rafmacalaba/data-use-mentions-extended huggingface dataset:
/sup> _p_ < 0.01

# **5 Discussion and conclusion**

A growing number of Malawian households are experiencing food and nutrition insecurity because of recent COVID-19 threats, supply chain disruptions caused by wars like the one between the Russian Federation and Ukraine, and climate shocks that result in the loss of produce, livestock, and livelihoods. Furthermore, the recent devaluation of the Malawi kwacha relative to the US dollar has made the macroeconomic environment even more dire, making it difficult to afford nutritious foods. Public programs to achieve food and nutrition security include provision of input subsidies, establishment of irrigation infrastructure and direct welfare transfers in form of food or cash. Prior evaluation of these programs found a positive association with food and nutrition security; however, there is a dearth of studies evaluating the impacts of participating in multiple programs (Tirivayi et al., 2016). We extend the literature by comparing and assessing the combined effects of social protection and irrigation investments using integrated household panel surveys for three years.

Overall, the findings suggest weak and insignificant joint effects from participating in several programs. In particular, the availability of irrigation infrastructure in a community and its use by households that receive food/cash transfers or input subsidies have no influence on food and nutrition security outcomes. This is contrary to evidence demonstrating stand-alone positive effects of irrigation infrastructure on production, livestock and livelihoods (Fan, Gulati, & Thorat, 2008; Nkhata et al., 2014), but these are localized studies and did not focus on food and nutrition 18
with at least primary education. On the other hand, these girls also reduce their participation in the labor market less than girls living in rural areas and with parents that have no education. Unfortunately, with the data available, it is not possible to tease out the underlying reasons that may help reconcile the lower school attainment and lower labor force participation identified for girls in these rural, less-educated households (Table 10).

> 30 A caveat of this analysis is that the follow-up survey has a significant number of missing values for the number of births. Further calculations show that this issue is equally prevalent in both stipend and non-stipend districts. Moreover, the problem of missing values does not appear to be systematically correlated with some determinants of fertility.

> 31 Estimates from the DD and RDD models shown in tables 4 to 8 were obtained from a DD framework for the whole sample with a control function of district literacy rates that determine program eligibility. The fact that the results are very similar may suggest that there is low impact heterogeneity across districts. Yet, this does not imply that there is not impact heterogeneity for different types of subgroups defined based on some socioeconomic characteristics.

22
Figure 3: Exposure of commuting zones: input tariffs <!-- Start of picture text --> Mean = 1.67 p.p., std = 2.40<br>6.84 − 37.60 5.17 − 6.84 4.02 − 5.17 3.24 − 4.02 2.74 − 3.24 2.16 − 2.74<br>1.82 − 2.16 1.39 − 1.82 0.96 − 1.39 0.46 − 0.96 0.00 − 0.46 No data<br><!-- End of picture text --> _Notes_ : This map displays the change in the baseline employment-weighted input tariff exposure for each commuting zone between December 2017 and December 2018. Darker colours represent more exposed areas.

ment composition as discussed in Section 3.3. Relative to the other two exposure measures, export tariff exposure shows a much greater concentration in the Great Plains and West, likely reflecting the concentration of agricultural products included in the retaliatory tariffs.

# **3.5 Classifying job adverts by occupation and skill**

The BGT job postings data provide information on the US SOC code of each of the postings (96% coverage). Examples at the 2-digit level include ‘Business and Financial Operations’ or ‘Food Preparation and Serving Related’; the full range is presented in Table 6 in the Appendix.

In order to investigate the impact of the tariffs on job postings by skill level, it is necessary to allocate postings into skill categories. Although the BGT data includes a variable for the minimum education level required in each job advert, the coverage is relatively low: only approximately 51 percent of postings have positive assigned minimum values of education.<sup>13</sup> In order to include the widest possible range of postings in the skill classification, we therefore instead use the available minimum education data to classify each SOC
Mel et al., 2008, 2009; Bardasi et al., 2011), there is a sizeable profitability gap (as measured by profits per worker) between male and female-owned businesses (with female-owned businesses earning approximately 45% less in monthly profits per worker than their male-owned counterparts). A similar pattern emerges when productivity is measured by sales per worker; female-owned businesses earn on average 266 USD per worker per month,<sup>19</sup> 72% of the level for male-owned businesses.<sup>20</sup> In five of the countries where both the Informal Sector Enterprise Surveys and micro-enterprise surveys are available, informal businesses are much less productive than micro-enterprises. Importantly, the size of this gap differs by country: in Zimbabwe, the average sales per worker for informal businesses is just a tenth of the corresponding value for micro-enterprises, while in Somalia, it is about 60%. Figure 12 plots the kernel densities of the log of sales per worker for informal and micro-enterprises. In Zambia and Somalia, there appears to be considerable overlap in the productivity of these two types of businesses. In contrast, in Zimbabwe, the majority of micro-enterprises earn more per worker than the top decile of informal businesses. The reasons for these relative differences between countries are left for future research. Overall, these results are consistent with previous studies that documented that informal businesses are less productive than their formal counterparts (La Porta and Shleifer 2014; Ulyssea 2020; Aga et al., 2021).

