rafmacalaba/data-use-mentions-extended huggingface dataset:
developed a research program on the economics of poverty in developing countries that became highly influential, both within the Bank and in scholarly circles.<sup>20</sup> Poverty is not inequality, however. Indeed, the Bank generally continued to be rather careful to speak of the former while avoiding mentioning the latter, unless strictly necessary. But under any rigorous analysis of poverty, sustaining this dichotomy is hard to do. Changes in poverty are mathematically determined by changes in average incomes and changes in the Lorenz Curve, which anchors all meaningful (relative) measures of inequality. (See, e.g., Ferreira and Ravallion, 2009.) Once one starts to be interested in poverty, inequality becomes increasingly hard to avoid. Indeed, as Figure 1 illustrates, the relative frequency of the word inequality in World Bank documents – which had spiked in the early 1970s and then flatlined near zero throughout the 1980s – revived precisely in the lead-up to the WDR 1990.

Aside from the internal dynamics leading to a resumption of interest in poverty, three other factors combined to make the 1990s the decade when inequality gradually became an issue that both the Bank and, soon after, the Fund were prepared to discuss and study seriously. These were, first, the growing availability of household survey data, which was then indispensable for any empirical assessment, measurement and analysis of inequality in incomes or consumption expenditures. Whereas Ahluwalia et al. (1979) had disaggregated household consumption or income data for 25 countries when they first tried to estimate global poverty, by the end of the 1990s, the estimates from Chen and Ravallion (2001) were based on 297 national surveys from 88 countries.

Second, the political authorizing environment changed once again, away from the starkly rightwing Reagan / Bush period. The fall of the Berlin Wall in 1989 and the collapse of the Soviet Union in 1991 marked the end of the Cold War.<sup>21</sup> Bill Clinton replaced George H.W. Bush
information would clearly enhance the relative effectiveness of the transfer scheme over the simple untargeted scheme considered here.

Which policy has more impact on poverty is unclear on _a priori_ grounds. The selftargeting mechanism in a workfare program is expected to assure that the gains from an EGS are not uniform but tend to be higher for the poor (although the extent of self-selection is an empirical question).<sup>8</sup> Against this, the EGS incurs deadweight losses associated with the foregone incomes of participants and the extra non-wage costs incurred for non-labor inputs and supervision. Our empirical results to follow will reveal which scheme has the greater impact on poverty, given the trade off between targeting performance and efficiency cost.

# **3. Data**

Our analysis is based on the Employment-Unemployment Schedule (“Schedule 10”) of India’s National Sample Survey (NSS) for 1999-00. At the time of writing, this is the most recent available “thick sample” NSS with the complete version of Schedule 10.<sup>9</sup> In addition to standard data on household characteristics (religion, caste, land ownership, demographics, schooling), Schedule 10 includes detailed information on employment characteristics for all members of every household. For each person, information is collected on her principal activity in the year preceding the survey and daily activities during the week preceding the survey.

Schedule 10 also collected consumption expenditures (including imputed values for consumption in kind), which we use in studying the impacts on poverty. However, Schedule 10 used an abbreviated version of the main consumption module used by the NSS (including in the

> 8 On the comparative targeting performance of workfare schemes see Coady et al. (2004).  For a more general discussion of the incentive properties of such schemes see Besley and Coate (1994).

> 9 The larger “thick” samples are surveyed every five years. Smaller (“thin”) samples are surveyed annually, with an abbreviated version of Schedule 10
the associated childcare activities. Given the long school closures, someone should stay at home to look after the children and help them with the school chores. For different reasons, including deeply rooted cultural factors, women are more likely than men to play this role, and then suffer the consequences in the labor market. In that scenario, only mothers with the ability to work from home might be able to reconcile their labor market activities with their family responsibilities.

We study these issues with the help of one of the most ambitious data sets collected immediately after the onset of the pandemic: the World Bank’s high-frequency phone household surveys (HFPS). These surveys were carried out in three waves between May and August 2020 in over 100 countries around the globe, including 13 in Latin America. Variables in the HFPS were harmonized by the World Bank, which helped foster a growing literature (Ballon et al. 2021; Cucagna and Romero 2021; Khamis et al. 2021; Kugler et al. 2021; Mejia-Mantilla et al. 2021).

We contribute to this initiative by codifying the occupation variables in the HFPS. These variables were not previously codified because the surveys include only open questions for occupations and hence the codification becomes very arduous. In this paper 2
, particularly during kharif, whereas much of the south and east will be spared the worst of the temperature increase in the principal growing season.

