rafmacalaba/data-use-mentions-extended huggingface dataset:
Bangladesh, Egypt, Indonesia, Pakistan) where the women’s questionnaire was only asked of evermarried women, we use the current age from the household roster, the day and month of the interview, and randomly assign a month of birth to calculate the year of birth.

> 13 We used the DHS weights, which account for the sampling strategy (selection probability) and household response rate in the stratum in order to be representative (Rutstein & Rojas, 2006). We calculate the share of the population (all individuals) in each second-level administrative geography and multiplied this by the national

7
vehicle use. A second car usually brings additional use; this not only substitutes the restricted trips but also expands the possibility of using the entire car capacity for other purposes. On the other hand, used cars tend to be less fuel-efficient and more polluting than new ones.<sup>32</sup> In Bogotá, the lifetime of the TWC has already expired for some used cars, and more than 50 percent of the regular cars do not have a TWC. Thus, the use of old cars may have contributed to the lack of decline in CO for a given total stock of cars.

# VII. TRANSPORT COSTS

Here I provide some calculations of the transport costs imposed by PYP. As Davis (2008) indicates, one might consider a model that takes into account people’s transportation choice set to analyze the costs induced by suboptimal choices due to driving restrictions. In that model, individuals derive utility from air quality, a nonpolluting composite consumption good, and transportation goods. Under the assumption that the marginal utility of the composite good is constant, the social costs imposed by the program would be equal to the willingness to pay (WTP) to avoid the restriction (see Davis 2008). WTP values are not available for Bogotá, but recently, Blackman et al. (2015) used contingent valuation to obtain the WTP for being exempt from HNC in Mexico. Analogous to the benefit transfer approach, I used Blackman et al.’s WTP values to estimate the driving restriction costs of the drastic PYP phase. Costs are not computed for the moderate phase as Blackman et al.’s estimates were obtained in 2013 when HNC had been in force for many years—that is, WTP is conditional on households’ past adaptation investments. This setting is much closer to the drastic phase as households already had some experience facing restrictions.

32. Basic statistics reported by Secretaría de Movilidad (2009) show that used vehicle sales increased four times more in the first month of the drastic phase
. The increase in effect size from the monsoon and kharif seasons to the start of the rabi season may be a consequence of rainfall, that predominantly falls during the monsoon, stored in surface infrastructure having had more time to percolate into the aquifers by the start of the rabi season.

We can also examine the impact of MNREGA implementation using continuous measures for the number of each type of project a given state implemented. In Table A4 we show a continuous measure of the quality of MNREGA implementation - the cumulative total of conservation or irrigation projects completed - that yields similar results to our binary treatment indicator. An additional 100,000 completed conservation projects reduces depth to groundwater by between 4% and 6% depending on the season. Irrigation projects reduce depth to groundwater by a similar 4% to 7%.

> 16These problems are exacerbated by using smaller units of government administration, such as blocks or villages (e.g. Gulzar and Pasquale (2017)).

> 17We also test if districts in different phases of the MNREGA had significant differences in the number of groundwater extraction structures prior to the program. The Fourth Minor Irrigation Census conducted in 2006-07 provides a count of all dug wells and tube wells present in the country. Using these data, we do not detect significant differences in pre-program well characteristics (Table A3).

> 18These results and all of our subsequent difference-in-difference estimates are based on implementing the methodology described in Callaway and Sant’Anna (2021) using their Stata package `csdid` .

9
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

# 4. EMPIRICAL ANALYSIS

This section summarizes the results of the analytical methodology described above. The specifications and the estimation techniques have been systematically adapted according to the different assumptions characterizing each model. The baseline model estimates the average empirical associations for all the countries included in the dataset, and is then adjusted to assess heterogeneity between country income groups. Finally, the model was tested on a sub-sample of resource-rich countries<sup>15</sup> , with a view to test the robustness of the empirical association.

## 4.1 The Baseline Model: Pooling All Countries

Table 6 presents the results from the recursive baseline model. The two equations have been estimated separately using a fixed-effect estimator and simultaneously using SURE plus country-level dummy variables. We find that, on average, nonrenewable resource dependence is negatively associated with both income inequality and GDP per capita. In the simultaneous estimation both coefficients increased because of the correlation between the error terms of the two equations.

Table 6: Estimated Results from the Recursive Model Specification (baseline model)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc **|**Gini index**|**GDPc**|
|Non Renewable resource dependence|-0.059**|-0.087**|-0.149***|-0.132**|
|High-low education ratio|-0.639***|-|-0.203|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (2005)
relationship disappears when industry dummies are included, indicating a spurious effect in the previous regression. However, the strong negative relationship with firm size persists, providing evidence in favor of our framework. Separate regressions by industry (not reported) yielded similar results.

