rafmacalaba/datause-encoder-data huggingface dataset:
17 Figure 2 (continued): Rice NRAs and international rice price, South and Southeast Asia, 1970 to 2005 (left axis is int’l price in current US dollars, right axis is weighted average NRA in percent)

# (c) Sub Saharan Africa

<!-- Start of picture text --> 600 50<br>500 30<br>400 10<br>300 -10<br>200 -30<br>100 Intern. Price in -50<br>USD<br>0 -70<br>NRA (%)<br>Intern. price (USD)<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Correlation coefficient is -0.72 Note: Countries included are Cote d’Ivoire, Ghana (except for 1983), Madagascar, Mozambique (except for 1970-75, 1997-98, 2002-04), Nigeria, Senegal, Tanzania (except for 1970-75), Uganda, Zambia.

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008).
liabilities between 2002 and 2007 was similar across the two areas, the net external liabilities of the Euro Periphery continued to grow rapidly after 2007, whereas emerging Europe has seen a much smaller deterioration during the crisis.

**Table 3.1. Net Foreign Assets, Percent of GDP**

||Country|2002|2007|2009|
|---|---|---|---|---|
|**New Member States**|Bulgaria|-30|-95|-117|
||Czech Republic|-18|-45|-47|
||Estonia|-60|-79|-84|
||Hungary|-74|-96|-129|
||<br>Latvia|-44|-80|-87|
||Lithuania|-37|-60|-62|
||Poland|-38|-58|-66|
||Romania|-24|-50|-66|
||Slovak Republic|-27|-61|-73|
||<br>Slovenia|-2|-25|-39|
|**Candidates and Potential Candidates**|Albania|-14|-28|-50|
||Bosnia and Herzegovina|-29|-36|-57|
||<br>Croatia|-30|-103|-86|
||Macedonia, FYR|-40|-47|-66|
||Serbia|n/a|n/a|n/a|
||Turkey|-37|-49|-45|
|**Eastern Partnership Countries**|Armenia|-64|-23|-48|
||Georgia|-64|-84|-121|
||<br>Moldova|-93|-62|-76|
||Ukraine|-29|-23|-39|

_Source_ : Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: This table reports foreign asset minus foreign liabilities.

**Table 3.1a. Net Foreign Assets, Percent of GDP**

|Country|2002|2007|2009|
|---|---|---|---|
|Euro Periphery|-48|-77|-102|
|Emerging Latin America|-47|-24|-24|
|Emerging Asia|-15|0|-10|
|New Member States|-35|-65|-77|
|Candidates and Potential Candidates|-30|-52|-61|
|Eastern PartnershipCountries|-63|-48|-71|

Source: Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: These tables report foreign asset minus foreign liabilities.

It might be argued that the debt component of the external balance sheet poses more problems than the equity component in relation to overhang issues. Accordingly, we show the net debt and net equity positions in Table 3.2. For the new member states, Table 3.2 shows sizeable growth in net debt liabilities between 2002 and 2007, from 2 percent of GDP to 18 percent of GDP. There has been further expansion during the crisis, rising to 27 percent of GDP in 2009. Table 3.2 also shows that net equity liabilities are far greater than net debt liabilities for the new member states. Moreover, the relative stability of net equity positions between 2007 and 2009 also highlights the stabilizing role played by equity-type liabilities - the value of these liabilities naturally falls with a decline in performance in these economies.

A similar message holds for the other countries. Indeed, net debt liabilities for this group actually fell between 2002 and 2007, before increasing during the crisis period. Between 2002 and 2007, these countries saw rapid growth in net equity liabilities. Figure 3.1 shows the scatter of net equity versus net debt positions for 2009 for the whole sample of countries.

9
# **Appendix A: Details of Question Design, Administration, and Item Responses Rates**

## **A.1 Wording of Questions in English and Spanish**

|**ASCD.9**|When making<br>**effective** **in re**|agreemen<br>**solving or**<br>|ts with**sup**<br>**preventin**|**pliers**, please in<br>**g**problems.**[R**|dicate to<br>**EAD OUT**<br>|what degree ea<br>**OPTIONS]**|ch of the following is<br>**SHOW CARD 8**<br>**’**|
|---|---|---|---|---|---|---|---|
|||Not at<br>all|Slightly|Moderately|Very<br>much|Extremely|**DONT KNOW**<br>**(SPONTANEOUS)**|
|Personal relations<br>**ASCd9a**|hip and trust|<br>1|2|3|4|5|**-9**|
|Mutual interest in<br>business relations<br>involving others|maintaining<br>hip, without<br> **ASCd9b**|1|2|3|4|5|**-9**|
|Paid, private dispu<br>**ASCd9d**|te resolution|1|2|3|4|5|**-9**|
|Assistance of gov<br>officials|ernment<br> **ASCd9e**|1|2|3|4|5|**-9**|
|Intervention of ot<br>(excluding paid, p<br>resolution<br>and government o<br>**ASCd9c**|her third-parties<br>rivate dispute<br>fficials)|1|2|3|4|5|**-9**|
|Legal system|**ASCd9f**|1|2|3|4|5|**-9 **|

