rafmacalaba/data-use-mentions-extended huggingface dataset:
firm, and makes about $71 per month. Taken together, the evidence shows that our sample is composed of established and profitable firms that employ workers who hold stable, regular, and well-paying jobs by Ugandan standards.

# **4.4 Road Network Data**

We supplement the pollution measures and firm survey with data on the network of Ugandan roads published by the World Food Program following the United Nations Spatial Data Infrastructure (UNSDI) for Transport standards.<sup>23</sup> The WFP shape-file distinguishes between 5 distinct road types in Uganda: _track/trail_ , _tertiary roads_ , _secondary roads_ , _primary roads_ , and _highways_ , by converting Open Street Map (OSM)’s highway tag.<sup>24</sup> The geo-referenced nature of the dataset allows us to match roads with both the pollution and the firm survey data. We create an ordinal measure of road size. To do so, we rank road types by size such that _track/trail_ are assigned the number 1, and _highways_ are assigned the number 5. We use these values when

> 23Source: `https://geonode.wfp.org/layers/ogcserver.gis.wfp.org:geonode:uga_trs_roads_osm/ metadata_detail.`

> 24The WFP classification is a mapping of the 18 OSM’s highway tag into 7 categories (the five categories mentioned above, as well as Residential and Path/Footway, which are absent from the Uganda road shape-file). Details of the mapping can be found on the WFP website.

15
a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR’s data only include IDPs protected or assisted by the agency.
sup>_∗∗_</sup>|(0.25)<sup>_∗∗∗_</sup>|
|Adjusted _R_<sup>2</sup>|0.013|||0.167|||
|_F_|||1.660|||3.056<sup>_∗∗∗_</sup>|
|Hansen _J_|||62.669|||71.109|
|AR(2)|||-0.634|||-0.487|
|Estimator|OLS|RE|D-GMM|OLS|RE|S-GMM|
|Instruments|||58|||70|
|_N_|592|592|451|555|555|555|

> * Growth rates calculated from GDP data in constant 2005 international purchasing power parity-adjusted dollars. Sampling frame selected to maximize sample size, and is hence unbalanced. Data periodicity are five-year averages. All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. A lagged dependent variable (GMM only), period fixed effects, and a constant term (all specifications) were included in the regressions, but not reported. _∗_ indicates significance at 10 percent level, _∗∗_ indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

34
workers. Indeed, 19 of these firms employ more than 1,000 workers.<sup>3</sup> Of these employees, 74 percent are engaged in apparel production. In absolute terms, the number of employees in the parks is still relatively small. However, since 2015, employment in industrial parks increased by 78 percent, comparable to what we saw in Vietnam over the period 1990-2000.

All but three of the new industrial parks are owned by the Ethiopian government: two of the other owners are Chinese and one is Bangladeshi. Almost all the firms in these parks are foreign-owned; 79 percent are owned by Asian investors, with China in the lead at 66 percent, 12.4 percent are owned or partially owned by Ethiopian investors, and 8.6 percent are owned by EU and UK investors.

Within Ethiopia’s industrial parks, 83 percent of employment is in firms with more than 1,000 employees. Women account for 74 percent of total employment; firm managers tend to hire young female workers who have completed a 10<sup>th</sup> grade education. These women typically work 8 hours a day for 6 days a week plus an average of two hours overtime each day bringing the average hours worked per week to 60. Ultimately, the sustainability of these parks depends in part on working conditions in the parks. Using a phone survey of firm managers, Meyer et al. (2021) find that the base salary in most of the firms in these industrial parks exceeds the cost of basic needs as measured by the local poverty line. When bonuses, overtime pay, incentive payments and in-kind benefits are included, total compensation is roughly four times the cost of basic needs. However, they also find that 21 percent of firms in the industrial parks report paying a base wage below the local poverty line. Based on the information in the report, it is not clear how far below the poverty line these wages fall and what happens to total compensation for
1999|2001|2003|2005|2007|2009|2011|2013|2015|
||||Math|Portu|guese|Enrol|lment (mil|lion)|||

_Note._ Minimum score on the SAEB scale that characterize adequate learning for students in 3<sup>rd</sup> grade of secondary education was 300 points for Portuguese and 350 points for math. _Source._ School Census and SAEB – INEP/MEC.

The literature has established the central role of teachers in student learning, but there is still no consensus on how teachers’ pre-service training affects teacher performance. Studies that measure the value-added of teachers in a school year find that students with a good teacher can achieve an average gain of one year; while students with great teachers might gain 1.5 grade levels (Hanushek and Rivkin 2010). In addition, teachers critically impact not only children’s immediate learning progress, but also their longterm development and life choices (Bruns and Luque, 2015). However, studies based on the educational production function, estimated from cross-sectional data and on regression analysis of aggregated levels of student performance, fail to find statistically significant effects of teacher education measures (Hanusheck, 2003). Other series of studies developed since the 2000s, based on student-level longitudinal data and involving broader sets of variables, and more recent research that addresses non-observed heterogeneity

> 1 Repealed by Law No. 12796/13, which reintroduced the secondary level Normal course as minimum level of training for teaching in early childhood education and in the first years of elementary school.

