rafmacalaba/datause-encoder-data huggingface dataset:
The number of exporters is clearly very low, but it is hard to compare with other countries because similar administrative data is rarely available. As a basis of comparison, we can compare the export propensity of Rwandan and other African firms in the World Bank Enterprise Surveys (WBES), see Van Biesebroeck and Mensah (2019) for details on that dataset. In the case of Rwanda, 9.0% and 9.5% of surveyed firms report to be exporting in, respectively, the 2006 and 2011 surveys. The average across other sub-Saharan African countries is 10.6% in both years, but 15.5%, which is quite a bit higher, over the full 19922018 period. However, limited to firms in the manufacturing sector the export propensity is 22.9% in Rwanda, which is barely distinguishable from the full sample average over all African countries of 22.1%. However, the two survey years count respectively 13 and 19 exporters for Rwanda. It would suggest that either almost all formal firms are included in the WBES survey or the administrative data misses some exporters.

# **4.2 Trade in manufacturing products is becoming relatively more important**

Not only the number of firms has increased over the sample period, the same is true for the absolute export volumes. The solid lines in Figure 7 show rising export values for all three sectors. The absolute growth is most pronounced for the mining and agriculture sectors, but both sectors also experienced large downturns. We only plot the absolute exports to 2015 as the aggregates are lower in 2016 and we cannot rule out entirely that this is at least partly due to incomplete recording at the time when we received the data.

Comparing average exports in the first two years (2008-2009) to the last two years (20142015), exports have increasing by 67 million USD for mining and by 32 and 26 million USD for agriculture and manufacturing. The percentage increase shows a rather different pattern as the initial export volumes differed a lot by sector. Exports more than doubled for agriculture and mining, growing by 120% and 137% from the first two to the last two years. Manufacturing exports, however, soared by almost 750%. It highlights both the very low base of manufacturing exports ten years ago, but also the growth potential. If not for the civil unrest in neighboring countries, the performance at the very end of the sample period would most likely have been even better. Apart from the decline at the end, the export evolution is least volatile in manufacturing.

The growing importance of manufacturing exports is also apparent from the dashed lines that show the relative share of the three sectors in the total. Given that incomplete reporting is likely to affect all sectors similarly, this series is shown up to 2016. Over the 9-year period that we observe, the manufacturing export share rose from 6% to 21%. And this was in a context of rapidly rising exports for the other sectors as well. Of course, the impact of this evolution should not be overstated as even in the final years manufacturing only accounted for less than one fifth of total exports and its share stopped increasing in the last three years.

13
# **Figure A.8. Predicted USAID Poverty Gap Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>20<br>40<br>18<br>38<br>16<br>36<br>14<br>34<br>12 32<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>36<br>30<br>35<br>28<br>34<br>26<br>24 33<br>22 32<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>35<br>38<br>36<br>30<br>34<br>25<br>32<br>30 20<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicate that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

65
anecdotal evidence that some villages have difficulty recruiting committee members, and that some longerserving officers are experiencing “volunteerism fatigue”.

|**Table 3. Village Fund Membership a**|**nd Number of Borrowers, 2010**|
|---|---|
||Median<br>Mean|
|Number of VF members per village|113<br>132|
|_Of which:*_||
|_Men_|_50_<br>_57_|
|_Women_|_60_<br>_73_|
|Number of VF borrowers per village|77<br>84|
|_Of which:*_||
|_Men_|_30_<br>_35_|
|_Women_|_43_<br>_48_|
|**_Memo items:_**||
|Number of households|210<br>314|
|Population|850<br>1,170|

Note: * The subtotals do not sum up to the total because the sample sizes differ; not all villages provided a breakdown of VF membership by gender. Source: Village Fund Survey, 2010.

|**Table 4. Size of**|**Village Fund comm**|**ittees**|||||||
|---|---|---|---|---|---|---|---|---|
|Size|<9<br>9|10|11|12|13|14|15|>15|
|% breakdown|3.5<br>39.9|8.8|14.2|5.7|7.2|4.3|15.4|1.0|

Source: Village Fund Survey, 2010.

