rafmacalaba/datause-encoder-data huggingface dataset:
|Country|a|6|SsTT<br>O|urSample|Country|ce|8|STT<br>O|urSample|
|---|---|---|---|---|---|---|---|---|---|
|Albania|47.1|58.2|11.2|0|Kuwait|55.0|48.9|-6.1|0|
|Algeria|33.4|33.2|-0.2|0|Latvia|404|41.1|0.8|Lt]|
|Argentina|64.5|707|6.2|1|Lebanon|184|$113|-7.1|Lt]|
|Armenia|56.6|67.5|10.9|it]|Libya|28.7|24.9|-3.8|Lt]|
|Australia|19.2|23.3|41|it}|Lithuania|28.0|32.6|4.7|0|
|Austria|22.9|28.0|5.1|1|Luxembourg|21.1|248|3.7|1|
|Azerbaijan|435|454|3.0|0|Macedonia, FYR|25.4|27.22|18|0|
|Bahrain|43.5|48.1|4.6|it)|Malaysia|61.1|56.9|-4.2|0|
|Belarus|30.9|33.6|2.8|0|Malta|23.2|23.6|0.4|0|
|Belgium|29.9|279.3|-0.6|1|Mauritius|56.5|68.8|12.3|Lt]|
|Bolivia|29.1|39.6|10.5|0|Mexico|718|75.8|4.0|1|
|BosniaandHerzegovina|26.1|33.4|7.3|0|Mongolia|45.3|51.5|6.3|0|
|<br>Botswana|7e4|F729|-5.5|it]|Montenegro|219|30.8|6.9|0|
|Brazil|53.9|56.6|2.7|1|Morocco|S17|36.5|-15.2|0|
|Bulgaria|32.9|S71|24.2|it)|Namibia|63.7|S75|-6.2|0|
|Canada|16.0|16.1|O12|0|Netherlands|21.6|26.0|44|1|
|Chile|56.6|64.8|8.2|1|NewZealand|18.6|25.1|6.5|0|
|China|38.9|447|5.8|0|Norway|7.0|6.5|-0.4|1|
|Colombia|62.8|62.1|-O.7|1|Pakistan|515|66.4|1495|Lt]|
|Costa Rica|S40|S87|47|it]|Panama|53.8|60.3|4.5|Lt]|
|Croatia|473|32.2|4.9|it]|Paraguay|447|Ss/7.7|13.0|Lt]|
|Cyprus|35.8|43.2|7.4|it}|Peru|51.6|57.7)|6.0|1|
|CzechRepublic|36.4|48.4|12.0|0|Philippines|48.5|48.2|-0.3|0|
|<br>Denmark|13.9|15.6|18|1|Poland|60.5|62.8|2.2|0|
|DominicanRepublic|477|519|42|0|Portugal|43.2|46.0|2.8|1|
|<br>Ecuador|56.1|63.3|7.2|0|Romania|314|38.8|7.4|Lt]|
|Egypt,Arab Rep.|59.7|60.7|1.0|it)|Russian Federation|39.9|47.6|F.7|0|
|ElSalvador|55.6|37.1|-18.5|it)|SaudiArabia|51.6|45.3|-6.3|0|
|Estonia|27.3|316|4.2|it]|Serbia|24.9|35.6|10.7|Lt]|
|Finland|122.9|14.2|13|1|Singapore|28.7|38.3|9.6|0|
|France|244|294|5.0|1|Slovak Republic|27.8|33.2|54|0|
|Gabon|68.9|54.7|-14.2|it)|Slovenia|27.3|36.0|8.7|0|
|Georgia|$2.1|60.2|38.1|0|South Africa|70.7|74.3|3.6|0|
|Germany|117|185|6.8|1|Spain|18.1|16.8|-1.3|1|
|Greece|43.9|48.2|4.3|1|Sri Lanka|58.6|60.0|14|Lt]|
|Guatemala|55.8|56.0|0.2|0|Sweden|98|11.3|15|1|
|HongKongSAR,China|25.9|37.7|118|0|Switzerland|17.6|14.0|-3.6|0|
|<br>Hungary|447|46.9|2.2|it}|Taiwan, China|38.2|39.2|1.0|0|
|India|53.4|53.3|-0.1|it}|Thailand|48.0|53.8|5.9|0|
|Indonesia|51.7|49.3|-24|0|Trinidad andTobago|39.2|39.1|0.0|0|
|Iran, Islamic Rep.|614|64.0|2.6|cf]|Tunisia|60.2|63.2|3.0|if]|
|Iraq|58.9|49.8|-9.1|0|Turkey|32.9|29.5|-3.4|0|
|Ireland|29.6|31.7|2.2|1|Turkmenistan|25.8|15.0|-10.8|0|
|Israel|16.9|15.6|-1.3|0|Ukraine|23.3|27.3|3.9|0|
|Italy|28.9|28.3|-0.6|1|UnitedArab Emirates|46.7|60.5|13.8|0|
|Japan|19.1|18.9|-0.2|0|United Kingdom|18.9|22.1|3.1|1|
|Jordan|33.2|36.6|3.5|it}|UnitedStates|26.9|28.8|1.9|1|
|Kazakhstan|49.7|504|0.8|0|Uruguay|516|65.4|13.8|0|
|Korea,Rep.|19.9|22.1|2.3|cf]|Venezuela,RB|53.1|49.2|-3.9|if]|
|<br>Kosovo|23.2|29.2|6.1|0||||||
# TECHNICAL APPENDEI

