rafmacalaba/data-use-mentions-extended huggingface dataset:
, sovereign default history and economic indicators from 1960 to 2008.<sup>4</sup>

# **2.1. Data Description and Source**

The first key measure of government characteristics is government polarization (polar), which captures how different are the preferences of the governing parties on certain issues, as well as

> 4 Our main data set spans from 1960 to 2008. However we use sovereign default crisis data from 1800 to 1959 to construct a measure of default history.

5
first language, international tourist arrivals, etc. We leverage that we have an estimate of the number of articles not using data – referred to as qualitative articles – to control for all factors that are likely to influence overall research output in a country unrelated to statistical performance. The number of qualitative articles is calculated as the number of total articles minus any articles using data, based on our NLP model.

Countries that tend to be over-represented in quantitative articles also tend to be overrepresented in qualitative articles, conditional on GDP per capita and population (Figure A.3). The elasticity is around 0.6, suggesting that a 1% increase in the number of qualitative articles associated with a 0.6% increase in the number of articles using data. We take this to imply that quantitative articles do not crowd out qualitative articles and vice versa. Returning to Equation (2), one prediction from the model is that countries with particularly high costs related to using data, (such as a lower statistical performance score), should see reductions in the amount of quantitative research, but it would not follow that it would see lower levels of qualitative ii XX research. This would predict the share of quantitative articles to total articles increasing in SPI score. Contrary to this, there is almost no relationship between SPI overall scores and the share of qualitative papers to total papers produced overall (Figure A.4). This suggests to us that some omitted variable may in part be driving the impact of statistical performance on academic articles using data, and that the number of qualitative articles can control for such possibly omitted factors.

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# **1. Introduction**

The paper forecasts growth trends in the developing world, and for a large sample of countries, identifies the main forces likely to underpin growth in the period 2005-14. The paper presents real per capita GDP growth forecasts for all developing countries for the next ten year period. For 55 of these countries,<sup>1</sup> representing all major world regions except Europe and Central Asia and accounting for close to 80% of the developing world’s GDP, the paper presents real per capita GDP growth forecasts that reflect the growth effects of various growth determinants assuming that these evolve following past trends.

The forecasting exercise contributes to the literature a collection of econometrically derived country forecasts of real per capita GDP growth for the next decade and all developing countries in the world. Such forecasts are often an input into various types of economic sector work – from debt sustainability analysis to scenario building for macroeconomic and policy modeling exercises, and we could not find a set of growth forecasts of this kind in the literature. The IMF’s World Economic Outlook, the World Bank’s Unified Surveys and CAS documents publish country growth forecasts for only the next 3 to 5 years, while the World Bank’s Global Economic Prospects report presents growth forecasts at the regional, not the country level.

Our analysis, based on a cross country regression framework, suggests that for the average developing country the growth effects of continued improvements in public infrastructure will be larger than those of improved secondary school enrollment, trade openness, and financial deepening. The joint contribution of increases in these growth factors to average, annual real per capita GDP growth in the developing world in the next decade is

