rafmacalaba/data-use-mentions-extended huggingface dataset:
disaggregation level is presented in Table 1 along with the corresponding formula.<sup>28</sup> The Database is publicly available for download free of charge at http://data.worldbank.org/datacatalog/exporter-dynamics-database.<sup>29</sup> For the measures of firm dynamics, firm-product and firm-destination market dynamics, we use the following definitions:

- Exporter t: any firm that exports in year t;

- Entrant t : a firm that does not export in year t-1 but exports in year t;

- Exiter t : a firm that exports in year t-1 but does not export in year t;

- Incumbent t : a firm that exports in both years t-1 and t;

- 2-Year Incumbent t : a firm that exports in years t-1, t, and t+1;

- Survivor t :  a firm that does not export in year t-1 but exports in both years t and t+1;

- 2-Year Survivort: a firm that does not export in year t-1 but exports in years t, t+1 and t+2;

- 3-Year Survivort: a firm that does not export in year t-1 but exports in years t, t+1, t+2 and t+3.

To protect the confidentiality of the firms in the raw datasets at the exporter-productdestination-year level, some of the measures in the files with product or destination disaggregation (CYH2.dta, CYH4.dta, CYH6.dta, CYD.dta) are missing when the underlying

> 28 Table 1 also describes the nomenclature used to name the different measures in the stata files consistently. For example, the variable ‘B1’ refers to the Herfindahl index; the variable ‘A7’ refers to the Export Value per Incumbent exporter, etc. While not shown in Table 1, note that we used suffixes to identify means (i), medians (ii), and standard deviations (iii) of certain measures in the stata files. For example, the variable ‘
a dummy variable of exposure.<sup>33</sup>

# [Table 1 here]

# **C. Specification**

I exploit the geographic — by municipality — and temporal variation — by date of occurrence and gestational age — of violent attacks against civilians to identify the effect of violence on newborn health. One key challenge to the evaluation strategy is the influence of municipal characteristics on newborn health. A particular concern is the correlation of violence with poverty and with a weak local state, on the one hand, and between the latter two and newborn health, on the other, for instance through a lower state capacity to provide adequate health services<sup>34</sup> or via poor maternal nutrition (Amarante et al. 2016). Poor economic conditions can lead to conflict as they lower the opportunity cost for young unemployed men to take up arms, limit the capacity of governments to repress insurgencies, and exacerbate grievances between the state and its citizens, and across groups within a country (Collier and Hoeffler 2004; Fearon and Laitin 2003; Miguel,

> 28 In the sample, 80% of neonatal deaths occur by the first week of life.

> 29 When there is no information about pregnancy length, one could subtract 9 months (or 40 weeks) from the date of birth. However, this assigns all births the same gestation length, as if they all reached full term. This is especially problematic in the case of pre-term births, as these pregnancies would be assumed to start much earlier than they actually did, resulting in a misrepresentation of their exposure to violence during the early part of the pregnancy.

> 30 It is reported in months in the 2005 DHS, and in weeks in the 2010 and 2015 rounds, where months are multiplied by 4.3 to calculate the length in weeks.

> 31 Although because both the date of birth and the pregnancy length are reported by the mother in the survey, there can be some recall error leading to imprecision
earlier: -0.5, -1.0, and -1.8.<sup>15</sup> With the survey data, poverty falls from 39 percent in 2002 to 35 percent in 2015. With the elasticity approach, in the most favorable case (an elasticity of -1.8), poverty falls from 42.3 percent in 2002 to 26.1 percent in 2015. However, with a partial elasticity of -0.5 (which is close to the estimate provided by Christiansen, Demery, and Paternostro (2003) for Ethiopia, as noted earlier), the poverty rate falls from 43.7 percent to only 38.4 percent. Thus, whether a significant and sustained reduction in poverty can be achieved in Ethiopia may depend crucially on the country’s ability to improve its growth performance. We now turn to an examination of these issues, with a focus on the impact of foreign aid and public investment.

