rafmacalaba/datause-encoder-data huggingface dataset:
others to produce random normal deviates, as described in the manual (StataCorp 2009). The algorithm produces numbers only from the positive half of the normal distribution, so the final step is to randomly assign the number a positive or negative sign with equal probability. This final step was the source of the problem. While this happened correctly on average, 95.1 percent of the 2<sup>31</sup> seeds that could be used to initialize the generator produced first and second draws with the same sign, yielding the pattern in Figure 1. No matter what seed was used, the signs of the draws from `rnormal()` followed almost exactly the same pattern.

This is illustrated using a cumulative sum of signs in Figure 2. After 3,400 draws, the sum of signs is near zero: these 3,400 draws include almost an equal number of positive and negative values, regardless of the seed. A consequence of this pattern is that the distribution of the mean of the first 3,400 draws has a lower variance than it should, had these draws been i.i.d. _N_ (0 _,_ 1). Likewise, since there is always an imbalance between positives and negatives in the first 7,600 draws, their mean is bimodally distributed. To illustrate, Figure 3 compares several distributions: the left side shows the distribution of the mean of the first 3,400 draws from `rnormal()` , while the right shows that of the mean of the first 3,400 draws from `invnorm(uniform())` . The upper panel shows this comparison for Stata 10.1 (before the bug fix), while the lower panel shows it for Stata 12.1 (after the 2011 bug fix). Seeds 1 through 100,000 are used to produce the figure; Figure 4 is produced analogously, but for the first 7,600 draws.

This could easily “interfere constructively” with a statistical procedure: a simple case is a T-test of whether the mean of a collection of normally distributed values is significantly different from zero. No matter what range of seeds is used, resetting the seed immediately before making 3,400 draws and taking their mean yields a what should be a 5-percent level test statistic that falsely rejects less than 0.3 percent of the time. Similarly, no matter what range of seeds is used, resetting the seed immediately before making 7,600 draws and taking their mean yields a statistic that falsely rejects more than 13 percent of the time. Neither of these phenomena occur with fully updated Stata 11, or with Stata 12.

4
because of the prudent management of the fiscal, monetary and exchange policies, Costa Rica has been able to guarantee a neutral exchange rate (as measured by the real effective exchange rate index - REER) and relatively low inflation levels to its export sector (Figure 1.4).

<!-- Start of picture text --> Figure 1.4 Costa Rica: Evolution of Inflation and REER<br>1983-2003<br>115<br>105<br>95<br>85<br>75<br>65<br>55<br>45<br>35<br>25<br>15<br>5<br>1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>REER Inflation<br>Percentage FTZ Law Reform Intel’s Arrival<br>Export Promotion Import Liberalization<br>Capital Account Liberalization<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

Attempts have been made to minimize the significance of the response of Costa Rica’s business community to the liberalization of the economy, by pointing out that the increase in Costa Rica’s exports is mainly associated with the foreign sales of the multinational companies under the free trade zone regime. The truth is that, although multinational companies are currently an important source of wealth for the country—by generating new and better employment sources and technology transfer to domestic businesses, among other positive effects—the results shown in the previous figures underline the importance of the liberalization in achieving a better allocation of the resources of Costa Rica’s economy, along with the entry of multinational companies since the second half of the 1990s.<sup>4</sup> The sustained growth of FDI inflows into Costa Rica is another positive outcome of economic liberalization, as it is an evidence of the foreign investors’ confidence in the new development model (Figure 1.5). Such investments have become not only an important source of jobs, but also a major destination market for products manufactured by domestic micro, small and medium-sized enterprises, turning them into indirect exporters (Monge _et al._ , 2005).

> 4 For a detailed discussion on the positive effects of the companies under the protection of Costa Rica’s free trade zones, see Monge _et al._ (2004).

8
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their effects on male and female migration outcomes. Both sets of findings prove relevant. First, female migrants can substitute for male migrants when households lack sons, suggesting that the demographic structure of the household may help to shape the incentives and constraints imposed on female migration. Second, female migration is found to be more responsive than male migration to household-level shocks. In particular, household health shocks provide a large deterrent and household property loss shocks provide a large incentive for female migration whereas neither shock has any impact on male migration. Thus, while female migration remains more tied to human capital factors, it remains simultaneously more tightly bounded by household-level considerations. Further tests also reject the possibility that female agency in migration is strengthening over time, despite ever increasing numbers of female migrants.

