rafmacalaba/data-use-mentions-extended huggingface dataset:
the owned plots, 15 percent have joint ownership, of which the predominant form is male-female. The remaining 38 percent and 47 percent of the owned plots are under sole male ownership and sole female ownership, respectively.

25 The IHS3 identified 18,917 plots that were reported to have been owned and/or cultivated during the reference rainy season (2008/09 or 2009/10). 618 plots are not considered for analysis since they lacked either GPS-based plot coordinates or GPS-based plot area. 1,314 plots are dropped since they are either fallow or missing production information. 199 plots are not included in the sample since unit values could not be computed reliably for at least one of the crops reported to be cultivated on the plot. 11 plots do not have a manager identified and 67 plots have at 9
from the WTO after the Russian Federation was formally invited to join the WTO on December 16, 2011. The Russian Federation is scheduled to implement its commitments to the WTO in stages, beginning with the summer of 2012, with the longest transition period eight years after accession, or 2020. We calculate the impact of the phased implementation of the commitments, starting from the impact of the commitments in 2012.

> cm3; and 870324 is for cylinder capacities exceeding 3 000 cm3. Within each six digit category, we take a simple average of the lower and upper bounds as the expected value of the cylinder capacity, i.e., for 870321 we have 500 (0+1000)/2=500).  Therefore, the following expected engine volumes apply: 500 cm3 for 870321, 1250 cm3 for 870322, 2250 cm3 for 870323, and 3000 cm3 for 870324.  Then we know that all ten digit tariff lines with the same 6 digit code have this engine capacity and we can apply the specific tariff to the numbers of cars in the import data based on the engine capacity that applies to these ten digit tariff lines from the tariff schedule.

> 11 This document is available from the website of the Customs Union, http://www.tsouz.ru/.

6
firms where sufficient information on innovation and employment is available.<sup>10</sup> Firms report both their sales and employment in the year the survey was conducted, which for most firms surveyed was in 2013 or 2014, as well as three years prior. In addition, firms report information about their innovation activities, such as product and process innovations but also including organization innovations which forms a key contribution of our paper. Because firms also report the share of sales attributed to new innovations, we are able to decompose employment growth into its respective components driven by old products and new products growth.

sured in terms of size, age and sector. Overall, we observe that the firms in our sample tend to be small, with fewer than 20 employees, and at the same time tend to be older, with the majority of firms operating for more than 10 years.

# **5 Results**

## **5.1 Employment and sales growth among innovators and non-innovators**

Before proceeding with the econometric analysis, we look at some descriptive statistics and check differences between innovators and non innovators, and other groups. Figure 1 displays employment growth rates annualized over the three-year period based on the most recent Enterprise Survey questionnaire by firm size groups. In general we observe that a large proportion of firms display no employment change and there is some tendency for younger firms to display higher rates of employment growth in line with predictions from the theoretical firm dynamics literature. For firms with less than 10 employees in the base year, we plot histograms of change in employees in lieu of growth rates. We observe that very few firms - less than two percent of the sample - display growth rates above the 20 % threshold (or an increase in roughly 8 employees for firms with less than 10 employees), which would classify them as high-growth the year in which the survey was completed, according to whether firms are non-innovators, process-innovators only, product innovators, and if so, whether they are also
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _16_ long-run averages estimated here) and over 20% of the official 5.33% long-run average. The lowest imputation _R_<sup> ̃2</sup> of 0 _._ 71 introduces only an additional 12% measurement accuracy compared to such a golden standard, which is not negligible but suggests that the real-time imputation of price surveys produces a viable alternative to official statistics, particularly when the timeliness and costs of both approaches are pitted against one another.

Another result that is worth commenting on is that the average correlation between the market-level food price indexes is low. At a monthly level the correlation is only around 0.18-0.26, and 0.33-0.57 when the price data is annualized. This means that there is on average little price transmission over short time spans. In this setting of weakly integrated markets, a single CPI index may not be representative of actual location-specific price dynamics.

