rafmacalaba/datause-encoder-data huggingface dataset:
2006)to finance “infrastructure development.” Since 1986, four different Nigerian states have made five issues, ranging from 15 million to 5 billion naira, to finance a variety of infrastructure development projects including water and drainage improvement, urban and rural road construction, and rural electricity. But none of the federal government’s bonds, which make up 95.6 percent of all Nigerian government bonds outstanding, had infrastructure development as their designated purpose. Consequently, the percentage of total government bonds outstanding that were issued specifically to finance infrastructure was very small: 0.93 percent.

**Table 5.2 Local government bond markets and infrastructure financing as of end-2006**

|Country|Amount of total<br>outstanding<br>government<br>bonds as % of<br>GDP|Amount of<br>government bonds<br>issued to finance<br>infrastructure as % of<br>total government<br>bonds outstanding|Maximum term of<br>government bond (in<br>years) /1|
|---|---|---|---|
|Benin|0|0|5|
|Burkina Faso|0|0|4|
|Cape Verde|29.2|—|14|
|Côte d’Ivoire|2.1|0|3|
|Ghana|8.3|0|10|
|Kenya|14.4|0|15|
|Lesotho|n.a.|0|10|
|Madagascar|n.a.|n.a.|1|
|Malawi|1.2|0|25|
|Mozambique|2.1|0|10|
|Namibia|11.2|0|20|
|Nigeria|4.2|0.93|28|
|Senegal|1.1|100|5|
|South Africa|24.7|1.2|30|
|Tanzania|3.9|0|10|
|Uganda|5.7|0|10|
|Zambia|4.0|n.a.|5|

_Sources:_ Data on outstanding government bonds were obtained from national authorities, except for Ghana (Standard Chartered Ghana). _Note:_ No data were available for Cameroon and Sudan. Organized bond markets do not exist in Chad, the Democratic Republic of the Congo, Ethiopia, Niger, or Rwanda.

a. For Kenya and for countries listing bonds on the Bourse régionale des valeurs mobilières—Benin, Burkina Faso, Côte d’Ivoire, and Senegal—the applicable date for outstanding bond amounts is April 30, 2007.

— = Not available; n.a. = Not applicable.

An estimated 1.2 percent of all South African government bonds outstanding were specifically issued to finance infrastructure. St South African Reserve Bank (SARB) staff estimated that all municipal bonds outstanding at end-2006 (valued at just over $760 million) were for infrastructure.

The actual share of government bond financing used for infrastructure development in Nigeria, South Africa, and other focus countries may be higher than shown in table 5.2, because some of the financing raised through government bond issues may have been applied to infrastructure development. This is difficult to gauge, however, because of the difficulty of differentiating within the government bond 45
We turn these assumptions into predictions for China's net foreign asset position as follows. Our assumption on average per capita GDP growth allows us to project forward per capita GDP. Our assumption on future saving rates then gives us the gross change in wealth for each future year. We then estimate per capita wealth by cumulating these gross changes in wealth and subtracting 6 percent depreciation per year. This gives us per capita wealth in 2025 which, as we have seen in Sections 3 and 4 of the paper, is a key determinant of China's future net foreign asset position. In the calibration exercise the relevant variable is China's per capita wealth relative to that of the rest of the world. We assume that the growth rate of per capita wealth of the rest of the world is equal to its historical rate over the period 1980-2000. This gives us China's relative per capita wealth in 2025. In our first and third scenarios, which feature continued very high saving rates, we set the saving rate at 25 percent of GDP, which is roughly equal to the current saving rate in PPP terms. This is of course much lower than the rate in local currency terms, reflecting the relatively high cost of investment goods in China. We use the lower PPP saving rates since our estimates of wealth are measured in international prices. In the second scenario with lower saving we reduce the saving rate to its historical average between 1980 and 2005 of 20 percent. Our assumptions for per capita GDP growth for the three scenarios are 6, 5, and 4 percent respectively, where 6% is the historical growth rate of GDP per capita between 1980 and 2005 as reported in the Penn World Tables.<sup>10</sup> We are more eclectic in our approach to future productivity levels, which is the other key determinant of the net foreign asset position. For the calibration model, the relevant variable is π which measures ROW's productivity advantage. In our first two scenarios we assume that relative productivity growth is equal to its historical average growth rate. Measured relative TFP from our crude development accounting exercise was 1.2 in 1980, and 1 in 2000. Extrapolating this annual average growth rate in relative productivity of 0.8 percent per year into the future gives us a value of π=0.87 in 2025. We note that this is a fairly conservative estimate of TFP growth for China. In our development accounting exercise average TFP growth for ROW between 1980 and

> 10 The PWT includes a more or less arbitrary, but not unreasonable, downward adjustment in its estimates of China's GDP growth, reflecting a variety of concerns about the quality of Chinese national accounts data.  See Young (2003) for a very thorough discussion of the issues.  The PWT reports data only through 2000 for China, we extrapolate to 2005 using (unadjusted) constant local currency growth rates.

