rafmacalaba/data-use-mentions-extended huggingface dataset:
day after the initial bank visit, pre-transfer expenditures come from the survey conducted at the initial visit, on day _t_ = _−_ 2 as indicated in Figure 2. Spending in the week after the transfer (including day of the transfer) is measured in Survey 1, conducted on day _t_ = 7 for the immediate-transfer group and day _t_ = 8 for the one day delay group. For the eight day delay group, Survey 1 measures spending in the week after the announcement, but before the transfer was made. As we will discuss, households may spend in anticipation of receiving a large transfer. Survey 2, conducted on day _t_ = 15, measures post-transfer expenditures for this group. The only exception to a one-week recall period is this survey, which includes an eight day recall period to capture spending on the day of the transfer.

Transfers were implemented in March and April 2014. They were timed to coincide with the end of the lean season, just before many households harvest and sell crops.

# **3 Data**

The household survey described above is adapted from Malawi’s Third Integrated Household Survey (IHS-3) and contains its detailed expenditure module. In addition to asking about the quantity purchased and total paid for 218 consumption goods, durable goods and services, we also ask whether each purchase was planned before the respondent arrived at the store or market, or was made on the spot.

We also use survey data collected between June and Aug 2013, before bank accounts were opened. These data include information about household demographics, expenditures, asset ownership and time preferences.

Panels A and B of Table 1 provide summary statistics for variables collected during baseline in 2013 and during round 0 of the midline interviews just prior to the transfer experiment. The majority of respondents are male. Sixty-three percent are married. Households have 4.7 members on average. Households own 1.5 acres of land on average and non-fixed assets worth MK 177,697 (about USD 555). During the
)|(0.229)|(0.272)|(0.364)|(0.332)|(0.296)|
|Observations|1,796|1,776|1,307|1,796|1,776|1,427|1,796|1,776|1,307|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

_Source:_ Authors’ estimations. _Note:_ First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World _Development_ Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

# **6. Implications of demographic change for poverty and shared prosperity**

The macroeconomic dividends previously described can affect poverty reduction by boosting economic growth (Dollar et. al. 2002, 2015). However, there are also direct channels through which demographic change affects households in the bottom of the income distribution. Particularly, due to the positive association between fertility rate and income level, it is likely that 18
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE African Development Community (SADC) (table 4.1). This finding reflects the complete absence of submarine cables on the eastern side of Africa at that time.

Regional indicators of information and communication technologies (ICTs) mask significant variations across countries (table 4.2). Mobile (GSM) signal coverage ranged between 60 to 90 percent of the population in most countries but was as low as 10 percent in Ethiopia. Notwithstanding high signal coverage, only a handful of countries (Kenya, Sudan, Tanzania, Uganda) had achieved mobile penetration of around 20 percent or more. Initial uptake of mobile telephony in Sudan was slow, with less than 4 percent of the population owning a mobile phone in 2003. The slow uptake is partially attributable to a lack of coverage, poor availability of prepaid services, and the price incurred by customers receiving calls. Yet as these impediments have eased, penetration has increased, growing by 500 percent since 2005.

Prices vary widely among countries. Most recent estimates for mobile prices range from less than $3 per month for a standard basket of services in Ethiopia and Sudan to almost $10 per month in Kenya. The median price for a standard basket of services in the region is around $9. The price of dial-up service (based on 2007 estimates) reveals wide variation—by a factor of 10—across countries, ranging from $15 per month in Ethiopia to $150 per month in Tanzania.

