rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Greater concentration of economic activity in a few places is part of the spatial transformation that accompanies development. In fact, the main message of the World Bank’s latest World Development Report “ _Reshaping Economic Geography_ ” (WDR2009, World Bank 2008) is that economic growth will be geographically unbalanced, and trying to spread out economic activity is tantamount to discouraging it. On the other hand, living standards can spatially converge if policies facilitate economic integration – between lagging and leading places. And the WDR2009 identifies that investments to improve health, education and information in lagging areas along with efforts to encourage labor mobility are most effective for economic integration.

However, policymakers in many countries do not view increasing economic concentration as a beacon of progress. Rather, they actively try to stimulate economic growth in areas not favored by the market in an attempt to balance economic activity across the national territory. Place specific investments such as infrastructure to reduce transport costs and improve accessibility of peripheral regions are viewed as integral components of territorial development policies. _But are these policies fighting market forces of concentration? Or are they adding net value to the national economy by tapping underexploited resources?_ Answers to these questions are rarely reflected in spatial policy design— often because information on regional constraints to growth are limited and policymakers lack empirical evidence to inform their decisions. This paper contributes to the debate on the spatial allocation of infrastructure investments by examining _where_ infrastructure investments will generate the highest economic returns – “spatial efficiency”, and identify if there are tradeoffs when infrastructure coverage is made more equitably across regions – “spatial equity”. To empirically identify spatial efficiency – equity tradeoffs, we examine the factors that entrepreneurs value when deciding where to locate

-  2
**Table 1: Descriptive Statistics**

|**Variable**|**Obs.**|**Mean**|**S.D.**|**Min.**|**Max.**|
|---|---|---|---|---|---|
|_Explanatory_||||||
|Female|1,394|0.63|0.48|0.00|1.00|
|Age|1,394|31.77|8.80|18.00|50.00|
|Speaks indigenous as mother tongue|1,394|0.10|0.30|0.00|1.00|
|Lima region|1,394|0.29|0.45|0.00|1.00|
|Coastal region|1,394|0.28|0.45|0.00|1.00|
|Andes region|1,394|0.22|0.41|0.00|1.00|
|Jungle region|1,394|0.22|0.41|0.00|1.00|
|Years of schooling|1,394|11.40|3.18|1.00|19.00|
|Mother has less than primary education level|1,331|0.18|0.38|0.00|1.00|
|Mother has incomplete or complete primary education level|1,331|0.43|0.49|0.00|1.00|
|Mother has incomplete secondary education|1,331|0.09|0.29|0.00|1.00|
|Mother has complete secondary education|1,331|0.20|0.40|0.00|1.00|
|<br>Mother has incomplete higher education|1,331|0.02|0.16|0.00|1.00|
|Mother has complete higher education|1,331|0.08|0.27|0.00|1.00|
|Father has less than primary education|1,307|0.08|0.27|0.00|1.00|
|Father has incomplete or complete primary education|1,307|0.41|0.49|0.00|1.00|
|Father has incomplete secondary education|1,307|0.10|0.31|0.00|1.00|
|Father has complete secondary education|1,307|0.25|0.43|0.00|1.00|
|Father has incomplete higher education|1,307|0.03|0.16|0.00|1.00|
|Father has complete higher education|1,307|0.13|0.34|0.00|1.00|
|<br>Dummy for taking less than 1/2 hour to go to school|1,391|0.88|0.32|0.00|1.00|
|<br>_Skills_||||||
|Z‐Score of cognitive measure‐ numeric skills|1,394|‐0.06|1.02|‐3.37|2.34|
|Z‐Score of cognitive measure ‐ working memory|1,394|‐0.06|1.00|‐2.96|3.58|
|Z‐Score of cognitive measure ‐ verbal ability (PPVT)|1,394|0.07|1.00|‐4.19|2.76|
|Z‐Score of cognitive measure ‐ verbal fluency|1,394|0.04|1.01|‐2.51|4.62|
|Z‐Score of Big 5 ‐ Conscientiousness|1,394|0.12|0.95|‐4.02|1.71|
|Z‐Score of Big 5 – Kindness|1,394|0.04|1.01|‐5.65|2.09|
|Z‐Score of Big 5 – Extraversion|1,394|0.02|1.00|‐3.10|1.96|
|Z‐Score of Big 5 ‐ Openness to experience|1,394|‐0.02|1.01|‐3.37|1.83|
|Z‐Score of Big 5 ‐ Emotional stability|1,394|0.02|1.01|‐3.41|1.75|
|Z‐Score of Big 5 – Cooperation|1,394|0.01|0.98|‐5.01|2.96|
|Z‐Score of Grit ‐ Perseverance of effort|1,394|0.06|1.00|‐3.77|1.72|
|Z‐Score of Grit ‐ Consistency of interest|1,394|0.01|1.00|‐1.92|2.98|
|Z‐Score of Big 5 ‐ Stability (alpha)|1,369|0.05|0.74|‐3.56|1.28|
|Z‐Score of Big 5 ‐ Plasticity (beta)|1,392|0.02|0.72|‐2.38|1.42|
|<br>Z‐Score of Grit|1,394|0.04|0.75|‐2.86|2.12|
|Z‐Score of Cognitive measure|<br>1,394|0.00|0.76|‐2.24|2.43|
|<br>_Outcomes_||||||
|Log of hourly wage|824|1.25|0.88|‐3.26|5.15|
|Employed|1,394|0.62|0.48|0.00|1.00|
|Wage worker|869|0.51|0.50|0.00|1.00|
|White‐collar worker|868|0.60|0.49|0.00|1.00|
|Formal worker|869|0.49|0.50|0.00|1.00|

