rafmacalaba/data-use-mentions-extended huggingface dataset:
onwards, and covers 154 countries for which we have trade data. Here coverage is almost complete for all years. Using two independent sources which employ different methodologies contributes to testing the robustness of our results. A full list of countries can be found in the appendix. We use the sum of the index for a country-pair as a regressor; that is, _Iijt_ = _Iit_ + _Ijt_ . This reflects the aforementioned symmetry built into our data.<sup>23</sup> Our measures of infrastructure quality are the number of telephone lines (landlines plus cellular phone lines) per 1,000 people, and the total country road length per 1,000 people; they are taken from the World Development Indicators and are available for the entire sample.

We obtained bilateral tariff data from the TRAINS dataset (via the World Bank) for 1988-99. Use of this data immediately eliminates observations from 1984-87. Although this is the most elaborate bilateral tariff dataset we are aware of, the coverage of the bilateral tariffs of all trade pairs in this sample is patchy (in the later years the coverage is significantly better than in the early years) and considerably reduces the sample size in regressions in which it is used. As a regressor, we use (trade-weighted average across goods) bilateral tariffs, averaged for each country-pair. As with institutional quality, the averaging reflects the symmetry assumption. We chose to average rather than to sum in order to make results easy to interpret. For many countrypairs the tariff data was available for only one country. In these cases, we treat the tariff of that country as the “average” of both. This procedure is used in order not to lose too many data points in the estimation of specifications with tariffs. Even so, the tariff data cover only 43% of the entire sample for which trade and institutional data are available. This restriction hardly changes the overall representation of country-pairs in the panel. However, using tariff data the sample is

> 23 We chose not to use
Policy Research Working Paper 10512

# **Abstract**

This paper offers a nontechnical review of selected applications that combine survey and geospatial data to generate small area estimates of wealth or poverty. Publicly available data from satellites and phones predicts poverty and wealth accurately across space, when evaluated against census data, and their use in model-based estimates improve the accuracy and efficiency of direct survey estimates. Although the evidence is scant, models based on interpretable features appear to predict at least as well as estimates derived from Convolutional Neural Networks. Estimates for sampled areas are significantly more accurate than those for non-sampled areas due to informative sampling. In general, estimates benefit from using geospatial data at the most disaggregated level possible. Tree-based machine learning methods appear to generate more accurate estimates than linear mixed models. Small area estimates using geospatial data can improve the design of social assistance programs, particularly when the existing targeting system is poorly designed.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at dnewhouse@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
122, World Bank.

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23
|2,842|
|Unaccompanied children|34.0|53.7***|41.2|16.0|31.5***|21.6|57.75|97.28|296|
|Siblings|36.2|55.8***|42.8|8.0|24.2***|13.4|54.24|203.44|566|
|Extended and other|44.5|29.5***|38.0|9.4|6.1**|8.0|−33.75|−34.47|1,367|
|All Nontraditional|19.0|50.9***|35.6|4.6|12.7***|8.8|167.86|177.52|20,963|

_Source:_ Own calculations based on ProGres and JD‐HV database. Gender gap is calculated as the percentage difference between female and male poverty rates.

_Note_ : PA = principal applicant; HH = household. Nontraditional family types are all family types apart from couples with children. Differences between male and female PA poverty rates by marital status and family type are statistically significant at *10 percent level, **5 percent level, or ***1 percent level.

18
be some subsectors more protected than others; and so one would ideally like to see even more detail. The risk of a high level of aggregation is that it would hide large disparities between industries grouped under the same broad heading. For example, in the tariff data for the food industry (the HS 2-digit codes 16 to 24) the simple average is over 200 percent, and Lederman reports ERPs for beverages based on average tariffs of 1600 percent. The reason for such large average tariffs at this level of aggregation is seen in Table 1, where imported alcoholic beverages are taxed at an almost prohibitively high rate. The highest tariff for non-alcoholic and non-automobile products is 30 percent, and is usually reserved for finished products produced in Egypt. But within industry groupings tariffs range from lows of zero and 2 percent to 10, 20 and 30 percent based on the degree of valued added that might be done in Egypt; several sectors show clear cases of tariff escalation. When reading the ERP estimates reported below, the reader should be aware of this aggregation problem and interpret the result appropriately.

