rafmacalaba/data-use-mentions-extended huggingface dataset:
# Appendix A2: Do Reform Efforts Focus on Lagging Policy Areas?

The World Economic Forum’s Global Competitive Index (GCI) measures each country’s performance across the policy pillars through unit-less ‘progress scores’ on a scale of 0 to 100 (100 being the highest performance). Formally, each indicator is scaled according to the formula below: p = <u>, ,</u> R R <u>, ,</u> ∗100 R R R ii EE tt ii EE tt vvaav RR −wwR pptt ppR aaIIssRR ii,EE,tt We scale these progress scores into distance-to-frontier metrics as: ppR ppR ppssR RR RRIIttiiR ii,EE,tt −wwR pptt ppR aaIIssRRii,EE,tt p The GCI has all twelve indices for each of the six GCC countries from 2007 to 2017. This period is the only IIiippttaaIIssRR ttRR R RRIIttiiR ii,EE,tt = 100 − ppR ppR ppssR RRii,EE,tt The GCI has all twelve indices for each of the six GCC countries from 2007 to 2017. This period is the only time where the GCI scores are methodically consistent and comparable across years.

The final source, UNCTAD’s Export Concentration Ratio (also known as the Herfindahl-Hirschmann Index), measures a country’s export market concentration as follows: where Hc is the index for country _c_ , xi is the value of exports for product _i_ , X is the sum of export values across all products, and n is the number of products. We rescale the index so that 0 is the least concentrated portfolio while 100 is the most concentrated. This transformation allows us to include diversification as an additional ‘pillar’ to complement the GCI’s ten. We obtain for each GCC country for the years 2007 to 2017. Table A2.1 shows the average Distance to Frontier. Overall, the GCC performs well in Macroeconomic Environment, Health and Primary Education, and is underperforming in Diversification, Innovation and Technological Readiness.

**Table A2
|0.448|0.448|
|6|10145.308|37.418|0.765|0.765|185.839|37.418|0.455|0.455|
|7|6260.320|37.418|0.756|0.753|415.819|37.418|0.455|0.453|
|8|409160.290|37.418|0.835|0.810|221.977|37.418|0.461|0.447|

Note: Montiel-Pflueger's (2013) robust weak instruments test calculated for each instrument one at the time. See Section 6 for details. Source: Author's calculation. See Appendix for a detailed description of data sources.

Table 4. Linear predictability of the instruments AECRR’~~

||and|AIR"|||
|---|---|---|---|---|
||PanelA.|AECRRexo|g||
||Argentina|— Brazil|Uruguay|Panel|
|Observations|96|91|98|285|
|R?|0.230|0.315|0.148|0.069|
|R? adjusted|0.074|0.167|-0.020|0.014|
|F|1.074|1.356|0.356|3.450|
|Prob >F|0.394|0.188|0.988|0.225|
||Panel B|. AlRexog|||
||Argentina|Brazil|Uruguay|Panel|
|Observations|89|90|97|276|
|R2|0.170|0.113|0.081|0.048|
|R2 adjusted|-0.015|-0.081|-0.103|-
technology acquisition and use may be much more relevant than additional public R&D funding.

Put differently, the current R&D system is simply not a viable or effective instrument for creating a modern, innovative economy. For example:

- The Latvian innovation system shrank dramatically since the early days of transition.  With 17,000 researchers, Latvia was a major center of R&D in the former Soviet Union.  Most of these scientists and engineers were engaged in defense related activities.  But as defense orders dried up, the number of research personnel declined dramatically.  Unfortunately, shrinkage is not the same as structural reform.

**Table 7 Changes in R&D Personnel in Latvia, 1989-1999**

|**Employed in Science**|1990|1993|1996|1999|2000|
|---|---|---|---|---|---|
|Total number of employed persons|30,700|8,536|4,744|4,301|4,280|
|Total number of researchers|17,700|3,999|2,839|2,626|2,590|
|Researchers with degrees|3,710|1,977|1,491|1,492|1,495|

_Source:_ European Trend Chart on Innovation, 2001

- Latvia’s remaining innovation system is small, with only about 4000 personnel. Therefore, to generate a critical mass of R&D with this small base, Latvia will need to focus its R&D efforts in a few key areas.

