rafmacalaba/datause-encoder-data huggingface dataset:
# **<mark>Summary</mark>**

**Introduction** : In Latin America, there are about two million people living with HIV, and the epidemic is concentrated in key populations that have a high risk for infection. Various sources of information have been used in recent years to understand the region’s HIV epidemic and its determinants.  Recognizing the crucial value of evidence as the basis for strategic planning and the monitoring of results, ASAP has worked with Ministries of Health in eight Latin American countries to compile regional HIV data generated over the past 15 years.  ASAP organized the information from each country in a document entitled “ _Database and References for an Evidence Based Strategy”._ In a second phase, ASAP analyzed the methodological quality of this data to ascertain its scope and limitations, its reliability, and the extent to which it is feasible to compare findings across multiple studies. In order to carry out this assessment, a tool developed by ASAP was used, in both a questionnaire and checklist format.

**Objectives:** To present the conceptual framework, methodology and application methods of a tool that analyzes the methodological quality of data; to share, through practical examples, the results of implementing this methodology in the Andean Region.

**Methodology:** For the characterization and analysis, the studies first identify: (i) the type of document; (ii) the status and means of publication; (iii) the epidemiological design used; and (iv) the priority population.  Next, the document is analyzed against a verification list based on the following eight methodological criteria:

- 1) Clear and coherent objective, in accordance with the development of the study;

- 2) Definition of inclusion and exclusion criteria;

- 3) Sample size;

- 4) Sampling technique;

- 5) Strategies to reduce data collection errors;

- 6) Data analysis technique;

- 7) Evaluation of limitations imposed by the methodology; and

- 8) Expressions of confidence intervals and/or statistical significance.

One point is awarded if the feature described in each criterion is present, and zero if it is not. Criteria 3, 4 and 5 carry double weight. The scores are then added up, obtaining a total score or quality index on a scale from 0 to 11 points.

**Implementation in the Andean Region:** In 2009, ASAP utilized the tool in Bolivia, Ecuador and Peru. In all three countries, the findings reflected that the studies done in most-at-risk populations had quality scores lower than the scores from general population studies. The methodological characteristics that clearly contributed to these differences in scores were selection method bias and inadequate sample size.  Both of these characteristics represent challenges that must be taken into account in future research. There were also significant differences in the operational definitions of indicators—especially in HIV-

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# **I. Introduction**

There are three main reasons for informal employment to raise concerns. The first reason is related to the equity aspect, namely that informal workers’ well-being could be vulnerable if they are outside the formal social protection system and that poverty and inequality can increase if informal workers are systematically underpaid. Second, the informal sector operates largely outside the state’s regulation where tax evasion is the norm, both by firms and workers, which challenges the sustainability of the fiscal system. Third, informal employment could undermine productivity and economic growth if workers or firms choose to adopt small-size/low-productivity organizational and technology settings mostly to avoid tax payments and government regulations, or due to failures in other markets such as access to capital or entrepreneurial skills.

Rigid labor market institutions could lead to higher informality, but previous studies suggest that a combination of weak enforcement and universal access to basic pension and health care are also important drivers of informality in the Russian Federation. A large body of literature has shown that informal and formal labor markets are integrated rather than segmented, implying that workers tend to choose to be informally employed in Russia. In contrast to the large amount of research on the causes and drivers of informality, the welfare consequences of informality are much less researched and understood.

This paper has one main objective: to estimate the impact of informal employment on workers’ individual well-being. In order to do so, it also assesses the comparability of available sources of data on informal employment and updates the trends in informal employment through 2016. Several data sources are available for the purpose of studying the impact of informal employment on well-being in Russia. For the estimation of levels and trends of informal employment, we utilize four surveys to see how the trends compare across data sources and definitions. We then focus on one survey, the Russia Longitudinal Monitoring Survey (RLMS), which has rich and detailed information on employment, and complement the analysis with other surveys when appropriate. For all data sources, we maintain a definition of informal employment that is consistent with the legalistic view of informal employment. This definition is in line with the majority of Russian literature and is deemed more appropriate for transition economies.

