rafmacalaba/datause-encoder-data huggingface dataset:
Muinhos, M. K. and M. Nankane (2006). “Comparing Equilibrium Real Interest Rates: Different Approaches to Measure Brazilian Rates”. Central Bank of Brazil Working Paper No. 101.

Nakane, M. and S. Koyama (2002). _Search Costs and the Dispersion of Loan Interest Rates in Brazil_ , Anais do XXXI Encontro Nacional de Economia, Associacao Nacional do Centros de Pos-Graduacao em Economia.

Pazarbasioglu, C., S. Byskov, M. Bonomo, I. Carneiro, B. Martins and A. Perez (2017). Brazil Financial Intermediation Costs and Credit Allocation. World Bank Discussion Paper.

Poghosyan, T. (2012). “Financial Intermediation Costs in Low-Income Countries: The Role of Regulatory, Institutional, and Macroeconomic Factors”. IMF Working Paper WP/12/140. International Monetary Fund.

Rajan, R. and L. Zingales (1998). Financial Dependence and Growth. _American Economic Review,_ 88: 559-586.

Rajan, R. and L. Zingales (2003). Saving Capitalism from the Capitalists. Random House, New York.

Rodano, G., N. Serrano-Velarde, E. Tarantino (2012). “Bankruptcy law and the cost of banking finance”, Oxford University Centre for Business Taxation.

Rogoff, K. (2005). _Strategies for Bringing Down Long-Term Real Interest Rates in Brazil_ , Presentation prepared for the Central Bank of Brazil, August 30, 2005.

Safavian, Fleisig, and Steinbuks (2006), “Unlocking Dead Capital: How Reforming Collateral Laws Improves Access to Finance,” World Bank, Public Policy for the Private Sector Series, Note Number 307.

Safavian, M. and B. Zia (2018). “The Impact of Interest Rate Caps on the Financial Sector: Evidence from Commercial Banks in Kenya.” Policy Research Working Paper 8393. World Bank.

Saunders, A. and L. Schumacher (2000). The determinants of bank interest rate margins: an international study. _Journal of International Money and Finance_ , 19(6): 813-832.

Segura-Ubiergo, A. (2012). “The Puzzle of Brazil's High Interest Rates.” IMF Working Paper 12/62. International Monetary Fund.

Stiglitz, J. and A. Weiss (1981). Credit Rationing in Markets with Imperfect Information _, American Economic Review_ , vol. 71(3): 393-410.

