rafmacalaba/datause-encoder-data huggingface dataset:
|Table 4:|Impacts on|households’ li|ght sources a|nd expendit|ures (OLS)||
|---|---|---|---|---|---|---|
|Dependent variables:|Per-day h|ours using lig|ht sources (s|um of all ow|ned & zero if|not own)|
||Flashlight<br>(rainy)|Flashlight<br>(rainy)|Flashlight<br>(of-rainy)|Flashlight<br>(of-rainy)|Kerosene<br>lanterns<br>(rainy)|Kerosene<br>lanterns<br>(rainy)|
||(a)|(b)|(c)|(d)|(e)|(f)|
|All products|-0.234***|-0.232***|-0.331***|-0.326***|-0.714***|-0.717***|
||(0.073)|(0.072)|(0.106)|(0.104)|(0.103)|(0.114)|
|One product|-0.060|-0.057|-0.028|-0.028|-0.546***|-0.546***|
||(0.090)|(0.088)|(0.134)|(0.128)|(0.110)|(0.116)|
|All = one (p-val.)|0.016|0.015|0.004|0.003|0.037|0.043|
|All + one = 0 (p-val.)|0.054|0.054|0.111|0.103|0.000|0.000|
|Mean _y_ (control)|0.316|0.316|0.460|0.460|0.767|0.767|
|R-squared|0.052|0.060|0.061|0.069|0.148|0.179|
|No. of obs.|847|847|847|847|847|847|
|Dependent variables:|Per-day h|ours using lig|ht sources (s|um of all ow|ned & zero if|not own)|
||Kerosene|Kerosene|Kerosene|Kerosene|Kerosene|Kerosene|
||lanterns|lanterns|lamps|lamps|lamps|lamps|
||(of-rainy)|(of-rainy)|(rainy)|(rainy)|(of-rainy)|(of-rainy)|
||(g)|(h)|(i)|(j)|(k)|(l)|
|All products|-0.925***|-0.928***|-3.708***|-3.713***|-4.458***|-4.464***|
||(0.117)|(0.126)|(0.194)|(0.188)|(0.270)|(0.256)|
|One product|-0.713***|-0.713***|-1.586***|-1.561***|-1.692***|-1.667***|
||(0.129)|(0.131)|(0.210)|(0.201)|(0.213)|(0.206)|
|All = one (p-val.)|0.032|0.035|0.000|0.000|0.000|0.000|
|All + one = 0 (p-val.)|0.000|0.000|0.000|0.000|0.000|0.000|
|Mean _y_ (control)|0.974|0.974|4.265|4.265|5.192|5.192|
|R-squared|0.145|0.170|0.459|0.484|0.448|0.468|
|No. of obs.|847|847|847|847|847|847|
|Dependent variables:|||Log of ex|penditures|||
||Education|Education|Kerosene|Kerosene|Total|Total|
||(m)|(n)|(o)|(p)|(q)|(r)|
|All products|0.018|0.016|-1.301***|-1.313***|-0.055***|-0.059***|
||(0.040)|(0.034)|(0.127)|(0.135)|(0.020)|(0.014)|
|One product|0.004|0.036|-0.693***|-0.686***|-0.032*|-0.016|
||(0.052)|(0.049)|(0.069)|(0.072)|(0.018)|(0.017)|
|All = one (p-val.)|0.791|0.659|0.000|0.000|0.361|0.035|
|All + one = 0 (p-val.)|0.772|0.476|0.000|0.000|0.003|0.002|
|Mean _y_ (control, not log)|3135.69|3135.69|1600.50|1600.50|81279.41|81279.41|
|R-squared|0.115|0.220|0.417|0.439|0.091|0.474|
|No. of obs.|842|842|631|631|847|847|
|HH baseline control|No|Yes|No|Yes|No|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each village. (3) The baseline controls include household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

