rafmacalaba/datause-encoder-data huggingface dataset:
# **_Mortgage Financing_**

While Sofoles and Infonavit dominate the mortgage market, there has been a resurgence of commercial bank-financed mortgages since 2004. As can be seen in figure 23, the outstanding stock of mortgage financing (excluding non-performing loans) in 2005 was only around 1 percent of GDP above its 2000 level. The rapid repayment of commercial banks’ restructured UDIs-denominated mortgage portfolio understates the actual growth in peso-denominated mortgage originations that has taken place in the last few years. Evidence for the increased attractiveness of this market segment for banks can also be found in their acquisition of mortgage Sofoles in 2004-2005 to allow them to reach the moderate to lower-income households that Sofoles typically target<sup>31</sup> . Commercial banks’ re-entry in this market segment is still overshadowed by Infonavit, which had significantly increased its market share to around 60 percent by end-2005. The contribution of the public sector in this market is reflected in the considerable reliance of Sofoles on development banks (especially the _Sociedad Hipotecaria Federal_ or SHF) for their funding, as well as the existence of mortgage lending by other public sector retirement programs such as FOVISSSTE (the equivalent of the Infonavit for public sector employees). Fifteen mortgage-backed securities (MBS) have also been issued – primarily by Sofoles – since 2003 totaling MXP$11.5 billion, but their size is still very small compared to GDP or to the total stock of mortgage credits.

Figure 23: Evolution of Mortgage Financing by Provider (2000Q1-2005Q3) <!-- Start of picture text --> 9%<br>Commercial Banks - Non-<br>8% 1.0% performing<br>7%<br>Infonavit<br>6% 3.6% 5.2%<br>5% Sofoles<br>4%<br>0.4%<br>3% Commercial Banks -<br>2% 2.8% 1.6% Restructured Performing<br>0.5%<br>1% Commercial Banks - Non-<br>0.8% 1.2% restructured Performing<br>0%<br>1Q00 3Q05<br>% of GDP<br><!-- End of picture text --> Source: BOM.

Note: Certain public sector retirement programs financing mortgages (e.g. FOVISSSTE) are excluded due to lack of data. Mortgage-backed securities issuance is also excluded, but it is small in size.

Favorable demographics and macroeconomic stability have led to increased demand for mortgages. The overall growth in the housing market has been underpinned by strong pent-up demand for housing due to favorable demographics. While mortgage origination has risen, a housing deficit remains in excess of 4 million units, and production still falls well short of the government’s target of 750,000 new housing units per year, a pace that

> 31 Sofoles’ more labor-intensive business model and more flexible underwriting standards have permitted them to succeed in mortgage lending to segments that banks traditionally have ignored. Sofoles have targeted households that earn between three and eight times the monthly minimum wage.

24
“conditions of competition” that prevail in an economy. Although such structure and performance data are not policy-specific, they can be employed to bolster monitoring and surveillance (Djankov and Hoekman, 1998) provided by the WTO. This type of outcome monitoring is distinct from an evaluation of the impact of specific policies as it makes no attempt at matching outcomes to policies. However, it is a useful complement to policy monitoring and assessment by providing information on the state of trade and competition.

Indicators such as import penetration ratios, changes in market structure and the size distribution of firms, measures of entry and exit over a given period, domestic industry concentration ratios or Herfindahl indices, and data on trends in price-cost margins all suffer from the same drawback: it is difficult to relate any of these measures unambiguously to a specific policy or change in that policy. Nonetheless, such measures provide information on the effect of the set of prevailing policies and have the virtue of being easy to calculate. They also do not require the use of models or calculation of indices that require (political) acceptance of a set of underlying assumptions. If used in regular multi-country exercises on the basis of identical industry classifications, the resulting panel datasets can be used for analytical purposes as well as cross-country comparisons.

# **5. Conclusion**

Developing countries are rarely challenged in formal WTO litigation for failing to live up to WTO commitments and obligations. While this lack of enforcement activity can be explained in part by the fact that developing countries have made fewer commitments than developed ones, there are numerous WTO disciplines independent of tariff bindings that apply to developing countries. These include disciplines ranging from rules on products standards to customs valuation. The weak incentives for trading partners to enforce commitments reduce the relevance of the WTO for trade constituencies in all countries, but especially those in developing economies.

While the first-order cost of failing to enforce WTO commitments in poor countries falls on consumers _within_ these countries who do not realize the economic welfare gains associated with importing, there are dynamic and externality costs as well. The enforcement failure likely has a dynamic cost of creating disincentives to negotiate additional, welfare-enhancing WTO commitments. First, there is little incentive for constituencies in the developing country that might gain from trade to organize politically in order to mobilize support for commitments.

