rafmacalaba/data-use-mentions-extended huggingface dataset:
check their current balance at any time. Jisomee Mita data contains water consumption, invoice amounts, payment history, current balance, and contact information of the property owner. When payments or balance checks are submitted, the Jisomee Mita data are updated automatically. However, monthly standing charges are applied to each account independent of whether a property owner made a payment or billing enquiry which means that each property owner’s balance is updated at least once a month.

The billing data is complemented with tenant and property owner survey data. A short baseline listing phone survey of property owners was conducted in August and September 2018. This captured ownership and water/sanitation connection status, property owner residency and number of paying tenants in the compound.

From August to October 2019 a follow up survey of both property owners and tenants included in the enforcement intervention captured data on rent, service-level satisfaction, political engagement, property owner-tenant interactions, water use and practices, mental well-being and general demographic measures of one randomly selected tenant and the corresponding property owner from each compound in the sample.

We use utility billing data to generate payment outcomes. This includes: (1) the proportion of property owners making a payment for water/sewer charges since the intervention, (2) the total amount paid by property owners’ post-intervention and (3) the proportion of outstanding service charges paid post-intervention. The data spans the entire period from when the first property owners were connected in 2014 up to nine months after the interventions were implemented (September 2019).

We rely on the follow up tenant and property owner survey to measure a range of outcomes that assess the possible welfare effects of the enforcement intervention. We collect measures related to water and sanitation infrastructure and use to assess how the intervention may have affected access: whether the compound had a pour-flush toilet and piped water connection at the time of the endline, whether the water connection was working, the main source of water used by compound residents, the amount of time spent fetching water in the last
in the video a basket of essential supplies worth JD 20, as in proposal 2 (unconditional in-kind transfer). Thus, the treatment increased transparency of the redistributive proposals by alleviating participants’uncertainty about the delivery of the transfer to the intended beneficiaries and about the value of the JD 20 in the case of an unconditional food transfer. The treatment was chosen as a result of a focus group on the perceived barriers to redistribution as well as consultations with Jordanian experts to offer concrete recommendations on measures to implement as part of a fuel subsidy reform.

# 2.5 Data

The data used in this paper were collected between late May and June 2012. The quantitative data from Jordan Gives includes decisions made by each individual participant

> 16 Indeed, in some cases, participants in the treatment group did decide to follow the facilitator and, as highlighted by participants in the focus group discussion, the mere availability of this option sent a credible commitment signal of trustworthiness.

9
and hence the quick ratio is alternatively (receivables + cash and cash equivalents)) / current liabilities. A firm’s debt is considered “at risk” if the QR is less than or equal to 1, namely liquid current assets cannot cover current liabilities. In normal times, firms may be able to operate with a QR less than 1 in the expectation that they can raise funds quickly through lines of credit with banks or by issuing in capital markets to cover short-term obligations. However, the pandemic has impaired funding conditions considerably, particularly in EMDEs. We define D as the total debt associated with companies that are “at risk” in country _C_ in quarter _t_ according to indicator _Y_ (i.e. ICR or QR). Next, we define D 1,C as the debt at risk in YY,C country _C_ in quarter DD _t_ according to one or more indicators. DD Where _i_ denotes individual firms and 1(.) is an indicator variable which is equal to one if the logical statement inside the bracket is true and zero otherwise. To quantify the impact of COVID-19 related earnings and liquidity shocks on non-financial corporate vulnerabilities, we calculate D , D Q ,C and D 1,C for each shock scenario. IICCII,C iiQ 4. DDData DD DD

**Our sample consists of 2019Q4 balance sheet and income statement data of 16,934 listed nonfinancial companies in 8 industries in 73 countries spanning all six World Bank regions (see Annex).** We use pre-pandemic, 2019Q4 data as the basis of our stress tests since this is the latest, most complete information available. Bloomberg is our primary data source.