Figure 13 provides a more formal test of the equality of the distributions of sales per worker of informal businesses and formal micro-enterprises.<sup>21</sup> This figure presents the (unconditional) cumulative distribution function (CDF) of sales per worker for informal businesses and formal micro-enterprises. The thick horizontal line along the horizontal axis highlights the values of sales per worker where the two distributions are statistically significantly different from each other (using the familywise error rate, FWER, at 5%
where 1 stands for the vulnerability line. The latter is derived empirically at population level after setting the vulnerability index , the highest probability of falling into poverty that we are willing to vv accept before we no longer consider a household to be economically secure. For example, we might PP<sup>VV</sup> set the index at a value of 20 percent. If a household faced a probability of falling into poverty greater than 20 percent, we would consider this household to be vulnerable. Formally, the vulnerability PP<sup>VV</sup> line is then derived from this index as follows<sup>10</sup> : This approach contrasts with other approaches in the sense that the vulnerability line is not set arbitrarily, for example at 1.5 times the poverty line, but derived empirically from the vulnerability index. In practice, this requires a recursive algorithm that goes over the synthetic panel data to arrive at the vulnerability line that yields the desired vulnerability index. We acknowledge that setting the vulnerability index is still an arbitrary decision, but it is based on a transparent weighing of risks that evokes the insecurity that is inherent to vulnerability.

the level of precision of the estimates. The main reason for this is that the results already consist of bounds that incorporate uncertainty about the errors in our income model. The bounds are highly likely to contain the true value of the probability estimates, as was confirmed in other settings by Dang et al. (2014) and Hérault and Jenkins (2019). In addition, sample sizes for each round are large, so that the standard errors of our estimators are generally small.

The second approach is based on a method for analyzing vulnerability introduced by Chaudhuri (Chaudhuri et al. (2002); Chaudhuri (2003)).<sup>11</sup> Here, we again conceive of vulnerability as a certain probability of future poverty, with the important feature that a household that is currently poor may not necessarily be
types of entrepreneurs may be operating in the informal sector, demanding similar heterogeneity among informal sector businesses (Amin, 2009). Businesses in the informal sector may also have varying levels of output, rely on differing levels of human capital, and use public resources differently.

This heterogeneity can exist even within closely defined areas or neighborhoods. Indeed, informality tends to be highly clustered, even within urban areas. A recent World Bank study using city-based data for Kampala from the Uganda Bureau of Statistics found that the vast majority of informal business activity was centered around three locations near the central business district, and that 84% of informal businesses sold to customers thought to be within a 30-minute walk to their business (World Bank, 2018). Informal businesses may also be differentiated by household-level factors. Unsurprisingly, members of poorer households tend toward informality. At least some (4)
Child health and household resources in South Africa: Evidence from the Old Age Pension program,” _American Economic Review Papers & Proceedings_ , 2000, _90_ (2), 393–398.

- **, R. Glennerster, and M. Kremer** , “Using randomization in development economics research: A toolkit,” in T.P. Schultz and J. Strauss, eds., _T.P. Schultz and J. Strauss, eds._ , Amsterdam: North-Holland, 2007, pp. 3895–3962.

- **Dupas, P. and J. Robinson** , “Why Don’t the Poor Save More? Evidence from Health Savings Experiments,” _American Economic Review_ , 2012, _forthcoming._

- **Edmonds, E.V. and N. Schady** , “Poverty alleviation and child labor,” _American Economic Journal: Policy_ , 2012, _forthcoming._

- **Edwards, A. Cox and M. Ureta** , “International migration, remittances, and schooling: evidence from El Salvador,” _Journal of development economics_ , 2003, _72_ (2), 429–461.

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The rest of the paper is structured as follows. Section 2 describes the data and methodology. Section 3 presents the analysis of how the business environment affects African firms, Section 4 does the same for pollution, and Section 5 briefly focuses on the effects of improving these key variables. Section 6 concludes and hints to some possible course of action for African cities.

# 2. Data and Methodology

Because this paper builds on Reyes, Roberts, and Xu (2017), the main data source we use is also the World Bank Enterprise Survey Dataset. This survey, conducted by the World Bank, takes place in 709 cities, in 128 countries, and collects information on the business climate to understand how this affects firms’ performance.<sup>8</sup> This data set includes information on firm characteristics (such as size, age, number of employees), firm performance (such as employment growth rate, labor productivity, total factor productivity), and business environment variables (such as obstacles to grow, overdraft facility). A list of key variables and sources is presented in Table 1. Summary statistics are shown in Table 2.