28
# **4 Roads and Armed Conflict**

## **4.1 Hypothesis and Descriptive Evidence**

While we focus our analysis on PRIO-grid cells, we first examine the broad correlation between violence and road projects at the provincial level. The mainstream assumption is that road quality is a proxy for government reach and, therefore, correlates with a reduction of violence and increased ‘peaceful’ economic activities (see discussion in sections 2.1 and 2.2). That is, improved connectivity in areas of operation of government versus rebel groups leads to a reduction in violence (M ̈uller-Crepon et al. 2021). But there is also the obverse hypothesis, namely, that all-weather roads are spaces where armies and rebels concentrate extraction (and competition for extraction), increasing the likelihood of violent incidents, meaning that remote/rugged areas and areas with bad roads experience fewer incidents (Nunn and Puga 2012; Gonz ́alez et al. 2023). Given the complicated and convoluted landscape of armed actors in Eastern Congo (Stearns 2022), road rehabilitation interventions may only temporarily reshuffle patterns of violent incidents, as Schouten et al. (2022) have hypothesized (cf. Bachmann et al. 2022a).

Note that some of the earlier results leading to opposite hypotheses could come from different thresholds of road access at which impacts on violence were estimated. In principle, no region is ever fully isolated: even if roads are completely impracticable, people still manage to go around on foot–and much of Congolese army and rebel movements rely on such ’unconventional’ logistics (Schouten 2022). If a region is therefore never completely insulated, there could be a reduction of violence as lower-quality roads see improvement. From an econometric standpoint, this is a question of whether we observe correlations of roads and violence _within_ or _between_ regions or provinces. As regards the regional (or between) variation, the positive relationship between roads and violence at the province level is shown in Figure 5. This positive correlation is not entirely surprising, given the relationship between mines, roads, and violence discussed in section 3.4. If roads facilitate mining activity and mines attract violence, then this can establish a positive association in the cross-section.

Given a significant cross-sectional bias, it is, therefore, important to look at the time variation. When we look at the available time variation at the country and provincial levels, the picture changes dramatically. One of the first pieces of evidence is provided in Figure 9, which shows a negative association between completed road projects and violence. Years in which many road projects are completed in the DRC and provinces tend to be more peaceful than years with fewer completed projects.

18
been sufficiently tested. The hybrid rules will need to be further assessed using cost-benefit analysis and feedback from lenders. Hybrid systems should in any case include a maximum period (as in the case of Morocco) to avoid too much uncertainty for guarantee users.

**Table 11: Payment Rules in MENA**

|Iraq|Single payment 30 days after the bank initiates legal procedures.|
|---|---|
|Jordan|After the bank initiates legal procedures to recover the debt. Single payment once the<br>claim is presented and validated.|
|Lebanon|Payment 90 days after 3 unpaid installments. Single payment once the claim is presented<br>and validated. The guarantor is mainly responsible for recovering the collateral|
|Morocco|Advance payment of a 50% once the claim is presented and validated, followed by the<br>balance once legal procedures have been exhausted (maximum 3 years)|
|Palestine|Single payment after six months from default date|
|Saudi Arabia|After a fixed number of days following default. Single payment once the claim is<br>presented and validated|
|Syria|Money is deposited in an escrow account at the bank until the legal procedures are<br>exhausted.|
|Tunisia|Advance payment of a 50% once the claim is presented and validated, followed by the<br>balance once legal procedures have been exhausted|
|UAE|Single payment once the claim is presented and validated. The guarantor is mainly<br>responsible for recovering the collateral|

# _4.6 Collateral and Down-payment Rules_

_General guiding principles and international experience_

**Guarantee schemes should be allowed to require collateral and down payments subject to reasonable limits.** One of the main roles of guarantee schemes is precisely to compensate for the lack of collateral hindering SME access to finance. However, the complete absence of collateral may generate adverse selection and moral hazard effects and ultimately result in large losses for the scheme. To mitigate this risk, the scheme should be allowed to require whatever collateral is available up to reasonable limits.  For example, in France and in Canada, the schemes are allowed to require personal guarantees but these guarantees are capped respectively at 50% and 25% of the loan value.

