rafmacalaba/data-use-mentions-extended huggingface dataset:
|2.42|20.43|
|2008|1.29|1.31|1.28|1.59|2.68|21.99|

_Source: own estimates based on panel HIECPS survey 2005-2008._ The widening of the distribution is well illustrated by growth rates for different percentiles. Figure 2 shows growth-incidence curves (GICs) for Egypt overall, and for urban and rural areas, over 2005-2008. Such curves show the annual growth rate for household welfare at each percentile from the poorest (1<sup>st</sup> percentile, on the left) to the richest (100<sup>th</sup> percentile, on the right).

The positive slope of the growth incidence curves suggests that the rich gained more than the poor, especially in rural areas, which show even a fall in real welfare for the poorest percentiles in contrast to overall positive growth. In urban areas the growth incidence curve has a characteristic U-shape, suggesting that the richest and the poorest had the highest growth rate, while the middle, especially those between the 20<sup>th</sup> and 60<sup>th</sup> percentiles, experienced the slowest growth, bordering just 1 percent per year.<sup>16</sup> Taking bottom 20 percent as poor in the country, one can see that in absolute sense the poor as a group had a small gain (with the extreme poor in rural areas experiencing a fall in welfare). Taking into consideration confidence intervals, one can say that for Egypt and for rural areas, the poor have experienced growth rates below the mean. In fact, the poor in Egypt had the lowest growth rate, hence the growth was not pro-poor in a relative sense.

> 16 It is important to remember that the period of high growth during 2005-2008 was preceded by five years of losses. For the lower-middle class, 1 percent per annum gain does not even compensate for losses
exporter in 2020 (see <u>https://www.eia.gov/energyexplained/us-energy-facts/imports-and-exports.php), at times</u> imports electricity from its neighbors Canada and Mexico. Unfortunately, we do not have highfrequency electricity imports and exports data, nor is it possible for us to compute “net imports” as the difference between generation and final consumption, as we lack data about the latter.

Imports and exports of electricity are commonplace within EU countries, and both Norway and Switzerland export electricity to EU countries. Only a small share of the total electricity consumption is however imported into the EU from non-EU countries (e.g., Russia<sup>9</sup> ), suggesting that EU aggregate generation serves as a good approximation to final consumption of electricity in the EU.

> 9 See https://view.news.eu.nasdaq.com/view?id=b62aadb50d4d3bd34550a8d2b09b7f6df&lang=en

20
developing countries, the following points matter for this choice. First, the TiVA data separates the various digital services sectors of interest. The ITPD-E dataset instead takes the various sub-sectors of the digital economy together. Second, many developing countries included in the ITPD-E database show few observations after aggregating into sector groupings in line with Annex Table A3. This points out to the fact that the ITPD-E data is not in a “squared” or balanced format, missing many observations in services, which in TiVA are present (even though they may be estimated). Hence, even though the limited number of developing countries in TiVA does not match the total number of countries with an identified data model, this database is still considered a preferred option. Nonetheless, data from the ITPD-E database is used for a robustness check and find largely similar results.

25
# Future prospects

There are now a wide variety of survey instruments that are being designed for or adapted to measure income or consumption among FDP populations. Home visits initially designed by the UNHCR to question FDPs for protection purposes are being revised to ask questions on income and consumption, becoming in this way viable instruments for poverty measurement. Large home visit exercises are conducted every year in countries such as Jordan and Lebanon and these data have been used to conduct poverty assessments of Syrian refugees (Verme et al., 2016). The WFP conducts vulnerability assessments that have been used by the WFP, UNHCR and World Bank to make gross poverty estimates using the consumption modules of these surveys, even if these modules are typically very short, with few items. The UNHCR conducts Multisector Needs Assessment such as the one conducted in Cox’s Bazar, Bangladesh, in 2018, Socio-economic assessments such as the one conducted in Zimbabwe in 2017, or nutrition surveys such as the one conducted in Tanzania in 2017.<sup>12</sup> All these surveys contain some information on income, consumption, or expenditure that is being used to assess the well-being of FDPs. The World Bank has conducted welfare assessments of Venezuelans in various Latin American countries including Colombia, Peru and Ecuador, with a forthcoming study expected for Chile. The welfare of Afghan refugees has now been studied in their main host country, Pakistan, and in Afghanistan upon return, providing elements to compare the living conditions of this population in the two locations. Again, all these surveys and studies are either very recent or were not used before for poverty/welfare analyses.

