (PMG) estimation method, introduced for panel estimates by Pesaran et al. (1999). It, too, is based on this principle of short-run deviations from the longrun trend. This method can be used to estimate the long-run relationship between the credit-toGDP ratio and other variables, which is identical for all countries, whereas the short-run deviations from this relationship can differ across countries. The PMG model, therefore, allows heterogeneity of the estimates for individual countries in the short run. However, the long-run relationship of the cointegrated variables is common to all the countries in the sample.

The data used for the OOS method were obtained from the International Monetary Fund’s International Financial Statistics (IFS) database, which provides the required macroeconomic data with a sufficient history (which is vital for estimating long-run relationships). For this reason, we used data for a 30-year period (1980–2010). The available statistics on bank loans to the private sector were used as the credit indicator. These statistics slightly underestimate the total credit of the private sector, as they do not include nonbank financial intermediaries (for example, leasing) and cross-border loans. Data on aggregate household consumption, government debt, short-term interest rates, unemployment, inflation measured by the GDP deflator, and GDP per capita in dollar terms were also used.

A long-run cointegration relationship between the credit-to-GDP ratio, the household consumption-to-GDP ratio, and GDP per capita in US dollars was identified for the OOS set of countries. The GDP per capita variable in the long-run relationship captures the different degree of wealth of the economy, which therefore also influences the equilibrium private credit level (Terrones and Mendoza 2004).

The following equation gives estimates of the coefficients of the long-run relationship between the co-integrated variables and the values of the coefficients in the short run, which are given as
evidence of a correlation between individuals‟ behavior in the public goods experiment and their willingness to cooperate with others in participatory accountability systems and civic engagement.

The paper is arranged as follows. Section 2 describes the Albanian context focusing, in particular, on the education sector. Section 3 describes the school survey and the public goods experiment. Section 4 presents descriptive statistics about the surveyed parents and explores individual and community characteristics affecting parents‟ cooperativeness in the public goods game. Section 5 reports our empirical results, and Section 6 provides some conclusions.

# **2. The Albanian context**

# **2.1 A new democracy**

Albania was the last country in Europe to participate in the “third wave of democracy” (Huntington, 1991). Despite the high levels of economic growth experienced during the democratic transition, Albania remains one of the poorest in Europe, with a per capita GDP of 8,000 USD (2010 international PPP $). Albania‟s relative underdevelopment has been attributed, in part, to the slowness of its transition to democracy relative to other Eastern European countries. The new constitution, which established that the “sovereignty in the Republic of Albania belongs to the people” (article 2) and that “governance is based on a system of elections that are free, equal, general and periodic” (article 1), was adopted in 1998. Since then, there have been three parliamentary elections – not without controversies concerning electoral fraud, protests and boycotts by the losing party. In each, voter turnout has been around 50%.

Albania‟s slow transition to democracy has been attributed to the unique characteristics of its communist regime: its isolation from other countries, including the Soviet bloc, for half a century; the elimination of intellectuals (including western-educated Albanians); the abolition of religious practices; and the harsh persecution of opponents to the regime. With the end of communism, a new era began. According to Elbasani (2004), the recently
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

within the margin of error to that found by Deville et al (2014) for France and Portugal, 0.77 ± 0.055, suggesting a stable relationship across countries. In addition, the R^2 value suggests that 85 percent of the variation in density in the World Pop data is explained by variation in the CDR data. The R^2 is lower for the census, given that the data is older. These results confirm that CDR data are valid for examining the population distribution in terms of their residential locations. By extension, shifts in CDR data can capture both short term and long-term shifts in population over time, with implications for disaster risk management and urban planning.

<mark>Table 2.</mark> Correspondence between known population data and CDR data

||(1)<br>Log (WorldPop density)|(2)<br>Log (Census density)|
|---|---|---|
|Log (Subscriber density)|0.675***|0.596***|
||(0.066)|(0.12)|
|Constant|2.98***|4.76***|
||(0.619)|(1.339)|
|Regional fixed effects|Yes|Yes|
|R^2|0.846|0.640|
|N of observations|45|45|

a Standard errors are reported in parentheses. *, **, and *** indicate significance at the 90%, 95%, and 99% level, respectively.

