rafmacalaba/data-use-mentions-extended huggingface dataset:
factories in control districts).

First, consider the minimum wage increase. While the minimum wage increase arose out of the post Rana Plaza protests (as described in section 2.3) it applied to the whole industry, and thus any effects that apply equally in treated and non-treated districts are netted out of the treatment effects given in equation 1. However, it is possible that international scrutiny leads to increased enforcement of existing labor laws given the role of multinationals in enforcing labor law (Boudreau, 2019). In this case, our identification strategy would correctly attribute this aspect of the minimum wage increase to international scrutiny.

Worker flows might affect garment workers in control districts if workers either voluntarily left (or were fired from or laid off) garment sector jobs in treated districts and went to work in garment factories in control districts. While we cannot use the panel component of our labor force survey data to speak to the empirical frequency of this event (as workers are not tracked if they move), a separate survey conducted by Heath (and collaborators Laura Boudreau and Khandker Wahedur Rahman) speaks to the phenomenon. In that survey, workers surveyed in 2017 by Kabeer, Haq and Sulaiman (2019) were recontacted in either November 2020 or March-April 2021. The workers all initially worked in treated districts. Of the workers who were found and still worked in the garment industry, 94% (266 of 283) were still in the same district. Among those workers who had moved, only 1 (i.e., .003 %) had moved to a control district. Thus, this survey presents one piece of evidence that flows of garment workers between treated and control districts are relatively small.

Next, we consider indirect effects through subcontracting, or spillover effects of international scrutiny. In particular, garment workers in control districts might be impacted if they had previously been in factories that subcontracted with export factories, and this subcontracting was discouraged by the international scrutiny following Rana Plaza. Or their factories might fear
0.071) ***|
|5th quantile|||||||0.455|(0.094) ***|
|t=2018|0.155|(0.051) ***|0.151|(0.050)|***<br>0.133|(0.050) ***|0.155|(0.051) ***|
|constant|0.305|(0.216)|0.301|(0.215)|<br>0.279|(0.213)|0.436|(0.212)**|
|No. of obs.|5766||5792||5819||5766||
|Wald chi|657.4||656.99||659.63||658.56||
|No. of district dummies|113||113||113||113||

Note: The dependent variable is _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

To interpret the results better, the marginal effects are computed by using the delta method ( **Table 5** ). By reducing the travel time required to visit a health facility by 1 percent, the probability of visiting a health facility could be increased by about 2.9 percent. Although the costs of improving transport connectivity vary depending on circumstances, this estimated increase in health care access could bring significant socioeconomic benefits to the country. Recall that Mozambique is estimated to spend nearly US$300 million or 2 percent of GDP for malaria treatment every year.
are invited to two days of standardized skill assessments. Some candidates are invited to further job readiness training based on their assessment results and residential location, but only 0.2% of candidates in our sample get jobs through this training. Our sample consists of all candidates who arrive at Harambee for the second of these two testing days, on 84 operational days.

We conduct three surveys to measure workseekers’ labor market outcomes, job search, and beliefs about their skills and the labor market. The baseline is a self-administered questionnaire that candidates complete on desktop computers at Harambee under supervision. This is administered after candidates have done skills assessments but before they receive information about their results. 10Throughout the paper, we use Statistics South Africa’s definition of an employed person as someone who did any income-generating activity, for at least one hour, during the reference week. Unemployment rates exclude those in full-time education or not in the labor force.

> 11Candidates are eligible to work with Harambee if they are aged 18-29, have legal permission to work in South Africa, have completed secondary school, have at most 12 months of formal work experience, have no criminal record, and are from disadvantaged backgrounds. This information is self-reported but checked against administrative data for some candidates.

9
# **1 Introduction**

_Mobile phones have changed the world, for better or worse._ _Alex Clark_ Mobile phones play an important role in modern life. Aside from the basic function of facilitating communication, mobile phones have become a tool for financial intermediation<sup>1</sup> and the source of broadband Internet to millions of people around the world. The significant increase in adoption of mobile phones across countries over the past two decades underscores the relevance of the technology to livelihoods.

The impact of mobile phones has become a topic of growing interest among researchers and policymakers (Aker and Mbiti, 2010; Manacorda and Tesei, 2020; Guriev et al., 2021). However, available studies have almost entirely focused on the microeconomic impacts of mobile phone uptake (Aker, 2010; Suri and Jack, 2016; Suri, 2017; Batista et al., 2018) and the effects on political economy (Manacorda and Tesei, 2020; Guriev et al., 2021; Gonzalez, 2021). Very little is known about the extent to which mobile phones stimulate economic development. While cross-country correlation between mobile phone penetration and GDP per capita as shown in Figure 1 suggests a positive relationship, evidence on the causal impact of mobile phones on economic development is scant.