29
20 covered by the project and would aim to provide a similar set of interventions in nonproject provinces to those being provided to project provinces through the two projects.<sup>12</sup> This, it was argued, made it impossible to conduct a formal impact evaluation of the project.<sup>13</sup> The same point was made by the authors of a report on the population and family health project by the Bank’s Independent Evaluation Group (World Bank 2006), who were forced to resort to examining national trends in key indicators without trying to determine what could be attributed to the project.

The behavior of the Vietnamese government appears to have been precisely that illustrated in Figure 1. It apparently responded to the World Bank projects by scaling back its planned increase in spending in the project provinces (area 1), relying largely on the Bank to finance extra spending (compared to the initial level) in these areas. The Bank projects thus allowed the Vietnamese government to achieve higher levels of spending (gross of Bank-provided funds) in both project _and_ nonproject provinces. In such a situation, the use of differences-in-differences would indeed fail to provide a reliable estimate of the projects’ overall benefits.

The approach outlined above provides a better way of approaching the estimation of the combined benefits of the two Bank projects. The benefits are twofold. First, the two projects combined allowed government spending nationally to increase by more than it would have otherwise done. If the donor assistance had stuck with the health sector, the two projects combined—including the spending targeted at specific provinces and other spending—would have added around US$ 200 million over the course of the seven-year

> 12 See pp. 2 and 7 of the population and family health project report (World Bank 2004). 13 See p.2 of the population and family health project report (World Bank 2004).
# **I.   INTRODUCTION**

Over the last two decades, many countries liberalized bank activities that traditionally had been heavily regulated and protected from competition. As part of this process, foreign banks, which had previously played only a marginal role, have established a substantial presence in the banking systems of several middle-income and developing countries. That presence has taken a variety of forms, ranging from the acquisition of domestic institutions with extensive branch networks to the establishment of isolated representative offices aimed at serving niche market segments. However, in contrast to the growing debate on the merits and pitfalls associated with an extensive foreign bank presence in emerging markets, little attention has been paid to how that presence is established. In particular, the literature has largely ignored what determines whether banks operate as locally incorporated and independently capitalized subsidiaries or as branches when going overseas.

In this paper, using a newly put together database on the activities of the top 100 international banks worldwide, we try to fill this gap by examining what factors affect the type of organizational form under which banks operate in foreign markets. We focus on foreign bank operations in Latin America and Eastern Europe, the two regions that have witnessed the largest increase in foreign bank participation over the last decade. In both regions, current levels of foreign bank participation exceed 50 % of banking system assets in many countries.

There are at least two sets of reasons for which policy makers, bank users, and bankers should care about how foreign banks operate in host markets. First, the organizational form of foreign bank operations may affect the competitive structure of the local banking systems, threatening the profits and market share of domestic banks and affecting the price and quality of banking services in the host country. For example, foreign subsidiaries with extensive networks are in direct competition with local commercial banks for retail clients, while single-branch
<!-- Start of picture text --> Figure 12. Synchronisation of recessions<br>(in percent)<br>100<br>80<br>60<br>40<br>20<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Source: Claessens et al (2012a).<br>Note: The share of countries experiencing a recession is presented. The figure includes completed as well as<br>ongoing episodes. The sample contains quarterly data for 21 advanced economies over the period Q2 1960 to<br>Q4 2010.<br><!-- End of picture text --> 72
# **Annex 2. Cross-country comparisons of the progressivity of direct and indirect taxes**

