rafmacalaba/datause-encoder-data huggingface dataset:
34

**Figure 15  Cumulative Returns (i.e. Cumulative Log Returns)**

1000 100 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 MSCI CH Bond Index Performence (Pictet) MIR BVG fic. PF R emp PF R Data-Source: SNB, MSCI and Pictet

## **Table** **<u>9  Ratio of Investment Portfolio Returns to MIR</u>**

||Ratio over MIR|Annual Excess Return|
|---|---|---|
|MIR BVG|100%||
|Bond Index Performance (Pictet)|121%|1.12%|
|Empirical Portfolio|142%|2.07%|
|<br>Simulated Portfolio|184%|3.66%|
|MSCI CH|279%|6.23%|
|Source: A|uthors’ Calculations||

# **_3.2.2 How Is the Minimum Interest Rate Determined?_**

As of today, the Swiss Federal Council sets the Minimum Interest Rate in a discretionary way. This procedure has disadvantages as to the adequacy of this requirement. Firstly, the discretion may lead (and has led) to considerable pressure on the Swiss Federal Council from insurance companies, pension funds and contributing workers. The last 20
We aggregate the three outcomes in Table 5 into a perceived quality of care index and then instrument this index with our pooled treatment indicator to estimate its effect on reported Ebola cases. In Table 6, we report a large first-stage effect; the F-statistic (9.86) approaches the rule-of-thumb for a strong instrument. When we scale our earlier reduced-form result by this first stage, we find that a one standard deviation change in the perceived quality of care corresponds to an increase in weekly case reports of 0.43.<sup>41</sup>

**Table 6:** Perceived Quality of Care and Ebola Cases

|First-Stage|Perceived Quality of Care|
|---|---|
|Pooled (CM or NFA)|0.401<br>(0.128)<sup>_∗∗∗_</sup>|
|Two-Stage Least Squares|Total Cases|
|Perceived Quality of Care|0.430<br>(0.249)<sup>_∗_</sup>|
|F-statistic|9.86|
|Observations|5,440<br>5,440|

_Notes_ : “Perceived Quality of Care” is an equally weighted average of the variables in Table 5. Matching-triplet fixed effects included in both first- and second-stage regressions. Standard errors are clustered by section. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

As with the full sample, we do not find consistent positive effects for families focused on supply-side variables in the Ebola sample. Pooling the treatments, we see no significant effects on health service delivery or clinic quality (see Table E.14).<sup>42</sup>

# **4.3 Alternative Explanations**

## **4.3.1 Transmission**

We attribute the increase in total and confirmed cases in treated sections to reporting, not a difference in Ebola incidence. As the true incidence of Ebola in Sierra Leone is unknown — the WHO and CDC assumed they were missing half or more of all cases (Enserink 2014) — we use a number of indirect approaches to support this argument.

The interventions concluded in December 2013, five months before the first Ebola case in Sierra Leone.

> 41 Our research design does not separately manipulate treatment status and the level of the mediator. Table 6 quantifies the effect of quality of care on reported Ebola cases only under a strong assumption about the exclusion restriction.

> 42 Separating the two treatments, clinic quality increases in NFA. Unpacking the clinic quality index, the improvement does not reflect improved conditions at the clinic (e.g., cleanliness), more staff on duty, or additional hours open; rather, the increase is driven by an increase in the proportion of required services provided and a reduction in charges for out-of-stock medicine.

33
ment to gains in wages or consumption are two recent papers by Bleakley (2010) and Cutler _et al_ (2010), who estimate effects of early-life exposure to malaria on adult wages in the Americas and on consumption in India, respectively.<sup>1</sup> Our paper links the early-life disease environment directly to adult wages, and applies our estimates to compute consequences for government revenue.

# **1.2 Consequences of Open Defecation in India**

Among potential health insults, exposure to poor sanitation is particularly likely to have quantitatively important effects on adult economic outcomes because its effects on early-life health are so large. Water and sanitation have long been known to be important determinants of health outcomes (for recent examples in economics, see Cutler and Miller, 2005; Galiani, Gertler, and Schargrodsky, 2005; and Watson, 2006). Nonetheless, the associations between human capital and sanitation and disease have received relatively less research attention than human capital gradients in income and education. However, a recently active literature has shown that sanitation can be particularly important in human capital accumulation in – developing countries, especially in India, where open defecation without using a toilet or – latrine is particularly widespread (Spears, 2013).

