rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 14: Average years of education by gender and age. Cambodia Labor Force Survey 2001**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>0 to 3 4 to 8 9 to 13 14 to 18 19 to 23 24 to 28 29 to 33 34 to 38 39 to 43 44 to 48 49 to 53<br>Age in 1975<br>Males Females<br>Years of schooling<br><!-- End of picture text --> 34
income is never negative so that if the income is less than the transfer, the latter is adjusted to avoid the definition of income being negative.

# **_Estimated value of health care and education services received by households_**

Using data from the SLCHBS we identify individuals who used public education and health services and imputed values for the use of these services. Based on the utilization of health services by the individuals who responded to the survey, a distinction is made between those who received inpatient care and those who received outpatient care in hospitals and community health centers. We then found data on average unit costs by type of care and imputed it to the individuals who used the service to proxy for the state’s current expenditure on health services. As for education, we assume that the public education system serves the entire preschool, primary, secondary, and postsecondary school population. The current expenditure on education was derived from the costs per level of education specified by the Grenada government. Once the annual cost of each level of education was obtained, it was imputed to each student in a household according to the level at which they were enrolled.

Due to the information limitations on household income structure and national accounts in Grenada, we could only perform a basic application with direct effects, omitting considerations on agents’ behavior. As more information and parameters on fiscal interventions become available, they could be added to the estimates of fiscal incidence.

# **5. Main Results**

A set of poverty, income distribution, and indicators of progressivity measures were constructed to answer the questions posed above. Next, we present the main results of the simulations.

# **5.1. How Does the Fiscal System Affect Poverty?**

The fiscal system has a high potential for poverty reduction in Grenada (table 1), with the highest impact being associated with in-kind transfers. After adjusting the disposable income, the poverty rate in Grenada for 2018 reached 22.77 percent under the definition of consumption per
1. To display the list of the directly available ICIO tables and their releases:<sup>4</sup>

``` .icio_load,info ```

|`table`|`version`|`from`|`to`|
|---|---|---|---|
|`wiodn`|`2016`|`2000`|`2014`|
|`tivan`|`2018`|`2005`|`2015`|
|`eora`|`199.82`|`1990`|`2015`|
|`wiodo`|`2013`|`1995`|`2011`|
|`tivao`|`2016`|`1995`|`2011`|

In this way the user can always recover which ICIO tables are directly available via the `icio` command. As can be seen from the previous output, at the time of writing, the following tables have been made available: the “2013” and the “2016” releases of the WIOD tables, the “2016” and “2018” of the TiVA tables, and the “199.82” version of the Eora Global Supply Chain Database tables.

2. To load a specific year of the ICIO table of interest; for example the following syntax allows to load the year 2014 of the WIOD tables released in 2016 (i.e. `wiodn` ):<sup>5</sup> ``` .icio_load,iciot(wiodn)year(2014) ``` 3. To load a user-provided ICIO table, by specifying `user` , instead of a specific ICIO table’s code, in the `iciotable(` _table_ _~~n~~ ame_ `)` option. The user-provided ICIO table and the related country list must be provided using two different comma separated files (i.e. files with extension `.csv
gravity variables are as expected: the larger the size of the sub-region and the trade-partner economy, the larger the exports to the trade partner; the further away in distance is the trade partner, the smaller the exports.

Next we examine if there are spillover effects from the banks from other neighboring trade-partner countries. The results in Table 3 indicates that the presence of banks from countries within the region of the trade partner continues to promote exports. However, a higher share of regional banks from neighboring countries reduces exports to the trade partner. The results suggest that foreign banks from neighboring countries have a trade diversion effect, where the sub-region trades more with regional countries that have a larger bank presence and substitute away from trade with the trade-partner country. When both the shares of bank branches from the trade partner and neighboring countries are included in the regression, the trade diversion effect is present but the presence of bank branches from the trade partner mitigates this effect. A one standard deviation increase in the share of regional bank branches decreases _Log(Exportsijt)_ by 0.06 standard deviations. In contrast, a one standard deviation increase in the share of bank branches from the trade partner increases _Log(Exportsijt)_ by 0.08 standard deviations. The net effect on _Log(Exportsijt)_ is positive. The role of the level of financial development remains unchanged from baseline results. A one standard deviation increase in financial development increases _Log(Exportsijt)_ by 0.03 standard deviations.