# **IV. POLICY EXPERIMENTS**

We now examine four types of policy experiments: changes in the level and composition of foreign aid (which imply also changes in the level of public investment), changes in the composition of public spending coupled with a reallocation of public investment (for a given level of foreign aid), and an evaluation of the level of aid needed by Ethiopia to achieve a 50 percent reduction in poverty between 2003 and 2015. All results are displayed as percentage changes (for variables in levels) or absolute differences (for variables in percentage form) from baseline values.

# **1.  Change in the Level of Foreign Aid**

Our first experiment consists of a permanent increase in the aid-GDP ratio by one percentage point relative to the baseline scenario. This implies that both categories of aid increase in the same proportion, given that the shares of food and nonfood aid remain constant.

> 15Note that these calculations do not account for the possibility of asymmetric effects in the relationship between growth and poverty
**Figure 8:  The Human Capital Index, With Uncertainty Intervals**

<!-- Start of picture text --> Human Capital Index<br>(With Uncertainty Intervals)<br>Singapore<br>Kor ea , Rep.<br>Germ any<br>Un ited S tates<br>Rus s ia n Fe deration<br>Vietnam China<br>ColoThailmbiaanTud rk ey<br>Ke ny a PhiliM o rop ccpiIn nedoo snesi a<br>India<br>Mala wi Benin S o ut h Africa<br>Mozamb iqu e C ôt e d'Ivoire<br>Chad<br>6 8 10 12<br>Log Real GDP Per Capita<br>Note: Dataset version 21 Sept 2018<br>Note: vertical range indicates uncertainty interval around HCI estimate<br>1<br>.8<br>.6<br>.4<br>Productivity Relative to Benchmark<br>.2<br><!-- End of picture text --> Notes: This figure reports the Human Capital Index. The vertical axis measures productivity relative to the benchmark of complete education and full health. A value of x on the vertical axis means that the productivity as a future worker of a child born today is only x�100 percent what it would be in the benchmark of complete education and full health. Uncertainty intervals around estimates are shown as vertical ranges for each country. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for 157 countries. Selected countries are labelled for illustrative purposes.

17
_Figure 3. Number of state-owned firms identified by country and source of information_ <!-- Start of picture text --> ORBIS baseline<br><!-- End of picture text --> <!-- Start of picture text --> Overlap Orbis & External sources<br><!-- End of picture text --> <!-- Start of picture text --> External (supplementary sources)<br><!-- End of picture text --> <!-- Start of picture text --> ORBIS baseline Overlap Orbis & External sources External (supplementary sources)<br>The Gambia<br>Seychelles<br>Senegal<br>Sao Tome and Principe<br>Rwanda<br>Nigeria<br>Niger<br>Namibia<br>Mauritania<br>Mali<br>Malawi<br>Madagascar<br>Lesotho<br>Kenya<br>Eswatini<br>Côte d'Ivoire<br>Comoros<br>Chad<br>Cameroon<br>Cabo Verde<br>Botswana<br>Benin<br>Angola<br>Sri Lanka<br>Pakistan<br>Nepal<br>Maldives<br>Bhutan<br>Bangladesh<br>Afghanistan<br>Morocco<br>Egypt<br>Uruguay<br>Peru<br>Paraguay<br>Ecuador<br>Dominican Republic<br>Costa Rica<br>Colombia<br>Chile<br>Argentina<br>Uzbekistan<br>Slovenia<br>Serbia<br>Russia<br>Poland<br>North Macedonia<br>Montenegro<br>Lithuania<br>Latvia<br>Kosovo<br>Hungary<br>Estonia<br>Croatia<br>Bosnia and Herzegovina<br>Albania<br>Vietnam<br>Samoa<br>Philippines<br>Indonesia<br>Cambodia<br
the labor force in the country. The survey covers about 16,500 households distributed over all governorates. Since 2017, the survey collects data that are representative of Jordanians and nonJordanians. As we cannot directly identify Syrians in the data, we proxy them as all non-Jordanians who arrived in Jordan after 2011 and have refugee status. Furthermore, we define as other immigrants as the rest of the non-Jordanians. In so doing, we follow the standard approach previously used for conducting analyses on Syrian refugees using Jordan LFS at the Poverty Practice (World Bank, 2016).

