rafmacalaba/data-use-mentions-extended huggingface dataset:
.028<br>|-27.619<br>|-|-|
||(32638)|(41821)|-|-|
|Lag9|.<br>-|.<br>-|-9.744|-20.598|
||-|-|(31.496)|(27.281)|
|Constant|918.692***|549.824***|1266.384***|892.179***|
||(78.346)|(87.163)|(153.775)|(154.173)|
|Observations|243526|243526|848983|848983|
|Number of pair id|44792|44792|120298|120298|
|R-squared|0.011|0.020|0.017|0.027|

Note: Estimation was conducted on the monthly trade data using a fixed effects estimator that accounts for cross-country-product heterogeneities. Robust standard errors are reported in parentheses. Asterisk indicate the level of significance, *** _p <_ 0 _._ 01, ** * _p <_ 0 _._ 05, _p <_ 0 _._ 1.

34
cities and rural townships.

# **3 Data**

This paper relies on three types of geospatial data for our treatment variables and the three outcomes of interest (local economic activity, urban land, and cropland). First, we rely on digitized road improvement data shared by the ERA to capture the evolution of RSDP. Second, we rely on nighttime lights to capture the evening use of light and electricity as a proxy for local economic activity. Third, we rely on annual landcover data from the European Space Agency (ESA) GlobCover program to capture the evolution of the surface area of urban land and cropland. The following four sections describe the data sources (Section 3.1); how data are aggregated to different units of analysis (Section 3.2); how units are separated into different groups of economic activity for exploring heterogeneity of results (Section 3.3); and, lastly, we present descriptive statistics for the area and period of interest (Section 3.4).

8
|1|0|0|0|1|
|Freight density (1,000 tonne-km/km)|**902**|469|827|90|270|663|364|475|2,427|406|
|Passenger density (1,000 passenger-km/km)|**166**|—|—|38|103|44|44|33|60|92|
|Labor productivity (1,000 traffic units per<br>employee)|**390**|580|722|131|710|281|—|484|3,308|502|
|Locomotive productivity (million traffic units<br>per locomotive)|**8**|30|41|3|25|13|—|25|33|25|
|Carriage productivity (1,000 passenger-km per<br>carriage)|**—**|4,046|2,391|1,176|3,333|750|—|—|—|3,286|
|Wagon productivity (1,000 net tonne-km per<br>wagon)|**195**|950|987|82|260|476|—|805|913|377|
|Freight yield (US cents/tonne-km)|**—**|—|—|6|5|3|3|—|—|4|
|Passenger yield (US cents/passenger-km)|**—**|—|—|1|0.9|0.5|1|—|—|1|

_Source:_ Bullock 2009. Derived from AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ * With 2.5 passenger-km equivalent to 1 traffic unit, 1 tonne-km equivalent to 1 traffic unit. — = Not available.

Zimbabwe‘s railways
# **IV. Impact of taxes and social spending on poverty and inequality.**

This section presents the general results of the impact of the tax system and social spending in Colombia on poverty and inequality. Likewise, some comparisons are shown with the results obtained for other countries and with the results of the exercise previously carried out in the country. Likewise, the main methodological differences between the present work and the previous exercise that sought to assess the distributional impacts of the tax policy and social spending for Colombia are presented.

## i. Methodological comparison

This section discusses some of the methodological differences between the present and the previous Commitment _to Equity_ (CEQ from now on) made for Colombia. The first and main limitation of this comparison lies in the non-existence of a public document that sets out the details of the assumptions made in the previous exercise. This comparison is based on the work of Lustig, Meléndez, and RodriguezCastelán (2014). To the extent that the assumptions were clear, and the existence of programs allowed, the calculations were made following the previous methodology. However, in many other cases, the lack of available information or the elimination of certain programs made comparison impossible.