The table provides some indication that imputation accuracy goes down when covering longer time periods. This may on the one hand be driven by increased price heterogeneity in the past, as indicated by the lower cross-market correlations, or by lower data availability as indicated by table 2. It is hard to gauge from the basic results whether there is erosion in performance taking place, which is part a reason why the trust score from section II.C is useful.

# _B. Prediction results_

The graph in figure 1 shows the modeled food price index using the 2017-2022 data selection, along with the obtained inflation rates and the trust scores. Graphs for the 2014 and 2009 starting points are available in the appendix.

The results show that for each data selection, a similar inflation rate of approximately 17% is obtained for the final period. A 1% difference is sensible since the food baskets are different. All three results also highlight a sharp price rise, with inflation reaching well above 20%, during 2019
of ninis are Iraq (51 percent), Guyana (44 percent), Nigeria (43 percent), and Bangladesh (41 percent), each with shares of more than 40 percent. At the other end of the spectrum, Sweden (4.2 percent), Slovenia (4.7 percent) and Luxembourg (4.8 percent) have the lowest shares of ninis. The region with the highest share of ninis is Middle East and North Africa, where one in three youth are out of school and out of work, while the lowest share is in high-income countries (11 percent). The share of ninis in Latin America is close to the global average of 22.4 percent. The GIDD data also reveal that the gender breakdown of the nini problem varies greatly across country groups: in East Asia and the Pacific, in Europe and Central Asia, and in high-income countries, woman are no more likely to be ninis than are men. Meanwhile, in South Asia women account for 82 percent of total ninis, and in the Middle East and North Africa and Sub-Saharan Africa the female share of ninis is similar to that of Latin America (two-thirds).

> 8 For details on the GIDD data and methods, see: <u>http://go.worldbank.org/YADEAFEJ30.</u>

> 9  Note that this global estimate is broadly consistent with the figure of 224 million calculated in a recent unpublished paper by Newhouse (2013).

12
**Figure 4. Skill content of jobs (STEP-based) by level of GDP per capita, all countries, latest year.**

<!-- Start of picture text --> Non-Routine Analytical, STEP Non-Routine Interpersonal, STEP Routine Cognitive, STEP<br>LB LB YE<br>SB NL<br>ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDVNGYPKINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOZACNCORSTHCRMEIQBGBRPAVEMXMUUYARLVTRCLRULTPLHUSCEESKGRPTSICZESCYPRITGBFRCAISFIBEDEIESEDKATUSNOLU ETKMZWGMBFUGSBNPKHBDCMCIZMGHMMNIMDVNGYPKINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOZACNCORSTHCRMEIQBGBRPAVEMXMUUYARLVTRCLRULTPLHUSCEESKGRPTSICZESCYPRITGBFRCAISFIBEDEIE SE DKATNLUSNOLU ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDVNGYPKINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECPEMVDOZACNCORSCRMEIQBGBRPAVEMXMUUYARLVTRCLRUHUPLLTSCEESKGRPTSICZDEESCYPRITGBFRCAFIBEIESEDKATUS LU<br>SLTZ SB ISNL NO<br>YE YE LB<br>700 800 900 1000 1100 700 800 900 1000 1100 700 800 900 1000 1100<br>GDP level, log points GDP level, log points GDP level, log points<br>Routine Manual, STEP Non-Routine Manual, STEP<br>PA<br>YE<br>LB<br>ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDVNPKGYINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMN AL BATNLKECMVPEDOZACNCORSTHCRIQBGBRLBVEMXMUUYARLVTRCLRUHUPLLTSCEESKGRPTCZSIESCYPRITGBFRCAISFIBEDEIESEDKATUSNOLU ETKMZWGMBFUGSBNPTZKHBDCMZMGHMMNIMDVNGYPKINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOCNZACORSTHCRMEIQBGBRPAVEMXMUUYARLVTRCLRUHULTPLSCEESKGRPTCZDESIESCYPRITGBFRCAISFIBEIESEDKATNLNOUS LU<br>NL CI<br>SB ME SL YE<br>700 800 900 1000 1100 700 800 900 1000 1100<br>GDP level, log points GDP level, log points<br>.2<br>.1<br>.2<br>0<br>.1 .1<br>-.1<br>-.2 0<br>Skill index Skill index Skill index 0<br>-.3 -.1<br>-.4 -.2 -.1<br>.2<br>.2<br>.1 .1<br>0<br>0<br>Skill index Skill index<br>-.1<br>-.1<br>-.2<br><!-- End of picture text --> Note: Each point shows the skill content of jobs using STEP-based indexes per country and year. The horizontal axis measures GDP per capita in PPP from WDI.