The LFS data cover a wide range of topics related to employment, unemployment, and labor force participation. It includes information about individuals' demographic characteristics, educational attainment, occupation, industry, employment status, and other relevant laborrelated variables.

For this study, we built a repeated cross-sectional dataset between 2017 and 2019. We harmonized industry codes to ISIC Revision 2 classification. We refer to other data sources and harmonization used for this study in the Appendix.

A sectoral disaggregation of occupations (ISCO) across sectors shows the tertiary sector absorbs most laborers in Jordan’s employment structure. Consistently, further disaggregating the occupational categories by main industry shows most workers are engaged in the _Community, Social, and Personal Services_ sector (see Figures 1 and 2). This sector includes activities in Education Services, Health and Social Services, Public Administration and Defense, and Cultural and Recreational Activities, among others.

<!-- Start of picture text --> Figure 1: Occupations, by broad economic sector<br>100%<br>Primary 80%<br>60%<br>Secondary<br>40%<br>Tertiary 20%<br>0%<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from UNCOMTRADE and Jordan LFS 2017-2019.

5 | P a g e
# **Electrification increases economic output and employment**

Focusing on electricity infrastructure specifically, Rud (2012) investigates the effect of electrification on industrialization in India between 1965–1984. This state‐level analysis shows that an increase in electrification of one standard deviation is associated with an increase in manufacturing output of around 14%. In South Africa, electrification has been shown to increase employment in newly‐electrified communities between 1996 and 2001, an effect that is especially strong among women. The study suggests that this is mainly driven by an increase in small enterprises and self‐employment, rather than larger‐scale industrialization of rural regions (Dinkelman, 2011).

Using data on the Indian wholesale electricity trade market, Ryan (2017) simulates the effect of additional transmission infrastructure in regions where limited transmission capacity is obstructing trade and economic activity. He finds that investments in transmission capacity can lead to social benefits (i.e. additional consumer and producer surplus) that exceed the investment costs, especially by facilitating interregional trade, increasing market harmonization, and increasing competition. In other words, new transmission infrastructure can pay for itself by reducing the negative effects of market power. Similarly, in the context of the Indonesian manufacturing sectors between 1990 and 2000, Kassem (2018) demonstrates a link between electrification and industrial development. Electrification is shown to increase the number of firms, employment, competition among firms, and the establishment of firms with higher average productivity in electrified areas.

# **Transport infrastructure increases productivity and creates jobs**

Transport infrastructure has been shown to determine firm productivity through various channels, both at the micro and macro levels. Common links identified in the literature include effects on employment through higher mobility of high‐skilled labor, decreased transportation costs and resulting increases in trade and allocative efficiency, reduced transaction costs through lower inventory requirements, increased market entrance of new firms, and agglomeration economies through spill‐overs in knowledge and human capital caused by spatial proximity.<sup>1</sup
075)|(0.075)|(0.082)|(0.079)|(0.082)|
|Relative wealth|0.243***|0.231***|0.166***|0.311***|0.216***|0.107*|0.251***|
||(0.067)|(0.068)|(0.062)|(0.062)|(0.068)|(0.063)|(0.068)|
|MHI-5 score|1.426*|1.480*|0.582|2.204***|1.000|0.035|1.800**|
||(0.783)|(0.790)|(0.721)|(0.721)|(0.791)|(0.756)|(0.772)|
|**Observations**|1824|1,824|2,000|2,000|1,776|1,776|1,776|

Notes: This table reports the results from three methods to test the sensitivity of our results to attrition. Column 1 reports the estimates from Table 2 for reference. Column 2 reports the results weighting the households by their inverse probability of responding to the survey (Inverse Probability Weighting or IPW). Columns 3 and 4 replace outcome values for the attrited households with semi-extreme values as in Kling and Liebman (2004). Missing treatment and control observations are set to have a 0.5 SD difference in their outcomes: that is we impute mean + 0.25 SD for missing treatment and mean – 0.25 SD for missing controls, and vice versa (KL bounds). Columns 5-7 use the approach proposed by Behaghel et al. (2015) to identify
; and Statement by the Executive Director for Liberia. Country Report No. 11/174. July.

