rafmacalaba/data-use-mentions-extended huggingface dataset:
these two effects are examined in turn.

# **5.1 Total Emissions by Income Decile**

Total industrial emissions are estimated here in three steps.

First, totaL industrial employment, by sector and plant size class, in each municipio, is obtained by summing across individual plants in the Brazilian data set.

Second, total employment within each sector-plant-size category is multiplied by the relevant pollution intensity (emissions per employee) computed from Mexico's SNEFF database.

Finally, emissions are aggregated across Brazil's municipios by income decile.

Columns 2-5 of T'able 3 record the resulting estimated total emissions by decile; columns 6-8 tabulate emissions shares by size class; and columns 9-11 show the estimated share of total emissions by plant size and income decile.

Table 3 presents a striking contrast to the information in Table 2. It shows that most of Brazil's industrial emissions are in relatively affluent municipios, and most of the emissions in all income deciles come from large plants. Municipios in the top two income deciles account for about 68% of total emissions. The large-plant emissions share is always high, varying from a low of 67% to a high of 93%.

Inspection of columns 6-8 of Table 3 shows that the variations in shares are not random. The smalland medium-plant shares are quite small in the first three income deciles; rise to a peak in the sixth decile; and decline sharply in the top deciles. Column (5) suggests that most of this variation is due to large fluctualions in emissions from large facilities. While emissions from small and medium facilities display a steady upward trend, large-plant emissions fall sharply from the second to the sixth deciles before increasing to very high levels in the ninth and tenth deciles. Table 3a shows why this occurs: large-plant employment in the middle of the municipio income distribution is simply lower than in the tails. This translates to the observed discontinuity in emissions.

13
.05|
|n predict|_Igt_ = 0|0.19|_−_2.42|2.22|0.862|0.544|0.828|2.661|21.8|3.31|_−_42.98|_−_23.92|_−_45.05|_−_26.63|8.76|_−_7.14|_−_1.75|0.13|
|ferences i|_Gt_ fxed|0.15|_−_1.74|1.59|0.917|0.720|0.896|1.60|12.66|2.01|_−_32.31|_−_17.26|_−_34.08|_−_19.40|6.04|_−_4.74|_−_1.39|0.09|
|Table 7: Di|Variable|GVA share of agriculture (%)|GVA share of manufacturing (%)|GVA share of services (%)|GVA share of agriculture, _R_2|GVA share of manufacturing, _R_2|GVA share of services, _R_2|Ave. no. of workers in agriculture|Ave. no. of workers in manufacturing|Ave. no. of workers in services|GDP percentage change|Agricultural output % change|Manufacturing output % change|Services output percentage change|Private consumption share (%)|Private investment share (%)|Public investment share (%)|Fixed-costs share (%)|

52
al., 2014) with the subset of intrapersonal and relational variables that showed significant treatment effects from one or both of the Initiative interventions. Table S9 shows the resulting indirect effect estimates for the intrapersonal and relational composite indices.<sup>14</sup>