**ASCD.9** Por favor indique en qué medida las siguientes circunstancias son efectivas para resolver o evitar <u>problemas en los acuerdos con proveedores.</u> **<u>LEER OPCIONES -MOSTRAR TARJETA 8</u>**

||Para<br>nada|Ligera-<br>mente|Modera-<br>damente|Bastante|Extreme-<br>damente|**NO SABE**<br>**(ESPONTÁNEO)**|
|---|---|---|---|---|---|---|
|Relaciones personales y confianza<br>**ASCd9a**|1|2|3|4|5|**-9**|
|El interés mutuo de mantener una<br>relación de negocios sin tener que<br>involucrar a terceros**ASCd9b**|1|2|3|4|5|**-9**|
|Mecanismos de resolución<br>privados ofrecidos por terceros y<br>que son pagados**ASCd9d**|1|2|3|4|5|**-9**|
|Ayuda de funcionarios del<br>gobierno**ASCd9e**|1|2|3|4|5|**-9**|
|Intervención de otros terceros<br>(excluyendo entes privados y<br>pagados y personas del gobierno<br>**ASCd9c**|1|2|3|4|5|**-9**|
|Recurso al sistema legal<br>**ASCd9f**|1|2|3|4|5|**-9**|

Appendixes, page 1
To begin we produce a score for each dimension within a given pillar, which, unless otherwise stated, is an unweighted average of the indicators within that dimension.

where S c c is an indicator _i_ (e.g. population census score) in dimension _d_ , pillar _p_ , time cc period _t_ , and country SS. SSIIDD _c_ , and is the number of indicators in dimension _d_ . For instance, the score II for the Standards and Methods dimension is obtained by taking the unweighted average of all II indicators in this dimension, including the indicators for the system of national accounts in use, national accounts base year, classification of national industry, CPI base year, and classification of household consumption (Appendix A, Table A1).

After computing a score for each dimension, a score for each pillar is computed as the average score of the dimensions in in that pillar. For pillars 1, 2, 4, and 5, the unweighted average of the dimensions within each pillar is taken. For pillar 3 on data products, we take a weighted average of the dimensions, where the weights are based on the number of SDGs in each dimension (6 SDGs in dimension 3.1 on social statistics, 6 SDGs in dimension 3.2 on economic statistics, 2 in dimension 3.3 on environmental statistics, and 2 in dimension 3.4 on institutional statistics).<sup>10</sup> This reflects a perspective that all SDGs are of equal importance, and therefore the dimensions are weighted accordingly. Additionally, for Pillar 4 on data sources, censuses and surveys are given separate weights, so that censuses, surveys, admin data, and geospatial data each receives a weight of 1/4. While censuses and surveys are in the same pillar in the framework, and therefore each would typically only receive a weight of 1/6 in this dimension, because of their importance in

> 10 SDG 14 - Life Below Water - is omitted because land-locked countries do not report on these indicators.

9
# **Normative Indicators Combining Poverty and Mortality: A Survey**

Benoit Decerf<sup>1</sup> World Bank Keywords: Poverty measurement, Mortality paradox, Poverty-adjusted life-expectancy, Multidimensional poverty JEL Codes: D63, I32

## 1 World Bank, Development Research Group. Email : <u>bdecerf@worlbank.org.</u>

Acknowledgments : This survey will appear as a chapter in the forthcoming Handbook of Research on Measuring Poverty and Deprivation (Silber 2022). I am grateful to Jacques Silber and to an anonymous referee for useful comments and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and should not be attributed in any manner to the World Bank, its affiliated organizations, or members of its Board of Executive Directors or the countries they represent.
Table 5: Policy intervention/shock: Climate Change Regional Differential Impact on Rainfall and Water Supply Using the CGE Model Simulation Results for Mexico

