rafmacalaba/data-use-mentions-extended huggingface dataset:
.

# **1.6.- The problem of externalities at airports**

When users of airport infrastructures impose a cost/benefit upon non-users (or even upon other users on the system), it is said that there is an externality. In other words, airport users are not bearing all the costs generated by the services they require. Regarding negatives or bad externalities, it is noise to which most airport externalities studies have been devoted (see Walters, 1978; Nelson, 1980; Levesque, 1994). However, pollution and congestion are also bad externalities that cannot be neglected from a regulator's point of view.

The main economic problem of externalities is quantification and subsequent valuation. For example, a highly sophisticated technology is required to measure aircraft pollutants emissions. Regarding noise, measurement is not an easy matter either. However, accousticians have devised ordinal scales constructed as weighted averages of the high frequency peak noise and the number of times at which the noise of an aircraft is heard.'° In order to measure congestion," this has to be linked to airport capacity (see above). Taking capacity as a given, there is an international standard for aircraft movements of an average delay not exceeding four minutes. Longer aircraft waiting times would indicate congestion problems, which may be also measured by using weighted averages.

9 See Doganis (1992). For example: The Noise Number Index in the UK, the Composite Noise Rating and the Noise Exposure Forecast in the US, and the Isosophique in France.

" Congestion is a type of negative externality imposed upon other users in the system. 9
|IDP|_Male_|16.50|2,911|0.250|_(0.226,_<br>_0.274)_|42.9|_(38.9,_<br>_47.0)_|58.3|_(57.6,_<br>_58.9)_|
|---|---|---|---|---|---|---|---|---|---|
|Sudan<br>Population|_Female_|16.05|2,832|0.260|_(0.236,_<br>_0.283)_|44.2|_(40.4,_<br>_48.1)_|58.7|_(58.0,_<br>_59.4)_|
|<br>Non-IDP|_Male_|33.53|5,916|0.056|_(0.036,_<br>_0.077)_|9.8|_(6.3,_<br>_13.3)_|57.4|_(55.6,_<br>_59.3)_|
|Population|_Female_|33.93|5,986|0.051|_(0.029,_<br>_0.073)_|9.0|_(5.2,_<br>_12.8)_|56.6|_(55.0,_<br>_58.2)_|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

There is
Despite this divergent view on the implication of South-South and North-South imported capital and intermediates for developing countries, we know little about how they affect economic transformation (here operationalized as structural change and productivity convergence) in Africa. The current paper fills this knowledge gap and by extension contributes to the burgeoning discussion on pathways to Africa’s economic transformation agenda. Our analysis relies on a panel sample comprising 44 African countries for the period 2000–2022. We assemble country-level indicators of economic transformation (i.e., structural change and productivity convergence) and imported capital and intermediate. For the latter, we use detailed bilateral trade data to identify imported capital and intermediate goods as well as distinguish these imports by their origin vis-àvis the Global North and Global South. A major empirical challenge of our analysis is endogeneity, resulting among others from reverse causality and omitted variable bias. To address this concern, we employ 2-stage least squares and rely on an instrumental variable technique that exploits exogenous changes in trading partner conditions to identify the effects of imported capital and intermediates. Our approach draws insights from <mark>Frankel and Romer (1999) and</mark> Blanchard and Olney (2017).

Overall, our results show that imported capital and intermediate goods are strong predictors of economic transformation, with imports of capital and intermediate goods from both the Global North and the Global South being equally important in this regard. However, the contribution of imported capital and intermediate goods from the Global South or the Global North to economic transformation depends on the considered channel. Imported capital and intermediate goods from the Global South play a dominant role in promoting structural change. For productivity convergence to the global best practices, however, there is a reversal of this trend with imported capital and intermediates from the Global North playing a more dominant role. Ultimately, capital and intermediate imports from the Global South or Global North to Africa appear to serve different purposes on the economic transformation agenda of the continent. As attaining any meaningful economic transformation in African countries entails that
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006 for countries for which data was collected in the first phase of the project. For some of the countries, considered phase 2 countries like Sierra Leone, data collection began later. In the particular case of Sierra Leone, data is collected predominately for the 2007–2009 period. Most technical data presented are typically 2006 for the benchmarks, while financial data for comparator countries are averaged over the available period to smooth out the effect of short-term fluctuations.

Second, in order to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In common with the rest of the continent, Western Africa‘s growth performance improved markedly in the 2000s. _._ The overall improvement in per capita growth rates has been estimated at 1.19 percentage points, of which 1.12 percent is attributable to better structural policies and 0.88 percent to improved infrastructure, with stabilization policies and external factors retarding growth by 0.81 percent.