35
Table 4: Disaggregated education and health aid

||Public edu<br>|cation exp.<br>|Public h<br>|ealth exp.<br>|
|---|---|---|---|---|
||OLS|FE|OLS|FE|
|Education IP|0.091|0.12|||
||(0.25)|(0.12)|||
|Education SP|2.53<sup>_∗_</sup>|1.21<sup>_∗∗_</sup>|||
||(1.35)|(0.55)|||
|Education TC|0.032|-0.0070|||
||(0.10)|(0.082)|||
|Education ONM|0.14|0.021|||
||(0.21)|(0.19)|||
|Health IP|||0.40|0.20|
||||(0.34)|(0.21)|
|Health SP|||1.19<sup>_∗_</sup>|0.84<sup>_∗∗∗_</sup>|
||||(0.60)|(0.31)|
|Health TC|||-0.12|0.0067|
||||(0.35)|(0.32)|
|Health ONM|||0.74<sup>_∗∗_</sup>|0.41<sup>_∗_</sup>|
||||(0.36)|(0.23)|
|General aid|-0.0012|0.031|0.023|0.0055|
||(0.051)|(0.029)|(0.031)|(0.019)|
|Support to NGOs|-0.56<sup>_∗_</sup>|-0.39<sup>_∗∗_</sup>|-0.15|-0.16|
||(0.30)|(0.19)|(0.16)|(0.11)|
|Other non-education aid|-0.0081|-0.0055|||
||(0.022)|(0.018)|||
|Other non-health aid|||0.014|-0.013|
||||(0.017)|(0.011)|
|GDP per capita|0.084|0.29<sup>_∗_</sup>|0.17<sup>_∗∗∗_</sup>|0.15<sup>_∗_</sup>|
||(0.060)|(0.15)|(0.048)|(0.085)|
|GDP per capita growth|-0.051<sup>_∗∗∗_</sup>|-0.029<sup>_∗∗∗_</sup>|-0.028<sup>_∗∗_</sup>|-0.021<sup>_∗∗∗_</sup>|
||(0.015)|(0.0091)|(0.011)|(0.0072)|
|Urbanisation|-0.0089|0.085|0.0026|0.055<sup>_∗_</sup>|
||(0.0081)|(0.055)|(0.0053)|(0.031)|
|Trade|0.016<sup>_∗∗∗_</sup>|-0.013<sup>_∗∗_</sup>|0.011<sup>_∗∗∗_</sup>|-0.0071<sup>_∗_</sup>|
||(0.0039)|(0.0067)|(0.0032)|(0.0040)|
|PV debt|-0.0040|-0.0027<sup>_∗_</sup>|-0.000074|-0.000092|
||(0.0034)|(0.0016)|(0.0021)|(0.00059)|
|Public debt service|0.052|-0.065<sup>_∗∗∗_</sup>|-0.039<sup>_∗∗_</sup>|-0.022<sup>_∗_</sup>|
||(0.062)|(0.021)|(0.019)|(0.011)|
|_R_<sup>2</sup>|0.187|0.215|0.302|0.183|
|Hausman||0.000||0.000|
|_βSP_ ⩽0|0.032|0.015|0.026|0.004|
|_βSP_ ⩾1|0.870|0.645|0.621|0.307|
|_βT C_ ⩽_−_1|0.000|0.000|0.006|0.001|
|_βT C_ ⩾0|0.621|0.466|0.363|0.508|
|Countries|108|108|108|108|
|Observations|1082|1082|1087|1087|

Note: OLS and fixed effects (FE) results, annual data, 1990-2003. All regressions include time dummies, coefficients not reported. Heteroskedasticity-robust standard errors, clustered by country, in brackets. _∗_ , _∗∗_ , and _∗∗∗_ denote significance at 10, 5 and 1%, respectively. In the case of FE estimation, _R_<sup>2</sup> refers to the within _R_<sup>2</sup> . Hausman shows the p-value of a generalized Hausman test of the null hypothesis that _ηi_ is uncorrelated with the regressors. _βSP_ ⩽ 0 ( _βSP_ ⩾ 1) and _βT C_ ⩽ _−_ 1 ( _βT C_ ⩾ 0) are p-values for the test of full (no) fungibility for sector programme aid and technical cooperation, respectively. Source: author’s analysis based on data described in the text.

17
- Cameron, Lisa.  2002 “Did social safety net scholarships reduce drop-out rates during the Indonesian economic crisis?”  World Bank Policy Research Working Paper 2800.

- Card, David, Jochen Kluve, and Andrea Weber.  2009.  “Active Labor Market Policy Evaluations: Meta-Analysis.”  IZA Discussion Paper Series 4002

- .    Caballero, R. J. and M. L. Hammour.  1994. “The Cleansing Effect of Recessions.” _American Economic Review_ 84(5): 1350-68.

- Caballero, R. J. and M. L. Hammour.  2005.  “The Cost of Recessions Revisited: A ReverseLiquidationist View." _Review of Economic Studies_ 72(2):313-341.

- Eduardo A. Cavallo & Alberto Cavallo, 2008. “Are Crises Good for Long-Term Growth? The Role of Political Institutions.”  Inter-American Development Bank Research Department RES Working Papers 4589.

- Casacuberta, C. and N. Gandelman.  2009.  “Productivity, Exit and Crisis in Uruguayan Manufacturing and Services Sectors.”  Mimeo.

- Collier, P. and B. Goderis.  forthcoming.  “Structural Policies for Shock-Prone Developing Countries.” Oxford Economic Papers.

- Davis, S., J. Haltiwanger and S. Schuh.  1996. _Job Creation and Destruction_ .  Cambridge: MIT Press.

