rafmacalaba/datause-encoder-data huggingface dataset:
that are in line with transfer of purchasing power at critical junctures. This should be regarded as the third major reason for sustained import demand in certain LMIC regions.

Chart 10. Contribution to World Import Growth by Major Recipients of Purchasing Power Transfer <!-- Start of picture text --> Percent<br>2.0 -<br>1.5 Surce In Ca<br>1.0 1st Oil Crisis /<br>0.5 - / //<br>[\\t\ZVl\;,,<br>| <> / ~~~~Mexico0's<br>40.5 - 0.0~~~~~~~~~~~~~~~~~~S pla o<br>Debt&Cisis<br>72 74 76 78 80 82 84 86 88 90 92 94 96<br>Asia (East Asia + South Asia)<br>-Latin America<br>---<br>Middle East & North Africa<br>Source: Intemational Economics Department, World Bank, August 1995.<br><!-- End of picture text -->

In the case of Middle East and North Africa, abrupt changes in contribution to world import demand, as shown in Chart 10, correspond to fluctuations in oil prices and the resulting transfer of purchasing power in the form of increased (or decreased) oil revenue. In fact, increased demand in oil exporting countries and circulation of oil dollars into Latin America and Asia were the main causes of sustained import demand in LMICX, as shown in Chart 7 for two periods of oil price hikes. Among other reasons for the sustained import demand, purchasing power was transferred away from OECD to these regions.

For Latin America, Chart 10 shows surges in imports in 1974-75 and 1980-81, when oil dollars complemented increasing amounts of private loans to this region. Aggregate net resource flows to Latin America peaked in 1980-8 1, augmenting the region's import capacity. Oil revenue rose for the region's oil exporting countries (Mexico, Venezuela), but import demand (import capacity) dropped rapidly in the wake of the 1982 debt crisis. In fact, as showli in Chart 11, during the height of the debt crisis in 15
|**Irrigation**Project||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||FY01|FY02|FY03|FY04|FY05|FY06|FY07|FY08|FY09|FY10<br>|FY11|
|Implementationperiod||||||||||||
|Operatingperiod||||||||||||
|GrossRecurrentCosts||||||||||||
|OperatingCosts||6.0|120|18.0|24.2|25.6|27.2|28.8|30.5|32.4|34.3|
|fuel||5.0|10.0|15.0|21.0|22 3|23.6|25.0|26.5|281|29.8|
|spares||1.0|2.0|3.0|3.2|3.4|3.6|3.8|4.0|4.3|4.5|
|Maintenance|||20|7.0|3.0|70|3.0|7.0|3.0|8.0|3.0|
|routine|||2.0|3.0|3.0|3.0|3.0|3.0|3.0|3.0|3.0|
|periodic||||4.0||4.0||4.0||5.0||
|Total||6.0|14.0|25.0|27.2|32.6|30.2|35.8|335|40 4|37.3|
|Recurrent Cost recovery||||||||||||
|feesfromfarmers||1.0|2.0|3.0|4.0|4.0|4.0|4.0|4.0|4.0|4.0|
|NetBudgetaryRequirement||5.0|12.0|22.0|23.2|28.6|26.2|31.8|29.5|36.4|33.3|

# **_Example_** _2: A_ **_School Project_**

The second example is a project to build a secondary school. The school is built in FY01 and opens in FY02. So the implementation period is FY01 and the operation period is from FY02 on. However the project actually lasts two years because it includes a component for training for teachers during the first year of operation. Ongoing training will be required every year to maintain the teacher's skills and this is reflected in the stream of recurrent costs under teacher training. However, since training in the first year is included as part of the project budget, the project provides one year of cost recovery for this recurrent cost as shown under FY02.<sup>2</sup> For subsequent years the training has be paid for out of the annual recurrent budget. Fees collected from students provide a further offset to the recurrent budget. The amount of recurrent costs that that will have to be funded out of the budget is given in the last line - Net Budgetary Requirement 2 In effect the 1.0 for training in FY02 is already accounted for in the capital budget (PIP) since it is part of the project.