The VF committee is typically headed by a small group of officers – usually a President, Vice-President, Secretary, and Treasurer. These officers are normally elected directly by the VF membership; in 70% of villages, this is the only method used. In 14% of villages, the officers are elected by the VF committee; occasionally (1% of cases) the officers are selected by the village; and in the remaining 15% of cases some combination of these methods is used.

# _Professional Background of VF Committee Members_

Most VF committee members have no more than a high school diploma, but on average about one committee member has a university degree, and another member has some higher education (see Table 5). It is not uncommon for VF committees to include school teachers, or retired army officers. However, 30% of committees have no member with any higher education.

On average, VF committees have 1.5 members with experience in accounting, 2.4 with managerial expertise, and 1.1 with “banking experience” (see Table 5). This helps equip many VFs with the knowhow to manage 8
# **Table 5c—Trade Flows by Trading Partner of Uganda (in percentage)**

|||||I|mports|||||||||Exports|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Kenya|Tanzania|Rwanda|COMESA|SADC|USA|EUR|China|ROW|Kenya|Tanzania|Rwanda|COMESA|SADC|USA|EUR|China|ROW|
|**Business Services**|||||||||||||||||||
|Air Transport|0.4|0.0|0.0|2.2|1.1|18.0|44.5|2.2|32.7|0.0|0.0|0.0|0.5|1.1|9.0|22.9|1.9|64.5|
|Communication|0.5|0.1|0.0|1.6|0.0|15.7|59.2|2.5|20.5|0.2|0.0|0.0|0.6|0.6|11.5|57.1|1.9|28.0|
|Insurance|0.0|0.0|0.0|0.2|0.2|18.9|50.5|1.2|29.1|0.0|0.0|0.0|0.8|0.8|33.7|24.1|6.2|34.3|
|Business Services nec|0.0|0.0|0.0|0.2|0.4|9.8|54.5|2.3|32.8|0.0|0.0|0.0|0.4|0.8|12.5|37.7|2.5|46.2|
|Financial Services nec|0.1|0.0|0.0|0.4|0.7|0.0|55.5|2.0|41.4|0.0|0.0|0.0|0.2|0.5|16.0|51.8|0.7|30.8|
|Transport nec|0.1|0.1|0.0|1.6|1.4|0.0|41.2|4.7|50.9|0.0|0.0|0.0|0.5|0.7|13.8|45.0|3.7|36.2|
|Water Transport|0.1|0.1|0.0|1.2|1.0|4.8|26.1|7.8|58.8|0.0|0.0|0.0|0.6|1.1|9.1|21.9|2.0|65.4|
|**Dixit-Stigliz Goods**|||||||||||||||||||
|Chemicals Mineral and Metal Products|29.5|0.5|0.2|1.0|12.0|0.9|20.7|4.5|30.7|1.8|2.9|19.1|21.0|1.7|3.1|2.8|1.1|46.5|
|Energy and Minerals|68.9|0.3|3.7|0.1|1.5|0.7|2.6|2.2|19.9|0.1|0.0|0.0|0.0|0.5|25.8|27.2|5.2|41.2|
|Food Products|44.7|8.7|0.0|2.0|19.7|6.6|11.7|0.4|6.2|9.1|3.8|10.9|11.8|6.8|1.3|37.0|0.6|18.5|
|Petroleum and Coal Products|15.2|0.0|0.0|1.2|0.6|0.6|0.9|0.1|81.4|0.1|1.0|10.2|1.2|23.5|7.5|30.8|2.0|23.7|
|Other Manufacturing|5.5|0.2|0.0|0.7|7.7|4.5|37.9|11.0|32.5|4.7|2.5|6.5|10.6|25.7|2.1|14.9|0.7|32.3|
|Textile and Apparel|22.9|2.3|0.0|0.2|2.2|0.5|3.2|46.1|22.6|11.9|4.5|7.0|12.6|2.5|5.0|8.8|24.4|23.3|
|Wood and Paper Products|32.8|0.5|0.1|4.4|9.0|0.7|21.4|5.9|25.3|6.5|1.7|25.0|13.9|2.7|4.1|11.3|1.6|33.1|
|**CRTS**|||||||||||||||||||
|Agriculture and Forestry|16.1|2.8|0.5|0.9|1.9|26.4|9.8|0.2|41.4|11.5|0.1|1.6|9.6|1.6|2.5|45.4|0.8|27.0|
|Other Services|0.3|0.1|0.0|0.7|1.2|33.8|31.4|3.3|29.2|0.1|0.1|0.0|0.8|0.9|26.7|32.2|2.4|36.9|
|Trade|0.0|0.1|0.0|0.2|0.5|5.3|47.5|9.3|37.1|0.0|0.0|0.0|0.4|1.0|8.2|47.3|7.5|35.6|
|Utilities|0.0|0.0|0.0|0.2|1.8|3.7|59.8|3.8|30.6|1.4|0.0|0.2|0.1|0.3|7.6|55.2|1.2|34.0|

