rafmacalaba/data-use-mentions-extended huggingface dataset:
>(turn-round time) - average<br>(hours)|5|5.5|6.0|2.5|8.0|6.0||5.0|4.0|
|General cargo vessel pre-<br>berth waiting time - average<br>(hours)|48|3|48|3|9.6|36|38|24||
|General cargo vessel stay<br>(turnaround time) - average<br>(hours)|12|3|48|2|48|41|46|60||
|Container crane<br>productivity* (container per<br>hour)||||18|13|12||||
|General cargo crane/gang<br>productivity - average (tones<br>per hour)|12|16|15|16|14|9|8||23|
|_Tariffs_**:**||||||||||
|Average general cargo<br>handling charge, ship to<br>gate (US$/tonne)|NA|200|180|260|168|155||160|220|
|Average dry bulk handling<br>charge, ship to gate or rail<br>(US$/tonne)|5.5|10.5|8.5|13.5|10|8|8|15|9|
|Average liquid bulk handling<br>charge (US$/tonne)|3.3|4|5|5|3|||5|5|

* Data for the Freeport of Monrovia relate to 2008, but no data were available for the Port of Buchanan. Source: Ocean Shipping Consultants 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU
chemicals – comprise only one product. As shown in Annex Table A3, global trade in electronics, transport, clothing and footwear largely takes place mainly within the context of GVCs and seldom outside that context (93 percent of footwear and 88 percent of clothing exports are traded as part of GVCs).

**Table 2: Number of GVC and Non‐GVC Product Codes by Sector**

|**HS Category**|**GVC**|**Non‐GVC**|**Total**|
|---|---|---|---|
|01‐05 Animal|0|193|193|
|06‐15 Vegetable|0|312|312|
|16‐24 Foodstuffs|0|178|178|
|25‐27 Minerals|0|147|147|
|28‐38 Chemicals|1|735|736|
|39‐40 Plastic/Rubber|12|177|189|
|41‐43 Hides,Skins|5|58|63|
|44‐49 Wood|1|218|219|
|50‐60 Textiles|320|194|514|
|61‐63 Clothing|233|58|291|
|64‐67 Footwear|42|13|55|
|68‐71 Stone/Glass|6|181|187|

> 6 We use importer records tabulated from UN Comtrade considered to be more accurate than the corresponding exporter records.

> 7 Athukorala, Prema‐Chandra (2010). “Production Networks and Trade Patterns in East Asia: Regionalization or Globalization” ADB Working Paper Series on Regional Economic Integration, No. 56.  This was compared to another set of products coded by Sturgeon and Memedovic (2011). Athukorala coded a greater number of network products, although mainly focused on parts and components. There was only minor overlap between the two coding systems.

> 8 The original lists by Athukorala in HS 1996
margin is less or more pronounced for intra-Africa exports compared to exports to destinations outside the continent. To do this, we interact an indicator variable for Africa destinations ( _AFRd_ ) with our main variable of interest. The results are reported in columns 4 to 6 of Table 4. The coefficient of the interaction term ( log _NTMpcdt × AFRd_ ) is positive but statistically insignificant in all three cases, implying that the trade-reducing impact of NTMs doesn’t vary significantly between intra-Africa trade and Africa’s trade with other regions of the world.

As is evident in Table 1 and Figure A2, the specialization patterns and the trends in exports differ markedly across the different African countries we consider. Exports from Burkina Faso are concentrated in the manufacturing sector, exports from Malawi are concentrated in the agriculture sector, and Senegal’s exports display a mix of both. Since the use of NTMs to regulate trade is different across sectors, this section tests whether the NTM-trade effect varies based on origins given the observed product specialization patterns. We define two dummy variables and interact them with our variable of interest. The first dummy variable indicates whether the origin country is Burkina Faso ( _BFAc_ ), and the second indicates whether the origin country is Malawi ( _MWAc_ ). The reference country is thus Senegal. We find that trade reducing effect of NTMs does not differ significantly between the two countries and Senegal, with the only exception being the agriculture sector, where we find a marginally less pronounced negative effect in Malawi compared to Senegal (see columns 7 to 9 of Table 4).

