rafmacalaba/data-use-mentions-extended huggingface dataset:
as a result of the logistic regression.

2. With the coefficent estimates, the probablity of access to the finanical service was precited for each individual in the sample. <mark>Probability (p</mark> �<sup><mark>) is based on the predicted relationship of</mark></sup> <mark>�</mark> �� <mark>, and a vector of the circumstances �</mark> ��<sup><mark>.</mark></sup> 3. The coverage rates, C, were computed

> 5 The mathematical framework of the Equity Adjusted Coverage Index is based on “Do Our Children Have A Chance?Index”, Molinas Vega et al. (2010) pg 49

28
Lendorfer, 2015).

> 4 All data from this survey can be downloaded from <u>http://www.gisse.org/pages/enquete-continue-sur-les-deplacesrefugies/enquete-sur-les-deplaces-et-les-refusgies.html</u> and from: <u>http://bit.ly/2nsxSd6.</u> 5 More precisely, the subsequent interviews were conducted in August 2014, September/October 2014 and then from November 2014 every month. More information on high frequency panel data using mobile phone interviews can be found in (J. Hoogeveen, Croke, Dabalen, Demombynes, & Giugale., 2014) and (Dabalen et al., 2016). 6 It should be noted that the definition of IDPs used in this survey is different from the one adopted from the UNHCR. This agency considers as IDPs also people displaced in the northern part of the country.

5
ambiguity ex ante about which gender is likely to benefit more. Women’s access to markets, services, and information tends to be weaker than men’s (World Bank, 2012). Consequently, men may benefit more since roads address only one of the several overlapping constraints that women face (we will refer to this scenario as a complementary constraints hypothesis). On the other hand, because women face a greater time constraint in accessing economic opportunities,<sup>2</sup> the marginal value of their time may be higher and they could benefit more than men for an equivalent decrease in travel times (we will refer to this scenario as a tightness of constraint hypothesis).

If the tightness of constraint hypothesis is correct, road improvements may constitute an effective policy tool in achieving the EAP WDR 2012 recommendations of “increasing women’s access to markets, factor inputs, locally available economic opportunities and services such as electricity, schools, hospitals and postal services.” Conversely, if the complementary constraints hypothesis is true, improving road connectivity alone may not be sufficient to achieve these development outcomes and other interventions may be needed to ensure that the benefits of infrastructure improvements flow to both genders equally.

Knowing which of these hypotheses is true has the potential to inform policy and development programming at a large scale. Estimates of global investment in transport, which are not easy to compile,

> 1 The Pradhan Mantri Gram Sadak Yojana or PMGSY.

> 2 This is because women are typically responsible for a greater share of the household and child care burden than men. See World Development Report 2012 for a discussion of this literature.

4
, female and children’s hours of family labor on each plot during the last major/minor seasons.

> 5 Other examples are multiple rounds of nationally representative LSMS-ISA surveys in Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda (World Bank 2015) or specialized surveys with a focus on agriculture (e.g. Walker 2009 - Ghana, Winowiecki et al. 2016 – Tanzania, Uganda). Another example would be the Agricultural Integrated Survey (AGRIS) designed by the Food and Agriculture Organization of the United Nations (FAO). They ask the holding about the total number of months worked within the last N months, followed by questions on the average number of days per month and average number of hours per day. These questions are at the agricultural holding level and asked to every household member.

> 6 This definition may be, for example, a “continuous piece of land on which a unique crop or a mixture of crops is grown, under a uniform, consistent crop management system” (World Bank 2016).

5
|14.1|
|Taxi cab drivers and chauffeurs|28.1|42.5|12.7|

**Table 4. Estimates Based on Equation (1)**

|**EDUCATION INDEX 1**||**DECADE: 1970-19**|**79**|**DECADE**|**: 1980-1989**|
|---|---|---|---|---|---|
|**VARIABLES**|**CENSUS 1980**|**CENSUS 1990**|**CENSUS 2000**|**CENSUS 1990**|**CENSUS 2000**|
|**Age**|0.00316<br>[0.00410]|0.00684<br>[0.00482]|-0.0069<br>[0.00624]|0.00884***<br>[0.00163]|0.00568***<br>[0.00207]|
|**Age squared**|0.0000214<br>[0.0000700]|-0.000106*<br>[0.0000610]|0.0000401<br>[6.32e-05]|-0.000113***<br>[2.38e-05]|-9.29e-05***<br>[2.37e-05]|
|**Years since arrival (dummy with value 1**<br>**for second half of the decade)**|-0.0596***<br>[0.00573]|-0.0300***<br>[0.00490]|-0.0352***<br>[0.00502]|-0.0413***<br>[0.00344
The benefit of the growth in remittances appears to have percolated deep into the economy. In 1995, approximately one in four households received some form of remittances. This became one in three by 2003 and more than one in two by 2010. Figure 8 shows the incidence of remittances at different points of the consumption distribution in 1995, 2003, and 2010. It is remarkable that the rapid increase in remittance receipts occurred at every spectrum of the consumption distribution. Not only did more Nepali households start receiving remittances, the average amount received also increased.<sup>21</sup>

