rafmacalaba/data-use-mentions-extended huggingface dataset:
|Income from farm|0.332|0.358|0.326|0.371|0.290|0.273|0.292|0.287|0.338|
|Other income|0.307|0.254|0.320|0.289|0.293|0.265|0.304|0.280|0.295|

Method: Decomposition of the Gini coefficient into components, ranking by total income. * Before taxes and benefits. ** After taxes and benefits *** Household ranked by original income.

_Source:_ Authors’ calculations based on HBS.

# **The Role of Taxes and Social Benefits**

While increased inequality in original income is clearly an important component of the dispersion of consumption, it is not the full explanation. As the second part of Table 5 shows, the growth in the Gini coefficient for original income (7 percent) was just over half of the increase in the Gini coefficient for consumption (13 percent). Thus, other factors must be at play. Amongst these factors, of potential importance are changes in the tax and benefit system which, for any given distribution of original income, may affect the distribution of the income that households actually have to spend (i.e., their disposable income).

The top section of Table 6 shows that the concentration index for taxes increased by 32.8 percent in the period 1994-2002, which suggests that the tax burden has become more concentrated. At the same time, the social benefits system evolved toward the poor more strongly. This would suggest that since the mid-1990s changes in the tax and benefit system have counteracted the increased inequality in original income, leading to a much smaller growth of inequality in disposable income (only 3 percent, from 0.308 to 0.316).

11
in export destinations lead to increased exports and sales, with the former showing stronger responses than the latter. The stronger effects on exports would be expected given the source of variation we are exploiting: unforeseen growth shocks in export destinations. We take these results as reassuring confirmation that our strategy for identifying unexpected export shocks is valid. We also observe that the magnitude of the effects of the forecast error on both exports and sales is somewhat larger than that of the forecast growth. In Table A4 of the appendix, we split firm-level export values into averages export quantities (in kilograms) and average export prices per kilogram exported. The results reveal that the effect of the demand shock on exports is entirely a quantity effect, so our estimates should be interpreted with this evidence in mind.

Columns (3) to (8) of Table 2 also show significant effects on investment (in both tangible and intangible assets), employment, value added, value added per worker, and average labor costs. For all these variables, the effects of the weighted forecast error are consistently stronger than those of the weighted forecast growth.

# [ Table 2 here ]

In Table 3, we use the employer-employee data to examine the effects on the various components of worker compensation and on the skill composition of the workforce. Regarding worker compensation, the total monthly wage consists of three components: _base wage_ , _overtime pay_ , and _other pay_ (e.g., various types of bonuses). We also have information on the number of hours worked per month, which allows us to calculate the total hourly wage. We report effects on firm-level averages of each of these variables.

# [ Table 3 here ]

The estimates in columns (1) and (2) of Table 3 indicate positive and significant effects of the forecast error on both monthly and hourly wages. Columns (3)-(4) show that this positive average effect is driven by significant increases in both base wages and overtime pay. The _other pay_ coefficient in
Pipeline comparisons might also be combined with discontinuity designs. Although I have not seen it used in practice, a possible identification strategy for projects that expand along a well defined route is to measure outcomes on either side of the project’s current frontier. Examples might include projects that progressively connect houses to an existing water, sanitation, transport or communications network, as well as projects that expand that network in discrete increments. New facilities (such as electrification or telecommunications) often expand along pre-existing infrastructure networks (such as roads, to lay cables along their right-of-way). Clearly one would also want to allow for observable heterogeneity and time effects. There may also be concerns about spillover effects; the behavior of non-participants may change, in anticipation of being hooked up to the expanding network.

# **7. Higher-order differences**

So far the discussion has focused on single-difference estimators that only require a single survey. More can be learnt if we track outcomes for both participants and non-participants over time. A pre-intervention “baseline survey” in which one knows who eventually participates and who does not, can reveal specification problems in a single-difference estimator. If the outcome regression (such as equations 4 or 5) is correctly specified then running that regression on the baseline data should indicate an estimate of mean impact that is not significantly different from zero (Heckman and Hotz, 1989).

