rafmacalaba/datause-encoder-data huggingface dataset:
POLICY RESEARCH WORKING PAPER 3028

# Abstract

Establishing an investment promotion agency has become a central part of most countries' development strategies. Today there are more than l50 investment promotion agencies worldwide. Yet very little is known about what these agencies have been really doing, notably in emerging countries, and whether they have been effective **in** influencing investors' decisions.

Using data from a new survey on 58 countries, Morisset shows that greater investment promotion is associated with higher cross-country foreign direct investment (FDI) flows, on top of the influence of the country's investment climate and market size.

But this result has to be qualified on several couIIts. First, the effectiveness of the agency depends on the country's environment **in** which **it** operates. An agency in a poor investment climate **is** less effective at attracting investment. Second, the scope of activities that an agency undertakes influences its performance. Morisset's empirical analysis indicates that agencies devoting more resources on policy advocacy are more effective because such activity is not only beneficial to foreign investors but also to domestic investors. In contrast, investment generation or targeting strategies appear expensive and risky, especially in countries with poor investment climates.

Finally, certain internal characteristics of the agencies are associated with greater effectiveness. The agencies that have established reporting mechanisms to the country's highest policymakers (the president or prime minister) or to the private sector have been systematically more efficient at attracting foreign direct investment. Such institutional links are crucial because they contribute to strengthen the government's commitment as well as reinforce the agency's **credibility** and **visibility** in the business community.

This paper-a product of the Foreign Investment Advisory Service-is part of a larger effort **in** the Bank to understand foreign direct investment flows. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Monte Feghali, room 19-110, telephone 202-473-6177, fax 202-522-3262, email address mfeghahi@ifc.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jmorisset@worldbank.org. April 2003. (22 pages)

> _The Policy Research Working Paper Senes dtsseminates the findings of work_ **_in_** _progress to encourage the exchange of ideas about development issues. An objective of the series_ **_is_** _to get the finditgs out quickly, even if the presentations are less than fully polished. The papers carry the names of the auithors and shotld be cited accordingly. The_ **_findings,_** _interpretations, and concluszons expressed in this paper are entirely those of the authors They do niot necessanly represent the_ **_view_** _of the World Bank, its Executive Directors, or the countries they represent._

Produced by the Research Advisory Staff
dren are household heads because if the children are living with parents, we may falsely attribute the permanent income of children to parents. Then we merge the household expenditure of the fourth wave (2007) with the household expenditure of the fifth wave (2014) using the household tracking ID. We adjust inflation taking 1993 as the base year.

**Role model data:** The role model in our study are individuals who were born in the same district as the child and belong to the top 50 percent of education distribution of fathers’ reference age group. To calculate the role model education, we first needed to obtain fathers’ age. We check if the father has co-resided with children in any of waves. If we can find the father, we obtain the age from the household roster and convert that value to what his age would be in 2014. For the non-resident father, we obtain the age from the non-resident module. The IFLS asks age at death and year of death, if the father is not alive. Using this information, we calculate what would have been fathers’ age in 2014. We then obtain the birth district information of the children. Separately, next we use the roster data of each wave to obtain years of education and age of all individuals who are older than 22 at the time of the survey. We choose older than 22 because by age 22 the individuals would have completed their education. Then we convert the age of these individuals to the value it would be in 2014. Using the birth district information, we obtain the birth district of all the individuals in the roster. For any given age, we calculate the average of years of education of the top 50 percent achievers within the _±_ 3 ( _±_ 5 for robustness check) age group of a district. We then merge the district ID information and age information with the birth district ID and father age information of the data above.

**Peer Data:** We select individuals who are within _±_ 3 ( _±_ 5 for robustness check) of the age group of our children sample and born in the same district

5
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa‘s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Curtis, Barney. 2009. ―The Chirundu Border Post: Detailed Monitoring of Transit Times.‖ SSATP Discussion Paper No. 10, Regional Integration and Transport—RIT Series, Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

- USITC (United States International Trade Commission) 2009. ―Sub-Saharan Africa: Effects of infrastructure conditions on Export Competitiveness. Third Annual Report.‖, USITC Publication 4071, Washington DC.low-income countrie

# Water resources

# Irrigation

FAO (Food and Agriculture Organization). 2004. ―Aquastata: Zimbabwe Country Profile.‖ www.fao.org/nr/water/aquastat/main/index.stm.

