rafmacalaba/datause-encoder-data huggingface dataset:
# **_3.2 Explanatory Variables_**

Among the individual-level characteristics in z1ij , z2ij, z3ij, and z4ij, we include a number of socioeconomic variables that we speculate might affect the use of bank accounts. All these variables come from the Gallup World Poll (2012).<sup>23</sup> _Female_ indicates whether the respondent is female. To the extent that it is harder for women to have bank accounts, we expect this variable to have a negative relationship. _Age_ and _Age Squared_ are both in years. We expect the use of bank accounts to first increase and then decline with age, so in order to capture this we also include age squared.

_Rural_ takes the value 1 if the respondent lives in a rural area and 0 otherwise, where a rural area is defined as a town or village with less than 50,000 inhabitants. If this information is not available, a rural classification is based on the interviewer’s perception of whether a respondent lives in a rural area, on a farm, in a small town, or in a village. In general, the presence of financial institutions is more limited in rural areas, so we expect this variable to have a negative impact.

The _Income Quintile_ variables are indicators of within-country relative income, based on the income of the respondents in a country. There are five such dummies (the top quintile is the excluded category in the regressions), which range from the poorest 20 percent to the richest 20 percent.<sup>24</sup> Overall, we expect bank account use to increase with income.

Each respondent falls into one of three education categories, represented by three variables: _0-8 Years of Education_ corresponds to completion of elementary education or less, _915 Years of Education_ corresponds to completion of secondary education and some education beyond that, and _> 15 Years of Education_ corresponds to four years of completed education after

> 23 Table 2 provides a list of all individual and country-level variables with definitions.

> 24 We use these income quintiles because we do not have complete data on the actual income of individuals.

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# **I. INTRODUCTION**

The telecommunications industry is experiencing one of the most rapid and dynamic developments in developed and developing countries, owing to recent regulatory evolution and technological advance, particularly in mobile telecommunications and the Internet. Without exception, all regions have increased telecommunications service provision, compared with other infrastructure services (Figure 1).<sup>1</sup> Even in Sub-Saharan Africa, which has been largely lagging behind in infrastructure development, the number of subscribers to telecommunications services increased more than five times in the past 10 years, despite the levels of service access remaining very low.

Figure 1. Recent Developments in Infrastructure by Region, 1992-2002 <!-- Start of picture text --> 300<br>200<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>GDP ElectricityTelecom Roads Water<br><!-- End of picture text --> <!-- Start of picture text --> GDP ElectricityTelecom Roads Water Sanitation<br><!-- End of picture text --> <!-- Start of picture text --> GDP ElectricityTelecom Roads Water Sanitation GDP<br><!-- End of picture text --> <!-- Start of picture text --> ElectricityTelecom Roads Water Sanitation GDP Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Telecom Roads Water Sanitation GDP ElectricityTelecom<br><!-- End of picture text --> <!-- Start of picture text --> Water Sanitation<br><!-- End of picture text --> However, these aggregate figures are somewhat misleading. In Africa, particularly, there are significant divergences in development of telecommunications network. As shown in Table 1, the access rates to telecommunications services do not seem to have converged across African countries. Relative to the regional average, the standard deviation of telephone penetration rates in Sub-Saharan Africa has only marginally changed. By contrast, the declining standard deviation in Latin America and Caribbean implies the

> 1 In the figure, “GDP” reads GDP per capita in constant 2000 U.S. dollars; “Electricity,” electric power consumption in kWh per capita; “Telecom,” fixed line and mobile phone subscribers per 1,000 people; “Roads,” percentage of paved roads in total; “Water,” percentage of population with access to improved water sources; and “Sanitation,” percentage of population with access to improved sanitation facilities. The road data are in 1999 (1990=100). The water and sanitation data are also based on 1990=100.

