rafmacalaba/data-use-mentions-extended huggingface dataset:
2007‐ three times more than Spain or Greece.

Ireland’s economy is distinguished from that of the others by the presence of large multinational corporations and global financial institutions that produce much of value‐ added in the country. The economies of Greece, Portugal and Spain are much more reliant on domestically‐ owned and smaller firms. At the same time, substantial differences in the business climate or governance did not matter in terms of preventing or mitigating a crisis; the World Bank’s Doing Business ranking placed Ireland 7<sup>th</sup> and Greece 106<sup>th</sup> in 2008. On the Global Competitiveness Indicator, Ireland placed 22<sup>nd</sup> and Greece 65<sup>th</sup> . Portugal (46<sup>th</sup> , 40<sup>th</sup> respectively), with a lower GDP per capita and Spain (43<sup>rd</sup> , 29<sup>th</sup> ), with a similar GDP per capita in 2007, had far better performance on several measures of the business climate and institutional quality than did Greece. The dynamic evolution of these indicators pre‐crisis provide deeper insight into the situation prevailing in GIPS relative to other countries. The worldwide governance indicators show rankings of all countries declining for some measures: government effectiveness, and political stability. All except Ireland, have deteriorating regulatory quality and corruption. GPS also fell in the global competitiveness ranking.

Cost developments and export composition provide insight into domestic structural differences that affect the post (2010) crisis path the countries took. Labor costs, as measured by compensation per employee (in PPP US$) rose an amazing 47% in Greece during 2000‐08, as compared with 27.3% for the Eurozone countries on average. Spain was the only one among the four to contain labor compensation around the Eurozone average‐ in Ireland the increase was over 48
# **6.1 Overall impact on formalization**

As discussed in section 3.4, our main measure of formalization is registration of the business with the chamber of commerce at GUFE (i.e. the registration was found in GUFE data). We think that this definition of formalization is preferable over others that use follow-up survey data because administrative data included information on the whole study population, whereas survey data only have information on those who were surveyed. Moreover, survey data are subject to declaration bias. However, the correlation between survey data and administrative data was high (0.7), and we show similar results using the survey data as well.

Table 3 presents the results on formalization two years after the program started. The impact of the program on the formalization rate was 9.6 percentage points in group 1, 13 percentage points in group 2, and 16.3 percentage points in group 3. All these effects are statistically significant at one percent level. The effects in treatment groups 2 and 3 are higher than in treatment group 1 (although the test is only statistically significant for group 3), and the effect in treatment group 3 is significantly higher than in group 2: both sets of additional incentives included in package B (counseling, trainings and bank services) and in package C (tax mediation) seemed to be valued by informal businesses as incentives to register.

The formalization rate in the control group was only 2.3 percent. Therefore, in the absence of the program, only a few businesses would have formalized. Alternative measures of formalization that combine survey and administrative data show consistent results on businesses surveyed during the follow-up survey. Impact rates in groups 2 and 3 are always significantly higher than in group 1 (for group 2 the test is only significant for declared formalization), and impact rates in group 3 higher than in group 2.

Figure 1 presents trajectories of impacts in time with formalization rates by group in the months following the first visit
schools allows us to match household and school inputs in an environment where complications arising from school choice are eliminated. We use the entire sample of 172 schools to estimate the relationship between test scores and cash grants to schools (rule-based and discretionary). We use the sub-sample of 34 schools matched to 540 households to estimate the relationship between rule-based cash grants to schools and household expenditures on education.

Table 1 presents summary statistics separately for rural and urban schools, as well as for schools that are in our "remote" sample and matched to households. As might be expected, there are significant differences between rural and urban areas, with the latter having a better-off student body, but not necessarily better school supplies per student. Our sample of "remote" schools is not significantly different from rural schools on most measures, but they attract poorer students than the other rural schools, and have relatively more books and desks per student (though each desk or textbook is still shared by two students). Per student funding from the predictable rule-based grant increases as we go from urban to rural to remote schools, which is consistent with a fixed rule-based grant being distributed among fewer students in rural and remote areas.