Whereas individual and household data on refugees are now more systematically collected, data collection for IDPs remains extremely scarce when compared to refugees, mostly limited to head counting. Refugees and IDPs are very different in that they have a different legal status, which leads to different access to public services, labor markets, government, and international assistance. Surveys for IDPs are often more difficult to conduct because
proposed PPML approach in the gravity model literature. Subsequent papers build upon the PPML approach and establish its appropriateness for gravity model estimation (Santos Silva and Tenreyro 2010, 2011, 2015).

In this paper, we apply a panel-data PPML estimator that includes the multilateral resistance terms, pair-specific fixed effects, and time-varying country effects. As a result, many of the traditional gravity variables drop out of the estimation. Standard errors are clustered on trading pairs.

# _Construction of the Main Data Set_

The dataset used in the estimation combines two separate datasets. The first dataset is the Handbook of Deep Trade Agreements (HDTA) database. The HDTA database includes a list of 279 agreements along with the year of entry and the agreement type (e.g. Free Trade Agreement, Customs Union, Economic Integration Agreement and PSA). Each chapter of the HDTA includes the relevant data tied to the name of the agreement. The HDTA also includes many, but not all, of the relevant country pairs. To complete the data, I identified the year of entry and countries included in the agreement and merged them into the HDTA database so that they could be included in the subsequent merges.

The second dataset is the 2020 version of the CEPII gravity model database (Head and Mayer 2010).<sup>6</sup> These data include common language, same country, common religion, distance between countries, and contiguity. These data include several measures of trade flows that come from either the International Monetary Fund (IMF), the United Nations COMTRADE database, and the BACI (Base pour L’Analyse du Commerce International) revisions to the COMTRADE data for both total and manufacturing trade (Gaulier and Zignago 2010). The advantage of the BACI data over the raw COMTRADE data is that the applied revisions reconcile the discrepancies found between the exporter-reported and importer-reported trade volumes and adjust for data reliability. The data also include information about whether missing values are the result of no trade (“true zeros”) and
Malawi Longitudinal School Survey, 2021. Malawi Longitudinal School Survey Endline data. Unpublished.

Ministry of Education, Science and Technology (2018). _Primary Teacher Management Strategy._ Lilongwe: Government of Malawi.

Ministry of Education (2021a). Education Management Information System (EMIS) data, 2019/20. Unpublished.

Ministry of Education (2021b). _Malawi Education Statistics 2019/20_ . Mimeo.

Ministry of Education (2022). Education Management Information System (EMIS) data, 2020/21. Unpublished.

Malawi Longitudinal School Survey (2021). Unpublished endline data.

Mulkeen, A., 2010. Teachers in Anglophone Africa: Issues in Teacher Supply, Training, and Management. Washington, D.C.: World Bank Publications.

Muralidharan, K.; Sundararaman, V. 2013. “Contract Teachers: Experimental Evidence from India.” NBER Working Paper No. 19440.

Pugatch, T and Schroeder, E. 2014. “Incentives for teacher relocation: Evidence from the Gambian hardship allowance.” _Economics of Education Review_ 41, 120-136.

Ramachandran, Vimala, Tara Béteille, Toby Linden, Sangeeta Dey, Sangeeta Goyal, and Prerna Goel Chatterjee. 2018. _Getting the Right Teachers into the Right Schools: Managing India’s Teacher Workforce._ World Bank Studies. Washington, D.C.: World Bank Publications.

