rafmacalaba/data-use-mentions-extended huggingface dataset:
neighbors in the graph by calculating the haversine distance between the OD point and all other nodes in the graph.

In each travel simulation run, we compute the shortest path (i.e., fastest route) using the Dijkstra’s algorithm<sup>78</sup> (minimizing total travel time along the network) and record the travel trajectory, time, and distance of the optimal route between each OD pair. We leverage the free-flow travel speed for all road types that are currently available from the OSM data set and assign it to the road segments in the network. During the simulation process, we calculate the average travel time = ∑ after the simulation run and track its change ������ over time. When ∆ reaches a threshold nn =0.01s<sup>10</sup> , the simulation<sup>�</sup> nn ii=1 ii tt tt nn<sup>tth</sup> runs are suspended (Fig. 5b). This approach achieves a stable or highly converged solution with reasonable<sup>�</sup> nn nn−1 ∆= tt −tt εε computational effort for large urban road networks. The mean travel time across all clusters is 18.25 min (1,095.46 s) with a maximum of 545.39 min (32,723.47 s or 9 hrs.), a minimum of 1.77 min (106 s), and 23.22 min (1,393.26 s) as the standard deviation.

In general, we find that the average travel time and distance increase with population less than proportionately (Supplementary Figure 3). Specifically, when population doubles, average travel time and distance increase by a factor of 1.35 and 1.38 respectively. This is globally aligned with the key findings from Angel and Blei<sup>79</sup> which demonstrate that when US cities double in population,
The international equilibrium price, free of taxes and tariffs, is obtained by solving the market equilibrium conditions for each product: The supply and demand parameters are calibrated as follows: All supply and demand parameters are calibrated at the 6-digit level of the harmonized system using data provided by TRAINS (2010). With the estimates of _Ai_ and _Bj_ as supply and demand parameters, the model estimates the levels of imports and exports that arise because of the preferences contemplated in the offers originally submitted by AfCFTA countries.

32
likely to have access to _Bolsa Familia_ programs. However, on average, integration have continuously improved over the last 4 years, as Figure 13, shows, with Venezuelans being 0.7 times as likely to be registered as Brazilians in July 2020, while they were only 0.2 times as likely to be registered in December 2018, with an F-index of 17 in December 2018 and 42 in December 2020. Venezuelans still constitute a small fraction of the vulnerable people registered in _CadUnico_<sup>_ ́_</sup> , with 0.01 Venezuelans registered for each Brazilian in 2018, which increased to 0.10 Venezuelans registered for each Brazilian in 2020. Integration in _CadUnico_ varies across states. Relative probability and the F-index of registering in _CadUnico_ is the highest 40
AIDS among one of the groups that is most at risk—the Armed Forces and Police—is done through a special Committee called COPRECOS, supported by health information systems that allow for a continuous epidemiological vigilance for HIV/AIDS.

> 52 Enzyme-linked immunosorbent assay (ELISA). This test is usually the first one used to detect infection with HIV. If antibodies to HIV are present (positive), the test is usually repeated to confirm the diagnosis. If ELISA is negative, other tests are not usually needed. This test has a low chance of having a false result after the first few weeks of infection.

50
Jain and Jayachandran (forthcoming), who engaged adolescents in classroom discussions about gender equality.<sup>17</sup> Table A6 further shows that improvements in attitudes were larger among boys who enrolled in Grassroot Soccer, with an improvement in the Violence Attitudes index of 0.442 standard devia17Table A5 shows that these shifts in attitudes are concentrated among males who were already sexually active at baseline, precisely the group of males who would be perpetrating IPV and consistent with female reports of greater reductions of IPV among females who were already sexually active at baseline.

26
# **1. Introduction**

The occupational and sectoral choices of the millions of rural households in developing countries are important determinants of economic growth. In the context of traditional growth models where a rural economy is identified with activities solely in the agricultural sector, sectoral diversification is synonymous with rural to urban migration. However, the view that rural economies are purely agricultural has recently been questioned. Reardon et al. (2006) review survey evidence from a large number of developing economies and show that on average rural nonfarm income constitutes about 40 percent of household incomes. In Bangladesh, the share of nonfarm household income grew from 42 percent in 1987 to 54 percent by 2000 (Reardon et al 2006, Hossain, 2004).