## **5.2 Putting the pieces together**

With predicted temperature changes and estimated price responses in hand, it remains only to calculate the weights and the other components of the formulae of section 2 to implement the distributional analysis. This is easier said than done, however, in that households in the NSS61 expenditure survey, for whom we have consumption data, are not the same as those in the 61st round employment-unemployment survey, nor are they the same as those in the 59th round farm household survey. We must, therefore, impute several key parameters based on observable characteristics common across surveys.

18
|Activity data for<br>different sectors|•<br>National Statistics Office of Georgia<br>•<br>Ministry of Economy and Sustainable Development<br>study<br>•<br>Ministry of Internal Affairs<br>•<br>Electricity and gas distribution companies<br>•<br>EC-LEDS survey|
|---|---|
|Power Sector|•<br>Ministry of Energy|
|Demand Drivers<br>(e.g., GDP,<br>population)|•<br>Country Basic Data and Directions for 2013-2016 ,<br>Ministry of Finance<br>•<br>IMF GDP projections<br>•<br>World Bank GDP projections<br>•<br>NationalStatistics Office ofGeorgia|

35
perceptions that firms have about the costs and benefits of selling in international markets. They found that treated non-exporters become less likely to export, whereas treated exporters become more likely to export, suggesting that the provision of information can have an impact on firms’ behaviour.

The advantage of the literature using firm-level data is that it can better identify the type of firm or worker that is benefiting from the program, and the channels through which export promotion affects export growth (e.g., extensive versus intensive margins). The disadvantage of microdata is that it is not clear how to aggregate results from individual firms or workers to obtain an impact on total exports or GDP. This is important, because the cases for and against export promotion are often based on externalities (positive and negative). By simply observing that firms benefiting from export promotion tend to export larger amounts than firms that do not benefit from the program, it is not possible to assess how big the aggregate impact is and not even the sign of that impact. It is potentially conceivable that badly designed export-promotion schemes will lead to a larger fall in exports of firms not benefiting from the program than the increase in exports of firms that benefit from the program. This paper takes the alternative route, which is to work with aggregate data directly. But it should be clear that these two types of analysis complement each other as they help address different questions.

Section 2 discusses the surveys of EPAs used to construct the dataset, and provides some descriptive statistics regarding budget, sources of funding, governance, and activities of EPAs. Section 3 presents the empirical strategy followed to estimate the heterogeneity in returns to export-promotion programs in terms of both exports and GDP per capita. Section 4 presents the results, and section 5 concludes.

# **2. Data Sources and Summary Statistics**

The study merged information from three rounds of EPAs’ surveys. The first survey was conducted in the fall of 2005 by the World Bank and was the base for the study
reduction in state and non-state violence.

In additional results in Appendix table A4 we show that ruggedness, measured by strong slopes in a cell, do not seem to interact strongly with roads. However, for one-sided violence we find that the effect of road project is dramatically increased in rugged terrain.

As we do not have forecasts for ACLED fatalities or sub-categories of the UCDP data, we do not apply the PreMDiD method to other violence types as the control group cannot be properly matched.

# **5.2 The Role of Mining Revenues**

Access to mining revenues is a driver of violence, and improved roads facilitate access to mining resources. We first control for the presence of mines as a robustness check for our main estimates of the impact of roads and then interact the presence of mines with road projects to understand how the impacts of road projects vary depending on the presence of mines.

First, we add changes in mining revenues in the first specification (Table 1). We multiply the number of mines in a cell times by the commodity price of these mines to get to a proxy of revenue and add it as controls. We show the results for artisanal gold mines and the two most common types of large mines: copper and cobalt. The main estimates for the impacts of road completion and the stock of roads remain similar (Table A5). The results of the PreMDiD are also robust to additional mining controls.

Second, we interact mining revenues with the road stock variable in the first specification to understand the interactions of roads and mines on violence. We show results when 26
<mark>Overall, Internet surveys are biased in the demographic coverage of the population, in comparison to nationally representative data from the GMD. The sample is young, more educated, and more male compared to the national averages. The sample is not necessarily more urban than GMD, and the pattern of the bias depends on the country. This lack of a pattern in the urbanization bias could be because, as explained above, COVID-19 itself could have impacted urbanization that is not being reflected in the slightly dated GMD data.</mark>

**Figure 3.1. Absolute deviation of Online Surveys from GMD**

**<mark>Figure 3.2. Absolute deviation of Online Surveys from the Internet using sample in GMD</mark>**

14
# **5 Results**

This section presents the results, both from a country-pair and country-centric perspective. Our goal is to analyze the link between GVC participation and both the level (for each country) and the synchronization (for each country-pair) of inflation.