**Table 4.1 . Benchmarking ICT infrastructure across Africa’s regional communities**

||ECOWAS|CEMAC|COMESA|EAC|SADC|
|---|---|---|---|---|---|
|Broadband subscribers (per 100 inhabitants)|0.03|0.01|0.04|**0.02**|0.36|
|International Internet bandwidth (per capita)|
industry and employ alternative measures based on the observation of the conduct of firms in the market such as the Lerner index, the Boone indicator or the Panzar‐Rosse H‐statistic. There is some evidence that market concentration is not a good proxy of bank competition (Claessens and Laeven, 2004; Carbo‐Valverde et al., 2009), and therefore policy implications may be sensitive to the measure of competition employed. Along these lines, Claessens and Laeven (2005) find support for the _market power hypothesis_ in a sample of 16 economies, showing that sectors heavily reliant on external financing grow faster where there is fierce competition. Liu et al. (2014) confirm this finding for a sample of 48 countries. Love and Martinez‐Peria (2014) also find that bank market power reduces access to finance for a cross‐section of firms across 53 countries, mainly developing economies. Leon (2015) and Ryan et al. (2014) reach similar conclusions in favor of the _market power hypothesis_ , for a sample of developing countries and 20 European countries, respectively. Fernandez de Guevara and Maudos (2011) find opposing evidence that the exercise of market power enhances the growth of financially dependent firms in for a sample 53 sectors in 21 countries, in line with the _information hypothesis_ . Hoxha (2013) provides similar findings.

This paper investigates the impact of bank competition on external financing constraints in the countries of the Gulf Cooperation Council (GCC). While displaying some significant differences in terms of size and population, the GCC countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates) all share high dependence on the hydrocarbon sectors and a long‐term policy objective to diversify their economic structures. Financial sector development is as important for economic growth for resource‐based economies as for other countries (Beck, 2011). The financial sector in the GCC is dominated by banks and is as deep as that of many advanced economies. Yet it falls short of providing
market-based relative to our estimated financial structure for each economy. Tables 1, 2, and 3 provide information on the **actual Financial structure ratio / Optimal financial structure ratio** , where we discuss the construction of the Optimal financial structure ratio below. This variable indicates that the average financial structure of countries such as Guatemala, Namibia, and United Arab Emirates are highly distorted relative to their estimated optimum in that they are too bank-based (>4.91; 90<sup>th</sup> percentile), while the financial structure of countries such as Denmark, Japan, and United Kingdom, are highly distorted relative to their estimated optimum in that they are too market-based (<0.03; 10<sup>th</sup> percentile). Appendix 1 contains a list of countries and provides an overview of the period medians for GDP per capita, Private credit, Stock Value Traded, the Financial structure ratio, and the Financial structure ratio / Expected-Optimal financial structure.

# **1.2 Other data**

As a measure of economic activity, we use **Log Real GDP per capita** , which equals the logarithm of GDP per capita in constant 2000 U.S. dollars. And, to assess the independent link between finance and economic development, we control for many other country characteristics that have been employed in the development literature. In some specifications, we use “standard controls” to evaluate the independent relationship between finance and economic activity. These standard controls include: years of schooling, openness to trade, inflation, government size, the initial GDP per capita of the economy in 1980, and dummy variables for the 5-year periods of analysis. Table 1 provides the specific definitions. In other specifications, we use “exogenous controls,” which include dummy variables for the legal origin of the country along with the country’s distance from the equator. Table 1 gives the detailed definitions and sources of these data and Table 2 provides descriptive statistics

#
from LASSO procedure. Selected control regressor set includes mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Robust standard errors in parentheses.

32
free trade agreements (FTAs) in other parts of the world, so far there has been little systematic work that could inform the continuing and expanding negotiations in South Asia. There have been several important studies of regional South Asian trade, some completed in the mid-1990s, prior to the recent drive toward SAFTA and the formation of bilateral FTAs within South Asia.<sup>2</sup> There is a general lack of information about the structure of recent trends in trade and what they imply about the prospects for further regional integration.

