rafmacalaba/datause-encoder-data huggingface dataset:
# {INSERT FIGURE 2 HERE}

Assuming strong social sanctions against default, an individual’s preference for loan default is represented by the curve _IGBIG_ , instead of _IOAIO_ , meaning a lower probability of default and a higher rate of recovery (Figure 2).<sup>6</sup> It thus follows that loan recovery is higher under a group-based, versus an individual, lending plan if the group incentive not to default helps to improve an individual member’s loan recovery behavior.

However, the converse is also possible; that is, other group members’ loan default may create a disincentive (group collusion) for an individual member to repay, resulting in a higher loan default and lower recovery rate (e.g., situation _C_ , instead of _B_ or even _A_ ) (Figure 2). In this case, the group loan default rate would be higher under group, versus individual, lending. It is also possible that the default incentive under group lending overwhelmingly reflects the default incentive observed under an individual lending scheme (e.g., situation _A_ ) (Figure 2). However, under group lending, it is expected that loan recovery will be determined by the default incentive of other group members, as well as that of the individual.

In theory, group lending could yield benefits of low loan-default cost via peer pressure, screening, and monitoring. In practice, however, documenting why loan repayment rates are higher or lower with group, versus individual, lending is not so easy, in part, because so few programs practice both group and individual lending. Not surprisingly, most empirical studies attempt to verify what makes group lending work in terms of group attributes, meaning that the group’s role is assessed in terms of the effect of observed group characteristics on loan repayment rates. But conclusions can also vary widely on the effects of group composition. For example, group cohesiveness (a form of social capital measured

> 6 Strong social sanctions against loan default may prevail, for example, if group members see groups as a way to entitle institutional credit and its future access.

7
**Table 5. Regression results. Dependent variable: Weight for age**

**<u>(standard errors adjusted for sampling structure)</u>**

||(1)<br>OLS<br>Individual<br>and<br>household|(2)<br>OLS<br>Individual,<br>household<br>and<br>community|(3)<br>OLS.|(4)<br>Instrumental<br>Variable|(5)<br>Individual<br>Random<br>Effect with|
|---|---|---|---|---|---|
||variables|variables|Children 6-9|Regression|instruments|
|||**Co**|**mmunity Varia**|**bles**||
|Partage or feeding posts|0.130|0.158|-0.102|0.553|0.456|
||(1.11)|(1.30)|(0.88)|(1.88)*|(2.14)**|
|Urban||-0.020|-0.002|-0.019|0.072|
|||(0.16)|(0.01)|(0.15)|(0.86)|
|Health worker in the village||<br>0.084|<br>-0.061|<br>0.145|<br>0.080|
|||(1.23)|(0.57)|(2.32)**|(1.63)|
|Minimum distance to the closest||<br>0.003|<br>0.012|<br>0.011|<br>0.006|
|health center||||||
|||(0.31)|(0.79)|(0.98)|(0.76)|
|Is motorable road near||<br>0.496|<br>0.415|<br>0.478|<br>0.553|
|||(5.81)***|(3.91)***|(6.18)***|(3.53)***|
|Road impassable||<br>-0.131|<br>-0.142|<br>-0.167|<br>-0.196|
|||(1.54)|(1.33)|(2.04)**|(2.89)***|
|Ratio of children vaccinated||<br>0.310|<br>0.002|<br>0.398|<br>0.166|
|||(2.36)**|(0.01)|(2.67)**|(1.65)*|
|||<br>**Househol**|<br>**d and individua**|<br>**l variables**||
|Log per capita consumption|0.209|0.170|0.142|0.209|0.117|
||(4.19)***|(3.38)***|(2.32)**|(3.59)***|(2.98)***|
|Farm income/total income|<br>-0.340|<br>-0.335|<br>-0.359|<br>-0.281|<br>-0.163|
||(2.37)**|(2.40)**|(2.51)**|(1.91)*|(1.93)*|
|Household size|0.004|0.004|-0.016|0.002|0.006|
||(0.60)|(0.63)|(1.74)*|(0.27)|(0.87)|
|Aged 12_23 months|<br>-0.965|<br>-0.999||<br>-0.997|<br>-0.932|
||(12.05)***|(13.15)***||(12.80)***|(16.78)***|
|Aged 24_35 months|-1.110|-1.099||-1.101|-1.174|
||(7.81)***|(7.65)***||(7.67)***|(12.50)***|
|Aged 36_47 months|<br>-1.128|<br>-1.114||<br>-1.119|<br>-1.189|
||(7.92)***|(7.90)***||(7.87)***|(12.40)***|
|Aged 48or more months|-1.069<br>|-1.072<br>||-1.076<br>|-1.152<br>|
||(8.05)***|(7.81)***||(7.81)***|(11.88)***|
|Aged 8 years|||-0.012|||
||||(0.17)|||
|Aged 9 years|||-0.085|||
||||(1.11)|||
|Born in rainy season and aged|-0.246|-0.203||-0.208|-0.537|
|<br>less than 2||||||
||(2.56)**|(2.09)**||(2.16)**|(6.35)***|
|Born in rainy season and aged|<br>-0.049|<br>-0.041||<br>-0.039|<br>-0.213|
|more than 2||||||
||(0.43)|(0.38)||(0.36)|(2.53)**|
|Father dead|<br>-0.095|<br>-0.129|-0.177|<br>-0.123|<br>-0.102|
||(1.04)|(1.45)|(1.54)|(1.37)|(1.26)|
|Mother dead|<br>-0.077|<br>-0.060|<br>-0.142|<br>-0.067|<br>-0.030|
||(0.54)|(0.42)|(1.12)|(0.47)|(0.26)|
|Male|<br>-0.148|<br>-0.148|<br>-0.036|<br>-0.153|<br>-0.203|
||(2.31)**|(2.24)**|(0.48)|(2.29)**|(3.31)***|
|Father’s height|<br>0.033|<br>0.032|<br>0.009|<br>0.031|<br>0.032|

