rafmacalaba/data-use-mentions-extended huggingface dataset:
between income shocks and conflict is predictably strong. Miguel, et al. (2004) show that negative income shocks, instrumented by rainfall, have a large, positive effect on the probability of civil war in SubSaharan Africa. They emphasize that this region is appropriate for their test because it relies heavily on rain-fed agriculture. It is also appropriate, however, because in this region, armed groups are particularly unable to enforce claims to future rents. In settings where they can, income shocks would be expected to have a smaller effect.

In OECD countries, the armed forces are exceptionally well-organized and yet do not make large claims on economic rents. The conditions under which armed forces exercise restraint in predating on the population are another area where more research is needed. However, one promising explanation for this restraint is the level of organization of unarmed citizens. In these countries, the capacity for collective action by citizens, whether through well-established political parties or local governments, allows them to more easily resist military efforts to extract a share of future rents. It also allows them to punish leaders who tolerate undisciplined armed forces, however, an explanation for high levels of organization of security forces.

The evidence in Besley and Persson (2009) can be interpreted in a similar light. They demonstrate that higher prices for a country‟s commodity exports increase the risk of conflict only in countries with low scores on the Polity IV index of executive constraints. They take this as support for their argument that rents cause conflict when political checks and balances are absent that would compel governments to share rents with the opposition. The alternative interpretation the emerges from the discussion here is that weak executive constraints are an indication that citizens cannot act collectively to restrain leaders, so that leaders refrain from sharing rents with everyone, and not only the opposition. In such
One, Two, and Three 4

**Table S3.** Oster Test

|(1)|(2)|
|---|---|
|�<br>**_βc, β_(****_δ, Rmax_)**<br>�|**_δ|β_=0**|
|�<br>0_._023_,_ 0_._026<br>�|-12.00|

_Source_ : Author’s postestimation analysis based on table 1. _Note_ : Column 1 reports the identified set of elasticity estimates. _βc_ is the elasticity estimate obtained after adding all controls (column 4, table 1 while _β_ ( _δ, Rmax_ ) is computed at _δ_ = 1 and _Rmax_ = 0 _._ 9402. Column 2 reports the coefficient of proportionality that is required to explain away the treatment effect after adding the unobservables in a hypothetical regression.

**Table S4.** Effect over different program regimes

||(1)|(2)|
|---|---|---|
|_sinh_<sup>_−_1</sup>(_ed,t_)|2.342<sup>_∗∗_</sup>||
||[1.021]||
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=0||5.556<sup>_∗∗∗_</sup><br>[2.032]|
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=1||1.724|
|||[1.063]|
|All Controls|Yes|Yes|
|Observations|1340|1340|

_Source_ : Author’s analysis based on AWI data
Software is needed to develop digital innovations in its simplest form and with the help of software data are transmitted. In addition, more technology advanced transmissions of data over the internet are done with the help of cloud computing technologies which in themselves are a form of software. Hence, despite not entirely capturing how much data are really being used in sectors, using the intensity of each sector’s use of software is in our view the first‐best available proxy.

<mark>Figure 1 provides an overview of the data‐intensities for each sector calculated on the basis of non‐ capitalized expenditures of software.</mark><sup>3</sup> <mark>The data to construct these intensities are downloaded at various digits levels in NAICS given that the US Census records this information at mixed levels between 2‐digit and 4‐digit. All data are re‐concorded into the ISIC Rev 3.1 2‐digit level. Employment data are from the US Labor statistics and given in 6‐digit level and also re‐concorded into the ISIC Rev 3.1 2‐digit level. We have developed our own concordance matrix at the most disaggregated level between the two data sources and then aggregated up to 2‐digit level by taking the simple average. The reason for re‐classifying these data points is that our innovation variables are provided in 2‐digit ISIC Rev. 3.1. Since data are given at two different aggregations across the software and labor, we first concord all data into ISIC and then compute the intensities.</mark> The 15 sectors in Figure 1 show the ranking sectors that have the highest data‐intensities based on our proxy of software expenditure. Not surprisingly, telecommunication is the sector that shows the highest data‐intensity level and is therefore very software‐intense compared to labor. Other very high data‐intensity sectors are computer and insurance and finance, which is also unsurprising. They also use a high amount of software
aggregates in Lane and Milesi-Ferretti’s data might point to even greater financial integration of the South.

> 3 In appendix table 1, we consider a country to be a source (destination) of international investment if there are nonmissing data for at least one year in our sample period for that country as a source (destination) of that investment type. 4 For instance, the series “other investment” in the IIP data also includes some insurance and pension-related assets, such as technical reserves and entitlements (IMF 2009, table 6.1).

