rafmacalaba/data-use-mentions-extended huggingface dataset:
from these studies and uses a larger sample of countries, our results on the effects of a persistent inflation shock are broadly in line with theirs.

A few recent empirical studies on the effects of inflation shocks on public debt are summarized in Table 1(b). As far as we know, Afonso and Jalles (2019) is the only one that uses the local projection method as we do. While they use historical data before World War I and focus on a deflationary dummy, their results from the local projection are broadly in line with ours. Quantitatively, however, their results and End et al.’s (2015) results from panel regression, however, are smaller than ours. Cherif and Hasanov (2018) estimate a VAR model with the debt dynamics equation using the US data, and their estimated impact of an inflation shock on the debtto-GDP ratio is comparable to ours.

Besides the above-cited recent studies using simulation or estimation approach comparable to ours, there is a long strand of literature on the link between inflation and public debt. Using detailed historical data of public debt, Hall and Sargent (2011) and Abbas et al. (2011) provide an accounting decomposition of the evolution of public debt and calculate the contribution of inflation and find that inflation was not a consistent source of public debt reduction, but played an important role in certain periods, such as post-war United States until the 1960s. Giannitsarou and Scott (2008) explore the implications of rising public debt for inflation based on an accounting exercise of public debt. The literature on the fiscal theory of price level, including Cochrane (2011) and Davig et al. (2011), explores the theoretical possibility that a high level of public debt may lead to higher inflation. Doepke and Schneider (2006) study the impact of an inflation shock on wealth distribution across private holders of public debt.

# **III.   SIMULATION APPROACH**

# **A.   Methodology**

# **Debt dynamics equation**
, early career choices matter and have long-lasting effects on employment prospects (Oreopoulos et al. (2012)).

# **2.2 Formal vs. informal sector wage growth**

In addition to job stability, choices over different types of employment also affect earnings as different career paths offer different wage trajectories. Figure 1 uses social security data (IMSS) to plot the average wage growth for formally employed people in our study sample of school leavers. In order to test how much of the observed wage growth is driven by firm selection, we show wage increases separately for people who exit employment in a given month (light) versus those that retain their job (dark). The graph shows several interesting stylized facts. First, there is substantial wage growth at the beginning of school leavers’ careers. After six months, wages have increased by almost 25%. While the wage growth slows down slightly afterwards, we still see a large gain of 35% after 12 months and 50% after 18 months. (The average annual inflation rate over this period was below 5%.) Second, 8
.5460|0.4981|0<br>1|
|**Living under the poverty line**|1896|0.1065|0.3086|0|1<br>658|0.0821|0.2747|0|1|1238|0.1195|0.3246|0<br>1|

_Note:_<sup>_1_</sup> _For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 52
4 even have any say in whether they are covered. Currie and Gruber (1996) and Finkelstein and McKnight (2008) exploited the staggered rollout of the Medicaid and Medicare programs in the US: Currie and Gruber found that increased Medicaid eligibility led to “a sizeable and significant reduction in child mortality”, while Finkelstein and McKnight found that “the establishment of universal health insurance for the elderly had no discernible impact on elderly mortality”.

In this paper we exploit the staggered rollout of Thailand’s Universal Coverage (UC) scheme (cf. e.g. Pannarunothai _et al._ 2004) to estimate its impacts on one measure of health status – whether individuals report themselves as being too ill to work. By using general revenues to cover everyone not covered by the civil servants scheme or the social security scheme, Thailand achieved universal coverage within a period of 12 months. The fact that the expansion was achieved in a staggered fashion in four waves enables us to identify the effects of the UC reform on health status. We estimate the health effects of UC by linking an individual’s self-reported health limitations at a particular date to their length of exposure to the UC “regime”; this depends on the survey date and the person’s province of residence. Our statistical power comes from the fact that we have an average of 62,000 respondents in our labor force survey at each survey date and no less than 68 survey dates, most of which are just one month apart. The accuracy of our estimates is enhanced by the fact that 17 of the 68 surveys we use predate the launch of UC: this allows our regressions to capture the pre-reform trends in our health limitations variable.

