rafmacalaba/datause-encoder-data huggingface dataset:
# **Section 3c. Summary Statistics- Updated Well-Being Measures**

Table 3 presents estimates of these updated measures of well-being.<sup>22</sup> All of the indicators captured in the MPM aside from the educational attainment of adults have a higher share of the population deprived than is estimated to be monetarily poor. Approximately 42 percent of the population lives in a household with poor school attendance; 61 percent of the population does not have access to improved drinking water; and above 80 percent of the population has poor access to electricity and poor access to improved sanitation. Combined, the population in PNG experiences extensive deprivations across all dimensions of well-being.

At odds with the level of economic activity in the country, PNG ranks near the bottom in each indicator captured and in the overall MPM. Aside from the educational attainment of adults, for no measure reported in Table 3 does PNG rank above the 13’th percentile of countries reported in the World Bank’s PIP. And in one of those indicators, access to improved drinking water, PNG ranks last out of 112 countries for which the indicator is estimated; and in two other indicators- access to improved sanitation, and access to electricity- PNG ranks between the second and sixth percentiles of countries.

Given the widespread deprivation in PNG across all indicators, there is a significant share of the population for which these indicators overlap. Table 3 illustrates that 74.5 percent of the population are deprived in indicators equivalent to at least one dimension of the MPM. Very few countries in the world either have as high a share of the population that is multidimensionally poor, or are as consistently one of the most deprived countries in the world across all indicators. PNG has the 118th highest share of the population that is multidimensionally deprived out of the 122 countries for which the measure was able to be constructed; and PNG respectively ranks 120th across the 122 countries measured in the median rank across all indicators and the MPM itself.

Figure 3 reports the share of total deprivations by source among the population ex-

> 22For the household-level proxy of monetary poverty, we use the predicted expenditure level for each household using a regression of expenditure on all the household variables used to impute monetary poverty. Estimates of the regression model are available from the authors upon request.

12
The role of foreign policy variables, e.g., national security/defense relationships or the extent to which countries seek to give preferential treatment to (some) developing countries or former colonies is another potential research question. A feature of the implementation of export restrictions in the EU was that actors could request exemptions based on such considerations. But presumably there was also lobbying by countries, firms and interest groups (NGOs) for selective removal of measures. An assessment of the impact of trade measures on developing countries using the data set could be an input into such more granular political economy research questions.

**Table 5: Import liberalization conditional on export controls** (medical products)

|**Table 5: Import liberalization co**<br>Dependent variable:<br>|**nditional on export controls**(medical products)<br>I<br>I lllllll|
|---|---|
|EI lrllrllr<br>|<br>0.54***|
|<br>|<br>(0.18)|
|<br>ll(1 +TARj)|0.59**<br>(0.21)|
|<br>Constant|-1.69**|
||(0.83)|
|Observations|108|
|R-squared|0.59|

**_Note._** Regression includes controls for government effectiveness; revealed comparative advantage; population; GDP weighted geographic distance to global markets; number of COVID-19 cases and import dependence. Robust standard errors, clustered by country, in parentheses. Levels of significance: *10%, **5%, ***1%.

# **_4.2 Policy impact_**

A second set of potential questions revolves around the quantification of the effects of different trade policy strategies. Natural outcome variables varying at the country, sector and time level include trade flows, extent of/reliance on GVC participation and positioning, and (changes in) offshoring and reshoring. Observed trade policy strategies may also be associated with firm-specific performance, the timing and scope of fiscal policies or features of applied lock-down measures.

We take a first look at some of these effects by assessing the potential impact that decreases in the global supply of food and medical products could have on international prices because of an increase in export restrictions imposed on these products.<sup>13</sup> To calculate the impact of export restrictions imposed on product _k_ on its international price, we use a standard partial equilibrium approach and divide the changes in global supply of such product by its import elasticity: l l =<sup>∆GGl</sup> � (2) kk<sup>lll rrsssllsskk</sup> CChl IIl rrll kk Changes in global supply are captured by the share of world exports that are covered by exports bans EEl rrl rrl ss that have been imposed by exporters between January and October 2020.<sup>14</sup> Product elasticities are estimated for a panel covering 152 importing countries by Fontagné, Guimbar and Orefice (2019) and

> 13 For other applications see Espitia, Rocha and Ruta (2020a,b).