- _IMF. 2011b._ Liberia: Policy Note for the Seventh Review under the Extended Credit Facility Arrangement. August 25.

- _Jack, William and Maureen Lewis. 2009._ “Health Investments and Economic Growth: Macroeconomic Evidence and Microeconomic Foundations,” pp. 1-39 in eds. Spence, Michael and Maureen Lewis. <u>Health and Growth. Commission on Growth and</u> Development.

- _LISGIS (Liberia Institute for Statistics and Geo-Information Services). 2011_ . Liberia Supply and Use <u>Tables for the Year 2008: Handbook. March. Monrovia.</u>

15
# **<u>Appendix A: Variables used & definitions</u>**

# WDI variables:

|**Indicator Name**|**Definition**|_Original source_|
|---|---|---|
|Trade (% of GDP)|Trade is the sum of exports and imports of goods and services measured as a share of<br>gross domestic product. Variable gives average decadal value per country.|_World Bank national accounts data, and OECD National_<br>_Accounts_ _data_ _files._|
|Urbanization|Average growth rate of urban population/total population by decade for each<br>country.|_Fraction of the orginal variables in grey below._|
|Conflict|Average conflict intensity level by decade for each country. Intensity level is<br>categorized into:no conflict, minor conflict or war.|_UCDP/PRIO Armed Conflict Dataset v.4‐2014a, 1946 –_<br>_2013_|
|Urban population|Urban population refers to people living in urban areas as defined by national<br>statistical offices. It is calculated using World Bank population estimates and urban<br>ratios from the United Nations World Urbanization Prospects. Aggregation of urban<br>and rural population may not add up to total population because of different country<br>coverages.|_World Bank Staff estimates based on United Nations,_<br>_World_ _Urbanization_ _Prospects._|
|Population, total|Total population is based on the de facto definition of population, which counts all<br>residents regardless of legal status or citizenship‐‐except for refugees not|_(1) United Nations Population Division. World_<br>_Population_ _Prospects,_ _(2)_ _United_ _Nations_ _Statistical_|
||permanently settled in the country of asylum, who are generally considered part of<br>the population of their country of origin. The values shown are midyear estimates.|
income (See e.g. Fleurbaey 2009; Stiglitz, Durand & Fitoussi 2018;</mark> <u><mark>Aitken 2020). This paper offers initial elucidation on</mark></u> _<mark>the second question</mark>_ <mark>by continuing the evaluation of the costs of default beyond GDP per capita. The first variable we examine in this subsection is thus poverty headcount ratios indexed to 100 in the year prior to default.</mark><sup>8</sup> <mark>We find that these exceed their pre-crisis levels by 30 percent, and the counterfactual’s level by a staggering 70 percent one year after the default. The gap between the defaulter and its control’s indexed poverty headcount level remains wide even a decade after default.</mark> <mark>But poverty headcounts are not without caveats, the literature on multidimensional poverty (e.g. Alkire 2015,</mark> <u><mark>Ravallion 2011), which could be traced to A.K. Sen’s 1976 seminal paper,</mark></u> <mark>highlights multiple issues with them, such as their inability to measure the intensity of poverty</mark>

> 8 <mark>Unfortunately, the best data set available on poverty, PIP data from the World Bank, has a short history and most individual observations are based on imputed values. While these data contain survey observations, these are sporadic and cover a short period. Therefore, for this part of the analysis we are limited to the use of PIP data since 1988 and to rely on many observations which are imputed from growth in that data set.</mark>