- R&D personnel are ageing and not being replaced by new, younger cadres.  This is true both of R&D personnel as well as university professors in scientific disciplines.

43
richer than other states. Figure 2 shows the income residual by quintile of population, and we see the very strong income effect in the microstate bottom quintile of population. We also note that the favourable performance of small states carries over to other quality of life indicators. For example, if we in tum use under-five infant mortality and life expectancy at birth as the dependent variable in the above regression, we find that infant mortality is significantly lower in small states by 22 per thousand, while life expectancy is about four years higher. We are not sure why microstates are so much richer than their regional neighbours and have so much better human development indicators, but we see this as a decisive refutation of the macro arguments that microstates suffer from a development disadvantage.

Are microstates richer than others, controlling for location, because they save more or because they have a higher productivity level? We use the Mankiw-Romer-Weil (1992)
the lagged outcome variable is necessarily uncorrelated with treatment status, but serves to increase precision given the smaller sample size.

The estimates from these specifications are presented in Table 3 for both households and individuals. To account for intra-cluster correlations in the error structure, we cluster standard errors at the household/locality level depending on the unit of observation. The results from the means comparisons are largely replicated with some differences. Most notably, the estimated recall estimates are somewhat lower in the experimental data for the number of individuals reporting at least one acute illness during the month, and the effect on doctor visits declines from 18.3 percentage points to 8.7 percentage points. This decline happens primarily because respondents experienced unusually few health events in the second month of the data. Similarly, the labor impact of the recall period decreases to 1 percentage point since there were fewer work/school days lost in the second month. As a fraction of total work/school days lost in the second month, the recall effect is very similar to what we find in the observational data. It is likely that a longer experimental study would have yielded similar results as those using the observational estimates. The household level estimates for both the observational and experimental estimates are again similar to the means comparisons. Again, the estimated effect of recall on doctor visits and the labor impact in the experimental case is lower due to the inclusion of seasonal effects. In the experimental case, the significance is also lower due to the smaller sample size.

Table 4 explores whether ―forgetting‖ is higher among individuals with larger health burdens. Here, we include an additional interaction of the recall effect with the original morbidity burden in the first year of recall surveys. While for the experimental data, these effects retain their causal interpretation, temporal autocorrelation in the observational data limits our ability to move beyond correlations without imposing further assumptions on the nature of the lag structure. The experimental data strongly suggests that the recall effects are stronger among those with a higher initial burden
cities: Eldoret, Emu, Garissa, Kakmega, Kericho, Kisumu, Kitui, Machakos, Malindi, Mombasa, Nairobi, Naivasha, Nakuru, Nyeri and Thika. The sample is representative of households in both formal and informal (slum) settlements. Field data collection began in July 2012 and continued through to March 2013. Two caveats are important here. This survey was completed before the onset of devolution under the new Kenyan constitution – that is, prior to the establishment of counties and abolition of municipalities in Kenya in 2013. Although the process of reestablishing municipalities has commenced in Kenya in 2018, the new jurisdictions may not necessarily align with the ones that existed prior to 2013. Second, we use the term “city” broadly to refer to all the urban areas studied, rather than use administrative terminology, specified in Kenya’s Urban Areas and Cities Act, that distinguishes between municipalities, cities and towns.

Unlike prior research in Kenya, this survey provides a more comprehensive picture of living conditions and infrastructure service throughout urban Kenya. It offers previously unavailable data on formal and informal urban settlements in 15 of Kenya’s major urban centers and is one of only a few known multisectoral studies of this scale that covers urban Africa (Arimah 2003, Steckel et. al. 2017).