Finally, we hypothesized that networks are gendered and that family networks should play a larger role than community networks in migration behavior – both because of prior studies from Mexico and as well as the social turmoil associated with the end of the Hoxha regime in Albania – but that the importance of networks should decline over time. Our results show that family migration networks are gender-specific with male and female family networks associated with increased odds of migration for both men and women but that female migration depends more strongly on female family networks than male migration depends on male family networks.

Routinization of migration within Albanian society, as predicted, appears to reduce the role of both family and community networks. The overall impression provided by the network data is that the effects of the network variables are weakening over time, particularly from the mid1990s -- an important finding not consistent with expectations from cumulative causation or from analyses of the Mexico-US migration context. This finding supports our earlier claim regarding the circa 1996 shift in the value of networks as well as in the value of human and financial capital. This shift, which coincided with the failed pyramid schemes and Greek regularization of Albanian immigrants, may have generated a diffusion of migration-related information which fundamentally altered the migration decision process.

In conclusion, our study reveals a complex and dynamic picture– one that emphasizes both the distinctiveness and growth of female migration from Albania as well as the continued lack of female agency in migration. Female migration is undoubtedly increasing but this does not necessarily translate into equality, nor does it mean the absence of economic motives behind 20
A Road to Trust development project which targets resources to the poorest municipalities in the poorest provinces of the Philippines (Chase and Holmemo 2005).

We have information on whether each respondent trusts people in general (generalized trust) and trusts people in their village (bonding trust). The questions are similar to the usual World Value Survey question<sup>4</sup> : “ _In general do you agree or disagree with the following statement: most people can be trusted._ ” We follow Alesina and La Ferrara (2002) and classify individuals answering “agree somewhat” or “agree strongly” as trusting. Others are classified as being non-trusting. With this distinction, only 7.2 percent of individuals declared trusting strangers while 58 percent declared trusting members of their community in 2003.<sup>5</sup> Information is available on the households’ poverty status. We use information on asset ownership to build an asset index which serves as a measure of wealth ( _asset_ ). We also use data on the highest level of schooling achieved by the household head ( _No Edu, Primary, Secondary, College_ ) and on whether the household is engaged in farming ( _Farmer HH_ ). Further, we have information on the religion and ethnic group of the household head. We aggregate this information at the village-level to obtain the following village estimates: measures of ethnic diversity and of inequality, the share of

> 4 Responses to trust questions are correlated with real-life financial decisions (Karlan 2005) but there are some debates as to whether they measure trust or trustworthiness. Indeed, self-reported trust levels have been shown to be correlated with the second player behavior (i.e. trustworthiness) in a trust game but not always with the first player behavior (i.e. trust) in this game (Glaeser et al. 2000, Holm and Danielson 2005).  However, recent findings from trust games suggest that behavior from the first player in a trust game does not necessarily capture trusting behavior but rather risk-aversion (Schechter 2007).

> 5 Our village-level measures of trust are computed using the balanced panel of about 2,100 households.

7
Notes: Data pooled for all follow-up surveys, unless otherwise noted. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round. Z score index constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p-values and q-values are reported below standard errors (q-values in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

a Z-score on use of finance includes seven variables. Those are the amount that business can borrow in 2 weeks (U$S), the amount borrowed (U$S), the owner has insurance for business, the amount in bank savings (U$S), a high relative savings, owner does not take business money for the household, business keeps financial records.

b High relative savings is a dummy that takes value 1 if the owner total savings is over the median of total savings at baseline and 0 otherwise. c Sample size of bank contacted the firm includes only follow-up 3 and 4.

42
Policy Research Working Paper 7937

# **Abstract**

This paper uses loan-level data from 124 countries over 1995–2015 to examine the transmission of monetary policy through the cross-border syndicated loan market. The results show that the expansion of monetary policy increases cross-border credit supply especially to weaker firms. However, greater foreign bank presence in the borrower country appears to reduce the potentially destabilizing impact of lower policy interest rates on cross-border lending, as it attenuates increases in loan volume and maturity while magnifying increases in collateralization and covenant use. The mitigating effect of foreign banking presence in the borrowing country on the transmission of monetary policy is robust to controlling for borrower-country economic and financial development, and a range of borrower and lender country policies and institutions, including the strength of bank regulation and supervision, exchange rate flexibility, and restrictions on capital flows. The findings qualify the characterization of international banks as sources of credit instability, and suggest that foreign bank entry can improve the stability of cross-border credit in the face of international monetary policy shocks.

