rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5850

# **Abstract**

This paper examines the dynamics of poverty and vulnerability in Haiti using various data sets. As living conditions survey data are not comparable in this country, we first propose to use the three rounds of the Demographic Health Survey (DHS) available before the earthquake. Decomposing household assets changes into age and cohort effects, we use repeated cross-section data to identify and estimate the variance of shocks on assets and to simulate the probability of being poor in the future. Poverty and vulnerability profiles are drawn from these estimates. Second, we decompose vulnerability to poverty into various sources using a unique survey conducted in 2007 in rural areas. Using two-level modelling of consumption/income, we assess the impact of both observable and unobservable idiosyncratic and covariate shocks on households’ economic well-being. Empirical findings show that idiosyncratic shocks, in particular health-related shocks, have larger impact on vulnerability to poverty than covariate shocks. Third, asset-wealth is characterized for households after the 2010 earthquake based on a survey designed to provide a rapid assessment of food insecurity in Haiti after the quake. Whereas it is not possible to confirm the existence of poverty trap, it seems that those households who have lost the most due to the earthquake succeeded in recovering more rapidly from the shock, regardless of the effects of assistance, and probably more in line with coping strategies that are specific to households.

This paper is a product of the Social Protection Sector, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at damien.echevin@usherbrooke.ca.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table A13. Associations between SCP Quality Determinants and Labor Market Outcomes**

**_Using Individual- and Program-Level Data for Brazil_**

||**Graduation**|**Ever**<br>**employed the**<br>**year after**<br>**graduation**|**Percent of time**<br>**employed after**<br>**graduation**|**Log**<br>**average**<br>**monthly**<br>**wage**|
|---|---|---|---|---|
||(1)|<br>(2)|(3)|<br>(4)|
|_Infrastructure_|||||
|Program offers at least one online class|0.0467**<br>(0.0193)||||
|>30% classes can be taken online|||2.560*<br>||
||||(1.497)||
|Number of workshops or labs available for practice|0.00149<br>(0.00236)||||
||<br>-0.0211||-1.506||
|Has enough equipment or tools for practice|(0.0240)||(1.893)||
||0.0345*||0.0472||
|Maintenance of largest lab: every year|(0.0202)||(1.369)||
|_Costs_|||||
|HEI scholarships are used by some students|0.00990<br>(0.0197)||||
||0.0173||||
|Loans by HEI are used by some students|(0.0207)||||
||<br>0.0500**|0.0285*|4.994***|0.202*|
|Loans by banks are used by some students|(0.0223)|(0.0148)|(1.569)|(0.104)|
|Other loans are used by some students|0.0136<br>(0.0190)||||
|HEI has received fundin from overnment|<br>-0.0290||-2.682*||
|g g|(0.0211)||(1.438)||
|HEI has received funding from private sector|0.0342<br>(0.0221)||||
|_Training and curriculum_|||||
||||-1.558||
|Curriculum is fixed|||(1.459)||
||-0.0285|0.0382**|<br>4.177**|0.292**|
|Teaches numerical competencies|(0.0255)|(0.0190)|(1.822)|(0.133)|
||0.0177||||
|Teaches a foreign language|(0.0210)||||
||<br>0.00888|0.0290**|1.654|0.158|
|Teaches persistence in complex tasks|(0.0199)|<br>(0.0141)|(1.400)|(0.0996)|
|Offers remediation classes before starting the program|0.0171<br>|0.0228*<br>||0.132<br>|
||(0.0165)|(0.0118)||(0.0820)|
|Offers remediation classes durin the roram||-0.0512**|-0.614|-0.375***|
|g pg||(0.0208)|(2.026)|(0.140)|
|Off lbd dii|-0.0232||||
|er non-cass-ase remeaton|(0.0180)||||
|fil ii i f i|<br>0.0549*|0.0348**|7.062***|0.201*|
|Proessona assocaton test requred or graduaton|(0.0280)|(0.0172)|(1.763)|(0.114)|
|Thesis or research project required for graduation|0.0259<br>(0.0235)|0.0166<br>(0.0200)|1.941<br>(1.773)|0.104<br>(0.133)|
|Years since last update to curriculum|0.00675*<br>(0.00357)||||
|Government standards are very important to update program|-0.0359**<br>(0.0167)||||
|Employment outcomes or employers' requests are very important to|<br>-0.0152||-2.561||
|<br>update program|(0.0295)||(2.007)<br>||
|Ellt td itt t dt|||-1.152||
|nromen rens are very mporan o upae program|||(1153)||
||-0.0366*|0.0320**|.|0.241**|
|Student feedback is very important to update program|(0.0215)|(0.0144)||(0.101)|
|Analyze student performance to solve problems more than once per|<br>0.0919***|<br>0.0476**|5.854**|<br>0.267*|
|year|(0.0298)<br>|(0.0211)|(2.313)<br>|(0.146)|
|Collect student satisfaction data more than once per year|-0.0398**<br>(0.0199)||2.386*<br>(1.261)||

