rafmacalaba/data-use-mentions-extended huggingface dataset:
intuition is here that different stages of technological transition require distinct policy priorities. A sufficient coverage _and_ quality of primary education are regarded as the minimum prerequisite to adopt technologies. By contrast, in countries where basic skill requirements are fulfilled and firms are making significant adaptations or innovations, the creation of more specialized skills ought to be the priority. In addition, the results here partially confirm the earlier micro-level evidence for Guatemala.<sup>25</sup>

- 24 In principle, the inclusion of a time trend for 1999 and an interaction term for secondary and tertiary schooling would allow incorporating all three levels of education at a time. Tentatively, such an exercise yields similar qualitative results on the impact of each level of education on growth—albeit primary schooling becomes insignificant. In addition, due to the multicollinearity problem, this specification was found to be rather sensitive and performs less well than the results displayed in Table 6.

- 25 For Guatemala, Psacharopoulos and others have extensively investigated the returns to schooling, sometimes by level of education. Such exercises are summarized in Psacharopoulos and Patrinos (2002), Haeussler (1993) and World Bank (1995). The studies generally report high private returns to primary schooling, but are merely based on ENS (1989) or earlier data, and typically do not care about sample selection bias.

-35-
extension + video treatment; єij represents an individual level error term that is clustered at the mws level to allow for arbitrary correlation of households and individuals within clusters.

Although treatment indicators should be orthogonal to further explanatory variables due to randomization, all models are re-estimated including additional covariates in order to increase precision of the estimates and to control for small-sample imbalances: In these models, Xij0 represents a vector of control variables related to farmer and household characteristics captured at baseline, while Wj, adds community-level indicators for rainfall, temperature and remoteness. When available, the inclusion of the baseline level of an outcome Yij0 in the equation should reduce the overall variance, since some degree of path dependency on previously gained experience with a technology is assumed. This treatment effect model is appropriate in this case, since for some outcomes, baseline and endline measures are not completely identical, or baseline data is not available at all.<sup>17</sup> In addition, this specification has been shown to be more powerful than the difference-in-difference estimator in the presence of relatively low autocorrelation (McKenzie 2012a; De Brauw et al. 2018), which can at least be stated for some of the outcome variables. The main estimators of interest are _β_ and _λ,_ capturing the intent-to-treat effect (ITT). One-sided equality tests evaluate the additional impact of the video intervention, by testing whether the coefficient of T2 is significantly larger than that of T1.

> 17 Baseline data is available for adoption of compost, blended fertilizer, improved seeds and lime. Regarding blended fertilizer, we additionally control for ex-ante use of any inorganic fertilizer, since during time of baseline, blended fertilizer was largely unavailable; instead, farmers used the widely available DAP fertilizer (cp. Table 1). In the two years between baseline and endline, NPS/NPK fertilizer blends largely replaced other inorganic fertilizer types. Line seeding can only be proxied, since it was assessed on a more general level during baseline, asking farmers how they _usually_ plant crops,
increasing returns to scale sectors of our model into low, medium and high technology sectors. The classification is defined by the share of R&D expenditures in total sales, based on U.S. data. For low R&D intensive sectors, we assume that the elasticity of firms with respect to price is the same for the CIS region as for the EU, but the elasticity is only one-third of Rest of the World elasticity (trade with the CIS or EU regions misses out on trade with China or the U.S.). For medium and high R&D intensive sectors, we assume that trade and FDI with the CIS region is only one-eigth as valuable as trade with the Rest of the World (as discussed above), while trade with the EU is two-thirds as valuable as trade with the Rest of the World. Finally, we allow the elasticity of the Rest of the World to vary depending on the R&D intensity of the sector, where we allow for more technology diffusion in more R&D intensive sectors. The results of these assumptions are in table 6b.

> 43 Schiff and Wang do not compare lagged industrialized trade to lagged developing country trade, which may bias the results against the relative benefits of industrialized trade.