**_5_**
**Figure 5: Wages relative to the BREGS stipulated wage rate**

<!-- Start of picture text --> Round 1 Round 2<br>.5 1 1.5 .5 1 1.5<br>ratio of wage rate to BREGS stipulated wage rate ratio of wage rate to BREGS stipulated wage rate<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>.5 1 1.5 .5 1 1.5<br>ratio of wage rate to BREGS stipulated wage rate ratio of wage rate to BREGS stipulated wage rate<br>non-public works<br>public works<br>4 5<br>4<br>3<br>3<br>2<br>density density 2<br>1<br>1<br>0 0<br>cumulative density cumulative density<br><!-- End of picture text --> _Note_ : Based on questions about casual work done in the last week. Wage data are unweighted. _Source:_ Authors’ estimates based on BREGS Survey.

29
In many urban areas (particularly in developing countries), the prevailing wind direction is regularly recorded at only one location (typically the airport). Taken together, the scarcity of wind direction information and aggregation of capacity measures across dispersed facilities in different microclimates eliminate any chance of incorporating meaningful wind effects. Of course, all locations in some urban areas may be subject to identical wind-direction effects during some periods, in which case radially-symmetric inverse-distance weighting would generate errors for some observations. However, our study calculates daily averages from hourly data, with consequent summation over random changes in wind directions for multiple, widely-scattered points over five annual cycles. Some measurement error probably remains, but we believe that it compares favorably with measurement errors for other variables in our model (e.g., facility capacities, local heating degree days, neighboring fire locations).

# **3.4 Urban Scale**

How many grid cells should be assigned to each city in our sample? Comparative analysis must confront the difference between arbitrary administrative boundaries and the actual extent of urban economic regions. Any attempt to define the latter will also include an arbitrary element. For all cities in this exercise, we standardize by including grid cells that lie within the same radial distance from their centroids. Then we test the robustness of the model by varying the radial distance. Another possible approach involves identification of a functional urban area (FUA) for each city region, as in Schiavina et al. (2019). FUAs are identified from urban mobility data for the OECD and Colombia, and estimated for other cities using a machine learning algorithm trained from estimated travel times, population distributions and incomes. We provide a further robustness test by estimating the model for FUAs as well.

# **4.  CO2 Measures across Cities and over Time**

# **4.1 Annual CO2 Concentrations**

The global reference standard is provided by CO2 measurement at Mauna Loa Observatory, Hawaii (Keeling et al. 1976; Thoning et al. 1989). Table 1 compares mean annual December measures from Mauna Loa and our global OCO-2 database.<sup>3</sup> The results correspond closely, showing an increase from 399 ppm in 2014 to 414 in 2020.

**Table 1:  Annual global CO2 concentrations (ppm) (December)**

|Year|OCO-2|MaunaLoa|
|---|---|---|
|2014|399|399|
|2015|401|402|

> 3 Mauna Loa is in the Northern Hemisphere, so we compare its measures with Northern Hemisphere values from

> OCO-2.

9
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Roads

## **Achievements**

Liberia is in the process of reconstructing its degraded road network. The re-engagement of donors since the mid-2000s has helped to ensure a source of funding for much-needed road reconstruction, and as a result Liberia has been spending around 1.4 percent of GDP on roads per year—one of the higher levels of transport spending in West Africa. The most recent available information suggests that 60 percent of the classified network is now in good or fair condition. This is an improvement on the immediate postconflict situation but still falls well behind the values of 80 to 86 percent for the two benchmark groups.

Liberia has a relatively low share of paved roads, though its traffic is also rather low. Just under 40 percent of the country’s primary road network is paved, compared with 70 to 80 percent for the benchmark groups. Nevertheless, the average traffic flow on Liberia’s paved network is around 570 vehicles per day, which is less than half the paved network traffic for the benchmark groups (though this may reflect depressed traffic movements due to the post-conflict situation). While about one third of the main network looks to be over-engineered relative to traffic levels, it is likely that traffic levels in the country are artificially depressed due to the post-conflict situation.