8
39 _Note: This figure shows return periods of 2 (top), 5 (bottom left), and 10 (bottom right) years using the benchmark model (without recently built infrastructure)._

# **_Modeling Exposure With Socioeconomic and GIS Data_**

Social and economic exposure depends on the geographic distribution of the poor and non-poor and of economic assets throughout the study area. However, detailed models of exposure do not exist for Ho Chi Minh City and even data about present conditions is tenuous. We have developed simple models of exposure given the kinds of data and projections that are available.

We use district-level estimates of population and poverty rates to calculate the exposure of the poor and non-poor in each district. GIS data from the Steering Center for Flood Control describes the distribution of semi-permanent and permanent buildings at the sub-district level. Assuming that the poor live in semi-permanent buildings and nonpoor live in permanent buildings, we can then map the exposure of the poor and nonpoor in each subcatchment.

For economic exposure, we use as a proxy indicator the contribution of each district to the city’s overall GDP, which can be approximated given available economic data. We again use GIS building data provided by the Steering Center for Flood Control to map subcatchment-level exposure. We assume that the fraction a district’s economic value that resides in a particular subcatchment is proportional to the fraction of the district’s permanent (i.e. wealthier) buildings that are in the subcatchment.

## **Poor and Non-Poor Exposure**

We use several variables to calculate poor and non-poor exposure. We first calculate PopulationDi, the population in the study area in each district Di.

PopulationDi = PopulationHCMC x PercentPopulationDi x PercentAreaDi where PopulationHCMC is the total population of Ho Chi Minh City; PercentPopulationDi is the percent of the total Ho Chi Minh City population in district Di; PercentAreaDi is the percent of district Di that is in the study area; Knowing the poverty rate PovertyDi in each district, we can calculate PoorDi and NonPoorDi,
we use household-specific inflation rates to account for the impact of differential price trends and household consumption baskets.

# **2.3 Key Assumptions and Limitations**

The simulation approach depends on certain key assumptions on the underlying income generation process of household, labor markets, and their propensity to consume.

The first assumption is that individual labor incomes grow at the nominal rate of the employment sector, implying that there is a full passthrough to workers via wages. The effective passthrough, , rate is period-dependent as it is the result of the composition of the economy and the capacity of workers to adjust their wages, with evidence suggesting countries with large informal sectors θθ and high inequality present lower passthrough rates as economic growth is more likely to be captured by wealthier, and non-poor, deciles (Tateno et.al., 2021).<sup>13</sup> We assume a passthrough rate of one since labor income is modeled independently and not directly transformed into consumption. Changes in this assumption will attenuate the impact of sectoral growth on (θθ= 1) poverty, but only with a small net impact.

The second modeling assumption is also related to labor markets and implies that individual workers remain in their respective employment sectors, with only wages adjusting in the direction of sectoral growth. Furthermore, we assume no shifts in the level of unemployment in response to crises and lower growth/constant sector-level labor share. While both these assumptions stem from data limitations, as the lack of a labor panel implies we cannot test for sectoral transitions, we believe that adjusting for these dynamics would not be associated with a substantial shift in our projections. As highlighted in Appendix Figure B1, the labor force participation across demographic and spatial groups (Panels E-F), as well as the distribution across sectors and by employment type (Panels A-D), has been stable over the years. This is likely linked to a highly degree of informality which absorbs shocks to the labor market and allows for relatively fast adjustment of wages in the
from citizens by reducing the uncertainty of their returns to monitoring (Devarajan et al., 2011). While this channel remains a possibility, it is difficult to rule out a countervailing mechanism, wherein dividends serve to acquiesce citizens and consequently reduce their oversight of government spending. Ultimately, the validity of this channel remains an empirical issue.

The second channel through which DDTs could improve governance is if a portion is subsequently taxed back by the government in order to fund public expenditure. This represents an explicit construction of the fiscal contract, and is more likely to result in keen citizen oversight of revenue management. Indeed, Devarajan et al. (2011) and Devarajan and Giugale (2012) even discuss the possibility that this mechanism could result in more public goods than the equivalent expenditure on direct public investments, as increased scrutiny may improve efficiency throughout the chain from public outlays to the end-point delivery of public goods. Conversely, direct expenditure on public investments would remain prone to leakage, a feature common in many institutionally weak African states (Reinikka and Svensson 2004).<sup>1</sup> Of the four broad options discussed above, only a combination of direct dividends and subsequent taxation is likely to mitigate rentier behavior, as no other option makes explicit demands of citizens to contribute to public revenues. Moreover, accountability mechanisms aside, there is good reason to believe that the DDT option may be the least exposed to political rent extraction, in part due to relative directness of the distribution channel. As Sandbu and Humphreys (2007) note, sovereign funds are prone to premature embezzlement unless accompanied by rigorous regulations. Government lending in poorly governed countries is also subject to corruption, (Khwaja and Mian 2005). To illustrate the case for DDTs, Moss (2011) observes that, in Ghana, Public Expenditure Tracking Surveys (PETS) found that between 50% and 80% of public spending was leaked through the system; by comparison, one million fraudulent recipients of direct transfers would represent the equivalent of only
TTBs to import products in a number of different industrial sectors, including sizeable shares of imports in textiles and apparel, metals, electrical machinery, plastics and rubber, and stone and glass (Karacaovali, 2011).