87
# **Table A2: Financial Integration and the Sensitivity of Consumption to Income: Binary Measures**

# **_<u>All Countries</u>_**

||**IMF**<br>**[1]**|**BHL**<br>**[2]**|**WW**<br>**[3]**|**Export**<br>**Proceeds**<br>**[4]**|
|---|---|---|---|---|
|Output|0.846<br>[0.013]***|0.747<br>[0.033]***|0.906<br>[0.034]***|0.830<br>[0.016]***|
|Output × _De Jure_|**-0.034**<br>[0.015]**|**-0.099**<br>[0.026]***|**-0.178**<br>[0.037]***|**-0.052**<br>[0.022]**|
|Observations|3,833|2,048|2,730|3,835|
|Countries|88|64|66|88|

# **_<u>Advanced Economies</u>_**

|Output|1.075|0.752|0.742|1.045|
|---|---|---|---|---|
||[0.142]***|[0.178]***|[0.035]***|[0.169]***|
|Output × _De Jure_|**-0.390**|-0.006|0.000|**-0.318**|
||[0.151]***|[0.186]|[0.000]|[0.191]*|
|Observations|963|945|945|965|
|Countries|22|21|21|22|
|**_Developing Econo_**<br>Output|**_mies_**<br>0.835<br>[0.023]***|0.766<br>[0.058]***|0.875<br>[0.074]***|0.802<br>[0.015]***|
|Output × _De Jure_|-0.031|**-0.139**|**-0.144**|**-0.052**|
||[0.028]|[0.059]**|[0.068]**|[0.024]**|
|Observations<br>|2,870<br>|1,376<br>|1,785<br>|2,870<br>|
|Countries|66|43|45|66|

Note: Dependent variable is consumption growth. GMM Dynamic Panel Estimates. Lags 2-4 of consumption growth, all right hand side variables and lagged saving are used as instruments. “IMF” refers to the binary IMF indicator for restrictions in capital account. “BHL” and “WW” refer to the stock market liberalization from Bekaert, Harvey and Lumsdaine (2002) trade integration from Wacziarg and Welch (2003), respectively. Export proceeds refers to restrictions on repatriation of export proceeds. Sample period 1966-2011 as dictated by availability of data.

22
best regressed latent variable on the K asset primary indicators, since no other explained variable is more informative (Asselin, 2009).

Next, the methodology is developed in order to compare distributions of the asset index over time. The data sets for several years are then pooled and asset weights are estimated using factor analysis for the pooled sample. We obtain:

> where the factorial score coefficients _F_ 1 _k_ are supposed to be constant over time.

Results from multiple correspondence analysis for pooled data sets (Demographic and Health Surveys 1995, 2000 and 2005) are presented in Table 1. Several wealth items have been used: liquid assets (radio, television, refrigerator, bicycle, motorcycle, car), housing characteristics (tap water, surface water, flush toilet, no toilet, electricity, rudimentary floor, finished floor) and head of household’s education (no education, primary education, secondary education and tertiary education).

