rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2.2: Investment-to-Output Ratios by Country Income Groups, Average over 2006-2015 (percent)**

|**Incomegroup**|**P10**<br>|**P25**<br>|**P50**<br>|**P75**|**P90**|
|---|---|---|---|---|---|
||**P**<br>|**ublic investment-t**<br>|**o-output, **I<sup>G</sup>/Y<br>|||
|High|2.3|3.1|4.0|4.8|6.8|
|Upper middle|3.2|4.2|5.4|6.7|10.8|
|Lower middle|2.7<br>|3.6<br>|5.6<br>|7.7|11.5|
|Low|4.7<br>|5.8<br>|7.0<br>|8.6|11.5|
||**P**|**rivate investment-t**|**o-output, **I<sup>P</sup>/Y|||
|High|13.7|15.9|18.2|19.8|22.3|
|Upper middle|12.9|16.0|17.7|21.0|23.7|
|Lower middle|7.8|13.8<br>|16.3<br>|21.5|25.2|
|Low|7.6|11.2<br>|12.8<br>|18.4|22.6|
|||**Investment-to-o**<br>|**utput, **IY<br>⁄<br>|||
|High|18.9|20.3|22.5|23.8|27.3|
|Upper middle|19.4|21.3|24.2|27.3|32.6|
|Lower middle|15.9|17.9|23.0|28.2|31.1|
|Low|13.5|18.4|22.3|24.6|33.0|

Notes:

- Data reported are 10-year average (2006-2015) investment to GDP shares at various percentiles (from 10 to 90) of each income group distribution.

- The sample comprises 144 countries: 43 high income, 38 upper-middle-income, 34 lower-middle-income and 29 low income. Countries are categorized into income groups based on World Bank classification.

- ⁄ data is obtained from WDI. and are calculated by splitting the WDI data on ⁄ using public and private investment shares of total investment from the IMF Fiscal Affairs Department Investment and Capital Stock Database (which has data available up to 2015)IIYY II<sup>GG</sup> /YY. II<sup>PP</sup> /YY IIYY

- Our baseline projections for Malaysia places it between the 75<sup>th</sup> and 90<sup>th</sup> percentiles of the high-income country distribution for <u>/ , and at the 50</u><sup>th</sup> <u>percentile for /</u> II<sup>GG</sup> YY II<sup>PP</sup> YY.

The efficiency of public investment, as reflected by the Infrastructure Efficiency Index (IEI) in the LTGMPC, is high in Malaysia. Malaysia’s IEI score of 0.877 puts it at about the 50<sup>th</sup> percentile among highincome countries. This indicates that infrastructure in Malaysia is well-constructed and of high quality, which is supported by the World Economic Forum’s survey on infrastructure quality for its Global Competitiveness Index – Malaysia ranked 21 of 137 countries in 2017/18. Given the high base, the potential growth impact from improvements in this measure of efficiency is limited, and so we assume quality is constant at its current rate in the baseline and all scenarios.

The IEI is best thought of as capturing public capital construction quality, which does not capture other less quantifiable, but important, aspects of public capital quality like poor project selection or an inflated cost of construction. We are, thus, unable to properly examine the implications of improvements on these elements in the model, especially, in the context of cross-country comparisons. However, it does not mean that these elements are not important for Malaysia. For instance, IMF (2019) notes that fiscal 10
**Table 8:** The effect of visa provision on the bilateral migration stocks: Primary _versus_ other sector migration.

|Dep var:|Prima|ry sec.|Othe|r sec.|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Visa|0.274***|0.274***|0.119***|0.120***|
||(0.080)|(0.079)|(0.045)|(0.045)|
|RTA|-0.011|-0.013|-0.092**|-0.091**|
||(0.083)|(0.082)|(0.038)|(0.038)|
|Schenghen||0.085||0.061|
|||(0.135)||(0.092)|
|GDP diference (ln)|0.048|0.050|0.039*|0.040*|
||(0.038)|(0.039)|(0.020)|(0.020)|
|Estimator|PPML|PPML|PPML|PPML|
|Country-Year Fixed Efects|Yes|Yes|Yes|Yes|
|Country-Pair Fixed Efects|Yes|Yes|Yes|Yes|
|Sample period|2000-2010|2000-2010|2000-2010|2000-2010|
|Observations|2,494|2,494|4,270|4,270|
|Pseudo R-squared|0.99|0.99|0.99|0.99|

Note: Dependent variable is the stock of immigrants in country _j_ originating from _i_ . Data on bilateral stock of migrants by macro-occupation are from the OECD DIOC dataset `https: //www.oecd.org/els/mig/dioc.htm` . Primary educated immigrants are those in ISCO occupation 92, 93 and 81. Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.

