rafmacalaba/data-use-mentions-extended huggingface dataset:
estimating different specifications of equations 2 and 3. The reported coefficients measure the differences in road length between municipalities that the president’s party won and won candidates. We structure our barely municipalities barely by opposition findings into specifications without covariates in panel A, and specifications including both time-invariant and pre-treatment municipality characteristics in panel B. The available time-invariant features include a state capital dummy and the size of the municipal area in km<sup>2</sup> . The predetermined municipality features come from the Census, and include the share of the population aged 25 and over with higher education, the percentage of economically active people aged 15 and over, and log population size. Throughout the analysis, we also account for state, time, and state-interacted-with-time fixed effects and report robust standard errors clustered at the municipality level.

Columns 1-3 report coefficient estimates of a local linear regression using different bandwidths, while the remaining columns 4-6 present estimates of a spline polynomial approximation of various orders. Overall, estimates from specifications with and without covariates are both qualitatively and quantitatively similar. We find striking patterns of presidential pork in the geographic distribution of federal highways. The coefficients on the status are mean- president’s party positive, statistically significant, economically ingful, and robust across bandwidths and polynomial orders. For example, estimates from the fourth spline polynomial approximation with covariates suggest that municifor the in contested races receive on palities voting president’s party closely legislative average 1.5 km of extra highway, nearly doubling the length of federal highway built in opposition constituencies.

To ensure that our the of the federal as re- findings reflect influence government, flected in the executive-legislative relationship, we now examine how party alignment between the president and legislative candidates potentially affects the spatial distribution of non-federal roads. Because these roads are not primarily under the responsibility of the executive branch, we should expect no significant outcome differentials near the 16
Syrian Refugees on the Turkish Economy: Regional Labour Market Effects.” _Social Sciences_ 6(4): 1-12.

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Fallah, Belal, Caroline Krafft, and Jackline Wahba. 2018. “The Impact of Refugees on Employment and Wages in Jordan.” Economic Research Forum Working Paper No. 1189.

Foged, Mette and Giovanni Peri. 2015. “Immigrants' Effect on Native Workers: New Analysis on Longitudinal Data.” _American Economic Journal: Applied Economics_ 8(2): 1–34.

Fransen, Sonja , Carlos Vargas-Silva, and Melissa Siegel. 2018. ”The impact of refugee experiences on education: evidence from Burundi.” _IZA Journal of Development and Migration_ 8(6).

Friedberg, Rachel M. 2001. “The impact of mass migration on the Israeli labor market.” _Quarterly Journal of Economics_ (November): 1373-1408.

Ghosh, Sucharita, and Ali Enamib. 2015. “Do refugee-immigrants affect international trade? Evidence from the world’s largest refugee case.” _Journal of Policy Modeling_ 37: 291–307.

Gimenez-Nadal, Jose Ignacio, José Alberto Molina, and Edgar Silva-Quintero. 2018. “On the Relationship between Violent Conflict and Wages in Colombia.” _The Journal of Development Studies_ .

Glitz, Albrecht. 2012. “The Labor Market Impact of Immigration: A Quasi-Experiment Exploiting Immigrant Location Rules in Germany.” Journal of Labor Economics 30(1): 175-213.

Hercowitz, Zvi, and Eran Yashiv. 2002. “A Macroeconomic Experiment in Mass Immigration.” IZA Institute of Labor Economics Discussion Paper No. 475.

21
4. Repeat steps 1 to 3 a large number of times _M_ . Let _τc_<sup>_∗_(</sup><sup>_m_)</sup> be indicator obtained in mth replicate, for _m_ = 1 _, . . . , M_ . The extended Census EB estimate is just the average of the _M_ indicators, In contrast with the traditional ELL and CB-EB approaches, in the above MC procedure, the model parameter estimates _β_<sup>ˆ</sup> 0, _σ_ ˆ _η_<sup>2</sup> 0<sup>and ˆ</sup><sup>_σ_</sup> _e_<sup>2</sup> 0<sup>, as well as</sup><sup>_η_ˆ</sup><sup>_c_0and</sup> var [ˆ<sup>�</sup> _ηc_ 0], are kept fixed across the whole procedure; that is, there is a single DGP.

