rafmacalaba/data-use-mentions-extended huggingface dataset:
by trade ministries in industrialized countries. Table 2 lists the United States’ trading partners that have been cited in annual reports devoted to assessing discriminatory foreign procurement practices. No least developed countries (LDCs) are listed in table 2; only a few of the larger developing countries appear to attract the attention of the United States Trade Representative (USTR)—although one must be open to the possibility that the USTR used the (unfulfilled) threat of “naming” a country in these reports to induce changes in other countries’ procurement practices. Even so, there appears little to suggest that the state procurement markets of much of the developing world are large enough to attract much attention from the trade officials in leading industrialized countries.

**Table 2: Public procurement practices of countries cited as concerns in USTR** **<u>annual reports on discriminatory trade practices</u>**

|Date<br>or<br>year<br>the|Countries mentioned in the Uni<br>_G_|ted States’_Annual Report on Discrimination in Foreign_<br>_overnment Procurement_.|
|---|---|---|
|report was<br>published.|“Identified” cases of<br>discrimination.|“Issues of Particular Concern That Do Not Meet the<br>Criteriafor Identification.”|
|1992.|European Union:<br>government-owned<br>telecommunications in<br>selectedmemberstates.||
|30 April<br>1996.|Germany: Heavy electrical<br>equipment.|Japan: Public works.<br>Japan: Supercomputers.<br>Japan: Computers.<br>Australia: Pre-selection criteria for bidders.<br>Brazil: Expanded discriminatory procedures in 1994.<br>China: Non-transparent, closed, and uncompetitive<br>procedures.|
|30 April<br>1999.|None.|Korea: Construction of Inchon International Airport.<
nological innovation on employment can be ambiguous. While product innovation is typically aimed at increasing a firm’s demand through the introduction of a new product, process innovation by definition usually entails productivity enhancements that can be labor saving. The existence of competing mechanisms make the net effects uncertain. For instance, in the case of product innovation, even though a new product is associated with new demand and new markets, there remains the possibility that the new product may be produced more efficiently than old products. While in the short term it is possible that new product sales may cannibalize old ones, the extent to which this occurs will depend on the degree to which the new products are substitutes or complements in demand and production. Furthermore, the net effect will depend on the extent that competitors react by introducing new products themselves, resulting in dynamic competition. For process innovations, while the use of new technologies and capital may be labor saving, under competitive conditions where lower costs of production translate to lower final goods prices, increases in demand and sales may offset any initial labor displacement. Over time, process innovations can shift labor to more skilled, higher wage jobs and widespread adoption of new capital equipment would enlarge downstream sectors of the economy.

In a recent survey of the literature, Vivarelli (2012) suggests that the microeconometric literature tends to support a positive link between innovation, proxied as R&D and/or product innovation, and employment; especially when focusing on high-tech sectors. The study also finds significant evidence in favor of the skill-biased hypothesis across different OECD countries, different economic sectors, and different types of innovation. For example, in one micro-econometric study Harrison, Jaumandreu, Mairesse, and Peters (2014) study the impact of innovation on employment using a comparable data set of firms from France, Germany, Spain, and the United Kingdom. The authors find that product innovation has a positive impact on employment, but that process innovation has a displacing effect on employment. However, the positive impact of product
|0.149|[.053]***|