**The companies in our full sample represent US$ 22.1 trillion in total assets and US$ 6.05 trillion debt** . **For the median country, total assets and total debt of these companies stand at 25 percent to GDP and 7 percent to GDP, respectively.** Exhibit 2 shows our sample
cowife. Marrying a second wife is indeed costly to the husband: savings are needed to pay for both the wedding and the bride price, which has remained high and remarkably stable over time in Senegal, according to our survey data. As for women, they may have incentives to increase their own savings so as to protect themselves against the arrival of a cowife, which may be viewed as a negative income shock. Depending on their capacity to protect their savings from their husbands, we expect different strategies of monogamous wives in response to an increased risk of polygamy. This article thus intends to fill a gap in the literature on polygamy by exploring the impact of the threat of polygamy on monogamous spouses’ saving behavior and resource allocation decisions.

The case of Senegal is particularly interesting since previous studies have shown that the prevalence of polygamy is still high and declines at a slower pace than in most other sub-Saharan countries. The share of young women (aged 20 to 24) in a polygamous union has even increased in rural areas from 1986 to 1997 Antoine (2002). In 2006, according to our data, 38 percent of Senegalese married women aged 15 to 60 are in a polygamous union.

We use original individual panel data from a nationally representative survey ( _Enquête Pauvreté et Structure Familiale_ [PSF]) conducted in 2006–7 and 2010–12. Our data are unique and particularly suitable for this analysis. Indeed, while survey data in the developing world generally collect information on savings at the 1 In this article, we use the generic term polygamy for polygyny: polyandry does not exist in Senegal.

2
# **Firm-level Model**

In this section, we discuss how we test our hypotheses using firm-level panel data. This method has the advantage of enabling us to measure geographic diversification continuously and to add control variables for firm-level characteristics that might confound the relation between FDI and political conflict. However, the disadvantage is that for many of the observations in the original data set, firmlevel variables were not available. Although as many as 67.1% of all parent companies in the fDi Markets data set could be matched to firms registered in ORBIS, only for 1,413 of those firms (10.2%) data on firm-level characteristics were available. Although a sample of this size is not uncommon in IB research, it considerably decreases the precision of our estimates and the ability to detect statistically significant effects. In addition, a missing value logistic regression (Long & Freese, 2006) shows that firm-level data are not missing at random; the probability that data are missing depends on the sector and the level of political conflict. As this could considerably bias our results, we prefer the countrylevel estimates and show the estimates of the firm-level regressions merely for robustness.

The firm-level data set comprises data on the 1,413 MNEs that invested in a developing country between the years 2003 and 2012, according to the fDi Markets data set. For each MNE ( _m_ ) we report greenfield FDI to a host country ( _i_ ) in a certain year ( _t)_ , resulting in the following regression model: ������ � ∝��∝� ������� �∝� ������� � ∝� ������� �∝� �������� ��� � �� � �� � ����, (2) Similarly to our previous models, the independent variable of interest is political conflict _P_ for country _i_ in the previous year, while we control for
News" by Dow Jones to limit the potential noise stemming from the large pool of articles used initially (Appendix Table A17). The results remain robust and highly significant in this subsample, further supporting the results on the dynamics of reform attitudes presented in this section.

Next, we explore whether reform chatter is associated with reforms. Actual reforms are difficult to measure. Data on structural reforms for a large sample of countries are drawn from Ostry et al. (2009) and the World Bank’s Doing Business project. The results presented in this subsection are mere associations. However, the strong relationship between reform chatter and actual reform suggests that reform chatter is not “cheap talk”.

The main specification for testing the relationship between reform chatter and actual reforms is: R R R R R R R ii,tt SSaaRRcca ccRRa ss ii tt ii,tt ii,tt ii ii,tt R R = ααR + ββ captures the changes in the principal component of domestic, international + λλR RRha aaR + γγR RRha aaR × λλR RRaaRRa cc + εε and aggregate indices. and are country and time fixed effects. The main coefficients of ii,tt interest are SSaaRRcca ccRRa and , which highlight the potential heterogeneity in the impact of reform chatter on ss ii tt actual reforms in the data. αα ββ λλ γγ Using data on structural reform (Ostry et al. 2009), Appendix Table A20 Panel A shows that reform chatter at time _t_ is associated significantly with more domestic reforms (Column 2) but neither international reforms (Column 3) nor composite reforms (Column 1). However, Table A20 Panel B show that for more democratic countries, reform mentions are significantly associated with the composite index of actual reforms (see Columns 1-3). Columns 4-6 in Panel B show that the results are not significant for international reforms. Columns 8-9 in Panel show that domestic reforms are associated with reform chatter in democracies, though the result in Column 7 is not significant when using the democracy index.

