rafmacalaba/data-use-mentions-extended huggingface dataset:
and cybersecurity.<sup>5</sup> We attempt to control for the factors from these studies in the regressions presented below.

A smaller literature explores the drivers of DFS growth during the COVID-19 pandemic itself. Based on a household survey in India, those who switched to using digital payments during the lockdown were significantly more likely to be aware of digital payments modes, more highly educated, and with access to smartphones and debit cards (Saroy et al., 2022). These are similar to characteristics of DFS users in studies prior to the pandemic who were found to be richer (Cohen and Rysman, 2013; Fujiki and Nakashami, 2019), better educated (Koulayev et al., 2016), and more aware of digital payments and generally more financially literate (Wyman, 2017).<sup>6</sup> Other drivers of DFS adoption differed across the pre-pandemic and pandemic periods. For example, and perhaps not surprisingly, the increase in digital payments during the pandemic depended on social distancing norms and containment measures (De’ et al., 2020, Alber and Dabour, 2020). While studies that predated the pandemic found that younger respondents were more likely to be DFS users, Saroy et al. (2022) found that during the pandemic middle-aged survey respondents in India were the most likely to have become DFS users, and that many of them had earlier tried but subsequently abandoned digital payments. Similarly, Jonker et al. (2022) found that digital payments increased more sharply during the pandemic for older age groups, which had typically been slower to adopt DFS.

> 5 On approaches to regulating DFS, see Staschen and Meagher (2018), Gutierrez and Singh (2013), and Tarazi and Breloff (2010). Gutierrez and Singh (2013) also provide evidence of a positive association between mobile money usage and aspects of the regulatory regime related to e-contracting, consumer protection, and interoperability, among other factors, for a sample of thirty-five countries. On
All these databases allow for the estimation of the source(s) of value (domestic vs. foreign and/or by country and industry) that is added in producing goods and services for export. They recognize that growing global value chains means that a country's exports increasingly rely on significant intermediate imports (i.e., value added by industries in upstream countries).

Inter-country input-output tables can also be used to develop measures of the importance of GVCs in countries and industries, as recently done by the OECD.

# _Principal indicators based on input-output data_

The measure of the importance of GVCs in countries and industries could be summarized under three headings (OECD, 2012b):

# **(1)** **<u>The country’s participation in GVCs</u>**

The question is to what extent the country is involved in vertically fragmented production, both as a user of foreign inputs for its own exports (measured as the value of imported inputs in the overall exports of a country) and as a supplier of intermediate goods or services used in other countries’ exports (measured as the percentage of exported goods and services used as inputs to produce other countries’ exports). Both measures could be combined in a “participation” index developed by Koopman, Powers, Wang and Wei (2011) and quantified for 57 countries worldwide in OECD (2012b). Conceptually, this index can broadly be considered a GVC-specific measure of trade openness. The higher the foreign value added embodied in gross exports and the higher the value of inputs exported to third countries and used in their exports, the higher the participation of a given country in the value chain.

The OECD (2012b) results suggest that country size – in particular relative to regional peers – appears to matter. Both in advanced and emerging economies, smaller countries such as the Slovak Republic, Belgium, the Czech Republic, Chinese Taipei, and Singapore post participation rates between 60% and 80% (Figure
within an EA by clustering standard errors at the EA level.

The main concern our is that be en- regarding identification strategy polygyny may dogenous. Communes with a high proportion of polygynous households may have different characteristics (geographic, cultural, etc.) than less polygynous communes. This could affect For if the of communes are lo- crop yields. example, majority polygynous cated in fertile areas, households living in these areas will tend to have higher crop yields than households living in less polygynous communes. We could (wrongly) attribute the yield differential as the impact of polygyny. We address this potential threat to identification using EA fixed effects. These EA-level fixed effects, capture average differences in geographic, economic, and cultural factors that do not vary over time. Over a short period of time like that denoted by our data set (2014 and 2017), we can reasonably assume that the prevalence of polygyny in a commune is not affected by omitted time-variant variables.

