rafmacalaba/datause-encoder-data huggingface dataset:
32 Table 8. Population nd Percentage Distribution by Geographic Region and Income Group, 1985-2100

|||Po|pulation|(millio|ns)|||Percen|tage of|world p|opulati|on|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region|1985|1990|2000|2025|2050|2100|1985|1990|2000|2025|2050|2100|
|World|4.843|5,282|6,205|8,262|9.639|10,560|100.0|100.0|100.0|100.0|100.0|100.0|
|Less developed (a)|3,665|4,069|4,938|6,925|8,311|9,226|75.7|77.0|79.6|83.8|86.2|87.4|
|More developed (a)|1,179|1.213|1,267|1,337|1,328|1,332|24.3|23.0|20.4|16.2|13.8|<br>12.6|
|Africa|560|<br>652|<br>872|1.508|2,046|2,469|11.6|12.3|<br>14.1|<br>18.3|21.2|23.4|
|East Africa|231|<br>270|<br>365|<br>644|<br>885|1,078|4.8|5.1|5.9|7.8|9.2|10.2|
|West Africa|204|<br>239|,326|585|<br>810|<br>994|4.2|4.5|5.3|7.1|8.4|9.4|
|North<br>Africa|125|<br>143|181|279|<br>356|<br>404|<br>2.6|<br>2.7|<br>2.9|<br>3.4|<br>3.7|<br>3.8|
|America|667|<br>721|825|1,04|0 1,15|0 1,205|<br>13.8|13.7|13.3|12.6|11.9|11.4|
|Latin America and Caribbea|n 399|<br>442|<br>530|<br>721|<br>835|<br>894|8.2|8.4|8.5|8.7|8.7|8.5|
|Northern America|268|<br>279|<br>295|<br>320|<br>315|<br>311|5.5|5.3|4.8|3.9|3.3|2.9|
|Asia|2,821|3,093|3,657|4,815|5,540|5,967|58.2|58.6|58.9|58.3|57.5|56.5|
|East and Southeast Asia|1,648|1,779|2,050|2,535|2,765|2,879|34.0|33.7|33.0|30.7|28.7|27.3|
|South Asia|1,058|1,182|1,437|2,021|2,449|2,722|21.8|22.4|23.2|24.5|25.4|25.8|
|Southwest<br>Asia|115|<br>132|<br>170|259|<br>325|<br>367|<br>2.4|<br>2.5|<br>2.7|<br>3.1|<br>3.4|<br>3.5|
|Europe<br>and U.S.S.R.|m|789|821|<br>864|<br>865|<br>879|<br>15.9|14.9|13.2|10.5|9.0|<br>8.3|
|Oceania|24|26|<br>29|<br>35|<br>38|<br>40|0.5|<br>0.5|<br>0.5|<br>0.4|<br>0.4|<br>0.4|
|Income group (b)|||||||||||||
|Low|2,447|2,703|3,250|4,48|1 5,34|1 5,927|<br>50.5|51.2|52.4|54.2|55.4|56.1|
|Lower-middle|680|<br>766|953|1,41|1 1,74|9 1,959|<br>14.0|14.5|15.4|17.1|18.1|18.6|
|Upper-middle|592|<br>649|<br>769|1,031|1,194|1,287|12.2|12.3|12.4|12.5|12.4|12.2|
|Nigh (oil exporters)|19|<br>23|<br>32|54|<br>70|<br>80|0.4|<br>0.4|<br>0.5|<br>0.7|<br>0.7|<br>0.8|
|Industrial msrket|741|<br>760|<br>787|<br>807|<br>774|<br>754|15.3|14.4|12.7|9.8|<br>8.0|<br>7.1|
|Nonreportingm<br>runber'r|364|<br>381|<br>414|<br>478|<br>517|<br>552|7.5|<br>7.2|<br>6.7|<br>5.8|<br>5.4|<br>5.2|

(a) "More developedu comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed comprises the rest of the world.

