rafmacalaba/datause-encoder-data huggingface dataset:
In regression 4.1 to 4.4, we use the new calculation of variability of exports as a measure of economic instability. In the reproduction of the main equation as well as in the robustness checks, the impact of economic instability on the success of the projects is negative, and the coefficient in front of the multiplicative variable is significantly positive. Thus our results are robust to a modification of a measurement of the variability of exports; however, the impact of the stabilizing effect is now smaller: now each percentage point of ODA on GNI reduces on average 3.5% of the negative effect of vulnerability.

# _Macro economic regressions_

In this paper, the unit of observation has always been each project funded by the World Bank. We will now aggregate these observations by country and by four year periods to obtain average rate of success of the projects. The explained variable is equal to the average rate of success of all the projects carried out in the country during the period<sup>11</sup> . In regression 5.1 we run the same model as the one of the main equation pooling panel data from 1980 to 1999 with the 55 countries for which the data are available. In the following regression of table 5, we present the results with country fixed effects: we find evidences of the existence of a stabilizing effect of aid with a coefficient in front of the multiplicative variable significant at 5% and 10% with fixed effects. We also note that this time, using the same method as Isham and Kaufmann (1999) or Dollar and Levin (2005) we find similar results: the quality of institutions significantly improves the success of the projects.

> 11 When a project is divided into two or more periods, its rating is divided into these periods proportionally to the duration of the project in each period.

19
|Health Rehabilitation Project|Albertus Voetberg|
|---|---|
|Health Rehabilitation Project|Albertus Voetberg|
|Health Rehabilitation Project|Albertus Voetberg|
|Health Rehabilitation Project|Albertus Voetberg|
|Health Rehabilitation Project|Albertus Voetberg|
|Health Rehabilitation Project|Albertus Voetberg|
|Health Rehabilitation Project|Albertus Voetberg|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Nairobi Water SupplyProject(03)|Alain R. Locussol|
|Agricultural Sector Management Project(02)|Christine E. Cornelius|
|Agricultural Sector Management Project(02)|Christine E. Cornelius|
|Agricultural Sector Management Project(02)|Christine E. Cornelius|
|Agricultural Sector Management Project(02)|Christine E. Cornelius|
|Agricultural Sector Management Project(02)|Christine E. Cornelius|
|Agricultural Sector Management Project(02)|Christine E. Cornelius|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|EnergySector Reform and Power Development Project|Joel J. Maweni|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Protected Areas and Wildlife Services Project|Agnes I. Kiss|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Parastatal Reform and Privatisation TA Credit Project|Thyra A. Riley|
|Export Development Project||
districts outside the ACCs using the same three-stage sampling procedure. Thus, the survey was designed with the secondary aim of generating results that are representative at the national level, at least for the four main regions that account for more than 90 percent of the agricultural production in Ethiopia (Bachewe, Koru, and Taffesse 2018). The analysis in this study uses data from 4,315 distinct households in 206 districts that produced maize or wheat at least twice in any of the three survey rounds. We also used administrative records from the ATA to define DSM adoption cohorts in the survey districts by crops.

Table A1 in the appendix presents the characteristics of sample households. Most households are male headed (about 90 percent), and the average household head is about 47 years old and completed less than four years of formal education. The average household consists of six members, owns 1.8 hectares of land, and keeps two oxen. On average, about 46 percent of sample households are members of agricultural cooperatives and use 82 kgs of fertilizer each year. The mean comparison tests between treatment (DSM) and control (non-DSM) households show statistically significant differences for several household characteristics, especially among DSM cohorts from 2014 to 2019. We account for those variables in the main analysis, which is discussed in the next section.

Next, we examine the descriptive statistics for our outcomes of interest—seed purchases and crop yields. Table 2 summarizes the descriptive results for the binary measure of fresh seed purchases (extensive margin) and the quantity of seed purchased per hectare (intensive margin) for maize and wheat, disaggregated by DSM status, cohorts, and survey rounds. Two key insights emerge here. First, post-treatment differences relative to the pre-treatment 2012 baseline are more pronounced and strongly significant in 2019 compared to 2016. For example, four out of the seven treated cohorts reported a higher quantity of maize seed purchases per hectare in 2019 compared to 2012, whereas only one treated cohort showed an increase in 2016. On the extensive margin, all but one treated cohort reported an increase in the share of farmers purchasing maize seed in 2019 compared to 2012, while three cohorts showed no change in 2016. Second, increases in seed purchases, both on the extensive and intensive margins, are more common and strongly significant for maize than for wheat. For instance, none of the treated cohorts showed increases in wheat seed purchases on the extensive margin in 2019 compared to 2012, while only two cohorts exhibited increases in 2016. On the intensive margin, only one treated cohort in 2016 and one in 2019 demonstrated higher wheat seed purchases per hectare compared to 2012.