Two types of factors, pull and push, induce households to diversify between farm and nonfarm activities. The pull factors include increasing demand, higher wage rates, and higher returns from nonfarm activities. High returns allow households to accumulate capital and invest in farm and nonfarm activities with high returns. This type of diversification requires access to credit, as well as physical, human and social capital and leads to higher growth.

Higher growth could also be achieved by focusing on a single occupation or sector. In this case household members can increase productivity and growth by learning from each other’s experience. For example, two members of a household, say a father and his son, jointly decide on which sector to enter in order to maximize household welfare. If the son enters the same sector as the father, he will be able to pick up the necessary skills faster as he learns from his father’s experience (Menon and Subramanian, 2008). However, lack of access to credit and capital, and the presence of idiosyncratic and uninsured risks may push rural households away from focusing on a specific sector into diversified activities, thus compromising growth.

In rural areas, one important push factor is year to year variability in rainfall and associated farm-income variability. Households may engage in nonfarm activities
more tradable sectors, have a higher fraction of foreign-currency denominated liabilities (Bleakley and Cowan (2008)). If this mechanism is empirically relevant, the structure of trade should naturally shift towards industries producing goods that oer a natural hedge against exchange rate uctuations in countries with high exchange rate volatility, or as a result of sustained changes in exchange rate volatility such as those resulting from exchange rate liberalization. This is the hypothesis that is formally tested in this paper using data on the composition of exports of 129 countries across 749 commodities during 1984-2000, and the correlation of these commodities' global-unit-values and each of these countries' nominal exchange rates.

The idea that the price of some goods may be correlated with uctuations in the nominal exchange rate has been present in the recent literature on commodity currencies (Cashin et al. (2004), Chen and Rogo (2003)), which are currencies whose value uctuates with the average price of the commodities exported by the country. Typical examples of commodity currencies are New Zealand and Australia. Because of this correlation, the price in local currency of these commodities and of any other sector whose price is correlated with them will be stable relative to other products. Thus, if uctuations in the local currency price of goods matter for trade and resource allocation, rms in those sectors should become relatively more important after a sustained increase in exchange rate risk.

The importance of a commodity's natural hedge emphasized in this paper as a determinant of trade patterns may depend on the availability of other forms of hedging. For this reason, the paper controls for the availability of currency derivatives to assess the quantitative importance of the mechanism we emphasize. Moreover, in as much income uncertainty will impact nancially constrained rms the most, one would expect that un-hedged exchange rate uncertainty will have a larger eect in countries with underdeveloped nancial systems.

The results indicate that exchange rate volatility matters relatively more for those sectors that lack a natural hedge and are more exposed to it.
|7.5|4.76|2.35|10.41|4.12|
|South Sudan|10.19|6.08|26.43|11.19|25.72|29.82|16.24|28.53|26.45|27.4|24.64|34.47|14.02|12.66|28.83|
|Sudan|14.5|23.13|11.24|16.94|11.55|22.91|12.48|23.65|11.93|11.13|20.34|19.55|16.41|14.93|11.1|
|Syrian Arab Rep.|||||7.26|9.8|22.19|15.21|7.39|13.55|4.96|8.89|14.47|11.03|21.09|
|Yemen, Rep.||3.77|18.45|15.28|23.15|11.87|7.84|11.28|21.82|15.38|7.41|17.58|7.78|9.51|5.85|
|FCS|9.55|13.92|13.99|11.4|10.38|10.9|9.87|9.34|10.6|10.27|10.34|10.44|9.12|10.52|8.1|

_Note:_ Figures are annualized standard deviations of month-on-month price changes in percentages. Monthly price data is maintained at the World Bank Data Catalog page associated with the paper. FCS is a geometric average of country rates. The 2021<sup>_∗_</sup> figures are based
indicators has implications for how we interpret the error terms in the relationship between observed indicator scores and the underlying concepts of governance.

