rafmacalaba/datause-encoder-data huggingface dataset:
**_Table 6_** _– Marginal effects after Probit - Gifts_

|**Dependent variable = 1 if the househo**|**ld received a gift in the last 12 months**<br>[1]<br>[2]<br>[3]|[4]|[5]|[6]|
|---|---|---|---|---|
|**treated**|**-0.26 *** -0.258 *** -0.257 *** **|**-0.259 *** **|**-0.240 *** **|**-0.239 *****|
||[0.028]<br>[0.029]<br>[0.029]|[0.034]|[0.030]|[0.030]|
|**Village level shocks**|||||
|Lower yields due to drought or floods|**0.020**<br>**0.030**|**0.028**|**0.026**|**0.022**|
||[0.031]<br>[0.031]|[0.038]|[0.031]|[0.032]|
|Rise in food prices|**-0.076 ***<br>**-0.073 ***|**-0.066 ****|**-0.046**|**-0.045**|
||[0.031]<br>[0.031]|[0.037]|[0.031]|[0.031]|
|**HH level shocks**|||||
|Crop disease|**-0.024**|**-0.016**|**0.004**|**0.005**|
||[0.033]|[0.041]|[0.034]|[0.035]|
|Livestock died or stolen|**-0.076 ****|**-0.069 ***|**-0.064 ***|**-0.058 ***|
||[0.033]|[0.044]|[0.034]|[0.035]|
|Theft|**-0.007**|**-0.003**|**-0.012**|**-0.013**|
||[0.051]|[0.065]|[0.050]|[0.050]|
|House damage|**-0.007**|**-0.009**|**-0.010**|**-0.013**|
||[0.031]|[0.037]|[0.030]|[0.030]|
|Safety net (food)|**0.048**|**0.046**|**0.024**|**0.023**|
||[0.033]|0.038]|[0.033]|[0.033]|
|Safety net (agr. inputs)|**-0.013**|**0.000**|**0.022**|**0.016**|
||[0.030]|[0.037]|[0.032]|[0.032]|
|**HH assets**|||||
|Furnitures (mattress, table, chairs)||**0.005**|**-0.009**|**-0.022**|
|||[0.063]|[0.051]|[0.051]|
|Animal (chickens, pigs, etc)||**-0.071**|**-0.056**|**-0.069**|
|||0.0517|[0.043]|[0.042]|
|Agr tools (axe, sickle, hoe)||**-0.113 ****|**-0.096 ****|**-0.105 ****|
|||[0.060]|[0.054]|[0.056]|
|**HH characteristics**|||||
|HH size|||**-0.018 ****|**-0.013**|
||||[0.008]|[0.009]|
|Chronically ill adults|||**0.010**|**0.002**|
||||[0.031]|[0.033]|
|Presence of orphans|||**-0.017**|**-0.019**|
||||[0.034]|[0.035]|
|Deaths in the past 5 years|||**0.026**|**-0.004**|
||||[0.033]|[0.034]|
|Disable persons|||**-0.011 ****|**0.088 ****|
||||[0.040]|[0.041]|
|**Head of HH's charact.**|||||
|Female headed||||**-0.021**|
|||||[0.035]|
|Over 65 yrs old||||**0.043**|
|||||[0.035]|
|No education||||**-0.002**|
|||||[0.032]|
|Poor health status in the past year||||**0.007**|
|||||[0.039]|
|Employment in past year||||**-0.027**|
|||||[0.041]|
|**Nr. of obs.**|749<br>749<br>749|749|748|744|
|**R-sq**|0.1<br>0.1<br>0.11|0.12|0.14|0.15|

**Notes:** Table reports estimated marginal effects after probit.Significant at the *** 1% level, ** 5% level, * 10% level. Robust standard errors in squared brackets.