# **2.1 Survey Methodology**

Details of the survey sampling and data collection methodology can be found in the _Kenya State of the Cities Baseline Survey: Overview Report_ (World Bank 2016). Table 2.1 summarizes the household composition of the final sample by location and income poverty level.<sup>2</sup> Overall, the sample draws on a data set of 14,583 household surveys. Of these, 14,204 are classified as urban households; almost a third of the households interviewed (32.7%) were located in informal areas and over half the households (51.1%) surveyed were poor.

> 2 For the purposes of this survey,
and children remain indispensable labor inputs for many families. Many ethnic minorities do not speak Vietnamese, and-particularly among poor households-have not been to school. As in education, Vietnam has achieved much in raising overall health status. However, child malnutrition rates have been extremely high, especially among the poor. The prevalence of stunting for 25 to 36 month old children in the poorest two deciles was estimated to be close to 70 percent in the 1992-93 VNLSS. Low birth weight was also found to be common among the poor.<sup>8</sup> Isolation from information, trade and market networks, physical infrastructure, and market and institutional structures (credit, land and labor) tends to limit the opportunities for off-farm diversification and employment, and to worsen seasonality and income variability. These factors increase the vulnerability of poor households and weaken their ability to withstand shocks and smooth consumption adequately. The very same factors have prevented many households from participating in the benefits of market reforms. Meanwhile, many of the same households also suffered from some of the reforms in social sector and social welfare services provision brought on by the transition.

As in all poor rural agriculture-based economies, Vietnamese households face a variety of threats to the stability of their living standards. It is useful to distinguish between _covariate_ and _idiosyncratic,_ as well as _unanticipated_ and _anticipated_ sources of income variability. Fluctuations are said to be covariate when an entire community or region is simultaneously affected. Common examples in Vietnam include wars, severe floods, drought, typhoons, crop pest inFestations, pre-harvest lean seasons and economic recession and changes in the terms of trade. ][diosyncratic shocks affect a single household or individual leaving others unaffected.

8
outside the context of a healthcare visit. Most of this expenditure is likely to be direct purchases from a pharmacy. Because NHIS does not cover the costs of medicines and supplies acquired outside the context of a consultation, we think it unlikely that NHIS covers the OOP expenditure reported as such.<sup>20</sup>

# **4 Estimating the Distribution of Income Net of OOP Healthcare Expenditures**

There are several options for identifying NHIS members.<sup>21</sup> We use only those who were able to show a valid NHIS card to the GLSS 2016/17 interviewer. This produces an estimated 9.3 million beneficiaries, compared to 10.8 million registered beneficiaries in 2016 and 12.2 million in 2017 based on program administrative data obtained from the government.

To generate treatment and control groups, we use coarsened exact matching (Blackwell 2009). We match NHIS members and nonmembers on region and area of residence (urban versus rural), the log of household consumption per adult equivalent, and several individual-level characteristics that increase the likelihood of NHIS membership because they reduce or eliminate the annual registration and membership fees: those aged 70 years old or older; a child under 18 years; a woman who is currently pregnant, has been pregnant in the past year, or currently has an infant under 3 months old; a contributor to SSNIT; or a recipient of a SSNIT pension.

Table 2 reports the results from our matching exercise. L1 is a measure of distance between NHIS members and nonmembers, bounded by 0 (perfect match) and 1. The ex-ante data show that the two groups are well-matched except with respect to region of residence and the log of household consumption per adult equivalent. After matching, some imbalance with respect to consumption remains, but it is considerably less than in the ex-ante comparison. Overall, the criteria used for the match generate 141 strata, 95 of which are successfully matched. Only 313 individual records out of 58,844 respondents could
these practices influence, can provide important insight on how to improve business performance.