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L. 2008. ―Irrigation Investment Needs in Sub-Saharan Africa.‖ Appendix 2 Country Results, World Bank, Washington, DC.

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<!-- Start of picture text --> BASELINE SAMPLE: 671 eligible women<br>from 28 villages in Jaunpur<br>RANDOMIZATION<br>TO (Control): 350 T1 (Own): 156 T2 (BAF): 165<br>eligible women eligible women eligible women<br>671 women approached for follow-up<br>6 Could not be approached (no<br>working GPS coordinates,<br>directions, address)<br>665 women attempted in person or by phone<br>32 Could not participate af all<br>(phone number net working, 3<br>633 women contacted for follow-up attempts fo contact in person)<br>8 Only partially completedfollaw-<br>up survey — no outcome date<br>FOLOW-UP SAMPLE: 625 eligible women<br>10 Fouchers could not be delivered:<br>6 Own vouchers + 4 BAF vouchers<br>TREATMENT-ON-TREATED SAMPLE:<br>615 eligible women<br><!-- End of picture text -->
# **<u>Table 1:  The relationship between the business environment and entrepreneurship</u>**

The dependent variable in all regressions is average new density for 2004 to 2007. p-values are shown in parentheses. GDP per capita and one-year GDP growth rates are from WB-WDI statistics (2009). The ‘Starting a Business’ index (a lower number is a higher ranking), and the cost and number of procedures to start a business are from Doing Business (2009). The Governance Index is from Kauffman, et al. (2008). Asterisks *, **, and ***, indicate significance at the 10%, 5%, and 1% level, respectively.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|L GDP i|0.0017***|0.0010***|0.0014***|0.0009***|0.0005|
|og per capta|[0.000]|[0.000]|[0.000]|[0.000]|[0.186]|
|Log ‘Starting a Business’||-0.0013***||||
|Ranking||[0.000]||||
|Cost of starting a business|||-0.0008*|||
||||[0.093]|||
|Log number of procedures||||-0.0029***||
|to start a business||||[0.009]||
|Governance Index|||||0.0018***|
||||||[0.004]|
|C|-0.0122***|-0.0013|-0.0100***|0.0003|-0.0019|
|onstant|[0.000]|[0.633]|[0.000]|[0.933]|[0.525]|
|Observations|99|97|97|97|99|
|R-squared|0.308|0.473|0.332|0.453|0.368|

# <u>Georgia:  The Impact of Reforms</u>

The impact of regulatory reforms can be further assessed by studying Georgia, a top Doing Business reformer in 2007. For instance, beginning in 2004, Georgia undertook a series of dramatic regulatory reforms aimed to alleviate the excessive financial and administrative burden placed on entrepreneurs. The most important reforms included an unparalleled gradual elimination by 2006 of the minimum capital required to start a new business, as well as a reduction in the number of registration procedures from 9 in 2004 to 5 in 2006 and a reduction in the number of days required to register a business from 25 days in 2004 to 11 days in 2006. In addition, Georgia created ‘one-stop registration’ by delegating certain formalities such as tax and statistical registration to the company registrar. The impact of these progressive reforms – in combination with the significant simplification of the licensing regime and strengthening of investor protection 11
# 3.4 External **lrade**

As indicated earlier, firms in the private formal sector allocate their output to exports or the domestic market according to the production possibility frontier (PPF) specified in equation (11) and the relative price of exports _(PEp)_ vis-a-vis domestic goods _(PDp)._ Efficiency conditions require that firms equate this relative price to the opportunity cost in production. This yields: The agricultural traded good is fully exported, as also indicated earlier; given that this sector is the only one using only its own good as intermediate consumption, we have These equations indicate that the ratio of imports to domestic supply of both categories of domestic goods depends on the relative prices of these goods and the elasticity of substitution, _UQA_ and oQp, between these goods.