As a measure of noise of the extended CEB estimators, here the actual MSE under the nested error model is considered. To estimate this MSE, we propose to apply a parametric bootstrap procedure similar to the one in MR (2010), but adapted to the case where the survey is not necessarily a subset of the census. This procedure is also described in Molina (2019) for the CEB predictor, but here we apply it to the extended CEB predictor that includes heteroscedasticity and sampling weights:

1. Fit model (1) to the survey data. This yields the set of parameter estimates:

In the implemented version, the model is fit either via FGLS and decomposing residuals as in the traditional ELL fitting approach, or by H3.

2. Using the estimates obtained in step 1 as true values of the model parameters, simulate a vector of census welfare _yc_<sup>_∗_= (</sup><sup>_yc_1</sup>
*4.  CO2 Measures across Cities and over Time**

# **4.1 Annual CO2 Concentrations**

The global reference standard is provided by CO2 measurement at Mauna Loa Observatory, Hawaii (Keeling et al. 1976; Thoning et al. 1989). Table 1 compares mean annual December measures from Mauna Loa and our global OCO-2 database.<sup>3</sup> The results correspond closely, showing an increase from 399 ppm in 2014 to 414 in 2020.

**Table 1:  Annual global CO2 concentrations (ppm) (December)**

|Year|OCO-2|MaunaLoa|
|---|---|---|
|2014|399|399|
|2015|401|402|

> 3 Mauna Loa is in the Northern Hemisphere, so we compare its measures with Northern Hemisphere values from

> OCO-2.

9
incomes (Table 4, right panel). Given the low incomes of the poor, small monetary transfers can make an important difference in living standards. Conversely, substantial changes in the relative incomes of the rich would require comparatively larger transfers. What stands out in this respect is that public monetary transfers mainly have an impact on the relative household incomes of the very rich and very poor. Broadly speaking, gross income of the richest quintile is boosted by 11.1 percent largely as a result of pension transfers. Meanwhile, gross incomes of the poorest quintiles rise by 12.0 percent mainly because of conditional cash transfers. The income boost from public monetary transfers on the intermediary quintiles is less pronounced at about 3.5-6.0 percent.

# **4.2 Public Non-Monetary Spending**

41. **Social public spending on education, health, early childhood and other public services have important benefits for poor and non-poor households.** The monetary value of these benefits can be imputed and the incidence across the income distribution analyzed. These benefits will not be captured in the official summary statistic of the Gini coefficient, which measures the distribution of post-tax incomes, which includes public monetary transfers and direct/indirect taxes, but excludes non-monetary transfers.

# **_Education_**

42. **Public spending on education is progressive and the benefit is substantial relative to the incomes of poor households.** The poorest quintile receives 26.8 percent of the subsidy, which arises because the cost of education is covered by the government rather than paid by the households. The richest quintile, in comparison, receives 11.8 percent of the total subsidy. Although these benefits are non-monetary, it is possible to estimate the hypothetical Gini coefficient that would arise if they were monetary. Such estimations suggest that the equivalent subsidy, if converted to cash, would lower the Gini coefficient by 3.8 points. When compared to household incomes, the education subsidy is equivalent to 66.6 percent of incomes
), but we will only refer to the initial year for simplicity. This section describes some key features of these data for our study.<sup>3</sup> It is important to first define and characterize the distinction between the organized and unorganized sectors in the Indian economy. These distinctions in the Indian context relate to establishment size. In manufacturing, the organized sector includes establishments with more than 10 workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, composed of establishments which fall outside the scope of the Factories Act.

