rafmacalaba/data-use-mentions-extended huggingface dataset:
turnover, and (iii) value chains (i.e. demand and assistance effect, diffusion effect, availability and quality effect). The focus of this paper is on value chains.<sup>3</sup> Spillovers through global value chains emerge, e.g., when local firms become input or service suppliers of multinational firms. Specifically, FDI spillovers can be generated through the demand of multinationals for better and/or more diverse inputs ( _demand effect_ ). Hereby, multinational affiliates might help local producers to upgrade their technological capabilities directly through sharing of production techniques and product design and assisting with technology acquisition ( _assistance effect_ ) (Paus and Gallagher 2008). Spillovers to supplying industries may also be generated through personnel training, advance payment, leasing of machinery, provision of inputs, help with quality assurance and organization of product lines (Lall 1980; Crespo and Fontoura 2007; Javorcik 2008).

While the demand and assistance effects are intentional, unintentional knowledge spillovers can occur, e.g., through technology leakages to other supplying firms in the sector ( _diffusion effect_ ). Finally, while the previously described effects refer to backward spillovers from multinationals to suppliers, there is also the case where a multinational firm supplies to a local producer in downstream sectors. This increases the availability, variety, and reliability of higher-quality inputs ( _availability and quality effects_ ) (Javorcik 2008). Given our data sample which covers surveys of suppliers that produce inputs for multinationals, we are only able to examine demand and assistance effects in the following.

# **3 Which Foreign Investor Characteristics Increase the FDI Spillover Potential?**

This section focuses on the role of foreign investor characteristics for the FDI spillover potential. Section 3.1 presents the dataset being used in this section. Section 3.2 evaluates if there are differences between foreign investors and domestic producers in terms of their potential to generate positive spillovers. Section 3.3 examines if there are differences in the extent of FDI spillover potential
, 2012; Alder, 2015). Finally, a subset of this literature also relies on georeferenced data and GIS analysis to assess the trade and spatial effects of transport infrastructure (e.g. Roberts, Deichmann, Fingleton and Shi, 2010; Roberts, Deichmann, Fingleton and Shi, 2010).

The rest of the paper is organized as follows. Section 2 presents the GIS analysis. The econometric strategy and results are discussed in Section 3. Section 4 outlines the trade impact of the BRI. Concluding remarks follow.

> 4 Related work by de Soyres et al., Mulabdic and Ruta (2018) and Maliszewska and van der Mensbrugghe (2018) uses the data on trade costs from de Soyres et al. (2018) in a structural and CGE trade model respectively.

4
classes in 2009 (80.5%). The share to premium class trips in FY12 is far lower for CO-based travel; reasonably, a similar relationship existed also for CY09.

# **Table A1.1: Breakdown of WBG Travel Data by Booked Seat Class for FY 2012.**

|TripOrigin|Class|Trips|Total Miles|AvgLength|% of Miles|
|---|---|---|---|---|---|
|CO|Economy|85,678|113,975,613|1,330.3|41.07%|
|CO|Business|55,683|107,057,207|1,922.6|38.58%|
|CO|First|118|202,132|1,713.0|0.07%|
|CO|Unknown|34,087|56,270,643|1,650.8|20.28%|
|CO Sub-Total||175,566|277,505,595|1,580.6|100.0%|
|HQ|Economy|31,477|63,421,648|2,014.9|17.39%|
|HQ|Business|91,089|238,565,602|2,619.0|65.43%|
|HQ|First|194|459,406|2,368.1|0.13%|
|HQ|Unknown|24,005|62,175,433|2,590.1|17.05%|
|HQSubtotal||146,765|364,622,089|2,484.4|100.0%|
|Combined|Economy|117,155|177,397,261|1,514.2|27.63%|
|Combined|Business|146,772
yields similar results. The elasticity of the number of formal employees per commune with respect to the sum of lights is 0.563 and again measured with a small standard error. Again the nightlights explain only a rather small share of the between variation in employment ( _R_<sup>2</sup> = 0 _._ 103). The elasticity of per capita expenditure with nightlights is much smaller and estimated with a _β_ of 0.126. This indicates that (at least in the development country setting of Vietnam) nightlights are more responsive to differences in production but less so to consumption. The R-squared measure of expenditure is even lower at _R_<sup>2</sup> = 0 _._ 023, suggesting that nightlights are not very predictive of overall cross-section consumption patterns in the setting of Vietnam. This finding is in contrast to other studies that find higher prediction power for small geographic units of up to 40-50% in Africa (Jean et al., 2016) and of up to 60% in Sri Lanka (Engstrom et al., 2016).