Substantial parts of school spending are suitable for substitution by parents. On average 54% is spent on books, chalk (for slates), stationary and other school materials by the school while 23% is spent on sports materials and equipment. About 19% is spent on utilities, maintenance and infrastructure, and only 3% is spent on allowances and other costs linked to teachers.<sup>16</sup>

# **3.3 Empirical Methodology**

We first test whether there is crowding out of household educational spending in response to anticipated grants. We estimate a cross-section demand model for the 1195 children (from 540 households) matched to 34 schools in which household spending on school-related inputs is regressed on anticipated and unanticipated grants with and without a set of controls for child, household and school-level variables
-17-

was actually introduced at a time of _falling_ inflation, it is possible that the decision of the Hungarian government to reduce and finally abolish the taxation of interest in 1994 was motivated partly by the need to account for inflation. However, the discussions about the reintroduction of interest taxation through the year 1996 (see above) confused agents and once more highlighted the ad hoc nature of tax policy.21 To contrast Hungary with other Central European economies, table 5 shows effective tax rates in the Czech Republic, Poland, and Slovakia.

**Table 5: Inflation, Interest Rates, and Effective Tax Rates in Transition Economies**

||Czech R|epublic|Pol|and|Slovak|Republic|
|---|---|---|---|---|---|---|
||1994|1995|1994|1995|1994|1995|
|Inflation (CPI)|10.1|9.1|33.3|26.8|13.4|9.9|
|Nominal interest rate|9.0|9.7|31.1|26.2|10.7|9.0|
|Effective tax rate on interest|- 130.2|220.3|0.0|0.0|- 59.1|- 153.4|

All figures in %. In the Czech and Slovak Republics, nominal tax rates on deposits and securities differ. The effective tax rates correspond to the rate on deposits.

Source: IMF: International Financial Statistics, and own calculations.

In contrast to Hungary, real interest rates were negative in the Slovak Republic and Poland in 1994 and 1995. The patterns of inflation were different, however. The Slovak Republic reported inflation of 13% in 1994 and 10% in 1995, which, in combination with a final withholding tax of 15% on deposits, led to high effective tax rates
**Small Area Estimation of Child Malnutrition in Ethiopian Woredas**

Thomas Pave Sohnesen, Alemayehu Ambel,<sup>*</sup> Peter Fisker, Colin Andrews, and Qaiser Khan<sup>†</sup>

**Keywords:** Child malnutrition, Small Area Estimation, Demographic and Health Survey, Targeting, Ethiopia **JEL Classification:** I15, I32

> * Corresponding authors:  Thomas Pave Sohnesen  tpavesohnesen@worldbank.org and Alemayehu Ambel <u>aambel@worldbank.org</u>

> † <mark>Thomas Pave Sohnesen, World Bank and Visiting Professor with the Development Economics Research Group, University of Copenhagen; Alemayehu Ambel, World Bank; Peter Fisker, University of Copenhagen, Department of Food and Resource Economics, Changing Disasters; C</mark> olin Andrews, World Bank; and Qaiser Khan, World Bank.

<mark>The authors acknowledge financial support from the World Bank. The authors would like to thank: Phyllis Ronek for editorial assistance; Tesfaya Hailu (Ethiopian Public Health Institute), Kalle Hirvonen (IFPRI), Silvia Kaufmann (World Bank), and Lisa Saldanha (World Bank) for valuable comments and suggestions.</mark>
the average monthly wage data with the applicable GDP deflator. For each country we generate an index of real wages in local currency.

To estimate labor productivity, we divide GDP in constant price local currency by the number of persons employed, using the data in the WDI, and convert this into an index for each country.<sup>2</sup> This By dividing the index of real monthly wages by the index of labor productivity, the index of RULCs in local currency is obtained. To generate the index of RULCs in US dollars we convert the nominal monthly wage series in local currency into US dollars using the average annual market exchange rates in the WDI and then deflate teach time series with the US GDP deflator. We have set the indices for each country at 100 in 2003 because that is the earliest year for which we have the requisite wage data for all the countries.

Tables 2a, 2b and 2c show the RULCs for the three groups of transition economies identified in table 1, in both local currency (LC) and US dollars. We compare averages over five periods: 2000-04, 2005-08, 2009-11, 2012-14 and 2015-17. The final three-year period covers the period after the global oil price collapse which had a major impact on real exchange rates in many of the transition economies. The main issue that we address is how RULCs have evolved since the first half of the 2000s.