114
unique firm identifiers, we have merged these data with customs records on export transactions collected by _Secretaria do Com ́ercio Externo_ (SECEX) of the Ministry see Greenaway and Nelson (2000), Bastos and Silva (2009), Falvey, Greenaway and Silva (2008, 2010) and the references therein.

> 2In particular, we kept for analysis employees working more than 20 hours per week, aged between 16 and 65, and earning at least the national minimum wage.

5
# **4.1. Methodology**

The pass-through rate is estimated using equation 5 above. Following Ravallion (2003), the regression is estimated without an intercept. Improving on the earlier literature, we only include growth spells between household survey rounds that are comparable in order to focus on real changes between two survey rounds and ignore any spurious changes. Survey comparability is assessed according to various characteristics, including the method of sampling, the questionnaire design, the methodology used in the construction of welfare aggregates and the price deflation used over time and space.<sup>19</sup> We use the growth in per capita HFCE as opposed to growth in per capita GDP, as HFCE aims to capture household private consumption in national accounts, and so it is, in principle, more aligned with the consumption captured in surveys than GDP (Ravallion (2003), Deaton (2005)). Similarly, to derive reference-year estimates in the absence of a new household survey, PovcalNet uses HFCE over GDP in most countries, including India (Prydz, et al. (2019)).<sup>20</sup> A crucial question is how to define the relevant sample of survey growth spells. The existing literature has partitioned the sample along various dimensions, for instance by income level, level of inequality or by geographic regions. See, for example, Birdsall et al. (2014), Chen and Ravallion (2010), Chandy et al. (2013) and Corral, et al. (2020). Given that different choices of partitioning variables yield different pass-through rates, a systematic approach of partitioning the data is necessary.

To that end, we follow an approach that is identical to the machine learning algorithm used in Lakner et al. (forthcoming).<sup>21</sup> Here, we use per capita HFCE growth instead of per capita GDP growth as the main independent variable. The partitioning algorithm, referred to as model-based recursive partitioning (MOB
multilateral loans than bilateral based on the logic that multilateral loans are a greater part of the loan-level dataset sample and are disbursed more slowly.

# 5.2 Accounting for global shocks

financial cycle suggest that investment across countries may be subject to common global shocks (Miranda-Agrippino and Rey, 2022; Kose et al., 2009). While time fixed effects control for common shocks in the main results, as an alternative we include cross-sectional averages of public and private investment in our regressions. Based on guidance from Chudik et al. (2017) we include three lags of the cross-sectional averages. The multipliers presented in table 7 are consistent with the multiplier estimated in our main results, where time effect was used to account for common shocks. Specifically, we find a baseline estimate of 1.6, which rises to 2.3 after three years.

**Table 7:** The response of private investment to government investment, cross sectional dependence

||(1)<br>|(2)<br> <br>|(3)<br>Cumulative M<br>|(4)<br>ultipliers (hor<br>|(5)<br>izon h in years<br>|(6)<br>)<br>|
|---|---|---|---|---|---|---|
||Baseline|h=0|h=1|h=2|h=3|h=4|
|Change in government investment|1.558**|1.581**|2.237***|2.373***|2.300***|1.420|
||(0.623)|(0.658)|(0.863)|(0.782)|(0.815)|(1.092)|
War and Women‟s Work: Evidence from the Conflict in Nepal _Nidhiya Menon, Brandeis University_ _Yana van der Meulen Rodgers, Rutgers University_ _JEL_ Codes: O15, J22, R23 Keywords: conflict, added worker effect, Nepal, instrumental variables, female labor supply, migration.

