rafmacalaba/datause-encoder-data huggingface dataset:
detail below.

Careful study of news outlets’ reports, agencies’ press releases and Institute for the Study of War (ISW) maps allow us to assign individual oil wells to the Islamic State group’s control at the daily level. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors deployed on the NOAA/NASA Suomi NPP satellite, to assess the status of each of the sites and then impute the appropriate levels of production.

Our approach relies on the property that the natural gas dissolved in oil underground expands when it reaches the surface. In our study context, the gas is subsequently flared, hence generating heat that is captured by VIIRS sensors. Using pre-war data on oil production obtained from several public and private sources, we estimate the relationship between production and VIIRS detection and infer contemporaneous production of Daesh-controlled oil fields. Alternatively, oil extraction can take place without gas flaring –a process called venting– in which case we assign historic output values as there will not be sensor detection.

Our paper contributes to two streams of literature. The first is the emerging literature that uses remote sensing to assess behavior in extractive industries in low-governance regions.<sup>2</sup> Reliable external measures of resource forestry, mining, and oil production can enable better approaches to a broad range of challenges. In Colombia and Nigeria, for example, insurgent organizations have long controlled territory where oil is produced, and in many regions around the world reliable field-level production numbers are hard to come by. Estimating production remotely can enable governments and international organizations to identify illegal or untaxed production as well as to better understand the role production could play in post-conflict economies and the impact of sanctions, trade restrictions, and other policy interventions. Variants on the approach adopted here could be applied in a much broader set of places.

The second literature is the substantive one that investigates the role of natural resources in

> 2See, for example, [30] and [35] which use remote sensing to measure illegal mining activity in Myanmar and Colombia, respectively.

3
agreement, is also suggested to have expanded intra-regional trade, contributing to economic recovery.

While the trade and investment liberalization stimulated trade in the region, various bottlenecks to the movement of goods hindered trade expansion. In particular, in East Asia, port infrastructure has an important role in supporting increases in trade by removing such bottlenecks.

**Table 1: Growth of GDP and Trade in East Asia**

||_No_|_minal GDP_||_No_|_minal Trade_||
|---|---|---|---|---|---|---|
||95/90|00/95|06/00|95/90|00/95|05/00|
|_East Asia (ASEAN5 plus CJK)_|12.4|-0.6|4.2|13.2|5.0|11.5|
|_East Asia (exclu. Japan)_|14.6|3.9|12.3|17.5|6.7|14.2|
|_ASEAN5_|13.7|-2.4|10.0|17.8|3.7|7.7|
|_Indonesia_|12.1|-4.0|14.1|12.9|3.7|7.2|
|_Malaysia_|15.1|0.3|8.7|21.1|4.2|7.3|
|_Philippines_|10.8|0.5|7.5|16.5|13.0|1.8|
|_Singapore_|18.0|1.9|6.1|18.2|2.7|7.9|
|_Thailand_|14.5|-6.1|9.0|17.8|0.9|11.6|
|_Northeast Asia (CJK)_|||||||
|_China_|15.5|10.5|14.3|20.5|14.3|24.6|
|_Japan_|11.7|-2.4|-1.1|7.9|2.0|5.4|
|_Korea_|14.4|-0.2|9.6|14.4|5.7|10.2|
|_USA_|5.0|5.9|5.2|8.4|8.5|5.2|
|_Canada_|0.3|4.2|9.8|7.5|8.1|5.4|
|_EMU_|5.1|-3.1|9.2|5.9|2.1|10.4|
|_World_|6.3|1.5|7.2|8.0|4.8|9.8|

(Source) World Bank, _World Development Indicators_ .

(Note) Unit: percent. Nominal GDP is in the current $US. Trade means the sum of imports and exports.

# _Regional Trade in East Asia_

Intra-regional trade has increased as a proportion of the total exports in East Asia over the past decade. Table 2 below illustrates the shares of exports in selected sub-regions and countries in East Asia.