- Davis, S., and J. Haltiwanger.  1990.  “Gross job creation and destruction: microeconomic evidence and macroeconomic implications.”  In O. Blanchard and S. Fischer eds., _NBER. Macroeconomics Annual:_ 123–168.  Cambridge: MIT Press.

- Davis, S., J. Haltiwanger and S. Schuh. 1992  "Gross Job Creation, Gross Job Destruction, and Employment Reallocation," _The Quarterly Journal of Economics,_ 107(3): 819-63.

- De Ferranti, D., G. Perry, I. Gill, and L. Serven.  2000. _Securing Our Future in a Global Economy._ Washington, DC: World Bank.

- de Janvry, A., F. Finan, and E. Sadoulet.  2006.  “Can conditional cash transfers serve as safety nets to keep children at school and out of the labour market?” _Journal of Development Economics_ 79(2):349-373.

- Dercon, Stefan and Pramila Krishnan.  2000. "In Sickness and in Health: Risk Sharing within Households in Rural Ethiopia." _Journal of Political Economy_ 108(4):688-727.

- Dollar, David and Mary Hallward-Driemeier.  2000.  “Crisis, Adjustment, and Reform in Thailand's Industrial Firms.” _World Bank Research Observer_ 15(1): 1-22.

- Drazen, Allan and William Easterly, 2001. "Do Crises Induce Reform? Simple Empirical Tests of Conventional Wisdom," _Economics and Politics_ 13(2): 129-157.

- Drazen, Allan and Vittorio Grilli.  1993. “The Benefit of Crises for Economic Reforms.” American Economic Review 83(3): 598-607.

- Ezemenari, Kene and Kalanidhi Subbarao.  1999.  “Jamaica's food stamp program - impacts on poverty and welfare.”   World Bank Policy Research Working Paper Series 2207.

- Fretwell, David, Jacob Benus, and Christopher O’Leary.  1999.  “Evaluating the Impact of Active Labor Programs: Results of Cross Country Studies in Europe and Central Asia.”  Social Protection Discussion Paper 9915.

17
consistent and close to a randomized experiment.

Also, double difference estimates and a randomized experiment can miss on some of the behavioral effects that may arise from unobserved heterogeneity such as differences in individual abilities. During a period of sharp growth such as the one we observe, those who profit from and contribute to growth are probably the most skilled, with better contacts and social abilities, all attributes which are difficult to measure in household surveys. This motivates the third step in our analysis where we use longitudinal data to control for unobserved heterogeneity.

Tables 5a and 5b report random effects estimates using all 16 possible specifications and the balanced and unbalanced panels.<sup>13</sup> Table 5a illustrates results for the welfare equations and 5b for the poverty equations. For simplicity of exposition, we report only the coefficients and the _z_ statistics for the variables of interest<sup>_B_and</sup><sup>_P_</sup> . With a balanced panel (top panel in Table 5a), _B_ is significant with a positive sign in equations 2, 6, 9 and 13. In all these equations, the macroeconomic variables (district employment rate and annual growth - _d_ ) are omitted which would suggest that once we control for growth social benefits do not influence welfare. This same statement is not true if we consider the unbalanced panel. In 12 of the 16 welfare equations _B_ is significant, including four equations where the macroeconomic variables are present (Table 5a).

Similar results are found with the poverty equations (Table 5b). With the balanced panel, _P_ is significant in equations 2, 6, 9, 12, 13, 14 and 16. In all these equations the macroeconomic variables are omitted. When we control for the economic cycle, the significance of social benefits disappears. However, with the unbalanced panel, in eight of the 16 poverty equations _P_ is significant including four equations where the macroeconomic variables are present (Table 5b). Therefore, the unbalanced panel would suggest that the economic cycle is not sufficient to explain improvements in welfare and that social benefits may play a role.

Tables 5a and 5b used a random effects model. In order to test different estimation models, we have to exclude the time-invariant variables (rural and summer) which are not supported by the fixed effects model. Table 6a and 6b report the results for the balanced and unbalanced panel estimations and include the three estimation models considered (FE, RE and BE). As before, the exercise is repeated for the welfare and poverty equations. With the balanced panel estimations (Table 6a), none of the social assistance variables is significant in either the welfare or poverty equations. With the unbalanced panel (Table 6b) the social assistance variables are significant in equations 2, 3 and 4. Again, the difference is between balanced and unbalanced panel with the balanced panel suggesting no effect of social benefits and the unbalanced panel suggesting some effect.

