rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A4. Impact of living in the GMA and, once in the Bangweulu Park System GMA, -** **<u>of participating in the VAG/CRB on the non poor households</u>**

||Bangweulu|Treatment Regre<br>capita co|ssion mod<br>nsumptio|els for the impact<br>nexpenditure of<br>|on per<br>|
|---|---|---|---|---|---|
|||||Participating|in|
|Variable|Variable description|LivinginGM|A<sup>_a_</sup>|<br>VAG/CRB|<br><sup>_b_</sup>|
|||(1)||(2)||
|hage|Age of the household head|0.006<br>(0.003)|**|0.004<br>(0.003)||
|fhhh|Female-headed dummy|-0.289|***|-0.161|**|
|||(0.096)||(0.078)||
|maxedu|Education of the most educated member|0.068|***|0.030|***|
||(years)|(0.013)||(0.009)||
|c0to14|Number of children less than 15 years old|-0.166<br>(0.016)|***|-0.175<br>(0.015)|***|
|f15to60|Number of female members 15-60 years|-0.187<br>(0.043)|***|-0.130<br>(0.042)|***|
|m15to60|Number of male members 15-60 years|-0.191<br>(0.037)|***|-0.166<br>(0.029)|***|
|m61plus|Number of members older than 60 years|-0.222<br>(0.089)|**|-0.253<br>(0.092)|***|
|kroad|Distance to the nearest main road in km|-0.007<br>(0.004)||-0.005<br>(0.004)||
|kbsch|Distance to the nearest basic school in km|-0.007<br>(0.007)||0.004<br>(0.005)||
|kheal|Distance to the nearest health centre in km|-0.001<br>(0.003)||-0.004<br>(0.002)|*|
|vacast|Value of consumer durable assets in ZMK|0.097||0.104||
|||(0.092)||(0.092)||
|dcoop|Participation in cooperatives dummy<br>|0.495<br>(0.125)|***|0.189<br>(0.146)||
|gma or|Participation dummy variable<sup>_a,b_</sup>|0.729|***|0.858|***|
|dvcrb||(0.193)||(0.147)||
|cons|Intercept|12.960|***|13.566|***|
|||(0.136)||(0.137)||
|Number of|observations|513||380||
|Log-likelih|ood value|-696||-422||
|Goodness|of fit Chi-Square|222.31||.||
|Rho||-56|***|-0.56|***|

> _a_ Participation dummy variable refers to the GMA dummy, equal to 1 if the household is located in a GMA

> _b_ The participation dummy variable refers to the CRB/VAG dummy, equal to 1 if the household participates in the VAG/CRB. This model uses a sub-sample of households that are located in the GMA Significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

19
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 33: Price indexation is another way to gradually decreasing replacement rates* for teachers and reduce the fiscal impact of the current scheme****

<!-- Start of picture text --> 120.0%<br>100.0%<br>80.0%<br>60.0%<br>40.0%<br>20.0%<br>0.0%<br>ref I.2 ref I.3<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of average group wage; ** Comparison of replacement rates for teachers - Reform I.2 with I.3.

**Source** : WB calculations based on PROST.

# **_V.B Transforming PSPF into a mixed scheme (Reform II)_**

The reform from the current monopillar PAYG defined benefit scheme to a two-pillar scheme is modeled as one of the possible alternatives to a drastic shift to a pure fully funded defined contribution system. The first pillar in this provisional reform scenario is suggested to be a funded defined benefit component, the second – a fully funded defined contribution scheme similar to the previous reform scenario, though smaller in size.

Similar to the pure defined contribution reform scenario, simulations are done for two extreme cases of the transition path: 1) everybody moves to the new scheme in the year of reform (Reform II.1); and 2) only new entrants go to the new scheme with current (as of the year of the reform) active members staying in the old system (Reform II.2).

## **V.B.1 Reform II.1**

The main design features of this hybrid reform scenario are as follows:

- Reform starts in 2007.

- Everybody switches to the new system.

- Pension rights accrued under the old system are treated the same way as in Reform I.1.