We find that UC reduced the likelihood of people reporting themselves to be too sick to work. In our estimation, we allow the effect to change with increased exposure to the UC
In addition, we now have strong evidence that individuals within a household may not be equally vulnerable to shocks (Case, Paxson and Ableidinger 2004, Dercon and Krishnan 2000, De Vreyer and Nilsson 2019, Rose 1999).

Despite these efforts, since very few datasets permit researchers to measure individual consumption and thereby intra-household inequality, available estimates of poverty and inequality simply ignore this issue. Standard measures of poverty and inequality are calculated assuming that resources are shared equally within the household (with some normalization for size and demographic composition). From the conceptual standpoint alone, viewing poverty as essentially an individual state requires us to go beyond this approach. From the policy point of view, neglecting intra-household inequality is likely to introduce biases in the poverty and inequality measures. Less obvious is the fact that household-based consumption data collection could also lead to an underestimation of mean consumption, which might in turn bias poverty estimates upwards.

We conducted an unusual survey aimed at better understanding household structure and intra-household resource allocation in Senegal ( _Poverty and Family Structure_ survey, hereafter PSF, see De Vreyer _et al._ 2008). The consumption section of the survey has been designed to collect consumption expenditures at the level of small groups within the household, and involved the interview of several household members.<sup>1</sup> The data can be used to construct a measure of consumption at the cell level, allowing a more precise assessment of individual consumption than permitted by traditional surveys.

These data are very revealing on intra-household inequalities. In general, food expenditures appear equitably distributed as far as their actual inter-individual allocation is observable (Meals are mostly collectively taken from a single dish, see section 2.3). Differences emerge 4
<!-- Start of picture text --> Figure 19. Percentage of the population with service connections who do not pay for service<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Poorest quintile 2nd quintile 3rd quintile 4th quintile quintile<br>Source : Banerjee and others 2008.<br><!-- End of picture text --> Another possible measure of affordability is whether the _full economic cost_ of a _subsistence level_ of consumption falls above a _normative affordability threshold_ . Economic cost is defined as the tariff that would fully cover both operating and capital costs and is country specific; reaching an average level of US$0.18/kW for SSA as a whole. Subsistence consumption is defined as 50 kWh per month, which is enough to cover very minimal usage for lighting (roughly one light bulb for two hours per day). The affordability threshold is typically defined as spending on subsistence power needs of between 3 and 5 percent of the total household budget. These values are normative, and are informed by power spending patterns by low income households that have been observed across a wide range of household surveys (recall figure 18 above).

By looking at the distribution of household budgets, one can calculate the percentage of households for which subsistence consumption priced at full economic cost would absorb more than 5 percent of their budgets and thus prove unaffordable. For example, looking across the distribution of household budgets for all of SSA, monthly bills of $2 would be affordable for almost the entire population, whereas monthly bills of $10 would only remain affordable for the entire population of _middle-income_ African countries.

Based on existing average effective tariffs, the bill for subsistence consumption levels looks very affordable _for those that are already connected to the grid_ (Figure 20a). With a 3 percent affordability threshold, the subsistence consumption of 50kWh/month priced at
1990, ... 2013} using the following equations:

* (Eq 2) _Physicians per 1000 populationt_ = _α_ 0 + _α_ 1 _yeart_ + _εt_

* (Eq 3) _Nurses/midwives per 1000 populationt_ = _β_ 0 + _β_ 1 _yeart_ + _εt_

where _εt_ is the random disturbance term and α0, β0, α1 and β1 are unknown parameters, with the last two parameters representing the linear growth rates to be estimated from the model.

For countries where more than two historical data points are available for physician and nurse/midwife density, the future projections of worker density are predicted based on the model parameter estimates. More than two historical data points were available for physician and nurse/midwife density for 136 and 81 countries, respectively, out of the total of 165 countries in our sample.

