rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 3: Trust in the Tax Department (% of respondents)**

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Somewhat A lot<br> Algeria Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Côte d ́Ivoire Egypt Gabon Ghana Guinea Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Morocco Mozambique Namibia Niger Nigeria Sao Tome and Principe Senegal Sierra Leone South Africa Sudan Swaziland Tanzania Togo Tunisia Uganda Zambia Zimbabwe<br><!-- End of picture text --> _Notes_ : Respondents were asked: “How much do you trust each of the following, or haven’t you heard enough about them to say: The tax department?” (% who say “Somewhat” and “A lot”). Afrobarometer R6 2014/2015 (Algeria, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Côte d ́Ivoire, Egypt, Gabon, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe). _Source:_ Isbell (2017) based on Afrobarometer.

The central challenge facing reformers lies in identifying innovative strategies to strengthen revenue mobilization, trust, and fiscal contracts. Recent research points toward potential elements of a strategy to strengthen the links between revenues and public benefits, including a greater emphasis on building trust and tax morale among taxpayers, expanded attention to the political challenges of reform, efforts to better tailor reform to local contexts and needs, and clearer focus on empowering taxpayers. However, these strands of research have been somewhat fragmented, and have yet to be consistently applied in practice or translated into an overarching vision for reform.

The Innovations in Tax Compliance project develops an integrated framework to expand our understanding of more effective approaches to tax reform. This is best understood not as an effort to fundamentally rethink what we know about tax reform, but, rather, to provide a coherent framework for tax reform by bringing together – and consistently operationalizing – recent research on the drivers of tax compliance, trust and fiscal contracts, with lessons from the field.

Traditionally, approaches to reform have stressed the importance of technical measures to strengthen enforcement and, more recently, facilitate tax compliance. This focus has reflected early models of tax compliance, which presented the decision to comply as the product of a rational economic calculus: a taxpayer’s decision to comply is a function of the perceived probability of an audit, the extent of penalties, and a taxpayer’s risk aversion. Stronger detection and higher 4
# **Figure B17: Cotton**

<!-- Start of picture text --> Log scale<br>2.5<br>Log price Permanent component<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.00<br>Medium-term cycle Transitory component<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 58 —_
assume a 1:1 relationship between precipitation and runoff and apply these change factors directly to the 45-year monthly historical streamflow record. This results in 300 different sequences of 540 months of streamflow. We use these months of streamflow to emulate interannual variability at a single point in time, and not as a transient sequence of streamflow across time. That is, we treat all 540 months of data as samples from a single future climate regime in 2040, not as a 45-year sequence of streamflow from 2015 to 2060.

# Assessing Project And Portfolio Performance With Futures

We run each project and portfolio in 300 futures to test its performance under different uncertain demand and streamflow conditions. To evaluate a project or portfolio in a single future, WEAP calculates the monthly met demand as a percent of total demand in each of the 540 months. We post process these results to calculate our key metric—the 90th percentile of monthly met demand across the 540 months.

A key objective of the analysis is to summarize the conditions under which a portfolio achieves water reliability. While there are three exogenous demand and streamflow variables in each future, we describe performance in terms of demand and dry season streamflow because the system as currently envisioned is sensitive to changes in dry season streamflow but not wet season streamflow. This is because, in the wet season, SEDAPAL prioritizes filling of reservoirs to meet upcoming dry season demand, and streamflow in the wet season often exceeds reservoir capacity. Therefore most changes in wet-season streamflow do not affect water deliveries. In contrast, in the dry season, changes in streamflow can have immediate and direct impact on water deliveries and met demand. Changes in how SEDAPAL operates its system could change this sensitivity.

43
have some equalization effect (the slope coefficient is negative), but it is not statistically significant. The actual transfer scheme has the least equalization effect (the slope coefficient is almost zero).

Table 2. Regression Results under Four Transfer Schemes: PCTi = ao + a, PCGDPi

||1994 Actual<br>Transfer|20%<br>Equalization|_50%_<br>Equalization|100%<br>Equalization|
|---|---|---|---|---|
|**ao**|206.46|268.92|362.62|518.77|
||(1.10)|(1.36)|(1.66)*|(1.93)*|
|a,|-0.97|-17.4|41.95|-83.93|
||(0.075)|(-1.30)|(-2.81)*|(-4.54)*|
|R2|0.00|0.06|0.22|0.42|
|DF|28|28|28|28|

Note: the calculations use cost adjusted figures on fiscal needs. Numbers in parentheses are t ratios. A "*" indicates that the t ratio is statistically significant.