24
# **Figure A2: Correlation Between Alternative Measures of Excess Interaction**

<!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>-.1<br>-.1 0 .1 .2 .3 .4<br>Excess interaction share - binary logits<br>Excess interaction share - calibrated<br><!-- End of picture text --> _Notes:_ In the vertical axis excess interaction share measures are constructed using calibration methods. In the horizontal axis excess interaction share measures are based on estimates from inspector-semester logit models. The sample used is the regression sample used to generate baseline results (see e.g. Table 3). It covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

51
- 8 -

# **III. EMPIRICAL STRATEGY**

The current analysis relies on two household surveys carried out in Chipata and Katete before and after the ADSP. The project areas were selected based on local agricultural potential. Eastern Province is among the most important agricultural areas in Zambia, producing cotton, soybean, groundnuts and tobacco as well as maize. Chipata and Katete produce more than 20 percent of the nation’s total groundnuts. Based on agricultural productivity, about 200 km of feeder roads were selected in each of the districts ( **Table 1** ). These account for about 24 percent of total feeder roads in Chipata, and 38 percent in Katete. Before the project, almost all feeder roads were in poor condition in these two districts.

Under the project, 5-year OPRCs were made from March 2009 to March 2014. The awarded contractors were required to rehabilitate selected road links in the first year and maintain them by spot improvements as well as emergency works in the following 4 years. In the five years, a total of 385 km of roads were improved with gravel surface, box culverts and stone patching. Note that the OPRCs were targeted at a specific set of roads, but they did not prevent other road works elsewhere in the districts. Regardless of the OPRCs, the local governments actually implemented some other traditional road improvement and maintenance works wherever needed.
summarizes the data sources and variable de...nitions. To ensure adequate coverage for each country, we excluded information for nations that do not have at least three consecutive years of tari¤ and rainfall data. Table A.2 lists the countries and corresponding time periods covered in our sample, while Table A.3 details the product classi...cation we employ. To identify agricultural products we use the UN statistics division of the SITC classi...cation. For robustness, we also consider a broader de...nition that includes as well other agricultural products.<sup>16</sup> Irrigation data are available for 32 of the 70 countries initially considered. These data generally refer to the 2001-2009 period, but the extent of time coverage di¤ers across countries. We therefore use the average value of this variable among non-missing observations for each of the 32 countries as a (time-invariant) proxy for access to irrigation at the country-level.

Figure 1 provides illustrative evidence on the rainfall variable we employ for a subset of countries. The dashed lines in these diagrams refer to major, widely-documented droughts observed in each nation. Many of these extreme weather events were associated with the El Niño or La Niña phenomena, including Argentina (1999), Bolivia (1998), Brazil (1998, 2003), Colombia (1992, 1997), Chile (1998), Costa Rica (2001), Guatemala (1997, 2001) and Peru (2000).

# [Figure 1 about here]

Our theory applies to countries that have discretion to set import tari¤s on agricultural goods. A potential concern is that, in reality, tari¤ setting might be constrained by limits associated with GATT/WTO membership. As shown in Figure 2, however, countries typically set their tari¤s on agricultural goods well below the bound tari¤s imposed by such multilateral tari¤ agreements; see Gibson et. al (2001, pp. 20-21) for further data and discussion. This implies that policy makers have ample policy space to adjust tari¤s strategically in response to rainfall shortages. We will nevertheless address this concern directly in the econometric analysis below.

# [Figure 2 about here]

Table 1 reports summary statistics on the ...nal panel data set employed in the regression analysis. As can be seen, most observations refer to developing countries in Latin America and Caribbean, Asia-Paci...c and Africa.<sup>17</sup>

[Table 1 about here]

> 16 As shown in Table A.3, this latter de...nition includes as well SITC product categories "08", "09", "11", "12" and "29".

> 17 We adopt a broad de...nition of the term "Asia-Paci...c", comprising all nations in Asia and Oceania.

