rafmacalaba/data-use-mentions-extended huggingface dataset:
different trajectory relative to the forecasts. This is a valid concern. One way to address it is to assess how well the forecasts in this paper predict actuals for the year 2015.

Data recently made available on conflict intensity in recent months reveals a strong similarity between the actual intensity of conflict in 2015 and the forecasts from Model 1. The predicted number fell short of the actual by only 0.2 percent, suggesting that the proposed approach is quite accurate at the national level. There is also a reasonable closeness between predictions and actual figures at the provincial level, with the correlation coefficient between the two series reaching 0.96. However, there are also a few important discrepancies, with the model not fully anticipating the increase in conflict intensity in the Northeast, and overestimating conflict intensity in the Central region, and even more so in the Westcentral region (figure 12).

An out-of-sample validation can also be conducted in relation to expenditures per capita, Because Model 2 was estimated using data from 2011, forecasts and actuals can be compared for all the years since 2012. The exercise also allows to assess the performance of the empirical approach used in this paper relative to other forecasts produced building on the 2011 NRVA survey and other data sources issued at about the same time. Such analyses include the already mentioned landmark 24
**Table 2: Parcel Level Descriptive Statistics by Treatment Status**

||To|tal<br>|Con|trol<br>|Treat|ment<br>||
|---|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD||
|Parcel size in hectares|0.284|0.735|0.273|0.708|0.300|0.769||
|Number of years possessed|17.644|15.461|17.272|15.787|18.146|14.998|***|
|Parcel was purchased|0.449|0.497|0.463|0.499|0.430|0.495|***|
|Parcel was inherited|0.360|0.480|0.341|0.474|0.386|0.487|***|
|Parcel allocated by government|0.171|0.376|0.165|0.372|0.178|0.382||
|Acquired through other means|0.020|0.141|0.030|0.171|0.007|0.081|***|
|Number ofparcels|63|27|36|34|26|93||

**Source:** Own computation from 2010 World Bank Land Tenure Regularization Survey **Note:** Significance levels are reported for t-tests of the equality of the means for each of the variables between control and treatment areas (* significant at 10%; ** significant at 5%; *** significant at 1%).

18
Policy Research Working Paper 7843

# **Abstract**

This paper employs nationally representative household survey data on parents of adult individuals to analyze the intergenerational transmission of education in nine SubSaharan African countries. The paper provides the levels, trends, and patterns of intergenerational persistence of educational attainment over 50 years, with a special focus on gender differences. The study finds a declining cohort trend in the intergenerational educational persistence in all the countries, particularly after the 1960s. The increase in educational mobility coincides with drastic changes in educational systems and a huge investment in human capital accumulation in the region following independence.

Nevertheless, the education of parents’ remains a strong determinant of educational outcomes among the children in all the countries. Ghana, Guinea, Nigeria, and Uganda experienced the highest intergenerational mobility, and the Comoros and Madagascar the lowest. In all the sample countries, more mobility is observed in the lower tail of the distribution of education. Intergenerational educational persistence is strong from mothers to children, and the effect is more pronounced among daughters than sons. The results highlight the need for targeted redistributive policies that improve intergenerational mobility in the region.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at e.yitbarek@maastrichtuniversity.nl.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the
Policy Research Working Paper 10621

# **Abstract**

This paper provides a detailed descriptive analysis of patterns of urbanization across Sub-Saharan Africa for the year circa 2015. Despite the rapidity and importance of Sub-Saharan Africa’s urbanization, little is known about the anatomy of patterns of urbanization across the region due to a lack of detailed and accurate official data on urban settlements and populations. To address this gap, the paper applies a modified version of the “dartboard” algorithm to high-resolution gridded population data for the region, which is derived from digitized maps of the footprints of all buildings in the region from very high-resolution satellite imagery. This allows for a consistent definition of urban areas across all countries in the region, overcoming the measurement problems that arise from relying on official definitions of urban areas, which vary markedly across countries. Using this definition, the paper presents evidence on key empirical regularities that are related to disparities across the urban hierarchies, such as the extent of urban primacy and Zipf’s law, as well as on the internal structures of cities, such as population density gradients and the number of centers that cities possess. The paper also analyzes how these characteristics are related to key country characteristics. Finally, the paper compares the results with those that arise from the use of an alternative definition of urban areas—the degree of urbanization.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice and the Poverty and Equity Global Practice.. 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 mroberts1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
thus will induce governments to be more transparent and accountable to the populace thereby improving the quality of governance (Khazaeli and Stockemer, 2013).