### **_a) Direct taxes_**

Lustig, Meléndez, and Rodríguez-Castelán (2014) include direct taxes, those corresponding to income, property, betterment levies, and vehicles and directly take the payments reported by households for these items from the Quality of Life Survey. As far as it can be seen in the methodology of the previous version of the CEQ, social security contributions were not included as part of direct taxes. This new version of the CEQ for Colombia takes the sum of personal income tax, property tax and betterment levies, vehicle tax and social security contributions paid by both employers and employees as direct taxes. The property and betterment levies tax and the vehicle tax had the same treatment as in the previous CEQ, as the values reported in the survey were used. The main difference
government officials shape the relationship between mining and the assessment of government performance in resource-rich communities. Third, we explore whether the legal rights or the discretionary power of subnational governments over the tax and revenue system matters in the analysis of the effects of natural resources on the provision of local public goods and services. Although royalties from mining industries are mostly controlled by central government authorities, mining-related or induced business generate rents and revenue opportunities that can be exploited by local authorities.

To date, and to the best of our knowledge, there is little evidence on the mediating role of institutions, local governance, and decentralization in the relationship between natural resources and development at the local level. Most of the existing evidence on such confoundedness is provided in macro-level studies where it has been shown that the quality of institutions and governance are key explanatory factors of why natural resources might be a curse for some countries and a blessing for others (van der Ploeg, 2011). Our paper, then, makes a significant contribution to the growing literature on the local effects of mining in Africa.

For our analysis, we match the Afrobarometer survey geo-referenced data with the SNL Metals and Mining by the S&P that provide time-series information on industrial mines in countries in Africa. The SNL Metal and Mining data are provided annually, which facilitates the merging with multiple rounds of Afrobarometer surveys and the identification of residents who live within a certain distance to a mine, be it active or non-active at the time of the survey. We follow existing publications by considering a 50 km radius to a mine as the reference value.<sup>1</sup> We exploit the availability of a new dataset on decentralization – mainly the decision-making power of subnational governments over tax and revenue instruments, which, unlike existing works, covers a range of African countries as well as those that have been implementing decentralization since the early 2000s.

We employ a difference-in-differences strategy, similar to that used in recent studies (Knutsen et al. (2017); Goltz and
specific drought or flood. Nevertheless, the opportunity to assess the relative importance of idiosyncratic and covariate shocks in different regions of a country is particularly useful for determining insurance priorities and targeting social protection programs.<sup>12</sup>

# 4. Data

## **Data source and poverty measurement**

The main sources of data used in this paper are the twinned Household Consumption Expenditure Survey (HCES) and the Welfare Monitoring Survey (WMS) of 2016. HCES and WMS are the official poverty and welfare monitoring surveys collected by the Central Statistics Agency (CSA) of Ethiopia every five years; HCES focuses on monetary welfare as measured by consumption expenditure while the WMS covers non-monetary welfare including education, health, access to services and exposure to various risks among other things. The two surveys cover the same sample households and the sample is large enough for sub-national analyses including at the regional level and urban and rural areas within a region. Coverage of pastoral areas of Afar and Somali was sparse until the 2016 survey round because in prior rounds the zones (administrative units below region) in Afar and Somali where pastoralists are prevalent were not included in the surveys. Therefore, the prior rounds of the surveys systematically under-represented pastoralists rendering them unsuitable for vulnerability analyses focusing on drought-prone and pastoral areas. In 2016 additonal zones were included in the HCES/WMS survey in Afar and Somali to increase the representativeness of the surveys to pastoral areas.<sup>13</sup> Consumption expenditure includes both food and non-food items, regardless of whether these items are purchased on the market, come from own production, or were received as gifts. For ownconsumption and gifts, the quantities consumed are valued at prevailing prices in the enumeration area. To capture the effect of seasonal variations, the data were collected over a 12-month span (Hamle 1 to Sene 30, i.e. July 8 to July 7), by randomly allocating sampled households to different

> 12 Idiosyncratic shocks are, by definition, not correlated and
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- ———. 2009e. ―Niger Enterprise Surveys 2009‖. Washington, DC: World Bank. http://www.enterprisesurveys.org/ExploreEconomies/?economyid=142&year=2009.