- The contribution rate is 15%: 5% paid by employee, 10% by employer. Contributions are split between two pillars: up to a specified ceiling they go to the

- 65 -
43 stockl (Kotllkoff 1979: 409). Certainly there is no conclusive evidence that public benefits trade off one-for-one with private savings In developed countries, nor is there any apparent positive effect of public plans on savings.37 There is very little evidence on this point from developing countries.

Tuming to the question of whether private pensions affect savings, again theory offers little clear evidence. For Instance, If fully funded private pensions are perfect substitutes for nonpension private saving, monies Invested in these pensions would offset nonpension saving one-for-one. However, some workers covered by pension plans are likely to save more than they would have otherwise, due to myopia or Inadequate Information; In this Instance, pensions could increase savings. Tax preferences In many countries also alter the terms of trade between pension and other savings Instruments, making i clear that net effects will depend on specific institutional structures in each country. Adding leavening to this discussion is the recent suggestion that capital markets may be quite local in nature, so that commercial loans are sensibly provided by locally-operated, well-informed financial organizations. (This was concluded In the United States, though studies elsewhere remain to be conducted; see Hannan 1991). Perhaps private pension fund capital can beneficially add to supplies of local capital, and take advantage of Investment opportunities, that otherwise would not be noted by outside investors. Empirical studies on private pensions are modestly optimistic, though here too there are a variety of estimates (Munnell and Yohn 1992). In the United States, analysts tend to find that another dollar of private penslon benets reduces nonpension private savings by 70 cents; on net then, additional private penslon wealth appears to increase capital formation, by 30 cents on the dollar. It is not yet known if this small positive effect carries over to other countries, particularly developing economies with much less extensive capital markets.

Irrespective of whether new capital is generated, it is still possible that private pensions can channel existing savings to promote equiy and bond markets often absent in developing countries, and for this reason policymaker **s** ometimes favor private over public pensions. Unfortunately this preference begs the question of whether developing countries lack funds for truly worthwhile private Investments which somehow intemational investors fail to perceive, or whether capital markets are absent
# **Figure 5: Droughts, cereal imports, and inflation**

<!-- Start of picture text --> 1200 50<br>1000 40<br>30<br>800<br>20<br>600<br>10<br>400<br>0<br>200 -10<br>0 -20<br>Droughts Imports of cereals and cereal preparations<br>Inflation, consumer prices (annual %) (WDI)<br>Inflation; droughts<br>Cereal imports (million, $)<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> _Source_ : NBE database; IMF, Direction of Trade Statistics, International Financial Statistics.

This approach to modeling inflation accounts for these features of the agriculture sector in three ways. First, in addition to headline inflation, this analysis models and estimates the determinants of cereals, food, and non-food inflation. Within the food basket, cereals are singled out given their important share in production, food imports, and food consumption. Second, this study considers the cereal production gap instead of overall GDP gap as a determinant of inflationary pressures arising from the supply side. Third, this analysis includes dummies capturing sharp spikes or drops in inflation that are typically associated with shocks to food production.

# **_Demand-side factors_**

On the demand side, the two prominent approaches to inflation modeling are the monetary theory and the fiscal theory of inflation (Gordon and Leeper 2006; Sargent and Wallace 1981). The two theories are interrelated but are often treated separately in empirical studies of inflation. Under the monetary theory of inflation, movements in the price level are determined by movements in the money supply, given the demand for real balances. Under the fiscal theory, movements in the price level are driven by changes in government borrowing, given the fiscal deficit.

# _Monetary factors of inflation_

Under the monetary view, inflation arises from excessive expansion of the exogenously determined supply of money relative to demand for real balances. Put differently, inflation results from imbalances in the money market. Letting λ and -ρ be the elasticities of money demand with respect to output and the interest rate, respectively, the money market equilibrium can be represented as: −ρ , or ( + ρ = 0 (6) The variables _m, p, y,_ and _r_ are the logarithms of the stock of money, price level, output, and mm−pp= λ mm−pp) −λ The variables _m, p, y,_ and _r_ are the logarithms of the stock of money, price level, output, and interest rate.