> 3 For additional detail on the survey data, we refer the reader to Nataraj (2011), Kathuria et al. (2010), Fernandes and Pakes (2008), Hasan and Jandoc (2010), Dehejia and Panagariya (2010), and Ghani et al. (2011b).

4
**Figure 15. Concentration Coefficients by Available Instruments in Egypt 2015**

<!-- Start of picture text --> Gas excise<br>Gas subs.<br>Personal income taxes<br>Social contributions<br>Energy subs. (indirect)<br>Tertiary education<br>Market income Gini<br>Sales Tax (indirect)<br>Sales Tax (direct)<br>Electricity subs.<br>Kerosene excise<br>Kerosene subs.<br>Tobacco, tea, sugar excise<br>Clinic care<br>Secondary education<br>Hospital (inpatient care)<br>LPG subs.<br>Food credit<br>Hospital (outpatient care)<br>Primary education<br>Karama<br>Takaful<br>‐1 ‐0.8 ‐0.6 ‐0.4 ‐0.2 0 0.2 0.4 0.6 0.8<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and administrative data. _Note:_ LPG = liquefied petroleum gas. LPG subsidies only reflect only the direct effects.

**Figure 16. Impact Effectiveness Indicator: Inequality**

<!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>‐0.20<br>‐0.40<br>Gini Index points<br><!-- End of picture text -->

_Source:_ Based on HIECS 2015 and budget figures.

34
economywide performance of employment and output in the manufacturing sector is not driven by large firms in the formal sector. Instead, it appears that the growing share of small, less productive firms is dragging down the economywide productivity performance of the manufacturing sector.

To determine whether this interpretation applies to other African countries, we turn to the UN Industrial Development Organization data, which we use to compute growth in real output per worker, real value added per worker and real exports to gauge the health of the formal manufacturing sector. The results of this analysis are presented in Table 1. The data are organized into four columns: employment growth, growth in real output per worker, value added per worker, and exports. The data coverage across the countries of Africa is admittedly sparse, but we do have information for the six countries with at least 100,000 workers in the formal manufacturing sector. We also include four Asian comparator countries: Bangladesh, Lao PDR, Sri Lanka and Vietnam.

In considering Table 1, first notice that growth in real value added per worker (or output) is positive in all the African countries. This is consistent with what we reported for Ethiopia and Tanzania and suggests that the poor productivity performance of economywide manufacturing is not a result of subpar performance in the formal manufacturing sector. Second, apart from Ethiopia and Kenya, employment growth in formal manufacturing is considerably weaker in Africa than in Bangladesh, Lao PDR and Vietnam. This gap is despite the fact the levels of manufacturing employment are considerably higher in the Asian comparator countries; for example, total formal manufacturing employment was around 300,000 in Ethiopia in 2017, while it was more than 6 million in Vietnam.

The upshot of these comparisons is that the formal manufacturing sectors in the African countries for which we have data appear to be performing reasonably well. This evidence is consistent with a large literature of on the productivity of Africa’s larger manufacturing firms.<sup>2</sup> But the formal manufacturing sector in Africa
As the primary explanatory variable, we employ the “number of local media articles” mentioning _Doing Business_ . This country-specific variable captures the total number of local press articles discussing economies’ performance on the _Doing Business_ indicators over each _Doing Business_ publication year (for more information see the Methodology chapter).

With the clearly identified dependent and independent variables put in place, we selected a number of controls imperative for the analysis. First and foremost, the model controls for income differences across countries. To this end, it employs the classical control variable of log income per capita in current USD of the World Development Indicators, the World Bank Group. In addition, using Factiva search engine, we collected the data on the number of media outlets per country. As historical data on media outlets is not available, we use the data for the most recent years – 2017/18. The data show that high income OECD countries have on average over 400 media outlets per economy, compared to only 9 in Sub-Saharan Africa.