**Figure A2.1 Kakwani Index of All Direct Taxes Figure A2.2 Kakwani Index of All Indirect Taxes**

<!-- Start of picture text --> Sri Lanka (2010) Ethiopia (2011)<br>Peru (2009) Peru (2009)<br>el Salvador (2011) Mexico (2010)<br>Chile (2009) Sri Lanka (2010)<br>Croatia (2014) Chile (2009)<br>Guatemala (2010) el Salvador (2011)<br>Ethiopia (2011) Albania (2015)<br>Mexico (2010) Indonesia (2012)<br>Albania (2015) Brazil (2009)<br>Georgia (2013) Russia (2010)<br>Brazil (2009) South Africa (2010)<br>South Africa (2010) Guatemala (2010)<br>Russia (2010) Armenia (2011)<br>Armenia (2011) Georgia (2013)<br>0 0.2 0.4 0.6 -0.3 -0.2 -0.1 0 0.1<br><!-- End of picture text --> Sources: Armenia (Younger et al, 2014), Brazil (Higgins and Pereira, 2014), El Salvador (Beneke et al, 2015), Ethiopia (Woldehanna et al, 2014), Georgia (Cancho and Bondarenko, 2015), Guatemala (Cabrera et al, 2014), Mexico (Scott, 2014), Peru (Jaramillo, 2014), Russia (Lopez Calva et al, 2015), Uruguay (Bucheli et al, 2014), and South Africa (Inchauste et al, 2015). Note: contributory pensions treated as part of market income.

# **Figure A2.3 Change in Gini (from market to final income) and size of Government (government expenditures/GDP)**

<!-- Start of picture text --> 0.000<br>-0.020 0 5 10 15 20 25<br>-0.040<br>-0.060<br>-0.080<br>-0.100 R2 = 0.1858<br>-0.120<br>-0.140<br>-0.160<br>-0.180<br>-0.200<br>General government final consumption expenditure (% of GDP)<br>Market to Final Income<br>Chnage in Inequality from<br><!-- End of picture text --> Sources: Own estimates for Albania; for other countries see Figure 4. General government final consumption expenditure (% of GDP) from WDI.

Note: A negative value indicates a reduction in inequality. For Jordan, data on government expenditures using 2009 since data for 2010 is not available. For Ethiopia, data on government expenditure using 2011. Data for 2010 not available.

38
for households with no access to clean water.<sup>8</sup> CHWs are recruited locally and their training follows the C-IMCI (Rosales and Weinhauer (2003)), a set of guidelines for community healthworkers that incorporates the WHO’s and UNICEF’s “Integrated Management of Childhood Illness” recommendations for when to refer children to formal health care (WHO (2014); WHO Department of Child and Adolescent Health and Development (2005); see also below).

Note that the role of the CHW may be more expansive in locations where formal care is less easily accessible. In rural areas, the CHW may for example provide basic medications and administer rapid tests. This is not the case in the CHW program we consider here. While CHWs programs were initially conceived as a way to reach patients in remote locations, many CHW work in practice in urban or peri-urban areas. Comprehensive data is sparse, but even in national CHW programs that target rural populations, a significant share of healthworkers operate in urban areas (e.g. 20% of CHW in Pakistan, Brook (2009)). In Mali, 6% of CHW work in Bamako ((Pascal Saint-Firmin et al., 2021)).

The research design took advantage of the second planned roll-out wave of AfH. Mali Health conducted a census in their expansion area in mid-2012 to enumerate all families with children under five years of age (or a pregnant mother) who satisfied a proxy-means test designed to select approximately the poorest third of households. After the baseline survey in 2012, data were collected in two surveys in 2013 and 2014 during the period of highest malaria and diarrhea incidence (September-November). All households identified in the census that were found at baseline were included in the random assignment to the different treatment groups and revisited in 2013.

In this study we use demographic, location, and household asset data collected in the baseline survey, and daily health diary data on children collected during the 2013 follow-up. The unit of randomization is the compound. A compound may house more than one household,

> 8We analyze effects of the two interventions on preventive care and longer-term health in a separate paper. We find no impact on illness incidence in the 2013 data used here, and measured chlorination levels in 2014 do not significantly differ between treatment groups. For more detail, see section 5 below and Dean and Sautmann (2023).