3
Table 6. Militarization and Alesina et al.’s measures of fractionalization, 1988–2002

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Military<br>Expend.|Military<br>Expend.|Military<br>Pers.|Military<br>Pers.|Arms<br>Imports|Arms Imports|
|Ethfrac (Alesina et al.)|0.517|0.486|0.901|0.867|1.590|1.470|
||(0.51)|(0.48)|(1.12)|(1.06)|(1.01)|(0.95)|
|Relfrac (Alesina et al.)|-0.321|-0.314|-0.794*|-0.801*|-0.498|-0.428|
||(0.47)|(0.46)|(1.88)|(1.89)|(0.34)|(0.30)|
|Linfrac (Alesina et al.)|-1.464*|-1.473*|-0.943|-0.961|-2.688*|-2.698**|
||(1.77)|(1.78)|(1.53)|(1.57)|(1.96)|(1.99)|
|GNI p.c. (ln)|-0.187|-0.203|0.155|0.140|-0.437|-0.414|
||(0.73)|(0.78)|(1.40)|(1.24)|(1.13)|(1.11)|
|Economic growth|1.197*|1.182*|0.028|0.006|0.330|0.414|
||(1.83)|(1.83)|(0.04)|(0.01)|(0.14)|(0.18)|
|Polity|-0.037**|-0.037**|-0.012*|-0.011*|-0.108|-0.115*|
||(2.15)|(2.11)|(1.77)|(1.69)|(1.63)|(1.72)|
|Government|0.159***|0.158***|0.025**|0.025**|0.154**|0.157***|
|expenditures|||||||
||(4.50)|(4.54)|(2.11)|(2.08)|(2.49)|(2.65)|
|Population (ln)|-0.022|-0.033|-0.129**|-0.141**|0.393|0.405*|
||(0.19)|(0.28)|(2.08)|(2.05)|(1.63)|(1.70)|
|Contiguous<br>|0.129**|0.124**|0.569**|0.558**|0.114|0.123*|
|militarization|||||||
||(2.49)|(2.44)|(2.24)|(2.17)|(1.51)|(1.69)|
|Peace years (civil war)|-0.018***|-0.018***|-0.003|-0.004*|-0.016|-0.016|
||(3.33)|(3.32)|(1.51)|(1.71)|(1.16)|(1.21)|
|Peace years (intern.<br>conflict)|0.006|0.006|-0.009|-0.009|-0.039**|-0.035**|
||(0.64)|(0.67)|(1.51)|(1.49)|(2.47)|(2.40)|
|Civil war|0.629***|0.629***|0.034|0.032|2.482**|2.501**|
||(2.95)|(2.98)|(0.51)|(0.49)|(2.44)|(2.49)|
|International conflict|0.797|0.794|0.629*|0.632*|3.284**|3.208**|
||(1.30)|(1.30)|(1.81)|(1.82)|(2.17)|(2.16)|
|Observations|1560|1560|1360|1360|1373|1373|
|Countries|130|130|136|136|137|137|

Notes: Absolute t- and z-statistics in brackets. Constant and year-specific time-dummies included, but coefficients not reported. *, **, *** significant at .1, .05 and .01 level, respectively.

26
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

|||**C**|**ost recov**|**ery**||**C**|**ost recov**|**ery**||**C**|**ost recov**|**ery**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||Actual||||Actual||||Actual||
|Utility|Effective<br>price at<br>4m<br>3|O&M<br>(GWI)|<br>O & M<br>cost<br>(AICD)|Capital<br>cost<br>(GWI)|Effective<br>price at<br>10m<br>3|O&M<br>(GWI)|O&M<br>cost<br>(AICD)|Capital<br>cost<br>(GWI)|Effective<br>price at<br>40m<br>3|O&M<br>(GWI)|O&M<br>cost<br>(AICD)|Capital<br>cost<br>(GWI)|
|Tygerberg|0.00|n|n|n|0.35|n|n|n|0.83|y|n|y|
|eThekwini|0.00|n|n|y|1.04|y|n|y|1.14|y|n|y|
|Johannesburg|0.00|n|n|n|0.24|n|n|n|0.87|y|n|y|
|SWSC|0.30|n|n|n|0.30|n|n|n|0.34|n|y|n|
|LWSC|0.56|y|y|n|0.39|n|y|n|0.34|n|y|n|
|NWSC|0.25|n|y|n|0.26|n|y|n|0.29|n|y|n|
|Cost Recovery (%)||23|13|4||16|13|4||29|22|9|
|Average|0.55||||0.49||||0.63||||
|MIC|0.72||||0.89||||1.31||||
|LIC|0.50||||0.38||||0.44||||

_Source:_ AICD WSS Survey Database, 2007.

41
# A SUPPLEMENTARY FIGURES

Figure A1: Measure: Largest individual owner with 20% equity has more than 20% managers?