In this paper, I present causal evidence on the extent to which mobile phone access spurs local economic growth. By linking a global dataset on the expansion in the second, third, and fourth generation (i.e., 2G, 3G, & 4G) mobile network coverage and nightlights (a proxy for economic activities) in over 34,000 subnational districts in 120 countries between 1998 and 2018, I provide new evidence on the development impact of mobile phones. The identification strategy of the paper is structured in two parts. First, I use a two-way fixed effects (TWFE) estimator that leverages the gradual expansion in mobile network connectivity across districts and time to estimate the effects of expansion in mobile connectivity on local
The Puerto Rican import data are the source of the dependent variable in our pooled product-level gravity regressions. For independent variables, we calculate great circle distances from each country or U.S. customs district to the port of San Juan. For reasons that we describe later in the paper, we parameterize export supply in the shipments’ origin rather than relying on fixed effects to sweep out heterogeneity in supplying regions’ product-level export supplies. To do this we calculate each origin’s total export supply of a given HS6 product in a given year, and include it as a control in the gravity regression estimated at the HS6 level of aggregation. The export supply measures are calculated using the CEPII (2022) data for non-U.S. origins and U.S. export data for U.S. origins.

Our analysis also exploits variation across products in characteristics that relate to demand for particular kinds of shipping and/or the freight rates that might be charged for transporting a given dollar value of that product. We use four product characteristics to predict reliance on particular kinds of shipping: a) the value share of a product’s annual imports that moves by sea, b) the log weight-to-value ratio of imports in the commodity, c) the squared log weight-to-value ratio, and d) the value share of imports that are shipped in containers. We calculate all these measures with U.S. import data so that they are exogenous to the flows we observe involving PR.<sup>16</sup> We estimate the model separately over subdivisions of final and upstream products. Our primary tool for separating final and upstream goods is the upstreamness measure from Antràs et al. (2012), though we also use the United Nations’ BEC classification to identify a sample of consumption goods for use in a robustness check.

be imperfect, but quite reasonable.

> 16We exclude imports from Canada and Mexico in these calculations so the U.S. data we use reflect the air-vs-sea choice that is available
is always of the opposite sign across the two columns.

Column two of Table 2 shows the coefficients of features selected for the growth rate’s deviation from its 2019 projection. Besides three features selected in the previous column–the second price factor and two mobility indices–lasso selects the second labor search index. Together with the macro variables, these lasso-selected features explain 49.5% of the variation in the outcome. Finally, except for the education and sports search index, lasso selects all the indices for the stringency regression. The lasso-selected variables explain 68% of cross-country variation in stringency measures and a higher value of adjusted r-squared indicates a better fit compared to the unrestricted specification reported in column three of Table 1.

To assess the individual predictive power of every lasso-selected feature, we check the contribution of every variable to the r-squared in each model, expressed in percent. Table 3 reports the results of this decomposition. According to our findings, Google mobility and food prices have the largest explanatory power. The two groups of indices contribute to 72.5%, 87.2%, and 58.4% of the overall r-squared from growth rates, their deviations from projected values, and stringency index regressions, respectively. The search terms also contribute to a reasonably large share of r- squared in every regression reported in Table 2. Finally, satellite-derived data and macroeconomic variables account for the minor shares of the goodness-of-fit measures.

11
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1a. Infrastructure’s historic contribution to economic growth**

<!-- Start of picture text --> 2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>Congo, Rep Sierra Leone CAR East Africa All parts of Sudan<br>ICT Power Roads<br>Changes in per capita growth<br><!-- End of picture text --> _Source:_ Calderón 2009. _Note:_ ICT = information communication and technology; CAR = Central African Republic.

**Figure 1b.  Infrastructure’s potential contribution to future economic growth**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>All parts of Sudan CAR Congo, Rep of East Africa<br>ICT Power Roads<br>growth<br>Potential contributions of per capita<br><!-- End of picture text --> _Source:_ Calderón 2009. _Note:_ ICT = information communication and technology; CAR = Central African Republic.

Specific data for South Sudan indicate its predicament. Inadequate infrastructure belittles the productivity of firms. In the state of Central Equatoria, power outages force 100 percent of firms to own backup generators to self-generate power. At least 50 percent of firms (particularly large firms) and almost 70 percent of small firms report that electricity is a major constraint on doing business (World Bank 2009a). Further, high costs associated with ports, transport, and trade logistics impact the business environment and compound the productivity challenges faced in South Sudan.

