rafmacalaba/datause-encoder-data huggingface dataset:
**-17-**

_Gender disparities._ Girls in low-income countries generally have fewer chances _for_ schooling than boys. For example, a girl's school life expectancy in a country at a per capita **GNP** of $200 is, on average, only three-quarters as long as that of a boy's. Girls catch up with boys as incomes grow, and are on par with them by the time incomes reach about $3,000 per capita. Low entry rates to first grade, as well as low survival rates within primary education are the main reasons why girls have shorter school life expectancies. Figure 9 shows that girls' schooling generally improve with country wealth, but the path of improvement is diverse across countries. Among countries at a per capita income of $400, for example, girls in some countries enter first grade at the same rate as boys while in other countries they are only half as likely to do so as boys. Similarly, among countries at this income level, girls' survival rates range from only half as high as boys; to 1.3 times as high.

Figure **9: Relation between gender disparity in education and per capita GNP, circa 1993** <!-- Start of picture text --> (A) Entry rate to Grade I (B) Swuvival rate ir primary cycle<br>100 0 0 ODO 00 mmo M 2 0 0<br>0 0 0 125 0<br>0 '- 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>0 1~~~~~~~~~~~~~~00<br>kso 00 200 1500 400 3000 SOO IX 0 000000 400 0 000 oS 0500 0 30 CID 0 0<br>0 0 0 ~~0 S 0 0<br>o 0 075 0 0 0 0<br>0 0 0~~~~~~~~~~~~~06<br>60 -~ 0 Per capta ffNP (0 0<br>0<br>0<br>25-<br>200 400 800 1500 3000 1 00O 00�O 000 40 800 1500 3000 10000 200600<br>Per capita GNP (log -cle) Per capita GNP (log scale)<br><!-- End of picture text --> _Distribution of public spending on education._ Among many possible indicators we use two relatively simple ones for which data are available for most countries: (a) public spending per primary pupil as a percentage of spending per secondary pupil; and (b) the share of public spending on education benefiting the 10 percent best educated in a cohort. The latter is constructed from data on the structures of enrollments and public spending per student by level of education. It therefore is a succinct measure of inequities in the pattern of spending on lSThe constution of **the** indicator is explained in detail in Mingat and Tan 1986.
- 27 -

# Table 5 (Continued)

# Comparative Analysis of Nominal Transport Costs for MaJor Caribbean Food and Agricultural Raw Material Exports to the United States

||1986 Exp|orts|U.S.|Nom|inal Freig|ht Rate|
|---|---|---|---|---|---|---|
|Country/Product<br>1/|Value(SOOO)|%of<br> <br>Total 2/|Appiled<br>Tariff 3/|Caribbean<br>Exporter|Other<br>Exporters|Difference 4/|
|Suriname|||||||
|Fresh or Frozen ShriUp (1144545|)<br>1,669|4.3|0.0|4.9|4.2|0.7|
|Black Unground Pepper (1617720)|<br>401|1.0|0.0|3.0|3.7|-0.7|
|Fresh Fish, NES (1103570)|310|08|0.6|17.1|20.4|-3.3|
|Subtotals|2,380|6.1|0.1|6.3|6.4|-0.1|
|Trinidad & Tobago|||||||
|Fresh Tuna (1101012)|5,264|0.7|0.0|5.1|9.6|-4.5|
|Sugars and Molasses (1552045)|4,938|0.6|0.0|2.8|6.8|-4.0|
|Cocoa Beans (1561000)|849|0.1|0.0|;3.3|7.6|5.7|
|Bitters with Spirits (1681300)|<br>530|0.1|0.0|17.8|17.2|0.6|
|Crude Coffee (1601020)|433|0.1|0.0|.5.3|<br>3.7|1.6|
|Subtotals|12,014|1.6|0.0|4.8|8.5|-3.7|

1/ United States TSUSA classification numbers are shown in parentheses. The average applied tariff and average nominal freight rates have been computed using weights basedon the values for major Caribbean agricultural exports to the United States.

2/ These figures show the share of the product In total exports of all goods.

3/ Measured by the ratio of all tariff revenues collected on the product to the f.o.b. value of the import. This ratio Is then multiplied by 100.

4/ The nominal freight rate for Caribbean products minus that for other exports. A negative value Indicates a favorable Caribbean transport differential.

**.N** l

Note: The symbol -- designates a value less than 0.1.
# **1. Background**

Diarrheal diseases continue to be a major threat to child health in developing countries around the world. The latest estimates published by the World Health Organization indicate that diarrheal disease is responsible for approximately 800,000 deaths of children under the age of five per year, causing a higher number of under-age-5 deaths than malaria and HIV combined<sup>1</sup> (WHO, 2007).