Since the end of the 11-year civil war in 2002, Sierra Leone‘s economy expanded at annual average rate of close to 10 percent, led by agriculture, construction, services, and mining. Though the global economic crisis led to an almost 40 percent reduction in demand for diamonds—Sierra Leone‘s main export item—the economy continued to grow, albeit at a slower rate, and is expected to rebound (figure 1). Such strong performance is to a great extent a result of sound economic and fiscal policies, and good performance of the key sectors. In addition, recently discovered off-shore oil reserves
inent literature in development economics studies interlinked labor and patronage relationships in village economies (Bardhan (1983), Mukherjee and Ray (1995), Platteau (1995a, 1995b)). When faced with a negative income shock, poor workers may need a transfer or a loan from their employers. This demand for informal insurance may affect the terms of the labor contract, generating an interlinkage between the labor and insurance markets.<sup>12</sup> The program, by improving the outside option(s) of the poorest workers may enable them to transition from interlinked labor to casual labor and/or self-employment. Thus, the program may reduce their likelihood to be receiving transfers from their employers.

# **4 Data Description**

The dataset I use was collected in order to evaluate BRAC’s TUP program in Bangladesh. The data covers 1409 communities, with the average community consisting of 90 households (387 individuals).<sup>13</sup> At baseline, an initial census of all households was carried out in every community, covering 126,810 households. This census collected information on the identity as well as wealth, occupation, education and demographic characteristics of all the households living in these communities at baseline.

Following the census, a detailed household questionnaire was carried out on a representative sample of households. For the sampling, census data was combined with information on households’ socio-economic statuses from the community wealth appraisal BRAC conducted as part of their procedure of identifying the ultra-poor households (see Section 2). The sample for the household survey included all (ultra and other) poor households and a random sample of the rest of the community. This corresponds to 7953 ultra-poor households and an additional 19,012 non-ultra-poor households. Households in this sample were surveyed at baseline (between April and December 2007), at midline (2009) and endline (2011). As detailed in Section 2, the implementation of the program lasts at most two years – less for some of
To illustrate this weighting, consider a case where 150 original observations for log phone lines were available in 1970, and 50 observations were available for electricity. We attribute a weight 3/4 to the former and the latter gets a weight 1/4, while the remaining three infrastructure variables are not part of the composite index in 1970. This weighting reflects two aspects: statistical reliability and economic relevance. Fewer observations of a variable for a given year mean more predicted values for that variable, which are less reliable than the original observations. Statistically, one hence wants to attribute less weight to this ‘less reliable’ series. Fewer observations also reflect less relevance of the indicator. Secure internet connections or mobile phones were simply not available or not as important in the 1970s, their availability was hence not recorded in the data. This constitutes an economic reason for attributing less weight to series with fewer original observations.

Alternatively, our data also allows using those series individually (in logs), where _lphone_ has the largest coverage.

To include variables for **financial crises** , we rely on the database from Laeven and Valencia (2020), which have coverage until 2017. Our dataset contains dummy variables for individual years of banking, currency, or sovereign debt crises and a dummy variable, _dum_fincrisis_ , if either of those crises is reported. Alternatively, the variable _fincrisis_years_ captures the number of years with some financial crisis during a 5-year period (with a maximum of 3).

**Climate change** can affect economic activity through various channels (see e.g., Acevedo et al., 2020). We take data from the World Bank’s Climate Change Knowledge portal on historical monthly temperatures by country and calculate their standard deviation over a 5-year period, _sd_temperature_ . Alternatively, mean temperatures, _temperaturecelsius_ , are available in the data set as well. Both have shown to be negatively correlated with economic growth in the past (e.g., Burke et al., 2015
e¤ects of a change in the level of the standard, respectively, on the number of ...rms and ...rms’output. Assessing the relative strengths of these two e¤ects helps us determine the overall impact on imports of any regional initiatives that deal with standards.

We are particularly interested in exploring the possible asymmetric e¤ects of regional agreements on standards in a heterogeneous world. We assume that the world consists of two types of countries: type K and type L, which di¤er in terms of their ...rms’costs of complying with standards, i.e. ci and Fi. Two broad cases are possible: (i) cK < cL and FK > FL or (ii) cK < cL and FK 6 FL. Our analysis focuses on case (i) for several reasons. First of all, case (i) is more analytically challenging, and once the implications in this case are established, those in case (ii) can be worked out quite straightforwardly. More importantly, case (i) may more accurately depict situations where ...rms from both rich and poor countries produce the same product. It is likely that ...rms from richer capital-abundant countries (type K) tend to adopt a more capital-intensive technology that requires a relatively large Fi, while ...rms from poorer labor-abundant countries (type L) tend to adopt a more labor-intensive technology that requires a relatively large ci. In fact, the World Bank’s (2004) Technical Barriers to Trade Survey reveals that a ...rm’s percentage of compliance cost spent on product re-design for each export market (corresponding to Fi in our paper) is positively correlated with the GDP per capita of the country in which it is located, whereas that spent on hiring additional labor for production and testing (corresponding to ci in our paper) is negatively correlated with the GDP per capita of the country. This limited evidence does not
appreciation of the bilateral US dollar real exchange rate and half to the rise in local currency real unit labor costs.