Open defecation matters for health because it releases fecal germs into the environment which cause disease in growing children. According to Unicef and WHO (2012) statistics, over a billion people worldwide defecate in the open; most of these live in India, and most people who live in India defecate in the open.<sup>2</sup> As a large medical and epidemiological literature documents, ingestion of fecal pathogens as a result of living near poor sanitation is well-known to cause diarrhea (Esrey, Habicht, and Casella, 1992). Checkley _et al_ (2008) use detailed, high-frequency longitudinal data from five countries to demonstrate effects of childhood diarrhea on subsequent height. In addition to the obvious threat of diarrheal disease, open defecation can cause net nutritional insults through worm or other parasitic

> 1Barreca (2010) evaluates the long-term economic impact of exposure to malaria in the 20th-century United States, and finds that increased in-utero and postnatal exposure is associated with a substantial but not statistically significant reduction in income.

> 2According to the 2011 Census of India, 53.1% of households in that year did not use a toilet or latrine. Recent findings in Coffey _et al_ (2014) of open defecation by a substantial fraction of people in households that own latrines suggests this may be an underestimate of the the overall prevalence of open defecation.

3
initiate technology transfers and to provide greater market access for source countries’ exports. These different external conditions can also be influenced at the margin by policies: reduction in remittances fees, reunion policies, circular migration agreements or the development of institutional business links between the Diaspora and the source countries. Others reflect a different position of the source country on the path of development and demographic transition. Finally, some reflect different conditions in source countries, education and financial systems in particular (on which policies can have influence), as well as investment behaviors.

# _Higher Remittances per Diaspora Member_

In baseline simulations, initial remittances per Diaspora member equal GDP per capita in the source country (and two-fifth of the GDP per worker in the source country). This amount is also equal to the cost necessary to finance one year of tertiary education per student. As GDP per capita in developed countries is 18 times higher at market exchange rates than that of developing countries (low and middle income countries)<sup>22</sup> , this also basically means that each Diaspora member remits 1:18 of its income to his country of origin. In the next simulations (S1r, S2r, S3r), we multiply this amount by 2 (thus, remittances would represent 15 percent of GDP in the initial years of simulation). Indeed, the impact of increased migration on human capital accumulation could possibly be much higher if remittances per migrant were higher. From a policy perspective, such a move could be encouraged by the reduction of remittances fees.<sup>23</sup> Comparing the new set of simulations S1r, S2r and S3r (where migration possibilities are progressively raised like in the previous section, accounting for higher remittances per migrant) with baseline simulations (S1, S2, and S3) suggest that higher remittances can significantly raise simultaneously incomes, per capita education expenditures and skilled migration rates. But, within this setting, raising possibilities to migrate (comparing S1r with S2r and with S3r) does not lead to a net domestic accumulation of human capital. On the contrary, the proportion of domestic skilled workers in the population declines. Indeed, higher remittances raise households’ capacity

> 22 See World Bank (2008). The ratios of GDP per capita between high income countries and middle income countries is 1:12, and 1:56 between high income countries and low income countries.

> 23 See World Bank (2006) for a discussion of remittance fees and ways to reduce them.

13
Plus dummy can help show whether or not there is a striking degree of variation among the DAC bilateral in use of country systems.

“Vertical funds” (sometimes called “global funds”) comprise a last set of donors. These donors have limited sector-specific mandates, such as the environment, primary education, or particular diseases. In the SMPD, most aid from vertical funds is accounted for by the Global Fund to Fight AIDS, Tuberculosis and Malaria. It disbursed 1.9% of the aid represented in the survey (about the same amount as Spain, Denmark or Sweden), in 47 of the 54 SMPD recipient countries. Only five donors disbursed funds in more countries in the survey. Vertical-fund programs, particularly in health, are generally viewed as having weak country “ownership,” driven predominantly by concerns over global public goods (World Bank, 2006). They are often criticized for using parallel implementation units outside normal government structures, with overlapping or redundant “reporting systems, procurement policies and procedures not aligned to national guidelines..” (World Bank, 2006: 22). We therefore expect vertical funds to be associated with lower use of country systems in general. Vertical funds are not generally mandated with providing budget support, but they are often designed to be compatible with sector strategies and programs (in health, education or other relevant sectors).

For the DAC bilateral donors, we can go beyond these donor group dummies and attempt to measure domestic constituents’ trust in aid effectiveness in general ( α in the model) using data from public opinion surveys. We expect stronger public support for development aid to increase a bilateral donor agency’s use of country systems. Where support for aid is relatively weak, aid agency officials will be under more pressure to show that the funds they are provided produce visible results directly attributable to its efforts. These “results” may include not only development objectives but also commercial or national security objectives for the donor country. Project aid, often administered using parallel procurement and other systems, is more conducive than budget support for linking aid funds to visible, attributable results, including employment of donor-country aid contractors.

