rafmacalaba/datause-encoder-data huggingface dataset:
For this analysis, we focus only on RSDP I–III, which concentrated on trunk and link roads. In RSDP IV, Ethiopia began URRAP, which had the objective of connecting all of Ethiopia’s Kebeles to the nearest all-weather road (Ethiopian Roads Authority, 2015b; Iimi et al., 2018). As of 2016, the latest year in our roads dataset during RSDP IV, not all Kebeles were connected. However, it does not make sense to include RSDP IV when using this instrumental variable strategy, as all Kebeles were targeted for treatment as part of URRAP, and this analysis focuses only on non-targeted treated areas.

We rely on RSDP road improvement data to identify areas targeted by RSDP. The dataset is organized by road link and lists the names of cities at each end of the link. We use this list of cities and Ethiopia’s nine regional capitals as the targeted areas. This process yields 919 targeted locations (Appendix S8 shows sample sizes after removing targeted locations and the number of units near an RSDP road).

We estimate the following long-difference model using Kebeles as our main model. The same model is run at using the 1x1km grid to check the sensitivity of our main results, where we exclude units within 5km of targeted areas. ∆ _y_ is the change in the outcome variable from baseline (1996) to endline (2009, the last year of RSDP III), and _Near Improved Road_ is a binary variable that indicates whether a unit is near a road improved from RSDP. To explore heterogeneity in impacts, we estimate alternative versions of equation 1 where we interact _Near Improved Road_ with variables indicating whether the unit was in a Woreda with zero, low, medium, or high baseline nighttime lights.

Identifying the impact of RSDP from OLS (model 1) assumes that non-targeted areas that were connected to road projects were as good as randomly selected. However, this assumption is violated if regional states also choose to connect politically or economically important areas between the primarily targeted locations. To this point, non-targeted treated and control areas are not balanced across baseline levels and pre-trends of outcome variables (see Appendix S9).

To address this concern, we follow Faber (2014) and construct two minimum spanning trees to use as instruments for road placement. The MSTs are hypothetical road networks developed under the sole goal of connecting all targeted destinations subject to cost minimization. The first MST connects targeted areas in a way that minimizes the total Euclidean distance of the network, and the second MST minimizes the total construction cost of the network. Construction costs are proxied using elevation and land cover (additional construction costs are assigned to areas that are built-up or that contain wetlands or water; see Appendix S10, which provides details on how construction costs are computed and how the MSTs are constructed).

We compute two variations of the Euclidean distance and least-cost MSTs. First, we construct MSTs that 16
|ln(TFPR) (BKR)|22,061|0.56|0.44|-1.14|2.31|
|---|---|---|---|---|---|
|ln(Age)|35,723|2.72|0.69|0.00|5.61|
|ln(Employment)|35,730|4.02|1.18|2.30|9.61|
|Foreign owned?|35,730|0.002|0.04|0|1|
|**_Vietnam_**||||||
|ln(TFPQ)|142,630|5.18|1.82|0.04|9.2|
|ln(TFPR) (HK)|106,413|0.71|0.96|-2.99|3.41|
|ln(TFPR) (BKR)|106,413|0.56|0.85|-2.99|3.72|
|ln(Age)|106,967|1.98|0.69|0.00|4.23|
|ln(Employment)|142,630|3.93|1.30|2.30|11.35|
|Foreign owned?|142,630|0.18|0.38|0|1|
|SOE?|142,630|0.02|0.15|0|1|

Source: Country data as detailed in Table 1 and authors’ calculation.

# **4. Results**

# **4.1 Baseline results**

The descriptive statistics in Table 2 suggest a few discrepancies between TFPR obtained using HK or BKR. A more nuanced picture emerges from inspecting the full distributions of TFPR for each country (HK, in red, and BKR, in blue) in Figure 1. In line with the HK approach, a transformation T <u>s</u> of TFPR is plotted, log( ��������� ), that is the log of the ratio between firm-specific TFPR and its 4- T PPRR digit industry average. PPRRss 11
five then formed the maintenance group for the plot assigned to the threshold group payment treatment.<sup>5</sup> Upon completion of the recruitment and assignment process, all five members of each maintenance group were assembled on the reforestation plot assigned to them. They received training on tree maintenance including how to water the newly planted trees (and at what frequency), how to remove dead leaves and other flammable materials in saplings’ vicinity, how to set up firebreaks, and how to protect the newly planted trees from being eaten by wildlife or livestock. The members of each group were also informed of the mechanism via which they would be remunerated at endline – either the linear group payment scheme, or the threshold one. We ensured that any payment earned by the group would be shared equally between all members of that group, independent of how much effort they put in. We did so by transferring one-fifth of the group payment to each of the five group members via mobile money bank accounts, and we announced this payment procedure beforehand.