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# **2. Access to community-level disaster preparedness mechanisms**

Most households live in communities where communal-level preparedness is low. By the end of 2021, only 9 percent of households had access to an emergency plan, and less than 25 percent of households had committees or community-trained members for disaster response (Figure 6a). The most common, but still scarce community coping mechanisms, were the evacuation routes (61 percent) and emergency shelters<sup>10</sup> (47 percent). Between the end of 2021 and early 2023 there was an increase of at least 2 percentage points in the proportion of households that reported having access to all community coping mechanisms except for shelters, with a 2-percentage-point drop (Figure 6). Nevertheless, rural households are less prepared than urban households for some mechanisms. Even though the difference is often less than 5 percentage points, in 2023, this gap is closed, and rural households show a better level of preparation than in previous years.

**Figure 6: Community disaster mechanisms by household’s area of residence (%) – (2021-2023)** a) By sex of head of household (2021)                                    b) By sex of head of household (2022)

<!-- Start of picture text --> 61.259.660.6 66.665 66<br>47.74546.7 44.2<br>43.1 43.5<br>26.7<br>21.326.523.3 17.62219.3 26.3 26.4 2223.8 22.6<br>9.6 8.5 9.1 9.8 8.8 9.5<br>Evacuation Shelter Trained Committee Emergency Evacuation Shelter Trained Committee Emergency<br>routes community plan routes community plan<br>Urban Rural Total Urban Rural Total<br>c) By sex of head of household (2023)<br>63.3<br>63 63.1<br>50.1<br>42.7 45.1<br>25.825.225.6 23.7 23.5<br>23.2<br>11.7 11.5<br>11.3<br>Evacuation Shelter Trained Committee Emergency<br>routes community plan<br>Urban Rural Total<br><!-- End of picture text --> Note a): Results from the full sample of wave 2. Note b): Results from the full sample of wave 3. Note c): Results from the full sample of wave 4.

> 10 Subsequently referred to as shelter.

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||**Control**|**Expert**<br>**advice**<br>**treatment**|**Social**<br>**norms**<br>**treatment**|**Relative**<br>**safety**<br>**treatment**||||
|---|---|---|---|---|---|---|---|
|**Variable**|**Mean/SE**|**Mean/SE**|**Mean/SE**|**Mean/SE**|**(1)-(2)**|**(1)-(3)**|**(1)-(4)**|
|**Male**|0.731|0.707|0.734|0.704|0.025|-0.003|0.027|
||[0.016]|[0.022]|[0.023]|[0.023]||||
|**40 years or older**|0.227|0.224|0.274|0.293|0.003|-0.048*|-0.066**|
||[0.015]|[0.020]|[0.024]|[0.023]||||
|**Highlands region**|0.256|0.238|0.263|0.220|0.018|-0.007|0.037|
||[0.016]|[0.021]|[0.023]|[0.021]||||
|**Islands region**|0.204|0.240|0.205|0.230|-0.037|-0.001|-0.027|
||[0.015]|[0.021]|[0.021]|[0.022]||||
|**Momase region**|0.436|0.413|0.416|0.440|0.022|0.020|-0.004|
||[0.018]|[0.024]|[0.026]|[0.025]||||
|**Southern region**|0.104|0.108|0.116|0.110|-0.004|-0.012|-0.006|
||[0.011]|[0.015]|[0.017]|[0.016]||||
|**Observations**|741|416|361|382||||

Notes: ***, **, and * indicate significance at the 1, 5, and 10 percent critical level. The values displayed for t-tests are the differences in the means across the groups.