**Table 1. Asset index weights for pooled data**

|Asset variables|Weights|% Inertia|
|---|---|---|
|_Liquid assets_|||
|Radio|0.310|2.1|
|Television|0.976|7.4|
|Refrigerator|1.146|4.5|
|Bicycle|0.462|1.4|
|Motocycle|0.807|0.6|
|Car|1.216|2.2|
|_Housing_|||
|Tap water|0.392|2.0|
|Surface water|-1.145|21.5|
|Flush toilet|1.150|2.6|
|No toilet|-1.076|19.7|
|Electricity|0.805|8.0|
|Rudimentary floor|-0.590|0.1|
|Finished floor|0.351|2.9|
|_Head of household’s education_|||
|No education|-0.912|17.7|
|Primary education|-0.005|0.0|
|Secondary education|0.938|6.0|
|Tertiary education|1.309|1.5|
|Partial inertia||21.5|

Source: Own computations using DHS 1995, 2000, 2005 6
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# Box 3 Costs of Financial Repression

The-costs of.financial repression-can.be substantal. Mexico's experience illustrates the effects of financial repression under. rising inflation. Financial controls intensified after. 1981 as.inflation soared, and the ratio of private credit to GDP dropped below already low -: levels. Foliowing fiiancial liberalintion in 1990, the ratio- doubled in two years; In Ghana,;.. private crditiwas ata. very low level in the. late: 1980s, reflecting years of financial. repression. Countries that abstined from. repressive interest rate:-controls, such as. Chile and..;:: Thailand,.and.Indonesia:(after ..1983) had very high levels of private credit which may. partially explain their superior investment and- growth performance. in the 1980s. ..

Source: Willia Easterly andm Klms Schmidt-Hebbel. (1993) countries from a number of other studies (see for example Barro (1991); Barro and Lee (1993)). What are the implications of this result for Pakistan that has grown fairly rapidly but without commensurate progress in improving its human capital"?

44. One obvious implication of the relative neglect of human development has been the high rate of population growth in Pakistan. So, even with a growth rate over the past ten years that has been the highest in the South Asia Region, PakSlant'a per capita GDP growth has been below the region's average. Therefore, over the medium-to-long term, a major pay-off to substantial investnent in human capital, especially female education, would be a sharp reduction in the population growth rate.

_45._ The fact that Pakistan has sustained an average growth of 6.0% p.a. over the past twenty years even with the neglect of human capital formation has emerged as a development puzzle. One needs to look a bit more deeply at the sectoral sources of growth to understand this phenomenon While there is some positive evidence of structural change happening in. Pakistan over the past 20 years, especially in terms of the share of manufacturing exports in total exports, the picture does not suggest the making of a dynamic economy. The share of manufactming in GDP remains relatively small. Moreover, there is a strong dependence of the manufacturing sector on a small number of agriculture-related industies (cotton, foodstff). Given the relatively tiin manufacturing base, it is not surprising that skldls have not yet emerged on a major consraint on the rate of economic growth. Moreover, fim level analysis suggests that on-die-job traimng has been playing an miportan role in skill formation, making up for some of the deficiencies resulting from weak formal education In agriculture, where growth was also fairly robust (4.2% p.a.), a large part of the output expansion " See **Behnman** and Schneider (1991) for a compartive analysis of invesmen in human capital in **Pakistan** and **odier** developing **countries.**
-6 -

value (dT) of the hea;th effects due to the change in air pollution under consideration is the surnuation of all effects and can be represented by:

# dT = YV1<sup>dH,</sup>

Unfortunately, there is still a great deal of uncertainty and controversy about much of the research on which these estimates are based. Recognizing this uncertainty, upper and lower bound estimnaes are provided to indicate the ranges within which the actual health effects are likely to fall. In addition, the categories presented in this paper are not all-inclusive, since quantitative evidence is not available for every health effect suspected of being associated with air pollution. Also, air pollution has been associated with non-health effects, including materials damage, soiling, vegetation lr..ses and visibility degradation.

These types of omissions suggest that the results of this analysis are likely to underestimate the hea1th effects, and will certainly underestimate the total effects, resulting from air pollution.