**Table 2. Liberia’s road indicators benchmarked against African peers**

||Unit|Resource<br>rich|**Liberia**|Low-income<br>countries|
|---|---|---|---|---|
|Classified road density|Kilometers per 1,000 square kilometers of land area|57.1|**101.4**|55.9|
|Paving ratio|Percentage of primary network paved|82.1|**39.4**|71.6|
|GIS Rural accessibility|Percentage of rural population within 2 km of<br>an all-season road|19.7|**23.7**|34.1|
|Paved road traffic|Average annual daily traffic (vehicles per day)|1,408.2|**573.9**|1,287.7|
|Unpaved road traffic|Average annual daily traffic (vehicles per day)|54.2|**17.1**|38.5|
|Classified network condition|Percentage in good or fair condition|79.9|**60.0**|86.2|
|Overengineering|Percentage of main road network over-paved relative<br>to traffic flows|15.0|**32.2**|29.6|

Source: Gwilliam and others 2009. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

## **Challenges**

Current spending on roads still falls short of what is needed, despite recent efforts to increase it _._ Based on the physical configuration and condition of Liberia’s infrastructure network, it is possible to estimate the resources that would be needed to clear the current rehabilitation backlog and maintain the network in good condition. Comparing recent spending against these norms indicates that Liberia still has a substantial shortfall of around 40 percent both in capital spending for rehabilitation and in network maintenance spending (figure 4). This situation is not unusual in West Africa. Nonetheless, some countries—such as Ghana and post-conflict Rwanda—have succeeded in securing adequate resources for road network preservation.

8
researcher does the randomization by computer. Second, a related common use for these significant tests is seen in Ashraf et al. (2006a), who are only able to survey 1777 of the 4000 microfinance clients allocated to treatment and control. They test whether there are differences between the treatment and control groups amongst those surveyed.

# **5. Conclusions**

Our surveys of the recent literature and of the most experienced researchers implementing randomized experiments in developing countries finds that most researchers are not relying on pure randomization, but are doing something to pursue balance on observables. In addition to stratification, we find pair-wise matching and rerandomization methods to be used much more than is apparent from the existing literature. The paper draws out implications from the existing statistical, clinical, and social science literature on the pros and cons of these various methods of seeking balance, and compares the performance of the different methods in simulations.

Our simulation results show the method of randomization matters more in small sample sizes, such as 30 or 100 observations, and matters more for relatively persistent outcome variables such as health and test scores than for less persistent outcome variables such as microenterprise profits or household expenditure. Overall we find pairwise matching to perform best in achieving balance in small samples, provided that the variables used in forming pairs have good predictive power for the future outcomes. Stratification and re-randomization using a minmax method also lead to some improvements over a pure random draw, but in the majority of our simulations are dominated by pair-wise matching. With sample sizes of 300 we find that the method of randomization matters much less, although matching still leads to some improvement in balance for the persistent outcomes.

Our analysis of how randomization is being carried out in practice suggests several areas where the practice of randomization can be improved or better reported. This leads us to draw out the following recommendations: _1) Better reporting of the method of random assignment is needed._ Researchers need to describe clearly their choice of method, the reason for this choice, and whether or not the randomization was carried out in public or private. This is particularly

