rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

The #MeToo movement highlighted the pervasiveness of violence against women worldwide. In a survey of women in 22 countries, over 50% reported being physically harassed in public and over 70% reported being followed (Livingston, 2015). A recent literature shows that harassment and violence against women in the public space might altogether discourage women from participating in market activities (Jayachandran, 2020; Vel ́asquez, 2019; Field and Vyborny, 2022).

Commuting to opportunity presents a specific risk for women and girls. For instance, the risk of violence on the way to school may reduce or distort women’s and girls’ human capital investments (Borker, 2021; Muralidharan and Prakash, 2017). Yet, the recurring nature of this harassment and the costs faced by women who _do_ commute daily have not been formally documented. Moreover, there is a lack of evidence on policies capable of combating sexual harassment on public transit.

In this paper, we use high-frequency data on women commuters to study a reserved space policy on public transit in Rio de Janeiro, Brazil. In response to the increased public awareness of the high prevalence of sexual harassment in the public space, the creation of women-reserved “safe spaces” has surged.<sup>1</sup> This policy response is highly controversial.<sup>2</sup> On the one hand, advocates argue that providing women-reserved spaces allows women to avoid harassment. On the other hand, detractors make the case that, by playing into latent prejudice, these reservation policies may reinforce norms restricting women’s movement in the public, non-reserved space, ( _e.g., “women should not overstep their boundaries”; “to be safe, a woman should stick to her reserved space”_ ); if this legitimizes and increases harassment in the public space, it may be deleterious to women’s safety overall. Both sides of the argument are still wanting for rigorous evidence (Aguilar et
next 10 years are proposed. The first is dubbed the “optimistic” target. This assumes that the recent trajectory of success against poverty can be maintained; this can be thought of as the counterfactual in the absence of extra effort but with a measure of good luck. The second is an “ambitious trajectory”—a “high case” path that would be a real challenge, requiring substantial extra effort from the whole world. These benchmarks are not (at least now) dreams beyond imaginable reach; they stem from scenarios that are within the

> 2 To my knowledge, the only careful attempt to benchmark performance at country level is found in Newman et al. (2010) who draw on time series evidence across developing countries. They use the empirical distribution of absolute changes in the poverty rate as data for informing the setting of performance benchmarks for countries in Latin America and the Caribbean.

> 3 As Hume (2009, p.4) puts it the MDGs aimed to “stretch ambitions and mobilize political commitment and public support.” The MDGs were not the first such effort at goal setting; Hume identifies a number of antecedents including the UN Declaration of Human Rights.

2
# **Non-Technical Summary**

In this paper we analyze the effects of preferential trade agreements (PTAs) on multilateral trade liberalization. First, we develop a model that captures key aspects of the current multilateral trading system and different types of PTAs. Next, we structurally estimate the main predictions obtained from our model using product level tariff data for the European Union (EU) spanning the Tokyo and Uruguay trade rounds. We show that the EU reduced its multilateral tariffs by only about half the amount in the goods imported duty-free under a PTA as compared with similar non-PTA goods, hence the EU’s PTAs held back its multilateral liberalization. The results are economically significant and robust to potential endogeneity problems. If its PTAs had not been present, the EU would have reduced its multilateral tariffs on the PTA products by 1.6 percentage points more with an average price effect of 50-60%. The EU's multilateral tariff reductions were largest for products exported by countries that provided greater increases in market access, providing evidence of reciprocity.