This paper is part of a larger study under “South Asia’s Trade Integration: Policies and Prospects Program,” a joint initiative of the trade team of the World Bank and PREM Sector Unit on South Asia (SASPR). The main objective of this paper is to evaluate whether South Asia possesses certain fundamental conditions (defined by empirical evidence) to become a successful trading bloc. It should be noted, however, that this paper deals solely with static trade effects. Many economists have argued that the benefits of an FTA, such as SAFTA, stem from dynamic gains, such as increased foreign direct investment to smaller countries, greater political stability and cooperation, and/or enhanced credibility of reform efforts. These issues, however, are not under the purview of this paper. While this paper does not directly address the net welfare effect of trade creation and trade diversion, it relates to these effects as implied by the various conceptual propositions.

It must be noted at the outset that official accounts of South Asia’s international trade statistics are flawed by the high incidence of informal trade between India and its neighbors. Available estimates (for selected years) suggest that informal trade is even higher than officially recorded bilateral trade between India and its neighbors.<sup>3</sup> However, these estimates are based on surveys carried out prior to 1995. It should be noted that it is likely that unilateral trade reforms

> 2 Srinivasan and Canonero. (1994
. Monthly averages of weekly price surveys<br>conducted at 74 service stations in Managua for<br>gasoline, diesel, and kerosene, and monthly averages of<br>weekly official price ceilings in Managua for LPG.|www.ine.gob.ni|
|Niger|91 RON gasoline|www.sonidep.net|
|Nigeria|90 RON gasoline. Price of subsidized kerosene as<br>supplied by the Nigerian National Petroleum<br>Corporation.|www.pppra-nigeria.org|
|Pakistan|87 RON gasoline. For figures 9, 10, and 13, Islamabad-<br>wide average retail prices notified by oil companies. For<br>pass-through calculations and figure 11, gasoline, diesel<br>and kerosene retail sale prices posted on the web site of<br>Pakistan State Oil and LPG prices in Karachi.|www.ogra.org.pk, www.psopk.com|
|Panama|91 RON gasoline, LPG in 25-pound (11.4-kg) cylinders.<br>Maximum retail price of LPG in Panama City.|www.autoridaddelconsumidor.gob.<br>pa.|
|Peru|90 RON gasoline containing ethanol, LPG in 10-kg<br>cylinders. Lima-wide, monthly average prices.|www.osinerg.gob.pe.|
|Philippines|93 RON gasoline, LPG in 11-kg cylinders. Retail prices<br>in Manila averaged over Jan and over all companies.|www.doe.gov.ph.|
|Russian<br>Federation|92 RON gasoline. Moscow prices.|www.mfa.ru.|
|Rwanda|95 RON gasoline. Monthly average prices.|www.minicom.gov.rw|
|Senegal|87 RON regular gasoline, LPG in 6-kg cylinders. Retail<br>prices averaged over the month of Jan.|www.eltonoil.com for gasoline,<br>diesel, and kerosene<br>www.total-senegal
pre-requisite for improvements in the country's growth fundamentals to realize better development outcomes (Chen, Loayza and Reynal-Querol 2008). In other words, Sri Lanka will have to pay close attention to the growth fundamentals outlined in Box 1.

**Box 2: Doubling US dollar per capita GDP between 2009 and 2016**

<!-- Start of picture text --> Per Capita GDP, US$<br>3000<br>2500<br>2,089<br>2000<br>1500<br>1,032<br>1000<br>500<br>500<br>0<br>Source: CEIC data<br><!-- End of picture text --> Sri Lanka took 13 years to double its nominal dollar per capita GDP from US$ 500 in 1991 to US$ 1,000 in 2004. Five years later, in 2009, per capita income had doubled once again to roughly US$ 2,000 as shown in the figure. The Mahinda Chinthana‘s vision is to achieve another doubling of dollar per capita income by 2016.

To get a better sense of the challenge involved, we use the following identity: , where _G$_ is nominal US dollar per capita growth, g is real GDP growth, is the growth in the bilateral Sri Lankan rupee-US dollar real exchange rate (defined such that a positive value is an appreciation), is U.S. inflation and _pgr_ is the population growth (all compound annual average growth rates).