# **4.5. Conclusions**

Statistical evidence suggests that China's current fiscal transfer system performs almost no redistributive function. The illustrative example of a formula-based equalization transfer model presented in this appendix shows that an important improvement in redistribution can be made by introducing appropriate measures of fiscal capacities and needs with appropriate variables. One should notice that shifting from the current transfer system to a "full equalization" system may not be feasible in the short- or medium run. A pragmatic approach is to increase the magnitude of the new transfer scheme gradually over time in order to minimize political difficulties. The purpose of this illustrative example is not to provide the exact model that China is to use; the intent is to offer an alternative methodology to the ones that are being considered.

58
The difference in convergence between the eligible/non-eligible boys and girls is consistent with a program effect on the survival of eligible girls. Our formal empirical analysis captures this intuition in the context of multivariate regression.

**Figure 1. Comparing Survival at 36 months, Eligible vs Non- Eligible Girls and Boys**

<!-- Start of picture text --> Mean Survival Rate (36<br>months)<br>0.98<br>Girls,Eligible<br>0.97 Program start : October 1994 Girls, Non-eligible<br>Boys, Non-<br>0.96<br>Boys, Non-<br>0.95<br>0.94<br>0.93<br>Girls, Non-eligible<br>0.92<br>Boys,Eligible<br>Boys,Eligible 0.91<br>0.9<br>Girls,Eligible 0.89<br>0.88<br>1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br>Survey<br>year<br><!-- End of picture text -->

# **4.  Empirical Analysis**

# **4.1. Women’s Fertility Preferences**

To analyze women’s fertility preferences, we use repeated cross-sections of individuallevel NFHS data, collected from a representative sample of women ages 15-49 (childbearing age) in Haryana. The NFHS-1 sample contains 2846 individuals, NFHS-2 2908 and NFHS-3 2790. We highlight with interest the large increase in the percentage of women who report at least some secondary education across survey waves, which is also described in the NFHS final report (summary statistics are found in Table 2a). These changes underscore the need to control for household socioeconomic status in our specifications. We also point out the number of SC/OBC households is lower in NFHS-1, which we partially attribute to measurement error resulting from our calculation of the OBC category.

We examine the impacts of ABAD on the sex-ratio of living children and on the number and gender of children a woman reports as her ideal. The sex ratio of living children is computed by dividing (number of living girls/number of living boys). By construction, this requires a mix of male and female children within a household. The value is missing among women whose children are all girls (for whom the ratio is technically infinite) or 10
Figure 10a: Share of Services Trade in Total Trade (Resource Rich Countries) <!-- Start of picture text --> 0.35<br>2002/2003<br>0.3<br>2006/2007<br>0.25 2010/2011<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>AZE KAZ RUS TJK<br><!-- End of picture text --> Source: Authors’ calculations based on WDI. Note: Data for Turkmenistan and Uzbekistan, also resource rich, are not available.

Figure 10b: Share of Services Trade in Total Trade (Resource Poor Countries) <!-- Start of picture text --> 0.35<br>2002/2003<br>2006/2007<br>0.3<br>2010/2011<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>ARM BLR GEO KGZ MDA UKR<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations based on WDI.<br><!-- End of picture text -->

# **3. Export Diversification along the Destination Dimension**

## **Destination Diversification and the Commodity Boom**

The commodity price boom had some effects on diversification along the market dimension among the ECA countries. Using the same methodology as in the previous section, we look at whether some identifiable pattern could be extracted from how export diversification performance varied along the destination dimension between the resource-rich and the resource-poor countries in the region. Results from estimating equation (2) but using destination diversification indicators as dependent variables are reported in Table (3) in the Appendix. Though less striking than for the product diversification case, results suggest that the commodity boom could have acted as a barrier to diversification along the market dimension too.

Figure 11: Average diversification trends during the commodity boom.