**Corresponding author:** Yana van der Meulen Rodgers, Women‟s and Gender Studies Department, Rutgers University, New Brunswick, NJ 08901. Tel 732-932-9331, fax 732-9321335, email yrodgers@rci.rutgers.edu. Contact information for Nidhiya Menon: Department of Economics & IBS, MS 021, Brandeis University, Waltham, MA 02454-9110. Tel 781-736-2230, fax 781-736-2269, email nmenon@brandeis.edu. We thank Josh Angrist for initial discussions on research design, and Lakshmi Iyer and Quy-Toan Do for sharing their Nepal conflict data. Thanks also to Xiao-Yuan Dong, Maria Floro, Joyce Jacobsen, Ana María Ibáñez Londoño, Olga Shemyakina, and to workshop participants at Brandeis, Wellesley, Cornell, Rutgers, and the 2010 World Bank workshops on Gender and Conflict. We also thank our discussants at the World Bank workshops: Hasan Zaman, Quy-Toan Do, Helge Holterman, and Gudrun Østby. This research is supported by the World Bank.
is zero and the least 6. Through a bottom-up approach, these indicators comprise the basis for broader PMR indicators at a sector-specific and economy-wide level. It should be noted that the OECD’s PMR indicators are comparable across countries due to their _de jure_ nature. However, their comparability is limited due to the various _de facto_ implementations of these laws across countries.

In a recent summary of the empirical competition literature, Hong (2022) reviews the effects of competition policy on three key macroeconomic outcomes. He concludes that pro-competitive policies lead to productivity growth, a positive effect on total employment and real wages in the medium to long term, and reduced income inequality. A working paper by Causa et al. (2015), reaffirms the inequality-reducing effects of product market deregulation, highlighting the positive effects on households’ disposable income.

14
In other words, the benefit of the PSI-PMI program, as measured by students’ performance in grade 12, accrues to high-performing students. As such, additional measures are needed to ensure that the benefits of the program are more inclusive along the entire spectrum of student performance. Figure A1, in the appendix, provides the results for the between school control group.

**Figure 2** : Effects of the PSI-PMI Program on Student Learning Outcomes: By Baseline Performance

<!-- Start of picture text --> Impact of PSI-PMI Program on Students with Varied Baseline Performance<br>Program Students VS Within-School Control Students<br>1 2 3 4 5 6 7 8 9<br>GABECE Math Grade Score<br>Impact of PSI-PMI Program on Students with Varied Baseline Performance<br>Program Students VS Within-School Control Students<br>Credit and Above Pass Fail<br>GABECE Math Grade Score<br>3<br>2<br>1<br>0<br>Improvement/Effect WASSCE Math Standardized Score<br>-1<br>2<br>1<br>0<br>Improvement/Effect WASSCE Math Standardized Score<br>-1<br><!-- End of picture text --> _Note:_ The standardized WASSCE mathematics score is the dependent variable. The results are for the program group versus the within-school control group, controlling for covariates such as gender, baseline performance, and socioeconomic background, and school characteristics. GABECE = Gambia Basic Education Certificate Examination; PSI-PMI = Progressive Science Initiative and Progressive Mathematics Initiative; WASSCE = West African Secondary School Certificate Examination. The GABECE is graded on a score of 1 to 9, with lower values being better.

23
al. (2014) find that institutional quality, measured by the Country Policy and Institutional Assessment (CPIA) score, has a significant and positive correlation with project success.<sup>6</sup> There are contradictory findings elsewhere, however. Using the Freedom House scores for civil liberties and political rights, Feeny and Vuong (2017) find that the probability of Asian Development Bank projects' success appears lower in more democratic countries. And Blum (2014) finds no clear evidence that projects, at least in public sector management, are more likely to succeed in countries with higher administrative capacity, as measured by CPIA scores and International Country Risk Guides (ICRG) Bureaucratic Quality Ratings. Blum (2014) offers an explanation for this seemingly counterintuitive result, arguing that TTLs may adjust the ambitiousness of their project objectives to the level of administrative capacity in the respective country. Therefore, he argues, the likelihood of reaching these objectives across countries and institutional environments is relatively constant.

The idea that economic growth and stability are conducive to project success is intuitively appealing and supported by evidence. Higher GDP growth rates are associated with more successful projects (Denizer et al., 2013; Briggs, 2020; Bulman et al., 2017; Feeny and Vuong, 2017; Presbitero, 2016), while economically unstable environments – such as high export volatility (Guillaumont and Laajaj, 2006), inflation (Deininger et al., 1998) and GDP volatility (Caselli and Presbitero, 2021) – are associated with worse project outcomes. On the other hand, Presbitero (2016) finds that World Bank projects implemented during periods of public investment booms are less likely to succeed, comprising suggestive evidence of absorptive capacity constraints in recipient countries.

