rafmacalaba/datause-encoder-data huggingface dataset:
opportunities received different questions from control and treated investors. A control investor asked: Can I see a “marketing plan clearly highlighting the marketing strategies?”. In contrast, a treated investor asked the same founder: How will the company “manage delayed payments (Risk) in case the company decides to partner with county or National Government”? Both questions were about scaling, but treated investors were more likely to use a prevention-focus frame – similar to the frames all investors used when assessing startups with a female founder. Treated investors were equally likely to agree or strongly agree to the statement “I will conduct due diligence” whether the startup had a female founder or all-male team. A simple change to an evaluation template affected investors’ use of prevention-framing, and whether they wanted to take the startup through due diligence. Together, this suggests that if organizations systematize inquiry by prompting investors to inquire about risk and reward in evaluation templates, this causes investors to inquire consistently across all founders, paying attention to risk and reward regardless of founder gender, which reduces gender disparities in investment evaluation practices.

**5.6. Alternative Explanations.** Differences in outcomes were robust to alternative measures

of the dependent variable (score – using OLS or a weighted score by investor), the independent variable (analysis of prevention-focused questions by LIWC) and the female binary variable (presence in venture overview – see Appendix C). The results held for heterogenous investor types (male and female, and trainee and professional). We do not have a large enough sample to conduct heterogeneity analysis by other investor characteristics, but we observed no directional difference on the relationship of treatments with the score.<sup>10</sup> This suggests that results could hold for a diverse range of investors.

**<mark>5.7. Investment Outcomes.</mark>** <mark>This experiment revealed that organizations can</mark> change evaluation frameworks to prompt investors to inquire about risk and reward systematically, and that

> 10 Heterogeneity analyses are available on request.

21
# Table 6: Conditional estimations for manufacturing sector

||||DV|: Female-own|ed entrant sh|are|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Log total incumbent employment|0.010+++|0.005|-0.000|0.003|-0.001|-0.006+|-0.006+|-0.007++|
|in district-industry|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Log female-owned incumbent||||||0.027+++|0.027+++|0.023+++|
|businesses in district-industry||||||(0.002)|(0.002)|(0.002)|
|Local Industrial Conditions by Incu|mbent Firms:||||||||
|Index of labor market strength||0.002||-0.026+++|||-0.010||
|for district-industry||(0.008)||(0.009)|||(0.008)||
|Index of input-output strength||0.023++||0.006|||0.015||
|for district-industry||(0.011)||(0.012)|||(0.011)||
|Index of labor market strength,|||0.036+++|0.044+++|0.019+++|||0.016++|
|female-owned businesses|||(0.006)|(0.007)|(0.007)|||(0.006)|
|Index of input-output strength|||0.027+++|0.025+++||||0.010|
|female-owned businesses|||(0.007)|(0.008)||||(0.007)|
|Index of input market strength|||||0.137+++||||
|female-owned businesses|||||(0.033)||||
|Index of output market strength|||||0.010+++||||
|female-owned businesses|||||(0.004)||||
|Observations|4,336|4,336|4,336|4,336|4,336|4,336|4,336|4,336|
|Adjusted R-squared|0.321|0.322|0.339|0.341|0.343|0.355|0.355|0.357|

Notes: See Table 4. Conditional estimations include district-year and industry-year fixed effects.
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15
<!-- Start of picture text --> 3 5 0 0 0 Industry<br>3 0 0 0 0<br>2 5 0 0 0 C onstru ction<br>2 0 0 0 0<br>1 5 0 0 0<br>S ervices<br>1 0 0 0 0<br>5 0 0 0<br>F arm ing, F orestry, A nim al<br>0 H usb andry and F ishery<br>1 9 8 7 1 9 9 0 1 9 9 2 1 9 9 5 1 9 9 7<br><!-- End of picture text --> _Source_ : Hwa 2000 and World Bank staff calculations.

Considering China’s severe employment situation, to keep the current labor-intensive industries viable would be crucial to prevent more people from being laid-off. However, the huge productivity gaps between China and international competitors and the enhanced competition after China’s accession to the World Trade Organization would make it very difficult. Although the liberalization process will be gradual with time for adjustment (box 1), the Chinese economy will unavoidably face strong international competitive pressures, forcing it to restructure and optimize itself according to its international comparative advantage.

# **Box 1 Accession to the World Trade Organization Entails Important Changes**

The commitment of China to the World Trade Organization on a set of trade reforms that will have far-reaching impact on its economy:

- Eliminating the import quota by 2006.