# <u>4.3 Making inferences about the general population using the phone survey and online experiment</u>

The aim of the phone survey and online experiment was to be able to learn about how to address vaccine hesitancy among a broader population in PNG, beyond just the survey respondents. Both surveys collected samples of respondents that are somewhat representative of the population that has access to their respective communication channels (i.e. the phone survey is somewhat representative of the population with access to a phone and the online experiment is somewhat representative of the population with access to the internet). Efforts were made to oversample poorer respondents in the case of the phone survey (see Section 4.1 for details) and ensure respondents participated across PNG in the online experiment (i.e. survey quotas were applied at the regional level). As such both surveys did not rely solely on random sampling, which means they somewhat, but not perfectly, represent the population that has access to their respective communication channels. To be able to make inferences about a broader population, it was necessary to weight responses to better reflect the demographics of the national population (such as age, sex and location) (see Table 2). Reweighting does not mean either survey perfectly reflected the views of all people in PNG as those who have access to the internet and phones may be systematically different. However, to the extent that these differences are correlated with demographic characteristics, survey weights can correct for them.

**Table 2 – Characteristics of respondents in phone surveys and online experiment relative to general population**

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United Nations:<sup>11</sup> a) **Newsprint:** “Uncoated paper, unsized (or only slightly sized), containing at least 65 percent mechanical or thermo-mechanical wood pulp (percent of fibrous content), of the type used mainly for the printing of newspapers, usually weighing not less than 40 g/m2 and generally not more than 57 g/m2.” b) **Printing and Writing:** “Paper, except newsprint, suitable for printing, writing or other graphic purposes, made from a variety of pulp blends and with various finishes. It may be uncoated and have been subjected to sizing, calendering, supercalendering, glazing, water-marking or similar simple processes, or it may be coated on one or both sides with coating material such as barium sulphate, clay (beneficiated kaolin), gypsum or zinc oxide, often supplemented with supercalendering, etc.”; and c) **Household and Sanitary:** “Absorbent paper, creped or uncreped, sometimes embossed, made from bleached or unbleached pulps, recovered paper or combination of these. This type of paper should be sufficiently strong to avoid disintegration or tearing in use. Other important characteristics are high absorptive capacity, retention of absorbed fluids, softness, freedom from lint and unpleasant odours. It is made in white and a variety of colours and in single, double or more plies.” Our data set contains information for ten years from 1999 through 2008.<sup>12</sup> However, our panel is unbalanced and therefore the number of observations varies by paper category. In Table 1 we present summary statistics from the data on paper consumption. We computed the summary statistics presented in Table 1 using information from a balanced panel of countries due to the unbalanced nature of our data and in order to examine the yearly evolution of paper consumption. However, our regressions below will employ the entire unbalanced panel.

Column 1 in Table 1 shows that aggregate paper consumption has fluctuated during our study period, not showing a clear either positive or negative trend. However, the

> 11 ftp://ftp.fao.org/docrep/fao/010/i0226t/i0226t.pdf

> 12 The latest data available for employment is 2008. http://laborsta.ilo.org/STP/guest

13
comparison, a set of four key indicators was selected: (i) income, (ii) expenditure, (iii) moderate poverty, and (iv) extreme poverty. Both income and expenditure were in real terms (in 1991/92 Tk.). The cost-of-basic-needs method was used to establish the poverty line, which requires one to establish the cost of a minimum food basket or food poverty line, and then add an allowance for non-food expenditure to estimate the moderate poverty line. By contrast, extreme poverty was determined by comparing the household’s total consumption expenditure on food and non-food items against the food poverty line.<sup>8</sup> The sample for this descriptive analysis was restricted to eligible households from the 1991/92 survey, regardless of their actual participation status.<sup>9</sup> This was done to ensure that comparisons could be made between similar participants and nonparticipants.