> 1 We focused on the group of fifty five developing countries, which jointly with twenty three developed countries, is a part of the panel data used by Loayza _et al_ . (2005) to estimate the cross-country growth model employed in this study. The rest of the developing countries were not part of
(0.04)|(0.02)|(0.05)|(0.02)|(0.04)|(0.02)||||
|Relationship quality index|-0.07|-0.21***|-0.02|-0.21***|-0.04|-0.27***|0.11|0.04|0.01|
||(0.08)|(0.03)|(0.09)|(0.02)|(0.08)|(0.03)||||
|Public speaking confidence index|-0.02|-0.08***|0.01|-0.03***|0.00|-0.01|0.11|0.34|0.68|
||(0.04)|(0.01)|(0.04)|(0.01)|(0.04)|(0.01)||||
|Progressive gender attitudes index|-0.12*|-0.13***|-0.23***|-0.09***|-0.16**|-0.16***|0.91|0.11|0.95|
||(0.07)|(0.03)|(0.08)|(0.03)|(0.07)|(0.03)||||
|**Bargaining power measures**||||||||||
|Wife's age|0.00|0.00**|0.00|0.00|0.00|-0.00|
Our evidence is consistent with the view that earnings gaps between the self-employed and employees are due to efficiency wages and the sharing of quasi-rents. Because it is likely that firms in low income countries are less productive, compared to those in more developed countries, and offer fewer resources that boost worker productivity, these firms earn fewer rents that can be shared with workers. As countries develop and firms gain access to innovative technologies, the productivity of employees in firms increases and they are able to share more of their quasi-rents with workers, increasing the wages of employees relative to the self-employed. It is also reasonable to expect that the bargaining power of employees will increase as countries develop, due to the increasing prevalence of unions and the better enforcement of labor regulations. If the bargaining power of workers is positively correlated with level of development and labor productivity, then the relationship between the worker’s share of quasi-rents with development and productivity will be even stronger.

# **VII. Conclusion**

This paper uses data from 73 countries and multiple years from a comprehensive set of harmonized household surveys to estimate the proportion and wage differentials for selfemployed, informal, formal and salaried workers from around the world.

We find that approximately half of all workers in low income countries are nonprofessional own-account workers, while only 9% are formal employees, and only 2% of workers in low income countries are employers or professionals. As countries develop, the proportion of workers who are formal employees, employers and professional ownaccount workers increases, while the proportion of workers who are non-professional ownaccount workers falls. In high income countries, non-professional own-account workers make up 7 percent of all workers, less than 5 percent are employers and professionals, and almost 90 percent are employees.

Regionally, the proportion of self-employed is smallest in the developing economies of Europe and Central Asia. Our most comprehensive estimate is that approximately 95% of workers in Europe and Central Asia are wage and salaried employees, although half of these are informal employees. This is even higher than
45.78|58.77|12.17|80.05|
|Per capita expenditures***|31.18|28.97|24.25|13.59|41.71|0|191.83|

TWs is the "poverty" index used by FONCODES in 1996. It is a weighted mean of 8 variables--the rate of chronic malnutrition amongst schoolchildren, the rate of illiteracy, the proportion of children not in school, and the proportion of households who live in overcrowded housing, who have inadequate roofing, no access to running water, no sewerage, and no electricity, respectively. Higher values of the index are an indication of higher poverty. All values are normalized so that the province with the lowest amount of poverty has a value of the FONCODES index equal to 1.

** Summary statistics for the pro-Fujimori vote in 1990 and 1993 are population-weighted. They do not correspond exactly to the actual fractions voting for Fujimori because of differences between total population and the population of eligible voters. Moreover, electoral data for the second round of the 1990 presidential election is only available for 205 out of 224 provinces which existed at the time.

*** This refers to per capita expenditures made by FONCODES on community-based projects between December 1991 and March 1995, in 1992 soles.

Figures 2 and 3 show scatter diagrams and non-parametric (kernel) regressions of the logarithm of per capita FONCODES expenditures as a function of the proportions voting for Fujimori in the 1990 presidential election and the 1993 referendum, respectively."<sup>5</sup> Figure 2 shows a positive correlation between the 1990 Fujimori vote and expenditures in the December 1991--October 1993 period, while Figure 3 shows a negative correlation between the 1993 Fujimori vote and expenditures in the November 15 Non-parametric regression analysis can be thought of as a moving average. The expectation of Y (in this case, the log of per capita FONCODES expenditures) is estimated conditional on X (the
patterns are observed for the gap and severity indices with the most severe effects in the IS‐affected and Kurdistan regions. Interestingly, the poverty gap and severity would have increased more in the South than in Centre, Rest of North and Baghdad, likely reflecting the larger share of population living close to the poverty line there prior to 2014 (World Bank 2015). The adverse welfare impacts in Kurdistan and the IS‐affected governorates are likely underestimates because they do not take into the likely increase in the prices of basic needs in the IS‐ affected governorates or the lack of payment of public sector salaries in Kurdistan during some months of 2014.