2
_Social Capital, Household Welfare and Poverty in Indonesia_ _58_ the aggregate social capital index. If there is only one religious group in the village, it is not possible to have a religiously heterogeneous association. There is also no reason to assume that ethnic or religious composition would directly affect household income, nor, obviously, will income affect diversity.<sup>3 0</sup>

- (2) _The density and effectiveness of institutions in the village._ Clearly, the possibility for a given household to join an association increases as more associations exist in the village. The likelihood to join and to be active in the association can also be expected to increase as institutions are perceived to be effective. As we have already demonstrated earlier, the density of associations at the village level does not significantly affect the level of household expenditure directly.

- (3) _The village's involvement in the procurement of social services and infrastructure._ Such involvement is likely to incite people to join associations dealing with education, health, roads or other infrastructure. It does not however have a direct effect on household income (which stems from the use of such services or infrastructure).

Given the content of the available village data file, this leads to eight possible instruments: an index of ethnic and religious diversity, the number of existing associations in the village, the percent of iistitutions deemed effective, and indexes of

> 30 This position is not inconsistent with-the possibility that ethnic diversity may affect economic growth at the national level (as has been demonstrated for Africa by Easterly and Levine, 1995 and Collier, 1998a).
# 3.2.4 Step 4

A key innovation of the model is the incorporation of both private and public signals into agents’ decisions. Agents use these signals to predict the annual component of demand and yields. They have both private and public sources, and weight them proportional to their precision. The model makes use of USDA reports released monthly that predict future demand and yield per acre; they are considered be public signals since anyone can access them at no cost. The section then estimates private signals through a Kalman Filter, in which the observables are prices and ex post realized annual demand and yield values.

Formally: where �� and �" are covariance matrices for �t; "t: �� is calibrated by measuring the precision of public forecasts and the standard deviation of changes in price that remain unexplained by the model. Values for �";are the precision in private forecasts estimated previously. Matrices Ft; Ht; and Bt represent the laws of motion for private signals, the e¤ect of private signals on prices, and the e¤ect of the rest of state variables on prices, respectively. Also, the second and third rows of matrix Ht represent the relation between signals and ex post realizations. That is, they capture the fact that signals forecast ex post realized demand and supply shocks with some error.

Estimations can be seen in Figures X and XI, which show the estimated private signal. Although private and public signals tend to move together, as one would expect, there are several moments in which these di¤er, which may explain public information is not the only driver of expectations.

33
br>(0.000)|
|Secondary education|0.041***|0.107***|0.120***|0.015***|0.033***|0.036***|
||(0.002)|(0.002)|(0.001)|(0.001)|(0.001)|(0.000)|
|Terciary education|0.143***<br>(0.002)|0.172***<br>(0.002)|0.290***<br>(0.001)|0.059***<br>(0.001)|0.067***<br>(0.001)|0.099***<br>(0.001)|
|Survey-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Region FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|6,849,340|2,295,693|5,031,670|4,912,294|3,085,027|2,552,231|
|R-squared|0.037|0.044|0.059|0.023|0.029|0.027|

_Source:_ Authors’ calculations based on data from 12-months survey panels from Labor Force Surveys. _Note:_ Each column reports regression results from a linear probability model of the effect of the listed individual characteristics on the probability of switching to formal employment. Regressions include survey-year and region fixed effects. Columns 1-3 and 4-6 have different subsamples. The sample considered in columns 1-3 includes all panel individuals (interviewed in one survey and 12 months later) who worked in both periods, were 20-64 years old and were informal-employee at the initial
**<u>│</u> 29**

A snapshot of differences in the ratio of woman-owned or managed foreign multinationals relative to domestic firms highlights the lower numbers of women in managerial positions (see Fig. 12). Firm scale, skills mismatches, and the nationality of managers often expatriated could explain these patterns. Although in countries where high positive differences are observed local conditions seem to be an important driver: Malaysia and Indonesia are good examples of high differences in female managerial responsibilities.

Overall, operating with global labor standards in countries where women do not have the same opportunities stands to make a difference for the local populations. Yet while the direction of this effect on gender inequality is undisputed, its magnitude would inevitably vary (Shu, Zhu and Zhang, 2007; Aguayo-Tellez, 2012; Braunstein and Brenner, 2007; Glick and Roubaud, 2006; Siegmann, 2007).

