rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> political "muscle", i.e. the potential lobbying resources at its disposal. The second measure proxies<br><!-- End of picture text --> a different dimension of sectoral politics, namely the direct voting power of an industry’s workers. In addition, I capture the lobbying potential of national governments using Herfindahl indices of value added and employment across EU member states. These measures proxy the extent to which each sector is geographically concentrated in particular countries, and thus the extent to which governments have a perceived economic or political interest in protecting "their" industry.”4 Treating all independent variables in (15) except standards and tariffs as exogenous, I reestimate the equation using two stage least squares (TSLS). Second stage results are in the second column of Table 3. Rejection of the null by a Hausman test (1%) indicates that endogeneity is indeed a problem in these data, but correcting for it leaves the basic conclusions unchanged: the coefficient on the total number of standards remains negative, while the percentage of harmonized standards still enters the equation positively.”> The magnitudes ofboth coefficients change slightly— becoming larger for the percentage of harmonized standards, and smaller (in absolute value) for the total number of standards—but remain statistically significant at the 5% level (total number of standards) and the 1% level (proportion of harmonized standards). The tariff coefficient increases markedly in absolute value terms, but remains statistically insignificant. Amongst the remaining variables, only per capita GDP undergoes an (unexpected) sign change, but it remains statistically insignificant. How valid is this choice of instruments? Unconditional correlation coefficients between the endogenous explanatory variables and the instruments range from 0.14 to 0.68 for stds,;, 0.1 to 0.29 for ~~ae,~~ and 0.05 to 0.12 for In(1+ Tzs¢). First stage estimates in columns 3-5 of Table 3 suggest that when used jointly, the instruments do a relatively good job of explaining the variation export variety, and to satisfy the second stage exclusion restriction. Second, it is desirable for the final model to be overidentified, but it would be difficult to achieve this using antidumping data since only a small number of independent data series would be available.

4These data come from Eurostat annual enterprise statistics on industry and construction, freely downloadable from http://epp.eurostat.ec.europa.eu. Series codes are V12150 (value added at factor cost) and V16110 (number of persons employed). I construct the Herfindahl indices as the sum of the squared country shares for each indicator. For the textiles, clothing, and footwear sectors, I use NACE industry codes DB17, DB18, and DC193 respectively. 2>For this and the test of instrument validity below, I use the regression based tests set out in Wooldridge (2002, pp. 118-124).

20
unambiguously increased its use of trade remedies over this period. But its total use of these instruments is minimal.

**Table 10: Non-Tariff Measures (NTMs) and Trade Remedies by Selected Major Developed Country, 1996-2011**

||Aus|tralia|Can|ada **||EU **|Ja|pan|No|rway|Swit|zerland|United|States **|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Trade Measure|1996|2011|1996|2011|1996|2011|1996|2011|1996|2011|1996|2011|1996|2011|
|**NTMs (in % of total tariff lines)**|||||||||||||||
|Licencing, Non-Automatic|0|X|0|X|0.8|X|1.3|X|2.6|X|0|X|0|X|
|Tariff Quota|0|*|0|2.2|0|4.9|0.6|1.8|0|0.4|0.2|3.5|0|*|
|AD/CV and VEPRs|0.4|..|0.7|..|0.2|..|0|..|0|0|0|0|5|1.1|
|**Trade Remedies (no. of measure**|**s in Forc**|**e)**|||||||||||||
|Antidumping|..|22|..|33|143|140|0|6|0|0|0|0|..|253|
|Countervailing|..|3|..|9|2|11|0|0|0|0|0|0|..|50|
|Safeguards|..|0|..|0|0|0|0|0|0|0|0|0|..|0|
|Total AD/CV/SG|..|25|..|42|145|151|0|6|0|0|0|0|..|303|
||Aus|tralia|Can|ada **||EU **|Ja|pan|No|rway|Swit|zerland|United|States **|
|Trade Measure|2007|2011|2007|2011|2007|2011|2007|2011|2007|2011|2007|2011|2007|2011|
|**Trade-Weighted Share of Non-oil**<br>|**Imports**|**(in %) ****<br>|*****||||||||||||
|Antidumping|..|0.4|..|0.7|..|1.7|..|0.1|0|0|0|0|..|3.9|
|Total AD/CV/SG|0.5|0.5|0.7|0.7|3.8|1.8|1.1|0.1|0|0|0|0|3.3|4.0|

Notes: X = licensing for health, sanitary, safety, environmental and/or defense.

- = less than 1%.

** = antidumping measures in effect in 2003 for Canada, 91; EU, 156; and US, 279.

- *** = Data are taken from Bown, C. (2012), Emerging Economies and the Emergence of South-South Protectioniem, PRD #6162, World Bank. (Table 1 and Appendix Table A.1)

.. = data not available Sources: WTO Trade Profiles database, WTO TPRs, and OECD, 1997 (table 5.1); and Bown (2012).