For investors, quality of governance is an important driver of investment decisions, as it largely influences the cost of doing business and also reduces uncertainty in the business environment. To this end, we test the association between high-speed connectivity via the SMC arrivals and quality of governance using metrics such as regulatory quality, government effectiveness, control of corruption, political stability, and rule of law from the World Bank’s World Governance Indicator database.<sup>27</sup> Specifically, we run the following specification at the country-level regression where _Yct_ is a placeholder for the measure of the quality of governance in country _c_ at year _t_ ; 1 ( _Submar inect_ ) an indicator variable set equal to 1 if the country is connected to at least one submarine Internet cable and 0 if otherwise. We also control for represent baseline controls such as whether the country is anglophone or otherwise interacted with time trends ( _Xc × T_ ).<sup>28</sup> Standard errors are clustered at the country level.

The results in Table 6 show a positive relationship between the arrival of the SMCs and governance measures albeit, the relationship is statistically significant only with respect to regulatory quality, and government effectiveness. In column 2, for instance, high-speed Internet connectivity is associated with a 0.072 standard deviation (sd) increase in the regulatory quality index. This result is particularly important to the findings of the paper since the regulatory quality index captures "perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development". Hence, access to the Internet is associated with policies that support private sector development. Similarly, high-speed Internet connectivity is associated with a 0.12 sd increase in the government effectiveness index. Overall, the results herein provide suggestive evidence that Internet connectivity is associated with an improvement in the quality of
online appendix growth caused by licensing.

**PAYE** Firms with employees are required to declare tax withholding on behalf of their employees. Large firms declare PAYE on a monthly basis. With permission, small firms declare PAYE on a quarterly basis. Employers who compensate an employee by 30,000 RwF (30 USD) or more per month are required to declare and pay PAYE on behalf of the employee.<sup>A6</sup>

A PAYE declaration aggregates the total pay, allowances and benefit-in-kind (BIK) provided to the firm’s employees. In an annex to the declaration, firms enumerate each employee, and report the compensation paid to each individual.

In Appendix D we use firm reported employment and wage bill to understand the impact of licensing on firm growth.

**Firm registration** Firm registration data contains firm-level details including the ISIC sector classification, and the Province, District and sub-District in which the firm operates. This data is collected when the firm is formally registered at the Rwanda Development Board (RDB) and continuously monitored and updated by the RRA.

# **A.4 Time series of mask prices**

To construct our time series of mask prices, we estimate the following equations to document changes in both domestic prices and border prices of masks in Rwanda during the COVID-19 pandemic. For both domestic and imported masks, we consider transactions between November 2019 and August 2020.

> A6Additionally, firms who declare pension or other benefits on behalf of their employees may submit a joint declaration, including PAYE. In cases where the income of the employee is less than 30,000 RwF (30 USD) per month this will not give rise to a tax withholding liability.

A11
the banks because of their interest rate risk positions.

**Quality of Loan Assets** One of the striking features of Turkey's private-sector banks is the low ratio of loans to total assets, and the short-term nature of such loans. While this fact may cause some concerns, it has a beneficial liquidity effect - namely that a greater proportion of assets can be held in more liquid asset-types, such as securities, and the loans can be called and turned into more liquid assets more easily, The securities can provide liquidity via sale, repo, or when pledged against interbank borrowings. On the other hand, loans are still the largest asset item, and large credit risks have profound implications for system liquidity.

The level oif credit risk in the Turkish banking system appears to be moderately low as measured by barnks reported level of non-performing assets, and the banks appear to be
employment information for all household members and find that phone surveys overstate employment rates for the full population even once sampling weights are used. Encouragingly, their analysis suggests that phone surveys do reasonably well at tracking disparities and changes across gender, education, and urban/rural groups. However, for age group comparisons the bias was greater.<sup>8</sup> We use household sampling weights for all our analysis. Household sampling weights are used even for respondent level employment indicators, so these should be interpreted as the share of households with a respondent having that employment outcome, rather than the share of individuals.<sup>9</sup> Countries are weighted equally such that summary statistics are

> 7Surveys in Democratic Republic of Congo, Central African Republic and Mozambique were conducted only in urban areas or the capital city and are not representative of the rural population.

> 8This paper does not make comparisons across age groups.