- Eliminating import tariffs on computers, semiconductors, and related products by 2005.

- Reducing the import tariff on agricultural products from 22% to 17.5%.

- Reducing the import tariff on industrial products from 24. % to 9.4%.

- Reducing the import tariff on vehicles from 100% to 25% by 2006.

- Reducing the import tariff on vehicle parts by 10%.

- Allowing up to 50% foreign ownership of telecommunications and insurance.

- Allowing importers to own domestic distribution networks.

- Allowing full market access for foreign banks within five years of accession (foreign banks will be able to conduct local currency business with Chinese enterprises two years after accession).

13
Figure 21: Gross external financing requirement (percent of GDP) <!-- Start of picture text --> 18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: Authors’ elaboration using data from the Central Bank of Turkey .

**Financing of the widening CAD since the early 2000s was initially led by inward FDI, but increasingly it has become financed by shorter-term financing which is more volatile.** Sustained, sizeable CADs over the last 15 years have meant that Turkey’s international investment position has been negative and its stock of net foreign liabilities has grown (Figure 20). With continued CADs and growing external debt, the national gross external financing requirement (GEFR) has increased, especially post-GFC (Figure 21). Figure 22 and Figure 23 illustrate the composition of the financial account.<sup>2</sup> Net inward FDI was small until 2001, after which it expanded to reach a peak of 3.5 percent of GDP in 2006 (Figure 23) and declined afterwards. The gap post-2006 was filled by a mix of ‘other’ (bank loans and foreign currency deposits) and portfolio (mostly debt securities) financing. Since capital account liberalization, there have been four portfolio flow reversals leading to net outflows (Figure 22), but otherwise Turkey has generally been a recipient of net inflows. Overall, the use of shorter-term current account financing, which is often associated with greater volatility, increased sharply after the onset of the GFC, aligned with the period when very low interest rates in advanced economies led to inflows into many EMDEs in a ‘search for yield’.

> 2 The capital account has been zero or negligible in all years, while errors and omissions are small and without a clear trend.

16
Impact Evaluation (3ie). Additionally, given that the effectiveness of WASH interventions is often measured by the health outcomes of the target population, published material from the Cochrane Library (as well as the Database of Abstracts of Reviews of Effects [DARE], accessed via the Cochrane Library) was also included. Finally, published and unpublished materials were sourced from various research institutions including the World Bank, regional development banks (such as the Inter‐American Development Bank, IDB), research centers, universities, nongovernmental organizations (NGOs), and other organizations (see Appendix B for a full list of databases searched).

The initial search gave rise to approximately 850,000 studies. A keyword search was then conducted using the following terms and combinations of terms: “water and sanitation,” “water sanitation,” “water supply,” “sanitation,” “handwashing,” “water + hygiene,” and “water + sanitation + hygiene.” Study designs were restricted to RCTs, and experimental and quasi‐experimental evaluation methods. This refined search resulted in 136 studies<sup>3</sup> used to construct the data set for this meta‐analysis.

# **2.3 Data Collection and Coding**

WASH‐related impact evaluations have been published at a steadily accelerating rate over the past decade. Almost 50 percent of the evaluations included in this review were published after 2008 (Figure 1). Of those, almost half were evaluations of WASH programs that combined more than one intervention, reflecting their increased popularity in recent years.

**Figure 1. Big Uptick in WASH Impact Evaluations since 2009**

(Number of published WASH impact evaluations 1969–2013) <!-- Start of picture text --> 15<br>14 14<br>13<br>11<br>8<br>7 7<br>6<br>5<br>4<br>3 3 3 4<br>2 3<br>1 1 1 2 1 1 1 1 2 1 2<br>Source: World Bank GWSP‐SIEF WASH Impact Evaluation Database.<br>Number of publications<br>1969 1982 1985 1987 1988 1989 1990 1991 1993 1994 1995 1996 1997 1998 1999 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->

# **_Geography_**

Studies appear to be geographically biased; 80 percent are concentrated in just three regions: South Asia (34 percent), Sub‐Saharan Africa (27 percent), and Latin America and the Caribbean (18 percent) (Figure 2). This finding is not particularly surprising given the significant lack of basic services in these areas. However, the scant attention paid to other regions, notably East Asia and the Pacific, the Middle East and North Africa, and Europe and Central Asia, points to a gap that future research could fill. In addition, only five publications examined multicounty or multiregional interventions. The country with the largest number of studies was India (18), followed closely by Bangladesh (16) and Kenya (12). Approximately 67

> 3 The 136 impact evaluations reviewed in this study were used to create an updated database of research papers, systematic reviews, and meta‐analyses of the sector publicly available at: http://www.wsp.org/library.

