rafmacalaba/datause-encoder-data huggingface dataset:
**_Figure 2 Payments of Taxes and Benefits of Public Spending by Household Market Income Decile (Million JOD)_**

<!-- Start of picture text --> 400<br>300<br>200<br>100<br>0<br>-100<br>-200<br>-300<br>-400<br>-500<br>Decile 1 2 3 4 5 6 7 8 9 Decile 10<br>Household per capita market income decile<br>Direct Taxes Indirect Taxes – direct Indirect Taxes – indirect<br>Direct Transfers Total Indirect Subsidies – direct Total Indirect Subsidies – indirect<br>In-kind Spending (education) In-kind Spending (health) Total Impact<br>Figure 3 Payments of Taxes and Benefits of Public Spending by Household by Household Market Income Decile<br>(Percentage of Market Income)<br>100<br>80<br>60<br>40<br>20<br>0<br>-20<br>Decile 1 2 3 4 5 6 7 8 9 Decile 10<br>Household per capita market income decile<br>Direct Taxes Indirect Taxes – direct Indirect Taxes – indirect<br>Direct Transfers Indirect Subsidies – direct Indirect Subsidies – indirect<br>In-kind Spending (education) In-kind Spending (health) Total Impact<br>Millions<br>Fiscal Revenue / Expenditure (JD)<br>Income)<br>Fiscal Revenue / Expenditure (Percentage of Market<br><!-- End of picture text --> Notes: Households are grouped into per capita market income deciles. Direct taxes include personal income tax, property tax and border exit tax. Indirect taxes include GST and SST. Indirect taxes-indirect is the cascading indirect effect of GST exemptions on selected goods and services. It has been modelled using the 2010 IO table uprated to 2016. Direct transfers include NAF, bread subsidy compensation and other government transfers. Indirect subsidies include electricity and water. Indirect subsidies -indirect is the cascading impact of commercial and industrial tariffs on households. In-kind spending includes health and education, net of costs or user fees. In-kind spending on health based on use of inpatient and outpatient health care. Contributory pension contributions and receipts are treated as deferred savings and income. Total Cash Impact excludes in-kind benefits.
|R<sup>2</sup>|0.15|0.16|0.16|0.17|0.16|0.17|0.25|0.18|0.18|
|---|---|---|---|---|---|---|---|---|---|
|#obs.|6550|6550|6550|4334|4334|4334|2184|2184|2184|

Significance level, *: p<0.10, **: p<0.05, ***: p<0.01. KCPE = Kenya Certificate of Primary Education.

37
BANERJEE, Abhijit V. & DUFLO, Esther. ”Inequality and Growth: What Can the Data Say?”. Cambridge, Mass.: MIT (mimeo), 2003.

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30
Bank, 1998). In addition to the Apoyo evaluations, Moncada (1996) considers various aspects of FONCODES performrance; Schady compares the FONCODES index of unmet basic needs with other district-level mesures of welfare available in Peru, and analyzes political influences on the timing and distribution of FONCODES investments (Schady, 1998 and 1999, respectively,.

The analysis in. this paper complements the work done by Apoyo and others. The Apoyo evaluations are useful to assess the opinions that FONCODES beneficiaries have of FONCODES, but they have only lirmited value to measure the targeting and impact of FONCODES investments. The evaluations did not create a rigorous control group necessary for a comparison of FONCODES beneiaiciaries with others; they were not based on a sampling framework which would make it possible to apply the results from the sample to a larger population-even if this population were limited to FONCODES beneficiaries; and information was not gathered on a number of important variables, including actual figures on school attendance or grade repetition.

# **5. The targeting ,of FONCODES investments in education**

Social expenditures in Peru have traditionally favored vocal, urban middle-class constituencies. FONCODES was set up, in part, to redress this balance, and funding projects which benefited poor areas and poor households has been an important program objective from the outset.