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neighborhoods with different proportions of informal workers. We estimate this effect using different specifications: an OLS estimation of all projects that were sold within a year, a panel specification including only projects that spend more than one year being sold, and a panel specification considering all projects in the sample.

Residents in Mexico City value proximity to employment subcenters as an amenity on housing prices. For all specifications, being 1 percent closer to employment subcenters (and controlling for their employment level) increases housing values between one and almost three percentage points. As employment subcenters earn importance in the spatial structure of the city, the Central Business District loses significance. In two of the three specifications analyzed, being closer to the Central Business District is perceived as a dis-amenity that depreciates housing prices. Efforts to revive the center of the city would be required to change the current negative perception captured on the negative effects on house prices.

On the other hand, proximity to public transit stations has a different effect on housing prices. While being closer to a BRT station does not have an effect on housing values, being closer to a Metro station is considered a dis-amenity that negatively affects housing prices. The coefficient of the quadratic term is of opposite sign, suggesting that housing prices increase with distance to the stations at a decreasing rate.

This negative perception of public transport is problematic for a city the size of Mexico City, where congestion and pollution are exacerbated by the common use of private vehicles -over 20 percent of the population commutes by private vehicles compared to about 12 percent in a city like Tokyo.<sup>15</sup> These results are relevant for policy makers and urban planners when planning future public and private infrastructure projects. The fact that private accessibility is more important than public accessibility (measured by distance to a public transport station) poses a challenge for cities that experience fast and unplanned growth. The use of private vehicles (including individual and collective transport), despite the existence of Metro and BRT systems, is not sustainable, even if new roads and highways are built. As the polycentricity of cities increases, in terms of employment, policy makers should think about

> 15 Institute for Transportation and Development Policy (2012). “Transforming Urban Mobility in Mexico. Towards accessible cities less reliant on cars.”. March (2012) http://mexico.itdp.org/wpcontent/uploads/Transforming-Urban-Mobility-in-Mexico.pdf

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622 million, a reduction of 5 percent. The majority of the poor by 2015 are projected to be in Sub-Saharan Africa, and there the reduction would be 6 percent.<sup>8</sup> Under the Doha scenarios reported in Table 12, the poverty impacts are far more modest. The number of poor living on $1/day or less would fall by 2.5 million in the case of the core Doha Scenario 5 (of which 0.5 million are in SSA) and by 6.3 million in the case of Doha Scenario 6 (of which 2.2 million are in SSA). This corresponds to the relatively modest ambitions of the merchandise trade reforms as captured in these Doha scenarios. If only agriculture was reformed (Doha Scenario 1) there would be much less poverty alleviation globally and none at all in SSA. This shows the importance for poverty of including manufactured products in the Doha negotiations.

# **_Developing countries could trade off Special and Differential Treatment for_**

**_more market access._** If developing countries were to tone down their call for Special and Differential Treatment (see Josling 2005), in terms of wanting smaller cuts and longer phase-in periods, reciprocity means they could expect bigger tariff and subsidy cuts from developed countries. Similarly, if they were to forego their call for lesser cuts for “Special Products”, they could demand that developed countries forego their call for some “Sensitive Products” to be subject to smaller tariff cuts. A comparison of Scenarios 5 and 6 in Tables 7, 8 and 9 shows that the economic payoffs for low-income countries even if high-income countries do not reciprocate with larger offers is considerable. Moreover, by embracing those options to reform more in the context of the Doha round

8 The approach here has been to take the change in the average per capita consumption of the poor, apply an estimated income-to-poverty elasticity, and assess the impacts on the poverty headcount index. We have done this by calculating the change in the real wage of unskilled workers, deflating it by a food/clothing consumer price index which is more relevant for the poor than the total price index. That real wage grows, over all developing countries, by 3.6 percent, or more than four times greater than the overall average income increase. We are assuming that the change in unskilled wages is fully passed through to households. Also, while the model closure has the loss in tariff revenues replaced by a change in direct household taxation, the poverty calculation assumes – realistically for many developing countries -- that these tax increases only affect skilled workers and high-income households. While these simple calculations are not a substitute for more -detailed individual country case study analysis using detailed household surveys as in, for example, Hertel and Winters (2005), they are able to give a broad region-wide indication of the poverty impact.