4
likely to use condoms than in a more casual relationship. After contracting the virus, they might infect their wives or long-term partners when they return to their country.

Second, miners have traditionally been accommodated in single-sex, regimented, companyowned compounds or hostels, located close to the mining sites. As a result of this situation, and because miners earn higher incomes, an active sex industry has arisen in the areas surrounding the South African mines. Commercial sex workers are a key group at high-risk for HIV infection and transmission (Abdool Karim et al. 1995; Nagot et al. 2002, Ramjee et al. 2005).

Third, mining is a very dangerous occupation and AIDS may be considered a distant threat compared to the more immediate dangers that miners have to constantly face during their daily job. This might raise barriers to behavioral changes, such as more frequent use of condoms with commercial sex workers.

There may be also an indirect relationship between migrant labor in the mines and HIV infection. Miners’ wives or their long-term partners, who are waiting for their husbands to come back from the mines, may also engage in extra-marital sexual relationships.

The long absences from home, the dangerous work in the mines, and sex workers around the mining sites have created high-risk groups – namely miners and their partners – that have been overlooked in the literature thus far. These groups could be instrumental in spreading HIV/AIDS and other sexually transmitted diseases in Southern Africa.

# **3. The data: A descriptive analysis**

The three datasets used for this study are nationally representative and come from the standard Demographic and Health Surveys (DHS) conducted in Lesotho (2004), Swaziland (2006-2007) and 6
suppliers affect the trade pattern. Levchenko (2007) argues that industries that rely heavily on a larger number of input suppliers are more vulnerable to holdup problems and are hence more sensitive to imperfect institutions. A substantial body of literature that followed these seminal papers confirms the role of institutions in explaining international trade patterns (see Nunn & Trefler (2014) for detailed review of the empirical literature).

Notwithstanding the growing body of evidence documenting the role of contracting institutions as a source of comparative advantage, our understanding of the underlying mechanisms is still rather limited. There are many potential ways that domestic institutions could affect trade patterns. For example, weak contract enforcement may simply affect firms’ decision to enter into foreign markets by increasing the cost of relationship-specific intermediate inputs - increasing the threshold of exporting (extensive margin). In addition to restricting export entry, weak contract enforcement can also distort the level of firm exports as firms need efficient and cheap supply of intermediate inputs to expand their export sales (intensive margin). Such institutional deficiencies could also affect the survival of firms in foreign markets since new exporting firms could later discover that the cost of contract enforcement makes an important component of profitability calculations, as a result of which some of them may have to exit sooner than one would expect.

To understand the operating forces and underlying mechanisms, one needs a micro-founded theoretical framework and cross-country firm-level data. Manova (2013) developed a heterogeneous-firm model by building upon Melitz (2003) to explore the mechanisms through which credit constraints affect international trade patterns. She finds that credit constraints substantially reduce exports both at the extensive margin and at the intensive margin. Our study follows closely the work of Manova (2013) but differs at least in two main respects. First, while the author focuses on financial frictions, we take interest in how limited contract enforcement affects different industries based on their contract-intensity. Second, we explore the trade effect by utilizing unique harmonized firm-level data that are ideally suited to studying these operating forces and mechanisms. Thus,
in what follows that the elasticity of aggregate economic activity to nighttime light is 0.25. This elasticity implies that the relative change in economic activity , is only a quarter of the relative change in nighttime light. The raw nighttime light data are derived from the VIIRS-DNB Cloud Free Monthly Composites, version 1, yy<sup>AA</sup> The raw nighttime light data are derived from the VIIRS-DNB Cloud Free Monthly Composites, version 1, made publicly available by the Earth Observation Group at the National Geophysical Data Center of the National Oceanic and Atmospheric Administration (NOAA).

The released data are already adjusted to correct for saturation, blooming effects, and comparability, all issues that had been detected in a previous nighttime lights product known as DMSP-OLS. The data are further cleaned for this paper, taking advantage of the 2015 annual composite of stable VIIRS nighttime lights to define a background noise mask (Elvidge et al. 2017). Only cells lying outside this background noise mask are treated as stable lights, while those inside the mask are recoded with value zero, corresponding to no light. The VIIRS-DNB data are available from 2013 to 2016 on a monthly basis, with a 11
characteristics of these households are significantly correlated with worse poverty indicators than those of other non-poor households, and they bear a strong resemblance to poor households not belonging to Set A (see Appendix). These results suggest that the consumption data systematically overestimate poverty in the South and in urban areas, with a corresponding underestimation of poverty in the Central and Northern regions and in rural areas. This pattern appears to be mostly due to price differentials, which are magnified by the fact that the share of consumption from household production is higher in the Central and Northern provinces and in rural areas than it is in the rest of the country.