# **B. Patterns of phone usage remained near-constant in terms of the number of active subscribers**

Overall cell-phone use remains stable over the period of observation. We use the number of active subscribers computed for Indicator 3 to
perceived to be biased in favor of the rich and politically connected. These facts motivate the need for judicial reform. With the benefit of a quarter century of additional empirical research from the time that several major surveys of judicial reform were written, we find that robust evidence on the precise ways to increase judicial effectiveness is starting to emerge. Some of the commonlyimplemented reforms of the courts – for example, the increase in judges’ pay or the introduction of ADR mechanisms – are found to be tangential to subsequent court effectiveness. However, an accessible, swift and unbiased legal system constrains the executive by limiting expropriation and the misuse of public office for private gain. Judicial reforms disproportionately benefit the powerless and discriminated groups of society, a welcome finding.

The empirical evidence over the past quarter century lends support to the earlier findings in Posner (1998), Messick (1999) and Botero et al (2003) published in the WBRO. Transformative judicial reform is most likely to succeed in countries with extraordinary politics: these emerging from conflict and violence or in countries that are pursuing accession to either regional (for example EU) or international (for example OECD or WTO) groupings. Absent such conditions, reformers are better off focusing on the adoption of procedural rules that raise the effectiveness of the existing judicial system. A number of such promising procedural reforms are documented by case study evidence.

Several court-related reforms commonly practiced in the hope for improved effectiveness – for example, increased judicial independence, better judges’ pay, more courts and larger budgets – can benefit from further support in the empirical literature. In some cases, such reform brings about unexpected results, indicating that one must tread with significant caution when proposing institutional change or incentive structure change in the judiciary.

Access to justice and faster resolution times are associated with increased judicial effectiveness, highlighting the positive effect of reform. These links are often causal, further extending the opportunity to argue that court reform can be part of economic development strategies.

26
IPA’s CRM software is ‘mostly’ or ‘fully’ used by its staff|0.900<br>[0.100]|0.688<br>[0.120]|0.212|
|IPA has a system for gathering investor complaints or disputes|0.800<br>[0.133]|0.438<br>[0.128]|0.362*|
|IPA has standard operating procedures for investor queries, aftercare.|0.700<br>[0.153]|0.688<br>[0.120]|0.013|
|IPA has shared information system cataloging general and sectoral||||
|data and information for use by investors|0.600<br>[0.163]|0.688<br>[0.120]|-0.087|
|**IPA has electronic database with updated contact information**||||
|Database containing current foreign investors|0.900<br>[0.100]|0.938<br>[0.063]|-0.037|
|Database containing potential foreign investors|0.800<br>[0.133]|0.688<br>[0.120]|0.113|
|Database containing domestic supplier firms|0.400<br>[0.163]|0.500<br>[0.129]|-0.100|
|Database containing available local joint venture partners|0.200|0.188|0.013|
||[0.133]|[0.101]||

Note: The value displayed for t-tests are the differences in the means across the groups. High-performing IPAs are identified based on the sectoral gravity model with country interaction effects (see section 4.1). IPA = Investment Promotion Agency
6 In sum, useful indicators of an education system that contributed to sustained growth include coordination of the level and quality of education with the economy’s stage in the industrialization and modernization process; paying attention to the types of subjects that students are learning and the quality of their education – both at entry and at upper levels; paying attention to skills gaps as reflected by labor market demand for skills; and finally, ensuring equal access by different income and population groups.

In the next section we use these indicators to benchmark the systems in Malaysia and Thailand against that of South Korea.

# **The Cases of Malaysia and Thailand: Where Do They Stand?**

Malaysia and Thailand have accomplished much in the past 30 years in moving from lower- to upper-middle-income status. But there are also some worrying economic signs that their trajectories are not following the steady rise seen in the East Asian Tigers like South Korea. In light of the preceding analysis we now examine whether the systems in these two countries are producing the ‘right’ skills using the indicators that we have defined.

We rely on such indicators as enrollment rates and years of schooling to measure the quantity of education and international assessments to measure quality. These assessments include the Programme for International Assessment (PISA); Trends in International Mathematics and Science Study (TIMSS); and the cognitive skills and the quality of education perceived by employers from surveys by the World Economic Forum.