Table 3 presents a ranking of Turkey’s “top 10” TTBs in effect in 2011, by estimated size of impacted imports.<sup>18</sup> Not surprisingly, four of the top 10 of Turkey’s TTBs concern the global safeguards policy – a set of import restrictions that negatively affects multiple foreign export sources

> 18 These are upper bounds to the true amount of impacted trade given that this is based on bilateral import data at the 6-digit Harmonized System level and TTBs are frequently applied at a much more disaggregated level. Furthermore, while the approach takes care to base the estimates on bilateral data and application of policy, it does not adjust for the possibility that trade diversion from non-targeted sources may replace bilateral imports destroyed because of the imposed TTB.

15
sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01

# **4.2.3 Effects of interventions on coping mechanisms**

Table 8 presents panel Tobit estimates of the effects of social assistance and irrigation investments on food and nutrition security as measured by coping strategy index. The results indicate no significant combined effects from multiple program participation. However, receiving input subsidies alone has significant effect on a household’s ability to cope with food insecurity (Panel A & C). For example, households receiving input subsidies cope between 13-16 percent less with food poverty on average. Conversely, receiving food/cash transfers is linked to significant use of coping mechanisms. On average, beneficiaries of food/cash transfers cope 21-26 percent more with food and nutrition insecurity (p<0.05). A finding also noted in Table A6 where we use quantity of subsidized fertilizers received.

Table 8: Effects of interventions on coping with food insecurity

|Dependent var: CSI|Model2||
|---|---|---|
||Coef.|SE|
|Panel A|||
|Received subsidized farm inputs (1/0)|-0.135<sup>*</sup>|(0.074)|
|HH uses an irrigation scheme (1/0)|-0.318|(0.217)|
|Subsidized inputs*use of irrigation scheme (1/0)|0.267|(0.360)|
|Panel B|||
|Received subsidized farm inputs (1/0)|-0.115|(0.077)|
|Community has irrigation scheme (1/0)|-0.001|(0.108)|
|Subsidized inputs*community has irrigation scheme (1/0)|-0.094|(0.203)|
|Panel C||
Clemens and Hunt (2017), carrying out a Kronmal correction on the instrument used by Borjas and Monras (2017), find a statistically insignificant effect on unemployment.

# Refugees in Denmark

Foged and Peri (2015) studied the inflow of refugees from conflict areas to Denmark between 1991 and 2008 accounting for up to 4.7 percent of the labor force in 2008. The paper exploits labor market administrative data following individuals continuously over time in a panel setting and a refugee dispersal policy that allocated refugees across the country between 1986 and 1998. Using OLS and 2SLS models, the paper finds that immigration increases the complexity and wages of jobs for the low-skilled natives. An increase in refugee-country immigrants by 1 percentage point increases the complexity of native jobs between 1.3 and 3.1 percent and wages by 1-1.8 percent. The authors interpret these results as immigrants pushing natives to more complex and better paid occupations either within the same establishments or by migrating to other establishments. They also find that total labor supply of natives either increases or is stable. These effects are not very large when compared to the overall changes of these parameters over the period considered but they show complementarities rather than competition between immigrants and natives. Similar effects are observed for high-skilled natives. In this case, the effects are smaller in terms of occupational complexity but larger for wages. These results are also supported by the DD cohortmunicipality model proposed by the same authors. Those cohorts living in municipalities with higher immigration experience a larger shift towards more complex occupations and better wages with these effects persisting in the short and long-run. Young and low-tenure, low-skilled natives are also shown to respond to immigration with stronger transitions towards higher occupational complexity and better wages with no negative effect on employment supply.