Another prominent control included in the analysis is freedom of the press variable of the Freedom House. This variable was selected due to its comprehensive global coverage and availability of gap-free historical data. As discussed in the literature section, Freedom House data, and freedom of press variable specifically, are widely used in the existing literature on drivers of reforms. Other sets of variables that we considered and selected as controls only in simple non-panel OLS models include World Justice Project’s rule of law index, Polity IV data on democracy and autocracy, Transparency International’s corruption perception index, the Economist’s democracy classification score and the Worldwide Governance Indicators of the World Bank Group.

Prior to testing the aforementioned hypothesis, we first run a number of simple correlations and perform data robustness checks. First, we correlate year on year Factiva and Google data, which yields an average correlation coefficient of 0.6. This is not surprising as Factiva’s data only captures local media, while Google does not differentiate between foreign
19 between 1959 and 1965. This implies that, as shown before, all cohorts were exposed to a significant, although different, extent to the exemption. Moreover, because in 1981 individuals belonging to these cohorts were between 16 and 22 years old they were still cohabiting with their parents. This allows us to include controls for a number of parental characteristics at the town-cohort level<sup>22</sup> . Even in this limited sample and with parental controls, we still detect a rather significant increase in high school completion rates of about 2 percentage points and a decline in death rates of about 0.3 percentage points among younger exempt cohorts.

In column (6) we perform a complementary exercise and estimate equation (2) on the sample of males born in 1963-65 and in 1956-58. With respect to the previous exercise we thus exclude from the sample youths born in 1959-61 a quarter of whom was likely affected by the exemption. On the contrary, in the sample underlying column (6) older cohorts are completely unaffected by the exemption as all of them were at least 23 when exempt. However, this does not allow us to control for parental characteristics since as of 1981 many of the older cohorts were no longer living with their parents. Results confirm the increase in high school completion rates in young exempt cohorts and the decline in mortality between 1991 and 2001.

In column (7) we restrict the sample to youths from the 33 exempt towns that either did not record any damage or were at the lowest of the official damage scale and to their 41 direct neighboring towns. Because this subset of treated towns was only exposed to the exemption, the exercise allows us to rule out the possibility that previous estimates reflect the presence of cohort-specific effects of the quake unrelated to the exemption, for example due to the fact that only younger cohorts responded to the limited income transfers assigned to sampled treated towns. The increase in the high school

> 22The 1991 census does not provide information on family background. We
)<br>1,895.49<br>0.55<br>1,882.97<br>(1,181.91)<br>(398.83)<br>Observations<br>130<br>133<br>Note: This table reports average amounts of fertilizers and costs and balance checks for the 678 farm<br>izers in 2014 by the control group (SDs in parentheses). We regress each variable on our set of treat<br>Column 3 reports the average input recommendations and the corresponding input costs for farme<br>have received by reporting the p-values of an F-test analogous to those in column 2. Columns 5 an<br>_T_3 and _T_4. Column 7 compares the amounts of each fertilizer and costs self-reported by the contro<br>dummy that takes value of 1 for farmers in_T_1. It reports the p-value of a t-test that_T_1=0. Column<br>the sample of farmers in_T_1_−T_4 and regressing each variable on a dummy that takes value of 1 for f<br>given to farmers (and hypothetical recommendations to controls) and Baseline survey data collected|

33
0 70<br>-20 90 -20 60<br>-40 80 -40 50<br>-60 70 -60 40<br>60 30<br>-80 -80<br>50 20<br>-100 -100<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

40
use data from the enterprise surveys for 104 countries covered a total of 70,192 firms over the period 2002-2006.<sup>8</sup> Of these, 87 countries have data on number of power outages, 77 countries have data on generator and 34 countries have data on cost of electricity.<sup>9</sup> Table 1 presents general summary statistics, and Table 2 break these down by generator ownership.

> 8 See https://www.enterprisesurveys.org.  Unfortunately it is not possible to use the available 2006-09 data for such an exercise since key questions about power were dropped from the questionnaire.

