rafmacalaba/datause-encoder-data huggingface dataset:
achieved on other yielding assets, and 8 percent reflects an economic gain to society beyond the opportunity cost of capital (in computing the SDI, the latter is considered as an economic cost).

**Table 7. Annual Average Outstanding Loan Portfolio (AAOLP)—Decomposition by Society, Borrowers, and Banrural, 2002**

|**Reported as percentage of AAOLP**<br>|**_Fondos propios_**|
|---|---|
|Cost of annual servicing of OLP<sup>1</sup>|24%|
|Interest and fees earned as a % of cost of annual service of OLP|72.2%|
|Subsidy as a % of cost of annual service of OLP|–8.3%|
|Profits + net returns on other earning assets as a % of cost of annual service of|36.1%|
|OLP||
|_Memo item (complement to data in table 6):_||
|Annual cost of loans (Q millions)|510,585|
|Net revenue from other earning assets|329,316|

Note: The portion of financial costs assigned to loans was based on the relative share of OLP from total assets, about 90 percent. Of administrative costs, 95 percent were assigned to lending operations (OLP) as well as the full value of subsidies.

OLP = outstanding loan portfolio.

# **Banrural’s Output Index**

Loan size is taken as a proxy for the borrowers’ income, assuming that both are highly correlated, so that smaller loans are assumed to benefit poorer clients. Box 1 provides details on Banrural’s OI and the number of beneficiaries.

# **Box 1. The Output Index and the Number of Beneficiaries**

The OI must reflect the relevant target group that policy makers seek to support (for example, individuals, households, firms, municipalities, and so forth). In Banadesa’s analysis, because of a lack of data, it was assumed that each loan benefited a household composed of four individuals. However, in Banrural’s analysis, we made adjustments to the OI to reflect the fact that different types of loans benefit different borrowing groups.

It was assumed that microfinance, agriculture, and housing loans benefit a household of five individuals and that consumer loans benefit one individual. The number of beneficiaries of different types of loans has been chosen arbitrarily for illustrative purposes, and only household survey data could provide a more reliable figure. For example, it is very likely that a consumer loan could actually benefit an entire household.

In a DFI where a higher weight is assigned to smaller loans (that is, a proxy for loans to poorer people), increasing the number of beneficiaries per type of loan would reduce the OI in a manner similar to dividing the loan into smaller loans for each individual. This is <u>done by adjusting the</u> _OI_<sup>_w_</sup> <u>to scale it with respect to the reference unit, which is</u> 23
22

# VII. **CONCLUSIONS**

Our analysis exploits the phased rollout of the Thai Universal Coverage policy to estimate the labor market effects of the policy. We control for the effects of the Village Fund scheme which began to be rolled out around the same time; we are able to separate out the effects of the two programs through the spatial and temporal differences in program rollout. Our results point to labor market effects: UC appears to have encouraged employment especially among married women, to have reduced formal-sector employment at least among married men, and to have increased informal-sector employment especially among married women. We see the largest positive informal-sector employment effects in the agricultural sector. These results are consistent with our hypothesis that by eliminating the medical expenditure risks associated with informal-sector employment, UC has allowed couples to switch from a situation where the breadwinner works in an urban formal-sector job and the spouse co-locates but does not work to having both working in informal-sector jobs in a rural setting.

Our effects suggest that UC increased informal-sector employment by two percentage points initially rising to just under 10 percentage points after three years. This effect is larger than that found in recent work for Mexico’s _Seguro Popular_ universal health coverage reform – there the negative effects on formal-sector employment were around half of one percent. However, as indicated in the Introduction, there are some reasons to expect a larger effect in Thailand. The gap in the benefit package in Mexico means that in Figure 1 the individual still faces some risk in an informal sector job even after the SP reform. So while SP reduces _M_ it does not reduce it to zero. _U_ ( _II_ ) is not attainable and the
WPS4181

# **Financial Services and Trade Agreements in Latin America and the Caribbean: An Overview**

by

**MARILYNE PEREIRA GONCALVES AND CONSTANTINOS STEPHANOU**<sup>∗</sup>

**Abstract:** The objective of this paper is to briefly review the international framework governing trade in financial services, to describe the treatment of financial services in recent trade agreements involving Latin America and Caribbean countries, and to analyze the liberalization commitments made in three selected country case studies (Chile, Colombia and Costa Rica). Emphasis is given to free trade agreements because of the generally deeper level of liberalization and rule-making achieved to-date. The authors discuss some of the causes and potential implications of their findings.