> 1 A real unit labor cost series could not be estimated for Kosovo because there are no data on wages before 2012.

> 2 Labor production is usually defined as real value added per unit of labor. We follow the practice in much of the literature, where labor productivity is measured as GDP (or one of its sectoral components) divided by the total number of persons employed (e.g. Turner and Golub, 1997; Felipe and Kumar, 2011). In some countries, such as the United States, more sophisticated estimates of labor productivity are available which incorporate the number of hours worked
|The Internation-<br>al Bureau of Ed-<br>ucation (IBE)—<br>legal dataset|UNESCO|The IBE provides a country database that<br>includes profiles of education systems (in-<br>cluding laws and policies), a selection of<br>official reports from national education au-<br>thorities, information on national curricula,<br>and links to official information sources on<br>education (including Ministries of Educa-<br>tion).|Does not cover issues related to insti-<br>tutions, labor market, property, public<br>goods, social protection, and hate<br>crime or hate speech. Does not allow<br>for cross-country comparison.|
|---|---|---|---|
|European Equal-<br>ity Law Net-<br>work—Legal<br>Developments|European<br>Equality Law<br>Network|The network includes updates on legisla-<br>tive, policy, and case-law developments in<br>the field of nondiscrimination in the 35<br>states that participate in the network.|Does not cover non-European coun-<br>tries. Does not allow for cross-<br>country comparison.|
|WORLD Policy<br>Forum—Data<br>on Education|The WORLD<br>Policy Analy-<br>sis Center at<br>University of<br>California, Los<br>Angeles<br>(UCLA)|<br>The WORLD Policy Forum includes laws<br>and public policies in 191 countries cover-<br>ing poverty, discrimination, education,<br>health, child labor, child marriage, and pa-<br>rental care.|Does not cover institutions, property,<br>public goods, social protection,
where ����,���� is the Purchasing Power Parity conversion factor in year 2011 for country _c_ ; Π�,���� is the cumulative percent increase in the CPI from time _0_ to year 2011; Π�,� is the cumulative percent increase in the CPI from time _0_ to time _t_ ; and �����������,� is the correction factor for country _c_ in time _t_ , estimated from equation (9).

# **IV. Data**

The Engel curve method requires detailed monthly CPI data on food and non-food items at regional levels and comparable consumption surveys in multiple years. However, data in Sub-Saharan Africa are challenging in terms of availability and quality. Of the 48 countries in the region, only half has two or more HBSs since the early 2000s (Beegle et al 2016). Among these, only a limited number collect consumption data using the same (or at least similar) survey design and methodology, thereby rendering the consumption aggregates comparable over time. Obtaining detailed CPI series adds another layer of difficulty. In some of the countries with comparable HBSs, detailed CPI data are either unavailable during the periods of interest or the continuity of the series has been disrupted due to political unrest or other factors, as, for example, in the case for Sierra Leone.

For the purpose of this study we obtain household consumption and CPI data for 16 countries in Sub-Saharan Africa that account for 70 percent of the regional population.<sup>10</sup> These countries are Burkina Faso, Cameroon, Côte d’Ivoire, Democratic Republic of Congo, Ethiopia, Ghana, Madagascar, Mauritius, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Togo, and Uganda. For Nigeria and Tanzania we use panel data from the Living Standards Measurement Study–Integrated Survey on Agriculture (LSMS-ISA), partly to address quality concerns with other consumption surveys (World Bank 2014, 2015).
10 travel and transport; although the latter’s poor performance remains unanswered. It is not clear what explains volatility and collapse in their trade. It remains unclear whether unusual patterns in Turkish trade in services are simply a statistical mirage or they reflect deeper structural barriers to this trade. Given its performance in trade in goods, one is tempted to blame the quality of statistics rather than idiosyncrasies of Turkey’s mode of integrating into global economy. Whatever the answer might be, it needs to be found simply because services statistics provide important inputs to economic policy making.