The top panel of Figure 1 presents informal employment rates across countries from the ILOWIEGO database for the most recent year for which there exists an estimate for that country. Informal employment rates range between 40-70 percent in Latin American countries but are significantly lower in the Europe and Central Asia region. Russia ranks towards the bottom among all these countries – however it should be noted that this list conspicuously does not include any high-income countries where the share of informally employed is expected to be considerably lower.

1/67 Informal Employment and Worker’s Wellbeing in Russia
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relative (five-digit) 1968-69 quantities purchased and total imports, these associations fail to achieve statistical significance at the 99 per cent level (both are in fact significant at a 95 per cent confidence level). Thus, market size may have a relatively minor direct influence on price in 1968-69 as compared to the structure variables and related competitive factors. However, the indirect effects of size appear important in both periods since Table 4 shows this variable is correlated with market structure which, in turn, influences market prices. Aside from these relations, the correlations between relative prices and the association dummy are among the strongest in the table for both 1968-69 and 1986-87. This suggests that the special relations between French producers and the associated countries (see Appendix 1) isolates the former from active competition and allows prices to rise above levels dictated by market structure. As such, it would seemingly benefit the associated countries to encourage alternative trade contacts, or adopt policy measuras aimed at increasing competition in their import markets. However, they may be deterred from doing this by practices such as tying aid, or by the small size of their import markets.

While the correlations between relative prices and the explanatory variables are the primary focus of this analysis, some of the intercorrelations between the independent variables are also of interest. For example, there appear to be a number of specific factors working against a more favorable price position for the associated countries. Specifically, Table 4 shows that these nations had significantly fewer trade contacts in both time periods, are generally smaller markets and also have significantly higher concentration ratios. All of these factors undoubtedly contribute to higher import prices. Conversely, the developed countries have less concentrated markets, as well as a larger number of trade contacts. Thus,
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Survival plots for educational attainment by gender (Figure 3) show that the gender gap in access to education has closed over time. Only 27 percent of women born in the early 1950s received any education, versus 44 percent of men, and increases in education attainment between the first two cohorts were concentrated among men. Between the second and third cohorts, however, large gains were made for girls, so that for those born 1980-84, there are only small differences between the attainment profile of girls and boys in the first years of primary school. After fifth grade, however, attainment falls much more rapidly for girls than for boys.

Figure 4 shows the differences between quintiles of socioeconomic status, as measured by a wealth index.<sup>9</sup> It is important to note that because these curves are based on household wealth levels in 2005 (and not when the individuals were children), they reflect in part the effect of education on wealth levels. Despite progress, education attainment levels for the poorest Haitians are still exceedingly low. Only 30 percent of those in the poorest quintile born 19801984 completed primary education, and just 12 percent completed secondary school. For those in the same cohort in the top quintile, 89 percent completed primary school, and 75 percent secondary school. The figures for the top quintile compare favorably to the average attainment in wealthier countries of the region like Colombia, where in the cohort born 1976-85, 77 percent completed primary school. In contrast, the survival rates for those in the poorest quintile in Haiti are comparable to those of a poor sub-Saharan African country like Rwanda, where only 32 percent in the same cohort completed primary education.<sup>10</sup> This analysis is based on the education attainment of people living in Haiti in 2005. Consequently, the attainment profiles may differ from those of that of the original cohort. The fact that the less educated probably have lower life expectancy, and thus lower likelihood of surviving through 2005, means that the education levels shown here are probably biased upwards relative to those of the original cohort, particularly for the oldest generations. This would suggest that the increase in education attainments that has taken place over time is even greater than what this analysis suggests.