The results illustrate three main points. First, both intrapersonal processes (e.g., future expectations) and relational processes (e.g., household dynamics) predicted the economic composite index (i.e., business engagement, business performance, and food security), controlling for condition. Second, results are consistent with a model in which the Initiative interventions influenced economic outcomes through intrapersonal outcomes, based on significant indirect effects (IE) on the intrapersonal composite index (IEPI = 0.05, 95% CI [0.01, 0.09], IEII = 0.04, 95% CI [0.01, 0.07]). Specifically, evidence is strongest for the mechanism of raised future expectations, given that this variable showed significant indirect effects for both interventions (IEPI = 0.04, 95% CI [0.01, 0.07], IEII = 0.04, 95% CI [0.01, 0.07]). Third, only in the Interpersonal Initiative intervention was there evidence for relational mechanisms, given a marginally significant indirect effect on the relational composite index (IEII = 0.03, 95% CI [0.00, 0.06], and not in the Personal Initiative intervention (IEPI = 0.01, 95% CI [-0.01, 0.04]). Improved household dynamics (e.g., perceived closeness) was a possible candidate for a mechanism of those measured, given it showed significant treatment effects from the Interpersonal Initiative intervention, and indeed the indirect effect estimate was significant in this condition (IEII = 0.02, 95% CI [0.00, 0
points between 2019 and 2021 (INEP, 2022). In Uganda, the percent of 6</mark><sup>th</sup> <mark>grade learners rated proficient in English decreased by 4.7 percentage points between 2018 and 2021 (N</mark> <u>APE 202</u> <u><mark>1). In the Russian Federation, 6</mark></u><sup>th</sup> <mark>grade children experienced losses equal to 3-4 months of learning (Zvyagintsev 2021). At the secondary level, close to one-third of children in grade 8 experienced learning losses in Kenya (Whizz Education 2021), measured by knowledge in math topics. In Krasnoyarsk, Russian Federation, 8</mark><sup>th</sup> <mark>grade students experienced an equivalent of 1.5 years of lost learning in more than half a period of full school closures (Stanislavovich 2021). The Monitoring Impacts on Learning Outcomes (MILO) report, looked at COVID-19 end-of-primary learning losses in six countries in Sub-Saharan Africa</mark><sup>22</sup> <mark>(UNESCO 2022). In contrast to the results from most other countries, that study showed that in all six countries, students maintained pre-COVID-19 learning outcomes after the onset of the pandemic, at least until mid-2021. This may indicate that in contexts with a very low initial proportion of students meeting minimum proficiency thresholds, a decline in learning may be difficult to observe, among other factors (see</mark> <u><mark>UNESCO 2022</mark></u> for a full discussion) <mark>. The MILO results are also in alignment with our simulation results which indicate the lowest learning losses in Sub-Saharan Africa for the reasons previously discussed.</mark> Two, the learning loss estimates from some of these empirical studies are roughly in the ballpark
mobility in response to the expansion of roads and electricity, with residents moving from low-skilled jobs to high-skilled jobs. This latter finding is consistent with the negative effect of these two types of infrastructure expansion on agricultural employment (shown in Table 6) in rural areas.

# **6.3 Gender**

In this section, we examine the different effects on the employment of men and women that stem from investments in roads and electricity. We run separate regressions for each gender group, and present the results in columns 3 and 4 of Table 7 in the Appendix. We find that the direction in which these infrastructure investments affects employment does not differ by gender. However, the magnitude of the does The DHS sample results show that roads and electricity have greater marginal effects for women than for men. Columns 3 and 4 show that in locations along the grid, bringing roads 10 km closer marginally increases employment by 7.6 percentage points among men and by 39 percentage points among women, with these effects being statistically significant. The additional employment gain due to the joint investment in bringing both roads and electricity 10 km closer is larger for women (6.5 percentage points) than for men (2.2 percentage points).

Unlike in the DHS sample, the impacts from using the LSMS sample of countries are relatively similar between men and women.

The OLS results are reported in the Appendix Table 13. The main conclusions from these results qualitatively mirror those from the IV estimates, with a few differences. The OLS results show that electricity expansion increases employment both for men and women, and continues to increase employment in areas with high access to roads; this is consistent with the findings from the IV regressions. The OLS results show that, while the joint effect of roads and electricity is positive for both groups, its magnitude is larger for women.

The findings imply that employment gains from investment in road and electricity infrastructure in Africa accrue to a greater degree among women than among men. However, in
less clear that the outputs of the GCMs provide useful information with respect to daily variation in climate (Schlosser, 2011). As a result, it is inappropriate to directly take the raw output from GCMs and use these in, for example, crop models for Tanzania estimated on the basis of historical data. Some manipulation of the GCM output is required. Here, we combine the long run temperature and precipitation changes predicted by each GCM with historical data on daily climate variation. Specifically, we calculate a ten-year moving average of the percentage monthly changes in temperature and precipitation between the selected climate change scenario as predicted by the GCM and a baseline no climate change scenario from the same GCM. So, the percentage change in temperature for January 2030 is the average percentage change in temperature recorded by the GCM for the periods January 2026 to January 2035. We then overlay these changes on the baseline historical scenario. For example, when the moving average from a GCM predicts a five percent increase in January temperature and a three percent decrease in precipitation for a given year, then the January baseline temperatures and precipitations (drawn from the historical record) are augmented by those percentages.