> 13By 'all possible specifications' we mean inserting and omitting all groups of variables _a_ , _b_ , _c_ and _d_ one at the time.

14
very modest positive effects, around .04 standard deviations on an academic test. These effects are simply too small to be detectable in the Lucas and Mbiti (2014) study, and when compared with the results I show in Table 3, it is clear that attending _any_ secondary school could simply have a much larger effect than increasing the quality of the secondary school. de Hoop (2010) also finds no positive effects of secondary school quality on a standardized test outcome in Malawi, but this is in keeping with the aforementioned studies. On the other hand, the Lucas and Mbiti (2012) finding that increased primary schooling actually reduced average performance on the KCPE exam is driven by the setting: the universal primary education policy they study couples an increase in years of schooling with increased enrollment. While test scores might have risen for some students who received more schooling, Lucas and Mbiti (2012) note that the class size and compositional changes overwhelm any positive effect on test scores. Reconciling the results here with those of Filmer and Schady is more difficult; similarly to their setting, additional completed grades are associated with between 0.2 and 0.3 standard deviations on the tests in the KLPS instrument. The LATE that Filmer and Schady estimate in Cambodia may be for lower-ability students, less able to benefit from additional schooling, but I cannot rule out that any other difference between the Cambodian and Kenyan contexts is responsible for the difference in findings.

# **4.3.2 Self-employment and employment**

Next, I examine the impact of education on labor market outcomes. Because many of the younger respondents are still in school, and because men are 22
<!-- Start of picture text --> &wp8 20-<br>cv&B 15-<br>c-3<br>2o<br>‘S 10-<br>£oa<br>a®22 s-<br>o-<br>0 100 200 300 400 500<br>Distance to nearest river (km)<br><!-- End of picture text --> <!-- Start of picture text --> 60-<br>&28<br>cve<br>&a2o 40-<br>3°<br>mmm=S&o22 20-o- TTT TT<br>0 100 200 300 400 500<br>Distance to nearest city (km)<br><!-- End of picture text --> <!-- Start of picture text --> 228 30-<br>cvge<br>§o 20-<br>3°<br>o=<br>2o22 10-<br>o-<br>0 50 100 150<br>Number of drought months by PDS!<br><!-- End of picture text --> <!-- Start of picture text --> 230-2B<br>zzgea8 20-<br>6<br>*£oa©Oo22 10-<br>o- oi<br>0 25 50 75 100<br>Cropland suitability by GAEZ (0-100)<br><!-- End of picture text -->
# **The Effects of Land Title Registration on Tenure Security, Investment and the Allocation of Productive Resources: Evidence from Ghana**<sup>*****</sup>

Andrew Agyei-Holmes, Niklas Buehren, Markus Goldstein, Robert Osei, Isaac Osei-Akoto, Christopher Udry<sup>°</sup> _JEL classifications: I38, O18, Q15, R52._

> * We thank the Millennium Challenge Corporation for support and Kevin Barthel, Jenny Lisher and Jack Molyneux for comments on an earlier version. We are grateful to the World Bank Umbrella Fund for Gender Equality and other World Bank sources for funding the research. 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.

> ° Agyei-Holmes: Institute of Statistical, Social and Economic Research; Buehren: World Bank, nbuehren@worldbank.org; Goldstein: World Bank, mgolstein@worldbank.org; Osei: Institute of Statistical, Social and Economic Research, rdosei@ug.edu.gh; Osei-Akoto: Institute of Statistical, Social and Economic Research, ioseiak@ug.edu.gh; Udry: Northwestern University, christopher.udry@northwestern.edu.
PoLIcY **RESEARCH WORKING PAPER** 2714

# Summary findings

Wagstaff, van Doorslaer, and Watanabe propose a method for decomposing inequalities in the health sector into their causes, by coupling the concentration index with a regression framework. They also show how changes in inequality over time, and differences across countries, can be decomposed into the following:

* Changes due to changing inequalities in the determinants of the variable of interest.

