rafmacalaba/datause-encoder-data huggingface dataset:
work with an institution such as a chamber of commerce, NGO, or training organization that has pre-existing relationships with firms, and has thus observed firm performance over time, and developed a sense of which entrepreneurs are more likely to succeed. Information on entrepreneurs with potential for growth might also be solicited among neighbors (Hussam et al., 2017), an approach that has the advantage of using local knowledge, but which will be dependent on how useful the local knowledge is. An additional advantage of micro-lenders, chambers of commerce, and similar organizations is that they may have social capital with local firms. The investor may benefit, in terms of better reporting and repayment, if firms find value in maintaining relationships and reputations with these local organizations.

A third approach is to have firms apply for funding through a business plan competition or similar competitive mechanism. McKenzie (2017) shows that the firms that apply for such programs tend to be positively selected, and that winners grow rapidly after receiving capital. An alternative approach is to rely on large amounts of data and prediction models to predict which firms will be good investments. McKenzie and Sansone (2017) find machine-learning methods have very little power in distinguishing growth paths among semi-finalists in a business plan competition. But such models may be better at distinguishing between subsistence and growth-oriented firms among microenterprises. For example, Hussam et al. (2017) find machine learning helps predict which Indian firms have higher returns; Arráiz et al. (2017) find that the algorithm used by the Entrepreneurial Finance Lab is able to use psychometrics to identify borrowers without credit histories who borrow more when given the chance, and who repay at similar rates to other borrowers; and Björkegren and Grissen (2018) find mobile call record data help predict which borrowers will have better repayment trajectories.

The choice between these methods will depend on who the investor is, and on scale. These methods will differ in terms of their feasibility and cost-effectiveness, and a micro-equity investor may be able to fine-tune this selection process over time through experimentation and the collection of return data.

# **2.5 Additional contractual options**

13
# **S9 Balance Across Non-Targeted Treated and Control Areas**

We check balance in outcome variables across non-targeted treated and control areas. We use both levels of outcome variables at baseline (1996) and compare differences in pre-trends (using the difference between 1992 and 1996). Levels and trends of outcome variables tend to be unbalanced, emphasizing that simple comparisons between treated and non-treated areas are insufficient to understand impacts of road upgrades.

**Table S11:** Summary Statistics Across Non-Targeted Treated and Control Areas: Kebeles, RSDP I-III

|Variable|(1)<br>Treatment<br>(_<_5km RSDP)<br>Mean/SE|(2)<br>Control<br>(_>_5km RSDP)<br>Mean/SE|T-test<br>Diference<br>(1) - (2)|
|---|---|---|---|
|NTL (1996)|0.06<br>(0.02)|0.02<br>(0.01)|0.04<sup>_∗∗_</sup>|
|Urban (1996)|0.03<br>(0.01)|0.22<br>(0.04)|-0.19<sup>_∗∗∗_</sup>|
|Cropland (1996)|134.21<br>(2.66)|130.81<br>(1.78)|3.4|
|NTL (Pre-trends)|0.03|0.01|0.02<sup>_∗_</sup>|
||(0.01)|(0)||
|Urban (Pre-trends)|0<br>(0)|0.02<br>(0.01)|-0.02<sup>_∗∗_</sup>|
|Cropland (Pre-trends)|0.69<br>(0.09)|0.59<br>(0.07)|0.1|
|N|3,157|7,063||

**Table S12:** Summary Statistics Across Non-Targeted Treated and Control Areas: 1km Grid, RSDP I-III

||(1)<br>Treatment<br>(_<_5km RSDP)|(2)<br>Control<br>(_>_5km RSDP)|T-test<br>Diference|
|---|---|---|---|
|Variable|Mean/SE|Mean/SE|(1) - (2)|
|NTL (1996)|0.0496<br>(0.0024)|0.0056<br>(2e-04)|0.044<sup>_∗∗∗_</sup>|
|Urban (1996)|0.001<br>(1e-04)|8e-04<br>(0)|2e-04<sup>_∗∗∗_</sup>|
|Cropland (1996)|0.6086<br>(0.0014)|0.2338<br>(4e-04)|0.3748<sup>_∗∗∗_</sup>|
|NTL (Pre-trends)|0.016|0.0022|0.0138<sup>_∗∗∗_</sup>|
||(0.0012)|(1e-04)||
|Urban (Pre-trends)|1e-04<br>(0)|1e-04<br>(0)|0|
|Cropland (Pre-trends)|0.0013<br>(1e-04)|3e-04<br>(0)|0.001<sup>_∗∗∗_</sup>|
|N|113,908|1,157,186||