**18 |** P a g e
|||||Central African Republi|c|||
|---|---|---|---|---|---|---|---|
||Towns|São Tomé Príncipe (2.2)|Botswana (1.7)|(1.6)|Malawi (1.3)|Mauritius (1.2)|Seychelles (1.2)|
||Urban Cores|Seychelles (2.8)|São Tomé Príncipe (2.3)|Lesotho (2.1)|Guinea (1.2)|Senegal (1.2)|Togo (1.2)|
|Den. P90/P10|Urban Areas|Botswana (5.3)|Central African Republic<br>(3.6)|Kenya (3.1)|Malawi (1.8)|Mauritius (1.7)|Seychelles (1.4)|
||Cities|Namibia (11.9)|Zimbabwe (6.4)|Gambia, The (4.6)|Cabo Verde (1.7)|Seychelles (1.6)|Ghana (1.6)|
||Towns|Botswana (3.0)|Central African Republic<br>(2.5)|Namibia (2.5)|Lesotho (1.7)|Malawi(1.6)|Mauritius (1.4)|
||UrbanCores|Cabo Verde (6.7)|Rwanda (3.9)|Mauritius (3.8)|Benin(1.4)|Senegal(1.4)|Comoros (1.4)|
|Share largest|Urban Areas|Seychelles (65.9)|Gambia,The (50.0)|Mauritius (43.8)|Nigeria (6.7)|SouthSudan(6.5)|SãoToméPríncipe (3.0)|
||Cities|Seychelles (65.9)|Gambia,The (50.0)|Mauritius (43.8)|Nigeria (6.7)|SouthSudan(6.5)|SãoToméPríncipe (3.0)|
||Towns|Comoros (17.0)|Seychelles (2.8)|Cabo Verde (2.3)|Côte d'Ivoire (0.1)|Mali(0.1)|Nigeria (0.0)|
||UrbanCores|CongoBrazzaville (35.2)|Gambia,The (32.5)|Gabon(31.5)|Ethiopia (3.8)|SouthSudan(3.1)|SãoToméPríncipe (1.4)|
|Zipf's coef.|Urban Areas|Cabo Verde (-0.4)|Gambia,The (-0.5)|Lesotho (-0.5)|Chad (-1.1)|Niger(-1.3)|SãoToméPríncipe (-1.3)|
||Cities|Lesotho (-0.3)|Eswatini(-0.3)|Rwanda (-0.4)|Côte d'Ivoire (-1.1)|Niger(-1.1)|Mali(-1.2)|
||Towns|Botswana (-0.7)|Eswatini(-0.8)|Comoros (-0.8)|Liberia (-1.6)|SierraLeone (-1.6)|SãoToméPríncipe (-2.5)|
|**Notes:** _No units:_Nu<br>population._Dens. P_|UrbanCores<br>mber of units del<br>_90/P10:_Inter-dec|Botswana (-0.5)<br>ineated._Average pop.:_Average<br>ile ratio for population density._S_|Cabo Verde (-0.6)<br>population of the units, in person<br>_hare largest:_Population share o|Eswatini(-0.7)<br>s._Average area:_Average ar<br>f the largest unit._Zipf's coef._|Liberia (-1.1)<br>ea of the units, in km<sup>2</sup>._Pop. P75/P_<br>_:_Zipf's coefficient.|SierraLeone (-1.1)<br>_25:_Inter-quartile ratio for populati|Mali(-1.2)<br>on._Pop. P90/P10:_Inter-decile ratio for|