Domestic supply linkages The strength of linkages between foreign and domestic firms is often shaped by the motivation for foreign investment. In particular, market-seeking FDI is more likely to result in linkages with local firms than efficiency-seeking, natural resource-seeking, or strategic asset-seeking ‐ FDI (Farole and Winkler, 2014; Amendolagine et al., 2013; Sánchez Martín, de Pinies and Antoine, 2015). More generally, what multinationals do – i.e., their industrial activities – largely explains the extent of linkages with local suppliers. The food, wood and furniture, automobile and auto parts industries are structurally more dependent on foreign inputs ‐ (Sánchez Martín, de Pinies and Antoine, 2015). By contrast, in the apparel industry, the presence of rules-of-origin requirements limits their development (Farole and Winkler, 2014). Services may be the most promising sector for local firms to develop and benefit from linkages with FDI activities due to the prevalence of market-seeking motivation and their high labor intensity.

The Enterprise Surveys confirm that domestic linkages of foreign multinationals tend on average to be less strong than those of domestic firms, with only very
- Boatman, A. & Long, B. T. (2018), ‘Does remediation work for all students? How the effects of postsecondary remedial and developmental courses vary by level of academic preparation’, _Educational Evaluation and Policy Analysis_ , 40(1), p29–58,

- Bound, J., Lovenheim, M. F. & Turner, S. (2010). "Why Have College Completion Rates Declined? An Analysis of Changing Student Preparation and Collegiate Resources", _American Economic Journal: Applied Economics_ , 2 (3), p129-57.

- Bühlmann P., van de Geer S. (2011) Lasso for linear models. In: Statistics for High-Dimensional Data. Springer Series in Statistics. Springer, Berlin, Heidelberg.

- Carrell, S. E., Page, M. E. & West, J. E. (2010), ‘Sex and science: How professor gender perpetuates the gender gap’, _The Quarterly Journal of Economics_ , 125(3), p1101–1144.

- Carrell, S. E. & Kurlaender, M. (2016), ‘Estimating the productivity of community colleges in paving the road to four-year success’, _NBER Working Paper_ , w22904.

- Castleman, B. L. & Terry, B. (2016), ‘Long Looking beyond enrollment: The causal effect of need-based grants on college access, persistence, and graduation’, _Journal of Labor Economics_ , 34(4), p1023-1073.

- Cellini, S. R. & Turner, N. (2019), ‘Gainfully employed?: Assessing the employment and earnings of forprofit college students using administrative data’, _Journal of Human Resources_ , 54(2), p342-370.

- Cellini, S. R., & Grueso, H. (2021), ‘Student learning in online college programs’, _AERA Open_ , 7(1), p1– 18

- Chetverikov, D., Liao, Z. Chernozhukov,
Linear Methods: Replication, Sectoral Extension and Ratings_

We first replicate the specifications in health, education, and WASH from Mishra and Newhouse (2009) for health, Birchler and Michaelowa (2016) for education, and Ndikumana and Pickbourn (2017) for WASH. Using identical model forms, we are able to reproduce the results in each paper closely (see Appendix A). Since our primary interest is in replicating these models as a baseline, we do not extend them through instrumentation or other techniques, nor do we make more than associative claims based on them.

We then extend this prior analysis by varying controls to check for robustness and adding two sectors: energy, and fiscal management. For energy, we use as a baseline the same controls as in WASH. For fiscal management, we use a limited set of controls, for income level and institutional quality. The general form of the regression equations was as follows:

> 8The AidData is based on the Organisation for Economic Co-operation and Development (OECD) Creditor Reporting System (CRS) donor-reported data, with added granularity on purpose and activity coding.

> 9The scrapped dataset of public documents related to World Bank development projects is openly available at HuggingFace (https://huggingface.co/datasets/lukesjordan/worldbankproject-documentS).

6
in the responses to sensitive questions than in non-sensitive questions, and the differences were being driven by internet respondents being more likely to choose the sensitive option. Combined, the results suggest that anonymous internet surveys can be an additional tool to collect information that individuals would likely underreport in other survey modalities.

However, there are a number of caveats to these results and future work to be done. First, although the internet respondents are more likely to choose the sensitive option in this setting, that does not mean that there still is not a subset of internet respondents that are purposely not admitting to sensitive preferences, opinions, and actions. Additional work needs to be done to investigate what share of individuals might still not fully respond to sensitive questions in these types of surveys, potentially by comparing responses to internet surveys to observable outcomes (e.g., election outcomes, market outcomes, etc.) or by using internet surveys along with key informant interviews and other techniques to try and better triangulate how people feel about sensitive issues.