17
participation on attending village council meetings. For women without NREGS cards, we find that participation in _Mahila Samakhya_ significantly increases the ability to go out without permission. The positive effect on attending village council meetings is also significant at the 90 percent level. We thus find that _Mahila Samakhya_ improves the bargaining power of not only women who work, but also of those who do not have access to outside employment. This effect may come from a combination of increasing the woman’s reservation wage and increasing her information set about alternative choices. However, all but one of the first-stage F-statistics for these regressions are lower than 10, ranging from 7.27 to 8.54. The only exception is the physical mobility regression for women without NREGS cards, where the F-statistic is 11.75. While we might be concerned about weak instruments in this case, the Sargan test indicate that the instruments are exogenous.

Restricting the sample further to those women who do not work _and_ do not have NREGS cards, we have 73 observations in the physical mobility regression and 78 in the political participation regression. The first-stage F-statistic for the physical mobility regression is 8.23, but is only 6.33 for the political participation regression; as a result, we do not report the results from this regression. We find that program participation increases a woman’s ability to leave the house without permission; the associated t-statistic is 1.83, meaning that the estimate is significant at the 90 percent level. Although the sample is small and weak instruments are a concern, these results suggest that the indirect effect of _Mahila Samakhya_ , working through increased reservation wages and expanded information sets, can improve women’s physical mobility, and thereby empower them.

In summary, using PSM, 2SLS, and ITTs, we find that the _Mahila Samakhya_ program empowers women in a variety of ways, via both direct and indirect routes. In addition to directly increasing access to outside employment, political participation, and physical mobility, our results suggest the
respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In the pooled regressions (Panels A1 and B1) each observation is weighted using sampling weights calculated as the fraction of households surveyed from each wealth class (lower, middle and upper) relative to the number of households from the relevant wealth class in the community census.

15
<!-- Start of picture text --> Overall, Destination and Product Overall lockdown<br>100% 100%<br>Overall import survival No Lockown<br>Destination survival Lockdown<br>75% Product survival 75%<br>50% 50%<br>25% 25%<br>0% 0%<br>0 2 4 6 8 10 12 14 16 18 20 22 24 0 1 2 3 4 5 6 7 8 9 10 11 12<br> Survival time (months) Survival time (months)<br>Destination lockdown Product lockdown<br>100% 100%<br>No Lockown No Lockown<br>Lockdown Lockdown<br>75% 75%<br>50% 50%<br>25% 25%<br>0% 0%<br>0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12<br> Survival time (months) Survival time (months)<br>Estimated survival probability Estimated survival probability<br>Estimated survival probability Estimated survival probability<br><!-- End of picture text -->

**Figure 5: Philippine’s import survival before and under the lockdown** Source: Authors’ compilation using PSA data

rate across destinations, as observed in Figure 4, originated from the rest of the world. The mean survival period of exports to the top ten destinations of exports was 0.6 months higher than to the rest of the world during the lockdown.

The second part of Table 4 shows results for imports. Columns 2 and 3 indicate no major difference in survival rates of intermediate and Other Products. Roughly two-thirds of these goods survived beyond the first month of trading. The survival rates in the 6<sup>th</sup> and 12<sup>th</sup> months were 39% and 37%
Montreal Protocol and ODS commitments to trade policy under RTAs introduces the potential for additional punitive measures for non-compliance in the form of retaliatory trade policy allowed for under RTA dispute settlement mechanisms. Because these expanded punitive measures fall under the RTA rather than the Montreal Protocol, they potentially cover a broad set of goods and services rather than the narrow focus on ODS-related goods under the Montreal Protocol.