Aggregate inequality is expected to remain stable relative to the business as usual scenario for the whole population – with the Gini and Theil index increasing slightly almost 1 point between scenarios (Table 9). However, the average nation‐wide impact hides differences across space. Three regions ‐i.e. Kurdistan, Baghdad and IS‐affected‐ would have suffered an increase in their inequality levels as a consequence of the crises. Most of these movements are projected to be partially compensated by other regions producing a relatively stable inequality for the country.

# **Distributional effects**

One of the advantages of using the micro‐simulation approach is the possibility of fully taking into account the heterogeneity of individuals observed in the household survey. This allows us to identify with some precision who are likely to be the winners or losers of the shocks modeled. Indeed, as the results 17
18 To quantify the effect of external spreads shocks on output, we estimated the following multipliers<sup>2 0</sup> based on the impulse response functions (Appendix 4): In Argentina, a onestandard deviation shock has an immediate impact on growth, producing a slowdown of 3% on average that lasts about 10 months. This result is similar to that obtained by Agenor, Aizenman and Hoffmaister (1999), though their effect is more persistent (15 months) but of smaller magnitude (about 0.5% on average). In Brazil, a shock of similar magnitude implies a slowdown of 1.4%, but of greater persistence (21 months). In Mexico, a one-standard deviation shock reflects in a growth-slowdown on average of 1.6% and has an effect that lasts 18 months. In Chile, the same sized shock reduces growth by 1.1%. and its effect disappears after 15 months. In Colombia, a similar shock produces a slowdown of about 0.4% but persists around 18 months.

Domestic real interest rate shocks are a major source of output variability in Chile and Colombia. In Chile, a one-standard deviation shock to real interest rates is associated with a slowdown of 1.3% on average that lasts a bit over a year. In Colombia, a similar shock results in lower growth of 1.4% that lasts about 16 months. In Brazil a one standard deviation shock to the real interest rate is associated with lower growth of .9%21 during nine months. In Argentina, a one-percentage point shock lowers growth by about 2% during seven months. In Mexico a similar shock lowers growth by 0.6% during 17 months.

The real exchange rate shocks are the main explanatory factor of output growth variance in Brazil and Mexico. In Argentina, Chile, and Colombia it's the second or third most important factor accounting for the variance of output. In every case the impulseresponse functions shows (Appendix 4
earnings. Note that this data does not include information on worker hours; however, given the low flexibility of work hours (see Section 5.1), it is reasonable to use attendance as a meaningful measure of the extensive margin of labor supply. These administrative data are used by Olam to compute earnings, and the amount to be deposited into workers’ accounts in each paycycle.

We supplement this with data collected through two sets of phone surveys, which form the basis for our baseline data for Phases 1 and 2. These include information on perceptions about redistributive pressure. In addition, we use a more detailed in-person endline survey, which includes details about financial transfers with network members.

Table 2 provides descriptive statistics for our study sample, and checks for balance in baseline covariates across treatment arms separately for each Phase. Across treatments, the average worker in our sample has worked at the factory for 620 days, and has a baseline attendance rate of 68%. Almost all workers (98%) are women.

Our primary outcome measure, earnings, is balanced at baseline. Of the 25 comparisons presented, only the comparisons on attendance are statistically distinguishable across treatment arms at the 5% level. Note that our primary empirical specification controls for any baseline differences in levels using a differences-in-differences approach that includes individual fixed effects. In addition, as we show below, we find similar treatment effects using only endline data controlling for baseline covariates.

# **6.2 Estimation**

Our primary outcome is workers’ daily earnings (in FCFA). Given the linear piece rate incentive scheme, this is equivalent to examining effects on output, and serves as our measure of workers’ total labor supply. We also study effects on attendance to specifically examine the extensive margin decision of deciding to come to work.