Our model also incorporates domestic political economy motives for tariff determination and we find that tariff levels are inversely related to import penetration and import demand elasticity due to the extra weight governments place on producer surplus. Furthermore, these industry weights increase in regional concentration and employment shares.
|(5)<br>(6)<br>(7)<br>(8)<br>Average<br>p-value<br>p-value<br>recomm. in 2015<br>Individual<br>Control vs.<br>Mean<br>p-value<br>vs. Average<br>Individual<br>T2-T4<br>T2-T4 vs. C<br>recomm.<br>recomm.|138.90<br>0.94<br>0.00<br>0.00<br>(12.38)<br>18.67<br>0.91<br>0.00<br>0.00<br>(16.73)<br>30.34<br>0.98<br>0.07<br>0.00<br>(17.45)<br>1,672.20<br>0.94<br>0.00<br>0.55<br>(215.33)<br>404<br>sample. Column 1 reports self-reported average amounts applied and total cost of fertil-<br>nd report in column 2 the p-value of an F-test that the dummies are jointly equal to zero.<br>mn 4 we compare the individual recommendations to those that the control group would<br>s to columns 3 and 4 for the average input recommendations and costs for farmers in _T_2,<br>nd individual recommendations given to farmers in _T_1 by regressing each variable on a<br>amounts of each fertilizer and costs of individual and average recommendations by using<br>eports the p-value of a t-test that_T_1=0. We use administrative data on recommendations<br>4.|
|---|---|
|(4)<br>idual<br> in 2015<br>p-value
Comparison Program (ICP) suggests that machinery and equipment tend to be relatively more expensive in African countries than the rest of the world (Cruz, Cirera, Hjort, Lang, Konte, and Edet, 2024). For digital products, including machinery, equipment, and software, prices in Africa are up to 34% more expensive than in the United States, in US dollar terms.<sup>3</sup> In this paper, we use detailed data on international trade flows and tariffs to study Africa’s imports of digital goods. We first provide evidence on the relative quality and quality-adjusted prices of Africa’s digital goods imports, as well as on the degree of tariff protection that is currently imposed on them. We then estimate the likely impacts of the African Continental Free Trade Area (AfCFTA) on digital goods imports, which we compare with a benchmark scenario of full unilateral import tariff liberalization from AfCFTA members towards all countries.

We begin by outlining the key features of digital goods imports and tariff protection across African nations that are part of AfCFTA. In 2019, Africa imported $59.4 billion worth of digital goods, with 35 percent sourced from China and 27 percent from the European Union. Digital goods constitute a smaller share of African imports (about 13 percent) compared to the rest of the world (around 21 percent). A few countries – South Africa, the Arab Republic of Egypt, Nigeria, Morocco, and Tunisia – collectively accounted for 70 percent of Africa’s digital goods imports, while intra-African trade in digital goods remains minimal.

Despite this heavy reliance on imports, Africa imposes relatively high tariffs on digital goods globally, with an average tariff of approximately 5 percent, compared to an average of 2 percent in other regions. These tariffs remained relatively constant in recent years. Moreover, the level of tariff protection on digital goods correlates negatively with income per capita across African countries, with upper-middle-income economies like South Africa imposing tariffs as low as 0.9
and significant.<sup>20</sup> Especially, the stringency measure has a robust negative coefficient. Containment and health and government economic support are negative and insignificant. Therefore, stringency measures and containment measures implemented by the government that target any specific segment of population or geographical area rather than generalized containment may help prevent the domestic economy from falling into a further slump. If the government fails to deliver any effective pandemic policies, then the economy suffers and growth declines further.<sup>21</sup> Our empirical results on the effect of health indicators are mixed. Therefore, they fail to show any significant impacts of health indicators in the regressions. This could be attributed to the fact that measuring impacts via the health channel is challenging as COVID-19 is still a novel disease.<sup>22</sup> Empirical evidence supports the existence of a negative relationship between trade exposure and growth. The deleterious effects of trade exposure can be partly explained by vulnerabilities to commodity price volatility (e.g. international oil prices). The estimated coefficients of international trade openness have a negative and insignificant impact on economic growth (-0.0116 points in regression [1] of Table 1).

