rafmacalaba/datause-encoder-data huggingface dataset:
The finding that consultative procedures grant elites greater influence over allocation decisions results from the dominance of meetings by council members, who often overlap with pre-existing elites.<sup>17</sup> According to monitoring data collected during meetings, village council members had more influence than other attendees in the selection of projects in 98 percent of meetings. In 35 percent of meetings, the final choice of projects was made entirely by council members. This finding is consistent with Humphreys et. al. (2006), which observes discussion leaders‘ preferences to be a significant determinant of the outcomes of deliberative meetings.

One of the most important channels through which elites influence allocation outcomes is agenda setting. In both meetings and referenda, agenda-setting rules were identical, but the fact that the selection procedure was known in advance clearly affected the choice of the proposed projects. In particular, the council was more likely to propose projects that were preferred by elites if a meeting was mandated. This effect played a major role in these villages as, conditional on a project being proposed, male elite preferences had only a small effect on project choice.<sup>18</sup> As we would expect elites to be more likely to manipulate the list of proposed projects under referenda given their more circumscribed ability to influence the results thereafter, the result is somewhat surprising. Two complementary explanations are that, when setting agendas for meetings, elites compensate for expected attempts by villagers to change agendas during the meeting, while, when setting agendas for referenda, elites anticipate a broader political cost from diverging significantly from villager preferences given the immutable nature of the agenda.

The results indicate that female elites influence project choice only under meetings. Since female elites commonly share familial links with the male elites, this result is perhaps not entirely surprising. However, unlike male elites, who exhibit their influence primarily at the proposal stage, female elites have almost no influence on project proposal, but a significant impact on project selection and prioritization. The results also indicate that female elite influence does not have a positive effect on the satisfaction of ordinary females, suggesting that preferences of female elites diverge from those of female villagers.<sup>19</sup>

> 17 Beath, Christia, and Enikolopov (2012) finds that more than at least a quarter of males elected to the CDC were identified as village leaders before the CDC elections.

> 18 Preferences of male villagers and female elites had almost no effect on the choice of proposed projects.

> 19 Unfortunately, we don’t have measures of ex-ante preferences for the ordinary women in the village, so we cannot directly test how aligned the selected projects were with their preferences and if the choice of their most preferred project increased their satisfaction ex-post.

15
the objective for monetary policy is to achieve price stability, while maintaining output stabilization. Targets for inflation, interest rates, monetary aggregates, or the exchange rate are managed through open market operations or through non-market controls such as setting reserve requirements.

However, the effectiveness of policy decentralization and its credibility depend on two further conditions. The first is that complete policy decentralization is only possible under very restrictive assumptions, such as the availability of _n_ policy instruments when there are _n_ policy objectives (Tinbergen 1952) and these instruments need to be independent of one another, in the sense that the effects of any one instrument on the objectives are not proportional to those of another, or of any combination of others (Tobin 1993). Lump sum taxation would be one such example of an orthogonal instrument, as is state contingent debt.

While such strict instrument orthogonality is in practice rarely observed and unnecessary, the shortage of policy instruments tends to be more marked in less developed economies. For example, it is common to see both the monetary authority and the debt manager operating in the primary market to pursue their respective policy objectives.<sup>8</sup> Where the monetary authority issues its own debt and incurs direct interest cost, the public will perceive that it would have less incentive to raise interest rates to curb inflation, as it may wish to keep rates low in order to contain the cost of borrowing.<sup>9</sup> In essence, the shortage of instruments may force the authorities into potential conflict and weakens the credibility of their ability to achieve their main policy goal.<sup>10</sup>

> 8  See for example, World Bank (2007). In Costa Rica, Indonesia, Lebanon, and Nicaragua, both the central bank and the ministry of finance issue short-term debt in the primary market.

> 9  It has been observed in some countries that even if the central bank issues government debt on behalf of the ministry of finance through an agency agreement, the central bank feels compelled to keep the rates low to lower the costs of the debt.

> 10  One of the reasons governments encounter less instruments than the number of objectives is due to incomplete markets, either due to the underdevelopment of the domestic government debt markets or simply because it is too expensive to have complete markets.  For example, if the government lacks the credibility that it is willing or able to honor the debt, then the market may demand prohibitively high risk premia to issue long-term fixed rate debt in domestic currency.  Or it may simply lack a policy to developing the domestic market, resulting in no or little incentives for the private sector to transact in the secondary market. The discussion on instrument independence helps highlight the importance of developing the domestic debt market from the perspective of macroeconomic stabilization policy.

