rafmacalaba/data-use-mentions-extended huggingface dataset:
modernized 100 percent of their fleets, and as of today, the CAR is served by the newest fleet in the region (table 5).

**Table 5. Air transport indicators, benchmarked against select other countries**

|Country|Central<br>African<br>Republic|Chad|Congo,<br>Dem.<br>Rep. of|Cameroon|Niger|Congo, Rep.<br>of|
|---|---|---|---|---|---|---|
|Domestic seats (seats per year)|—|—|327,988|105,742|—|443,634|
|Seats for international travel within Africa (seats per year)|20,661|109,074|468,217|472,089|128,414|351,882|
|Seats for intercontinental travel (seats per year)|23,842|88,608|193,414|398,034|41,717|117,962|
|Seats available per capita|0.010|0.018|0.016|0.051|0.012|0.24|
|Herfindahl-Hirschmann Index—air transport market (%)|50.26|36.35|22.65|10.18|18.97|30.79|
|Percent of seat-km in newer aircraft|100|99.5|74.7|91.8|94.3|73.3|
|Percent of seat-km in medium or smaller aircraft|23.5|93.6|39.3|31.9|44.9|50.5|
|Percent of carriers passing IATA/IOSA audit|0|0|0|0|0|0|
|FAA/IASA audit status|No audit|No audit|Failed|No audit|No
sup>5</sup> which I exclude from my analysis.

My international coffee price data comes from the _International Coffee Organization_ , which maintains historical statistics on international coffee prices and trade. I use the monthly coffee price index for the variety of coffee known as _Brazilian Naturals_ , which is an Arabica species and one of the most widely traded coffee types, with Ethiopia as one of the major suppliers. Figure 1 plots significant swings in the international coffee prices over the study period, 2006-2016.<sup>6</sup> To shed some light on how domestic prices move in response to international prices, figure 2 plots international prices (adjusted by nominal exchange rate) together with domestic prices at major coffee production centers across Ethiopia, obtained from the Retail Price Survey (RPS). The

> 4 For more information, see `https://microdata.worldbank.org/index.php/catalog/2783/download/48264` .

> 5While this problem is also prevalent in the Demographic and Health Survey (DHS) dataset, the large sample size of DHS data means that one can still have good variation in the data after dropping these outliers.

> 6The period 2006-2016 is the study period because some of under-5 children in ESS 2011 round data are in utero in 2006, and some of the under-5 children in ESS 2015/16 data are in utero in 2016.

6
. An important limitation, however, is that the sample is not representative of the universe of IDPs in Colombia.<sup>15</sup> In terms of which outcomes to focus on with respect to displacement effects, we consider a broad range, which we group into six categories. Given prior work on the welfare-diminishing

> 14Generally, _performance_ evaluations differ from _impact_ evaluations in that the latter tend to have built-in randomized treatment and control groups for subsequent estimation of effects, while the former tend not to or are often more descriptive in their evaluation goals.

> 15While the distribution of respondent characteristics in the household survey largely resemble comparable respondent characteristics from the recent (nationally representative) rural waves of LAPOP in Colombia (see Figure A.3), we cannot strictly generalize our findings outside of these areas.

10
which is specific to each country and captures local variations in population density, better reflects the concentration of poverty in urban areas. This is particularly evident in countries with either high or low average population density.

From a methodological perspective, our study carries important implications. Our approach allows for the disaggregation of official global poverty statistics into globally consistent urban and rural areas, as well as more nuanced geographic categories along the urban-rural continuum. This is the first study to attempt such an endeavor. We do not exclusively recommend one over the other, as both the DOU and DB approaches have advantages depending on the purpose of the analyses. Additionally, our results highlight the importance of understanding the spatial distribution of poverty at a global scale in order to allocate resources more efficiently and effectively for poverty reduction and the achievement of the SDGs.