_Notes:_ The table reports intent to treat estimates of the effect of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. Heterosketastic robust standard errors are reported in brackets. Because there are two endline surveys per round per individual, each surveyed individual enters twice into the regressions, and standard errors are clustered by individual. *** p<0.01, ** p<0.05, * p<0.1 xxxvii
Trade liberalization has also been linked to reduced incidence of poverty and inequality.<sup>14</sup> In the case of India, this link has been spatially evaluated by Topalova (2007, 2010). A district with higher exposure to tariff liberalization experienced lower reductions in rural poverty, while urban poverty is unaffected by these reforms. Her work suggests that rural and urban inequality were also unaffected by liberalization.<sup>15</sup> Although a number of studies find small effects of trade reforms on wage inequality,<sup>16</sup> they have been associated with reduced incidence of child labor in India and Indonesia,<sup>17</sup> improvements in schooling and investments in human capital formation,<sup>18</sup> and decline in urban unemployment in Indian states with flexible labor markets.<sup>19</sup> Cross-country studies are mostly inconclusive on the impact of trade and globalization in promoting gender equality. For instance, the empirical relationship between trade flows and female labor force participation is often found to be contingent on the country’s income level and economic structure and the time period considered (e.g., Wood, 1991; Bussman, 2009; Cooray, Gaddis and Wacker, 2012). One of the shortcomings of cross-country studies is the lack of comparability of data across a range of countries. Studies using micro data at the level of households, employees and establishments offer a much broader scope for a detailed and meaningful analysis. Country-specific literature on the issue has documented a positive association between export-orientation and feminization of workforce.<sup>20</sup> Some studies go further to establish that the causality runs from liberalization to better outcomes for female employment, that is, trade liberalization is found to positively contribute to a convergence of male and female labor force participation and employment rates.<sup>21</sup> By comparison, using a panel of Indian formal manufacturing firms from 1998-2008, Banerjee
civil conflict. Countries with more intense violence in their past are much more likely to host firms that report being constrained by political instability. We illustrate this in two figures. Figure 11 shows the share of firms reporting that political instability is no constraint on the y-axis and the log of the sum of past battle related deaths per capita on the x-axis. Firms in countries like India, Turkey or Ethiopia with past violent episodes that were small compared to their population, report constraints close to the overall average. In these countries more than 50 percent report that instability is not a constraint on their activity. This changes dramatically in countries that were heavily affected by violence in the past. Firms in Yemen, South Sudan and Afghanistan are almost all somewhat constrained by political instability. In Figure 12 we show the share of firms reporting that political instability is the worst constraint. The share is increasing in past violence and, strikingly, is higher than 20 percent in the most heavily affected countries. We will return to this data after we have discussed a model of expectations in the following section.

# **6.3 Foreign Investment During Recovery**

Data on internal investments is quite patchy or nonexistent in a post conflict context. We therefore approach this issue from a foreign investment perspective. Foreign investments are both a proxy for investment more general and for the particular interest of foreigners in the local economy. We use four different datasets on foreign investments: a unique data set from the Dutch Central Bank, data from the OECD, data from the UNCTAD and data from the World Bank. Details are discussed in the appendix.

In what follows we focus on net-flows of foreign investments. These are relatively noisy compared to gross flows but we stick to them nonetheless for two reasons. First, it ensures comparability across investment from the different sources we use. Second, results can be interpreted directly as the net amount of foreign investment attracted by the country.

To give a first impression of investment flows Figure 13 plots mean positive net
take place, in contrast to the Asian Green Revolution, which was based on wheat and rice (Staatz and Dembele 2007).

In order to understand past developments in agriculture and predict future ones, the mechanisms behind agricultural development and growth must be identified. Schultz (1964) formulated the idea that peasants are rational decision-makers maximizing the returns from the resources at their command and responding to incentives. Many of them remain poor not because they are ―backward‖ but because their government provides them with few technical and

> 7 The source of agricultural growth also matters for its impact on poverty reduction. Agricultural growth in East Asia has been achieved with technologies increasing labor productivity and has led to large reductions in poverty. By contrast, in Africa, where labor productivity gains in agriculture have been small and most growth has come from land expansion, poverty reduction has also been low (de Janvry and Sadoulet 2009b).

> 8 This discussion owes much to Binswanger-Mkhize and McCalla (2010).  Numbers are from the 2008 World Development Report.

14
roads, health care, etc. They were added to the questionnaire in order to take advantage of the cross country survey and to gain additional information about service delivery. The quality of these services may only indirectly be related to the uncertainty of doing business.

# _Question 22: Efficiency of government in providing major services_

Question 22 asked businessmen to rate their overall perception of: efficiency of customs, roads, mail delivery, and public health provision. This question aims at an implicit evaluation of several major components of government services in the surveyed countries.

38
empowerment at different levels. Section 2.3 of this paper discusses work currently in progress on developing a survey module that can be added to a Living Standards Measurement Survey (LSMS) or other national household questionnaires.

Finally, using the ME framework concepts of domains and levels allows a comparative assessment of empowerment across countries. While some argue that inter-country comparisons are pointless, others indicate that information of this kind can encourage governments to improve performance. In fact, this has already happened as a result of the international governance reviews and databases now available to compare performance in governance across countries (Kaufmann, Kraay, and Mastruzzi 2003). Such information, particularly on trends, also helps international policymakers and donors decide strategy and financing priorities.

Two points are important in relation to these various applications. The first concerns the need for context-specific data collection and analysis. The second relates to conceptual 15
(Rolstad et al., 2011; Zezza et al., 2017).<sup>3</sup> Aggregating items also yields cost savings and reduces the length of the survey.