With baseline data one can go a step further and allow some of the latent determinants of outcomes to be correlated with program placement given the observables. This section begins with the double-difference ( _DD_ ) method, which relaxes the conditional exogeneity assumption of single-difference NX estimators by exploiting a baseline and at least one follow-up survey postintervention. The discussion then turns to situations — common for safety-net programs set-up to address a crisis — in which a baseline survey is impossible, but we can track ex-participants; this illustrates the triple-difference estimator.

**_The double-difference estimator:_** The essential idea is to compare samples of participants and non-participants before and after the
Global Inc.|Data on more than 62,000 public firms and<br>18.2 millionprivate firmsglobally.|Yes|Yes|
|Extractive Industries<br>Transparency Initiative<br>(EITI)|EITI|Firms in the extractive industries sector,<br>covering 60 SOEs in 20 EITI countries|No (revenue data<br>at the mining<br>project level)|<br>Yes (tagged as SOE)|

Source: Authors’ elaboration 41
2014, 59% of trips by EU residents traveling internationally relied on digital tools to book accommodations, and 67% for air transportation.<sup>3</sup> In the United Kingdom, the share of travelers using online accommodation services grew from 42% in 2007 to 52% in 2017.<sup>4</sup> Digital travel sales are expected to grow from US$471 billion to US$818 billion by 2020.<sup>5</sup> The flip side of the use of digital tools to plan trips and purchase travel services can be seen in the sharp decline in the number of physical travel agencies. For example, in the United States, the number of travel agencies declined from 25,975 establishments in 2000 to 14,797 in 2016, with a concomitant fall in employment from 183,143 to 108,984.<sup>6</sup>

> 2 All figures are from the World Development Indicators, World Bank Group.

> 3 Source: European Commission, _Statistics on ICT use in tourism_ . Online publication.

> 4 Source: Statista.

> 5 Idem.

> 6 Source: 2000 and 2016 _County Business Patterns_ , US Census Bureau.

Page **2** of **34**
Herbst (2014) argues that the difficulty African political leaders faced in governing countries divided into multiple ethnic groups, coupled with the dissociation of ownership and control of land, motivated them to develop infrastructure that would extend their authority as far as possible, see also Herbst (2000). The poor or absent state of road networks in most countries in sub-Saharan-Africa leaves a majority of rural people cut off from the centers of political power. Herbst (2000) argues that African governments have been building roads that connect political centers to previously inaccessible areas to expand their power.

Work by Muller-Crepon et al. (2020) relies on a theory according to which the accessibility of ethnic homelands by public authorities is a prerequisite for political leaders to extend their domination over remote areas (Tollefsen and Buhaug, 2015; Raleigh and Hegre, 2009; Buhaug and Rod, 2006; Herbst, 2000; Fearon and Laitin, 2003). Indeed Muller-Crepon et al. (2020) argue that political instability is predominant in areas where political power has very little control over the inhabitants; more specifically, they establish that the ethnic groups’ homelands that are difficult to reach from the national capital are also sites of rebellion and political conflicts. The authors use the map of road networks in African countries to measure access to ethnic homelands from national capitals, and the interconnectivity of ethnic groups. Estimating the effects of these two variables on the occurrence of political conflicts, they show that political conflicts reoccur in areas where there is a weak physical presence of the state. To demonstrate the robustness of this finding, they construct a theoretical network composed of hypothetical road segments that ensure the lowest travel time between the busiest ethnic groups; using these hypothetical networks, they construct instrumental variables estimating what the accessibility of ethnic homelands from political centers as well as the inter-connectivity of ethnic groups would be if these networks existed. .

Their results are robust to the use of these instruments, thus confirming the twofold role of road networks in extending the control of political powers and reducing
effectiveness, and the criteria attempt to capture the key factors that foster growth and poverty reduction. For each criterion, countries are rated from 1 (low) to 6 (high), and an unweighted average score of 3.2 or less is generally seen as the threshold of state fragility. The CPIA is also used by the IMF and other IFIs.