# **_Spatial Correlation_**

A serious problem confronting our identification is the potential presence of spatial correlation in the error term, due to the tendency of both droughts and polygyny to be clustered in space. Although droughts can happen randomly over time, they often tend to be clustered across neighboring geographic units (Hsiang et al., 2011). Likewise, polygyny, as a cultural practice, is often clustered in geographic units that share similar livelihoods and cultural values (Lawson et al., 2015). To account for this problem, we adjust the standard errors using the method developped by Conley (1999) and Hsiang et al. (2011). In particular, we correct standard errors by allowing for both spatial correlation across the EAs within a radius of 52 km (the median distance across EAs) and infinite serial correlation.

Furthermore, there is the potential presence of spatial correlation due to omitted weather variables (Auffhammer et al., 2013). To account for this problem, we construct a measure of drought during the
6.5|6.5|7.3|7.6|7.3|7.6|
|Weighted Average|FIN|6.1|7.2|7.3|8.8|9.7|9.7|10.0|
|Weighted Average|INDEP|6.0|6.7|7.0|8.4|9.1|8.4|8.2|

Source: HFSA raw data 43. Average total fees expressed as a share of assets have decreased significantly, from 13 percent in 1998 to 3 percent in 2004. This sharp decrease is typical of young pension systems and reflects the rapid growth of assets relative to contributions. Again, total fees expressed as a share of assets were lower for the MPFs sponsored by employers (2.1 percent), followed by independent MPFs (2.6 percent) and MPFs sponsored by financial
Based on these findings, the present study uses a baseline reduction in HAIs (and associated case fatality) of 50%, with a range of between 25% to 75% reduction. As presented in Watson et al (2019), before-and-after studies have reported higher percentage reductions in HAIs from the baseline, following prolonged implementation of the WASH measures. Hence, a baseline estimate of 50% reduction in HAIs is likely to be conservative.

# Cost of WASH and HCWM interventions

The study draws on Chaitkin et al, 2022 for costs to achieve basic WASH and waste management in healthcare facilities in 46 low-income countries. These include costs for water supply, sanitation, hygiene and HCWM, as per WHO/UNICEF Joint Monitoring Programme definitions for SDG monitoring. Costs are estimated from the provider perspective and include upfront investments (capital costs), annual operations and periodic maintenance costs for hospitals and non-hospitals in both urban and rural settings.<sup>14</sup> Eight out of the nine focus countries were included in these 46 countries (Kenya excluded). The average cost data for 46 countries<sup>15</sup> from Chaitkin et al (2022) were updated to 2022 values using the GDP deflator for each country.

# Benefit-cost ratio

The benefit-cost ratio is calculated as the average economic savings per capita (across the entire population) divided by the average cost per capita of achieving universal coverage of basic WASH in healthcare facilities across nine countries.

12
s gender and age, mobile phone ownership status, whether they lived with and cared for a child eight years old or younger, and the

> 17The eligibility criteria are described in footnote 12.

> 18We only find differences between the EHPM and the survey-mode experiment samples in educationallevel variables; the survey-mode experiment sample is more educated. This difference could also stem from the fact that only 60 percent of our sample completed the baseline survey. Therefore, educational attainment data is available only for a subsample of 358 (out of 599) individuals.

> 19In Figure 1 we present the different stages of data collection that we describe in this section.

10
, could not, in terms of their standard of living, differ from each other by more than the ratio of 4 to 1. And the bulk of workers who lived at less than their countries’ average income and closer to the subsistence, could not have incomes that differed by more than 2 to 1—with many of them living at approximately the same subsistence level. Indeed, Broadberry and Gupta (2006, Table 6, p. 17) show that in the period 1800-1849, the wheat-wage of an unskilled daily laborer in India (among the poorest countries in the world then) was about 30% of the wage of a similar worker in England. And comparing the Netherlands with the Yangtze valley, two relatively developed areas sharing a number of similar geographic features, Li and van Zanden (2010, p. 21) conclude

> 4 The data are derived from an Occupational Census, were consolidated by Javier Rodriguez Weber, and can be found on Peter Lindert’s Website ―Global Price and Income Histrory group‖ (see the previous footnote).