S18
# Impact of Investigations and Duties on Import Prices

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TABLE 6: IMPACT OF TRAININGS ON PSYCHOLOGICAL OUTCOMES

||Personal Initiative<br>(1-5)|Self Efficacy<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|Error<br>management (1-<br>5)|Attitude to Risk<br>(0=risk averse;<br>1=risk loving)|
|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|OLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**_DOT study_**|||||||
|DOT_after one year|0.0848**|0.0891**|-0.0177|0.0852|0.0273|-0.0117|
||(0.0371)|(0.0413)|(0.0491)|(0.0557)|(0.0371)|(0.0264)|
|DOT_after two years|-0.00284|-0.0246|-0.209**|-0.0463|0.0328|0.0213|
||(0.0575)|(0.0521)|(0.0964)|(0.0746)|(0.0590)|(0.0277)|
|Observations|1184|1184|1184|1184|1184|1184|
|Control Group Mean at Follow-up|4.548|4.333|4.619|4.046|4.621|0.529|
|I|(0.0284)|(0.0305)|(0.0343)|(0.0408)|(0.0269)|(0.0193)|
|p_value: DOT_after one year =<br>DOT_after two years|0.170|0.0601|0.0734|0.128|0.966|0.402|
||Personal Initiative<br>(1-5)|Self Efficacy<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|Error<br>management (1-<br>5)|Attitude to Risk<br>(0=risk averse;<br>1=risk loving)|
|**_PI/BSED_**|ANCOVA|ANCOVA|ANCOVA|ANCOVA|ANCOVA|ANCOVA|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Personal Initiative (PI)_after 1.5|-0.0126|-0.00302|0.0214|0.00501|0.00819|0.199|
|years|(0.0450)|(0.0502)|(0.0369)|(0.0447)|(0.0242)|(0.159)|
|Basic Skills (BSED)_after 1.5|0.0123|-0.0139|0.0323|0.0189|0.0483*|0.0378|
|years|(0.0362)|(0.0402)|(0.0505)|(0.0463)|(0.0242)|(0.145)|
|Observations|1,454|1,454|1,454|1,454|1,454|1,454|
|Control Group Mean at baseline|4.427|4.159|4.335|3.954|4.354|4.294|
||(0.0559)|(0.0411)|(0.0594)|(0.0821)|(0.0387)|(0.118)|
|Controls used|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_

* significant at 10% level ** significant at 5% level *** significant at 1% level

(1) Controls include: received WEDP loan at baseline, age of owner, marital status, education, household size, number of children and digitspan score at baseline.

(2) Analysis restricted to business owners who had businesses that were still operational at the time of the follow-up survey.

# **4.3.3 Impact on business practices**

Table 7 presents the impact on indices of business practices that is created by taking an average of the practices that business has done in the last 12 months - record keeping, marketing, stock control and financial planning practices. This is by no means an exhaustive list of all the measured business practices in the surveys but highlights a few of the more common cited practices presented in business studies (McKenzie & Woodruff, 2017). Overall in Ethiopia, we do not observe changes to business practices or
**Figure 3: FDI and non-FDI Capital Outflows**

<!-- Start of picture text --> Median outflows for all EMs in % of trend GDP<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>‐0.5<br>‐1<br>‐1.5<br>FDI non‐FDI<br>Figure 4: Components of non-FDI Capital Outflows<br>Median outflows for all EMs in % of trend GDP<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>‐0.5<br>‐1<br>‐1.5<br>Other flows Portfolio equity Portfolio debt<br>1990q1 1991q1 1992q1 1993q1 1994q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1<br>1990q1 1991q1 1992q1 1993q1 1994q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1<br><!-- End of picture text --> In Table 2 we compare consecutive five year periods. Portfolio debt inflows increased in 2006-10 and again in 2011-15. Less widely appreciated, FDI outflows from emerging markets rose strongly in 2006-10. Other flows also increased in 2006-10.