9
countries? Is bank regulation so much better now that one should not expect crises to follow from greater capital account liberalization? In addition to these important questions about the stability of financial systems, policy makers are also concerned about other features of their financial systems. Will the bank regulatory framework prescribed by the Basel Committee on Bank Supervision increase access to financial services? Have changes in regulation contributed to financial-sector development and the allocation of capital by banks to those firms most likely to promote growth and reduce poverty? And what about the efficiency of banks, or their corporate governance, and corruption in the lending process itself? These questions regarding the recent changes in the regulatory environment and their effects represent a natural area of inquiry.

More than ten years ago, a similar set of questions motivated us to start assembling the first cross-country database on bank regulation and supervision. Based on guidance from bank supervisors, financial economists, and our own experiences, we began putting together an extensive survey of bank regulation and supervision.<sup>1</sup> The original survey, Survey I, had 117 country respondents between 1998 and 2000. The first update in 2003, Survey II, characterized the regulatory situation at the end of 2002, and had 152 respondents. Survey III was posted at the Bank website in the summer of 2007 with responses from 142 countries. Survey III is special because barring a postponement in Europe on par with that in the United States; it represents the last look at the world before many countries formally begin implementing Basel II, the revised Capital Accord.

This paper is structured as follows. Section II will very briefly review the structure of the survey and discuss some issues that arise in the responses to the three surveys. Next, Section III looks at the state of bank regulation around the world in 2006, and importantly how it has changed in the last 10 years. Section IV then turns to a first analysis of the data, asking whether the changes in bank regulation are contributing positively to financial sector development (and thus we hope to the wider availability of financial services) and to the stability of banking systems around the world. Section V concludes with lessons for Basel II, and for countries that are grappling with a response to it.

> 1 As in Barth, Caprio, and Levine (2006), hereinafter BCL, we sometimes use the term regulation generically to apply to banking-sector policies and compliance mechanisms, while at other times to discuss particular, specific regulations or special aspects of supervision.

2
<!-- Start of picture text --> Real GDP Growth Real GDP Per Capita Growth<br>LO.0% -nenecneseccneecec cece cece cece ecccneeeceneecccneeeeeeneeeceneeceneessnaeecenneescnnaas 0.0%<br>5.0% Ce -5.0% Se > Ss nnn<br>0% SSS, a“<br>5.0% Mey 10.0% — ar<br>Be eeeSEER NEE NE scl cee Ao<br>-15.0% =——— Ney, _-- ;*- 20.0% ————-<br>0 a<br>2011 2012 2013 2014 2015 2016 2017 2018 2011 2012 2013 2014 2015 2016 2017 2018<br>USD bil, 2010 Real GDP Level USD, 2010 Real GDP Per Capita Level<br>60 3,000<br>50 2,500<br>40 “Sy 2,000<br>30 S—.-.yn...Ss ------ 1,500 Ses. onSS ==.<br>20 — =<br>1,000<br>10<br>0 500. ——<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br>=== Scenario (I!) - Central projection Note: GDP in 2010 amounted to USD 60 billion (IMF WEO October<br>--®-- Scenario (I!) - Lower estimate 2018). GDP per capita in 2010 amounted to USD 2,857, based on<br>----- Scenario (II) - Upper estimate population of 21.018 million (United Nations).<br><!-- End of picture text -->
Table A.4: **Correlates of worker attendance at the job fairs**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Background|Search Efort|Employment|All|
|Degree|0.0639|||0.0330|
||(0.198)|||(0.209)|
|Vocational|0.00802|||0.00559|
||(0.0395)|||(0.0398)|
|Post<br>~~s~~econdary|0.000127|||-0.0294|
||(0.191)|||(0.201)|
|Female|-0.0109|||-0.0115|
||(0.0307)|||(0.0310)|
|Migrant|0.0154|||-0.00141|
||(0.0362)|||(0.0358)|
|Amhara|0.00957|||0.0148|
||(0.0376)|||(0.0338)|
|Oromo|-0.0181|||-0.0164|
||(0.0506)|||(0.0488)|
|Experience|-0.0590|||-0.0433|
||(0.0547)|||(0.0533)|
|Age|-0.00861|||-0.00924*|
||(0.00528)|||(0.00518)|
|Certifcate|0.0984***|||0.0654*|
||(0.0304)|||(0.0357)|
|Distance (center)|0.00214|||0.00167|
||(0.00722)|||(0.00715)|
|Search<br>~~6~~months||0.0418||0.0155|
|||(0.0409)||(0.0469)|
|Plan Self Empl||0.0399||0.0297|
|||(0.0898)||(0.0891)|
|Search frequency||0.304***||0.293***|
|||(0.0497)||(0.0505)|
|Wage Empl (6 months)|||-0.0164|-0.0446|
||||(0.0304)|(0.0289)|
|Work frequency|||-0.0291|-0.00877|
||||(0.0496)|(0.0524)|
|**Employmentatthet**|**imeofthejob**|**fair**|||
|<br>Permanent Job|||-0.161**|-0.160**|
||||(0.0646)|(0.0692)|
|Any Job|||-0.00143|-0.00576|
||||(0.0338)|(0.0335)|
|Constant|0.748***|0.398***|0.631***|0.664**|
||(0.253)|(0.0376)|(0.0270)|(0.263)|
|Observations|1,006|1,006|1,006|1,006|
|R-squared|0.018|0.045|0.007|0.063|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 A.5
-29-