14
_Brazilian spatial labor dynamics Page 24_ Table 9: Regression results, Non-metropolitan Brazil

|Dependent Variable:<br>Delta ln Wage|2SLS Est.|2SLS SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|.09597151|.10607452|-.07212585|.15562966|
|ln Teacher qualification in 1991|.00297498|.00255803|-.00046191|.00367207|
|Years of Schooling in 1991|.06753124|.00544935|.06342638|.00746079|
|Total Rainfall|.00005995|.00001096|.00007959|.00001616|
|Government Accountability|.01715587|.01423606|.00466584|.02099178|
|Delta ln Employment|-.58726752|.07915421|-.72860634|.13080938|
|ln Transport Cost SP in 1995|-.05128457|.00834921|-.04954901|.01126003|
|ln Transport Cost Capital in 1995|-.06169838|.00863113|-.04358863|.0138009|
|Delta ln Transferences|.24978392|.04062804|.16556984|.06593844|
|Delta ln Market Potential|.33007705|.12288672|.67970574|.17956081|
|crit. fn. test of overid. restrictions|----|----|51.198551|51.198551|

|Dependent Variable:<br>Delta ln Wage|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|---|---|---|---|---|
|Intercept|-.04565513|.17729723|-.0494362|.21368676|
|ln Teacher qualification in 1991|-.00177951|.00383909|-.00199813|.00412284|
|Years of Schooling in 1991|.06576312|.00830697|.06912613|.00973923|
|Total Rainfall|.00008265|.0000173|.00007853|.0000177|
|Government Accountability|.00246969|.02073262|.00032706|.01966279|
|Delta ln Employment|-.73485869|.14010873|-.74297255|.15258759|
|ln Transport Cost SP in 1995|-.05022805|.01231244|-.04537549|.01413209|
|ln Transport Cost Capital in 1995|-.04676843|.01599272|-.04803535|.01860846|
|Delta ln Transferences|.16973922|.07140593|.16184129|.08098298|
|Delta ln Market Potential|.64660937|.19555643|.60036469|.22895648|
|crit. fn. test of overid. restrictions|35.984318|35.984318|22.100483|22.100483|
Table 4: Dependent Variable: Arms imports - COMTRADE

||(1)<br>All|(2)<br>USA|(3)<br>RUS|(4)<br>CHN|(5)<br>DEU|(6)<br>FRA|(7)<br>RoW|
|---|---|---|---|---|---|---|---|
|Discovery in past 5 years|0.398<sup>_∗∗_</sup>|-0.062|0.428|0.928<sup>_∗∗_</sup>|0.329|0.333|0.707<sup>_∗∗∗_</sup>|
||(0.176)|(0.286)|(0.513)|(0.425)|(0.432)|(0.530)|(0.245)|
|N|3392|3523|3523|3523|3523|3523|3523|
|R-sq|0.72|0.66|0.57|0.61|0.72|0.64|0.66|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||All|USA|RUS|CHN|DEU|FRA|RoW|
|Discovery in past 5 years|0.340|-0.016|0.121|1.001<sup>_∗_</sup>|1.126|1.043|0.941<sup>_∗∗∗_</sup>|
||(0.240)|(0.420)|(0.592)|(0.567)|(0.736)|(0.852)|(0.313)|
|_×_ Oil price|-0.119|0.091|-0.610|0.144|1.584<sup>_∗_</sup>|1.409|0.464|
||(0.301)|(0.838)|(0.973)|(0.857)|(0.784)|(0.929)|(0.560)|
|N|3392|3523|3523|3523|3523|3523|3523|
|R-sq|0.72|0.66|0.57|0.61|0.72|0.64|0.66|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

the whole study period, with a long list of other countries accounting for the remaining 25%. I thus check if discoveries trigger arms imports from these large arms exporters in particular. The first surprising result is that arms imports from the United States, the largest arms exporter in the world, do not react at all to giant discoveries. This is true across specifications and datasets. For other countries the results are different whether we look at COMTRADE or SIPRI data. Using COMTRADE data I find arms imports from China to be particularly responsive to giant discoveries, while arms from France and Germany react strongly to discoveries but only in times of high oil prices. SIPRI data on the other hand suggest that it is Russian arms that react most to giant discoveries. Overall these results suggest that different discovery countries might import arms from different countries.