The monthly series involved in the estimation of tax effort are:

- a) Real VAT revenue (T), defined as nominal VAT collection net of withdrawals, deflated by the average combined price index.

- b) VAT rate (t), applied to general goods and servicesl/.

- c) Collection delay (0) which is the legal delay, defined as the ratio "days **of** legal delay/days of period".

- d) The inflation rate (7r), measured by the variation of the combined price index2/.

- e) Real GDP (Y). Note that GDP data was only available on a quarterly basis. It was converted into a monthly series, using a monthly industrial production index as a proxy of economic activity.

The above information was obtained from the Tax Administration Agency (DGI), the Central Bank (ECRA) and the Institute of SLatistics (IINJDEC).

In the main text (see Figure 2), we decided to present tax effort as an index, which was constructed in two steps. The monthly percentage change in tax effort3/ was estimated following equation (5); the detailed results are shown in Table 1 of this appendix. We then calculated the tax effon index (with base January of 1983=100) as follows:

# E,+= EXP [ LN(E,) + nLN(Et+,)]

The resulting monthly index is presented in the last column of Table 1 of this appendix. The quarterly inde; in Figure 2 is the simple average of the monthly index.

The decomposition of total tax effort into exemptions reduction effort and tax evasion reductiorn effort has been calculated by using the procedure described in the main text (see section 2.4 and Figure 4). The results are summarized in Table 2 of this appendix. Again, we chose to present them as indices, following the methodology described above.

**1/** It is acknowledged that otber rates apply to some goods and services, though these were not -tvailable on a detailed basis throughout the period.

> **2/** This measure of inflation is commonly used in Argentina; the combined price index is the simple average of the consumer and wholesale price indices.

- 3/ Measured as the variation of -natura **l** ogarithms.
# **Reference**

Africa Development Bank. 2010. _Coffee Production in Africa and the Global Market Situation_ . Commodity Market Brief, Vol.1, No. 2, July, 2010.

Celasun, O., X. Debrun, and J.D. Ostry. 2007. _Primary Surplus Behavior and Risks to Fiscal Sustainability in Emerging Market Countries: A “Fan-Chart” Approach_ . IMF Staff Papers Vol. 53, No. 3.

Economic Intelligence Unit. 2013. _Burundi Country Report: 1st Quarter 2013_ . London. United Kingdom.

Economic Intelligence Unit. 2008. _Burundi Country Profile 2008_ . London. United Kingdom.