The third respect in which these indicators differ is in the sample of countries they cover. A number of indicators cover a very large and broad sample of developed and developing countries (EIU, DRI, HFWSJ, PRS and WDR), while others cover very narrowly-focused samples of countries (PERC for Asia, CEER and FHNT for transition economies). Some indicators cover primarily developed countries but also include major developing countries (WCR, GALLUP, BERI). This difference between indicators is perhaps the most important for the empirical work which follows. There is by now considerable evidence that govemance on average tends to be better in richer countries. This implies that the distribution of governance is likely to be very different in indicators which cover sets of countries with different average income levels. These differences need to be taken into account when placing the observed data from various indicators into common units and combining them into aggregate govemance indicators.

In order to distinguish between indicators in this dimension, we construct a simple coverage index which measures differences between the distribution of countries across income and regional classifications and the distribution of all countries in the world across these categories. In particular, we divide the world into a two-way classification by region and income, following the World Bank's 1998 World Development Report. For each of the sources of govemance data, we report one-half of the sum of absolute -deviations between the share of countries in each of the 45 region/income categories in that source and in the world as a whole. By construction, this measure ranges from zero to one, with low values indicating more representative indicators. We report this number in the fifth column of Table 1. The five indicators covering the largest number of countries (DRI, EIU, HF, PRS and WDR) are substantially more representative according to this measure than the others, with a value of the coverage index of less than 0
sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

# **References**

- **Abraham, Rosa, Amit Basole, and Surbhi Kesar.** 2022. “Down and out? The gendered impact of the Covid-19 pandemic on India’s labour market.” _Economia Politica_ , 1–28.

- **Allard, Jenna, Maulik Jagnani, Yusuf Neggers, Rohini Pande, Simone Schaner, and Charity Troyer Moore.** 2022. “Indian female migrants face greater barriers to postCovid recovery than males: Evidence from a panel study.” _EClinicalMedicine_ , 53.

- **Arora, Varun, Sujoy Chakravarty, Hansika Kapoor, Shagata Mukherjee, Shubhabrata Roy, and Anirudh Tagat.** 2023. “No going back: COVID-19 disease threat perception and male migrants’ willingness to return to work in India.” _Journal of Economic Behavior & Organization_ , 209: 533–546.

- **Bandiera, Oriana, R Burgess, I Rasul, M Sulaiman, and A Vitali.** 2020. “Worker heterogeneity and job search: Evidence from a six-year experiment in Uganda.” _PEDL Research Paper_ .

10
.783***|
||(0.392)|(0.224)|
|Per capita HH expenditure aggregate per month|-0.001**|-0.000***|
||(0.000)|(0.000)|
|Food Consumption Score|0.003<br>|0.001<br>|
||(0.001)|(0.001)|
|Reduced Coping Strategies Index|<br>0.005*|<br>0.003**|
||(0.002)|(0.001)|
|Livelihoods Coping Strategy Index|0.005|0.002|
||(0.016)|(0.010)|
|At least 1 HH member can speak Turkish|-0.167*|-0.099*|
||(0.082)|(0.047)|
|At least 1 HH member can read Turkish|-0.058|-0.036|
||(0.086)|(0.048)|
|Does anyone in the HH read or write in Arabic|<br>-0.104|<br>-0.066|
||(0.136)|(0.078)|
|Main income from skilled labor|-0.357|-0.215|
||(0.237)|(0.133)|
|Main income from unskilled labor|<br>-0.315**|<br>-0.186**|
||(0.103)|(0.058)|
|Debt as share of total expenditure (among those with debt)|<br>-0.005|<br>-0.003|
||(0.013)|(0.008)|
- OPEC (2021). _Average annual OPEC crude oil price from 1960 to 2021_ . url: https://www. .

- statista.com/topics/1830/opec/

- Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman (2018). “Distributional National Accounts: Methods and Estimates for the United States”. In: _The Quarterly Journal of Economics_ 133(2), pp. 553–609.

- Pomeranz, Dina (2015). “No Taxation without Information: Deterrence and Self-Enforcement in the Value-Added Tax”. In: _American Economic Review_ 105(8), pp. 2539–69.

- Quinn, Dennis (1997). “The Correlates of Change in International Financial Regulation”. In: _American Political Science Review_ 91(3), pp. 531–551.