**Keywords:** Chile, Colombia, Costa Rica, trade agreements, financial services, GATS.

World Bank Policy Research Working Paper 4181, April 2007 _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._

> ∗ Marilyne Pereira Goncalves (mgoncalves2@worldbank.org) is a consultant in the World Bank’s Financial Market Integrity department and Constantinos A. Stephanou (cstephanou@worldbank.org) is a Financial Economist in the World Bank’s Latin America and the Caribbean Region. This paper forms part of a broader project on trade in financial services in Latin America and the Caribbean Region. The authors would like to thank Pierre Sauvé for his excellent guidance, and express their gratitude to Augusto de la Torre, Carsten Fink, Juan Marchetti and Latifah Osman Merican for helpful comments and suggestions.
<!-- Start of picture text --> Figure 28: State Level Sanitation Deprivation by Region<br>a) b) c)<br>Sanitation Deprivation - by State Sanitation Deprivation - by State Sanitation Deprivation - by State<br>in North Central - MICS in North East - MICS in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Sanitation Deprivation - by State Sanitation Deprivation - by State<br>Sanitation Deprivation - by State<br>in South South MICS in South West- MICS<br>in South East - MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Abia Anambra Ebonyi Enugu Imo Akwa IbomDelta BayelsaEdo Cross RiverRivers Ekiti Lagos Ogun Ondo Osun Oyo<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis.

Children in some states are comparatively less severely sanitation deprived than in others, but it does not mean that they have access to high quality toilet facilities. Also, a household having access to a sanitation facility may not necessarily result in all children having access to it as qualitative evidence from Ekiti and Enugu state presented by Abramovsky et al. (2015) suggests scenario. Estimates for improved sanitation deprivation, presented in Appendix Figures A.5 and A.6, consider additionally those children deprived that only have access to open pit latrines, latrines without slaps, or toilets hanging over the water of any sort. In Ebonyi state 81 percent of children are deprived of improved sanitation facilities. In rural Ebonyi state this share is likely to be even higher, at the South West regional average around 80 percent of rural children are deprived of improved sanitation compared to in the most recent survey round solely 10 percent in the urban South West. Comparing the regional level Figures for severe and improved sanitation deprivation, Figures 26 and A.5, respectively shows that despite the inexistent severe sanitation deprivation in the urban North West, improved sanitation deprivation levels exceeds even the levels of the urban South West which leads the statistics of severe sanitation deprivation.

39
reluctance to participate in internationally benchmarked tests) and, if so, what would we find in terms of the average score and variance of the achievement distribution?

We propose a method that uses publicly released questions (items) from the Trends in International Mathematics and Science Study (TIMSS) 1999 8th-Grade Mathematics test to place Indian students on an internationally comparable achievement scale. The test, which consists of 36 items taken from the full TIMSS item bank, was administered to 6,000 students in public and private schools in two Indian states—Rajasthan and Orissa. Using the published item parameters for these 36 questions in conjunction with the Item Response Theory test-equating methods used by TIMSS, we construct a distribution of scores for the tested children that is directly comparable to the worldwide distribution; this allows us to compare the tested children to the international average and to place them in reference to the 51 other countries tested by TIMSS in 1999 and 2003.

The average scores of children in Rajasthan and Orissa place these states below 46 and 42 of the 51 countries tested in 1999 or 2003. After nine years of education, between 30 and 40 percent of enrolled children in these two states cannot pass a low international benchmark, described as “some basic mathematical knowledge.” Children enrolled in secondary schools in these two Indian states are 3.1 (OECD) standard-deviations below the OECD mean. Where children in these two states are relative to the rest of the world is harder to ascertain. On the one hand, the TIMSS sample is heavily biased towards relatively high-income countries. The median scores, for instance, in Rajasthan and Orissa do not look too bad compared to Philippines and Chile. On the other hand, secondary school enrollments in India are also lower—53 percent of the appropriate age group is enrolled, compared to more than 90 percent in South Africa, the worst performer in the TIMSS sample. To the extent that children currently out of school are less “motivated” or “able”, test scores would arguably look worse at higher levels of enrollment.