- ———. 2010a. ―Getting Electricity: A Pilot Indicator Set from the Doing Business Project‖ _._ Washington, DC: World Bank. http://doingbusiness.org/data/exploretopics/getting-electricity.

- ———. 2010b. ―Niger: Modernizing Trade during a Mining Boom Diagnostic Trade Integration Study for the Integrated Framework Program.‖ Poverty Reduction and Economic Management. Africa Region, World Bank, Washington, DC.

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Enhanced Integrated Framework. 2008. ―Niger: Modernizing Trade during a Mining Boom.‖ Diagnostic Trade Integration Study for the Integrated Framework Program, February 2008. Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean
**Natural Disasters and Household Welfare – Evidence from Vietnam**

Timothy Thomas, Luc Christiaensen, Quy Toan Do, and Le Dang Trung<sup>1</sup>

**Key Words** : Natural disaster, household welfare, adaptation, Vietnam **JEL codes:** C81, D12, O12

> 1 Tim Thomas is Research Fellow at the International Food Policy Research Institute (IFPRI) (tim@timthomas.net), Luc Christiaensen is Senior Research Fellow at the United Nations University‐World Institute for Development Economics Research (UNU‐WIDER) (luc@wider.unu.edu), Quy Toan Do

(qdo@worldbank.org) is Senior Economist in the Development Economic Research Group at the World Bank, and Le Dang Trung is Ph.D. student at the Institute of Food and Resource Economics at Copenhagen University (trung@foi.dk). The study has been initiated under the Economic and Sector Work umbrella conducted by the Social, Environment and Rural Division of the East Asia and Pacific Sustainable Development Department of the World Bank. The authors would like to thank Nguyen Van Viet and Ngo Tien Giang for providing weather station data and assistance in using the data for defining the drought events and Yabei Zhang for her diligent support in managing the project. They also express their gratitude for the insightful comments provided by Rasmus Heltberg, Stephan Klasen, Emmanuel Skoufias and Steve Jaffee. Funding by the Global Facility on Disaster Risk Reduction is gratefully acknowledged. All errors are the authors' alone. The denominations used and the boundaries shown on maps herein do not imply, on the part of the World Bank, any judgment on the legal basis of any territory or any endorsement or acceptance of such boundaries. The findings, interpretations, and conclusions 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.
RFSFAXRC1-H WORKING PAPER 2450 [i-icy

# Summary findings

Using data on mergers and acquisitions involving Korean firms, Freund and Djankov identify which sectors and firms attracted foreign investment after the liberalization of investment activity at the end of 1997.

They find that domestic acquisitions are similar to foreign acquisitions by sector (of both the target and the acquiring firm), but that international transactions are larger than Korean transactions.

This suggests that consolidation is a two-stage process: Firms consolidatc first domestically, then intcrnationally.

The authors also find that foreign investment is focused on high-value-added sectors, on larger and more profitable firms, on firms with low debt, and on firms that export a large share of output. Their results suggest that growth induces foreign investment.

This paper-a product of the Financial Economics Unit, Financial Sector Strategy and Policy Department-is part of a larger effort in the department to study the effect of financial liberalization. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contactRose Vo, room MC9-626, telephone 202-473-3722, fax 202-522-2031, email address hvolc,worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Simeon Djankov maybe contacted at sdjankovicbworldbank.org. September 2000. (25 pages) _The Policy Research W/orking Paper Series disseminates the findings of work in progress to encourage the exchange of ideas_ **_about_** _developmient issues. An objective of the series is toget the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and shouild be cited accordingly. The findings, interpretations, and con-lusions 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._ Produced by the Policy Research Dissemination Center
markets crash and foreign lenders and investors pull out their money. As all potential sources of funds collapse there may be “nothing to redistribute” in terms of trade credit; thus, contraction in the supply of formally intermediated funds lead to contraction in the supply of trade credit finance. Consistent with this argument, we also find that countries that experience a sharper decline in bank credit also experience a sharper decline in trade credit.<sup>5</sup> The remainder of the paper is as follows. Section II describes the data and presents basic descriptive statistics and graphical analysis. In section III we discuss our empirical strategy. In section IV we present the results and in Section V we conclude.

