rafmacalaba/datause-encoder-data huggingface dataset:
consumers postponing purchases of consumer durables and investment goods. Eaton et al. (2009) estimate that changes in demand for manufactured goods accounted for about 70 percent of the global decline in international trade (relative to gross domestic product). These authors cite the World Trade Organization’s estimate that merchandise trade dropped by 23 percent in 2009 relative to the previous year—the largest drop in trade by a factor of four since World War II.

The impact of the trade collapse was especially acute in Mexico. The decline in Mexican trade was highly correlated with the decline in U.S. GDP (Robertson 2009). Our data show that formal employment in the trade-intensive northern states fell more than 9 percent from September 2008 to March 2009. Real log wages of workers who did not lose their jobs (stayed with the same firm) fell on average by 0.1% from September 2008 to December 2008 and by 1.2% from December 2008 to March 2009. As exports and imports with the United States began to recover in the second quarter of 2009, employment and wages recovered as well. Since this shock originated outside of Mexico, and there have been few, if any, suggestions that factors within Mexico induced the crisis, we consider the shock to be reasonably exogenous from Mexico’s point of view.

Figure 1 shows total Mexican imports and exports for the 2000-2010 period. The effect of the 2008 crisis on trade flows is clear. Between the local peak (around April 2008) and the trough (January 2009) Mexican trade fell about 43% in real terms. The drop erased nearly a decade of trade growth in a few months, but recovery was also relatively quick. The very close relationship between Mexican imports and exports is not a new phenomenon. The close relationship between imports and exports is characteristic of a vertically-integrated economy that is heavily engaged in processing activities (offshoring).

6
vii. Based on the analysis of World Bank experience in MSWM, further research is recommended on: (i) the relative magnitude of damages and benefits versus the cost of providing solid waste services to better estimate the economic rate of return for MSWM components as well as to facilitate the ranking of MSWM in cumparison to that of other urban investments; (ii) various pricing strategies, separable demand, and intra-urban cross-subsidies to improve cost recovery; (iii) private sector delivery of solid waste services to determine how and under what circumstances the private role in the solid waste sub-sector can be expanded; (iv) the role of APEX institutions in MSWM to improve solid waste service delivery in large, intermediate, and small cities; and (v) strateg.c planning approaches to improve the full range of solid waste services and related management activities in large, intermediate, and small cities.
# **Table 5: Shares of Debt Denominated in Foreign Currency**

||Selected years||
|---|---|---|
|Country|Year|Share|
|Brazil|2001<br>2008|40%<br>23%|
|Chile|2002|93%|
||2009|37%|
|Estonia|2004|100%|
|Malaysia|2003|20%|
|Sth Afi|1994|20%|
|ou rca|2008|42%|
18 Table 5: Panel Data Fstimates: Equity Flows, 1988.01-1992.09 Latin America (9 countries) - Asia (9 countries).

||Lat|in America||Asia|
|---|---|---|---|---|
||Nominal interest<br>rates|Real interest<br>rates|Nominal interest<br>rates|Real interest<br>rates|
|Method|GLS|GLS|Within|Within|
|Credit rating|||.396**<br>(0.131)|0.359**<br>(0.017)|
|PCS|-1.367<br>(5.377)|2.458<br>(4.626)|||
|PH|-10.539*<br>(5.339)|-6.505<br>(4.587)|-2.347**<br>(0.380)|-1.924<br>(0.385)|
|Adjusted R<sup>2</sup>|0.292|0.289|0.065|0.042|
|X2|4-0.025|-0.032|6.976<br>(0.008)|_5.595_<br>(0.018)|
|NOBS|513|513|513|513|