We are interested in whether the volatility of flows, as measured by the coefficient of variation (adjusting the standard deviation by their mean in the same period) has risen significantly. In Tables 3 and 4 we therefore regress the coefficients of variation for a pooled sample of 5 year 7
Policy Research Working Paper 4748

# **Abstract**

Regulation allows microfinance institutions to evolve more fully into banks, particularly for institutions aiming to take deposits. But there are potential tradeoffs. Complying with regulation and supervision can be costly. The authors examine the implications for the institutions’ profitability and their outreach to smallscale borrowers and women. The tests draw on a new database that combines high-quality financial data on 245 of the world’s largest microfinance institutions with newly-constructed data on their prudential supervision. Ordinary least squares regressions show that supervision is negatively associated with profitability. Controlling for the non-random assignment of supervision via treatment effects and instrumental variables regressions, the analysis finds that supervision is associated with substantially larger average loan sizes and less lending to women than in ordinary least squares regressions, although it is not significantly associated with profitability. The pattern is consistent with the notion that profit-oriented microfinance institutions absorb the cost of supervision by curtailing outreach to market segments that tend to be more costly per dollar lent. By contrast, microfinance institutions that rely on non-commercial sources of funding (for example, donations), and thus are less profitoriented, do not adjust loan sizes or lend less to women when supervised, but their profitability is significantly reduced.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand the development and impact of microfinance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rcull@worldbank.org or ademirguckunt@ 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
Policy Research Working Paper 7479

# **Abstract**

Recent economic research documents a range of adverse welfare consequences from extreme heat stress, including health, labor productivity, and direct consumption disutility impacts. Without rapid adaptation, climate change will increase the burden of heat stress experienced by much of the world’s population in the coming decades. What will the distributional consequences of this added heat stress be, and how might this affect optimal climate policy? Using detailed survey data of household wealth in 690,745 households across 52 countries, this paper finds evidence suggesting that the welfare impacts of added heat stress caused by climate change may be regressive. Specifically, the analysis finds that poorer households tend to be located in hotter locations across and within countries, and poorer individuals are more likely to work in occupations with greater exposure to the elements not only across but also within countries. These findings—combined with the fact that current social cost of carbon estimates do not include climate damages arising from the productivity impacts of heat stress—suggest that optimal climate policy, especially when allowing for declining marginal utility of consumption, involves more stringent abatement than currently suggested, and that redistributive adaptation policies may be required to reduce the mechanical inequities in welfare impacts arising from climate change.

This paper was commissioned by the World Bank Group’s Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank Group’s flagship report: “Shock Waves: Managing the Impacts of Climate Change on Poverty.” 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://econ.worldbank. org. The authors may be contacted at jisungpark@fas.harvard.edu.

_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
Policy Research Working Paper 6424

# **Abstract**

Using newly collected survey data on direct suppliermultinational linkages in Chile, Ghana, Kenya, Lesotho, Mozambique, Swaziland, and Vietnam, this paper first evaluates whether foreign investors differ from domestic producers in terms of their potential to generate positive spillovers for local suppliers. It finds that foreign firms outperform domestic producers on several indicators, but have fewer linkages with the local economy and offer less supplier assistance, resulting in offsetting effects on the spillover potential. The paper also studies the relationship between foreign investor characteristics and linkages with the local economy as well as assistance extended to local suppliers. It finds that foreign investor characteristics matter for both.

The paper also examines the role of suppliers’ absorptive capacities in determining the intensity of their linkages with multinationals. The results indicate that several supplier characteristics matter, but these effects also depend on the length of the supplier relationship. Finally, the paper assesses whether assistance or requirements from a multinational influence spillovers on suppliers. The results confirm the existence of positive effects of assistance (including technical audits, joint product development, and technology licensing) on foreign direct investment spillovers, while the analysis finds no evidence of demand effects.

This paper is a product of the International Trade Department, , Poverty Reduction and Economic Management Network. 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://econ.worldbank. org. The author may be contacted at dwinkler2@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
standard) are greater for state-owned firms than that for non-state-owned firms in China. This finding, again, boils down to the size of the firms because state-owned firms in China account for a significant proportion of large-scale energy firms, and cleaner production standards have greater effects on the improvement of productivity of state-owned firms.