> 6 These scores comprise a set of criteria grouped into four clusters: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions.

7
|16.6|-14.7|-6.8%|7.9|2.5|-5.3|-11.8%|3.7|1.4|-2.3|-10.3%|

_a/_ Moldova and Romania measure moderate poverty at rates close to US$5-a-day, while Peru and Thailand measure at rates close to US$4-a-day. _Source:_ Author’s calculations with data from RIGA for Ghana and Nepal, and from household surveys for Bangladesh, Moldova, Romania, Peru, and Thailand.

25
0.620)|
|Polity Indext-1|<br>-0.037|<br>0.008|<br>-0.050|
||(0.044)|(0.047)|(0.036)|
|Exchange Ratet-1(ln)|-0.400|0.050|-0.319|
||(0.295)|(0.459)|(0.295)|
|Exchange Rate Volatilityt-1|-13.326*|-6.163|-4.232|
||(5.404)|(7.057)|(3.556)|
|Control of Corruptiont-1|1.000+<br>(0.568)|0.361<br>(0.778)|1.117*<br>(0.542)|
|Inflationt-1|<br>0.001|<br>0.011|<br>-0.002|
||(0.008)|(0.010)|(0.007)|
|Observations|1,234|1,234|1,234|
|Number of Countries|90|90|90|
|Country FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|

Robust standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 Table 2 shows the results for hypothesis 5. For these regressions we aggregated greenfield FDI, distinguishing between investments made by relatively diversified and undiversified MNEs. Since this results in a three-dimensional data set (country - year - diversification dummy), it is impossible to use 24
(the United States and India) data were purchased from D&B because nationally representative business registry data on corporations were otherwise unavailable.<sup>4</sup> Respondents completed either paper or electronic surveys. The database was checked for consistency across countries and over time. Quality assurance measures included comparing data to any published official data and/or related country case studies.<sup>5</sup>

# **3. Data and Summary Statistics**

# **_3.1 Entrepreneurship and the Business Environment_**

The analysis in our paper focuses primarily on one country-level indicator: Entry Density, calculated as the number of newly registered limited-liability firms in the corresponding year as a percentage of the country’s working age population (ages 15-64), normalized by 1,000.<sup>6</sup> We first summarize the data by region (Figure 2). The data show significant disparities across regions, ranging from an entry density of 0.58 in Sub-Saharan African (SSA) countries to 4.21 in industrialized countries (IND). In other words, there are on average about four limitedliability firms registered annually per 1,000 working age individuals in industrialized countries, as compared to less than one firm per 1,000 individuals in developing countries. This translates roughly to a national average of 55,000 newly registered limited-liability firms per year in industrialized countries, relative to about 35,000 in Latin America, 14,000 in South Asia, and 9,000 in Sub-Saharan Africa.

> 4 The complete list of sources is available upon request. 5

> The complete database is available at: **http://econ.worldbank.org/research/entrepreneurship.** 6 Average Entry Density for 2004-2009 is shown, by country, in Appendix 1

8
Table 1: Lagged shipment frequencies and shares of 2016 import statistics

|# of shipments|Share of|Share of|Share of|Share of|
|---|---|---|---|---|
|observed in 2015|shipment types|transactions|inspections|import value|
|0|0.539|0.188|0.350|0.191|
|1|0.131|0.051|0.047|0.039|
|2|0.075|0.038|0.033|0.032|
|3-5|0.108|0.084|0.068|0.071|
|6-10|0.064|0.089|0.068|0.086|
|11-100|0.079|0.374|0.291|0.392|
|_>_100|0.005|0.176|0.143|0.189|
|Totals:|671,548|3,889,775|285,611|1,597,102|

Table notes: The table shows, for frequency bins defined over 2015 imports, the share of shipment types ( _ofk_ triplets), import transactions, inspections and import value in Serbia’s 2016 imports. The final row reports the total associated with each variable. Import value is reported in millions of Serbian dinars.

calculated as the ratio of inspected shipments to shipments in year _t_ , for an _ofk_ triplet, multiplied by 100.