* Changes in the means of the determinants.

* Changes in the effects of the determinants on the variable of interest.

The authors illustrate the method using data on child malnutrition in Vietnam. They find that inequalities in height-for-age in 1993 and 1998 are accounted for largely by inequalities in household consumption and by unobserved influences at the commune level. **And** they find that an increase in such inequalities is accounted for largely by changes in these two influences.

In the case of household consumption, rising inequalities play a part, but more important have been the inequality-increasing effects of rising averat - consumption and the increased protective effect of consumption on nutritional status. In the case of unobserved commune-level influences, rising inequality and general improvements seem to have been roughly equally important in accounting for rising inequLality in malnutrition.

This paper-a joint product of Public Services for Human Development, Development Research Grouw, and the Development Data Group-is part of a larger effort in the Bank to investigate the links between health and poverty. Copies of **tht** paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please co-itact Hedy Sladovich, room MC3-607, telephone 202-473-7698, fax 202-522-1154, email address hsladovich@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at awagstaffCkworldbank.org, vandoorslaer@econ.bmg.eur.nl, or nwatanabe(aworldbank.org. November 2001. (19 pages) _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 polishea'. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed ili this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, Ot the countries they represent._ Produced by the Policy Research Dissemination Center
median as measured by the Worldwide Governance Indicator of corruption have on average 15% higher costs.<sup>31</sup> The effects of the other control variables are stable when comparing their coefficients and standard errors with table 4.

Table S.5 in the appendix shows the results without controls for conflict and corruption, and some of the omitted controls that are still of interest. Pilot countries have on average lower costs, but the coefficient is not significantly different from zero. There is substantial regional variation. Unit costs in East Asia and the Pacific, Latin America and the Carribean, the Middle East and North Africa, and South Asia are all significantly lower than in costs in the base category, Sub-Saharan Africa. Looking at column (1), these differences in costs range between 49% in East Asia and the Pacific and 20% in Latin America and the Caribbean.

# **Robustness**

We now perform a number of robustness checks on our results. First, we introduce country fixed effects in table 6 in order to control for unobserved time constant country heterogeneity in costs. This is a very limited test because most of our variables are country-specific and so drop out. As Kaufmann et al. (2010) point out, most countries in the Worldwide Governance Indicators have high persistence in these indicators over time, and changes in indicators are due to changes in measurement as well as in the performance of a country. However, we have within country level variation in the conflict variable due to the different timing of the road work activities and conflicts, and this variable does not suffer from changes in measurement. The coefficients on those variables that can be tested are not significantly affected. The scale effect remains significant, negative, higher in magnitude, and coincides even closer with the results of AFRICON (2008). The coefficient on the conflict variable remains significant and positive but slightly lower in size, suggesting that countries undertaking road works during times of violence face 20% higher costs.

Second, given the significance of population density, we have also estimated a speci-

> 31We also tested whether estimated or contracted costs are significantly lower compared to actual costs in countries which suffer from conflicts, or countries with high levels of corruption, but we do not find any evidence for this.

19
<!-- Start of picture text --> Mean Change in Rents ($B)<br>[go s4127 - 1 oso<br>: (BB 1 00252 1 a59722<br>BB 1 59723 - 2.045323<br>WB 2 esses - 4 issn<br>WB 6s 2eo0e 6 510577<br>L é n@<br>sasiv "5<br>an ee<br>deep<br><!-- End of picture text --> <!-- Start of picture text --> Mean Change in Rents | §R }<br>[9 saa8057 . e.ga7E77<br>( ega7era-tastaris<br>HE serge<br>HM is.cssr2e-ss00n5<br>MM cess - ss. cstas2<br>FE; ee]<br>—-s--ae an | ‘<br>Der<br><!-- End of picture text -->
<!-- Start of picture text --> 0.6 poe ~ on Me OP<br>504 ome — Po<br>S L : os<br>B03 40% Lo<br>i] : wre,<br>0.25 ae<br>0.01 0.1<br>Agglomeration. Potential,. 1/ 0.05 Probability of land grab, x2<br><!-- End of picture text --> <!-- Start of picture text --> 2 05 of a ee<br>fg ee<br>§ 044. oe<br>0.25 |<br>0.1 - an<br>0.01 on 01<br>0.05<br>Agglomeration Potential, 1/,<br>Probability of land grab, x2<br><!-- End of picture text -->
In Table III-2 which shows the correlation between credit cycles and non-oil GDP in oil exporters, the coefficient of interest is positive and significant for Saudi Arabia and the Islamic Republic of Iran, which implies an increase in GDP growth after a peak in credit cycle. Except for Kuwait where this number is negative and significant, for the rest of oil exporters the coefficient is nonsignificant showing the low correlation between credit cycle and GDP growth. Given the importance of oil in the oil-exporters’ economy, we control for real oil prices in all the regression for these countries. Additionally, we show the graphs of medium-term credit cycles and real-oil price cycle in Figure VI-8 in the appendix.