For countries with insufficient historical data points or where linear regression prediction yielded implausible values, several alternative approaches were used to estimate future supply (see Annex A for details). Briefly, for 72 and 118 countries, respectively, for physicians and 7
The results in the first column indicate that areas with more fighting have seen a larger increase in female primary attainment. Casting this result in terms of the distribution of conflict violence, this implies that an increase in violence by one standard deviation of the district-level distribution of casualties during the conflict (0.98 casualties per 1000 inhabitants) increases female primary schooling attainment by 5.6 percentage points. Put differently, this is roughly the effect of a dramatic move from the 5<sup>th</sup> to the 75<sup>th</sup> percentiles of the district-level conflict distribution of total casualties. The sign of this effect is confirmed when comparing cohorts born only five years apart observed at the same age (between 10 and 18 years old) in 2001 and 2006 (Column 5), although this effect is somewhat larger but less precisely estimated and so only marginally significant. When investigating separately exposure to conflict at ages 6-10, the “normal” primary schooling age when over 80% of children are enrolled in primary school (author’s calculations based on DHS 2006 data) and at 11-14, when the share enrolled decreases sharply from 70.5% to 16.1%, neither conflict exposure coefficient is statistically significant, nor are these coefficients statistically significantly different from each other.

Across all specifications in Table 4, conflict exposure does not appear to affect male primary schooling completion.

The estimates reviewed so far suggest that any positive factor contributing to an increase in female educational attainment is broadly common to all girls aged 6-14, which rules out an explanation due to a reduction in the incidence of early marriage and indicates that, although parents may be reluctant to send young female teenagers to school due to the risk of sexual violence, this effect is compensated by other channels favorable to girl schooling, as otherwise one would expect that conflict experienced while 11-14 would tend to decrease educational attainment. The results in Table 4 also suggest that such favorable factors are not shared equally by boys. Although
.8%. In addition to the generalized growth of foreign flows to emerging markets after the 2008 global financial crisis, there are two key related events in Colombia in the period that contribute to explain the surge of foreign investors: (i) Colombian sovereign debt recovered its investment grade in June 2011, and (ii) in 2013, taxes on foreign investors’ earnings from domestic securities were reduced from 33% to 14%. Between 2012 and 2017, foreign mutual funds accounted for 7.8% of the total traded value, with investment banks, ETFs, and government funds representing each 5.2%, 3.5%, and 2.8% respectively (Table OA2, Panel A).

According to the annual statistics of the World Federation of Exchanges (WFE, 2019), at the end of 2017, Colombia had the fourth largest stock market in Latin

> 9Since February 2012, the market hours are synchronized with the New York Stock Exchange trading session, with a closing batch auction during the last five minutes of each trading day.

10In Colombia, there are no individual or aggregate ownership limits on foreign investors. Foreign individuals and institutions are allowed to either reinvest or transfer earnings such as dividends or capital gains with little restrictions.

6
5|66.5|
|2012|18.8|68.2|41.3|61.9|60.2|56.5|64.4|
|2013|22.4|68.1|41.6|61.3|58.1|55.7|66.5|
|2014|16.8|62.8|47.1|59.3|57.2|56.5|66.3|
|2015|18.9|63.3|41.4|61.4|58.3|56.7|71.4|
|2016|19.2|62|46.5|61|53.3|57.1|67.5|
|2017|18.9|60.5|48.1|61.6|54.8|56.7|66.8|
|2018|20.4|62.1|41|61.4|54.3|55.8|68.1|
|2019|21.7|62.8|40.6|61.1|51|55.9|67.9|
|2020|21.1|58.5|44.3|60.2|53.1|55.5|67.3|
|2021|17.0|62.8|42.1|63.3|52.6|57.5|68.8|

_Notes:_ Based on data from EH 2007-2021.

Column (4) shows that approximately 60 percent of workers in Bolivia have an hourly income above the third decile of the income distribution of their respective cohorts. Moreover, the share of workers complying with this component has remained relatively stable over the decade, with a peak in 2021, reporting the highest percentage throughout the period. On the other hand
sup>11</sup> The average age of first marriage and first birth are 20.23 and 22.13, respectively. On average, each mother has 2.74 children and 7.75 years of education, or the equivalent of halfway through lower secondary education. There are multiple observations of preschool access per district, ideally equal to the multiple of 9 PODES rounds between 1990-2014; however, some districts in West Sumatra province are not included in the 2011 PODES and SUSENAS because probability sampling does not always cover all the districts. Ultimately, there are 2,559 districtyear observations of preschool access with an average of 0.24 public and 4.18 private preschool density.