Government of Burundi, Ministry of Public Service, Work, and Social Security. 2012. _Pour une Politique Salariale Equitable dans l’Administration Publique Burundaise_ . Mimeo, November 2012.

Hemming, Richard and Murray Petrie. 2000. _A Framework for Assessing Fiscal Vulnerability._ IMF Working Paper.

International Finance Corporation. 2012. _Burundi: Summary Analysis and Proposals for Reforming the Regime for Investment Incentives_ , Mimeo, September 2012.

International Monetary Fund. 2013. Burundi: Second Review Under the Extended Credit Facility—Staff Report; Staff Supplement; Press Release on the Executive Board Discussion, IMF Country Report No. 13/64, March 2013.

International Monetary Fund. 2012. _Global Risks, Vulnerabilities, and Policy Challenges Facing Low-Income Countries._ Mimeo, October 2012.

International Monetary Fund. 2011. _Burundi: Ex Post Assessment of Longer-Term Program Engagement_ . IMF Country Report No. 11/269, September 2011.

International Monetary Fund. 2009. _The Implications of the Global Financial Crisis for LowIncome Countries—An Update._ Mimeo, September 2009.

International Monetary Fund. 2008. _Food and Fuel Prices—Recent Developments, Macroeconomic Impact, and Policy Responses_ . Mimeo, June 2008.

28
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# Introduction

The aim of this study is to examine the differential impact of disasters and climate shocks on populations in conflict-affected regions, specifically investigating the repercussions of flooding in conflict versus non-conflict areas. Using remote sensing technology, we attempt to overcome the challenge of data scarcity in conflict-affected countries, allowing us to account for short-term impacts of recent disaster and climate shock events. Despite the inherent limitations of using nightlight intensity as an economic activity indicator, it provides an empirical foundation for the analysis and enough observations for an ex-post quasi-experimental impact evaluation. We employ a difference-in-difference econometric approach, using satellite imagery of nightlight radiance alongside geospatial data on flood and conflict events. This methodological framework is applied to assess the aftermath of the March-April 2019 Floods in Mozambique following Cyclones Kenneth and Idai, as well as the 2022 floods spanning July to October in Nigeria.

Results show significant disparities in the effects of disasters and climate shocks between conflictaffected and non-conflict-affected areas. Specifically, we observe a more pronounced decline in economic activities in conflict-affected regions.

attention to the interconnectedness of conflict and disasters and climate shocks, outlining the methodology and empirical strategy derived to estimate such ex-post impact. In the second section, we present the results and supporting data derived from the study, including the case studies on Mozambique and Nigeria. Finally, we discuss limitations as well as broader implications before concluding.

# Context: Flood impact and conflict affected population (Literature)

## 1.

Quantitative economic analyses have frequently used nightlight radiance as proxy for economic activity (Chen & Nordhaus, 2011; Henderson et al., 2012). These have also been used to estimate the impacts of weather variability and disasters and climate shocks (Bertinelli & Strobl, 2013; Elliott et al., 2015; Felbermayr et al., 2022; Heger & Neumayer, 2019; Miranda Montero et al., 2017) and, more specifically, floods (Kocornik-Mina et al., 2020) _._ Most analyses using nightlight data usually demonstrate a negative impact of disaster and climate shocks on nightlights but with effects resorbing within the year following the event (Bertinelli & Strobl, 2013; Elliott et al., 2015; Gillespie et al., 2014). Schippers & Botzen (2023) find that for a severe disaster such as Hurricane Katrina, the effect can be longer lasting.

However, there is a debate about the accuracy of nightlights as a proxy for economic activity. Critics argue that nightlight intensity may not capture economic activity accurately in all contexts, such as highly rural areas, where changes in lighting efficiency could affect the amount of light observed without necessarily reflecting changes in economic activity. Possibly other cultural and social factors or government policies on lighting could also influence the amount of nightlight observed.

