rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

This paper assesses the relationship between democracy and income inequality. It is reasonable to think that democracies reduce income inequality by allowing citizens to vote for political parties that privilege redistributive platforms. Thus, societies with a more egalitarian distribution of political power may have lower income inequality. Nonetheless, Asian countries, for example, display low income inequality and low-rating democratic institutions while Latin America, a region with higher-rating democratic institutions, displays extreme income inequality (Savoia et al., 2009).

The lack of a clear relationship between democracy and income inequality has two popular explanations. The first one is the political Kuznets curve. This hypothesis states that democratization first produces an increase in inequality before it produces an improvement in the distribution of income. However, Palma (2011) states that inequality does not decline after democratization in countries with historically high levels of inequality. Acemoglu and Robinson (2002) clarify that there are exceptions to the political Kuznets curve, for example, inequality might fluctuate if countries switch between less and more democratic regimes (e.g., Latin America). A second explanation suggests that democracies that begin with high inequality may develop poor economic and political institutions that perpetuate the unequal distribution of income. Indeed, recent research attributes high and persistent inequality to the presence of “bad” economic and political institutions, which are in turn a result of high initial inequality (Savoia et al., 2009).<sup>1</sup> Gerring et al. (2005; 2012) argue that that the best way to think about the relationship between democracy and social outcomes is as a time-dependent, historical phenomenon. Most recent empirical studies assess the link between democracy and inequality using cross-country panel data sets, traditionally: the Deninger and Squire data base; UNU-WIDER’s World Income Inequality data base; and the World Bank’s World Development Indicators.<sup>2</sup> In general they conceptualize this link as the effect of regime type on inequality at time _t_ plus some specified period. However, regimes are historically informed phenomena rather than contemporary variables. Gerring et al. find, for example, that a country’s level of democracy in a given year is not likely to affect its level of human development, but its stock of democracy over a very long-period of time does.

On top of the previous problem “traditional” cross-country studies have other considerable problems: i) Some surveys report data on income and some on expenditure, making cross-country comparisons difficult, ii) The degree of accuracy is still a problem; e.g., some surveys undertaken in the midst of civil wars in sub-Saharan Africa have “national” coverage, but polities were fragmented

> 1See Acemoglu et al. (2013) for further discussion.

> 2For example: Li et al. (1998), Gradstein et al. (2001), Reuveny and Li (2003), Chong (2004), Hamanaka (2008) and Timmons (2010), to name a few.

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through simulations, to identify production challenges early on (Buxey, 1991; Milgrom and Roberts, 1990; Groover and Zimmers, 1983; Evans, 1980).

We next assess whether quality gains are heterogeneous across new and existing exported varieties. Robots are also likely to be particularly important for new products, with many goods now explicitly designed to be assembled by robots. Many of the most modern product varieties, such as cellphones, modern cars and other electronics goods, heavily rely on automation in their production.

However, there is likely overlap between the initial quality gap and new varieties or complexity. New varieties in our data are particularly likely to be exported by developing countries and these countries also produce lower-quality exports (Henn et al., 2020). Developing countries also tend to both produce lower-quality exports and also less complex goods (Levchenko, 2007). In our data we find that developing countries are also particularly likely to produce complex goods at lower-quality - combining a wide-variety of intermediates without production errors may be a particular challenge (Costinot et al., 2013).

To disentangle the roles of complex products and new varieties, or initial product quality, we first estimate these relationships individually and second introduce a horse race between these product characteristics. We measure potential heterogeneous quality gains for complex goods by interacting log robot stock with product complexity developed by Levchenko (2007), defined by (the negative of) Herfindahl-Hirschman Index (HHI) of intermediate inputs.<sup>24</sup> We next examine the possible of an overlap with initial product quality by running a horse race by including both complexity and initial quality interactions. We follow the same steps for new products, thereby replacing the complexity interaction with a new product indicator. The new product dummy reflects whether the variety has been first exported by that country after 2000, and not at any point in 1990-1999 before our sample period.<sup>25</sup> Then we run a horse race with new product and initial quality interactions.