## **GVC and the synchronization of inflation across country-pairs**

In table 1, we use within country-pair variations and control for country-pair fixed effects as well as third country exposure to investigate the link between trade in intermediate inputs and the correlation of inflation for each pair of country. Results in column (1) show that the correlation of GDP deflator (GDPD) inflation increases when total trade proximity increases. This result is indicative that the determinants of GDPD inflation might not be domestic only, and that higher trade integration is associated with a link in the formation on prices. Moreover, we can dis-aggregate this results and separate total trade into trade in final goods and trade in intermediate inputs. Doing so, column (2) unveils an interesting insight about the specific role of Global Value Chains. While an increase in final good trade is _not_ associated with any significant change in inflation co-movement, the effect we observed through total trade is actually solely driven by trade in intermediate inputs.

This result is not very surprising since GDP deflator is defined net of imports. While imported final goods for consumption are simply not taken into account in the construction of GDP deflators, the effect of imported input can still have an impact through the effect of cost shocks on the prices of other firms down the production chain. Overall, our results show that GVC integration as proxied by trade in intermediate inputs creates a link between inflation fluctuation between countries and confirms the view that in a world with production sharing across countries, the determinants of inflation are not purely domestic anymore.

Following results presented in table 1, in table 2 we extend the analysis by using value-added data for trade flows instead of gross trade flows. Doing so is important because, for example, gross
best describe operations along the supply chain that one could place on a “smilecurve” as illustrated in Figure 3.

In Figure 4, we compare our source for bilateral FDI announcements data with bilateral FDI data from UNCTAD. Even though the correlation fluctuates over time, on average our chosen measure of FDI tracks official data on cross-border investments relatively well. We explain the discrepancy by the fact that our data is not based on declarations of firms’ balance sheets and therefore does not record the full set of balance of payment flows, which implies that our constructed measure of inward (or outward) FDI cannot perfectly match official measures. First, inward and outward notions require a reporting country, which is not the case for the fDi Markets data as its compilation is performed by experts from the Financial Times. Second, outward (inward) flows are computed by netting out any transactions that decrease the stake of resident (foreign) investors in foreign (resident) enterprises from transactions that increase it.

A crucial stage of the empirical work resides in dealing with the absence of announcements. As acknowledged in the trade literature, zero trade (or investment) flows do not occur randomly, which means that samples restricted on positive values may yield biased estimates. It is particularly important to account for zero flows when studying the effects of deep integration. The nature of our investment data requires assumptions concerning the presence of zero investment flows. Contrary to officially reported trade or investment data, we cannot apply the mirror method to complete missing observations, and lack of announcements in the news can be left to interpretation. Nevertheless, we assume that if a significant investment ever materializes, it will be covered in the news or announced in some other way that fDi Markets will be able to identify. We generate zero investment flows by doing the following: starting from the sample of countries included in the fDi Markets database, we generate all possible source/destination pairs across the sample period 12
04|
|Lease price paid for land (US$/ha)<br>||115.28|112.88||-0.02|
|Interested in leasing more land<sup>1</sup>||0.85|0.79||-0.07|
|Interested in buying land||0.79|0.74||-0.06|
|Max. price to be paid (US$/ha)||1,540|1,473||-0.04|
|Received credit last 12 months||0.25|0.22||-0.12|
|if yes, amount received||84,689|70,755||-0.16|
|if yes, interest paid||4.57|8.83||0.93|
|Number of farms|1,206|1,206|1,206|||

_Source:_ Own analysis of balanced household survey panel data as described in the text.

_Note:_ Profit is the value of output net of purchased inputs, that is, it includes remuneration for fixed factors and family labor. The six main crops are wheat, barley, rapeseed, soybean, maize, and sunflower. ha = hectare.

18
940|5,175|2,207|134|300|3,767|4,049|2,018|2,767|3,012|
|N Treatment 1|7,068|2,817|2,978|4,161|1,216|121|297|2,779|3,016|1,611|2,007|2,157|
|N Treatment 2|7,757|3,108|3,340|4,499|1,494|134|321|3,090|3,358|1,599|2,214|2,570|
|_R2_|_0.373_|_0.354_|_0.365_|_0.392_|_0.308_|_0.389_|_0.472_|_0.369_|_0.384_|_0.305_|_0.335_|_0.391_|

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Administrative data, school year 2016-2017, own calculations.

39
Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models labor supply upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality of the number of hours of work/week, unconditional on return migration. The predicted number of hours of work/week for non-migrants, illegal migrants, and legal migrants are reported in the last row.