One of the key factors contributing to the frequency and burden of diarrheal disease is the pronounced lack of water and sanitation in a majority of developing countries (Zwane and Kremer, 2007). According to the United Nations report, more than half of the population in developing countries still lacks access to the most basic form of sanitation (United Nations 2007) .<sup>2</sup> Somewhat more progress has been made in the water sector, but 21% of the population in developing countries still does not have access to adequate drinking water (UNDP, 2007/2008). The situation is most severe for Sub-Saharan African countries, where 63% of the population lacks access to basic sanitation and 45% of the population lacks safe drinking water supply (UNDP, 2007/2008).

From a public health perspective, the lack of access to water and sanitation infrastructure is disconcerting. Several studies have documented the significant positive effect of water and sanitation on reducing child diarrhea (for an overview see Esrey et al., 1991; Fewtrell et al., 2005; and Waddington et al., 2009). Moreover, improved water and sanitation has been shown to lower the health risks related to schistosomiasis, trachoma, intestinal helminthes and other water related diseases. In addition, improved water and sanitation is likely to reduce the burden of disease related to other major health issues by reducing the average stress level for the immune system, and thus strengthening the immune response to new infections. This phenomenon has been labeled the Mills-Reincke Multiplier in honor of Hiram Mills and J.J. Reincke, who first noted the health benefits of water-borne disease improvements on other disease-specific mortality rates (Cutler and Miller, 2005; Ewbank and Preston, 1990). Given the large potential

> 1 According to the WHO, Malaria and HIV AIDS caused 584,000 and 184,000 deaths under age 5 in 2002, respectively; the total under-age-5 death burden associated with diarrheal diseases was 841,000.

> 2 If flush toilets were considered the sanitation standard to be met, the number of people lacking proper sanitation today would even total 4 billion (Black and Fawcett, 2007).

2/36
21 Figure 5 OFFER CURVE ELASTICllY a) MEDIUM ECONOMY (EO=MO=25) Offer Cures With High and Low Omega <!-- Start of picture text --> 40 - ~ v gOmega(High)=2 0<br>- - a Omego(Low)= 02<br>30x<br>~20<br>10 -~__ _<br>10<br>-10<br>-20<br>-30<br>-40 A -L . , L , .-<br>0.2 0.4 O6 08 1 12 14 16 18 2<br>sigma Volues<br>b) CLOSED AND OPEN ECONOMY OFFER CURVES<br>With Omegn=1<br>20<br>16 i d(E 0 =M0 =10)<br>* 8 t-- Open (EJ=M0=40)<br>12<br>-8<br>-12<br>-14 ' ! * * .<br>0.2 04 06 08 1 12 14 16 18 2<br>Sgmao vatues<br><!-- End of picture text --> WoEd Bank - 43114:5
24 themselves.113 As argued in Section 2, the existence<sup>of monopsony power can</sup> lead to rent sharing.

This is separate from the issue of whether<sup>or not product markets are</sup> perfect on the sellers' side. Imperfect<sup>product markets per se do not imply</sup> rent sharing as we define it, although<sup>they do affect who gains and who loses</sup> from a quota. We focus only on rent<sup>sharing, which results from buyer power</sup> and not on rent appropriation, which results from seller power.

In regression (1), we regress the<sup>adjusted Hong Kong price on the US</sup> price, a constant, the quota utilization<sup>ratio, the quota level and the</sup> numbers equivalent of the Herfindahl<sup>Index, which measures concentration in</sup> the license holdings and proxies for market<sup>power in the license market. The</sup> numbers equivalent of the Herfindahl Index<sup>is defined as l/Zsi2, where s</sup> equals the share of license holder<sup>i in total licenses. Regression (1) is</sup> therefore of the form: (1) pit HK a + Pitus + rHit + 6Uit + oVit + fit The right-hand-side variables can be considered as exogenous<sup>variables.</sup> If the US is a large country, Pit s is properly taken<sup>as given. As quota</sup> license allocations are historically<sup>determined, Hit can be also taken as</sup> given though it does vary over time with the<sup>composition of exports. The quota</sup> level, Vit, is exogenously determined.<sup>The utilization rate, Uit, should</sup> be unity if the quota is binding, and<sup>any departure from unity is assumed to</sup> reflect exogenous difficulties in attaining<sup>full utilization due to frictions</sup> in the implementation system.

