rafmacalaba/datause-encoder-data huggingface dataset:
AIDS or saving a disability-adjusted-life-year (hereinafter, a DALY), as estimated by Marseille, Hofmann and Kahn (2002, Table 1) using various methods. These authors put the average annual cost per DALY saved of a diverse bundle of HIV-prevention interventions (e.g., targeting sex workers and condom promotion, voluntary counseling and testing, ensuring a safe blood supply) at $12.50, whereby it may be remarked that, for these particular interventions, the assumption that Gt produces a pure public good does not seem to be wide of the mark.

Following the purposive and determined implementation of the full battery of preventive measures, the remaining intervention is to treat the infected. Opportunistic infections can be treated in the later stages of the disease, and the onset of full-blown AIDS can be delayed for some years through the controlled use of anti-retroviral therapies. Such measures will do little to reduce qt as strictly defined here, but by keeping infected individuals healthier and extending life somewhat, they will raise lifetime income and improve the parental care enjoyed by children in the affected families. In the context of the model, therefore, it seems perfectly defensible to interpret these gains in longevity as a reduction in qt. The estimated average annual cost of the recommended first-line HAART regimens is about $360 per person (WHO, 2005). It is assumed here that the latter is the cost of the efficient, marginal form of intervention when determined and extensive efforts at prevention are already being undertaken.

With an additional assumption about how quickly diminishing returns to preventive measures set in as the prevalence rate falls (see the note to Table 4), the above conditions yield the values of the parameters at, bt, ct and dt. The years in which such a program might be undertaken fall into the decades beginning in 2000 and closing in 2040 (see below). The resulting values of the parameters are set out in Table 4, and the associated functions qt(Gt; D = 1) are plotted in Figure 3.

28
|||**Table 5:**<br>|**Getting Credit S**<br>|**core and Corru**<br>|**ption**||||
|---|---|---|---|---|---|---|---|---|
|||**Incidence of P**<br>|**etty Corruption**<br>|<br>||**Depth of Pet**<br>|**ty Corruption**<br>||
|Dependent variable: Financially<br>Constrained (dummy)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Incidence of Petty Corruption (Cell<br>average)*Getting Credit Score<br>(DB)|-0.287**<br>(0.130)|-0.399***<br>(0.112)|-0.395***<br>(0.130)|-0.470***<br>(0.152)|||||
|Depth of Petty Corruption (Cell<br>average)*Getting Credit Score|||||-0.296**|-0.431***|-0.436***|-0.514***|
|(DB)|||||(0.144)|(0.127)|(0.152)|(0.177)|
|Incidence or Depth of Petty|2.506**|3.602***|3.242|0.474|2.536**|3.924***|2.865|1.515|
|Corruption (Cell average)|||||||||
||(1.126)|(0.895)|(3.444)|(2.321)|(1.194)|(0.939)|(4.311)|(2.616)|
|Incidence or Depth of Petty|||0.246**|0.269**|||0.259*|0.270*|
|Corruption (Cell average)*Annual<br>Sales (2009 USD, logs)|||(0.108)|(0.116)|||(0.140)|(0.145)|
|Incidence or Depth of Petty|||-0.318||||-0.239||
|Corruption (Cell average)*GDP|||||||||
|per capita (logs)|||(0.458)||||(0.550)||
|Annual Sales (2009 USD, logs)||-0.084**|-0.161***|-0.166***||-0.084**|-0.154***|-0.156***|
|||(0.040)|(0.053)|(0.054)||(0.040)|(0.055)|(0.056)|
|Age of Firm (logs)||0.054|0.063|0.062||0.055|0.062|0.061|
|||(0.086)|(0.085)|(0.086)||(0.087)|(0.086)|(0.086)|
|Exports (proportion of sales)||-0.291|-0.358|-0.355||-0.270|-0.310|-0.305|

41
was leased out, the survey asks its owner about the characteristics of the tenant, including his principal assets, and about the duration of the tenancy. For reasons that will become apparent shortly, we focus only on those landlords who report at least two leased plots, which gives us a sample of 345 tenanted plots owned by 127 landlords in 52 villages. The median elapsed tenure in this sample is 4 years and the mean is 6 years.