Data for testing this hypothesis are available from three different public opinion surveys: Gallup International’s 2002 “Voice of the People” survey (equations 1 and 2), the 1995-1998 round of World Values Surveys (equations 3 and 4), and the 2004 Eurobarometer. These surveys each cover a somewhat different sample of donor countries, as shown in Table 2. Thirty donors are represented in one or more of the surveys, but only four (Finland, Germany, Spain and Sweden) are included in all three. The question inquiring about support for development aid is worded somewhat differently in each survey. The percentage of respondents indicating greater support for aid in the WVS is correlated at .85 with the corresponding percentage from Gallup 18
_3_ _Ways Out of Povertv - Diffusing Best Practices_<sup>_and Creating Capabilities_</sup>

# **II. HOPE AND ANGER**

_"First, I would like to have work of any kind." - An 18-year old man, Ecuador_

**The challenge.** The quotation is from<sup>"Voices of the Poor,"a World Bank survey</sup> capturing the perspectives of poor people<sup>around the world.'Poor people know what they</sup> need to escape poverty. Finding a decent<sup>job or establishing a small business is the key.2</sup>

But not any job or any business will lead<sup>out of poverty.If it weresimply a matter of</sup> creating jobs, having the state employ everyone<sup>would do the trick. This has been tried</sup> before in most parts of the world, notably<sup>in communist regimes.The key to poverty</sup> reduction is to create productive jobs and<sup>thuswealth, not just to compileodd jobs</sup> allowing people to scrape by. The basic<sup>sentiment comes froma man from Pakistan:"the</sup> rich have one permanent job; the poor are<sup>richin many jobs."3Neededespeciallyare jobs</sup> and productivity improvements where poor<sup>peoplelive, mostly in rural areas and the</sup> peripheries of cities.

**Hope.** The 20th century gave more hope<sup>that poverty could be eliminated than any other</sup> episode in human history. Average income<sup>per person,adjusted for inflation, increasedat</sup> least five times over the century, despite<sup>unprecedentedpopulationgrowthfrom about1.8</sup> billion to more than 6 billion.<sup>4</sup> At century's<sup>end, morepeople livedabove the basic</sup> poverty line of $1 dollar a day than even<sup>a decade before.5And when taking a definition</sup> of poverty broader than just income, human<sup>developmentin very poor countries,even in</sup> Mozambique, has advanced beyond that<sup>in Italy in thelate</sup> 19<sup>thcentury.6For thefirst time</sup> in history it appears that increases in income<sup>may notlead to a Malthusianincrease in</sup> population. Worldwide fertility rates peaked<sup>in the1960s, andpopulation growth has</sup> since started to slow.<sup>7</sup>

**Anger.** And yet, the absolute number of poor<sup>peoplehas not fallen,even though their</sup> share in total population continues to<sup>decline.Advancesare heavily concentrated in Asia,</sup> particularly China and India. In Africa, however,<sup>poverty rates increasedover the last</sup> three decades. In Eastern Europe and<sup>theformer SovietUnion they increasedduring the</sup> decade prior to the fall of communism<sup>andfaster still thereafter.Nor have two decades of</sup> reform in Latin America prevented poverty<sup>rates fromrising(figure1).Whetherall</sup> things considered global inequality has risen<sup>or declined duringthe last threedecadesof</sup>

'Narayan et al., 2000.

**2** Detailed studies making use of household<sup>survey dataconfirm this.The smglemost importantfactorsfor</sup> escaping from poverty are changing labor<sup>earningsand changes in ermploymentstatus.See, forexample,</sup> Fields et al. (2000).

> 3 Narayan et al., 2000.

4 de long, 2000. S Pfeffermann, 2000. **6** Crafts, 2000. 7 Umted Nations, 2001.
**Table 7**

||**Agr**|**Ser**|**Ind**|**Const.**|**LR-test**|**p-value**|
|---|---|---|---|---|---|---|
|**_HI_**|**0**|**1**||*(2)|X<br>= 10.17|0.01|
|_H 2_|1|0|||X (2)= 12.27|0.00|
|_H3_|1||0||X (2)=6.21|0.04|
|_H4_|II||.|0|2(1)=<br>0.90|0.34|

_Note:_ Table 7 summarises the findings of different hypotheses tests on the coefficients of a and _6B._ A 0 indicates that the coefficient of a variable, i, has been restricted to zero and is equivalent to a test of longrun exclusion, a I indicates the variable used for normalisation and a * indicates that a variable has been left unrestricted. All tests are joint tests for long-run exclusion and weak exogeneity, i.e., a, = **_A,_** = 0 . All tests are likelihood ratio (LR) tests which are distributed as _zx,_ conditional upon the rank and the number of restrictions imposed.