Controlling for potentially endogenous commune sizes confirms the strong positive relationship between nightlights and economic activity. Column (2) includes the log area (in square kilometers) as a regressor. The coefficient is strongly negative and indicates an elasticity of the number enterprise with respect to commune size of -0.631. The coefficient on log SOL increases to 0.589 and is still strongly significant. This is a natural consequence of the negative correlation between commune size and economic activity as outlined before. Similarly, in column (4) the employment elasticity increases to 0.806. For expenditure, the elasticity increases only slightly from 0.065 to 0.126. The considerable increase in the R-squared measure in all regressions indicates the predictive power of commune size independently of nightlights and confirms that commune boundaries are endogenous.

# **3.2.2 Landsat Spectral Indices**

The focus is now on the question whether
# **The Impact of Mobile Money on Poor Rural Households: Experimental Evidence from Uganda**<sup></sup>

Christina Wieser, _World Bank_ Miriam Bruhn<sup>§</sup> , _World Bank_ and _IZA_ Johannes Kinzinger, _International Finance Corporation, World Bank Group_ Christian Ruckteschler, _International Finance Corporation, World Bank Group_ Soren Heitmann, _International Finance Corporation, World Bank Group_ Originally published in the Policy Research Working Paper Series on _June 2019._ This version is updated on _December 2022._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u>

**JEL Classification:** C93; E42; G23; O12; O18.

**Keywords:** Mobile money, remittances, food security.

>  This study was funded by the Bill and Melinda Gates Foundation. It is implemented through and draws important experiences from the IFC-Mastercard Foundation Partnership for Financial Inclusion (“the Partnership”), which leverages IFC’s expanding Mobile Network Operators client networks in Africa and its emerging expertise in mobile financial services to catalyze innovative and low-cost approaches to expand financial services to low-income populations. We thank Siobhan Murray from the World Bank for her help with mapping and defining the clusters of enumeration areas used in this study; Xavier Gine and Emanuela Galasso from the World Bank for their inputs and suggestions; Richard Chamboko from the IFC for his comments and edits; and Richard Yego, Acting Director, Airtel Money, Uganda, for his operational support to control the agent roll-out and implement the study. This study is registered in the AEA RCT Registry and the unique identifying number is: "AEARCTR-0004605".

> § Corresponding author: The World Bank, 1818 H Street NW, MSN MC3-307, Washington, DC 20433; phone: (202) 458-2732; e-mail: mbruhn@worldbank.org

1
the first valuable contribution by challenging the commonly held view that country-specific standards act as a barrier to trade whereas harmonized standards encourage trade.<sup>2</sup> In a gravity framework, the author uses a panel data set with data on country-specific and bilaterally standards for 471 industries in 12 OECD countries during the period 1980-1995. He finds that bilateral share standards are favorable to trade while country-specific standards tend to hinder trade in simple goods (including agricultural products, food, beverages, and mineral fuels) and promote trade in complex goods (like machinery and electronics). The author then offers an explanation for the divergent effect of country-specific standards based on the dual impact of standards in production and trade costs: While standards may impose additional costs on exporters as it maybe necessary to adapt products for specific markets (cost-effect), they also can reduce exporter’s information cost if they convey relevant market information which would be costly to gather in the absence of the standard (informational-effect). Moenius (2004) results imply that the cost-effect outweighs the informational-effect for simple products while the opposite is true for complex products. In

> 2Moenius’ work focuses mostly in bilateral harmonization of standards rather than in international harmonization of standards.

