rafmacalaba/datause-encoder-data huggingface dataset:
_Oportunidades_ CCT program and 34 percent of Indonesia‘s _Kartu Sehat_ health insurance program benefited the poorest quintile of the population in those countries (Lindert 2005).

# **5.  Trends in Out-of-Pocket Payments for Health Care and Catastrophic Health Expenditures during Program Expansion**

This section assesses to what degree the expansion of the Green Card program led to an improved financial protection of beneficiaries by reducing households‘ out-of-pocket expenditures. As such, the measured impact of the program depends on the extent of out-ofpocket expenditures of beneficiaries before – or at the time of - program entry. Such out-ofpocket expenditures had been, for the most part, relatively moderate, reflecting that affordable access to public health services had existed before program roll-out.

Figure 3 provides the distribution of out-of-pocket health expenditures across the percentiles of the population in 2003 and 2008, respectively. The Figure shows the cumulative distribution of the population ranked by nominal per capita expenditures and maps per capita (gross) expenditures and expenditures net of health spending for each individual in the sample in 2003 and 2008. The vertical lines in the Figure show the nominal size of health expenditures for each individual in the sample. The horizontal dashed lines are the poverty lines for 2003 and 2008. Around the poverty line, the expenditures of the poor on health were relatively small in both of these years. Also, we can observe that relatively modest amounts of vertical lines crossed the poverty line in 2003, before the expansion of the Green Card took place.

If we compare Green Card households to those households without any insurance coverage, we observe a small difference in the share of total expenditures allocated to health in 2008. Households with a head holding a Green Card in 2008 spent on average 1.6 percent of total expenditures and 2.7 percent of non-food expenditures on health, while those households with no insurance spent 2.1 percent of total and 2.9 percent of non-food expenditures on outof-pocket health expenditures.

The difference in spending in terms of percentage of household expenditures between the two groups is 0.45 percent of total household expenditures. While this difference is statistically significant for the overall sample (at the 95 percent confidence level), it is not statistically significant when restricted to the poorest quintile. Welch test results for this hypothesis test are provided in Table 2. For the poorest households -- which already had low levels of health 8
||5.1|Legal Environment ...............................<br>30<br>|
|---|---|---|
||5.2|Economic Environment..........................<br>. .31|
||5.3|Host Country Credit Rating ......................<sup>31</sup>|
||5.4|Political Environment<br>.....<br>...............<br>. 33<br> <br>|
||5.5|Sector Characteristics<br>......<br>...........<br>...<br>. 33<br>|
||5.6|Cyclical Considerations<br>...<br>...................<br>. 34|
|VI.|COST I|SSUES...................<br>....<br>.....<br>.35|
||6.1|Cost of Senior Debt .<br>.<br>.................... 35<br>|
||6.2<br>|Cost of Equity<br>.<br>...............<br>. 36<br>|
||6E3|Project Efficiencies<br>......................<br>38<br>|
||6.4.|Other Benefits ....................<br>. 38<br> <br>|
||6.5|BOT vs.BOO<br>...........<br>. ..<br>.38|
|VII.|PROCURE|MENT ISSUES.....<br>........<br>40|
||7.1|Unsolicited Proposals.<br>40<br>|
||7.2|Competitive Bids...............<br>..41|
||7.3|Integrity<br>of the<br>Process ......<br>.<br>42|
|VInI.|ARGUMEN|TS<br>FOR AND AGAINST BOT PROJECTS ......<br>42|
||8.1|Additionality<br>................<br>,42|
||8.2<br>|Credibility .................<br>......... ........ 43<br> <br>|
||8.3|Efficiencies<br>.<br>..........<br>....<br>.44<br> <br>|
||8.4|Benchmark.<br>..44|
||8.5|Technology Transfer and Training....|
||8.6|Privatization .<br>..................<br>44|
||8.7|Complication and Cost<br>..<br>................<br>. 44|
|IX.|WORLD<br>|BANKGROUP INVOLVEMENT IN<br>|
||BOTPR|OJECTS TO DATE................<br>..<br>...........<br>. ....<br>45|
||9.1|World Bank....................<br>45|
||9.2|IFC.**.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>..<br>.<br>46|
|X.|FUTURE|ASSISTANCE<br>TO HOST GOVERNMENTS<br>...........<br>47|
||10.1|Education ................................<br>.<br>.<br>.<br>47|
||10.2|Advisers<br>.<br>...............................<br>47|
||10.3|Identification of Projects........................<br>. 47|
||10.4|Evaluation of Proposals ......................<sup>. ..</sup>.48|
||10.5|Local Regulatory and|
|||Financial Infrastructure....................<br>. 48|
||10.6|Direct Involvement................<br>..............<br>48<br> <br>|
||10.7|MIGA ..........................s * * *<br>.<br>.... 9-49|
|XI.|CONCLUS|ION ..........<br>..............|
|No earners|64%|29%|63%|51%|40%|50%|
|---|---|---|---|---|---|---|
|Remittance recipients|||||||
|only|19%|4%|16%|8%|7%|8%|
|Female single<br>earner|47%|15%|41%|52%|12%|47%|
|Male single earner|54%|27%|44%|23%|8%|19%|
|Majority female earners|66%|1%|43%|57%|3%|50%|
|Equal contribution|46%|6%|27%|24%|10%|20%|
|Majority male earners|41%|4%|16%|16%|5%|13%|
|Panel B. N.E Nigeria|||||||