While the appropriate level of disaggregation for FAFH is unclear, asking on a single line to account for all FAFH consumption for the entire household may result in high levels of underreported consumption, as well as an increase in measurement error (Fiedler & Yadav, 2017). One-line questions often give lower overall estimates of consumption and can be difficult for respondents to answer (Browning & Crossley, 2001; Gray et al., 2008). Still, around 24% of nationally representative household surveys collect FAFH data with just a single aggregated line (Smith et al., 2014). This extreme level of aggregation for a growing segment of household expenditures could likely lead to further measurement error.

The purpose of the current study is not to test the impact of each of these dimensions separately, but to identify scalable and cost-effective solutions that improve the accuracy of measures coming from modern surveys and data collection methods. The design of the experiment draws from pre-existing evidence and provides further insight on the measurement effects resulting from the level of reporting (household or individual), bounding methods, and the level of aggregation in order to identify the alternatives that work best when collecting FAFH consumption, and why they work best.

Our benchmark measure of ‘true FAFH consumption’ is that captured through an individual-level daily diary rather than asking about consumption in a given period (which is the recall method). With this approach, respondents are asked to keep a diary which allows them to record all FAFH consumption as it happens (or at least on a daily basis). Personal diaries have often been used as a benchmark for collecting information on food consumption (Beegle et al., 2012). Despite the “gold standard” status of diary methods, discrepancies can occur due to compliance or supervision issues, diary fatigue,<sup>
et al., 2014) showed that the density of unique users in a cell tower’s catchment area scales with population density, and can be plotted on a logarithmic curve. Researchers can therefore extrapolate shifts in the number of unique users to predict shifts in population densities over time, day by day or week by week. Accordingly, CDR aggregates provide insights on population movements, which is useful for estimating regional connectivity and the impact of mobility restrictions (Wesolowski, Buckee, Engø-Monsen, & Metcalf, 2016). It also helps identify areas with higher risks of importation due to population flows from other regions, and develop spatial epidemiological models (Aledort et al., 2007) (Wesolowski et al., 2012).

Combining CDR data with administrative and survey data offers insights on fast-moving health and well-being indicators. Drawing from survey data on the incidence of poverty in Rwanda, Blumenstock et al. use machine learning algorithms to predict poverty outcomes based solely on patterns in mobile network data (J. Blumenstock et al., 2015). Erbach-Schoenberg et al. combine CDR data with public health data sets in Namibia to link mobility and malaria incidence (Zu Erbach-Schoenberg et al., 2016). When compared with estimates using static maps, this leads to discrepancies of up to 30 percent. These applications showcase the value-added of high resolution, high-frequency proxy data like CDR in the context of an epidemic such as COVID-19.

CDR data can also be used to rapidly update estimates of population distribution when a natural disaster leads to widespread displacement. Bengtsson et al. used CDR data from a major telecommunications company to track displacement in Haiti after the 2010 earthquake (Bengtsson, Lu, Thorson, Garfield, & von Schreeb, 2011). This allowed them to track shifts in population distribution and estimate that up to 20 percent of the population of the capital city left in the 19 days after the earthquake. Lu et al. used

**3** | P a g e
and rural productive infrastructure. States may use the resources in localities exhibiting extreme poverty or large deficits in social infrastructure. Any other type of investment must be authorized by the federal government.

From 1998 until December 2013, the allocation of the FAIS among municipalities followed a mathematical formula.<sup>12</sup> The formula generated first a measure of household poverty, called the global poverty index, based on five indicators of well-being: income, education, housing, sewerage, and electricity. The index is multiplied by the number of people in extreme poverty in the household to obtain a deficient mass for each household. Subsequently, all the household masses are aggregated to obtain indexes of poverty at the municipal, state, and national levels. The percentage of the FAIS assigned to each state or municipality is thus proportional to the share of the deficiency mass by state (or municipality) with respect to the overall national deficient mass.

The unweighted average of municipal real revenue in Mexico has risen by a factor of 2.5 in 2000–14 (table 1). This increase in municipal fiscal revenues is reflected in the growth of spending on public works during the period. At the same time, there was an increase in current expenditure in the municipalities (salaries, services, and supplies). However, the ability to obtain resources remains limited. Conditional and unconditional state and federal transfers represent the largest source of income among municipalities.