(b) Criteria for frcome gras are gIven in World Bank 1988. See listing in Table 15 of this paper.
15 range in the 2018 HEFPI version to achieve definitory consistency between these survey families and the World Health Survey (WHS) that only collected pregnancy outcome data for women 18 and older. However, as the number of surveys in HEFPI that have maternal and child health data available from age 15 increases (e.g. by adding MICS 5 and 6 surveys) and as the WHS that was conducted in 2002-2004 becomes increasingly dated, the 2019 HEFPI database switches to the 15-49 age-group whenever the raw survey data permit it. Users of the 2019 HEFPI version’s skilled birth attendance and antenatal care use data should be mindful of the new age-group inconsistency in the WHS data points that continue to come from women age 18-49.

All other indicator definitions of the 2018 HEFPI database remain the same.<sup>15</sup> Table 3 shows the newly changed definitions of the pap smear, skilled birth attendance, and antenatal care indicators.

<u>Table 3: HEFPI indicators with changed definitions</u>

|Indicator|Definition|Main Data<br>Source<sup>16</sup>|
|---|---|---|
|Pregnancies with 4 or more<br>antenatal care visits (% of total)|Percentage of most recent births in last two years with at least 4<br>antenatal care visits (women age**15**-49 at the time of the survey)|DHS, MICS,<br>WHS|
|Births attended by skilled health<br>staff (% of total)|Percentage of most recent births in last 2 years attended by any<br>skilled health personnel (women age**15**-49 at the time of the<br>survey). Definition of_skilled_varies by country and survey but<br>always includes doctor, nurse, midwife and auxiliary midwife).|DHS, MICS,<br>WHS|
|Pap smear in last 3 years|Percentage of women who received a pap smear in the last**3**years<br>(preferably age**20-69**but age groups may vary)|DHS, EHIS,<br>STEPS,<br>WHS|

# 3.2.3. Changes in indicator values due to coding corrections

Coding corrections led to changes in indicator values for a number of data points, even if their underlying data source remained unchanged.

> 15 They are presented in Tables 1-3 of Wagstaff et al. (2018).

> 16 Main data sources are survey families with 15 or more data points for an indicator.
where BCRISISi is a dummy variable which takes the value of one if a country has experienced a banking crisis during the financial crisis.<sup>10</sup> β2 < 0 would indicate that a banking crisis amplified the response of the relative volume of outward FDI in financially vulnerable sectors to the global financial crisis in more financially developed countries. This would be in line with tighter credit conditions in countries experiencing a banking crisis than in other countries during the 2008-2010 period. Estimation of model 2 allows us to focus even more on a credit channel impact of the global financial crisis by considering the country-specific level of financial distress in the banking sector.

# **3 Data and estimation method**

## **3.1 FDI data**

Our proxy for FDIist by firms to establish a new production or processing manufacturing facility in a foreign country. This variable should capture a large fraction of the initial fixed costs incurred by multinational enterprises _ex nihilo_ .<sup>11</sup> Data come from the _FDI Markets_ to produce abroad when establishing a foreign affiliate database compiled by FDI Intelligence, a division of the Financial Times.<sup>12</sup> This database is the most

> 10 [αs · GFC0810t · BCRISISi] are fixed effects which account for sector-specific shocks affecting outward FDI in countries experiencing a banking crisis during the 2008-2010 period.

> 11As previously discussed, firms may incur other fixed costs before or after this cross-border investment. While our dependent variable does not include these costs in principle, they play a role in firms’ ability to self-finance their capital expenditures abroad.

> 12http://www.FDImarkets.com/

6
# Machine Learning in International Trade Research — Evaluating the Impact of Trade Agreements<sup>∗</sup>

Holger Breinlich<sup>†</sup> Valentina Corradi<sup>‡</sup> Nadia Rocha<sup>§</sup> Michele Ruta<sup>¶</sup> J.M.C. Santos Silva<sup>∥</sup> Tom Zylkin<sup>∗∗</sup> Originally published in the Policy Research Working Paper Series on April 2021. This version is updated on May 2022. To obtain the originally published version, please email prwp@worldbank.org.

KEY WORDS: Lasso, Machine Learning, Preferential Trade Agreements, Deep Trade Agreements.

JEL CLASSIFICATION: F14, F15, F17.