7
**Table 3: Environment for Institutional Infrastructure Investment**

||**INFRA**<br>**BONDS?**|**LEAD**<br>**INVESTOR?**|**MDB?**|
|---|---|---|---|
||**_Country A_**|**_Country B_**|**_Country C_**|
|**_Sovereign credit rating_**|**_At / close to_**<br>**_investment grade_**|**_Below investment_**<br>**_grade_**|**_No rating_**|
|**_Size pension fund AUM (%GDP)_**|**_> 10%_**|**_1-5%_**|**_Minimal_**|
|**_No. pension funds_**|**_10s or 100s_**|**_A few main funds_**|**_Dominant social_**<br>**_security fund_**|
|**_Quality of pension system_**<br>**_e.g. Mercer Global Pensions Index_**|**_D_**|**_C_**|**_E_**|
|**_Insurance penetration % GDP_**|**_High_**|**_Medium_**|**_Minimal_**|
|**_SWF_**|**_Yes_**|**_Yes_**|**_No_**|
|**_Development Fund or Bank_**|**_Yes_**|**_No_**|**_Yes_**|
|**_Level financial sector_**<br>**_- bank assets % GDP_**<br>**_- private credit % GDP_**|**_Medium_**|**_Low_**|**_Fledgling_**|
|**_Level capital market_**<br>**_-Stock market cap % GDP_**|**_High_**|**_Medium_**|**_Low_**|
|**_Business conditions_**<br>**_e.g. IFC Doing Business ranking_**|**_Good_**|**_Average_**|**_Difficult_**|
|**_Infrastructure investment conditions_**<br>**_e.g. EC Harris Global Infrastructure_**<br>**_Investment Index_**<br>**_e.g. Nabarro Infrastructure Index_**|**_Good_**|**_Average_**|**_Poor_**|

31. Figure 3 and Table 4 illustrate some of the factors (e.g., legal/ regulatory reform, capacity building, project preparation and credit enhancement / structuring of investment vehicles) that support different financing models. Such a framework can help countries assess their situation and identify the best combination of policies to help mobilize institutional investment.

11
We find evidence consistent with the hypothesis that stress in adolescence and early adulthood worsens later-life health. Specifically, a greater risk of military induction during the Vietnam War is associated with worse later-life health for men who did not serve in the military. These adverse health effects are modest, but are relatively robust, and include a higher risk of obesity and higher probability of reporting being in worse health. Falsification tests conducted with samples of women and male veterans do not show the same pattern for obesity and selfassessed health, which suggests that those results are not due to cohort effects or trends in unobservables. Moreover, it does not appear to be due to compositional effects (i.e., more healthy men being removed from the ranks of non-veterans when induction risk is high), because the negative correlation is still detectable (though, naturally, weaker) in a sample in which the non-veterans are pooled with veterans. The finding that stress is associated with a greater risk of obesity and worse self-assessed health in later life is robust to alternate proxies for stress (such as the probability of being killed in action) and various sample inclusion criteria.

For the outcome of mortality, however, the falsification samples exhibit the same patterns as the main analysis sample, which leads us to conclude that the correlation between induction risk and later-life mortality is due to cohort effects or other omitted variables.

This study has a number of limitations. First, we cannot directly measure stress. No largescale survey that we know of has measured the stress of individuals using cortisol laboratory measurements at the time of the Vietnam War. However, the measure we use, which we limit to those who did not serve in Vietnam, captures an important source of uncertainty faced by individuals at that time. For those who do not serve, we do not have baseline health measures to investigate whether they did not serve due to preexisting health conditions that would bias these individuals towards worse health later in life. However, this should not bias our results unless 24
variables have considerable correlations among them. However, if the correlations are driven by common underlying factors, then the factors become the main objects of interest for us.

If two variables share a common factor with other variables, their partial correlation, controlling for all remaining variables, will be small. The Kaiser-Meyer-Olkin (KMO) statistic, based on this idea, computes the ratio of (i) the sum of squared correlations of each variable in the analysis with every other variable to (ii) the same sum plus the sum of squared partial correlations of each variable with every other variable, controlling for all remaining variables. Large values for this “overall” KMO measure indicate that the partial correlations are small, that is, common underlying factors are responsible for the correlations among variables. A large value for the KMO measure indicates considerable gains from undertaking a factor analysis. Table 3 indicates that the overall KMO measure is 0.875, which provides solid support for proceeding with factor analysis of the KAM data. Further, the KMO statistic for each variable individually indicates that their high correlations are driven by underlying factors.<sup>9</sup>