21
**Policy Research Working Paper Series**

' "

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2227 The Sri Lankan Unemployment<br>Problem Revisited|Martin Rama|November 1999|S.Fallon<br>38009|
|WPS2228 Fiscal Contingency Planning for<br>Banking Crises|Patrick Honohan|November 1999|A. Yaptenco<br>38526~|
|WPS2229 Do School Facilities Matter? The Case<br>of the Peruvian Social Fund<br>(FONCODES)|Christina Paxson<br>Norbert Schady|November 1999|N. Schady<br>88247|
|WPS2230 Bankruptcy Organization through<br>Markets: Auction-Based Creditor<br>Ordering by Reducing Debts<br>(ACCORD)|David Hausch<br>S.Ramachandran|November 1999|L.Tsang<br>80516|
|WPS2231 What's Behind Mercosur's Common<br>External Tariff|Marcelo Olarreaga<br>Isidro Soloaga<br>L.Alan Winters|November 1999|L.Tabada<br>35555.|
|WPS2232 Market Access Advances and<br>Retreats: The Uruguay Round and<br>Beyond|J. Michael Finger<br>Ludger Schuknecht|November 1999|L.Tabada<br>35555|
|WPS2233 User's Guide to an Early Warning<br>System for Macroeconomic<br>Vulnerability in Latin American Countri|Santiago Herrera<br>Conrado Garcia<br>es|November 1999|C.Garcia<br>87969|
|WPS2234 The Green Revolution and the<br>Productivity Paradox: Evidence from<br>the Indian Punjab|Rinku Murgai|November 1999|M.Fernandez<br>33766|
|WPS2235 Beyond Capital Ideals: Restoring<br>Banking Stability|Gerard Caprio Jr.<br>Patrick Honohan|November 1999|A Yaptenco<br>38526|
|WPS2236 Valuing Water for Chinese Industries:<br>A Marginal Productivity Assessment|Hua Wang<br>Somik Lall|November 1999|R.Yazigi<br>37176|
|WPS2237 Reforming Tax Systems: The World<br>Bank Record in the 1990s|Luca Barbone<br>Arindam Das-Gupta<br>Luc De Wulf<br>Anna Hansson|November 1999|L.Barbone<br>32556|
|WPS2238 A Review of the Role and Impact of<br>Export Processing Zones|Dorsati Madani|November 1999|L.Tabada<br>36896|
|WPS2239 The EU Factorinthe Trade Policies<br>of Central European Countries|Bartlomiej Kaminski|November 1999|L.Tabada<br>36896|
# **Migration, Trade, and Foreign Direct Investment in Mexico**<sup>*****</sup>

# Patricio Aroca Gonzalez

_Universidad Católica del Norte, Antofagasta, Chile_ William F. Maloney _Office of the Chief Economist for Latin America The World Bank_

**Abstract:** Part of the rationale for NAFTA was that it would increase trade and foreign direct investment (FDI) flows, creating jobs and reducing migration to the U.S.  Since poor data on illegal flows to the U.S. make direct measurement difficult, this paper instead evaluates the mechanism behind these predictions using data on migration _within_ Mexico where the census data permit careful analysis.  We offer the first specifications for migration within Mexico incorporating measures of cost of living, amenities and networks.  Contrary to much of the literature, labor market variables enter very significantly and as predicted once we control for possible credit constraint effects.  Greater exposure to FDI and trade deters out-migration with the effects working partly through the labor market.  Finally, we generate some tentative inferences about the impact on increased FDI on Mexico-U.S. migration. On average, a doubling of FDI inflows leads to a 1.5-2% fall in migration.

# World Bank Policy Research Working Paper 3601, May 2005

_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._

> * This paper was financed by the regional studies program of the Office of the Chief Economist for Latin America at the World Bank.  Our thanks to Gordon Hanson and Raymond Robertson for insightful comments and Gabriel Montes Rojas and Lucas Siga for expert research assistance.
with higher gaps emerging in the main rainy and harvesting seasons relative to the short rainy season. However, we cannot reject the equality of reporting gaps across all three seasons. At the intensive margin of work, results are measured with less statistical precision as we do not find meaningful differential self/proxy survey design effects between girls and boys within and across seasons. A detailed power analysis shows that self-reporting appears to yield a good compromise between survey precision and costs for national surveys on child labor.