- Rodriguez, Francisco and Daniel Ortega (2006). “Are Capital Shares Higher in Poor .

- Countries? Evidence from Industrial Surveys”. In: _Working Paper_

- Rodriguez, Francisco and Dani Rodrik (2001). “Trade Policy and Economic Growth: A Skeptic’s Guide to the Cross-National Evidence”. In: _NBER Macroeconomic Annual_ 15, pp. 261–338.

- Rodrik, Dani (1997). “Trade, Social Insurance, and the Limits to Globalization”. In: _NBER Working Paper Series_ ( 5905).

- Rodrik, Dani (1998a). “Why do More Open Economies Have Bigger Governments?” In: _Journal of Political Economy_ 106(5), pp. 997–1032. doi: 10.1086/250038.

- Rodrik, Dani (1998b). “Why do More Open Governments Have Bigger Governments?” In: _Journal of Political Economy_ 106(15), pp. 997–1032.

- Romer, Christina and David Romer (2010). “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks”. In: _American Economic Review_ 100(1),
a look at the residual plot of the earlier estimates. They show moderate variations during these time periods, in particular since 1999.

The inclusion of the trend variables does not have a substantial impact on the significance level of the long-run elasticities, albeit the magnitude of the coefficients is moderately affected. While the schooling coefficient decreases minimally, the physical capital coefficient is augmented. The time trend for 1985 is significantly positive but there is a negative trend since 1999. Interestingly, both time periods are related to political events. 1985 is the transition year to civilian rule. 1999 is the election year of the Alfonso Portillo government, where compromised representatives of the former military nomenclature are suspected of wielding political power.

To the extent that this association is correct, a loose interpretation would suggest that in Guatemala the strengthening (weakening) of civilian rule has a significant positive (negative) impact on long-run growth. While at first sight this interpretation appears plausible, however, it is obvious that other factors are important as well. Moreover, the growth-enhancing channel of democratic rights might be operating indirectly on some independent variables, such as educational attainment. This complicates the analysis. Hence, further research is needed to strengthen this hypothesis.

# **_Alternative Capital Stock Data_**

Column 2 of Table 8 includes capital stock data with a 4 percent depreciation rate rather than the 5 percent thumb value assumed throughout this study. The data with 4 percent depreciation is essentially identical to the Nehru and Dareshwar (1993) capital stock series, despite some minor discrepancies? when compared with data from Banco de Guatemala? on investment. Assuming 4 percent depreciation of the capital stock has little impact on the results, although in the IV specification the significance of the capital coefficient is weakened. This suggests that a 4 percent depreciation is rather on the low side.

Column 3 includes the capital stock estimate built with disaggregated investment data originally constructed to compute the quality index for capital. This series is robustly correlated with growth. The long-run elasticities for physical and human
Procedures</mark>**

|Repaymentperiod|% breakdown|
|---|---|
|Weekly|0.2|
|Monthly|28.1|
|Quarterly|1.6|
|Yearly|62.0|
|At end of term|6.1|
|Other|2.1|

Source: Village Fund Survey, 2010.

The bulk (88%) of VFs have subcommittees, or designated committee members, to encourage loan repayment; nearly two-thirds (64%) allow overdue loans to be rescheduled; but just 6% are willing to consider further loans to individuals whose loans have been rescheduled.

VF officers and committees frequently complain that they do not get as much support as they would like from the regional or central office. Most say they would like help with bookkeeping (87%), management (83%), assessing the creditworthiness of borrowers (68%), or for other matters (98%). The need for maintaining and upgrading skills can easily be overlooked in such a highly decentralized organization.

