rafmacalaba/datause-encoder-data huggingface dataset:
the Bolsa Escola, where the means test is based on detailed household information, targeting is more efficient than in the Food for Education program, where targeting was not much better than a random distribution of resources to the rich and poor.

4.2 Screening In cases where means tests are logistically or politically infeasible, CCTs can be used as screening mechanisms. For screening to work, the net benefit of the scheme which is the benefit from the cash transfer net of the opportunity cost of satisfying the conditionality, must be positive for the targeted group while negative for others. If this asymmetry is satisfied, then only members of the targeted group self-select into the program.'’ An example of particular interest, which we return to below due to its wide use and applicability, is that of targeting the poor. In such cases, the requirements for self-selection can be expressed in terms of the income effect of the conditioned-on good so that targeting is successful if the conditioned-on good is inferior.‘ Then, the rich (who consume less of the good to start with) will find the costs of meeting the conditionality higher than the poor and will disproportionately opt out of the program.

In Figure 2, the conditionality Xo induces exactly this sort of screening. Type II and Type ITI individuals participate in the program, while Type I individuals voluntarily decide to opt out. If we are to think of this as targeting to the poor, Type II and Type III individuals are less wealthy and Type I individuals are more wealthy. The good is inferior because consumption decreases in wealth. Examples of CCTs as screening mechanisms include the well-documented workfare programs (Van de Walle, 1998, Galasso and Ravallion, 2003), rationing of food or health subsidies by queueing (Alderman, 1987) and the packaging of commodities that are unappealing to the rich (Alderman and Lindert, 1998, Jacoby 1997). Nevertheless, the requirements for CCTs as screening mechanisms may be hard to satisfy.

The Plan Jefes y Jefas program in Argentina was designed to increase employment by providing a subsidy for individuals trying to find employment. Household heads who were initially unemployed could apply for the program under which they would receive cash if they found employment and undertook “20 hours of counterpart activities such as basic community work, training activities, school attendance or employment in a private company with a wage subsidy for six months” (Galasso and Ravallion, 2003). Why was the conditionality of counterpart activities imposed?

13This “single-crossing condition” requirement is detailed in Spence (1973), and Rothschild and Stiglitz 14The consumption of an inferior good decreases with wealth. In contrast, the consumption of a normal good increases with wealth.

14
These undeniable differences between the linearly and exponentially weighted rankings indicate that some caution is warranted when the indices are applied. However, the results do show that total toxic intensity is a goud proxy for all the total toxic measures.

Table 4.2: RankCorrelationAnalysis for Six Indices of Pollution Intensity

# Pearson Rank Correlation Coefficients

|C<br>|e<br><br>|o<br><br>|oo<br>|||
|---|---|---|---|---|---|
|[roe||<br>||oes|<br>ee||nc||oe||os||
|<br>|<br>suns|<br><br>|oss|<br>|<br> <br>oe|oss <br>|<br>|<br>|<br>|<br>oe|<br> <br>|<br>ons|<br>|
|<br>|me<br>|<br>|<br>oe|<br>|<br>oes|ee|<br>|<br><br>vee <br>|<br> <br>|<br>|<br>oes|<br> <br>|
|<br> <br>[_rormse||<br>ocr||<br>oso|oss||<br>oes||<br><br>ozs|<br> <br>||

Key: ‘ ToTTRI - Total pounds of TRI substances released LinHum - Linearly weighted acute human toxicity ExpHum— - Exponentially weighted acute human toxicity LinAq - Linearly weighted acute aquatic toxicity ExpAq - Exponentially weighted acute aquatic toxicity TotMet - Total pounds of TRI metallic compounds released Table 4.2 also shows that the total toxic measures have much lower rank correlations with intensity in releases of bioaccumulative metals. The rank correlations do not rise above 0.51 and fall as low as 0.23. Clearly, the metalsgenerating sectors are not a random draw from all toxic 28
The survey defines shocks as adverse events that lead to a loss of household income and/or loss of productive assets. The “shock” module asks households to go through a list of adverse events and select those that affected them in the last year. We grouped this long list into a number of broad categories: economic, climatic, health and asset. Economic shocks include problems in terms of employment, reduced remittances, loss of business, and difficulty with selling agricultural products and buying inputs. Climatic shocks include crop damage and destruction of homes and assets due to floods as well as loss of land due to river erosion. Health shocks include death and/or ailment of an earning member, and severe disease of a household member. Asset shocks include theft and dowry related expenses.

