rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 3: Services Trade Restrictions Index (STRI) by sector and region**

<!-- Start of picture text --> GCC SAR MENA EAP AFR LAC OECD ECA<br> Financial Telecom Retailing<br> Transportation Prof.Services<br>Note: 103 countries included.<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Note: The services trade restrictions index (STRI) at the regional level is calculated as a simple average of individual country‘s STRIs. The STRI in the cross-border air passenger transportation subsector comes from the QUASAR database of WTO (2007). Regional abbreviations: HNO – High income non-OECD, SAR – South Asia, EAP – East Asia and Pacific, MENA – Middle East and North Africa, AFR – Sub-Saharan Africa, LAC – Latin America and Caribbean, ECA – Europe and Central Asia, OECD – High income OECD.

Apart from professional services, transportation services remain one of the few bastions of protectionism in high-income countries and are also subject to relatively high barriers in developing countries. In maritime transportation, even though international shipping is today quite open, entry into cabotage and auxiliary services such as cargo handling is restricted in many countries. In air transportation, most countries across all income groups require international services to be provided within the highly confining framework of bilateral air services agreements and also restrict investment in the supply of international and domestic air passenger services.

We can infer from the current state of policies that developing countries have liberalized a range of services sectors over the last couple of decades. However, even though markets for most services are now competitive and allow foreign participation, they remain in most countries some distance from being completely open. In telecommunications, public 23
20

|Country,<br>L<br>economy, or<br>territory|ast niublis<br>P<br>Date|hed censu<br>opulation<br>(1000)*|s<br>Source for population<br>(mId-1985)|Source for fertility<br>(1985-90, untess irdicated)|Source for mortality<br>(1985-90,<br>unless indicated)|
|---|---|---|---|---|---|
|Guam|Apr 80|106 J|Bank projection from official<br>1964 est.,UNPVSR4/87|Bank est.|Based on USBOC 1985|
|uatemela|Mar 81|6054 J|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|1980-85, U.N. 1988 revision<br>(pretlim.);<br>1985-90, Bank<br>projections|
|uinea|Feb 83|<br>5781 F|Bank projection<br>from census|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|
|uinea-Bissau|Apr 79|<br>768 F|Bank projection from census|Bank est.|Bank est.|
|uyana|May 80|<br>759 F|UNPVSR 7/86 (official<br>est.)|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|
|Haiti|Aug 82|<br>5054 J|U.N. 1988 revision<br>(prelim.)|Celade<br>printout<br>2/87|Celade printout<br>2/87|
|Noly See|Apr 48|<
distortions. However, voluntary savings programs are also likely to incur social costs, particularly when they involve fiscal incentives.<sup>8</sup> To develop measures to improve the financial well-being of the elderly and incorporate an element of self-control in retirement planning, policy makers must first understand worldwide retirement savings patterns. In the next sections, we evaluate these patterns using a binary measure of saving for old age, which capture whether an individual has saved for old age during the last year. Data limitations prevent us from measuring the volume of savings for old age. However, examining savings behavior can reveal a more complete picture of consumer characteristics and inform policy design.

# **3. Who saves for old age?**

# **3.1      The data**

We use data from the Global Findex database, which provides in-depth data showing how people save, borrow, make payments, and manage risk.<sup>9</sup> It is the world’s most comprehensive set of data providing consistent measures of people’s use of financial services across economies and over time. The data were collected in partnership with Gallup, Inc. and with funding from the Bill & Melinda Gates Foundation. The survey was carried out over the 2014 calendar year as part of the Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160

> 8 Such incentives involve tax exemptions and subsidies. These might require changes in fiscal policy that can be socially costly because they increase distortions. Moreover, such schemes might suffer from limited or lower coverage as well as volume of savings, particularly among the poor, compared to the remaining pillars. Valdés-Prieto (2002a) suggests that most third-pillar fiscal incentives are regressive because the incentives are taken up mostly by higher-income workers while the increase in general tax rates fall to some degree upon middle- and lower-income workers. There is already a rich literature on the effects of social security incentives and provisions
# **4. Evaluating the performance of the sample**

In this section, we evaluate the performance of the HIES 2016/17 sample in terms of accuracy. More specifically, we evaluate the sample’s ability to estimate accuracy statistics for the targeted variables at the level of precision we selected. As mentioned previously, the key parameters used for the estimation of the sample size and the allocation of PSUs across strata are all based on the HIES 2010 data. Table 11 summarizes district level RSEs of mean nominal per capita and total household expenditure. The table also reports SEs of mean poverty headcount ratios (i.e. the three accuracy statistics selected for the HIES 2016/17) estimated using Equation (1) but using ���<sup>�</sup> � as the left-hand side variable and _n_ as the final sample allocation.