_Reviewing Collateral Rules in MENA_

**Most MENA guarantee schemes allow banks to take collateral but do not impose ceilings** (Table 12). This may contradict the objectives of PCGs, **a** lthough there is little information on additional collateral provided by guarantee users in MENA. This information should be collected and disclosed.  Some enterprise surveys conducted by the World Bank indicate that banks in MENA tend to require high levels of collateral even when using

19
using very granular barcode level price data, and show that local prices increase in winning counties a year after the MDP establishment is announced. The reason we go down to looking at the barcode-level prices rather than using off-the-shelf regional price indexes is described succinctly in Handbury and Weinstein (2015). They point out that even though the variation in prices across locations is central in economic geography, regional price indexes are plagued with problems such as product heterogeneity bias (consumers can purchase different varieties within a product category across locations), retailer heterogeneity bias (consumers can shop in systematically different stores with different amenities), and purchaser heterogeneity bias (purchase prices may reflect different shopping intensities of consumers). Regional price indexes typically do not correct for these biases because without barcode data it is difficult to find the identical good in two different locations. By comparing the same UPC variety sold in the same store and by looking at the intensity of shopping time changes before and after the MDP establishment in winner compared to loser counties, we are able to address these biases pointed out by Handbury and Weinstein (2015).

We also show that wages only increased for skilled workers, and therefore rising local prices translate into a direct fall in real wages for unskilled workers. In terms of methods, we follow the literature that uses event study methods to study the effects of MDPs by comparing the outcomes in winning versus immediate runner up counties, such as in Bloom et al. (2019), Kim (2020), Greenstone et al. (2010), Patrick (2016), Patrick and Partridge (2019), Giroud et al. (2021), and Slattery and Zidar (2020).

Second, we micro-found this mechanism of rising local inflation due to the MDP establishment by introducing a new partial equilibrium model with non-homothetic consumer preferences over consumption and leisure, and retailers who respond to consumers’ falling price sensitivity to rising wages by increasing mark-up. This relates our work to the trade and macro literature that studies price and mark-up response to economic shocks. The concept of falling consumer shopping time and price sensitivity to wealth and income shocks has a rich history in macroeconomics studying the pro-cyclicality of mark-ups to recessions and the distribution of inflation across households (Butters, 1978; Burdett and Judd, 1983; Argente and Lee, 2021; Kaplan and Menzio, 2016; Aguiar and Hurst, 2007; Aguiar et al., 2013; Nevo and Wong, 2019; McKenzie et al., 2011; Coibion et al., 2015; Griffith et al., 2009). The trade literature has studied the relationship between consumer price sensitivity 5
WPS3725

# **The Impact on Russia of WTO Accession and The Doha Agenda: The importance of liberalization of barriers against  foreign direct**

# **investment in services for growth and poverty reduction**

Thomas Rutherford, University of Colorado David Tarr, The World Bank Oleksandr Shepotylo, University of Maryland Chapter 16 in _Poverty and the WTO: Impacts of the Doha Development Agenda_ , Thomas W. Hertel and L. Alan Winters (eds.) forthcoming from the World Bank, Washington, DC.

## World Bank Policy Research Working Paper 3725, October 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ We thank Maros Ivanic for the simulations from the GTAP model, Thomas Hertel and Mark Horridge for comments, and Maria Kasilag for help with the logistics. We gratefully acknowledge financial support from the World Bank-Netherlands Partnership Program and the United Kingdom’s Department for International Development (DFID).

.

i
is important to note that we assume perfect substitutability of Chinese exports and country _i_ ’s exports at the 4 digit level of aggregation.

We develop a similar index to calculate the increase in the import demand faced by Latin American economies due to China’s growth. This opportunity index is equal to where _Mg,t_<sup>_C_istheproduct</sup><sup>_g_importsofChinaattime</sup><sup>_t_and</sup><sup>_M W_</sup> _g,t_<sup>istotalimportsintheworld.</sup> This is the same index as (1) assuming that China’s exports are equal to the -additive- inverse of imports and that world’s imports equal world’s exports.