# **Figure 8: Remittance Incidence**

<!-- Start of picture text --> 100%<br>50%<br>0%<br>100%<br>50%<br>0%<br>100%<br>50%<br>Remittances Receiving Households<br>0%<br>3 7 11 15 19 23 27 31 35 39 43 47 51 55 59 63 67 71 75 79 83 87 91 95<br>Consumption Percentiles<br>NLSS 1995/96<br>NLSS 2003/04<br>NLSS 2010/11<br><!-- End of picture text --> Source: Nepal Living Standard Surveys 1995, 2003 and 2010

# **_Labor Income_**

In addition to households directly benefitting from remittances sent by migrant members, nonmigrant households also benefitted from the spillover effects of migration. The decomposition exercises suggest that growth in labor incomes explain as much as 50 percent of the observed

> 21 The share of remittances in total income increased from 6 percent in 1995 to 16 percent in 2010. See Table A2 in the Appendix.

16
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A second key contribution we hope to make is to establish the effectiveness of “accountability” interventions that boost the salience of anti-corruption efforts. While previous research has hypothesized based on cross-country correlations that beliefs in the legitimacy of government generally, and to some extent the quality of democratic institutions, affect tax morale, we are able to show that experimentally varying information about anti-corruption efforts has measurable effects – by far the strongest in our study – on tax morale.

However, while we are able to show using manipulation checks that respondents actually absorb the information given to them in the interventions, our analysis finds no conclusive evidence on the question of why these interventions work to increase tax morale. We view this, as well as the question of how long-lasting these effects on tax morale are, as a fruitful avenue for further research.

Overall, our view is that the kind of highly-scalable research design documented here could help answer both important policy questions about tax morale, as well as social scientific questions about the social and psychological mechanisms underlying tax compliance around the globe. Especially when used in tandem with more context-specific studies featuring administrative data on tax compliance, this could constitute a useful part of a composite approach aimed both at generating and optimizing interventions and then deploying them in the field.

19
# **<u>Appendix B: Variable definitions</u>**

Data used in this paper come from two surveys as well as from administrative records of our partner financial institution (OIBM). We conducted a baseline survey from March to April 2009 and an endline survey from July to September 2010.

All variables that are created from survey data are top coded at the 99th percentile for variables with a positive range and bottom and top coded at the 1st and 99th percentile respectively for variables with a range that spans both negative and positive values. All figures in money terms are in Malawi Kwacha (MK).

# <u>Baseline characteristics (from baseline survey):</u>

_Number of members per club_ is the number of listed club members per information provided by the buyer companies (Alliance One and Limbe Leaf). Not all club members were interviewed.

_Female_ equals 1 for female respondents and 0 for male respondents.

_Married_ equals 1 for married respondents and 0 for respondents who are single, widowed, or divorced.

_Age_ is respondent’s age in years.

_Years of education_ is the respondent’s years of completed schooling.

_Household size_ is the number of people counted as members of the respondent’s household at the time of the baseline survey.

_Asset index_ is an index based on the first principal component of the number of items owned of 14 common non-financial, non-livestock assets and indicators of presence of 4 major types of housing characteristics (iron sheet roof, glass windows, concrete floor, electricity connection).

_Livestock index_ is an index based on the first principal component of the number of animals owned of 7 common types of livestock.

_Land under cultivation_ is the total of area of land under cultivation, measured in acres, for the late-2008 planting season.

_Proceeds from crop sales_ is the sum of sales from maize and tobacco in the 2008 harvest.

_Cash spent on inputs_ is the total amount of cash spent – excluding the value of input packages that are part of
# **1 Introduction**