# _4. Firm dynamics_

To fully test the dynamics of our framework would require longitudinal data. Though the ENAMIN is the most comprehensive data source available to date, it lacks this dimension. Nonetheless, the cross sectional evidence it does offer is consistent with our framework. First, though the data is truncated at a firm size of five employees (fifteen in manufacturing), long lived firms exist across the revenue distribution, reflecting the underlying distribution of 0. Remarkably, average firm age is roughly the same across all deciles of the revenue distribution, ranging from a low of 7.9 years for the seventh decile to a high of 9.0 years for the second decile; the first and last deciles have respective means of 8.8 and 8.7 years.<sup>22</sup> Moreover, the relatively uniform revenue-age distribution is not an artifact of the upper limit on number of employees: average firm revenue in the last revenue decile is more than eighty times larger than the first revenue decile. This suggests that this population of firms may be in a steady state, with entry and exit rates roughly uniform across the revenue distribution.<sup>23</sup> Second, the observed patterns of firm entry and exit are consistent with those predicted by our framework and with those observed in the U.S. and in other developing countries. Numerous studies have documented high entry and failure rates among startups that decline with size and age of the firm.<sup>24</sup> Evans and Leighton's (1989) study of self-employment dynamics in 22 The difference in average firm age between the second and seventh deciles is statistically significant at a five percent level of confidence. The difference between the first and
|0.285|Regression coefcient_log_(_TFPR_)vs_log_(_TFPQ_)<br>|
|_τ _<sup>_e_</sup>|1.6|Average frm size in Ghana’s manufacturingsector relative to the U.S.|

_Source:_ The average size, the employment concentration at the top 10 largest firms, and the average size ratio between 21-25 year old and 1 year old firms in U.S.’ manufacturing are drawn from the BDS database for the year 2007. To make the U.S. data comparable with Ghana’s formal Manufacturing Census, from which we infer idiosyncratic distortions and entry barriers, we compute statistics conditional on firms with ten workers or more, which is the smallest firm size covered in the Ghanaian data. The average firm size in Ghana’s informal sector is computed from the full establishment census. The _log_ ( _TFPR_ ) and _log_ ( _TFPQ_ ) are computed exactly as in (Hsieh & Klenow, 2009), estimated from Ghana’s National Industrial Census of 2003. The entry barrier _τ_<sup>_e_</sup> is drawn directly from (Fattal-Jaef, 2022)’s estimate for Ghana.

# **6.2 Quantifying the Role of Entry Barriers and Idiosyncratic Distortions**

Our main quantitative experiment involves starting from a stationary equilibrium calibrated to the U.S. economy and introducing the measures of distortions targeted to Ghana. Can the distorted stationary equilibrium resemble the micro-level features of the Ghanaian economy? In particular, can the extent of informal production and its implied consequences on the shape of the size distribution generate a missing middle as in the data? If so, what are the aggregate implications?

Consider first the effect of the various combinations of distortions on the shape of the size distribution, reported in figure 15. The top-left panel replicates the model’s representation of the U.S. economy’s size distribution, an outcome of the calibration. The bottom right panel illustrates the size distribution
Second, as we observe investment from both the EU and the US, we can include destination country-time fixed effects j to control for any pull factors such as business-friendly reforms in destination markets . Finally, we include country-pair fixed effects to control αα for any time-invariant characteristics determining bilateral investment between and . jj DD jj

# _2.3. Data_

Information on bilateral investment comes from the Financial Times fDi Markets database. The data cover cross-border greenfield investment announcements, including new projects and the expansion of existing projects, at a monthly frequency. In addition to the number of projects, the database provides information on the value of investments. An important feature of the database is that it also tracks US inter-state investments, which allows us to study the impact of recent shocks on reshoring activity by US multinationals.<sup>5</sup> The fDi Markets data have been used extensively in recent research projects and for surveillance purposes by policy institutions, showing that the data tracks well official FDI flows (Aiyar et al., 2023; Alfaro and Chor, 2023; IMF, 2023; Toews and Vézina, 2022; UNCTAD, 2023). The data also cover information on the number of jobs associated with greenfield investment announcements. Toews and Vézina (2022) show that, in the case Mozambique, fDi Markets data is correlated with census data across different sectors, cities, and time but underestimates the total number of jobs created.

The greenfield investment data is combined with standard gravity variables from the CEPII’s gravity database. These variables include the bilateral geographical distance and information on whether countries share the same language, border, or colonial history. Finally, we also construct an indicator variable to capture if country-pairs are part of the

> 5 An important caveat is that the data does not cover intra-US state greenfield investments, which may account for a large share of greenfield investment at the state level.

8
), the average welfare loss caused by US sanctions on Iran over the period 1984-2005 was around $80 million, less than 1% of Iranian GDP over that period.