Trees had been planted in two reforestation plots in each of the 33 forest blocks, and 325 of our 330 participants participated in our baseline survey.<sup>6</sup> Unfortunately, we were not able to visit two of our 33 blocks at endline. Etouayou of the Nosebou forest in the western part of Burkina Faso was not accessible at endline because of a flood, and Matiacoali of the Tapoaboopo forest in the east could not be visited at endline because of armed unrest. We thus have endline tree survival information on 31 blocks (consisting of 62 reforestation plots); we have baseline survey information for 305 of the 310 participants in these blocks, and endline information for 290.

> 5Whether threshold group payments are able to outperform linear group payments obviously depends on the severity of the free-rider problem within maintenance groups, which, in turn, depends on both group size and on group composition. In laboratory experiments with random group assignment, four subjects in a group are enough to induce (strong) free-riding (Huck _et al._ , 2004). Still, compared to the practice of local forest management, our group sizes are small, and hence we decided to impose an exogenous group formation process (as opposed to allowing subjects to self-select into groups on the basis of pre-existing personal ties including kinship) to ensure that if we find that linear group payments outperform threshold group payments, this would not simply be the result of implausibly high levels of cooperation (among kin, or friends). Whether our design is indeed such that there is scope for threshold group payments to yield higher levels of cooperation than with linear group payments, is tested in Table 2 below.

> 6Due to logistical reasons the baseline survey was implemented in September and October 2017, about one month after the maintenance groups had been formed and the tree maintenance contracts had been signed. The enumerators were unable to interview five of our 330 participants, four of the linear group payment treatment and one of the threshold group payment treatment.

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23 1960 and 1996.<sup>24</sup> Table I shows the most frequent intervening actors during this time period and Table II the most frequent targets of intervention. Most of the frequent interveners are indeed democracies, and most of the targets are non-democracies.

The interventions were coded as democratic or autocratic. Following Ellingsen (2000) and others, states receiving a score of 6 or higher on Polity2 are coded as democracies. A country-year with an intervention is coded as having a democratic military intervention if at least one of the intervening actors is coded as democratic. Other interventions are coded as autocratic. The Pickering dataset codes not only states as intervening actors but also international organizations and multilateral forces. We define these as democratic if at least one country in the organization is a democracy. Appendix 4 shows how these actors have been coded. Among the organizations, only one (the Arab League) was coded as non-democratic. In addition, a non-independent country (Belize) was also coded as a non-democratic actor.<sup>25</sup>

**Table I. The Five Most Frequently Intervening States, 1960–96**

|Intervener|Interventions|
|---|---|
|USA|63|
|UN|45|
|France|41|
|Soviet Un-<br>ion/Russia|27|
|UK|25|

Computed from data in Pickering (1999).

> 24 We have left out 23 military interventions listed by Pickering, since the targets of these interventions are not included in Polity for that year or at all. See Appendix 3 for a list of these interventions.

> 25 Each military intervention has a unique case number with start and termination dates. If the termination date was missing, we have coded only the start year of the intervention as a countryyear with military intervention. Interventions with code 99999999 for termination date are ongoing. Due to a misunderstanding we coded these as missing data. However, this has no effect on the results of the analyses because all these interventions start around 1991, and the subsequent year is coded as 1 on _Recent intervention_ . The important factor is whether there has been a recent intervention, not whether an intervention is on-going. In two cases, the intervention started before 1991. These cases were modified and coded as ongoing military interventions through 1996. Included in the recent democratic intervention variable are also cases where the intervention stopped before 1960 but the end date is 1955 or later.
such as retail, electronics and equipment are most likely to report drops in demand as the main cause. Firms in less demand-sensitive sectors such as food are least likely to report a drop in demand. We examine the issue of demand sensitivity more carefully in the next section.

There is a very strong link between reporting demand shocks as the main cause and the actual sale performance. Firms that reported a drop in the demand as the main effects witness larger declines in sales. The decline in sales is 31 percent on average for firms that report “drop in demand of products and services” as the number one impact, whereas the decline is 20 percent for firms that do not do so. The difference is significant at the 1 percent level.

To sum up, we see a surprisingly overwhelming fraction of firms that claim the drop in demand as the most important factor for their worsening performance. Credit shocks, the usual suspect that was much more thoroughly examined in numerous studies, was reported by much fewer firms. This is robust for all firm sizes, for all sectors, for all three waves, and for all countries. While only subjective, the evidence is decidedly clear that demand declines are the number one concern for firms.