> ∗Research for this paper has been in part supported by the World Bank’s Multidonor Trust Fund for Trade and Development. 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 affi liated organizations, or those of the Executive Directors of the World Bank or the governments they represent. We gratefully acknowledge financial support through ESRC grant EST013567/1, and thank Scott Baier, Maia Linask, Yoto Yotov, and seminar participants at the World Bank Economics of Deep Trade Agreements Seminar Series for useful comments. Alvaro Espitia, Diego Ferreras-Garrucho, Jiayi Ni, and Nicolas Apfel provided excellent research assistance. The usual disclaimer applies. An R package (penppml) implementing penalized PPML regressions with high-dimensional fixed effects is available from CRAN.

> †University of Surrey, CEP and CEPR. Email: h.breinlich@surrey.ac.uk

> ‡University of Surrey. Email: v.corradi@surrey.ac.uk

> §World Bank. Email: nrocha@worldbank.org.

> ¶World Bank. Email: mruta@worldbank.org.

> ∥University of Surrey. Email: jmcss@surrey.ac.uk.

> ∗∗University of Richmond. Email: tzylkin@richmond.edu.
orphan living with their mother; and Kenya and Tanzania with mid-range prevalence rates saw reductions on the order of 4 or 5 percentage points. There also appears to be a weakly negative association between HIV prevalence and changes in the likelihood that maternal orphans live with their father. Here, however, the exceptions are more dramatic: in the three countries with the highest prevalence (Namibia, Zambia, Zimbabwe) there was virtually no change in the probability that maternal orphans live with their father. Consistent with the discussion above, there are many countries in which there is a fall in the share of maternal orphans living with their father, some of which have fairly low prevalence levels.

In Figure 6b the relationship between HIV prevalence and changes in probability of living with grandparents is presented. The associations are starkest for single orphans (top two panels), precisely where one would expect that grandparents are assuming greater caregiving roles because of sick surviving parents. Among paternal orphans it is clearly the countries with high HIV prevalence where the role of grandparents increased substantially. Among maternal orphans these hard-hit countries also exhibit large increases in the role of grandparents—but there are large increases in countries with lower prevalence levels as well (such as Cameroon, Ghana and Guinea for example) and small increases in at least one high prevalence country (Zimbabwe). Recall that in many countries there was only a small change in the role of parents; increased caregiving by grandparents is observed even when parental caregiving has not shifted—reflecting a shift in caregiving from other relatives to grandparents. This is especially pronounced for Zambia among paternal orphans, and for Namibia and Zambia among maternal orphans. As discuss above, there is a common trend towards caregiving by grandparents even among double orphans (which does not seem to be systematically related to HIV prevalence) and among non-orphans (which appears to be weakly correlated with HIV prevalence).

# _Household characteristics: Caregiver age_

Two characteristics of the households in which children live are further examined: caregiver age and the number of children. Table 5 reports the mean age of caregivers by a child’s orphan status, overall and by caregiver category (that is, living with a parent, grandparent, other relative, or non-relative) for the most recent round of survey data. For non-orphans the average ages of the mothers and fathers with whom they live is distinguished. Reflecting the age gap in couples, fathers are typically on the order of 10 years older than mothers—for non-orphans as 17
Policy Research Working Paper 6038

# **Abstract**

This research estimates the impact of macroeconomic fluctuations on import protection policies over 1988:Q1– 2010:Q4 for five industrialized economies—the United States, European Union, Australia, Canada and South Korea. There is evidence of a strong countercyclical trade policy response in the pre-Great Recession period of 1988:Q1–2008:Q3 during which increases in domestic unemployment rates, real appreciations in bilateral exchange rates, and declines in the GDP growth rates of bilateral trading partners led to substantial increases in new temporary trade barriers. It then applies this pre-Great Recession empirical model to realized macroeconomic data from 2008:Q4–2010:Q4 and find it predicts a surge of new import protection during the Great Recession—e.g., for the US and EU, the model predicts new trade barriers would cover an additional 15 percentage points of nonoil imports, well above the baseline level of 2-3 percent of import coverage immediately preceding the crisis. Finally, the research examines why the realized trade policy response differed from model predictions. While exchange rate movements played an important role in limiting new import protection during the Great Recession, there is also evidence of one particularly important change in trade policy responsiveness; i.e., in this period, governments refrained from imposing new temporary trade barriers against foreign trading partners experiencing their own weak or negative economic growth.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at cbown@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
The household survey is from 2009, while the rents derived from the real estate data refer to late 2013 - early 2015. There is no real need however to express the values in prices from the same year, i.e. to inflate the 2009 expenditures to 2014 prices or to deflate the house prices to 2009 prices. Instead we will be assuming that the Pareto tail index associated with the top tail of the income distribution is stable over the 2009-2014 period.