The regression analysis finds that countries that are more dependent on external financing could experience deeper declines in economic growth. For instance, in regression [1] in Table 1, the estimated adverse impact of (gross) capital flows on economic growth at -0.0832 is significantly deeper than the one of trade openness. Therefore, countries with greater dependence on external financing tend to have larger declines in growth. When looking at the composition of capital inflows, the regression analysis finds that both portfolio investment inflows and other investment inflows are significantly positive at 0.264 and 0.228, respectively in regression [7] of Table 1. Consequently, portfolio investment inflows and other investment inflows help prevent further declines in GDP growth. The coefficients of FDI flows have insignificantly negative impacts on economic growth
- 20 -

# **VI. CONCLUSION**

Procurement packaging has particularly important effects on not only the bidders’ bidding behavior, but also their entry strategy. Procurers can encourage or discourage market competition by designing contract packages differently. The performance of contractors is also affected by procurement planning. Some of the poor contract performance, such as cost overruns and delays, may be attributable to flaws of contract design. In practice, there is no single solution about how to package public contracts. Procurement planning is often fairly flexible.

The paper explores the optimal size of public road contracts with the procurement data from rural road projects in Nepal. It found that the procurement and contract performance could be improved by changing the size of public contracts. There are different optima, depending on policy objectives. To maximize the bidder participation, the length of road should be about 11km. To minimize cost overruns and delays, the contracts should be much larger at 17km and 21km, respectively. These point estimates are significantly larger than the current procurement practices. The current average length is only 7km. Therefore, procurers should take more advantage of enlarging road packages in this case, although there is risk that too large contracts could discourage firms from participating in public competitive bidding.
# (a) Developing countries

<!-- Start of picture text --> Coconut<br>Cotton<br>Sugar<br>Milk<br>Maize<br>Wheat<br>Pigmeat<br>Poultry<br>Beef<br>Rice<br>2005-09<br>Soybean 1980-84<br>Coffee<br>-50 50 150<br><!-- End of picture text -->

# (b) High-income countries

<!-- Start of picture text --> Rice<br>Sugar<br>Cotton<br>Milk<br>Pigmeat<br>Poultry<br>Beef<br>Maize<br>Soybean<br>Wheat<br>2005-10<br>Rapeseed<br>1980-84<br>Barley<br>-50 50 150<br><!-- End of picture text --> Figure 9. Comparison of earlier (1980–84) and more recent (2005–10) NRAs in developing and high-income countries, by product (%).

Source: Anderson, Cockburn, and Martin 2010, Fig. 2.6, updated using Anderson and Nelgen 2012b.

59
improvement in scores for height for age of children by 1.3 percent and 1.2 percent respectively. Results for weight for height are not conclusive.

Our results differ from much of the previous literature that finds that rising food prices pose risks to children’s health (see Woldemichael, Kidane, & Shimeles (2017); Arndt _et al.,_ (2016); Vellakkal _et al.,_ (2015); Miller and Urdinola (2011); Torlesse _et al.,_ (2003)). There are a range of linkages from rising crop prices to child nutrition that go beyond the direct impacts on household incomes to include access to health.

There are two important differences in our approach compared to the previous literature on food prices and child undernutrition. First, with regard to the price data used, the approach implemented here uses cereal price data collected from market stalls when the survey of child nutrition and household outcomes was conducted. Thus, our cereal prices may reflect more closely the prices households actually face compared to the price data collected by statistical agencies used in previous studies. The latter may be a less direct measure of the prices faced and typically suffer from the challenges of missing values and averaging. Second, our approach utilizes a panel data fixed effects model compared to the frequently used pooled regression analysis. Our results may be different since we track the same child between waves of the Living Standards Measurement Survey to examine the how changes in cereal prices are associated with changes in child undernutrition. Lastly, note that the majority of households in the LSMS survey come from rural areas and their welfare is likely to be closely related to agricultural development in terms of both farm and non-farm incomes.<sup>23</sup>

# **7. Conclusions**

The study investigated the association between food prices and child undernutrition in Ethiopia. The increase in crop prices between wave 2 (2013/14) and wave 3 (2015/16) in the LSMS survey created
serve significant changes for people with higher reservation wages (above 6,600 pesos). While the pattern of treatment effects is similar, the magnitude is much smaller and not significant.

The last piece of evidence in support of reservation wages as an underlying mechanism for the treatment effects comes from hourly wage data collected in the endline survey. Appendix Figure A2 shows how hourly wages evolve over the employment spell for participants with formal sector jobs. Compared to the control group, youths eligible for the wage incentive accept jobs that pay around 5 pesos (12%) less in the first few months (suggesting that they lowered reservation wages).

