rafmacalaba/datause-encoder-data huggingface dataset:
# CONTENTS

## **Acknowledgments  v Abstract  vi Overview  1**

## **Accounts  10**

### **Ownership of accounts  11**

How does account ownership vary around the world? How has account ownership changed over time? How does account ownership vary by individual characteristics?

### **How and how often financial institution accounts are accessed  17**

How often do account holders make deposits or withdrawals? How do account holders in developing economies make withdrawals? How many people own and use debit cards? How many people own and use credit cards? How many people access financial institution accounts through a mobile phone? How do people make direct electronic payments from financial institution accounts?

## **Payments  28**

### **Payments from businesses or government to people  29**

How do people receive wage payments? How do people receive government transfers? How do people receive payments for agricultural products?

### **Payments from people to businesses or government  33**

How do people pay utility bills? How do people pay school fees?

### **Payments between people—domestic remittances  34**

How do people send and receive domestic remittances? What is the most common digital payment channel for remittances?

### **Account holders’ use of digital payments  37**

## **Saving, credit, and financial resilience  43**

### **Saving for the future  44**

How do people save? How do account holders save? How has savings behavior changed over time? What are the main reasons for saving?

### **Credit and its purposes  48**

What are the sources of new loans? What is the role of credit cards? How has borrowing changed over time? What are the main purposes for borrowing?

### **Saving or borrowing for business?  53**

**Financial resilience  54**

> THE GLOBAL FINDEX DATABASE III
based on the probability of falling into poverty but using a regression-based framework (Ferreira et al., 2013; Lopez-Calva and Ortiz-Juarez, 2014); other options are to simply use some absolute cutoff thresholds such as between $2 and $10 PPP dollars (Banerjee and Duflo, 2008) or between the 40<sup>th</sup> and 80<sup>th</sup> percentiles of the income distribution (Alesina and Perotti, 1996).

# **III.3. Welfare Analysis**

We provide the empirical illustration in Table 3, where estimates are constructed based on the results from Dang and Lanjouw (2014).<sup>12</sup> We briefly review the most relevant studies about propoor growth in each country before discussing estimation results.

# _India_

India witnessed its GDP per capita increasing by almost half (47 percent) and poverty decreasing by 21 percent during the period 2004-2009 (World Bank, 2015b). Not much, however, is known about pro-poor growth for India during this period, but recent studies (e.g., Datt and Ravallion, 2011; Ravallion, 2011) suggest that economic growth has generally had a negative impact on poverty rates starting from the early 1990s. Our earlier estimates (Table 1) indicate that growth for the bottom 40 percent is slightly smaller than that of the whole population.

Estimation results shown in Table 3 confirm that growth in India in this period has been propoor, with the population share of the Poor category decreasing by 14 percent. However, this rate of decrease is slower than the growth rate of the Middle class at 19 percent. This period also saw the Vulnerable category expanding by 5 percent. The growth scenario for India in this period is

> 12 The figures shown in Table 3 are the relative changes of the different welfare categories. The absolute changes offer qualitatively similar results and are provided in Table 1.2 in the Appendix.

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interpretation is more problematic, as it does not tell us if the high ratio is due to substantial variation in the distribution of per capita GDRPs or the presence of outliers. Nevertheless, MMR provides a quick, easy to comprehend and politically powerful measure of regional income inequality.