All district level nominal per capita consumption expenditure and total household expenditure show RSEs of less than 9 percent, thus below the targeted threshold of 10 percent. SEs of estimated district level poverty rates are also all lower than the acceptable threshold of 5 percent.

**Table 11 Annual district level summary accuracy statistics**

|**Accuracy statistic**|**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|RSE of mean nominal per capita<br>expenditure< 10 percent, district level|64|3.34%|1.28%|1.70%|7.89%|
|RSE of mean nominal total household<br>expenditure< 10 percent, districtlevel|64|3.54%|1.44%|1.85%|8.58%|
|SE of mean poverty headcount ratios<br><5 percent
Policy Research Working Paper 10184

# **Abstract**

This paper uses the first round of the 2020 COVID-19 Disparities Survey to examine the impacts of the COVID-19 pandemic on the income and mental well-being of cismen, ciswomen, transgender, and non-binary individuals. The analysis shows that the pandemic led to high job and income loss expectations among transgender and non-binary individuals. The pandemic has also led to a disproportionate reported decrease in consumption for transgender and non-binary people compared to cismen, potentially related to a high rate of anxiety, loneliness, and depression observed in the group. The results highlight that the disproportionate economic impacts resulting from the COVID-19 pandemic might cause further declines in the economic conditions of transgender and non-binary people, thus exacerbating existing vulnerabilities in the community.

This paper is a product of the Social Sustainability and Inclusion Global Practice. 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 authors may be contacted at tahmed9@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
beet ($27) and tobacco ($22) were the highest payers. Dairy producers came in third at $18.

While their estimates are statistically significant, the effects are small relative to the total campaign contribution these committee members receive. The lobbying data we have analyzed suggests that a similar exercise for House Agriculture committee members would show committee-effects that are not only statistically significant, but also economically large.

To close, it is pertinent to ask whether informational lobbying might not be a useful alternative to the quid-pro-quo lobbying of the Grossman-Helpman model. It has been well documented, for example in Parker and Parker (1998) that congresspersons and lobbies interact continually on agricultural issues. Congresspersons have full knowledge about their constituents’ preference, which is why they seek out memberships on agriculture committees.

44
There is little doubt that economic growth reduces poverty (e.g., Dollar and Kraay 2002). However, sectorial growth rates might have a different impact on the poor. Ravallion and Datt (1996) do not find any impact of Indian manufacturing growth on the poor, even in urban areas, whereas rural growth reduced poverty both in rural and urban areas. Gallup, Radelet, and Warner (1997) find that a one percent growth in agricultural GDP per capita leads to a 1.61 percent increase in per capita income of the bottom quintile, whereas an equivalent increase in industrial GDP increases the income of the bottom quintile by only 1.16 percent.<sup>14</sup> Unfortunately the data used by these authors only included 35 developing countries and the differences in the sector effects were not statistically significant. Timmer (2002) studies the contribution of agricultural and non-agricultural output per worker to the income per capita across quintiles of the income distribution. Although he finds a slightly greater impact of agricultural output, it is unlikely that the sector differences in his estimations were statistically significant.

The data on income shares across quintiles come from Dollar and Kraay (2002) and the per capita incomes from the World Bank’s World Development Indicators 2003.<sup>15</sup> The resulting data span from 1960 to 2000. The econometric estimations utilize a panel of five-year averages. To study the contribution of agricultural GDP to the income of different quintiles, we estimate our specifications using GMM in order to deal with the endogeneity of the sector GDPs. This is the main difference between our work and what has been done so far in the literature. More specifically, the GMM IV system estimations, which were explained in Section III.C., use appropriate lags of the sectorial GDPs as instrumental variables.

Table 4 reports the impact of output per worker in agriculture and non- agriculture on the income per capita of each quintile while also controlling for regional heterogeneity. The results

>
of economic growth in urban areas. Therefore, we use the Global Human Settlement Layer (GHSL) constructed by Melchiorri et al. (2019) data to identify raster grid cells that are likely to be part of urban areas.<sup>18</sup> These data provide a lens through which we can better interpret changes in nighttime light emissions. In panels A-D in Table 4 we incorporate this data in different ways. Across all panels, we exclude rural provinces — provinces which contain no urban grid cells based on the 2000 GHLS data. Panel A in Table 4 sets a useful benchmark and is estimated by equation 3. We regress logdifferenced sectoral GVA or GDP ( ) on log differenced nighttime light luminosity (Δ ), pp pp which as in equation 2 also involves controlling for unobserved province level effects, province tt tt ΔΔOO ΔΔLL trends and has the standard errors clustered by province. = Δ pp pp pp tt tt pp tt ΔΔOO ΔΔLL + μμ + εε 16 Table 4 uses both measures in the same regression but includes year effects (which eliminates mean differences since the sensor measures are annual) and does not include changes in 2013 (which would have involved two different sensor measures).

