rafmacalaba/datause-encoder-data huggingface dataset:
_Table 4: Selected Plot-Level Means by Plot Distance to Household (Above versus Below 500 meters)_

|||**Malaw**|**i(IHS3)**<br>|||**Ethiopia**|**(ESS2)**<br>||
|---|---|---|---|---|---|---|---|---|
||**Entire**<br>**sample**|**Sample**<br>**[d < 500m]**|**Sample**<br>**[d >= 500**<br>**m]**|**x[d <500m] -**<br>**x[d >= 500m]**|**Entire**<br>**sample**|**Sample**<br>**[d < 500m]**|**Sample**<br>**[d >= 500**<br>**m]**|**x[d <500m] -**<br>**x[d >=**<br>**500m]**|
|Observations (Plots)|18,256|9,798|8,458||19,966|12,282|7,684||
|**_Plot Areas_**|||||||||
|GPS-based plot area (Ha)|0.394|0.377|0.412|-0.035***|0.197|0.163|0.249|-0.086***|
|Farmer-reported plot area (Ha)|0.414|0.397|0.432|-0.034***|0.193|0.162|0.239|-0.076**|
|**_Yields_**|||||||||
|Maize yield (Kg/Ha)|1,693|1,734|1,648|87|||||
|Value of output/Ha|||||29,303|25,373|34,563|-9,190|
|**_Plot Manager Characteristics_**|||||||||
|Female †|0.261|0.270|0.251|0.019|0.153|0.171|0.125|0.046***|
|Age (Years)|43.147|44.017|42.235|1.782***|46.817|47.508|45.759|1.749**|
|Education (Years)|5.028|4.961|5.098|-0.137|1.874|1.965|1.735|0.230|
|**_Household Characteristics_**|||||||||
|Household size|4.934|4.843|5.031|-0.188***|6.476|6.490|6.455|0.035|
|# of HH members - [0,5]|0.981|0.964|0.999|-0.035|0.916|0.929|0.897|0.032|
|# of HH members - [6,14]|1.396|1.369|1.424|-0.055|1.932|1.938|1.923|0.015|
|# of female HH members - [15,39]|0.901|0.868|0.936|-0.068***|1.111|1.092|1.142|-0.050|
|# of male HH members - [15,39]|0.837|0.797|0.879|-0.081***|1.212|1.207|1.219|-0.011|
|# of female HH members - [40,59]|0.270|0.269|0.270|-0.001|0.386|0.388|0.384|0.004|
|# of male HH members - [40,59]|0.269|0.264|0.275|-0.011|0.391|0.365|0.431|-0.066**|
|# of HH members – 60 & above|0.280|0.311|0.247|0.063***|0.527|0.571|0.458|0.113***|
|Household consumption expenditures per capita|50,431|48,099|52,876|-4,777***|5,723|5,787|5,625|161|
|Number of plots in the holding|2.374|2.337|2.414|-0.077**|15.857|16.160|15.392|0.768|
|**_Plot Characteristics_**|||||||||
|Owned by household †|0.904|0.927|0.879|0.048***|0.866|0.902|0.811|0.091***|
|Use of hired labor †|0.223|0.188|0.260|-0.071***|0.057|0.045|0.075|-0.030**|
|Use of organic fertilizer †|0.116|0.123|0.108|0.016**|0.183|0.246|0.088|0.157***|
|Use of inorganic fertilizer †|0.618|0.630|0.606|0.024**|0.404|0.419|0.383|0.036|
|Irrigated †|0.005|0.003|0.007|-0.004**|0.016|0.016|0.015|0.001|
|Soil quality good †|0.467|0.449|0.487|-0.038***|0.327|0.338|0.309|0.030|
|Soilquality poor†|0.113|0.112|0.114|-0.002|0.173|0.162|0.190|-0.028|

**Note:** † denotes a dummy variable. *** p<0.01, ** p<0.05, * p<0.1. Sample of plots within a 1-kilometer radius is the comparison group for the tests of mean differences.