15
**Table 2.A Variables for analysis of fiscal procyclicality**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Government spending|Real government expenditure|IMF_World Economic Outlook_<br>(WEO)|
|Political risk|Index based on 12 components with varying weights:<br>government stability, socioeconomic conditions,<br>investment profile, internal conflict, external conflict,<br>corruption, military in politics, religious tensions, law<br>and order, ethnic tensions, democratic<br>accountability, and bureaucracy quality.|PRS Group (database)|
|Bureaucracy quality|<br>Institutional strength and quality of the bureaucracy<br>is a shock absorber that tends to minimize revisions<br>of policy when governments change. In low-risk<br>countries, the bureaucracy is somewhat autonomous<br>from political pressure. Index measures the quality of<br>bureaucracy from 0 to 6.|PRS Group (database)|
|Control of corruption|<br>A measure of corruption within the political system<br>that is a threat to foreign investment by distorting<br>the economic and financial environment, reducing<br>the efficiency of government and business by<br>enabling people to assume positions of power<br>through patronage rather than ability, and<br>introducing inherent instability into the political<br>process. Index ranges from 0 to 6.|PRS Group (database)|
|Government stability|A measure of both the government’s ability to carry<br>out its declared program(s) and to stay in office. The<br>risk rating assigned is the sum of three<br>subcomponents: government unity, legislative<br>strength, and popular support|PRS Group (database)|
|Law and order|“Law” assesses
Adult survival rates are available in consistent form for a large cross-section of countries from 11 the WDI. The ASR has the advantage of measuring survival during working years (15-60), and thus seems likely to be a good measure of health during working years, which is what should be most relevant for determining the level of output per worker (Weil 2007). Weil (2007) estimates a value of  _ASR_ =0.653.

Table 2 reports descriptive statistics for years of schooling and human capital for all the countries in the sample (Panel A) and subsamples according to level of economic development and year (Panel B). Notice that while other capital assets in Table 2 are expressed in dollars, human capital is the 'physical' human capital embodied in a worker as expressed in (3).

# _2.3 Institutional capital_

As in World Bank (2006) our indicator for institutional capital is a rule of law index from Kaufmann, Kraay and Mastruzzi (2009). It measures the extent to which agents have confidence in and abide by the rules of society. In particular, it measures the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.<sup>12</sup> In their Worldwide Governance Indicators (WGI) research project, Kaufmann, Kraay and Mastruzzi, provide data on five additional dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality and control of corruption. These indicators are highly correlated -the lowest pair-wise correlation coefficient in our sample is 0.76 between political stability and regulatory quality.

An argument employed in World Bank (2006) to use the rule of law indicator, is that it captures well some of the features of a country's social capital, in particular trust. The correlation between a generalized trust indicator in Paldam and Svendsen (2006) and rule of law, 0.51, is larger than for
The first order condition for this problem is given by By combining the first order conditions of the minimization and maximization problems and replacing the output elasticity with the revenue elasticity in the equation for calculating markups, we obtain This result shows that the estimated markup, using the revenue elasticity, should always converge to one. Therefore, the true value of the markup cannot be recovered when using revenue data instead of data on physical quantities. In addition, Bond et al. (2020) show that markup estimates cannot be obtained when: (1) revenue/sales data are deflated by a sectoral deflator (data for physical quantities are necessary to obtain this estimate); (2) there exists residual demand or when demand is heterogenous; (3) using a large cross-section but short-time series assuming firms are heterogeneous.

**4) The Markov process for productivity is violated** , when regulation affects average firm productivity as formulated in ACF.