4
important implications not only for end-user safety, efficiency, and convenience, but also for the ability for account holders to access credit.

Historically, financial institutions managed the risks of lending by only issuing loans to people with a proven history of paying loans back as evidenced by their credit history, and by requiring collateral to secure the loan. These methods can be effective if a potential borrower has a credit history and valuable collateral to offer. Yet many low-income adults and women do not. The practical consequence of relying on credit histories, therefore, is that low-income adults, women, migrants, and other excluded groups are shut out of formal credit markets (Demirguc-Kunt, Klapper, and Singer, 2017).

Establishing a pattern of payments in and out of an account via digital payments can function as a form of “alternative data” that lenders can use to assess borrower risk. Using mobile money services to make digital payments creates an official record of income and spending. Users with a limited financial history can leverage this payment record to obtain credit under more favorable loan terms.

Examples of alternative data sources include bank deposits and withdrawals, mobile money transaction histories, airtime top-ups, utilities payments, payroll, rent, and taxes, among others (World Bank, 2022). Evidence shows that using alternative data and risk assessments results in lenders extending more loans to low-income borrowers. For example, studies in the United States suggests that including data on past utility and telecom payments in risk assessments reduced the share of adults that were assessed as having no credit score from 12 percent to just 2 percent (Turner et al., 2012; Turner and Varghese 2010). In China, an alternative data model resulted in increased credit access for borrowers who either would not have been offered loans or who would have been required to put up collateral (Feyen et al., 2020). Similarly, another study in the United States found that more individuals were offered loans at an affordable interest rate when the lender incorporated previous transaction data into risk assessments (Jagtiani and Lemieux, 2018).

While alternative data can help potential borrowers access credit, it also has been found to be beneficial to financial institutions, based on evidence that it is “at least as predictive as that held by credit bureaus” (World Bank, 2022). Evidence from Kenya found that using payment and transaction history in creditworthiness assessments reduced the share of nonperforming loans, 14
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to this decline and the remaining decline in poverty was due to the redistribution of income in favor of the poorer households. Thus, we may conclude that it was the lack of economic growth which led to a lower reduction in poverty in Ukraine between 1980 to 1992.'u

# 10. Comparing Ukraine with other Soviet Republics

It is alwavs very difficult to make intemational comparisons of inequality. The major difficulty is the availability of comparable data from different countries. Since the data from different republics of the USSR are quite similar, it will be possible to make a cross-republic comparisons of income inequality and welfare. The year book of the USSR provides data on income distribution for 1990 with seven income groups. We used a general interpolafion device to compute quintile shares and the Gini index. The results are presented in Table 20.

It is evident that income inequality varies quite widely among the republics. Inequality as measured by the Gini index is lowest in Ukraine. The highest inequality is found in Azerbaijan with the Gini index of 30.05. The other republics with very high inequality levels are Georgia, Kazakhstan and Turkmenistan. Ukraine and Belarus have quite smilar inequality patterns.

Table 21 presents real per capita family income in 1980 Ukmainian pric. Welfare in 1990 was computed using the formula given in (1). According to this table, it is found tbat the richest republics are Estonia and Latvia. The ranling of Russia was 13 in 1980 and 1985 which became 12 in subsequent periods. The poorest republic is Tlildstan followed by Azerbaijan. On the basis of per capita income, Ukraine's ranldng remained 9th throughout the period between 1980 and 1990. If we take into account income inequality in measuring welfare, Ukmraine's ranking rises to 11th in 1990.

The major conclusion that emerges from Table 21 is that there existed substantial regional disparities in the Soviet Union. Although the reduction of regional disparites was one of the major goals of the Soviet government, disparities between republics continued to exist. This observation has been made earlier by McAuley who states: wAlthough all republics in the USSR enjoyed increases in per capita incomes, there was little if any reduction in interrepublican variation. If the Soviet government did become more concemed about the extent of regional disparities in the standard of living in the late fifties, and if it actively sought to reduce them, its policies must be judged to have failed during the 1960s', (1979, pp.99).