<!-- Start of picture text --> ITA<br>.4<br>PRT<br>FRA<br>.3 ESPDEU<br>.2<br>GBR POL<br>.1<br>0<br>0 .1 .2 .3 .4<br>FOFM Share (Our Measure)<br>FOFM firms in WMS defined as those which family owned with a family CEO. FOFM according to our measure are firms<br>for which the largest owner with more than 20% equity has more than 20% of managers with the same last name.<br>Dashed line is 45-degree line<br>FOFM Share (WMS)<br><!-- End of picture text -->

# B SUPPLEMENTARY TABLES

Table A1: Correlation of Our Measures with WMS Measure

||(1)|(2)|
|---|---|---|
|Measure|Country-Level|Firm-level|
|All Managers same last name, own 20% equity|0.450|0.206|
|All Managers same last name, own 20% equity, Ind. Owners only|0.505|0.222|
|Any individual owner with 20% equity has manager?|0.442|0.354|
|Share of managers for largest owner with 20% equity|0.633|0.419|
|Largest owner with 20% equity has manager?|0.653|0.433|

1
Policy Research Working Paper 5158

# **Abstract**

This paper investigates the reasons for the low application of external fertilizers on farms in Kenya and Uganda. The analysis uses a large panel of household data with rich soil fertility data at the plot level. The authors control for maize seed selection and household effects by using a fixed-effects semi-parametric endogenous switching model. The results suggest that Kenyan maize farmers have applied inorganic fertilizer at the optimal level, corresponding to the high nitrogen-maize relative price, in one of the two survey years and also responded to the price change over time. In Uganda, even the low application of inorganic fertilizer is not profitable because of its high relative price. The authors conclude that policies that reduce the relative price of fertilizer could be effective in both countries, while the efficacy of policies based on improving farmers’ knowledge about fertilizer use will be limited as long as the relative price of fertilizer remains high.

This paper—a joint product of Japan’s National Graduate Institute for Policy Studies and the World Bank’s Agriculture and Rural Development Team, Development Research Group—is part of a larger efforts at both institutions to understand policies that promote productivity gains and growth in agriculture. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Tom.Matsumo@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
Table 13: Aid & bilateral OIL - CASH- Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.079***|-0.091***|-0.079***|-0.079***|
||(0.019)|(0.020)|(0.019)|(0.019)|
|_Ln(Affected)_|0.017**|0.009|0.009|0.012|
||(0.008)|(0.009)|(0.009)|(0.009)|
|_Ln(GDP p.c.)_|-0.170***|-0.244***|-0.181***|-0.172**|
||(0.063)|(0.077)|(0.067)|(0.068)|
|_Fuel exports (% of_|0.003|-0.004|0.001|-0.008*|
|_merchandise exports)_|(0.002)|(0.004)|(0.002)|(0.005)|
|_Regulatory quality_|0.299***|0.475***|||
||(0.112)|(0.145)|||
|_Fuel exports_×||-0.010*|||
|_Regulatory quality_||(0.005)|||
|_Corruption control_|||0.264**<br>(0.124)|0.347***<br>(0.128)|
|_Fuel exports_×||||-0.014**|
|_Corruption control_||||(0.007)|
|||NON-|OECD||
|_Ln(Fatalities)_|-0.006|-0.023|-0.008|-0.005|
||(0.048)|(0.051)|(0.049)|(0.051)|
|_Ln(Affected)_|-0.000|-0.017|-0.013|-0.019|
||(0.017)|(0.026)|(0.023)|(0.024)|
|_Ln(GDP p.c.)_|0.161|0.012|-0.020|-0.008|
||(0.152)|(0.208)|(0.199)|(0.196)|
|_Fuel exports (%_|-0.010*|-0.019*|-0.007|0.005|
|_of merchandise exports)_|(0.005)|(0.011)|(0.005)|(0.025)|
|_Regulatory quality_|-0.125|0.173|||
||(0.305)|(0.408)|||
|_Fuel exports_×||-0.013|||
|_Regulatory quality_||(0.013)|||
|_Corruption control_|||0.480|0.360|
||||(0.426)|(0.437)|
|_Fuel exports_×||||0.021|
|_Corruption control_||||(0.017)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