> 5 In  1990 international prices.

4
, and the like, which have been associated with social capital, are all epiphenomenal, arising as a result of social capital but not constituting social capital itself.” The last sentence of Fukuyama’s definition is especially important, because it underscores the relationship between social capital, on the one hand, and trust, networks, and civil society, on the other.

**Trust and networks uphold institutions and formal economies in many liberal democracies.** They have been similarly demonstrated to uphold informal economies in many African countries. According to the Arab Barometer (2018), interpersonal trust is low in Sudan; only 14 percent of the Sudanese surveyed believe “most people can be trusted.” About 84 percent felt “I must be very careful in dealing with people.” The Sudanese who were surveyed were equally wary of their country’s institutions. For example, when asked about their trust in the Parliament, 68 percent reported “not very much trust” or “no trust at all,” and for civil service, this figure was 64 percent. The trust in local governments is equally low with 63 percent reporting not much or no trust at all. When it comes to the private sector, the situation is similar with 65 percent of respondents claiming to have not much or no trust at all in domestic business people and 61 percent for private banks. Of the 16 institutions that the Sudanese who were surveyed were asked to assess, people had by far the greatest trust in the army: 31 percent reported “a great deal of trust,” while 33 percent reported “quite a lot of trust.” _Sources_ : Fukuyama 2001, 7; Odera 2013.

An important informal institution that affects women’s and youth’s ability to access the labor market in Sudan is _wasta_ . _Wasta_ is an Arabic term that means having a connection to someone who can ‘get things done’. Whereas patriarchal customs appear to exert more constraints on the acquisition of human capital, _wasta_ appears to
- 29 -

# (Gev"-erg and Swoboda 1987, Gupta and Islam 1983, Moran 1983). 7/

The reasons for tS.e discrepancy between the apparent quality of the results obtained using the ordinary least-squares method and those obtained using a structural model do not appear to have been explained clearly. One reason is perhaps the following: if the sample of countries is heterogeneous for certain relationships, there is a good chance that the estimation of the simultaneous model will detect it and that the quality of the adjustment will be affected thereby; in contrast, in the reduced form equation the effects of the heterogeneity of the sample on various underlying relationships may tend to compensate each other. Moreover, the fact that cross-sectional series analyses probably give rise to special heteroscedasticity problems (cf. supra) raises the question of whether the resulting loss of efficiency of the estimation is not greater in the double least-squares than the ordinary least-squares method.

# Conclusions

Four principal conclusions can be drawn from the foregoing analysis of the main methodological problems that arise in a crosssectional study of factors affecting growth.

The quality of the available statistical data appears to have a greater impact on the results when it affects the independent variables than when it affects the growth rate itself. The studies examined clearly brought out the need for better statistical knowledge of developing economies. This need is particularly important in the case of the economic policy indicators.

The results obtained appear to be very sensitive to the choice of periods and samples. However, the choice of periods seems to open the door less to arbitrary subdivision. Interpretation of the results of a cross-sectional study of factors affecting growth should be based on an analytical description of the composition of the sample (taking into account not only the number of countries but also their geographic location, development level, political system, etc.). The hypothesis of sample homogeneity must be suitably tested (coefficient stability and homoscedasticity tests).

Interpretation of the results must, moreover, be accompanied by close attention to the way in
# **1. Introduction**

International evidence shows that investing in high-quality early childhood programs can have large economic returns, especially for children from socially disadvantaged groups (Barnett 2011, Engle et al. 2011, Walker et al. 2011, Yoshikawa et al. 2013). In response, developing countries are looking to increase public investments in early education programs (World Bank 2016a). One of the challenges faced by policy makers is deciding what to fund given the wide range of programs that exist in local settings. In order to invest smartly in early education programs, there is considerable interest in understanding how children’s various early education experiences predict their success as they transition into primary school.<sup>1</sup> This understanding is particularly pertinent to on-going policy discussions in Indonesia regarding how best to prioritize investments in pre-primary education to meet the Sustainable Development Goals.