The data show that more than half of the sample households experienced one or more shocks over a one year recall period. Conditional on experiencing a shock, the average number of shocks across all households was 1.6 (see Table 3). Rural households were more likely to experience shocks (and also faced a higher number of shocks on average) and were more vulnerable: thirty percent of rural households who experienced one or more shocks report that income from their main source fell in 2009 compared to 21 percent of urban households who reported the same.

# Insert Table 3

Households experienced numerous types of shocks but the most common type of shock is idiosyncratic (Tables 4 and 5). Health-related shocks (severe disease or death of a household member) are the most common (24.1% of all respondents) followed by climatic and environmental shocks (15%), economic shocks (14.2%), and finally asset shocks (7.5%).

# Insert Table 4

To the extent that data from September 2009 can point to the impacts of the 2009 global financial crisis, the results suggest that this shock appears to have had limited impact in Bangladesh. The two main channels which were of concern were a drop in remittance income and job loss in export industries (especially garments). The household survey data suggests that these concerns did not materialize. When households were asked whether they had experienced a crisis due to a set of reasons, less than 1% of households said they had experienced a shock 6
# **4. Data and Descriptive Statistics**

The first wave of the Somali High Frequency Survey (HFS)<sup>6</sup> was conducted in 2016. The second wave conducted in 2017 extended coverage to include nomads and households in insecure areas. The sample of IDPs and non-displaced Somalians is representative of the Somali population living in secure areas.<sup>7</sup> We use the second wave of the HFS which samples 4,780 households (26,317 individuals). IDPs account for 33 percent of the survey population, most of whom reside in IDP settlements in the South West and in urban Jubbaland.

**Table 1. Household and household head characteristics, by displacement status and gender**

||Settl|ement I|DP|Non-se|ttlemen|t IDP||Non-IDP|||Total||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Female|Male|All|Female|Male|All|Female|Male|All|Female|Male|All|
|_Female-headed (%)_|||55.4|||41.7|||51.0|||50.5|
|De jure|31.0||31.0|25.6||25.6|26.7||26.7|||27.0|
|De facto|68.9||68.9|74.4||74.4|73.2||73.2|||72.9|
|_Household characteristics_|||||||||||||
|Urban (%)||||38|32|34|79|69|74|74|63|68|
|Rural (%)||||62|68|66|21|31|26|26|37|32|
|Household size|6|5|5|6|6|6|5|5|5|5|5|5|
|Number of children|3|3|3|3|4|3|3|3|3|3|3|3|
|Dependency ratio|2|1|1|1|2|1|1|1|1|1|1|1|
|Able bodied adults (%)|46|48|47|54|43|47|52|49|50|51|48|49|
|_Household head characteris_|_tics_||||||||||||
|Age|36|38|37|41|43|42|37|40|38|37|40|38|
|Education (%)|||||||||||||
|No education|88|64|76|90|69|77|51|38|44|68|50|58|
|Primary or below|9|24|16|8|21|16|24|26|25|17|25|21|
|Secondary and above|3|12|7|1|10|7|25|36|31|15|26|21|
|Number of households|226|184|410|186|263|449|1,998|1,923|3,921|2,410|2,370|4,780|
|Observations (number<br>of individuals)|1,254|970|2,224|1,009|1,426|<br>2,435|10,969|10,689|21,658|13,232|13,085|<br>26,317|

_Source:_ Estimated by authors using Somali HFS 2017-18. _Note:_ IDP stands for internally displaced persons. Notes HH = household head 6 Surveys were conducted by the World Bank in collaboration with the Somali statistical authorities. They can be accessed in the World Banks micro data library see https://microdata.worldbank.org/index.php/catalog/2738 and <u>https://microdata.worldbank.org/index.php/catalog/3181</u> 7 Rates of poverty and other markers of well-being may thus be different from those in different regions inaccessible to the HFS.

10
entertainment, construction, real estate, computer services, and a select number of other services.<sup>8</sup> Obtaining permission is difficult. According to a recent review of Tunisia’s Investment Policies by the OECD, since 2005, the CSI has been processing between two and three applications per year with roughly half of all applications being successful (OECD, 2012). In addition, foreigners are not allowed to own more than one-third of the capital of firms engaged in agricultural activities, while medical laboratories can only be owned by Tunisians. We also consider such activities to be subjected to FDI restrictions. The list of sectors subjected to restrictions on foreign investment overlaps considerably, but not perfectly, with those that are subjected to government authorization.