10
**2b. Group 2 and Group 3**

|**Gro**|**up2**||**Group 3**|
|---|---|---|---|
|**Plants**|**Arthropods**|**Plants**|**Arthropods**|
|_Albucapentheri_|_Clania macgregori_|_Pearsonia bracteata_|_Ableptina nubifera_|
|_Antimimagracillima_|_Colletes zygophyllum_||_Acollesisfraudulenta_|
|_Arctotheca marginata_|_Cryptolarynx estriatus_||_Acontia tanzaniae_|
|_Babianapapyracea_|_Idopompilus braunsi_||_Acontia tinctilis_|
|_Chamarea snijmaniae_|_Merostenus longistylus_||_Acontia torrefacta_|
|_Chrysocoma hantamensis_|_Moluris semiscabra_||_Afrobirthama hobohmi_|
|_Cyanella aquatica_|_Nesomyrmex nanniae_||_Aphilopota iphia_|
|_Cygnicollum immersum_|_Nixonia mcgregori_||_Aphilopota subalbata_|
|_Daubenya capensis_|_Opistophthalmus laticauda_||_Aspilatopsisgloriola_|
|_Daubenya stylosa_|_Patellapis bifurcata_||_Athetis xanthopis_|
|_Diascia insignis_|_Patellapis doleritica_||_Audeafumata_|
|_Diascia lewisiae_|_Patellapis minima_||_Chiasmia kirbyi_|
|_Euryops mirus_|_Patellapis tenuihirta_||_Clostera lentisignata_|
|_Geissorhiza divaricata_|_Patellapis timpageleri_||_Concavia transvaali_|
|_Geissorhiza subrigida_|_Prosoeca marinusi_||_Conchylia sesquifascia_|
|_Hesperantha rivulicola_|_Scrapter mellonholgeri_||_Cropera sericea_|
|_Hesperantha vaginata_|_Scrapter oxaloides_||_Desmeocraera vernalis_|
|_Ixia brunneobractea_|||_Drepanogynisglaucichorda_|
|_Lachenalia alba_|||_Epilepia melanobrunnea_|
|_Massoniapseudoechinata_|||_Epiplema inconspicua_|
|_Mesembryanthemum_<br>_tenuiflorum_|||_Eublemma albivena_|
|_Moraea aspera_|||_Eublemmafulvitermina_|
|_Moraeafragrans_|||_Eutrichafulvida_|
|_Moraea hesperantha_|||_Euxoa anarmodia_|
|_Moraeapseudospicata_|||_Gibbalaria sistrata_|
|_Oxalis argillacea_|||_Halseyia rufilinea_|
|_Oxalis callosa_|||_Idaea associata_|
|_Oxalisfilifoliolata_|||_Laelia extatura_|
|_Pelargonium aristatum_|||_Lamoria exiguata_|
|_Pelargoniumpachypodium_|||_Leucania cupreata_|
|_Pentameris dentata_|||_Lithacodia normalis_|
|_Romulea discifera_|||_Lymantriades xanthosoma_|
|_Romulea sabulosa_|||_Mauna ava_|
|_Selago spectabilis_|||_Metasia eremialis_|
|_Strumariapicta_|||_Metopteryx rattus_|
|_Trachyandraprolifera_|||_Mimasura innotata_|
|_Xiphotheca canescens_|||_Mimasuraquadripuncta_|
|_Zaluzianskya regalis_|||_Mythimna atrinota_|
||||_Naarda melanomma_|
||||_Nycteola malachitis_|
||||_Odontopera homales_|
||||_Omphalucha albosignata_|
||||_Panagropsis muricolor_|
||||_Pseudomallada sjostedti_|
||||_Scopula deserta_|
||||_Scotopteryx cryptocycla_|

20
Our main interest is in identifying if property values in riskier areas are lower, thereby capitalizing “dis-amenities” from potential risk. We consider two approaches for comparing property prices. First, we group properties into deciles of seismic risk, and compare prices for matched properties in two adjacent deciles. For example, comparable properties in the riskiest decile are valued 7114 pesos less than properties in the next riskiest decile (see comparisons between the 9<sup>th</sup> and 10<sup>th</sup> decile in Table 1). Similarly we see lower property values in locations with higher risk for most deciles of seismic risk.

