rafmacalaba/datause-encoder-data huggingface dataset:
is maintained, and per capita income loses its effect. In columns 4 and 5, we include the law and order institutional variable. The results indicate that countries with a weak legal system have a higher probability of civil war than countries with efficient courts. In column 5, we include per capita income, population and law and order. We find that per capita income is no longer significant, whereas law and order is still significant.

We next check whether the results are maintained when we control for the inclusion of other variables that have been used in different studies of civil wars. Collier and Hoeffler (2004) point out that the existence of natural resources provides an opportunity for rebellion since these resources can be used to finance war and increases the payoff if victory is achieved. We include variables that capture the percentage of world gold, iron, silver, zinc and oil reserves. Following Fearon and Laitin (2003) mountains are another dimension of opportunity since this terrain could provide a safe haven for rebels. And long distances from the center of the state's power also favors the incidence of civil wars, especially if there is a natural frontier between them, like a sea or other countries. Montalvo and Reynal-Querol (2005a, b) show that ethnic polarization explains the likelihood of conflicts and civil wars. Finally, many authors control for the level of democracy.

We include all these variables together with our core variables in Table 4, columns 1 through 5. In columns 6 to 10 we perform the same exercise but including our institutional variable. As before, the results indicate that once the institutional variable is included, per capita income loses its significant effect on civil wars, while the lack of economic institutions keeps its negative and significant effect.

In table 5 we perform the same analysis as in table 3 but using the sample of 113 excolonies. The results are similar to the ones obtained using the whole sample. In particular, if we go from the median of the explanatory variable to the 75<sup>th</sup> percentile, the probability of suffering a 8
educational attainment are available, age bounds are identical to those of 2000. The concept of citizenship must only be used for Germany and for all other countries where Census and register data are not available.

# 2.2 Step 2 - Emigration rates

The second step consists in comparing the emigration stocks to the number of remaining residents in the source country for the same three educational categories mentioned above. Calculating the brain drain as a proportion of the total educated labor force is a better strategy to evaluate the pressure imposed on the local labor market. It is indeed obvious that the pressure exerted by 1,096 thousand Indian skilled emigrants (4.5% of the educated total labor force) is less important than the pressure exerted by 76 thousand Surinamese (90.1% of the educated labor force).

This step requires using data on the skill structure of the adult population in the countries of origin. Denoting Nt;s<sup>jasthestockofindividualsaged25+,ofskillsin</sup> country j, at time t, we de...ne the emigration rates by In particular, m<sup>j</sup> t;h<sup>providessomeinformationabouttheintensityofthebrain</sup> drain in the source country j. It measures the fraction of skilled agents born in country j and living in other OECD countries<sup>10</sup> .

Data for Nt;s<sup>jare taken from Barro and Lee (2000). For 1990 and 2000, they provide</sup> the adult population structure by educational attainment for about 120 countries. Barro and Lee’s educational attainment measures are based on the highest level of schooling attained. Their measures are based on Census data when available. They provide perpetual inventory estimates based on historical schooling enrollment ...gures when Censuses are not available.

Combining Census and Survey data on immigration, we obtain the emigration stock for almost all the countries of the world (191 countries in 2000, 170 in 1990). For countries where Barro and Lee measures are missing (about 70 countries in 2000), we use the World Bank data on the population aged 25 and over and transpose the skill sharing of the bordering country with the closest GDP per capita. Note that the World Bank database provides the GDP per capita in PPP value for 155 countries. For a limited number of countries, the population aged 25 and over has been estimated by applying the share of the 25+ observed in the geographical area to

> 10For some countries, immigrants often travel back and forth between their new and old countries (e.g. Mexico). They are likely to be counted as still being residents in their home country. For that reason, Carrington and Detragiache (1998) provide an upper bound (m = M=N ) and a lower bound (m = M=(N + M )). Since the upper bound is not interpretable for a large number of countries (higher than one), we only report the lower bound.