It is worth noting that WBES data are a representative sample of the non-agricultural economic activity at the country level. Although the data are geographically stratified, this is done on a country-by-country basis, which results in no common stratification for all firms. Moreover, because the population of interest is limited to registered firms with more than 5 employees, most of these firms are clustered around the main urban centers. This could introduce some bias because more productive or fast-growing firms may be attracted to big cities. However, when estimating the model for different subsamples depending on city size, results do not show significant differences.<sup>9</sup> Furthermore, even though firms’ geographic distribution is representative for the country, representativeness at the city level is not automatically assured. Because this paper focuses on the correlation between pollution and firm performance -at the firm
- Fujita, Masahisa, Paul Krugman, and Anthony Venables, The Spatial Economy: Cities, Regions and International Trade (Cambridge, MA: MIT Press, 1999).

- Ghani, Ejaz (ed.) ThePoor Half Billion (Oxford, UK: Oxford University Press, 2010a).

Ghani, Ejaz (ed.) The Services Revolution (Oxford, UK: Oxford University Press, 2010b).

- Ghani, Ejaz, William Kerr, and Stephen O'Connell, “Promoting Entrepreneurship, Growth, and Job Creation”, in Ejaz Ghani (ed.) Reshaping Tomorrow (Oxford, UK: Oxford University Press, 2011a).

- Ghani, Ejaz, William Kerr, and Stephen O'Connell, “Sector Transitions in India”, Working Paper (2011b).

- Ghani, Ejaz, William Kerr, and Stephen O'Connell, “Spatial Determinants of Entrepreneurship in India”,Working Paper (2011c).

- Ghosh, Piyali, and Reena Cheruvalath, “Indian Female Entrepreneurs as Catalysts for Economic Growth and Development”,The International Journal of Entrepreneurship and Innovation 8:2 (2007), 139148.

- Glaeser, Edward, and William Kerr, “Local Industrial Conditions and Entrepreneurship: How Much of the Spatial Distribution Can We Explain?”, Journal of Economics and Management Strategy 18:3 (2009), 623-663.

- Glaeser, Edward, and William Kerr, “Entrepreneurship, Mines, and Urban Growth”, Working Paper (2011).

- Glaeser, Edward, William Kerr, and Giacomo Ponzetto, “Clusters of Entrepreneurship”, Journal of Urban Economics 67:1 (2010), 150-168.

- Government of India, Input flow matrix 2006-07, at factor cost from IOTT 2006-07 – Central Statistical Organization (CSO), Ministry of Statistics & Programme Implementation, Government of India.(2007)

- Greenstone, Michael, Richard Hornbeck and Enrico Moretti, “Identifying Agglomeration Spillovers: Evidence from Winners and Losers of Large Plant Openings”, Journal of
proportion of boys combining working with studying was twice as large compared to girls (18 versus 9 percent). About 6 to 7 percent of boys and girls were neither working nor studying.

# **6. Results: Short-Term Effects of the Ban of December 1998**

This section presents the results for the short-term effects of the ban. By short term we mean the effects for a few months after the change in the law. To estimate the impact of the ban in September 1999, nine months after the increase in the minimum legal age, we use the PNAD of 1999. Given that those who turned 14 in 1999 would still be out of the formal labor force in 2000, we could pool the surveys of 1999 and 2000 for greater efficiency. However, the year of 2000 was a census year and the PNAD was not collected. We thus pool the years 1999 and 2001 and perform the analysis with a narrower bandwidth.

# _Visual Check: Local Linear Regressions_

For the local linear regressions we use a twelve-month bandwidth. Estimates are obtained with a triangle kernel with a one-month bandwidth. The forcing variable is defined in months.<sup>23</sup> The cut-off value is defined at December 1998, that is, the forcing variable takes the value of 0 that month, 1 in January 1999, and so on. All figures show local linear regression with a 95 percent confidence interval.

Figures B.1 to B.9 are shown in the appendix. Figures B.1, B.4, and B.6 suggest that the proportion of boys doing paid work, only paid work, and paid work and studying, dropped. There is some strong indication that the proportion of those only studying increased. Based on these figures it seems that boys traded off work experience for more education. If that was a rationale behind the change in the law, it seems that it paid off at least in the very short term.

The parametric results discussed below confirm what is
ascribed to a large increase in the poverty line going from 2019 to 2022, particularly in the non-food element. If we apply either the 2019 or the 2022 poverty line and fix its real value over time, we observe a decrease in cross-sectional poverty from 2019 to 2022.