> 24 Product complexity is measured using Levchenko (2007) with 1997 IO tables for US NAICS 1997 corresponded to data. We use the negative of the HHI, so that higher complexity measures a wider variety of inputs.  We divide the HHI by 1,000 for legibility of the coefficients.  We demean product complexity interactions so the so non-interacted terms reflect the effect of robots at the mean complexity.

> 25 Broadly similar results are obtained using alternative years such as products introduced since 2005 or 2010.

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**<u>Table 15. Impacts of IPAs on US Exports to Related Parties by Sector</u>**

||<br>(1)<br>US IPA|<br>(2)<br>US/EU/EFTA<br>IPA|<br>(3)<br>WTO-X IPR<br>LE=2 IPA|
|---|---|---|---|
|Low-IP × IPA|0.302***<br>(0.102)|0.102<br>(0.105)|–0.090**<br>(0.036)|
|AI × IPA|0.097*|–0.033|–0.262***|
||(0.050)|(0.059)|(0.040)|
|BIO × IPA|0.508***|–0.002|–0.073|
||(0.173)|(0.209)|(0.132)|
|CHEM × IPA|0.029|–0.098|–0.146***|
||(0.064)|(0.063)|(0.035)|
|ICT × IPA|–0.404***|–0.211***|–0.258***|
||(0.127)|(0.077)|(0.073)|
|MED × IPA|0.301***|0.008|0.118|
||(0.077)|(0.111)|(0.164)|
|PT × IPA|–0.018|0.019|–0.156*|
||(0.065)|(0.102)|(0.081)|
|Other × IPA|–0.047|–0.636***|–0.400***|
||(0.129)|(0.137)|(0.103)|
|Observations|15,294|15,294|15,294|
|Country-sector FEs|Y|Y|Y|
|Year FEs|Y|Y|Y|

_Notes_ : Dependent variable is US exports to related parties. GDP and TRIPS controls included but not reported. Robust standard errors clustered by country × year in parentheses. *** _p_ < 0.01, _** p_ < 0.05, * _p_ < 0.1.

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captures the views and assessments of a majority of actors with direct stakes in priority setting and judicialization of health.

In order to generate our empirical results, we perform standard statistical techniques (rank sum tests of means, ordered probit regressions, etc.). Whenever the statistical technique is sensitive to the small sample size (i.e. maximum likelihood estimation), we also used alternative tests (ANOVA and bootstrap quintile regressions and Bayesian techniques) for robustness check.

# **A) Empirical findings for the generic implications of the formal model**

To briefly recap the formally generated hypotheses, we expect that for a given institutional configuration _S_ (  ,  ) **,** there will be significant divergence among the bliss points of various stakeholders according to their preferences on two dimensions (efficency and equity): a) healthcare producer, consumer or regulator; and b) the organized or unorganized nature of the lobbying group.

Hypothesis 1 implies that all healthcare policy consumers care about efficiency to a greater extent than the unified public sector agency.<sup>41</sup> Hypothesis 2 suggests that there should be systematic differences among unorganized groups and organized lobbies on the perceived _efficiency_ and _equity_ issues.

If our theoretical assumptions are correct, we should be able to observe stakeholder heterogeneity of perceptions in terms of their general evaluations of the efficiency and equity of the current Benefit Plan and priority setting strategies designed by the Government.

Our measures of efficiency and equity of benefit plans result from question 4 of the general survey, phrased as: “What is the perception of your organization in terms of: a) the quality of the Benefit Plan; b) The efficient distribution of resources within the Benefit Plan; c) The accountability of<sup>health</sup> care providers to patients; d) The capacity of the Benefit Plan to provide similar service quality and attention to both marginalized and non-marginalized groups; e) The capacity of the average patient to have access to prioritized healthcare services (...).” The table below summarizes the main findings:

> 41 Here, the _unified_ public sector agency includes the three branches of government: the executive, legislative and the judiciary. Another version of this model and empirical testing will explore divergences in the bliss points of stakeholders representing separately these three groups.

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Suppose one were to find that all village members benefit from the program because they redistribute the PROGRESA grant in the village regardless of the actual identity of the households that receive the transfer. This would mean that costly and time consuming household level targeting may not be required. Geographic targeting, using poverty maps to identify poor areas, could be a cheaper and faster alternative.