During the period 2004 – 2009, real GDP growth was 6 percent with the population growth rate at 1 percent, implying per capita growth of 5 percent per year. Yet dollar per capita GDP grew at a huge 15 percent per year. Assuming 2 percent US inflation, the above identity tells us that the big driver of _G$_ was a rapidly appreciating bilateral real exchange rate, recording an annual average appreciation of 7.5 percent.
A middle ground between individual household modeling and the RHG approach is offered by Hertel, Ivanic, Preckel, and Cranfield (2004). Their approach is geared towards shedding light on the impacts of global trade liberalization across a wide range of developing countries. This multi-country approach is inevitably less detailed for any given country, which is why they seek a middle ground between the full complexity of household-level micro-simulation, and the RHG approach.

These authors begin by stratifying households into seven groups, based on earnings specialization. They identify households that receive 95% or more of income from agricultural selfemployment, non-agricultural self-employment, wage labor (rural vs. urban) and transfer payments.<sup>11</sup> All other households are considered diversified, and are broken into rural and urban groups. Up to this point, this sounds like the RHG approach, but within each of the seven strata the authors aggregate the survey data into twenty vingtiles, based on initial income per capita. This results in 140 distinct households per country, and permits them to analyze changes in income distribution _within_ strata, as well as _between_ strata.

The authors estimate a common demand system for all households and use this to characterize consumption behavior as well as establishing a poverty level of utility. The micro-simulation model in each country consists of each of the 140 households maximizing utility subject to a budget constraint determined by their endowments and the commodity and factor prices obtained from the CGE model. They explore two different factor market closures, the first (limited factor mobility) is one in which only wage labor is mobile across sectors, and the second (full factor mobility) is one in which selfemployed labor and capital are also mobile. (Agricultural land is sector-specific in both cases.) In their analysis of the Indonesian poverty impacts of multilateral trade liberalization, the authors find that in the short run (limited factor mobility), the _between stratum_ earnings differences dominate, while the limited _within stratum_ differences derive mainly from consumption effects.

More specifically, Hertel et
138|2352|2490|
|80-84|0.902|0.935|0.896|0.9372|0.9|0.8669|
||[0.0035]|[0.0078]|[0.0039]|[0.0130]|[0.0037]|[0.0041]|
||6805|997|5808|344|6461|6805|
|85-89|0.912|0.944|0.907|0.9417|0.91|0.8848|
||[0.0029]|[0.0064]|[0.0032]|[0.0108]|[0.0030]|[0.0033]|
||9067|1258|7809|467|8600|9067|
|90-94|0.902|0.932|0.898|0.9278|0.899|0.8762|
||[0.0027]|[0.0063]|[0.0030]|[0.0079]|[0.0029]|[0.0030]|
||11714|1585|10129|1071|10643|11714|
|95-99|0.904|0.9117|0.903|0.9466|0.898|0.8921|
||[0.0031]|[0.0081]|[0.0033]|[0.0070]|[0.0034]|[0.0033]|
||8760|1224|7536|1007|7753|8760|

Standard errors in brackets, with sample sizes.

**_Source_ :** From birth histories, Cambodia Demographic and Health Survey (2000).

38
electric power machinery (77), and photographic equipment (88) (Table 8). These are drawn from Hummels (2001), which investigates how ocean shipping times and air freight times influences the probability that air transport is chosen. Those industries are associated with highest positive and statistically significant estimates of the days/rate ratio in Hummels (2001). This ratio measures the combined effect of ocean shipping times and air freights on the probability of choosing air transport. Similarly, three twodigit SITC 2 industries – textile yarns (65), cement (66), plumbing fixtures (82) – are chosen as time-insensitive as they are associated with the lowest negative and statistically significant estimates of the days/rate ratio in Hummels (2001).<sup>21</sup> We base our selection of time-sensitive agricultural products on the information of their storage life (Gast 1991). We focus on fruits and vegetables (HS 07 and 08). We classify products with a minimum storage life of 3 weeks or less, for example apricots, beans, currants, and mushrooms, as time-sensitive agricultural products. Time-insensitive agricultural goods are those with a minimum storage life of 4 weeks or longer, for example apples, cranberries and potatoes (Table 9). Since the data are at a very disaggregate level, we only use export data of those agricultural products of the most important exporters (Table A3).