# **3.3 Effects of innovation vs. adoption of green/clean technology on the productivity**

Whether the firm’s productivity change due to green investment is caused by the investment in the deployment of green/clean technologies or by their development (i.e., R&D investment)? While the increased adoption of green/clean technologies directly affects productivity even in the short run, innovation of new technology through R&D investment does so in the long run. However, the development of new technology does not guarantee its adoption. The discussions earlier are related mostly to investment in the adoption or deployment of clean technologies. In general, the countries with greater energy R&D investment intensity show higher levels of investment in the green technologies (Popp et al., 2011). Here, we briefly highlight the literature that exploits the relationship between R&D investment in clean technologies and firms’ productivity. Some existing studies empirically show that environmental knowledge spillover or the knowledge created through R&D investment on environmental or clean technologies are positively related to firms’ performance.

Based on a survey of small and medium-sized Italian manufacturing firms over the 20042006 period, Aldieri et al. (2021) show a positive but only slightly significant effect of process or product innovation on productivity. They also show positive and strongly significant effects if it is an organizational innovation.<sup>9</sup> Using firm-level data on inputs, outputs, and their patents registered from Japan, the USA, and European countries, Aldieri et al. (2020) show investment in environmental or clean technologies or knowledge, measured in terms of registered patents, affect firms’ productivity significantly and positively. Using firm-level panel data of Dutch manufacturing firms over the 2000-2008 period, van Leeuwen and Mohnen (2017) find a positive

> 9 Generally, product innovation includes new products, process innovation includes new technologies and organizational innovation includes new business practices (OECD, 2005).

14
**Figure 3 – Scatter plots of Compass and Rope vs GPS (left) and Self‐Reported (right) land area measures, acres**

<!-- Start of picture text --> Ethiopia<br>6 6<br>4 4<br>2 2<br>0 0<br>0 2 4 6 0 2 4 6<br>GPS SR<br>Tanzania<br>3 3<br>2 2<br>1 1<br>0 0<br>0 1 2 3 4 0 2 4 6 8<br>GPS SR<br>Nigeria<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>0 5 10 15 20 0 5 10 15 20<br>GPS SR<br>CR<br>CR<br>CR<br><!-- End of picture text --> _Note: red line is line of equality between measurements._ 44
For instance variables representing human capital are not available on annual basis and thus are not included in the full information set in the annual regressions.

The main findings can be summarized as follows. Increases in the flow of aid are not followed by improvements or declines in the quality of economic institutions. For the whole sample, and sub samples used in the robustness check, such as considering Sub-Saharan countries only, or East Asian countries, this result is robust across different estimation methods, and to variation in the choice of moment conditions. Not surprisingly the annual data results are less precise due to the instrumentation strategy but in all the cases we do not reject the restriction of b1+b2=0 representing a 2 year effect of the flow of aid on institutions (see Table 8).<sup>19</sup> For all the specifications we consider OLS, and Within Groups (WG) estimates, and two versions of the “Differenced” GMM estimator. All models include a full set of time dummies. The first version of the Dif-GMM (1) is the one-step version of the GMM estimator with WhiteHuber heteroskedasticity-robust standard errors, while the Dif-GMM (2) is the two step version of the GMM estimator. The Dif-GMM (2) estimator always uses the small sample correction for standard errors of the two step GMM estimates, (see Windmeijer (2005)). We also consider a two-step version of the System estimator, with standard errors robust to heteroskedasticity and autocorrelation and Windmeijer (2005) corrected. For all the different GMM estimators, for the sake of consistency of the results, we consider the same set of instruments following the selection rules discussed in the previous section.

Table 9 reports the first set of results on the legal structure/property rights variable from the Fraser Institute. As the table shows, there are reasons to believe that this model and the selection of the instrument is properly specified and that the first lag of the dependent variable is

> 19 Though the coefficients are individually significant and of opposite sign during the first and second years, for the annual data we believe that this reflects noise in the annual data rather than a systematic effect of aid on institutions that changes sign over the years. In any case, the 2 year effect is zero.

27
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