# **6. Conclusions**

Subjects who are ineligible for a particular treatment will often benefit from it indirectly in a wide variety of interventions. Measuring these spillover effects (or Indirect Treatment Effects) and understanding the mechanisms that generate them can be crucial to the design of effective policies. Failing to adopt an evaluation design that accounts for spillover effects can result in a double underestimation of a program’s impact, as the average effect on eligible subjects is underestimated and the effect of the treatment on ineligible subjects goes completely unnoticed. This double underestimation may lead to incorrect policy recommendations (for example, dropping the program because it does not seem cost effective).

This paper makes some key recommendations to aid the design of evaluations that account for the presence of spillover effects. Here is a summary of these recommendations:

1. Develop and/or adopt a theory about what may cause the spillover effects, who may be affected by them, and how. Knowledge of the local economy and of the types of interactions among its members is crucial for the design of an evaluation that at least accounts for the existence of spillover effects. Ideally, the evaluation should enable researchers to measure the effects and understand why they arise. Having a hypothesis about spillover effects often means having better assumptions about how the program works, which should help in the design of better policies.

2. To measure spillover effects effectively, the evaluation design must take them into account from the start. This often means collecting more data, for instance by surveying geographical areas that are not affected by the intervention. Evaluation designs that do not consider these issues from the very beginning are typically unable to measure spillover effects.
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systems on Uruguayan trade. A better indicator would be for example, that the value of textiles and apparel products covered by RPs equals one- third of total Uruguay's value added in manufactured goods.

3.2 Measurement of the extra protection provided by the HE and **ME** systems in **UJruguay.** The costs of administered protection enforced in Uruguay at a time that Uruguay was undertaking trade liberalization can be disaggregated into three main components. First, the RP and MEP systems auzmint or magnify the tariff protection against selected imports. Second, the systems C the trade liberalization policy implemented during recent years. ITird, the systems tend to di=rt the relative prices of cheap and expensive imports of the same item. Table 2 presents the findings that support these conclusions.

_a. Zhe Droteaion augmentaion effect._ The RP and MEP systems magnify the impact of the nominal tariffs faced by imports with prices lower than the floor prices. This effect is likely to be substantial in that administered protection affects goods that already face relatively high nominal tariffs of 50 percent to 58 percent in IMo6, _45_ percent in 1987, 44 to 45 percent in 1988, and 37 percent to 39 percent in 1989 for the sample of nontextile goods examined here-or more than one-third of all the nontextile items involved in administered protection. The true tariffs faced by cheap imports are thus likely to be high.

In the case of the RP mechanism, the real tariff is derived from equation [1] above by dividing and multiplying tF by p:

[1'J P = p(l + tca), where - = F/p measures the protection augmentation effect introduced by the reference price mechanism. Data made available by the Central Bank of Uruguay allow us to distinguish between transactions cleared at prices below the floor price and transactions cleared at prices above the floor price. The average prices of the two types of transactions have been computed for each item. Dividing the floor price by the average price of the transactions cleared at prices lower than the floor price for each item gives the augmentation coefficient a, which enables the real tariff ta to be computed.' Ihe average true tariffs based on these computations are higher than the average apparent tariffs by 28.9, 27.6, 24.9, and 18.1

**Te** method of **avenging prices** for **the two tpes** of tnactions **has the advantago** of avoiding **the risk** of **overeotmating** protection by focusing on **the unactions** with the lowest prices. Indeed, **the** price **differenial betwee the two types** of **tansctons ae** a multiple of 2 **and 3,** a 'nonnal' figure by any standard.
perform physical activity, or to handle moving objects, such as vehicles or machines; requiring workers to repair large mechanical or electronic equipment; entailing worker exposure to diseases or infections; involving work in which email is very seldom used. This measure, however, is based on six-digit-level disaggregation of the Standard Occupational Classification (SOC) system. Given that the EU Labour Force Survey has a coarser disaggregation of occupations, we aggregate our telework measure to three-digit SOC occupations using employment weights based on the Current Population Survey of the US Bureau of Labor Statistics. Therefore, the six-digit-level variable used by Dingel and Neiman (2020) becomes a continuous variable between zero and one in our data; this variable reflects the share of jobs within each broad occupation category that is amenable to telework. We then transform this classification to the International Standard Classification of Occupations (ISCO08) used in the EU labor force survey.