Note: Mothod: estimation technique; PH: first prinoipal component of tde five types of interest rates and US industrial production (it explains 93 % of the variance of these variables if nominal interest rates are used and 79% if real interest ratw ar used); PCS: first principal component of country's credit rating and secondary market prices obtained by stacking the corresponding individual first principal components (the least explained PCS is 85%); figures in parentheses are standard errors of coeffioients or p-value for kl, which tests for independence of either PCS or credit rating with the latent variable; columns 2 and 4 use nominal interest rates; column 3 and 5 use real interest rates; NOBS: number of observations; '"o, **_"*',_** and **_"**":_** significant at tde 10%, _5%_ and 1% level, respectively; and for Ladn Ameica, 3 dummnies are used for equity flows to capture the privatization phenomenon (Telmex). Toh choice of estimation technique depends on the value of _x_<sup>_2_</sup> in the following way: if p < level of significance (1 % or 5%), we refect H. and use either 2SLS or within-estamtor. If 2 is negative or p 2 level of significance, we use GLS.
without adjusting for missing districts), 24 percent of the existing humanitarian database would be eligible for referral to the MoLSA’s CT program with a high confidence level (90 percent or more). The referral rate increase with the lower referral confidence, about 54 percent of the humanitarian caseload would be eligible for the referral under at a low confidence level (50 percent or higher). To allow flexibility depending on the availability of budget, the analysis is also carried out by expanding the coverage of the national program and changing the referral confidence rate. In general, the number of referral cases increase with higher coverage and lower referral confidence. Given a large proportion of the humanitarian database that could potentially be referred for MoLSA CT program, the results suggest a sequencing of smaller numbers with a high degree of likelihood to qualify could be referred first, with larger numbers being referred later when budgets expand.

The remainder of the paper is structured as follows. Objectives of the joint activities are outlined in section 2. Section 3 presents the analytical methodology, including the proposed pseudo-PMT (PPMT), its performance criteria, and theoretical results. Cross-eligibility results from applying the PPMT on the legacy database and potential eligible households’ profile are presented in section 4. Concluding remarks and discussions of next steps are outlined in section 5.

# 2. Objectives and Activities

The main objective of this initial desk analysis is to understand the extent to which beneficiaries of humanitarian cash assistance would be eligible for MoLSA social protection programs under the MoLSA targeting approach. This means both understanding:

1. The percentage of the existing humanitarian database of current and past beneficiaries identified under the current vulnerability assessment approach who are likely to be eligible;

2. The percentage of new beneficiaries who would be identified under the new vulnerability assessment approach who would be likely to be eligible.

In addition, it is desirable to understand who would be eligible and who would not. This requires understanding where eligible (and ineligible) households live, as well as their demographic characteristics.

In order to meet these objectives, three activities have been undertaken:

1. An analysis has been conducted of the targeting overlaps between the MoLSA approach and the historical humanitarian approach.

2. A similar analysis has been conducted of the MoLSA approach and the new humanitarian approach which had recently been developed.

3. Geographic and demographic profiles of the likely eligible (and likely ineligible) are analyzed.

# 3. Methodology

The challenge in assessing the likely percentage of beneficiaries of humanitarian cash assistance who could be referred to the SSN program is that the humanitarian database lacks all the variables necessary 4
# Table 1

||**Real**GDP Growth<br>1965-80<br>1980-85|Export Share<br> <br>of GDP<br>1965-80 1980-85|Growth of<br>Real GDI*<br>1965-80 1980-85|
|---|---|---|---|
|SSA**|5.3<br>-0.7|25<br>21|9.0<br>-11.4|
|DC$***|6.0<br>3.3|13<br>21|8.2<br>0.8|
||Indez of Food|Percent||
||Production<br>per Capita|Share of Food<br>in Merchandise|Gross International<br> <br>Reserves|
||(79-81-100)<br>1983-85|Imports<br>1965<br>1985|(months of imports)<br>1985|
|SSA**|107|15<br>18|1.7|
|DCs***|<br>116|16<br>11|3.5|
|||Debt Service a|s Percentage of:|
||External|Exports||
||Debt/GDP|of Goods||
||Ratio|and Services|GNP|
||1970<br>1985|1970<br>1985|1970<br>1985|
|SSA**|14.2<br>39.1|5.3<br>21.5|1.2<br>4.8|
|DCs***|<br>13.5<br>31.5|11.2<br>19.7|1.5<br>4.3|

* GDI denotes Gross Domestic Investment.