3
there was almost a doubling in the number of beneficiaries as well as in the program budget (Government of India 2000). By 1999-2000, the budgetary allocation for the program was around $170 million. This is a substantial budgetary outlay, even if not large in per capita terms. It will increase rapidly in the near future, as the government aims to cover all administrative blocks.<sup>7</sup> It is perhaps the largest program of the kind in the world.

The combination of economic growth, agricultural surpluses, and a slew of programs aimed at increasing access to food might be expected to yield more than a modest decline in child malnutrition. To begin to unravel this puzzle, we examine the functioning of the ICDS program, which seeks to directly provide nutritional supplementation to children in need. Given the importance of the program’s objectives and the size of the budget, it is important to assess whether it is effective in its main objective of enhancing child nutritional status.

One of the crucial determinants of program success is program placement: whether the ICDS centers are allocated to the areas with the highest level of malnutrition. Despite the importance of this problem, there has been little formal analysis of program placement. The effectiveness of program placement is therefore the main focus of our analysis.

Program efficacy depends also on how well it is implemented once it is in place. This is more difficult to evaluate formally, due to a lack of prospective data on recipients and non-recipients of the program. Several studies have sought nevertheless to evaluate program impact, but most compare outcomes between areas where the program is present and those where it is not ⎯ without controlling for differences in the characteristics of the children, households and villages that could bias the estimates of program effect.<sup>8</sup> We use a more rigorous methodology to ensure that the children in the “treatment” and “control” villages are matched along a wide range of dimensions, but conclusive impact evaluation depends
effective to ensure that they are not subjected to penalties or fines. So any increase in the effectiveness of an already effective admninistration should result in _adecrease_ in enforcement collections. Low enforcement collections could, therefore, indicate either a very ineffective or a very effective administration.

_PIs in the sam;ple._ Annex Table A2 lists performnance indicators used in the 12 TLSRs and 5 SALs/SECALs where perfornance indicators (other than compliance with covenants and the tax/GDP ratio) were specified. In addition, 1 SAC had a condition that the governnent would develop a set of performance indicators cdunng the project. The table shows that performance indicators specified in TLSRs were considerably more detailed than those in SALs/SECALs. Nevertheless, even among TLSRs, corrlprehensiveness varies and indicators specified lack uniformity across projects. There is also significant use of qualitative information in lieu of performance indicators. Judging from the table, the design weakness identified by Datta-Mitra (1997) in SALs/SECALs is only somewhat lower in TLSR loans. A case in which there is dramatic variation in the quality of "PIs", f.or customs and tax reform components, even in within the same project, is in Box 8. Box 8 also gives details of a tax administration reform project, where the set of PIs is relatively comprehensive even if some of them are "qualitative PIs".

> 2 World Bank, Operations Policy Department (1996).

18
surveys have information on the educational level of the mother, the presumed caregiver, there is no consensus on how to translate that information to a measure of the mother’s care giving abilities and thus we do not include it in our measure.

**Table 4: Components of Care**

|**IDEAL**|**AVAILABLE**|
|---|---|
|Workload and time availability of caregiver|No|
|Social support for caregiver|No|
|Psychosocial care|No|
|Caring Behaviors: Breast‐feeding|Yes|

> 12 For Bolivia and Peru initial breastfeeding can only be identified in the first 100 minutes (instead of 60 minutes) and the measure is not available in the IFPRI Bangladesh data.