The global rollout of COVID-19 vaccines has been substantially faster than any other vaccine rollout in history, however, progress has been very uneven. By mid-2022, low-income countries (LICs) still had relatively limited coverage of their populations and the take-up of COVID-19 vaccines had begun to slow down considerably (Figure 1). These trends are alarming as the possibility of many of these countries reaching herd immunity for COVID-19 seems far beyond reach (Kwok et al, 2020). This is at a time when many supply constraints for COVID-19 vaccines have been eased, which suggests limited demand is likely contributing to slower progress towards reaching the rest of the population. The drivers of people’s demand for COVID-19 vaccines have received relatively little attention in LICs as policy makers have tended to focus on procuring vaccines and making them as widely available as possible. There is a knowledge gap about the role intra-household dynamics may play in shaping people’s attitudes towards COVID-19 vaccines. This issue warrants close consideration as the rapid rollout of COVID-19 vaccines and near universal eligibility means that almost all members of the same household can get vaccinated at the same time, which is truly distinct from what has occurred with any other vaccine rollout in LICs.<sup>1</sup> We examine this issue of intra-household dynamics and attitudes towards vaccines by conducting novel randomized survey experiments and drawing on a unique nationally representative survey in Zambia. Specifically, we use individual level surveys for all household members in a nationally representative sample of over 10,000 households that were conducted in-person immediately prior to the widespread rollout of COVID-19 vaccines in mid-2021. Building on this initial survey, we conducted a randomized survey experiment through Facebook involving almost 3,000 participants in March and April 2022. This was followed by another randomized survey experiment conducted via UNICEF’s U-Report SMS messaging platform covering over 2,000 participants in August and September 2022. The randomized experiments tested how people’s self-reported willingness to receive the COVID-19 vaccine

> 1For instance,
10 Given the dominance of trade-distorting policy instruments, what does the World Bank panel data reveal with respect to policy reforms? Figure 10(a) shows the phasing out of export taxes by most developing countries. This reform has been particularly striking, although it has been reversed in a few developing countries—most notably, in Argentina following a major devaluation in late 2001 (Sturzenegger and Salazni 2008). In sharp contrast, as assistance to import-competing agricultural subsectors of developing countries has grown (Figure 10[a]), the relative importance of import taxes has increased dramatically (Figure 11). In Western Europe, the growth of decoupled, more-direct income-support measures, along with the virtual abolition of all support measures in Australia and New Zealand, reveals a far different pattern than in high-income countries in East Asia, where border-measure supports continue to dominate (Figure 12).

Input subsidies are a relatively minor component of most countries’ assistance to farmers. But they lingered on in Australia and New Zealand when most other forms of assistance were being phased out, and such subsidies have also remained about one-fifth of the total NRA in the United States (Anderson 2009, Chaps. 4–5). With two notable exceptions, input subsidies are even less common in developing countries, where funds for such direct subsidies are scarcer. The important exceptions are India and Indonesia. In India, input subsidies contributed 7 to 9 percentage points to the agricultural NRA in the 1990s and 10 points in 2000–04. In Indonesia, such subsidies have contributed 2 to 4 percentage points to the agricultural NRA since 1990. (They also contributed from 5 to 9 points in the 1970s and 1980s—even at times when the overall agricultural sector of those countries had a negative NRA.) Up to the 1980s—and in some cases the early 1990s—it was quite common for developing-country governments to intervene in the market for foreign exchange. Such interventions added to the anti-trade biases that were targeted at tradable sectors, including agriculture. However
Under this definition also, the textile and apparel industries are integrated to GVCs across all external comparators. The set of countries in the region that are in the same position is also unchanged, as textile and apparel manufacturers are integrated to GVCs in Kenya and Uganda among the non-resource rich group in the region, in Ghana, South Africa, and Zambia among the non-oil resource rich group, though none of the countries in the oil exporting group.

The use of the alternative definition significantly alters our assessment of the degree of integration of specific countries to other categories of manufacturing GVCs. Thus, Indonesia and Vietnam are assessed to be significant players in the manufacturing GVCs in transport equipment among the external comparators as are Malawi and Côte d’Ivoire among non-resource rich countries of the region, and the Democratic Republic of Congo, Ghana, and Zambia among the non-oil resource rich group. The group of significant players along GVCs of electrical goods and machinery also changes with only Bangladesh among the external comparators, only Côte d’Ivoire among the non-resource rich group of countries of the region, with South Africa and Zambia among the non-oil resource rich group and none among oil exporters.

Three of the four external comparators, namely, Bangladesh, Cambodia, and Vietnam are all integrated to manufacturing GVCs in metal products under the alternative definition along with Kenya among non-resource rich countries of the region, South Africa and Zambia among the non-oil resource rich group, and none among the oil exporting group. The corresponding lineup of participants in GVCs in the manufacturing of chemicals and non-metal mineral products now includes only Indonesia among the external comparators; Malawi and Kenya, among the nonresource rich group of countries in the region; the Democratic Republic of Congo, South Africa, and Zambia, among the non-oil resource rich group, and Cameroon among the oil exporting group.