# CHAPTER III

# BINARY COMPARISONS OF REAL OUTPUT AND PURCHASING POWER BRAZIL/USA AND MEXICO/USA IN 1975

# Introduction

This study applies the "industry of origin" approach to 3 countries, namely Brazil, Mexico and the United States. The sample covers 17 industries representing 20 to 39 per cent of manufacturing output. This chapter explains the methodological and empirical problems which we encountered, and the procedures we used to overcome them. We have tried to make our procedure completely transparent to facilitate the task of those who wish to criticise, replicate, augment, truncate or otherwise modify it.

The year 1975 was chosen as the basis for comparison, so that the results can be compared with ICP Phase III (1982). One of the major problems in comparisons with the United States is that the nearest US census figures refer to 1977, so that price and volume adjustments had to be made to bring the US estimates to a 1975 base. This problem does not arise in the binary Mexico/Brazil comparisons which are presented in chapter IV.

# Scope of the Production Census and Definition of the Products

Detailed analysis of production census material was the basis for comparing the manufacturing sector. Census data have a distinct advantage over other sources. Large amounts of information on gross output values and quantities, input values and (sometimes) quantities, and employment are available from a single source covering the same establishments. The reliability of the information is backed up by legal penalties for non- or inaccurate reporting. A higher degree of internal consistency can, therefore, be assumed than when making use of different sources to compare sectors. Census material, however, is not perfect or always ideally suited for our purposes, as will be shown below.

With regard to the scope of the production census, it should be emphasized that physical quantities and output values are not specified sepazrately fOr all individual commodities of an industry. For some items only value figures are provided, and some items are not specified individually at all. This latter pc.int is one reason for differences sometimes found between total output values in the summary volume of the census and the values specified in the detailed volumes. These census limitations require an adjustment of the "matched" output to total output of an industry, which we will discuss below.

With regard to the definition of "industries" and "commodities" Rostas already recognized difficulties when he wrote in 1948:

- "These difficulties are mainly due to the fact t.iat individual industries, as classified by the censuses, each produce a group of products and by-products which are not identical in the different countries, either as regards type or quality or as regards the. relative importance of individual types within the group."

- (Rostas, 1948, p. 11).
Ivkovic, Z., C. Sialm, and S. J. Weisbenner, 2008, “Portfolio concentration and the performance of individual investors,” Journal of Financial and Quantitative Analysis 43(3): 613-655 Ivkovic, Z., J. Poterba and S. J. Weisbenner, 2005, “Tax-Motivated Trading by Individual Investors,” The American Economic Review, 95(5): 1605-1630 Lakonishok, Josef, and Baruch Lev, 1987, “Stock Splits and stock dividends: Why, who and when,” Journal of Finance 42, 913-932 Lesmond, D. A., J. P. Ogden and C. A. Trzcinka, 1999, “A New Estimate of Transaction Costs,” Review of Financial Studies 12 (5): 1113-1141 Li X., Z. Geng, A. Subrahmanyam, and H. Yu, 2016, “Portfolio concentration and performance of institutional investors worldwide,” Working Paper, UCLA Lin, D.Y. and L.J. Wei, 1989, “The robust inference for the cox proportional hazards model,” Journal of the American Statistical Association, 84, 1074–1078 Lo, A. W., H. Mamaysky, and J.Wang, 2004, “Asset prices and trading volume under fixed transaction costs,” Journal of Political Economy, 112, 1054–1090 Lynch, A. W. and S. Tan, 2011, “Explaining the magnitude of liquidity premia: The roles of return predictability, wealth shocks and state dependent transaction costs,” Journal of Finance 66(4): 1329–1368 Odean, T., 1999, “Do investors trade too much?,” American Economic Review, 89, 1279–1298.