The effect of migration is harder to assess. The population of Haitians living in the United States was 420,000 in 2000, according to U.S. Census data, and very rough estimates

> 9 The wealth index was constructed using the approach of Filmer and Prichett (2001). The wealth index was generated using the pooled dataset with all three survey years. The coefficients of the wealth index can be found in Table A.4 in the appendix.

> 10 The figures for Colombia and Rwanda are based on calculations from the 2005 DHS surveys.

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**Table 11: The OLS results of the Quality of Life: Community Security and Health Status in 2000**

||Car<br>accidents<br>(per 1000)|Homicides<br>(per 1000)|Children in<br>poverty (%)|Gini|Life<br>expectancy|Child<br>mortality<br>(up to 1 year)|
|---|---|---|---|---|---|---|
|log(population)|0.339|-2.907|1.359|0.004|-0.503|1.365|
||(2.23)*|(2.78)**|(4.63)**|(1.56)|(2.64)**|(2.81)**|
|log(population)^2|-0.013|0.095|||||
||(2.29)*|(2.44)*|||||
|log(per capita income)|0.144|0.867|-26.894|-0.052|3.414|-13.686|
||(1.88)|(1.66)|(13.05)**|(2.61)*|(2.55)*|(4.01)**|
|Avg. years of schooling|-0.057|0.311|2.31|0.012|0.281|-0.078|
||(2.22)*|(1.76)|(3.32)**|(1.83)|(0.62)|(0.07)|
|Dummy (Southern)|-0.013|-0.871|-6.459|-0.04|2.653|-14.22|
||(0.33)|(3.20)**|(6.12)**|(3.87)**|(3.87)**|(8.14)**|
|Constant|-2.415|18.147|141.886|0.766|53.635|95.873|
||(2.36)*|(2.58)*|(18.10)**|(10.08)*<br>*|(10.54)**|(7.40)**|
|Observations|123|123|123|123|123|123|
|Adjusted R^2|0.05|0.18|0.91|0.46|0.59|0.81|

Absolute value of t statistics in parentheses; * significant at 5%; ** significant at 1%

**Table 12: The OLS results of the Quality of Life: Local Government expenditure in 2000**

||log(expenditure<br>on education),<br>per 1000|log(expenditure<br>on security),<br>per 1000|log(expenditure<br>on health),<br>per 1000|log(expenditure<br>on housing),<br>per 1000|log(expenditure<br>on<br>transportation),<br>per 1000|
|---|---|---|---|---|---|
|log(population)|0.018|0.178|0.091|0.136|0.166|
||(0.58)|(1.05)|(1.52)|(1.89)|(1.56)|
|log(per capita income)|0.797|2.239|1.278|0.723|1.005|
||(3.55)**|(1.65)|(3.02)**|(1.44)|(1.32)|
|Avg. years of schooling|-0.228|-0.578|-0.252|-0.183|-0.247|
||(3.00)**|(1.31)|(1.77)|(1.08)|(0.98)|
|Dummy (Southern)|0.331|0.72|0.114|-0.019|0.985|
||(2.86)**|(1.01)|(0.53)|(0.07)|(2.54)*|
|Constant|8.08|-5.172|4.486|5.976|2.663|
||(9.44)**|(1.01)|(2.79)**|(3.12)**|(0.93)|
|Observations|122|94|122|122|120|
|Adjusted R^2|0.39|0.12|0.22|0.06|0.22|

Absolute value of t statistics in parentheses; * significant at 5%; ** significant at 1%

# **6. PROXIMATE DETERMINANTS OF GROWTH**

We have examined population growth and productivity as two measures of success among Brazilian agglomerations. On the supply side, our underlying model assumes growth of the labor 29
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political influence could explain the differences across the former colonies in economic performance in the 20<sup>th</sup> century (a survey is Hoff, 2003): the initially richest areas—rich because of plantations and mines—were characterized by the highest levels of inequality and became the poorest in modern times. There was a “reversal of fortune”:

- 4) _The colonial origins of underdevelopment—_

Across the regions once colonized by European powers, the vast differences in the 18<sup>th</sup> and 19<sup>th</sup> centuries in the fraction of the population with access to the vote, schooling, financial markets, and property rights help explain the vast differences in economic performance today. This work focuses on the impact of history on macro-performance.