5
, if attrition is negatively correlated with income, so that children in poorer areas are more likely to drop out during the school year than their counterparts in rich: r areas, our estimates could _underestimate_ the effect of FONCODES expenditures on attendance. The LSMS surveys provide some evidence that attrition does affect measures of district-level school attendani ce rates. The 1994 LSMS was conducted between June and August, and the 1997 LSMS was conducted between September and November. In the 1994 LSMS, there is a drop in attendance rates of primary-ag -d children of two percentage points, from 96.6% to 94.6%, between June and July. This decline is concentrated among children in districts with a higher value of the FONCODES index. For example, for children in districts with a value of the FONCODES index greater than 14 (roughly the median), the attendance rate declined from 96% to 92%. However, there is no systematic change in attendance rates across months after July in either the 1994 or 1997 LSMS. Since the 1993 census was conducted in June, a portion of the high rate of attendance in 1993 shown in Figure 6.1 may reflect the higher attendance durittg the earlier part of the school year. Furthennore, given that attrition after June is more likely for poorer children, the results may undlerstate the gains made by children in poor districts relative to those in rich districts between 1993 and 1996. As a double-check on our results, we repeated the analysis of Figures (. I and 6.2 using the 1994 and 1997 LSMS surveys, including and excluding observations from June. Although the results are somewhat noisier, since there are fewer districts, we found that the results based or the LSMS surveys are similar to those using the census and INEI, and that excluding June has almost no effect on the relationship between the gain in attendance and the FONCODES index.

**18** The standard deviations of the FONCODES index and per capita expenditures are 7.6 points and 9
(across countries and during 1993‐ 2009) are calculated. Let D<sup>75</sup> s and D<sup>25</sup> s be the 75<sup>th</sup> and 25<sup>th</sup> percentiles of the deviations from trend output in sector s.

Next, the global trend for each sector is used to calculate predicted Russian output for that sector using RUSLANA data. The percent deviation of actual output from this predicted output is calculated for each sector‐year. A sector s is defined to be in slump in year _t_ if its deviation from trend in year _t_ is above D<sup>75</sup> s, and to be in slump if its deviation from trend is below D<sup>25</sup> s. The sector is in a “normal” year if its deviation is between D<sup>75</sup> s and D<sup>25</sup> s.

> 18 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=8&Lg=1</u>

> 19 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=2&Lg=1</u>

> 20 NACE is the acronym used to designate the various statistical classifications of economic activities developed since 1970 in the European Union (EU). NACE provides the framework for collecting and presenting a large range of statistical data according to economic activity in the fields of economic statistics (e.g. production, employment, national accounts) and in other statistical domains. This classification was designed to delineate broad economic categories, into large economic classes of commodities, distinguishing food, industrial supplies, capital equipment, consumer durables and consumer non‐durables.  It is broadly used to stand for sectors.  The higher
design or fieldwork protocols include Beegle et al. (2012), Browning, Crossley and Winter (2014), Engle-Stone et al. (2017), Jolliffe (2001), Pradhan (2009), Schündeln (2018), and Winter (2003). Most of these papers find a downward bias in estimated mean consumption compared to a benchmark. Consumption by logic (and instrument/questionnaire design) has a lower positive, nonzero bound, but has no symmetric upper bound. In part for this reason, measurement error in consumption, whether downward biased or mean preserving, tends to reduce the positive skewness in the distribution. Korinek et al. (2006) find higher non-response rates among the rich. Similarly, Banerjee and Piketty (2005) find that a substantial part of the HHS - NAS gap in India can be attributed to missing top incomes from surveys. Farfán et al. (2017) find significant underreporting of food-away-from home which would have the effect of compressing the positive tail of the distribution. Gibson et al. (2015) find that reporting error is negatively correlated with true consumption (accounting for which would require changing the distribution). The weight of the evidence is that measurement error in surveys is not distribution neutral and typically biases downward both mean consumption and the density of the upper tail of the distribution.

Within the limited literature of reconciling household surveys and national accounts there is sometimes a presumption that national accounts are the benchmark for comparison (i.e. they are free of measurement error). This inclination towards national accounts estimates over household surveys has been somewhat heightened recently by the assertion of Pinkovskiy and Sala-i-Martin (2016) that GDP per capita is better correlated than survey data with data on nighttime lights and non-monetary development outcomes, such as schooling and health status. Yet since the founding of national accounts in the 1940s, there has been an acknowledgement of measurement error in national accounts by those working in this field. As one of the seminal papers on this, Stone et al