# _Access to education_

Malaysia and Thailand have a relatively good track record on access to basic education—both countries have achieved universal primary school since the 1980s. But school enrollment rates at secondary and tertiary levels remain below what might be expected. In 2009, Malaysia and Thailand’s gross secondary enrollment rates were 68 and 76 percent, respectively, compared to the average of 83 percent for upper middle-income countries and 101 percent for high-income OECD countries. Tertiary gross enrollment rates for the two countries (36 and 45 percent) remained below the high-income OECD average (72 percent)
Elaborating on this point, Kremer and Clemens (2016: 71) note “[i]f one believes that the primary impact of the [World] Bank comes from its specific investments, then one might reasonably evaluate the Bank by assessing what proportion of its investments yield, for example, a 7 percent annual rate of return. If one believes, as we and many other observers do, that the biggest effects of the World Bank arise through its role in influencing developing country policy, then one’s assessment of the overall impact of the [World] Bank will hinge primarily on one’s beliefs about the effects of these types of policies. A regular bank hopes to obtain a positive return on the vast majority of its investments. In contrast, the [World] Bank could potentially achieve a high social rate of return through a few big wins [. . . ]. Clearly, if one believes that the [World] Bank has reduced poverty primarily by using its financial resources as part of a bargaining process to promote a few important policy reforms, measuring the Bank’s impact becomes difficult. Running a regression with country-level outcomes as the dependent variable and World Bank lending as a key explanatory variable will miss the point if a substantial share of the Bank’s impact came from the spread of conditional cash transfers, or reforms inspired by the Doing Business reports, or the impact of former Bank officials on Indian economic reforms in the 1990s.” To help close this evidence gap, we designed and implemented the _2014 Reform Efforts Survey_ in the summer of 2014 in partnership with the National Opinion Research Center (NORC) at the University of Chicago. It provides a unique source of micro-level survey data on the policy influence of nearly 150 other aid agencies and development banks, as observed and experienced by the local counterparts of these institutions in 126 low-income and middle-income countries.<sup>11</sup> Unlike traditional elite surveys that are based on convenience sampling (where a population of interest
’s report, whereby head teachers provided independent reports of teacher absence over the last 30 days for the entire matched teacher sample. As an indicator for teacher shocks, this is a noisy measure and as usual, measurement error implies that our estimates are likely to be biased towards zero.<sup>13</sup> Table 1 summarizes the school, teacher and student samples. The schools are evenly divided among rural and urban locations, with an average of 23 teachers teaching 912 pupils in every school. There are more female teachers, the majority have teaching certi fi cates and about half have been teaching for fi ve years or more. Absenteeism is a big problem. Head-teachers reported that 304

> 10 As in a number of other countries in the region, student teachers are typically used to teach a class for a year before returning to teacher training college to complete their training.

> 11 In schools with less than 20 students in grade 5, the entire grade was sampled.

> 12 Ideally we would like to measure the time that teachers spend away from the classroom when they should be teaching. This would include absence episodes while teachers are in school. Glewwe, Kremer and Moulin.(2001) fi nd that teachers are in school but absent from class 12 percent of the time. We focus only on time away from school.

> 13 Absences and their reasons are broadly similar for different methods used to collect absenteeism data. Appendix 1 provides a discussion of these alternative measures.Despite the measurement error associated with a 30-day recall period as a measure of year-long shocks, there are established precedents in household surveys. Most household survey modules on illness, for instance, restrict themselves to recall periods of 30-days or less. Nevertheless, these measures have been extensively used and validated in the literature on health and economic outcomes.

13
Policy Research Working Paper 9900

# **Abstract**

Across Sub-Saharan African countries with customary tenure systems and low levels of documented land ownership, there are limited nationally representative insights on men and women landowners’ rights over land. Variations in institutions and norms governing land ownership further complicate cross-country comparisons. Using machine learning techniques and nationally representative, intrahousehold survey data elicited in private from men and women on their ownership of assets, this paper creates unique profiles of landowners in Ethiopia, Malawi, and Tanzania, anchored in a range of constructs related to self-reported rights and control over land parcels. The analysis reveals a high degree of cross-country consistency in the new insights. Landowners, particularly women, often do not have full rights and decision-making power over land. Multiple correspondence analysis demonstrates that transfer rights (rights to bequeath, sell, rent out, and use as collateral) contribute the most to the variation in the composition of the constructs related to rights and control over land. Hierarchical clustering shows that landowners can effectively be clustered into three categories: (1) owners with mostly exclusive transfer rights, (2) owners with mostly joint transfer rights, and (3) owners with no/limited transfer rights. Owners with transfer rights tend to have all other rights and measures of control. Women are overrepresented in the cluster of landowners with no/limited transfer rights, and in moving from the cluster with mostly joint transfer rights to the one with mostly exclusive transfer rights, the increase in the share of individuals not needing permission to exercise any right is considerably greater among women than men.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at ardinah@umich.edu or tkilic@worldbank.
4).