Finally the data set allows us to investigate whether the role of foreign banks changes as financial conditions tighten. We include a dummy for the crisis period and interact it with the variables for financial development and the information channel. Tables 4 and 5 report the results. Again the intensive margins play a more significant role in boosting trade. When the _Share(Trade-partner bankijt)_ grows by one standard deviation during the crisis, _Log(Exportsijt_ ) increases by 0.05 standard deviation. The corresponding effect for either the financial development
# **I. Introduction and motivation**

A large social science literature suggests that the larger and more established the middle class, the better off society is, or will be (Easterly 2001; Banerjee and Duflo 2008). This suggestion is not new: Aristotle argued, in _Politics_ , that the middle class serves as a mediator between the rich and the poor, preserving values conducive to stability and prosperity. Among academics but also in the public discourse, a narrative of increasing inequality and a crisis of the middle class has emerged (Fukuyama 2012; Besharov and others 2016; Sitaraman 2018; Bussolo and others 2018). Remarkably, the most common way in which the crisis of the middle class has been represented is by its shrinking.

This paper examines the _health_ of the middle class by using on a novel definition of the middle class. In this new definition, introduced by López-Calva and Ortiz-Juarez (2014), being in the middle class requires absence, or a low risk, of vulnerability to poverty. The risk of experiencing poverty –defined as having income levels below a given poverty line– is what makes an individual _vulnerable_ or insecure in an economic sense. Since economic security is a hallmark of the middle class, being vulnerable means not being in the middle class. So rather than focusing on what happens to the size of the middle class, this paper investigates changes in the risk of vulnerability to poverty. In essence, this vulnerability is assessed by estimating a level of household income associated with a low probability (defined as less than 4 percent over four years) of falling into poverty. By using European panel data covering 22 countries during the period from the 2008 global financial crisis to just before the onset of the COVID-19 pandemic, this paper assesses, for the first time, whether the crisis of the middle class manifested itself as an increase of the risk of falling into poverty or, equivalently, as an increase of the minimum income needed to be (almost) immune from this risk
studies use considerably smaller and more homogeneous country samples than the sample used in this paper. This helps to explain why, in their studies, the global factors account for larger shares of inflation variation (Figure 1). Nevertheless, the shares presented here are within the range— although toward the low end—of those reported in the literature.

Another group of studies focus on the evolution of global inflation synchronization over time. Neely and Rapach (2011) extract a global factor and seven regional factors from CPI inflation in 64 mostly advanced economies during 1950-2009. They find that the global factor, on average, accounted for 35 percent of inflation variance, and the regional factors accounted for 16 percent— and these shares have risen substantially since the 1980s. Mumtaz and Surico (2012) estimate global and regional factors from 164 inflation indicators for 13 OECD economies during 19612004. They find that, in most countries, the degree of inflation synchronization has strengthened since the 1980s. Mumtaz, Simonelli, and Surico (2011) focus on an unbalanced panel of 36 countries between 1860 and 2007. They show that the share of inflation variation due to the global factor has grown since 1985.