> 15Education has expanded in Middle Eastern and North African (MENA) countries faster than in any other region of the world (Source: World Bank). Some countries such as Iran, Turkey, Egypt, and Jordan experienced more growth in education.

> 16The youth literacy rate is the percentage of people ages 15 to 24 who can read, write, and understand a short simple statement about their everyday life.

> 17Based on the article 30 of the Constitution of the I.R. of Iran, “the government is obliged to provide free of charge education for all individuals up to the end of the secondary level of education and to facilitate free higher education up to achieving self-sufficiency” (Source: UNESCO, the World Education Forum report for Iran (2015)).

> 18Tertiary education was nearly all public until the 1980s. In 2006, about half of all university students were enrolled in public universities.

> 19Only 10% of students who take the university entrance exam, win that scholarship.

> 2058% of pre-university students receive private tutoring, which is a significant item in households’ education expenditure (52%), to increase their probability of success at the university entrance examination (source: calculation by the author using HIES).

> 21The Ministry of Education’s school ranking is based on teachers’ quality standard, level of social and cognitive activities, school facilities and environment, school management, etc.

8
# **1 Introduction**

How does extortion affect political behavior? Extortion not only is a form of economic hardship; it also can be a form of traumatic victimization. Yet, existing literature has reached mixed conclusions about the relationships between political engagement and both economic hardship and victimization. For example, socioeconomic shocks may generate grievances and lead to increased turnout, or they may reduce participation because people have fewer of the resources required to engage in politics (e.g. Brady et al., 1995; Burden et al., 2017). Similarly, some research suggests that people exposed to violence will be more pro-social and thus more politically engaged (e.g. Bateson, 2012; Bauer et al., 2016). On the other hand, victimized individuals may lose faith in governmental institutions and participate less in public life (e.g. Collier and Vicente, 2014; Ley, 2018). In the aggregate, these studies may have varied results partly because of the difficulty disassociating traumatic experiences, whether related to violence or economic costs, from the economic and security environment in which the traumatic experiences occur. This environment can itself influence or correlate with unobserved factors shaping victim behavior. For example, violence and economic coercion are more likely to occur in places with high crime rates, and local criminal networks may shape how and to what degree one is willing to take civic action.

In this paper, we leverage unique data from Guatemalan deported migrants to disentangle the effect of extortion on political behavior from the potentially confounding local context in which extortion occurs. Deported migrants are a unique population for this inquiry because deportation involuntarily moves individuals, changing their local political and behavioral context so that it is distinct from where the extortion occurred. Furthermore, we show that extortion experienced by coyote-using migrants is quasi-random. Thus, we use the variation in experiences of extortion during the migration journey among individuals returned to Guatemala by the U.S. government to better isolate the relationship between extortion and civic engagement.
Krafft, C., Assaad, R., Marouani, M. A., Cheung, R., & LaPlante, A. (Forthcoming).

Are labour markets in the Middle East and North Africa recovering from the COVID-19 pandemic? _IZA Journal of Development and Migration._ Krafft, C., Razzaz, S., Keo, C., & Assaad, R. (2019). The Number and Characteristics of Syrians in Jordan: A Multi-source Analysis. In C. Krafft & R. Assaad (Eds.), _The Jordanian Labor Market between Fragility and Resilience_ (pp. 173–202). Oxford University Press. OAMDI. (2021). _COVID-19 MENA Monitor Household Survey (CMMHH)_ (Version 5.0 JordanCMMHH) [dataset]. http://www.erfdataportal.com/index.php/catalog.

OAMDI. (2022). _Survey of Young People in Jordan—UNICEF_ (Version 1.0) [dataset]. Egypt: Economic Research Forum (ERF).

Obi, C. T. (2021). _The Impact of Living Arrangements (In-Camp versus Out-of-Camp) on the Quality of Life: A Case Study of Syrian Refugees in Jordan_ . https://doi.org/10.1596/18139450-9533 Razzaz, S. (2017). _A Challenging Market Becomes More Challenging: Jordanian Workers, Migrant Workers and Refugees in the Jordanian Labor Market_ .

Ruiz, I., & Vargas-Silva, C. (2013). The Economics of Forced Migration. _Journal of_ _Development Studies_ , _49_ (6), 772–784. https://doi.org/10.1080/00220388.2013.777707 Ruiz, I., & Vargas-Silva, C. (2017). Are Refugees’ Labour Market Outcomes Different from Those of Other Migrants? Evidence from the United Kingdom in the 2005-2007 Period: Refugees and Other Migrants Labour Market Outcomes: Evidence from the UK. _Population, Space and Place_ , _23_
in favor of the developed economy. On the developing economy’s side, Li and Liu (2015) and Zi (2014) developed models in which LBE drives gains for the host countries. This relates to an older strand of literature that examined the effects of the North American Free Trade Area. Most prominently, Feenstra and Hanson (1996) showed that offshoring and host countries benefit from production fragmentation since the average skill intensity rises in both countries when the initial technology gap between the two is sufficiently large.