We estimated panel data fixed effects and GLS regressions for the local currency real unit labor costs in which the independent variables were remittances as a share of GDP, the capital output ratio, and net ODA receipts as a share of GDP. The regressions were estimated on both the sample of 28 transition economies and on a sub sample of 11 high remittance recipients. In all four regressions, the estimated coefficient on remittances was both positive and significant at the 1 percent confidence level. Although problems of reverse causality cannot be entirely discounted, this suggests that remittances have a causal effect on the evolution of real unit labor costs in the transition economies.

What are the policy implications of our findings? Estimates of changes in real exchange rates are often used as proxies for external price competitiveness, but for economies which receive substantial remittances, real exchange rate indices may not fully capture the extent to which they have incurred an erosion of external price competitiveness, especially with regard to labor intensive industries. Trends in real unit labor costs also need to be taken into account in analyzing developments in external competitiveness. The trends in real unit labor costs have potentially profound implications for feasible long term growth and development strategies because of their impact on the economy’s potential comparative advantage.

The very steep rises in real unit labor costs, incurred by some of the high remittance recipients in the South Caucasus and Central Asia, may undermine the feasibility of development strategies which focus on labor intensive industries, such as manufacturing. Instead, it may be more realistic for development strategies to focus more on traded goods industries whose competitiveness is less dependent on low cost labor. Tradeable services may offer a more viable alternative for some of these countries because tradeable services tend to be more heterogenous products than manufactured goods and thus their competitiveness may be less sensitive to relative costs. However, most tradeable services are dependent on skilled labor and so resources will have to be
# **6 Conclusion**

This study sought to analyze the effect of COVID-19 lockdown policies on export and import trade in the Philippines. We applied an event-study analysis on a monthly-product-country data series ranging from January 2019 to December 2020.

We found that both exports and imports dropped upon imposition of the lockdown by trading partners of the Philippines. We find no evidence that the domestic lockdown (i.e., by the Philippines) affected exports and imports of the Philippines. Restrictions on internal movements and international travel controls in partner countries were responsible for the drop in exports. The slump in imports was because of workplace closure, stay-athome requirements, restrictions on internal movement, and international travel controls in trading partners of the Philippines.

Further analysis revealed that lockdowns in the Philippines’ smaller trade partners were key in driving the decline in exports to those countries while larger trading partners (top ten partners) were responsible for the fall in imports. In addition, the drop in exports can be attributed to the slump in Other Products (non-intermediate products), while intermediate goods (particularly Consumer Electronics, Machinery transport equipment) and Other Products were responsible for the decline in imports. Therefore, export GVC trade was robust to the lockdown while backward GVC trade declined during the pandemic. Finally, both exports and imports were more affected at the extensive margin than the intensive margin when the lockdown was imposed. This indicates that lockdown measures hindered interactions among people, in turn reducing the potential of businesses to create new relationships and launch new products in foreign markets.

Since lockdowns affected entire countries and regions, domestic production or regionally based supply chains would not have prevented the disruption to production systems. GVCs through extensive supply chain networks with diversified and geographically dispersed suppliers can help countries adjust better and contribute to speedy recovery and possible resilience to future shocks.

36
<mark>80% in all sectors except for natural resources provision. Communications and business services are among the services sectors that benefit the most from cheaper intermediates as the initial share of imported intermediate inputs is above 23%.</mark><sup>14</sup> <!-- Start of picture text --> Figure 4. Output changes, EU-Indonesia FTA (USD)<br><!-- End of picture text -->

**<mark>Figure 4. Output changes, EU-Indonesia FTA (USD)</mark>**

<!-- Start of picture text --> Social services<br>Communication and business services nec<br>Finance and other business services<br>Trade and transport<br>Construction<br>Utilities<br>Other manufacturing<br>Machinery and equipment nec<br>Electronic equipment<br>Transport equipment<br>Metals<br>Chemical, rubber, plastic products<br>Wearing apparel and leather<br>Textiles<br>Food, beverages, tobacco<br>Natural resources / mining<br>Other agriculture<br>‐10000‐5000 0 5000 10000150002000025000300003500040000<br><!-- End of picture text -->

> 14 The bulk of these intermediate services is composed of trade and transport services, communications and business services, and social services.

13
use of the two submetrics allows one to distinguish between diets which are nutrient inadequate due to insufficiency in the diet, and diets which are nutrient-inadequate due to unhealthy food choices. However, this metric has not yet been widely integrated in national surveys and data availability for LMIC are very limited.