> 2 A longer historical data series would be desirable.

4
In order to address the potential endogeneity in the model, we instrument the EBA Regulation index with the legal origin of a country’s commercial code or company law.<sup>3</sup> This reflects the overall substantive and procedural aspects of a legal system, and therefore is fundamentally related to the complexity of legal aspects measured under the EBA Regulation index. Meanwhile, it is arguable that legal origin, usually established centuries ago, does not have a direct impact on individuals’ recent financial behavior. Column (3) in table 4 shows that the effect of more a comprehensive and supportive regulatory framework on financial inclusion remains positive and significant under the instrumental variable method.

**Table 4.** Regression results

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|FINC|FINC|FINC|
|REGULATION|0.00926<br>||0.590***<br>|
||(0.00676)||(0.0235)|
|REGULATION||||
|_--II quartile_<br>||0.358<br>(0.267)<br>||
|_--III quartile_||0.654**||
|||(0.285)||
|_--IV quartile_||0.757**||
|||(0.309)||
|FEMALE|-0.266***|-0.267***|-0.141***|
||(0.0559)|(0.0567)|(0.0136)|
|AGE|0.101***|0.101***|0.0515***|
||(0.0101)|(0.0102)|(
.006 0.022|0.019|0.015|0.233|
|Air Transport|0.044|0.029|0.003 0.105|0.032|0.052|0.592|0.091|0.116|0.032 0.358|0.117|0.082|1.402|
|Communica�on|0.006|0.002|0.004 0.023|0.004|0.008|0.145|0.024|0.012|0.008 0.083|0.021|0.031|0.545|
|Financial services nec|0.020|0.008|0.005 0.108|0.009|0.016|0.330|0.016|0.010|0.004 0.042|0.013|0.011|0.243|
|Insurance|0.006|0.005|0.004 0.055|0.005|0.009|0.191|0.006|0.003|0.001 0.020|0.003|0.004|0.066|
|Business services nec|0.065|0.027|0.052 0.160|0.034|0.060|0.903|0.097|0.145|0.089 0.364|0.103|0.137|2.393|

# **Source: GTAP 9 Database**

49
Harris (1954), Hanson (2005), Emran and Shilpi (2012), Dorosh, Wang, You, and Schmidt (2012), Jedwab and Storeygard (2016), and Blankespoor et al. (2016).

> 3 We use 1 �∑��� ��,����,��� instead of ∑��� ��,����,���  so as to be able to calculate the natural logarithm of the market access index even when the weighted sum of the populations is equal to zero, which can occur as we restrict the calculation of the market access index to travel times of six hours or less.

> 4 We use time indexes _t_ and _t-_ 1 to refer to the years in our data (1970, 1980, 1990, 2000 or 2005; see Section 3). Note that in Formula (1), we exclude the population of the locality and use travel times based on roads prior to _t-_ 1 (see Appendix), which addresses endogeneity concerns in the regressions.

5 Major ports are defined as ports that include direct or trans-shipment capacity as measured in the AICD report (Foster and Briceño-Garmendia 2010).

> 6 The problem is attenuated by the fact that we use the _lagged_ value of the market access variable.

7 The number of people residing in each location is determined from LandScan and UNEP/GRID-Geneva. The urban/rural dichotomy is based on a density threshold. These information are given at the 30 arc second (approximately 1 by 1 km). The spatial model to allocate national or subnational GDP across space uses subnational population data from LandScan within urban and rural strata and does not make any direct use of roads or cropland. 8 The average market access index grows at 3.95 per cent annually.

9 We have 20.088-1�0.063.

> 10 The estimates show that a marginal increase in Δ���� ln ���,� has a smaller
during the pandemic-induced crisis: first, when ports were affected by lockdown and staff availability, then a longer period from the end of 2021, when the pressure from demand challenged handling throughput first. In comparison, a port such as Durban in South Africa experienced bursts of stress prior to the pandemic, which may be related to known systemic in-country infrastructure management issues.

