rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 13. Consumption thresholds for each economic category for Moldova in the consumption distribution, 1998-2004 and 2006-2011**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1998 1999 2000 2001 2002 2003 2004 2006 2007 2008 2009 2010 2011<br><$2.50 $2.50-$5 $5-$10 Over $10<br><!-- End of picture text --> Source: Authors’ calculations based on harmonized regional HBS data (consumption aggregate excludes housing, durables and health expenditures). Notes: The Household Budget Survey for Moldova had a substantial change in methodology between 2005 and 2006. Changes included improvements to the questionnaire and the sampling. As a result, the poverty series for Moldova cannot be used to assess long-term poverty changes, but the analysis is divided into two periods: pre 2006 and post 2006.

# **4. Profiling Socioeconomics Groups in Moldova**

## **Characteristics of households differ significantly across socioeconomic groups, particularly in**

**education and sector of employment** (Table A1).  In 2011, the poor have larger households, higher dependency rates for both young and old dependents (but particularly for children), and are significantly more concentrated in rural areas. However, some of the largest differences between the group in poverty and the rest of the groups are in the share of household members with incomplete secondary education or less (40 percent for those under the $2.5/day threshold compared to, for example, 8 percent for those with consumption over $5/day). Similarly, although household employment rates do not differ greatly across groups, the poor have a higher dependence on the agriculture sector (with a household employment rate in agriculture of 53 percent vs. 19 percent for these same groups). Consistent with this, there is a higher share of households receiving wage income in the better-off groups than those in poverty. In terms of public and private transfers, the key difference lies in a larger share of better-off households receiving remittances, compared to the poorest.

17
Table 1. Proportion of FSD Schools (%) by Administrative Region, 1998–2011

|Region|1997|1998|1999|2000|2001|2002|2003|2004|2005|2006|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|I|2|47|46|50|57|50|52|52|56|56|60|61|71|71|71|
|II|2|42|44|53|58|52|55|59|67|67|63|64|62|64|64|
|III|53|62|61|68|68|67|70|72|70|75|79|84|80|80|83|
|IV|2|53|57|60|64|59|64|62|69|72|74|76|82|82|82|
|V|12|32|39|42|47|53|57|60|61|63|67|68|71|74|74|
|VI|2|39|43|48|55|59|62|64|68|71|73|75|79|78|77|
|VII|3|50|55|58|59|65|68|70|72|73|75|77|80|80|81|
|VIII|4|41|47|50|51|56|61|62|63|64|66|67|73|72|72|
|IX|8|73|76|81|85|84|82|84|85|84|85|86|88|89|88|
|X|40|79|80|84|84|86|86|87|87|88|88|89|92|92|92|
|XI|9|64|65|70|77|71|79|81|79|83|88|87|76|75|76|
|XII|11|33|48|53|62|45|49|49|59|61|64|62|60|64|61|
|RM|19|24|28|37|41|44|50|49|51|57|60|62|69|70|70|
|Average|13|49|53|58|62|61|64|65|68|70|72|74|76|76|76|

_Note:_ The table includes the proportions of primary schools in each of Chile’s administrative regions that had implemented the FDS program in at least one of its grade levels. RM=Santiago metropolitan region.

_Source:_ Authors’ estimates using Ministry of Education FSD database.

30
WPS3159

# **Does Judicial Efficiency Lower the Cost of Credit?**

Luc Laeven and Giovanni Majnoni*

## **Abstract**

We investigate the effect of judicial efficiency on banks’ lending spreads for a large cross section of countries. We measure bank interest rate spreads for 106 countries at an aggregate level and for 32 countries at the level of individual banks. We find that, after controlling for a number of other country characteristics, judicial efficiency, in addition to inflation, is the main driver of interest rate spreads across countries. This suggests that in addition to improving the overall macroeconomic climate in a country, judicial reforms, through a better enforcement of legal contracts, are critical to lowering the cost of financial intermediation for households and firms.

JEL Classification: D23,G21,G32,K42 Keywords: Interest Rate Spreads, Financial Intermediation, Law and Finance

## World Bank Policy Research Working Paper 3159, October 2003

_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 Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ __________________ * The authors are from the World Bank. Email: <u>llaeven@worldbank.org</u> and gmajnoni@worldbank.org. We are grateful to Guillermo Noguera for excellent research assistance.
employment rate between men and women, is around 29% if we consider paid work only, and around 6% if we take into account unpaid work as well. When we consider people having experienced migration, though, the gender gap significantly decreases, mostly because of an increase in the female employment rate. If persons have one current international migrant in their family, the gender gap is 28%, while if they experienced migration either directly or through another household member the gender employment gap is respectively 22% and 16%. In case of past migration of others, though, employment rates decrease both for males and females, suggesting that those who stay behind are either more likely or more willing to withdraw from the labor market (men relatively more than women). Moreover, the paid plus unpaid employment rate decreases in all cases but for return migrants: women upon return are significantly less engaged in unpaid work and more in paid employment (this sub-group is very small, though).