_Source:_ Authors' calculations.

Since H4 cannot be rejected, which says that the constant can be excluded from the cointegration space and, when normalising on service sector GDP, _Ser,_ the long-run reduces to:

# **4.4 A Dynamic Short-run Growth model**

To combine short-run and long-run information for the three sectors in a growth model we estimate a parsimonious dynamic model, which contains the cointegration relationship and up to 1 lag of _Ind, Agr_ and _Ser_ in first differences.<sup>8</sup> Following Hendry's general-to-specific system reduction approach all insignificant variables are removed from the system based on F-tests and the resulting system is then estimated by full information maximum likelihood (FIML) to fiurther improve its robustness. The resulting model is presented in Table 8.

The final model passes the Hendry-Mizon LR test of over-identifying restrictions, X2 (3) = 3.4835 [p=0.3229], and therefore represents a valid reduction of the initial system. All variables that appeared to be strongly significant in the long-run, also retain -

> 8 A specification of I lag is adequate because the two lags in levels used for the cointegration analysis correspond to one lag in first-order differences.

14
**<u>Table 7: FIML estimate of switching regression model: Effects of credit constraint on crop productivity (Wollo Sample)</u>**

|||**Model 1**|||**Model 2**||
|---|---|---|---|---|---|---|
||**Constrained**|**Unconstrained**|**Selection**|**Constrained**|**Unconstrained**|**Selection**|
|Liquidity x 10<sup>3</sup>|-0.089|-0.101*||-0.085|-0.099*||
||(-1.398)|(-1.849)||(-1.287)|(-1.830)||
|Owned land in hectares|-0.137***|-0.256***|0.062|-0.171***|-0.337***|0.061|
|(log in model 2)|(-2.944)|(-3.137)|(0.859)|(-3.151)|(-3.199)|(0.755)|
|<br>Number of male adults|0.003|0.038|0.056|0.005|0.040|0.057|
||(0.105)|(0.690)|(1.182)|(0.158)|(0.717)|(1.200)|
|Number of female adults|-0.038|0.074|-0.002|-0.036|0.072|-0.002|
||(-0.997)|(1.326)|(-0.033)|(-0.959)|(1.322)|(-0.041)|
|Number of oxen owned|0.034|-0.005|0.050|0.039|0.010|0.043|
||(0.870)|(-0.067)|(0.853)|(0.976)|(0.128)|(0.737)|
|Value of non-farm income x 10<sup>3</sup>|-0.016|-0.043|0.044*|-0.017|-0.044|0.047**|
||(-0.657)|(-1.587)|(1.919)|(-0.692)|(-1.588)|(2.017)|
|Age of household head|<br>0.016|<br>-0.022|<br>0.004|<br>0.016|<br>-0.016|<br>0.003|
||(0.966)|(-1.044)|(0.187)|(0.971)|(-0.740)|(0.157)|
|Age of hh head squared x 10<sup>2</sup>|-0.016|0.024|-0.012|-0.016|0.019|-0.011|
||(-1.032)|(1.152)|(-0.585)|(-1.034)|(0.914)|(-0.571)|
|Formal education by head (years)|-0.005|0.006|0.031*|-0.005|0.009|0.028|
||(-0.352)|(0.305)|(1.724)|(-0.359)|(0.529)|(1.564)|
|No of hh members < age 15|<br>0.002|<br>-0.002|<br>0.024|<br>0.006|<br>0.010|<br>0.019|
||(0.103)|(-0.046)|(0.709)|(0.297)|(0.274)|(0.585)|
|Share of good soil quality land|-0.023|0.016||-0.021|0.137||
||(-0.113)|(0.061)||(-0.105)|(0.528)||
|Share of medium quality land|-0.159|0.014||-0.171|0.079||
||(-0.730)|(0.053)||(-0.786)|(0.308)||
|Proportion of flat land|<br>0.475**|<br>0.480||<br>0.464**|<br>0.428||
||(2.231)|(1.578)||(2.183)|(1.394)||
|Proportion of gently sloped land|0.335|0.429||0.330|0.405||
||(1.504)|(1.427)||(1.482)|(1.334)||
|Proportion of irrigated land|0.265**|-0.099||0.260*|-0.132||
||(1.974)|(-0.509)||(1.950)|(-0.681)||
|Number of years possessed|-0.004|-0.006||-0.003|-0.007||
||(-0.777)|(-1.042)||(-0.734)|(-1.356)||
|Value of household assets x 10<sup>3</sup>|||0.004*|||0.004*|
||||(1.700)|||(1.700)|
|Value of other livestock x 10<sup>3</sup>|||0.031**|||0.032**|
||||(2.182)|||(2.273)|
|Adult children in village|||0.020|||0.019|
||||(1.185)|||(1.172)|
|Member has a political position|||0.120|||0.110|
||||(1.349)|||(1.230)|
|Member of service cooperative|||0.234**|||0.241***|
||||(2.492)|||(2.607)|
|Member of microfinance inst.|||0.401***|||0.390***|
||||(2.904)|||(2.710)|
|Sigma|0.782|0.968||0.779|0.964||
|<br>Rho|-0.586|-0.803||-0.574|-0.821||
|Log-Likelihood||-1,429.55|||-1,415.94||
|<br>chi2||31.553|||33.267||
|LR test of independent equations||19.309|||17.177||
|Number of observations||846|||842||