We begin by showing the distribution of import and inspection activity across the lagged shipment frequency bins. Table 1 reports, for 2016 Serbian imports at the HS10 level, a collection of summary statistics for each lagged shipment frequency bin. We report each bin’s share of shipment types ( _ofk_ observations), total transactions, total inspections and import value. The final row of the table reports totals for each outcome
and women traders, which results in many such traders engaged in informal trade.

Table 5: Road harassment in selected West African countries

||Number of<br>per 100 km|Road Controls<br>|Bribes per 1|00 km|Delays per|100 km Trip|
|---|---|---|---|---|---|---|
||2005|2013|2005|2013|2005|2013|
|Burkina Faso|5.5|1.6|4,410|2,140|22|17|
|Côte d’Ivoire|-|1.9|-|2,675|-|8|
|Ghana|2|1.8|1,960|679|21|18|
|Mali|4.6|2.6|12,250|3,775|38|26|
|Senegal|-|1.3|-|1,614|-|14|
|Togo|1.5|0.9|1,470|597|16|7|

_Source:_ World Bank, 2018 calculations using IRGT Initiative data

- Cross-border trade is expensive and inefficient due to the difficulty of obtaining import and export licenses. It takes an inordinate amount of time to file for licenses, and bribes or informal payments may be expected.

- Unorganized and Fragmented Domestic Transportation Market: The transport sector in these three countries is dominated by a large number of individual or

> 5 UN-OHLRSS (2019).

> 6 Behar and Manners (2008).

17
# The Democratic Republic of Congo’s Infrastructure: A Continental Perspective

## **_Vivien Foster and Daniel Alberto Benitez_**

### **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Marie Françoise Marie-Nelly (country director), Franck Bousquet (sector leader), Alexandre Dossou (roads), Pierre Pozzo di Borgo (railways), Michel Layec (power), Franck Bousquet (water and sanitation), Jerome Bezzina (ICT), and Johannes Herderschee (macro).
interest to explore LTRinduced effects in this regard. Table 8 highlights that, most likely because it requires an explicit record of who will inherit the parcel, the process significantly reduces succession-related uncertainty. We find a 9 percentage point increase in the respondent‟s response that she/he now knows who will inherit the parcel. Column 2 shows that greater certainty along these lines strongly benefits respondents‟ the children and that gender bias has virtually been eliminated, at least for couples. In LTR areas, children are 13 points more likely to inherit the land and, as columns 4 and 5 document, girls both benefit almost equally. Column 4 suggests that girls in female headed households are significantly less likely to be designated as an heir compared to their counterparts in male headed households. This result deserves further research, but it is not inconsistent with data from Mexico which shows that women exhibit strong and persistent preferences for male versus female heirs (Deere and Leon 2003).

# **5.3 Land market activity**

It is often claimed that, by reducing in the transaction costs of transferring land, interventions to improve land tenure may set off a wave of distress sales whereby households who lack access to credit and

> 10 While our questionnaire did not elicit information on disputed claims, it would in principle be straightforward to use the dispute register to explore how many of these were able to register disputed claims and how many of these had actually been resolved.

14
|Age 30-64|59%**|61%**||
|---|---|---|---|
|By Region||||
|North|37%**|25%**|**|
|Central|44%**|46%**||
|South|57%**|59%**||
|West|38%**|76%**|**|
|Crop Sales|63%**|62%**||
|By Age||||
|Age 15-29|70%**|58%**|**|
|Age 30-64|61%**|62%**||
|By Region||||
|North|47%**|10%|**|
|Central|51%**|33%**||
|South|59%**|57%**||
|West|70%**|75%**||

The values were computed using the 2018/2019 EHCVM and the 2014 LSMS of Burkina Faso. * indicate significance at 10 percent, ** at 5 percent.

36