The difference-in-difference gravity regression we estimate is

> 21 Time-sensitive products are more likely to be shipped by air, while our measure of time delays is from factory to ship.  Much of the time delay in exporting (about 75 percent on average) is due to administrative costs, which are nearly identical for sea and air.

19
first QAT after the first major expansion of the program: phase-3, which increased the number of program schools from 194 to 676 schools or by 248%.

The sample for the learning dynamics analysis is restricted to phase-3 and phase-4 program schools. This is for two reasons. First, large numbers of schools entered into the program in these phases, and these schools have been in the program for a sufficiently long period to examine short-run dynamics (phase-3 schools have been subjected to five QATs and phase-4 schools four). Second, by phase-3, PEF had formalized the program entry qualification process, introducing standardized application forms, unannounced school inspections by PEF staff with standardized inspection forms, and an entry test, called the Short-Listing Quality Assurance Test (SLQAT), which is a pared-down version of the QAT. As a result, pre-program information on schools from the entry qualification stages is available for phase-3 and phase-4 schools, allowing the examination of the correlations between pre-program characteristics and the learning levels and changes, as well as their use as a form of specification testing in the causal analysis of the effects on learning from program design features that introduce stick and carrot incentives.

The data were provided by PEF in QAT-specific databases. Each QAT database is at the student level, with the number of points scored and/or the percent score in each relevant subject. Information on the school and grade of the tested student is also provided. Schools in the databases for QAT 5−QAT 8 were identified by both their names and unique identification numbers provided by PEF. School-level summary statistics on QAT performance—such as the number of test takers, test score means, test score standard deviations, and test pass rates— derived from the student-level data were linked across QAT rounds using the unique school identifiers. There were no issues in the linking process: when a school in a given QAT was expected to be also found in another QAT, it was. Program schools in the QAT 4 database were however only identified by school name (at
can contribute significantly to the economy of a State (ITF/OECD, 2015), changes in port throughput will impact the economic performance of a State or region.

UNCTAD (2012) suggests three strategies to reduce maritime freight rates for States, with two of these strategies focused on developing port competitiveness and porthinterland connections and one on efficient linkages between inter-continental, regional, and national shipping services. The difference in States’ policies and capacities to develop their port will inevitably bring different impacts on the transport costs from and to the individual States. Hence, even though GHG mitigation measures may be implemented uniformly at the global level, the resulting economic impact on States could be far from uniform.

# **2.2.3 Import prices of goods**

According to OECD data, transport costs vary widely between various products and countries of origin and destination (OECD, 2011). For instance, on average, 5% of the imported value of manufactured goods can be attributed to maritime transport costs, compared with 11% for agricultural goods, and 24% for industrial raw materials. Furthermore, according to UNCTAD data (UNCTAD, 2017), this variation is also reflected across different country groupings such as SIDS, LDCs, landlocked developing countries, and developed economies. For the first three country groups, international transport costs generally account for higher shares in import values of goods compared to the last country group (Figure 4).

6
|-0.02|
||(0.11)|(0.11)|(0.07)|(0.12)|(0.12)|(0.10)|(0.12)|
|Observations|2062|2062|2073|2059|2062|1970|2068|
|ITT Leader + ITT leader X Agreeingleader = 0|0.12|0.58|0.96|0.77|0.11|0.82|0.19|

_Note:_ Agreeing leader is a binary variable indicating that the closest leader agreed with at least 4 out of 5 program messages on parenting practices at baseline. All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 43