rafmacalaba/datause-encoder-data huggingface dataset:
5 financial sector appears to have<sup>beenhitthehardestamongthesamplecountries withNPLs</sup> amounting to over 50 percent of<sup>bankingsystem assets (Caprio and Klingebiel2002).</sup>

**Figure 1. Peak Level of Nonperforming**<sup>**Loans (NPLs)asPercent**</sup> **of Total Banking System Loans**

<!-- Start of picture text --> Thandk (198) _<br>Pobnd (1M3)<br>Norway (1r1<br>Japan (1998)<br>Hunguy (1994)<br>Chdie (19_6)<br>Argoftina (9I9 S1) _4<br>0 10 20 30 40 50 60<br><!-- End of picture text --> Note: * denotes NPLs as percent of<sup>totalbanking assets.</sup> Source: Caprio and Klingebiel 2002:<sup>IntemationalMonetary Fund,International FinancialStatistics</sup> The increase in the number of bankruptcies<sup>(Table1)alsoreflectsthe extentof corporate</sup> distress. Among the countries<sup>forwhichdatawereavailable,themostsignificantincreasein</sup> bankruptcies was recorded in Argentina,<sup>where bankruptciesrose by 76%in thefirstyear of the</sup> financial crisis (Balino 1991).<sup>In Hungaryand Poland,over 50 percentof the bankingsystem's</sup> non-perforning loans were owed by<sup>loss-making state enterprisesthat madeup over half of the</sup> enterprise sector and were related<sup>tothe transitionfromcentralplanning toamarketeconomy</sup> (World Bank 1996).
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household income of 35 percent, with no effect for treated households served by nonmotorable roads.

The paper will cast light on such heterogeneity and complementarities across different types of roads. Our data come from five rounds of household surveys carried out in Ethiopia for the period 2012 to 2016. In recent years, the Government of Ethiopia has been investing heavily in both primary and feeder roads. Under the Road Sector Development Program (RSDP), many Trunk and Link Roads have been rehabilitated. There are also rural road initiatives, such as Universal Rural Road Access Programme (URRAP), which is focused on feeder road improvement at the village (kebele) level. Different levels of connectivity are likely to contribute to the economy in different ways.

The paper is organized as follows: Section II provides a brief overview of the country context, specifically focused on the agriculture and road sectors. Section III develops our empirical strategy. Section IV provides more details on our data. Section V presents main results and discusses policy implications. Then Section VI concludes.

# **II. AGRICULTURE AND ROAD SECTORS IN ETHIOPIA**

In Ethiopia, agriculture remains among the most important economic sectors. It produces about one-third of GDP, employs 70 percent of the workforce and accounts for 80 percent of the country’s merchandise exports (Ethiopian Agricultural Transformation Agency, 2014). Ethiopia produces about US$6 billion of crops a year, including teff, maize, coffee, beans, sorghum and wheat. Coffee is one of the traditional export crops of the country. Agricultural productivity is generally low but compares favorably to neighboring countries. Earlier studies (e.g., Taffesse et al., 2012; Spielman et al., 2012) show that agricultural growth was largely attributed to expansion of land area cultivated during the 1990s, but productivity growth has also been contributing since the early 2000s.
head under age 5, 0.36 children under age 15 that are not the biological children of the head, and 1.18 other members that include grandparents, aunts, uncles, and other extended family members. Only 12 percent of the sampled children have a parent that ever attended school.

We use the Raven‟s Colored Progressive Matrices (CPM) to measure a child‟s cognitive ability. The Raven‟s CPM is a measure of fluid intelligence or problem solving ability, and it does not require formal schooling to be able to answer the questions (Raven, Raven, and Court 1998). The test does not depend heavily on verbal skills, making it relatively “culture free” (Borghans, Duckworth, Heckman, and Weel 2008). In the Raven‟s test, the child respondent is asked to select the image that is missing in order to complete a picture. This type of question is novel to the children in Nahouri province, thus providing a truer measure of problem solving skills. We ask 18 questions from the Raven‟s CPM and on average, children in our sample answer 4.7 questions correctly. Younger children answer fewer questions correctly than older children (the average number correct for children age 5 is 2.7 and for children age 15 is 7.3).<sup>6</sup> To control for this relationship between age and raw test scores, we calculate a z-score for each child measured as the child‟s raw test score minus the average score for the same age children divided by the standard deviation of test scores for children of that age.<sup>7</sup> Therefore, the mean of the Raven‟s z-score is zero and the standard deviation is one for each age and across all ages.

In Table 1, we present summary statistics about children‟s schooling. Few households in rural Burkina Faso ever enroll all of their children. For households with at least two primary school-aged children, 59 percent of these households experience variation in enrollment among

> 6 During extensive pretesting of the Raven‟s test, results were consistent whether children were asked the entire set of 36 questions or only the odd-numbered questions, so to save interview time we only administered the 18 oddnumbered questions (Sets A, Ab, and B). The average number of questions answered correctly for children ages 6, 7, 8, 9, 10, 11, 12, 13, and 14 is respectively 2.6, 3.6, 4.5, 5.0, 5.5, 5.8, 6.0, 6.4, and 6.3.