Authorization requirements and FDI restrictions enable the government to regulate entry and impact market structure and competition. Since both prospective entrants and incumbents need to be authorized in order to operate, entry regulations can be abused both to deter prospective entrants and to stifle competition from existing firms. Anecdotal evidence suggests this may have happened in the case of the closing of the Bouebdelli School, a highly respected private school from which many of Tunisia’s elite have graduated in one of the most heavily regulated sectors. This school was perceived to be in direct competition with the International School of Carthage, which was founded by Ben Ali’s second wife, Leila Ben Ali. In spite of widespread public protests, the Minister of Education ordered the school to close for failure to comply with registration regulations.<sup>9</sup> Should regulatory capture occur, then it is arguably most likely to happen in sectors subject to both types of entry restrictions, as opportunities for capture increase with the regulatory burden. Moreover, these regulatory restrictions can be mutually reinforcing—raising the cost to domestic entrepreneurs protects the firm in the home market, while regulating foreign competition can help steer foreign franchises toward the connected group. Since authorization requirements can in principle be used to limit competition from both domestic and foreign firms, sectors that are subject to both types of regulation could be argued to be overregulated. We focus on such sectors not only because they are the most intensely regulated but also because connected firms turn out to be important players in sectors subject to both types of restrictions as shown below.

> 8 These FDI restrictions apply to greenfield investments only; all mergers and acquisitions in which foreign investors obtain majority ownership of a firm are subject to approval of the _Commission Superieure d’Investissement._

> 9 Wikileaks cables 09TUNIS372_a and 07TUNIS1489-a: see https://wikileaks.org/plusd/cables/09TUNIS372_a.html, https://wikileaks.org/plusd/cables/07TUNIS1489_a.html, accessed February 23, 2013.

9
# 2 **The EU asd the world banana market**

The European Union is the world's largest importer of bananas. It imports around 40 per cent of world trade. Bananas are the most traded of all fruits.

Banana exports are important sources of foreign exchange for many small developing economies, a large number of which are small island economies. Latin America exports most of the world's bananas, accounting for almost 75 per cent of world exports (chart 2.1). ACP countries and overseas EU territories account for 15 per cent of world exports only.

**Chart** 2.1 **Latin America dominates banana exports**

<!-- Start of picture text --> _ _~~~~~~~~~~~~~~~~~~~~~~ ........................... .. .... ........ ........................... _ _<br>AfrVY Philippines<br>lie Caribbean :xx. . ~t9' , Othe A<br>M.~ ~ ~ r~t. Oversaw EU terfltodes<br>R ~~~~~~Latin AIeda<br>.f O_es!* : . .<br>a.dnto. 1TWL Gtrt&alo. hMox co. 1ccsa;ua. WIbhmn and Vuneukab.<br>Daft so:wca FAO 1991).<br><!-- End of picture text --> The pattems of trade between banana exporters and the EU have been long established (see chart 2.2) and have shown only minor changes since the EU was established in _1957._ They have been tightly defined by import policies and preferences granted by separile national govermments, notwithstanding the EU's common commercial policy. It imports from African, Canbbean and Latin-American sources but does not import from big Asian countries such as the Philippines.

# **Efficiency of export suppliers varies widely**

Latin American exporters are efficient, low cost suppliers with the potential to expand output The ACP countnes and the overseas EU territories are less efficient than Latin American suppliers (see chart 2.3).

Climate, topography and soils favor Latin American countries over ACP countries and EU ternitories. The resul, productivity in Latin America is up to double that of other producing countries and non-labor production costs afe less than half of those in other countries. Quality in Latin America is more reliable.

_5_
<!-- Start of picture text --> Posterior Distribution of Human Capital Coefficient<br>Omega = Infinity<br>Omega = 10<br>1.5 2.3 3.1 3.9 4.7 5.5 6.3<br>Coefficient<br>Source: Authors' calculations<br>1000<br>500<br>Frequency<br>0<br><!-- End of picture text --> Figure 2: Posterior distribution for coefficient of human capital, with alternative assumptions about the validity of the exclusion restriction. Lower values of _ω_ indicate greater prior uncertainty that the instrument satisfies the orthogonality condition. Even with high levels of uncertainty, the posterior distribution of the slope coefficient does not include zero.

financial management, alongside the human capital measure. For policy, we utilize the first principal component of an index of primary balances, gross debt, and inflation, similar to the policy measure proposed in Burnside & Dollar (2000). For public financial management, we adopt an index of the quality of public investment management developed by Dabla-Norris, Brumby, Kyobe, Mills & Papageorgiou (2011).<sup>16</sup> By and large, these variables display low correlations with human capital ( _−_ 0 _._ 20 for macro policy, 0.19 for structural policy, and 0.19 for financial management), which alleviates concerns of multicollinearity. We find that, in regressions where we include both human capital and another alternative channel, the coefficient on the human capital variable remains significant, while those for either macroeconomic policy or financial management are insignificant.<sup>17</sup> Thus, we are fairly confident that these other main alternative channels are not responsible for the effect of government effectiveness on income.