While our approach is innovative, it is important to acknowledge its limitations. Firstly, our data is cross-sectional in nature, which means that we cannot fully control for unobserved heterogeneity, such as sorting based on individual abilities, by adding individual fixed effects. Although it is theoretically possible to add more time points to our dataset using GHSPOP and WorldPop data from other years, the comparability of these datasets and the consistency of consumption and poverty measures in HBS over time need to be carefully assessed, which is beyond the scope of this study. Another methodological limitation is the availability of data in the HBS. Our approach can only analyze countries where the location information of geographically disaggregated units is available in the HBS. This data availability in the HBS is crucial to the approach. Furthermore, there is inconsistency in the spatial deflation approach within the current global poverty monitoring system. Many countries measure global poverty without adjusting for subnational cost of living differences. While we apply spatial deflators to such countries, the spatial deflation approaches are not consistent across countries. The quality of the underlying population layers and HBS can also impact our results. The lack of availability of a recently conducted population census, which is common in low-income
|Number of firms that exist in the same province|N|903 25340|15785|<br>2104|40638|
|Number of years since the firm was established|YEAR|903 14.86|12.61|<br>1|<br>110|
|Dummy for firms having a female owner||903 0.25|0.43|<br>0|<br>1|
|Dummy for owner’s education level: Secondary||903 0.34|0.47|<br>0|<br>1|
|Tertiary||903 0.45|0.50|<br>0|<br>1|
|Share of exports in total sales (%)||903 5.89|18.68|<br>0|<br>100|
|Dummy for firms importing goods and materials||903 0.21|0.41|<br>0|<br>1|
|Dummy for firms owning a power generator||903 0.22|0.42|<br>0|<br>1|
|Sector dummy variable:||||||
|Agro business (food and wood products)||903 0.22|0.41|<br>0|<br>1|
|Chemical and metal||903 0.19|0.39|<br>0|<br>1|
|Light manufacturing||903 0.22|0.41|<br>0|<br>1|
|Service||903 0.24|0.43|<br>0|<br>1|
|Dummyfor 2018 data|_t_|903 0.44|0.50|<br>0|<br>1|
|1In constant 2018 metical.||||||

2 Divided by 1 million
or somewhat difficult<br><!-- End of picture text --> Small firm owners are also less likely to have achieved a high level of education, although their average educational attainment is similar to the population average. On average, firm owners have about 10 years of education, very similar to the average years of education for adult employed men (10.2 years) and women (10.7 years) in the four cities reported in the June 2011 round of the Ecuadorian household survey ENEMDU. However, while 22 percent of employed adults have complete tertiary education and 38 percent have at least some tertiary education, just 7 percent of firm owners in our sample report having completed tertiary education. This suggests that the distribution of skills among firm owners is much narrower than in the overall population.

# **2.3 Firm performance**

Survival rates among surveyed firms are consistent with the rest of Latin America but are higher than OECD averages. It is well documented that the firm selection process tends to be intense and many firms do not survive for long; however, those that survive tend to grow fast and generate enough employment to partly offset the loss from firms exiting the market. However, more than 80 percent of firms interviewed for EMES have operated for more than two years, which is on the higher end of estimates by Bartelsman et al. (2004), who report that 61 to 87 percent of firms that enter the market in a given year still operate after two years in a sample of 14 countries (Figure 3:A). The median age for EMES firms is 10 years and the mean is 12 years, which is consistent with evidence on formal urban firms with 1-50 employees in Ecuador (10 and 15 years, respectively) and in all of Latin America (14 and 19 years, respectively) from 4
system and the way incentives are shared. Financing reforms are generally most successful when members of the dominant, or powerful group are convinced that they will benefit and those who are adversely affected are less influential (Varghese 2004).

Resistance may also arise from those that will be better off as a result of the reform. Perhaps this group is not well informed, or dislikes change and uncertainties. Thus, information and actions to demystifying suggested changes to institutional funding is important in the preparatory phase. One strategy to build consensus is to offer financial compensation. When groups that tend to lose from the reform receive some form of compensation, they might be willing to give up their resistance. This strategy is often used in practice, but naturally lowers the efficiency gains from the reform. Another approach to garner institutional support is to introduce performance funding, at least for the first few years, as additional funding for tertiary education.