# **3. A Quick Review of the Algebra of Effective Protection**

Because subsidies are important in the case of Egypt, both on outputs and especially on energy inputs, the algebra for estimating effective rates of protection requires an extension to that usually found in textbooks. We quickly review below the manner by which we calculate effective rates of protection, first incorporating output subsidies, then input (energy) subsidies. The ERP is defined in terms of the percent deviation of an industry’s value added from what it would otherwise be in the absence of protection (both formal applied tariffs and NTBs). (See, for example, Corden, 1971, Vousden,

> 3 Two cases where preferences might make a difference among most-traded goods are HS 270900 (Petroleum oils and oils obtained from bituminous minerals, crude)  and  HS 090240 (Black fermented tea and partly fermented tea, whether or not flavoured, in immediate packings of
is reported for a year between 2010 and 2015 for 5 countries and after 2015 for 68 countries. These countries are divided into four income groups: 24 are in the High-income group, 27 are in the Upper middle-income group, 34 are in the lower-middle-income group, and 20 countries are in the Low-income group. Expenditures are disaggregated by expenditure quintiles within each country and between urban and rural locations.

Transport expenditures are classified into three subcategories: non-durable private transport, durable private transport, and transport services. Non-durable private transport expenditures include fuels and lubricants for personal transport equipment, maintenance and repair of personal transport equipment, and other services linked to personal transport equipment. Durable private transport expenditures include durable good purchases like cars, motorcycles, bicycles, and animal-drawn vehicles. Transport services expenditures include: passenger transport by railway, passenger transport by road, passenger transport by air, passenger transport by sea and inland waterway, combined passenger transport, and other purchase transport services. Total transport expenditures are the sum of both private transport types of expenditures and transport services expenditures.

**LSMS household surveys.** The Living Standards Measurement Study (LSMS) is a household survey program by the World Bank’s Development Data Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys. This paper uses the latest LSMS surveys conducted in 18 countries from 2010 to 2018 (Table 2).<sup>3</sup> Most of the countries are from Sub-Saharan Africa, but there are also 3 countries from the South Asia region, one country from Europe and Central Asia region, one country from East Asia Pacific region, one country from Latin America and the Caribbean region, and 1 country from the Middle East and North Africa region. In terms of income groups: 10 countries are in the low-income group, 6 countries are in the lower middle-income group, and two countries are in the upper middle-income group.

The typical LSMS household survey contains questions on expenditures on private transport and transport services
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case of Venezuelan migration to Colombia. Over the last decade, Venezuela has experienced a multifaceted humanitarian, economic and social crisis that has led to a large outflow of its citizens. Official figures from the R4V platform indicates that, by the end of October 2021, 5.91 million Venezuelans had left the country (about 16% of the country’s population), with over 1.75 million migrating to neighbouring Colombia<sup>1</sup> This case is important for empirical and theory development because both populations share ethnolinguistic, social and cultural features. These similarities potentially present divergent possibilities: on the one hand, if Colombians and Venezuelans possess similar occupational skills—then they may be close substitutes in the labour market, giving rise to more restrictive policy preferences on the basis of greater economic

> 1 Statistics obtained from the R4V website – The platform for interagency coordination for refugees and migrants from Venezuela: http://www.r4v.info

2
# **5.2 An Instrument for Related Lending**

Though ethnic fractionalization poses fewer endogeneity problems than rule of law, the substitution of these variables does not address the potential endogeneity of the related lending policies themselves, which could also be driven by similar unobservable political factors. The difficulty, of course, is finding an appropriate instrument for those policies.<sup>19</sup> We propose as a candidate the index of official supervisory powers constructed by BCL (2001). That index, which is described in detail in Appendix 2, is based on sixteen questions about the powers granted to supervisors in monitoring and disciplining banks. We view this as an indication of a society’s general propensity to use official mechanisms to monitor market activities. To the extent that a society views its banking sector as being tightly regulated and supervised, fears about the negative effects of related lending might be diminished. We would therefore expect a positive relationship between our related lending index and the BCL index of official supervisory powers, and indeed the correlation between the two is 0.42, which is significant at the 1 percent level.