# Refugees in the United States

Similar to Foged and Peri (2015), Mayda et al. (2017) look at the long-term labor-market impacts of refugee inflows and exploit a refugee placement policy in the country of destination. They study the impact of resettled refugees
</u> (LHS) <u>and the nearest road segment to each centroid (RHS).</u> To compute a large number of routes, I configure a server with the Open Source Routing Machine (OSRM)<sup>5</sup> and a copy of the Africa OSM from the 5th of June 2023, and compute fastest car routes between all cell centroids. This amounts to 12,016<sup>2</sup> _−_ 12,016 = 144,372,240 routes, for which I obtain both the road distance in _m_ and the estimated travel time in minutes. OSRM ’snaps’ cell centroids to the nearest road, yielding divergent route start/endpoints. Figure 3 shows the outcome of this process. For some cells in the Sahara, the Congo Basin, and Madagascar, colored in light grey on the left-hand side (LHS), OSRM cannot find the nearest road and returns ’NA’. For many further cells, the nearest road snapped to is outside of the cell, as shown on the RHS.

I clean the data by first mapping inaccessible cells to the nearest route start/endpoints. I then follow Graff (2024) in adding the spherical centroid-start/end distance to all routes. This increases the length of routes by 0.53% on average. I also adjust travel time estimates by assuming a travel speed of 10km/h on these straight passages, increasing travel time by 3.4% on average.<sup>6</sup>

> 4Large computer programs used for navigating (routing) through digitized transport networks.

> 5A high-performance open routing engine in C++ optimized for OSM. Website: https://project-osrm.org/.

> 6In this regard I differ from Graff (2024), which assumes walking speed of 4km/h. My main reason is that the centroid of such cells is unlikely of economic significance, and assuming a travel speed of
by 9%; older plants with above median capital intensity show no effect while those below show a contraction of 8.5%. More sophisticated young plants grow with proximity to the highway network, while sophisticated older plants, contract less. Plant size (employment) also follows the same pattern, albeit with less of an age “twist”.<sup>18</sup>

> 17 The only significant coefficient in the census scheme suggests this gradient as well with young firms, again an insignificant 4.3% and older firms with a significant 14.4% (Annex table 4).

> 18Young firms with above median skills are 7.4% larger and those below are 6% (insignificant), very similar to older firms. Those above average in labor productivity show increases of 9.2% and those below, 6.9%. A similar pattern appears, although again, more mutedly, for mature firms for skills, capital intensity and labor productivity. Similar effects appear for labor productivity for small firms (capital intensity), mature firms (skills) although signs flip for above and below average labor productivity suggest perhaps mechanical mean reversion effects dominating. Similarly, TFPR for small and mature firms shows the same pattern for capital intensity differences, and perhaps mean reversion effects for labor productivity.

20
Households in both the “Mother Leaders” arm and the “Mother Leaders and Affirmation” arm report experiencing significantly fewer dimensions of food insecurity over the past seven days, with the effect generally being the largest for those in the Cash + Mother Leaders arm. Interestingly, as seen in Appendix Table 2, the provision of cash by itself has no statistically significant effect on this measure of short-term food insecurity.

Finally, while point estimates all suggest that households in the three behaviorally enhanced arms all reported experiencing fewer months without enough food during the preceding 12 months than households in the cashonly arm, this effect is only statistically significant for households in the arm where the cash is supplemented by “Mother Leaders and Planning”. Once again (see Appendix Table 2) cash by itself does not lead to a statistically significant diminution in this measure of longer-term food insecurity (except in the specification where we are able to control for baseline values for the smaller sub-sample which was surveyed at both points in time). Overall, self-reported food insecurity is only significantly reduced when cash is supplemented by the Mother Leader groups and additional behavioral nudges.

# **6.3: Long-Term Outcomes**

As Table 5 shows, only the treatment arm where cash is augmented with the Mother Leaders groups shows statistically significant positive differences in overall child development as measured by the MDAT, as adapted to the Malagasy context. It is worth noting that the size of this effect is approximately as large as the effect from the provision of cash alone (see Appendix Table 3). Thus, the addition of the Mother Leaders treatment roughly doubles the effect on early childhood development as the provision of cash alone – a striking finding.

When we look at the component indices that make up the overall MDAT score, we see that children in several of the enhanced arms have significantly higher social skills than children in the cash-only arm. Once again, a comparison with the corresponding section of Appendix Table 3 shows that the magnitude of the effect from the enhancements is almost
1=yes)|0.118|0.139|0.109|0.114|0.030|0.025|-0.005|
||(0.323)|(0.346)|(0.312)|(0.318)|(0.078)|(0.067)|(0.062)|
|N|2382|539|532|1311|1071|1850|1843|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ HH stands for household. Calculation of food insecurity status based on self-experienced events of food insecurity, based on Household Food Insecurity Access Scale (HFIAS). MWS stands for microwatershed. Kebele is the lowest administrative unit in Ethiopia. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

7