# **3.5 Robustness of Benchmark Specification**

In the benchmark models, we did not introduce any additional controls to explain cross-country income per capita. Here, we allow **X** to include variables that the literature has identified as important. More specifically, we draw on a selection of the variables that Levine & Renelt (1992)

> 16We also consider several alternative measures of each. For policy, for example, we used the World Bank’s CPIA rating for macroeconomic management as well as structural policies; for public finance, the World Bank’s CPIA ratings of quality of budget management and efficiency of revenue mobilization.

> 17These results are available on request.

14
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48
**Table 5. Effects of foreign ownership on productivity of SADC domestic firms**

|largest owner|TFP<br>0.016|TFPtran<br>0.070**|TFP<br>0.012|TFPtran<br>0.066**|
|---|---|---|---|---|
||(0.039)|(0.028)|(0.038)|(0.026)|
|ln(firm age)|-0.003|-0.021|-0.004|-0.023|
||(0.016)|(0.014)|(0.016)|(0.015)|
|loss_crime|-0.014***|-0.021***|-0.015***|-0.022***|
||(0.001)|(0.001)|(0.002)|(0.001)|
|ln(GDPpc)|0.091***|0.094***|0.079**|0.081***|
||(0.029)|(0.026)|(0.031)|(0.029)|
|firing_costs|-0.002***|-0.002***|-0.002***|-0.002***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|interest spread|-0.003**|-0.003***|-0.003**|-0.003**|
||(0.001)|(0.001)|(0.001)|(0.001)|
|government effectiveness|-0.108*|-0.147***|-0.092|-0.129*|
||(0.059)|(0.057)|(0.067)|(0.067)|
|Industry-level employment-weighted|0.213**|0.232***|||
|average foreign ownership|(0.096)|(0.082)|||
|Number of observations|2,215|2,210|2,215|2,210|
|Adjusted R2|0.076|0.066|0.074|0.062|

Note. Standard errors in parentheses, clustered at the country level.

***, **, and * represent statistical significance at the 1, 5 and 10 percent levels. The sample consists of domestic firms in SADC countries..

27
the remaining two blocks in the fall of 1985. The fact that measles vaccination was introduced in this phased manner makes it possible to evaluate its impact upon subsequent mortality.

Thf first evaluation of the impact of the Matlab measles immunization program was a retrospective case-control study consisting of 536 deaths to children aged 10-60 months of age between early 1982 and the end of 1984 (Clemens, et al. 1988). These cases were randomly matched by age and sex with 1072 controls (i.e., children who survived)from the two blocks where vaccination was not offered, and compared in terms of their prior measles immunization status. Measles immunization was found to be associated with a child mortality rate which was 36 percent lower during the study period.

The second study based upon the Matlab measles vaccination program consisted of a longitudinal cohort analysis following children through late 1985 (Koenig, et al. 1990). All 9-60 month children in these two blocks who received measles vaccination during 1982-85 were eligible for inclusion in the study. These children were randomly matched with unvaccinated children of similar age from the other half of the intervention area, resulting in a final analysis cohort of 8135 vaccinated and 8135 unvaccinated children. Through a comparison of the relative mortality experiences of these two cohorts of children from the date of vaccination to the end of the observation (death, outmigration, exceeding five years of age, or the end of the study period, October, 1985), it was possible to evaluate the effect of measles vaccination upon short and longer-term survival. As Figure 2 shows, a striking differential in cumulative mortality risks according to vaccination status was evident. At 42 months from' the date of vaccination, vaccinated children experienced mortality risks which were 40 percent lower than their unvaccinated counterparts.

# Replacement Mortality

An issue of direct relevance to the impact of measles vaccination programs, but one which remains under considerable debate, is the replacement mortality hypothesis. According to this hypothesis, children 'saved' from death from a specific cause may continue to be at elevated risk of death from other unaddressed causes, resulting in little improvement in overall survival levels. While potentially relevant to many health interventions, this hypothesis has most commonly been applied to the issue of measles and measles immunization (see The Kasongo Project Team, 1981; Mosley, 1985. If substantiated, then the contribution of measles vaccination may be largely to delay rather than prevent childhood mortality, calling into question 7