> 7 We do not use the international Raven‟s norming standards since we asked a subset of the Raven‟s test and what is most important here is how the children in rural Burkina Faso compare to each other, not internationally. Note that in Section 4.3, to test the robustness of the results using the Raven‟s age-adjusted z-score, we estimate alternative specifications using the Raven‟s raw test score and results are consistent.

7
marginal cost is significant, firms adjust to cost shocks partly by reducing the future quality of their product.

Finally, the coefficients on initial quality realizations indicate that unobserved heterogeneity is often important, and that Wooldridge's correction matters. On the other hand, coefficients on initial marginal cost realizations are usually insignificant, so our representation of unobserved heterogeneity (8) is probably more general that it needs to be for most industries. Nonetheless, the covariance matrices for our compound disturbances imply that in many cases, persistent unobserved heterogeneity remains after conditioning on these variables.

Consider now the variables of primary interest-dummies for the various activity bundles. 1<sup>4</sup> Wald statistics reported at the bottom of each table test the joint null hypothesis that last period's activity bundles have nothing to do with current quality. Tellingly, all but three of these X<sup>2</sup> statistics have ap-value greater than 0.05, so in 16 of the 19 country/industry cases, one cannot reject the null hypothesis that previous international activities have no effect on current product quality. Experiments with additional lags on activity bundles (not reported) left this basic message intact.

Further, in the three instances where activities are significantly related to quality, the dominant relationship is negative. In Moroccan plastics and Colombian fertilizers/pesticides, any combination of international activities leads to _worse_ product quality than no international activities. In the other significant case, Moroccan paints and varnishes, firms that export and firms with foreign ownership also do worse than firms without international activities, but firms that have both types of international relationship 24
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have analyzed the UNPS. Sheehan and Barrett (2017) provide a broad overview of input use in six Sub‐ Saharan African countries using the 2010 wave of the UNPS. Their work highlights the relatively low input use in Uganda compared to Ethiopia, Malawi, Niger, Nigeria, and Tanzania, and that the average amount of inorganic fertilizer applied per hectare of 0.7 kilograms for Uganda is substantially lower than the cross‐ country average of 56 kilograms per hectare (see Figure 2.3).

**Figure 2.3. Use of inorganic fertilizer and application rates, 2010**

<!-- Start of picture text --> 90<br>77.3<br>80<br>70 64.3<br>60 56.3 55.5 56.9<br>50<br>41.4<br>40 35.2<br>30 25.2<br>17 16.9<br>20<br>7.7<br>10 1.7 3.2 0.7<br>0<br>Malawi Ethiopia Nigeria Niger Tanzania Uganda Average<br>Fertilizer use (% of HH) Average nutrient application rate (kg/ha)<br><!-- End of picture text --> Source: Sheehan and Barrett (2014), using comparable LSMS‐ISA data.

The potential gains to input use appear quite high in Uganda given cultivatable land and output prices compared to other LSMS‐ISA countries. Binswanger and Savastano (2014) develop a measure of agro‐ ecological potential (AEP) using international crop prices, the share of land under each crop, and the potential of the crop in that area from the 2005/06 and 2009/10 LSMS‐ISA waves. The AEP is based on attainable crop yields across all agricultural zones using data from the International Institute for Systems Analysis and the Food and Agriculture Organization for medium input levels (Tóth et al., 2012). Uganda has the _highest AEP_ (and the 4<sup>th</sup> highest AEP per capita) compared to all other countries at $1,878 per hectare, twice as high as the second runner up, Malawi, at $999 per hectare. However, these calculations assume the use of improved varieties, adequate fallows, and some mechanization, fertilizer application, chemical pest, disease and weed control. Smallholder farmers, who may be stuck in poverty traps, comprise more than 50% of Uganda’s farmers and are precisely the individuals who may not be purchasing inputs, and who face variable input quality ‐ a key element in explaining the lack of returns to input use. Similarly, McCarthur and McCord (2017) also predict high potential returns to input use, but also under the assumption that inputs are of high quality.

As in Suri (2011), the differences in input use are large across consumption quintiles and regions, depending on the predominant crops grown in the region.<sup>5</sup> Overall, poorer households have lower input

> 5 The consumption aggregate used here is the aggregate constructed by the Uganda Bureau of Statistics, which uses the same method to generate the consumption aggregate used in their official poverty measures. The majority of the income aggregates come from the Rural Income Generating Activities (RIGA) database, which uses standardized

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mortality, stock recruitment, number of vessels, selectivity of the fishing gear, costs of gear.

Introducing the catchability coefficient into the model, it is shown how this coefficient affects the possibility to agree on cooperation and to establish a grand coalition. The authors consider two different situations with two different catchability coefficients, high and low.