This paper is a product of the 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 Ademirguckunt@ worldbank.org; balint.horvath@bristol.ac.uk; H.P.Huizinga@uvt.nl.

_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
balances to open accounts, the annual fee and the documents to open checking accounts, the minimum amount for consumer loans, the days to process SME and consumer loans, and the fees for using ATM cards. On the other hand, private credit to GDP is positively and significantly correlated with the number of locations to submit loan applications. Interestingly, the fees on consumer and SME loans, the cost to transfer internationally and the locations to open deposit accounts are not significantly correlated with economic or financial development.

To gauge the relationship between barriers and aggregate measures of financial sector outreach, we utilize recently compiled data on branch penetration and the number of loan and deposit accounts (Beck, Demirguc-Kunt and Martinez Peria, 2007). These are country-level indicators, compiled from regulatory surveys and publicly available information. We would expect countries with banks that impose higher barriers on their customers to invest less in the number of branches and higher barriers to be also reflected in fewer deposit and loan accounts per capita.

The correlations in Table 6 suggest that higher barriers are indeed associated with lower outreach. Specifically, banks in countries with a higher demographic branch penetration demand lower minimum balances to open accounts, require fewer documents to open accounts, are more likely to accept loan applications in branch-like offices or over the phone or Internet, set lower minimum loan amounts, are quicker at processing loan applications, and charge lower fees for using ATM cards. Similarly, banks in countries with higher loans per capita have lower minimum loan amounts, are quicker in processing loan applications and are more likely to accept these applications outside headquarters and through non-traditional channels such as phone or Internet. Banks in countries with more deposits per capita demand lower minimum balances and lower fees, require fewer documents to open such an account, set lower minimum amounts for 16
Previous cross-country empirical research had shown that bad governance and corruption reduce the quality of publicly provided services and investment in the public sector. Moreover, bad governance, by promoting mis-allocation of resources and poor accountability, can raise service’s costs (both official and unofficial) and limit user’s access to public services. Users with lower income or firms of different size may pay less or more in term of bribes than others to obtain the same public service. The micro-level analysis presented in this paper permits to shed some light into the 'black boxes' elicited by the empirical findings from aggregate cross-sectional analysis on the impact of bad governance (Gupta, Davoodi, Alonso-Terme, 1998; Davis, 2004) corruption is found to be associated with higher income inequality.

This work also begins to focus on the role of alternative policy tools that can be used to improve governance, and in particular on the importance of strengthening voice mechanisms, such as feedback mechanisms and freedom of the press, to promote better governance and greater access to public services. These voice mechanisms can be fostered by supporting NGOs on the ground and promoting the use of social accountability tools, as for example, the scorecard method introduced first in Bangalore by Samuel Paul and his NGO, Public Affairs Center<sup>20</sup> . This paper also provides preliminary evidence on the importance of social networks, especially in presence of weak governance institutions.

As we emphasized, this is a work in progress and further analytical work is needed, especially at the micro level to address possible omitted variable issues in our analysis and to test the robustness of our results for other countries.

# **REFERENCES**

Ades, A. and R. Di Tella (1999), “Rents, Competition and Corruption”, _American Economic Review_ , Vol. 89, No.4, pp.982-993.

Alesina, Alberto (1999), “Do Corrupt Government Receive Less Foreign Aid?” _NBER Working Paper Series,_ No. 7108, May 1999.

Bardhan, P. (1997) “Corruption and Development” _Journal of Economic Literature_ 35 (3) pp.1320-46.

Bearse, P., G. Glomm and E. Janeba (2000), “Why Poor Countries Rely Mostly on Redistribution In-kind”, _Journal of Public Economics,_ Vol.75, March 2000.

Broadman, H. and Recanatini, F. (2002) “Seeds of Corruption” _MOCT-MOST, Economics of Planning,_ March 2002.