There are various ways of measuring offshoring, but in most cases, it is the share of imported inputs as percentage of total inputs, value added, or output. Since these measures are typically based on inputoutput tables, these types of studies are often performed at the industry-level. However, the focus was more on the labor market effects. Shifting the focus on productivity growth, Egger and others (2001) found that materials offshored to Eastern European countries had a significantly positive impact in 20 Austrian manufacturing industries.

In the 2000s, this stream of literature gained momentum when the focus shifted on services offshoring and its impact on TFP or labor productivity. Several studies at the sectoral level found evidence that services offshoring significantly increases productivity, while the effect of materials offshoring is smaller or insignificant: including Amiti and Wei (2006, 2009) for U.S. manufacturing between 1992 and 2000, Crino (2008) for nine EU countries between 1990 and 2004, Winkler (2010) for German manufacturing industries covering the period 1995–2006, and Michel and Rycx (2011) for Belgian manufacturing industries between 1995 and 2004. The last study also examined the impact of inter-industry spillover effects, but only found little evidence of it. Other studies, by contrast, only found significant productivity gains from materials offshoring, including Daveri and Jona-Lasinio (2008) for Italy between 1995 and 2003 and Ito and Tanaka (2010) for Japanese manufacturing over the period 1988–2004. Focusing on low-skilled workers, Egger and Egger
indicators affect changes in winter crop area; in fact, reductions in winter crop area in VCs unaffected by conflict are higher (though not significantly so) than in those affected by conflict. Determining whether the decline in winter crop area is due to conflict-related shortages of labor, fuel, or other inputs needed to carry out the necessary crop management activities or climatic factors will require econometric analysis.

As the literature suggests that for winter wheat, satellite-based estimates of NDVI can reliably approximate yield,<sup>16</sup> we use the crop masks obtained from the above land cover analysis for 2019-22, respectively, to compute NDVI on all fields planted to winter wheat for each of the 10-day periods ending on May11, 21, 31, and June 11 and aggregate these values at village level. Calculations were performed on the Google Earth Engine cloud computing platform.<sup>17</sup> Figure 3 illustrates NDVI densities for different periods in each year. For all years except 2021, peak NDVI values are attained in the first 10 days of June. Tabulating VClevel figures in panel C of table 2 confirms that NDVI in 2022 was uniformly lower than in earlier years.

# **3.3 Weather conditions**

<mark>To control for time-varying effects that are unrelated to conflict, we use information on precipitation as well as minimum and maximum temperature derived from the United States National Centers for Environmental Prediction Global Forecast System (GFS) that produces weather forecasts every 6 hours. For the 2019-22 growing seasons, we use the first 6-hour interval of each forecast as a “nowcast” to derive daily aggregated values a</mark> t rayon level using GEE scripts <mark>. Temperature data is then converted into growing degree days using the sinusoidal distribution as suggested by d'Agostino and Schlenker (2016).</mark> Data on cumulated GDD>0 days computed based on daily values of _Tmin_ and _Tmax_ in figure 4 illustrates a marked seasonal pattern and clear differences across years.
0.13|0.15|0.10|0.09|
||**Totals and weighted averages**|**11,504,336**|**9,575,226**|**4,055,836**|**5,639,889**|**0.26**|**0.37**|**0.17**|**0.25**|
||**Unweighted averages**|**547,826**|**455,963**|**193,135**|**268,566**|**0.17**|**0.23**|**0.12**|**0.20**|

Notes: Descriptive statistics taken from National Sample Statistics.
# Table A1: Livelihood Diversification Indices Summary

<!-- Start of picture text --> Standardized<br>Index Time<br>Across Description Pre-COVID-19 Kernel Density Graph<br>Type Period<br>Countries<br>Ethiopia Malawi<br>4<br>4<br>For this index we collapse variables into income 32 32<br>categories that are consistent across rounds within a 1 1<br>Pre- and country but vary across countries. As a result, this 0 .2 .4 .6 .8 1 0 0 .2 .4 .6 .8 1<br>Post- index allow us to observe income sources at the most<br>Fraction No<br>COVID- granular level available over multiple waves for each 4 Nigeria 4 Uganda<br>19 country individually. This fraction index considers 10 3 3<br>income source categories in Ethiopia, 7 in Malawi and 21 21<br>Nigeria, and 8 in Uganda. 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Specialization Index Specialization Index<br>Density<br>Density<br><!-- End of picture text --> Continued on Next Page...