Our paper contributes to the measurement of agricultural data in some domains. First, we contribute to recent randomized studies in developing countries that analyze the effects of survey design on outcomes such as adult labor (Bardasi et al 2011), health (Das et al 2012), and education (Baird and Ozler 2012).<sup>2</sup> For child labor, Dillon et al. (2012) document the extent of variation in child labor statistics across respondent type and length of the child labor module in Tanzania. Dillon et al. (2012) reported that using a proxy or asking the child survey questions directly does not affect child labor statistics while using a short labor module generates lower child labor statistics by defining more precisely what work means, thus filtering out children that report labor market activities instead of domestic activities. While our experimental research design is motivated by and broadly similar to Dillon et al.’s (2012) study, we focused on agricultural activities and collected data in three different seasons of the agricultural calendar to account for the seasonality of self/proxy discrepancies.

Second, this study also contributes to the small but growing literature on seasonality and recall in agricultural data that, through its focus on adult labor, agricultural inputs and harvest measures, has cautioned that the degree of distortion in agricultural statistics depend, among other things, on the level of data aggregation and the seasonal timing of household surveys (e.g., Beegle et al 2012, Arthi et al. 2018, Gaddis et al 2019). In this study, we report variation in child labor 6
# **Table of Contents**

||**List of Acronyms**|**Page**|
|---|---|---|
||<br>**Executive Summary**||
|**I.**|**Introduction, Objectives and Structure of the Paper**|I|
||Background and objectives|1|
||Structure|2|
|**II.**|**Using Financial Statements in Determining the Need for Regulation**|4|
||Characteristics and types of microfinance institutions (MFIs)|4|
||The financial statement approach|6|
||Importance of financial statements in determining the extent ancd impact of regulation|7|
||<br>Impact of fragmented regulation on MFIs' institutional developrnent and transforrnation|9|
|**III.**|**Issues in Proposals for Regulation of Microfinance Institutions**|**10**|
||Is there a need to regulate microfmance institutions?|10|
||What activities of MFIs should be regulated?|10|
||Basic considerations|I I|
||Regulatory thresholds|12|
||Who should regulate MFIs? -- monitoring regulatory triggers|13|
||Internal regulation and governance|14|
||External regulation and prudential guidelines|16|
||Sanctions|17|
|**IV.**|**Basic Considerations in Risk Management**|19|
||<br>Fundamental considerations|19|
||Risk-management, rather than ratio-management|1 9|
||<br>Risks that have to be managed by financial intermediaries including MFIs|19|
||<br>Allocation and sharing of risk management responsibilities|20|
||Application of risk management principles|20|
||Risks in the balance sheet structure|25|
||Solvency and capital adequacy|26|
||<br>Income statement structure|27|
||Liquidity risks|28|
||Credit risk|29|
||Interest rate, market and currency, and operational risks|30|
|V.|**Summary and Conclusions**|31|
||Summary of issues addressed|31|
||<br>Major conclusions and recommended action|31|
||<br>Considerations for governments, donors and MFIs|32|
||Information, reporting and disclosure requirements and standards|33|
||Final observations|34|
|**Ap**|**pendix**|35|
||<br>Schedule 1. Sample of South African Exclusion Notice|36|
|**Re**|<br>**ferences**|39|
|**Li**|**st of Charts and Tables**||
||Chart 1. Distinguishing characteristics of types of microfinance institutions|_5_|
||<br>Table 1. Sample threshold values of liabilities|8|
||<br>Table 2. Regulatory Framework Model: regulatory thresholds of microfinance activities|11|
||Table 3. Risk management partnership among key players|15|
||Table 4. Major categories of risk|20|
||<br>Table 5. Key risk management factors and indicators|22|
||Daily res. wage<br>(inv.<br>hyperbolic<br>sine,<br>USD)<br>(5)<br>0.104<br>(0.077)<br>-0.0200<br>(0.091)<br>-0.0820<br>(0.106)|805<br>0.103<br>3.690<br>Yes<br>2.250<br>0.118<br>(0.081)<br>-0.118<br>(0.089)<br>-0.154<br>(0.101)<br>746<br>0.121<br>2.060<br>Yes<br>2.220<br>0.345<br>0.557<br> in Table 6. All regressions control for<br>age-level variables, measured at base-<br>olarship (Treatment=1, 0 otherwise).<br>lt of testing the equality between the|
|---|---|---|
|s by Gender|Family index:<br>SES/Well-being<br>(4)<br>0.188**<br>(0.084)<br>-0.199*<br>(0.118)<br>-0.0270<br>(0.127)|T<br>804<br>0.182<br>6.710<br>Yes<br>0<br>0.0420<br>(0.119)<br>-0.247<br>(0.163)<br>-0.00100<br>(0.168)<br>743<br>0.120<br>4.510<br>Yes<br>-0.0300<br>0.0220<br>0.882<br>. Column (5) is the same variable as <br>el (EMIS) variables and 5 census vill<br>s who were eligible for the poverty sch<br>rresponding p-value below) is the resu|
|Treatment Efect|Family index:<br>Socioemotional<br>(3)<br>-0.0180<br>(0.096)<br>-0.195<br>(0.127)<br>0.0260<br>(0.146)|AF<br>807<br>0.102<br>3.480<br>Yes<br>0.0300<br>-0.0860<br>(0.097)<br>-0.289**<br>(0.119)<br>-0.0250<br>(0.136)<br>749<br>0.107<br>2.820<br>Yes<br>0.0100<br>0.103<br>0.748<br> indices from Tables 3 to 5 and 7<br>riables from baseline, 6 school-lev<br>herwise) and Panel B respondent<br> * p<0.1. The Chi-square (and co|
|9: Heterogeneous|Family index:<br>Cognition<br>(2)<br>0.133<br>(0.096)<br>-0.337***<br>(0.110)<br>-0.0410<br>(0.131)|813<br>0.225<br>8.520<br>Yes<br>0.0700<br>0.168*<br>(0.098)<br>-0.254**<br>(0.117)<br>-0.309**<br>(0.141)<br>752<br>0.202<br>4.380<br>Yes<br>-0.0300<br>2.891<br>0.0890<br> columns (1) to (4) are the family<br>ndividual-level socio-economic va<br>it scholarship (Treatment=1, 0 ot<br>ol level). *** p<0.01, ** p<0.05, <br>m the poverty sample.|
|Table|D<br>Family index:<br>Education<br>(1)<br>**Panel A. Merit**<br>Treatment<br>0.142*<br>(0.085)<br>Female<br>-0.00700<br>(0.114)<br>Female and treatment<br>-0.0220<br>(0.115)|Observations<br>814<br>R-squared<br>0.167<br>F-statistic<br>4.910<br>Covariates<br>Yes<br>Control mean<br>0.130<br>**Panel B. Poverty**<br>Treatment<br>0.257***<br>(0.096)<br>Female<br>-0.146<br>(0.132)<br>Female and treatment<br>0.0120<br>(0.133)<br>Observations<br>753<br>R-squared<br>0.174<br>F-statistic<br>3.690<br>Covariates<br>Yes<br>Control mean<br>-0.0100<br>**Joint test: Poverty vs. Merit**<br>Chi-square<br>0.0670<br>p-value<br>0.796<br>Notes: Estimated treatment efects. The dependent variables in <br>district fxed efects, baseline test score, baseline poverty score, i<br>line. Panel A includes respondents who were eligible for the mer<br>Robust standard errors are in parentheses (clustered at the scho<br>interaction term from the merit sample and interaction term fro|