Davis, J. (2004) “Corruption in Public Service Delivery: Experience from South Asia’s Water and Sanitation Sector”, _World Development,_ Vol.32, No.1 Ehrlich, I. and F. Lui (1999), “Bureaucratic Corruption and Endogenous Economic Growth”, _Journal of Political Economy,_ Vil. 107, No.6, December 1999.

> 20 For additional details on this work, please visit <u>http://www.pacindia.org/aboutus</u>

19
in La Paz live on less than $0.90 per capita per day. Poverty rates are significantly higher in El Alto than La Paz, due to geographical segmentation of poverty. Moreover, within La Paz, poverty tends to be concentrated in the northern neighborhoods and ‘laderas’ or steep cliffs surrounding the city.

**<u>Table 1.2: Poverty statistics</u>**

||_Extreme Poverty_|_General Poverty_|
|---|---|---|
|Poverty lines (US$ pc pm)<br>Headcount poverty rates (%)|26.05|42.92|
|•El Alto|41.16|59.47|
|•La Paz|27.53|48.37|

_Source_ : Encuesta Nacional de Hogares, 1999 Access to utility services makes an important contribution to the welfare of poor households. Indeed, household survey evidence indicates that the poorest 20% of urban households in Bolivia are willing to pay significant rental premia to live in dwellings that have utility connections. These premia are equivalent to 12% of their income (or $93 per year) for water, 2% of their income (or $21 per year) for sewerage, and 2% of their income (or $19 per year) for electricity (Estache _et al_ ., 2001).

Moreover, changes in utility tariffs can have a substantial impact on the budgets of poor households. Utility charges account for 10% of the expenditure of the poorest urban households in Bolivia (World Bank, 2001). About two thirds of this goes on electricity, and a further quarter on water and sewerage services (Figure 1.1). If these direct expenditures are added to the aforementioned rental premia, utility spending accounts for more than a quarter of the budget of the poorest households.

**Figure 1.1: Utility expenditure shares by quintile**

<!-- Start of picture text --> 12<br>10<br>8<br>Water and sewerage<br>6 Communication<br>Electricity<br>4<br>2<br>0<br>1 2 3 4 5<br>Income quintile<br>18<br>16<br>14<br>12<br>10 Spending via rent<br>8 Direct spending<br>6<br>4<br>2<br>0 7<br>Electricity Water and sewerage<br>Expenditure share (%)<br>Expenditure share (%)<br><!-- End of picture text -->

**Figure 1.2: Direct expenditure versus rental premia for first quintile**
bX SPECiAL TOPIC 1: PROJECTING AIDS MORTAUTY Table SI. HIV transmission factors and their values used in simulations

|Variable|Values|
|---|---|
|Starting year of spread of HIV|1970, 1975, and 1980|
|Condom use|low (1%M),<br>medium (2%), and high (3%);<br>increased gradually after 1990|
|Genital ulcers prevalence|medium: 0.001-7.5%, depending on risk<br>subgroup; decreased gradually after 1990|
|____**__**|___<br>high: double the medium value|
||low: half the medium value|
|Blood screening|low (5%), medium (10%), high (20%);<br>increased gradually after 1990|
|Sexual behavior|Number of new sexual partners per year<br>varied to achieve 1990 HIV prevalence of 1%,<br>3%, and 6%|

Regression analysis is also used to establish the relationships between the level of adult HIV infection and mortality indicators in the future. The effects for adult males and adult females are estimated separately, and the effects on children's mortality is estimated from female infection rates. Table S4 shows regression equations, with life expectancy at age 10 the dependent variable for the equations including HIV prevalence, HIV prevalence squared, and the starting year of the epidemic as independent variables for adults. For children, under-five mortality is the dependent variable, and HIV prevalence for women the only independent variable. Given that these regressions use data generated with simulations, eliminating the effects of extraneous factors, it is not surprising that the R<sup>2</sup> values are so high.

As stated above, these results apply to a hypothetica! population with overall mortality levels as in Eastern Africa. The equations would underestimate the impact of AIDS in countries with lower mortality from other causes and overestimate the Impact where mortality is higher. Further analysis showed that for the projection purposes, the application of an adjustment factor equal to the squared ratio of a given life expectancy and the hypothetical population's life expectancy would produce consistent results. This adjustment factor is applied to the number of years lost due to AIDS.