14
academic publications about open data impacts as a proxy for existence of impacts. Researchers were asked to score the extent of impact on a 0 – 10 scale. The scoring guidance directed assignment of the highest scores for peer-reviewed studies showing impact and emphasized the importance of sources making a direct connection between open data and observed impacts. For scores over 5, researchers were asked to cite at least two separate examples in the given category. To calculate each score for each component an average of the variables in that component is taken and the average of components is used to generate each sub-index. The weighted average of the sub-indexes is used to generate the overall ODB score.</mark> Like the ODB, the GODI also assesses government data based on the open definition and the <mark>open data charter. However, unlike the ODB, GODI limits its inquiry to the publication of national government data and ignores other aspects of the common open data assessment framework such as context, use or impact. It covers 94 countries including 3 MENA countries- the Islamic Republic of Iran, Tunisia, and Oman- in its most recent version. GODI data categories are refined</mark>

> 25 These 10 questions are as follows: <mark>Does the data exist? Is it available online from government in any form? Is the data set provided in machine-readable formats?; Is the machine-readable data available in bulk?; Is the data set available free of charge?; Is the data openly licensed?; Is the data set up to date?;  Is the publication of the data set sustainable?; Was it easy to find information about this data set and;</mark> ; <mark>Are (linked) data URIs provided for key elements of the data?</mark>

> 26 Excerpt from ODB methodology report- ‘”By putting forward categories of data, rather than specific named data sets, we allowed researchers to exercise judgement as to the extent to which countries were making data
data for a few Latin American countries which require frms to account separately for domestic and imported inputs that inventories of imported inputs are much higher. Second, a poorly functioning market can lead to shortages of certain goods; firms expecting these shortages would stock up on inventories in anticipation. In the Soviet Union, firms were known to maintain a high ratio of raw materials inventories to finished goods inventories for this reason.<sup>9</sup> Third, higher uncertainty of demand should lead firms to keep higher inventories according to the stockout model. Finally, the interest rates at which firms can borrow working capital determine the holding cost of the inventories. The higher the interest rates, the costlier are inventory levels; thus one would expect lower levels in equilibrium. Since developing countries have higher interest rates than developed countries, it is then, on that account surprising that their inventories are higher.

There are a number of additional factors that ideally should be included in the study but cannot due to a lack of cross-country data. If developing countries were more likely to use FIFO accounting while developed countries used LIFO accounting, their inventory stocks would appear to be higher, especially in cases of high inflation. Although we do not have evidence by country on this issue, our research on this topic indicates that LIFO, although allowed in the United States for tax purposes, is rare in both developing and other developed countries.<sup>10</sup> Other relevant factors are the degree of vertical integration, the concentration of upstream suppliers, production to stock vs. production to order, and the type of production technology. 1 ' We do s Since any additional orders would incur some fixed ordering costs, the firm may actually prefer a higher safety stock than indicated.

> 9 Chikan [1991] shows that socialist countries held a larger ratio of raw materials inventories to finished goods inventories.

> **10** See, for example, Nobes and Parker [1995], p.<sup>
141|
|Cotton|Zambia|1990-94|1995-99|47|79|152|
|Tobacco|Tanzania|1991-95|1996-00|22|35|52|

The key constraint on the commodity sector performance was insufficient producer incentives. By eliminating most of the intermediate agencies and reducing and eliminating export taxes and restrictions, the agricultural policy reforms led to higher prices for the farmers. They fit into the categorization of the growth accelerations through the elimination of major constraints, and as expected they lead to spurts in output growth.

We also include the production levels for the last five years in Table 1 as a proxy for the longerterm sustainable growth outcomes. They are indicative of whether the initial supply responses were maintained and improved upon. Of course, given the cyclical behavior of production in many cases, output in the last five years might not give a clear sense of the sustainability of the growth process.<sup>11</sup> It is just another indicator in addition to our judgments on status of the sector around 2008. The case studies and the discussions below indicate that four cases of can be thought of as showing sustained growth. These are Zambia cotton, Kenya and Tanzania tea, and

> 9 The details of the output movements along with the behavior of producer prices are analyzed in the following sections. For the further details, the reader is referred to the original papers.