From Equation 1, we can express the conditional mean of productivity as follows By replacing Equation 23 with the expected value of Equation 15, we obtain _E_ (Φ _jt −_ ˆΦ _jt|It_ ) = _xjt_ , which is correlated with regulation _zjt_ . This problem can be handled by reformulating the second step in ACF such that

12
+ta and rec+ta+ deviate from rec+t in that additional foreign grant aid is received: in each year 2023-2026 foreign grants increase by 1 percent of 2022 GDP (i.e., reaching 4 percent in 2026); the increases are reversed during the period 2027-2030 so that, in 2030, foreign grants are back at the level of the base scenario.

Similarly, the assumed increase in social transfers from the government to households would permit Yemen to reach the 90<sup>th</sup> percentile among LICs in terms of spending on social safety nets, assuming that Yemen’s initial level was in the bottom half among LICs (more specifically in the 25<sup>th</sup> percentile).<sup>11</sup> For remittances (i.e., transfers to Yemeni households from abroad expressed in foreign currency), the base scenario assumes that they grow at the same rate as the population, 2 percent, i.e., that they are unchanged in per-capita terms. For the rec+ scenarios, it is assumed that, starting from 2023, the annual growth rate (in foreign currency) increases by 1 percentage point. Such a development may require that the Gulf Cooperation Council (GCC) countries give preferential

> 9 More specifically, in each year the health and education spending increases for the rec+ and rec- scenarios are computed relative to base GDP and translated into increase in real service provision and real additions to the capital stock. Thus, the real changes are the same for all rec+ (rec-) scenarios, not depending on changes in GDP or prices in the individual rec+ (rec-) scenarios.

> 10 More specifically, the spending levels by LIC percentile were computed using World Development Indicators data for 2019 (World Bank 2020). The link between the spending percentile, the HCI, and labor productivity is based on data in World Bank (2021).

> 11 LIC percentile
, by identifying gaps in the legal and regulatory frameworks governing rental contracts and tenant-landlord relations, then analyzing the tax environment.

- _Third_ , by discussing the broader housing policy context of tenure choice policies in the studied countries, giving specific attention to the privatization, “communalization,” and restitution processes, as well as to the current strategies of retaining and redeveloping a social housing sector.

> 5 Countries represent various groups: Armenia, small post-Soviet countries; Lithuania, small EU accession countries; Poland, large EU accession countries; Romania, medium EU pre-accession countries; Russia, its own category; Serbia, postconflict southeastern Europe.

- 6 The LSMS and HBS are administered by the statistical agencies within each country with technical assistance from donor organizations. In Russia a one-time survey, the NOBUS, was used. This survey, administered by the statistical authorities, was intended to improve on the questionnaire design of the annual HBS.

- 7 For more detailed discussions of available theories and evidence see Annex 1.

3
>(3.75)**|
|Constant|-7.669<br>(512.47)**|-4.474<br>(115.18)**|-4.058<br>(139.82)**|-0.611<br> <br>(25.92)**|-0.014<br>(0.50)|-1.418<br>(57.40)**|-2.571<br>(112.49)**|-7.243<br>(132.87)**|
|Obs<br>|829|807|798|828|753|829|812|659|
|R<sup>2</sup>|0.00|0.01|0.01|0.00|0.00|0.01|0.00|0.02|

Absolute value of t statistics in parentheses significant at 5%; ** significant at 1% Our results suggest that IPM may be more profitable than conventional farming, since no IO coefficient is significantly higher for IPM and several are significantly lower. For pesticides, the savings are clear: Conventional farmers use an average of 2.33 kg of

> 6 We have also estimated these regressions for a sample limited to the five regions where we collected data on IPM farmers, and for specifications that include the IPM determinants in Table 2.  The results are indistinguishable from the full-sample estimates in Table 3.

9
# **Annex 2 – Further details on data coverage and sourcing**

Despite initial data collection for 4,348 Investment Project Finance (IPF) projects closed between fiscal year (FY) 1995 and FY2017, the paper’s analysis concentrates only on those approved between FY1995 and FY2009. This interval minimizes both missingness in select measures, which is more prevalent in projects that were approved before FY1995, and censoring, which is an issue for projects approved after 2009 as many of them had yet to close or be rated at the time of data collection. Accordingly, the paper focuses more on relatively recent World Bank IPF lending, and the analytical data set comprises 90% of projects approved between FY1995 and FY2009, which is 89% of projects that exited between FY2001 and FY2015.<sup>1</sup> Table A2.1 summarizes the distribution of projects by fiscal year.