Column (1) of Table 2 reports a regression of log duration on an array of tenant, landlord, and plot characteristics, and on geographical dummies for each of the 18 tehsils (administrative units below a district). Less than a third of the total variance in log(duration) is explained by these covariates. Tenants with larger land holdings appear to be less mobile, staying with a given landlord longer. But tenants’ ownership of a tractor or plow has no effect on duration, nor do any of the landlord asset variables.

The regression in the second column of Table 2 includes landlord fi xed effects. All the landlord characteristics and tehsil dummies consequently drop out. Remarkably, this regression explains 80 percent of the variance in log duration. This means that a landlordspeci fi c unobservable, which is uncorrelated with landlord assets, accounts for at least half of the variation in tenancy durations in our sample.<sup>32</sup> Evidently, a large portion of the variation in μoc consists of heterogeneity in the behavior of landlords toward all their tenants taken as a group. One interpretation of this heterogeneity is that there are those who can acquire reputations as ‘good’ landlords who do not opportunistically evict their tenants and others who cannot. If so, then not only can we use duration data to test proposition 2, but we can interpret any tenure security effect as arising largely from landlord commitment problems.

# 4.3 Main results: The tenure security effect

Among the 563 tenanted plots in our sample, the median number of years that the same tenant has cultivated the plot is 7; 8 years for share-tenants and 4 years for fi xedrent tenants. Results reported in rows (5) and (6) of Table 4 strongly support a tenure security effect. The negative impact of leasing on FYM use declines signi fi cantly with tenure on the plot, whether leasing itself is treated as exogenous (p-value = 0.0015) or as

> 32 Note, however, that part of the landlord fi xed effect includes landlord means of unobserved plot and tenant characteristics. Since these unobservables are likely to have high intra-village correlation, we also compared the fi t of a regression with village fi xed effects to that of one with landlord fi xed effects on a sample of 263 plots (95 landlords in 20 villages) for which there were at least two landlords per village. The R<sup>2</sup> ’s are, respectively, 0.351 and 0.784. This suggests that unobserved plot and tenant characteristics that vary across villages account for very little of the landlord effect.

18
We consider estimators based on onefold and twofold nested error models, including unit-context (UC) models with only aggregated covariates, and direct estimators. Concretely, we consider the following:<sup>39</sup>

# **Direct Estimates**

1. Direct estimates from the survey samples for each municipality. These are calculated with weights from the considered design. Specifically, the calculation of FGT indicators under the two-stage design detailed in subsection 4.1 using the inclusion probabilities is obtained as:

- _τ_ ˆ _a_<sup>_direct_</sup> =<sup>�</sup> _h_ _<u>pach</u>_ <u>�</u> _pach_<sup>_Fach_;</sup>

where, _pach_ is the inclusion probability for household _h_ in cluster _c_ and in municipality _a_ , and _Fach_ is the FGT or welfare measure of interest for household _h_ in cluster _c_ and in municipality _a_ .<sup>40</sup>

# **Unit-level models:**

1. _CEBa_ : Model fit is done using Henderson’s Method III (with sampling weights) and estimates are obtained using CensusEB as noted in CMN (2020). The fitted model reads:

- _yach_ = _xachβ_ + _zacα_ + _taω_ + _gsλ_ + _ηa_ + _εach_ ;

- where _xach_ is a vector of household specific characteristics, _zac_ contains cluster level characteristics, _ta_ includes municipality level characteristics and _gs_ is composed of state level characteristics. The random effects, _ηa_ , are specified at the municipal level.

2. _CEBc_ : Model fit is done using Henderson’s Method III (with sampling weights) and estimates are obtained using Census EB as noted in CMN (2020). The difference with model (1) is that random effects are specified at the PSU level. That is, the fitted model is:

- _yach_ = _xachβ_ + _zacα_ + _taω_ + _gsλ_ + _ηac_ + _εach_ ;

where, _ηac_ , is a random effect for cluster _c_ within municipality _a_ .

3. _CEBac_ : Model fit is done using REML and estimates are obtained using CensusEB as noted in Marhuenda et al. (2017). Fitted model follows:<sup>41</sup>

- (a) _yach_ = _xachβ_ + _zacα_ + _taω_ + _gsλ_ + _ηa_ + _ηac_ + _εach_ ; random effects are specified at the municipality and PSU level.