Figure 1 shows our estimated population shares of the four poverty transition outcomes over time under the main scenario. Headline transition results are qualitatively the same, irrespective of which of the three poverty lines is used.<sup>17</sup> This means that all three scenarios show a consistent image of declining chronic poverty. In the main scenario, we observe a decrease from 22-28 percent of the population in chronic poverty over the 2004-2007 interval to a 2-3 percent share over 2019-2022. Moreover, the share of the population that is consistently non-poor increased to around 88 percent of the population. Over time, the share of the population that escapes poverty is decreasing, which is not surprising given that a much smaller share of the population lives in poverty. Similarly, the share of the population that falls into poverty has also decreased much, to between 3-4 percent of the population. Together, these result in much less chronic poverty. Further below, we investigate to what extent these results hold across different groupings of Malaysians.

It should be noted that social assistance policies instituted in response to the Covid-19 pandemic will have influenced these results. For example, we could have hypothesized that the pandemic would have led to higher poverty entry figures. However, we do not observe such an outcome over the 2019-2022 interval. Obviously, we cannot draw any conclusions about the years in between, when the Covid-19 pandemic hit hardest. Still, it seems that Covid-19 did not lead to largescale permanent poverty and it is likely that government relief programs have contributed to this. Total budgets of relief packages for households and businesses equaled 23 percent of GDP in 2020 and 15 percent in 2021 (World Bank, 2021). For example, a World Bank survey indicated that about
All the analyses for cities in the report were run with the following identical variables:

- 4km/h walking speed for pedestrians accessing transit

- 7AM to 9AM on a weekday – peak travel time

- Within 60 minutes travel time between departure and arrival Where GTFS feeds with specific stops were unavailable, 400 meters was assumed as the distance between stops.

Data produced by WhereIsMyTransport<sup>7</sup> were used for five cities – Lusaka, Kampala, Harare, Douala and Cape Town. WhereIsMyTtransport is a South African company which collects and digitizes data from formal and informal public transport networks to produce GTFS feeds. Data collected include information on fares, stops, routes, frequencies and travel times and speeds. This mapping is carried out with local partners and identification of all available modes of transport within the city and local teams of data collectors. Routes and data are validated in the field by the local team during daily feedback sessions with data collectors.

The transport data for Nairobi are based on the Digital Matatus Project. This was carried out in 2012-2014 by the DigitalMatatus consortium comprising the Universities of Nairobi, Columbia, MIT and Groupshot, to produce a map of the Matatu minibus network in Nairobi. University of Nairobi students collected information on bus routes, schedules and stops with GPS technology based on their phones. Students and the Civic Data Design lab at MIT analyzed the data, and created a public transit map and a GTFS feed (Williams et al. 2015; Klopp et al. 2015).

The transport data for Dar es Salaam were a combination of sources, collected and aggregated by the World Bank office. The transport system includes the DalaDala routes, mapped as an initiative of the Ramani Huria team, the Tanzania Open Data Initiative (TODI) team from the World Bank, and Ally.<sup>8</sup> The transit information for the operating BRT routes where provided the by DART transit agency.

For Conakry, the route map is based on maps produced for the ongoing Plan de
offset, composting,<br>remanufacturing, greenhousegas,LEED|
|Most common environmental words in<br>O*NETgreen tasks|Environmental, energy, solar, green, waste, wind, carbon,<br>biofuels,recycling,hazardous,repair,landfill|
|ESCO taxonomy S3.3.2 complying with<br>environmentalprotection laws and standards|environmental impact, erosion control, sea pollution, protect<br>trees, protect wilderness areas,conserve natural resources|
|Eurostat manuals for data collection and<br>analysis|Air pollution control, solid waste management, noise control,<br>wastewater management, soil remediation, hazardous waste<br>collection, waste recovery and recycling, resource-efficient,<br>energysaving,eco-tourism,renewable energy|
|Commonly mentioned terms the in<br>environmental economics literature|Climate change, global warming, environmental, sustainability,<br>recycling,renewable energy|
|Emsi-Burning Glass Technologies dictionary|Clean energy, renewable energy, wind energy engineers,<br>recycling coordinator, climate change policy analyst,<br>conservation scientist|
|Janser (2019) dictionary|Lithium ions, smart grid technology, passive house, tree care,<br>bicycles,lightweight construction|

Once the first version of the dictionary was completed, it underwent several robustness checks. First, it was manually examined through more than 50 rounds of random tests to ensure that the dictionary correctly identified the task statements in the occupational database. As a result of this process, 104

> 46 Those within S3.3.2 complying with environmental protection laws and standards.

> 47 Topic areas were include  Agriculture, forestry, and fish production, Clean energy, Climate change common terms, Energy efficiency, Environmental certifications, Environmental knowledge, Environmental regulations and compliance, Environmental software, Greenhouse gas reduction and pollution reduction & removal, Low-carbon mobility, Low-polluting construction, Natural resource conservation, Recycling and reuse of waste and materials, and Repair.

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