rafmacalaba/data-use-mentions-extended huggingface dataset:
economies (e.g., Brown et al. 2008, Ulimwengu et al. 2009). A related literature considers non-transportation infrastructure investments in developing economies (e.g., Duflo and Pande 2007, Dinkelman 2011).<sup>2</sup> Recent papers in these literatures have grappled with concerns regarding the endogenous placement of infrastructure that prevents a causal interpretation of infrastructure’s role. As Duranton and Turner (2011) highlight, the endogenous placement could bias findings in either direction. Infrastructure investments may be made to encourage development of regions with high growth potential, which would upwardly

> 1 ‐ The impact of highways has been studied for other developed countries as well. For example, Holl and Viladecans Marsal (2011) study the impact of highways on Spanish cities while Hsu and Zhang (2011) work with Japanese data. 2 More broadly, a number of studies find high elasticities of private output with respect to public capital, often greater than 0.3, but some more disaggregated studies cast some doubt on these elasticities by observing that infrastructure has not been necessarily related to productivity in sectors that should have benefited the most. See, for example, Aschauer (1989), Munnell (1990), and Otto and Voss (1994).

5
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discussion about

> 5 Another direction is to study mobility across generations: asking questions about the extent to which children’s living standards are associated with those of their parents, and often interpreting findings as offering a window on the extent of equality of opportunity in society (i.e. the extent to which children’s fortunes are unrelated to the circumstances of their parents). The nationally representative data that allow an investigation of intergenerational income mobility are simply not available for India (although see van der Weide and Vigh (2018) for an analysis of intergenerational educational mobility in India).

8
For example, we do not discuss behavioral responses, social stigmas, or implications for social cohesion and political support for poverty programs.<sup>20</sup> A few caveats are also in order. The paper uses existing household consumption surveys to simulate the targeting performance of various methods and the likely impacts on poverty of using the methods to allocate a fixed budget. In a sense, we are creating a “laboratory.” While this is inevitably somewhat artificial, we believe it to be essential for properly testing the performance of the various targeting methods found in practice. It must be acknowledged, however, that things happen in the real world that cannot be predicted in our laboratory setting. None of the methods considered here could be implemented flawlessly. While we do allow for lags in implementation, idiosyncratic mistakes will no doubt also be made. Most likely the methods will perform less well than our calculations suggest. We present and compare the best case results for the various methods reviewed, unaffected by potentially differential costs, ease of implementation, and susceptibility to manipulation and corruption.

In addition, there are potential benefits of each method in practice that we cannot identify in our laboratory setting. Our purpose here is to judge the methods solely on how accurately they reach the poor and by how much transfers targeted in these ways would reduce poverty. These are undeniably important as goals for targeted programs and are usually explicit in the justifications provided by donors and governments. Nonetheless, there may be other objectives in practice such as capacity building or building systems. Donors, governments and NGOs implementing these methods do sometimes make considerable effort to include communities in the selection of beneficiaries, build robust grievance and redress procedures, and continuously monitor and evaluate the performance of the targeting systems. Such efforts could well be important in building administrative capacity for social protection over the longer term and also improve targeting. It is not obvious how an accounting for such potential benefits would alter the relative assessments of alternative methods based on our simulations.

The
**Table 11 – Poverty over time**

||**Multidim**<br>**Poverty**|**ensional**|**Monetar**<br>**Poverty**|**y**<br>|
|---|---|---|---|---|
||2001|2007|2001|2007|
|Urban|9.4|7.4|7.6|4.8|
|Rural|42.3|18.3|25.1|14.4|
|Total|23.9|12.1|15.3|8.9|

Source: NSLS 2006/07. Totals are simple weighted averages of the urban and rural statistics.

36
randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. Locus of control is an index measured in standard deviation units, generated by standardizing the raw Locus of control score to the mean and standard deviation among females in control communities who were not assigned to the _Goal_ treatment at baseline and at endline separately. Very confident can complete task is a binary indicator for the female reporting being very confident she can complete any task she starts, Discount factor measures the extent to which females discount the future and ranges from .1 to 1, and Chose riskiest option is a binary indicator for the female selecting the riskiest option in a risk game implemented in the field. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

75
NW/TL) of privatized banks appear to have declined quite remarkably during the

> We did also have credit allocation data for December, 1995. However, for the five categories of banks in Table 9 the qualitative differences between that data and the 1996 data were not great. As a result, we present only the 1996 data.

> 34 Of course, because all new loans are good loans, it will more telling to observe privatized provincial banks' portfolio quality in coming years. They do, however, appear to be starting on very solid footing.