We consider two aspects of issue linkages between trade policy and environmental policy using detailed data on trade agreement content from the World Bank’s Deep Trade Agreement database. First, we empirically investigate the use of trade agreements to induce accession to international environmental agreements—a phenomenon referred to as “participation linkage” in the literature. Because the Montreal Protocol limits trade in ODS-related goods with non-member countries, member countries may lobby to include Montreal Protocol accession as a requirement in trade agreements with non-members in order to more effectively open trade while avoiding any barriers under the Montreal Protocol. Second, we consider “enforcement linkage” between trade agreements and reductions in use of ozone depleting substances. In contrast to participation linkages—in which commitments bind via the Montreal Protocol—we also consider ODS commitments directly enumerated in trade agreements themselves that bind via RTA dispute settlement mechanisms. In particular, we study whether such enforcement linkages are effective at mitigating ODS emissions. This is a critical question in light of recent evidence that China has increased production and consumption of CFC-11 in violation of its Montreal Protocol commitments (Rigby _et al._ , 2019).

We also examine the role of substitution in the apparent success of the Montreal Protocol. We study whether reductions in regulated ODS were, in part, accomplished via substitution to other unregulated or less stringently regulated substances. While policy linkage in trade agreements can support commitments prescribed by international environmental agreements, exposure to potential trade remedies may exacerbate or accelerate policy spillovers such as substitution away from regulated substances to potentially harmful alternatives.

We find evidence that RTAs and ODS provisions are effective in promoting participation linkage. While the Montreal Protocol and
village size; strata and interviewer fixed effects. Dimensions considered for benefit score: improved health and safety for women, household status, and happiness, increases in labor supply and time saving. Dimensions considered for cost score: toilet unhealthiness, missing time with others, getting sick more easily, spending more time fetching water, difficulty and cost of emptying the pit.

# **7 Conclusion**

This paper provides some of the first rigorous evidence on the effectiveness of micro-credit in improving the adoption of an important lumpy human capital (health) investment - a household toilet. Drawing on a cluster randomized controlled trial in rural Maharashtra, India, rich data from a primary household survey and two novel sources of administrative data on borrowing (loan-level data on lending by the partner MFI, and credit bureau reports) we show that providing joint-liability credit labelled for sanitation is an effective approach to motivate toilet construction. Two and a half years after intervention rollout, we find that the newly introduced sanitation loans were taken by around 18 percent of eligible clients, and toilet ownership, and usage of these, increased by 9 percentage points.

Through a theoretical framework and supporting statistics from our data, we show that it is not just the provision of credit that matters, but that credit attributes are important. While this is a well-established finding, the novelty of this study is to show that the label itself plays a significant role in affecting loan uptake and investment decisions of poor households. The simple theoretical framework we present, explains how loan labels can distort household borrowing and investment choices towards the labelled investment. Moreover, in the absence of a product labelled for a specific investment, a household may be discouraged from making the specific investment even if it has access to credit. The introduction of a loan earmarked for sanitation would thus allow it to make the investment. And indeed, our empirical evidencesupports this mechanism: we find that a large proportion of households that borrows from the micro-finance partner take more expensive business loans rather than the sanitation loan
between 2006 and 2011, but corresponds to only one point in time, which does not allow for examination of city height evolution.

As mentioned before, for each city, the data from the DLR provides average height of every pixel inside the defined urban extent, with a resolution of 90x90 meters. Table 3 provides summary statistics on building heights across cities at different income levels. These are average heights, covering the entire urban extent.

**Table 3: City Heights Measures - Summary Statistics by Income Group**

||**Low**<br>**Income**|**Lower-**<br>**Middle**<br>**Income**|**Upper-**<br>**Middle**<br>**Income**|**High**<br>**Income**|
|---|---|---|---|---|
|Ave Height (in m)|2.37|2.34|3.58|3.34|
||(0.53)|(0.77)|(1.32)|(1.14)|
|Ave q90 (in m)|3.63|3.72|6.24|5.93|
||(1.21)|(1.56)|(2.73)|(2.62)|
|Ave q95 (in m)|4.26|4.35|7.56|7.69|
||(1.58)|(2.00)|(3.43)|(3.88)|
|Ave max (in m)|10.52|13.26|25.12|31.78|
||(6.65)|(12.26)|
# **1. Introduction**

Unemployment benefits (UB) and active labor market policies (ALMP) have gained widespread recognition in both developed and developing economies to improve labor market conditions and address social challenges.<sup>1</sup> Despite their effectiveness in cushioning vulnerable groups from shocks, their role in significantly bolstering overall employment and productivity remains ambiguous. Theoretically, UB and ALMP have the potential to increase both employment and productivity by extending job search time through income support, offering active job search support, and enhancing skills through training programs, among other benefits. However, these policies can also strain government budgets, displace or substitute productive jobs, and result in deadweight loss. Therefore, their collective impact on labor market outcomes hinges on which effects dominate at the macro level (Gautier et al. 2018).