Walter, T. F. 2018. “Misallocation of State Capacity?” PhD Thesis. London School of Economics and Political Science, London. Available at: http://etheses.lse.ac.uk/3852/1/Walter <u>misallocation-ofstate-capacity.pdf [9.1.22]</u> 24
firm, for a total number of observations in their study of 76,903.<sup>10</sup> This study is an important contribution to the literature, since there are no studies of the impact of services sector _liberalization_ on firm level productivity using firm level data across several countries. The closest study to this one is the study by Arnold, Mattoo and Narcisco (2008). Arnold et al. (2008) found that improved services sector _performance_ (as opposed to services liberalization in the Klishchuk and Zelenyuk study) increased total factor productivity in 1,000 firms in 10 SubSaharan African economies. The Klishchuk and Zelenyuk study is an advance of the literature for several reasons. First, Klishchuk and Zelenyuk examine the impact of services sector liberalization, rather than performance, on firm level productivity. Second, it uses panel data that allows the use of fixed effects in the empirical work; fixed effects allow the authors to capture firm heterogeneity that is not captured by other variables in the model, and this is important in the results of their paper. Third, the large sample size in this study justifies the asymptotic distribution theory in some of the estimators they employed. Finally, it is the first such study to be conducted on transition countries, and provides support for the applicability of the casual link between services liberalization and productivity on data from transition countries.

The authors use seven separate categories of services performance from the EBRD Transition Indicators as their measures of services sector performance. The EBRD performance measures they use are for the following sectors are: banking, non-bank financial services, telecommunications, railways, electric power, roads, water and waste water. They control for liberalization of the trade and foreign exchange regime, and several other variables of importance to firm level productivity. They estimate labor productivity as a function of these many variables in both a pooled ordinary least squares regression and in a model with fixed effects for each firm in the sample. Their statistical tests indicate that the fixed effects model is the
||No Rights|Rights (License Required)|Rights (No License)|
|---|---|---|---|
|Backhaul|27.5%|63.75%|8.75%|
|Cabotage|82.5%|17.5%|0%|
|Transit|22.5%|71.25%|6.25%|
|Triangular|47.5%|47.5%|5%|

Source: World Bank Survey Notes: This table presents the percentage of countries which allow a type of travel (backhaul, cabotage, transit, or triangular), and the degree to which that travel requires prior licensing.

Table 2: Regulatory Practices foreign carriers cannot complete domestic jobs, and set minimums on the share of international jobs performed by truckers from each country (Teravaninthorn and Raballand (2009)). Similarly, in Central America and Belize, prevailing regulation prohibits cabotage, despite trucking being a major mode of international trade (Guerrero and Abad (2013)). In contrast, the European Union features a more integrated regional trucking market where truckers are allowed to pick up as many as three cabotage loads after completing an international trip (Blancas and Briceno-Garmendia (2020)). Even in this more relaxed context, Commission (2014) reports that vehicles traveling outside their registration country were empty more than 45 percent of the time, while vehicles within their registration country were empty just over 25 percent of the time. This difference is present in both specialized and general freight markets, suggesting that cabotage restrictions, rather than search frictions, is the culprit.

Based on a 2018-2019 World Bank survey of regulatory practices, restrictions on cabotage are very common - over 80 percent of countries do not allow cabotage rights. Backhaul, transit, and triangular rights are more common, but in the vast majority of countries, they require particular licenses.

Across countries, higher income countries tend to have
response probability to construct the response weight variable for each IHPS adult household member that was interviewed in private; (2) winsorize the response weights at the top 3 percent to account for potential outliers; and (3) set it equal to 1 for all adults in the IHS4 sample. Henceforth, all statistics are weighted using the response weight.