|Panel B. N.E Nigeria|%<br>|of monetary po<br>|or<br>|% of m<br>|ultidimensiona<br>|l poor<br>|
|---|---|---|---|---|---|---|
||IDPs|Non-IDPs|All|IDPs|Non-IDPs|All|
|Female hhh|91%|91%|91%|28%|8%|14%|
|Male hhh|90%|76%|80%|21%|22%|22%|
|De jure female hh|99%|77%|96%|32%|9%|20%|
|De facto female hh|85%|80%|89%|25%|7%|11%|
|No earners|83%|98%|94%|50%|62%|58%|
|Remittance recipients<br>only|100%|99%|96%|69%|76%|73%|
|Female single earner|98%|91%|92%|38%|24%|32%|
|Male single earner|96%|93%|94%|14%|4%|7%|
|Majority female earners|88%|86%|83%|28%|9%|14%|
|Equal contribution|93%|81%|83%|16%|17%|17%|
|Majority male earners|78%|56%|57%|19%|15%|16%|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

# **8. Conclusion**

This paper contributes to the literature by analyzing multidimensional poverty among refugees and internally displaced populations. We observe that forcibly displaced communities are poorer than host communities in each of the five countries’ sub-populations covered in the surveys, with the difference in incidence between displaced and non-displaced population ranging between 15 and 19 percentage points in South Sudan and Somalia to over 30 percentage points in Ethiopia and Sudan. Displaced communities also experience greater deprivations in nearly every indicator, although there is significant variation in which indicators are the most salient, with having a bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria showing the largest differences between the two populations. The results also indicate gender differences in the experience of multidimensional poverty, with female-headed households more likely to be poor than male-headed households in most of the countries. In addition, displaced households headed by women have a higher incidence of poverty and MPI than non-displaced female-headed households. Particularly, female-headed households in camps have higher multidimensional poverty and intensity compared to their counterparts living outside camps. Dissaggregating further, we find heterogeneity among de facto and de jure female heads. This variation lends itself to further research questions about 28
**TABLE 1 Global growth forecast** **_uncertainty_ weights**

||**V**<br>|**AR-based weigh**<br>|**ts**<br>||**OLS-based**<br>**weights**|
|---|---|---|---|---|---|
||6-months<br>ahead|12- months<br>ahead|18-months<br>ahead|24-months<br>ahead|(All horizons)|
|S&P500 (β1)|0.25|0.22|0.14|0.11|0.23|
|Term spread (β2)|0.01|0.03|0.06|0.15|0.48|
|Oil price (β3)|0.01|0.17|0.40|0.42|0.27|
|Other factors (βε)|0.73|0.58|0.40|0.30|-|

Source: World Bank staff estimates.

Note: VAR-based results are variance decomposition weights derived from a VAR of oil prices, S&P500, term spread and global growth—they are calculated as the share of variance of global growth explained by selected risk factors and other risk factors not included in the analysis. OLS-based weights are the absolute values of the coefficients obtained from an OLSregression of global growth on oil prices, S&P500 and global term spreads. Regressions use annual data for 1982-2014.