The
from the 68<sup>th</sup> round conducted during the period from July 2011 to June 2012, specifically survey data collected from the questionnaire referred to as "Schedule 10: Employment and Unemployment". The number of households surveyed was 101,724 (59,700 in rural areas and 42,024 in urban areas). These sample households correspond to 456,999 individuals (280,763 in rural areas and 176,236 in urban areas).

# **4.1 Matching household survey data with tariffs and NTMs**

The paper uses a novel approach to assess barriers that individual producers across the income distribution face in international markets. In doing so, it combines household survey data on income and consumption from India with information on lowest applied tariffs in the top 15 destination markets (EU counted as one) for Indian products in 2012.<sup>6</sup> These importers account for more than 75% of world imports from India. This is done by matching India’s National Industrial Classification (NIC) – which is based on the International Standard of Industry

> 6 These importers account for more than 75% of world imports from India.

7
# **1. INTRODUCTION**

Global trade has experienced significant expansion buoyed by technological progress and trade-supporting policy shifts. IMF data highlight that, by the year 2000, trade value had increased 60 times since 1960, reaching US$13 trillion. During this period, exports increased 61 times while imports rose 58 times.<sup>4</sup> Since then, global trade has seen curve-shifting expansion. There is an apparent correlation between the accelerated growth in trade and economic growth. According to the World Bank World Development Indicator data (WDI), world exports of goods and services increased from US$8 trillion in 2002 to over US$31 trillion in 2022 and as shown in Figure 1 below, the growth in exports was accompanied by global gross domestic product (GDP) growth. The patterns of trends in global trade tend to be similar for both advanced economies (AEs) and Emerging Markets and Developing Economies (EMDEs), albeit higher for the former. International trade has enabled EMDEs to integrate with the global economy, which has mirrored their growth. Figure 1 shows that EMDEs have increased their participation in global trade significantly since the 1960s as well as their share of global trade (from 19 percent in 1990 to 41 percent in 2021). Between the late 1990s and 2022, manufacturing exports from EMDEs increased eight-fold — from approximately US$ 629 billion in 1995 to US$ 5.5 trillion in 2022. Countries that trade more tend to achieve higher income, as illustrated in Figures A2, A3, and A4 in the Appendix.

**Figure 1. The relationship between trade and growth and the share of EMDEs trade**

<!-- Start of picture text --> a) Trade vs GDP (1970-2021) b) Share of EMDEs trade (1970-2021)<br>120 100%<br>100<br>80%<br>80<br>60%<br>60<br>40%<br>40<
**Figure 1. Average NRA and the timing of political reforms.**

.

<!-- Start of picture text -->
-10 -5 0 5 10<br>Years relative to democratisation<br>NRA 3-year moving average<br>average predemocratisation average postdemocratisation<br>30<br>20<br>10<br>0<br>Nominal rate of assistance<br>-10<br><!-- End of picture text -->

_Source:_ Own calculations based on data from the World Bank’s agricultural distortions database and the Polity IV database

**Figure 2. Average RRA and the timing of political reforms.**

<!-- Start of picture text -->
-10 -5 0 5 10<br>Years relative to democratisation<br>RRA 3-year moving average<br>average predemocratisation average postdemocratisation<br>20<br>10<br>0<br>-10<br>Relative rate of assistance<br>-20<br><!-- End of picture text -->

_Source:_ Own calculations based on data from the World Bank’s agricultural distortions database and the Polity IV database.

6
As expected, the randomization procedure was successful in ensuring statistical balance between treatment and control groups. Table 1 below presents means, normalized differences,<sup>13</sup> and _t_ -statistics for several important variables using baseline survey data. Among the variables listed, mean differences are always smaller than 13 percent of the standard deviation.

# **IV. Hypotheses**

The main goal of this paper is to use the aforementioned identification strategy afforded by randomized evaluation of NSP to test the “hearts and minds” theory of counterinsurgency, which posits that development projects will increase economic welfare, improve attitudes to government, and reduce insurgent violence. The three hypotheses below formalize these predictions:

**Hypothesis 1:** Levels of economic well-being will be higher among people living in villages that have received a development project.