20
comprehension test for the guidance manual and paper-based questionnaires at the end of the halfday training. Table 1 summarizes the main questionnaires used to collect data.

Table 1. Summary of data sources

|**Data source/**<br>**Questionnaire**|**Date**|**Sample**|**Type**|**Key information**<br>**collected**|**Response**<br>**rate (%)**|
|---|---|---|---|---|---|
|**SIRE**<br>**administrative**<br>**data**|2016<br>(placebo),<br>2017<br>(placebo /<br>prediction)<br>; 2018<br>(baseline),<br>2019<br>(follow-<br>up)|All<br>schools<br>(4,000)|Online|Key administrative<br>student-level data<br>(including Students’ IDs,<br>detailed grades, age,<br>gender, and dropout<br>status)|100%|
|**Complementary**<br>**baseline data**|May 7<sup>th</sup>-<br>May 29<sup>th</sup>|All<br>schools<br>(4,000)|Online|Perceptions on school<br>dropout, returns to<br>education and influences<br>on the dropout decision,<br>as well as targeted<br>support and tasks<br>prioritized by principals.|70%|
|**Training**<br>**feedback**|May 29<sup>th<
or political structures which may impact agricultural production.

As discussed in the prior section, several restrictions are placed on the data set to ensure accuracy. First, only gridcells with at least 30 percent cropland according to the ESA data set are included. In robustness checks, this threshold is changed to 75 percent and 90 percent. Agriculture in gridcells must also be irrigated, as described in section 2.3. Gridcells must be no more than 100 kilometers from their matched monitoring station, to ensure that water quality at the station accurately reflects irrigation water quality. This threshold is somewhat arbitrary and chosen to balance two competing factors: higher distance thresholds reduce the average relationship between measured upstream water quality and actual water quality in the gridcell, while lower distance thresholds reduce the number of observations in our study. In section 5 we present results where this threshold is varied for robustness.

In order to avoid bias caused by reverse causality, gridcells are carefully matched to monitoring stations which are strictly upstream from them. It is well known that if drainage is not carefully managed, irrigated agriculture can impact the salinity of discharged water. Thus, downstream water salinity can be impacted by upstream agricultural production. In addition, water extractions for agriculture or other uses can also impact downstream salinity levels. Thus, to ensure that the impact of water salinity on agricultural production is properly identified and that salinity levels are entirely exogenous, a careful matching of gridcells to upstream monitoring stations was done. In the Mekong and India analyses, this was done through the use of a hydrological connectivity approach, where the streamflow from each gridcell was traced upstream until a monitoring station was found. The hydrological connectivity approach is a process in which areas of land (such as districts or grid cells) are connected to water quality stations through the drainage network based on the direction of surface runoff. To achieve this process, the following steps 6
Nonvide, 2019) in addition to PSM methods (Dillon, 2010; Palmer-jones, 2012). Authors with time series data have also used lagged variables to address endogeneity (Akber, 2020; Akber, Paltasingh, & Mishra, 2022) in a systems estimation approach including seemingly unrelated regression and 3SLS; and the single equation approach (OLS and 2SLS). To compare and assesses the interactions of impacts of (i) input subsidies; (ii) irrigation infrastructure; and (iii) food/cash transfers implies that our modeling must address three endogenous variables. We could not, however, find an applicable econometric approach that can address selection bias of three variables at once. Therefore, the study resolved to assess individual effects and paired effects.

Table 1: Instrumental variables used in the models.

|Endogenous variable|Instrumental variable used|
|---|---|
|Input subsidies|Member of Parliament visited the community in the past three<br>months.<br>Household head has always lived in village.<br>Deviation of particular-year rainfall from long term median.|
|Cash transfers/food aid|Deviation of particular-year rainfall from long term median.<br>Member of Parliament visited the community in the past three<br>months.|
|Irrigation infrastructure|Deviation of particular-year rainfall from long term median.<br>Whether the village chief receives payment for land use or sale<br>Member of Parliament visited the community in the past three<br>months.|

For models assessing the impact of production and social assistance programs on household DD, the Poisson model is appropriate because the outcome of interest is a non-negative count variable (Woodridge, 2010). Models are estimated using instrumental variable Poisson regression (IV Poisson) via the generalized method of moments (GMM) consistent with a prior study by Karamba (2013). This approach further allowed us to account for two endogenous variables in the estimation and assess joint effects of the interventions.
# **IV.  Data from China and Brazil**

The analysis in this paper focuses on China and Brazil. These countries have two of the largest economies in the developing world and present interesting contrasts in terms of economic history and development strategy. Brazil has a long history of trade orientations, starting as a commodity-based export economy, followed by a long experiment in import substitution that has recently given way to a more liberal trade policy with export-led growth strategies in some sectors. However, Brazil has higher barriers to investment than most Latin American countries surveyed. In particular, dealing with three levels of government (state, province, and municipality) is a big burden to firms (World Bank 2002).