**TABLE 2 Global growth forecast** **_skewness_ weights**

||**V**<br>6mnth|**AR-based weights**<br>12 mnth||**OLS-based**<br>**weights**|
|---|---|---|---|---|
||-os<br>ahead|- os<br>ahead<br>18-months ahead|24-months ahead|(All horizons)|
|S&P500 ( α1)|0.36|0.40<br>0.49|0.47|0.23|
|Term spread (α2)|0.1|0.14<br>0.41|0.52|0.48|
|Oil price (α3)|-0.06|-0.21<br>-0.76|-0.82|-0.27|

Source: World Bank staff estimates.

Note: Impulse responses-based weights are derived from a VAR including in this order: oil prices, S&P500 Index, term spread and global growth. The responses of global growth to own innovations are not presented here. OLS-based weights are derived from OLS-regression of global growth on oil prices, S&P500 and global term spreads. Regressions use annual data for 1982-2014 14
the conventional practice that younger teachers, with relatively less political and social capital, tend to be placed in rural schools.

# **References**

Angrist, J. and V. Lavy, 1999, Using Maimonides’ rule to estimate the effect of class size on scholastic achievement, _Quarterly Journal of Economics_ 114: 533-575.

Bruns, B., D. Filmer, and H. Patrinos, 2011, Making Schools Work: New Evidence on Accountability Reforms, The World Bank.

Card, D. and A. Krueger, 1996, Labor market effects of school quality: Theory and evidence, NBER Working Paper No.5450, National Bureau of Economic Research.

Case, A. and A. Deaton, 1999, School inputs and educational outcomes in South Africa, _Quarterly Journal of Economics_ 114: 1047-1084.

Chaudhury, N. and D. Parajuli, 2010, Giving it Back: Evaluating the Impact of Devolution of School Management to Communities in Nepal, Manuscript, World Bank.

Duflo, E., P. Dupas, and M. Kremer, 2009, Additional Resources versus Organizational Changes in Education: Experimental Evidence from Kenya, Manuscript, MIT.

Dustman, C., N. Rajah and V. Soest, 2003, Class size, education and wages, _Economic Journal_ 113: F99F120.

Gertler, P., H. Patrinos, M. Rubio-Codina, 2011, Empowering Parents to Improve Education: Evidence from Mexico, _Journal of Development Economics_ 99: 68-79.

Hanushek, E., 1998, Conclusions and controversies about the effectiveness of school resources, _Federal Reserve Board of New York Economic Policy Review_ 4: 11-27.

Hoxby, C., 2000, The effects of class size on student achievement: New evidence from natural population variation, _Quarterly Journal of Economics_ 116: 1239-1286.

9
**Table 3: Maximum Likelihood Estimates for Total Non-Fuel** Exportsa

|Model||(I)||(II)|
|---|---|---|---|---|
|Equation|Probit|Gravity|Probit|Gravity|
|_Intercept_|-6.631|-10.791|-6.766|-11.170|
||(-27.77)|(-29.31)|(-27.10)|(-29.55)|
|_GDPi_|_0.556_|1.374|0.556|1.374|
||(33.86)|(60.26)|(33.85)|(60.38)|
|_GDPj_|0.458|1.017|0.443|0.945|
||(29.84)|(46.85)|(25.93)|(35.11)|
|_Populationi_|-0.052|-0.320|-0.052|-0.320|
||(-2.84)|(-12.18)|(-2.83)|(-12.20)|
|_Populationj_|-0.153|-0.137|-0.137|-0.070|
||(-8.15)|(-4.90)|(-6.57)|(-2.17)|
|_Distance_|-0.473|-1.114|-0.467|-1.100|
||(-13.55)|(-23.69)|(-13.34)|(-23.41)|
|_Border_|-0.393|0.301|-0.381|0.328|
||(-2.54)|(1.52)|(-2.47)|(1.65)|
|_Language_|0.588|0.826|0.588|0.826|
||(8.96)|(8.95)|(8.97)|(8.98)|
|_EC_||-0.068||-0.096|
|||(-0.24)||(-0.34)|
|_EFTA_||-0.137||-0.152|
|||(-0.28)||(-0.31)|
|_LAIA/LAFTA_||0.822||0.944|
|||(3.73)||(4.26)|
|_ASEAN_||2.352||2.442|
|||(4.78)||(4.97)|
|_CACM_||2.127||2.267|
|||(4.28)||(4.56)|
|_IpRSb_|||0.047|0.176|
||||(1.92)|(4.46)|
|||2.113||2.109|
|_obs._|7309|5294|7309|5294|
|||0.005||0.002|
|-21nA(p=0)||0.016||0.003|
|-2_InA (_{y,,/} = 0)c||8520.968||8544.524|

a t-statistics in parentheses _b_ Park and Ginarte index of the destination country of the trade flow, that is countryj in the case of exports and country i in the case of imports.

