rafmacalaba/data-use-mentions-extended huggingface dataset:
in 2018 remains below the regional average (at about 76 percent), and far lower than many MENA or SSA countries which have a smaller share of urban population (Figure 3).<sup>7</sup> This low national coverage rate implies that the vast majority of the rural population (which account for 23% of the total population) has no mobile broadband coverage in Djibouti.

Further evidence of the disparities of access can be found in EDAM4 conducted in 2017. Results from the survey show the extent of the digital gap between the richest population and the poorest population. Eighty-nine percent of the households in the richest quintile (the top 20 percent of the households with the highest consumption levels) own at least one mobile phone, a figure similar to the households in the third quintile, where 81 percent own at least one mobile phone. However, the figure drops at 70 percent for the fourth quintile, and 41 percent for the poorest quintile (i.e. the bottom 20 percent). The discrepancy is higher in relative terms for computer ownership: whereas 27 percent of the households in the top quintile own a computer, there are virtually no households in the bottom 20 percent that own one (Figure 4). As the richest households are mostly located in urban areas and the poorest households are mostly located in rural areas, there is a

> 6 World Bank, World Development Indicators.

> 7 Incidentally, the number of fixed broadband subscriptions in Djibouti is also low. In 2017, there were 2.54 broadband subscribers per 100 people (World Development Indicators [accessed January 6<sup>th</sup> 2019]).

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|**Variable**<br>**Appendix Table 1: Definition of variables**|**Survey**|**Description**|
|---|---|---|
|_Panel A: Village survey_|||
|**Village level data**|||
|||Number of farmers in the village that fulfill the 2 main program criteria: (i)|
|Number of eligible farmers in 2009/10|Baseline|cultivate no more than 1 hectare of maize or rice, (ii) not have used chemical<br>fertilizer or improved seeds in the previous 5 years|
|Share of beneficiaries among eligible farmers in<br>2009/10|Baseline,<br>Follow-up|Number of farmers in the village who are program beneficiaries in 2009/10, as a<br>share of number of farmers in the village that are eligible in 2009/10 (see variable<br>above)|
|Number of HHs involved in agricultural activities in<br>2009/10|Baseline|Number of households involved in agricultural activities as their main occupation<br>in 2009/10|
|VVC has 3 women and 3 men|Baseline|Equals 1 if Village Voucher Committee (VVC) has 3 female and 3 male members.|
|VVC members were elected|Baseline|Equals 1 if VVC members were selected though an election|
|Rains were good in 2009/10|Baseline|Equals 1 if the long rains (masika) in 2009/10 in the village were as expected or<br>more than expected.|
|Maize price at the beginning of harvest 2009/10<br>(TSH/Kg)|Baseline|Maize price (TSH/Kg) at the beginning of the long rains harvest in 2009/10|
|Program subsidy as percent of input package|Baseline|Average price of input package discounted the top-up amount (TSH
other studies that exploit bilateral data, our paper analyzes a greater number of countries, particularly in the South. In addition, the longer sample period allows us to examine developments before and after the GFC.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 documents the rise of the South in the value of total investments. Section 4 focuses on the growth of new links and the relative importance of old and new links. Section 5 examines three extensions to assess the robustness of our findings. Section 6 concludes. The appendix describes in detail the global data sets we use and the treatments we apply to align the sources with each other for the analysis.

# **2. Constructing Bilateral Investment Positions**

In their work on the “External Wealth of Nations,” Lane and Milesi-Ferretti (2001, 2007, 2018) use external assets and liabilities to measure international financial integration for a large cross-section of countries vis-à-vis the rest of the world. They provide the most comprehensive database on each country’s IIP, covering 212 countries for 1970–2015 in the latest public version. In our paper, we focus instead on bilateral investment positions to study the sources and destinations of international investment, allowing us to analyze the network structure of international financial integration.

This section describes the four data sets we use and addresses common concerns related to data on bilateral investment. Throughout the paper, “international” means cross-border investment between any pair of countries or jurisdictions.<sup>2</sup> International investments are reported on a residency basis, in line with statistics on national accounts and the balance of payments. The resulting notion of financial integration thus focuses on the geography of investment, not on the nationality of lenders (McGuire and von Peter 2012) or ultimate borrowers (Coppola et al. 2020).

> 2 In this paper, we mostly use the term “countries” to denote sovereign countries as well as territories (or jurisdictions) that are usually reported
Authors’ calculations using data from CAPMAS Annual Bulletin of the Labor Force Survey for LFS series from 1977 to 2005 (CAPMAS, n.d.), from the LFS public use microdata (OAMDI 2023) for LFS series from 2007 to 2021, and from public use microdata series (OAMDI 2019) for ELMPS 1998, 2006, 2012, and 2018.