# **3.3 Avoiding a Pitfall**

Performing factor analyses separately on the 1995 variables and the 2002 variables is a pitfall one should avoid if the purpose of the factor analysis is to compare factor scores across the two periods. Separate analyses produce factor scores (that is, the quantity of a factor contained in each country) that are not strictly comparable. For example, in PC analysis separate analyses produces factors with different variances so that their magnitudes are not comparable (that is, their “units” are different). In order to solve this problem, we proceed as follows. First, we combine the 1995 and 2002 variables into one set of 12 variables. To be consistent, the same factor analytic method is used to combine each pair

> 9The variables in Table 3 are an amalgam of each variable-pair over the two years, see below.

17
<mark>lasting impacts for children living in receiving areas, whereas Maystadt and Verwimp (2014) find a limited but positive impact of refugees on health outcomes, driven by the improvement of infrastructure including health services in refugees’ hosting areas. We are not aware of studies that looked at the intergenerational health effects of refugee crises on children in Tanzania or elsewhere.</mark> <mark>This paper intends to contribute to the relatively small but growing literature on the long term and intergenerational impacts of early childhood exposure to shocks by expanding this literature to refugee crises. We empirically examine and hypothesize that the already documented health impacts of early life shocks such as natural disasters, famine, war or pollution on the second generation of children also hold for temporary population shocks such as a refugee influx. More specifically, we study if Tanzanian children born to parents who spent their early childhood in high refugee-receiving areas are more likely to be stunted. We focus on under five years old Height for Age Z score (HAZ) and the probability of stunting as our key health indicators. This choice stems from the critical role of these measures in capturing long-term health effects and socio-economic development prospects along the life course.</mark> <mark>The paper finds that children of mothers exposed to the 1993-2001 refugee crisis in Northwestern Tanzania are more likely to be stunted. We use the latest geocoded 2015-2016 Tanzanian Demographic and Health Survey (DHS) capturing migration histories of mothers and fathers. Our results suggest that, almost 15 years after refugees’ arrival, under five years old children born to mothers who were between 0 and 5 years old during the refugees’ influx (1993-2001) and living closer to refugee camps have lower Height-for-Age Z-scores and are more likely to be stunted than a comparable sample of children whose mothers were more than five years old at the time of the shock. We also find that mothers who were inutero and less than 24 months at the time of the high refugee influx period from 1993 to 1996 are more likely to give birth to children who are stunted. No differentiated impacts were found between girls and boys.</mark> <mark>Several plausible mechanisms explain our main results. We found that early childhood “refugeesexposed” mothers are less likely to complete secondary education in their adulthood. Our results also show that those mothers have a lower likelihood of land and house ownership more than a decade after refugees’ arrival. Most importantly, our findings reveal higher participation in the labor market of grandmothers of children who were living in refugees’ receiving areas at the time of the influx, potentially suggesting a trade-off with child care at home, especially for girls. This corroborates findings from Alvi and Dendir (2011) who reported that earlier-born girls are more likely to perform domestic work in Ethiopia. Our results are in line with findings emanating from recent studies showing that the long-term consequences of shocks are more pronounced among females than males (Zhibo et al.,2015;</mark> 4
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the capital account. Unfortunately, savings thus obtained does not correspond with fiscal data since the financing flows from the monetary sector are inconsistent with tnose data. Given savings from the capital account, the only variable that is left to close the current account is factor income (FIo). This factor income id defined as the return to capital employed by the SEEs. Therefore, it includes retained profits and depreciation but not wage payments. Again, this figure does not match the data obtained from the fiscal accounts.

**A1.4.** National Accounts

# The national accounts identity can be expressed as:

(A1.14) OFIb + Flo + VAp + TI - Sub = E*Xt - E-IMt + Cb + Cp + + Ig + Id + Ip Both sides of the equation must sum to GDP at market prices. The left hand side af the equations sums value added, indirect taxes and subsidies (-). Given other factor income of the budget and factor income of the SEEs (Flo), we can derive a concept of value added of th; private sector using GDP at market prices as hard data. The concept of value added of the private sector is not a pure concept as it also includes wage payments received from other sectors, non-retairned profits and the value of free services provided by the iublic sector. Consequently, VAp is the residual.