> 9 This results in a sample of 62 countries with data on generator and number of power outages, and 32 countries with data on generator, number of power outages and cost of electricity.
be representative and includes KUR borrowers with a variety of different characteristics. These qualitative interviews covered the following topics: perception and information received about KUR, KUR loan application process, use and benefits of KUR loans, business productivity and access to financial services, alternative financing sources available, the impact of COVID-19 on the business, and suggestions for improving the KUR program. Due to the COVID-19 pandemic, the qualitative interviews were conducted using phone interviews. To avoid challenges of survey length and respondent fatigue, the depth of probing on each topic varied by respondent.

Qualitative interviews were also conducted with 100 individuals who did not receive KUR but who had been identified as potential debtors in the program’s database.<sup>18</sup> Among them, some firms had applied

> 16  Strata included gender of KUR recipient, size of KUR loan, financial institution that issued the KUR loan, and geographic region.

> 17  These modules were not asked of all respondents due to concerns about survey length and respondent fatigue. The modules to be asked were randomly assigned and balanced across sampling strata to ensure all modules included nationally representative information.

> 18  97 percent of individuals in the SIKP who were successfully contacted for quantitative interviews with KUR borrowers had received KUR, so the sampling for these interviews was a convenience sample of entrepreneurs who were screened but not eligible for the surveys of borrowers.

**13**
||||avg.<br>||
|---|---|---|---|---|
|SOE revenue share|-0.333<sup>***</sup>|-0.146<sup>**</sup>|-0.353<sup>*</sup>|-0.252|
||(0.126)|(0.056)|(0.211)|(0.209)|
|Constant|0.196<sup>*</sup>|0.104<sup>*</sup>|0.364<sup>***</sup>|0.219<sup>**</sup>|
||(0.103)|(0.054)|(0.122)|(0.084)|
|Lagged avg. age|Yes|Yes|Yes|Yes|
|Lagged avg. employment|Yes|Yes|Yes|Yes|
|Sector size in economy|Yes|Yes|Yes|Yes|
|Industry effects|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Observations|704|704|704|704|
|Within R-squared|0.021|0.019|0.031|0.032|

Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. Market-level measure of allocative efficiency is the static Olley and Pakes (1996) measure computed as the within market covariance between firm size (i.e., market share) and (labor) productivity. SOEs are defined as firms where national or subnational governments have ownership stake of at least 10%. All regressions are at the 2-digit NACE industry level and include industry and year fixed effects. Standard errors clustered at the industry level are
five percentage points since 2000, compared with the 2011 PPP series. This represents 27 million more poor people in LAC, driven by 12 million and 5.5 million in Brazil and the Andean region, respectively.

A significant increase in the poor population is mirrored by a decrease in the size of the vulnerable group by around 5.4 pp in 2017 with the revised lower-middle-class bound of $14 in 2017 PPP. The regional vulnerability trend is due to declines in the Andean region and Central America, offset by increases in countries in the Southern Cone, including Brazil, since 2012. The update of the upper-middle-class threshold of $81 in 2017 PPP increases the size of the middle class regionally by around 0.9 pp. This change in lower and upper middle-class limits provides a similar positive trend for this group over the last two decades.

The rest of the paper is organized as follows. Section II presents the methodological details for updating both the vulnerability and middle-class lines in terms of the 2017 PPPs. Section III describes the data used in the study. Section IV presents the estimation results for the middle-class upper and lower bounds and some sensitivity analysis. Section V documents changes observed in regional and country-level poverty, considering the vulnerable and middle-class estimates with the 2017 PPP. Finally, Section VI discusses the final remarks.

> 4 The Andean region is the aggregate of Bolivia, Colombia, Ecuador, and Peru; Central America is the aggregate of Costa Rica, Guatemala, Honduras, Nicaragua, Panama, El Salvador, and the Dominican Republic, and the Southern Cone is the aggregate of Argentina, Chile, Paraguay, and Uruguay.

3