4. _CEBsa_ : Similar to 4, however random effects are specified at the municipal and state level. The goal here is to borrow strength from the state for municipalities that are not in the sample which takes a cue from Marhuenda et al. (2017).

> 39Every model includes a constant term.

> 40Note that depending on the sampling strategy, it is likely that we do not have direct estimates for all areas.

> 41All twofold nested error methods are fit without the use of probability sampling weights and are thus not comparable to those that use survey weights.

24
for the imputation of energy poverty indicators and energy spending shares. The literature so far has mainly focused on income-based or education-based indicators.<sup>6</sup> Moreover, to the best of our knowledge, we are the first to explore statistical matching techniques in Bulgaria's case.

# **Moreover, we make an important contribution by investigating three different multiple imputation**

**approaches and comparing them to each other.** This assessment can shed light on the quality, validity, and sensitivity of data fusion methods. To facilitate statistical matching in the future, we recommend including auxiliary variables – variables that are highly correlated with energy expenditure – in both surveys. Elevating data on energy spending for a small subsample of the EU-SILC could also improve the quality of data fusion in the context of energy spending shares.

**The paper at hand is subject to some important limitations.** First, we show that significant differences in the distribution of the matching variables between surveys persist after the intent to harmonize them. Future research could address this shortcoming by analyzing how to better harmonize variables between both surveys. Second, similar to most papers in the area of statistical matching, our results rely on the assumption of conditional independence, which is untestable in the current setup. Third, future research could analyze if our results differ from an approach that combines EU-SILC 2020 (with income reference year 2019) with HBS 2020.

**The paper is organized as follows.** Section II describes the methodology and data. Section III summarizes the main results of the imputation and how energy and income poverty overlap. Section IV concludes.

6 Most of the work to date focuses on imputing income data collected as part of EU-SILC surveys into HBS. Leulescu et al. (2013) apply a similar research question to the EU-SILC and EU-LSF while Kaplan and McCarty (2013) use statistical matching techniques in the case of educational data from PISA and TALIS surveys. Similarly, Wiest et al. (2019) explore the potential of statistical matching for the analysis of wider benefits of learning in later life.

7
financial closure, among the active PPP projects in South Asia is 3.22. Debt financing makes up more than 70 percent of total physical investments in India, Nepal, and Pakistan, while it is between 60 and 70 percent of total physical investment in Bangladesh, Bhutan, and Sri Lanka (figure 8). Capital subsidies from government play a larger role in Afghanistan than in the other countries in South Asia.

**Figure 8 Composition of public-private partnership financing for active projects in South Asia, by country, 1990–2018**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Afghanistan Bangladesh Bhutan India Nepal Pakistan Sri Lanka<br>Debt Equity Capital Subsidy<br><!-- End of picture text --> Sources: Authors, based on data from PPI database and own data collection.

# **4.3. Value at risk**

The losses given distress from debt and equity obligations depend on the causes of distress. There are three major reasons for early termination of PPPs: the government’s default or voluntary termination of the project, private partner’s default or breach of contract, and force majeure. In the first case, based on market practices the private party expects to be compensated for the full amount invested in the project (debt and equity) plus the equity return it had forecasted (World Bank 2019).

In the case of termination due to the private partner’s default or breach of contract, the market practice is to provide some amount of compensation. The justification for compensation is that if there is no compensation, the government might be seen as enjoying windfall gains unfairly and would have a hard time attracting lenders and investors for PPP projects in general (EPEC 2013; World Bank 2019). Even in the case of the private partner’s default, the private partner may legally allege government responsibility, so the government becomes liable to compensate the private party, or otherwise incur additional legal costs (World Bank 2019).

In the case of force majeure, because the distress event is outside both parties’ control, the risk should be shared between both parties. As such, the government is liable for less than full compensation and has the right to take over the relevant asset, while the private partner loses any 19
population and the average years of secondary education of the female population. Our third model (M3) focuses on standard policy indicators (Barro, 2000). It includes the inflation rate (GDP deflator) as an indicator of macroeconomic stability, the adjusted ratio of the country’s volume of trade to its GDP as an indicator of the degree of openness of the economy, and the ratio of public consumption to GDP as an indicator of the burden imposed by the government on the economy.<sup>22</sup> Lastly, the fourth model (M4) is taken from López and Servén (2009). It includes the inflation rate, the average years of secondary female education, and a lagged composite index of public infrastructure.<sup>23</sup>