# **Source:** GTAP 8.1 data set.

61
elevations exceeding 700 meters. About 20 percent of RN is considered suitable for cultivation, and 75 percent is either under pasture or has potential for such use.

Geographically, the state is rather heterogeneous, comprising two great ecosystems—the Coastal Zone and the semiarid Sertão Zones. The Coastal Zone is a humid area, with average precipitation of 2,000 millimeters per year. Although it only accounts for 10 percent of the state’s area, more than half the state’s 2.8 million inhabitants resided in the Coastal Zone in 2000. Major cities and industry are located in this area, as well as some high-value agriculture. Coastal soils are formed from sedimentary rock and are thus deep and permeable, making them suitable for agriculture. Despite substantial degradation of the flora in the coastal area, there are still some forests and wetlands remaining.

The rest of the population lives in the Sertão, which has relatively few natural resources and scattered caatinga vegetation. Soils in the interior are generally shallow and impermeable, and underground aquifers are scarce and often saline. Agricultural activity predominates, but climatic variability makes it risky. Precipitation ranges between 300 and 800 millimeters per year. However, rainfall is irregular and tends to be concentrated in a few, brief storms, resulting in high rates of erosion and water loss. Much of the inland area experiences water deficits lasting 8 to 12 months. Droughts and floods are frequent. On average, only two years in every decade are free from drought. Droughts cause crop failures and unemployment, which exacerbate poverty and cause social problems such as malnutrition and migration (Magalhães 1992).

# **Economy and People**

RN has a population of 2.8 million (1.6 percent of Brazil’s total). The people are called _Potiguars_ , after an Indian tribe that once resided in the state (Box 1.1). In 1999, the state’s GDP was about R$7.6 billion, equivalent to about 6 percent of the Northeast’s GDP, and well under 1 percent of the country’s GDP. About two-fifths of the state’s GDP is derived from services and another one-third from industry. Agriculture accounts for only 7 percent of GDP but about 18 percent of employment.<sup>2</sup>

> 2 _Contas Regionais do Brasil_ , ( _Instituto Brasileiro de Geografia e Estatística_ , IBGE).

4
**<u>Table 2.2: External debt: Highlighting recent accumulation and final debt stocks</u>**

|(a) Low growth and low final debt<sup>1/</sup>|Benin<br>Burkina Faso<br>Burundi<br>Cameroon<br>CAR<br>Comoros<br>Congo, Republic of<br>Côte d'Ivoire<br>Ethiopia<br>Guinea<br>Guinea-Bissau|Liberia<br>Madagascar<br>Mali<br>Rwanda<br>Sierra Leone<br>Tanzania<br>Togo<br>Uganda<br>Zambia<br>Chad|
|---|---|---|
|(b) Moderate growth and moderate final debt<sup>1/</sup>|DRC<br>Ghana<br>Niger<br>Senegal||
|(c) High growth and/or high final debt<sup>1/</sup>|The Gambia<br>Malawi<br>Mauritania<sup>2/</sup><br>Mozambique<br>Sao Tome & Principe<br>Sudan||

1/ See footnotes 6 and 7 for the classification criteria. 2/ Mauritania is classified in category (c) wholly on the basis of its high initial indebtedness (above 70 percent).