These results consistently suggest that reservation wages are binding for job seekers, which is in line with evidence from the U.S. by Krueger and Mueller (2016). It raises the question of why youths in our setting hold these high reservation wages in light of rapid skill and wage growth they would experience in formal employment. Next, we will explore the role of time preferences, false beliefs and liquidity constraints.

# **5.3 Discount factors**

One explanation for why job seekers set high reservation wages and search little for jobs in the formal sector relative to the informal sector is that they heavily discount future benefits of formal employment including strong wage growth and being covered by social security programs.<sup>27</sup> We elicit participants’ discount rates in two ways. First, as part of the inperson endline survey we use the traditional method of asking hypothetical choices between payments in one month or one year.<sup>28</sup> Second, we implement an incentive-compatible way to elicit time preferences for 665 respondents in our second endline survey. Specifically, respondents are invited to participate in a lottery. Their payout, if they win, is determined by their choices between receiving 2,000 pesos in one month or a larger amount in one year. We elicit time preferences through a set of binary choice questions and compute individual
We selected two representative housing types: a high-value two-story home constructed with brick walls, concrete floor and clay roof (referred to as “Masonry”) and a middle-income two- to-three story home constructed with mixed wall, concrete floor and asbestos roof (referred to as “Mixed Wall”). The replacement value of the representative high-value building is assumed to be 19,200 USD and 7,000 USD for the middle-income one (based on estimates from Silver, 2007).

# **_Exceedance Probability Curve and Selected Disaster Risk Reduction Measures_**

Given flood hazard data available for Jakarta, our analysis is based on approximate flood extent maps and limited depth estimates for two past floods in January/February 2002 and February 2007 (Dartmouth Flood Observatory, 2008). Our hazard analysis also uses a 30-year monthly rainfall time series, observed at the Jakarta Observatory (NOAA global database) within the catchment of the Ciliwung and an elevation map based on data from the NASA Shuttle Radar Topography Mission. Based on these inputs, two probabilistic flood depth curves are generated, representing a higher (‘max hazard’) and lower (‘min hazard’) hazard location. Using two curves also allows us to test the sensitivity of findings to the hazard approximation.

<!-- Start of picture text --> 0.2<br>0.2<br>Mixed Wall in Max Hazard<br>0.2<br>0.1 Mixed Wall in Min Hazard<br>0.1<br>0.1 Masonry in Max Hazard<br>0.1<br>Masonry in Min Hazard<br>0.1<br>0.0<br>0.0<br>0.0<br>0.0 5.0 10.0 15.0 20.0 25.0<br>Loss Amount (Thousands USD)<br> Exceedance Probability<br>Annual<br><!--
and Reinvestment Act of 2009, about two-thirds came from increased federal government spending, with the Department of Transportation receiving US$ 48 billion.<sup>4</sup> Chi and Baek (2016) document the bidirectional relationship between transport infrastructure and GDP for the period 1960-2012. Their findings suggest that expanding transport infrastructure improves aggregate economic output, and enhanced economic output increases public investment in transport infrastructure in the period. However, the magnitude of the impact of transport infrastructure (0.55) on GDP is smaller than that of non-transport public infrastructure (1.02), implying that non-transport infrastructure investment is a more effective long-term fiscal stimulus than expanding transport infrastructure.

Pereira (2000), based on the US highway spending data for the period between 1956 and 1997, finds a long term multiplier of 1.97 for investment in transportation infrastructure (the lowest across different types of infrastructure investments) well below 4.5, the long run multiplier for total public investment. This finding implies a 3.4 percent rate of return on public investment in transportation infrastructure. Similarly, Leduc and Wilson (2012) estimate 2.7 and 6.2 for the short- and long-run multiplier for the period 1993-2010, while Perotti (2004) finds 1.47 in the short-run and 0.37 in the long-run for the period 1960-2001.