Transshipment hubs such as Algeciras, Singapore, and Tanjung Pelepas appear to exhibit similar erratic fluctuations in their stress patterns compared to gateway ports serving as final vessel destinations or departures. Therefore, port-level stress data can serve as valuable complementary information and provide insights alongside other established indicators and data on port and/or logistics performance (like the World Bank’s Container Port Performance Index (CPPI) and the Logistics Performance Index (LPI)).

A port is heavily dependent on its hinterland connections to facilitate cargo flows. Port congestion has notable impacts on the availability of assets like drayage trucks and rail ramps as connections to inland destinations. A shortage of chassis to haul containers by road can severely limit capacity, as experienced on the United States West Coast during the COVID-19 pandemic for example. These chassis are also utilized for container storage at some rail yards and distribution facilities.

There exists a divergence between the increasingly demanding punctuality and flexibility of modern supply chains, such as e-commerce fulfillment, and the rigidity inherent to maritime shipping networks optimized for economies of scale through post-Panamax vessels. Shippers and cargo owners often respond by increasing inventory holdings and placing additional orders as a buffer, creating a demand-amplifying “bullwhip effect” that propagates backward through supply chains. This surge in demand, driven by actual consumption compounded by precautionary stockpiling, can overload shipping resources, especially the available container equipment pool. Container availability and shortages became the primary propagation and backpropagation mechanism disrupting maritime logistics networks. In this context, containers were spending 20 percent more dwell time immobilized within the logistics system on vessels, chassis, and container yards.

The stress stemming from the declining velocity of container movements initiates a
uses surveys collated and harmonized by the Luxembourg Income Study for 38 middle- and high-income countries to show that non-response rates are correlated with GDP per capita. Countries with PPP GDP per capita of around $10,000 had an average non-response rate of around 10%, whereas this was around 25% for those with per capita incomes of around $35,000.

When measuring the top of the income distribution, the concern is the unit non-response rate at the top of the distribution. Ravallion (2022) notes that direct evidence that rich people are missing from surveys is scarce. As evidence that they are missing, he cites a paper by Székely and Hilgert (2007), who used household survey data on 18 South American countries which showed that the highest income individual in each survey had incomes that were similar to that of managers in medium-sized companies, meaning that the top of the distribution was missing. Ravallion (2022) notes that one reason that the rich are assumed to be missing is that rich people have a higher opportunity cost of time and may therefore be less like to respond. Choumert-Nkolo et al. (2023) focus on non-response and assert that high income households in low and middle-income countries (LMICs) often live in gated 5
In addition, the results are likely to be also valid for children who had an older sibling in school but whom the older sibling forgot to list at the time of the interview, and to children who had a sibling in school but whose household was omitted during the enumeration. Those children share the most important characteristic of baseline children, which is that they were either in school themselves or that they had an older sibling in school. Therefore it is reasonable to assume that their elasticity of preschool attendance with respect to preschool conditions was similar to the elasticity for children in the baseline. Children who were not in school themselves and did not have an older sibling in school might have a di¤erent elasticity of preschool attendance, so the e¤ects of the school meals program could have been di¤erent for them<sup>25</sup> .

# 4.2 Measuring school participation

The ...rst outcome of interest is school participation. In this paper, a child is de...ned to be participating in school on a given day if she is present in school on that day. School participation can quite di¤erent from o¢ cial enrollment. For example, children might be enrolled in school but miss class because they are ill. Since we are interested in the amount of time children spend in school, participation is the right outcome to look at. If the program in‡uences the probability that a child will show up in school given that she is enrolled, for example because it a¤ects the probability that a child is ill, the change in enrollment will be a biased estimator of the change in school participation. In the Kenyan context, there is an additional reason why one should not trust enrollment data as indicators of school participation. As described in section 3, even when a child is enrolled in school, the teacher has a lot of discretion in deciding whether to keep a child in class when the parents have not paid “enough.
any other region except Sub-Saharan Africa.<sup>4</sup> On average, in the MENA countries, 34 percent of firms indentified access to finance as major constraint to economic activities.<sup>5</sup> For countries such as Algeria and Lebanon, this constraint is even more severe (50%<sup>6</sup> and 42%<sup>7</sup> ), for others, such as Oman (25%<sup>8</sup> ), the constraint is lower, but a barrier to business activities nonetheless. Improving secured transactions regimes has helped alleviate this constraint in other countries and can reasonably be expected to do the same for the MENA countries.