# **3.3 Does the household survey indeed omit the rich?**

One way of illustrating whether the household data under-represent the top part of the distribution is to compare some of the characteristics of the top 1 percent the household survey with those of senior Egyptian executives. For the purpose of this exercise, household income is imputed from household expenditures in the survey using the average savings rate in Egypt for 2009.<sup>14</sup> The data on executive pay come from Payscale, an online information company providing current information on salary, benefits, and compensation by type of job, location, and other characteristics. The numbers are presented in Table 1.<sup>15</sup>

||% surveyed population|Minimum|Median|Maximum|
|---|---|---|---|---|
|Household income|Top 1%|11,995|14,666|98,080|
|CEO total pay|Top 1.2%|23,723|68,970|168,545|
|CFO total pay|Top 0.8%|22,551|54,563|212,393|

Table 1: Annual income of top earners in Egypt (USD, nominal, 2009 prices) We focus on the total compensation of senior executives, who represent 2 percent of survey participants and have the highest reported median compensation among survey participants.<sup>16</sup> Therefore, in principle, these households should be in the top 1% of households in Egypt’s household survey. However, since the median senior executive income is closer to the maximum income than to the median income of the richest 1 percent in the household survey, and the maximum income earned by senior executives is much higher than the maximum income in 14 We assume that household income reflects mainly the income of the household head and that the top households save at the average rate. The source for the average savings rate is the World Bank’s World Development Indicators (WDI).

> 15Household income is imputed based on information on household expenditures in Egypt’s 2009 household survey and the average saving rate in Egypt in 2009. The total pay of senior executives in Egypt is obtained from a global database of salaries and compensation for 2015. If anything we expect the data on executive pay to be on the conservative side. The values in the table are deflated and converted from EGP into USD using annual average inflation and exchange rate data from the World Bank’s World Development Indicators.

> 16The senior executives surveyed by Payscale are either chief executive officers (CEOs) or chief financial officers (CFOs) in Egyptian firms.

15
# **Results**

First, we measure the impact of SHGs on the number of different problems a WM deals with. The results are shown in Table 7. The columns (1) and (2) provide the estimates using OLS (equation 20), and the columns (3) and (4) using instrumental variables (equation 21). Regressions without WM controls are provided in the odd-numbered columns and those with controls in the even-numbered columns.

WMs operating in wards where SHGs have started undertaking collective actions deal with 1.5 extra issues compared to WMs who operate in wards where SHGs are not present or do not undertake actions. The result confirms our prediction: an increase in collective actions within the ward causes the WM to tackle a greater variety of issues. The coefficients are significant, but slightly smaller in size in the OLS regressions. We also find that, overall, male WMs deal with fewer issues.

Next, we examine the type of extra issues the WM starts dealing with. To do that, we replace the left hand side variable of the equations (20) and (21) by _Ti jtk_ , a dummy equal to one if WM _i_ , elected in ward _j_ in year _t_ , deals with issue _k_ . The independent variables are the same. The results are given in Table 8. The odd numbered columns provide the results using OLS, and the even numbered columns using IV.

Once SHGs undertake collective actions, WMs are 29% more likely to deal with alcohol problems, 31% with school problems and 35% with forestry issues. WMs are also more likely to start dealing with welfare schemes. Thus, the estimates confirm that WMs start dealing with public goods that are of interest to SHGs (see Section I). The bias is similar to the previous regression: The IV coefficients are close to or larger than their OLS counterparts.

Interestingly, male WMs are less likely to take care of alcohol issues, school problems and welfare schemes, but they are more likely to deal with public infrastructure.