> 10 Cashew production in Tanzania is higher in the last five years but it is much lower than the peak production attained in the post-reform period (see Fig. 9).

> 11 Commodity prices have been increasing for the last few years which might skew the results of the last five years.

5
, a time period that corresponds to the onset of the BH insurgency, the national rate of IPV fell from 18 to 16 percent. However, this progress was uneven and in two regions, North East and South West, IPV prevalence rates increased. The BH insurgency was focused in northeast Nigeria and so the rise in IPV could be associated with conflict but equally could be driven by other factors that create variation in IPV prevalence rates in different locations in Nigeria. Our paper examines whether the conclusion that conflict has led to increased IPV can be supported by further analysis of the DHS data and we set out below some background to the conflict and identify the locations where communities were most impacted.

Boko Haram (BH) carried out its first known terrorist activity in the Northeastern state of Borno, Nigeria in 2009.<sup>4</sup> BH’s activities were initially focused mostly in Northeastern Nigeria but from 2014 onwards its terrorist activities spread to the neighboring countries of Cameroon, Chad and Niger (UNHCR 2018). Between 2009 and 2017, <mark>the Armed Conflict Location and Events Database (ACLED) reports 2,378 conflict events</mark><sup>5</sup> <mark>across Nigeria with BH as an actor. During this period, the Northeastern states of Borno, Yobe and Adamawa experienced the highest number of conflict events associated with the BH insurgency with 1,678, 219 and 179 events respectively.</mark> The insurgency is responsible for large-scale forced migration and an unprecedented regional and

> 2Ethnicity is often used as a proxy for culture because it entails social norms that determine behaviors as well as the gender roles and power dynamics within the household and community as well as women’s contribution to household decision-making (see Oyediran and Isiugo-Abanihe 2005).

> 3 Reasons include: arguing with him, refusing sex, burning the food, neglecting the children, or going out without telling him.

> 4 Armed Conflict Location & Event Data Project (ACLED
by Kathleen Beegle, Aline Coudouel, and Emma Monsalve. Washington D.C.: The World Bank.

- Bottan, <mark>Nicolas</mark> , <mark>Bridget Hoffmann, and Diego A.Vera-Cossio.</mark> 2021. Stepping up during a crisis: The unintended effects of a noncontributory pension program during the Covid-19 pandemic. _Journal of Development Economics_ 150(May): 102635.

- <mark>Breisinger,</mark> Clemens, Yumna Kassim, Sikandra Kurdi, Josée Randriamamonjy, and James <mark>Thurlow. 2022. From Food Subsidies to Cash Transfers: Assessing Economy-wide Benefits and Trade-Offs in Egypt. MENA RP Working Paper 34. Washington, DC: International Food Policy Research Institute (IFPRI).</mark>

- Brooks, <mark>Wyatt, Kevin</mark> Donovan, <mark>Terence</mark> Johnson, and <mark>Jackline</mark> Oluoch-Aridi. 2021. Cash Transfers as a Response to COVID-19: Experimental Evidence from Kenya. Economic Growth Center Discussion Paper No. 1082. New Haven, CT: Yale University.

- <mark>Brown,</mark> Caitlin, Martin <mark>Ravallion, and D</mark> ominique van de Walle <mark>. 2018. A poor means test? Econometric targeting in Africa.</mark> _<mark>Journal of Development Economics</mark>_ <mark>134(September): 109-124.</mark>

- Bundervoet, T., M.E. Dávalos, and N. Garcia. 2022. The Short-Term Impacts of COVID-19 on Households in Developing Countries: An overview based on a harmonized dataset of high-frequency surveys. _World Development_ 153(May): 105844.

- Celasun, Oya, Jungjin Lee, Mico Mrkaic, Allan Timmermann (2021). An Evaluation of World Economic Outlook Growth Forecasts, 2004-2017. IMF Working Paper No. 2021/216. https://www