**Notes:** _No units:_ Number of units delineated. _Average pop.:_ Average population of the units, in persons. _Average area:_ Average area of the units, in km<sup>2</sup> . _Pop. P75/P25:_ Inter-quartile ratio for population. _Pop. P90/P10:_ Inter-decile ratio for <u>population.</u> _<u>Dens. P90/P10:</u>_ <u>Inter-decile ratio for population density.</u> _<u>Share largest:</u>_ <u>Population share of the largest unit.</u> _<u>Zipf's coef.:</u>_ <u>Zipf's coefficient.</u>

14
of trade for Sub-Saharan African countries have fared much more favorably in the current oil price cycle, relative to their respective starting points (figure 8).<sup>29</sup> <!-- Start of picture text --> Figure 7: Commodity and oil prices Figure 8: Terms of Trade Index in SSA<br>(Price indices, 2000=100) 1973-1980 and 1999-2006<br>(1973, 1999 = 100)<br>350<br>160.0<br>300 1999-2006<br>140.0<br>250 mineralsMetals and 120.0<br>200 100.0<br>1973-1980<br>150 Oil 80.0<br>100 60.0<br>Agriculture 1974 1975 1976 1977 1978 1979 1980<br>2000 2001 2002 2003 2004 2005 2006<br>50<br>Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07<br>Source: World Bank various years b. Source: AFRCE various years; World Bank various years.<br>Figure 9: Nominal and Real Price of Oil in SSA Figure 10: Official Development Assistance to SSA<br>(Price indices, 2000=100) (In 2000 US dollar and percent of GDP)<br>350<br>8 35<br>300 7 30<br>World nominal 6<br>250 price [USD] ODA as % of GDP 25<br>5<br>20<br>200 4<br>15<br>3<br>150<br>10<br>2 Total ODA to SSA (In 2000 US$)<br>100 Sub Saharan Africa 1 5<br>oil importers, real price<br>0 0<br>50<br>Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07<br>Source: World Bank, DECPG.<br>Source: World Bank various years b. Source: OECD DAC various years; World Bank various years h.<br>1970197219741976197819801982198419861988199019921994199619982000200220042006<br>O D A as % o f G D P<br>T o tal O D A , b illio n $U S<br><!-- End of picture text --> For oil-importing countries, the weakening of the U.S. dollar has also meant that the oil price in euros or local currency has not risen as much. For Sub-Saharan Africa in

> 29 The recovery of terms of trade in non-oil-exporting developing countries since the late 1990s is, however, still below the peaks of the early 1980s. Data are from the World Bank’s _World Economic Indicators_ .

24
The results show that mobility variables, when selected by LASSO, are generally powerful predictors of income and employment changes. When examining the initial impact, they explain between 27 percent (for farm income declines) and 40 percent (for work stoppage) of the explained variation. For the subsequent evolution, they explain between 34 percent (Nonfarm income decrease) and 46 percent (current employment) of the explained variation.

The importance of different Google search terms varies considerably by indicator. When predicting initial impacts in the first wave, searches related to food, money, and jobs account for 80 percent of the explained variation. When examining the same variable (the prevalence of total income declines) over time, important variables include food, exercise, and religion, which combined account for over 90 percent of the explained variation. When modeling work stoppage in the initial wave, search related to food, money, and religion contribute 36 percent of the variation. When modeling variation over time in current employment, searches related to food and religion contribute 27 percent of the variation, while jobs and stopping school contribute 5 percent each.