17 Gibson and Boe-Gibson (2021) show that NTL changes are weakly correlated with GDP changes, controlling for year fixed effects, for counties in the United States. Their specification is in levels, while our specification uses log-differences (i.e., growth rates). Further, the US has a very large service sector; therefore, a greater fraction of economic activity is intangible, but it is does have better quality data. We are unsure how this result would transfer to countries that are less developed than the US.

18 https://ghsl.jrc.ec.europa.eu/download.php.

11
, with much greater effects for countries in closer competition with China.

# **VI. Conclusion**

To our knowledge, this paper is the first attempt to quantify the effect of exchange rate changes on the exports of competitor countries to third markets—the spillover effect—that both exploits

> 24 Broda and Weinstein (2006) argue that with more disaggregated data, one is likely to find higher estimates of the elasticity of substitution.

23
2024 (World Bank 2024); (ii) they are independent countries with GDP of at least $100bn in 2023; (iii) they have data for GDP, inflation and interest rates from at least 1980; and (iv) they have not primarily set interest rates in order to target the exchange rate since 1999.<sup>9</sup> Depending on the analysis, we include individual countries that are currently in the euro area, or consider the euro area as an economic entity.

We collect monthly data on the nominal interest rate defined as the policy rate by the central bank from January 1970 through May 2024. Our main source is the BIS, but when data are unavailable or there are gaps, we augment it with information from Haver Analytics, FRED and the OECD. For members of the euro area, we use the policy rate for individual member countries through the end of 1998, and then use the European Central Bank (ECB) policy rate starting in January 1999. It is worth noting that interest rate that the central bank identifies as the policy rate has changed over time in most countries.<sup>10</sup> Also, in many economies the policy rate was substantially more volatile in earlier periods when it was not the central bank’s operating target (e.g., in the 1970s and 1980s some central banks allowed their policy rates to adjust more flexibly 7 See Forbes (2019a) and Kiley (2020) for discussions of the challenges associated with using the neutral interest rate to measure the monetary policy stance, as proposed in Jordà and Taylor (2019). Estimating the neutral rate relies on estimates of the output gap in real time, which is itself subject to significant difficulties (Orphanides and Norden 2002 and Garrat et al. 2008). For a discussion of the determinants of the long-run neutral rate, see Ferreira and Shousha (2023).

8 Countries have adjusted their frameworks and targets meaningfully during the sample, but classifying different
**Table 9 -  CWI versus Expenditure Poverty**

|||**CWI**|**Poverty**||
|---|---|---|---|---|
|||**Poor**|**Non poor**|**Total**|
||**Urban**||||
||**Poor**|2.2|2.6|4.8|
||**Non poor**|5.2|90|95.2|
|**Expenditure Poverty**|**Total**|7.4|92.6|100|
||**Rural**||||
||**Poor**|6|8.4|14.4|
||**Non poor**|12.3|73.3|85.6|
||**Total**|18.3|81.7|100|

Source: NSLS 2006/07. Totals are simple weighted averages of the urban and rural statistics.

34
.

Thus, there is a dearth of literature focused on sub-Saharan Africa’s manufacturing experience, and those that do have utilized a limited number of SSA countries due to data inadmissibility. This paper attempts to fill this void by assembling data on 41 sub-Saharan African countries from multiple sources including the Groningen Growth and Development Center 10-sector Database, the Maddison Project Database, the World Development Indicators, and the International Labor Organization’s ILOSTAT database. Our data spans 1960-2016, which is the longest panel data on output and employment shares on Sub-Saharan African countries to our knowledge. If sub-Saharan African countries are experiencing prematurely deindustrialization, this has potentially negative ramifications for the region’s economic development.

> 1See also Dasgupta & Singh (2005).

2
Figure 2 shows that when the better employment rates are very low, the full UHCI converges to 0.2 ( = H m ), regardless of the value of the HCI. This is because even if people are potentially more productive due to a high mm HH human capital, they are not able to utilize that human capital. The UHCI increases as better employment expands, as people can increasing use their human capital to increase productivity, with a faster rate of increase for those countries with a high HCI.

# **Section 3. Data and Measurement**

The 2020 HCI data are available for a large number of countries (174), and one of the goals of the UHCI is to maintain the wide cross-country coverage of the HCI.<sup>22</sup> To calculate the UHCIs also requires a range of employment data, which are taken from the International Labour Organization (ILO) and the World Bank’s Jobs Indicator Database (JOIN), generally using the most recent year if multiple data sources are available.<sup>23</sup> Both the ILO and JOIN also provide splits by gender.