38
(1 _,_<sup><u>1</u></sup> 2<sup>_,_</sup> 4<sup><u>1</u></sup><sup>_,_0</sup><sup>_,_0)and(1</sup><sup>_,_</sup><sup><u>1</u></sup> 2<sup>_,_</sup><sup><u>1</u></sup> 4<sup>_,_</sup><sup><u>1</u></sup> 8<sup>_,_0).</sup> Table 5: Pair-wise dominance tests for changes in poverty over time in Bangladesh and its divisions

|||2007–2011|||2011–2014||
|---|---|---|---|---|---|---|
||_P_|_P_1|_PG_|_P_|_P_1|_PG_|
|Barisal|No|No|No|Yes|Yes|Yes|
|Chittagong|Yes**|Yes**|Yes**|No|No|No|
|Dhaka|Yes**|Yes**|Yes**|Yes**|Yes**|Yes**|
|Khulna|Yes*|Yes*|Yes*|Yes|Yes|Yes|
|Rajshahi|Yes***|Yes***|Yes***|Yes|Yes|Yes|
|Sylhet|No|No|No|Yes*|Yes*|Yes*|
|Bangladesh|Yes***|Yes***|Yes***|Yes*|Yes*|Yes*|

Sources: Authors’ own computations. A “Yes” implies reduction of the poverty levels within each region over time evaluated at the four extreme weights; a “No” implies otherwise. The levels of statistical significance for the dominance tests are: *** for 1%, ** for 5% and * for 10%.

Table 5 presents the pair-wise dominance tests over time for Bangladesh and its divisions.