Table 1 presents the descriptive statistics on selected individual and household attributes. Individuals in the IHS4 are slightly older, more likely to be the household head or spouse, female and less educated, and they live in smaller households. Although these are statistically significant differences, the magnitude of the differences is negligible.<sup>12</sup>

**<u>Table 1. Descriptive Statistics by Survey Approach</u>**

|IHPS<sup>(1)</sup>|IHS4<sup>(2)</sup>|T-test<br>(1)–(2)|
|---|---|---|
|Age (years)<br>33.01|33.46|**|
|Female<br>0.52|0.53|**|
|Never attended school<br>0.09|0.13|***|
|No primary degree<br>0.52|0.51||
|Primary degree<br>0.12|0.12||
|Lower secondary degree<br>0.12|0.12||
|Higher secondary degree<br>0.15|0.12|*|
|Married<br>0.68|0.66||
|Household head or spouse<br>0.73|0.76|**|
|Log household size<br>1.61|1.52|***|
|Dependency ratio<br>0.89|0.93
</sup>

> 23 In Bloom et al. (2019b), only four questions from the U.S. MOPS were selected for microenterprises. The correlation between the management score calculated with this subsample of questions and the overall management score for SMEs and large firms is 0.86, indicating that this short score is a good measure of management practices. This result is similar to McKenzie and Woodruff (2017) who find an important role for management in micro-firms in developing countries.

> 24 As annex table A.6 shows, the overall explanatory power of firm characteristics in explaining the management score is still quite limited: the most complete specification (column 8) explains only 21% of the overall variation in the management score, implying that nearly 80% of the variation in management practices is explained by idiosyncratic, firm-specific factors.

25
, carrying the generalized lessons from cross-country regressions to inform policy-making in any one country can be highly problematic. There are also a number of promising examples of how simulation tools for economy wide policies such as Computable General Equilibrium models can be combined with household-level survey data to assess impacts on poverty and inequality.<sup>62</sup> These simulation methods make it far easier to attribute impacts to the policy change, although this advantage comes at the cost of the need to make many more assumptions about how the economy works.

**_Is the evaluation answering the relevant policy questions?_** Arguably the most important things we want to learn from any evaluation relate to its lessons for future policies. Here standard evaluation practices can start to look disappointingly uninformative on closer inspection.

> 62 See, for example, Bourguignon et al. (2003) and Chen and Ravallion (2004).

55
# **6. Conclusion**

This paper has examined the relationship between changes in a country’s external trade policy offered toward non-PTA partners as a function of changes in the country’s trade policy offered toward PTA partners. Our empirical setting has focused on the two major economies of MERCOSUR – Argentina and Brazil – and we estimate different effects for the period in which MERCSOSUR was an FTA (1990-1994) from when it was a customs union (1995-2001). Our primary innovation is to consider measures of trade policy that are more expansive than tariffs and that also include the temporary trade barriers of antidumping and safeguards. When considering all available policy instruments, we present evidence of an economically significant stumbling block effect arising during the customs union period, and our results also tend to reverse any potential evidence of a building block effect that may have been thought to arise (based on tariffs alone) during its FTA period.

These results are novel; however, they do not completely resolve the puzzles identified by the existing literature. Furthermore, our analysis of the highly detailed policy data raises a number of other interesting and yet unaddressed questions worthy of additional empirical research.

First, our results call into relief the need for additional work to construct more “complete” product-level measures of import protection that take into consideration all available trade policy instruments. This is something that we have also noted in other settings (Bown and Tovar, 2011) and for which some efforts have been made, albeit only in the cross section (e.g., Kee, Nicita and Olarreaga 2008, 2009). One potential explanation behind why our estimates are not stronger is our reliance on categorical variables for some of the policy outcomes; construction of measures of the ad valorem equivalents for the imposed TTBs may improve upon the estimation considerably.