# **Coefficient of variation [CV]**

The coefficient of variation is one of the most widely used measures of regional inequality in the literature.<sup>2</sup> The CV is a measure of dispersion around the mean. This dispersion can be calculated in a few different ways. Several authors have used the standard deviation of the logarithm of real per capita GRDP.<sup>3</sup> In this study, however, the CV as defined below attempts to capture the dispersion of per capita GRDP. This measure is standardized and can be used to make comparisons between countries and over time (especially if GRDP data is only available in current prices). In the following analysis, the CV is calculated in two ways. The first, the "simple coefficient of variation" is an unweighted measure as given below: <!-- Start of picture text --> E(y' -Yu) 2<br>cv i N<br>CV = (1)<br>Yu<br><!-- End of picture text --> where **Y,** is the income per capita of state i, N is the number of regions and _yu_ is the mean per capita GRDP. _Yu_ is computed as the mean of the regional incomes per capita without weighting them by population as follows: This is slightly different from Williamson's (1965, 11) formula for the unweighted CV, where the mean income, y , is taken as the national mean per capita GRDP. The Williamson measure 3
of the indicator (imbalance) varies from -100 to +100; negative values indicate a negative evaluation (shortage), positive values indicate excess supply, zero represents an equilibrium.23 The major reservation to conjuncture tests is that they do not take into account "forced" substitution and the effect of parallel markets, as do the Pindak and Winiecki methods.' The perceived imbalances may therefore be relevant in the partial equilibrium framework but they need not generate an aggregate disequilibrium. In other words, the reported values (sec Table V) are related to the A ant disequilibrium in specific markets before some form of adjustment takes place such as "forced" substitution.

Further, it must be born in mind that the conjuncture tests are the result of surveys on expert opinion and thus are subject to the usual problems of sampling and other survey difficulties. On the other hand, they are product-specific and highly disaggregate. That is to say, they provide specific information about individual markets. The authors of the test believe that to the extent that bias exist - either in sampling methods or in responses - the bias should be consistent over time.

The tests have been compiled for many markets, but Tible V shows the division into foodstuff and durables only. Rising positive numbers indicate the perception of rising surpluses in shops and vice-versa. The interesting point is that the conjuncture tests in the second half of the 1980s confirm our conclusions from the previous section - namely, that there has been intensive shortage since 1988, reaching a peak in 1990. From this point of view, we should concentrate on non-perishable goods where a sharp worsening of shortage is evident.

# _2.5_ Purchasing **Pos~er Parity** (PPP) **Estimates**

So far we have been considering direct estimators of inflation, but it can be also estimated indirectly. One such indirect method is the purchasing power parity technique that leads to estimates of inflation as a by-product of comparisons of purchasing power parties over time. The PPP technique involves a comparison of price levels among countries by comparing the purchasing power of currencies, which leads to an implicit exchange rate of currencies. In 23As far as we know, Czechoslovakia is the only country which used this kind of indicator. The investigations were performed quarterly and covered the whole retail sector; in 1990, the question about the option of salesmen on demand satisfaction was sent only to firms with over 100 employees. The surveys, which have been conducted by Vyzkumny ustav obchodu (Research Institute of Trade), discontinued after 1990.

'Recall, however, that parallel markets were not sufficiently large in Czechoslovakia and that only a small part of the population participated in them. Nonetheless, two general effects are possible. First, transactions in the "second" economy require more cash, and second, those who become better off can buy more luxury goods that would otherwise not be accessible.

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|ct|Max|35.000<br>1.000<br>18.000<br>1.000<br>1.000<br>1.000||50.000<br>1.000<br>10.000|1.000<br>1.000|nt spent<br>mbers of|
|---|---|---|---|---|---|---|
|al confi|Min|0.000<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000||0.000<br>0.000<br>0.000|0.000<br>0.000|al amou<br>itive nu|
|a judici|Median|0.650<br>1.000<br>0.250<br>0.000<br>0.000<br>1.000||0.700<br>1.000<br>0.450|0.000<br>0.000|ase. Tot<br>ated pos|
|f having|StD|3.651<br>0.156<br>3.226<br>0.429<br>0.492<br>0.346||8.447<br>0.204<br>2.258|0.400<br>0.471|ecent c<br>o indic<br>arge.|
|or frms o|Mean|1.755<br>0.975<br>1.392<br>0.241<br>0.404<br>0.863||3.386<br>0.957<br>1.288|0.198<br>0.327|he most r<br>ndants wh<br>tify but l|
|tions f|N|131<br>122<br>46<br>141<br>141<br>131||89<br>93<br>28|101<br>101|er to t<br> Respo<br>o quan|
|ative implica||FCFA)<br><br>stments<br>ourt again?||FCFA)|stments|the frst ref<br> payments. <br> impossible t|
|on neg|if|nfict<br>million <br> confict<br>ing inve<br> go to c|ant:|nfict<br>million|confict<br>ing inve|ns after <br>formal <br> it was|
|escriptive statistics|th case(s) as plaint|(in million FCFA)<br>t due to judicial co<br>ork hours lost (in <br> due to the judicial<br>ped frm from mak<br>d situation, would|h case(s) as defend|(in million FCFA)<br>t due to judicial co<br>ork hours lost (in|due to the judicial<br>ped frm from mak|and B all questio<br> court fees, and in<br>ving a number said|
|Table A8: D|PANEL A: Firms wi|Total amount spent <br>Work hours were los<br>Monetary value of w<br>Had to take on debt <br>Judicial confict stop<br>Same type of case an|PANEL B Firms wit|Total amount spent <br>Work hours were los<br>Monetary value of w|Had to take on debt <br>Judicial confict stop|Notes<br>: In panels A <br>includes lawyer fees, <br>hours lost without gi|