Although unlikely to be registered in national data bases, when required HEs do register with local authorities. In many countries, including Ghana and Tanzania, registration is optional, as it is legal to do business as a HE in one‟s own name without license or registration. In other countries, such as Rwanda, national legislation requires all HEs and MEs to register with local authorities, (World Bank and IPAR, 2011a). In Ghana, nationally representative data show an increasing tendency of HEs to register in the capital city (40 percent), but not outside (only 13 percent). MEs in Ghana are more likely to register in both areas – hence scale of enterprise matters. In Rwanda and Uganda, focus group surveys revealed that 61 and 58 percent of NFEs were registered with local authorities. These surveys are not nationally representative and are likely to be biased toward registration as the NFE interviewed were more likely to be urban and operating in markets than HEs at large.

A requirement to pay taxes and fees to local authorities is a common reason for registration. 61 and 55 percent of focus group respondents reported paying fees or taxes in Uganda and Rwanda, respectively. The reported taxes vary substantially from 30 to 50 percent of revenues in Uganda, 19 percent in rural Tanzania and 6 percent in Rwanda (Fox and Pimhidzai (2011), Kweka and Fox (2011) and World Bank (2011a)). In Uganda, the high rate of taxation was partially triggered by the need for revenue for local authority budgets after other sources were gradually abolished (Fox and Pimhidzai, 2012). In R. Congo, municipal authorities have the right to impose ten different taxes, including a fee for authorization to open an HE in a fixed location and annual poll taxes (on both the owner and the shop, called census taxes). A security fee is also imposed even though the security is rarely provided. HEs report that they often are not given a receipt for payment of taxes
.6<br>.8<br>1<br>Specialization Index<br>Malawi|
||||Given the level of detail provided in the pre-COVID<br>|0<br>1<br>2<br>3<br>4<br>Density<br>Ethiopia|0<br>1<br>2<br>3<br>4<br>Malawi|
|HHI<br>|Yes<br>|Pre-<br>COVID-<br>19<br>|survey data, we are able to generate an HHI to capture<br>household income diversity more precisely. For this<br>index, we use the same 12 income categories used in<br>the simple fraction index but consider the amount<br>earned from each source.<br>|0<br>.2<br>.4<br>.6<br>.8<br>1<br>0<br>1<br>2<br>3<br>4<br>Density<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>Nigeria<br>|0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>|

_Note_ : The table summarizes the two livelihood diversification indices used in the main analysis. Higher index values indicate more household specialization (less income diversification). Appendix A contains similar summary information regarding the other four indices we pre-specified.
-|---|---|---|---|---|
|Apr-20|May-20|Jun-20|Jul-20<br>Aug-20|Sep-20|Oct-20|Nov-20|Dec-20|Jan-21|Feb-21|Mar-21|

Source: CFUWBES Implementation Reports and COVID-19 MENA Monitor Household Survey Study Description. Notes: The CFUWBES asked employee levels at the end of the “last completed month”, and the CMMHH asked individuals about their main activity in the “past month.” _Figure 8: Georgia: Timeline of Surveys_

|||CFUW<br>BES1|||||||
|---|---|---|---|---|---|---|---|---|
|||||CFUWBES2|GHFPS1||||
|Apr-20|May-20|Jun-20|Jul-20 Aug-20<br>Sep-20|Oct-20 Nov-20|Dec-20|Jan-21|Feb-21|Mar-21|

Source: CFUWBES Implementation Reports and GHFPS round 1 main findings. Notes: The CFUWBES asked employee levels at the end of the “last completed month”, and the GHFPS1 (Georgia High-Frequency Phone Survey) asked individuals about whether they lost jobs due to COVID-19 since March 2020.

For Georgia, unfortunately, the available data do not permit us to perform the same type of validity check. As shown in Figure 8, there was a High-Frequency Phone Survey (GHFPS) conducted in December 2020, just a month after CFUWBES2. However, this survey does not follow a sufficiently similar sampling approach to a labor force survey. The World Bank’s high-frequency phone surveys generally only capture data on the individual responding to the phone call, which in some countries has skewed samples toward the household head. In addition, the GHFPS does not permit one to net out job gains by other workers that may have

> 40 Moreover, both approaches to estimating job losses depend upon the modeled assignment of production versus nonproduction