Table 1. Trends in major municipal resources and investments

|_Category of municipal public finance_||_Annual aver_<br>|_age per capita_<br>|||_Annual chang_<br>|_e_<br>|
|---|---|---|---|---|---|---|---|
||_2000_|_2005_|_2010_|_2014_|_2000–05_|_2005–10_|_2010–14_|
|Investment in public works|519|915|
20<br>15<br>10<br>5<br>0<br>Government Pupil-teacher ratio<br>expenditure<br><!-- End of picture text -->

# **D. Attracting and retaining talent**

<!-- Start of picture text --> Score<br>5 2016-2017 OECD average<br>4<br>3<br>2<br>1<br>0<br>Azerbaijan Tajikistan Kazakhstan Russia Ukraine Armenia Georgia Turkey Bulgaria Montenegro Poland Albania Kyrgyz Republic Hungary Macedonia, FYR Croatia Bosnia & Herz. Moldova Serbia<br><!-- End of picture text --> Sources: Haver Analytics, World Bank, World Economic Forum.

A. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 19 ECA economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 22 ECA economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 22 ECA economies. Latest data available during 2011-15.

B. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 10 ECA economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 20 ECA economies. Latest data available during 2011-15.

C.D. The score is from 1 to 7 (best). The OECD and EMDE average are the simple averages of all the countries in the respective subgroupings.

E. Regional aggregated number. Data are for 2014 or latest available. GDP data are in constant 2011 “international dollars.” F. Share of renewable energy consumption as percent of total final energy
16|0.15|0.19|0.13|0.06|
|Center|0.57|0.38|0.72|0.62|0.57|0.49|0.56|0.38|0.35|0.46|0.54|0.70|
|South|0.19|0.38|0.24|0.05|0.13|0.38|0.37|0.46|0.49|0.35|0.33|0.23|
|No. obs|868|8|29|422|96|192|121|17,672|14,408|2,954|156|154|

_Source:_ Own computation from 2006/07 NACAL. Other crops for estates include beans, cotton, sugar cane. Other crops for smallholders include wheat, millet, beans, velvet beans, ground beans, pigeon peas, cow peas, cassava, sweet potato, irish potato, cotton, and sugar cane.

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The simulations of other fiscal elements are less precise. This applies to such instruments as the salary tax (23.1 percent of the administrative aggregate) or means of transportation tax (35.3 percent). These ratios reflect lack of representativeness of the data at the higher end of income distribution and lack of detailed information in the data with the parameters of the fiscal system. The underrepresentation of richest households has consequences both on the direct side of taxes, given the high tax base and the progressivity of both the salary tax and means of transportation taxes, and the indirect side since expenditure of the top income households might be conducted more specifically in the formal sector and correspond in value with their high incomes.

The limited precision in modelling SSC/HI and salary taxes stems from the high degree of progressivity of tax contributions and lack of highest earners in the data. For example, there are only 13 people in the data who would be modeled to pay income tax at the highest rate of tax. While detailed information on the number earners who fall in the top income tax bracket is unavailable for detailed sensitivity analysis, it is clear that information on top incomes is not as precise in household data and the corresponding simulation of taxes simply cannot match the information in administrative statistics.

The observed difference between survey estimations and macro data is not unusual for taxes on several of the modeled consumption items.<sup>16</sup> The simulated values of indirect taxes less closely match administrative records. Estimations from the survey capture 59.2 percent of the total government revenue from VAT.<sup>17</sup> The undersimulation of some indirect taxes like the public lighting tax or alcohol tax in all sections of the income distribution might reflect underreporting of specific categories of goods such as tobacco and alcohol. Further, the undersimulation for expenditures also reflects the fact that not all final consumption is conducted by private households. VAT is also paid by public institutions and by the corporate sector for which the final sales
|||
|B3. Share of the system affected over observed<br>period (24 months)|49.2%|44.8%|60.7%|48.0%|38.3%|
|C3. Mitigation effectiveness (0 to 100%)|10.5%|15.0%|10.0%|7.0%|5.0%|
|D3. HLO decrease (points) =<br>_B3*(A*((Total school weeks/43.3)*(1-C3))_|32.3|35.4|41.7|27.0|14.5|
|**GEP* (GDP per capita growth %) [g]**|3.3|4|5.1|1.5|1.1|

_Note: Values represent country-level averages._

- - Notes: (*) Global Economic Prospects January 11th 2022 update (https://www.worldbank.org/en/publication/macro <u>poverty outlook), with the regional average imputed if no country value was available for 2020 or 2021.</u>

16 The table provides an overview of parameters by income, though simulation parameters are applied at the country level.

> 17 The World Bank’s Harmonized Learning Outcome (HLO) puts learning data from international and regional assessments on a comparable scale. The data can be accessed here. We assume the learning gains will vary from 20 to 50 learning points depending on the country’s income level, as explained in Azevedo, Hasan et al. (2021).

8