rafmacalaba/datause-encoder-data huggingface dataset:
We defined several variables to assess entrepreneurs’ repayment behavior: a binary variable equal to one if, 12 months after being screened by the EFL tool, their classification was poorer than “normal,” and zero if their classification was “normal”; a binary variable equal to one if the maximum number of days was 90 days or more 12 months after being screened by the EFL tool, and zero if it was less than 90 days; and a binary variable equal to one if the maximum number of days in arrears was 90 days or more at any time during the 12 months after being screened by the EFL tool, and zero if it was less than 90 days during the same period. We also used the total number of days in arrears 6 and 12 months after applicants were screened by the EFL tool.

We examined the use of credit as well. The SBS data do not specify which financial institution has issued entrepreneurs a loan—they only report the total amount of debt each month. We thus used coded binary variables equal to one if any increase in the total amount of debt was detected one and six months after being screened by the EFL tool (compared to one month before being screened by the EFL tool) and zero otherwise. We also used a binary variable equal to one if the person had any classification in the SBS’s credit bureau 12 months after being screened by the EFL tool, and zero if the person does not have any classification (i.e., does not have credit from a formal financial institution subject to credit risk).

Table A2 in the Appendix shows the correlations between our repayment behavior and access to credit indicators with the EFL and credit bureau scores, in the sample of entrepreneurs for which we have both scores. For both scores, a higher value is associated with better repayment behavior. Entrepreneurs with a higher EFL score also have a lower probability of using credit.

# **_3.3 Methodology_**

We estimate linear regression models of the following form: �� ������ ���, �∈�.

Where �� is either a continuous variable (for example, total days in arrears for entrepreneur _i_ ) or a binary variable (for example, an indicator equal to one if the entrepreneur _i_ has a classification worse than “normal” in the public credit registry, and zero otherwise). In the binary case, our model is a linear probability model; _xi_ is an indicator defined differently depending on the hypothesis we are testing. For example, for hypothesis 1 the indicator is equal to one if the entrepreneur was rejected based on his or her EFL score and accepted under the traditional 12
||(1.805)|(0.039)|(0.054)|(0.029)|(0.021)|(0.029)|(0.021)|
|---|---|---|---|---|---|---|---|
|Active|0.007|0.001|0.050**|0.034**|-0.032|0.054**|-0.023|
||(1.012)|(0.026)|(0.024)|(0.017)|(0.021)|(0.026)|(0.022)|
|Observations|9,790|9,790|12,226|12,216|12,227|12,227|12,227|
|R-squared|0.128|0.180|0.453|0.148|0.208|0.171|0.095|
|mean of dep var|0.407|14.84|0.565|0.652|0.151|0.732|11.6|

_Note:_ Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. *** p<0.01, **p<0.05, *p<0.1. OLS = ordinary least squares.

Overall, we estimated in table 8 that a household’s access to electricity decreased when a mine became active. This finding is surprising if we assume that electricity infrastructure is seldom destroyed, so that with time, access has generally been increasing.<sup>13</sup> Figure A1 (Annex) further decomposes the effect, and panel Aa of the figure confirms that the coefficient for electricity access is negative using the treatment distance 20 km. However, with a treatment distance of 10 km, the effect is marginally positive and insignificant. If we use a treatment distance of 50 km, we no longer see a significant effect. In panel Ab, the results are replicated using a spatial lag model, meaning that we allow for nonlinear effects with distance. In reality, it seems like the electricity rate is much higher before a mine (dashed line) than with an active mine (the blue line). However, when the results are decomposed by migrant status in panel Ac of figure A1 (Annex) we find that migrants are driving the lower electricity rate. In fact, among nonmigrants, the electricity rate is higher 0–10 km from an active mine, although it is slightly lower 10–20 km away.<sup>14</sup>

# **6.3 Distributional effects on wealth and inequality**

Table 9 presents the effects of mining on asset wealth and on asset wealth inequality. Wealth data are available in the form of a wealth index, but only for the two last DHS surveys. Following Fenske (2015) and Flatø and Kotsadam (2014), we calculate inequality by means of a Gini coefficient (recoding the wealth variable to be positive only, and using the command

> 13 It is also possible that mining companies compete with households for electricity if supply cannot be increased in the short run.

> 14 In panels Ba, Bb, and Bc of Appendix figure A1, we analyze access to radio. We learn that access to radio is higher close to active mines, and that this seems true according to both the first method (Ba), according to the spatial lag model (Bb), and for both migrants and nonmigrants (Bc). The difference in effects between electricity and radio access might be due to electricity being more dependent on public infrastructure, and that electricity access may come with a time lag to other development indicators such as employment and access to radio, since a battery radio can be bought and used instantly, and easily moved.