****** Sub-Saharan Africa. These numbers are weighted averages, usually weighted by GDP. Since Nigeria accounts for roughly half of GDP of SSA, it has a large influence on the numbers.

*** All developing countries.

Source: World Bank, 1987, p. 204, 211. 209, 213, 225, 231, 239.

34
**Table B3. All scenarios: Structure of GDP at factor cost in final year (%)**

||**2012**|**base**|**agrpweff in**|**dupweff**|**transeff**|**fuelpwm **|**remitdecr **|**trnsfrpoor**|**allgrw**|**allgrw-tr**|
|---|---|---|---|---|---|---|---|---|---|---|
|**a-agr**|9.80|8.88|11.22|6.31|7.50|8.05|9.02|8.87|7.71|7.75|
|**a-mining**|0.52|0.53|0.41|0.35|0.50|0.51|0.52|0.53|0.37|0.37|
|**a-food**|3.63|3.77|3.88|2.84|3.42|3.62|3.77|3.76|3.00|3.01|
|**a-bevetob**|2.90|4.03|8.06|1.06|2.56|3.67|4.22|3.88|1.99|1.97|
|**a-garment**|4.41|1.32|0.14|16.08|0.45|2.90|1.68|1.07|11.93|11.71|
|**a-woodpap**|0.81|0.83|0.68|0.56|0.75|0.83|0.83|0.82|0.60|0.60|
|**a-fuel**|0.25|0.31|0.10|0.07|0.21|0.22|0.32|0.29|0.08|0.08|
|**a-metplast**|2.78|2.90|2.04|2.21|2.76|2.72|2.82|2.88|2.32|2.32|
|**a-machequip**|1.62|1.75|0.88|1.19|1.59|1.82|1.71|1.76|1.22|1.23|
|**a-enewat**|2.60|3.09|2.84|2.82|3.35|3.10|3.05|3.12|2.94|2.97|
|**a-constr**|5.32|5.22|5.74|5.66|5.57|5.00|4.90|5.19|5.99|5.96|
|**a-trade**|14.73|14.28|16.07|14.86|14.15|13.99|14.16|14.20|15.94|15.91|
|**a-hotrest**|1.51|1.60|1.12|1.00|1.47|1.63|1.60|1.61|1.06|1.05|
|**a-transp**|6.82|7.18|5.75|4.50|6.60|6.73|7.20|7.15|4.04|4.04|
|**a-posttele**|7.00|7.45|4.87|4.24|12.38|8.14|7.46|7.76|3.99|4.03|
|**a-finance**|7.05|6.69|6.56|6.30|7.03|6.68|6.69|6.71|6.60|6.61|
|**a-realest**|5.24|4.61|4.52|4.43|4.84|4.62|4.55|4.70|4.59|4.66|
|**a-itserv**|1.60|1.84|1.70|1.93|1.84|1.84|1.77|1.83|1.93|1.93|
|**a-othbusiserv**|2.54|2.85|2.92|2.65|2.95|2.85|2.81|2.91|2.81|2.86|
|**a-pubadm**|4.96|5.90|5.84|6.05|5.65|5.94|5.87|5.91|6.03|6.03|
|**a-educpriv**|0.64|0.61|0.60|0.59|0.63|0.61|0.61|0.61|0.60|0.60|
|**a-educpub**|5.71|6.33|6.27|6.48|6.04|6.40|6.36|6.33|6.44|6.44|
|**a-healthpriv**|3.27|3.53|3.46|3.46|3.37|3.57|3.56|3.56|3.46|3.48|
|**a-healthpub**|1.05|1.20|1.20|1.23|1.13|1.22|1.21|1.21|1.22|1.22|
|**a-sanipriv**|0.21|0.19|0.19|0.18|0.20|0.19|0.19|0.19|0.19|0.19|
|**a-sanipub**|0.34|0.36|0.36|0.37|0.34|0.36|0.36|0.36|0.36|0.36|
|**a-organiz**|0.78|0.79|0.61|0.56|0.73|0.81|0.81|0.80|0.55|0.55|
|**a-recrea**|1.42|1.54|1.56|1.59|1.55|1.54|1.54|1.55|1.61|1.62|
|**a-othperserv**|0.48|0.43|0.43|0.43|0.44|0.42|0.43|0.43|0.44|0.44|
|**total**|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|