For a definition of the likelihood ratio test statistics, see Appendix A.

15
o 'K t '--a 20

-

}e S

-.

Tabk 7. ndices of <!-- Start of picture text --> !<br><!-- End of picture text --> <!-- Start of picture text --> tz<br><!-- End of picture text --> Applicatio for Major Bpont Products of the Fower Soviet Uion-

|-<br>.L<br>-- --<br>STota<br>Product Grotip (SMt)|1991<br>OBCD<br>Imort(SmilL)|<br>EEC(12)|Shaic Of allfffhl<br>Finn|;D<br>prdu<br>tha<br>Jaan|t faceatriffb<br>d|e<br>(%**)**<br>.S<br>||
|---|---|---|---|---|---|---|---|
|AU Food Produc (0 + 1 + 22 + 4)|<br>940|<br>|<br>|p<br>|swe|Switerlad|U.S.A|
|<br>Mea and live animal (01)|<br>3|39<br>so|77<br>0oo|68<br>33|76<br>|46<br>|33|
|Fres ad frozen fisIk (03)|710|<br>26|<br>63|<br>|S0<br>|8<br>||
|FnCS mndpreservCdfzit(05Ito053)|<br>56|<br>47|<br>_55_|100<br>0|78<br>|0<br>|0<br>|
|Sugar amd preparations (06)|16|<br>80|<br>0|<br>|100|6|0|
|Animad Feeds (08)|<br>7|<br>20|to<br>100|0<br>0|.<br>-|||
|BevCWagCs<br>(11)<br>|<br>59|<br>46||50||||
|O0led and nuts (22)|34|<br>0|0||100<br>|SO|94<br>|
|Animad and vegetable os (4)|9|<br>11|<br>0||100<br>||0|
|Agriculural Materi<br>a(2<br>- 22 " 27 - 28)|<br>2.115|<br>26|<br>0|t00<br>23|100<br>|100<br>||
|Wood and lumber (24)<br>|1,380|<br>21|<br>0|<br>0|7<br>0|20<br>|6<br>|
|Ptdp and paper (25)|<br>125|a|<br>|<br>|<br>|0<br>|0|
|TCKetifibers (26)<br>|<br>435|<br>14|v<br>0|0<br>0|0|0<br>||
|Orcs, MinoS and h5Ctai (27 + 28 + 68)|4022|3||<br>||0<br>|13<br>|
|Crude Feizer<br>(271|,<br>51|<br>0|0|U<br>0|29<br>|9|0|
|Metal Ore and srap (28)|493|<br>0|<br>0|<br>|S0<br>|||
|Nonferru metals (69)|<br>3434|<br>6|<br>|0<br>|67<br>|0<br>|0|
|AlD ManUfaCured<br>_goods (5_a S - 68)|,<br>5048|<br>18|1<br>3|0<br>1|20<br>|17<br>|0<br>|
|Chanical Cements (51)|,<br>1085|<br>4|<br>0|0<br>|26<br>|18<br>|1|
|Manufacturcd<br>frtilizer (56)|,<br>293|<br>:7|<br>U|13<br>|30|16|_5_|
|L,ather nd goods (61)|<br>34|<br>90|<br>0|100<br>80|||0<br>|
|Wood manufiatures (63)|132|<br>0|<br>0|<br>|0<br>|100<br>|0|
|TCxtile yam and SbriC (65)|<br>112|<br>93|<br>0|0<br>38|0<br>|0<br>|0<br>|
|Fearus metals (67)|925|<br>68|<br>0|<br>|71<br>|67<br>|20|
|Metal Manufactures (69)|<br>30|<br>1|<br>0|0<br>|94<br>|10<br>|0|
|Nonlectric nachiinory<br>(71)|<br>254|<br>0|<br>0|0<br>0|11<br>|0<br>|0<br>|
|Electica machin y 1(72)|126|<br>3|<br>0|<br>|0<br>|6<br>|0|
|Transport equipmnt (73)|<br>895|<br>0|<br>0|0<br>|3<br>|9<br>|0|
|Fumniturn<br>(82)|<br>71|<br>0|<br>0|0<br>|13<br>|60<br>|0<br>|
|Cbhng (84)|<br>_45_|<br>83|<br>|0<br>|0<br>|0<br>|0|
|Footwear M)|<br>27|<br>94|0<br>0|71<br>|65<br>|100|0<br>|
|SeiiC<br>insruomes (86)|<br>50|<br>3|<br>|0<br>|_75_<br>||0|
|<br>Misc. Manufcturcs (69)|<br>167|<br>17|0<br>0|0<br>|0<br>|22<br>|0|
|AD non Cnergy goods (O to 9 -3)<br>|<br>13,654|<br>19|<br>4|2<br>18|19<br>|7<br>|0<br>|
|AU Goods (0 to 9)|<br>29443|<br>19|<br>|<br>|29<br>|21|5|
||,||_5_|19|30|24|6|