26
experienced a change in Polity IV of three points or more in the previous three-year period. Additionally, to proxy instability we measure a country’s history of rioting in the past 5 years (Banks 2010).<sup>5</sup> We also use ethnic fractionalization (Fearon & Laitin 2003) and a direct measure of group exclusion from government based on ethnicity (Wimmer et al. 2009). While the ethnic fractionalization index does not vary over time, the Wimmer et al. (2009) measure of ethnic exclusion changes over time, reflecting the idea that leaders build their coalitions endogenously in response to changing circumstances.

To proxy the risk of coup we use the coup risk measure proposed by Belkin and Schofer (2003) which is a composite score based on the strength of civil society, legitimacy of the regime and the coup history of the country in the past ten years.<sup>6</sup> This measure is the most appropriate at capturing coup risk distinctly from political instability and Belkin and Schofer argue plausibly that, by itself, the history of a country’s successful and attempted coup d’état does not capture high risk regimes that manage to have effective coup-proofing strategies. Still, the Belkin and Schofer measure is only available from 1960 to 2000 and, therefore, we also proxy coup risk with the history of attempted and successful coup d’état in the past five years which has been shown to be a good indicator of future coups (Londregan and Poole 1990).<sup>7</sup> Additionally we use 5 Riots are violent demonstrations or clashes of more than 100 citizens involving the use of physical force. We count the number of years with riots in the past five years and for the first five years in the series, we count the number of riots for the available years only.

6 The correlation of coup risk with anocracy is relatively low at 0.21.

7 To further minimize the loss of observations, for the first five years of a country for which we have a measure of coups we compute history of coup for the available years only rather than turn the observation to missing. We update the data to 2003 using Powell and Thyne (2011).

16
<!-- Start of picture text --> —#-—Actualadoption —*—Cumulative adoption<br>1400<br>o<br>a]<br>Oo 1200<br>=<br>& _ 1000<br>a2<br>£ = 800<br>eS<br>a 3 600<br>° = 400<br>3S<br>= 200<br>a<br>= 0<br>a) f& So) We) “A Cs) 2) 9 y<br>2 & S %) & QS & “+ “ty<br>re Ff Ff LF TF YF FT YY F<br>Year of SHS adoption<br><!-- End of picture text -->
larger increase in poverty particularly in Africa. This was, among other things, due to the high stock of poor people in the continent, the relatively muted economic growth leading up to the pandemic, and the relatively limited health infrastructure available in the continent. Over the course of the pandemic, the World Bank has come up with various projections for the increased poverty due to the pandemic in 2020. For SubSaharan Africa in 2020, the predicted additional poor due to the COVID-19 pandemic has ranged from 26 million – projected in April 2020 – to 34 million – projected in June 2021 (Lakner et al., 2021). Other methods had predicted poverty in Africa to be even worse. For example, simulations accounting for a negative GDP growth shock of between 5% and 20% suggested that the pandemic could push between 28 million and 120 million people into extreme poverty in Sub-Saharan Africa in 2020 (e.g., Sumner et al., 2020). Similarly, our country-level poverty estimates are much smaller than early projections (Table A1). For example, our poverty impacts in Nigeria are smaller than those early estimates by Andam et al. (2020) and poverty impacts for South Africa are smaller than those implied by income losses in Arndt et al. (2020). Overall, macro-based evidence suggests that the economic impacts of the pandemic in Africa have been muted compared to what was feared initially.

# **2.3 Heterogeneity across countries**

There is quite a bit of variation in poverty changes across countries due to the COVID-19 pandemic. Figure 4 shows the changes in extreme poverty across the continent grouped into four categories: (i) countries with less than 1 percentage point increase in extreme poverty, (ii) countries with increases between 1 and 2 percentage points, (iii) countries with between 2 and 3 percentage points increase, and (iv) countries with larger than 3 percentage points increases. In general, countries in North Africa had the smallest changes in extreme poverty – likely because they tend to be less poor and hence have fewer people living close to the international poverty line. Countries in southern and central Africa were moderately affected, while a few countries in west Africa and a couple in southern Africa were most severely affected.