We estimate effects for Phase 1 (comparing the Private treatment with Control) and for Phase 2 (comparing the Private treatment with the Non-Private treatment). To measure treatment effects, we use the administrative
A second set of problems arises because, while physicians and hospitals may correctly charge for what they actually do, they might not perform or recommend the appropriate procedure, despite knowing what that is: the know-do gap. Health insurance alters the relative price of different procedures, potentially influencing provider behavior in a way that could result in lower health care quality.

# Insurance Fraud and Administration

Health Insurance fraud ranges from 3-15 percent of program costs in OECD countries and 3-10 percent in the United States (Morris 2009). It probably accounts for a larger fraction of program costs in LMICs, as periodic reports (Ngetich 2021; Begue 2018) and audits suggest, but program-wide estimates are difficult to come by. Most countries have not made their health insurance claims data public.

Two problems that may be more salient in LMICs are denial of care and “surprise” or double billing. Denial of care refers to a situation where insurance cards are not honored at participating hospitals. Dercon, Gunning and Zeitlin (2019), who were the first to study this issue, show that denial of care is frequent in their setting in Kenya. Denial of care has two important implications. First, the decision to participate in the health insurance scheme depends on trust in the insurance system. Second, since denial introduces a new risk of the insurance not being honored, the overall risk with insurance may be higher than without. There is a state of the world in which the individual has paid the premium without receiving compensation in the bad state. If individuals are forward-looking, this implies that less risk averse individuals will be _more_ likely to take the insurance. By combining lab measures of trust and risk aversion with take-up decisions for an insurance product, Dercon, Gunning and Zeitlin (2019) show that both predictions hold in their data.

Surprise or double billing is a situation whereby health care providers charge the insurance company the reimbursable amount, but then levy additional (and illegal) top-ups from patients. Rather than providing an administratively mandated price for
For the purpose of this study, we utilize data from as many as 89 countries for which a nationally representative welfare survey from 2009 or later was conducted and obtained by the World Bank, and for which 2013 poverty rates have been published.<sup>10,11</sup> Figure 1 presents the number of surveys in the final sample, according to their survey year. The final sample contains 104 surveys. In 15 of the 89 countries there are two available surveys bracketing 2013, which are both utilized as described below. The year 2009 was selected as the cut-off year to balance the competing goals of maximizing geographical coverage of the sample and minimizing error when “lining up”, or extrapolating, country-level poverty figures to 2013 as discussed in Section 2.2.

**Figure 1: Number of surveys by survey year**

<!-- Start of picture text --> 15 10<br>15<br>2009 2010<br>24 2011 2012<br>10<br>30 2013 2014<br><!-- End of picture text --> In total, the 89 countries in the sample used in this paper represent 84% of the developing world’s population in 2013 and all geographical regions (Figure 2).<sup>12</sup> The 650 million in the sample that are classified as extremely poor amount to 85.4% of the estimated number of extreme poor worldwide in 2013, according to the World Bank.<sup>13</sup> Table 1 lists the number of countries and the share of the 2013 population represented in the sample, by region and income classification. Except for the Middle East and North Africa (MENA) and Sub-Saharan Africa (SSA) regions, the sample used in this study provides excellent coverage of the global population and the extreme poor. While the extremely low data coverage in the MENA region is a concern, it is not unique to this study. In
For the implementation and analysis, we use 2500 replications, with a sample size of 1,000 observations. Replications using sample sizes of 500 and 2,000 are provided in the appendix, with results that are qualitatively similar. We focus on the comparison of conditional quantile regressions for the 10<sup>th</sup> , 50<sup>th</sup> and 90<sup>th</sup> quantiles, as well as for the 10<sup>th</sup> , 50<sup>th</sup> and 90<sup>th</sup> unconditional quantiles. Quantile regressions were estimated using the fast algorithm developed in Chernozhukov et al (2022) and implemented via the Stata command -qrprocess-, whereas the unconditional quantile regressions were estimated following Firpo, Fortin and Lemieux (2009) and implanted via the Stata command -rifhdreg- (Rios-Avila, 2020). Finally, the simulation was implemented using -parallel- (Vega Yon and Quistorff, 2019). Finally, our imputation method is implanted with a new user-written program -intreg_mi-, which is available upon request.