Research in the colonial origins of underdevelopment draws on a large sample of economies. But many questions about development cannot be studied across large samples. To make it possible in those cases to build cumulative knowledge, economic historians introduced an approach called “analytic narratives.” A theoretical model is built based on a specific historical problem. For example, Greif (1994) explains why the city-state of Genoa in the early medieval period established a rule of law to solve the problem of overseas agents behaving opportunistically during long-distance trade, whereas the Maghribis, who traded all over the Muslim Mediterranean world, did not. Although based on specific cases, models in analytic narratives generate testable hypotheses applicable to many cases (Bates et al., 1998).

# _5)  Analytic narratives—_

Economic historians enlarged neoclassical theory by deductive rational choice modeling of large-scale historical phenomena. History influences the salience of the possible equilibrium outcomes and, therefore, which equilibrium is selected.

Most work in NIE and analytic narratives retains rational choice theory while bringing social factors back into economic analysis. As the precision of empirical work in economics grew, it became increasingly clear that there remained much of importance that rational choice 5
We replace missing values and large outliers of observed real interest rates with predictions from a Kalman smoother based on local level models. Broadly following Fiorentini _et al_ . (2018) and Probst (2019), we identify observed real interest rates above 20 percent in absolute terms as outliers. The outlier removal, and the specific threshold in particular, has only a minimal effect on the estimates of r*, but ensures convergence of the model.<sup>17</sup> Table 1 shows the total number of observations in our sample, as well as the share of outliers and missing values.

**Table 1** Sample characteristics

||Full Sample|Advanced<br>economies|<sup>EMDE</sup>|
|---|---|---|---|
|Number of countries|50|23|27|
|Observations|2318|1129|1189|
|Outliers (%)|1.12|0.09|2.1|
|Missing (%)|0.13|0|0.25|

In Table 2 we provide summary statistics for the main variables over the whole period and during different episodes - the post-war period, the Great Moderation, and the Great Recession and its aftermath. As expected, both nominal interest and inflation rates were highest in the period before the Great Moderation, with quite similar values between advanced economies and EMDEs. In both ex-ante real interest rates were negative, though more so in EMDEs. In advanced economies both the average nominal interest rates and differences across countries decreased significantly afterwards. Since inflation declined even more, ex-ante real interest rates increased to 2.7 percent on average between 1984 and 2007. In EMDEs, nominal interest rates increased while inflation decreased, resulting in similar average ex-ante real interest rates of 3.1 percent during this period. With the global financial crisis, nominal interest rates in advanced economies dropped strongly from 6.6 percent between 1984 to 2007 to only 0.8 percent between 2008 and 2019. At the same time, inflation declined only from 3.4 percent to 1.5 percent, resulting in negative ex-ante real interest rates. There has been a similar trend in EMDEs, but their average ex-ante real rates remained positive during this period. Over the entire sample, ex-ante real rates have been 1.0 percent in advanced economies and 1.6 percent in EMDEs, with both nominal interest rates and inflation much higher in EMDEs.

> 17 In the robustness section, we show that inferring r* from the observed real interest rate without replacing missing values and outliers leads to very similar results.