16
Only some of the most recent studies have looked at whether aid raised the export capacity of _recipient_ countries. Cali and te Velde (2009) regressed trade costs and the value of exports on control variables and lagged AFT disbursements, using data from the OECD’s Credit Reporting System. Their sample was a panel of countries covering 1995-2007, and they dealt with endogeneity and measurement errors in AFT flows by instrumenting them with the World Bank’s index of civil liberties. The message that seems to emerge across their various specifications is that aid to trade facilitation and infrastructure seems (by and large) to have a significant effect (the former on trade costs, the latter on export values), while aid to productive capacity is insignificant. When looking at sectorally-targeted aid, controlling for country _×_ sector fixed effects (comparative advantage) they found, again, that aid to infrastructure had a significant impact but aid to productive capacity didn’t. Brenton and von Uexkull (2009) combined mirrored product-level (HS4) export data with export-development (ED) aid data from the GTZ (covering 1975-2005) and from the OECD/WTO Trade Capacity Building Database (covering 2001-2005) for 48 countries. They useda differencein-differences (DID)<sup>2</sup> approach where the performance variable was exports and regressors included lagged exports, country and year _×_ product fixed effects, and contemporaneous and lagged aid coded in binary form (ED program in force = 1). By and large, once outliers were dropped, matching yielded insignificant coefficients in spite of the relatively large sample size, suggesting that once selection effects were taken into account, export-development programs provided little significant boost to exports. All in all, it is fair to say that, as the literature stands, the effect of AFT on the export performance of beneficiary countries has not been clearly established on the basis of aggregate numbers.

Among TRA programs, technical-assistance ones have scarcely been evaluated.<sup>3</sup> A brochure published by the EU Commission (EC 2006) tells the interesting story of a Kenyan fruit and vegetables exporter who got assistance from the EU’s Pesticides Initiative program (PIP); the case study presents the program as “[...] to provide support to companies like Myner [the Kenyan exporter], to help them get up to speed with European food safety and traceability requirements. [...] Since it began in July 2001, the PIP has had a positive effect on more than 26’000 ACP producers, many of whom are small-scale farmers. Nearly 6.25 million Euros has been

> 2See section 3 below for a discussion of the DID and matching approaches.

> 3Marcano and Ruprah (2009) is one of the few papers looking specifically at technicalassistance programs. Using a multiple-treatment approach, they found a positive impact to Chile’s Neighborhood Improvement Program and Guatemala’s Social Investment Fund (both water-access program) over and above public-works spending.

4
comparisons are conducted for the same individuals over time, messages would have to affect personspecific desirability bias in some way to affect the results. This is an implausible explanation for the within-respondent patterns I find.

**Table 2: Sample Distribution and Risk Reducing Behaviors**

||Total<br>Int.|Rur|Urb|Total<br>Beh.|Hand<br>Wash|Greet|Trav.|Visit|Work|Mask|Soc.<br>Dist|Stock<br>Food|Quar|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Apr|219|166|53|4.36|92%|37%|61%|67%|38%|87%|23%|17%|13%|
|<br>May|1,278|950|328|6.40|99%|81%|85%|81%|68%|98%|61%|34%|34%|
|Jun|1,198|899|299|6.62|98%|94%|93%|85%|59%|98%|64%|34%|34%|
|Jul|1,205|909|296|6.74|99%|98%|97%|96%|50%|97%|68%|35%|35%|
|Aug|1,194|904|290|6.44|97%|92%|92%|88%|50%|95%|64%|34%|33%|
|<br>Sep|1,143|856|287|5.62|98%|71%|70%|68%|43%|89%|57%|33%|33%|
|Oct|1,174|874|300|6.06|98%|69%|69%|68%|65%|98%|67%|36%|36%|
|Nov|1,195|896|299|5.57|97%|67%|67%|65%|41%|94%|58%|35%|34%|
|Dec|1,191|901|290|5.55|98%|70%|69%|69%|38%|97%|39%|37%|37%|
|Jan|1,166|884|282|5.04|96%|62%|61%|62%|32%|95%|32%|32%|32%|
|Total|10963|8239|2724|5.91|98%|76%|77%|75%|48%|95%|55%|34%|34%|

Between 75 and 77 percent of respondents reported reducing risky greetings (such as kissing, handshakes, and hugging), reduced traveling, and less visiting with friends and others outside of their immediate family. Other behaviors fluctuated more over the course of the pandemic, and were on average less common, including social distancing (55 percent on average), changes in typical work habits (48 percent) increasing the stock of reserve food (34 percent) and quarantine measures (34 percent).