The authors construct the characteristic function of the game that assigns a value to each coalition (or union of countries). They consider at first the situation in which the catchability coefficient is high.

The characteristic function is as follows: where _v_ ( _i_ ) is the value of a single-player coalition; _v_ ( _i_ , _j_ ) the value of a two-player coalition; _v_ ( _M_ ) is the value of the grand coalition. Term _FNS_ denotes the strategy chosen in Nash equilibrium by coalition _S_ , and _F_ * denotes the optimal full-cooperative strategy that maximizes the value of the grand coalition.

Three situations are presented: the first when the countries play a three-player noncooperative game as single-coalition. The net present values for these countries are calculated with the characteristic function and it is showed that in this situation their gains are low and became negative after three years; the herring stock is rapidly driven to extinction. The study is done using the Nash reaction curves (these are also referred as best reply curves) and two basic concepts of coalition stability: the core and the individual rationality.

The second case is when the countries create two-player coalition. The paper adopted the Chander and Tulkens (1995) approach in which the country outside the coalition will play non-cooperatively against the coalition members. All the possible combinations are simulated. It is demonstrated that this is a more stable situation because the net revenues for the countries involved are higher that in the first situation. The stock will stabilize at a positive value in all two-player coalition cases. However, the spawning stock biomass is below the safe minimum biological level. In this case the reaction functions of the couples of players are simulated also. Finally, the full cooperative situation is studied and the net values for this coalition are calculated.

It is shown by the authors that the two-player coalitions are stable, but others are not. Even the grand coalition is not stable, the authors say, since the sum of the revenues accruing to its members, if they free ride, is higher than the value of the grand coalition.

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each different tax per filing cycle is used to compute the (1) time spent on VAT per submission and (2) total time spent on VAT per year.<sup>25</sup> All other details, regarding to other controls are presented in summary statistics, Table R.SA1.

# **3.1.2 Nepal**

As was the case in South Africa, we try to consider the most relevant part of TCC that may be affected by e-filing. However, due to differences in the surveys performed for the different countries, the same sub-divisions of TCC are not always available. We consider total TCC in terms of money and time as well as total TCC with respect to tax accounting<sup>26</sup> in money and time.<sup>27</sup> Moreover, as the VAT

> 25 For further details, see the appendix.

> 26 Tax accounting activities are defined in the survey as calculating, filling out and submitting tax reports, paying taxes, finding and analysis if relevant legislation and so on. See TCC Survey Nepal (2011), page 8.

> 27 We refer reader to the IFC (2011) report for the details on computing TCC.

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variables were constructed; Section 4 provides the theoretical model and Section 5 reports results from the difference‐in‐difference regression analysis. Finally, Section 6 provides a discussion and conclusion for the study.

# 2. Related Literature

From civil wars to riots and violent mass protests, past and present violent conflicts result in lost opportunities for human and economic development and have significant effects on the welfare, resilience and behavior of individuals, households and communities. Due in part to security studies’ traditional focus on the state and state agency, research on violent conflict has until recently relied mostly on standardized macro‐level measures of conflict such as the number of battle deaths per country per year. Over the last 15 years, initiatives such as the Households in Conflict Network (HiCN), housed within the Institute of Development Studies at the University of Sussex, and MicroCon (Micro Level Analysis of Violent Conflict), an EU‐funded multi‐institution partnership, have spearheaded micro‐level conflict research. Some studies have conducted systematic empirical analysis of the mechanisms linking interactions of individual, household, and community units of analysis to processes of violent conflict (Brück, Justino, Verwimp, and Alexandra Avdeenko, 2010; Bozzoli, 2009, Bundervoet et al., 2009; Dupas and Robinson, 2012; Miguel and Roland, 2011).

Brück (2011), Justino (2012), and Bozzoli (2011) differentiate between two main approaches to using data in micro‐level conflict research: (1) using purposively designed surveys; and (2) employing existing socio‐economic data sets from conflict‐affected regions. Of the two, the former aims to use specifically collected data to uncover causes and functions of conflict at the micro‐level. This more uncommon approach so far, this includes ex‐combatant surveys, genocide and atrocities surveys, displaced people surveys, surveys of civilian populations affected by conflict, and standardized conflict surveys (I.e. ICRC’s People on War Surveys) (Blattman 2007; Calderón, Gáfaro and Ibáñez 2011; Justino, Gupte, and Tranchant 2012; Mvukiyehe and Samii 2008/9). The second direction uses micro‐ level data sets that were not explicitly collected for the analysis of conflict processes or consequences, but which can be used for that purpose when merged with conflict event data (Bozzoli and Brück 2009; Bozzoli, Brück and Muhumuza 2011; Douarin, Litchfield and Sabates‐Wheeler 2011; Moya 2015; Nasir, Rockmore, and Tan 2015). Beyond these two directions, research on the causes and drivers of conflict at the individual and household level also includes qualitative and smaller scale quantitative analysis based on small samples and limited geographic locations (Boothby, Crawford, and Halperin 2006; 5