# **Jobs with Greater Income Risks**

Using these criteria, we categorize a job as vulnerable to income risks if it is neither essential nor able to be undertaken via telework. Workers in these jobs are exposed to higher risks of dismissal and loss of income given that mobility restrictions impact their ability to perform their duties at their workplaces.

# **Jobs with Greater Health Risks**

When assessing job-specific exposure to health risks, as our fourth measure, we first quantify the extent of face-to-face interactions required by occupations. We follow Avdiu and Nayyar (2020) who create an index based on O*NET surveys. In this case, we assume that a job requires high face-to-face interactions if it involves tasks such as establishing and maintaining personal relationships, assisting and caring for others, performing for or working directly with the public, or selling to or influencing others through extensive personal interaction (Garrote-Sanchez et al., 2020). We categorize jobs as exposed to health risks, if they require above average face-to-face interaction level, are deemed essential and cannot be performed from home.<sup>5</sup> These jobs, such as those in healthcare or public transportation, are generally shielded from economic risks as they are considered essential, but they are highly exposed to the COVID-19 contagion given their high personal interaction and lack of amenability to work from home.

# **Jobs That Are More Vulnerable to Demand Shocks**

COVID-19 affected not only the labor supply (by restricting mobility) but also labor demand by changing consumption patterns (Rio Chanona et al., 2020; Baker et al., 2020, Brinca et al., 2020). While certain sectors, such as the health care sector, experienced an increase in demand, overall

> 5 Non-essential jobs that require a high degree of face-to-face interactions suffered more from income risks rather than from health risks; this is because mobility restrictions in many situations prevented workers from performing their duties.

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# Table 1. **SELECTED** INTEZVENTIONS AT **PRIMAY*** AND FIRST ROMMAt" LEVS TO REDUCE MUATRNAL MOTALITY AND YOREIDITY IN **DEVELOPING COUNTRIES**

|Cause of<br>Maternal Mortality<br>Morbidity|/<br>Intervention|He|alth System<br>Level|
|---|---|---|---|
|ALL|Family Planning<br>Primary <br>Prenatal care<br>Supervised delivery|&1st|referral<br>Primary<br>Primary|
|HEMORRHAGE|Risk screening/referral||Primary|
|(before,<br>during or<br>after delivery|Other prenatal care, including<br>treatment of anemia (diet,<br>iron/folate,<br>anti-malarials)<br>Oxytocics when placenta delivered***<br>Intravenous fluids<br>Transportation<br>to 1st referral level<br>Manual removal of placenta***||Primary<br>Primary<br>Primary<br>Primary<br>Primary|
||Blood typing of donors|1st|referral|
||Blood transfusion|1st|referral|
|SEPSIS|Risk screening/referral||Primary|
||Tetanus toxoid immunization<br>Clean delivery<br>Antibiotics when membranes ruptured if<br>not delivered within 12 hours***<br>Transportation<br>to 1st referral level<br>Hysterectomy|1st|Primary<br>Primary<br>Primary<br>Primary<br>referral|
|TOXEMIA|Monitor symptoms, blood pressure and<br>urine (for protein)||Primary|
||Bed rest/sedatives<br>Transportation<br>to 1st referral level<br>Induction or caesarean section|1st|Primary<br>Primary<br>referral|
|COMPLICATIONS|Antibiotics***||Primary|
|OF ABORTION|Transportation||Primary.|
||Oxytocics<br>Evacuation|1st|Primary<br>referral|
||Hysterectomy|1st|referral|
|OBSTRUCTED LABOR|Risk screening/referral||Primary|
|(including|Partogram||Primary|
|ruptured uterus)|Transportation<br>to 1st referral level||Primary|
||Symphysiotomy|1st|referral|
||<br>Caesarean section|1st|referral|

> * Primary level includes outreach programs, and health dispensaries, posts or centers.