# **C** **_._** _Trade in goods and services in comparative perspective: expanding aggregate trade openness_

Looking solely at trade turnover per capita does not provide a good base for an assessment of a country’s involvement in world trade (Figure 3). Turkey’s trade turnover per capita in 2004 puts it close to the bottom of EU-8, SEE-3 and EU candidate economies. It is higher than only trade turnover per capita of Macedonia, Serbia and Montenegro and Albania. On the other hand, however, Turkey’s turnover is pretty close to Romania’s trade turnover per capita. But this is highly misleading, as Turkey’s population is almost double that of the largest country among the EU-8, Poland; more than triple of that of Romania (the second most populous country); and 52 times larger than the least populous, Estonia. Larger population implies a larger domestic economy less dependent on foreign trade.

**Figure 3:** _Trade in goods per capita in Turkey and other ‘EU-related’ European countries in 2004 (in US dollars)_

<!-- Start of picture text --> 16,766<br>13,009<br>11,48910,516 9,979<br>6,304<br>5,460 5,076<br>4,244<br>3,136<br>2
poor participant households compared to poor non-participant applicants (Alix-Garcia et al., 2015). In this

> 1 Applicants with scores within 25 points of the scoring cutoffs represent 97% of the possible applicants.  However, for the environmental analysis, we further restrict this group to only include individual polygons with more than 50% forest cover in 2000.  We will discuss this further below in the section on the environmental analysis.

> 2 The study was designed to use higher resolution land cover change data being prepared under the new MAD-MEX MRV system. Unfortunately, post-processing of that data was not completed in time to use.

4
2020 World Development Report was to attempt to provide a bridge between this case-study-based literature and the broader empirical literature on GVC participation. The idea was to develop firm-level and product-level measures of the extent to which the GVC participation of firms is relational in nature, and the extent to which international transactions associated with a given product tend to be relational in nature. More specifically, the aim was to exploit the panel nature of some of the datasets available from the World Bank’s Exporter Dynamics Database (EDD) to construct measures of the observed persistence of firmto-firm transactions, perhaps partialling out various natural determinants of persistence (such as exchange rate volatility). This is very much in line with the measure of “relationship stickiness” proposed by Martin et al. (2018), which they compute using detailed firm-to-firm export data from France. The hope was to “scale up”this approach and apply it to various countries, with the initial goal of verifying whether there was a significant positive rank-correlation in product-level stickiness across countries. Such a finding would indicate that there is a significant product-level dimension to stickiness that is likely to render GVC participation in certain goods particularly prone to be relational in nature. With such finding at hand, one could use standard industry concordances to construct a global Input-Output table that separates trade flows associated with traditional 21
should be completed for each country.

Template 4 sets up an indicative listing of levels and trends of ODA in infrastructure as a percentage of GDP. The OECD Credit Reporting Database is a comprehensive information source for the completion of this template.

> 4 With the exception of the telecommunications sector, the PPI database records commitments as opposed to actual disbursements. The difference between commitments and disbursements is often substantial. For instance, in multiyear projects the initial commitment to an investment is an aggregate, while disbursements correspond to annual flows.

21
- **International migrants:** This is the total number of people who are reported by their households to be outside El Salvador in 2007 for each census tract.

- **International migrants in the war period:** This is the total number of people who left El Salvador between 1979 and 1990 and are reported by their households to be outside El Salvador in 2007 for each census tract.

- **Remittances rate:** This is the share of households in a given census tract that report receiving monetary help from a member outside El Salvador in 2007.

- **In-migration during the war period:** This is the total number of individuals who reported in 2007 that they arrived in a given census tract between 1979 and 1990.

- **Moving population:** This is calculated as the number of people in a given census tract who reported in 2007 any relocation in their entire life.

- **Moving population share:** This is calculated as the moving population in each census tract over the total population in the same tract.

# **A.D** **_Presidential election results_**

All data related to elections was provided by the Tribunal Supremo Electoral of El Salvador, which included the list of results and coordinates for each polling station.

- **Left voting share:** This is calculated as the total votes for the FMLN party over the total valid votes for each polling station in El Salvador.

- **Right voting share:** This is calculated as the total votes for the ARENA party over the total valid votes for each polling station in El Salvador.