11
# **Table 13: Potentially important background variables and their partial effects on mean WTP in the expert ́s own country for implementing plan A in rounds 1 and 2.**

# **(* = significant at level 15%, ** at level 10%, *** at level 5%)**

|Variable|Rou|nd 1|Rou|nd 2|
|---|---|---|---|---|
||Log-linear|Linear|Log-linear|Linear|
|How close is Delphi estimate<br>to true value (1-10)|0.040|-2.24|0.063|-0.76|
|How difficult to implement<br>CV study on Amazon (1-10)|-0.111 (*)|-1.42|-0.088|-0.63|
|How difficult to implement<br>CV study on domestic<br>biodiversity (1-10)|-0.030|-4.29 (*)|-0.038|-4.13 (*)|
|Familiarity with CV<br>literature. (1-10)|-0.137|-1.96|-0.197 (**)|-1.76|
|Familiarity with CE literature<br>(1-10)|<br>0.064|-1.68|0.066|-2.99|
|Number of CV and CE<br>surveys on biodiversity done|-0.037|-0.78|-0.017|-0.53|
|Number of papers on CV and<br>CE published, last 5 years|0.007|1.26|-0.015|1.40|
|Number of papers on CV and<br>CE reviewed, last 5 years|-0.006|-0.26|-0.020|-0.27|
|Fraction of research time<br>spent on CV and CE, %|0.013(**)|0.20|0.016 (***)|0.15|
|Read JEEM regularly<br>(dummy)|1.00 (***)|34.5 (***)|0.903 (***)|30.8 (***)|
|Read AJAE regularly<br>(dummy)|-1.14 (***)|-31.2 (***)|-1.03 (***)|-24.0 (***)|

22
# **4.1 Is CCI Persistent over Time?**

In order to look at the persistence of currency cyclicality, we split our sample in to two equal sub-periods, 1985-1999 and 2000-2013, and we compute the CCI in each of them. The scatterplots for the CCI in Figure 9 indicate a significant degree of persistence. Most of the countries that were countercyclical in the first period remained countercyclical in the second, while only a few moved from being countercyclical in the first period to procyclical in the second, one such notable exception being India. While some countries that were procyclical in the first period became countercyclical in the latter period, most of these are countries that changed their status after joining the Eurozone.<sup>17</sup>

**Figure 9: Persistence of Currency Cyclicality Index Over Time**

<!-- Start of picture text --> CCI = -0.02 + 0.33*CCI(-1)<br>(0.40) (2.91) GBR<br>AR G SWECHL TURNZL KOR<br>IND CAN ISR R OMB RAISL MEX<br>COL AUS<br>POL HRV RUS CZEPHL<br>THA<br>SVKHUN<br>NOR IDN<br>IRN<br>SVN<br>MLT PE R MYS<br>ESTJPNIRL HKGLVANLDAUT DEUBOLFRA MARITAZAF BG GEOCYPR LUX ESPFIN<br>CHE<br>BELT UL D NK USA<br>PRT<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>CCI: 1985-1999<br>Countercyclical both periods Procyclical both periods<br>Acyclical both periods Other<br>Fitted values<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>CCI: 2000-2013<br>-.4<br>-.6<br>-.8<br>-1<br><!-- End of picture text --> Note: CCI is the correlation between the HP filtered NEER and HP filtered real GDP. The black dashed line is the 45<sup>0</sup> line. The t- statistics are reported in parentheses.

> 17 Interestingly, most of the Eurozone periphery countries (including those that experienced financial difficulties in the recent past such as Italy, Spain, Portugal, and Cyprus) are countries that had a procyclical currency in the first period but, with the adoption of the euro, they inherited the countercyclicality of the old German Mark, which did not change with the euro adoption. For what it is worth, also notice that, while the US and the UK were both acyclical in the first period, they became, respectively, countercyclical and procyclical in the second period.

14
|Marginal effect of<br>withinUShh1<br>Marginal effect of<br>postAskari_IDH|||(0.008)<br>0.032***<br>(0.011)<br>0.033***<br>(0.009)|||
|---|---|---|---|---|---|
|Observations|25,145|25,145|25,145|25,145|25,145|
|LR chi2|2079.87***||2091.45***|4877.09***|3823.37***|
|Degrees of freedom|29||31|28|28|
|Log likelihood|-5613.97||-5613.99|-11481.24|-6991.08|
|McFadden R2|0.185||0.186|0.212|0.273|
|R-squared||0.074||||
|% Predicted right|92.46%||92.43%|88.16%|91.8%|
|Governorate of<br>origin*governorate||||||
|of residence FE|Yes|Yes|Yes|Yes|Yes|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# 3.3.2 IV strategy to identify the impact of forced displacement

In this section, we present the findings from the models formulated to correct for potential endogeneity. The results are presented in Table 6. Since our instrumental variable is only available from 2003 onwards, our sample size is reduced to 23,983.<sup>20</sup> Column (1) displays outcomes from the auxiliary regression of the two-stage residual inclusion (2SRI) model where we instrument for the IDH dummy with the IBC variable – two months fatalities count<sup>21</sup> – and run a generalized linear model (GLM) regression. Results from the first regression show a positive correlation between the total fatality at IDPs’ governorates of origin – two months prior 20 We are left with 865 IDHs of the 2,027 IDHs in our full sample.