Finally, sectoral, goods, or subnational factor decompositions have been used to identify more granular patterns in inflation synchronization. Monacelli and Sala (2009) estimate the contributions of a global factor to inflation variance in a large cross-section of sectoral price data (948 CPI products) for four OECD countries during 1991-2004. They find that, on average, the global factor explains 15-30 percent of the variation in disaggregated consumer price inflation, the share being higher in sectors with greater trade openness. Auer, Levchenko, and Sauré (2017) show, for disaggregated producer price index (PPI) inflation for 30 mostly OECD countries and 17 sectors during 1995 to 2011, that the global factor explains nearly half the fluctuations in PPI inflation in the average economy. They argue that this PPI synchronization across countries is driven primarily by common sectoral shocks and amplified by input-output
40|.53|.64|.67|
|Average mandatory-plan share||Not Av|ailable||.64|.62|.60|.58|.57|.57|
|% firms with wage discretion|.01|.01|.01|.02|.05|.09|.12|.20|.32|.35|
|% firms under MRS|.00|.00|.00|.01|.02|.04|.08|.42|.83|.88|
|Average firm-level pay sensitivity<br>for firms under MRS|Too|Few O|bservat|ions|.51|.39|.41|.46|.43|.42|
|%investmentfmancedby<br>government funds|.27|.26|.24|.21|.20|.17|.14|.13|.12|.10|
|% investment fnanced by<br>bank loans|.17|.18|.20|.22|.25|.33|.32|.34|.33|.28|
|% investment fmanced by<br>profit retention|.08|.08|.09|.12|.12|.12|.12|.12|_.15_|_.15_|
|% firms with managemen**t**urnover|.09|.01|.06|.10|.16|.14|.07|.15|.10|.09|

Source: _A Survey of State Enterprises: 1980-89._ 4It may be useful to know that there were two complementary conditions that allowed SOE reforms to work. First, SOEs became increasingly active in both the input and product markets. Between 1980 and 1989, the share of material inputs purchased through the market rose from 32% to 59%
# **1. Introduction**

Many developing countries face severe electricity constraints driven by various electricity supply- and demand-side factors that limit their economic growth by restricting the productive capacity of industries and firms while also lowering the labor productivity of households that are unable to meet their basic needs. Among the supply-side constraints are low electrification rates, insufficient power generation, and frequent outages that significantly affect the population’s welfare. According to the World Development Indicators (WDI), less than 50 percent of the population in 2018 had access to electricity in nearly 60 countries, with the lowest access rates registered in the Sub-Saharan Africa region, including Burundi (11 percent), Chad (12 percent), and Burkina Faso (12 percent).<sup>2</sup> At the same time, the demand-side challenges, such as high electricity tariffs, connection costs, and low willingness to pay, generate demand shortages and have become equally or more important than the supply-side constraints. For example, Sievert and Steinbuks (2020) found that even with low tariffs and no connection charges, the uptake will still be low because people cannot afford energy-using appliances and, hence, have a low willingness to pay for electricity access. In fact, the share of households in the Sub-Saharan Africa region that lives near the electric grid but has no connection is high, with a median uptake of 57 percent for 20 countries, according to the World Bank.<sup>3</sup> Electricity challenges impose considerable obstacles for private-sector development in low-income countries, along with other infrastructure constraints. According to the World Bank,<sup>4</sup> raising Africa’s infrastructure to some regional or international benchmarks could increase per capita growth by one or two percentage points. Among infrastructure constraints, firms in low-income countries consider electricity as one of the top obstacles for their expansion. In particular, the World Bank Enterprise Surveys Indicators data show that 70 and 50 percent of firms in low-income and lower-middle-income countries, respectively
**Table A.2: Land Market Restrictions, Female Labor Force participation and Wages: Robustness Checks for IV Results: Estimates from 2SLS**

|||**L**<br>Ad<br>|**abor Force Pa**<br>ditional Contro<br>|**rticipation**<br>ls<br>|||
|---|---|---|---|---|---|---|
||Farm Size|Populaion<br>Density|Pop. in 5 hrs<br>travel time|Non-farm<br>Share|Irrigation|LDO less<br>than 30%|
||1|2|3|4|5|6|
|**Area Under LDO**|**1.042*****|**0.971****|**1.061*****|**1.149*****|**0.847****|**1.732*****|
||**(2.639)**|**(2.433)**|**(2.743)**|**(2.802)**|**(2.236)**|**(3.245)**|
|**Instrument Strength**|||||||
|Angrist Pischke F|11.98|11.27|13.24|11.83|10.84|9.326|
|Stock-Yogo 10% bias|9.08|9.08|9.08|9.08|9.08|9.08|
|No. Of Observations|10850|10850|10850|10850|10850|9767|
from international financial institutions, particularly the World Bank, and this aid surge is unlikely to be sustained. After high domestic spending during COVID-19 and now the economic implications of the Ukraine war, the largest bilateral donors are likely to have reduced development aid budgets (McCord et al., 2021).