<!-- Start of picture text --> Wage emp. Paid self-emp.<br>0 1 2 3 4 0 1 2 3 4 5<br>log hours w log hours w<br>Men Women Men Women<br>Unpaid work<br>0 1 2 3 4<br>log hours w<br>Men Women<br>1.5 1.5<br>1 1<br>.5 .5<br>prob density prob density<br>0 0<br>1.5<br>1<br>.5<br>prob density<br>0<br><!-- End of picture text -->

**Figure 3.** Hours of work distribution, by type of work and gender

|**TABLE 4: Employment rate and gender **|**gap by migration e**|**xperience (**|**% of working a**|**ge pop.)**<sup>**a**</sup><br>|
|---|---|---|---|---|
||**Total**|**Men**|**Women**|**_Gender Gap_**<sup>**_b_**</sup>|
|**All**|||||
|Paid employment rate|0.53|0.65|0.36|**0.29*****|
|Unpaid employment rate|0.31|0.21|0.44|**-0.23*****|
|**Current migrants in the hh**|||||
|Paid employment rate|0.47|0.60|0.32|**0.28*****|
|Unpaid employment rate|0.37|0.24|0.50|**-0.26*****|
|**Past indiv.migration**|||||
|Paid employment rate|0.67|0.68|0.46|**0.22*****|
|Unpaid employment rate|0.18|0.17|0.23|**-0.06*****|
|**Past migration of hh members**|||||
|Paid employment rate|0.35|0.47|0.31|**0.16*****|
|Unpaid employment rate|0.49|0.39|0.53|**-0.14*****|

* significant at 10%; ** significant at 5%; *** significant at 1% _Notes:_ (a) Persons aged 16 to 64 years. (b) The difference in the employment rate between men and women.

12
# 1. INTRODUCTION

Mental health problems make the leading contribution to the global disease and injury burden among adolescent women (World Health Organization 2004).<sup>2</sup> Mental health problems developed during adolescence are not only of intrinsic immediate importance to young women, but they can also have severe long-run health consequences. They often persist into adulthood, increase the risk of contracting communicable and non-communicable diseases, and are strongly associated with reproductive and sexual health (Patel et. al., 2007; Prince et. al., 2007). In addition, mental health problems are linked to adverse developmental outcomes, such as lower educational achievement (see, e.g., Currie and Stabile, 2006; Eisenberg and Golberstein, 2009; Fletcher, 2008; Fletcher, 2010; Fletcher and Wolfe, 2008; Kessler et. al. 1995; Stein and Kean, 2000). These adverse health and developmental effects can in turn have a long-term impact on socioeconomic outcomes and thus result in a vicious circle of poverty and poor mental health (Patel and Kleinman 2003).

Das et. al. (2007; 2008) suggest that unexpected life events (either positive or negative shocks) can have a strong influence on mental health. Friedman and Thomas (2008) and Stillman, McKenzie, and Gibson (2009) are prominent examples of studies confirming the impact of shocks on mental health. Friedman and Thomas (2008) show that the Indonesian financial crisis of the 1990s had a detrimental effect on the psychological wellbeing of the Indonesian people. Stillman, McKenzie, and Gibson (2009), on the other hand, show that individuals who were selected by lottery to migrate from Tonga to New Zealand (which has higher living standards) exhibited significantly improved mental health outcomes.

> 2 In a review of 11 epidemiological community studies from developed and developing countries, Patel et. al. (2007) show that the prevalence rate of mental disorders among adolescents ranges from 8% in the Netherlands to 27% in Australia. Women are more likely than men to suffer from poor mental health and depressive disorders (Das et. al. 2008; Patel et. al. 2007). This gender differential is likely to be the result of a combination of genetic and environmental risk factors (Patel and Kleinman 2003). Poor mental health also demands a heavy toll among young women in Sub-Saharan Africa: after HIV/AIDS and abortion, depression makes up the leading contribution to disability adjusted life-years (DALYs) among this demographic group.