Targeting education resources is important in Peru because there are large difference ; in measures of educational attainment across regions and income groups. We use data from tY e 1996 INEI survey to illustrate this point. In Figure 5.1 we graph the average number of years of schooling attained by children of different ages who live in the poorest 25% of districts, up to those who live in the richest 25% of districts, when districts are ordered by mean per capita income.<sup>6</sup> Figure 5.2 presents a comparable graph for household income quartiles. Figures :i. and 5.2 clearly show that children in poor districts and poor households lag behind in the years of schooling they attain for any given age. The differences across quartiles increase with age, ,o that by age sixteen there is almost a two-year difference between children in the poorest and richest districts, and almost a one-year difference between those in the richest and poorest **iir** scome quartiles.

A host of factors probably contribute to differences in the educational attainment of children, including differences in the education of other household members, income,

> 6 INEI combined information from the 1993 population census and a household survey conducted in I1995 to estimate mean district per capita income. This is done by first, using the household survey to regress income per capita on a set of variables common to the census and the survey, and second, predicting mean income per capita for each district by applying the coefficients from the regression to the census (see INEI, 1996, and Hentschel et. al. for a discussion of the methodology).

7
The estimates presented here are conservative estimates of the likely environmental costs of the problems analyzed. Policy recommendations based on these estimates have to be made with extreme caution. A much more detailed analysis of the data provided, as well as of the applicability of the U.S. experience to the Mexican conditions, would have to be made. From the calculations, we draw the following conclusions.

(1) Where human lives are at risk, the costs of environmental problems rise sharply, even attributing the most conservative values to them.

(2) Water- and solid-vwaste related health problems are significant only if one assumes current diarrheal disease mortality rates, without reference to the potential benefits of oral rehydration therapy (ORT). Guaranteeing water supply, sewage supply, water treatment, and solid and toxic waste disposal are all vital. But given the potential benefits of ORT, the government should urgently promote campaigns to advertise ORT, its proper administration, and its benefits, and attempt to guarantee access of all those at risk to serum ingredients or laboratory compounds.

'3) If diarrheal infant mortality were controlled or significantly reduced, air pollution in Mexico City would be the country's major environmental problem. This would reflect both the large urban population and the incredibly high pollution level.

(4) Particulate matter is Mexico's most damaging pollutant, more dangerous than ozone and lead. Lead should soon cease to be a major health hazard-although the issue of use of leaded and unleaded gasoline in MCMA, their prices and effects on the use of catalytic converters are still critical issues for government.

_(5)_ Water supply to both Mexico City and to irrigation in agriculture are rarely mentioned as major Mexican environrmental problems. But underpricing water has led to overuse. We calcul- ad only the subsidies implicit in the charging systems. Massive pumping underground waters is also causing the terrain to sink, threatening infrastructure. Although the effects of subsidence in Mexico City were not even calculated, the cost estimates here suggest that adjustment of p.i^ing is urgent.

(6) Soil erosion is often considered the major environmental problem in Mexico and some Central American countries. The calculations suggest that after water pollution and related problems this could well be the case. We only considered on-farm costs (impaired crop yields), but off-farm costs (siltation of dams, for example) must eventually be taken into account. We also could not estimate the costs from increased fertilizer applications. Estimation based on applying U.S. parameters to Mexican soil data may be particularly invalid because the effects of soil erosion are very site specific. Such issues deserve much further analysis.

3
|Parameter<br>Value|
|---|
|Mekong– BWI model|
|pr1<br>384.660 (0.314)|
|pr2<br>124.496 (0.087)|
|pr3<br>504.480 (1.302 *)|
|Lag time (months)<br>2|
|Observations<br>50|
|DF<br>47|
|RMSE<br>963|
|R2<br>0.597|
|Mekong– Precipitation model|
|pr1<br>640.873 (1.844 **)|
|pr2<br>22.521 (2.072 **)|
|pr3<br>0.00593 (0.0915)|
|Lag time (months)<br>2|
|Observations<br>50|
|DF<br>47|
|RMSE<br>930|
|R2<br>0.624|
|Zambezi- BWI model|
|pr1<br>749.262 (3.379 ***)|
|pr2<br>-1358.335 (-4.054 ***)|
|pr3<br>886.249 (8.009 ***)|
|Lag time (months)<br>3|
|Observations<br>201|
|DF<br>198|
|RMSE<br>611.915|
|R2<br>0.688|
|Zambezi – Precipitation model|
|pr1<br>255.066 (1.914 **)|
|pr2<br>8.996 (3.024 ***)|
|pr3<br>-0.001 (-0.0440)|
|Lag time (months)<br>3|
|Observations<br>201|
|DF<br>198|
|RMSE<br>886.463|
|R2<br>0.346|