The regression is run on pooled data across the ten<sup>apparel groups for</sup> the years 1981 through 1988. If there is<sup>no rent sharing and the goods are</sup> homogeneous, we should expect to observe PitHK<sup>-</sup> PitUS. In other words, in
smaller UHS. However, the latter survey includes too few DB households for our purposes. Chen et al. (2006) describe the survey data in greater detail.

The population with which we are concerned is all actual or “potential” participants in a targeted antipoverty program. We can readily identify actual participants (denoted _D_ =1) but potential participants are more difficult. We used a model of participation conditional on covariates _X_ to identify non-participants with a probability of participation greater than some min critical value, _P_ ( _Di_ = 1 _X i_ ) > _P_ . The sample we use is thus all those households who are actual participants in DB in the base year plus a sample of the same size comprising those with the highest predicted probability of participation based on their covariates in that survey round.

We drew samples of all 1,040 _Dibao_ participant households and 1,029 “high propensity” non-participants from the 2007 UHSS for seven cities. The high propensity households were those with the highest propensity scores (predicted probabilities) for DB participation in the UHSS, based on probit using a large number of explanatory variables; this was essentially the same probit reported in Chen et al. (2006). These 2069 households were resurveyed in 2009 and 2010. The surveys were done by the Urban Household Survey Division of the NBS. In addition we had numerous informal, open-ended, interviews in 2007, 2009 and 2010 with DB officials (central and local) and DB households in Beijing, Chongqing, Tianshui and Wuhan.

The UHSS measured household income from responses to a series of questions on income by broad categories (formal salary income, business income, casual work, selfemployment, private transfers and DB). Measurement errors in the reported incomes must be anticipated although more so for some components than others. It should also be noted that survey-based incomes may differ from income at the time of assignment for DB eligibility. Checks on the latter are done by local authorities/neighborhood committees and there is also a community appeals process. And it should be noted that there is more than one way to assess “income.” For example, there are differences in the time period deemed relevant (current income vs. longer-term income). Possibly DB officials use a different time period to the survey.

Table 2 summarizes the overall participation rates and the entry and exit rates for the combined sample of actual (2007) participants and the high-propensity participants. There is significant “stickiness” as indicated by the dominant diagonals in these joint distributions. Over the period 2007-10, we find that a greater number of households left the program than joined. This was confirmed by administrative data from MOCA.

13
Table 4. Multinomial Logit Model: Labor Market Transitions: 2001-2002 (Standard errors in parentheses)

||Employme|nt Transitions|Unemploym|ent Transitions|Inactivity|Transitions|
|---|---|---|---|---|---|---|
||to Inactivity|to Unemployment|to Inactivity|to Employment|to Unemployment|to Employment|
|**_Men_**|-0.815***|0.278|-0.926**|0.823**|0.779***|0.892***|
||(0.165)|(0.229)|(0.432)|(0.350)|(0.157)|(0.174)|
|**_Age (years)_**|||||||
|25-44|-0.951***|-0.472|-0.200|0.328|0.542**|1.100***|
||(0.315)|(0.329)|(0.325)|(0.409)|(0.263)|(0.288)|
|44-54|-0.630*|-1.333***|0.246|0.784|-0.118|0.953***|
||(0.335)|(0.432)|(0.535)|(0.533)|(0.288)|(0.352)|
|55+|0.290|-1.442**|2.410**|1.783|-2.616***|-0.269|
||(0.340)|(0.610)|(1.201)|(1.310)|(0.421)|(0.399)|
|**_Single_**|0.146|0.708***|0.121|-0.821**|0.269|0.374|
||(0.262)|(0.244)|(0.341)|(0.346)|(0.247)|(0.308)|
|**_Education_**|||||||
|Vocational|-0.657***|-0.333|-0.070|-0.080|1.205***|0.771***|
||(0.213)|(0.257)|(0.347)|(0.352)|(0.203)|(0.172)|
|High School|-0.547*|-0.703**|0.057|0.172|0.646***|0.336|
||(0.324)|(0.282)|(0.453)|(0.425)|(0.198)|(0.226)|
|College|-1.269***|-1.070**|-0.574|0.274|1.713**|0.133|
||(0.411)|(0.539)|(1.120)|(0.895)|(0.705)|(0.614)|
|University|-3.675***|-1.108**|-33.627***|0.818|0.257|1.180**|
||(1.038)|(0.453)|(1.010)|(1.133)|(1.042)|(0.550)|
|**Constant**|-0.224|-1.873***|0.108|-0.191|-2.573***|-2.037***|
||(0.422)|(0.505)|(0.629)|(0.669)|(0.392)|(0.380)|
|Number of Obs||2126||422||2439|

_Note_ : The following are base groups: Female, 15-24 years of age, elementary or unfinished elementary school education, Banja Luka municipality. Standard errors are corrected for stratification and intra-cluster correlation.