Around 1987, the ILO conducted Methods Test Surveys in Egypt and India designed to test the effects of questionnaire design, sex of interviewer, labor force definition, and self/proxy status on the reporting of women’s labor force participation. The Egypt Methods Test survey was carried 4
capital stock reduces the coefficient on the training / grant treatment from 1758 to 971, suggesting that as much as half of the initial effect is due to the grant. As a final check, given the grant was 15,000 Rs, the TOT of 1786 Rs in the pooled sample would represent a monthly return on the grant of 11.9 percent if the effect was due to the grant alone. This is similar to the return on capital found for the average male microenterprise owner in Sri Lanka, but considerably higher than average returns for female business owners found in de Mel et al. (2008, 2009a). It therefore appears plausible that some of the short-term effect may be due to the combination of training and the cash grant, rather than just the cash grant alone. However, the fact that this effect appears to only be temporary suggests that the combination is speeding up convergence to a steady state, but not dramatically changing the trajectory of the enterprise.

# **5.3 Impact on starting a business among potential business owners**

We now turn to the potential business owner group. Table 5 examines whether the treatments affect the likelihood that an individual enters self-employment or engages in wage work during our various survey rounds. We present ITT estimates from a regression of equation (1) with self-employment as the dependent variable (and no lagged dependent variable since none of this group operated a business at baseline), and TOT estimates instrumenting receipt of the treatment with random assignment.

We see that 38 percent of the control group operates a business at the time of the round 2 survey, increasing to 56 percent by the round 4 survey and 57 percent by the round 5 survey. This high rate of start-ups may reflect both the screening of our sample, and the rapid growth of the economy during this period, improving the environment for starting a new business.

The combination of cash and training results in a 29 percentage point increase in the likelihood of running a business in round 2 for those
vary by year, province, and indicators, rendering comparisons difficult. The Early Childhood Development Index (ECDI),<sup>1</sup> the best available measure of child development at scale across provinces, shows substantially lower-than-average scores for children in Pakistan relative to the 75.1 percent average seen across 60 low- and middle-income countries (Lu et al., 2020): 59.4 percent in Punjab (2017–18), 47.5 percent in Sindh (2018–19), and 54.6 percent in Khyber Pakhtunkhwa (2019) (Tomlinson et al. forthcoming). There are no recent ECDI scores from Balochistan, the poorest province in Pakistan, and except for health surveys that provide proxies for ECD, there are no national ECD data sets for children from birth to age 3. Smaller-scale or province-specific studies shed light on patterns and rates of parental distress or depression, psychosocial stimulation, participation in ECE, and similar variables of interest, such as integration of parenting and nutrition interventions in community health settings in Sindh (Yousafzai et al., 2018) and Idara-e-Taleem-o-Aagahi (ITA)-Early Learning Partnership (ELP) studies of ECD during COVID disruptions in Punjab (ITA, 2021). This study aims to broaden the knowledge base by providing nationally representative data.

# Young children in Pakistan are exposed to a host of risk factors that impede development

Child development occurs through a series of biological and psychological processes that are influenced by risk and protective factors that have the potential to alter brain structure and have implications throughout the life course. Common risk factors include social determinants of health, such as poverty, poor water and sanitation, and lack of maternal education, the health of the caregiver and child, including physical and mental health of the caregiver and malnutrition and infections for the child, and caregiver psychosocial health, including parental distress. Exposure to multiple risk factors reduces children’s chances of staying on track developmentally, and the rates of multiple exposure are high in Pakistan
use these differential mining sites attributes for estimating the heterogeneous effects of post-WTO accession on local economic development. Ultimately, the constructed dataset contains over 40 mineral types matched across 2339 districts in 37 countries in Africa.

One key feature of the USGS data is the presence of historical mineral prices data (measured at 1998 constant US dollars and average US prices) available since 1900 for the United States. We employ<sup>10</sup> these data to control for the volatility in world mineral prices as we later detail in the empirical specification. Two facts guide the choice of US historical prices data. First, the US has a large economy whose mineral prices are good proxies for world mineral prices. Second, compared to the commonly used World Bank Global Economic Monitor (GEM) price data, the USGS price data covers a wider range of mineral commodities. The overall (unreported) price correlations between GEM and USGS, when matched across minerals, is 0.93, which suggests that the use of US minerals’ prices is not unreasonable. However, we use the GEM price data for robustness checks of our main regression estimates.

The third data source is the Satellite Assessment of Rainfall for Agriculture in Tropical Africa (TAMSAT),<sup>11</sup> which provides monthly rainfall data in Africa since 1983. We use this data source to construct a measure of rainfall (mean and standard deviations) and later use them as proxies for climatic shocks and agricultural productivity variations; this is similar in spirit to Miguel et al. (2004).