**Table 6 : Parameters of the BWI and Precipitation models for both basins: Mekong and Zambezi.  In parentheses are t-values. *** (p<0.01); ** (p<0.05); * (p<0.10).**

34
29 Hightower, A., R. Kiptui, A. Manya, A. Wolkon, J. L.V Eng, M. Hamel, A. Noor, et al. 2010. ―Bed net ownership in Kenya: the impact of 3.4 million free bed nets.‖ _Malaria Journal_ 9 (1): 183.

IMF. 2010. _Regional Economic Outlook Sub-saharan Africa, April 2010_ . International Monetary Fund.

Jann, B. 2008. ―A Stata implementation of the Blinder-Oaxaca decomposition.‖ _Stata Journal_ 8 (4): 453–479.

Kleinschmidt, I., C. Schwabe, L. Benavente, M. Torrez, F.C. Ridl, J.L. Segura, P. Ehmer, and G.N. Nchama. 2009. ―Marked increase in child survival after four years of intensive malaria control.‖ _The American journal of tropical medicine and hygiene_ 80 (6): 882.

Kenya National Bureau of Statistics and ICF Macro. 2010. _Kenya Demographic and Health_ _Survey 2008-09_ . Calverton, Maryland: Kenya National Bureau of Statistics (KNBS) and ICF Macro.

Kolaczinski, J., and K. Hanson. 2006. ―Costing the distribution of insecticide-treated nets: a review of cost and cost-effectiveness studies to provide guidance on standardization of costing methodology.‖ _Malaria journal_ 5 (1): 37.

Lengeler, Christian. 2004. ―Insecticide-treated bed nets and curtains for preventing malaria.‖ In _Cochrane Database of Systematic Reviews_ , ed. The Cochrane Collaboration and Christian Lengeler. Chichester, UK: John Wiley & Sons, Ltd, April.

Magesa, S. M, Ch. Lengeler, D. deSavigny, J. E Miller, R. J A Njau, K. Kramer, A. Kitua, and A. Mwita. 2005. ―Creating an ̳enabling environment‘ for taking insecticide treated nets to national scale: the Tanzanian experience.‖ _Malaria Journal_ 4: 34.

Ministry of Public Health and Sanitation. 2001. _Kenya National Malaria Strategy (KNMS) 2001– 2010_ . Ministry of Public Health and Sanitation, Nairobi, Kenya.

Ministry of Public Health and Sanitation. 2009. _Kenya National Malaria Strategy (KNMS) 2009– 2017_ . Ministry of Public Health and Sanitation, Nairobi, Kenya.

Mueller, D. H, V. Wiseman, D. Bakusa, K. Morgah, A. Daré, and P. Tchamdja. 2008. ―Costeffectiveness analysis of insecticide-treated net distribution as part of the Togo Integrated Child Health Campaign.‖ _Malaria Journal_ 7: 73.

Mulligan, J., J. Yukich, and K. Hanson. 2008. ―Costs and effects of the Tanzanian national voucher scheme for insecticide-treated nets.‖ _Malaria Journal_ 7: 32.
<!-- Start of picture text --> Figure 7 - PCG adjusted parameters during the pandemic<br>(In percentage of programs)<br>LAC US EU<br>Maturity<br>100<br>80<br>Program extension 60 Grace period<br>40<br>20<br>0<br>Economic sector Interest rate ceiling<br>Coverage Fees<br><!-- End of picture text -->

**Source:** Own elaboration. **Note** : For the US and the European Union (TF, individual countries ruled on grace periods and interest ceilings<sup>19</sup> ), the radar takes the maximum value (100) if the parameter was modified/included in the PCG program, 0 otherwise.