27
WPS3751

# **THE IMPACT OF THE STRONG EURO ON THE REAL EFFECTIVE EXCHANGE RATES OF THE TWO FRANCOPHONE AFRICAN CFA ZONES**

**Ali Zafar**

## World Bank Policy Research Working Paper 3751, October 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Policy Research Working Paper Africa Technical Families Poverty Reduction and Economic Management 3
Table 2. Size of the first pricing block, and differential between that and second

|1st<br>block<br>monthly<br>consumption threshold<br>(kWh)|Countries|Price differential between first and<br>second block (%)|
|---|---|---|
|300|Ghana, Zambia|Between 30 and 100|
|200|Mali|Less than 30|
|100|Mozambique|Over 100|
||Congo,Dem. Repof|Less than 10|
|50 or less|Uganda, Madagascar, Kenya, Nigeria, Tanzania,<br>Chad, Cote d’Ivoire, South Africa, Zimbabwe|Over 100|
||Ethiopia,Benin,Malawi|Between 30 and 100|
||Cameroon,Cape Verde,Burkina Faso|Less than 30|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database The second question is whether tariff levels on successive blocks rise steeply enough to ensure that costs can be fully recovered on higher volumes of consumption. This principle holds in many cases. Most countries with a first-block threshold set at subsistence consumption levels (50 kWh or less), have a price jump of over 100 percent to the second block (Uganda, Madagascar, Kenya, Nigeria, Tanzania, Chad, Côte d’Ivoire, South Africa, Zimbabwe), signaling a clear intention to differentiate among customers so that larger consumers contribute to cost recovery. But the tariff structures of a few countries—for example, the Democratic Republic of Congo, Cameroon, Cape Verde, and Burkina Faso— are not highly differentiated by consumption level. This makes the implementation of targeted subsidies difficult (table 2).

Thereafter, prices among higher consumption blocks do not rise as steeply as they do between the first and second block. In Burkina Faso, the second block is priced only 6 percent higher than the first one and the third block only 7 percent above the second one. In Ethiopia the price increase from the first block to the second is 31 percent and from the second to the third, 40 percent. In Malawi, the increase is 47 percent in the first case and 42 percent in the second one.

Despite the fact that IBTs already incorporate a “lifeline” principle, a number of countries have felt the need to introduce parallel “social tariff” that provide an even larger discount to qualifying customers. For example, the Democratic Republic of Congo, Madagascar, Mali, and Benin (whose block pricing structure does not differentiate low-consumption, low-income customers) provide such a parallel “social tariff” (table 3). Most of these social tariffs are linear in nature and include fixed charges; albeit modest ones. The criteria for determining social tariff eligibility are usually based either on total consumption or technical characteristics of service (voltage, load). The very existence of such parallel “social tariffs” – most of which have eligibility criteria based on low consumption – in itself may be a signal that the main IBT is not managing to perform its intended social function.

8
ratio of children to working age population will decline, while the number of retirees will remain small. As a result, not only will dependency ratios fall, but also the amount the state must spend on expanding the quantity of social services will decline. This will free up resources to spend on improving quality and other poverty reduction efforts.

|**Table 2.7:**|**Share of t**<br>**Pará,**|**he Population per A**<br>**1980-2000**|**ge Group**|
|---|---|---|---|
|**Age Group**|**1980**|**1991**|**2000**|
|0 - 6 years old|23.29|21.73|17.64|
|7 - 14 years old|21.34|20.87|19.44|
|15 - 64 years old<br>|52.19|54.22|59.07|
|65years or older<br>|3.19|3.17|3.85|
|Total|100.00|100.00|100.00|

Source: SEOF/DIEEST/BDE from IBGE.

Fecundity the number of children per mother dropped from 3.9 in 1992 to 3.1 in 1999 and is still among the six highest in Brazil. Women’s increased participation in the labor market is an important factor contributing to the reduction in the fertility rate, which also produced a sharp drop in the dependency rate to 69.2 in 2000 compared to 94.4 in 1980. However, fecundity did not fall for all age groups and fertility rates are much higher for the 15 to 19 and 20 to 24 age groups.

# **3.  Data and Methodology for Poverty Analyses**

This section presents some of the methodology and data used to analyze the poverty and quality of life in Pará, but does not attempt a more comprehensive quantitative and qualitative analysis of other forms of deprivation.

## **METHODOLOGY**

The income-poverty measures are designed to count the poor and to diagnose the extent and distribution of poverty. The income-poverty measures proposed by Foster, Geer, and Thorbecke (1984) are used throughout the report. These are the headcount rate (P0), poverty gap (P1), and squared poverty gap (P2) measures. The former measures the magnitude of poverty and the latter two poverty measures assess both poverty magnitude and intensity.