- The Fund for Peace’s Fragile States Index (FSI), which has broader coverage, consisting of five groups of indicators measuring state cohesion, economic conditions, political legitimacy and public service provision, social conditions, and external intervention.

**Regardless of the indicator used, state fragility appears to be widespread and persistent** . The various aggregate indicators tend to agree on which countries are at the bottom and the top of the list, but there is less agreement in the middle of the rankings. Nevertheless, fragile states are home to nearly 1 billion people. They exist in most parts of the globe, including Africa, the Middle East, and parts of Asia and Oceania. Although fragile states exist in both low- and middle-income countries, they are more prevalent among the former: about half of the world population living in extreme poverty resides in fragile states.<sup>6</sup> Fragility is also hard to shake off: once a state becomes fragile, it may take several years to exit fragility, which has led many to talk about a “fragility trap.”

Fragility, economic performance, and financial development

**State fragility is unambiguously associated with a lower level of economic performance** . In fragile states, the formal sector tends to be small relative to the size of the economy and dominated by small and inefficient firms. In many cases, crime and theft are prevalent and private firms have to cover security costs, as well as deal with power outages or water insufficiency. Fiscal capacity is weak and governments have difficulty collecting taxes to finance essential public services, often relying on volatile commodity revenue.
**|**16.3**|**10.5**|**5.9**|**104.1**|<br>**6.5**|**4.2**|**8.4**|
|The agricultural group|5.4|2.9|9.6|6.8|3.8|16.2|5.3|2.4|16.9|
|The low-skilledgroup|5.6|4.1|12.4|6.0|4.3|80.6|4.0|3.0|4.9|

Source: Own calculations based on ENEU survey 1/ Following OECD, it is assumed that teachers work on an average of 41 weeks per year and non teachers work on an average of 49 weeks per year.

After using several definitions of teacher salaries and payments, it is clear that real salaries and real labor earnings for teachers in basic public education are significantly above other occupations and groups salaries.

# _Income Sources_

With respect to income sources, Table 11 shows the income source shares for primary teachers, lower secondary teachers and other occupational groups. From this table, one can see that salaries contribute close to 82 percent of the teachers’ total income. Notice that non-monetary income is the second highest income source, especially housing imputed rent and gifts (8.14 percent and 13.14 percent for primary teachers in public and private schools, respectively; 6.4 percent and 14.2 percent for lower secondary teachers in public and private schools, respectively). Financial income is also important for teachers; on average, its contribution to total income is about 5 percent<sup>_6_</sup> .

Yet half the income of _the mixed-skilled group_ and _the low-skilled group_ incomes comes from salary, while in _the agricultural group_ salary contributes just 22.8
allow for better quantification of changes in physical activity levels related to travel and the intensive (per hour) and extensive (number of hours) physical margin of domestic and market work that are harder to quantify with the methodology and data adopted here. Another promising alternative to costly household surveys is the use of technology for the purpose of capturing TEE. Most modern smartphones can track physical activity levels reasonably well and simple devices that monitor heart rates offer the potential to estimate variation in activity levels for larger samples, greatly increasing the scope for estimating activity levels for populations outside the laboratory relative to the prohibitively expensive chemical methods that prevail in the nutrition literature.