In general, Google mobility and search terms explain a much larger share of the variation than the satellite-based measures. Night-time lights, although included in the candidate set of variables, was not selected by lasso for any of the 18 models listed in Table 8. There are a couple of cases however where EVI and NO2 contribute significant variation, particularly EVI in the case of stopping school and not eating all day. NO2 is a significant determinant in particular of non-farm enterprise income gains, which is suggestive of a particularly strong association between household enterprises and mobility. Combined, the Google mobility measures and NO2 explain 51 percent of the variation in the prevalence of non-farm income losses and 59 percent of the prevalence of non-farm income gains.

# **5. Conclusion and Discussion**

This paper assesses the ability of real-time indicators to predict household survey-based wellbeing measures. Reference outcomes are provided by high-frequency phone surveys administered from the second quarter of 2020 to February 2021. The real-time data are publicly available and derived from the internet (Google mobility and Google search) or satellites (night lights, air pollution, and EVI).

22
Policy Research Working Paper 6798

# **Abstract**

This paper uses regression analysis to identify which country context, reform content, process, and project management variables predict the performance of public sector management projects, as measured by the Independent Evaluation Group’s project outcome ratings. The paper draws on data from a large sample of World Bank public sector management projects that were approved between 1990 and 2013. It contributes to an emerging literature that uses cross-country regressions to analyze public sector management reform patterns. The findings suggest that political context factors have a greater impact on the performance of public sector management projects than on other projects. Specifically, public sector management projects perform better in countries with democratic regimes than autocratic ones. They fare better in the presence of programmatic political parties and in more aid-dependent countries. Project managers’ subjective risk assessments predict performance in public sector management operations better than objective risk indicators. These findings suggest that the performance of public sector management projects would benefit from a better alignment of project design with political context and from a more open dialogue about risk between task team leaders and management.

This paper is a product of the Governance and Public Sector Management 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://econ.worldbank.org. The author may be contacted at jblum@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, 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 International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
43

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ED4<br>size|ED4<br>literacyrate|ED4<br>civilwars|BP5<br>small|BP5<br>literacyrate|BP5<br>civilwars|
|LIT||0.088|||0.072||
|||[0.078]|||[0.066]||
|RES*WAR|||0.098|||0.379|
||||[0.473]|||[0.498]|
|EXTD *WAR|||-0.101|||-0.028|
||||[0.096]|||[0.087]|
|WAR|||-0.439<br>[0.217]**|||-0.419<br>[0.212]**|
|Observations|1214|1071|1214|1191|1065|1191|
|R-squared|0.31|0.29|0.31|0.23|0.21|0.22|

_Notes_ : see note to Table 5. DREM is a dummy variable that takes the value 1 if in a given year the level of workers’ remittances over GDP of a country is above the 75th percentile value of that year’s (cross-country) distribution. * significant at 10%; ** significant at 5%; *** significant at 1%
# **2. Basel II: What’s new and is it relevant to developing countries?**

# **2.1 An Overview**

At first glance, the Basel II documentation is daunting. The New Accord currently runs to 216 pages, but there are literally thousands of pages of supporting documents depending on definitions<sup>9</sup> . The new Accord consists of 3 Pillars: (1) Minimum Capital Requirements, (2) Supervisory Review Process and (3) Market Discipline. However, to understand what’s new about Basel II, the Capital Accords need to be placed the context of the (wider) Basel Core Principles for Effective Banking Supervision (BCPs).

Figure 2 gives a very simple graphical representation of the Basel standards. The idea of the figure is the following. The Basel Core Principles cover a great deal of ground and more than encapsulate Basel I<sup>10</sup> . This is indicated in the figure as Basel I is depicted as totally within the space of the BCPs. Indeed, BCP number 6 on capital adequacy is normally understood as more general than any particular capital adequacy standard and arguably calls for the application of Basel I as a minimum<sup>11</sup> .