13
Policy Research Working Paper 9445

# **Abstract**

This study takes advantage of a publicly salient policy sphere—road quality—in the Russian Federation’s capital city to explore the use of digital technologies as means of aggregating information and demonstrating government capacity and effectiveness. It focuses on the potential linkage between road quality based on citizens’ complaints and electoral outcomes in two rounds of Moscow mayoral elections in 2013 and 2018. The data on more than 200,000 online potholes’ complaints were collected and combined with local election data. The causal relationship between these two processes is established, making use of an arguably exogenous variation in the differences across local weather conditions during the heating season that differentially affects pothole creation but is uncorrelated with electoral outcomes. The results indicate that greater use of digital technologies (measured by pothole complaints) results in an increased number of votes and a higher margin of victory for the incumbent. They highlight digital technologies’ role as a tool to create participatory governance mechanisms and convey to the public an image of a transparent, responsive, and capable government.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jsteinbuks@worldbank.org.

_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 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._ Produced by the Research Support Team
Scale. In Sub-Saharan Africa, this scale has been used in studies of hospital patients (e.g., Kretchy et al. 2014 in Ghana) and adolescent students (e.g., Celis et al. 2014 in Rwanda and Astrom and Blay 2002 in Ghana). Results generally support the cross-cultural validity of the scale, though gender patterns are less clear.

Specific locus of control scales for the economic domain also exist, most notably the Spector (1988) “Work Locus of Control Scale” and the Furnham (1986) “Economic Locus of Control Scale”, which includes items such as “whether or not I get to become wealthy depends mostly on my ability” and “although I might have the ability, I will not become better off without appealing to those in positions of power.” Plunkett and Buehner (2007) found that scores on the Economic Locus of Control Scale correlate with economic parameters, such as discount rates for financial outcomes, which were not predicted by Rotter’s scale. Although this scale has been validated in a South African sample (Van Delen et al. 1987), to our knowledge this scale has not been included in questionnaires in Sub-Saharan Africa, at least not in its entirety.

Overall, unlike psychologists, economists and researchers working in Sub-Saharan Africa have typically focused on generalized measures of LOC, which are largely independent of the context and conceptualize internality or externality as a general personality trait.

# **4.1.2     Self-Efficacy**

While means-ends relationships refer to beliefs about whether outcomes are contingent upon certain actions, agent-means relationships regard beliefs about whether one can _produce_ the relevant actions. Control within the agent-means relationship has been most frequently measured through selfefficacy, the belief in one’s capabilities to act effectively towards a goal. This should be distinguished from _outcomes expectations_ , which are assessments of future outcomes that are largely based on perceived selfefficacy. The construct of self-efficacy was introduced by Bandura and represents a core aspect of his social-cognitive theory (Bandura 1977; 1995). In reaction to theories that focused on locus of control, Bandura pointed out that even if individuals believe that outcomes can be influenced by behaviors or responses, they will not attempt to exert control unless they also believe that they themselves can produce the requisite responses.

There are two main conceptualizations of self-efficacy, which result in two main measurement methods. As Bandura originally envisaged it, self-efficacy is a context-specific judgment about one’s ability. Thus, self-efficacy should be measured by asking the respondent about their confidence in completing specific actions, as illustrated in Box 5. For example, when evaluating self-efficacy for selfregulated learning, the student should be asked about specific actions such as “remember information presented in class and textbooks” and “arrange a place to study without distractions”.