> 9Individual level sampling weights are only available in some countries with RDD surveys, and it would be

8
):**|||||||||
|Not in labor force|0.09|0.07|0.08|0.08|0.21|0.02|0.06|0.09|
|Unemployed|0.06|0.00|0.00|0.00|0.04|0.02|0.01|0.00|
|Employed in Agriculture|0.34|0.66|0.52|0.72|0.14|0.42|0.41|0.47|
|Employed in Industry|0.09|0.08|0.09|0.05|0.14|0.14|0.15|0.10|
|Employed in Services|0.42|0.18|0.31|0.15|0.48|0.40|0.38|0.34|
|**Access to basic services**|||||||||
|Improved water|0.68|0.81|0.78|0.57|0.88|0.59|0.79|0.78|
|Improved sanitation facility|0.46|0.30|0.33|0.04|0.32|0.20|0.29|0.37|
|Access to electricity|0.47|0.53|0.80|0.36|0.91|0.81|0.44|0.58|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the GHSPOP 1km dataset and the DOU method. 64
merely declining to accept the e-invoice. The derogation for the buyers’ acceptance of e-invoicing was inevitable under compulsory ETI. However, there could also be criticism about such derogation. Kim and Sur contended that such ETI-issuing process without the buyers’ acceptance only favors suppliers when a legal dispute pertaining to a transaction, a payment, or an invoicing arises (2012). In this respect, Korea’s legal solution will be subject to future legal proceedings and rulings.

# **IV. ASSESSING KOREA’S SUCCESS WITH ETI**

Although only four or five years have passed since the introduction of compulsory ETI in Korea, this paper attempts to analyze what it has achieved and what effect it has had on tax compliance. First, an analysis is undertaken on ETI adoption rates, tax compliance cost reduction, and combating tax evasions and frauds using data provided by the NTS and previous research findings. Second, descriptive and statistical analysis are conducted on how Korea’s ETI affected tax compliance, the transparency of business transactions, tax service, and taxpayers’ burden in ETI compliance using survey results taken from private tax practitioners and business taxpayers in Korea.

## **1. Analysis of NTS Data**

## **Pace of ETI Adoption**

After the introduction in 2011, ETI adoption reached nearly 100 percent as shown in Table 3, a remarkable achievement from around 15 percent before the adoption.

Table 3: Pace of ETI Adoption

|Year|Number of<br>Compulsory<br>issuers (A)|companies<br>ETI<br>participation<br>(B)|B/A<br>(%)|Transacti<br>Total value<br>of<br>transactions<br>(C)|on value (KRW,<br>Value of ETI<br>transactions<br>(D)|trillions)<br>D/C (%)|
|---|---|
Large firms have higher productivity growth. Our results are robust to sub-sample analysis by country income group and by looking at countries with large versus small informal sectors. When we examine the role of age conditional on firm size, we find that while large mature firms (firms with over 100 employees and 11+ years) have the largest proportional share of total employment compared to other size-age groupings, job creation rates are highest amongst small young firms (firms with <20 employees and ≤ 5 years old). We also find that young firms (≤ 5 years) have higher employment growth, sales growth, and productivity growth rates than older firms in regressions controlling for firm size, country, industry, and year fixed effects.

Our cross-country database improves upon existing databases along several dimensions. First, the data are comparable across countries since they are all sourced from the World Bank Enterprise Surveys (ES) database which samples formally registered firms from over 100 countries to study the business climate constraints to private sector growth and performance. The surveys use standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across countries.<sup>3</sup> Second, for the first time ever, we are able to compute statistics on SMEs for a large sample of developing countries. While statistics on size distribution are more easily available for the developed countries from sources such as the OECD, there is little to no information available for many developing countries. Finally, the data set allows us to compute contribution to total employment, labor productivity, and employment generation across the entire size distribution and conditional on size, we can also compare the role of young versus mature firms.

> 3 The mean response rate across our sample of countries is 70% which is superior to most other survey based studies. For instance, Campello, Giambona, Graham, and Harvey (2010) report response rates of 3 to 7% in their survey of how companies use credit lines during a financial crisis
the waters” may be substantially less than the cost of locking in major exporting contracts” (Eaton et al., p. 17).

We will attribute an export dummy to firms only that exceed a certain threshold _ζ_ of export intensity. Assumptions (i) and (iii) ensure that applying a threshold _ζ_ covers established exporters only that have paid the bulk of sunk entry costs, but not new exporters. Assumption (ii) makes sure that exiting exporters wouldn’t fall under the category exporter if their export intensity is below the threshold _ζ_ . If all three assumptions hold, lagged exports – the measure for sunk entry costs – and the dependent variable would be identical. Since lagged exports would perfectly predict exports in such a case, lagged exports as a measure of sunk entry costs couldn’t be included into the regression analysis.

Equation (2) then translates into: Pr( _exit_  1)  Pr(  _it_   _firmit_  _regrt_  _aggrt_  0) (3) 17
and liberalization of trade and capital flows, including for foreign direct investment. Extensive restrictions on domestic financial systems were eased, and market access to foreign institutions was increased.