# 3.5 Prices

As we have seen previously production requires both factor inputs and intermediate inputs; we therefore define the net or value added price of output as: 30
**Table 3. Overview of Cross-Sectional & Panel Regressions Estimated with Alternative Sample & Dependent Variable Definitions**

|_Cultivation_|_Source of Dependent Variable_|**Ob**<br>|**servation Numbers**<br>|**by Regression Ty**<br>|**pe **<br>|
|---|---|---|---|---|---|
|_Domain_|<br>_Maize Yield (Kg/Ha)_|_MAPS I_<br>_Cross-Sectional_|_MAPS II_<br>_Cross-Sectional_|_Household_<br>_Panel_|_Parcel_<br>_Panel_|
||Self-Reporting‡|440|240|480|316|
||Sub-Plot CropCutting‡|N/A|240|480|316|
|All|Sub-Plot CropCutting (All Plots)|440|440|880|594|
||Remote Sensing|440|In Progress|In Progress|In Progress|
||Full Plot Crop Cutting|N/A|200|N/A|N/A|
||Self-Reporting‡|201|70|118|64|
||Sub-Plot CropCutting‡|N/A|70|118|64|
|Pure Stand|Sub-Plot CropCutting (All Plots)|201|117|186|114|
||Remote Sensing|201|In Progress|In Progress|In Progress|
||Full Plot Crop Cutting|N/A|47|N/A|N/A|
||Self-Reporting‡|239|170|240|170|
||Sub-Plot CropCutting‡|N/A|170|240|170|
|Intercropped|Sub-Plot CropCutting (All Plots)|239|323|430|312|
||Remote Sensing|239|In Progress|In Progress|In Progress|
|**Notes:**‡ MAP|Full Plot CropCutting<br>S II cross-sectional regressions exclu|N/A<br>de plots subject to f|153<br>ull-plot crop cutting.|N/A<br>|N/A|

**Table 4. Overview of Log Plot Area (GPS, Ha) Regression Coefficients**

|_Cultivation_<br>_Domain_|_Source of Dependent Variable_<br>_Log Maize Yield (Kg/Ha)_|_MAPS I_<br>_Cross-Sectional_|_MAPS II_<br>_Cross-Sectional_|_Household_<br>_Panel_|_Parcel_<br>_Panel_|
|---|---|---|---|---|---|
||Self-Reporting‡<br>Sub-Plot CropCutting‡|-0.708***<br>N/A|-0.731***<br>0.161*|-0.730***<br>0.063|-0.790***<br>0.116|
|All|Sub-Plot CropCutting (All Plots)<br>Remote Sensing|0.066<br>0.273*|0.138**<br>In Progress|0.093<br>In Progress|0.155*<br>In Progress|
||Full Plot Crop Cutting|N/A|-0.093|N/A|N/A|
||Self-Reporting‡|-0.561***|-0.805***|-0.346*|-0.890***|
||Sub-Plot CropCutting‡|N/A|-0.169|0.084|-0.124|
|Pure Stand|Sub-Plot CropCutting (All Plots)<br>Remote Sensing|0.159**<br>0.047|-0.071<br>In Progress|-0.092<br>In Progress|-0.126<br>In Progress|
||Full Plot Crop Cutting|N/A|0.018|N/A|N/A|
||Self-Reporting‡|-0.829***|-0.702***|-0.705***|-0.872***|
||Sub-Plot CropCutting‡|N/A|0.260**|0.146|0.208|
|Intercropped|Sub-Plot CropCutting (All Plots)|-0.087|0.201**|0.117|0.148|
||Remote Sensing|0.470|In Progress|In Progress|In Progress|
||Full Plot CropCutting|N/A|-0.101|N/A|N/A|

<!-- Start of picture text --> Full Plot Crop Cutting N/A -0.101 N/A<br><!-- End of picture text -->

**Notes:** ‡ MAPS II cross-sectional regressions exclude plots subject to full-plot crop cutting. MAPS I regressions controls for plotlevel (1) soil quality index, (2) maize genetic heterogeneity, and (3) edge effects (specifications 2, 4, and 6). MAPS II regressions based on self-reported maize yield exclude observations subject to full-plot harvest. ***/**/* denote statistical significance at the 1/5/10 percent level, respectively.