** 1st referral level would usually be a district or cottage hospital with 20 beds or more, with capability for blood transfusion and caes;rean sections. *** Recommended experimental approaches at community level

Source: Herz and Measham, 1987.
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# **Table0: LENDUING FOR POPUIATION BY REGIONa:** FY86-88

Commitment in US$ million

|Fiscal<br>year|Region|No. of<br>projects|No. with<br>population<br>componentb|Total PHN<br>lending<br>|Total lending<br>to population|<br>Lending to<br> <br>population<br>as % of total<br>PHN lending|
|---|---|---|---|---|---|---|
|1986|AFRICA|5|5|81.1|9.7|12.0|
||ASIA|4|2|242.4|129.0|53.0|
||EMENA|0|-|-|||
||LAC|-- I|1|96.0|0.3|0|
|Sub|total|1!|8|419.5|139.0|33.0|
|1987|AFRICA|4|4|30.8|7.9|26.0|
||ASIA|0|-|-|||
||EMENA|1|0|13.3|-||
||LAC|==1|1|10.0|6.8|68.0|
|Sub|total|6|5|54.1|14.7|27.0|
|1988|AFRICA|5|3|121.4|19.9|16.0|
||ASIA|2|2|74.5|62.3|84.0|
||EMENA|0|-|-|||
||LAC|J|0|109.0|||
|Sub|total|8|5|304.9|82.2|27.0|
|Tot|al|25|18|713.5|235.9|33.0|

Source: Annex 11

- a Post-Reorganization country groupings b "Free-standing" population projects and projects with population components.

# Regional experience

70. Although the largest population loans are made to long-standing population/family planning programs in Asia, lending to Africa has been increasingly emphasized recently. In the last three years, 12 of the 18 population and population/health loans have been to African countries.

71.7 Africa, Population activities in Africa further the Bank's efforts to assist governments to improve economic development, health and individual well-being. The region's average annual rate of population growth of 3% is not expected to decline until around the year 2000, 20 to 30
being surveyed in larger economies and is chosen to make each country’s sample representative of the non-agricultural private economy.

From the Enterprise Survey we construct the following four access to and use of financial services indicators: (i) _access to finance_ is an indicator variable ranging from 1-5 with 1 indicating access to finance is “no obstacle” to the operation of firm to 5 indicating a “very severe obstacle”; (ii) _account_ is a dummy variable equal to one if the firm has an account at the time of the survey and zero otherwise; (iii) _overdraft_ is a dummy variable equal to one if the firm has an overdraft facility at the time of the survey and zero otherwise; (iv) _loan_ is a dummy variable equal to one if the firm has a line of credit or loan from a financial institution at the time of survey and zero otherwise.

We match the two samples by building a cross-sectional dataset that matches the firm characteristics with the average of the available data from the FSAP reports. Maximum country overlap between the two data sources is 54 countries with over 25,000 firm level observations. Appendix Table A1 lists the countries in our sample, a breakdown of the firm distribution by country, and by-country summary statistics of the FSAP variables we will use in the subsequent analysis. Table 1 provides descriptive statistics and Table 2 correlations on the country-level.

The descriptive statistics in Table 1 show that over 90% of firms in our sample have an account. This percentage, however, varies significantly across countries. While in the Slovak Republic, 20.8% of firms have an account, 99.8% do so in Croatia. Almost 50% of firms have an overdraft facility and 45% have a loan. Behind this average, however, are again large crosscountry variations. While only 1.3% of firms have an overdraft facility and 3.1% a loan in Guinea-Bissau, 87.5% and 74.5%, respectively, do so in Chile.

We also use information from the Enterprise Surveys to control for firm-level characteristics that might affects a firm’s ease of access to financial products. In particular, we construct dummy variables for firm size (small, up to 19 employees; medium, 20-99 employees; large, 100 or more employees), being a subsidiary, and being publicly listed, and control for the percentage of the firm owned by private foreign owners and the percentage of a firm owned by the state, as well as the firm’s age. The descriptive statistics in Table 1 show that 47.4% of all firms are small, 34.3% are medium-sized and 18.3% large. 13% are subsidiaries of other firms, 8