Current research investigating the interlinkages between UB and labor market outcomes generally suggests that longer UB periods lead to prolonged unemployment duration and lower formal employment in both developing and developed economies (e.g., Filiz 2017; Hagedorn et al. 2015). The evidence regarding the impact of UB on job quality and earnings primarily concentrates on developed countries and generally indicates a positive association (Caliendo et al. 2013; Tatsiramos 2009). Only two studies have evaluated the relationship between UB and productivity and both indicate a positive relationship between them (Soltes 2017; Acemoglu and Shimer 2000).

Most studies analyzing ALMP use micro-level data to evaluate the effects of a specific program on participants' employment and earnings. Findings indicate that the effects of ALMP on labor market outcomes vary based on program type and design. For instance, evidence from both developed and developing countries suggests that job search assistance, placement, and training programs generally increase participant’s employment rates. However, these effects tend to be contingent on program design and differ across participant groups (e.g., Levy Yeyati et al. 2019; Autor et al. 2017; Card et al. 2018; Groh et al
|Emotional Stability|0.002|0.038|0.005|-0.034|0.017|-0.008|0.053*|
|---|---|---|---|---|---|---|---|
||(0.03)|(0.04)|(0.03)|(0.04)|(0.03)|(0.03)|(0.03)|
|Grit|0.049*|-0.012|-0.036|-0.067|0.028|0.033|-0.037|
||(0.03)|(0.05)|(0.03)|(0.05)|(0.03)|(0.03)|(0.04)|
|Decision-making|-0.031|-0.028|0.063**|0.004|0.040|-0.019|0.004|
||(0.03)|(0.05)|(0.03)|(0.04)|(0.03)|(0.03)|(0.03)|
|Observations|1,557|847|1190|1464|1454|1370|1948|

_Note_ : Standard errors in parentheses (* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01). The sample includes wage and self-employed workers. _Schooling_ is measured as completed years of education. Reference categories for _Self-employed_ and _Gender_ variables and for computer complexity are those not self-employed, males, and no computer use, respectively. Scores on the reading proficiency assessment and personality and behavior traits have been standardized to a mean of 0
# **3.2 Sample Descriptive Statistics**

We begin by reporting basic pre-treatment descriptive statistics regarding demographics, education, skills and labor market outcomes for our sample in Table 2.<sup>18</sup> We do not distinguish between treatment and control groups in this section, but we show statistics for our pooled sample and separately for the sample in Argentina and Colombia. We do this to get a sense of how our sample compares across countries and, within country, to the overall target populations in each location. Following a similar criterion of the program targeting of beneficiaries, for the overall population in Argentina, we compute descriptive statistics for women between 18 and 40 years old, living in Buenos Aires city and Greater Buenos Aires Area. We get this data from the 2018 Permanent Household Survey ( _Encuesta Permanente de Hogares_ , INDEC). Equivalently, for Colombia we compute descriptive statistics for women between 18 and 50 years old in Bogotá. We get data from the 2018 National Household Survey ( _Gran Encuesta Integrada de Hogares_ , DANE).