25
This linkage between corruption, weak institutions, and offshores was also confirmed in a crossnational study of petroleum rents. Andersen et al. (2016) found that petroleum rents originating in countries with weak institutions (mostly autocracies) have a much higher probability of ending up in tax havens than those originating in non-autocracies. The authors suggest that “unanticipated increases in petroleum rents are partly captured by political elites and transferred to private bank accounts in havens, either directly or through sham corporations based in other havens” (Andersen et al. 2016, 28). They find a similar pattern at work in these polities in the face of political instability: elites transfer part of their domestic wealth to tax havens prior to elections and coups d’etat (Ibid.). Besides petroleum rents, offshores enable laundering the proceeds from other types of corruption and illicitly obtained wealth. Another recent study by Andersen et al. (2020) reveals that foreign aid disbursements from the World Bank and the Bank for International Settlements to poor countries coincide with significant increases in the value of bank deposits in secrecy jurisdictions. This study suggests that foreign aid might be diverted by economic and political elites into privately held accounts in jurisdictions that enable anonymity and non-transparency, specifically places like Zurich, a wellknown center for private wealth management (Ibid., 3).

The link between political instability and capital flight to secrecy jurisdictions is confirmed by the study of the patterns of financial flows in Ukraine. Earle et al. (2019) reveal that the Ukrainian oligarchs who are better connected politically tend to be more secure about their assets relative to those less connected (or associated with the competing political group). Their perception of the security of their assets is demonstrated by the wealth-hiding patterns: those less politically connected tend to use secrecy jurisdictions to a greater degree than the rest of the oligarchs. Ukraine’s 2004 Orange Revolution that resulted in the competing political group coming to power provided a context approximating a natural experiment that enabled this team of researchers to trace the
levels of productivity. However, these productivity gains are somewhat offset by a lack of worker reallocation into more-productive firms, at least in the short run. One year after the minimum-wage increase, a minority of workers from exiting firms were employed in formal firms, and indicative evidence suggests that most had exited the labor force or were unemployed.

The paper also shades new light on the long-standing debate on the employment effects of the minimum wage. To the best of our knowledge, this is the first paper to explicitly distinguish between employment effects originating from employment cuts made by surviving firms (the intensive margin),

> 3 To the best of our knowledge, recent exceptions are Mayneris, Poncet, and Zhang (2018), who focus on the impact of a minimum wage increase on value added, productivity, and profits in China. Hau, Huang, and Wang (2016) also examine the substitution of labor for capital by Chinese firms in response to the minimum wage.

> 4 To the best of our knowledge, the only recent paper using employer-employee linked dataset to look at the impacts of the minimum wage in the context of a developing economy is Engbom and Moser (2021), although their dataset does not include detailed information on firm outcomes as in this paper. In the context of an advanced economy, Dustmann et al. (2020) also look at the joint effects of the minimum wage on firms and workers.

> 5 Prior contributions looking at the minimum-wage impacts on firm exits are restricted to the restaurant industry in the US (Aaronson et al., 2018; Luca and Luca, 2019; Ashenfelter and Jurajda, 2021).

> 6 Dustmann et al. (2020) have recently looked at this question in the context of Germany.

4
However, there have been recent innovations in how job training and job search programs in developing countries are designed, targeted, and implemented.<sup>1</sup> We argue that there is a real, if more limited, role for government in providing these programs. For example, government action may be warranted when private firms are underinvesting in training of workers due to the possibility workers will leave, and when search and matching frictions slow down or prevent the reallocation of workers across sectors and geography that are critical for structural transformation. Equity concerns may also justify the use of these policies to help disadvantaged jobseekers find jobs. We begin with an overview of the worse and better reasons why governments in developing countries have become involved in job search and job training. We then outline lessons from recent experiences in developing economies with vocational training, policies designed to overcome spatial and informational search frictions, interventions aimed at overcoming psychological barriers to job search, and government efforts to encourage the development and use of online jobs platforms.

# **Good and Not-So-Good Reasons for Governments to Provide Job Training and Search Assistance**

Firms typically have strong incentives to find and retain good workers, and many workers likewise have strong incentives to look for jobs and seek training in the skills demanded by the market. As a result, the vast majority of jobs are filled without government intervention. Table 1 illustrates this point, using some of the relatively few available labor force surveys that directly ask employed workers in developing countries how they found their jobs. The vast majority of jobs are found by workers either learning about jobs through their social networks of friends and relatives, or through workers directly approaching employers in their business or worksite and asking if they have openings. Public (government) agencies and private employment agencies are

> 1 We focus on job training and job search assistance as two of the most common types of active labor market policies undertaken by governments. There are several other types of active labor market policy that we do not cover