The fourth data source is the Gridded Population of the World (GPW. V4).<sup>12</sup> At a grid cell resolution of 30 arc-seconds (approximately 1 km at the equator), this source contains two main sub-sources: population count grids for the years 1990, 1995, and 2000, and population count grid future estimates for the years 2005, 2010, 2015 and 2020 and the data are adjusted to reflect
Limiting the samples to only children, defined in the previous section as individuals below 18 years of age, leads to sample sizes of about 13,000 children in the GHS surveys and 89,033 in the MICS survey. Table 1 provides an overview of the exact sample sizes for each wave and poverty measure. Sample sizes vary following the definition of the various deprivations, data availability only for a limited age group and response rate. In the case of the GHS, the survey limits itself to individuals of households with consumption data.

As the table below shows, the non-response rates are particularly high for the severe health and food deprivations and hence, results of these deprivations should be treated with caution. Moreover, these large non-response rates or responses falling beyond the biologically possible in terms of weight and length or height for the food deprivation measurement, urges for more emphasis on quality data collection.

Another, more general regional limitation relates to the ongoing conflicts in the country. Any results based on the data sources used here in particular from the North-East and to a certain extent the South-South regions need to be treated with caution, as both the GHS and MICS data collection were limited to only the accessible areas. Even though (Azad et al. 2018) did not find a correlation between conflict and poverty status, as conflict seems to be affecting also non-poor households, there is a concern that conflict increases deprivations, such as educational deprivation suggested in work by Bertoni et al. (2018) and that excluding highly conflict affected areas may underestimate results. While conflict is a concern in three regions of Nigeria, the North East (Boko Haram insurgency), North Central (farmer – herder conflict over land access) and South-South (militant and pirate groups targeting in particular the oil sector), it is only in the North East and to some, but much lesser extent, in the South-South that enumeration areas were declared not accessible for data collection, due to security concerns. This limits in especially the representativeness of the
the treatment group had access to CPS and many treatment groups’ villages had access to HBP. There are reasons to believe that these early interventions are unlikely to affect our estimation. baseline results do not pick up any significant cognitive differences between the treatment groups and the control, indicating that early enrollment in preschool intervention had not given a significant benefit to the treatment group. Relatedly, expressed in number of days at school, early enrollment in preschool programs is only marginally higher in the treatment groups. At baseline, treatment children had approximately only 10 extra days of preschool compared with the control. It further means that the roll-out of our baseline survey was on average 10 days too late compared to the roll-out of the the CPS construction. Given the size of the program, we consider executive function) were then summed into a domain score and standardized again. The individual tests and scoring methods are summarized in Appendix 6.

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.<sup>7</sup> Once a split-off individual was located, the new household that he/she may have joined vis-a-vis the prior survey round was brought into the IHPS sample. Based on these protocols, the dynamically expanding IHPS sample included 3,181 households in 2019, which can be traced back to 1,491 original households that had been interviewed in 2010. The IHPS 2019 fieldwork was conducted from April to December 2019, and the households that were determined to have owned and/or cultivated land during the 2018/19 rainy season were attempted to be visited twice, once in the post-planting period and once

> 6 For more information on the IHPS rounds, please consult the publicly-available basic information document for each survey - IHPS 2010: https://bit.ly/ihps2010; IHPS 2013: https://bit.ly/ihps2013; IHPS 2016: <u>https://bit.ly/ihps2016; and IHPS 2019: http://bit.ly/ihps2019.</u>

> 7 The individuals who were residing in prisons, police compounds or army barracks were excluded from tracking.

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two categories. First, there are indirect methods where researchers can infer the population level responses to sensitive questions and protect the anonymity of the respondent through subtle changes in survey questions or through the introduction of randomization of responses to survey questions. These methods include endorsement experiments (e.g., Lyall et al. 2013), list experiments (e.g., Imai 2011; Blair 2See both the background section on the Republic of Yemen and the data section for a discussion of the difficulties we had inserting some sensitive questions in a mobile phone survey of Yemeni households conducted outside of the country.

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# 5.4.2 RDD Results

In this section, we examine the RDD estimates of the impact of the INDH on economic outcomes, allowing for a more flexible relationship than in the regressions above. Estimates are presented for a number of different bandwidths in Table 5. Figure 4 and Figure 5 show the RDD graphs for the 50 percent bandwidth (those for larger bandwidths are essentially more smoothed versions of the 50 percent estimates). Impact estimates are generally positive, but they are not consistently statistically significant and are very sensitive to the bandwidth used. Higher estimated impacts typically occur for smaller bandwidths. Although it is plausible that the relationship between the poverty rate (the forcing variable) is nonlinear, in a range of 27–32 percent, it seems unlikely that there is a radically different relationship right at the 30 percent mark. For this reason, estimates should be interpreted with caution, particularly for smaller bandwidths. As Figure 4 and Figure 5 show, substantial fluctuations by poverty rate are estimated at the 50 percent bandwidth that do not have any apparent explanation. Those fluctuations at the discontinuity do not appear to be substantially different from those fluctuations elsewhere.