21 We did some robustness checks using total fatalities 1 month and 3 months prior to migration and the results did not change much – see Tables D3 and D4 in Appendix D.

23
16 The data used for empirical analysis are obtained from a cross sectional survey of 201 pension systems conducted in 1991, covering a total of 269 separate retirement plans. The PENDAT file created from this survey was provided by the Government Finance Officers' Association (Zom 1991). As of this writing, there s no larger, more up-to-date, and more representative survey of state and local pension plans in the country; the federal government collects no centralized information of this type (though many have suggested it should). Respondent systems in..iuded in the PENDAT file represented 73% of state and local active pension plan participants, and 71% of state and local plm assets in 1990 (Zom 1991). These systems represent the vast majority of the PERS-covered population, but are not necessarily representative of all plans since they are among the largest in the nation, and probably better managed and funded than many smaller plans; as a consequence, interpretation of results must bear this caveat in mind. PERS plans responding to the survey accounted for about a tenth of the estimated universe of state and local pension plans nationally.

The PENDAT datafile is quite extensive, including variables reflecting all aspects of the systems' management, investment, and funding practices as well as plan participant and benefit mixes.'° Two indicators of investment performance are used as dependent variables for the multivariate analysis -- the actual investment yield rates, and the variability of plans' investment yields -- and two measures of pension funding -- stock and flow funding patterns.

Taking the investment outcomes first, three different ways were chosen to Olivia S. Mitchell - Public Pension Governance and Performance
with the Objective Economic Decile being the decile of consumption or the decile of asset ownership. The sample consists of the repeated cross-section of the three rounds of the Life in Transition Survey. Limits to this method have been discussed by Ravallion (2012). Given the ordinal nature of the dependent variable, we follow Schroder (2017) by comparing both ordered logit and OLS results of the estimates. The variables that included in the regression are:

- Individual characteristics: gender, age, education, rural versus urban, family characteristics (number of members, marriage, share of females, share of old persons), the work profile (having worked in the last 12 months or not).

- Individual beliefs and perceptions: type of religion, the main reason why there are people in need, the most important factor to succeed in life, views on the market economy, perceptions about past intergenerational mobility and perceptions that the next generation will do better.

- Objective ranking: per-capita consumption per country or assets.

- Country and time dummies: _δc_ for country c and _δt_ for time t.

The results of table 4 show that, as expected, there is a correlation between the objective economic decile and the subjective ranking in the economic ladder - but most of the non-income factors included in the regression also have a significant correlation with the subjective ranking. In this sense, individuals’ perception about their economic welfare is associated to a combination of individual and social circumstances beyond their objective economic situation. The next paragraphs provide a more detailed description of these results.

Table 4: Perceived decile and non-income factors

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||O. Logit<br>Dep. var|O. Logit<br>iable: perc|OLS<br>eived econo|OLS<br>mic decile|
|Objective decile (consumption)|0.124**||0.0956**||
||(0.00911)||(0.00728)||
|Objective decile (assets)||0.192**||0.146**|
|||(0.0131)||(0.00921)|
|Ln(age)|-0.154**|-0.137**|-0.115**|-0.102**|
||(0.0420)|(0.0422)|(0.0327)|(0.0329)|
|Female|0.0418|0.0459|0.0284|0.0326|

17
level of significance of some coefficients, most of the variables remain significant when incorporating additional randomness. Nonetheless, we also observe that the randomness issue becomes more important as the proportion of missing values increases (in the cases of Turkey and Tanzania).