# **4. Demand versus credit factors: An econometric analysis**

This section provides evidence on the impact of the change in demand on the performance and operation of firms in the crisis. The main indicators for firms’ performance and operation are firms’ sales, employment, and capacity at the first wave of the crisis. We find that the change in firm sales and the change in capacity are significantly and negatively correlated with firms’ demand sensitivity; and this is robust across specifications. We do not find a significant and robust relationship between firm sales and capacity with various proxies for credit dependence before the crisis. We also look at the change in fulltime permanent employment, but do not find a relationship between it and either demand nor credit sensitivity.

The details of the dependent variables are below:

1. Current change in sales: the percentage change between a firm’s sales in June/July 2009 versus sales for the same month in 2008. We obtained this variable from the first wave of the Financial Crisis Survey.

2. The change in a firm’s capacity between June/July 2009 and the end of 2007 fiscal year. This is the difference between capacity asked at the first wave of the Financial Crisis survey and capacity asked in the 2007 BEEPS. Capacity indicates the current utilization rate (%) of all resources at the time of the interview.

8
**<u>Table 4: Publicly listed companies in agribusiness value chains</u>**

|**Item**|**Global agric. value chain (%)**|**Number of companies**|**Market Cap (%)**|
|---|---|---|---|
|Suppliers|22.7|103|39.6|
|Farming|22.2|7|0.2|
|Processing|14.8|60|9.7|
|Logistics|14.7|26|9.7|
|Packing and<br>distribution|25.6|88|36.8|
|Integrated|n.a.|16|4.0|
|**Total **|**100**|**300**|**100**|

_Note:_ Global market capitalization is in US$ millions as of October 2009. _Source_ : Own computation based on Brookfield 2010 27
industry level (mainly public but also private outlays) relative to the small magnitude and lack of statistical significance at the firm level suggest that a more disaggregated and more carefully posed set of questions on research expenditures and particularly on development outlays (the “D” side of R&D) may be required to better capture different types of R&D expenditures at the firm level. Finally, aggregated outlays across different sub-types of investments in reputation & branding and global collaboration are not statistically significant. But more disaggregated outlays allow the relative impact on domestic versus export sales to be disentangled—suggesting the importance of collecting data on more narrowly-defined intangible variables in better understanding the various drivers of within-firm productivity.

The methodologies developed to measure and analyze public and private investments in knowledge capital at the industry and firm levels are more broadly applicable. First, a modified version of the firmlevel questionnaire developed and tested as part of this study should in principle be relevant for a better understanding of the within-firm knowledge-related productivity drivers of other agriculture, manufacturing and service industries. This may be particularly insightful for commodity-based industries—such as agro-food processing industries including key agricultural crops, meat and milk products as well as forestry and mining processing industries—where there may not be a sufficient appreciation of the value for business competitiveness of investing in knowledge assets. It is hoped that this study may provide inspiration for future work seeking to better understand the association between knowledge capital and industry- and firm-level productivity. Second, the public-private dimension of the paper raises questions about the appropriability of the publicly-generated benefits by individual firms. There is little doubt that a public program to increase the visibility of Chilean wines in world markets, if successful, would help the representative private firm, but by how much and in what form? Would a positive impact of such investment be captured in the return to existing categories of knowledge capital or would this be more appropriately measured through a new additional category of knowledge capital? Should investments in public asset stocks for the industry be measured separately from the private stocks, and if so, how do they overlap with the private stocks? These are questions to be addressed in further work. Finally, it is hoped that this study may assist in the design of more effective policies to stimulate productivity in a way that helps build shared prosperity for the bottom 40 percent of each country’s population – by spurring further studies to better understand the linkages between support policies and investments in particular types of knowledge capital that help generate more and better jobs over time for the less well-off people in each country.

26
required, and if a PSU was selected three times, 12*3=36 interviews and blocks were required. All other criteria for block delineation remained in place (Figure 4).

**Figure 4: Example of EA delineated into blocks**

Source: Flowminder / WorldPop.

Enumeration blocks were selected with equal probability. In the general case of 12 blocks per enumeration area, every single block was selected as 12 interviews per EA were required (and equivalently for PSUs with 24 or 36 required interviews in special case (iii)). In PSUs where more than 12 (or 24, or 36) blocks had been delineated due to the high number of visible structures (special case (ii)), selection of 12 (or 24, or 36) blocks with equal probability was implemented using equal probability random sampling. In PSUs with less than 12 (or 24, or 36) visible structures (special case (i)), two selection mechanisms were possible: First, if field teams found that there were indeed less than 12 structures in the PSU (as the satellite imagery suggested), all structures were interviewed. Second, when field teams found that the number of structures was higher than the satellite imagery suggested, enumerators counted the number of structures and randomly selected 12 (or 24, or 36) households to be interviewed with equal probability.