Second, we also take a first step toward examining the role of import market power on the interplay between the relative changes in trade policy affecting PTA partners and PTA non-partners. Given our desire in this
combustion is associated with a range of other toxic pollutants, including sulfur dioxide and nitrogen oxides. Overall, our findings suggest that coal prices do not significantly and systematically alter urban PM2.5 concentrations in our sample of 133 countries. However, while this might hold globally, the relationship may be significant in different subsamples, such as regions or national income classifications.

# 5. Discussion

Exposure to air pollution is widely accepted as a leading health issue in many countries, regardless of income classification (WHO 2021). In urban environments, fossil fuel combustion is a significant contributor to airborne PM2.5, making fossil fuels an attractive target for regulation to improve health outcomes. While the relationship between fossil fuel prices and consumption has had ample attention in empirical economics, the cascading effect on air quality is less thoroughly studied. At a fundamental level, one would expect higher fuel prices to lead to lower consumption, which, in turn, would result in better air quality outcomes. Earlier works on the price-pollution channel have been at the city level and found mixed results regarding price impacts on air pollution; our contribution to the literature is in examining whether a relationship exists at the global level by using the CCE estimator with a novel panel dataset that covers 133 countries over 19 years.

Our results indicate a significant relationship between PM2.5 concentrations and the price of two transport fuels. For gasoline, a US$1 increase in prices yields 22.2 μg/ m<sup>3</sup> reduction in PM2.5 concentrations; for diesel, the reduction is 31.05 μg/ m<sup>3</sup> . This trend seems to mirror findings in the literature, which has found that diesel prices in particular are effective in mitigating emissions. Both fuels are frequently used in public transit and freight transportation, and diesel is a major source of urban air pollution. Their consumption tends to be variable, as consumers can switch to alternatives, such as public transit, or invest in
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|4.80|
|Mongolia|5.03|4.91|11.61|2.70|3.86|4.82|11.96|3.88|
|Pakistan|0.52|6.63|14.37|2.35|3.94|2.61|14.26|3.17|
|West Bank and Gaza|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Peru|5.01|5.30|9.56|3.60|7.49|2.88|9.29|4.42|
|South Africa|2.20|3.96|12.46|5.63|3.04|2.15|12.38|6.80|
|Timor|2.73|1.94|n.a.|n.a.|3.06|1.32|n.a.|n.a.|
|Turkey|1.94|4.08|11.22|4.75|3.53|0.78|11.24|4.99|
|Uruguay|6.20|6.79|9.11|1.72|7.33|3.22|8.91|1.90|
|USA|4.32|3.01|10.57|5.14|4.52|2.48|10.35|6.16|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18. West Bank and Gaza survey only distinguishes between married and not married people.

32
data well suited to analyzing this issue; data drawn from evaluations of Ethiopia’s Productive Safety Net Programme (PSNP). The PSNP is the second largest safety net program in Africa reaching about eight million chronically food insecure people. A feature of the program is that its beneficiaries receive their payments either in cash or food – or in some combination of both. Further, the share of cash and food payments in total payments have varied over the years since the program began in 2005. Household and locality panel data were collected bi‐annually over five survey rounds fielded between 2006 and 2014. In each round, beneficiary households were asked whether they would rather receive payments in cash or in‐kind (food). These data make it possible us to describe predilections for cash and for food, show how these change over time, and allow an assessment of associations between them and household and locality characteristics. Ethiopia is an especially appropriate country to situate such a study as, since the beginning of the PSNP, there has been an active debate on whether the payments should be made in cash or in‐kind. Thus, while speaking to the international literature on this topic, this study also contributes to this local policy debate in Africa's second most populous country.

# **2. Context and data**

This section provides contextual information. It describes the data used in the paper including information on sampling, household and market characteristics, PSNP payments, market prices and beneficiary views on cash and food payments.

> cash. Beneficiaries who belong to villages that are very far from a bicycle store were less likely to prefer cash over kind as were older beneficiaries.

6