10
Policy Research Working Paper 6143

# **Abstract**

This paper outlines and evaluates several intellectual property monetization strategies available to patent holders in developing countries that help generate domestic innovation and knowledge-driven growth by promoting more active technology markets. Based on a review of World Intellectual Property Report indicators, the patent ownership gap between a sample of developed and developing countries has narrowed gradually for more technologically-sophisticated developing countries. However, based on complementary International Monetary Fund Balance of Payments data, the patent commercialization divide (as indicated by licensing income) has been widening. The paper argues that patents, and all forms of intellectual property, are an enabling mechanism rather than a defensive right: an intangible asset class that can be proactively nurtured and managed for greater value extraction to stimulate knowledge-based entrepreneurship and growth in developing countries. The paper presents multiple case studies of alternative monetization strategies to address the commercialization divide. These strategies range from private, market-driven options to those requiring a greater amount of public policy support: from patent securitization and patent exchanges (focusing on the United States-initiated Intellectual Property Exchange International and the Shanghai Silicon Intellectual Property Exchange), to the strengthening of technology transfer and commercialization infrastructure (focusing on the experience of the Association of University Technology Managers and Taiwan, China’s Intellectual Property Rights Institute), to patent litigation support (including South Korea’s support of patent infringement lawsuit costs for small and medium enterprises). The paper also highlights areas where further policy research would be helpful.

This paper is a product of the Economic Policy and Debt 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 roya.ghafele@oxfirst.com.

_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
# IPF Results: Road Transport

As with water catchment projects, transport projects are ranked by SEI and FEI scores separately. The calculations for SEI and FEI scores are detailed in Annexes 6, 7, and 8, while the graphical results by each index are summarized in Figures 10, 11 and 12. Figures 10 and 11 show the SEI ranking based on the simple poverty indicator and the multi-criteria poverty index, respectively.

## Road Transport SEI and FEI Rankings

Figure 10. Road Transport SEI Ranking Results from Input and Weighting with Pov_c <!-- Start of picture text --> 100<br>90 Social and Environmental Indicator (SEI) - Pov_c<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Figure 11. Road Transport SEI Ranking Results from Input and Weighting with Pov_multi<br>100<br>90 Social and Environmental Indicator (SEI) - Pov_multi<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Figure 12. Road Transport Project FEI Ranking Results<br>100 Financial and Economical Indicator (FEI)<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>P31 P12 P10 P26 P34 P4 P46 P19 P23 P13 P39 P28 P14 P17 P44 P5 P21 P25 P18 P29 P8 P16 P30 P6 P9 P43 P22 P20 P35 P49 P11 P32 P42 P48 P3 P33 P2 P41 P47 P1 P50 P15 P38 P36 P27 P37 P40 P45 P7 P24<br>P31 P19 P12 P10 P34 P26 P5 P18 P14 P46 P28 P39 P23 P44 P17 P4 P25 P21 P1 P13 P20 P11 P35 P6 P30 P22 P9 P48 P29 P49 P50 P3 P42 P2 P8 P16 P41 P32 P33 P47 P43 P15 P27 P40 P37 P36 P38 P45 P7 P24<br>P21 P46 P42 P8 P14 P3 P39 P26 P27 P5 P25 P16 P10 P22 P31 P17 P12 P50 P18 P48 P7 P23 P49 P44 P19 P9 P32 P45 P40 P35 P2 P6 P4 P33 P28 P15 P34 P41 P30 P47 P11 P29 P43 P13 P20 P37 P24 P1 P38 P36<br><!-- End of picture text --> Source: Authors’ calculations 38
# Table V.4

# Interest Rates in Korea's Fragmented Financial Markets, 1965-1990

|Year|General Bank<br>Loan Rate<br>(1 yr-Maturity)|Special<br>Funds|Loans in<br>Unregulated<br>Financial (Curb)<br>Markets|Deposit<br>Rate<br>(1 year)<br>_|Inflation<br>Rate|
|---|---|---|---|---|---|
|1965|18.5||58.9|30.0|11.6|
|1970|24.0||50.2|22.8|16.1|
|1975|15.5||40.5|15.0|25.3|
|1978|17.1|13.0|41.7|16.9|14.4|
|1980|16.0|12.0|46.1|15.1|28.3|
|1982|15.0|10.0|29.7|11.4.|9.8|
|1985|12.0|10.0|24.6|10.0|2.5|
|1988|12.5|10.0|23.8|10.0|7.1|
|1990|11.0|10.0|23.0|8.0|6.5|

Sources: 1965-1978 from David C. Cole and Yung Chul Park. Financial Develooment in Korea. 1945-1978, Harvard University Press. Subscquenz years. Ministy or Finance.