18
|**Table 4. Effect of earmar**|**ked relations on log vol**<br>(1)|**ume of workin**<br>(2)|**g capital loan**<br>(3)|**s (cross-sectio**<br>(4)|**n comparison)**<br>(5)|<br>(6)|
|---|---|---|---|---|---|---|
|Earmarkfbt* Riskft|-0.006**<br>(0.002)|-0.018***<br>(0.004)|-0.028**<br>(0.014)|-0.008**<br>(0.003)|-0.023***<br>(0.005)|-0.037**<br>(0.015)|
|Earmarkfbt|0.023**|0.027***|0.004|0.023**|0.027**|0.013|
||(0.009)|(0.009)|(0.022)|(0.009)|(0.009)|(0.025)|
|Earmarkfbt* Riskft*MPft||||0.008***|0.017**|0.033**|
|||||(0.003)|(0.008)|(0.013)|
|Riskft* MPft||||-0.003|-0.018***|-0.018*|
|||||(0.004)|(0.005)|(0.010)|
|Earmarkfbt* MPft||||0.002*|0.004|-0.032**|
|||||(0.001)|(0.006)|(0.013)|
|Observations|259,709|179,786|182,955|259,709|179,786|182,955|
|R-squared|0.868|0.910|0.916|0.868|0.910|0.916|
|Mean loan volume (logs)|11.05|11|11.08|11.05|11|11.08|
|Firm*Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Location*Sector*Month FE|No|Yes|No|No|Yes|No|
|Firm*Year FE|No|No|Yes|No|No|Yes|
|Notes: The table shows estimates of OLS regres<br>relationship with a bank. The dependent variable<br>consecutive periods after a firm receives its first<br>prior to the start of their earmarked relationship<br>prior to the start of their earmarked credit relation<br>(measured by the Herfindahl Index) is above the<br>loan characteristics at the time of issuance (matu<br>effects for the ten loan rating categories establis<br>Brazil's CDI). Standard errors are doubled cluster|sions on our loan-level data<br>corresponds to the volume of<br>earmarked loan from a bank,<br>above the median and zero o<br>ship._MPft_is an indicator vari<br>median and zero otherwise.<br>rity, interest rate spread and a<br>hed by regulators, as well as<br>ed at the bank and time level.|for the sample of f<br>working capital lo<br>and zero otherwise.<br>therwise. Credit ris<br>able that equals on<br>Other controls incl<br>dummy variable i<br>fixed effects for th<br>|irms that at any<br>ans (in logs)._Ea_<br> _Riskft _is an indic<br>k is measured by<br>e if the market po<br>ude the length of<br>ndicating if the l<br>e type of loan (w|time in our sampl<br>_rmarkfbt_is an indi<br>ator variable that<br>the average ratin<br>wer in period_t_of<br>the relationship d<br>oan was collateral<br>hether the loan h|e period begin an<br>cator variable that e<br>equals one for firm<br>g of loans issued t<br>banks operating in<br>uration of each fir<br>ized). All regressio<br>as a fixed rate or a|earmarked credit<br>quals one for all<br>s with credit risk<br>o firms one year<br>firm_f_'s zip code<br>m-bank pair and<br>ns include fixed<br>rate indexed by|

22
> that are beset by deep divisions. We willinow turn to the quantitative data explore these issues greater detail.

> We will examine the determinants of participation, and see the extent to which community interactions and

> the capacity for collective action have been affected by the social fund. We will therefore understand the extent to which our qualitative findings can be generalized to the population. We will also attempt to examine the causal impact of the social fund process on the community's capacity for collective action and participation.

# 3. **Ouantitative Data and Methodoloav**

Each of the five pairs of communities described above have been matched on the basis of observable characteristics such as the availability of public services and levels of poverty, but the field visits also helped match them on "unobservables" such as their geography, political culture and social structure. Within each community, fifty households were selected at random to be administered the questionnaire. Within each household an attempt was made to interview two adults: the head of the household and one another randomly chosen member of the opposite sex to the household head. In practice, Jamaica's family structure, which tends to be have a large proportion of single parent households, made it difficult to locate the second adult in many households. Therefore we have a sample of about 500 households with 684 individuals, spread evenly between social fund and non social fund communities.

The questionnaires that were administered to these households covered issues that ranged from socio-economic characteristics, experience with community based activities and participation in projects prior to the introduction of the social fund, sources of information prior, networks, perceptions of problems in the community, information and knowledge about the social fund, level of participation in community and social activities, and a series of questions that asked respondents to evaluate changes from five years ago to the present on a series of outcome variables (the social funds started operating in these communities in 1997). The questionnaires for the social fund and non-social fund communities were similar, the only difference being that social fund communities were asked an additional set of questions that were unique to the social fund process. The analysis will focus on two sets of dependent variables - one set will analyze targeting and participation focussing on data from the social fund communities, the second set will compare social fund to non-social fund communities in order to tease out the impact of the social fund on changes in various indicators of the capacity for collective action.

> Our data suffers from two important problems, common to many impact evaluations, that affect our ability to determine the causal impact of the social fund program:

> 5 Aniruddha Dasgupta who manages urban CDD projects in Indonesia.

23
# **6. Robustness and Discussion**

We have performed several robustness checks to test the reliability of our results. First, we checked our data for the coverage they provide in terms of the total banking market in each country. Second, we checked how many countries have the representativeness that would enable cross country comparisons. We observe that we have a more complete dataset in the last few years, as almost all the countries had 100 percent of their banking sector, in terms of assets, represented in the sample (Figure 15b).