In Table 6 I look at how different types of discoveries affect arms imports. I focus on three dimensions, i.e. oil vs gas, onshore vs offshore, and by field distance to the nearest border. The top panel shows results when I include separate dummies for oil and gas discoveries 20
**Table 6:** Knowledge of Public Goods Needs, Heterogeneity by Baseline Road Quality

||Balance<br>_Podes_2014|Knowledge<br>Roads Quality|Receive<br>Roads Complaint|Take Action<br>on Roads|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|||**Panel A: Baseline**|**Roads Quality = High**||
|New village head|0.117|0.635<sup>_∗∗_</sup>|0.052|-0.149|
||(0.108)|(0.292)|(0.116)|(0.161)|
||[0.128]|[0.332]|[0.137]|[0.196]|
|Observations|496|525|525|213|
|Control mean|0.49|2.49|0.68|0.76|
|Robust p-value|0.391|0.0155|0.870|0.344|
|MSE-opt. bandwidth|28.2|12.3|18.8|21.8|
|Effective obs.|306|187|250|106|
|||**Panel B: Baseline**|**Roads Quality = Low**||
|New village head||-0.226|0.387<sup>_∗∗∗_</sup>|0.355<sup>_∗_</sup>|
|||(0.491)|(0.112)|(0.166)|
|||[0.590]|[0.134]|[0.204]|
|Observations||510|510|215|
|Control mean||2.42|0.67|0.70|
|Robust p-value||0.563|0.001|0.086|
|MSE-opt. bandwidth||16.2|17.3|17.1|
|Effective obs.||205|219|90|
|High vs. low p-value||0.245|0.145|0.144|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are villages in column 1, bureaucrats in columns 2 and 3, and village heads in column 4. In column 1, the dependent variable is an indicator for whether a village is below or above the median of a standardized index of transportation costs constructed using the 2014 Podes data. In column 2, the dependent variable is bureaucrats’ rating of road quality measured on a 5-point Likert scale. In column 3, the dependent variable is an indicator equal to 1 if the bureaucrat reports receiving complaints about roads from village citizens. In column 4, the dependent variable is an indicator equal to 1 if the village head took action on roads. Road quality is measured by a standardized index of transportation costs to the subdistrict office and the regent or mayor’s office, where lower transportation costs indicate better road quality. Panel A restricts the sample to villages with high baseline road quality (transportation costs below the sample median), while Panel B restricts the sample to villages with low baseline road quality (transportation costs above the sample median). The bottom row reports the p-value from the test of Calonico et al. (2025) that robust RD estimates are significantly different across the two panels. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Conventional standard errors in parentheses and robust bias-corrected standard errors in brackets. Significance levels are based on the robust bias-corrected standard errors.

38
and 11% in Sierra Leone. These low values suggest that large-scale utilization of azithromycin as a COVID19 treatment would be difficult without substantially increasing stocks in facilities.

While antibiotics (other than potentially azithromycin) are not viable treatments for COVID-19, examining a composite indicator of availability of any of a wide range of antibiotics (amoxicillin, azithromycin, ampicillin) in the facility can serve as a proxy for availability of key medicines more broadly. Encouragingly, over 90% of health facilities in Kenya had at least one of these three common antibiotics and over 80% in Sierra Leone and Tanzania, however, it was rare for a facility to have all three. Figure 9 indicates that the availability of all three of these antibiotics is highly variable, with most of the variation coming from differences in availability between hospital and lower-level facilities (as opposed to urban/rural location) (Appendix Tables 3 and 4).

Information on other relevant treatment equipment and supplies (such as oxygen cylinders, pulse oximeters, etc.) is not available from recent SDIs, and as described above, the depth of stocks is also unknown. While this could be estimated based on direct observation or patient exit interviews, for example, the frequency of utilization of equipment, supplies, and medications is also not quantified.

**Figure 9** : Availability of amoxicillin, azithromycin, ampicillin by facility type (hospital vs. non-hospital) and urban/rural location* in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Hospitals Non-hospitals<br>Kenya - Rural<br>Kenya - Urban<br>Sierra Leone - Rural<br>Sierra Leone - Urban<br>Tanzania - Rural<br>Tanzania - Urban<br>0 1 2 3<br>Number of antibiotics available<br><!-- End of picture text -->

> * Sierra Leone sample did not have any rural hospitals, and therefore a single dot for the “Sierra Leone – Rural”

Given that treatment options for both children and adults with COVID-19 often include ensuring adequate hydration,<sup>20</sup> it is prescient to examine availability of rehydration supplies. For children, Oral Rehydration