- 32 -
possible to identify the impact of teenage childbearing among teenagers who become pregnant using miscarriages as a natural experiment. Not uncommon during pregnancy, miscarriages present a methodologically-advantageous characteristic: they are neither related to the mother‟s demographic condition, nor appear to be substantially connected with the characteristics that impact adult outcomes such as education, income, and so forth (Lang, 2007). This paper takes advantage of the 2006 round of the Mexican _Encuesta Nacional de la Dinamica Demografica_ (ENADID), which records not only the birth histories of its respondents, but also their miscarriage and abortion histories. By focusing on the population of women who became pregnant as teenagers, this paper circumvents the issue of selection bias inherent to any inquiry into the consequences of teen pregnancy. Using miscarriages as a natural experiment, however, requires caution, and results must be interpreted carefully.<sup>5</sup> Although teenage pregnancy has received much attention in social science and popular media alike, rigorous evidence on the consequences of early childbearing is scarce. Hotz et al. (2005) find that, contrary to popular perception, adolescent childbearing in the US has negligible if not positive consequences on the overall welfare and economic opportunities of teenage mothers. The authors look at the consequences of early childbearing on a sample of women who became pregnant as teenagers, providing the estimate of the effects of early childbearing on that selected population only. Geronimus and Korenman (1992) estimate the effect of adolescent childbearing by comparing outcomes of sisters. They find little difference between the sister who gave birth in adolescence and the sister who did not. Using age at menarche as an exogenous source of variation in the age at first birth, Ribar (1994) does not find significant effects of teenage childbearing on high school completion. Ashcraft and Lang (2006) provide narrow bounds of the effect of adolescent childbearing combining IV estimates as in Hotz et al. (2005) with OLS estimates. They find generally adverse, but modest effects. To our best knowledge, the impact of adolescent childbearing in developing countries has yet to be rigorously evaluated.

> 5 The literature points out that the use of miscarriages as a natural experiment to estimate the effects of giving birth may be subject to under or overestimation problems when accounting for abortion, as described by Lang (2007). Results may also be estimating the cost of a miscarriage (i.e. the emotional toll on the mother), vis-à-vis the cost of child bearing. We address each of these issues in the paper.

3
adjusted its baseline deleveraging estimates upward from $2.6 to $2.8 trillion by end-2013.<sup>23</sup> In June 2012, the ECB produced a more conservative estimate of $2.1 trillion or €1.6 trillion (Exhibit III.5). These numbers are consistent with plans banks announced in 2011 which sum up to a multi-year asset reduction of over €2 trillion.

**Credit outside the Euro Area could be hit disproportionately** —the IMF estimates as much as $400 billion could be affected, twice as much as in the Euro Area. This figure is consistent with an illustrative World Bank estimate of $500 billion in emerging markets which was derived by simulating a reduction of the simple asset-to-equity ratio of European banks from 18 to 12 times, assuming i) they are not able to raise any equity and ii) conduct the required asset reduction to reach the target ratio for 50 percent via their loan books (Exhibit III.6).<sup>24</sup>

<!-- Start of picture text --> III.5 The IMF increased its Euro bank deleveraging III.6 ... indeed, emerging credit will fall significantly<br>estimate in Oct 2012 to $2.8 tr. European bank credit if European banks are not able to raise sufficient<br>in the rest of the world will be severely affected... equity and have exhausted shrinking other assets<br>Total European Bank Deleveraging Estimates Impact on EM Credit of Lower Euro Bank Leverage<br>$ trillions (2011Q3-2013Q4) € billions<br>3 2.6 €1,000 978<br>0.2<br>0.4 €800<br>2 676<br>€600<br>2.8 525<br>1 2.0 €400<br>1.5<br>€200<br>0<br>€0<br>ECB estimate IMF estimate IMF estimate<br>10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>(Jun 2012) (Apr 2012) (Oct 2012)<br>Necessary asset deleveraging via loan book (%)<br>Reduction in euro area credit<br>Equity increase: 20%<br>Reduction in rest of world credit<br>Equity increase: 30%<br>Asset sales and reduction in interbank lending<br>Total deleveraging Equity increase: 0%<br>Source: IMF, ECB Source: Feyen, Kibuuka, and Otker-Robe (2012)<br><!-- End of picture text -->

# **IV. Deleveraging Mechanisms and Drivers**

**European banks are confronted with various deleveraging options which could affect lending, particularly longer-term credit.** There are several ways for banks to delever and derisk their balance sheets. Via the liability side, banks can boost capital by issuing equity, converting debt to equity, and buy back their own bonds if they trade at a discount. They can also increase retained earnings by raising prices, reducing dividends, and cutting costs. Via the asset side, banks can reduce their (risk-weighted) size by scaling back activities with higher risk weights and shedding assets including non-core operations and trading assets, reducing lending

> 23 IMF (October 2012).