Many of the COVID-19 related PCG schemes were designed for a short period, and the end of these programs coincided with the peak of the pandemic. This is at odds with the experience of high-income economies, which have been extending the programs introduced during the crisis or launching new ones. In detail, 13 of the surveyed PCGs launched COVID-19 oriented programs, most of them during April 2020 (6).<sup>20</sup> The most successful PCGs have extended their COVID-19 programs (Peru, Paraguay, Brazil, Uruguay, and Chile). As of December 2020, COVID-19 programs in Peru, Argentina, Mexico (Bancomext), Paraguay, and Brazil were closed as they met the pre-determined termination date or exhausted the available capital. This contrasts with the experience of advanced economies, as the European Union extended the TF until December 2021, the United Kingdom replaced CBILS, CLBILS and BBLS with a new Recovery Loan Scheme (RLS) that will run until December 2021 and the United States launched a Second Draw PPP in January 2021.<sup>21</sup> Successful experiences during the pandemic point to loan standardization as a key feature to facilitate the use of guarantee programs **.** Ten of the 16 surveyed schemes standardize the contractual conditions of the loans. This is a key feature, and in some instances, explains the successful implementation of some programs. This was the case of Chile, where PCGs’ loan standardization matched the central bank's liquidity facilities conditions to the banking system. The survey shows that ten PCGs set grace periods, and

> 19 The EU TF also established that the minimum all in interest rate (base rate plus the credit risk margins) for SMEs and large enterprises receiving state-sponsored guarantees should be at least 10bps per year.

> 20 During 2021 some PCGs continued to create new programs for supporting borrowers. This is the case of FOGAPE Reactiva in Chile, which is targeted to financing for working capital, investment, and refinancing for MSMEs. The program, which will be valid until December 31, 2021, considers a coverage rate of up to 90 percent with no fees for the borrower. For additional details, see <u>http://www.fogape.cl/sitio/2021/02/23/participacion-programa-fogape-2-</u>

> <u>0-reactivacion/</u>

> 21 PPP ended in May 2021 after exhausting the funds allocated to the program.

13
Overland Transport Costs: a Review<sup>_∗_</sup> A. Kerem Coşar<sup>_∗∗_</sup>

**Keywords:** Freight costs, road transportation, gravity regression. **JEL Classification:** L9, F1, R4.

> _∗_ This paper was prepared as a background paper for the Global Transport Practice Flagship Report _Shrinking Economic Distance_ , led by Matias Herrera Dappe, Aiga Stokenberga, and Mathilde Lebrand. I thank them for helpful comments and feedback. For their insightful discussions of an early draft, I thank Roman Zarate and Théophile Bougna.

> _∗∗_ Department of Economics, University of Virginia; e-mail: kerem.cosar@virginia.edu
relationship is also apparent for the period as a whole. Landiyanto et al. (2005), in their study of East Java, also found that districts surrounded by wealthy neighbors tended to grow faster.

Topography can also make a difference to growth. Districts that have many villages on the coast or in the hills (as opposed to in valleys or on plains) tend to be poorer. Between 2001 and 2005, they also tended to have lower growth rates, too. However, this relationship does not appear to hold prior to the economic crisis.

The possession of significant oil and gas resources does not necessarily boost district growth. Prior to the crisis, districts that relied heavily on oil and gas resources grew almost 2 percentage points more slowly than districts without oil and gas. The same is also true for the immediate post-crisis period and for the decentralization era, although the gap in growth rates has narrowed. Interestingly, this is only the case for districts in which oil and gas is always the largest sector; the growth rates of districts that have some oil and gas in their GRDPs are generally similar to those of districts that do not have oil and gas.