10
<!-- Start of picture text --> Dictator Game - Baseline to Endline - Refugee<br>Effect on Giving<br>Effect on Group Bias Oh<br>—___?<br>><br>2 1.5 A -5 0 5<br>° oLs © Controls<br>® Weights © Controls + Weights<br>© Cluster<br><!-- End of picture text --> <!-- Start of picture text --> Dictator Game - Baseline to Endline - Host<br>Effect on Giving<br>Effect on Group Bias<br>1 -5 0 5 1<br>© oL“s © Controls<br>® Weights © Controls + Weights<br>© Cluster<br><!-- End of picture text --> <!-- Start of picture text --> Stag Hunt - Baseline to Endline - Host<br>Effect on Giving<br>Effect on Group Bias<br>-4 -2 0 od 4<br>e®oL“s ® Controls<br>© Weights © Controls + Weights<br>© Cluster<br><!-- End of picture text --> <!-- Start of picture text --> Stag Hunt - Baseline to Endline - Refugee<br>Effect on Giving<br>Effect on Group Bias<br>1 -5 0 5<br>© oL_s ® Controls<br>® Weights © Controls+ Weights<br>© Cluster<br><!-- End of picture text -->
<!-- Start of picture text --> Percent Percent<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>30<br>20<br>10 20<br>0 10<br>pe © SD 2)/ 8 86 SF 2)/ 2 B GS @ 0<br>9 9 SF SFS/B ®B SF F/B B SF S CBE SR Be SR Be eR Ge eR Sle K SSS<br>Advanced economies EMDEs LICs<br>EAP ECA LAC MNA SAR SSA<br>mg Agriculture = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others mw Agric. = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>100 Percent Percent<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>te) te) fo} © re) w © (oe) wo te) (oe) (oe)<br>~ a = = N fo) ro) — NS fp) ro) — ONOCDNNODANNDODANNODANNHNODANNOM<br>Oo= Oo= fo)nN oOro (o>)= (o>)= oOnN oOnN (o>)= Oo= lo)“ oOnN BwWBROrrMDHDDOODDOCODDOCODHDDCODHRDOCODHCORh OrrROrrROrrThROrr hR Ort<br>Advanced economies; EMDEs LICs TUTNNEAP TH ECATK NNT LACTH NN TH MNAKH NN TOKO SARKHON NTO SSAONG<br>m Agriculture = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others m Agric. = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others<br><!-- End of picture text -->
The first term on the right-had side is the growth component of the change in poverty, and is constructed as the proportional difference between the initial poverty measure and a hypothetical poverty measure computed using the second period mean but the first period Lorenz curve. The second term is the distribution component which is computed as the proportional difference between the initial poverty measure and a hypothetical poverty measure constructed using the first period mean but the second period Lorenz curve. These two components are the discrete-time analogs of the two terms in Equation (3). Unlike Equation (3), however, there is also a residual term because the decomposition is done over a discrete and not an infinitesmal interval. I measure the proportional changes on the left- and right-hand side of Equation (5) as log differences and normalize by the length of the interval to get average annual percent changes in poverty and its growth and distribution components for each spell.

Tables 2 and 3 report the results of applying this decomposition to the two datasets of spells. Throughout these two tables, I use the following variance decomposition to summarize the relative importance of the various components. If X and Y are two correlated random variables, then I define the share of the variance of VAR(X) + COV(X, Y) X+Y due to variation in X as .<sup>7</sup> The top panel of each VAR(X) + VAR(Y) + 2 ⋅ COV(X, Y) table documents the importance of the residual relative to the sum of the growth and distribution components of the change in poverty. The first column shows the variance of the sum of the growth and distribution components, the second column the variance of the residual, and the third the covariance between the two. The final column reports the share of the variance of changes in poverty due to the growth and distribution components, which is virtually one for all poverty measures. This simply reflects the fact that the variance of the residual term is tiny relative to the variance in measured changes in poverty. This can also be verified visually from the top panel of Figure 4, which

> 7 When X and Y are normally distributed, this variance decomposition has a very natural interpretation.  It tells us how much the conditional expectation of X increases for each unit that we observe the sum (X+Y) to be above its mean value.

15
**Table 3. Changing Indebtedness, first 4 years of 10**<sup>**th**</sup> **Plan period**

## **(% points of GDP)**

||2002/03|2003/04|2004/05|2005/06|
|---|---|---|---|---|
|1.Increaseindebt|5.1|0.0|-2.1|-2.0|
|2. Primary Deficit|3.2|2.7|1.4|1.3|
|3. Real GDP growth|-2.8|-6.6|-6.7|-6.7|
|4. Real interest rate|4.0|3.5|3.1|3.2|
|5. Real exchange rate change|-0.3|-0.5|-0.3|0.1|
|6. Financial Sector Recapitalization|0.1|0.2|0.0|0.0|
|7.Divestment|-0.1|-0.6|-0.1|-0.1|
|8.Residual(1.minus sumof 2.to 7.)|1.1|1.4|0.4|0.1|

_Source_ : Authors’ calculations based on Handbook of Statistics on Indian Economy, Reserve Bank of India

# 2.1.c. From Rising Debt to Rising Growth

From Tables 2 and 3, it is obvious that the resumption of strong growth in 2003/04 has been the most significant factor driving the debt-to-GDP ratio in recent years. This growth has been broad-based and most significantly, has included manufacturing, which stagnated during the Ninth Plan period, as shown in Table 4. It is also the most tax-buoyant sector of the economy and could explain, via increased taxes, the big fall in the primary deficit also evident in Table 3.