12
# **1. Introduction**

The adverse impacts of climate change<sup>2</sup> and extremes represent a serious challenge to development efforts around the globe and are likely to exacerbate the incidence, severity and persistence of poverty in many countries. The global mean surface temperature of the earth has been rising as a result of increased emission of greenhouse gases, particularly carbon dioxide (DFID 2004). Climate change and extremes are expected to affect mostly climate-sensitive sectors of the economy and in turn influence the pattern of household income and consumption. It is estimated that three-quarters of the world’s poorest whose standard of living falls below $2 per day rely mostly on natural resources for their livelihoods (WRI 2008). The degradation of natural resources induced by climate change thus places significant stress on these livelihoods. As for agriculture, an important sector of activity for the poor, yields from rain-fed agriculture could be cut by half by 2020 in some parts of the world. It is feared that climate change could reduce soil fertility by 2 to 8 percent inducing a significant reduction in yields for a variety of crops.

However, very little is known about the welfare losses that households experience from these phenomena. Households at low levels of income are believed to be the most vulnerable to the impacts of climate change and extremes. This is due to their geographical locations, limited assets, limited access to resources and services, low human capital and high dependence upon natural resources for income and consumption. While there is wide recognition of this impending threat of climate change upon the poor, limited attention is given upon quantifying the poverty and distributional effects of climate change and identifying _adaptation_ strategies and targeted measures that could mitigate the poverty impacts.

> 2 According to the Intergovernmental Panel on Climate Change (IPCC) a narrow definition of climate refers to the statistical description in terms of the mean and variability of quantities such as temperature, precipitation and wind over a period of time ranging from months to thousands of years.  The norm is 30 years as defined by the World Meteorological Organization (WMO).  In a wider sense, climate refers to the _state_ and the _statistical description_ of a system composed of the following five components: atmosphere (gaseous envelope around the Earth), hydrosphere, cryosphere (snow and ice), land surface, and biosphere (all ecosystems and living organisms).  For more details, please see Parry et al.( 2007).  Climate is different from weather which refers to atmospheric conditions in a given place at a specific time.  The term “climate change” is used to indicate a significant variation (in a statistical sense) in either the mean state of the climate or in its variability for an extended period of time, usually decades or longer (Wilkinson 2006).

2
a relative success, in lower-income countries their performance has still lagged behind expectations, with c-efficiency ratios (which measure collection relative to potential) in Africa far below other regions (Figure 5).<sup>15</sup> Corporate tax collection continues to be plagued by extensive and poorly managed exemptions, despite decades of international pressure for enhanced transparency, and recent estimates suggest that revenue losses as a result of international tax evasion and avoidance are larger as a share of GDP in developing countries than in OECD member states.<sup>16</sup> Meanwhile, institutional reforms aimed at strengthening IT systems,<sup>17</sup> limiting political interference, reducing corruption,<sup>18</sup> and improving transparency and expanding engagement<sup>19</sup> have frequently underperformed. Finally, surveys reveal that trust in tax systems remains sharply limited, undermining compliance and the potential for constructing broader political support for reform (Figure 3, above).<sup>20</sup>

# **Figure 4: Composition of tax revenue in developing and OECD countries, 1990-2016**

<!-- Start of picture text --> Developing Countries OECD Countries<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>Trade Tax GST CIT PIT Trade Tax GST CIT PIT<br><!-- End of picture text --> _Source:_ ICTD /UNU-WIDER Government Revenue Dataset

> _prospects_ . Cambridge, MA: Lincoln Institute for Land Policy; van den Boogaard, V., Prichard, W., & Jibao, S. (2018). Informal taxation in Sierra Leone: Magnitudes, perceptions and implications. _African Affairs_ 118(471): 259–284.

> 15 Keen, M. (2013). The anatomy of the VAT. _IMF Working Paper 13/111_ . Washington: International Monetary Fund.

> 16 Cobham, A., & Jansky, P. (2017). Global distribution of revenue loss from tax avoidance: Re-estimation and country results. _WIDER Working Paper, no. 2017/55._ Helsinki: United Nations University World Institute for Development Economics Research.

> 17 Bird, R., & Zolt, E. (2008). Technology and taxation in developing countries: From hand to mouse. _National Tax Journal_ , 4 (2):791-821.

> 18 Ahlerup, P., Baskaran, T., & Bigsten, R. (2015). Tax innovations and public revenues in Sub-Saharan Africa. _Journal of Development Studies_ , 51(6): 689-706; Dom, R. (2019). Semi-autonomous revenue authorities in Sub-Saharan Africa: Silver bullet or white elephant. _The Journal of Development Studies_ , _55_ (7), 1418-1435

> 19 Moore, M. (2014). Revenue reform and state building in Anglophone Africa. _World Development,_ 60: 99-112.