# **II. Data**

# **II.1. Sample**

We study two of the four major crises that occurred during the nineties, i.e. the Mexican devaluation in late 1994-early 1995, and the South East Asia currency crisis in mid-1997 including Indonesia, Korea, Malaysia, Philippines and Thailand.<sup>6</sup> We use the Worldscope database, which contains data on publicly traded firms around the world and represents about 95% of the world’s market value. Since it focuses mostly on the firms for which there is a significant interest of international investors, the sample represents the largest firms in each country. Our study excludes all financial firms.

> reduce it (Gertler and Gilchrist (1994)). Such access to alternative sources of finance in the US is likely to be behind the aggregate increase in trade credit (during monetary contractions) observed by Nilsen (2002). 5 Our results are also consistent with patterns observed in Demirguc-Kunt and Maksimovic (2001). They find that the provision of trade credit across countries is positively correlated with the level of development of financial intermediaries.

> 6 We are not able to include the analysis of the Russian default occurred in 1998 and the Brazilian devaluation in early 1999 due to lack of data
|Fish<br>56.1<br>64.0<br>57.7|
|---|
|Fresh Fruits – Vegetables<br>72.2<br>77.1<br>74.8|
|Legumes and pulses<br>67.2<br>78.9<br>44.0|
|Meat<br>70.0<br>74.3<br>78.0|
|Nuts and derived products<br>75.8<br>68.9<br>89.6|
|Oil-bearing<br>49.6<br>55.8<br>58.5|
|Stimulant crops<br>24.1<br>42.8<br>54.1|
|Sugar<br>48.8<br>37.1|
|Vegetable and animal oils<br>78.5<br>71.5<br>75.3|
|_Source: _Espitia, Rocha, Ruta (2020).“Database on COVID-19 trade flows and policies”.|
|Note: The concentration of imports is calculated as the simple average by product group, across all import food dependent<br>countries of that group, of the sum of the import shares from top-3 exporters that are most affected by covid-19.<br> <br> <br><br><br><br>|
|I<br>d dFddIcIpFdi= 100 ∗(∑<br>∑<br>pIdiiTFI pIdi)/N<br>⁄<br>K=3<br>i=1<br>N<br>i=1<br>, where i, j, k, and n are respectively importer, exporter,<br>exporter rank and product. For definition of import food dependence see footnote 11. Low, medium and high import food|
test the relevance condition of the instruments, this section examines in more depth this condition through the first-stage estimation. Table 7 reports the first-stage results linking sex-mix and fertility, where columns (1) to (3) show the results for the just-identified models without covariates, columns (4) to (6) show the results after adding covariates, and column (7) shows the results for the over-identified model with covariates.

> 24The primary sampling units (PSUs) are geographically defined area units, which are selected with a probability proportional to size and they are very similar to a village level unit. The PSUs are already constructed in all the LSMS surveys and also in the DHS.

19
**Table 1. Number of conflicts and conflict‐countries by macroeconomic variable and definition**

|Variable|Definition 1|Definition 2|Definition 3|Definition 4|
|---|---|---|---|---|
|Government consumption as % of|64 conflicts|24 conflicts|36 conflicts|39 conflicts|
|GDP (PPP)|60 countries|23 countries|33 countries|36 countries|
|Public debt as % of GDP|32 conflicts|8 conflicts|22 conflicts|24 conflicts|
||32 countries|8 countries|20 countries|22 countries|
|Annual inflation rate (%)|46 conflicts|17 conflicts|30 conflicts|32 conflicts|
||43 countries|16 countries|26 countries|27 countries|

Source: Authors’ calculations based on data from UCDP/PRIO Armed Conflict Dataset. See text for details.