Note: For activity (sector) notation, see Table 3.

44
**Figure 5 Gini Coefficient for LAC Countries, Actual vs. Projected**

<!-- Start of picture text --> 5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2017 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA CHL COL CRI ECU SLV HND PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2018 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA COL CRI DOM ECU SLV HND MEX PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2019 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA COL CRI DOM ECU SLV HND PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2020 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA CHL COL CRI DOM ECU MEX PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

Note: actual data excludes COVID-19-related mitigation measures when the survey includes the program beneficiaries and the amount received.

21
Policy Research Working Paper 10036

# **Abstract**

This paper estimates intergenerational mobility in education using data from 91 censuses that span 24 countries in Latin America and the Caribbean over half a century. It measures upward mobility as the likelihood of obtaining at least a primary education for individuals whose parents did not finish primary school, whereas downward mobility is the likelihood of failing to complete primary education for individuals whose parents completed at least primary school. In addition, the paper explores the geography of educational intergenerational mobility using nearly 400 “provinces” and more than 6,000 “districts”. It documents wide cross-country and within-country heterogeneity. The paper documents a declining trend in the mobility gap between urban and rural populations, and small differences by gender. Within countries, the level of mobility is highly correlated with the share of primary completion of the previous generation, which suggests a high level of inertia. In addition, upward (downward) mobility is negatively (positively) correlated with distance to the capital and the share of employment in agriculture, but positively (negatively) correlated with the share of employment in industry.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at emunozsaavedra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Manufacturing employment in India<br>©<br>_<br>g=<br>fe}<br>=<br>=<br>a<br>Ss<br>co<br>1990 1995 ae 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing employment in India<br>aw & ~~ ~~<br>~ a ied<br>“”<br>22 oo<br>S a<br>= a<br>= foe) ae<br>o e—--------«77<br>Eo<br>2a<br>=<br>a<br>+<br>N<br>1990 1995 2000 2005 2010<br>feat<br>—+*—- Formalsector —-—#-—- Informal sector<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing employment in India<br>[<e)<br>2<br>5° PP ~ e<br>= a<br>-<br>< -<br>i= + err<br>SB Ueeo7<br>= aoe<br>5S2 e---T _-e77 es— .- — : — —cee*<br>oO<br>1990 1995 2000 2005 2010<br>Year<br>“@--- Formal tradables — ®— - Formal non-tradables<br>——#—- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing firms in India<br>+ e<br>a<br>“<br>ae oo<br>S “7<br>= Poe<br>weor<br>oan veo<br>== _" ue<br>th= e eo e-<br>1990 1995 2000 2005 2010<br>a<br>ve @-o- Formal tradables — ®— - Formal non-tradables<br>—-—#—- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text -->
- 51 -