23
bias due to unequal group sizes – as the model also assumes equal group sizes (Cameron et al., 2008; Oshchepkov and Shirokanova, 2020). Unequal group sizes are part and parcel of our data set since we have a few metropolitan areas with many observations and many metropolitan areas with a limited number of observations. Because our goal is to estimate the impact of the group level variable – urban density – the Mundlak model seems to be the best reasonable choice. In addition, the Mundlak estimation accounts for omitted variable bias by incorporating average firmlevel characteristics in the model and thus factoring in regional firm demography and socioeconomic conditions.

Although the Mundlak model addresses part of the endogeneity problems in agglomeration economics models, this estimation technique does not control for endogeneity arising from reverse causality between firms’ productivity, agglomeration economies, and mobility. Specifically, the location choices of firms resulting in lower or higher levels of urban density and mobility in some places could be induced by the migration of firms between places of low productivity to places of high productivity (Melo et al. 2009). The most common technique to deal with this reverse causality is to isolate the effect that runs from urban density to labor productivity by means of a two-stage least squares (2SLS) estimation. Based on earlier work of (amongst others) Ciccone and Hall (1996), Combes et al. (2010), and Chauvin et al. (2017), we used historical and geography variables which can be thought to only affect labor productivity through their effect on current urban density: (1) population residing in the metropolitan area in 1975 and (2) average altitude of the main center. The 1975 historical urban density variable is constructed in exactly the same way as our 2015 variable, using the delineation of that period, obtained from the Global Human Settlement Layer. The altitude variable is also obtained from the Global Human Settlement Layer and is measured as the number of meters above sea level. We perform Wu-Durban-Hausman tests after our estimations to gauge whether urban density can be treated as exogenous variable. As shown in the tables in Section 4, the used instruments are relevant (not weak) and exogenous.

Although there may be a problem of reverse causality from a theoretical point of view, this does not necessarily mean that there is a problem from an econometric point of view (Combes et al, 2008). If indeed the independent variable of interest is exogenous, then the Mundlak estimation, which is a more efficient estimator, should be preferred over its 2SLS counterpart. In addition, estimating 2SLS models including interaction effects is a challenge in and of itself without having good instruments for the interaction effects.

15
Table 6: Differential impact of electricity access on components of tax compliance

||||All||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Frequency of electricity availability|||||
|Level 1|-0.022|-0.170***|0.086**|-0.072*|
||(0.039)|(0.041)|(0.040)|(0.038)|
|Level 2|0.173***|0.062*|0.065*|-0.128***|
||(0.034)|(0.033)|(0.035)|(0.033)|
|Level 3|0.120***|0.066|0.186***|-0.153***|
||(0.041)|(0.041)|(0.040)|(0.039)|
|Level 4|0.196***|0.089***|0.060**|-0.031|
||(0.029)|(0.029)|(0.030)|(0.028)|
|Level 5|0.171***|0.073**|0.036|0.088***|
||(0.032)|(0.033)|(0.034)|(0.031)|
|Constant|-1.575***|-0.008|-0.570***|-1.267***|
||(0.166)|(0.150)|(0.159)|(0.145)|
|Observations|43,560|43,560|43,560|43,560|