39
revolutions - 0.0309 * Number of assassinations - 0.0148 * Deviation from the PPP in 1960. For simplicity, the last three regressors are considered to be zero. We collect data for the other variables using the Barro-Lee and Summers-Heston databases. We use GDP per capita and the enrollment rates for primary and secondary school for three years before the start of the crisis. For government consumption scaled by GDP, we take the average ratio for the period between eight years and three years before the start of the crisis. The quadratic form in the model of GDP per capita implies a positive relation between GDP per capita level and growth for values of GDP per capita above $10,800.<sup>11</sup> We therefore restrict the realizations of the GDP per capita level variable to be no larger than $10,800. For each country, we insert the realizations of the variables at three years before the start of the crisis into the Barro equation and adjust for population growth rates to obtain the predicted GDP growth rates. Finally, we compare this predicted rate with the actual growth rates during the crisis and, following the IMF methodology, summarize the differential growth rates into an output loss variable.<sup>12</sup> For a country’s institutional and legal environment, we use three indicators. QUALITY OF INSTITUTIONS is a broad measure of institutional quality developed by

> 11 The quadratic form can be viewed as an approximation to a functional form that asymptotically approaches a zero relation between growth and level of per capita GDP, with the relation coming close to zero when real GDP is above $ 10,800 (Barro 1991).  For simplicity, we cut off the GDP per capita data at $10,800, thus forcing a flat relationship for higher actual GDP per capita.

12 There are also a number of other measures that can be used to estimate the output loss. These differ in terms of the assumptions made about trend growth and the timing of the pre-crisis and post-crisis period. Generally, we got similar results for the fiscal outlay, policy, and institutional variables. Still, it is worthy to note there are many other factors determining the potential GDP level of a certain country at a certain time. Furthermore, since it is not possible to isolate the effect of the banking crisis on GDP from other shocks, none of these output loss measures correctly captures the effect of the banking crisis on GDP and the full economic costs of a banking crisis. This caveat is the more important as there can be large differences between the measures. In his discussion of the analysis of Hoggarth, Reis and Saporta (2002), for example, Honohan (2002) shows that their cumulative loss in output has a correlation of only 33% with the IMF-loss of growth measure. Hoggarth et al. also show that using a lost cumulative output—instead of the lost cumulative growth measure, output losses during crises in developed countries are as high, or higher, on average than those in emerging economies, a conclusion which contrasts with other research.

18
||Table 6: Alt|ernative mor|ality proxies||
|---|---|---|---|---|
|Column #|6-1|6-2|6-3|6-4|
|Morality proxy|Work is duty<br>to society|Child quality:<br>responsibility|Child quality:<br>tolerance &|Progress toward<br>more<br>|
||||respect|humane society|
|**OLS**|||||
|Morality|-0.755|1.238*|0.565|0.886|
||(-1.26)|(1.96)|(0.72)|(1.06)|
|Morality-ability|-3.460***|1.585*|1.496*|3.953***|
|correlation|(-3.53)|(1.84)|(1.71)|(3.54)|
|No. observations|72|84|84|76|
|**2SLS**|||||
|Morality|0.115|3.075**|1.924|0.293|
||(0.05)|(2.04)|(1.21)|(0.15)|
|Morality-ability|-3.319*|8.566**|6.380***|8.161***|
|correlation|(-1.81)|(2.40)|(2.93)|(3.59)|
|Overid p value|.02|.65|.43|.41|
|**LIML**|||||
|Morality|0.570|2.799|2.803|-0.243|
||(0.11)|(1.06)|(1.11)|(-0.09)|
|Morality-ability|-5.816*|14.822|8.485***|9.209***|
|correlation|(-1.74)|(1.48)|(2.58)|(3.12)|
|Overid p value|.44|.92|.58|.48|

Dependent variable is log of GDP per capita (PPP-adjusted), 2008. Test statistics (from robust standard errors) are in parentheses. A * ,** and *** respectively indicate significance at .10, .05 and .01 levels for two-tailed tests. OLS models use the specification of Table 2, equation 2-4 but with different proxies for morality as indicated in the column headings. 2SLS and LIML follow the specification of Table 5. Results are shown only for morality variables for space reasons, but complete results are available from authors on request.

41