# B. Baseline Assumptions

An important question in all of the health effects estimates is whether a threshold level exists, below which health effects no longer occur, or whether the slope of the dose-response function diminishes significantly at lower concentrations. There is a presumption by some that a threshold exists at the EPA air quality standard, or at the WHO ambient guidelines for criteria pollutants. Most of the studies reported here have estimated linear or log-linear functions suggesting a continuum of effects down to the lowest levels, and have not specifically identified a threshold level. When efforts have been made to identify a threshold, little conclusive evidence has been found that one exists. In fact, many recent epidemiologic studies report an association between air pollution and health at ambient concentrations at or below the current federal standard. The former Administrator of the EPA has stated, 'in a heterogeneous population it is unlikely that, for any pollutant, there will be a single scientifically defensible threshold applicable to all people. Instead, there will be a series of thresholds for different
large strides still have to be made to bring all children up the education ladder. Efforts are needed to improve access for the poor to basic, quality education. Among the different age groups there is a huge variation. Among young children (5 year olds) only 4.3 percent attend formal education such as pre-school (Table 7.1). Among 6-11 year olds 77 percent attend formal education and the attendance rate increases to 81.5 percent for children aged 12-14. Location matters for school attendance in Haiti. In the metropolitan area, 82.1 percent of 6-11 year olds attend school. In urban areas, 84.8 percent of 6-11 year olds attend school, but the number falls to 73.3 percent in rural areas.

Children in rural areas often face a long travel time to go to school and for the poor this is especially so, as they go to school on foot. Of youth aged 18-24 only 39.1 percent attend formal education in Haiti, but the variation across location is large. Of 1824 year olds 46.6 percent are in school in the metropolitan area compared to only 32.8 percent in the rural areas.

Figure <u>7.1: Average Years of Schooling for Household Heads by Decade of Birth, 2001</u> <!-- Start of picture text --> 8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>1900s 1910s 1920s 1930s 1940s 1950s 1960s 1970s 1980s<br>Decade of Birth<br>Years of Schooling<br><!-- End of picture text --> _Source_ : Own calculations based on HLCS 2001.

Table 7.1: School Attendance by Age 2001 (percent)

|Age|Metropolitan|Urban|Rural|Total Haiti|
|---|---|---|---|---|
|5|4.7|5.7|3.8|4.3|
|6-11|82.1|84.8|73.3|77.0|
|12-14|84.6|87.0|78.6|81.5|
|15-17|79.3|79.9|71.3|74.8|
|18-24|46.6|44.1|32.8|39.1|

_Source:_ Own calculations based on HLCS 2001.

Large differences also exist in school attendance across regions and children and youth in the poorest regions fall behind their peers in the richer regions. For example, in Northwest and Artibonite 71.3 and 68.1 percent of the 6-11 year olds attend school compared to 80.3 and 78.7 percent in the West and Center regions in 2001 (Table 7.2).

What causes the falloff in school enrollment after 14 years of age? Is it a supply constraint or lack of demand? Is the reason cost of schooling in dispersed areas or lack of 38
**Table 3.  Means and Standard Deviations of Explanatory Variables in the Expenditure System, by Household Migration Status**

|Variable|Households With<br>|out Migrants<br>|Households<br>International<br>|With<br>Migrants<br>|Households wi<br>Migra<br>|th Internal<br>nts<br>|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|Household size|4.049|1.954|3.795|2.011|3.748|1.919|
|Number of children|0.636|0.932|0.372|0.759|0.366|0.770|
|Age of Household head|43.801|15.012|56.271|13.129|59.441|14.067|
|Schooling of Household<br>head|5.168|3.870|3.302|3.043|2.928|3.101|
|Number of household<br>members with six<br>years of schooling|1.646|1.368|2.708|1.516|2.450|1.482|
|Number of household<br>members with nine<br>years of schooling|0.746|0.987|1.021|1.221|0.888|1.161|
|Number of household<br>members with ten or<br>more years of<br>schooling|0.402|0.822|0.417|0.843|0.496|0.933|
|Landholdings|4.416|26.330|7.692|32.184|4.571|10.696|
|Frequency of Transport|7.873|5.990|8.375|5.211|9.300|5.990|
|Inaccessibility During<br>Weather Shocks<br>(Dummy)|0.127|0.334|0.146|0.354|0.156|0.363|