12
|**var0308_skills**|0.0015***|0.0010***|0.0013***|0.0004*|0.0013***|0.0025***|0.0011|0.0011|0.0010|0.0009|0.0011|0.0008|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|**var0308_all_DB**|0.0052***||||||0.003**||||||
||(0.000)||||||(0.000)||||||
|**var0308_Busin. Oper**||0.0041***||||||0.002**|||||
|||(0.000)||||||(0.000)|||||
|**var0308_Permit**|||-0.0163***||||||-0.0024||||
||||(0.002)||||||(0.009)||||
|**var0308_Tax**||||0.0192***||||||0.0129**|||
|||||(0.001)||||||(0.000)|||
|**var0308_Trade**|||||0.0030***||||||0.002**||
||||||(0.000)||||||(0.000)||
|**var0308_Empl**||||||0.0031***||||||0.0029**|
|||||||(0.000)||||||(0.000)|
|**_cons**|1.0152***|1.0143***|1.0137***|1.0149***|1.0134***|0.9790***|0.9571***|0.9570***|0.9566***|0.9569***|0.9567***|0.9576***|
||(0.049)|(0.049)|(0.049)|(0.049)|(0.049)|(0.049)|(0.034)|(0.034)|(0.034)|(0.034)|(0.034)|(0.034)|
|**NACE dummies**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Country dummies**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**R-squared**|0.1318|0.1318|0.1327|0.1321|0.1324|0.136|0.1115|0.1115|0.1115|0.1115|0.1115|0.1115|
|**N. obs**|16,800|16,800|16,800|16,800|16,800|16,800|28,360|28,360|28,360|28,360|28,360|28,360|

> ~~1~~ (10-49) is the omitted size category.

2 (1-5) is the omitted age category.

3 Purely domestic is the omitted ownership category.

4 var0308_instock_gdp(var0308_outstock_gdp) is related to the stock of inward (outward) FDI in the manufacturing industry. var0308_instock_gdp(var0308_outstock_gdp) is related to the stock of inward (outward) FDI in the services industry.

_Note 1_ : The variables for infrastructure and stock of outward FDI were excluded due to multicollinearity.

_Note 2_ : All PCA indices of business regulation (all_DB, DB_business_startup, DB_business_operations, and DB_institutional_environment) were included in the analysis.

However, only all_DB and DB_business_operations were statistically significant. The sub-indicators for DB_business_operations (permit, tax, trade, and employment) are also included in the results above.

Significance: *** 1%, ** 5%, * 10%.

16
households in our survey, and of the DoL office location, and based on these, the (log) of the distance between each household and the DoL office was calculated.<sup>19</sup> Comparing migrants to non-applicants, we find log distance to be a very strong predictor of migration, with a first stage F-statistic of 21.9. Row 5 in Table 4 and column 7 of Appendix 1 show the resulting estimate of the income gains from migration using log distance as an instrument. The estimated gain is $305, which is 11% higher than the experimental estimate. Since migration status is a binary variable, an alternative approach to the two-stage least squares (2SLS) model is to use full information maximumlikelihood (FIML) to estimate a treatment effects model with an endogenous binary treatment.<sup>20</sup> Column 8 in Appendix 1 shows the models coefficients are similar to those from 2SLS. Row 6 of Table 4 shows a slight reduction in the estimated gain from migration, down to $298, which is only 9% higher than the experimental estimate.

# **5. Exploring Selection Directly**

The non-experimental methods all overestimate the income gains from migration compared to the estimate obtained from the lottery. This suggests that lottery applicants are positively selected in terms of unobserved individual characteristics which determine

> 19 A possible threat to the exclusion restriction needed for distance to be a valid instrument is that individuals who lived further away from the DoL office would have lived in more isolated, less urban areas. If this makes them less able to adapt to city life in New Zealand, then we would expect an upward bias in the IV estimator, since it would be in part capturing the returns to more urban experience. To investigate this possibility, for the group of migrants in New Zealand we regressed income on our set of controls, including past income, and log distance. The coefficient on log distance is positive (9.0) and insignificant (p-value of 0.76). Thus there is no strong effect of living in a location closer to the DoL office in Tonga on labor income, and if anything, migrants that lived further away earned slightly more in New Zealand. Based on this it is likely that one would conclude that this was a reasonable instrument, even without reference to the experimental comparison.

> 20 This was carried out using the treatreg command in STATA.