# **_III. C. Descriptive evidence_**

Figures 2 and 3 (with panels a, b and c) cover two historical episodes of armed conflict and compare the macroeconomic dynamics of a conflict‐afflicted economy to an unafflicted neighboring economy. These figures illustrate the intuition behind the difference‐in‐difference estimator and anticipate some of the main econometric results discussed further below.

# **_Example 1. Nicaragua and Costa Rica in the late 1970s_**

Between 1978 and 1979, the Sandinista National Liberation Front led an insurrection in Nicaragua that ousted the 43‐year old Somoza family rule in the country. Costa Rica, Nicaragua’s neighbor with a long history without armed conflict, celebrated in 1978 both presidential and legislative elections followed by a peaceful political transition after two consecutive National Liberation Party governments.

From the early 1970s to the early 1980s, Costa Rica maintained the GDP share of government consumption (at current purchasing power parities) stable at around 18% (Figure 2a). In Nicaragua, despite having also a stable share of government consumption of
: “Are decisions on specific investments made by the Managers?” to construct the index of political fund. They classify all funds with managerial independence score of less than 1 as “political funds” and all others as “non political funds.” They then define a dummy variable that takes the value of 1 for “political fund” and 0 if not.<sup>7</sup> The religion variable includes the share of Christians and Muslims in a given country using the new database of the PewResearch Center.<sup>8</sup> In terms of control variables, we use the following variables: Private credit and bank costs to take into account the level of financial development and bank efficiency, respectively. Private credit is calculated as the credit provided by the banking sector, including all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net and bank costs are measured by overhead costs to total assets (%), defined as operating expenses of a bank as a share of the value of all assets held, and cost to income ratio (%), defined as operating expenses of a bank as a share of the sum of net-interest revenue and other operating income.<sup>9</sup> We also control our results using school, inflation, macroeconomic policies such as budget balance and trade taken from the World Bank WDI.<sup>10</sup> _School_ is measured by the total enrollment in secondary education, regardless of age, expressed as a percentage of the population of official secondary education age. _Inflation, consumer price index_ , measured by the consumer price index, reflects the annual percentage change in the cost to the average consumer of acquiring a

> 6We also use the average per-capita GDP growth rate to test the robustness of our results.

> 7The database of political SWFs are from Bortolotti _et al._ (2015).

> 8The database are publicly available at `http:/
Percentage of firms that perceives business regulations and crime as a major or very severe obstacle to current operations**

<!-- Start of picture text --> 25<br>20<br>20 17 14 16 14 15<br>15 12 11<br>10<br>5<br>0<br>Customs & Tax Licensing & Crime, Theft &<br>Trade Administration Permits Disorder<br>Regulations<br>Male-owned firms Female-owned firms<br><!-- End of picture text --> Source: BEEPS, 2009 Given that BEEPS interviews commonly involved not only business owners but also senior executives (not necessarily female), it is unlikely that differences in survey responses can be explained by perceptional differences of the business environment between men and women. It is possible that firms owned by women are subject to somewhat greater scrutiny by public officials (potentially because women are perceived as being less assertive in negotiations and considered an easy prey) than those owned by men, which increases business start-up and development costs for women and restricts the growth of women-owned businesses.

# **2.2 Career Advancement among Men and Women**

There are pronounced gender differences in the career advancement of men and women in Serbia, with men having a higher representation in leadership positions and women being over-concentrated in the group of professionals, technicians, clerks, service and sales workers as well as in elementary occupations (Table 5).

Women comprise only 30% of the people in the category of legislators, senior officials and managers in public and private organizations. The data from the BEEPS 2009 survey shows that females constitute just 16% of firms’ top managers. Interestingly, female-owned companies are significantly more likely to have women among top managers than male-owned companies: 41% of female-owned firms vs. 6% of 8