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The productivity gap highlighted above occurs mostly at the top of the distribution. In Figures A.4 and A.5 we plot the differences in sales per worker between Ethiopia and each competitor country at five different percentiles. Differences tend to be small up to the median firm. However, at the 75th and 90th percentiles of the distribution, we observe a large productivity gap. Thus, what is distinctive about Ethiopia is the lack of high productivity firms.

**Fact 2.** _Ethiopia has the lowest labor costs in the sample, but faces intermediate material input costs._

Manufacturing firms in Ethiopia also face low labor costs. This confirms a wide perception that abundant labor and competitive wages can help Ethiopia expand its manufacturing sector. Monthly labor costs per worker are about 69 USD in Ethiopia, 79 USD in Bangladesh, 175 USD in Kenya, 180 USD in India, and 201 USD in Vietnam. In the garment sector, the differences are even more striking: average labor costs in Bangladesh and India are 20 percent and 128 percent higher than in Ethiopia, respectively.

On the other hand, Ethiopian firms are in the middle of the distribution in terms of the cost they pay to source inputs of production. This is not surprising, given that Ethiopia is the only land-locked country in the sample, with limited options to access the sea. Further, material costs may also reflect the mix of sectors of the economy. If I restrict attention to the garment sector, for example, I find that material costs are higher than neighbouring Kenya, but lower than Bangladesh (India and Vietnam have much higher material costs, probably due to specialisation in higher quality garment).

**Fact 3.** _The value added of Ethiopian firms is above that of Bangladesh, but below India, Kenya and Vietnam._

Overall, low labor costs cannot fully compensate for low productivity. As a result, Ethiopian firms are among the least profitable (in terms of value added per worker) in our sample (Figure 2 and A.6). However, value added per worker in Ethiopia is actually relatively close to those of the next most profitable country – Bangladesh. If we look at the whole manufacturing sector, value added per worker is higher than in Bangladesh. If we focus on the garment sector, then we find that the profitability of Ethiopian firms is just below that of firms in Bangladesh.

There are three important caveats related the value added data I have just presented. First, the large number of missing values in the material costs variable means that I am working with a selected sample. Second, the surveys for Ethiopia and Vietnam do not record data on rental payments for land or capital. I impute these payments by using 6
<!-- Start of picture text --> Demographics 3%<br>New Employment 7%<br>Salaries 4%<br>Self-empl. earng. 1%<br>Pensions 17%<br>TSA 29%<br>Agric. activ. 4%<br>Inter-hh transfers 1%<br>Other regular inc. 11%<br>Other soc. ass. 4%<br>Irregular inc. 13%<br>-20% 0% 20% 40% 60% 80%<br><!-- End of picture text --> _Source_ : Own elaboration based on 2009 and 2015 WMS and components micro-decomposition method described in Azevedo et al. (2012). Bars add up to total household income growth 2009-2015.

<!-- Start of picture text --> Demographics -7%<br>New Employment 34%<br>Salaries 33%<br>Self-empl. earng. 8%<br>Pensions 16%<br>TSA 3%<br>Agric. activ. 7%<br>Inter-hh transfers 5%<br>Other regular inc. 4%<br>Other soc. ass. -1%<br>Irregular inc. 35%<br>-20% 30% 80%<br><!-- End of picture text --> _Source_ : Own elaboration based on 2009 and 2015 WMS and components micro-decomposition method described in Azevedo et al. (2012). Bars add up to total household income growth 2009-2015.