The headcount rate is defined as the proportion of people below the poverty line. One concern applying the P0 measure is that each individual below the poverty line is weighted equally and, therefore, the principle of transfers is violated. Hence, it is possible to increase social welfare by transferring money from the very poor to lift some richer poor out of poverty. P0 takes no account of the degree of poverty and it is unaltered by policies that lead to the poor becoming even poorer.

One measure of poverty that takes this latter point into account (at least in weak form) is the poverty gap measures. P1 is the product of incidence and the average distance between the incomes of the poor and the poverty line. It can be interpreted as a per14
reported in Table A17 and A18 in the Appendix. Despite losing more than one third of our observations, unexpected demand shocks still have a significantly positive effect on sales and exports, with coefficient magnitudes that are very similar for exports and even larger for sales. Furthermore, we find significantly positive effects on monthly and hourly wages that are larger than our baseline estimates. Overall, these results suggest that our results are not particularly driven by exceptional adjustments during the global financial crisis.

# **6 Conclusion**

In this paper, we examined the effects of unexpected demand shocks on worker compensation. We proposed a new methodology to identify the unexpected component of demand shocks at the firm-level, exploiting errors in the official forecasts in the GDP growth of export markets. In each destination, the unexpected component of demand shocks was measured as the difference between the GDP growth actually observed and recent forecasts published in the World Economic Outlook of the International Monetary Fund. We then aggregated these shocks at the firm-year level, weighting by the initial share of destinations in firms’ total sales. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets.

In the empirical analysis, we found that unexpected demand shocks have significant effects on sales, employment, investment and average wages. Using employer-employee panel data, we reported evidence that revenues from unexpected demand shocks are partly transmitted to workers in the form of higher average wages, but in a highly unequal way, with most of the wage increases occurring close to the top of the within-firm wage distribution. These wage increases, which include an increase in average base wages, occur despite some evidence that the skill composition of the workforce is negatively affected by unexpected demand shocks.

These results suggest that wages to some extent reflect rent sharing between firms and their workers, and our analysis indicates several different channels through which such rent sharing occurs. One channel is collective bargaining power, which is corroborated by our finding that positive base wage responses to demand shocks are much stronger in firms with firm-level wage agreements than in firms that are subject to more centralized bargaining schemes. Another channel is individual bargaining power created by hiring or training costs, which is consistent with our finding that wage responses tend to be stronger for longer-tenured workers. Finally, for a subset of the firms, we also found significant increases in bonus-related wage components in response to an unexpected demand shock, which indicates rent-sharing through the use of 23
ACS) and providing a detailed discussion of the questionnaire. In the process of data collection, businesses that operate without registration are considered to be informal. Throughout the paper, we refer to the unit of analysis as 'business' rather than 'firm' to avoid the legal connotation of the latter. Importantly, as noted above and discussed in detail in the companion paper, Aga et al. (2022), the samples of respondents of the Informal Sector Enterprise Surveys are representative of the underlying population of informal businesses at the city level. With proper weighting, which is well established in the literature and is applied in this paper, population estimates are unbiased.

Descriptive findings from the Informal Sector Enterprise Surveys data are enriched by comparing informal businesses with formally registered micro-enterprises (employing fewer than 5 employees) interviewed in some of the same countries and around the same time through the World Bank Micro-Enterprise Surveys. Section 3 presents these findings, organized as the following set of stylized facts across a wide range of topics and contributing to several branches of literature on informality: (1) Owners of informal businesses are generally young, having started the business out of necessity rather than as an opportunity for growth, and less skilled than their formal counterparts (consistent with Maloney, 2004; La Porta and Shleifer, 2014, 2016). Representation of women among the owners varies considerably across cities, with women generally experiencing a sizeable productivity gap compared to male-owned informal businesses.

(2) A demographic analysis of informal businesses reveals that they are very small, largely one-person operated, younger than their formal counterparts, and mostly in the retail sector (consistent with Kanbur, 2017; La Porta and Shleifer, 2014).

(3) External finance is difficult to access for informal businesses, perhaps due to the very nature of informality (see, for instance, Koeda and Dabla-Noris, 2008), with widespread use of mobile money (consistent with Islam and Jolevski, 2019).

(4) Informal businesses frequently operate from within households, more so than their formal counterparts, with limited exposure to harassment or crime (in contrast with Francis, 2019; Dube and Casale, 2016), perhaps due to their choices of location and activities.

> - Enterprise Surveys page of the Enterprise Surveys website: https://www.enterprisesurveys.org/en/informal <u>businesses.</u>

3