Prior to nation-wide introduction, results-based funding models can often be improved through small-scale experiments. Pilots and phased implementation create opportunities for further refinement and adaptation of the mechanism and scaling up based on evidence and lessons learned. Specialized training in data collection, reporting and analysis may be needed in tertiary institutions and ministries prior to implementation. Also, management structures may need to be updated to operate the system. Careful specification and a common understanding of all elements of the system are key to success. Clearly defined responsibilities make it apparent to everyone involved what, when, how and by whom performance data will be collected and reported to supervisory authorities.

- 20 -
We calculate the z-score for a sample of 1,752 banks across 57 countries, with the number of banks included in our sample varying from a high of 315 in the United States and a low of 3 in the Republic of Korea, Oman, Sri Lanka, Sweden and Trinidad and Tobago.<sup>11</sup> We calculate the return on assets, its standard deviation and the capital-asset ratio over the period 1997-2003. In the regression analysis, we include the log of the z-score to control for non-linear effects and outliers.<sup>12</sup> Since z-scores might vary with the time period over which they are measured, we will test the sensitivity of our results to the time period over which z-scores are computed. All bank data are from Bankscope, a commercial database of financial statements of financial institutions around the world. While it does not provide a perfect coverage of banks, it usually covers around 90% of countries’ banking systems in terms of assets.

Table 1 shows a wide variation in bank fragility across countries. Here we present the unweighted average of z-scores across all banks for each country in the sample. The z-scores indicate that for banks’ losses to deplete equity, profits have to fall about fifty times below the average level of profits in the United States, but only less than two standard deviations in Korea. The average bank in the average countries has a z-score of 24.

# **3.2 Control variables**

We include several bank-level variables to control for bank characteristics that might influence the fragility of individual banks. Specifically, we include the log of total assets in U.S. dollars for the first available year. Larger banks might be better able to diversify risk and thus have more stable earnings, reducing their risk of insolvency. On the other hand, larger banks

> 11 We imposed a requirement of at least three banks per country.  Our results, however, are not affected if we use a broader cross-country sample including countries
to the exclusion cost. It is worth mentioning that calculating _ECTB_<sup>undertheobserved</sup> transfer schedule is equivalent to calculating the optimal local reform (based solely on the targeting trade-off). For example, if we observe a ratio greater than 1, _EC_<sup>_<u>TB</u>>_1,this</sup> suggests it is optimal to increase the CCT and decrease the UCT from their observed values. Likewise, if this ratio were less than 1, this suggests it is optimal to decrease the CCT and increase the UCT from their observed values.

Finally, the optimal CCT (based on the targeting grounds alone) satisfies _EC_<sup>_<u>TB</u>_(</sup><sup>_t_</sup> _c_<sup>_∗, tu_(</sup><sup>_t∗_</sup> _c_<sup>)) =</sup> 1, i.e., the targeting benefit just offsets the exclusion cost. In order to determine the optimal CCT, we need to observe income and price effects at all values of the transfer schedule ( _tc, tu_ ), not just at the observed schedule.

# V Estimating Income and Price Effects in Rural Mexico at the time of Progresa

The remainder of the paper will focus on estimating the importance of the targeting benefit in the context of Progresa, a large CCT program in rural Mexico. In this section, we will focus on estimating the quantities necessary to recover curvature of utility - the income and price effect schedules - using evaluation data from Progresa. We will assume that the utility function is CRRA ( _u_ ( _c_ ) = _<u>c</u>_ 1<sup>1</sup> _−_<sup>_−_</sup> _γ_<sup>_γ_)and,therefore,willusetheaverage</sup> ratio of income
9

# **111.2 Fiscal implications of duty-free electronic commerce**

We have seen that if international trade in digitizable products shifts from a physical to electronic form because of duty-free treatment for the latter (and not because the latter is a more efficient means of delivery), then the loss in tariff revenue is a measure of the fiscal costs of trade diversion. Ideally we should calculate the tariff revenue that could be raised from aggregate imports of products for which electronic means are less efficient than alternative means of delivery. But there are serious empirical difficulties in undertaking this exercise. First, it is not possible to measure the trade that has already switched to electronic means to avoid customs duties. Secondly, it is not easy to establish product-byproduct whether electronic means are more or less efficient than the alternative means of delivery - a judgement that is in any case bound to change with technological developments. We choose therefore to determine the maximum fiscal costs of future trade diversion by calculating the total tariff revenue currently raised by countries from all digitizable products.