A similar second‐stage sampling strategy was employed for IDP strata. Each IDP settlement was segmented manually into enumeration blocks with approximately 10 structures each. Where sensible, 12 enumeration blocks were combined into one enumeration area. In some cases, however, IDP settlements consisted of geographically dispersed pockets within urban areas, each far away from the next. To keep enumerator travel time in check, facilitate supervision, and ensure safety, the construction of IDP EAs followed these geographical contingencies to some extent. Hence, some EAs were created to contain more than 12 blocks and others contained less than 12.

Several of the most recent IDP settlement boundaries provided by UNHCR were a few years old, while the recent drought caused perturbations to the size, composition, and localization of the IDP population. Thus, each selected IDP enumeration area was inspected to ensure that it was still inhabited by displaced 12
events and measurement error on the other hand. If the wardrobe had been upgraded, Nve would expect future ratings on average to be as nice as today's. However, if today's nice rating was caused by an idiosyncrasy or measurement error, we would expect future ratings to revert to the old, lower levels. Hence, both transitory events and measurement error would show up as many movements up and down the fashion hierarchy but all these movements would tend to be undone in the next period. This intuition underlies the procedure that distinguishes persistent and transitory shocks. This procedure is presented formally in the next section.

Second, one might obtain three independent measures of the quality of a person's outfit; for example the outfit's price, age and the interviewer's rating. If there were no measurement error, all three measures would always move in the same direction. We can then find the measurement error in each measure by seeing how often that measure moves in a different direction than the other two. Once we know the amount of measurement error in the interviewer's ratings, we can calculate the likelihood that an increase in the ratings was due to the person wearing a nicer outfit. One can interpret two of the measures of fashion quality as instruments for the third measure. This instrumental variables procedure is developed formally in the next section.

# 3. **Data and Methodology**

## **_Data_**

The data for Russia come from the Russian Longitudinal Monitoring Survey (RLMS), which is a nationally-representative socioeconomic survey of the Russian Federation. We use the panel component of waves 5 through 8 which were fielded in the falls of 1994, 1995, 1996 and 1998. This yields a sample of 7,382 individuals in 2,256 households with complete demographic, income and expenditure information. More details about this dataset can be found on the website of the University of North Carolina at Chapel Hill (www.cpc.unc/projects/rlms/project.html) and in Lokshin and Popkin, 1998.

_5_
countries shows that the gender gap in math is present as early as 4th grade (Bharadwaj et al. 2012). This is not the case for Mexico. In order to assess whether our sample is representative of the Mexican population, we follow the nationwide cohort of 6th grade ENLACE takers in the year 2007 over to 9th (in 2010) and 12th grade (in 2013). Girls do consistently better than boys in language and the gap stays constant throughout the different grades. Neither in 6th grade nor in 9th grade is there evidence of a gender gap in math, but girls’ 12th grade score in math is 30 points (0.30 _σ_ ) lower than boys’ (Table 1). The gender gap in 12th grade may be partly explained by differential selection: 28.6% of the boys who took the ENLACE 6th grade in 2007 completed the 12 grade exam in 2013, as opposed to 34.9% of the girls.

# **3.3 Follow-up Data**

Students enrolled in 10th grade in 2009 were supposed to complete high school in 2012.<sup>16</sup> We use data from the 2012 12th grade ENLACE exam to measure the three main outcomes of interest: the probability of taking the test, math scores, and Spanish scores. We interpret the probability of taking the 12th grade test in 2012 as a proxy for the probability of completing upper secondary on time. 61% of the students surveyed at the baseline took part in the 12th grade ENLACE exam three years later. Not observing a student who was originally enrolled in EMS in 2009 take part in the 12th grade ENLACE exam in 2012 has four possible explanations: 1) the student dropped out of school at any point between 9th and 12th grade, 2) the student repeated one or more semesters, 3) the student did not show up for the exam but regularly completed the EMS, 4) or potential merging problems. Using 2013 data from 12th grade ENLACE we find that 205 students from the original sample (4.9%) took the exam in 2013, most likely because they had to repeat two full semesters.<sup>17</sup> The probability of taking ENLACE in 2013 is not statistically different for treatment and control schools. In order to provide a measure of the no-showing up rate, we collected the 2012 lists of all the potential 12th grade ENLACE takers that each school had to send to the central authority roughly two months before the test. On average, 10% of the students reported on the list did not show up for the exam, but they are likely to complete EMS regularly. Reassuringly, the no-showing-up rate is not statistically different for treatment and control schools. Finally, only for five students the quasi- _CURP_ was not sufficient to identify them since it was not unique. Therefore, we interpret the difference in probability of taking the 12th grade exam between the treatment and control groups as a good measure of the intervention’s effect on

> 16The northern State of Nuevo León is an exception since public upper secondary schools follow a two-year program.

> 17Students who repeat either one or three semesters leave school without taking the test.

10