The test-score distribution is also highly unequal—the difference between the top 5 percent and bottom 5 percent in both states is among the highest in the world, next only to South Africa. Students at the bottom of the distribution in both states score similarly or worse than the bottom students in the three worst performing countries. At the same time, students at the top of the distribution score higher than the top students in other low performing countries, and higher than the median student in all but the best countries. The top 5 percent of students in 2
# **Box 2: Is Income Inequality in Europe Much Lower Than in the United States?**

Conventional wisdom suggests that the United States, with its relatively small size of government and lower share of expenditures on public transfers than comparator European OECD countries, has much higher income inequality than Europe. Indeed most cross-country comparisons of income inequality show the United States to have higher inequality than any individual country in Europe. For instance, the 2007/08 UN Human Development Report shows the Gini Coefficient in United States to be 0.41, considerably higher than the corresponding estimate for Latvia (0.38)—the EU member state with the highest income inequality. Similarly, the LIS data also confirm that income inequality in European countries is in general quite low (Gini of around 0.25 – 0.32) and considerably lower than in the United States. When making such comparisons, however, it is important to bear in mind the much larger size of the United States compared to the typical EU member state—the population of the US is more than three times as large as Germany, the largest EU member. While inequality within European countries is lower than in the United States—indeed considerably lower in some cases—this isn’t necessarily true for Europe as-a-whole, since between-country inequality in Europe is higher than between-states inequality in the US (Chart).

<!-- Start of picture text --> Per-Capita Output: States in the US vs. European Countries<br>0 10 2 0 30 4 0 50<br>S tate s/C oun tries fro m P oo re st t o R ic hes t<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br><!-- End of picture text --> Note: 2005 US$ GSP per capita of the 50 US states is represented by green dots; 2005 GDP per capita of the 25 EU members is represented by blue (in Euros) and red (PPS) dots.

How is this possible, given that virtually all EU member states devote a considerably larger share of GDP to social transfers, which in turn are widely accepted to be quite well-targeted to the poor within these countries? A number of factors help explain this apparently paradoxical finding. First, most of social assistance in the United States is provided through federal welfare programs targeted at the country’s poorest regions. In Europe, by contrast, comparable transfers to the poorest regions would require substantially greater inter-governmental transfers within the European Union than is presently the case. Second, despite the relatively recent (and limited) opening-up of cross-border employment within the European Union, labor mobility is still considerably lower than in the US, and so plays a much smaller role in equalizing incomes across countries. Third, employment rates in the US are considerably higher than in the EU, and also play a major role in equalizing incomes across families.

23
# **Figure 7-2.  Infrastructure Investment across Countries**

**(percentage of GDP)**

# **( a) Total Investment**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1980 1985 1990 1995 2000 2005<br><!-- End of picture text -->

## **(b) Public Investment**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>1980 1985 1990 1995 2000 2005<br><!-- End of picture text -->

## **(c) Private Investment**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>1980 1985 1990 1995 2000 2005<br>EGY TRK SA<br><!-- End of picture text --> Data source Calderón, Odawara, and Servén (2008).

39
-6-

(SIPRI). _]/_ We define the "Peace Dividend" narrowly<sup>as the percentage</sup> difference between the level of real capacity output<sup>per capita that would</sup> result from a given sustained reduction in the military<sup>spending ratio, and</sup> the baseline path of capacity output that would have prevailed<sup>in the</sup> absence of such a reduction.

# 1. Trends in military spending. 1972-1990

The descriptive literature on trends in military expenditures<sup>(see</sup> Hewitt (1993) and the primary data sources cited there)<sup>indicates the large</sup> extent to which the world's productive resources have<sup>been devoted to the</sup> military throughout the period since the Second World War. _1/_ Hewitt's data show important differences in military spending across country groupings as well as over time. On average during the period 1972-90,<sup>for</sup> example, over 5 percent of world resources as measured<sup>by the combined GDP</sup> of the 124 countries considered by Hewitt, were devoted to military spending. Table 1 summarizes the main patterns of military<sup>expenditure for</sup> industrial countries and for developing countries in various geographic regions. The entries in this table are weighted averages of national military spending ratios, where the weights are each country's share of<sup>the</sup> regional total GDP level measured in U.S. dollars using official exchange rates.