On the other hand, China was until recently relatively closed to market economies, but has recently experienced meteoric growth in manufacturing sectors that produce both for export and for the domestic market.

# <u>China</u>

As Table 1 shows, the data for China includes 975 usable observations that are about evenly distributed across five cities: the rapidly growing coastal cities, Shanghai and Guangzhou; Beijing and the nearby port of Tianjin; and the interior provincial capital, Chengdu. They are roughly equally distributed across five industry groups: Apparel and Leather Goods; Consumer Products; Electronic Components; Electronic Equipment and Vehicles and Parts (Table 2). About two-fifths of all firms in the survey sample are state-owned – a proportion that is quite consistent across industry groups.

As an indicator of the importance of international trade, Tables 3 and 4 break the firms down into those that export at least part of their output and those that do not. At least 30% of firms in all industry groups export, but the share of exports is substantially higher in Apparel and Leather Goods and Electronic Components. Not surprisingly, a very high proportion of firms in Shanghai and Guangzhou export, while a high proportion of firms in Chengdu do not.

Ownership status is also related to export behavior. About 50% of privately
in the presence of asymmetric information about their quality and reliability. Rating systems that signal product quality on these platforms further enhance buyers’ trust in unfamiliar suppliers. For example, evidence from advanced economies shows that better-rated sellers on eBay have higher prices and higher revenues (Livingston, 2005; Lucking-Reiley et al. 2007; Melnik and Alm 2002), and that sellers on eBay are more likely to exit the platform as their ratings fall (Cabral and Hortacsu 2010). Similarly, cloud computing disproportionately benefits small firms by eliminating upfront capital expenditures associated with hardware needs for file storage, data backup, and software programs. Using data from the United Kingdom, DeStefano et al. (2023) find that cloud computing was associated with a 13 percent annual increase in employment in young firms between 2008 and 2015.

There is direct evidence of digitalization spurring market entry in developing economies. In South Africa, around a quarter of the increase in jobs associated with the staggered arrival of submarine cables and the subsequent rollout of terrestrial fiber networks is explained by net firm entry (Hjort and Poulsen 2019). This arrival of faster internet across Africa is also associated with internetinduced entrepreneurship. In areas with internet availability, the probability of a household establishing a nonfarm business is 17 percentage points higher compared to areas without availability (Houngbonon, Mensah, and Traore 2022). Further, experimental evidence from Africa showing that smaller firms taking part in a training program about how to sell to bigger corporations, governments, and other large buyers win about three times as many contracts as nonparticipants, but only if they have internet access (Hjort, Iyer, and de Rochambeau 2020). In rural China, e-commerce platform adopters obtain significantly higher income than non-adopters, as their sales income increases significantly (Liu et al. 2021; Li and Qin 2022).

However, there are concerns around large firms benefiting more as many digital technologies require significant capital investment. Based on data from Belgium, Dhyne et al. (2018) find that large firms experience
the agricultural sector is around 97%. This high fraction has not changed noticeably over time. It is not surprising that this share is high in agriculture, but this share for Rwanda is higher than for the rest of sub-Saharan Africa, where it fluctuated between 95% and 96.4% over the 1995-2015 period. The share for sub-Saharan Africa is already higher than in other regions of the world, which is a result of the importance of smallholders.

The corresponding share for the manufacturing sector, i.e. the sum of value added and domestically sourced intermediates, was 88% in 1995 and this declined by one percentage points by 2015. Again, the share for Rwanda is higher than for the whole sub-Saharan African region where it was approximately 85%.

The right panel of Figure 2 shows the most important regions in the world from where Rwandan manufacturing firms source their intermediates. The EU has become the most important region with an import share of 39%, while previously the most important origin was the rest-of-the-world (ROW) aggregate. Two regions/countries experienced an increasing share over time. In absolute terms, the other sub-Saharan countries saw the largest increase and in 2015 they provided a fifth of imported inputs. In relative terms, it is the rise of Chinese intermediate inputs that really stands out. This latter trend is shared by every single country on the continent and Rwanda is lagging slightly behind, sourcing only 4.5% of inputs from China, while the regional average is 6.2% in 2015.

> 6 This full EORA data is available online at http://worldmrio.com/

5
‘star’ pattern of accessibility in the city and its low levels of average accessibility and high inequality – but also high maximum access in the center.