# **3.1** **<u>Growth in Income, Expenditure, and Non-food Consumption</u>**

Between survey years 1991/92 and 2010/11, real per capita income increased by 5.2 percent a year for program participants, compared to 6.2 percent for non-participants. However, the differences in per capita income for participants and non-participants were not significant for any year, as the t- statistics suggest. As Table 3 shows, participants experienced significantly higher growth in nonfarm income than did non-participants over the 20-year period. As a share of total income, participants’ non-farm income was 62.7 percent in 1991/92, compared to 61.3 percent for nonparticipants, which increased to 76.5 percent in 2010/11, as opposed to 72.6 percent for non-

> 8 The calculation of moderate poverty is based on the official poverty line, which includes the food poverty line and an allowance for non-food expenditures.  The food poverty line is calculated by estimating the cost of a food basket needed to maintain the per-capita daily caloric requirement (2,120 calories) recommended by the Food and Agriculture Organization (FAO) and the World Health Organization (WHO) (FAO and WHO 1973).  For Bangladesh, the food basket contains mainly rice, along with such other food items as pulses, milk, meat, fish, fruits, and vegetables in specific quantities.  The cost of the food basket is calculated from the local prices for the food items.  By contrast, extreme poverty is defined by the household’s total consumption expenditure on food and non-food falling short of the food poverty line.

> 9 Eligible households are those that participated in microcredit programs and those that could have participated but did not, constituting 83 percent, 87 percent, and 99 percent of the households surveyed in 1991/92, 1998/99, and 2010/11, respectively.

9
how long is the father's shadow cast on the siblings? A major focus of the literature on developed countries has been on the share of sibling correlation accounted for by father's education or income (more broadly, parental education or income). A substantial literature on sibling correlation in income in developed countries suggests that the share of the intergenerational transmission may be small (see Bjorklund and Salvanes (2011) and Solon (1999)). A small intergenerational share would imply that the focus of research and policy should be on the neighborhood and school factors rather than family (Solon (1999)). However, the recent analysis by Bingley and Cappellari (2019) suggests that this low estimate is due to the restrictive assumptions of homogeneity and independence in the estimates of the intergenerational transmission from the parents to the children. They develop an approach that relaxes these assumptions, and nd that the share accounted for by the intergenerational transmission of income in sibling correlation in income is much higher. We provide estimates of intergenerational share in sibling correlation in schooling using the classic methods (Solon (1999), Bjorklund et al. (2010), Mazumder (2008)) along with the Bingley and Cappellari (2019) approach.

The evidence suggests four key conclusions. First, the estimates of sibling correlation in our sample of developing countries are, in general, substantially larger than the existing estimates for the developed countries. The average in our 53 country sample is 0.59, and the average for the top half of the distribution is 0.65. Based on 56 estimates available for developed countries, the average sibling correlation in schooling is 0.41.<sup>15</sup> Our estimates thus suggest a considerable gap in educational opportunities between the developing and developed worlds.<sup>16</sup> Second, there is signicant spatial heterogeneity at the regional level and across countries within a region. The Latin America and Caribbean region experienced the worst educational

> 15The 56 estimates are for the same birth cohorts as our sample: 1960s to 1990s birth cohorts. Bjorklund and Salvanes (2011) report a range of 0.40-0.60 for developed countries with the estimates for the United States among the highest. Prag et al. (2019) report an average of 0.49 from a meta analysis of the studies on sibling correlation in income and education published between 1972-2018 (includes both developing and developed countries).

> 16The existing cross-country evidence for developing countries based on father-child correlation in schooling also nds lower mobility in developing countries (see, for example, Neidhofer et al. (2018)). However, the existing evidence cannot answer whether this pattern holds for broader measures of socioeconomic status. We provide the rst evidence that this conclusion holds even when we capture a broad set of observed ad unobserved family and neighborhood factors.