The “hearts and minds” theory focuses on attitude-driven behavior. Accordingly, subjective perceptions of one’s economic situation are as important as objective outcomes. To test this hypothesis, we look both at objective measures of economic well-being and at subjective perceptions, such as whether people report that their economic situation has improved in the past year and whether they expect it to improve in the future. The first hypothesis is consistent not only with the “hearts and minds” theory, but also with the “opportunity cost” theory, since an increase in income and in employment raises the cost of participation in the insurgency.

**Hypothesis 2:** Attitudes towards the government and allied entities will be more positive among people in villages that have received a development project.

This hypothesis asserts that economic benefits arising from development projects will improve attitudes toward the government. Since NSP is managed by the government of Afghanistan, but is funded by international donors and implemented by NGOs, we are also interested in the potential effect on levels of support for these other entities. The second hypothesis is important for distinguishing between different theories, since the “hearts and minds” approach is the only one that
income on consumption. It is consistent with the findings reported by Adams (2005) based on a different modeling approach and data from rural Guatemala.

An overarching conclusion of this research is that criticisms of migration for not stimulating productive investments may be misplaced; they may be more a result of modeling and data limitations than actual differences in expenditure patterns between migrant and nonmigrant households. As rural incomes increase, expenditure patterns change. This is true regardless of whether the income gains are from migration or other sources. The key question that should be of interest to researchers and policy makers is whether expenditure patterns change differently for households that participate in migration, and if so, why. This requires a more complex modeling approach than has been used in past research exploring the impacts of remittances on expenditures. Migration’s potential impacts include influences besides those of remittances; expenditure patterns in migrant households must be compared with those in _otherwise similar_ households without migrants while controlling for the endogeneity of migration choices. Our findings reveal that migration does indeed significantly influence expenditure patterns in rural areas, but not in the ways that most past studies of remittance use predict. In particular, the propensity to invest appears to be considerably larger for households with migrants.

30
## **Table A0: Literature review (continuation)**

|**Paper**|**Fiscal Adjustment**|**Succesful fiscal adjustment**|**Multivariate analysis?**|**Sample**|
|---|---|---|---|---|
|Kaplanoglou|Attempts are defined as an|At least one of the two following criteria holds: 1) in the|Logit model.|29 (OECD),|
|et al. (2015)|improvement in the CAPB of at<br>least 1.5% of GDP taking place in<br>one year or taking place over<br>three years, if in each and every<br>year the CAPB does not<br>deteriorate by more than 0.5% of<br>GDP.|three years after the attempt, the ratio of the cyclically<br>adjusted primary deficit to GDP is on average at least<br>2% of GDP below the attempt year, 2) three years after<br>the attempt, the ratio of the debt-to-GDP is at least 5%<br>of GDP below the levelk of the attempt year. (following<br>Alesina Perotti 1997)_||1971-09|
|Gupta et al.<br>(2017)|Updates data of Devries et al.<br>(2011).|Analyses difference between the size of planned fiscal<br>adjustment and the size of the realized fiscal<br>adjustment as measured by changes in the primary<br>budget balance (all expressed in percent of GDP)|Analyses difference<br>between planned and<br>actual fiscal<br>consolidations.|17 (OECD),<br>1978-15|
|Wiese et al.<br>(2018)|BP test on cyclically adjusted fiscal<br>balance
Figure 1: Industry Compositions in Urban Ethiopia (percent), 2006–16 <!-- Start of picture text --> 100<br>24 24 25<br>80<br>8 8 6<br>4 7<br>60 5 3 5 6<br>40 32 28 28<br>1 6 1 8<br>20 2 7<br>14 12 12<br>0<br>7 0 9 6 0<br>0<br>2006 2010 2016<br>Public<br>Agriculture Construction Administration<br>Other<br>Mining Commerce Services<br>Transport<br>Manufacturing Communications<br>Financial<br>Public utilities<br>Business Services<br>Sources: Urban Employment and Unemployment Survey (UEUS) 2010/2016<br><!-- End of picture text --> In addition, the analysis of poverty dynamics based on the three waves of Ethiopian panel data demonstrates that self-employment is associated with frequent movement into poverty compared to wage employment (World Bank 2020). Among households headed by self-employed workers, informal self-employment is particularly volatile in that they are relatively more likely to fall into poverty.