For households in Set B, auto-consumption<sup>13</sup> prices for key items (maize [corn], rice, other cereals, cassava, groundnuts [peanuts], chestnuts, bananas and tomatoes) in the rural Northern and Central regions exceed both the purchase prices for these commodities and the auto-consumption prices defined for farmers in other areas; In sum, both of these surveys present significant evidence of measurement error: in 2002/03 and 2008/09, respectively, 27 percent and 21 percent of observations record a poverty status that is inconsistent with all other household characteristics. Among these, households in Set A are likely affected by underreporting of consumption, while the results for Set B appear to be skewed by the overestimation of prices for household production. In Section 4 we re-estimate these observations by using sample estimates based on the 1996/97 survey.

# **2.3 Using out-of-sample estimates to re-estimate unreliable observations**

The fact that the 1996/97 survey seems to be less affected by consumption underestimation and more consistent with other welfare indicators suggests that consumption-prediction models based upon it can produce a less biased fitted measure. Thus, for each survey (1996/97, 2002/03, and 2008/09) an identical consumption model has been estimated (Equation 1), the dependent variable for which is household per capita consumption deflated using the national
Figure 6: Expenditure Shares of Flour versus Subsidies per Capita <!-- Start of picture text --> 8 a. Expenditure share 50 b. Benefits per capita<br> Djibouti Djibouti<br> Iran, Islamic Rep. Iran, Islamic Rep.<br> Libya 40 Libya<br>6<br>30<br>4<br>20<br>2<br>10<br>0 0<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 0 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Percentiles of population Percentiles of population<br>US$-PPP<br>Flour expenditure share (in %)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

# Simulations of Subsidy Reforms

In order to simulate comparable reforms across countries, we consider a flat reduction of unit subsidies by 30 percent across all products and all countries. We measure the impact of these reforms on household welfare, inequality, and the government budget in this order. We also consider the cost for the government of compensating the population to reach the pre-reform level of welfare. The implied changes in prices of the proposed simulations are large for most countries and products, which makes the standard linear approach to subsidies simulations inappropriate. We therefore model the demand function using Cobb-Douglas preferences.<sup>2</sup>

## Welfare

Figures 7 and 8 show the impact on household welfare (measured in terms of household expenditure per capita). For each product in the figures we have two panels. The top panel illustrates the welfare impact in annual per capita US$-PPP terms. The bottom panel illustrates the welfare impact in terms of share of total household expenditure. Therefore, the top part of the figures is the absolute welfare effect, and the bottom part is
# **2 Background and Data**

## **2.1 Regional Trade Agreements**

Regional trade agreements, beyond eliminating trade barriers between countries, increasingly address non-trade policy areas in their content such as competition policy, intellectual property, and environmental concerns. Non-trade commitments within RTAs have proliferated in recent decades, both in number and in policy scope—the average RTA in the 1950s covered eight policy areas; in recent years they have averaged 17 (Hofmann _et al._ , 2017). The increasing number of policy commitments has been accompanied by an increase in regulatory and enforcement requirements. The changing nature of RTAs is documented in a new database by the World Bank detailing the content of all RTAs in force and notified to the World Trade Organization (Mattoo _et al._ , 2020). Since the signing of the Montreal Protocol in 1987, many RTAs have included policy commitments tied to the Montreal Protocol and/or ozone-depleting substances in general.

Our data on environmental provisions in RTAs have been collected as part of the broader World Bank project on the content of trade agreements and are described in detail in Monteiro and Trachtman (2020). This is the most extensive effort to date to document environmental provisions in trade agreements. The environmental provisions coded include environmental goals, specific commitments, compliance with multilateral environmental agreements, enforcement mechanisms, and external assistance and collaboration. Within this set of environmental provisions, there are two that address ozone depleting substances:

- Does the agreement require states to control ozone-depleting substances?

- Does the agreement require states to comply with the Montreal Protocol on ozone depleting substances?

Figure 1 displays the frequency of RTAs over time, as well as the inclusion of provisions related to ODS. There are 44 agreements that include at least one of the two provisions related to ODS.<sup>3</sup> The first trade agreements to enter into force that included ODS provisions were the Common Market

> `3` The list of trade agreements containing provisions on ODS use