8
# PRE Workina Paper Series

|:k~<br>fU|r|Dtfor|Contact<br>"per|
|---|---|---|---|
|WPS632 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of Argentin|Carlos Alfredo Rodriguez<br>a|March 1991|R. Luz<br>34303|
|WPS633 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of Thailand|Virabongse<br>Ramangkura<br>Bhanupongse<br>Nidhiprabh|March 1991<br>a|R. Luz<br>34303|
|WPS634 Trends in Developing<br>Country<br>Exports, 1963-88|Bela Balassa|March 1991|WDR Office<br>31393|
|WPS635 Exchange<br>Rates and Foreign Trade<br>in Korea|Bela Balassa|March 1991|C. Cuskelly-Young<br>39413|
|WPS636 Economic Integration<br>in Eastem<br>Europe|Bela Balassa|March 1991|C. Cuskelly-Young<br>39413|
|WPS637 Poverty in Poland, 1978-88|Branko Milanovic|March 1991|WDR Office<br>31393|
|WPS638 Researching<br>the Trade-Productivit<br>Link: New Directions|y James Tybout|March 1991|D. Ballantyne<br>37947|
|WPS639 The High Cost of Protecting<br>Uruguay's<br>Automotive Industry|Wendy E. Takacs|March 1991|D. Ballantyne<br>37947|
|WPS640 The Impact of Policy in African<br>Agriculture: An Empirical Investigat|William Jaeger<br>ion|March 1991|A. Yideru<br>34663|
|WPS641 Intertemporal<br>Substitutio**n**<br>i<br>Consumption:<br>Evidence<br>for Some<br>High- and Middle-income<br>Countries|Karsten N. Pedersen|April 1991|M. Hileman<br>31284|
|WPS642 How a Change in Brazil's Sugar<br>Policies Would Affec**t**he World<br>Sugar Market|Brent Boi rell|April 1991|A. Kitson-Waiters<br>33712|
|WPS643 Regional Integration<br>among<br>Developing<br>Countries,<br>Revisited|Andras lnotai|April 1991|D. Ballantyne<br>37947|
|WPS644 Trade and Payments<br>Arrangements<br>in Post-CMEA<br>Eastern and Central<br>Europe|Constantine<br>Michalopoul<br>David Tarr|os<br>April 1991|M. Colinet<br>34698|
|WPS645 Poverty, Policy, and Industrializa-<br>tion: Lessons from the Distant Past|Ben Polak<br>Jeffrey G. Williamson|April 1991|WDR Office<br>31393|
|WPS646 The Developmental<br>Effectiveness<br>of Aid to Africa|Tony Killick|April 1991|S. King-Watson<br>33730|
|WPS647 Growth Rates and Aggregate<br>Welfare: An Intemational<br>Comparis|Nanak Kakwani<br>on|April 1991|B. Rosa<br>33751|
effects—–often neglected in trying to understand ‘which’ tax rate matters for cross-border investment—require some qualification of the results for both the cross-border differential in statutory rates and the host marginal effective rate, but do not overturn the broad empirical conclusions on each. Requiring much less qualification is the finding that closed economy marginal effective tax rates outside the host country have little if any impact on inward FAI, and this is so even when some impact might be expected given fixed location costs impacting the extensive margin of investment. The implication is that league tables of marginal effective tax rates cast very little light on the relative tax attractions for multinationals of investing in alternative countries.

Applying these results to the analysis of the global minimum tax, due for implementation from 2024, the strength of the spillover effects through statutory tax rates mean that FAI into the (relatively high rate) host countries in our sample would be expected to increase with the imposition of a minimum rate at the proposed level of 15 percent. This is in sharp contrast to a view that increases in marginal effective rates as a consequence of the minimum tax (leaving aside the question of whether and when that will indeed be the consequence) will tend to depress real investment. Indeed while inward tangible investment into some host countries in the sample would fall at higher levels of the minimum, the total across all is predicted to increase up to a minimum of close to 30 percent.

There remain, of course, many open issues. These include, for example, understanding and testing the implications of the potentially very different commercial structures of multinational enterprises for the appropriate weighting of statutory rates in evaluating crossborder spillovers. The direction of the interaction effects noted above also remain conceptually puzzling; and, on the policy side, the characterization of the minimum tax reform taken here is highly simplistic. Nonetheless, the analysis casts some light on the surprisingly under-studied questions of how, and how much, the international tax system and the fundamental changes to it with which policymakers now need to grapple affect aggregate tangible cross-border investment.

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