# **Table III-1: Oil Exporters and Overall GDP Growth**

This table show the result for the regression where the main dependent variable is real GDP growth for the oil exporters and the main explanatory variable is the local peak of medium-term credit cycle growth. The definition of the local peak of credit cycle growth is when the credit cycle growth is in its maximum volume compared to past and next five years. In all the regressions we control for oil-prices. All regressions are estimated using OLS. Standard errors appear in parentheses, and ***, **, * correspond to one, five, and ten percent levels of significance, respectively.

||1|2|3|4|5|6|7|8|
|---|---|---|---|---|---|---|---|---|
|**VARIABLES**||||**GDP**|**Growth**||||
|Saudi Arabia|-3.7<br>(3.41)||||||||
|Bahrain||0.67|||||||
|||(1.55)|||||||
|Algeria|||-1.6||||||
||||(2.44)||||||
|Iran, Islamic<br>Rep.||||-5.8**<br>(-2.4)|||||
|Kuwait|||||0.64||||
||||||(0.21)||||
|Oman||||||-3.1|||
|||||||(4.6)|||
|United<br>Arab<br>Emirates|||||||-1.31<br>(2.7)||
|Qatar||||||||2.6<br>(5.8)|
|Observations|49|57|57|57|52|52|52|17|
|R-squared|0.027|0.221|0.416|0.207|0.272|0.304|0.073|0.04|

12
# **Full Esteem Ahead?**

# **Mindset-Oriented Business Training in Ethiopia**

**Authors** : Salman Alibhai<sup>a</sup> , Niklas Buehren<sup>b</sup> , Michael Frese<sup>c</sup> , Markus Goldstein<sup>d</sup> , Sreelakshmi Papineni<sup>e</sup> and Kathrin Wolf<sup>f 1</sup>

_JEL codes_ : J16 L25 L26 O12 _Keywords_ : Gender, Firms, Training, Psychology and Ethiopia.

> 1 This paper is a product of the World Bank Africa Gender Innovation Lab (AFRGIL). Alibhai (email: aalibhai@worldbank.org); Buehren (email: nbuehren@worldbank.org); Frese (email: michfrese@gmail.com); Goldstein (email: mgoldstein@worldbank.org); Papineni (email: spapineni@worldbank.org); and Wolf (email: kawolf@leuphana.de). This study is on the Women Entrepreneurship Development Project (WEDP), an ongoing lending operation of the World Bank's Finance, Competitiveness and Innovation (FCI) Global Practice. We thank Seblewangel Ayalew Woreta and Tsedey Asheber for superb field assistance and the Government of Ethiopia Federal Urban Job Creation and Food Security Agency (FUJCFSA), the implementing agency of the WEDP project. We are grateful to the World Bank Group’s Umbrella Facility for Gender Equality and the Government of Canada for financial support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

<mark>a, b, d and e: W</mark> orld Bank Africa Gender Innovation Lab (AFRGIL), 1818 H Street, NW, Washington, DC, 20433. USA. c: Head of Department of Management & Organization, National University of Singapore (NUS) Business School and Professor at Leuphana University of Lueneburg, Germany.

f: PhD Psychology Leuphana University of Lueneburg, Germany.