> 14 The trade flows used to compute export shares are based on UN Comtrade statistics for 2019 or most recent year available. These calculations do not account for the total duration of the export bans imposed by countries.

15
**Table 3: Decomposition of Changes** in **Growth Rates from 1986-90 to 1991-93** - **Investment Included**

|Country|Actual<br>change<br>|Predicted<br>change in<br>|Contributio|n to predicted ch<br>rates from|ange in growth|Regression<br>residuals|
|---|---|---|---|---|---|---|
||in|growth rates|||||
||growth<br>rates||Six refonn<br>variables|Time effect'|Other<br>variables||
|Argentina|7.464|5.619|6.304|-1.73|1.044|1.845|
|Bolivia|1.561|2.836|3.308|-1.73|1.258|-1.275|
|Brazil|-0.252|-1.643|-0.344|-1.73|0.43|1.392|
|Chile|0.901|-1.501|0.944|-1.73|-0.715|2.402|
|Colombia|-0.049|0.109|0.644|-1.73|1.195|-0.157|
|Costa Rica|0.923|-0.41|0.712|-1.73|0.608|1.333|
|Ecuador|1.584|0.069|0.291|-1.73|1.507|1.516|
|Guatemala|1.197|1.187|2.142|-1.73|0.775|0.01|
|Honduras|1.141|0.698|1.393|-1.73|1.035|0.443|
|Mexico|1.116|2.424|3.393|-1.73|0.761|-1.308|
|Panama|9.178|3.722|3.34|-1.73|2.112|5.455*|
|Peru|4.504|5.399|5.289|-1.73|1.84|-0.895|
|Paraguay|-0.92|0.927|1.727|-1.73|0.93|-1.847|
|El Salvador|1.714|1.232|2.906|-1.73|0.056|0.482|
|Uruguay|0.255|-1.077|1.358|-1.73|-0.706|1.333|
|Venezuela|2.261|-0.408|1.346|-1.73|-0.024|2.668|
|**Regional**<br>**Average**|2.036|1.199|2.172|-1.73|0.757|0.837|

**1** The time effect corresponding to the last period is equal to the overall constant plus this period's dummy coefficient.

***** Statistically different from zero at the 0.10 level of significance on a one-tail test.

****** Statistically different from zero at the 0.05 level of significance on a one-tail test.

22
poor” (PPPP) is a greater concern. That raises the issue of how the use of a PPP that is more appropriate for comparing poverty lines would affect the economic gradient in Figure 2.

Intuitively, the impact on the gradient depends on the correlation between mean consumption and the error made in using the aggregate PPP for the poverty lines rather than the PPP for the poor.<sup>35</sup> It is unclear what one would expect on _a priori_ grounds; possibly we might even find that the economic gradient in poverty lines largely vanishes if we use a PPPP. In this section we try to test to see how the use of various PPPP’s might affect our results.

One candidate for a PPPP is the food component of the aggregate consumption PPP’s for 2005, as available in World Bank (2008a). It has been argued by Reddy and Pogge (2008) that it would be better to use the PPP for food consumption alone. This is not obvious; as we have noted, the mean food share for the national poverty lines (mean ratio of food component to total poverty line) is 0.564 (n=28), while the mean for the poorest 20 countries (ranked by PCE) is 0.653 (n=7). So the food PPP leaves out a sizable share of consumption by people living near the national poverty lines, even in the poorest countries. Nonetheless, it is of interest to see how this alternative PPP affects our results.

Figure 7 gives the corresponding graph to Figure 2 using the food PPP’s. We use the same PPP for measuring PCE in international $’s; the claim that this PPP is appropriate for mean consumption from the NAS is not at issue—rather the issue is what PPP is most appropriate for measuring poverty. The pattern in Figure 7 is similar to Figure 2, with a marked economic gradient in the poverty lines above a critical level of consumption, although the poverty lines are generally lower. On re-estimating equation (3) for _f_ linear, we found that the consistent reference group using this PPP needed to be slightly larger (n=17) (the estimated cut-off point is $64.26; t=4.07), the slope is slightly lower (0.292, t=11.23) and the estimated value of _Z_ * is appreciably lower at $22.74 (t=13.62). The least squares elasticity of the poverty line to mean consumption rises slightly; the regression coefficient of log poverty line at the food PPP to log PCE per capita is 0.78 (t=15.63), as compared to 0.65 using the mean consumption PPP.