**Note:** Robust t statistics in parentheses; *, **, and *** denote significance at 10%; 5%; 1%. Constant included but not reported.

22
Figure 11 Years of active exports with OECD as a function of waiting time <!-- Start of picture text --> 20<br>18<br>SA<br>16<br>14 Eur.&<br>12 CA<br>10 MENA<br>8<br>EAP<br>6<br>4<br>SSA<br>2<br>0 LAC<br>0 5 10 15 20 25 30 35 40<br>waiting time<br>years<br><!-- End of picture text --> Notes: SA: South Asia; Eur. & CA: Europe and Central Asia; MENA: Middle East and North Africa; EAP: East Asia and Pacific; SSA: Sub-Saharan Africa; LAC: Latin America and Carribeans.

# **6.   Concluding remarks**

Looking at the evolution of OECD imports, at a high degree of disaggregation (over a thousand product lines) on a forty-year period where data are available, we found striking evidence of geographical diversification at the product level. That is, OECD countries have been sourcing each good from increasingly large pools of suppliers. We also found evidence of re-concentration of imports in the last five years or so, but this trend reversal is attributable to the growing share of China in OECD imports. Put together with Besedes and Prusa’s (2006a, 2006b) findings of high churning rates among exporters, our findings suggest that OECD markets are increasingly contestable for developing-country exporters.

As for the drivers of diversification vs. re-concentration, we find that geographical concentration is higher but also more volatile for differentiated goods where quality presumably matters more and is more heterogeneous across suppliers. Moreover, when re-concentration takes place, it tends to be on 40
moment, we estimate non-parametric regressions of cashew shares on the log of per equivalent adult expenditures.

Figure 9 shows the average cashew share at different levels of income for rural and urban BissauGuinean households. In rural areas, the curve slopes down steeply. This means that poor households would tend to benefit more from price increases than richer households would. For instance, at the bottom left of the log per equivalent adult spectrum, the average cashew share is around 50 percent. On the top right side of the distribution, the average share drops to 40 percent. For urban households, we find that the average cashew share is also higher for poor than for rich households. In this case, differences are much deeper. For example, poorer urban households derive on average about 20 percent of their income from cashews. The average share for richer households is much lower, around 5 percent.

**Figure 9. Average cashew share: Rural and Urban Households**

<!-- Start of picture text --> 10 11 12 13 14 15<br>log per equivalent adult expenditure<br>rural urban<br>.6<br>cashew share .4<br>.2<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Source: QUIBB 2010.<br><!-- End of picture text --> From this descriptive analysis, we can make some inferences about the potential welfare effects of cashew price changes. Concretely, the average Bissau-Guinean household will gain from higher cashew prices, ceteris paribus. The gains are likely to be much higher for rural households than for urban households. Further, the gains are likely to be larger for poor households than for richer households. Finally, the gains are likely to be larger for the rural poor, they are likely to be comparable for the rural rich and for the poorest urban households, and they are going to be small for richer urban households.

Our simulation results, reported in Tables 13 to 16, indeed confirm these intuitions. They also provide a quantification of the magnitudes involve. For instance, in Table 13 we compare the current situation with FUNPI to a baseline situation without FUNPI (column 2). As we showed 27