Much of the existing literature on early education in developing countries compares children who attended preschool to those who did not, or compares children who attended preschools with improved quality to those who attended non-improved preschools (Engle et al. 2011, Nores and Barnett 2010, and the references cited therein). While these are valid comparisons in understanding whether a specific type of preschool is effective, such comparisons may not necessarily capture the reality of children’s early educational experiences, which are often not as binary. We use rural Indonesia as an example to show that there is considerable heterogeneity in the _sequence, timing,_ and _duration_ of early childhood education participation among children and argue that these factors matter in predicting children’s success in primary school.

We use a uniquely rich data set from Indonesia that allows us to link children’s primary school test scores with their entire enrollment histories in early education programs. The data were collected in 2013 and sampled children in 310 poor villages in Indonesia as part of an evaluation of the Indonesia Early Childhood Education and Development project. We focus on test scores in language, mathematics and general cognitive skills
**_<u>________________________________________________________</u>_**

# **_2.4.4 Geography_**

Transport costs are partially determined by geography. The distance between the origin and destination points of a transport journey affects the variable cost of shipping in the form of fuel, wear and tear of vehicles, and the amount of time goods are traveling.

_<u>Limao and Venables (1999) Working Paper on Infrastructure, Geographical Disadvantage, and Transport Costs</u>_ includes the shipping company quotes for the cost of shipping a standard container from Baltimore to select destinations. While this measures reflects the true cost of a homogenous good, and provides the city of origin, landfall and destination; the charges can be effected by specific routes, frequencies etc.

_-_ _<u>UN Center for Human Settlements (UN Habitat) Global Urban Indicators Database</u>_ The Global Urban Indicators Database maintained by UN-Habitat provides statistical information on cities throughout the world, from 1993 and 2001. It contains a set of 23 key indicators and nine lists of qualitative data. It covers urban poverty, urban human development, city investment potential, urban environment, urban governance and overall quality of urban life. Two transport indicators are included under urban environment. These are “travel time” (average time in minutes for a one-way work trip, which is formed as an average over all modes of transport) and Transport modes to work (percentage of work trips undertaken by a certain mode). It is available on the web.

Indicators provide a comprehensive picture of cities, which, with other indicators which may be chosen by countries, will provide a quantitative, comparative base for the condition of cities, and show progress towards achieving urban objectives.

Key indicators are both important for policy and relatively easy to collect. They are either numbers, percentages and ratios; Qualitative data or checklists, which give an assessment of areas which cannot easily be measured quantitatively. They are audit questions
**Figure 6. Income and Urban Apartment Tenure in Poland**

<!-- Start of picture text --> Poland<br>35<br>30<br>25<br>Ow ners<br>20<br>Public Rental<br>15<br>Private Rental<br>10<br>5<br>0<br>1 2 3 4 5<br><!-- End of picture text --> _Source:_ Household surveys (Table 1 _)_

**Figure 7. Income and Urban Apartment Tenure in Russia**

<!-- Start of picture text --> Russia<br>35<br>30<br>25<br>Ow ners<br>20<br>Public Rental<br>15<br>Private Rental<br>10<br>5<br>0<br>1 2 3 4 5<br>Source: Household surveys (Table 1).<br><!-- End of picture text --> 32
- Auten, Gerald, Gee, Geoffrey, & Turner, Nicholas. 2013. Income Inequality, Mobility, and Turnover at the Top in the US, 1987-2010. _American Economic Review_ , **103** (3), 168–172.

- Azevedo, Joao Pedro, Inchaust, Gabriela, & Sanfelice, Viviane. 2013. _Decomposing the Recent Inequality Decline in Latin America_ . Policy Research Working Papers. The World Bank.

- Bergolo, Marcelo, Londo ̃no-V ́elez, Juliana, & Tortarolo, Dar ́ıo. 2023. Tax progressivity and taxing the rich in developing countries: lessons from Latin America. _Oxford Review of Economic Policy_ , **39** (3), 530–549.

- Birdsall, Nancy, De La Torre, Augusto, & Caicedo, Felipe Valencia. 2011. The Washington Consensus: Assessing A “damaged Brand”. _Page 0 of:_ Ocampo, Jos ́e Antonio, & Ros, Jaime (eds), _The Oxford Handbook of Latin American Economics_ . Oxford University Press.