In this paper, we study the causal links between business training, practices, and outcomes, and identify two separate pathways to profits through a randomized control study of 852 small firms in South Africa. We distinguish between two types of training programs, marketing and finance, in order to understand the process by which training influences business practices and, thus, has an impact on firm performance. Importantly, we find that certain outcomes of interest to policy

> 1 See: http://data.worldbank.org/indicator/SL.EMP.SELF.ZS

2
While the Pass Laws Act had already been repealed in 1986 and although identification would be cleaner if data from before 1986 were available, this timeline does not pose a major threat to our identification strategy. This is because the Group Areas Act and the Population Registration Act were still in place, and as such the black population was still severely constrained in its choice of residence until June 1991.<sup>10</sup>

#

Distance to the nearest homeland is used as an instrument for migration flows in order to causally identify the effect of migration on population distribution. Figure 4 shows that the relationship between this distance and population growth between 1991 and 2011 is strongly negative, both at short and longer distances. In this figure we pool neighboring observations into discrete bins to improve clarity. We specify 100 bins in total, which puts around 20 observations into each bin. The log linear specification seems a good fit for the data. The validity of the instrument relies on the conditional quasi-random allocation of homelands, which has been argued for in Section 2. The assumption may be violated for areas adjacent to the homelands however, as they are likely to be affected by economic spillovers from the neighboring homeland in a variety of ways that are not related to the cost of out-migration from the homelands. To adjust for this problem, we exclude areas within 10 km from the homelands as a robustness

> 10The data from 1991 does not cover the entirety of South Africa. One general drawback of the dataset is that it does not cover the homelands. This does not affect the analysis since we only look at areas outside the homelands. Another more relevant drawback is that there are a few areas that are not covered within South Africa (see Figure 7 in appendix B). This is due to two reasons. First, Statistics South Africa only has a partial map of the census enumeration areas in 1991. Therefore, part of the census data cannot be geographically referenced. Secondly, due to violent turmoil at the time, some
# **4.2 Labor Market Scenario**

In theory, the influx of forcibly displaced lead to an increase in labor supply. The effect on the labor market depends not only on the degree of substitutability between the local and the displaced workers but also on the socio-economic characteristics of both the groups. Only if the displaced workers are substitute to local workers, local employment may fall leading to a rise in unemployment and/or a fall in wage. This detrimental effect may well be negated and even exceeded by the humanitarian agencies and the government recruiting local workers to provide and accommodate for the influx of forcibly displaced and the increase in consumer demand which generates a multiplier effect generating new employment opportunities across the economy. However, since this paper concentrates only on the short-term effects, it is not capturing the multiplier effects that takes time to ripple through the economy.

The quarterly data of PNADC (Continuous National Household Sample Survey) from 2012 to 2020 is used to look into the labor market consequences of the Venezuelan influx in Roraima. The survey sample of PNAD is representative of the states and the federal level of Brazil and aims to monitor dynamism in the labor force. Unfortunately, it does not collect information on nationality or immigration status and as a result, this paper focuses on the overall labor market variables and not solely on the impact on native community. Figure 8 shows that the working age population, defined as those above the age of 14 years, increased from 372,000 in last quarter of 2017 to 383,000 in the first quarter of 2018 – an increase of about 11,000, which is about a 3% increase, suggesting a labor supply shock in Roraima due to forcibly displaced Venezuelans. This shock in working age population of this magnitude in the first quarter of 2018 is only being witnessed in Roraima as Figure A5 reveals and most of the other northern states see a slight decline in the population in 2018, adding evidence that the influx of forcibly displaced Venezuelans are responsible for the population
of 2020 than in 2019:Q4. The P90 for nonperforming loans is 1.42 percent in 2019:Q4, and is lower than that for every subsequent quarter except 2020:Q2, when it reaches 1.51 percent, which is only slightly larger.

It is likely the case, as argued elsewhere (e.g., Beck and Keil, 2021), that reported loan performance would have been significantly worse were it not the case that the bank portfolios included hundreds of billions of dollars of Paycheck Protection Program (PPP) loans. These loans are largely not intended to be paid back, so should contribute little to nothing to charge-offs and nonperforming loans. As discussed in more detail below, this suggests that part of the “financial surprise” of a lack of banking crisis or significant financial distress is likely due to government stimulus during COVID-19 in addition to prior fortification of the industry in response to the GFC. Some of the improvements may also be illusory due to the forbearance rules discussed above, but it is not possible to determine how much this may be the case. Nonetheless, it is reassuring that reported loan performance problems did not significantly worsen during the crisis.