**Insufficient Suitable Collateral is Among the Top Reasons for Difficulty in Accessing Finance** _._<sup>_9_</sup> Firm level surveys conducted in developing countries help explain why obtaining finance is difficult. A common trend among the firms indicates that credit applications are mostly rejected due to insufficient collateral, i.e. unacceptable or unsuitable collateral. In many cases, business owners did not even bother applying for loans, because they were certain that they could not meet the collateral requirements often requested by banks.<sup>10</sup> As illustrated by the table below, more than 80% on average of the loans granted in MENA by financial institutions require some type of collateral. An in-depth analysis indicates that availability of collateral is frequently not the source of the problem; it is the ability of translating valuable assets into productive use. While in the developing world 78% of the capital stock of a business enterprise is typically movable assets such as machinery, equipment or receivables and only 22% immovable property, financial institutions are reluctant to accept movable property as collateral. Banks heavily prefer land and real estate as collateral (suitable collateral).<sup>11</sup> This dichotomy is clearly present in the MENA region,
Baltimore, Kleinert and Spies (2011) use shipping prices from the UPS. These data are not generally available and focus on the cost of shipping for standardized units of cargo instead of the cost of shipping specific products.

# **3.4 Other Useful Data Sources**

In studying the determinants of shipping costs to the U.S. and port efficiency, Clark et al. (2004) used quality of port infrastructure and port handling costs from the Global Competitiveness Report. The Global Competitiveness Report, published annually by the World Economic Forum, provides the quality of port infrastructure index for 140 countries between 2007 and 2017. The index measures business executives’ perception of their country’s port facilities in terms of development and accessibility. These data are collected through online surveys or in-person interviews.

The Service Trade Restrictiveness Index developed by the OECD measures 44 countries’ restrictions on service sectors, including maritime transportation, from 2014 to 2020 (the value of the index has little variation over time). This index allows researchers to study the cross-country variation in maritime sector policy restrictions and their impact on transport costs (Bertho et al., 2016).

Using data from surveys of logistic professionals, the World Bank provides The Logistic Performance Index (LPI) that ranks countries on six dimensions of trade. The index includes information on customs infrastructure and timeliness of The LPI is more use- performance, quality, shipments. ful for a cross-country comparison in trade facilitation. It does not allow researchers to identify the international transport aspects of trade separately from the domestic infrastructure.

1. We end this section by providing basic facts about selected datasets in Table

21
- 18 -

# **VII. CONCLUSION**

36. Understanding how land prices are determined is of particular importance for policy makers; however, there is little evidence in the developing world, particularly in Africa. Many African countries are currently experiencing very rapid urbanization. The paper examined the relationship between land prices and locational characteristics using data from Antananarivo, the capital of Madagascar, where over 2.6 million people live.

37. It was found that the land prices in Antananarivo are much elevated in the center of the city and taper off quickly toward suburban areas. Since spatial data, such as land prices, are normally autocorrelated, the spatial autoregressive model was applied. The land value gradients were found to be relatively steep. The elasticity is -0.402 with respect to travel time to the CBD and 0.122 with respect to the accessibility to jobs, i.e., JAI. This confirms the facts that the land and housing prices in the middle of the city overshoot and push the poor away from the city to suburban areas, because they are not affordable.

38. The results also indicate that transport infrastructure and services, such as taxi-be and suburban bus systems, are underdeveloped, restricting people’s mobility. With wider suburban development, the land price gradients would be less steep. In particular, our estimation results suggest that an extensive bus network needs to be developed to meet the people’s needs. The proximity to taxi-be services was always found to be an important determinant of land value in Antananarivo.

39. Not only transport connectivity but also other factors, such as proximity to amenities and administrative centers, were also found to be important to determine the land values. The climate vulnerability also has a negative impact on land prices. These elements cannot be ignored to stimulate urban development in Antananarivo. On the other hand, the impact of rice fields looks inconclusive.