- Blanchet, Thomas, Flores, Ignacio, & Morgan, Marc. 2018. _The Weight of the Rich: Im-_ .

- _proving Surveys Using Tax Data_ Tech. rept. hal-02878315. HAL.

- Bobilev, Roman, Boschini, Anne, & Roine, Jesper. 2019. Women in the Top of the Income Distribution – What Can We Learn From LIS-Data? _LIS Working papers_ , Sept. Number: 773 Publisher: LIS Cross-National Data Center in Luxembourg.

- Boschini, Anne, Gunnarsson, Kristin, & Roine, Jesper. 2017. Women in Top Incomes – Evidence from Sweden 1974-2013. _Working Paper Series_ , Aug. Number: 5/2017 Publisher: Stockholm University, Swedish Institute for Social Research.

- Bruil, Arjan, Essen, C ́eline van, Leenders, Wouter, Lejour, Arjan, M ̈ohlmann, Jan, & Rabat ́e, Simon. 2022. Inequality and Redistribution in the Netherlands. _CPB Discussion Paper_ ,
non‐random non‐response) of who responds to the survey methodology and the averages analyzed here can only speak to the actual sample as opposed to populations in which we might have more interest.<sup>16</sup> Although this is a difficult issue to directly address, there is evidence that the WFP surveys detected many of the significant changes in welfare relative to the pre‐conflict population estimates that large segments of the population have been reported to have experienced during the conflict, and the survey is able to roughly identify the areas that are receiving the largest amount of food assistance as identified by 2018 population‐level estimates by the UN and the humanitarian community (IPC 2018; Tandon and Vishwanath 2019). Thus, there is some corroboration 13 Approximately 85 percent of the population resided in a household that owned a mobile phone and would be represented in the household‐level survey conducted by the WFP. In no governorate was this share lower than 60 percent, and the share was 77 and 81 percent respectively for poor households and rural households.

14 The survey reaches a large share of struggling households, where approximately one‐third of the respondents report being internally displaced and upwards of 40 percent of the population reports receiving food assistance. Additionally, surveys of food aid recipients performed by the WFP and the registration procedure for the World Bank’s cash transfers program being implemented by UNICEF (which covers nearly one‐quarter of the entire population and is aimed at relatively poorer households) both find that very large shares of beneficiaries have access to mobile phones. For example, see (accessed January 2019): <u>http://documents.wfp.org/ stellent/groups/internal/documents/projects/wfp278006.pdf and http://www.worldbank.org/en/news/press‐ release/2017/05/19/new‐world‐bank‐support‐to‐address‐food‐insecurity‐in‐yemen‐aims‐to
customs clearance times, reliable infrastructure and good financial services are associated with the likelihood to export. Eifert, Gelb and Rachamandran (2005) and Eifert, Gelb and Rachamandran (2008) find the importance of high indirect and business environment cost impact negatively the productivity of African firms relative to those in other countries.

Likewise, Doing Business data has led to a vast literature on structural and policy factors linked with participation in international trade and investments flows. Early works include Djankov, Freund and Pham (2006), who measure how border policies impact the ability of firms to trade, showing notably that time lost at the border is especially costly. Sekkat and Varoudakis (2007) examine the impact of investment climate on FDI.

In the empirical literature on the effects on trade of institutional and structural variables, the predictive power of the gravity model introduced by Tinbergen (1962) has made it the most popular tool. Gravity modeling helped establish firmly the empirical truth about the role of distance and economic size as key factors of bilateral international trade flows. The model has been refined over the years, as well as grounded to the main theories of international trade (see for a comprehensive review Head and Mayer, 2013). From its inception, the gravity model has been used to test the existence of policy dimensions linked to trade, and it has been essential in furthering the understanding of the drivers of international trade and transaction costs (transport costs, and trade policies) directing trade flows. The gravity approach has also been applied with success to other bilateral economic flows: services (Kimura and

> 13 Hidalgo, Klinger, Barabasi and Hausmann (2007).

5