Roll, R., 1984, “A simple implicit measure of the effective bid-ask spread in an efficient market,” Journal of Finance, 39, 1127–1139 Schultz, Paul, 2000, “Stock Splits, Tick Size, and Sponsorship,” Journal of Finance 55,429-50 Seru, A., T. Shumway, and N. Stoffman, 2010, “Learning by trading”, Review of Financial Studies 23 (2): 705-73 Simutin, M., 2013, “Cash Holdings and Mutual Fund Performance,” Review of Finance, 18 (4): 1425-1464 Stoffman, N., 2014, “Who trades with whom? Individuals, institutions, and returns,” Journal of Financial Markets 21: 51-75 Vayanos, D., 1998, “Transactions costs and asset prices: A dynamic equilibrium model,” Review of Financial Studies, 11, 1-58 39
designed and supervised the data collection and the implementation was done by the Institute of Economics in Hanoi.

In each of the six provinces, samples of project and non-project communes were drawn for a total of 200 surveyed communes. Road links covered by the project all pass through communes, and a majority link up commune centers ⎯ where facilities and services are located ⎯ with the road network. Data are often, and more easily, collected at the commune level in Vietnam. For these reasons, a road’s zone of influence is defined as the commune through which the road passes. The project communes were randomly selected from lists of all communes with proposed projects in each province. A list was then drawn up of all remaining communes in districts with proposed sub-projects from which a random sample of non-project communes was drawn. Comparison areas chosen this way should share many of the same characteristics as the project areas. However, we cannot be confident that they are a good comparison group on a priori grounds. For this reason, we will use matching techniques to test the selection of comparison groups **.** A detailed commune-level data base was created in part by drawing on annually collected records ⎯ both current and retrospective ⎯ at the commune level and augmenting this information with other data. A household questionnaire was also administered to 15 households in each sampled commune.<sup>8</sup> The baseline data collection began in June 1997. Subsequent rounds followed at two year intervals, in the summers of 1999 and 2001, tracking the implementation process followed initially as closely as possible. Surveying of districts is timed across months to coincide with the schedule followed during the baseline. In this paper, we use only the baseline and 2001 waves.

We also use a project level database constructed from provincial and central records. This provides information on project location, timing, costs, the condition of the road pre- and post-project, whether bridges were rehabilitated, and kilometers rehabilitated. Some of these data were not carefully recorded, or are missing entirely. However, the aggregates do accord well with kilometers recorded by the center; the means

> 8 Given constraints, no attempt was made to measure a household level welfare indicator such as consumption.  However, the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households (van de Walle, 2006).

8
- 11 -

# 1.2.1 Country set

The estimation of an econometric equation using the data for a set of countries assumes that the relationship studied does not differ significantly between the countries of the sample. In other words, as far as we are concerned here, it assumes that the differences between the observed growth rates are explained by the different values assumed by the independent variables for the various countries.

This implicit hypothesis of structural homogeneity of economic relationships would call for systematic tests. But the econometric studies using cross-sectional data are generally accompanied by a much more limited number of diagnostic tests than the studies done using time data. Most of the customary software produces, for example, the Durbin and Watson statistic, which has no clear significance in cross-sectionul analysis. However, the pertinent tests for this type of research, such as the coefficient stability test between two subsamples due to Chow (1960), or the homos- 'sticity tests that bring out the variability of the residual variances between the different observation points -- and therefore that of the coefficients, according to Breush and Pagan (1979) -- generally call for supplemental work by the investigators. They would therefore be very helpful for justifying econometric analysis using cross-sectional e3ta by means of a given model or, on the contrary, for excluding .ertain countries from the sample.

Whereas including a country in a sample implies the hypothesis of homogeneity, the fact of excluding a country does not necessarily imply a hypothesis of structural heterogeneity. It may simply reflect the investigator's tastes or limits on the availability of data. To be tested, the homogeneity hypothesis requires that the model be estimated with and without the countries in question.

Howe;er, the econometrician is tempted to include the largest possible number of observations in the sample in order to have sufficient degrees of freedom to apply statistical tests under satisfactory conditions. The temptation to do 3o can sometimes conflict with the homogeneity requirement stressed earlier. It can also be pointed out that the exclusion of a country from the sample used (taking into account the cost in terms of degrees of freedom that that implies) might reveal an implicit heterogeneity hypothzsis. Cenerally speaking, however, the literature does not contain detailed justification of the choice of the sample analyzed, and a wide variety of criteria have been applied.

# Sets and subsets

Cross-sectional analyses of growth are generally based on a sample composed exclusively of developing countries (cf. table 1, column 3). However, some studies include industrialized countries in the sample (Fry and Lilien 1986, Genberg and Swoboda 1987, Hwa 1983,