Note that we use the increase in China’s share in world imports instead of using the increase in country _i_ ’s exports to China. We avoid using bilateral data because they are affected by both import demand and export supply shocks. The objective is to isolate the exogenous import demand shock from export supply shocks.

In order to have an estimate of the increase or decrease in net exports of a sector in country _i_ , we calculate the difference between the opportunity and vulnerability indices. In reality, the actual change in country _i_ ’s net exports can be quite different from the amount implied by the indices. Because the indices only capture the shocks caused by the growth of China, assuming there are no other exogenous changes, particularly in local market conditions. Note that we are not interested in calculating the observed change in exports _per se_ , but the change in exports that can be attributed to China, net of all other exogenous shocks.

It is useful to discuss the underlying assumptions under which the indices can capture changes in country _i_ ’s net exports: (1) All products from all origins within category _g_ are perfect substitutes produced in perfectly competitive markets, and they are unrelated to products in other categories. The level of aggregation of the trade data might affect the validity of this assumption. For example, 12
tion by adding interacted dummies for the exact number of non-white, white, female, and male workers in the peer group before the death.

Table 5 shows results separately for non-white female, white female, non-white male, and white male incumbents. We observe the strongest effects for non-white females. For them, the death of a female coworker lowers the likelihood of staying at the firm by 1.9 percentage points, and the death of a non-white coworker reduces retention by even 2.5 percentage points. However, the interaction of the deceased worker being female and non-white is positive and almost offsets the main effects. Adding up all coefficients, the loss of a female non-white worker does not significantly affect the retention of female non-white incumbents. They are only affected by losing a female white or a male non-white coworker (all relative to our omitted category: the loss of a male white worker). Apart from female non-white incumbents, we also find that male non-white incumbents are more likely to leave the firm after the death of a non-white coworker, but for them, the gender of the deceased coworker does not matter. Moreover, for white (female and male) incumbents we do not see any effects.

These results align very closely with the findings from Linos _et al._ (2024) for new hires in a professional services firm in the United States. There, the only race and gender group whose turnover is affected by the share of white coworkers is Black women. Moreover, it is mainly the share of white male coworkers, not of white female coworkers, that is found to increase the turnover of Black women. This is consistent with our result that only the death of a non-white male, instead of a non-white female, affects the retention of non-white females. Finally, finding larger effects for female rather than male incumbents is also in line with Elvira and Cohen (2001) who find that a higher proportion of women leads to a significant reduction of turnover among women, whereas an increase of men does not cause a decrease in quitting among men.

# **5 Conclusion**

We study the impact of having same-race coworkers on workers’ retention at the firm. Using matched employer-employee data from Brazil, we exploit the deaths of workers from different racial groups as an exogenous shock to the racial composition of peer groups. We find that the death of a non-white worker – relative to the death of a white worker – decreases the likelihood of staying at the same firm for non-white incumbents. In contrast, we find little evidence that the racial coworker composition matters for white incumbents. The death-induced decline in the non-white share of the peer group makes non-white incumbents more likely to quit their current job – while not affecting their likelihood of being laid off – and to move to a new job where the non-white share is again higher than in their old job. Together, these results suggest 21
the GHS-FUA spatial package (Moreno-Monroy et al., 2020) and it is measured as the population living in urban centers in a metropolitan area divided by the land area of the urban centers in that metropolitan area.