- 32 -
not as intuitive as a monetary value. Table 1 presents the means of the variables used in our model for each country.

The final set of variables from the PISA dataset used in this analysis are re-sampling replicate weights used in the calculation of standard errors. Intra-cluster correlation violates an assumption needed for the unbiasness of the analytical method of calculating standard errors based on the variation of the sample. Re-sampling methods such as bootstrapping, Jackknifed Repeated Replication, and Balanced Repeated Replication serve as alternative means of calculating standard errors. These methods calculate sampling variance by re-sampling the same sample to mimic re-sampling of the original population. Replicate weights are alternative sample weights which represent a subsample based on the original sampling design. PISA provides replicate weights compatible with Fay’s adjusted Balanced Repeated Replication. These weights were constructed to reflect the sampling design including any country specific modifications, as well as non-response by students or schools (OECD 2002: 89 – 98).

# 3. Model

This paper quantifies wealth and gender inequity in cognitive skills as the dependence of cognitive skills on wealth and gender. While the concept of dependence on gender is relatively clear, the concept of dependence on wealth needs to be relevant to the overarching problem of intergenerational transmission poverty and upward immobility. Consequently, the dependence of a child’s cognitive development on wealth should not 12
Table 14: Monitoring and Sanctioning Processes

|Civil SocietyLea<br>|Mean Control<br>der’s Status - Incr<br>|Effect of VH<br>Training<br>eased Direct Pre<br>|Effect of VH<br>+ CL Training<br>ssure<br>|CL Effect<br>||
|---|---|---|---|---|---|
|VH usually meets CL at CL’s home|0.076|-0.075**|-0.060|0.015|191|
|(0/1) (CL survey)|(0.267)|(0.038)|(0.038)|(0.043)||
|CL’s perceptions of whether respected|0.685|-0.048|0.001|0.046|193|
|by all in village (0/1) (CL survey)|(0.467)|(0.083)|(0.085)|(0.097)||
|Fire Alarm<br>|- Increased Press<br>|ure from Above<br>||||
|VH’s perceived independence from govt|2.448|0.037|0.081|0.044|244|
|(1-4) (VH survey)|(0.996)|(0.149)|(0.159)|(0.182)||
|No. times per year exchange information|0.287|0.613**|-0.243|-0.857**|191|
|btwn CL and district administrator (CL survey)|(0.911)|(0.283)|(0.301)|(0.341)||
|CommunityMobili|zation - Increase|d Pressure from|Below|||
|No. community meetings organized|5.323|0.337|-1.511|-1.848|193|
|by CL (CL survey)|(7.643)|(1.231)|(1.262)|(1.430)||
|HH’s knowledge test score (0-1)|0.597|-0.005|0.001|0.006|2151|
|(HH survey)|(0.228)|(0.014)|(0.013)|(0.016)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

35
9 where TO is an index representing the degree of trade openness of an economy, α is a constant, and Xi is composed of log A, log (ni+g+ δ ), log sK,i and log sH,i. OLS, IV, or GMM are commonly applied over a single cross-section of data including averages of each variable for the entire period, or over a panel of averages of 5-10 year subperiods. Several variables that have an impact on annual economic growth but not on the steady-state rate (e.g. fiscal balance, real exchange rate distortions, natural disasters) are left out of the analysis, implicitly and arguably assuming that their fluctuations even out over periods longer than 5 years. As mentioned in the previous section, the main obstacle to estimate equation (4) is defining an accurate measure of TO.

An alternative route taken to simplify the above specification and bypass the difficulty of measuring TO is to use fixed effects regressions on annual growth data (e.g. SW and WW). Since fixed effects or within group estimation transforms the variables in the above equation into deviations from the mean, those growth factors that are time invariant are dropped out from the equation. Geography is clearly a time invariant factor, while the overall institutional quality changes only gradually. Factors that generate fluctuations of annual growth around its steady-state are again assumed to even out over the periods under observation. Given these considerations the specification used for the fixed effects exercise is simply: in which the subindex t represents time, and POSTLIB is a dummy variable equal to zero in the years prior to trade liberalization, and one afterwards. Thus, π measures the increase in GDP per capita growth attributed to trade reforms.

The present paper takes into account two important violations in SW and WW to the assumptions needed for the validity of their fixed effects estimations of π : incorrect identification of liberalization episodes and changes in omitted variables. In general, we address these issues through a new identification of trade liberalization episodes based on reviews conducted under the World Bank’s Trade Assistance Evaluation (IEG, 2006); a sample selection that eliminates cases of extreme volatility in the