Checking next for insufficient loss allowances for the industry, this also does not appear to be problematic. The mean allowance of 0.81 percent in 2019:Q4 is slightly exceeded in every quarter of 2020, ranging between 0.82 and 0.84 percent. Similarly, the P10 values of the allowance also modestly exceed the 0.36 percent in 2019:Q4 in 2020, ranging between 0.37 and 0.38 percent. We cannot say whether the increase in allowances in 2020 are sufficient to cover the increased risks from the pandemic, but the banks at least made some effort to maintain slightly higher reserves to absorb these risks.

Turning to the credit performance data for the G-SIBs, the story is much the same – no substantial problems. Mean charge-offs are low in 2020, between 0.05 and
# **3. Data**

This paper uses data from the World Bank Global Findex (Demirguc-Kunt et al. 2022) to document current trends in account ownership and usage and identify ongoing barriers to financial inclusion.<sup>1</sup> The Global Findex is the world’s most comprehensive database on financial inclusion and the only data source collected directly from users of financial services that allows for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. The data can be used to also identify gaps in access to and usage of financial services by specific groups. Since 2011, the database has triennially collected indicators on access to and use of formal and informal financial services and digital payments, and behaviors that enable financial resilience.

The 2021 edition is based on nationally representative surveys of 128,000 adults in 123 countries, including both high income and developing countries. Fewer than 4 percent of the unbanked live in high-income countries. Thus, in our empirical analysis we focus on developing countries.<sup>2</sup> Table 1 presents the summary statistics of the individual characteristics for the 79 developing countries used in our multivariate regression analysis. In our sample, 50 percent of adults are women and the average age of adults is 37.25 years. Just over half of adults in the sample (51 percent) have primary education only. The average household size is 4.61. In terms of employment status, 26 percent of adults are employed for a wage, 25 percent are selfemployed, 9 percent are unemployed, and 39 percent are out of the labor force.

> 1 The country and individual-level data, along with the questionnaire, glossary, survey methodology and other relevant materials, are available at: https:/globalfindex.worldbank.org.

> 2 See Table A.1, Panels A and B, for a summary of the percent of the unbanked around the world.

7
country-level variables (i.e. GDP, internet and broadband). These bbc aaeebbaaiiee country-level variables are helpful to capture the link between the economy-wide level of digital and economic development with the level of computer use at work.

In the next step, we use the estimated equation (1) to predict the probability of using a computer at work at the individual level in the SEDLAC database.<sup>22</sup> The probability of not using a computer at work is simply 1-Pr(computer=1). When choosing the reference years of the country-level variables of the model, we use the reference year of the SEDLAC surveys. Then, the probability of an individual being exposed to AI and of using a computer at work will be the multiplication of both individual probabilities. For example, take the case of a group of workers (e.g. workers with high education) that, on average, has a 0.23 GenAI augmentation exposure probability (i.e. the average of the binary exposure measure at the 4-digit level). Then let’s assume that, based on the individual predictions from the Logit model, on average, individuals in such group have a 0.7 (0.3) likelihood of using (not using) a computer at work. As a result, we conclude that workers in such group have a 0.161 (=0.7 x 0.23) probability of being exposed to GenAI augmentation and of using a computer at work, while they have a 0.069 (0.3 x 0.23) probability of being exposed to GenAI augmentation and of not using a computer at work.<sup>23</sup>

> increased dramatically since then. In addition, some developed countries with more recent surveys do not have ISCO 08 information at the 4-digit level (e.g. United States). In contrast, New Zealand and Slovenia’s surveys were collected during the second round (2014-2015