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29 and under, where women are more affected, and youngest and oldest employees experience higher wage increases and job losses. The minimum wage increase significantly impacts the accommodation and food services sector, affecting almost half of employees. In contrast, fewer than 1% of employees in the electricity, gas, steam, and air conditioning supply sectors are affected. Interestingly, sectors with moderate employment impact tend to experience the most substantial wage hikes. Across all scenarios, wage growth remains close to zero.

**Similarly, increasing the statutory minimum wage to a living wage estimate is expected to result in a slight short-term increase in average wages. Still, it could lead to significant job losses, particularly among the youngest workers.** The impact of the minimum wage increase on income inequality in this scenario depends on the approach chosen, with a more substantial impact than in Scenario 1, resulting in a reduction in the Gini index and an increase in the bottom 50% income share. However, the policy simulations under both scenarios show that the minimum wage increase does not result in significant wage hikes among the formal employee population, with average wage increases of 0.40% in the short term and 0.04% in the long run.

# **Several policy recommendations arise from this analysis:**

**First, the legal minimum wage should be set based on an assessment of its adequacy, among many other factors. It is also critical to analyze the dynamics of the leading macroeconomic indicators to mitigate the potential negative impact on employment.** A living wage assessment is a key tool to determine how the existing minimum wage covers the basic consumption basket, encompassing food and non-food essentials **.** Moreover, evolving, these adjustments should at least be anchored to inflation rates to uphold the purchasing power of individuals. Our results show that increasing the minimum wage to the level of the consumption basket can generate significant income gains for employees who keep their jobs. However, a significant formal employment reduction may also be recorded. Therefore, the government should implement labor market policies to support the transition of those who lose their job back to employment and minimize job mismatches. This approach ensures that the minimum wage remains attuned to the changing economic landscape while safeguarding the real value of earnings **.**

# **Second, monitoring the labor market is essential, as well as accurately estimating the wage elasticity**

**of labor demand.** The minimum wage can have different effects depending on the country's context at a certain point in time, particularly during different stages of the economic cycle.  Therefore, to set a minimum wage and impose an adjustment formula, a detailed monitoring of the labor market and the estimation of the critical parameters that determine the effect of the minimum wage are needed. In this note, we consider several scenarios based on the estimates of the wage elasticity of labor demand from international evidence, which

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particularly in lower-income countries. We use the most recent measurement reported for each country. In our sample, the average number of hospital beds per 1,000 people is 0.6, 1.6, 3, and 4.15 in low-, lower-middle, upper-middle, and high-income countries, respectively.

**Non-COVID-19 mortality** Baseline mortality rates _π_  ̄ _n_ 1, _π_  ̄ _n_ 2 and _π_  ̄ _n_ 3 are computed from country-specific life table data obtained from the Global Health Observatory Data Repository of the World Health organization.<sup>22</sup>

In terms of elevated mortality due to shortfalls in aggregate income, several papers have estimated the relation between economic shocks and infant or young child mortality (Baird et al., 2011; Bhalotra, 2010; Cruces et al., 2012; Friedman and Schady, 2013; Maruthappu et al., 2017). For low and middle-income countries, the population groups most vulnerable to declines in aggregate income are young children and, perhaps, the elderly (Cutler et al., 2002). We focus on mortality impacts among children under-5 as this population group has been the most extensively studied. We estimate the effect of short-term aggregate income shocks on mortality following the methodology of Baird et al. (2011). We use data on GDP per capita from the World Development Indicators. The values are adjusted for purchasing power parity, corresponding to 2011 US dollars. Data on infant and child mortality are taken from retrospective birth histories as reported in the Demographic and Health Surveys (DHS) conducted in 83 low- and middle-income countries between 1985 and 2017. The combined sample is of 5.2 million births in low- and middle-income countries. We run regressions of the following form: where _Dict_ is a binary indicator that takes the value 1 if child _i_ in country _c_ died in year _t_ , log GDP is the natural logarithm of per capita GDP, _fc_ ( _t_ ) is a country-specific flexible time trend, _δc_ is the country fixed effect, and _εict_ is the error term. Standard errors are clustered at the country level.