2. Several facts are worth describing from Table Regarding demographics of our sample, the average age of eligible women in our sample is about 29 years old. Even though only 19 percent of our sample are mothers, the mean age in our sample is remarkably similar to the mean age of mothers at first childbirth, which is 29.5 years old for Buenos Aires (Direccion General de Estadistica y Censos, 2019), and 27 years old for Bogota (OECD, 2019). About 14 percent of women in our sample are married. The household income distribution in our sample is somehow skewed towards upper deciles of the income distribution, with 44 percent of our individuals having a household income in deciles 4-7 and 38 percent in the upper 3 deciles. Age and marital status of eligible candidates are extremely similar across bootcamps locations. Our sample in Argentina has a somewhat smaller share of mothers relative to Colombia, and a household income distribution relatively skewed to the
# Introduction

The spending on public infrastructure (SPI) exercise is a methodological tool to support the collection and preparation of standardized, comprehensive information—comparable across countries—on public spending on infrastructure services. Particular attention is paid to the level, composition, and planning and releasing mechanisms.

Originally, the SPI exercise was developed to support the Africa Infrastructure Country Diagnostic (AICD) study<sup>1</sup> in recognition of the fact that the infrastructure data in the fiscal accounts as collected by the International Monetary Fund (IMF) were scattered and incomplete. While Africa‘s infrastructure needs are widely debated, very little was known about the existing levels and composition of public expenditure on infrastructure subsectors. Such information is important in order to structure financing options to close any funding gap. Infrastructure outlays—if at all—are reported in an aggregate manner without the economic breakdown that would delineate their nature: that is, fixed capital, operations and maintenance, interest, subsidies, the compensation of employees, and so on. In addition, such outlays are not reported by function, which would allow costs to be attributed to specific infrastructure subsectors, such as irrigation, water resource management, electricity, transport, and so on.

Between 2000 and 2005, the IMF Government Financial Statistics,<sup>2</sup> under the general government section, reported infrastructure spending data for only Uganda out of the 42 Sub-Saharan African countries. These data indicated an implausibly low spending level of 0.2 percent of gross domestic product (GDP) in 2001. Since government statistics reported by the IMF focus primarily on central government accounts, they provide incomplete coverage of infrastructure expenditure, much of which is undertaken by subnational and parastatal entities. Delivery of infrastructure services relies heavily on public (or para-public) agencies outside the purview of the central government. A significant share—if not the majority—of public expenditure in infrastructure is channeled through nonfinancial public enterprises, local governments, and (other) off-budget vehicles (
points respectively, with similar effects for both heavy and light overdrafters. SSP also significantly increased the share of individuals who strongly agreed that the product helps – manage transfer requests from people within the household (column 1) this is broadly consistent with results in Appendix Table A9.

However, we cannot reject the null that the effects in Appendix Table A10 are the same for heavy and light overdrafters. Why are effects on downstream outcomes so different by overdrafter status when perceived benefits of the product are so similar? One possibility is that differential treatment effects are not driven by underlying behavioral mechanisms so much as they are driven by differences in financial fundamentals. Consider, for example, the “lumpy savings goal” hypothesis: in order for SSP to have an impact on the likelihood of meeting a savings goal, a saver’s baseline probability of meeting a goal would have to be neither too low nor too high. It may be that light overdrafters, by virtue of being in a better savings position to begin with, were more likely to be marginal savers.

Ultimately, data constraints prevent us from testing the hypotheses discussed in this – section more directly we therefore leave further investigation of these questions to future research.

# **4 Conclusion**

We use six rounds of detailed survey data as well as administrative bank data to study the impact of a commitment savings product for salaried workers in Ghana. Overall, the product increased savings at the partner bank, both during and after the commitment period, with limited crowd out of cash savings held in other places. These savings gains are offset by an – increase in debt as a result, the point estimate on net savings is small and insignificant. This suggests that people with access to liquidity may fail to benefit from commitment savings products because they can undo their commitment with other financial instruments.

However, we find divergent results when we examine heterogeneity in treatment effects – by baseline propensity to take overdrafts with our partner bank. Heavy overdrafters who – had less liquid savings and
including knowledge) within suitable production technologies, but also managerial and technological innovations geared at responding to demand, for instance, by upgrading product design with a view to raise quality and appeal as needed to seize and develop market niches with auspicious demands. Importantly, Lederman and Maloney recognize that moving from concept to empirics is not a simple matter, not least because of the difficulty in adequately measuring quality (the “what”) based on the 3- to 6-digit classifications available in trade statistics.

8