Each cell of the table reports whether a _reduction_ or _increase_ in poverty within each region over time in Table 4 is robust (or not) to alternative ordering weighting vectors within the class (i.e., _P_ ) and subclasses (i.e., _P_ 1 and _P_ 2) to which the ordering weighting vectors used in Table 4 belong.<sup>17</sup> For example, it is evident from Table 4 that, according to _PM_ , poverty in Bangladesh has fallen from 37.5 points in 2007 to 30.1 points in 2011. So we could ask: is this reduction between 2007 and 2011 robust to all _P ∈P_ 1? The corresponding dominance test reported in Table 5 shows that the reduction is certainly robust with 1% significance

- 17 We implemented a standard intersection-union test (IUT) whose alternative hypothesis is that poverty levels at the four extreme points are jointly lower in year-region “A” (e.g. Bangladesh in 2011) vis-`a-vis “B” (e.g. Bangladesh in 2007). We reject the null (i.e. at least one poverty level in “A” is equal or higher than in “B”) in favour of this alternative only if every poverty level is lower in “A” than in “B” with a given significance level _α_ . As explained by Berger (1997, p. 226), the IUT’s overall significance level is also _α_ and no correction for multiple comparisons (e.g. Bonferroni, etc.) is required.

32
schools allows us to match household and school inputs in an environment where complications arising from school choice are eliminated. We use the entire sample of 172 schools to estimate the relationship between test scores and cash grants to schools (rule-based and discretionary). We use the sub-sample of 34 schools matched to 540 households to estimate the relationship between rule-based cash grants to schools and household expenditures on education.

Table 1 presents summary statistics separately for rural and urban schools, as well as for schools that are in our "remote" sample and matched to households. As might be expected, there are significant differences between rural and urban areas, with the latter having a better-off student body, but not necessarily better school supplies per student. Our sample of "remote" schools is not significantly different from rural schools on most measures, but they attract poorer students than the other rural schools, and have relatively more books and desks per student (though each desk or textbook is still shared by two students). Per student funding from the predictable rule-based grant increases as we go from urban to rural to remote schools, which is consistent with a fixed rule-based grant being distributed among fewer students in rural and remote areas.

Substantial parts of school spending are suitable for substitution by parents. On average 54% is spent on books, chalk (for slates), stationary and other school materials by the school while 23% is spent on sports materials and equipment. About 19% is spent on utilities, maintenance and infrastructure, and only 3% is spent on allowances and other costs linked to teachers.<sup>16</sup>

# **3.3 Empirical Methodology**

We first test whether there is crowding out of household educational spending in response to anticipated grants. We estimate a cross-section demand model for the 1195 children (from 540 households) matched to 34 schools in which household spending on school-related inputs is regressed on anticipated and unanticipated grants with and without a set of controls for child, household and school-level variables: respectively anticipated (rule-based) and unanticipated (discretionary) grants per student in

> 16 Looking at average spending shares by households, 27% is on books and stationary, and other materials for school while 19% is spent on cash contributions of various forms (although PTA fees were formally abolished) and other direct cash payments to the school. The remainder, 54% of household expenditure, is on school and sport uniforms and shoes, and for sports activities at school.

12
# **Table 8. Transitions in and out of poverty**

|VARIABLES|(1)<br>Becamenonpoor|(2)<br>Becamenonpoor|(3)<br>Becamenonpoor|(4)<br>Became poor|(5)<br>Became poor|(6)<br>Became poor|
|---|---|---|---|---|---|---|
|_a. Events by type_|||||||
|Conflict events|-0.040*<br>(0.020)|||-0.005<br>(0.009)|||
|Conflict events property||-0.070*<br>(0.039)|||0.004<br>(0.033)||
|Conflict events violence|||-0.129***<br>(0.048)|||0.004<br>(0.024)|
|Observations|493|493|493|1,582|1,582|1,582|
|R-squared|0.548|0.547|0.547|0.350|0.350|0.350|
|Controls|YES|YES|YES|YES|YES|YES|
|LGA FE|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|
|_b. Events by perpetrator_|||||||
|Insurgents|-0.057**<br>(0.027)|||-0.008<br>(0.013)|||
|Bandits/criminals||-0.029|||-0.010||
|||(0.076)|||(0.037)||
|Pastoralists/nomads|||0.019<br>(0.063)|||0.004<br>(0.020)|
|Observations|493|493|493|1,582|1,582|1,582|
|R-squared|0.547|0.543|0.543|0.348|0.348|0.348|
|Controls|YES|YES|YES|YES|YES|YES|
|LGA FE|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variables are the variables denoting becoming nonpoor after round 1 and becoming poor after round 1. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 40