# **IV. Growth, poverty and inequality: Estimation results**

We turn to the estimation of (12) under alternative model specifications and using different econometric approaches. As summarized above, the GMM estimators of Arellano and Bover (1995) and Blundell and Bond (1998) attempt to deal with the endogeneity of regressors typical of dynamic panel data models like (12) using internal instruments. However, when the cross-sectional dimension of the sample is not large relative to its time dimension -- a common situation with macroeconomic panel data -- these GMM estimators can behave poorly (Bun and Sarafidis, 2015). In this setting, it is not obvious that GMM should be preferred to more conventional estimation methods, such as OLS with time and/or country dummies. For this reason, we report both sets of estimates, which helps also assess the robustness of the results.

# **IV.1. Pooled-OLS and Within-Group estimates**

Table 2 presents robust pooled-OLS and within-group (WG) estimates of the four models considered (M1, M2, M3 and M4). Time dummies are included in all models. As noted above, we use the headcount poverty rate (with poverty line of US$ 2) as our baseline measure of poverty.<sup>24</sup> For ease of comparison with the existing literature, we also report estimates of (12) omitting the poverty rate and including only inequality. For each model, we can then assess the contribution of

> 22 We use the residuals of a regression of the openness index on country size and two dummies indicating whether the country is landlocked and whether it is an oil exporter (Loayza et al., 2005).

> 23 The infrastructure index is an updated version of that used by Calderón et al. (2015).

> 24 Following López and Servén (2009), we drop Nigeria and Eswatini (formerly Swaziland) from the sample because of the poor quality of their PWT7.1 GDP data. Moreover, for models M2, M3 and M4, we found several big outliers for the investment price and inflation times series. For instance, there are 3 observations above 4 and even 5 standard deviations for the domestic investment price, and 6 big outliers for inflation. These anomalous observations affect mainly the specification tests of system GMM (the Hansen and m2 tests), bringing them closer to rejection in some cases, but have only minor incidence on the estimation results.

14
10 protected from any adverse effect of an activity, including habitat alteration. The most prominent examples of such activities are dam or other construction, and mining or logging on federal land. On private land, it is primarily forms of direct harm that are restricted. Direct harm is defined specifically in the Act and includes such obvious examples as shooting, trapping, and selling. 'Recovery' measures give the governmen **t** he power to improve the condition of listed species. The Act provides FWS with the authorization to develop and implement plans to preserve and improve the condition of listed species. More importantly, the Act gives FWS and other federal agencies the authority to purchase significant habitat sites and to aid state agencies that have agreements with FWS.

B. **Regression #1:** Factors in **the Listing Decision** Since listing a species is the crucial first step in its protection, it would be helpful to gain a better understanding of the determinants of the government's decision. What role, if any, is played by the "relevant and measurable" objectives discussed in **tfie** previous section? To answer this question, we constructed a sample of all vertebrate "full species" which might possibly be considered for listing. This sample excludes all taxonomic units below the "full species" level; that is, we do not include any subspecies or populations. Such a sample is possible because the Nature Conservancy database contains an exhaustive list of _aU_ U.S. vertebr-ate (full) species."5 We restrict our sample to all full species, listed and unlisted, - that meet a minimum threshold of endangerment the NC endangerment rank of 3 or lower. This leaves us with a sample of 511 full species, of which almost half are fish. Using this sample, we esfimate a logit regression with a dependent dummy variable, LISTED, which is set to 1 if **the** (fll) species was listed as of March 1993 and to O otherwise. The independent variables are Nature Conservancy degree of endangerment rank (NCRANIQ, log of physical length (LNLENGTH), dummies for the taxonomi **c** lass (MAMMAL, BIRD, - REPTILE, and AMPHIBIAN fish is the benchmark), and a dummy for monotypic genus (MONOTYPIC).

**"We exclude subspecies from this analysis because the NC does not track a complete list of U.S. sibspecies We do not even know how many non-listed subspecies exist, much less what they arm.**
region or country, respectively. For ease of exposition, we group funds into two categories: “global funds” and “specialized funds.” Global funds encompass world funds and foreign funds. All other fund types are called specialized funds, investing in a subset of the assets available to global funds. Naturally, funds with a wider investment scope (global funds) are always able to invest in the stocks held by specialized funds, but not vice versa.