- **Blank voting share:** This is calculated as the total blank votes over the total valid votes for each polling station in El Salvador.

- **Turnout share:** This is calculated as the total valid votes over the total number of people registered to vote in each polling station in El Salvador.

57
3|1,044.4|0.054|1.334|96.1|2,491.9|
|Morocco|_Flour1_|1.197|0.168|12.3|14.0 %|n.a.|n.a.|n.a.|n.a.|
||_Flour2_|0.479|0.342|41.7|71.5 %|||||
|||||_Sugar(kg)_|||_Veg _|_etable oil(l_|_iter)_|
|Djibouti||0.865|0.061|6.5|7.0|1.422|0.171|10.7|12.0|
|Iran, Islamic Rep.||n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Libya||0.362|1.545|81.0|427.2|0.868|4.054|82.4|467.0|
|Morocco|_Sugar1_|1.393|0.682|32.9|49.0|n.a.|n.a.|n.a.|n.a.|
||_Sugar2_|1.393|0.682|32.9|49.0|||||
||_Sugar3_|1.077|0.682|38.8|63.3|||||

_Source:_ World Bank estimations from Household Budget Surveys. _Note_ : Subsidized flour and sugar in Morocco have different prices depending on varieties and forms; kg = kilogram; PPP = purchasing power parity.

# A Distributional Analysis of Subsidies

As indicated in the introduction, we use the microsimulation model SUBSIM to
, life insurance, non-life insurance, reinsurance), telecommunications services (fixed-line, mobile, Internet), distribution (retail, wholesale), transportation (air freight domestic, air freight international, passenger domestic, passenger international, rail freight, road freight, maritime freight transport, maritime auxiliary services, maritime intermediation and other), professional services (accounting, auditing, legal services on foreign law, legal services on domestic law, advice and representation services).<sup>8</sup> See Annex 2 for more details on the sectoral and modal coverage.

For the purposes of data collection, economies and information sources were divided into two groups. For 25 of these economies the information underlying the updated STRI was collected through a survey conducted jointly by the World Bank and WTO in 2016-2017, while information for the remaining 43 economies was sourced from the OECD STRI regulatory database (2016 information), thanks to the cooperation of the OECD, which is gratefully acknowledged.

Drawing from the World Bank's experience with its original Services Trade Restrictions Database (STRD) and developments since then, notably the OECD STRI, we developed a new classification of measures affecting trade in services. We organized measures into four main policy categories, namely a) conditions on market entry, b) conditions on operations, c) measures affecting competition, and d) regulatory environment and administrative procedures. Relevant policy measures that potentially affect trade but do not fit neatly into any of these four categories are included in a fifth category – miscellaneous measures (see Box 1).<sup>9</sup>

> 5 For an overview of prior initiatives, please refer to Borchert et al (2019).

> 6 While strictly speaking we do not cover “consumption abroad”, a mode of delivery that is particularly important in services like tourism, education and health – sectors not covered by the database – we take in fact a broad view of 'cross-border supply', covering measures that apply directly to the consumer,
_Table 1 Descriptive statistics on income and poverty per Household Income Survey (HIS) round (selected years)_

|||**2004**|||**2009**|||**2016**||
|---|---|---|---|---|---|---|---|---|---|
|Mean income (2016 MYR)|974|||1,103|||1,690|||
|Median income (2016 MYR)|654|||750|||1,256|||
|Poverty headcount ratio|0.400|||0.338|||0.105|||
|_N_|36,483|||43,026|||78,297|||
|Poverty headcount (age 25–55)|0.409|||0.340|||0.107|||
|N (age 25–55)|25,893|||30,774|||54,993|||
|**_Poverty figures subgroups_**|Pop. share|Pov. ratio|Pov. share|Pop. share|Pov. ratio|Pov. share|Pop. share|Pov. ratio|Pov. share|
|Bumiputera|0.663|0.491|0.814|0.685|0.405|0.821|0.681|0.131|0.853|
|Chinese Malaysian|0.248|0.177|0.110|0.233|0.149|0.103|0.218|0.024|0.050|
|Indian Malaysian|0.084|0.334|0.070|0.077|0.305|0.070