# **4. Empirical Strategy**

> 10 In other instances, years 1988-1990 of a constructed panel are matched to 1990 PODES, years 1991-1993 to 1993 PODES, and years 2012-2015 to 2014 PODES.

> 11 IFLS surveys span over 21 years. However, the employment history which extends as far back as 1988 allows a maximum of 27 years of observations between 1988 and 2015.

8
the ICRG index, public or external debts, the interaction terms of ICRG with the data transparency indicator and (public or external) debt with the data transparency indicator, lagged trade and financial openness variables as instrumental variables.

# _Transparency Data_

Data transparency indicators are proxied by public data transparency indices from the World Bank and IMF. The World Bank’s statistical capacity indicator (SCI) captures the availability, collection and practices in the production of official statistics by the country, and the IMF’s subscription and compliance to the Special Data Dissemination Standard (SDDS) are binary variables. We refer to the indicators of the World Bank as the WB data transparency and the indicator of the IMF as the IMF data transparency.

The World Bank’s Statistical Capacity Indicator (SCI) measures a country’s ability to collect, analyze, and disseminate high quality public data of an economy. This indicator is a composite score that evaluates the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: (i) methodology, (ii) data sources, and (iii) periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. Therefore, the overall Statistical Capacity score is calculated as a simple average of all three dimensions (i.e. practice, collection, availability) with a scale of 0- 100. Higher scores mean that a country has a stronger statistical capacity. The methodology indicator (or practice score) measures the country’s ability to adhere to internationally

> 4 Banisar (2006).

7
Bank’s Doing Business Report in 2006 (WBDBR) shows Morocco and Tunisia to have rigid labor markets compared to other developing countries (fig 4).<sup>2</sup> <!-- Start of picture text --> Fig 4: Rigidity of Employment Index<br>70<br>60<br>60<br>54<br>50<br>44 45 46<br>40 37 37 39<br>30<br>24 24<br>20<br>10<br>10<br>0<br>Malaysia Chile Czech Hungary Poland Slovak Bulgaria Philippines Pakistan Tunisia Morocco<br>Republic Republic<br><!-- End of picture text --> Rigidities in other factor markets in Tunisia include: delays in securing finance, land and construction permits; difficulties in closing businesses due to lengthy bankruptcy procedures; restrictions on majority capital ownership by foreign investors in key sectors; and general restrictions to entry (foreign and national) in certain activities (World Bank 2004). The recent Morocco ICA also points to difficulties faced by firms in raising finances from banks as well as in accessing land. The high cost of finance and access to industrial land was considered to be a major constraint by some 80% and 43% of firms surveyed, respectively. The 2006 WBDBR shows that the minimum capital requirement (as a percent of income per capita) in Morocco was 700.3% compared to an average of 177.3% for all countries included in the survey. The figure for Tunisia declined from 327.8% in the 2004 WBDBR to 29.8% in the 2006 WBDBR, showing improvement in this area.

# **Model**

It is against this background of existing factor market rigidities that we consider how improved flexibilities in the factor markets could affect the impact of trade reforms. A

> 2 Available at: <u>http://www.doingbusiness.org
unsophisticated investors, this might boost the bonuses of the financial engineers and executives associated with marketing the new-fangled instruments while simultaneously distorting the allocation of society‘s savings and impeding economic prosperity.

Although the evidence on the role of the financial system in shaping economic development is substantial and varied, there are serious shortcomings associated with measuring the central concept under consideration: the functioning of the financial system. Researchers do not have good cross-country, cross-time measures of the degree to which financial systems (a) enhance the quality of information about firms and hence the efficiency of resource allocation, (b) exert sound corporate governance over the firms to which they funnel those resources, (c) provide effective mechanisms for managing, pooling, and diversifying risk, (d) mobilize savings from disparate savers so these resources can be allocated to the most promising projects in the economy, and (e) facilitate trade. Instead, researchers have largely—though not exclusively— relied on measures of the size of the banking industry as a proxy. But, size is not a measure of quality, or efficiency, or stability. And, the banking sector is only one component of financial systems.