71
# **D.B Impacts by Age**

Table D.4. Heterogeneous Impacts on Educational Attainment

||Educatio|n Index|Literat|e [=1]|Some Edu|cation [=1]|Primar|y [=1]|Second|ary [=1]|
|---|---|---|---|---|---|---|---|---|---|---|
|Age in 2017|Age_≤_25<br>(1)|Age_>_25<br>(2)|Age_≤_25<br>(3)|Age_>_25<br>(4)|Age_≤_25<br>(5)|Age_>_25<br>(6)|Age_≤_25<br>(7)|Age_>_25<br>(8)|Age_≤_25<br>(9)|Age_>_25<br>(10)|
|_Panel A. Taliban Exposure_|||||||||||
|_ETi_|-0.011|-0.240***|0.058|-0.193**|-0.027|-0.326***|0.120|-0.121*|-0.196|-0.321***|
||(0.040)|(0.059)|(0.037)|(0.078)|(0.062)|(0.089)|(0.087)|(0.072)|(0.122)|(0.111)|
|R-squared|0.060|0.143|0.043|0.182|0.075|0.216|0.226|0.029|0.070|0.050|
|_Panel B. Contested Territories Exposure_|||||||||||
|_ECi_|-0.012|-0.103***|0.017|-0.087***|-0.011|-0.140***|0.062*|-0.052*|-0.117**|-0.134***|
||(0.015)|(0.023)|(0.014)|(0.031)|(0.024)|(0.035)|(0.033)|(0.028)|(0.046)|(0.044)|
|R-squared|0.060|0.144|0.043|0.182|0.075|0.217|0.227|0.029|0.071|0.051|
|_Panel C. U.S-Allies Exposure_|||||||||||
|_EAi_|0.009|0.084***|-0.014|0.070***|0.008|0.114***|-0.046*|0.042*|0.087**|0.108***|
||(0.012)|(0.018)|(0.011)|(0.024)|(0.018)|(0.027)|(0.025)|(0.022)|(0.036)|(0.034)|
|R-squared|0.060|0.145|0.043|0.183|0.075|0.217|0.227|0.029|0.071|0.051|
|Observations|2,801|2,791|2,801|2,791|2,801|2,791|2,801|2,791|2,801|2,791|
|_Controls in All Panels_|||||||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables are described in the Appendix D. Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
**-** **<u>Table 6: Impact on SHG participants and non participants</u>**

||**Pa**<br>|**Untrimm**<br>**rticipant**<br>|**ed sam**<br>**s**<br>|**ple, sim**<br>**Non-**<br>|**ple DD**<br>**participants**<br>|
|---|---|---|---|---|---|
|Consumption p.c. (Rs/year)|1103|(400)|***|195|(423)|
|Food (Rs/year)|257|(175)|(*)|-89|(190)|
|Non-food (Rs/year)|846|(296)|***|283|(343)|
|Energy intake p.c. (Kcal/day)|94|(86)||-132|(91)|
|Protein intake p.c. (g/day)|1.96|(1.86)||-2.58|(2.03)|
|Non-financial asset p.c. (Rs)|711|(289)|***|340|(345)|
|No. of obs.|920|+191=11|11|962|+333=1295|
||**T**|**rimmed s**|**ample,**|**PS weig**|**hted DD**|
||**Pa**<br>|**rticipant**<br>|**s**<br>|**Non-**<br>|**participants**<br>|
|Consumption p.c. (Rs/year)|864|(384)|*|-19|(446)|
|Food (Rs/year)|345|(219)|(*)|-181|(210)|
|Non-food (Rs/year)|519|(252)|*|163|(365)|
|Energy intake p.c. (Kcal/day)|210|(110)|*|-84|(102)|
|Protein intake p.c. (g/day)|6.99|(2.31)|***|-1.61|(2.40)|
|Non-financial asset p.c. (Rs)|720|(311)|**|231|(399)|
|No. of obs.|66|4+176=84|0|908|+332=1240|

* significant at 10%; ** significant at 5%; *** significant at 1% for two-sided test;

(*) significant at 10% for one-sided test 22
Table 4a: OLS estimates of the share of each sector in **wage** employment Share of agriculture in wage emplovment

|Variable|Coefficient|Std. Error|T-ratio|**P>ItI**|Number of observations|99|
|---|---|---|---|---|---|---|
|Log of GNP per capita|10.22|<br>24.81|0.41|<br>0.68|F( 11. 87)|4.6|
|Square of log of GNP per capita|-0.15|<br>1.54|-0.09|<br>0.93|Prob > F|0|
|Log of population|-3.89|<br>2.87|-1.35|<br>0.18|**R-squared**|**0.3675**|
|Square of log of population|0.37|0.48|0.78|<br>0.44|Adj R-squared|0.2875|
|Europe and Central Asia|8.10|8.99|0.90|<br>0.37|Root MSE|21.185|
|Latin America and the Caribbean|28.10|7.28|3.86|<br>0.00|||
|Middle East and North Africa|22.50|9.05|2.49|<br>0.02|||
|High-income OECD|-1.44|10.59|-0.14|0.89|||
|South Asia|11.87|<br>12.72|0.93|<br>0.35|||
|Sub-Saharan Africa|11.54|<br>9.42|1.23|<br>0.22|||
|Table I a dummny|-3.34|8.64|-0.39|0.70|||
|Constant|-46.30|99.93|-0.46|0.64|||