"<sup>2</sup> Note that poverty in Ukraine reduced by 29.28 percent in the 1980 to 1991 period and about 70 percent of the reduction is explained by the growth in per capita income and the remaining 30% was due to income redistribution in favour of poor households. But during the subsequent period (1991 to 1992) a large deterioration in economic growth changed this situation.

41
Our modifications to the standard GTAP model focus on features that enhance analysis of agricultural reforms and simulation of distributional impacts. We retain the simplistic yet empirically robust assumptions of constant returns to scale and perfect competition typically featured in agricultural trade studies.<sup>4</sup> Our modifications are aimed at permitting us to shed new light on the distributional consequences of WTO reforms – focusing particularly on the seemingly intractable problem of agriculture liberalization in the industrial countries. We turn now to these modifications.

_Factor Markets:_ Since the work of T.W. Schultz (1945), economists have recognized the importance of off-farm factor mobility in determining farm incomes. Significant wage differentials between farm and non-farm employment persist in the United States and other high income economies (Gardner, 1992; Kilkenny, 1993). The limitations of agricultural labor markets have also been prominently featured in the development economics literature, as an explanation for the very low level of agricultural supply response (de Janvry _et al_ ., 1991). The common CGE assumption of perfect mobility of labor and capital from agriculture to non-agriculture forcing wages to equalize at each point in time for farm and non-farm workers with comparable skills, is at odds with historical observation.

Effectively modeling the complex processes leading to limited farm/non-farm, rural/urban mobility for the full range of countries in our model would be a lifetime project. Instead, we specify a constant elasticity of transformation function which

> 4 Francois et al. (2004) introduce monopolistic competition in the manufacturing sector into their analysis of WTO reforms. The resulting variety and scale effects generally boost the gains to rich countries and

9
invest in both types of investment. We see a stronger and more consistent of Table C-1 in the Appendix shows the impact of inflation on the machinery investment decision of large firms and small firms. The first five regressions follow the first specification, while the last five follow the second one. The _SmallInflation_ coefficients are significant across most of the regressions. The coefficient value of _−_ 0 _._ 023 implies that an additional 1% inflation makes small firms 2.3% less likely to invest in machinery and equipment, relative to large firms.

Table C-2 shows the impact of inflation on the land investment decisions. Overall, we see some evidence that small firms are relatively less prone to invest in land and buildings at higher rates of inflation. Most of the significant coefficients range from _−_ 0 _._ 018 to _−_ 0 _._ 034, implying that an additional 1% inflation makes small firms 1.8-3.4% less likely to invest in land and buildings relative to large firms. However, note that in two regressions - the ones with NoCredit (columns 3 and 8) - the _SmallInflation_ coefficients have positive values which are highly significant. It is probably driven by the addition of _SmallNoCreditInfl_ variable, which has large, negative and significant coefficients ( _−_ 0 _._ 053). The sum of the _SmallInflation_ and _SmallNoCreditInfl_ is still negative, implying that in high inflation environments, small firms with no credit line are less likely to invest than large firms with no credit line.

# **4.3 Results: Intensive margin**

We now proceed in investigating the differential impact of inflation on investment in large and small firms, conditional on positive investment. The dependent variable is therefore the log of investment (in real local currency). We gradually add control variables and access to finance variables interacted with inflation. We are mostly interested in the coefficients of the interaction, and we highlight significant coefficients. Overall, we see that inflation 21
<!-- Start of picture text --> 0,6<br>0,5<br>0,4<br>0,3<br>0,2<br>0,1<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>BRUNEI CAMBODIA INDONESIA LAO REP MALAYSIA<br>MYANMAR PHILIPPINES SINGAPORE THAILAND VIETNAM<br><!-- End of picture text --> Figure 1: Time varying weights between China and the ASEAN countries to move upward in the near future.

# **3.4 Contemporaneous effects**

Contemporaneous effects show how variables of each country are affected contemporaneously by the combined effect of the same variables of the rest of the countries. These results are presented in table (2). Values of the coefficients show the response of the domestic country variables to a 1% change in their foreign counterparts. For example, in Indonesia, a 1% increase in trade in the rest of the countries (foreign trade variable) brings 0.63% increase in growth in the domestic economy. These results can also be interpreted as impact elasticity and they give a good indication of how interconnected the countries are.

In table (2), coefficients are also presented with their corresponding Newey-West heteroskedasticity and autocorrelation consistent t- ratios. Values in bold font indicates they are statistically significant at the 5% level. Results show a high degree of contemporaneous 12