As presented in table 3, television was by far the most common mode of receiving any information about COVID-19, with about 96 percent of respondents listing it as a primary source. In-person sources were next most common, with information from health workers more common than other individuals. The third most common source was SMS, averaging about 21 percent over the full period, and a peak of 29 percent during the height of project messaging. The only official SMS messages being sent during this time were from the Mobile Engage project, suggesting that many of these responses referred to these. This interpretation is bolstered by the fact that in a companion study in neighboring Uzbekistan, where no mass text-messaging campaign was conducted, only 8.9 percent of respondents listed SMS as a primary source over the same period. About 12 percent of respondents listed radio, 5 percent social media, and only 2 percent newspapers (either electronic or physical) as primary sources of information.

7
# Gross Capital Flows by Banks, Corporates, and Sovereigns<sup>_∗_</sup>

Stefan Avdjiev Bank for International Settlements S ̧ebnem Kalemli- Ozcan<sup> ̈</sup> University of Maryland, CEPR, NBER

## Bryan Hardy

University of Maryland Luis Serv ́en World Bank

### **JEL-Codes** : F21, F41, O1

**Keywords** : Quarterly Capital Flows, Business Cycles, External Corporate and Bank Debt, Sovereign Debt, VIX, Systemic Risk, Emerging Markets

> _∗_ We thank Luis Cat ̃ao, Eugenio Cerutti, Stijn Claessens, Branimir Gruic, Gian Maria Milesi-Ferretti, and Philip Wooldridge for useful comments and suggestions and Bat-el Berger for excellent assistance with the BIS IBS data. All errors are our own. The views expressed here are ours only and do not necessarily reflect those of the Bank for International Settlements or the World Bank. This work was partly funded by the World Bank’s Knowledge for Change and Strategic Research programs.
# _2.3.1 Predictions of Soil Properties from Spectra_

Following the methods designed by Shepherd and Walsh (2002) the results of the CSA were used to predict soil properties onto the full sample based on the spectral signatures, an example of which is found in Annex II. Figure 5 illustrates the predictive power of the mid-infrared spectroscopy on key soil properties, while Table 2 summarizes selected predicted properties, disaggregated by top- and sub-soil. The predictive models are successful in that, of the variables predicted, the lowest correlation between predicted value and actual value (using the reference sample upon which CSA was conducted) was 0.946 (prediction of zinc concentration using Mehlich 3 method). The highest correlation was in the prediction of aluminum concentration by TXRF, with a rho of 0.989. Key soil properties such as total carbon (percent), total nitrogen (percent), clay (percent), and pH were strongly predicted with correlation coefficients of 0.984, 0.983, 0.988, and 0.985, respectively. The near-perfect predictions lend confidence to our assumption that laboratory results obtained through spectral analysis are strong proxies for true measures.

Table 2 illustrates that significant differences are observed between the top- and sub-soil samples, motivating the need to analyze both separately if study objectives and resources allow. The rooting depth of the crop(s) of interest should be considered when determining if top and/or sub-soils should be tested, as it is preferable to test the soil properties at the level at which the plant absorbs the majority of its nutrients (Lorenz and Lal, 2005). Levels of all presented properties are significantly different between top- and sub-soil at the 1 percent level, with the exception of sand percentage, which is significant only at the 10 percent level. In addition to variation across soil depths, levels of key soil properties vary across administrative zone. Figure 6 illustrates that distribution of total carbon and pH by administrative zone. Carbon levels are highest in the West Arsi zone, followed by East Wellega and Borena (means across zones significantly different at the 1 percent level). High carbon and pH variability is observed in Borena, likely due to the great variation in agroecological zones enclosed within its borders. East Wellega has more acidic soils, which could be suitable for maize production (FAO, 1983).

12
rule was breached every year after its adoption but the deviations became smaller over time (IMF 2007).