2
Table 1: Contact farmers' characteristics by treatment status

|Dierence<br>in mean|-1.302|-0.097<br>-1.252*<br>-0.410|-1.066<br>0.011<br>-1.309<br>0.240<br>2.473<br>3.362<br>0.047<br>39.873<br>23.206<br>-0.446<br>0.242||
|---|---|---|---|---|
|N|50|47<br>22<br>49|50<br>50<br>46<br>50<br>50<br>50<br>50<br>42<br>32<br>42<br>42||
|SD|10.559|0.503<br>3.202<br>2.570|14.333<br>9.064<br>26.441<br>17.203<br>11.573<br>12.034<br>0.624<br>265.410<br>426.578<br>2.255<br>0.935|statistics.|
|Control<br>Mean|40.160|0.447<br>3.409<br>2.653|19.100<br>10.860<br>38.370<br>22.240<br>12.340<br>12.960<br>1.242<br>244.548<br>436.063<br>3.286<br>1.167|level for t|
|N|148|140<br>51<br>144|147<br>147<br>133<br>148<br>144<br>143<br>144<br>126<br>108<br>137<br>137|.<br>ritical|
|SD|9.348|0.479<br>2.239<br>2.401|16.095<br>9.659<br>28.320<br>15.145<br>12.726<br>12.498<br>0.655<br>267.037<br>430.130<br>2.362<br>1.210|vey, 2012<br> percent c|
|Treated<br>Mean|38.858|0.350<br>2.157<br>2.243|18.034<br>10.871<br>37.060<br>22.480<br>14.813<br>16.322<br>1.289<br>284.421<br>459.269<br>2.839<br>1.409|ousehold sur<br> 1, 5, and 10|
||||days<br> last 7 days<br>0 days<br> last 30 days<br>eek<br>unity<br> before 2010<br> before 2010|rvey, 2010; H<br>cance at the|
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|||l t<br>ce|rs <br>ar<br>rs <br>ar<br> C<br>or<br> cu<br>ho<br>in <br>Rec<br>tec<br>tec|fa<br>nd <br>on|
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32
-  51  -

# **References :**

Bernard, Andrew B. and J. Bradford Jensen. 1999. “Exceptional exporter performance: cause, effect, or both?” _Journal of International Economics_ . 47:1-25.

- Blomström, Magnus and Ari Kokko. 1998. “Multinational Corporations and Spillovers,” _Journal of Economic Surveys_ , 12(2): 1-31.

- Blonigen, Bruce A. 2001. “In search of substitution between foreign production and exports” _Journal of International Economics_ , 53: 81-104.

- Bruno, Michael. 1994. “Stabilization and Reform in Eastern Europe: A Preliminary Evaluation,” in Olivier Jean Blanchard, Kenneth A. Frost and Jeffrey D. Sachs, eds. _The Transition in Eastern Europe_ . Vol. I: Country Studies, The University of Chicago Press, Chicago and London, pp. 19-49.

- Claessens, Stijn and R. Kyle Peters, Jr. 1997. “State Enterprise Performance and Soft Budget Constraint: the case of Bulgaria,” _Economics of Transition_ No. 5. European Bank for Reconstruction and Development, November, pp. 305-322.

- Dobrinsky, Rumen. 1997. “Transition Failures: Anatomy of the Bulgarian Crisis,” _The Vienna Institute for Comparative Economic Studies Paper 236_ , Vienna, April.

- Dobrinsky, Rumen. 1996. “Monetary Policy, Macroeconomic Adjustment and Currency Speculation under Floating Exchange Rate Regime: The Case of Bulgaria,” _Economics of Transition_ , vol. 4, no. 1 (May), pp. 185-210.

- Dobrinsky, Rumen. 1995. “Economic transformation and the changing patterns of European EastWest Trade?,” in Rumen Dobrinsky and Michael Landesmann, eds., _Transforming Economies and European Integration_ , Edward Elgar, Aldershot, UK and Brookefield, US (86-115).