There are also two diet quality metrics derived from modeling approaches. The **Global Dietary Database** has sought to consolidate existing quantitative data and produce modeled estimates of individual food and nutrient intake for 188 countries (Miller et al. 2021). These estimates have been used to describe dietary quality at country, regional and global levels using various metrics (Miller et al. 2022) and quantify disease burdens attributable to diet, such as for type 2 diabetes (O’Hearn et al. 2023).

The **Global Burden of Disease Project** uses a set of **15 dietary risk factors** : diets low in: fiber; fruits; legumes; nuts and seeds; polyunsaturated fatty acids (PUFAs); seafood omega-3 fatty acids; vegetables and whole grains; milk; calcium; and diets high in: processed meat; red meat; sodium; sugar-sweetened beverages; and trans fatty acids; these include most of the recommendations from WHO with some additional ones for cancer from IARC (GBD 2017 Diet Collaborators, 2019). They develop Global Burden of Disease estimates of the contribution of dietary risks to the burden of NCDs in adults over the age of 25 across 195 countries, along with their associated impact on mortality and DALYs.

One critique of these modeled estimates points to the fact that evidence on dietary intake from representative populations in LMIC is currently inadequate to produce robust and reliable estimates of intake across countries (Beal et al. 2021). The relevance and face validity of modeled data for country-level work are also often questioned by national stakeholders. Another concern is that GBD 2017 Diet Collaborators’ (2019) estimates of adverse effects of dietary factors (11m deaths annually) far exceed the estimates
33|
||town pop|8.03|35.32|38.23|11.67|3.29|2.66|0.79|
|Henan|in-migrant|3.58|17.59|37.37|15.19|13.65|8.95|3.67|
||town pop|5.94|27.12|41.31|14.17|5.39|4.92|1.16|
|Sichuan|in-migrant|3.80|20.09|29.79|24.26|12.79|7.18|2.09|
||town pop|6.19|32.20|36.21|12.94|6.96|4.41|1.09|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table 1-8b, and Table L7-6-1b and Table L7-6-2b.

note: 1, literate; 2, primary school; 3, secondary school; 4, high school; 5, vocational school; 6, college; 7, university or above.

In representing the findings on educational attainment of those who migrated into towns, and of general population in towns in three provinces (Table 9), we have found that in-migrants generally possess higher level of educational attainment than those of general population. In Zhejiang, for example, there were 17.8 percent of all in-migrants who have completed high school or professional technical school, whereas only 14 percent of town general population who have completed high school or professional technical schools.

10
**Figure 2: Sales decline gap at the various quantiles**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>10th 20th 30th 40th 50th 60th 70th 80th 90th<br>Sales decline gap 95% confidence interval<br>Sales decline gap (percentage points)<br><!-- End of picture text --> Source: Authors’ own calculations based on data from World Bank’s Enterprise Surveys. Note: Sales decline gap is the decline in sales due to the pandemic as a percentage of the prepandemic sales level of SMEs minus the same for large firms. A positive value of sales decline gap implies that the percentage decline in sales is bigger for SMEs than large firms.

47
Our application of this procedure to data for 1996, 2001 and 2007 led to results reported in both table 2.6 and figure 2.2. The decomposition for 2001 and 2007 is based on the same groups listed in table 2.5. As noted earlier, the data from the 1996 survey has a different grouping. These results confirm the conclusion we reached earlier on the basis of Shapley analysis of regional differences in Poverty. Between group inequality is indeed a major component of overall inequality (as measured by the Gini Coefficient) in Cameroon. This component has increased from 39 percent of the total in 1996 to almost 50 percent in 2007. It represented 43 percent of total inequality in 2001. These results also reveal that there is significant overlapping between regional distributions and a low level of within group inequality. In addition, within group inequality has been declining significantly over time. It accounted for about 30 percent of total inequality in 1996, 20 percent in 2001 and dropped to about 3 percent in 2007.

Table 2.7. Contribution of Location to Income Inequality

||Rural-Urban|Region|Rural-Urban &Region|
|---|---|---|---|
|1996|0.03|0.10|0.10|
|2001|0.18|0.17|0.22|
|2007|0.30|0.35|0.40|

Source: Authors’ Calculations Finally, we use simple regression analysis to decompose the variance of the logarithm of per adult equivalent expenditure. To do this, we run regressions the logarithm of per adult equivalent expenditure only on a set of dummy variables indicating the area of residence of the household. It is known that the R-squared from such a regression measures the proportion of the variation in the dependent variable (log of expenditure) explained by the location dummies (Benjamin, Brandt and Giles 2005). We consider three different specifications for each of the three years: the rural-urban