> 35 Consider (for example) the least squares elasticity of the national poverty line to mean consumption. It is readily verified that if the log of the ratio of the PPP for the poor to the PPP for mean consumption is positively (negatively) correlated with log PCE per capita (at PPP) then the elasticity of the poverty line to mean consumption will be over (under) estimated by our analysis. Essentially the log ratio of the two PPP’s is an omitted variable in the regression of log poverty line on log PCE.

21
|Criteria for buildup and release|Institution-<br>specific (loan<br>quality)|Institution-<br>specific (loan<br>quality)|Institution-<br>specific (loan<br>quality)|Discretion<br>(may change)|Systemic<br>(GDP<br>growth)|
|---|---|---|---|---|---|
|Requirement based on riskiness<br>bytype of exposures|Yes|Yes|Yes|Yes|Yes|
|General or specific provision|General|General|General|Specific<br>(maychange)|General|

_Source_ : Fernández de Lis and Garcia-Herrero (2010) and the author‘s analysis.

Because of their very short history, it is still too early to fully evaluate the effectiveness of existing dynamic provisioning regimes, despite some limited experience in Spain. The experience of the Spanish dynamic provisioning system (see box 1) shows that the system did work as designed to cover ―latent‖ losses and improve banks‘ solvency through the cycle,<sup>30</sup> by building up buffers in good times that can be drawn down during bad times. The generic provision fund grew steadily from 2000 to late 2007 and then started to be drawn down at the end of 2007, when specific provisions began to surge (see figure 1). Although it helped strengthening the resilience of the overall banking sector before the crisis, it had very little impact on credit expansion during good times, which was not the primary objective to begin with. Realistically speaking, the policy objective of a provisioning regime would seem to have to be modest and focused. For developing countries, dynamic provisioning at least can be a way of building buffers in good times for the financial system to be better prepared for bad times. As shown in appendix A, the system can be designed in different ways and to various degrees of sophistication depending on available data and national circumstances.

**Figure 1. Spain: Buildup and Release of Generic Provisions by Credit Institutions**

<!-- Start of picture text --> 80<br>70 Total provisions<br>60 Specific<br>Generic<br>50<br>40<br>30<br>20<br>10<br>0<br>Billion euro<br><!-- End of picture text --> _Source_ : Saurina (2009b); Bank of Spain.

> 30 See Poveda (2000) for a discussion of the rationale of the Spanish statistical provisioning system.

16
# WPS4091

# **An Assessment Of Reform Options For The Public Service Pension Fund In Uganda**

Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles<sup>†</sup>

## The World Bank

#### Abstract

This paper analyzes the future liabilities that the Ugandan Public Service Pensions Fund might accumulate under the provisions of CAP 286, unless it is reformed. It then discusses alternative reform options that can be used as input in designing an educated homegrown reform of the fund. The paper supports a hybrid (two-pillar) reform option composed by a small defined benefit scheme and a complementary defined contribution scheme, instead of a pure defined contribution (monopillar) reform option discussed by policymakers in the country. The main reason for this is related to the fact that hybrid and pure defined contribution reforms will have the same impact on reducing pension expenditure (for same grandfathering rules and surplus in the first pillar). In addition, everything else being equal, the hybrid reform is likely to produce higher average replacement rates due to the redistributive and pooling properties of the small DB pillar.

#### World Bank Policy Research Working Paper 4091, Revised February 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

### **JEL Classification Codes** :

### **Keywords** : Uganda, Pension Reform, Civil Service Scheme, Pension System

† Tatyana Bogomolova (tbogomolova@worldbank.org) is Social Protection Specialist, Gregorio Impavido (gimpavido@worldbank.org) is Senior Financial Economist and Montserrat PallaresMiralles (mpallaresmiralle@worldbank.org) is Social Protection Specialist, all at the World Bank. This paper reflects the view of its authors, not necessarily those of the affiliation institution.
and telephones. They are also computed on a scale of zero to 100 with zero being the most efficient and 100 being the least efficient. Finally, the trade-cost index is taken from the same source and is defined as a measure of national protectionism, or efforts to prevent importation of competitive products. It also runs from zero to 100, with 100 being the highest trade costs. All of these indices are based on extensive surveys of MNE managers. Finally, distance is the number of kilometers of each country's capital city from Singapore.