**Figure 8: District growth is positively correlated with the growth of neighboring districts**

<!-- Start of picture text -->
-.5 0 .5 1<br>-.6 -.4 -.2 0 .2 .4 Weight Average Growth of neighboring districts 1999-2001<br>Weight Average Growth of neighboring districts 1993-1997<br>-.2 0 .2 .4 .6 -.5 0 .5 1 1.5<br>Weight Average Growth of neighboring districts 2001-2005 Weight Average Growth of neighboring districts 1993-2005<br>.2<br>.6<br>.1 .4<br>.2<br>0<br>0<br>-.1<br>-.2<br>Geometric Average Growth 1993-1997 Geometric Average Growth 1999-2001<br>-.2 -.4<br>.15 .2<br>.15<br>.1<br>.1<br>.05<br>.05<br>0<br>0<br>Geometric Average Growth 2001-2005 Geometric Average Growth 1993-2005<br>-.05<br>-.05<br><!-- End of picture text -->

Source: Authors’ calculations based on GRDP data from BPS. Real data.

Note: Graphs for 1993-97 and 1999-2001 use constant 1993 prices; graph for 2001-05 and 1993-2005 use constant 2000 prices.

15
# **Figure 4. Stylized facts on lead times from discovery to production for gold and copper deposits**

_The bulk of gold deposit discoveries are developed more quickly than the average copper mine. However, almost 60 percent of high quality (Tier 1) copper deposits are also developed within the first 10 years following discovery._ <!-- Start of picture text --> B. Lead times to production, by<br>size/quality of deposit (copper and gold)<br>Percent<br>35 Tier 1 Tier 2/Tier 3 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>0 20 40 60<br><!-- End of picture text -->

## **A. Lead times to production, by type of deposit**

<!-- Start of picture text --> Percent<br>35<br>Copper Gold<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0 20 40 60<br><!-- End of picture text -->

**D. Lead times to production, by size/quality of gold deposit**

# **C. Lead times to production, by size/quality of copper deposit**

<!-- Start of picture text --> Percent<br>35 Tier 1 Tier 2/Tier 3 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>0 20 40 60<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>35 Tier 1 Tier 2/Tier 3 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>0 20 40 60<br><!-- End of picture text --> Source: World Bank Staff, MINEX Consulting, B, C, D. Tiers refer to the size and quality of the deposit with Tier 1 deposits referring to the highest quality deposits based on criteria that include concentration of ore and size of find.

10
Table 5: Economic outcomes at time _t_ + 1

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_Panel A: First Stage_|||||||
|||payment|= 1||||
|Below trigger|0.341<sup>_∗∗_</sup>|0.330<sup>_∗∗_</sup>|0.351<sup>_∗∗_</sup>||||
||(0.121)|(0.117)|(0.143)||||
|_Panel B: Reduced form_|log|expenditu|res p.c.|lo|g income|p.c.|
|Below trigger|0.209|0.180|0.277<sup>_∗_</sup>|0.290<sup>_∗_</sup>|0.205|0.307<sup>_∗_</sup>|
||(0.140)|(0.126)|(0.138)|(0.165)|(0.153)|(0.153)|
|_Panel C: 2SLS_|log|expenditu|res p.c.|lo|g income|p.c.|
|�<br>% ha receiving payment|0.613<sup>_∗_</sup>|0.544<sup>_∗_</sup>|0.789<sup>_∗∗_</sup>|0.852<sup>_∗∗_</sup>|0.622<sup>_∗_</sup>|0.876<sup>_∗∗_</sup>|
||(0.333)|(0.314)|(0.356)|(0.361)|(0.369)|(0.353)|
|Bandwidth|50|70|all|50|70|all|
|Polynomial|linear|linear|quadratic|linear|linear|quadratic|
|N|2494|3538|5879|2494|3538|5879|

_∗ ∗∗_ Note. Standard errors are clustered at the state level. Asterisks indicate statistical significance: _p <_ 0 _._ 10, _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Observations are at the household-municipality-level. The treatment variables vary only at the municipal level. Panel A shows results from the OLS estimation of the first stage, which corresponds to estimating equation 1. Panel B shows results from the OLS estimation of the reduced form, which corresponds to estimating equation 2, and panel C displays 2SLS estimates. Columns 1 and 4 are estimated with a linear polynomial on a sample of observations within the optimal bandwidth of 50 mm. Columns 2 and 5 are estimated with a linear polynomial on a sample of observations within an alternative bandwidth of 70 mm. Columns 3 and 6 are estimated using a quadratic polynomial on the entire sample.

29