In summary, while indebtedness has decreased in SSA countries as a whole, at least compared to the very high levels shown in the late 1990s, debt burden indicators for some countries appear to have deteriorated following the global financial crisis. The improvement shown when compared with the very high levels of indebtedness before debt relief masks an important degree of heterogeneity across countries. In fact some countries appear to have contracted significant levels of new debt after debt relief, a development that if left unchecked could reverse the benefits of debt relief. This development warrants a more detailed analysis of the underlying factors explaining the changes in debt and the potential risks and vulnerabilities associated. This is undertaken in the following two sections.

# **Section 3. Underlying Debt Dynamics for Public and External Debt**

This section explores the following questions: what are the key drivers of debt buildup (or reduction)? And, have debt dynamics changed over time? The analysis looks at public sector debt and external debt dynamics since 2006, thus covering the global financial crisis years, highlighting significant differences between periods and across country groups. It measures the role played by exogenous and endogenous factors in explaining debt dynamics (i.e., primary deficit, changes in the real exchange rate, real GDP growth, real interest rate and “others” including debt relief). The analysis is based on 33 SSA countries, and the data for each country was obtained directly from the latest DSA undertaken jointly by World Bank and IMF staff.<sup>10</sup>

> 10 There are 33 SSA countries covered by a DSA in either 2013 or 2014.  For 27 of these countries, DSAs were carried out during the 2014 fiscal year, while six were covered during the 2013 fiscal year.

9
developments have been pro-poor, pro-rural areas and also multidimensional determining an inversion of trend vis-à-vis the previous decade.

Have these positive economic outcomes been accompanied by visible changes in the structure and performance of the labor market? One of the problems that have been affecting developed and developing economies alike during the past decade is the question of jobless growth. Most emerging economies grow at significant annual rates and many of these economies also deliver in terms of population welfare but this seems to occur with no real growth in employment (Bhalotra, 1998, Verme, 2006, Wolnicki, 2006).

Morocco has experienced a similar path of jobless growth. Key indicators of the labor market are on a downward trend since 2000. The Labor Force Participation Rate (LFPR) at the national level fell from 54.5% in the late 1990s to 49.2% in 2011, a decrease of 5 percentage points over the period. The Employment Rate (ER) follows the same trend, especially in rural areas. At the national level, it declined from 46.9% in 1999 to 44.8% in 2011, representing a decline of almost two percentage points over a period of more than ten years. A more positive picture is conveyed by the Unemployment Rate (UR), which declined persistently throughout the decade from 13.8% in 1999 to 8.9% in 2011. However, the unemployment rate remains a poor indicator of the labor market performance in a country like Morocco characterized by a very large informal sector. And, given the decline in the LFPR, the decline in the unemployment rate may be explained by a growth of the discouraged unemployed.

**Figure 2 – Main Labor Market Indicators 2000-2011**

<!-- Start of picture text --> LFPR UR ER<br>2000 2005 2010<br>Year<br>Male, Urban<br>Female, Urban<br>Male, Rural<br>2000 2005 2010 2000 2005 2010<br>Year Female, Rural Year<br>80.0 80.0 80.0<br>60.0<br>60.0 60.0<br>40.0<br>40.0 40.0<br>20.0<br>20.0 0.0 20.0<br>0.0 0.0<br><!-- End of picture text --> Source: Authors’ estimations from LFSs 1999-2011.

To better appreciate the relative changes of employment (E), unemployment (U) and inactivity (I), we plotted in the figure below each labor market status on one side of the triangle and normalize the sum of the three labor market statuses (WAP) to 100. Each point in the triangle represents a particular E-U-I structure in one particular year. We plotted these points for three years (1999, 2005 and 2011) and for different population groups constructed along gender and location lines.

5
REGRESSION **III.3** ITAX 0.929 - 1.26 D76 + 0.697 PD + 0.027 IE (1.4) (-1.4) (6.78) (0.08) MA(1) -0.62 (T-Value:-2.36) Adj.R2= 0.68 D-W Stat.:1.59 Sample Period: 1963-1987 In order to determine the effects of the Public Sector deficit on inflation tne next natural step is to ascertain the relation between the inflation rate and the revenue from money creation. Such link is provided by the demand for real cash balances that has the inflation rate as one of its determining variables (taken as a measure of the opportunity cost of holding money). Precise estimates of money demand for Argentina will be derived in the next section but we can already derive some preliminary estimates by running a regression of the inflation rate on the series of revenue from money creation.