33
34 Figure 9: Shares in different financial protection categories under different assumptions <!-- Start of picture text --> 100% 4.0<br>90% 3.9<br>80% 3.8<br>70% 3.7<br>60% 3.6<br>No expenditures<br>No use<br>50% 3.5<br>Use but no expenditures<br>Not catastrophic<br>40% 3.4<br>Catastrophic<br>Impoverishing<br>30% 3.3<br>Immiserizing<br>FP index<br>20% 3.2<br>10% 3.1<br>0% 3.0<br>$1.25 $2.00 $5.00 $1.25 $2.00 $5.00 $1.25 $2.00 $5.00 $1.25 $2.00 $5.00<br>OOP > 40% of HH pushed to Household Individual<br>disposable below 120% of PL<br>consumption<br>Definition of catastrophic spending Household or individual questionnaire<br><!-- End of picture text -->
( _Population_ ) and exports plus imports in 2005 expressed as a percentage of GDP per capita ( _Trade_ ). The data source for population and the trade-to-GDP ratio is World Development Indicators, World Bank. Data on landlocked countries are collected individually for each country through various website searches.

The quality of the overall macroeconomic and business environment is likely to have a direct effect on the growth rate of a country and it could also have an indirect effect by serving as a proxy for the quality of the overall institutional environment. If gender inequality also happens to be systematically correlated with the macroeconomic and business environment then our main estimation results could be biased. To check for this possibility, we control for overall macroeconomic and business climate measures that include year 2005 values of consumer price inflation ( _Inflation_ ) taken from World Development Indicators, World Bank; and Heritage Foundation’s sub-indices on fiscal and financial freedoms (2005 values).

Our last set of controls focus on human capital or the level of education in the country. We use two separate controls here. First, the overall level of human capital in the country is widely considered to be an important determinant of growth (see for example, Lucas 1988, Krueger and Lindahl 2001). If gender inequality and the level of human capital happen to be systematically correlated then our main estimation results could be biased. To guard against this possibility, we control for overall level of gross enrollment rates (for males and females) in primary, secondary and tertiary education in 2005 ( _Education_ ). The data source for the variable is the United Nations. Second, as discussed above, we check if our results for the gender inequality and growth relationship are due to gender inequality in education or other factors. To this end, we control for the gender gap in education and its interaction term with _Income_ . The gender gap in education is measured by year 2005 values of the ratio of the female-to-male 11
WPS3660

# **Do Incumbents Manipulate Access to Finance During Banking Crises?**

Erik Feijen*

## **Abstract**

This paper tests the hypothesis that during systemic banking crises, access to finance is opportunistically tightened by incumbents to eliminate or weaken competition from mainly young firms. We find this to be especially true in more corrupt countries. To do so, we employ a methodology similar to Rajan and Zingales (1998) on three digit manufacturing industry-level data provided by the United Nations Statistics Division for about 15 developed and developing countries in over 20 industries on average. We show that price-cost margins in externally _more_ financially dependent industries are _higher_ during crisis than in externally _less_ dependent industries in countries with _higher_ levels of corruption. We find the opposite relationship for the change in the industry-level number of establishments during a crisis. The results withstand an array of robustness checks, including using different indices of corruption, different controls, and robust estimation techniques.

JEL Classifications: D73, G1, G2, G3, and P48.

Keywords: Access to Finance; Systemic Banking Crises; Corruption.