Turning to the energy sector, the report of Blyth _et al._ (2014) surveys the literature on the employment effects of selected policies and note that renewable energy has a higher jobs multiplier in the short run, while it requires less labor for operation and maintenance in the long-run. Comings _et al._ (2014) show that for each megawatt of renewable energy capacity added, photovoltaic projects would add 5 to 9 jobs depending on the size of the project. The contribution of wind projects, on the other hand, is around 1.5 jobs for each megawatt of capacity added.

Broadband infrastructure has been identified as an important area of public investment during
sparking growth and consolidating future fiscal positions.

The empirical analysis relies on a new database we assembled, which contains 1912 infrastructure projects financed by the World Bank between 1979 and 2008; these include detailed project performance evaluations on which we build this paper. Anticipating on the results, three main insights emerge from this research: 1) the quality of project implementers (borrowers) is the most important proxy for project success, regardless of most macroeconomic variables; 2) successful project implementation is positively related to the performance of the 2
9 these cities, the employment in tradables is neither too low nor too high, implying a relatively balanced distribution of employment between tradables and non-tradables. Unsurprisingly, 8 of the 13 neutral mega cities are capital cities, where the government sector generates substantial employment in urban non-tradables.

Finally, we show how, for a given size, cities in Latin America and the Caribbean (LAC) have experienced sectoral employment changes over time.<sup>12</sup> We use FUA data for 8 countries to estimate the mean population-weighted urban employment share of tradables for the whole region and the periods “pre-1980” (obs. from 1962 to 1982; mean = 1978), “early 1990s” (1990-1994; 1991) and “c. 2010” (2001-2012;2009). As seen in Figure 3, employment in tradables (MFG+FIRE) has been declining over time. The evolution of LAC’s urban system was driven by its largest cities, as the decline at the “top” is 15 percentage points. Large LAC cities have thus increasingly become consumption cities.<sup>13</sup>

**Robustness.** We obtain similar results if we (Web Appx. Section B): (i) include the square, cube, and perfect fourth of the urban share, and their interactions with the population dummies, in case there are non-linearities; (ii) use log per capita GDP instead of the urban share or control for the urban definition used by each country;<sup>14</sup> (iii) compare the raw (i.e. non-residualized) employment shares; (iv) use other weights or ignore them; (v) consider other classifications for the population dummies; and (vi) study urban non-tradables or informal employment. Since our residuals are estimated _relative to_ similar cities in the world, we may worry that adding more countries to the analysis could change the results. However, our sample captures close to three-fourths of
03|0.09|0.02|
|93|Other service activities|2,246,662|2,022,703|237,157|287,952|0.10|0.12|0.09|0.12|
||**Totals and weighted averages**|**12,183,718**|**12,529,347**|**1,056,693**|**1,152,673**|**0.08**|**0.08**|**0.09**|**0.09**|
||**Unweighted averages**|**870,266**|**894,953**|**75,478**|**82,334**|**0.14**|**0.11**|**0.13**|**0.10**|

Notes: Descriptive statistics taken from National Sample Statistics.
only two attempts have been made in this direction, notably by Santos et al. (2019) and Balasubramanian et al. (2023). Both employed unbalanced panel data and a firstdifference estimator (FDE) to uncover a negative correlation, albeit focusing on different indices of multidimensional poverty and study periods.

Our paper endeavors to contribute meaningfully to this relatively uncharted but essential literature. We begin our investigation by constructing a panel dataset that integrates Multidimensional Poverty Index (MPI) data with indicators of economic growth. Following the methodological footsteps of Santos et al. (2019) and Balasubramanian et al. (2023), we employ a first-difference model to elucidate the fundamental relationship between MPI and economic growth.

We begin the analysis by exploring the fundamental relationship between the MPI and economic growth, focusing on variations across different income groups and regions. This initial analysis replicates existing findings from the literature, providing a solid foundation for our research. Building upon this, our study contributes significantly to the literature by examining the subcomponents of MPI—namely education, health, and living standards—to identify any heterogeneity within these subcomponents. Furthermore, we investigate economic growth across various dimensions and components to provide deeper insights and distinguish our work from previous studies. In the second part of our study, we analyze the effects of economic growth by 2