While population parameters for conditional quantile regressions for some of the data generating exists, there are no close form solutions for the population parameters corresponding to the RIF regressions. Because of this, we assume that average estimates using fully observed data represent the population parameters, for the calculation of the relevant statistics. Thus, by construction, the bias of the model estimations using fully observed data is zero.

# **3.2. Results**

Tables 1 to 3 provide a summary of the results for the Monte Carlo simulations using the different data generating processes. In each table, we present the bias of our imputation procedure compared to the average parameters using fully observed data as if they were the asymptotic population parameters. For both conditional and unconditional quantile regressions, the bias observed using the multiple imputation data is small when the homoscedastic error is assumed to follow
# **4. Data**

We require data with considerable variation in life expectancy at birth and information on lifetime human capital investment and earnings. We exploit the World Bank’s _International Income Distribution Database (I2D2)_ for this purpose. I2D2 is a harmonized collection of household surveys conducted in 111 countries from all regions and income groups. A list of the countries and survey years is presented in Table B1 in Appendix B. Of our 111 countries, 30 are developed countries, 11 in Asia and Pacific, 17 in Central Asia and Eastern Europe, 23 in Latin America, 4 in the Middle East and North Africa, and 26 in Sub-Saharan Africa and South Asia.

From each survey, we include only those individuals who have complete information on both education and wages. Since our interest lies in how life expectancy at birth affects human capital accumulation and earnings, we focus on those individuals who were working at the time of the survey and reported positive incomes. We limit our analysis to the age group 25-60 years. Focusing on individuals over age 25 limits the probability that they would still in school;<sup>9</sup> the upper bound of 60 years avoids selection issues related to retirement and rising mortality with age.

Because life expectancy at birth sets the trajectory for lifetime human capital investment and earnings, we do not aggregate across individuals with different life expectancies, but instead define each cohort in each country as the unit of observation. Our earliest available survey is in 1970 and the latest is in 2012. To fit our age range of 25-60, we include 77 birth cohorts born between 1911 and 1987.

Many of the countries have multiple surveys, giving us repeated observations for many cohorts. However, completed schooling will be the same for the same cohort across surveys.

> 9 Both Barro and Lee (2010) and Cohen and Soto (2007) assume that years of schooling are fixed by age 25.

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# **Table 2. FinScope Survey Results on Access to Finance and Financial Literacy in Sub-Saharan Africa and Pakistan**

|COUNTRY<br>|USEF<br>|INANCIALP<br>|RODUCTS<br>|CURRENTLY<br>SAVE ORPUT|BORROWED<br>MONEY INPAST|USE AN<br>INSURANCE|AWARE<br>SAVINGS|NESS(“HASNEV<br>INTEREST ON|ERHEARDOF...<br>|”)<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|(YEAR OFSURVEY)|TOTAL|FORMAL|INFORMAL|MONEYAWAY|12 MONTHS|PRODUCT|<br>ACCOUNT|<br>SAVINGS|INSURANCE|LOANS|
|Southern Africa|||||||||||
|Swaziland(2011)|63%|50%|13%|66%|33%|22%†|--|--|--|--|
|Lesotho (2011)|81%|58%|23%|51%|66 % (“have<br>borrowed”)|61%†|--|--|--|--|
|South Africa (2010)|77%|63%|14%|37% (“save<br>regularly”)|33% (“have credit<br>product”)|50%†|--|--|--|--|
|Mozambique(2009)|22%|13%|9%|20%|8%|5%|82%|90%|88%|22%|
|Botswana(2009)|67%|59%|8%|--|--|