# **7. Conclusion**

Based on the EORA database (UNCTAD 2013), this paper shows that African economies have reasonably high rates of linkages compared with those of China, the rest of
more in the past.|Global|1990-2000|
|Castaldo et al. (2018)|Positive correlation between fixed broadband and<br>economic growth in 23 OECD countries between 1996<br>and 2010.|OECD|1996-2010|
|Arvin and Pradhan<br>(2014)|Causal positive relationship between broadband adoption<br>and GDP growth using data of 19 countries between 1998<br>and 2011.|G-20|1998-2011|
|Gruber et al. (2014)|Broadband adoption has had a significant positive effect<br>on GDP growth in 27 countries part of the European<br>Union between 2005 and 2011.|27 EU<br>member<br>countries|2005-2011|
|Edquist et al. (2018)|A 10 percent increase in MBB adoption causes a 0.8<br>percent increase in GDP.|Global|2002-2014|
|Bahia et. al (2019)|A 10 percent increase in the adoption of (any) mobile<br>technology increases GDP between 0.5 and 1.2 percent.|Global|2000-2017|
|Haftu et al. (2019)|The expansion of mobile phone subscribers in SSA had a<br>significant positive effect on the region’s per capita income<br>between 2006 and 2015.|40 SSA<br>countries|2006-2015|
|Pradhan et al. (2019)|Co-development of innovation, diffusion, and ICT<br>penetration fosters economic growth in the long run, in<br>Europe|Europe|1961-2016|

9
# **1 Introduction**

A large literature in macroeconomics and development explores the ‘misallocation’ of resources across firms, i.e., dispersion in static marginal products of inputs, and the implications for crosscountry differences in aggregate productivity and per capita income/output.<sup>1</sup> Because marginal products are not directly observed, the extent of such misallocation is typically inferred from dispersion in the _average_ product of these inputs (revenue or value-added/input ratios) – under standard assumptions, the dispersions in the two are identical (for example, with common production technologies and demand elasticities). Recently, attention has turned towards examining the role of particular factors in generating the observed misallocation. Examples include technological frictions such as adjustment costs or imperfect information, unobserved heterogeneity at the firm level, for example, in production technologies or markups, as well as firm-specific ‘distortions’ stemming from economic policies or other institutional features.<sup>2</sup> From a policy standpoint, disentangling the role of these forces is paramount – any prescription for economic reforms aimed at reducing misallocation must be based on a careful understanding of the underlying determinants and nature – e.g., efficient or inefficient – of the observed average product dispersion.

In a recent contribution, David and Venkateswaran (2017) develop an empirical methodology to disentangle a number of sources of _measured_ capital misallocation – dispersion in the average (revenue) product of capital ( _arpk_ ) – using observable data on firm revenues (or value-added) and inputs. This paper applies their method to a number of countries, with a particular focus on developing ones. Specifically, we use the Orbis database in conjunction with a number of country-specific firm-level censuses/surveys to compile data for 11 countries – nine developing and two developed (Japan and the US) – and implement that methodology on this set of countries. In addition to furthering our understanding of the sources of capital misallocation, our analysis also aims to provide a broader perspective on
**Box 2:** A Revolution in Public Distribution Systems?

Any transfer program must include two basic elements – unique personal identification to indicate who is eligible for the transfer, and a system for making payments. New biometric and electronic transfer technologies have opened up an option of broadly based, transparency and potentially criteria based transfers that have only been a theoretical possibility up till now, except for high-income regions with small populations like Alaska. Applied well, these may provide important instruments for buttressing state-society relationships in MENA‘s states in a way that meets popular and legitimate objectives while limiting the proneness of human based identify systems to local capture and discrimination. Greater recourse to reformed ―blue-chip‖ transfer programs has the potential to substitute for inefficient fuel, food transfer systems and welfare public employment.

Over 450 million people in developing countries have had their biometric data recorded for a variety of purposes, and over the next five years this number is expected to triple, or even quadruple. In at least eleven countries it has been used to provide the identifier that underpins a transfer program. Some of these programs have been on a relatively small scale, experiments financed by donors with no link to any nation-wide registration system. Others, such as the Watan card issued by the Government of Pakistan, have drawn on national biometric population registries to enable the rapid creation of emergency support. The Watan card program is targeted to 1.5 million flood-affected households and draws on Pakistan‘s National Database & Registration Authority (NADRA) fingerprint database that covers some 96 million citizens. In Nigeria, biometric audits reduced the number of Federal Pensioners from 97,000 to 60,000, a cut of almost 40%. India is now poised to create the largest biometric database in the world. In early 2009, the government announced that they would create a national ID program for its 1.1 billion people, with the goal to enroll 600,000 people in the first five years.

In December 2010 Iran launched a massive program
and United States (1960Q1-2019Q4).

> 38 IMF (2017). Quarterly National Accounts Manual. Statistics Department, International Monetary Fund, 2017 Edition, Washington, D.C.

> 39 Since the series on unemployment insurance spending are not available at quarterly frequency, we proceed to interpolate it based on annual frequency data for each country. As a robustness check for this approach, and assuming that short-term ‡uctuations in unemployment insurance spending are mainly driven by changes in the number of unemployed workers, we use such information on unemployment (which is available at quarterly frequency) to guide the weights used in the interpolation procedure. This alternative approach leaves our ...ndings virtually unchanged.