# **5.3 In-between Cities**

# _5.3.1 Bamako_

The transport data are based on the SOTRAMA network, which is composed of privatelyowned minibuses traveling on different routes. The maps of the Sotrama network were produced and provided by the “Cellule de Préfiguration de l’Agence Urbaine de Bamako” (CPAUB) housed in the mayor of Bamako’s office. The original data, in Autocad format, were converted into a shapefile for the purpose of the accessibility analysis. While the routes are identified in the shapefile, public transportation speeds were not determined. So, the analysis makes some assumptions on average speeds that were discussed and validated with local experts. The Sotrama speeds are assumed to be linked to the road class they circulate on as follows:

||**SOTRAMA- rush hour speeds**|
|---|---|
|**Primary**|21 km/h|
|**Secondary**|11.2 km/h|
|**Tertiary paved**|9.8 km/h|
|**Tertiary unpaved**|7 km/h|

TABLE 6: SPEED ASSUMPTIONS DURING RUSH HOUR BASED ON ROAD CATEGORY IN BAMAKO The employment data for Bamako are based on a business registry carried out in 2015, listing 8,808 firms for the Bamako urban area. The registries include all formal firms, and the wage bill is reported at the level of the firm (and not the plant). To account for this, the top 5% of firms with the biggest wage bills were dropped from the analysis, as these would run the greatest risk of reporting firm (and not plant) level data. In addition, large mining, extractive and security firms were dropped from the analysis, since it can be safely assumed that a
_en douane_ ). In a typical semester, there are on average 45 brokers who each handle 173 declarations from 33 different importers.<sup>9</sup> The overwhelming majority of the 3,660 importers in our sample work exclusively with one broker, as is shown in Appendix Table A2. Brokers must have a license, which is issued by the customs administration and they administer the customs clearance process on behalf of the importer by fulfilling customs formalities and submitting documentation.<sup>10</sup> Brokers are accountable for the payment of taxes, duties and potential tax adjustments and are penalized (with a fine) in case of non-compliance. In principle, repeated non-compliance can result in the revocation of the broker’s license. In practice, suspension of brokers due to misconduct is rare. Customs officials and brokers frequently socialize and are part of the narrow elite in the small town of Toamasina. Many brokers either have served as customs officials themselves, or deliberately recruit former customs officials because of their expertise and networks. Thus, there is extensive repeated interaction between inspectors and brokers, both inside and outside of the customs premises.

There is significant information asymmetry between importers and brokers given that the latter are much better informed about customs procedures and are the first point of contact for customs in case disputes arise. Some brokers have transparent pricing schemes which typically depend on the size and contents of the cargo, but others charge a fixed amount (inclusive of potential tax liabilities) per container cleared, irrespective of its content, which implies that their profits directly depend on the amount of tax they remit on behalf of the importer.

To understand how corruption may happen it is instructive to consider the customs clearance process, a stylized version of which is depicted in Figure 1.

1. **Registration.** The first step in the process is the electronic registration of an import declaration by the broker on behalf of the importer via the Automated System for Customs Data (ASYCUDA)++ customs
Buzby and Roberts (2010) analyze similar data on US import refusals to that used in the present paper, but only provide descriptive statistics. Baylis et al. (2009) is the first paper to investigate US import refusals empirically. In order to test whether import refusal decisions are biased, and what are the reasons for it, they look at a set of potential factors triggering refusals. They find that contrary to prior expectations, countries with experience in exporting products to the US are actually subject to relatively more refusals, thereby suggesting some degree of “stickiness” in the refusal determination process. Their findings also suggest that refusals are influenced by political pressure.

Related work has also been conducted on European food standards. Baylis et al. (2010) use data on EU import alerts—closely related to refusals<sup>2</sup> —in a gravity model to show that they tend to decrease trade.<sup>3</sup> However, they do not examine the determinants of import refusals, and in particular the potential for reputation effects, which is the focus of our paper. Baylis et al. (2011) use information on EU notifications and find that increased use of notifications is linked with a decreased level of protection through tariffs. They also see that European countries that would intuitively be demanders of protection tend to be at the origin of more notifications than their EU partners. Finally, the first stage of the empirical approach taken by Cadot et al. (2009), which tests whether import alerts contribute to increase trade costs and thus supplier concentration, uses EU import alerts as the dependent variable, but the cross-sectional setting of their regressions means that they are unable to account for reputation effects of the type we are interested in here.

In this paper, we use newly collected data to focus on one important example of the de facto implementation of product standards: refusal of entry into the US market for imported foods on safety 2 The terminology for the European and US