Notes: Robust standard errors in parenthesis and clustered at the PSU level. Estimations are done using OLS. The survey sampled across 7,137 communities/towns (4,435 in rural area, 2,702 in urban areas). Dependent variables are dummy variables used to construct the tax morale index: whether the respondent agrees that people must pay taxes, sees refusing to pay taxes as a legitimate protest action, thinks the government should have the right to collect taxes, and trusts tax officials/administration. All regressions in the table control for individual/household-, community-, and country-level variables. Individual/household-level variables include the respondent’s gender, age, education, quality of housing, employment status, size of the household, and ownership of a TV or mobile phone. Community-level variables include the presence of piped-water systems, paved roads, schools, and hospitals. Country-level variables include total population, GDP PPP, share of the urban population, population density, value added of agriculture and service as a share of GDP, total natural-resources rents (% of GDP), IIAG score, and ethno-linguistic fragmentation score.

> _∗_ Significant at 10 percent level

> _∗∗_ Significant at 5 percent level

> _∗∗∗_ Significant at 1 percent level

28
# **Surveys for regulators, local banks and global banks**

# _Objective and respondents_

Currently, evidence of the reform spillover impacts on (non-G20) EMDEs remains mostly anecdotal, reflecting, inter alia, the ongoing implementation and adjustment and challenges in separating the effects of reforms from broader post-crisis developments. In this context, one global bank respondent commented that: _“A comprehensive assessment of the aggregate impact of the various reforms is challenging and ongoing; the adjustment of business models and plans is likely to take another 5-10 years.”_ To help fill a gap in the monitoring of the impact of regulatory reforms on EMDEs, particularly those that are not a member of the Financial Stability Board or any of the standard setting bodies, and to provide a more granular understanding of the scale and drivers of spillover effects and the capacity to deal with them, the World Bank has undertaken a survey of senior regulators and bankers. The surveys were designed to cover a wide range of topics, and are not well suited to provide in-depth technical answers and solutions.

The surveys were also intended to complement the continuing FSB impact monitoring and evaluation work (Box 1) as well as the work conducted in the FSB Regional Consultative Groups. In doing so, the World Bank aims to bring to the attention of the international community the perspectives of those of its member countries that are not members of the FSB and therefore have not been part of FSB deliberations.<sup>5</sup> The exercise targeted seven countries that were selected on the basis of the size of their crossborder activities, geographic dispersion and diverse characteristics of their financial sectors: Bangladesh, Colombia, Kenya, Morocco, Peru, Romania, and Tanzania. Separate surveys were sent to senior officials at supervisory agencies, selected local banks (29 in total, including a few local affiliates of internationally active banks), and to the head offices of six global banks

> 5 Note that a subset of non-G20/FSB EMDEs are involved through the FSB’s Regional Consultative Groups.

8
As for funding, the basic issue is that IEs have tended to be on the expensive side, although there may be scope to reduce costs. Gertler et al. (2011) show that, for a sample of World Banksupported programs for which IE and program cost data was available, IE costs represented on average between 4% and 5% of total program costs, ranging between 0.2% and 13.3%. This is because project costs in the sample ranged between $11 million (Rwanda) and $86 million (Colombia). Trade-related projects rarely attain such levels. If we take DIME’s estimate of a minimum of $300’000 for a feasible IE, a ratio of 5% would require a project of $6 million. By the standards of World Bank projects in social development, poverty, or health, this is a small project. By the standards of trade-related assistance, it is very large.

These rough calculations provide one reason for the slow spread of IE in trade-related assistance and suggest that IE templates must be adapted to the area of trade assistance in order to make IE an acceptable proposition for donors. Clearly, quasi-experimental methods using statistical data instead of original household surveys are the way to go. We now turn to a few examples of recent IEs in that spirit and how they have contributed to our understanding of the effectiveness of trade interventions.

_c. Early Results: Does Export Promotion Make a Difference?_ So far, there have been very few impact evaluations of trade-related interventions, and only a thin, “early” literature can be reported on. However, the performance of export-promotion agencies, which is one of the few areas of “clinical-type” interventions that have been extensively studied, provides a good testing ground to evaluate the contribution that IE can bring to policy debates and dialogue with developing countries.

18
take place between countries without bilateral aid flows. Moreover, we also expect to find a nonzero correlation between the two error terms in the statistical model in a way that is consistent with the truncation of potential outcomes. Alternatively, when investment decisions are not subject to agency selection – that is, when firms optimize over Ω ( _st_ ) -- we expect no correlation between the error terms of the statistical model and any observed correspondence between investment flows and aid flows is attributed to coincidence.