Source: OECD tRad sbaistis fiom COMTRADE reCords. Tariff records from the SMART Daa bas. Bklaks icdicatc tha no tradCe occuredm it product group.
# LINKING PARTICIPATORY POVERTY ASSESSMENTS TO POLICY AND POLICYMAKING: EXPERIENCE FROM VIETNAM

By Carrie Turk, World Bank

# **Introduction**

It is estimated that in the mid-1980's seven out of every ten Vietnamese were living in poverty. A little more than a decade later - a decade of reforms and rapid economic growth - the incidence of poverty has halved. Latest household survey data suggests that 37% of the population is now under the poverty line and 15% of the population is so poor that consumption is inadequate to meet nutritional needs. Social and other indicators have also improved significantly. But many of the newly non-poor households are still nearly poor, hovering close to the poverty line. Their vulnerability to shocks and crises coupled with the recent slowdown in economic growth means that the gains in poverty reduction cannot yet be considered robust.

1999 was an important year for poverty-related research and policy development in Vietnam. Household survey data from the second Vietnam Living Standards Survey had been collected by the General Statistics Office (GSO) the previous year and became available for analysis in 1999. In addition, four participatory poverty assessments (PPAs) were implemented in Vietnam during the year. _The Vietnam-Sweden Mountain Rural Development Program_ carried out a PPA in two Districts of Lao Cai Province, an upland area with a high proportion of ethnic minorities living in remote villages. _ActionAid_ coordinated a PPA in six Districts of Ha Tinh Province in the north central coastal region, a typhoon-prone area with very poor natural endowments. _Oxfam GB_ carried out a PPA in two Districts of Tra Vinh Province, a coastal region with a large ethnic minority population and growing problems of landlessness. In Ho Chi Minh City, _Save the Children Fund (UK)_ coordinated a study of three poor, urban Districts of Vietnam's most prosperous city .

This case study describes the work that took place collaboratively between Government agencies, donors and NGOs in implementing the PPAs. Although there has been a considerable amount of qualitative information on poverty produced in Vietnam over the past 8-10 years, it has rarely grabbed the attention of policymakers who have tended to view such information as "unscientific" and lacking in credibility. By contrast, the PPAs implemented in 1999 have been widely circulated, used and quoted. It is possible to identify a number of factors that have profoundly influenced the potential to use this participatory research for both program development and policy formulation. These include: Leadership and a significant commitment of resources - financial and other - by the World Bank and other donors; 'The four agencies named here will be called the "PPA study agencies" for the remainder of this case study. I
**Figure 8.  Total Fertility Rate and Infant Mortality Rate, Brazil 1950-2005**

<!-- Start of picture text --> 7.0 160<br>Total fertility rate<br>140<br>6.0<br>Infant mortality rate<br>120<br>5.0<br>100<br>4.0<br>80<br>3.0<br>60<br>2.0<br>40<br>1.0<br>20<br>0.0 0<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Year<br>Infant Mortality Rate (per 1000 births)<br>Total Fertility Rate (births per woman)<br><!-- End of picture text --> 34
||0.02|0.02|0.03|
|---|---|---|---|
|**Imports**|0.46|0.48|0.02|
||0.03|0.02|0.03|
|**Private investment**|0.41|0.40|-0.01|
||0.04|0.05|0.07|
|**Public investment**|0.46|0.46|0.01|
||0.05|0.08|0.09|
|**Public expenditures**|0.34|0.37|0.03|
||0.05|0.04|0.08|
|**Public revenues**|0.41|0.44|0.03|
||0.05|0.03|0.06|