In 2008, for the 50 percent and 100 percent bandwidths, there are significant differences in income, log income, and log consumption. Consumption itself is also significantly higher in 2008 with the 50 percent bandwidth. However, only the log income result persists at the 200 percent bandwidth, where log incomes in treatment areas are predicted to be 22.9 percent higher. Significant differences in log income do not persist in 2013; by 2013, values for the difference in levels have also dropped substantially. The differences in log income fall in 2011 and rise in 2013 but are only statistically significant at the 50 percent bandwidth, where they are a rather improbable 56.1 percent higher. Differences in wealth and log wealth remain insignificant. Although there are significant consumption and log-consumption differences in 2011, these fall with increasing bandwidth to a 23.9 percent higher consumption rate
distributional aspects of the housing market within a given economy. These distributional price data are very relevant for additional analysis as they can, for instance, be paired with household-level income data for affordability assessments. Finally, an overview of the entire housing market can reveal supply-demand mismatches particularly regarding in which price segment formal housing market activity is generally low or absent altogether.

We start the web scraping process by identifying the most up-to-date and complete websites that list private property prices for sale in the five EMDEs. We identify up to three relevant listing websites per economy. Websites were selected based on the following aspects: (i) websites with the most comprehensive number of up-to-date listings, (ii) websites that offer broad ranges of properties and do not only cater to the luxurious segment (i.e., avoiding websites exclusively targeting expats etc.); (iii) websites that offer structured data entries on housing attributes including price and size. We limit ourselves to up to three websites since we notice considerable cross-postings in additional, usually less comprehensive, websites. We then scrape all residential properties that are listed at one point in time for sale on these websites along with all available housing features, including price, size, type (i.e., whether the property is an apartment or a house), location, number of bedrooms, number of bathrooms, and sometimes amenities such as garage, time of construction, number of floors, etc.

We extracted online listing data for the entire formal housing market of five EMDEs at one point in time, between April 2020 and August 2020. While this falls within the onset of the COVID-19 pandemic, insights on how house prices were impacted in EMDEs are qualitative and largely anecdotal. Commentary on the matter focuses on the affordability challenges for HHs rather than specifically on changes in property prices.<sup>11</sup> Beyond qualitative insights, comprehensive analyses on price changes due to the COVID-19 crisis are preliminary and focused mostly on developed economies (e.g.
0.827|
|Age household head|2,512|41.39|41.25|0.759|0.825|
|<br>Literate household head|2,512|0.74|0.74|0.756|0.898|
|Number of men in the household|2,512|1.58|1.56|0.914|0.720|
|Number of women in the household|2,512|1.66|1.61|0.311|0.205|
|Number of boys (age 7-14) in the household|2,512|0.57|0.52|0.106|0.101|
|<br>Number of girls (age 7-14) in the household|2,512|0.49|0.47|0.415|0.444|
|Number of young boys (age 0-6) in the hh|2,512|0.66|0.64|0.070|0.466|
|<br>Number of young girls (age 0-6) in the hh|2,512|0.60|0.57|0.284|0.286|
|<br>Number of rooms in house|2,511|1.91|1.84|0.139|0.111|
|**Network**||||||
|Distance (in min) to closest cellphone signal|2,505|32.48|29.65|0.193|0.233|
|<br>Distance (meters) to closest cellphone signal|2,343|2,116.53|1,979.93|0.832|0.478|

Baseline survey characteristics of children and households who were visited at endline. The sample includes all baseline children with cognitive tests at follow-up and
|172|109|
|August|70|28|180|112|
|September|80|34|183|114|
|October|75|33|149|107|

**Notes:** The number of active trade policies is computed at the 15th of each month. Roughly 7% of total policies recorded by GTA involve both Food and Medical products. These measures have been counted in both sectors.

Similar patterns of relative importance across sectors and categories emerge when looking at the value of trade covered by recorded measures. To provide a rough estimate of such values we have used UN Comtrade data for 2019 (or latest year available) to estimate the amounts of trade which may be implicated by the interventions in our data set. For the inclusion into the GTA database, the products affected by each intervention are classified according to the Harmonized System (HS). For our analysis, we use HS codes at the 6-digit-level, the most granular product breakdown for which there is comparable international trade data available through UN Comtrade. To estimate the amount of trade that may be implicated by each intervention, we sum over the trade flow values in the affected products in the year prior to the intervention. The estimates account for geographic restrictions on the potentially affected trading partners such as FTA exemptions or targeted interventions. Furthermore, to remove de minimis trade flows, we apply a minimum threshold of USD 1 million per HS code – trading partner combination.

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