# **Table 10.** **<u>OLS estimation of project delays</u>**

||Coef.<br>|Std. Err.|Coef.<br>S|td. Err.<br>|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|Coef.<br>S|td. Err.|Coef.|Std. Err.|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_securityduring_|1.131|(0.273) ***|||||||||||||1.133|(0.243) ***|
|ln_unexpectedsecurity_|||0.047|(0.191)|||||||||||||
|ln_rainduring_|||||0.326|(0.150) **|||||||||0.043|(0.162)|
|ln_unexpectedrain_|||||||0.170|(0.090) *|||||||||
|ln_num_|||||||||-0.509|(0.346)|||||-0.504|(0.355)|
|ln_lowbid_|||||||||||0.089|(0.039) **|||0.105|(0.035) ***|
|ln_dayscontracting_|||||||||||||0.820|(0.252) ***|<br>0.497|(0.369)|
|ln_cost_|1.000|(0.299) ***|1.424|(0.452) ***|<br>1.449|(0.297) ***|<br>1.707|(0.275) ***|<br>1.776|(0.280) ***|1.519|(0.305) ***|<br>1.286|(0.317) ***|<br>0.706|(0.325) **|
|ln_length_|-0.962|(0.283) ***|-0.002|(0.340)|-0.851|(0.312) ***|<br>-0.980|(0.344) ***|<br>-0.943|(0.340) ***|-1.423|(0.295) ***|<br>-0.759|(0.314) **|-1.072|(0.283) ***|
|ln_lane_|0.902|(2.356)|0.725|(2.036)|1.500|(2.157)|1.422|(2.215)|1.339|(2.044)|1.844|(2.053)|1.599|(2.042)|1.388|(2.416)|
|ln_thickness_|-0.326|(0.182) *|0.173|(0.350)|-0.393|(0.202) *|-0.521|(0.223) **|-0.365|(0.201) *|-0.329|(0.242)|-0.290|(0.188)|-0.138|(0.193)|
|ln_gravel_|-0.024|(0.116)|0.067|(0.127)|0.022|(0.116)|0.048|(0.113)|0.035|(0.114)|0.063|(0.121)|0.020|(0.125)|-0.072|(0.116)|
|ln_bitumen_|-0.113|(0.081)|-0.526|(0.160) ***|<br>-0.123|(0.094)|-0.094|(0.095)|-0.126|(0.098)|-0.067|(0.102)|-0.125|(0.087)|-0.086|(0.086)|
|ln_earth_|-0.195|(0.118) *|-0.392|(0.135) ***|<br>-0.262|(0.105) **|-0.298|(0.104) ***|<br>-0.274|(0.100) ***|-0.253|(0.105) **|-0.316|(0.101) ***|<br>-0.107|(0.128)|
|ln_brick_|-0.122|(0.127)|-0.029|(0.157)|0.042|(0.125)|0.080|(0.125)|0.068|(0.124)|0.171|(0.115)|0.030|(0.115)|-0.064|(0.115)|
|ln_gabion_|-0.112|(0.084)|0.012|(0.119)|-0.135|(0.090)|-0.121|(0.093)|-0.101|(0.099)|-0.126|(0.095)|-0.125|(0.089)|-0.083|(0.084)|
|ln_excavate_|0.051|(0.058)|0.070|(0.094)|0.061|(0.058)|0.070|(0.055)|0.081|(0.055)|0.061|(0.061)|0.043|(0.059)|0.022|(0.059)|
|ln_cement_|-0.207|(0.090) **|-0.183|(0.165)|-0.280|(0.096) ***|<br>-0.297|(0.097) ***|<br>-0.332|(0.093) ***|-0.279|(0.096) ***|<br>-0.226|(0.099) **|-0.136|(0.090)|
|D(_postqualify_)|0.720|(0.866)|2.142|(0.956) **|0.474|(0.846)|0.707|(0.811)|0.649|(0.782)|0.936|(0.917)|0.464|(0.773)|0.761|(0.973)|
|Constant|-12.081|(3.499)***|-17.331|(5.110)***|-17.007|(3.405)***|-18.727|(3.370)***|-18.616|(3.396)***|-16.374|(3.602)***|-14.692|(3.730)***|-10.597|(3.559)***|
|Obs.|152||95||155||154||155||142||154||138||
|R-squared|0.607||0.513||0.562||0.553||0.548||0.613||0.572||0.709||
|_F_-statistics<br>No. of dummy<br>variables:|18.40||94.16||12.91||13.46||12.16||12.77||12.85||23.39||
|Project districts|18||18||18||18||18||18||18||18||

Note: The dependent variable is the number of days of project delays in logarithm, ln _delay_ . Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.
achieve scale in export markets. Government support could also extend to so-called “quality infrastructure,” or the system of public and private institutions, legal frameworks, and practices supporting metrology, standardization, accreditation, and conformity assessment. Without such support, the rise in quality regulation as in Latin America could increase global inequality, as smaller firms in lower-income countries are excluded from more lucrative export markets.