> 24 For details, see Feyen, Kibuuka, and Otker-Robe (2012). The simulation targets a reduction of the simple asset to equity ratio of European banks from around 18-20 times to 12 times. The necessary adjustment is assumed to impact emerging market credit proportionately. The geographical distribution of European bank credit is taken from the IMF’s Financial Soundness Indicators.

15
# Table B5. Distribution of Manufacturing Establishments Vehicles for Shipment by Employment Size, Nigeria

VEHICLES (OM VEHICLES FOR SHIPMENT) FIRMSIZE (EMPLOYEES per FIRM) <!-- Start of picture text --> Percent<br>Raw Pet<br>Cot Pft 1-19 j20-49 150-99 1OO-199 1200-499 1500-999 p1000 £<br>~~~~. I . . .......... I ~ .. ~~I .. . I ........ over ....... ITotaL<br>YES I II 21 1 26 1 25 1s 111 41 113<br>4 474r II .n | 14S3 | 13.sr 97 10.06 6.15 2 23 63A3<br>| .08 18.58 23.01 22.12 15.93 9.73 3.54<br>50.00 I 60.00 59.09 71.43 69.23 73.33 I 50.00:<br>...... +.. ..... . ...... ....... ..... .. .... ...... ........-....<br>NO I .14 1 10 oo1 8 41 4' 66<br>4.47 7.821 10.061 5.59 '.*' 2.23 2.23 36.87<br>12.12 21.21 I 27.27 I 15.15 1 12.12 6.06 6:06<br>50.00 40.00 1 40.91 1 28.57 1 30.77 1 26.67 1 50:00<br>.... .... - ^ e @ w e-@. > + w @ ............................................................ + .................. .........<br>Tota 16 35 44 35 26 15 a 179<br>8.94 19.55 24.58 19.55 14.53 8.38 4.47 100.00<br><!-- End of picture text -->
# **4.  Research Database**

Figure 1 displays the four regions that comprise our study area in southern Bangladesh: Barisal, Chittagong, Dhaka and Khulna. The study area spans the coastal regions, with extensions to permit assessment of current and future salinity further inland. Our research combines spatially-formatted salinity information with municipality-level data on revenue, development income, road networks and road maintenance expenditures. Our study teams have collected time series data from the 20 municipalities highlighted in Figure 1, which provide a sampling from all parts of the coastal region.

# **4.1  Salinity Measures**

The Bangladesh Soil Research Development Institute has published monthly measures from 41 soil salinity monitoring stations for the period 2001-2009. In Dasgupta et al. (2014b), we extend these measures using projections of river salinization, temperature and rainfall through 2050. Our results (Figure 2) indicate that many areas in the coastal region of Bangladesh will have very significant increases in soil salinity during the coming decades. Monitoring stations are color-coded using standardized ranges for soil salinity in 2001, 2009 and 2050:<sup>5</sup> Blue (0-0.75 dS/m); Green (0.75-1.50); Yellow (1.50-2.25); Orange (2.25-4.50); Red (4.50-6.00) and Purple (6.00+). In 2001, Khulna has the greatest variance among the four regions, with northern stations uniformly Blue and central stations heavily Red and Purple. Stations in Barisal vary from Blue to Orange, while stations in Chittagong vary from Green to Red.

By 2009, a general pattern of salinity increase is already apparent: All stations in northern Khulna have increased from Blue to Green; nearly all stations in Barisal (one

> 5 The standard sample-based measure for soil salinity is electrical conductivity (in dS/m -- deciSiemens per meter).

7
<!-- Start of picture text --> 0.1<br>0<br>=> 0.1<br>Qu<br>Sf 02<br>><br>oO -0.3<br>x<br>o -04<br>coc<br>O -05<br>oO.<br>—<br>06<br>07<br>08<br>06 05 04 03 0.2 01 0<br>(Core) b./J<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>lay -0.1<br>—<br>SS 02<br>Ww -03<br>—<br>S 04<br>-0.5<br>-0.6<br>-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0<br>(Core) b./¥<br><!-- End of picture text -->