# **5. Data Description**

# 5.1. <u>AIJ Investments</u>

Our dichotomous measure of AIJ investment is taken from data on AIJ projects reported to the UNFCCC and compiled by the SBSTA. As of February 2002, the UNFCCC reported on 156 projects. Among these, we include in our sample the 147 projects in which a letter of intent was signed with the host country. The projects began between 1992 and 2001 and were distributed among 22 investor countries and 42 host countries (table 1).<sup>16</sup> For our initial analysis we focus on the 65 unique investor-host partnerships that evolved over the study period; that is, for our preferred model we define an investment event as the joint agreement by an investor country and a host country to participate in any number of AIJ projects. We do so for two reasons.

First, this approach is more in keeping with descriptive studies that suggest agency preferences, sometimes tied to national policy objectives, were influential in screening outcomes. The related point of interest then is whether the agency approval hurdle is cleared for any given potential partnership, rather than how frequently it is cleared. Second, and importantly, in the few instances where the number of bilateral projects in a partnership is large, for example between Sweden and Estonia, many projects are replications of a common approach and distinguished primarily by differences among second-tier counterparts.<sup>17</sup> Consequently, it is not clear that giving greater weight to this type of investment adds to the analysis. Even so, it is possible to modify the statistical model to make use of the data in count form and we do so to check that our results are robust. We return to this topic later.

> 16 The Bonn Decision, taken in 1995, gave rise to several new national programs. However, the Decision also brought several established programs into the common AIJ framework. Consequently, some projects under the Dutch, Norwegian, Swedish and US programs were negotiated prior to the Bonn decision.

> 17 Many of the projects between Sweden and Estonia involved up-grading public heating systems. These projects were organized under a common national framework, although local government counterparts differed among projects.

11
# | Poticy ReseARCH WORKING PaPeR 2283

## Summary findings

This paper introduces a large new cross-country database on political institutions: the Database on Political Institutions (DPI).

Beck, Clarke, Groff, Keefer, and Walsh summarize key variables (many of them new), compare this data set with others, and explore the range of issues for which the data should prove invaluable.

Among the novel variables they introduce:

* Several measures of tenure, stability, and checks and balances.

* Identification of parties with the government coalition or the opposition.

* Fragmentation of opposition and government parties in legislatures.

The authors illustrate the application of DPI variables to several problems in political economy.

Stepan and Skach, for example, find that democracy is more likely to survive under parliamentary governments than presidential systems. But this result is not robust to the use of different variables from the DPI, which raises puzzles for future research.

Similarly, Roubini and Sachs find that divided governments in the OECD run higher budget deficits after fiscal shocks. Replication of their work using DPI indicators of divided government indicates otherwise, again suggesting issues for future research.

Among questions in political science and economics that this database may illuminate: the determinants of democratic consolidation, the political conditions for economic reform, the political and institutional roots of corruption, and the elements of appropriate and institutionally sensitive design of economic policy.

This paper — a product of Regulation and Competition Policy, Development Research Group — is part of a larger effort in the group to understand the institutional bases of poverty alleviation and economic reform. The study was funded by the Bank’s Research Support Budget under the research project “Database on Institutions for Government Decisionmaking”TM (RPO 682-79). Copies of this paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room MC3-422, telephone 202-473-8526, fax 202-522-1155, email address psintimaboagye@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/ research/workingpapers. The authors may be contacted at theck@worldbank.org, gclarke@worldbank.org, pkeefer@worldbank.org, or pwalsh@worldbank.org. February 2000. (46 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 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 Bark, its Executive Directors, or the countries they represent.

Produced by the Policy Research Dissemination Center
to capture the differences between the 10 Leamer commodity clusters, and country population to account for scale.

# **_Export Booms_**

Export booms are identified using the identical product-level export data used to identify discoveries. A product export boom is defined as a product experiencing at least 10% growth in at least 6 years between 1997 and 2003. There are 3782 product-level export booms identified in the data, listed by country in the Appendix. As with the discovery data, counts of these booms are aggregated to the SITC 3-digit level, and the Gini coefficient for the Leamer commodity cluster is calculated, providing our measure of concentration.

The control variables included in our examination of export booms are historical boom counts for the country/Leamer commodity group to capture country/commodity group fixed effects and persistence, GDP per capita (in quadratic form) to capture the possibility of more export accelerations in poorer countries due to convergence, population level to control for scale, and commodity cluster indicator variables.

# **3. Results**

Figures 2, 3, and 4 show the relationship of performance, measured by total counts, to concentration<sup>3</sup> . Among all three search processes (on-the-frontier innovation, discoveries, and export booms), there is a clear negative correlation between concentration and performance.

> 3 For illustrative purposes, performance and concentration at the country level across commodity groups is shown. The estimation, however, is at the country/commodity group level, as described in the methodology section.

11
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