<!-- Start of picture text --> Figure 24: LAC Growth and Inflation<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006<br>-10<br>Growth (GDP per capita) Inflation (GDP deflator)<br>Annual % change<br><!-- End of picture text --> Source: World Development Indicators <!-- Start of picture text --> 180 Figure 25: Public Debt of Selected Latin American Countries<br>160 Total (domestic plus external) gross central government debt (% of GDP)<br>140<br>120 Argentina<br>100<br>Brazil<br>80<br>Chile<br>60<br>40 Mexico<br>20<br>Uruguay<br>0<br>Source: Reinhart and Rogoff (2011).<br><!-- End of picture text --> 82. In the first half of the 1990s, these stabilization policies succeeded in bringing down inflation—an achievement that has endured since. With the debt overhang resolved and reforms under way, private capital flows resumed. In conjunction with more liberalized domestic financial markets, domestic spending rose, real growth accelerated, and social indicators began to improve.

83. Yet, towards the end of the 1990s signs of fragilities became evident with Mexico‘s ― tequila‖ crisis and contagion to other major economies in the region. In parallel, in the wake of the Asian and Russian crises, investor appetite for global risk declined; and the consequent sudden reversals of capital inflows accentuated inherent vulnerabilities in many Latin American economies
# **Table A2. Survey data**

|**Country**|**Market surveys and descriptions**|**Data sources**|
|---|---|---|
|BRA|Expectations for output, inflation, interest rates, and FX rates,<br>based on_Focus Market Readout_forecasts of about 160 banks,<br>asset managers, and other institutions|Central Banco Brazil|
|CHL|Expectations for inflation and interest rates based on monthly<br>surveys of academics, consultants, and executives or advisors of<br>financial institutions and corporations.|Central Bank of Chile|
|HUN|Professional forecasts on future inflation, output, FX rates|Consensus Economics|
|IND|Professional forecasts on future inflation, output|Consensus Economics|
|IDN|Professional forecasts on future inflation, output, FX rates|Consensus Economics|
|MEX|Expectations for output, inflation, interest rates, and FX rates<br>based on monthly surveys of private-sector economists|Bank of Mexico|
|PER|Median expectations for output, inflation, interest rates, and FX<br>rates, based monthly survey of macroeconomic expectations|Central Reserve Bank of<br>Peru|
|PHL|Consumer expectations surveys on future inflation and output<br>growth|Central<br>Bank<br>of<br>Philippines|
|POL|Consumer expectations for future Inflation and output growth|National Central Bank of<br>Poland,<br>Central Statistical Office|
|ROM|Professional forecasts on future inflation and output|Consensus Economics|
|UKR|Professional forecasts on future inflation, output, FX rates|Consensus Economics|
|RUS|Monthly inflation expectations for the next year|Central<br>Bank<br>of<br>the<br>Russian Federation|
|TUR|Monthly survey of expectations for future output, inflation,<br>interest rates, and FX rates,
Figure A7: Distributions of poor populations across urban versus rural areas by country, $3.65 poverty line <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used.

Figure A8: Comparison of urban and rural poverty rates <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 60 MWI<br>MWI<br>NER<br>40 40 TZA UGA 40 TZA UGA<br>NER<br>20 UGATZA MWI 20 ETH 20 ETHTCD LSO<br>GABMRTSENCIVGIN GNBGHAETHBFATCDAGOLSO NER GABMRTGINCIVSENGHA GNBTCDBFA LSO AGO GABMRTSENCIVGINGNBBFAGHA AGO<br>20 40 60 80 20 40 60 80 20 40 60 80<br>Poverty rate in rural areas (%) Poverty rate in rural areas (%) Poverty rate in rural areas (%)<br>Poverty rate in urban areas (%) Poverty rate in urban areas (%) Poverty rate in urban areas (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines.
that firms that are reliant on high‐skilled labor, specialized inputs, and geographically distributed markets are especially sensitive to congestion, as it obstructs their access to these factors (Weisbrod, Vary and Treyz, 2003). Sweet (2013) uses panel data from 2001 to 2007 and 88 metropolitan areas in the US and finds that, amongst effects on economic growth, a one percent increase in congestion – measured by daily 12
. P. J. (2024). Monthly energy price estimates by product and market (Version 2024-01-25). WLD_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. <u>https://microdata.worldbank.org/index.php/catalog/6134</u>

Andrée, B. P. J. (2024). Monthly food price estimates by product and market (Version 2024-01-25). WLD_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. <u>https://microdata.worldbank.org/index.php/catalog/6160</u> 3