- Dobrinsky, R. and I. Yaneva. 1997. “Impediments to exports in small transition economies: the case of Bulgaria,” _Moct – Most: Economic Policy in Transitional Economies_ (Netherlands) No. 2 (33-55).

- Falcetti, Elisabetta, Peter Sanfey and Sladjana Tepic. 2004. “South-Eastern Europe: Opportunities and Potential for Growth,” paper presented at ONeB Conference on European Integration, 28-30 November.

- Feenstra, Robert C. 1998. “Integration of Trade and Disintegration of Production in the Global Economy.” _Journal of Economic Perspectives_ , vol. 12, number 4 (31-50).

- Fischer, Stanley and Ratna Sahay. 2000. “Economies in Transition. Taking Stock.” _Finance & Development_ . September (2 – 6).

- Hamilton, Carl B, and L. Alan Winters. 1992. "Trade with Eastern Europe," _Economic Policy_ , April.

- Kaminski, Bartlomiej. 2005. “Bulgaria’s Institutions and Policies: Integrating into Pan-European Markets,” mimeo, The World Bank, March.

- Kaminski, Bartlomiej. 2004. “Production Fragmentation and Trade Integration in Enlarged Europe: How MNCs Have Succeeded Where CMEA Had Failed.” _Papeles del Este: Transiciones poscommunistas_ . No. 9.

- Kaminski, Bartlomiej. 1993. “How the Market Transition Affected Export Performance in the Central European Economies.” Policy Research Working Paper 1179, The World Bank, Washington DC, September.
by trade ministries in industrialized countries. Table 2 lists the United States’ trading partners that have been cited in annual reports devoted to assessing discriminatory foreign procurement practices. No least developed countries (LDCs) are listed in table 2; only a few of the larger developing countries appear to attract the attention of the United States Trade Representative (USTR)—although one must be open to the possibility that the USTR used the (unfulfilled) threat of “naming” a country in these reports to induce changes in other countries’ procurement practices. Even so, there appears little to suggest that the state procurement markets of much of the developing world are large enough to attract much attention from the trade officials in leading industrialized countries.

**Table 2: Public procurement practices of countries cited as concerns in USTR** **<u>annual reports on discriminatory trade practices</u>**

|Date<br>or<br>year<br>the|Countries mentioned in the Uni<br>_G_|ted States’_Annual Report on Discrimination in Foreign_<br>_overnment Procurement_.|
|---|---|---|
|report was<br>published.|“Identified” cases of<br>discrimination.|“Issues of Particular Concern That Do Not Meet the<br>Criteriafor Identification.”|
|1992.|European Union:<br>government-owned<br>telecommunications in<br>selectedmemberstates.||
|30 April<br>1996.|Germany: Heavy electrical<br>equipment.|Japan: Public works.<br>Japan: Supercomputers.<br>Japan: Computers.<br>Australia: Pre-selection criteria for bidders.<br>Brazil: Expanded discriminatory procedures in 1994.<br>China: Non-transparent, closed, and uncompetitive<br>procedures.|
|30 April<br>1999.|None.|Korea: Construction of Inchon International Airport.<br>Japan: Computer products and services.<br>Japan: Design and consulting and construction<br>services.|
|30 April<br>2000.|None.|Japan: Public works.<br>Taiwan (China): General procurement procedures.<br>Canada: Provincial price preferences.<br>Mexico: Implementation of New Procurement Laws<br>and NAFTA-related tendering periods.<br>Korea: Airport construction.<br>Germany: “Sect Filters” (to ensure that the principles<br>of Scientology are not used or spread in fulfillment of<br>a contract.)|
|30 April<br>2001.|None.|None in addition to prior years.|

_Source_ : Website of the Office of the United States’ Trade Representative.

8
the effect, and only when raising the poverty line from its initial 17 percent to over 65 percent of the population.