We also collect data on the time series of return/price data on mutual funds. Since these are open-ended funds, the value of each fund each day reflects the value of their underlying holdings or the net asset value (NAV). To be able to compare returns across funds within families, we restrict our focus to large families with several types of funds. We thus use daily returns at the fund level between September 1989 and June 2006 for 36 mutual fund families, as reported in Table 1. We work with a total of 722,885 daily observations, encompassing all returns for all funds in our sample. We include all funds within a given family of funds. On average, each family has ten different mutual funds.<sup>21</sup>

# **3. How Do Mutual Funds Allocate Their Portfolios Globally?**

The U.S. mutual fund industry’s activity in international capital markets has expanded sharply since the early 1990s. For example, in 1991 there were fewer than 170 mutual funds established to invest in international equity, while in 2005 there were almost 700 funds. While the number of global funds increased steadily until the early 2000s, that of specialized funds increased until 1998 and declined since then (Figure 2). This was likely driven by the Asian and Russian crises that seem to have generated a desire to hold funds that can invest more freely around the world. At the end of 2005, there were 490 global funds and 186 specialized funds. In terms of assets under management, the differences are even starker. Global (specialized) funds managed US$29 ($7) billion in 1991 and US$781 ($160) billion in 2005. This pattern holds across more specific fund types. Foreign funds had the most noticeable increase, with assets under management increasing from US$10.3 billion to US$540 billion between 1991 and 2005. In sum, the data show a

> 21 See Appendix Table 1 for a detailed description of the sample coverage of the price data for each mutual fund family.

12
five growth variables at period _t_ + 1 as well as the time-invariant parameters 1 _− β_ and _δ_ from the observed data. Furthermore, to apply growth equation (14) to the next period at period _t_ + 2, we need to calibrate _γt_ +1 also. Typical neoclassical growth models assume that _A_<sup>�</sup> _L,t_ +1 and � _Nt_ +1 are constant for all periods (we may make similar assumption on<sup>�</sup> _ht_ +1), but they are silent about the changing rates of _γt_ +1, _S_<sup>�</sup> _W,t_ +1, _S_<sup>�</sup> _E,t_ +1 and<sup>�</sup> _ht_ +1. In fact, we cannot make such nonzero constant growth assumption for _γt_ +1, _S_<sup>�</sup> _W,t_ +1 and _S_<sup>�</sup> _E,t_ +1 because they are ”share” variables which are upper-bounded. Thus, we need to choose a way to predict the path for _γt_ +1, _SW,t_ +1 and _SE,t_ +1 during the targeted future period for the simulation purpose. Furthermore, these three variables are labeled as ”time-varying policy parameters” which would change depending on demographics and policies.

# **4.3 Calibration 1: Status-quo Simulation Approach**

To evaluate the neoclassical growth model as a simulation tool as the World Bank’s LTGM project does, we would like to vary the calibration method and compare the patterns as well as the performance of the prediction of the model to seek the best way to choose the calibration � objects, i.e., the future growth rates _Nt_ +1 _, A_<sup>�</sup> _L,t_ +1 _,_<sup>�</sup> _ht_ +1 and the time-varying policy parameters � � ( _γt_ +1 _, SW,t_ +1 _, SE,t_ +1), in order to simulate the growth path of GDP per capita. Regarding the labor share and the depreciation rate parameters, we will fix them at the same values as in the decomposition analysis of the actual Korean economy in Section 3 in order to isolate the effects of the calibration method on simulation.<sup>11</sup> The first and the most straightforward way of calibration is to simply follow the canonical neoclassical growth model, where the productivity and population grow at constant rates � � _AL,t_ +1 = _gAL_ , _Nt_ +1 = _gN_ for all periods. We may make similar constant growth rate assumption for the human capital as well such that<sup>�</sup> _ht_ +1 = _gh_ for all periods. The canonical neoclassical growth model also assume that investment rate is constant such that _γt_ +1 = _γt_ = _γ_ 0. This assumption of ”constant rates” in fact can be a reasonable one when the economy is near the steady state and the economy grows close to the balanced growth path, along which the growth rates are determined mainly by the fundamental parameters of technology and preferences. Consistent

> 11That is, 1 _− α_ = 0 _._ 602 and _δ_ = 0 _._ 053.

21