> 20 Kritz F and Huang P. “Coronavirus Symptoms: Defining Mild, Moderate, and Severe.” March 13, 2020. <u>https://www.npr.org/sections/goatsandsoda/2020/03/13/814691018/coronavirus-symptoms-defining-mildmoderate-and-severe</u> and World Health Organization. “Seasonal Influenza: Key issues for case management of severe disease.” 2011. <u>http://www.euro.who.int/__data/assets/pdf_file/0020/153380/flu_case_management.pdf%3Fua%3D1</u>

18
BOX 1.3 TEXT REFERENCE FOR THIS BOX Who are the newly banked? A look at China and India Both China and India saw strong growth in account ownership between 2011 and 2014—in China account penetration increased from 64 percent to 79 percent, and in India from 35 percent to 53 percent. Translated into absolute numbers, this growth means that 180 million adults in China and 175 million in India became account holders—with the two countries together accounting for about half the 700 million new account holders globally. A closer look at who the newly banked are in these two countries reveals differences in how that growth was distributed across groups of individuals.

In China, while account penetration increased by 15 percentage points on average, the growth varied substantially across different groups (figure B1.3.1). For example, account penetration grew by 26 percentage points among adults in the poorest 40 percent of households but by only 8 percentage points among those in the richest 60 percent. It grew faster among those in the poorest 40 percent of households in part because there was more room for growth in that group; by 2011, 76 percent of adults in the richest 60 percent already reported having an account. Account penetration also grew more strongly among adults living in rural areas and among older adults. There was no clear difference in rate by gender, however; account penetration grew by about 15 percentage points among both men and women.

For India, by contrast, the data show little such variation: the overall growth in account penetration of 18 percentage points is evenly reflected across all groups of individuals for which the data are broken down.

# FIGURE B1.3.1

Account penetration in China and India by individual characteristics, 2011 and 2014 Adults with an account (%) <!-- Start of picture text --> 2011 2014<br>China All adults 79<br>Male 81<br>Female 76<br>Poorest 40% 72<br>Richest 60% 84<br>Ages 15–24 74<br>Age 25+ 80<br>Rural 74<br>India All adults 53<br>Male 63<br>Female 43<br>Poorest 40% 44<br>Richest 60% 59<br>Ages 15–24 43<br>Age 25+ 57<br>Rural 50<br><!-- End of picture text --> _Source:_ Global Findex database.

> THE GLOBAL FINDEX DATABASE **25**
Estimates were obtained using the computer program QFIVE (United Nations 1983). The full technical details and assumptions made in their derivation are contained in the Appendix. With the indirect method one obtains separate mortality estimates for women in different age groups, the age bands typically used in these exercises being 15-19, 2024, 25-29, 30-34, 35-39, 40-44 and 45-49. Obviously, the rates arnong the older age groups capture mortality rates in earlier periods than those among the younger age groups. Typically, the rates among the youngest group-and often those among the second-youngest group as well-are ignored on the grounds they reflect the higher risks associated with pregnancies arnong younger women.

**Figure 1. Indirect estimates of IMR, Vietnman**

<!-- Start of picture text --> 70 --- - - --- -4-- --- --- -*- 1993 VLSS<br>60 - -2000 MICS<br>@ 50 - . 1999 Census<br>>40 40- -4- 1993 VLSS adjusted<br>30 -- 1997 DHS<br>, 20 - / 1998 VLSS weighted<br>E ~~45-49 age group/<br>10 - ag / - 1989 Census from<br>25-29 age group VDHS 97 tb 3.5<br>0 - lE) - 1994 ICDS94 from<br>1975 1980 1985 1990 1995 2000 VDHS 97 tb 3.5<br>Reference year<br><!-- End of picture text -->

**Figure 2. Indirect estimates of U5MR, Vietnam**

<!-- Start of picture text -->
--  1993 VLSS<br>120  1  --<br>l)  100  - -*-2000  MICS<br>1999 Census<br>80-<br>.4+1993  VLSS  adjusted<br>8  60-<br>-. x1997  DHS<br>40-<br>El-  - 1998 VLSS  weighted<br>L  0  45-49 age group<br>20  - 5-49 a  g  - -1989  Census  from<br>25-29 age group  I  VDHS  97 tb 3.5<br>0  - I  I  1994  ICDS94  from<br>1975  1980  1985  1990  1995  2000  L  VDHS  97 tb 3.5<br>Reference  year<br><!-- End of picture text -->

4