# **6. Concluding Remarks**

This paper employs recent sources of data to estimate and reassess the impact of the pandemic on countrylevel poverty in Africa. We combine national accounts data along with High-Frequency Phone Surveys for several countries to estimate the impact of the pandemic on poverty. These data can help refine and update early poverty projections which were built on several assumptions about expected changes in income as well as trends about the evolution of the pandemic and public health measures. We also review and synthesize empirical evidence on the role of social protection systems to mitigate the adverse impact of the 29
Developing East Asia has undergone dramatic transformation over the past few decades thanks to a combination of policies that fostered outward-oriented and labor-intensive growth, investments in basic human capital, and sound economic governance. However, slowing growth and shifting patterns in global trade, rapid technological change, and evolving country circumstances present challenges to sustaining past productivity growth and ensuring that future growth remains. Thus, understanding the extent of misallocation and the elements that correlate with it is an important step towards identifying the types of policies that can improve domestic productivity and the competitiveness of firms.

<mark>Our results are three-fold. First, measurement error is substantial. A</mark> mong the data for the three countries, the Vietnamese data have the largest measurement error. <mark>This results in an overestimation of misallocation by the HK approach.</mark> In other words, resource misallocation estimated using the BKR approach is overall smaller than that estimated using the HK approach. We discuss the potential measurement error that BKR corrects in Section 4. <mark>Second, after the correction for measurement error, resource misallocation across firms within a sector is large</mark> , albeit declining over time. Third, examining the firms' characteristics that correlate with distortions, we find that firms with higher productivity face larger distortions, pointing to potential structural constraints and inefficient allocation of resources. Foreign firms (in Indonesia and Vietnam) and state-owned enterprises, or SOEs (in Vietnam) have lower distortions (or “taxes”), which reflects either a less constrained environment or a more privileged status.

The rest of the paper is organized as follows. Section 2 summarizes the theoretical framework by HK and BKR. Section 3 presents the firm level data for the three countries. Section 4 discusses the results. Section 5 concludes.

# **2. Methodology framework**

- **2.1 Hsieh and Klenow (2009) framework**

5
TTO personnel payment system, 2010**

**Figure 10: Sources of TTO Figure 11: Additional services income, 2010 provided by TTO or parent institution, 2010**

<!-- Start of picture text --> 8%<br>20.4 21%<br>20% 39.5<br>13% 51%<br>54% 18.4<br>18% 15%<br>9.9 7.3<br>0.4<br>3.6<br>0.5<br>from parent institution<br>univ. pay scale and<br>bonus license royalties incubator and prototyping services<br>equity in new companies<br>only univ. pay scale<br>only incubator services<br>broker for research<br>rent of space<br>only bonus<br>only prototyping services<br>rent of equipment<br>other payment system consulting fees no additional services<br>other<br><!-- End of picture text --> Source: World Bank/MOES survey, 2011.

Russian TTOs reliance on public support is not unusual but the income from technology transfer activities should increase in the future. Some level of public support to the TTO in the initial years may be 24
8 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE but now for the extended sample of countries. In the preferred specification with origin-year and destination-year fixed effects, the IME is 0.38 among origin-destination pairs with at least 100 exporting firms and 0.52 among all origin-destination pairs.<sup>11</sup> To make sure the IME is not driven by small exporting firms, we re-estimate it after excluding firms whose annual exports fell below $1,000 in any year. The corresponding IME estimates in Table 3 (core sample) and Online Appendix Table I4 (extended sample) change only slightly.

A separate concern is measurement error. Since total exports is the sum of firm-level exports, classical measurement error in exports per exporter _x_ would bias the IME upward, but classical measurement error in the number of exporters _N_ would bias the IME downward. Depending on their relative importance compared to the true IME, classical measurement error could bias the IME upward or downward. If the measurement error is serially uncorrelated, then instrumenting total exports with its leads and/or lags should yield an unbiased estimate of the IME. As shown in Online Appendix Table I5, the instrumented IMEs are very close to the OLS IME, both economically and statistically.