2
<!-- Start of picture text --> Polley Research<br>Trade Policy<br>WPS 1025<br><!-- End of picture text --> This paper- a product of the Trade Policy Division, Country Economics Department-is part of a larger effort in the department to assess the experience of Sub-Saharan Africa with trade liberalization. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Dawn Ballantyne, room N1O-019, extension 38004 (November 1992, 38 pages).

In a cross-country study for 1967-87, Matin tests whether the finding that increased openness improves perfomnance holds true for SubSaharan Africa as a subgroup among developing countries.

Econometric analysis - based on the augmented production function that includes labor. capital stock, and a measure of openness -shows that openness exerts a significant positive impact on economic performance of countries in Sub-Saharan Africa. The relationship is especially strong in "fixed-effect" estimates that use annual panel data with country dummies to capture unobserved country-specific differences.

Matin finds the evidence of a positive link between openness and performance surprisingly robust to different measures of openness, to different periods, and to the inclusion of other policy variables. All four measures of openness, for example, are significant for 1967-87. For the shorter period, 1980-87, three are significant. Also, the size and significance of the openness coefficients do not change when one controls for macroeconomic policy.

**ThePolicyResearchWorking PaperSeriesdissebinates thePindings** of work underwayin theBank. Anobjectiveofthescries **is to got these** fndings **out quickly, even if presentations are less than fully** polished. The rtndings. interpretations, **and conclusions in these papers** do **not necessarily represent of ficial** Bank **policy.**

**Produced by the** Policy Research Dissemiination Center
# Aid, Disbursement Delays, and the Real Exchange Rate

Alexandra Jarotschkin Aart Kraay* The World Bank

## **JEL Classification Codes:** F31, F35

**Keywords:** aid, real exchange rates, Dutch disease

_____________________________________________________________________________________

> *  Corresponding author, 1818 H Street NW, Washington, DC 20433, USA, akraay@worldbank.org.  We are grateful to Oscàr Jordà and Luis Serven for helpful comments, and to Nanasamudd Chhim, Evis Rucaj, Shelley Lai Fu, and Ibrahim Levent for their assistance with the Debtor Reporting System data used in this paper.  Financial support from the Knowledge for Change Program (KCP) of the World Bank is gratefully acknowledged.  The views expressed here are the authors' and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.
Ziliak, 1996). Even if the p‐values indicate that certain factors impact the WTP distribution, it does not tell us if the impacts are meaningful in economic terms.

In this concluding subsection, we investigate the economic significance of determinants of demand by conducting a simulation exercise.<sup>9</sup> This simulation involves using the regression coefficients to predict the number of households who would connect to the improved service for different sub‐groups and under different scenarios. The regression model and the relevant parameters used in these simulations was re‐estimated by including the 11 statistically significant interaction terms discussed in the previous sections. The coefficients are reported in Table 3. Technically, we can interpret the output of the probit model, obtained as fraction between 0 and 1, as the predicted percentage of a specified population who will connect under a specific scenario, where the scenario is created by inserting specified values for the independent variables. For example, if we want to specifically predict the connection rates for the poor who are already connected to services in Greater Negombo when the consumption charge is Rs. 200, then we set the poor household, Greater Negombo, and connected household dummy variable equal to one and the consumption charge variable equal to 200. The prediction simultaneously accounts for all other service, preferences, location, and socio‐economic characteristics by similarly specifying their value and using the estimated regression coefficients. This is the essence of our uptake model. We consider three simulations.

For all the simulations reported and discussed below, we define 8 subgroups of the population. These subgroups are defined according to whether the households are poor or non‐poor, connected or unconnected to services, and live in Greater Negombo or Kalutara‐Galle. Table 4 reports the proportion of the overall study sample of 1818 that fall into each subgroup category. The largest subgroup, representing 28 percent of the sample, is the unconnected non‐poor in

> 9 The economic significance can be measured by multiplying the normalized coefficient for each variable by the household value of each of those variables.  Each coefficient is normalized by dividing by the negative of the monthly bill coefficient to convert the utility values into money terms.