# **3.1 Data — Basic UHCI**

For the basic UHCI, the main extra data series required is the employment-to-population ratio of 15-64-year-olds. Employment data are available for 185 countries (182 after conditioning on the availability GNI per capita).<sup>24</sup> Combining the employment rate data and HCI data, I can calculate the basic UHCI for 169 countries/regions.

The main source of employment-to-population data is the ILO series “Employment-to-population by sex and age (%) – Annual”, Age (Youth,Adults): 15-64, using the latest period available.<sup>25</sup> The ILO employment-to-population data are the most recent for 150 countries. Some of these countries do not have HCI data, so ILO employment-topopulation data are used for 83% of the basic UHCI sample (140 countries)
Table B2: Evolution of APA indicators vs. survey indicators

||Change APA|Change IE Survey|
|---|---|---|
|Accountability|0.23|0.04|
|Fiduciary|-0.42|0.21|
|Improved infrastructure|-0.57|0.41|
|Urban planning|0.67|0.44|

_Note:_ This table reports changes in APA scores between the 4th and 6th APAs (column (1)) and survey indices between first and second rounds (column (2)), for APA component and similar survey index. Changes are normalized to be interpreted as standard deviations of baseline. Indicator for "Increased revenue" is not presented since it was not collected on the 6th APA.

We present how variations in APA scores and equivalent household survey measures vary between the 2016-2018 period in the 18 LGAs receiving ULGSP in Figure B3. For the infrastructure and urban planning dimensions, we observe a positive correlation - although with a fair amount of noise as should be expected from 18 observations. The linear relationship between the two variables is also positive for accountability, but the scatter plot makes clear that most LGAs do not observe any variation in the DLI scores. Finally, for the fiduciary responsibility dimension we actually observe a negative correlation between changes in DLI and survey measures. Additionally, we present correlations for baseline, endline and changes in subcomponents for household survey measures in Figure B4.

We also present correlations between APA scores and government officials’ survey indices in Figure B5.

58
-109-

# STATISTICAL APPENDIX (NOTES)

# Introduction

This appendix presents the basic census material we have used and the detailed procedure for matching products. It is intended to be fully transparent in the sense that it gives enough detail for other scholars to replicate or modify our procedures. The tables are arranged by industry, and numbered Al to A17. Their order follows their sequence within the ISIC major division "manufacturing".

# The industries covered are:

|ISIC<br>Code|ISIC Three DigitMajor<br>Industry Groups|Our<br>Code|<br>Our Sample Industries<br>|
|---|---|---|---|
|311/2|Food Products|Al<br>A2|Grain Mill Products<br>Sugar and Sugar Products|
|313|Beverages|A3|Malt and Malt Beverages|
|314|Tobacco|A4|Tobacco and Tobacco Products|
|321/2|Textiles, Wearing Apparel|A5|Textiles|
|323/4<br>331|Leather Products, Footwear<br>Wood Products|A6<br>--|Footwear and Leather Products<br>not represented|
|332|Furniture|--|not represented|
|341|Paper and Paper Products|A7|Pulp and Paper|
|342|Printing and Publishing|--|not represented|
|351/2|Chemicals|A8<br>A9|Soap and Detergents<br>Paints|
|||A10|Agricultural Fertilizers|
|353/4|Petroleum Refining.<br>Petroleum and Coal Products|All|Petroleum Refining and Products|
|355/6<br>361/2/9|Rubber and Plastic Products<br>Stone Clay and Glass|A12<br>A13|Tires and Inner Tubes<br>Hydraulic Cement|
||Products|A14|Bricks, Tiles and Clay<br>Refractories|
|371
# **Figure 21:** **<u>Cargo Dwell Time, by the Main Factors in Selecting C&F Agents, 2011</u>**

<!-- Start of picture text --> 12 10.75<br>9.50<br>10 9.10<br>8.67<br>8<br>6<br>4<br>2<br>0<br>long term relationship/no cost reliability and other factors<br>will to change agent professionalism<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

**Figure 22: Cargo Dwell Time in Select African Countries, by the Main Factors in Selecting C&F Agents, 2011**

<!-- Start of picture text --> long term relationship/no will to change agent cost<br>reliability and professionalism other factors<br>20<br>15<br>10<br>5<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br>Source: Authors based on firm surveys.<br><!-- End of picture text -->

# **Analysis by Product Category**

The overall distribution of imports by category, shown in Table 2, indicates that machinery and electrical products are the largest category (27 percent), followed by chemicals and allied industries (14 percent) and transportation (13 percent).<sup>11</sup> Machinery and electrical is the largest category in Nigeria, South Africa, and Uganda and the smallest in Zambia. While the distribution of most product categories is rather balanced among countries, important differences are evident for transportation, which accounts for 30 percent of imports in Zambia, but only five percent in South Africa. The differences are also important for textiles, which account for only zero to four percent of imports in all