9 There are 216 pages to the English language .pdf version of the Third Consultative Paper (CP3), although there are 226 if one downloads the new Accord section by section. In other languages, the Accord is somewhat longer. The BIS website lists 103 documents published by the Basel Committee for Banking Supervision (BCBS) responsible for Basel II. While not all these documents are strictly relevant to Basel II, more than 50% would probably be deemed relevant and the average document length is well-over 20 pages.

10 The Basel Core Principles for Effective Bank Supervision are generally subdivided into 7 areas, (i) Objectives, autonomy powers and resources (BCP 1.1-1.6), (ii) Licensing and structure (BCP 2-5) (iii) Prudential regulations and requirements (BCP 6-15) (iv) Methods of on-going supervision (BCP 16-20) (v) Information requirements (BCP 21) (vi) Remedial measures and exit (BCP 22) (vii) Cross border banking (BCP 23-25)..

11 A counter argument is that Basel I is only intended for “internationally active banks” and hence may be considered to go beyond what would be required under the capital adequacy assessment for the BCPs. However, if Basel I is the minimum for larger, more diversified institutions, then in the context of a developing country, it seems reasonable that it should also be considered a minimum for smaller and more concentrated institutions. Many developing countries have implemented Basel I for all banks and, with stricter limits, reflecting this view. This is evident analyzing the World Bank, bank regulation database available on www.worldbank.org.

8
|Appendix2.FirstStageProbit|
|---|
|Variables|

|Variables|ProbitModel|
|---|---|
|Pre-privatization profits|_-0.7573_a<br>|
||(0.168)|
|Politicalaffiliationofunions|-0.1823b|
||(0.091)|
|LatinAmerica|-0.1941<br>|
||(0.243)|
|Asia|0.1790<br>|
||(0.298)<br>|
|Africaand MiddleEast|0.3652|
||(0.304)<br>|
|DevelopedCountries|0.3681<br>|
||(0.263)<br>|
|EnglishCommonLaw|-0.4983c|
||(0.277)<br>|
|German Commercial Code|-0.3351<br>|
||(0.213)<br>|
|ScandinavianCode|-1.0645b|
||(0.463)<br>|
|Openness|0.0011|
||(0.003)|
|Numberofobservations|308|
|PseudoRSquared|0.205<br>|
|F-statisticsonexcluded instruments|4.32|
|Prob>F|0.000|

_Note:_ This appendix p;esents the first-step<sup>regressionof the two-step procedure for oneof the potentially</sup> endogenous variables. Robust standard errors<sup>are givenin parentheses. Regressionsinclude agentbank</sup> dummy (not reported) a significant at I percent;<sup>bsignificantat 5 percent;</sup><sup>**c**significantat10percent.</sup> 40
Labor Demand Elasticities: Empirical Evidence from India,” <u>The Review of Economics and Statistics, August 2007, 89 (3), 466–481.</u>

- Helpman, Elhanan and Paul R. Krugman, <u>Market Structure and Foreign Trade: Increasing</u>

- <u>Returns, Imperfect Competition, and the International Economy, Cambridge:</u> The MIT Press, 1985.

- , Marc J. Melitz, and Stephen R. Yeaple, “Export Versus FDI with Heterogeneous Firms,” <u>The</u>

- <u>American Economic Review, March 2004, 94 (1), 300–316.</u>

- Manova, Kalina and Zhiwei Zhang, “Export Prices across Firms and Destinations,” November 2010. Working Paper.

- Melitz, Marc J., “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” <u>Econometrica, November 2003, 71 (6), 1695–1725.</u>

- and Saso Polanec, “Dynamic Olley-Pakes Decomposition with Entry and Exit,” December

- 2010. INNODRIVE Working Paper No. 11.

- Menezes-Filho, Naercio Aquino and Marc-Andreas Muendler, “Labor Reallocation in Response to Trade Reform,” November 2007. Working Paper.

- Nataraj, Shanthi, “The Impact of Trade Liberalization on Productivity and Firm Size: Evidence from India’s Formal and Informal Manufacturing Sectors,” September 2010. Working Paper.