Our results for the IME could be coming from country differences in industry composition of exports combined with industry differences in average exports per firm. In Figure 2 we plot the (demeaned) intensive and extensive margins against total exports at the origin-industrydestination-year level using HS 2-digit industries. The pattern here is similar to that in Figure 1. Table 4 shows that the IME actually increases when moving to industry-level data. At the lowest level of aggregation available (HS 6-digit), for the core sample of countries the IME is 0.51 with origin-year-industry and destination-year-industry fixed effects. The results also hold in the extended sample, for which we calculated IME disaggregated at HS2 product level. As reported in the Online Appendix Table I6, this IME is also close to 0.52.<sup>12<
*|0.00|0.64|0.15|**0.099**|

Note: The p-values reported in the last column are generated by either one-tailed tests of equality of means or Chi-square tests.

77. Simple pair-wise correlations between the different measures of accountability and transparency reveal that not keeping a teacher absence register is associated with underperforming school directors not being subject to penalties. Table A.2.3, in the Appendix, compares the schools in the different REDs with respect to accountability and transparency of school administrations. The data show that the number of RED inspections is on average significantly higher in the REDs of Fier and Shkoder, and significantly lower in Korce, where it is also most likely that a bad performing school director is not subject to penalties. Perceptions of corruption in the appointments of school director and teachers are most severe in Vlore, Diber and Elbasan.

78. To contrast with the indicators above, Table 4 provides information about the bodies that have been established for participatory “bottom up” accountability and parental involvement in the school. The top panel of the table reports statistics on the composition and meetings of the school board (SB). The number of SB meetings in the previous semester (January – May 2009) varies from a minimum of zero to a maximum of five, with no significant differences between rural and urban schools. Contrary to the MoES directives, in about 30% of schools, the school director participates in school board meetings and expresses his or her vote, again with no significant differences between rural and urban schools. In about 81 percent of the schools, according to the answers of parent SB members, parent class representatives are elected by parents rather than appointed by teachers; and in 43% of the schools teacher school board members are elected by teachers rather than appointed by the school directors. These proportions are significantly higher in urban schools as compared to rural schools.

79. The second panel of Table 4 reports the proportions of parents
- 49 -

# Sources

Population and labor force data are taken from the World Bank's Social Indicators of Development 1996 and refer to 1994, unless otherwise indicated. Unemployment rate taken from CIA Factbook 1995, unless otherwise indicated.

Military employment data are taken from International Institute for Strategic Studies: The Military Balance Survey of 1995-96, unless otherwise specified.

Wages and salaries are taken from IMF Government Finance Statistics, 1995 and GDP from World Tables 1995, unless otherwise specified.

## **_Afcka_**

|Angola|Data on Central Government and military employment relate to 1995 and are broken down as follows: 120,000 civil<br>service (central and provincial governments) and 82,000 police. Data on Health employment relate to 1990.<br>(Source: Health Project of October 23, 1992 -- Staff Appraisal Report). The report states that the number of MOH<br>Workers was 27,771, of which 662 were physicians, 9,145 paramedical (mainly nurses), 1,691 traditional birth<br>attendants, 4,165 health promoters (and others with little or no health training). Data on Education are taken from<br>Peter Ngoba, Education Specialist for Angola, and are broken down as follows: 31,900 teachers for the first four<br>grades, 3,200 teachers for fifth and sixth grade, 1,100 for seventh and eighth, 170 for pre-university, 300 for normal<br>(teacher training), 280 for technical education and 650 for higher education. Data are for 1992. Data on Local<br>Government employment are an estimate of provincial government from AFlMI and relate to 1995. Angola's<br>situation is greatly affected by the Civil War that has engulfed that country for the past two decades. Data must be<br>handled with great care. Data on Wages and salaries as percentage of GDP are taken from IMF Background paper<br>No