|**Impact on**|**North**|**Central**|**Southwest**|**Southeast**|**Rio Bravo**|
|---|---|---|---|---|---|
|Land value(Dry)||||||
|Land Value(Irrig)||||||
|Employment(Rural)||||||
|Household income<br>(Rural,all classes)||||||
|**Comments**|Climate ch<br>different f<br>on the fiv<br>RBB, Nor<br>33% in So<br>prices are<br>other dura<br>rainfed re<br>holds for r<br>rents also|ange is exp<br>orce. A dif<br>e regions, n<br>th, Center a<br>uth West.<br>increased, d<br>ble commo<br>place irrigat<br>ural wages<br>dropfor th|ected to affect d<br>ferential water a<br>amely reduction<br>ns South East, r<br>Most crop yields<br>iary and livestoc<br>dities and servic<br>ed crops. Urban<br>in the North an<br>e North,the RB|ifferent regions<br>vailability change<br>of 50, 50, 4, and<br>espectively, and<br>and area are red<br>k prices are redu<br>es. More croppi<br>real wages drop<br>d in the RBB. Irr<br>B,and the South|of Mexico in a<br>was imposed<br>6.5 % on<br>an increase of<br>uced and crop<br>ced as well as<br>ng under<br>and the same<br>igated lands<br>east.|

49
another 20%. The survey was focusing on nonfarm enterprises, but did not cover commercial farms. A stratified multi-stage cluster sampling was used for each survey module. To ensure representation of all geographical and climatic zones, mainland Tanzania was initially stratified into seven zones based on agro-ecological characteristics. One region from each geographical zone was selected into the sample. Thus, Morogoro, Kilimanjaro, Tabora, Kagera, Kigoma, Mtwara and Mbeya were selected respectively from the East, Northern Highland, Central, Lake Victoria, West, Southern and Southern Highland zones.<sup>4</sup> By focusing on rural enterprises beyond major urban centers, the survey overcomes some of the biases inherent to traditional analysis. Moreover, inclusion of community data facilitates the understanding of objective factors shaping the investment climate, rather than relying on perceptions only.

The paper provides several methodological and emprical insights. At a methodological level, thanks to subtantive innovations in data collection, we can build on pioneering work that has highlighted the importance of informal private sector development in rural and urban areas in Africa (Liedholm and Mead 1999; Liedholm, 2002). In this paper we focus at the determinants of _rural_ enterprise growth and the _rural_ investment climate. Furthermore, at the emprical level, we are able to identify and rank major constraints to private sector growth. Our main findings suggest that private sector constraints in rural Tanzania mainly operate from the supply-side. In particular, better access to finance, infrastructure and cell phone communication is significanlty correlated with higher enterprise employment growth. Demand-side contraints are relatively less important due to the good performance of the agricultural sector during 2000-2005. We show that enterprises operate mainly in agricultural retail and wholesale trade. Small and young enterprises grow faster, a powerful finding

> 4 A comparison of mean for standard indicators was made between the 2005 Rural Investment Climate Survey (RICS) and the 2001 Household Budget Survey (HBS) to evaluate sample population validity (World Bank, 2007a). The main findings suggest that the 2005 RICS can be considered as semi-representative at the national level, though sample weights were not used for the present analysis. Throughout the paper we therefore selectively complement our analysis of the 2005 RICS with the 2001 HBS.

4
shifts probably varied across countries and sectors. For example, Latin American manufacturing exporters probably faced lower prices and increased competition as China increased its market share in world markets. On the other hand, the increase of Chinese raw material imports probably contributed to the recent increase in commodity prices. As world manufacturing prices decreased, workers employed in manufacturing sectors faced downward pressures on their wages, forcing some of them to leave their sectors of employment. Unlike the manufacturing sector, as raw material prices increased, wages and employment in mining and agricultural sectors might have risen. These effects vary across countries and across sectors within a country. Moreover, the final effect on wages and employment depends on both the size of the shocks and the size of each sector as well as the costs workers face when moving between sectors of employment.

In the absence of reliable estimates of the impact of China on product-level prices, we develop vulnerability and opportunity indices for manufacturing, mining and agriculture. The objective of these indexes is to quantify the impact of China on each sector’s trade balance in a large sample of Latin American economies. Put differently, these indices give us the size of country-specific trade shocks that can be attributed to China’s increasing weight in world trade. In turn, we estimate (and partially calibrate) a neoclassical small open economy model with labor market adjustment costs using Argentine, Brazilian and Mexican data. Finally, we bring these two pieces together: We shock and calibrate the model using the vulnerability and opportunity indexes to calculate the effects of China’s export growth on wages, labor allocations, and informal employment.