The response of the inflation rate to the printing of money in order to finance the deficit need not be instantaneous as there may be lags En the adjustment of prices to changes in the money supply. We therefore include inflation lagged one year as an explanatory variable for current inflation in addition of current revenue from money creation. The results are presented in Regression 4 that indicates a clear association between the revenue from money creation and the resulting inflation rate. Here we have assumed a linear relation between ITAX and the inflation rate, a fact that may not be valid for high inflation rates because as inflation raises the base of the tax, real cash balances falls and actual revenue may actually fall. In the next section we derive the precise non-linear relationship using an estimate of the demand for money. Cur results here are therefore an approximation valid for inflation rates to the left of the maximum of the revenue curve.

The results of Regression III.4 indicate that the long run effect of raising 1% point of GOP from money creation is associated with an additional 97% inflation rate.

22
We use Hausman-Wu specification test to determine whether the fixed or<sup>random effects</sup> model is appropriate for the given data and present results accordingly.

# Data and Variable Description

The data used in this paper are drawn from 85 districts of India for 1972/73 to 1980/81. A more complete discussion of the data can be found in Binswanger et al,<sup>1988.</sup> The number of observations varies depending on the data available for each dependent variable. Thus, 765 observations (85 districts for nine years) are used for the<sup>output supply</sup> and wage equations, 738 (82 districts for nine years) observations for the fertilizer equation, 228 (76 districts for three years) observations for the investment equations, and only 170<sup>(85</sup> districts for two years) observations for the farm and nonfarm employment equations.<sup>The</sup> investment data are computed from livestock censuses of 1966, 1972, 1976 and 1982,<sup>while</sup> fertilizer, crop output and wage data are from yearly fertilizer, wage and agricultural statistics published by the Ministry of Agriculture of India. Cm output<sup>is the aggregate</sup> index of 17 major crops using 1965/76 as the base year. Fertilizer is measured<sup>in nutrient</sup> tons of nitrogen, phosphate and potash. The wage rate is the daily wage rate<sup>of agricultural</sup> field workers. The irvesment varie* are the net additions over each census<sup>interval to the</sup> stock of draft animals (male bullocks and male buffalos), milk animals (female<sup>bullocks and</sup> female buffalos), small stocks (sheep and goats) and pumps (both diesel and electric).Empoyment daa, drawn from the population censuses of 1970 and 1980,<sup>are comparable</sup> I/ A second-age equation for tractors could not be estimated because none of the explanatory variables has a sigificant effect an the tractors investment. lTus, the tractor vaniable was<sup>dropped.</sup> 17
Policy Research Working Paper 10004

# **Abstract**

Ethiopia is currently embroiled in a large-scale civil war that has continued for more than a year. Using unique High-Frequency Phone Survey data, which spans several months before and after the outbreak of the war, this paper provides fresh evidence on the ex durante impacts of the conflict on the food security and livelihood activities of affected households. The analysis uses difference-in-differences estimation to compare trends in the outcomes of interest across affected and unaffected regions (households) and before and after the outbreak of the civil war. The findings show that seven months into the conflict, the outbreak of the civil war increased the probability of moderate to severe food insecurity by 38 percentage points. Using the Armed Conflict Location and Event Data on households’ exposure to violent conflict, the analysis shows that exposure to one additional battle leads to a 1 percentage point increase in the probability of moderate to severe food insecurity. The conflict has reduced households’ access to food through supply chain disruptions while also curtailing non-farm livelihood activities. Non-farm and wage-related activities have been the most affected by the conflict, while farming activities have been relatively more resilient. Similarly, economic activities in urban areas have been much more affected than those in rural areas. These substantial impact estimates, which are likely to be underestimates of the true average effects on the population, constitute novel evidence of the near-real-time impacts of an ongoing civil conflict, providing direct evidence of how violent conflict disrupts the functioning of market supply chains and livelihoods activities. The paper highlights the potential of phone surveys to monitor active and large-scale conflicts, especially in contexts where conventional data sources are not immediately available.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ktafere@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team