**<u>Table 4. Real GDP growth at factor cost (%)</u>**

||85/86<br>-89/90<br>(avg.)|8th<br>Plan<br>(avg.)|9th<br>Plan<br>(avg.)|10th<br>Plan<br>02/03<br>-05/06<br>(avg.)|02/03|03/04|04/05|05/06|
|---|---|---|---|---|---|---|---|---|
|Agriculture, forestry & fishing|3.1|4.7|2.3|1.9|-6.9|10.0|0.7|3.9|
|Industry|7.6|7.6|3.4|8.0|7.0|7.6|8.6|8.7|
|of which, manufacturing|7.8|9.8|1.2|7.7|6.8|7.1|8.1|9.0|
|Services|7.6|7.6|8.6|8.9|7.3|8.2|9.9|10.0|
|GDP|6.0|6.7|5.5|7.0|3.8|8.5|7.5|8.4|

_Source_ : Authors’ calculations based on Handbook of Statistics on Indian Economy, Reserve Bank of India We hypothesize that the growth rebound since 2003/04 stems from a more efficient microeconomic environment than has prevailed at any time since 1991 (and probably since independence). IMF (2002) suggests that India’s trend growth had fallen to 5-6% of GDP during the late 1990s, and to perhaps as low as 5% in 2001/02, reflecting lingering structural distortions, infrastructure bottlenecks, and remaining industrial and agricultural controls. However, the transitional recession of the late 1990s led to a process of creative destruction that lay the basis for the subsequent manufacturing growth. The main impetus to growth has come from increased competition and hardened budget constraints as a result of the economic liberalization and cuts in trade and financial repression taxes, which have forced firms and banks to become more efficient, the microeconomic counterpart of the macroeconomic slide during the 1990s. The efficacy of hard budgets and competition in stimulating micro restructuring has been convincingly established in firm-level studies in the transition countries of Central and Eastern Europe.<sup>16</sup> 16 The first systematic evidence was presented in Pinto, Belka and Krajewski (1993), later confirmed by a 3000-firm survey conducted by EBRD reported in Carlin, et al. (1999).

9
# **Trade and Innovation in MENA**<sup>1</sup>

Matteo Ficarra (Graduate Institute), Meryem Gökten (CEPS), Péter Harasztosi (EIB), Zsóka Koczán (EBRD), Roberta Lesma (EIB), Rozália Pál (EIB), Christoph Weiss (EIB) JEL code: D22, F14, 014, 031 Keywords: Trade, innovation, firm performance, business environment, COVID-19

> 1 Ficarra: PhD student, Graduate Institute. Email: <u>m.ficarra@eib.org. Gökten: Researcher, CEPS. Email: meryem.goekcegoekten@ext.eib.org. Harasztosi: Economist, European Investment Bank. Email: p.harasztosi@eib.org.</u> Koczán: Associate Director, Senior Economist, European Bank for Reconstruction and Development. Email: <u>koczanz@ebrd.com. Lesma: Trainee, European Investment Bank. Email: r.lesma@eib.org. Pál: Economist, European</u> Investment Bank. Email: <u>r.pal@eib.org. Weiss: Senior Economist, European Investment Bank. Email: c.weiss@eib.org.</u> Matteo Ficarra and Meryem Gökten were trainee and consultant at the European Investment Bank while working on this paper. This paper was commissioned by the World Bank Middle East and North Africa Chief Economist office as part of the activity “Assessing the MENA formal Private Sector.” The activity is task managed by Asif M. Islam. The study is a background paper for the Chapeau report titled “Unlocking Sustainable Private Sector Growth in the Middle East and North Africa: Evidence from the Enterprise Survey.” The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors or the Governments they represent. The opinions also do not reflect the views of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) or other institutions the authors may be affiliated with. All errors and omissions are the authors’ responsibility. The authors thank Çağatay Bircan, David Francis, Sergei Guriev, Barbara Marchitto and Debora Revoltella for helpful discussion and comments.