Since detailed and comprehensive tariff revenue data are not available, we need to estimate the tariff revenue countries collect from these products.<sup>7</sup> Table 3 provides an estimate of the weighted average tariff rates applied to digitizable products currently traded as goods, the import values for these products, and the estimated tariff revenue for various countries. The estimates take into account duty-free treatment of intra-EU, intra-NAFTA and Australia-New Zealand trade, and the reduced rates for intra-MERCOSUR trade. However, they are still likely to overestimate tariff revenue as they do not take into account other tariff reductions or exemptions.

Table 3 indicates that the average applied tariff is below 10 per cent in most countries. Of the countries included in the table, only Thailand, Morocco, Korea and India apply tariff rates above 20 per cent. Total estimated tariff revenue, therefore, adds up only to about US$ 732 million for the world as a whole8 The EU and Korea are estimated to
government spending.<sup>14</sup> This results in a core regression sample of 2804 country-year observations covering 102 countries listed in Table 2, and averaging 28 annual observations per country. Averaging

> 13 Specifically, I draw on the total government spending series reported in the dataset accompanying Clements, Gupta, and Nozaki (2011).   While the resulting merged dataset on government spending based on these various sources remains highly imperfect, it is important to keep in mind that the inevitable measurement error in government spending will not bias my estimates of the spending multiplier as long as it (plausibly) is uncorrelated with fluctuations in my instrument based on past loan approval decisions.  If this is the case, the only consequence of measurement error in government spending is to reduce the strength of my first-stage relationship between changes in spending and changes in predicted disbursements, and accordingly also the precision of my 2SLS estimates of the multiplier.

> 14 In addition, to prevent a relatively small number of extreme changes in output, government spending, and the predicted disbursement instrument from unduly influencing my estimates, I trim the sample at the first and 99th percentiles of the distributions of these three variables.

14
share of budget devoted to childrenrelated expenditures is the same no matter whether it is the mother or the father that migrates, even if the two have different preferences over investments on children.

The empirical analysis, based on Indonesian data, shows indeed that, when the mother migrates and the husband is left behind with the children, the share of income spent for exclusively adult goods increases but that devoted to children-related expenditures does not change significantly. The results are obtained through a two-stage least squares estimation procedure that exploits a set of instrumental variables derived from the theoretical setup, that is, the expected value and volatility of previous migrants’ earnings. Note that these results do not necessarily imply that children are no worse off when left behind with the father instead of the mother because other mechanisms, different from that of the allocation of the budget may play a crucial role; to shed light on this, I further estimate the effects of maternal versus paternal migration on children’s health and education indicators and find no difference in health measures but a significant decrease in the amount of time spent at school for children left with their fathers mirrored by an increase in the amount of time devoted to housework. This type of disruptive effect is likely to explain the result found by Cortes (2015) that children of migrant mothers are more likely to lag behind at school than children of migrant fathers.

> 4 A similar reasoning has been applied to the case in which one of the spouses passes away by Gertler et al. (2004).

3
Mexico was chosen to participate in the experiment. Randomization is being implemented at the locality rather than at the household level because PROGRESA benefits such as improvements in local schools and

> 16 ENCEL00N: Encuesta de Evaluación de los Hogares, November 2000; ENCEL99N: Encuesta de Evaluación de los Hogares,  November 1999; ENCASEH97: Encuesta de Características Socioeconómicas de los Hogares, November 1997.

> 17 Programa de Educación, Salud y Alimentación (http://www.progresa.gob.mx/).