**Source:  Analysis of ENHRUM data**

33
# **Chapter 4: Marketing**

|**Misconception**|**HB Response**|
|---|---|
|"I have sold the same products for a long time now, there is no need to<br>change my assortment."|In our survey we found that shops that introduced at least one new product in the<br>last three months were associated with significantly higher sales and profits than<br>those that did not.|
|"If a customer decides to quit buying, I cannot do anything."|From our conversations with shop owners, it became apparent that many do not<br>simply give up on an absent customer, and try to follow up to understand the<br>reasoning.|
|"I already make the best possible prices, giving discounts is not<br>necessary."|You may want to give discounts for products that are on the verge of spoiling. If your<br>supplier offers you good terms, you can pass on a bit of discount to customers. Also,<br>considergivingdiscounts forgoods bought in bulk.|
|**Reasons**|**to Adopt the Practice**|
|From our survey of small and mid-size retail shops, we gathered that succ<br>customers and win over new ones.|essful shops were the ones that adopted some simple practices to help retain|
|Shop owners who decide to get back to former customers to see the reas<br>the sales of businesses whose owners just let it go. What is more, their m|ons why they quit buying at their shop associated with monthly sales 24% higher than<br>onthly profits are 23% higher.|
|From the survey we know that shops that use discounts to attract new an<br>the sales of shops that do notgive discounts. Also, their monthly profits a|d retain loyal customers are associated with monthly sales that are 40% higher than<br>re 29% higher.|
- 10-

air pollution. The air pollution levels in Jakarta are well within the range of those observed in the epidemiologic studies used in this report.

# B. Development of Dose-Response Estimates

# 1. Particulate Matter

Epidemiologic studies provide dose-response relationships between concentraons of ambient particulate matter and several adverse health outcomes including: mortality, respiratory hospital admissions, emergency room visits, restricted activity days for adults, lower respiratory illness for children. asthma atacks, and chronic disease. Among these sudies, statstically significant relationships have been found using several alternative measures of particulate matter, including TSP, fine particles (particles less than 2.5 microns in diameter), British smoke. coefficient of haze (COH) and sulfaes. Frw have involved measurement of PMIO. the metric used by the EPA in the national ambient air quality standard. The studies have been conducted in several different cities and seasons, thereby inoorporating a wide range of ciimates, chemical compositions of particulate matter, and populationsFor comparison of results and the calculation of final dose-response functions, alternative measures of particulate matt were converted into PMIO. Ideally, this would be accomplished by comparing co-located monitors at each study site. Unfortnately, for many of the measures, these data are not available and we are forced to use broad estimates of the relationships between alternative measures of particulate matter. The results of our analysis of consistency, however, indicates that the findings are generally robust to these assumptions. To convert from TSP to PMIO, we relied on the EPA estimate<sup>7</sup> that PMIO is between 0.5 and 0.6 of TSP, and use the mean of 0.55. Using the reported averages from 100 cities in 1980, we assumed that sulfates constitute approximately 0.14 of TSP.9 Therefore, the ratio of sulfate to PM10 is 0.25. The "British Smoke" (BS) measurenent is based on the amount of light reflected through a filter paper stained by ambient air flowing through the paper. Since monitors for BS do not admit particles greater than 4.5 microns in diameter, they are indicators of
Second, each individual in the JLMPS restricted sample is matched to the 2010 Jordanian school census. The matching process determines for each individual the number of sexappropriate public basic and secondary schools per 1,000 individuals available in the individual’s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level).<sup>7</sup> A school is considered sex-appropriate for a female if it is a girls’ or a mixed school and for a male if it is a boys’ or mixed school. The empirical analysis is also performed by entering boys’, girls’, and mixed schools separately.<sup>8</sup> Measuring the local supply of public schools at the subdistrict of birth of the individual (i.e., the child) mitigates potential endogeneity originating from parents who had a higher taste for schooling moving to subdistricts where public schooling was more abundant when their child was of school age, although it is not possible to rule out that parents might have moved across subdistricts _prior_ to the birth of their child.

The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father’s schooling, mother’s schooling, and local supply of schools in subdistrict of birth.<sup>9</sup> These exclusions resulted in a sample of 4,139 males and 4,131 females, which are referred to as the male and female full samples, respectively.

> 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts.

> 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA.

> 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8,312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father’s schooling), and 7 observations (missing mother’s schooling).

13