The right hand side of the equation sums the various components of expenditure. All but Cp and Ip are known. In order to consistently close the model we take private investment (Ip) from national accounts data and leave private consumption as the 28
Panels E and F of Figure 12 show the first-year exporter survival rates in sectors with and without comparative advantage in each country during 2005-2007 and 2008-2009. Survival rates were higher in sectors with comparative advantage in all LAC countries except Costa Rica during the steady growth period. The differences were in some cases very large: e.g., in Ecuador a 22 percentage point difference separated the survival rates in sectors with and without comparative advantage. Again this was not a LAC specific pattern, as it was verified also in Bangladesh, Cambodia, and South Africa. In absolute the magnitudes are striking: whether a sector has comparative advantage or not, more than 75% of new exporters in that sector do not make it beyond their first year. Panel F shows that the global recession did not affect exporter survival rates across sectors with and without comparative advantage in LAC countries.

These patterns suggest that exporters in the LAC region are engaging in a high degree of experimentation in ‘non-traditional’ sectors where they do not have comparative advantage. However, the longer-lasting new export relationships were observed in sectors with revealed comparative advantage, which may be linked to learning spillovers due to the presence of networks of established exporters in each country. These patterns are, however, not a feature of the LAC region per se since they are also observed in comparator countries.

# **_8. Conclusion_**

Using novel exporter-level data from customs for 11 countries in the LAC region – Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, and Peru – covering the period 2005-2009, we uncover a wealth of new findings on export entrepreneurship in the LAC region.

A few of the findings are worth highlighting. First, the region appears to be no less entrepreneurial in terms of the extensive margins of exports relative to other countries, and relatedly, entry rates across sectors are quite similar but survival rates appear to be highest in agriculture sectors. Second, the size of new exporters tends to be relatively smaller (in comparison to incumbent exporters) in natural resource-abundant countries. Third, entrepreneurship is driven to some extent by comparative advantage. More specifically, although firm entry rates into exporting are higher in sectors in which countries do not have comparative advantage than in sectors with comparative advantage (in the sense of Vollrath 1991), firm exit rates as well as survival rates of new exporters are notably higher in sectors with comparative 38
Table A.3: Balance at baseline by phone survey

||In|-person||Phone|In-person – Phone|
|---|---|---|---|---|---|
||N<br>(1)|Mean/SD<br>(2)|N<br>(3)|Mean/SD<br>(4)|Diference<br>(5)|
|Own Voucher|541|0.233|123|0.220|0.013|
|||(0.423)||(0.416)||
|BAF Voucher|541|0.238|123|0.285|-0.046|
|||(0.427)||(0.453)||
|Age|541|25.601|123|26.024|-0.424|
|||(2.623)||(2.744)||
|Years of schooling|541|9.616|123|9.252|0.363|
|||(4.400)||(4.721)||
|Hindu|541|0.933|123|0.935|-0.002|
|||(0.249)||(0.248)||
|Scheduled Caste or Tribe|541|0.449<br>|123|0.407<br>|0.043|
|||(0.498)||(0.493)||
|Other Backward Class|541|0.429<br>(0.495)|123|0.488<br>(0.502)|-0.059|
|Wears ghunghat|541|0.882|123|0.894|-0.013|
|||(0.323)||(0.309)||
|Worked last year|537|0.140<br>|122|0.131<br>|0.009|
|||(0.347)||(0.339)||
|Co-residence with MIL|541|0.673|123|0.699|-0.026|
|||(0.470)||(0.460)||
|Mobility score|541|0.935|123|0.772|0.163|
|||(1.686)||(1.487)||
|Asset score|541|0.031|123|-0.124|0.155|
|||(1.660)||(1.490)||
|Number of general peers|541|1.682|123|1.520|0.162|
|||(1.030)||(1.019)||
|Number of close peers inside village|541|0.545<br>(0.618)|123|0.593<br>(0.651)|-0.048|
|Number of children|541|1.928<br>(0.888)|123|2.041<br>(0.995)|-0.113|
|Wants another child|537|0.501|123|0.480|0.021|
|||(0.500)||(0.502)||
|Using any FP method|540|0.470|123|0.488|-0.017|
|||(0.500)||(0.502)||
|Using modern FP method|540|0.169<br>(0.375)|123|0.220<br>(0.416)|-0.051|
|Ever visited a clinic for FP|541|0.342<br>(0.475)|123|0.390<br>(0.490)|-0.048|
|MIL opposed to FP|539|0.148<br>(0.356)|121|0.190<br>(0.394)|-0.042|
|F-test of joint signifcance: p-value|||||0.569|
|F-test: Observations|||||650|

_Notes:_ Standard deviations (SD) are in parentheses. BAF denotes Bring-a-Friend, FP denotes family planning, and MIL denotes mother-in-law. Variable definitions are in the Online Appendix.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 51