**Figure 15a. Assets of banks/total Assets**

**Figure 15b. Countries with 100% of total Assets**

<!-- Start of picture text --> 250 100%<br>100% 87% 90%<br>90%<br>90% 79% 82% 83%<br>200 75% 72% 73% 75% 80%<br>80%<br>70%<br>70% 61% 62% 63% 64% 62% 64%<br>150 60%<br>60%<br>50%<br>50%<br>100 40%<br>40%<br>30%<br>30%<br>20% 50 20%<br>10% 10%<br>0% 0 0%<br>1995 1997 1999 2001 2003 2005 2007 2009 1995 1997 1999 2001 2003 2005 2007 2009<br>No. of Countries with 100% of Assets (left axis)<br>Total No. of Countries (left axis)<br>Countries with 100% of Assets as % of total no. of<br>countries (right axis)<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data For Brazil, we had a representative sample of the banking system in terms of total banking assets, for all years. From 2004-09 we had a completely representative sample (Figure 15a), which makes us confident of the robustness of our results. Since financial assets in Brazil are mainly held by the banking sector, our sample is consequently strongly representative of the whole financial sector.

Furthermore, we use the R<sup>2</sup> test to check the robustness of our regressions. We find that a high R<sup>2</sup> for our regressions means that micro-factors play a significant role in the total spread (NIM), such as in Brazil. In general R<sup>2</sup> s are high, but when they are not, this means micro-factors are not important in explaining the NIM (Table 2); in this case we test the significance of the residual constant term, the macro-factors. This is the case for quite a few developed countries, since they have already dealt with many micro issues. In these countries the main drivers of NIM spreads are macro-factors, for example Denmark with an R<sup>2</sup> of 0.32 and macro-factors significance at the 1% interval in 2009. However, having a competitive banking sector, such as the one in the U.S.(R<sup>2</sup> of 0.02 in 2009), and significant macrofactors does not prevent the whole banking sector from entering into a crisis by underestimating the total risk it is taking.

28
stan(lard error of . 4d, whlile the defau'rers paid 1.22 percentage points. 'n 'lie group of coun:ries that newlv acquired soveriegnitv (43 countries), African countries (2.. countries) were charged. the highest spreads. The average spread for the African countries is 1.41 (with a standard error of 40) while **it** is 1.20 (with a standard error of .49) for the remaining countries Next, we defined a repayment record dummy variable **as in Lindert and** Morton (1987) (LD3020 in Table 2). For this purpose, we collected the countries that were not sovereign at any time during the **period of 1820** through 1930s, with the countries that were scvereign but had no repayment problems, and assigned zeros to all those countries (countries **with** "unblemished repayment records"). Countries that were sovereign and had repayment problems were assigned ones. It is interesting to observe that when countries are grouped in this way are the spreads of "good" countries (LD30 20 zero) higher than those of "bad" ones.

In contrast to above procedure, we next look at only the countries that were sovereign and compare the mean spreads of defaulters to those of nondefaulters, as they were described for various historical episodes in Table1. Table 2 indicates that defaulters paid higher spreads than nondefaulters for each definition of the dummv variable.16 (An "F" test rejects the null hypothesis that the means of the two samples are equal.) One may suspect that the above results ar. a consequence of timing of borrowing for the countires in our sample, since the entry dates of countries to the market under consideration differ and that the spreads varied over-time. However, the finding that defatilters paid higher average spreads than non-defaulters hold separatelv for each year of the sample. For everv vear in the data, the **nuil** hvpothesis that the means of the two
Policy Research Working Paper 8074

# **Abstract**

This paper uses unique data collected in rural Pakistan to assess the extent to which consanguinity, which is widespread in North Africa, Central and West Asia, and most parts of South Asia, is linked to child cognitive ability and nutritional status. As economic benefits of marrying cousins may lead to upward bias in estimates of the effects of consanguinity on child outcomes, prior work likely underestimates the negative impacts of consanguinity on child outcomes.

This paper finds that children born into consanguineous marriages have lower test scores, lower height-for-age, and a higher likelihood of being severely stunted. After controlling for current household wealth and parent education, the effects of endogenous consanguinity on child cognitive ability and height-for-age are identified by (current and past) grandfather land ownership and maternal grandparent mortality as instruments for consanguineous marriage of parents.

This paper is a joint product of the Social Protection and Labor Global Practice Group, the Jobs Cross Cutting Solution Area, and the the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cmete@worldbank.org.

_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 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._ Produced by the Research Support Team