## World Bank Policy Research Working Paper 3660, July 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* University of Amsterdam and World Bank. Email: <u>e.h.b.feijen@uva.nl. I thank the World Bank’s</u> Financial Sector Operations and Policy Department for its hospitality while this paper was written. Without implication, I would like to thank the following people. Stijn Claessens, Rocco Huang, Luc Laeven, Enrico Perotti, Ernesto Revilla, and Konstantinos Tzioumis for detailed discussions and/or comments on earlier drafts. Furthermore, I appreciate useful comments and suggestions of participants during the conference on Endogenous Institutional Change at Stanford, March 2005.

1
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supervising agent (the World Bank in our case) and 3) successful implementation can create an impact only in presence of structurally solid projects, therefore selectivity and project governance are the tools through which successful infrastructure investment can be achieved.

Table 1: Infrastructure spending per Multilateral Development Bank in billion dollars<sup>*</sup>

|Multilateral Development Bank|Total<br>Lending|Infrastructure<br>Lending|% of Total|
|---|---|---|---|
|World Bank|58.70|26.41|44.9%|
|African Development Bank|7.52|3.91|51.9%|
|Asian Development Bank|11.46|7.51|65.5%|
|European Bank for|7.72|2.53|32.7%|
|Reconstruction and Development||||
|Inter American Development<br>Bank|13.04|10.0|76.6%|

Note: * for the World Bank the figures refer to its 2010 operations, refer to the Annual Report webpage; for the African Development Bank the figure refers to its 2009 operations, refer to the Annual 2010 Report, page 18; for the Asian Development Bank the figure refers to its 2010 operations, refer to the Annual 2010 Report, page 34; for the European Bank for Reconstruction and Development the figure refers to its 2009 operations, refer to the Annual 2009 Report, page 16-20; for the Inter American Development Bank the figure refers to its 2010 operations, refer to the Annual 2010 Report, page 15-25.

These results lead us to suggest that high development performance cannot be achieved unless higher investment on project screening and elaboration is pursued. In our opinion, a substantial message emerging from this paper states clearly that projects need to be solid and well-designed since their proposal, otherwise they do not succeed in terms of development outcome even in presence of good implementation. Throughout this paper we will often refer to “selectivity” as a key term, with this we intend the selection of projects presenting good design and solid structural features. In the empirical analysis we find a clear separation between: ex-ante weak projects, that show very low probability of success even in presence of good implementation; and ex-ante well-designed projects, which have a good chance of succeeding conditional on good implementation. Therefore in this work our reference to selectivity does not recall that of David Dollar and Victoria Levin (2006), whom defined “selective” in terms of democracy and property rights and rule of law.

3
**Figure 17: Middle class’ incidence elasticity of growth and the Gini coefficientin developing countries.**

<!-- Start of picture text --> KAZ<br>UGA<br>UKR<br>GEO<br>MRT<br>IDNALB<br>YEM<br>PER<br>ARM<br>KHM<br>TUR BFA<br>IND VNM<br>RUS MDA<br>BLR CIV ECU HND<br>UZB NPL JAMVEN<br>THA NIC<br>LKA DOMMEX DG<br>JOR KEN PRY<br>URY PAN BRA ZAF<br>AZESEN SLVCRICHL GTM BOL<br>COL<br>GUY ARG<br>ETH<br>GIN CMR<br>LAO<br>.2 .3 .4 .5 .6<br>Gini<br>6<br>4<br>Elasticity<br>2<br>0<br><!-- End of picture text --> _Source:_ World Bank staff calculations.

Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America

# **5. Conclusion**

This paper analyses, in an ex-ante fashion using the GIDD macro-micro modeling framework, the effect of demographic and economic changes over the 2005-2030 period on the income distribution around the world. It focuses especially on the shifts in the middle class’ size and composition in Latin America and the Caribbean. Under a baseline scenario based on past economic trends and UN population projection, GIDD – using a collection of household level surveys covering more than 90% of the world population – is able to generate scenarios for the distributional effects of growth not only between countries but also within countries (with a high degree of heterogeneity). The resulting simulated income distribution should not be seen as a _forecast_ of what the future distribution might look like; instead it should be interpreted as the result of an exercise that captures the _ceteris paribus_ distributional effect of demographic, sectoral, and economic changes.

34