# **3 Data**

## **3.1 Towns and Populations**

The main data sets used in this study are the 2012 Urban Employment and Unemployment Survey (UEUS) and the 2013 Labor Force Survey (LFS) collected by the Central Statistical Agency (CSA) of the Ethiopian government. Although core labor-related questions are common in both surveys, the LFS includes additional information, such as migration and secondary jobs. In addition, though both surveys are nationwide, the coverages of towns in the 2013 LFS data sets is larger than that 7
# **Applied Services Trade Policy**

A Guide to the Services Trade Policy Database and the Services Trade Restrictions Index Ingo Borchert (1) Batshur Gootiiz (2) Joscelyn Magdeleine (3) Juan A. Marchetti (3) Aaditya Mattoo (2) Ester Rubio (3) Evgeniia Shannon (3)<sup>**1,2**</sup>

**JEL codes:** F13, F14, F23, L80

**Keywords:** services, trade policy, investment, STRI, trade restrictions, quantification

_(1) University of Sussex_ _(2) World Bank_ _(3) World Trade Organization_ For Batshur Gootiiz and Evgeniia Shannon, affiliation at the time of writing.

> 1 Acknowledgments:  The authors would like to express their gratitude to all WTO and World Bank colleagues who assisted in the process of creating this Database; in particular Laura Baiker, Renjie Ge, Cecilia Heuser, Giulia Jonetzko, Justine Lan, Anastasiya Letnikava, Clemence Moreau and Fatima Anjum Quraishi. Cooperation by the OECD Secretariat, who made available data from the OECD Services Trade Restrictiveness Database (2016 vintage), which was used as source of information for 43 economies in the World Bank-WTO Services Trade Policy Database, is gratefully acknowledged. This paper is part of a World Bank research project on trade in services, supported in part by the Umbrella Trust Fund for Trade and Development.

> 2 Disclaimer:  This is a working paper, and hence it represents research in progress. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They are not intended to represent the positions or opinions of the WTO or its Members and are without prejudice to Members' rights and obligations under the WTO. They also do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, the Executive Directors of the World Bank, the governments they represent, or any of the aforementioned individuals. Any errors are attributable
in Table A1 in the Appendix.</u>

|provided in Table A1 in the Appendix.|Pane<br>|l A. Loans-Go<br>|vts,b,m<br>|Panel<br>|B. Loans-Firm<br>|sf,b,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm*DebtStates,16Q1*BankExposureGovs,b,Mar16|-1.87***|-2.89***|-4.61***|0.189*|0.279*|0.734*|
||(0.56)|(0.64)|(1.09)|(0.113)|(0.142)|(0.391)|
|Postm*DebtStates,16Q1|-0.08|0.15||0.016***|0.022***||
||(0.12)|(0.11)||(0.006)|(0.006)||
|Postm*BankExposureGovs,b,Mar16|1.33***|1.65***|2.81***|-0.224|-0.363*|-1.263**|
||(0.29)|(0.35)|(0.44)|(0.200)|(0.199)|(0.539)|
|Observations|7,568|7,512|7,499|1,136,616|1,136,616|469,
,” Vol. 5, Elsevier, 2015, pp. 247–348. Esch, Thomas, Elisabeth Brzoska, Stefan Dech, Benjamin Leutner, Daniela Palacios-Lopez, Annekatrin Metz-Marconcini, Mattia Marconcini, Achim Roth, and Julian Zeidler, “World Settlement Footprint 3D-A first three-dimensional survey of the global building stock,” _Remote Sensing of Environment_ , 2022, _270_ , 112877. Florczyk, AJ, Michele Melchiorri, Christina Corbane, Marcello Schiavina, M Maffenini, Martino Pesaresi, Panagiotis Politis, S Sabo, S Freire, Daniele Ehrlich et al., “Description of the GHS urban centre database 2015,” _Public release_ , 2019, _1_ , 1–75. Zhou, Yuyu, Xuecao Li, Wei Chen, Lin Meng, Qiusheng Wu, Peng Gong, and Karen C Seto, “Satellite mapping of urban builtup heights reveals extreme infrastructure gaps and inequalities in the Global South,” _Proceedings of the National Academy of Sciences_ , 2022, _119_ (46), e2214813119.