18
4 coordinating tax reforms: can lost tariffs be recouped?

to minimize or evade payments, which if substantial could render trade taxes part of a _first_ -best tax package.

In this paper, I combine trade theory, cross-national evidence, and an in-depth case study of a low-income country using a unique data set on all import transactions at the border in Nepal.<sup>8</sup> I find that low-income countries have had a mixed record of achievement in offsetting reductions in trade tax revenue. This is partly because of their weak enforcement of domestic taxes like Value-Added Tax (VAT). In principle, a strict enforcement of a positive, single-rated VAT with no exemptions is a highly effective form of modern taxation, and can negate substantial losses in tariff revenue. I confirm this by using a partial equilibrium model to simulate reforms using data from Nepal on tariffs and up to _ten_ additional domestic taxes imposed on more than 400,000 import transactions between January 1 and December 31, 2008.<sup>9</sup> The paper proceeds as follows. Section 2 uses panel data from selected lowincome countries to assess whether they have succeeded in replacing trade taxes with domestic sources over a period of 25 years. Given the limitations for countryspecific policy inference from cross-country regressions, sections 3 and 4 cover a country case study. Section 3 begins by adapting conditions for welfare-enhancing tariff cuts to a revenue-enhancing result from a _coordinated_ tariff and tax reform in the presence of an informal sector. Two sets of plausible policy reforms are then simulated: i) different tariff cutting approaches are matched by domestic tax reforms with and without the assumption of a large informal sector; and ii) tariffs and other discriminatory charges on imports from members party to the Agreement on the South Asian Free Trade Area (SAFTA) are eliminated with and without Sensitive Lists that exempt a subset of products from tariff cuts.<sup>10</sup> I check for

> 8 “Border” in this paper refers to a generic port
literature contains many theoretical and applied papers arguing how households can use migration to respond to shocks, and so relying on fixed effects requires having a compelling story as to why one household suddenly changed migration status at the time that it did, and why this is not otherwise related to the outcome of interest.

Fixed effects may potentially raise fewer concerns when migration or migrant outcomes like employment and wages are the dependent variable, and the fixed effects are being used to help ensure that impacts are being identified from shocks in an independent variable. A first example is Mckenzie et al. (2014). Their analysis involves panel data on migration from Philippines to various countries. The unit of observation is the destination country of migrants, and outcomes of interest are counts and wages of migrants going to those countries. Fixed effects for destination country account for time-invariant omitted variables at the destination level. The focus is on the impact of changes in destination log GDP on migrant flows and migrant wages. Reverse causation is plausibly ruled out because Philippine migrants are a small share of destination labor forces. Omitted variable concerns that remain are about whether changes in GDP _per se_ are the causing factor, as opposed to changes that happen to coincide with GDP changes (such as changes in public policies). Changes in public policies (such as immigration policies) are not necessarily identification concerns, however, if they are mechanisms through which GDP shocks lead to changes in migration. A second example of a fixed effects approach is Mahajan and Yang (2020), who examine the effect of hurricanes on international migration to the U.S. The unit of observation is combinations of origin-country and year from 1980 to 2002, and the outcome of interest is immigration to the U.S. (expressed as a share of origin country population). The analysis regresses immigration on an origin country’s hurricane index (a measure of the origin country’s hurricane exposure) in a given year, including origin-country fixed effects, year fixed effects, and differential
been largely studied, primarily using the extended knowledge production function approach pioneered by Griliches (1979). A key element in the estimation of production functions is the need to control for the endogeneity of investment (in knowledge stocks or physical capital stocks) and productivity (Parisi, Schiantarelli, and Sembenelli 2006, Doraszelski and Jaumandreu 2013, Harrigan, Reshef, and Toubal 2018, Siliverstovs 2016).

With the expansion of Community Innovation Surveys (CIS) in the European Union (EU)— and later in other countries—Crépon, Duguet, and Mairesse (CDM) proposed a framework (Crepon, Duguet, and Mairesse 1998) that models the interdependent relation between investment in R&D, innovative outputs, and firms’ productivity. This framework has been 6