# _Moderator variables for firm and sector characteristics_

We assess the moderating effect of tradability by looking at sector, exporting behavior, and foreign ownership. The sector classification provided in the enterprise surveys is based on the International Standard Industrial Classification of All Economic Activities (ISIC). In line with the taxonomy of De Gregorio et al. (1994), all firms active in manufacturing (ISIC 15-37) are part of the tradable sector, while firms active in services, including construction (ISIC 45), wholesale trade (ISIC 50-51), retail trade (ISIC 52), hotels and restaurants (ISIC 55), transportation services (ISIC 60-64), business services (ISIC 70-74) and personal services (ISIC 80-98) are part of the nontradable sector. Utilities, mining, and financial services are not included in the Enterprise Surveys. Our _manufacturing_ dummy is therefore 1 if a firm operates in the manufacturing sector and 0 otherwise.

We then explore the moderating effect of exporting and foreign ownership. The _exporter_ dummy is set at 1, if a firm exports at least 10% of its sales, and 0 otherwise. The _foreign-ownership_ dummy also takes the value 1 if at least 10% of the establishment is foreign owned, and 0 otherwise. While all exporting firms belong to the tradable sector, in our sample about one fifth of all manufacturing establishments, which represent the tradables sector in our analysis, are exporters and only around 10% are foreign owned. By contrast, only 6.6% of services firms, which in our classification belong to the non-tradables sector, are exporters. Therefore, our non-tradable sector includes mostly firms serving the domestic market, while our analysis of the moderating effect of exporting and foreign ownership considers to the extent possible the influence of services firms operating in the tradable sector. Details are provided in Appendix Table B2.

# _Mobility and congestion variables_

We obtain measures of mobility and congestion indexes from Akbar et al. (2018; 2021), who use travel time predictions from millions of simulated trips to compute city-level speed indices. Specifically, the database of Akbar et al. (2021) makes use of roughly 538 million trip instances in 1,228 cities<sup>8</sup> that have been queried on Google Maps between June and October 2019. In each

> 8 Countries in which Google Maps is restricted (e.g., China and the Republic of Korea) and a few other cities that are missing credible variation in real-time travel data were excluded.

10
Table D.8. Placebo Test for All Pairs of Neighbors Whose Difference in Altitude is between the Following Thresholds

||_Altitude_|_difference betwee_|_n 15 and 20 masl_|_Altitude_|_difference between_|_20 and 100 masl_|
|---|---|---|---|---|---|---|
||Altitude|Night Lig|ht- Arcsine (2013)|Altitude|Night Lig|ht- Arcsine (2013)|
||Any neighbor|Any neighbor|Both neighbors outside|Any neighbor|Any neighbor|Both neighbors outside|
||pair|pair|guerrilla area|pair|pair|guerrilla area|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Difference|17.83***|0.0206***|0.0239***|47.71***|-0.0114***|-0.0172***|
||(0.0322)|(0.00521)|(0.00525)|(0.201)|(0.00384)|(0.00430)|
|Neighbor pairs|2,914|2,914|2,515|11,811|11,811|8,742|
|||_Wealt_|_h Index (2007)_||_Wealt_|_h Index (2007)_|
|||(7)|(8)||(9)|(10)|
|Difference|-|0.0149|0.0202**|-|-0.0456***|-0.0468***|
||-|(0.00921)|(0.00980)|-|(0.00501)|(0.00583)|
|Neighbor pairs|-|2,910|2,513|-|11,729|8,733|
|||_Years of_|_Education (2007)_||_Years of_|_Education (2007)_|
|||(11)|(12)||(13)|(14)|
|Difference|-|0.0818***|0.0964***|-|-0.0540***|-0.0513***|
||-|(0.0307)|(0.0336)|-|(0.0144)|(0.0172)|
|Neighbor pairs|-|2,911|2,513|-|11,758|8,734|

_Note:_ The table presents the placebo test results. The unit of observation in Columns 1 to 3 is the pair of neighboring census tracts conditional on having a difference in altitude between 15 and 20 masl. The unit of observation in Columns 4 and 5 is the pair of neighboring census tracts conditional on having a difference in altitude between 20 and 100 masl. Columns 1, 2, 4, and 5 show the mean difference for all neighbor pairs in the sample. Columns 3 and 6 do the same for pairs in which both neighboring tracts are outside the guerrilla-controlled area. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

83