As this paper has as a central objective, the intention to focus further on the design and preparation phase of the project cycle, an extensive data collection exercise was required in order to capture elements of the preparation task team not systematically stored in the Bank’s centralized data systems, and match them to their corresponding projects. By design, the World Bank’s administrative and project-level data systems store several fields of information in its current format only, overwriting historical data. This includes the TTL who prepares and takes a project to Board, and their Practice Manager and Country Director at that time. For example, if, as is often the case, the project’s Practice Manager at closing is not the same as the project’s Practice Manager at entry, then system data cannot be used as it will only provide information for the last known Practice Manager. To circumvent this issue, data collection turned to text scraping to avoid a manual data collection of 4,348 projects’ preparation staffing teams from the projects’ Project Appraisal Documents (PADs).

Doing so clearly proved more efficient than manual collection,
, weighted mean, manufactured products (%)|1.308|0.662|-1|
|10|Tariff rate, most favored nation, simple mean, all products (%)|2.258|0.693|-1|
|10|General government final consumption expenditure (% of GDP)|2.056|0.510|-1|
|11|Inflation, consumer prices (annual %)|-1.058|0.597|-1|
|11|General government net lending/borrowing, Percent of GDP|3.299|0.653|1|
|11|General government gross debt, Percent of GDP|1.723|0.433|-1|
|11|Voice and Accountability|2.181|0.540|1|
|11|Business costs of crime and violence, 1-7 (best)|2.161|0.389|1|
|11|Public trust in politicians, 1-7 (best)|2.243|0.470|1|
|12|Foreign Direct Investment: Inward stock (USD per capita)|1.862|0.701|1|
|12|FTA in % of world gdp|2.749|0.744|1|

Note: The table reports the results for variables with statistically significant estimated coefficients (at the 10% significance level). The first column profiles the variables with respect to groups outlined in Table 1. The results are obtained using the outlined methodology with country/sector panel data. The dependent variable is the binary indicator of RCA over 2015-19 period. The regressors are the policy indicators (average value over the 2010-14 period).

Lastly, one variable is significant but does not behave as expected: the level of inflation. As noted earlier, we assigned the model how to interpret each independent variable expected influence on the probability of success. In the case of inflation,
where investors evaluated startups as normal. After analyzing 87 startups – resulting in 1,341 judgestartup investment decisions on continuing diligence – treated investors sought consistent information across startups while control investors asked more risk-focused questions to startups with a female founder. This reduced gender disparities in evaluation, compared to the control group.

In the second stage, we leveraged a unique facet of Vilcap’s investment thesis – it trains local investors to invest $320,000 into 16 startups over three months. We tested the effect of an additional treatment to systematically inquire about startups’ progress, added to the first treatment. We assessed this treatment’s effect on investment outcomes of Vilcap’s actual investment capital. Our analysis of a panel dataset of 1,530 decisions (from 510 investor-startup dyads over three time periods) suggests that treated investors assessed startups in a dynamic fashion rather than static – this changed how they evaluated startup competence and its future potential. These small changes in evaluation templates produced changes in investors' processes of inquiry, which spurred differences in evaluation that eliminated, even reversed, the gender gap in investment outcomes.

Rather than focus on how to prepare startups for evaluation, our research explores a more systemic question: what is the effect of organizations’ evaluation practices on investments? We contribute to a growing literature in entrepreneurship that considers how to level the playing field for female entrepreneurs. This research highlights a novel mechanism to reduce gender disparities in investment outcomes: organizations can change evaluation templates to structure investor inquiry. This can affect which startups continue in due diligence, or receive investment. We extend prior research on designing organizational evaluation practices to reduce gender disparities in outcomes, to contexts where performance data is limited, and inquiry is a core part of evaluation. More broadly, we theorize how processes of inquiry can sustain or reduce status-based inequities in society.

5