Taking into account the investment income generated by its assets, the size of the fund has grown at a rapid pace both in absolute terms and as proportion of GDP. The fund accumulated assets equal to 1,784 billion NOK in 2006, equivalent to about 285 billion USD (216 billion EUR) or 83 percent of GDP. The assets of the Government Pension Fund - Norway amounted in 2006 to 107 billion NOK or 5 percent of GDP.

|Table2.1:A|nnual Tran<br>|sfers and <br>|Total As<br>|sets ofG<br>|PF,2000<br>|-2006<br>||
|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|
|Transfers (NOK bn)|149.8|251.2|125.4|103.9|138.2|220.3|288.3|
|Percent of GDP|10.2|16.5|8.3|6.6|8.0|11.6|13.4|
|Assets (NOK bn)|386.5|613.7|609.0|845.3|1016.4|1399.0|1783.7|
|Percent ofGDP|26.3|40.2|40.1|53.6|59.2|73.5|83.0|

Source: NBIM and IFS

# **2.3 Institutional Structure and Fund Governance**

Norway did not establish the GPFG as an independent legal entity.<sup>16</sup> The fund itself has no rights or obligations against private sector entities or public authorities and may not institute, or be subject to, legal proceedings (Government Pension Fund Act, 2005, chapter 1, section 6). Responsibility for managing the fund is vested in the Ministry of Finance, which makes all strategic decisions, formulates investment policy objectives, and sets the strategic asset allocation and benchmarks.

Apart from the 4-percent fiscal rule, no other special measures have been adopted to insulate the fund from political interference. The fiscal rule limiting the use of accumulated balances is set by the government and must be approved by Parliament. The strategic asset allocation is also subject to parliamentary approval.

The Ministry selected Norges Bank as the operational manager of the fund. The assignment is open-ended and is subject to a one-year period of notice of termination by either party. The Ministry supervises the operations of the bank and uses independent consultants to evaluate its performance. Norges Bank exercises the voting rights of the fund.

The fund is required to operate with a very high level of transparency and public accountability. The Ministry reports to Parliament, through national budget documents, on the performance of the fund and on the Bank's management. Norges Bank holds press conferences every quarter to explain its results and compare them against the benchmarks set by the Ministry. It posts on its website comprehensive reports on the performance of

> 16 A decision was taken in 2007 to organize the National Insurance Fund, which manages the Government Pension Fund - Norway, as a separate legal entity. Thus, the operational managers of the two funds will be legal entities, but the funds themselves will not.

27
# **1. Introduction**

During the past 30 years, civil conflict affected almost three-fourths of all countries in SubSaharan Africa. Recently academic economists and policy makers have begun focusing on this topic, specifically trying to understand the causes of war and its role in reducing growth and development (Collier and Hoeffler, 1998; Miguel, Satyanath, Sergenti, 2004; Guidolin and La Ferrara, forthcoming). Several researchers argue that wars do not have negative long-run economic consequences (see Davis and Weinstein (2002) for Japan; Brakman, Garretsen, and Schramm (2004) for Germany; Miguel and Roland (2006) for Vietnam; and Bellows and Miguel (2006) for Sierra Leone). Despite the suffering and deaths caused by these various conflicts, there is limited research that examines the microeconomic impacts of war, although this is slowly changing as data from war regions become available (Shemyakina, 2006).<sup>4</sup> The 1990s were a particularly violent decade in Central Africa’s history. Burundi and Rwanda experienced several episodes of mass murder and genocide, and the regional civil war in the Democratic Republic of Congo created an enormous loss of life and socio-economic destruction. Most of the recent work on Burundi focuses on the causes of the latest episode of civil conflict (Nkurunziza and Ngaruko, 2000), the progression of the crisis (Chrétien and Mukuri, 2000), the year-by-year political dimensions of the conflict (Reyntjens and Vandeginste, 1997; Reyntjens, 1998), or the possible solutions to it (Ndikumana, 2000).