# **A Estimation of Resting Energy Expenditures (REE)**

The best anthropometric predictor of REE is fat-free mass, but as this is rarely measured in the field, prediction equations have been developed based on regressing laboratory measured REE on more commonly observed variables ((St. Jeor and Stumbo 1999)). A standard approach, adopted by FAO (2001) and (Indian Council of Medical Research 2009), is to use prediction equations that regress REE on gender, age, weight, and in some cases height. Estimating separate equations by age and sex, and incorporating height, helps account for systematic average differences in fat-free mass, e.g. women, older, and shorter individuals tend to have lower fat-free mass for a given weight. Equations provided in (Henry 2005) use height and weight and are estimated separately for several age/sex groups. Results are not very sensitive to the use of alternative prediction equations. In Appendix Table S.1 Panel A, alternative REE estimates are generated using the prediction equations used by (Indian Council of Medical Research 2009). Similar results can be obtained using the prediction equations from (St. Jeor and Stumbo 1999). There have been laboratory measurements of REE in India but none for a large or representative sample of individuals and none generating specific predictive equations. (Ferro-Luzzi et al. 1997
Figure 1: Violence against women, rates per 100,000 women <!-- Start of picture text --> 2011 2012 2013 2014 2015 2016<br>year<br>Hospitalizations Homicide<br>9<br>90<br>7.5 75<br>6 60<br>45<br>4.5 Report<br>30<br>3<br>15<br>Hospitalizations and Homicides 1.5<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Hospitalizations<br><!-- End of picture text --> <!-- Start of picture text --> Homicide<br><!-- End of picture text --> _Note:_ Women ages 15 – 49. Municipal averages, unweighted, N = 841.

Figure 2: Histograms of wage ratios, 2011 <!-- Start of picture text --> .5 1 1.5 2<br>Leave-out wage ratio Raw wage ratio<br>Note: N = 841 municipalities. Both gender wage ratio measures are Winsorized at percentiles 1 and<br>99. Bin width = 0.01. Weighted by population of women ages 15 – 49.<br>Frequency<br>2.0e+06 4.0e+06 6.0e+06 8.0e+06<br>0<br><!-- End of picture text --> 27
for 14 “countries” (13 non-EU countries, plus the composite EU region) and 14 industries, which are listed in Table 1.<sup>36</sup>

# **3.2 Bilateral**

We construct bilateral, industry-level tariffs on final goods for four benchmark years: 1995, 2000, 2005, and 2009. We briefly describe the data sources and procedure here; see Appendix B for details.

We start with national government, product-level tariff schedules collected by UNCTAD (TRAINS) and the WTO, which we obtain via the World Bank’s WITS website [ `http: //wits.worldbank.org` ]. Multilateral MFN applied tariffs are typically available in the WTO data, while bilateral applied tariffs are from TRAINS. Combining these sources and aggregating product lines yields a data set of bilateral tariffs at the Harmonized System (HS) 6-digit level.

To identify final goods tariffs in the data, we use the Broad Economic Categories (BEC) classification. We retain HS 6-digit categories classified as consumption and capital goods, discarding both mixed use and intermediate input categories.<sup>37</sup> We then concord these HS 6-digit final goods categories to WIOD industries using a cross-walk from HS categories to ISIC Revision 3 industries to the WIOD industry codes. We take simple averages across HS categories within each industry to measure industry-level applied bilateral and MFN tariffs.

# **3.3 Temporary Trade Barriers**

We obtain data on temporary trade barriers (TTBs) — antidumping, safeguards, and coun— tervailing duties from the World Bank’s Temporary Trade Barriers Database [Bown (2014)]. These data identify the importing country imposing the TTB, the countries and product lines on which the TTB is imposed, and the timing of when TTBs are imposed and removed.<sup>38</sup> Following Trefler (1993) and Goldberg and Maggi (1999), among others, we construct import coverage ratios to track TTB use over time
metric, attention to inequality at the IMF has been clearly and unambiguously on the rise, in contrast to a more cyclical pattern at the World Bank. Much as in the case of the World Bank, IMF research staff were responding to the rising profile of inequality in the academic literature (and, perhaps, at the World Bank),

> 30 Important precursors included work on changes in inequality during the economic transition from socialism, e.g., Keane and Prasad (1999).

> 31 Critics have argued that it is based on a problematic data set (Jenkins, 2015) and suffers from methodological problems (see Kraay, 2015).