_1/_ Appendix IA provides definitions and sources for all<sup>data used in this</sup> study. A detailed discussion and analysis of the<sup>SIPRI data on military</sup> spending, as well as that provided by other sources,<sup>is given in Hewitt</sup> (1992, 1993). Based on his detailed analysis, Hewitt<sup>concludes that SIPRI</sup> data are to be preferred to other sources for empirical<sup>work of the sort we</sup> undertake here.

_.2_ Our paper makes use of the data on military expenditures presented in Hewitt (1992, 1993), which are based mainly on statistics published<sup>by</sup> SIPRI. Hewitt's data cover 124 industrial and developing<sup>countries.</sup>
||**Y=**|**RIF estimate**|**of Log [ Self-r**|**eported Pro**|**ductivity (XO**|**F/ha)]**||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|Household head has Primary<br>education|<br>(0.106)|<br>(0.065)|<br>(0.048)|<br>(0.047)|<br>(0.041)|<br>(0.038)|<br>(0.037)|<br>(0.043)|<br>(0.059)|
|Household head has Secondary|-0.013|0.058*|0.033|0.043**|0.034**|0.021|0.017|0.003|-0.018|
|<br>education|(0.017)|(0.031)|(0.023)|(0.019)|(0.017)|(0.015)|(0.012)|(0.012)|(0.019)|
|Household head has Tertiary|-0.004|-0.003|-0.003|-0.002|-0.001|-0.000|-0.002|0.006|0.006|
|<br>education|(0.004)|(0.004)|(0.003)|(0.002)|(0.001)|(0.001)|(0.002)|(0.006)|(0.007)|
|Household head disability|0.136*|0.036|-0.014|-0.021|0.014|0.035|0.058*|0.004|-0.008|
||(0.080)|(0.047)|(0.042)|(0.042)|(0.034)|(0.033)|(0.034)|(0.037)|(0.049)|
|Dependency ratio|0.075|-0.003|0.108|0.056|-0.009|-0.122*|-0.128*|-0.166*|-0.182**|
||(0.149)|(0.095)|(0.086)|(0.082)|(0.076)|(0.073)|(0.071)|(0.089)|(0.092)|
|Household Male Labor Use|0.239|0.210**|0.114*|0.031|-0.010|-0.031|-0.037|-0.004|-0.108*|
|(Number)|(0.175)|(0.086)|(0.065)|(0.056)|(0.050)|(0.044)|(0.045)|(0.048)|(0.060)|
|<br>Household Female Labor Use|0.143|-0.027|0.016|0.102|0.120|0.075|0.096|0.148|0.013|
|(Number)|(0.217)|(0.136)|(0.116)|(0.132)|(0.107)|(0.095)|(0.094)|(0.098)|(0.138)|
|Household Child Labor Use|-0.073|-0.010|-0.022|0.012|0.017|0.004|0.010|0.046**|0.028|
|(Number)|(0.046)|(0.031)|(0.026)|(0.021)|(0.022)|(0.020)|(0.019)|(0.023)|(0.032)|
|<br>Hired Labor Use (Number)|0.081|-0.032|0.009|-0.010|-0.050|-0.067|-0.034|-0.077*|0.002|
||(0.075)|(0.047)|(0.049)|(0.048)|(0.046)|(0.049)|(0.049)|(0.044)|(0.058)|
|Number of Fields Managed by the|-0.154|0.272|-0.448**|-0.222|-0.190|0.100|0.170|0.079|-0.084|
|<br>household|(0.285)|(0.215)|(0.195)|(0.162)|(0.157)|(0.160)|(0.154)|(0.169)|(0.207)|
|Total number of crops produced|-0.180|-0.377*|0.303|0.013|-0.027|-0.086|-0.225|-0.251|-0.604***|
||(0.335)|(0.227)|(0.190)|(0.172)|(0.180)|(0.171)|(0.187)|(0.171)|(0.208)|
|Household used any purchased seed|0.026|0.010|0.042|0.056|0.057|0.092*|0.017|-0.046|0.030|
||(0.102)|(0.069)|(0.063)|(0.064)|(0.051)|(0.047)|(0.049)|(0.053)|(0.063)|
|Plots that Use Chemical Fertilizer (%|0.028|0.046*|0.042|0.035|-0.007|-0.048*|-0.044*|-0.031|-0.051|
|<br>of total)|(0.032)|(0.025)|(0.027)|(0.023)|(0.021)|(0.026)|(0.025)|(0.025)|(0.036)|
|<br>Plots that Use Pesticide (% of total)|-0.318**|-0.383***|-0.199***|-0.227***|-0.208***|-0.035|-0.033|0.021|-0.102|
||(0.139)|(0.089)|(0.075)|(0.067)|(0.074)|(0.071)|(0.061)|(0.076)|(0.107)|
|Export crops dummy (1 if Household|<br>-0.150|<br>0.051|<br>-0.089|<br>-0.205*|<br>-0.173*|<br>-0.089|<br>-0.175*|<br>-0.181|<br>-0.009|
|<br>produced any export crop)|(0.260)|(0.157)|(0.124)|(0.104)|(0.095)|(0.105)|(0.098)|(0.122)|(0.152)|
|<br>Constant|<br>8.737***|<br>2.059*|<br>0.477|<br>0.054|<br>0.235|<br>0.254|<br>-0.072|<br>0.026|<br>1.069|
||(1.413)|(1.107)|(0.878)|(0.706)|(0.662)|(0.639)|(0.569)|(0.580)|(0.802)|
|Observations|3936|3936|3936|3936|3936|3936|3936|3936|3936|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively._ 26
Using the threshold PPMT method presents a trade-off; while almost all of those picked by the method at a very high confidence level would accurately be referred for the government support, it may exclude a significant number of cross-eligible households. The share of cross-eligible households referred by the method, however, would increase by decreasing the confidence level and/or extending the government program to less poorer households (increasing eligibility thresholds).