The vulnerability and opportunity indexes show the potential effect of China on Latin American exports. For instance, the results suggest that China’s increased demand for mining products could have increased Brazil’s exports (of these goods) by 33% between 2001 and 2011, while Chile’s exports of mining products could have increased by 26%. Sizable increases in agricultural exports are expected for countries like Paraguay, Argentina, Uruguay and Brazil. Moreover, China’s competition in manufacturing could have significant effects on countries exporting textiles like Honduras, El Salvador and Haiti as well as countries exporting a more 3
composition of the board of directors? What percentage of board members represents management? Major owners (more than 5% shares)? Small shareholders?

3. Theory suggests that debt and equity contracts give shareholders and especially creditors control rights to intervene when a contract is violated. Bank surveys, however, in general do not provide information on the financial structure and the rights of stakeholders. _Suggested questions: (1)_ What is the share of outside equity? Share of inside equity? Share of debt financing? Debt-equity ratio? What type of institutions or individuals are the main shareholders (bank, pension funds, a holding company, a foreign investor, or a domestic investor)? The main creditors? (2) What are the control rights of the equity-holder, of the debt-holder? Would a firm become bankrupt if it defaults interest payment for debts? Does the firm post collateral when borrowing from banks? What is the share of tangible assets? What are the shares of long- and short-term debts in total debts respectively''

# **Internal Incentives**

Internal incentives help align managerial and employee objectives (see Holmstrom and Tirole, 1989, for a survey). There are many ways to motivate workers:

- Promotion, In theory, a large wage differential between workers at different levels improves performance by spurring employees to work hard in order to advance to the next layer, but this benefit has to be balanced against the loss of control and of information. The tournament literature suggests measuring the number of layers within the firm and wage differential between production workers and managers (Lazear and Rosen, 1981; Nalebuff and Stiglitz, 1983; Rosen, 1986).

- The threat of firing. Presumably, internal incentives are stronger when involuntary turnover is a real and credible threat. This may be achieved by allowing a certain percentage of contract workers. A high share of temporary workers, however, may have unintended consequences: The threat cf firing as a disciplinary option spurs worker incentives, but temporary workers face reduced incentives to accumulate firm-specific humlan capital. Since the net effect is unclear, surveys will provide the best source of data to answer this question and provide policy advice.

8
**Figure 10.  Return of Assets and Equity**

_(percentage, 2009)_ <!-- Start of picture text --> ROA ROE<br>80%<br>15%<br>60%<br>10%<br>40%<br>5%<br>20%<br>0%<br>0%<br>0 20 40 60 80 100<br>0 20 40 60 80 100<br>-5%<br>-20%<br>-10% -40%<br>-15% -60%<br>-80%<br>-20%<br>DFIs DFIs<br><!-- End of picture text -->

# **6. Corporate Governance Arrangements**

Good corporate governance is a critical component for the success of DBs. In broad terms, corporate governance refers to _“...the process and structure for overseeing the direction and management of a corporation so that it carries out its mandate and objectives effectively_ ”.

The governance in a DB can be more challenging than in private financial intermediaries. To begin with, the structure of DB ownership and control can be more complex, involving a large number of governmental institutions (ministries of finance, agriculture, housing, trade, labor, etc.), and sometimes even the legislature. These entities all have their own legitimate (and sometimes conflicting) expectations regarding the goals the DB should accomplish.

When the mandate of the DB is stated only in general and broad terms, senior government officials or elected politicians have more room to influence the direction and activities that a DB pursues. Unless the institutional framework of a DB is strong enough to withstand undue political pressure, a DB can become vulnerable to political interference or be captured by interest groups exerting pressure on it to take excessive credit risks, thus causing future financial losses for the DB.

Moreover, as government owned institutions, DBs rarely face the risk of takeover. This, combined with the absence of a sound monitoring and evaluation framework, can cause the board and management of DBs to become progressively tolerant with DBs‟ inefficiencies and weak performance.

19
-3-

# T'able 1. Growth in Pakistan's GDP - An Intemational CompariRon

||1970-80|1980-91|
|---|---|---|
|Pakistan|4.9|6.1|
|India|3.4|5.4|
|Bangladesh|2.3|4.3|
|Nepal a/|2.1|4.7|
|Sri Lanka|4.1|4.0|
|South Asia|3.5|5.4|
|East Asia and Pacific|6.6|7.7|
|Latin America and the Caribbean|_5.5_|1.7|
|Middle East and Norh Africa|5.2|2. L|
|Sub-Saharan Africa|4.0|2.1|
|World|_3.5_|3.0|

a/ Estimated from World Tables 1993.