Additionally, this is a single conflict setting in which pre-conflict mobile phone usage and internet usage were high relative to conflicts in other parts of the world.<sup>27</sup> Furthermore, the surveys used here were small, and larger and more detailed surveys could better vary the source of the sensitivity, can better compare different issues where the source of sensitivity is similar but the degree of sensitivity might vary substantially, and so on. Additional work can shed light on these and other issues, and further illustrate how these results might generalize to other conflict settings.

> 27For example, see estimates of penetration of mobile phones and internet in the World Development Indicators at https://datatopics.worldbank.org/world-development-indicators/.

21
We double-checked the list for consistency against country details provided in IMF Article IV reports.<sup>11</sup> Episodes identified by an alternative criterion where the sudden stop ends when capital flows recover to the average of the past 20 quarters are listed in the Appendix as well.

> 6 The full list of countries and the periods for which their data are available is in Appendix A.

> 7 Cavallo et al (2013) show that the sudden stops in flows from non-residents tend to be larger and have larger impacts on economies than those which are driven by outflows by residents.

8 Calvo et al (2004), in an early influential study, use monthly data for 20 advanced and emerging markets over the period 1990-2001. Since capital flow data are unavailable monthly, they instead use the change in reserves and the trade balance. According to their definition, a sudden stop begins when capital flows so measured fall one standard deviation below the mean for the past 24 months; the episode continues until flows recover to above the earlier mean. In addition they require that in at least one month during the duration of the episode capital flows fall 2 standard deviations below their earlier mean. Forbes and Warnock (2012) define sudden stops similarly but use data on actual capital flows available at a quarterly frequency. A sudden stop is said to occur when the year-on-year change in capital flows over four quarters is at least one standard deviation below the average in previous five years and when in at least one quarter flows are two standard deviations below that prior average. They discard episodes lasting only one quarter.

9 In addition, we provide some limited comparisons with other categories of capital movements (FDI flows and portfolio flows by residents), which reinforce the contrast and help to justify the focus.

10 In some cases where the criterion of capital flows declining by 2 standard deviations below mean was missed by a whisker, we still identified that episode as a sudden stop. One could of course measure capital flows and their volatility
quartile. This indicates that sorting countries into even broad categories is subject to significant margins of error for most countries,.

The rather large size of these confidence intervals has important implications for the use of these aggregate governance indicators. It is clear that small differences in point estimates of governance across countries are not likely to be statistically significant. This mirrors the reality that it is in fact difficult to distinguish small differences among countries using this type of data on govemance. As a result, users of this data should focus on the range of possible governance for each country as summarized in the 90% confidence intervals shown in Figure 1. For two countries at opposite ends of the scale of govemance, whose 90% confidence intervals do not overlap, it is reasonable to conclude that there are in fact significant differences in govemance between these two countries. For pairs of countries that are closer together and whose 90% confidence intervals overlap, one should be much more circumspect about the significance of estimated differences in govemance between two such countries.

Despite the imprecision of these aggregate indicators, they are still very useful, for several reasons. First, since each of these aggregate indicators spans a much larger set of countries than any individual indicator, it is possible to make comparisons - however imprecise -- across a much larger set of countries than would be possible with any single indicator. Second, although imprecise, each aggregate indicator provides a more precise signal of its corresponding broader governance concept than do any of its component indicators, and moreover provides a convenient and consistent summary of the available evidence. Third, the measures of precision for each country are useful because they enable formal statistical tests of cross-country differences in governance instead of arbitrary comparisons. Fourth, it is possible to use information in the estimates of the precision of each aggregate to quantify the effect of measurement error in regression analyses that use governance indicators as right-hand side variables.

_11_
plugs etc.)|7721|44611|0.6|0.0|0.2|0.0|0.2||
|Mineral or chemical fertilizers, nitrogenous|5621|44419|0.6|0.0|0.1|0.0|0.0||
|Tinned sheets and plates, of steel|6747|43542|0.5|0.9|4.9||0.1||
|Otherparts & accessories of motor vehicles|7849|43273|0.5|0.1|0.2|0.0|0.0|105.5|
|Cyclic hydrocarbons|5112|42935|0.5|0.3|0.1||||
|Parts of and accessories suitable for 751.2-|7599|42698|0.5|0.1|0.1|0.0|1.0||
|Other tyres, tyre cases, inner tubes|6259|40622|0.5|7.8|1.1||18.2||
|Engines & motors, such as water turbines etc.|7188|40298|0.5|0.0|0.1||0.0||
|Iron ore agglomerates(sinters,pellets etc)|2816|40079|0.5|37.4|2.2||||
|Other non-electric parts & accessories of machines|<br>7499|13017|0.2|1.8|0.5|0.0|0.0||

_Source_ : Calculated using UN COMTRADE Database _Notes_ : A value of 0.0 represents less than US$100 in exports; blanks represent zero exports..

32