Moreover, as discussed, the PPMT can also be exploited by applying the more flexible ranking method, which would overcome such a trade-off. The high and significant rank correlations (0.7) between the PPMTs and full PMT (Table A1) suggest that the two formulas order the households similarly and it is appropriate to employ the “ranking” approach. We set the “pre-set” targeting rate same as the MoLSA simulated program size. For instance, for the program targeting households below the poverty line i.e., bottom 18 percent nationally, we set 18 percent as the “pre-set” targeting rate for the ranking method, i.e., the bottom 18 percent of the households in the PPMT score would be regarded as eligible.<sup>8</sup> Under such scenario, both the PPMT-SEVAT and the PPMT-Legacy VA models would refer about 36 percent of the extremely vulnerable households to the government support. Part II of Table 5 shows that a high referral success (e.g., 77 percent in the PPMT-SEVAT model) and a low failure to refer (30 percent in the PPMT-SEVAT model) is achieved with the ranking method. That is, simply ranking referrals based on their score but not placing a maximum referral threshold means many more households will be referred and with relatively low referral failure.

Given the significant difference between the scope and hence the survey methodologies between the SWIFT on which the PPMT is designed and the CLCI’s verification field-test survey to which the PPMT is applied, the ranking method is more appropriate; a small reduction in successful referral rates compared to a much greater number of successful referrals and a slightly larger number of disappointed referrals is arguably preferable.

_Table 4: Share of humanitarian beneficiary households (extremely vulnerable groups) that are referred for government support by the PPMT model_

|||GOI social assist<br>|ance sim<br>|ulatedpro<br>|gram cov<br>|erage<br>||
|---|---|---|---|---|---|---|---|
|PPMT cases|Referral<br>confidence<br>level|Referral<br>poverty line<br>(log)|Btm<br>15%|Btm<br>18%|Btm<br>25%|Btm<br>30%|Btm<br>35%|

8 Ideally, one should use the rate based on the targeting criteria for the government’s program that is specific to the group or region that the data represents. Therefore, the ideal rate for this field-experiment data would be the poverty rate among the IDPs and returnees in the Northern governorates for a MoLSA program targeting households below the poverty line. Unfortunately, we do not have such information.

19