> 20 Aiko, R., & Logan, C. (2014). Africa's willing taxpayers thwarted by opaque tax systems, corruption. _Afrobarometer Policy Paper 7_ .

6
the two parties (PRI and PAN) in power during our study period had large plans for infrastructure development, we also examine whether they similarly resorted to _machine politics_ (Dixit and Londregan, 1996; Cox and McCubbins, 1986) using the distribution of federal highway spending. We find no differential effect of political bias in highway investment between the PRI and PAN.<sup>4</sup> This is consistent with the idea that any party will make use of the it has to local allocations as just possibility influence public spending a tool to strengthen its political position. Overall, our findings indicate that in addition to electoral competition, legislative stability—a rather understudied topic in the political economy literature applied to developing countries—is an important determinant of the geographic distribution of public capital.

To our knowledge, we make four contributions to the literature. First, to overcome misreporting issues inherent to road expenditure data that most studies rely on, we combine election outcomes with actual physical investments at a politically-relevant geographic scale. The granularity of our data allows us to consider heterogeneity effects based on primary funding sources (federal vs. non-federal) for different types of roads. Second, our quasi-experimental approach makes it possible to causally infer the impacts of political incentives on road construction projects. Our paper is one of the rare studies, if not the first, to establish a causal relationship between tangible highway construction and political outcomes.<sup>5</sup> Third, we offer new insights into the debate about the institutional and political environment in which political favoritism thrives. While the development literature acknowledges the role of democracy (Hodler and Raschky, 2014; Burgess et al., 2015) and electoral competition (Brollo and Nannicini, 2012; Asher and Novosad, 2017), we provide strong evidence that a divided government is a significant

> 4 The Institutional Revolutionary Party (PRI) and the National Action Party are two major political parties in Mexico. The PRI held the executive power uninterruptedly from its creation in 1929 to 2000. In 2000, a coalition led by a candidate from the PAN won the presidential election allowing the PAN to rule Mexico until 2012.

> 5 Burgess et al. (2015) document a strong relationship between ethnic favoritism and road construction in Kenya, using both road expenditure data and the actual physical extent of roads, but acknowledge the limitations of their data to address potential challenges to causal identification.

3
# Table 6

Income Gains and Inequality Costs without Trade Policy Preference Reversals

||Income Gains|Equ|ality G<br>|ains<br>|
|---|---|---|---|---|
||_μ_1_−μ_0<br>_μ_0|_μ_1<br>_μ_0|_I_0(_ε_)_−I_1<br>1_−I_0(_ε_|_ε_)<br>)|
|||_ε_= 0_._5|_ε_= 1|_ε_= 10|
|A) Countries without Trade-ofs|||||
|Guinea-Bissau|2.0|0.5|0.7|0.8|
|Central African Republic|4.2|0.4|0.8|2.1|
|Jordan|4.1|0.4|0.7|0.3|
|Yemen, Rep.|2.7|0.2|0.3|0.1|
|Mongolia|0.1|0.1|0.2|0.5|
|Comoros|-0.3|-0.1|-0.2|-1.8|
|Rwanda|-0.2|-0.2|-0.2|-4.9|
|Madagascar|-1.1|-0.3|-0.7|-2.4|
|Ghana|-1.9|-0.4|-0.8|-2.3|
|B) Countries with Trade-ofs|||||
|Indonesia|1.9|0.1|0.2|-0.4|
|Pakistan|2.4|0.0|0.3|-0.2|
|Azerbaijan|2.5|0.0|0.0|-0.6|
|Zambia|5.9|-0.0|-0.1|-0.0|
|Moldova|0.7|-0.0|-0.0|0.6|
|Kyrgyz Republic|0.6|-0.0|-0.1|-0.0|
|Ukraine|3.2|-0.0|-0.1|-0.7|
|Egypt, Arab Rep.|2.9|-0.1|-0.2|-0.8|
|Tajikistan|1.9|-0.1|-0.2|-0.6|
|Iraq|1.6|-0.1|-0.1|-0.3|
|Armenia|2.5|-0.1|-0.2|-1.0|
|South Africa|2.5|-0.1|-0.4|-1.2|
|Guinea|2.8|-0.3|-0.6|-0.8|
|Uganda|1.9|-0.3|-0.5|0.3|
|Uzbekistan|3.5|-0.4|-0.7|-2.4|
|Cameroon|6.8|-0.5|-1.1|-5.0|
|Cambodia|-3.0|0.0|-0.1|-0.8|
|Bhutan|-0.8|-0.1|-0.5|-7.5|