When we use the official supervisory powers index as an instrumental variable in a twostage least squares regression, the index of related lending is not statistically significant at conventional levels (Table 5, column 3). When we then restrict the sample to countries that began our period of study with relatively low levels of credit (private credit/GDP < 0.7 in 1990), the index of related lending is negative and significant at the 5 percent level (column 4). This filter excludes nine countries from the active observation set, eight of which are industrialized.<sup>20</sup> Similar results hold when we restrict the sample using other variables that measure institutional or financial development. The results from the instrumental variable regressions therefore indicate that related lending is more strongly associated with slower private credit growth in less developed countries, which offers support for the notion that
<sup>_th_</sup> quintile and 0 otherwise. Again, to isolate the direct effect of lightning on economic development from confounding the relationship between the instrument and the outcome variable, I include the district-level lightning intensity interacted with time trends.<sup>30</sup> In this set-up, the main identifying assumption is that the differential effects of regional expansion in mobile network coverage across places with varying lightning intensity affect local economic development only through its effect on expansion in (local) mobile network coverage. Estimates are weighted by population. Robust standard errors are also clustered at the district level.

30The interaction with time trends is necessitated by the fact that the lightning intensity data is cross-sectional.

15
A challenge for those interested in assessing how different households are impacted by trade policy is that tariff data are typically classified using the Harmonized System, whereas household survey classifications have historically been somewhat ad hoc. To render them compatible and comparable across countries, we aggregate goods in the household surveys to common 4- and 2-digit categories using separate expenditure, autoconsumption and income templates. We cover spending on, income derived from, and autoconsumption of 53 4-digit agricultural and food items. These include Staple Agriculture, such as corn and rice, and Non-Staple Agriculture, such as oils, cotton and tobacco. We also categorize spending on five classes of manufacturing items. In addition, we keep track of spending on five (non-tradeable) services and on four other expenditures. The fact that we have much more granular data on agricultural products than on manufacturing services reflects the nature and structure of the household surveys we are standardizing. Note that not all categories are populated in all surveys, which reflects both survey design and local consumption patterns (e.g., pork not being consumed in the majority of predominantly Muslim countries). The expenditure template is shown in Figure 1.

On the income side, we keep track of income derived from the sales of the same 53 food items we cover on the expenditure side. In addition, whenever the survey design allows it, we also split wage income by sector, defined roughly at the 1 digit level, and keep track of non-farm household enterprise sales across 10 sectors, as well as various types of transfers. The income template is shown in Figure 2. We also keep track of production for home consumption using the autoconsumption template, which is shown in Figure 3 and contains 53 agricultural products and a select few categories for other goods.

Since many of the surveys are subject to confidentiality agreements, we aggregate the households and offer statistics for each percentile of the household per capita real income distribution. The database thus has 5,400 observations (54 countries and 100 observations per country),
**Table 1: Regional variation in Manufacturing Value Added (% of GDP)**

||1960s|1970s|1980s|1990s|2000s|2010s|
|---|---|---|---|---|---|---|
|West Africa|0.08|0.12|0.11|0.11|0.09|0.07|
|Observations|24|63|75|99|121|82|
|Number of countries|3|7|8|10|14|14|
|East Africa|0.07|0.09|0.09|0.09|0.09|0.08|
|Observations|30|62|88|90|90|38|
|Number of countries|4|7|9|9|9|9|
|Central Africa|0.16|0.25|0.17|0.13|0.1|0.09|
|Observations|12|32|50|53|76|61|
|Number of countries|3|4|5|6|9|9|
|Southern Africa|0.11|0.13|0.14|0.14|0.15|0.14|
|Observations|26|69|80|89|90|40|
|Number of countries|5|7|8|9|9|9|
|Sub-Saharan Africa (All SSA)|0.11|0.15|0.13|0.12|0.11|0.10|
|Observations|92|226|293|331|377|221|
|Number of countries|15|25|30|34|41|41|