16
In addition to data at the firm level, anonymized person-level data enable us to track inflows and outflows of workers from these firms, and to examine the gender and age composition of the workforce, as well as the monthly salaries paid to workers. Column 1 of Table A8 looks at the proportion of workers who were working in firms in January 2013 who remained in the firm five years later, at the end of December 2017. In the control group, only 47 percent of workers are remained this length of time. The point estimate suggests a 5 percentage point increase in this retention rate in the group treatment firms, but this is not statistically significant. Columns 2 and 3 show that 74 percent of workers are male and the average worker is age 43, with neither treatment having large, nor statistically significant impacts on these worker characteristics. Finally, Column 4 examines the treatment impact on mean worker monthly wages. The group treatment results in a 36,526 COP (3%) point estimate increase, but this is not statistically significant.

|**Table A8: Impact on Employment Composition**<br>Five‐Year Retention:<br>Proportion of Jan 2013<br>|Wor|ker Charac|<br>teristics|
|---|---|---|---|
|workers remaining in<br>firm in Dec 2017|Proportion<br>Male|Mean<br>Age|Mean Monthly<br>Salary (COP)|
|Individual Treatment*During Intervention|0.008|‐0.052|‐38509|
||(0.009)|(0.362)|(29109)|
|Individual Treatment*Post Intervention<br>‐0.031|0.009|0.130|‐37387|
|(0.052)|(0.014)|(0.485)|(36636)|
|Group Treatment*During Intervention|‐
on variation across an average of 122 organizations while still constraining the comparisons of errors within a restricted geographic and institutional environment. We also follow standard practice in studies of the World Management Survey indices, and include ‘noise controls’, which aim to soak up aspects of the enumeration of the management survey.<sup>26</sup> Finally, to focus the analysis on the benefits of acquiring information, we condition on proxies of the marginal cost of that acquisition.<sup>27</sup> To ensure the results of Table 2 tolerate

> 25 Whilst we have focussed on official’s beliefs about the populations they serve, we also collected data on their beliefs over the rules of the public service. Constituency features could be regarded as ‘soft’ information, whilst public service rules, written in the governing documents of all organizations, could be regarded as ‘hard’ information. Indeed, as laid out in the Appendix, we find that centralized officials have superior information on this hard information in line with theoretical predictions (Myerson, 1981; Mookherjee, 2006; Dessein, 2002).

> 26Noise controls are composed of the time of day of the survey, day-of-survey fixed effects, enumerator fixed effects, an indicator for the enumerator’s subjective assessment of the quality of the interview, an indicator for the duration of the interview (decile fixed effects), controls for the average tenure and gender of the managers interviewed.

> 27Marginal cost variables are the proportion of managers in the organization that state that a management information system is in place; the organization average response to “In what proportion of projects would you say information flows effectively between individuals in the same organization?”; the organization average response to “In what proportion of projects would you say information flows effectively between organizations?”; the organization average response of managers to “How many personnel do you manage?”; the organization average response of employees to “How many people would you

21
using forward-looking firm level data, find that the COVID-19 induced shocks lead to 3 new hires for every 10 layoffs. They also project that the total number of entire working days performed from home will triple after the end of the pandemic and that between 32% and 42% of all layoffs will be permanent.

Findings of research focused on the Canadian labor market are in line with the results in the United States. Using the Canadian Labor Force Survey up to April 2020, Béland et al. (2020b) document substantial increases in unemployment (approximately 5 percentage points) and drops in labor force participation (3.7 percentage points), hours worked (1.5 percentage points) and wages (0.4 percentage points). These impacts were less severe for essential workers or workers who can work remotely, while they were more pronounced for younger and less educated workers. Differently from the results for the United States, they do not find evidence of differential effects by gender or of disproportionate impacts of the crisis on labor market outcomes for migrants.

Adams-Prassl et al. (2020) compare the impacts of COVID-19 on jobs, earnings and hour worked in three countries that have introduced different policies in response to the pandemic, i.e. the United Kingdom, United States and Germany. They find substantial differences across and within countries. Job losses in the United States and the United Kingdom were substantially higher (18% and 15%) than in Germany (5%), a country with a well-established short-time work scheme. They point out that the United Kingdom also introduced a similar scheme, which however does not allow furloughed workers to do any work for their employers, thereby potentially discouraging firms from applying. Not surprisingly, the study also finds that furloughing was more prevalent in the United Kingdom (43%) than in the United States (31%), a country that strongly relied on the expansion of unemployment benefits to respond to the crisis. The study also finds that in all countries,