Source: WDR, 1993.

Table 2. Growth in Pakistan's GDP Per CaDia - An International Comparison

||1970-80|1980-91|
|---|---|---|
|Pakistan|1.8|3.0|
|India|1.1|3.3|
|Bangladesh|-0.4|2.1|
|Nepal<br>l|-0.2|2.2|
|Sri Lanka_|2.4|2.6|
|South Asia|1.1|3.2|
|EastAsia and Pacific|4.8|6.1|
|LatinAmerica and the Caribbean|2.2|-0.3|
|MiddleEastand North Africa|1.7|-1.1|
|Sub-Saharan Africa|0.9|-1.0|
|World|1.6|1.3|

a/ Estimated friom World Tables 1993.

Source: WDR. 1993.
Figure 7. Summary of regression model (eq. 10) estimates: coefficient for being ina vocational programme by country <!-- Start of picture text --> E @ statistically significant (10% level)<br>‘<br>B14 not statistically significant<br>S<br>o<br>4<br>;<br>1S) * ba &<br>=4<br>5<br>po || a<br>3& -2 ca 5 7 e<br>=<br>Ei + 5<br>E Upper midd +<br>= pper: micate High income<br>= income *<br>-4<br>GDP per capita (constant 2010 USS)<br><!-- End of picture text -->

Source: authors’ calculationsusing PISA 2015 data
difference-in-differences specification that estimates the impact of SPS and TBT provisions in PTAs on exports of a firm-HS 6-digit product before and after the entry in force of a PTA in a given destination (panel dimension) relative to exports of a firm-HS 6-digit product in destinations with no PTAs (cross-section dimension) While our empirical strategy captures the effect of the content of PTAs in terms of provisions included, we are not able to study the impact of their effective applicability, which is not observable. However, this concern is mitigated for two reasons. First, our fixed effects structure controls for any systematic under-enforcement bias in PTAs signed by Chile, Colombia, and Peru. Second, the previous version of the Deep Trade Agreements database described in Hofmann et al. (2017) reveals that the SPS and TBT chapters included in 75 and 80 percent, respectively, of the PTAs signed by Chile, Colombia and Peru are considered as legally enforceable .<sup>22</sup> The inclusion in our specifications of _τ_ , the tariff rate imposed by destination _j_ on exports of product _k_ by firms from a given origin, and of the _Depth_ variable capturing the normalized number of provisions in all 16 policy areas (other than SPS and TBT) discussed in Section 2.1 allows us to isolate the impact of SPS or TBT provisions from tariff concessions and other forms of integration provided by the PTA. Equations (1) and (2) are estimated by PPML introduced by Silva and Tenreyro (2006) with the recent command of Correia et al. (2020). Inference is based on Huber-White standard errors robust to heteroskedasticity clustered by origin-destination.

Equations (1) and (2) include destination GDP to control for time-varying destination-specific demand. A variant of our equations includes destination-year fixed effects which are a more flexible way to account for demand and macroeconomic shocks in destination markets (Appendix D.2).<sup>23</sup> Due to computational limitations, we are only able to estimate this variant using a more aggregated version of our data at the exporting country-firm-destination-

> 22Legally enforceable means that (i) the language used is sufficiently precise and committing and (ii) the chapter has not been excluded from the PTA’s dispute settlement procedures.

> 23These fixed effects account in particular for the Global Financial Crisis that is part of our sample period.

14
# **TABLE OF CONTENTS**

**INTRODUCTION** .......................................................................... ......4 Purpose and Structure of Report What Is At Stake for India **PART I:  IDENTIFYING PUBLIC LAND APPROPRIATE FOR UNLOCKING VALUE** .........................................................8 Public Land Inventories Identifying “Surplus” Land Creating Administrative and Financial Incentives for Land Disposition The Entrepreneurial City:  Using Public Land Values to Finance Urban Infrastructure Investment Lessons from International Experience:  Part I Further Readings for Part I **PART II:  SPECIALIZED INSTITUTIONS AND INSTITUTIONAL ARRANGEMENTS FOR DISPOSING OF PUBLIC LANDS** ..............25 Liegenschaftsfonds, Berlin, Germany Canada Lands Company Lessons from International Experience:  Part II Further Readings for Part II **PART III:  EARMARKING REVENUES FOR INFRASTRUCTURE INVESTMENT** .........................................................................33 Current Practice Regarding the Use of Revenues Generated

2