- Olley, G. Steven and Ariel Pakes, “The Dynamics of Producticity in the Telecommunications Equipment Industry,” <u>Econometrica, 1996, 64 (6), 1263–1297.</u>

- Pavcnik, Nina, “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants,” <u>Review of Economic Studies, 2002, 69 (1), 245–276.</u>

- Sivadasan, Jagadeesh, “Barriers to Competition and Productivity: Evidence from India,” <u>The B.E. Journal of Economic Analysis & Policy, 2009, 9 (1 (Advances)), Article 42.</u>

- Topalova, Petia and Amit Khandelwal, “Trade Liberalization and Firm Productivity: The Case of India,” <u>The Review of Economics and Statistics, forthcoming.</u>

- Trefler, Daniel, “The Long and Short of the Canada-U.S. Free Trade Agreement,” <u>The American Economic Review, September 2004, 94 (4), 870–895.</u>

30
2 and Krugman (1985). The present paper is not concerned with this topic.

Secondly there are theories which allow for oligopoly and strategic interactions among firms, and which introduce the idea that government policies, such as export subsidies or tariffs, may shift profits from a foreign firm to its domestic competitor, and that this may yield a national gain, at least provided the foreign government does not retaliate. Hence these theories have normative, implications, with possible policy relevance. This "profit-shifting" concept originated with a series of papers by James Brander and Barbara Spencer (1981, 1983, 1984, 1985) and it has generated a massive and highly sophisticated literature.<sup>2</sup> It has been a genuinely new contribution to the theory of trade policy to attempt to allow for oligopoly and strategic interactions among private firms, as distinct from governments. A few earlier papers - notably Johnson (1953) - have dealt with oligopolistic interactions among tariff-imposing governments in the theory of tariffs and retaliation. But there has not been a 2Two comprehensive books on this subject are Krugman ed. (1986) and Helpman and Krugman (1989). In addition, there are several very useful surveys. One which is very much in the spirit of the present paper, is by Deardorff and Stern (1987). The outstanding critique is by Grossman (1986). A comprehensive survey, with many references and discussing also the broader issues, is by Stegemann (1989). Other surveys of aspects of the subject, apart from those cited in the text, are by Dixit (1984), Grossman and Richardson (1985), Greenaway and Milner (1986, Chapter 12), Caves (1987), and Richardson (1989). With regard to policy implications, in particular, see Bhagwati (1989), who concludes "I am strongly opposed to strategic trade policymaking: whether to shift profits to oneself or to retaliate against rivals allegedly doing this."
6 The Dooley method can, thus, be described as: Stock of Unreported Foreign Claims **=** A + B + C - D

## where

# A. Cumulative Recorded non-equity Balance of Payments Assets

= AI + A2 + A3 + A4 + A5 + _So_ + A7 + A8 o/w Other Long-term Capital (Assets) Al ?ortfolio investment (IMF BoPY **lines** 53,36) A2 Resident official sector (IMF BoPY lines 62..64) A3 Depo0t money banks (IMF BoPY lines 69..71) A4 Other sectors (IMF BoPY lines 77..79) o/w other Short-term Capital (Assets) AS Resident official sector (IMF BoPY lines 84.. 85) A6 Deposit money banks (IMF BoPY line 89) A7 Other sectors (IMF BoPY lines 93..94) A8 Reserves (IMF BoPY lines 98.. 109)

- B. Cumulative Errors and Ormissions (IMF BoPY line 112)

- C. Adjustmenit for Unrecorded Claims

- Stock of external debt as reported to the World Bank (see Section 4) Cumulative Recorded Balance of Payments Liabilities

where: Cumulative Recorded Balance of Paymients Liabilities = C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8 o/w Other Long-termn Capital (Liabilities) Cl Portfolio investr"!nt (IMF BoPY lines 54,55,57,58)

- C2 Resident officiai aector (IMF BoPY lines 65..68)

- C3 Deposit money banks (IMF BoPY lines 72..76)

- C4 Other sectors (IMF BoPY lines 80..83)

- o/w Other Short-term Capital (Liabilities)

- C5 Resident official sector (IliF BoPY lines 86. .88)

- C6 Deposit money banks (IMF BoPY lines 90..92)

- C7 Other sectors (IMF BoPY lines 95..97)

- C8 Reserves: IMF credit (IMF BoPY lines 110.. 111)

Consequently, flow figures are more meaningful than stock figures because they do not depend on any assumptior for **the** initial stock value.