Note: Standard errors below statistics. * p<0.10, ** p<0.05, *** p<0.01

# **Other controls**

In this section we extend our analysis to control for other possible factors affecting business cycles. Thus, one of the key characteristics distinguishing developed and developing countries is their level of institutional development. In this section we investigate whether the effects of size on countries’ business cycles properties survives after we control for the level of institutional development. This analysis also allows us to see if the significant effect of development we uncovered in the previous section is driven by the differences in institutional quality across countries. We proxy the latter by a subjective index of investor perceptions, the Composite Risk Rating index from the International Country Risk Guide (ICRG). This index is a composite of political, financial, and economic risk ratings and can take values from 0 to 100, with a rating of 100 indicating the lowest risk, and a rating of 0 indicating the highest risk.

In the previous section we noted that the incidence of fiscal rule adoption varies across country size and level of development, with larger and more developed countries being more likely to adopt some form of fiscal restraint. Therefore, we investigate whether differences in volatility, cyclicality and persistence of various variables between small and large countries may be driven by their different propensity to adopt fiscal rules. For this purpose, we include into our regressions a dummy variable taking a value of 1 if a country has ever adopted fiscal rule(s) and 0 if not. We also check how robust our results are to the definition of a fiscal rule adopter. Specifically, we define a country to be a fiscal rule adopter if it has had fiscal rule(s) in place for at least 5 years. We find that the results remain robust to this alternative control for the fiscal rule(s) presence. This is not surprising given that the average duration of fiscal rule policies is more than 5 years in the majority of countries in our sample. For instance, expenditure rules are in place for just over 10 years, on average; while revenue rules, budget balance rules and debt rules have an average duration of 14 years across countries in our sample.

We also control for the regions in which countries in our sample are located using a set of regional dummies. Our seven regions include South Asia (1); Europe and Central Asia (2); Middle East and North Africa (3); East Asia and Pacific (4); Sub-Saharan Africa (5); Latin America and Caribbean (6); and North America (7).

35
<!-- Start of picture text --> 70 64 66<br>60 59<br>53<br>50<br>50<br>40 42 40<br>° 34<br>30 26 27<br>23<br>20 20<br>10 as<br>0<br>Uganda Rural Urban Central Eastern Northern Western<br>Areas Regions<br>| Poverty lm Vulnerability<br><!-- End of picture text -->

<!-- Start of picture text --> 100<br>TM Poverty m Vulnerability 8&7<br>80 75 oe<br>62 63 63<br>67 7<br>60 56 gy 5H so 56<br>44 4 4<br>40 35<br>27 2 2 2<br>19 20) 2 2<br>20 1<br>o ==42 ]<br>22228E £8 22 2°Q2 2§3-« £3 $€£3 eo822 5sS$ Ge& 8 23Es9# =B<br>= 4acES =$sa © « * sacES a @ £e <<br>Sub-regions<br><!-- End of picture text -->
**FIGURE 2: Housing Stock in Transition Countries, 1991**

<!-- Start of picture text --> Housing Units /1000 Population<br>450.<br>400 350 ** _ _ _<br>fl_<br>2503 f * f fl<br>200 j f l f *<br>150V f l f<br>****<br>50 *f<br>0<br>Estonia Hungary Bulgaria CSFR Romanla Poland Russia<br><!-- End of picture text --> _Source: UN-ECE, Annual Bulletin of Housing & Builcing Statistics, November 1994_

# **2.1 Credit Risk**

In developed mortgage markets, credit risk--that is, the risk of default--on a real estate loan is usually measured by two ratios, with a lower value of either ratio indicating less credit

# risk:

- The _loan to value ratio_ is the ratio of the loan amount to the property value.

- a The _payment to income ratio_ is the ratio of the annual mortgage payment (including payments for insurance, property taxes, and the like) to the borrower's annual income.

# Loan to Value Ratios

The loan to value ratio is generally considered the more basic determinant of credit risk because, as long as the loan amount is less than the property value, the lender can always recover the loan principal by taking over the property and selling it. In transition economies, however, this is unlikely to be the case for two reasons. First, the property rights and foreclosure procedures that are needed for real estate to function as loan collateral are not well established in 7