The table reports SSA regional mean shares of manufacturing output shares in GDP (constant prices) for each
.9|61.6|61.8|
|Other Pan-Euro|3.2|3.2|3.9|3.6|3.8|4.4|41.8|42.1|43.7|
|ROW|30.6|28.8|26.6|42.7|41.1|40.3|33.7|35.0|32.5|

Note: Total networks include T&C, footwear, furniture, automobile and ICT chains. Source: Computations based on Turkey data from UN COMTRADE Statistics.

Network trade, accounting now for almost three-thirds of Turkey’s EU-destined exports, has also driven Turkey’s integration into EU markets in a pattern and sequence similar to that observed earlier in several EU-8 economies. While Turkey has not as yet moved fully to the second stage, the process appears to be at a full swing. The value of exports of parts and final
the so-called _J_ -curve pattern following devaluation. In other words, the long-term improvement in Ethiopia’s external balance would be achieved without any significant shortterm deterioration in the trade balance (see Annex I).

Further analysis reveals that a 1 percent real devaluation increases Ethiopia’s manufacturing exports by 1.06 percent and agricultural exports by 0.33 percent (Haile, 2015b). The results in Table 2 (columns 3

**Figure 11. Contributions to Changes in the Current Account Balance (2010-13) (% GDP)**

<!-- Start of picture text -->
1.0%<br>0.5% Credit growth<br>0.0%<br>-0.5% FDI Lag NFA (share in GDP)<br>-1.0% Terms of trade Fiscal balance instrumented (share in GDP)<br>Relative openness (lag)<br>-1.5%<br>Real Aging speed<br>-2.0%<br>effective  Relative GDP growth<br>-2.5%<br>exchange Lag CA<br>-3.0% rate Inflation volatility (3 year average, lag)<br>-3.5% Relative income<br>-4.0%<br><!-- End of picture text -->

change compared to previous period (2010-2011) <!-- Start of picture text --> Source: Portugal and Zildovic (2014).<br><!-- End of picture text --> <mark>Source: Portugal and Zildovic (2014).</mark> 15
# **6. Discussion and Conclusion**

The present study uses data from the 2003 and 2007 TLSS surveys to evaluate the long-term impacts of the 1992-1998 armed conflict in Tajikistan on education and labor market outcomes for men and women. The analysis of education focuses on those born in 1966-1973 and 1976-1985 (war-cohort). These cohorts should have completed at least secondary school by 2007. The analysis of labor market outcomes focuses on those aged 22-49 in 2007. The results of difference in difference regressions suggest that residence in the region more affected by conflict during an individual’s youth had a significant negative impact on the educational attainment of women. These women are also more likely to participate in the labor market, but their wages are not statistically different from the wages earned by the rest of the sample. No significant relationship between residence in the more affected region during one’s secondary school and early labor market years and employment or total earnings in 2007 is found for men.

The results are robust to alternative specifications and the inclusion of additional household and community controls that purport to have a significant impact on employment, such as migration and remittances and the employment status of household heads and spouses. Men’s labor supply is more responsive to changes in non-labor income than female labor supply.

The higher employment among young women in the more affected areas could be attributed to a “persistence” factor. It is possible that entry into the labor force is rather costly. The main share of the cost is not necessarily in terms of money or investment in acquiring education (as younger women received lower education in the conflict affected areas) but the intrinsic cost of adjusting to employment. Anecdotal evidence suggests that there is relatively little socialization among young women in Tajikistan once they stop attending schooling. Thus, employment may provide an avenue for such socialization, an extra income and a break from the drudgery of housework. An employment during the time of war may also have provided women with a valuable labor market experience and connections that help