Notes: Authors’ calculations. The table presents the decomposition of the inequality-adjusted gains from trade _G_ ( _ε_ ). The first column reports the average income gains from trade (the proportional change in real household expenditures). The three remaining columns show the equality gains (due to changes in inequality) for different values of inequality aversion ( _ε_ = 0 _._ 5, _ε_ = 1, and _ε_ = 10). The inequality-adjusted gains from trade is the _sum_ of the income gains and the equality gains.

50
Our analysis augments the most basic administrative data with additional data to improve the quality of prediction (in the case of Guatemala, with periodic national exam data for primary students; in the case of Honduras, with household survey and census data). This augmentation could affect the consistency of the prediction from year to year, as the availability of household survey and other data varies over time. It could also make the approach more difficult to replicate for practitioners within ministries of education, who may not be familiar with accessing and analyzing these other data sources. However, as mentioned above, in both countries more and more variables are being routinely collected over time, which will reduce the need to pull data from other sources and also could serve to improve the quality of prediction. In addition, as mentioned for Guatemala, a greater focus on obtaining complete data for each student would be critical to improving the quality and usefulness of the predictions.

While this paper has focused on the feasibility of making accurate and replicable predictions of who is most likely to drop out, this is only a first step towards developing an effective early warning system, and a natural question immediately arises – what to do with the data? Maintaining confidentiality, avoiding negative labeling of students, and identifying resources to act appropriately on the predictions are all among the potential important concerns. Moreover, the predictions in and of themselves do not identify the main factors that put any given student at risk of dropping out, information which is critical to providing effective interventions. A complete discussion about setting up an effective early warning system merits its own paper, but many lessons learned have emerged from the experiences of school districts in the U.S. These include clearly communicating the meaning of the predictions; defining roles at all levels in terms of who should receive what information and who is responsible for taking what actions; empowering local school officials to identify and implement relevant, customized prevention measures; and taking an iterative approach to facilitate learning from initial pilots (O’Cummings and Therriault 2015). These and other lessons learned from international experiences, combined with a strong knowledge of local contexts, show a path forward for responding effectively to the dropout crisis in Guatemala, Honduras, and many other countries.

17
# **Table of Contents**

|1.<br>Introduction ................................................................................................................. 1|
|---|
|2.<br>The Expenditure Surveys ............................................................................................ 3|
|3.<br>The NSSO versus NAS Expenditure Estimates .......................................................... 4|
|4.<br>The Official Poverty Lines .......................................................................................... 5|
|5.<br>Controversies regarding Poverty Lines ....................................................................... 7|
|6.<br>Poverty at the National Level ................................................................................... 10|
|7.<br>Poverty in the States: Rural and Urban ..................................................................... 17|
|7.1.<br>Rural and Urban Populations ............................................................................ 17|
|7.2.<br>Rural and Urban Poverty .................................................................................. 18|
|8.<br>Poverty in the States by Social Groups ..................................................................... 21|
|8.1.<br>Population Distribution by Social Groups within the States ............................ 22|
|8.2.<br>Poverty by Social Groups ................................................................................. 24|
|9.<br>Poverty in the States by Religious Groups ............................................................... 27|
|10.<br>Inequality .............................................................................................................. 30|
|11.<br>Concluding remarks .............................................................................................. 32|

2
<!-- Start of picture text --> 15<br>3 i!lL<br>=I<br>a<br>2<br>=40<br>5<br>E<br>g<br>£<br>5<br># 5<br>ac]<br>3<br>5<br>_<br>;<br>-100 -50 0 50 100<br>Annual Returns<br>MM 1961-2023 [C7 2010-2023<br><!-- End of picture text -->