The number of countries and years included for manufacturing (1994-2003) is lower than that of the full sample (1984-2003).<sup>5</sup> The full sample was also estimated for the period 1994-2003 using the same countries as in the manufacturing sample for comparison purposes. As discussed earlier, the substantial improvement in Singapore’s skill base and investment climate should have raised the incentives of MNEs to locate regional headquarters there, from which to increase outward vertical FEI.

Summary statistics on all variables are available on request. To overview them, industrialized countries make up about two-thirds of the sample. Overall total outbound FEI stocks to developing countries were far higher than the reverse activity. In contrast, total inbound FEI stocks from industrialized countries were much higher than Singapore’s outbound FEI to those nations. Average FEI stocks were considerably larger after 1988 than before in both directions. It is important to note that there are a number of years early in the period where either inward or outward investment stocks were reported to be zero, almost always with developing countries. Thus, our estimation procedure is Tobit.

> 5  The countries included in the full sample (1984-2003) are Australia, Canada, France, Germany, the Netherlands, Japan, Switzerland, Taiwan, UK, USA, Hong Kong, China, India, Indonesia, Malaysia, the Philippines, South Africa and Thailand.  There were no manufacturing FEI data for South Africa.  For India, data were available only after 1998. Hence both countries are excluded from the 1994-2003 analysis.

17
# **Annex I. Description of Unified Land Tenure Questionnaire Modules**<sup>**16**</sup>

Due to the various survey designs in which this module could be integrated, multiple versions of the module have been designed to facilitate its inclusion in a number of different survey designs and types. The appropriate version selection for a given survey depends on: (i) whether one person responds on behalf of other household members (proxy respondent approach) or each individual is asked specifically about his or her land tenure rights (selfrespondent approach); and (ii) whether data is collected at the parcel level or the household/farm level. Five versions of the questionnaire module are provided, to account for the various combinations of respondent selection and level of data collection (refer to the attached questionnaire modules and guidance note for more detail). The versions are differentiated as follows:

- **Version 1:** Parcel-level data; self-respondent approach; no parcel roster elsewhere; assumes separate household member roster which records individual sex; administered to (a) one or more randomly selected individuals or (b) all adult household members.

- **Version 2:** Parcel-level data; self-respondent approach; assumes parcel roster elsewhere which can be fed forward into either (a) the interview of one or more randomly selected individuals or (b) the interviews of all adult household members; assumes separate household member roster with sex.

- **Version 3:** Individual-level data; self-respondent approach; not reported at parcel level; administered to (a) one or more randomly selected individuals or (b) all adult household members.

- **Version 4:** Parcel-level data; proxy respondent approach; no parcel roster elsewhere; assumes separate household member roster with sex.

- **Version 5:** Individual-level data; proxy respondent approach; not reported at parcel level.

Each version offers advantages and disadvantages. The use of proxy respondents has the potential to result in biased results but can simplify fieldwork operations and reduce fieldwork time. Similarly, collecting data at the parcel level will produce the most valuable, disaggregated data on land tenure, but may be costlier than asking questions at the individual or farm level.

> 16 This annex is an excerpt from FAO, World Bank, and UN-Habitat (2019).

22
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|ST. LUCIA HIV/AIDS PREVENTION & CONTROL<br>Shiyan Chao|
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|ST. LUCIA HIV/AIDS PREVENTION & CONTROL<br>Shiyan Chao|
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|ST. LUCIA HIV/AIDS PREVENTION & CONTROL<br>Shiyan Chao|
|ST. LUCIA HIV/AIDS PREVENTION & CONTROL<br>Shiyan Chao|
|ST. LUCIA HIV/AIDS PREVENTION & CONTROL<br>Shiyan Chao|
|ST. LUCIA HIV/AIDS PREVENTION & CONTROL<br>Shiyan Chao|
|ST. LUCIA HIV/AIDS PREVENTION & CONTROL<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|KN: HIV/AIDS PREVENTION AND CONTROL PROJECT<br>Shiyan Chao|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
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|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena<br>|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
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|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
|ST. VINCENT SNF THE GRENADINES HIV/AIDS PREVENTION AND CONChristine Lao Pena|
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|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|
|DO: Health Reform Support(APL)<br>Fernando Montenegro Torres|