Our final robustness check considered the impact of moving from a labor market in which unskilled labor is reasonably mobile between rural and urban employment, and a strongly segmented labor market specification based on parameters estimated for China, where there are formal barriers to mobility such as the residence permit system. Under the strongly segmented labor market assumption, rural unskilled wages rose much more than urban wages in response to increases in agricultural prices, and rural poverty rates rose less with a single labor market. However, urban poverty rose by more, and the impact on national poverty rates was essentially the same.

# **7 Concluding Remarks**

Our study has attempted to shed some light on the important linkages between higher global food prices and poverty. By applying a simple approach of calculating the first-order welfare changes of households covered in ten detailed surveys, it was able to provide a very detailed assessment of the impact of higher food prices on national welfare and poverty in the sample of the countries. Even though the methodology employed in the study is simple, it is powerful enough to give us a good understanding of the underlying mechanisms and the expected magnitude and direction of change of the poverty rate of the developing countries resulting from the changes in the global food prices.

The findings of the study suggest that the overall impact of higher food prices on poverty is generally adverse. Despite certain variation by commodity and by country, the fact is that most poor are net consumers of food and as such tend to be hurt by higher food prices. This conclusion is much more obvious for urban households where farming is much less dominant. Even though many rural households gain from higher food prices, the overall impact on poverty remains negative.

18
10 standard deviation (z score) units.<sup>5</sup> Most effect sizes (shown<sup>**in**Appendix2,Tables 2-9,Figure 2)are</sup> moderate to large.

Figure 2. Effect Sizes of Various T1reatment Combinations <!-- Start of picture text --> Percent Correct on Syllable Identification:<br>Effect Sizes at Top of Bar, t At .05.<br>292-- 26D<br>S- 04-- |1<br>75'1X<br>45 _ |_ 1 0 - 2<br>PA RR PA+RR Control<br>85 197<br><!-- End of picture text -->

<!-- Start of picture text --> Percent Correct on Word Identification:<br>Effect Sizes at Top of Bar, c" At .05.<br>337 397<br>5 124L -<br>75' , .. M =.9 _<br>4 5J' l l -<br>PA RR PA+RR Control<br>asi.<br><!-- End of picture text --> <!-- Start of picture text --> Percent Correct on Letter Identfication:<br>Effect Sozes at Top of far, llAt.05.<br>ass. 369-- 323--<br>75'1 l,<br>PA RR PA+RR Contro<br><!-- End of picture text --> <!-- Start of picture text --> Percent Correct on Sentpulaio enrtofication:<br>Effect Sizes at Top of Bar, At .05.<br>ss 391 472-- 603--<br>75<br>PA RR PA+RR Contrd[<br><!-- End of picture text -->

# **Q;uestionnaire Results**

The data fr7om the questionnaire provided a portrait of the<sup>population enrolling in the literacy</sup> programs. Some of the pertinent findings follow.

_Few questionnaire variables showed significant relationships with reading_<sup>_scores._The only</sup> learner characteristics significantly related to performance on the paper-and-pencil<sup>test were the</sup> number of years of prior schooling and the age at which some learners<sup>attended Koranicschool;those</sup> who had attended Koranic schools had better performance if they had attended at a younger<sup>age.</sup> However, only 6 percent of the learners had attended Koranic schools, and<sup>the correlationshould be</sup> interpreted with caution (Appendix 2, Table 12).

5. An effect size is the mean of the treatment group mnus the mean of the control<sup>groupdivided by thestandard deviationof</sup> the control group. Effect sizes may show how the average treatment group<sup>student wouldscore IF thestudent werein the</sup> control group. Sizes of 0.4-0.6 are considered small, 0.6-0.8 moderate,<sup>and above0.8large.For example,aneffectsize of</sup> 0.5, located at a statistical table of cumulative normal probabilities<sup>(area</sup> of<sup>the normal curveencompassedby various z</sup> score values), would be found to encompasses 69% of the normal distribution,<sup>hence theaveragetreatmentgroupstudent</sup> would score at the 69th percentile IF they were in the control group. An<sup>effectsize of 1.5means thatthe average treatment</sup> group student would score at the 93rd percentile of the control group.