22
Table 2: OLS Estimation of Information Treatment Effects

|||**Panel A**|**: Average **|**Treatment **|**Efects**||
|---|---|---|---|---|---|---|
||He|alth Insuranc|e||Pension||
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|
|Treated|0.008|0.011|0.011|-0.004|0.000|-0.000|
||(0.012)|(0.012)|(0.012)|(0.013)|(0.013)|(0.013)|
|Lagged Dependent Variable|0.656***|0.546***|0.534***|0.627***|0.519***|0.504***|
||(0.016)|(0.025)|(0.026)|(0.017)|(0.025)|(0.026)|
|R-squared|0.488|0.519|0.523|0.453|0.501|0.505|
|No. of Observations|4,587|4,587|4,587|4,560|4,560|4,560|
||He|**Panel B: H**<br>alth Insuranc|**eterogeneo**<br>e|**us Treatme**|**nt Efects**<br>Pension||
|Treated|-0.030|-0.024|-0.028|-0.028|-0.020|-0.023|
||(0.020)|(0.019)|(0.019)|(0.020)|(0.019)|(0.019)|
|Treated*No-Contract|0.062**|0.056**|0.060**|0.042|0.032|0.035|
||(0.026)|(0.025)|(0.025)|(0.027)|(0.025)|(0.025)|
|No-Contract|-0.187***|-0.161***|-0.140***|-0.219***|-0.171***|-0.144***|
||(0.023)|(0.026)|(0.026)|(0.024)|(0.027)|(0.026)|
|Lagged Dependent Variable|0.562***|0.545***|0.533***|0.519***|0.519***|0.504***|
||(0.021)|(0.025)|(0.025)|(0.022)|(0.025)|(0.026)|
|R-squared|0.505|0.519|0.524|0.479|0.501|0.506|
|No. of Observations|4,587|4,587|4,587|4,560|4,560|4,560|
|No-Contract Treatment Efect|0.032**|0.032**|0.032**|0.014|0.012|0.012|
|Treated+[Treated*No-Contract]|[0.045]|[0.049]|[0.042]|[0.405]|[0.476]|[0.452]|
||A|**Panel C: P**<br>verage Efect|**ension wit**<br>s|**h Restricte**<br>Heter|**d Sample**<br>ogeneous Ef|ects|
|Treated|0.002|0.011|0.012|-0.035|-0.028|-0.030|
||(0.016)|(0.015)|(0.015)|(0.022)|(0.021)|(0.021)|
|Treated*No-Contract||||0.072**|0.066**|0.071**|
|||||(0.032)|(0.031)|(0.031)|
|No-Contract||||-0.195***|-0.157***|-0.124***|
|||||(0.029)|(0.032)|(0.032)|
|Lagged Dependent Variable|0.641***|0.496***|0.480***|0.544***|0.496***|0.480***|
||(0.018)|(0.031)|(0.032)|(0.027)|(0.031)|(0.032)|
|R-squared|0.489|0.530|0.536|0.507|0.531|0.537|
|No. of Observations|2,925|2,925|2,925|2,925|2,925|2,925|
|No-Contract Treatment Efect||||0.037*|0.038*|0.039*|
|Treated+[Treated*No-Contract]||||[0.097]|[0.078]|[0.059]|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: The additional variables included in Model 1 are a vector of city fixed effects; in Model 2 we also add individual level controls (age, gender, education, marital status, number of children, dummies for self-employment, dummy for working and an interaction between self-employment and lagged (2015) dependent variable and dummy for “willingness to remain in the city permanently if policy permits.”), while Model 3 adds firm level controls (pre-treatment firm size and ownership indicator variables ) as well. Robust standard errors clustered at workplace of household head are in parentheses. P-values are in brackets. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