42
Table 3 gives the participation rate, demand rate and the rationing rate by rural household quintiles defined on household consumption per person from the survey.<sup>13</sup> As expected, we see that demand for work on MGNREGS declines with consumption per person. Richer households are less likely to want to do this work, although there is demand even amongst the richest quintile in rural areas. Consistently with the incidence of expressed demand, we also see that the proportion of households who have obtained job cards declines with consumption per person. But notice that the demand rate is higher than the proportion with job cards; there are many households who express demand for work who have not obtained job cards.

Across India as a whole, the rationing rate tends to rise with consumption per person. The local-level processes of deciding who gets work amongst those who want it entails that poorer households are less likely to be rationed, although the difference is modest. Thus we see that the participation rate declines with household consumption per person even more steeply in proportionate terms than the demand rate.<sup>14</sup> By contrast the absolute gap between the participation rates for the poorest quintile and the richest is attenuated by rationing, given that its overall incidence (the excess demand rate) falls with consumption (Table 3).

Quintile averages lose a lot of detail. Figure 4 provides a finer representation of the data using the household-level data from the NSS. Panel (a) gives the non-parametric regression function—a locally smoothed scatter plot—of the participation rate against consumption per person, with the latter converted into ranks and normalized to be between 0 and 100. Thus the horizontal axis gives the percentile of the consumption distribution.<sup>15</sup> The lower panel (b) gives the rationing rate. (The demand rate shows a similar pattern to panel (a), though at higher levels given the rationing.) In both cases we also give the regression functions with controls
Binding transferability restrictions, especially for bequest, are likely to reduce investment incentives even if tenure is secure (Deininger and Jin 2006) while sales limits will undermine emergence of credit markets. In light of Zambia’s history of requiring Presidential consent for any land transfer and a doctrine of land having no value that prevailed until recently (Adams 2003), understanding of the basis and nature of these restrictions will be essential to assess how these should feature in discussions of the new land policy.<sup>16</sup> A total of 31% of households report having access to some document, a level that is higher for urban (72%) as compared to rural areas (15%). Documents are most prevalent in Lusaka (67%) with shares between 11% in the East and 18% in the South. Yet, at about 11% overall (31% in Lusaka, 6% in the South and about 2% in the remainder of the provinces), less than half of these documents are full titles. While virtually all are in the name of household members, only 35% list a woman as a land owner. Incomplete titles, that have not been formalized by paying required fees and certificates by traditional authorities on customary land, each account for about half of these documents. Cross-checking these figures against administrative data (Kaunda 2017) suggests that they are plausible, though at the high end.<sup>17</sup> Substantively, high levels of informality may be due to three issues. Cost of service delivery may be elevated by outdated survey regulations requiring high precision sporadic surveys, institutional inefficiencies related to sporadic title issuance, and a rather dysfunctional IT system. A need to pay fees (of at least ZMK 4,000 per urban parcel, in most instances much higher) upfront rather than in installments may be unaffordable for credit constrained households, limiting transition to the formal regime and reducing pubic revenue (as property tax can be levied only on titled
# **Introduction**

The Middle East and North Africa (MENA) region has the second largest gender economic gap, after South Asia, with an average population-weighted score of 63.4%. The regional average score on the Economic Participation and Opportunity sub-index is 46% in spite of an educational attainment score of 99.5% (Global Gender Gap Report, 2022). Female labor force participation (FLFP) is the lowest worldwide with a rate of 19.6% in 2021 (World Development Indicators, 2022).

The gender economic gap is driven by several factors, including individual and household socioeconomic characteristics, the structure of economic growth, educational attainment, trade openness, and population growth (Assaad et al, 2014; Rodgers and Zveglich, 2012; ElborghWoytek et al, 2013; Nazier and Ramadan, 2017; Bursztyn et al, 2018; Efobi et al, 2018; Assaad et al, 2018; Osundina, 2019; Kooli and Al Muftah, 2020). Informal institutions such as social norms, traditional gender roles, motherhood, and work–family responsibilities may explain the gender gap in labor market outcomes, especially in conservative societies such as the MENA region (Kleven et al, 2019; Nazier and Ramadan, 2020; Patricia and Cortés, 2020). Additionally, formal institutions promoting more equal gender roles and redistributing child responsibilities equally between fathers and mothers are significant determinants of FLFP (Patricia and Cortés, 2020).

Another key driver of FLFP is digital inclusion that ensures equal access to, use, and benefit of information and communication technology (ICT). As the World Bank (2021) has highlighted, universal adoption of digital technologies would double FLFP by 20 percentage points over a 30-year period. Digital technologies would provide women information and knowledge, economic opportunities, and flexible arrangements. Such arrangements may be more compatible with the family-work responsibilities in conservative societies where women are the main caregivers and their mobility is limited (Chen, 2004; Antonio and Tuffley, 2014; World