The rest of the paper is organized as follows. Section 2 describes our data sources and discusses the rise of quality regulation. Section 3 presents a conceptual framework to guide our empirical strategy. Section 4 presents our empirical strategy and discusses our key findings. Section 5 concludes.

# **2 Data and Context**

## **2.1 Data Sources**

This paper uses a panel of NTMs in 16 Latin America countries collected annually by ALADI from 2012 to 2017.<sup>7</sup> For each country, the data set includes information on the inventory of active multilateral NTMs governing imports each year, classified according to the highest level of disaggregation of the 2019 international classification of NTMs (UNCTAD, 2019).<sup>8</sup> Importantly, we observe the year in which the NTM was introduced and the products (classified using 6-digit HS codes) that it regulates. The initial 2012 data collection allows us to construct a panel of NTMs by product prior to 2012. Annual data collection thereafter allows us to update this panel each year. This annual frequency is particularly important given the acceleration of NTMs after 2010 (Figure 1). Our novel data allow us to observe the implementation of NTMs over time during the two decades ranging from 1996 to 2017 in different countries in Latin America, for different types of NTMs introduced in Section 1 and discussed further in Section 2.2.

To explore the relationship between NTMs and imports, we merge the NTM panel with import flows at the country and product level from 1996 to 2017 obtained from the World Integrated Trade Solution (WITS). This process requires the conversion of HS codes in the NTM database and in WITS to a consolidated version of the HS.<sup>9</sup> We focus the analysis

> 7The information can be accessed through the UNCTAD trade portal (TRAINS): https://trainsonline.unctad.org. The countries included are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Peru, Paraguay, El Salvador, and Uruguay.

> 8The information also includes NTMs applied to exports and NTMs applied in a bilateral fashion. As these represent a negligible share of measures, we do not consider them in our analysis.

> 9We follow the consolidation procedure described in Fernandes, Freund and Pierola (2016).

7
of this small percentage difference on how investors actually assessed startups. To provide more insight, the first author conducted 45-minute semi-structured interviews with investors in one region, to examine how investors conducted evaluation, and how they decided to give startups a high or low score. After interviewing all investors in the treatment and control program in one region, we found that many investors evaluated static startup elements of the venture to assess its potential. For example, a male investor explained: “I gave AA a four [top score] in most of the categories... I really like their solution... it has a lot of potential for scaling... I went through their website... I was quite impressed with the profiles of people that work in the team... their business model too.” A female investor explained that she rated a startup well when the problem was convincing: “I rated BB high... because I think the business idea is really necessary... I see its use and purpose.” These investors used similar criteria to that elaborated by Gompers and coauthors in their survey of earlystage investors (2020). In the control group, all nine investors evaluated static criteria (100%). As shown in Table 6, in the treatment group, three of the seven treated investors (43%) also described how they gathered data on static criteria in at least one of their responses.

Some investors took a more dynamic lens, focusing on progress or improvement when scoring ventures. A male investor described how he had ranked a startup well because he had observed improvement over time: “The question is, have you seen improvement in them during this business program? ... I’ve been in a breakout room with YY twice. And the suggestion I had noted to her in the first breakout room ... was repeated with a group of mentors... they asked a similar question.... I noted that when she’s answering the question, she’s answering it differently... it sounded much better than the first time... once you see those things from people you can see that they are improving and changing.” 29