A key contribution of this paper therefore lies on the data front. The effort to put together, improve, and analyze data on financial systems is an important motivation for this paper, and for the associated online database tools. In recent years, substantial efforts have gone into improving various aspects of these data, and the paper makes use of these improved data. This paper is accompanied by a newly launched Global Financial Development Database, an extensive worldAugust 31, 2012 (9:27 PM)
and Measurement, which are part of the Instruments field of technology. On the other hand, there is no Russian comparative advantage in chemistry sub-fields such as Micro-structural and Nano-technology or Furniture, Games and Consumer Goods. As well, there is no RPA in Instruments sub-fields such as Optics and Control. Finally, none of the 8 sub-fields of Electrical engineering Russia show a RPA. However some sub-fields of Electrical engineering are part of government priorities such as Computer Technology, Telecommunications and Digital Communications. (The complete RPA/RTA is in Annex II.)

**Figure 5: Russia’s RTA in Sub-fields of Technology, 2002–07**

<!-- Start of picture text --> 7 6.3<br>6<br>5<br>4<br>3.2<br>3<br>2<br>1<br>2.4 2.2 2.1 1.9 1.9 1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.2 1.2 1.0 1.0 0.9 0.8 0.8 0.8 0.6 0.5 0.4 0.4 0.3 0.3 0.2<br>0<br><!-- End of picture text --> Source: Authors’ elaboration using WIPO 2011 data (http://www.wipo.int/ipstats/en/statistics/patents/).

Contrary to more advanced economies where the business sector plays a leading role, the bulk of R&D expenditures in Russia is funded by the public sector (66.5 percent in 2009) (Figure 6). This share has continuously increased in the last few years, while that of both private and foreign sources has decreased to 27 percent and 6 percent respectively, probably as a result of the global financial crisis. It is estimated
|0.337|0.119|
|Mozambique|0.086|0.371|0.049|
|Namibia|0.140|0.176|0.081|
|Niger|0.154|0.355|0.153|
|Nigeria|0.111|0.326|0.163|
|Rwanda|0.070|0.365|0.025|
|Senegal|0.222|0.159|0.090|
|Sierra Leone|0.090|0.327|0.082|
|Swaziland|0.033|0.226|0.020|
|Tanzania|0.113|0.355|0.040|
|Togo|0.070|0.214|0.062|
|Uganda|0.117|0.281|0.039|
|Zambia|0.102|0.343|0.055|
|Zimbabwe|0.070|0.256|0.028|
|Mean|0.114|0.321|0.086|

Note: Observations with missing values and pregnant women are dropped. Means are population weighted. Women between 15 and 49 years of age and children between 0 and 5 years of age are included. A woman is underweight if she has a BMI less than or equal to 18.5. A child is stunted if she is two standard deviations below median heightfor-age and wasted if she is two standard deviations below median weight-for-height. GDP and literacy are taken from the World Bank’s <u>World Development Indicators. Literacy rate for 2011 or closest available year to 2011 in</u> 2007-15; more recent year for ties.

39
# **_Including China_**

The baseline results exclude China from the regressions, since Chinese trade data are reported jointly for January and February 2020. In addition, China does not report information feeding the Google mobility data. As a robustness check we combine trade flows data on January-February for our 28 countries and measure Covid-19 related shocks with the industrial production variable to be able to include China as exporter in our regressions. That is, monthly growth rates that were previously computed for February only are now computed for January and February combined. Work mobility changes are replaced with annualized IPI growth in the exporting countries and retail mobility with annualized IPI growth in the partner countries. The results are reported in Table 6.

**Table 6: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics,**

**including China, OLS regression results**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|
|**Exporter supply**|**_ipiit*remoteik_**|**1.309*****|||**0.772***|
|**sector**||(0.440)|||(0.469)|
||**_ipiit*gvcil_**|**2.104*****|||**2.571*****|
|||(0.616)|||(0.638)|
|**Partner demand**|**_ipijt*durablek_**||**0.475