On the other hand, provided we control for the same set of IC variables in all the specifications, the results under the complete case and the ICA method are reasonably consistent between the two. Even in those cases in which the complete case represents less than half of the original sampling frame, the estimated parameters of production function (PF) and IC variables is within a reasonable range of values. This illustrates the importance of using the large set of IC variables, in order to control for the data generating process in the estimation.<sup>4</sup> Likewise, the ICA method shows reasonable robustness to the endogenous sampling case. Heckman’s lambda is non-significant in all cases, which does not support the endogenous sampling selection hypotheses. The results of the ICA method are similar to those of the Heckman regressions, indicating that even if there were an endogenous sampling selection problem, this would not be serious enough to bias the final results. In this sense, replacing only those RHS variables and not the dependent variable (sales in our case) does not change the results, provided the endogenous sample selection is not supported by the models and the robustness in the results.

As the use of Investment Climate Surveys becomes more and more important among policy makers, scholars and applied researchers, thorough research into the causes of the missingness problem in order to improve the quality of the data is becoming a requisite. The parsimonious methodology we propose here is intended to be a first step in helping prepare the way forward and delve further into this line of research.

> 4 In order to pursue this issue more deeply, further research is needed. Nonetheless, once the relation between IC variables and the MDM is proved, using them to gain independency between our model and the MDM is a requisite. We believe that this procedure is what balances the results, in the sense that it is what allows us to get robust results in specifications.

91
# **A Regional Currency Regime**

Arising out of the Asian crisis and related to the issue of financial system reforn,<sup>272</sup> is an ongoing debate on the appropriate currency regime for East Asian economies, extending as far as a discussion of the suitability of a regional monetary arrangement for East Asia.<sup>273</sup> Prior to the crisis, currencies of the crisis-affected countries were "effectively" pegged to the U.S. dollar.<sup>274</sup> (See Table 5.17 below for "official" pre-crisis exchange rate regimes for the crisis-affected countries.)<sup>2</sup> 75 Large capital inflows into these countries caused rapid appreciation of their exchange rates, especially after 1994. When the dollar appreciated against the yen beginning in 1995, the currencies became overvalued. The eventual reversal of capital flows in mid-1997 put downward pressure on these currencies, which were vigorously defended by their respective central banks. In the end, after their foreign exchange reserves were depleted (Thailand and Korea), the currencies could no longer be defended and were allowed to float and, thus, depreciate. This depreciation, combined with rising interest rates, negatively impacted banks. Their nonperforming loans increased as borrowers (particularly real estate developers)<sup>276</sup> went into bankruptcy, and they suffered direct balance sheet losses (to the extent they were net dollar borrowers). The only exception was Malaysia, which switched from a managed float to a conventional fixed peg and imposed capital controls.

Table 5.17

|**CrisisCountry**|**Pre-CrisisExchangeRate**<br>**Regime***<br>**(12/31/91)**|
|---|---|
|Indonesia|CrawlingPeg|
|Korea|Managedfloatwithnopre-<br>announcedexchange ratepath|
|Malaysia|Peggedrateinhorizontalband|
|Philippines|Managedfloatwithno pre-<br>announced exchangeratepath|
|Thailand|Other ConventionalFixedPeg|
|*SeeAppendixfordesc|riptionofexchangerateregimes.|
|Source: IMF AnnualRe|port 2000andFischer(2001:Tables2,3,and 4)|

Radelet and Sachs (1999) believe that if Thailand had switched to a float earlier in 1997 and moderately tightened monetary and fiscal policies, foreign exchange reserves 98
influenced by the distribution of welfare, their usefulness as indicators of distribution is quite unclear. For example, they may not be as responsive to the well-being of the ultra-poor as we would like. And the low participation of this group in many policies ostensibly designed to help them (including some applauded in HPA) has been of serious concern (Lipton, 1983; Jimmenez, 1935, Chapter 5; World Bank, 1990, Chapter 5).

Real progress - or lack of it - in reducing the persistent. poverty underlying chronic hunger may show up little in these aggregate social indicators. Data inadequacies are not now a good enough justification; for example, there is a marked contrast between HPA and the broad set of measures of the well-being of the poor assembled in various chapters of the recent World Development Report (WDR) on poverty (World Bank, 1990). Well designed household based surveys have proved to be a potentially powerful tool for studying many of the dimensions of well-being discussed in HPA, such as access to social services, as well as command over private consumption goods.