In this paper, we examine the impact of the Burundian civil war on the health status of children.<sup>5</sup> We focus on chronic early childhood malnutrition and stunting measured by heightfor-age z-scores, and we examine if and how shocks, such as violent conflict, affect childhood health outcomes for those exposed children. We combine data from a nationally representative household survey (the 1998 Priority Survey carried out by the World Bank and the Burundi Statistics Institute) with event data on the timing and evolution of the conflict between 1994 and 1998. The empirical identification strategy exploits variation in the timing and duration of armed conflict across the Burundi provinces and the related variation determining which birth

> 4 The number of people killed in direct violence is often less than the number killed by a conflict’s indirect consequences, such as the breakdown of the economic and health systems and the spread of infectious diseases (WHO, 2002; Eck, 2003). Ghobarah, Huth, and Russett (2003) use cross-country data to assess the impact of civil wars on the loss of life and find that wars “continue to kill people indirectly, long after the shooting stops.” 5 Between 1990 and 2002, per capita income in Burundi fell from $210 to $110 leaving Burundi as the world’s poorest country.  The proportion of people living below the nationally defined poverty line increased from 35 percent to 68 percent, while international assistance declined from $300 million to less than $100 million.

1
**Table 4: Weight-for-age Z-score (Nepal)**

**Model 1- Without Controls**

|VARIABLES|OLS|Un<br>|conditionalqu<br>|antile regressi<br>|on<br>|
|---|---|---|---|---|---|
|||10th|25th|50th|75th|
|Per capita kcal|0.016|0.060|0.055*|0.046|-0.014|
||(0.022)|(0.037)|(0.032)|(0.028)|(0.030)|
|1-SSR|0.185***|0.132***|0.188***|0.197***|0.165***|
||(0.026)|(0.043)|(0.036)|(0.032)|(0.035)|
|1 - SSEXR|0.193***|0.168***|0.206***|0.202***|0.189***|
||(0.025)|(0.040)|(0.035)|(0.030)|(0.033)|
|FCS|0.164***|0.106***|0.174***|0.187***|0.174***|
||(0.025)|(0.040)|(0.033)|(0.030)|(0.033)|
|HDDS|0.110***|0.073*|0.133***|0.136***|0.091***|
||(0.025)|(0.038)|(0.034)|(0.030)|(0.033)|
|Per capita consumption|0.279***|0.221***|0.241***|0.281***|0.316***|
||(0.047)|(0.048)|(0.044)|(0.047)|(0.050)|
|Food share|-0.215***|-0.166***|-0.206***|-0.191***|-0.233***|
||(0.027)|(0.039)|(0.034)|(0.032)|(0.037)|

|||**Mod**<br>|**el 2- With Con**<br>|**trols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|U<br>|nconditionalqu<br>|antile regressi<br>|on<br>|
|||10th|25th|50th|75th|
|Per capita kcal|0.019|0.055|0.054*|0.053*|0.003|
||(0.022)|(0.039)|(0.032)|(0.029)|(0.031)|
|1-SSR|0.017|-0.036|0.034|0.013|0.004|
||(0.029)|(0.053)|(0.043)|(0.037)|(0.039)|
|1 - SSEXR|0.057**|0.040|0.069*|0.044|0.065*|
||(0.026)|(0.044)|(0.039)|(0.034)|(0.035)|
|FCS|0.068**|0.012|0.081**|0.081**|0.080**|
||(0.027)|(0.046)|(0.038)|(0.034)|(0.037)|
|HDDS|0.029|-0.008|0.053|0.047|0.012|
||(0.026)|(0.043)|(0.038)|(0.034)|(0.037)|
|Per capita consumption|0.139***|0.082**|0.097***|0.132***|0.193***|
||(0.036)|(0.041)|(0.036)|(0.036)|(0.045)|
|Food share|-0.075**|-0.018|-0.069|-0.024|-0.111**|
||(0.032)|(0.051)|(0.045)|(0.041)|(0.045)|

# **4.3 Pakistan**

Results for height-for-age and weight-for-age for Pakistan are presented in Tables 5 and 6, respectively. In the parsimonious model, unlike the results from Bangladesh and Nepal, most of the food 22