**Table 1: Propensity score matching results**

|10th decile: most riskyneighborhoods. 1st decile: least riskyneighborhoods|
|---|

|Propensity Score|||Difference<br>in property||
|---|---|---|---|---|
|<br>Values|Treated|Controls|<br>values|Standard Errors|
|9th vs. 10th decile|107,864.21|114,981.45|(7,117.24)|438.65|
|8th vs. 9th decile|123,985.18|124,605.55|(620.37)|292.96|
|7th vs. 8th decile|135,456.52|183,383.70|(47,927.18)|561.85|
|6th vs. 7th decile|183,383.70|177,011.83|6,371.87|683.37|
|5th vs. 6th decile|194,592.08|207,884.16|(13,292.08)|755.68|
|4th vs. 5th decile|207,884.16|189,487.95|18,396.21|663.00|
|3rd vs. 4th decile|193,627.91|234,565.17|(40,937.26)|2,166.93|
|2nd vs. 3rd decile|232,855.67|188,860.65|43,995.02|2,043.64|
|1st vs. 2nd decile|188,860.65|193,513.79|(4,653.14)|454.74|

Source: estimates for this paper.

Second, we identify ten neighborhoods facing the highest seismic risk, and examine if property prices are significantly higher in locations that are further away (Figure 5 shows where these neighborhoods are located). These include La Picota Oriental, San Juan Bautista and La Arbolada Sur. Estimates in Table 2 confirm that difference between properties in the riskiest neighborhoods and others locations increase with distance. For instance, property values per unit of construction are higher by 13,434 pesos in locations closest to the riskiest neighborhoods, and the difference increases to 28,265 pesos for locations in the 20<sup>th</sup> -204<sup>th</sup> percentile of distance. For the furthest 20 percentile, the 14
# <u>Appendix table 3: Effects of Cash on Long-Term Outcomes</u>

||Language<br>|Learning<br>||Fine Mo<br>|tor Skills<br>||Social Sk<br>|ills<br>||Composi<br>|te Developm<br>|ent<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(I)|(II)|(III)|(I)|(II)|(III)|(I)|(II)|(III)|(I)|(II)|(III)|
|Treatment|||||||||||||
|Effect (Cash)|0.08|0.07|0.18**|0.14|0.11|0.09|0.14*|0.14*|0.03|0.14*|0.13*|0.14**|
||(0.08)|(0.07)|(0.07)|(0.09)|(0.08)|(0.07)|(0.07)|(0.07)|(0.09)|(0.07)|(0.07)|(0.07)|
|Intercept|-0.15**|-0.2|-0.09|-0.1|-0.25*|-0.2|-0.18**|-0.31*|-0.18|-0.18**|-0.32**|-0.17|
||(0.06)|(0.15)|(0.24)|(0.08)|(0.15)|(0.17)|(0.05)|(0.16)|(0.25)|(0.05)|(0.14)|(0.22)|
|N|1353|1353|624|1353|1353|625|1353|1353|625|1353|1353|624|
|R-squared|0.00|0.01|0.10|0.00|0.02|0.06|0.00|0.01|0.02|0.01|0.02|0.07|