5
Appendix Chart 5 Contributions of Shocks to Inflation (Percent of total variation) <!-- Start of picture text --> By global and domestic shocks<br>Global Domestic<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By structural shocks<br>Oil GS GD GMP DS DD DMP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By types of shocks<br>Oil and supply Demand MP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through December 2023 for the G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Forecast error variance decompositions of domestic CPI inflation based on FAVAR models (as explained in Section IV) that consist of four global variables (global output growth, inflation, monetary and oil prices) and three domestic variables (domestic output growth, inflation, and monetary policy rates). “OP” = oil price shock, “GS” = global supply shock, “GD” = global demand shock, “GMP” = global monetary policy shock, “DS” = domestic supply shock, “DD” = domestic demand shock, “DMP” =domestic monetary policy shock, “Oil and supply” = OP+GS+DS; “Demand” = GD+DD; “MP” = GMP+DMP 87
# _Regulation_

A second major element of 1<sup>st</sup> advantage concerns the degree to which government regulation impedes entrepreneurship and the emergence of new activities. Economic theories which saw the state as playing a central role in directing and coordinating economic development led to a huge variety of regulations being put in place. However, there is increasing empirical evidence that (well-intentioned as the architects of regulation may have been), regulation has been neither an engine of economic development nor a boon for the poor.

One problem is that government regulations can be exploited to expropriate entrepreneurs. Surveys of start-up firms, for example, reveal that more than 90% of Russian managers had to make “extralegal” payments to secure government services or a business license (Johnson, McMillan and Woodruff, 2002a, 2002b). Those firms who were most concerned about corruption invested 40% less than those who were least concerned. Government regulation of entry can also impede entrepreneurship and innovation. Djankov and others (2002) collect data on the time and number of procedures an entrepreneur must complete to officially open a business in 85 countries. They find pronounced variation across countries with the number of regulations to set up a business being larger in poorer countries. Heavy regulation of entry is associated with less democratic governments, greater corruption and larger unofficial economies. They argue that this is in line with a public choice view of regulation as being put in place by officials or insiders intent on extracting rents.

Findings for labor regulation point in a similar direction. Botero and others (2003) study labor regulations in 85 countries and find that heavier regulation of labor is associated with a larger unofficial economy, lower labor force participation and higher unemployment. Besley and Burgess (2002) find that pro-worker labor regulations in Indian states are associated with lower output, employment, investment and productivity in registered or 14
changes. The change is wage is a driver for labor supplies and migration flows across three different labor markets: rural, informal urban and formal urban. The other micro model, NIDS, estimates the consumer expenditure shares of the CGE model. The advantage of using a separate micro model NIDS over the standard CGE specifications for households (e.g., Cobb–Douglas or the linear expenditure specification) is that price and income elasticities are allowed to be different across household categories.

Morales et al. (2017) incorporate the informal sector in their CGE model when they analyze the impact on Bolivian wage and labor policy of decreasing international prices of its main exports. The definition of the informal sector in this study covers a wider spectrum of labor supply. It includes all non-remunerated workers, self-account workers without tax registration, and workers without long-term social security or contracts. The informal sector accounts for 80% of Bolivia’s labor force. The CGE model used in the study represents the labor mobility between the formal and the informal sectors through a labor supply function supplemented with a reservation wage curve. It allows the both-way (formal to informal and vice versa) migration of the labor force. The study assumes the unemployment elasticity of real wage is negative for the informal sector and zero for the formal sector. This means the wage curve is downward sloping in the informal labor market and horizontal in the formal labor market. The study defines four types of labor: formal, informal, private and government workers. But, the public sector does not hire informal workers. Labor supply between the formal and the informal sector is distributed through a CET functional form.

One of the biggest challenges to incorporate the informal sector into the CGE modeling is the lack of data. The social accounting matrix (SAM), the main database for CGE modeling, accounts data for the formal sector or aggregate sector, which includes both formal and informal activities without distinction. Some existing studies have illustrated how to incorporate the informal sector into the SAM. Rada (2009) is an example. In the absence of official statistics that differentiate the formal and informal economic activities, the study uses the shares of regular and irregular wage income in a given sector to derive an approximate size of informal and formal activities. Because there is no universal definition of the informal sector, for China, the study considers formal workers are those employed by both formal and informal enterprises as defined in the official statistics. Irregular employment, which are not included in the official statistics, is classified as informal labor. In India, the formal sector consists of those enterprises which are 7