# Share of industrv in wage employment

|Variable|Coefficient|Std. Error|T-ratio|P>Itl|
|---|---|---|---|---|
|Log of GNP per capita|49.48|17.63|2.81|<br>0.01|
|SquareoflogofGNP per capita|-2.73|<br>1.10|-2.49|<br>0.02|
|Log ofpopulation|-0.64|2.04|-0.31|0.75|
|Square of log of population|-0.34|0.34|-1.00|0.32|
|Europe and Central Asia|3.56|6.39|0.56|0.58|
|Latin America and the Caribbean|-6.97|5.17|-1.35|0.18|
|Middle East and North Africa|-1.83|6.43|-0.28|0.78|
|High-income OECD|4.76|7.53|0.63|0.53|
|SouthAsia|-13.51|9.04|-1.49|0.14|
|Sub-Saharan Africa|-13.511|<br>6.691|<br>-2.02|0.05|
|Table Ia dummy|3.79|6.14|0.62|0.54|
|Constant|-132.55|71.031|<br>-1.87|0.07|

|Number of observations|99|
|---|---|
|F( 11, 87)|10.55|
|Prob > F|0|
|R-squared|0.5716|
|Adj R-squared|0.5174|
|Root MSE|15.059|

Share of services in wage emplovment

|Variable|Coefficient|Std. Error|T-ratio|**P>Itl**|
|---|---|---|---|---|
|L og of GNP per capita|41.34|14.63|<br>2.83|0.01|
|Square of log of GNP per capita|-2.24|<br>0.91|-2.46|<br>0.02|
|Log of population|-4.1S|<br>1.69|<br>-2.47|<br>0.02|
|Square of log of population|0.29|0.28|1.04|<br>0.30|
|Europe and Central Asia|12.75|<br>5.30|2.41|0.02|
|Latin America and the Caribbean|-1.35|<br>4.29|-0.32|<br>0.75|
|Middle East and North Africa|7.34|5.33|1.38|<br>0.17|
|High-income OECD|11.13|<br>6.24|1.78|<br>0.08|
|South Asia|3.53|7.50|0.47|0.64|
|Sub-Saharan Africa|-9.73|<br>5.55|-1.75|<br>0.08|
|Table Ia dumnmy|-3.15|<br>5.09|.<br>-0.62|0.54|
|Constant|-105.61|<br>58.91|-1.79|0.08|

|Number of observations|99|
|---|---|
|F( 11, 87)|13.66|
|Prob > F|0|
|R-squared|0.6334|
|Adj R-squared|0.587|
|Root MSE|12.49|

30
7 countrirt on their debt. Second, the model assumes the 'uncovered interest parity" which links the interest rate differentials between the United States and any industrialized country to the movements in their exchange rates.

# D. Estimatlca and Solution

Most of the parameters of the model are estimated using historical data. In many instances, a single equation is estimated for all five industrialized country blocks using pooled cross-section method and in some cases common coefficients were imposed across the blocks if the hypothesis of equal coefficients could not be rejected. Such a procedure, which is an attempt to standardize macroeconomic relations, reduces the variation of responses across di'ferent blocks but does not eliminate them.

One feature of the model that complicates its solution is the presence of expectation variables in some of the stochastic equations. These variables are: (1) nominal exports of the developing country block (with four period leads), (2) exchange rates, inflation rates and nominal exchange rates of the industrialized country blocks (each with one period lead), and (3) interest rates (with one period lead). The forward-lookingproperty of the model consists in constraining the expectations of these variables to be identical to their respective values in the model solution.

The expectations variables are solved for using the Fair-Taylor algorithm and simulated using the TROLL 'Forward-Looking Simulator".<sup>11</sup>

# E. Behavioral Equations and Estimated Coefficients

The behavioral equations in MULTIMOD along with their estimated coefficients are shown in Table 1.12 The model has a total of 308 equations. Table 2 gives their break-down by type (behavioural, empirical and definitional), by sector of the economy, and by country or region.

Stee Fair and Taylor (1983), Fair (1984: Chapter 11), and Peter Hollinger (1987).

9Vor the explanation of the choice of variables in each equation see Masson et al (May 1988).