_Note: Standard errors in parentheses. '*' denotes significance at p<.1 level, '**' at p<.05. Model (I) is a simple regression with clustered standard errors, model (II) adds demographic and explanatory controls including household size, gender of household head, age of household head, education of household head, age of youngest child, weeks since last payment, and distance of the household from the nearest school, and age of the child (in months) and model (III) adds additional baseline controls_ 30
while non-lifeline customers enjoyed a 50% subsidy for the first three months. Specifically, we adopt two strategies: First, we replicate the main analysis by estimating equation 2 separately for lifeline and non-lifeline residential customers and examine the trends in consumption over time. This allows us to see the trends in consumption for both lifeline and non-lifeline customers, especially the response of non-lifeline customers after the removal of their partial subsidy. The results as shown in Figure 6 reveal interesting trends in the responses to the subsidy. We see that relative to the pre-subsidy (COVID) period, electricity consumption by lifeline customers increased by between 35% and 70%. On average, between April and December 2020, electricity consumption of lifeline customers (100% subsidy) increased by about 64% relative to the average consumption in the pre-subsidy period (see Table 10 column 2<sup>24</sup> ). On the contrary, for non-lifeline customers (50% subsidy) demand increased only during the first two months of the subsidy, falling sharply after the subsidy removal. In fact, for the most part of the post-subsidy period, monthly consumption was below the pre-subsidy (covid) period (see Figure 6).<sup>25</sup> During the three-month period (April-June) where the partial subsidy was offered to non-lifeline customers, average consumption increased by 11% relative to the pre-subsidy period and declining thereafter by 4.3% during the post-subsidy (see Table 10 column 4). Thus, comparatively, the results provide suggestive evidence that the electricity subsidies played a key role in the relatively high increase in electricity consumption during the COVID period in Ghana. At the same time, the decline in consumption among non-lifeline residential customers after the removal of the subsidies relative to the pre-COVID era is indicative of the potential effect of COVID on household electricity consumption if the subsidies were not offered.

Our second strategy exploits the differences in the duration of the subsidy across residential user types to estimate the effect of the subsidy on consumption. Given that the subsidy for non-lifeline customers expired after the third month, while lifeline customers continued to enjoy the subsidy, we leverage the removal of the subsidy for the non-lifeline customers to conduct a "conventional" difference-in-difference analysis by using consumption data of the two groups between April-December, 2020 and comparing the differences in consumption between lifeline and non-lifeline customers before and after July 2020 when the subsidy for non-lifeline customers expired. So essentially, we estimate the effect of subsidy removal to identify the extent to which subsidies contributed to the stark increase in electricity consumption in Ghana as shown by the preceding results. Note that

> 24 _._ 494 ( _exp −_ 1) _×_ 100 = 63 _._ 8%

> 25An exception is month _t_ = 8, i.e. December 2020, where we see a spike in consumption.

14
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# **_2.1 The 2011 PPPs and the global poverty estimates_**<sup>**_12_**</sup>

In 2014, the ICP released the 2011 PPPs, reflecting some key changes in coverage and methodology. Country coverage of the ICP increased from 146 economies in the 2005 round to 199 economies in the 2011 round. The 2011 PPPs are based on data from these 199 economies, which represent 97 percent of the world’s population and approximately 99 percent of the world nominal GDP (in U.S. dollars using exchange rates). In addition to improved coverage globally, the 2011 PPPs also increased coverage within countries. In particular, World Bank (2015a) reports that the 2011 ICP saw improved coverage of China. The 2005 PPP data for China are based on collecting data from 11 cities (and surrounding areas), but the 2011 PPP data come from nationwide surveys covering urban and rural areas in all provinces. In terms of methodological changes, the ICP 2011 aimed to improve linkages across regions of the world by introducing a “global core list” of 618 items for all countries to include.<sup>13</sup> Once again, potentially due to these changes in ICP methodology, there is scope for the new price data to alter the overall profile of global poverty. The 2011 PPPs significantly revise relative price levels, particularly between rich and poor countries. In broad strokes, the main difference is that relative prices are substantially lower in developing countries according to the 2011 ICP round. As a result, incomes in PPP-adjusted dollars are correspondingly higher in poor countries than previously indicated by the 2005 PPPs. This has fostered a new debate about the relative merits and quality of these two ICP rounds.

Deaton and Aten (2014) argue that the 2011 PPPs are superior to the 2005 PPPs, and in part reverse a bias in the 2005 PPP estimates. The 2005 PPPs are essentially the product of two price indices – one index established purchasing power parity _within_ regions and the other compared _across_ regions. The cross-region index is based on data from a set of 18 so-called “ring” countries in which a distinct commodity list was used to price out goods that were not unique to any particular region. Deaton and Aten argue that the cross-region ring index is the key source of error in the 2005 PPP data, resulting in an overestimation of the relative price levels in Africa, Asia and Western Asia by 20 to 30 percent. Along similar lines, Inklaar and Rao (2014)

> 12 Parts of this section draw heavily from Jolliffe and Prydz (2014).

> 13 Countries were asked to price as many products as possible from the 618 items in the global core list, provided they were available and not unusual in their economies.

8