# _4_ **_Famines_**

A large part of HPA is devoted to policies for preventing famine, building on Jean Dreze's recent work (particularly Dreze 1990 a,b), as well as Amartya Sen's well-known earlier writings, particularly Sen (1981a). The latter book made an enormous contribution in exposing the limitations of the simple-minded view that famines are caused by a decline in aggregate food availability. The main information gathered by researchers attempting to understand the causes of a particular famine, or to anticipate future famines, has usually concerned aggregate food availability. Indeed, this has sometimes been the only information deemed necessary. Sen (1981a) has argued that this 13
intervention. The randomized design of the original deworming project at the community level permits its use for estimation in this study, as shown in Figure 1: In communities where deworming began in 1998, the children who were born in 1998 (and who I find in 2009 or 2010 as 11- or 12-year-olds) began experiencing the effects of community deworming in their year of birth; thus their age at the time of deworming, _Aid_ , is less than one year (see Panel A, middle column, top row). I consider them “treated” for the purposes of the present study. Children from the same birth cohort but in a community where deworming only began in 2001 had to wait until age three for school-based deworming to arrive; I consider them “untreated” for the purposes of the present study (see Panel A, middle column, bottom row). The figure outlines these definitions of treatment and comparison, by community and by treatment arm.<sup>12</sup> Because deworming started in different communities at different times, I can control for age at observation separately from age at the time of community treatment.

Summary statistics on the study population are shown in Table 1. Roughly half the sample is female, the average age is between 11 and 12, and average height is roughly what would be expected for these ages, if a bit low. Roughly 28 percent of the sample had migrated since birth. In-migration to these communities in response to Kenya’s 2008 postelection violence left school populations inflated with recent migrants from urban areas; for my results, I exclude those migrants from all regressions, since they were not present in these communities at the time of deworming in the late 1990s. Out-migration is much less of a concern, since these rural areas are moderately ethnically homogeneous, and did not experience notable conflict.

In Panels B and C of Table 1, I restrict attention to the sample of non-migrants. Panel B shows that the non-migrants are demographically much the same as the full sample, and goes on to tabulate several other characteristics: 21.6 percent of this population is stunted<sup>13</sup> ; respondents had an average of 1.45 older siblings who attended the same primary school; 22.5 percent had at least three such siblings, while 37 percent had no older siblings who attended the same primary school. These measures are used to assess the likely intensity of

> 12For the borderline case of children whose age was approximately 1 when deworming arrived, I consider them neither clearly treated nor untreated, as I only measure age up to a precision of one year increments, and the literature is not definitive on how these cases should be handled, had measurement been precise. Empirically, this is absorbed through an additional dummy variable, as discussed further below.

> 13Stunting: height-for-age Z-score less than -2

8
although the rate does increase from about 3% to 7% by 2004. The average household size of the elderly sample is larger than younger age groups in the KHDS and declines from 6.7 persons to 5 persons over the panel. Most of the 2004 elderly individuals are living with their sons, daughters or grandchildren in 2004 (see Table 1 for a summary of living arrangements for the elderly).

# **2.3. Adult death**

Our main independent variable is a binary variable indicating whether or not there was a prime-age death in the older individual’s household between baseline (1991) and 2004. We define “prime age” as ages 15 to 50, inclusive.<sup>6</sup> Since the data set spans thirteen years, the term “household” cannot be well defined. Even though the elderly are less geographically mobile (i.e. are more likely to live in same village than younger respondents), the composition of their household changes over the course of the panel. Of the 613 elderly individuals in 2004, only 84 have a household of the same size, and only 14 live in a household with exactly the same group of panel respondents as at baseline. It is thus clear that living arrangements change for the elderly over the course of the panel (as they do, in fact, for respondents of all ages). These changes reflect life-cycle events like children moving away after marriage, as well as responses to shocks (for example, the fostering in of an orphaned child).

Our variables for adult death must thus take into account the changing nature of the household over time. To this end, we use three indicator variables reflecting different types of adult deaths which can potentially impact the socioeconomic situation of the elderly. The first equals one if all the following conditions are satisfied: 1) the adult who died was of prime age (15-50) at the time of death; 2) the adult was part of the elderly individual’s baseline household; and 3) the elderly individual and the prime-age adult were living together at the time of death. We refer to these deaths in our results as “Coresident PHHM who died ages 15-50,” where PHHM stands for previous household member. The majority of these deaths are close relatives of the respondent (spouse, child, sibling, or niece/nephew).

> 6 We do not specifically try to ascertain the cause of death. Rather, we focus on mortality among prime-age adults; other studies have found that a majority of these deaths are caused by HIV/AIDS (Centers for Disease Control and Prevention 2000).

8