We run the regression separately for countries of different income levels, as classified by the World Bank 2020 income groups. The main result is that a 1 percent decrease in per capita GDP is associated with a 0.15 increase in under-5 mortality per 1,000 children in low-

> 22who.int/gho/data

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36. The University has generated a limited amount of internal revenue to -- assist its financing. In real absolute terms, the revenues have increased solely due to an increase in 1989 -- at an average annual rate of 17.6% between 1987-90, with special revenues growing much faster than those for service. However, Table 3 shows that when the internal revenue is compared to the rapidly increasing overall budgetary expenditure, the proportion has been declining by an average rate of 10.8%. Internal revenue currently accounts for only 3.9% of the total University expenditure, leaving the University almost totally dependent on Government and donor assistance for finance.

Table 3: University of Sana'a Annual Internal Revenue as Percentage of Total Expenditure (YRls Thousands: 1987 Constant Prices) *

||||||Average|% Annual|
|---|---|---|---|---|---|---|
|ITEM|1987|1988|1989|1990|rate of|change|
|Internal Revenue as %|5.5|3.9|4.1|3.9|||
|of total Expenditure||(-28.9)|(6.2)|(-4.5)||-10.8|

Source: University of Sana'a Figures in parentheses are percentage change on previous year

# (c) Staff

37. The University appoints members of its academic and administrative staff either on tenure or contract. All Yemenis are appointed to tenure positions, while non-Yemenis are on contract. The shortage of middle and high-level manpower forced the University to employ expatriate teaching and non-teaching (technician and administrative) staff from neighboring Arab country universities -- predominantly from Egypt, and to a lesser extent from Syria, Iraq, Sudan, Jordan and Morocco. For foreign language teaching, and in those fields where English is the medium of instruction (Medicine and Engineering), non-Arab expatriates are recruited. The University has not faced any difficulty in recruiting them, for two reasons. First, the relatively attractive salaries and fringe benefits of the expatriate staff are largely covered by funding from Kuwait. Second, the working conditions are reasonable. Teaching loads are determined by rank. Professors are expected to teach between 8-10 hours a week, while the norm for assistant professors, lecturers and assistant lecturers is between 10-12, 12-14 and 14-16 hours respectively. However, student "contact-hours" has not been stipulated as obligatory.

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# **5. SIMULATION OF ALTERNATIVE SOCIAL POLICIES**

5.1 The serious targeting deficiencies observed in existing social policies motivate the search for alternative targeting mechanisms that might be more effective in channeling resources to the poor. The basic presumption is that any alternative mechanism should be based on an accurate targeting device and be limited to subsistence consumption levels. On this basis, a variety of alternative social policy mechanisms can be conceived, each of which is designed to remain within the current budget envelope for infrastructure subsidies. Both use of service subsidies and connections subsidies (where applicable) are considered in the simulation exercise.

## **TARGETING MECHANISMS**

5.2 In order to improve the targeting of subsidies, it is necessary to develop an accurate poverty proxy that can be used to identify low-income households. In Argentina, a number of potential targeting mechanisms have been publicly debated, including the use of consumption levels as a targeting device, as well as the allocation of utility subsidies to households that are already beneficiaries of other social programs (such as the Heads of Household program<sup>12</sup> ). Furthermore, provincial experience illustrates the possibility of developing multi-dimensional poverty proxies based on aggregating a number of different indicators of structural poverty. Each of these are now considered in turn.

## **Consumption**

5.3 In order to evaluate the hypothesis that physical consumption might be a good poverty indicator, a simulation was conducted using data on household electricity consumption in the Metropolitan Area, since this was the only service and geographical area for which there were sufficient reliable data points on household consumption. The simulation explores the possibility of allocating subsidies only to those households that consume (successively) less than 50, 100, 150, 200, 250 kilowatt-hours per month (Figure 13).

> 12 The Heads of Household program is a major welfare program introduced to attenuate the impacts of the severe 2002 economic crisis. The program makes a monthly payment of US$67.7 to unemployed heads of household that participate in approved workfare schemes.

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