30
# Table 6. Change in the Consumption-to-Income Ratio

||Bangl|adesh,2|000-2010|Ghan|a,199|8-2005|Mold|ova,200|1-2010|Nep|al,1996|-2003|Per|u,2005-|2009|Roma|nia,200|1-2009|Thail|and,200|0-2009|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Income|Initial|<br>Final|<br>Annual|Initial|Final|Annual|Initial|Final|Annual|Initial|Final|Annual|Initial|Final|Annual|Initial|Final|Annual|Initial|Final|Annual|
|deciles|period|period|change|period<br>|period|change|period|period|change|period|period|change|period|period|change|period|period|change|period|period|change|
|Decile|1.8|2.3|3%|22.6|34.8|6%|15.3|18.4|2%|3.3|2.9|-2%|1.6|1.9|5%|4.51|2.37|-8%|1.5|1.2|-3%|
|2|1.2|1.5|2%|5.9|8.3|5%|4.0|1.9|-8%|1.9|1.6|-2%|1.3|1.4|2%|1.89|1.16|-6%|1.1|1.0|-2%|
|3|1.0|1.3|2%|3.3|4.6|5%|2.9|1.5|-7%|1.5|1.4|-1%|1.2|1.2|1%|1.44|0.99|-5%|1.0|0.9|-1%|
|4|1.0|1.1|1%|2.5|3.4|5%|2.2|1.3|-6%|1.2|1.2|0%|1.2|1.1|-1%|1.26|0.88|-4%|0.9|0.9|-1%|
|5|1.0|1.0|0%|2.2|2.5|2%|1.8|1.2|-5%|1.1|1.1|0%|1.1|1.0|-1%|1.10|0.82|-4%|0.9|0.8|-1%|
|6|1.0|0.9|-1%|1.7|2.2|3%|1.6|1.1|-4%|1.0|0.9|-1%|1.1|1.0|-2%|1.01|0.74|-4%|0.8|0.8|0%|
|7|1.0|0.8|-2%|1.5|1.6|0%|1.3|1.0|-3%|1.0|1.0|0%|1.0|0.9|-1%|0.92|0.71|-3%|0.8|0.8|0%|
|8|0.9|0.7|-2%|1.5|1.2|-2%|1.1|0.9|-2%|0.9|0.9|0%|0.9|0.9|-2%|0.85|0.65|-3%|0.7|0.7|0%|
|9|0.9|0.6|-4%|1.3|1.0|-3%|0.9|0.9|-1%|0.9|0.8|-1%|0.8|0.8|-1%|0.77|0.60|-3%|0.6|0.6|0%|
|10<br>_Source_:|0.8<br>Author|0.5<br>’s calcu|-5%<br>lations wit|0.8<br>h data fr|0.5<br>om RI|-6%<br>GA, and|0.7<br>National|0.7<br>Househo|0%<br>ld Surveys|0.8<br>.|0.8|-1%|0.7|0.7|0%|0.64|0.49|-3%|0.5|0.5|0%|

30
> - 26 -

place in JapanL!/, and have yet to be eliminated. The more accomodating stance of MOF--Japan's powerful Ministry of Finance--in recent years also reflects Japan's new prominence as a leading financial power and world's largest international creditor.22/ Paradoxically, if Japan were to maintain the present pace of deregulation and allow interest rates to be fully market-determined, yield spreads between yen and dollar assets would be likely to narrow. This would put further downward pressure on the dollar, intensify the need for defensive interest-rate incrfases in the United States, and could precipitate a domestic recession in that country. The task of correcting payment imbalances among the leading industrial countries would also become infinitely more complex.

By contrast, financial liberalization is still lagging and patchy in the EC and in other European countries. Admittedly the Netherlands has traditionally had an open capital market and the Dutch authorities' regulatory stance has become even more flexible in recent years. Similarly, the German authorities have made major strides and France has made considerable progress in modernizing what used to be a relatively archaic banking and financial system. But much progress is needed in other EC members. For example, 21/ Remaining barriers--and protections--between specialized financial insLitutions (such as between banks and securities houses, and between trust banks, regional banks, and mutual S&Ls--"sogo" banks) remain in place, particularly as to how each category of banks can conduct its funding. The Banker, January 1988, p. 49.

22/ Japanese financial institutions have become the most powerful players in the euromarkets and are the major foreign presence in several domestic markets. Through the combination of exchange-rate effects and of the country's position as the main external creditor, Japan now counts seven among the world's ten largest commercial banks. (The Banker, July 1987, p.<sup>91</sup> .) These have completely displaced US money center banks from their former dominant positions. In late 1986, Japanese banks held over $1 trillion in international assets, or about one-third of the world total. At that time, the sahre of US banks had dropped to only 20 percent of such assets. (World Economic Survey 1987, United Nations, 1987, p.83.)
|||tabulation of restrictions on cross-border financial transactions reported in the<br>IMF's_Annual Report on Exchange Arrangements and Exchange Restrictions_<br>_(AREAER)._ http://web.pdx.edu/~ito/Chinn-Ito_website.htm|
|---|---|---|
|Primary<br>schooling|WDI|Gross primary school enrollment rates (percent of population)|
|Gini coefficient<br>on educational<br>attainment|Barro-Lee<br>dataset|The Barro-Lee dataset provides data on the percentage of the population that<br>attained different levels of education: No education (0 years), complete primary (6<br>years), complete secondary (12 years), and complete tertiary (16 years). For non-<br>complete primary, secondary, or tertiary we assume respectively 3 years, 9 years,<br>and 14 years of schooling. With this information, we can construct a Lorenz curve<br>measuring which percentage of population attained which percentage of total<br>years of schooling. With this information, we construct a Gini coefficient that<br>measures educational inequality analogous to the standard income inequality<br>measure.|
|Agricultural<br>Share in<br>GDPproductivity|WDI|WDI Indicator: NV.AGR.TOTL.ZS, “Agriculture, value added (% of GDP)”.|

34