The lower panel of Table 10 shows the changes that have been in the use of trade remedies by developed countries over the period 2007-2011. The trade weighted share has increased in this period for the US primarily because of the use of additional countervailing measures, but has decreased both for the EU and Japan.<sup>26</sup> The overall conclusion from this analysis of the frequency of use of trade remedies by developed countries is that, while still substantial, it is certainly not more extensive than in the nineties. Combined with the earlier discussion of the declining trends in the use of other core non50
**Table 1.A. Literature review of forecasting methods for crude oil prices (continued)**

|**Authors and**<br>**year**|**Crude oil**<br>**prices**|**Methods**|**Main findings**|**Real or**<br>**nominal**<br>**prices**|**In-sample or**<br>**out-of-sample**<br>**forecasts**|
|---|---|---|---|---|---|
|Zeng and<br>Swanson<br>(1998)|Crude oil<br>price|Random walk,<br>VAR, VECM|Error-correction models<br>perform better in shorter<br>forecast horizons.|Nominal<br>prices|Both types of<br>forecasts|
|Zhao, Li and<br>Yu (2017)|WTI|Deep learning<br>neural network<br>model,<br>multivariate<br>forecasting<br>models|The deep learning approach<br>outperforms multivariate<br>forecasting models.|Nominal<br>prices|Out-of-sample<br>forecasts|

_Note_ : AR (Autoregressive Model), ARMA (Autoregressive–Moving-Average Model), ARIMA (Autoregressive Integrated Moving Average Model); BVAR (Bayesian Vector Autoregressive Model); GARCH (Generalized Autoregressive Conditional Heteroskedasticity); Least Absolute Shrinkage and Selection Operator (LASSO); SVAR (Structural Vector Autoregressive Model); VECM (Vector Error Correction Model); VEC-NAR (Vector Error Correction Model and nonlinear autoregressive neural network); VAR (Vector Autoregressive Model); WTI (West Texas Intermediate).

27
# **Better Loans or Better Borrowers?**

## **Impact of Meso-Credit on Female-Owned Enterprises in Ethiopia**

_By_ Salman Alibhai, Niklas Buehren and Sreelakshmi Papineni<sup>_∗_</sup>

**Keywords** : Gender, Entrepreneurship, Firms and Ethiopia.

**JEL** : J16 L25 L26 O12

> _∗_ This paper is a product of the World Bank Africa Gender Innovation Lab (AFRGIL). Alibhai (email: aalibhai@worldbank.org); Buehren (email: nbuehren@worldbank.org); Papineni (email: spapineni@worldbank.org). This study is on the Women Entrepreneurship Development Project (WEDP), an ongoing lending operation of the Finance & Markets Global Practice, and we thank the Team Leader Francesco Strobbe for his support.We thank Seblewangel Ayalew Woreta for superb field assistance. We are grateful to the World Bank Group’s Umbrella Facility for Gender Equality and the Government of Canada for financial support.
**Table 11: Estimated Costs of Producing Jatropha Oil in India**

||Yr 4|Yr 5|Yr 6|Yr 7<br>($/lt<sup>a</sup>)|Yr 8 Yr 9 Yr 10|
|---|---|---|---|---|---|
|**Annual variable plantation costs**||||||
|Lease|0.15|0.04|0.02|0.02|0.02 0.02 0.02|
|Harvesting|0.08|0.08|0.08|0.08|0.08 0.08 0.08|
|Maintenance|0.37|0.09|0.06|0.05|0.05 0.04 0.04|
|Retainership (including irrigation costs)|0.41|0.10|0.06|0.05|0.05 0.05 0.05|
|Sub-total|1.01|0.31|0.22|0.21|0.20 0.18 0.18|
|**Annual variable logistics costs**||||||
|Seed collection center|0.00|0.00|0.00|0.00|0.00 0.00 0.00|
|Wharehousing|0.04|0.01|0.01|0.01|0.01 0.01 0.01|
|Transport|0.02|0.02|0.02|0.02|0.02 0.02 0.02|
|Sub-total|0.07|0.04|0.04|0.04|0.04 0.03 0.03|
|**Annual extraction operating costs**||||||
|Seed preparation|0.01|0.01|0.01|0.01|0.01 0.01 0.01|
|Decorticator and oil extraction unit operations|0.07|0.07|0.07|0.07|0.07 0.07 0.07|
|Sub-total|0.08|0.08|0.08|0.08|0.08 0.08 0.08|
|**Oil distribution (to biodiesel production plant)**|0.01|0.01|0.01|0.01|0.010.010.01|
|**Total**|**1.16**|**0.44**|**0.36**|**0.33**|**0.33 0.31 0.31**|

Source: Kukrika (2008).<sup>a</sup> The original figures are in 2007 Rs/lt, and were converted to $/lt using and exchange rate of 42.4 Rs/$ as of 2007 and inflation adjusted to 2008.

# _3.1.2.2  Microalgae_

The economic viability of some projects dedicated to the production of higher value products (such as taxanthin and nutraceuticals) has already been demonstrated (Schenk et al. 2008). However, economics are the main current impediment to large scale cultivation of microalgae for lower value uses. Simply stated, in order for microalgal biodiesel to be commercially viable, production costs need to be sharply reduced from current levels.

Assessing the costs of producing algal oil is a challenge, mainly because of existing uncertainty of potential yields and evolving technologies. Early estimates (Benemann and Oswald 1996) placed achievable cost for open ponds in the $51-90<sup>19</sup> per barrel range, for two different yield levels. A summary of their calculations is presented in Table 12. These calculations considered two alternative production systems. In the first system, the power plant and the algae farm are located nearby to allow for direct flue-gas utilization. In the second system, the flue gas captured from a remote power plant is purified to CO2 and transported to the algae farm. The second option would obviously

> 19 Inflation adjusted to 2008.

23
The Financial Services Board (FSB), the statutory regulator of the non-banking financial sector in South Africa, undertook a nationally representative study on financial literacy levels in South Africa in 2011.<sup>7</sup> The survey focused on four key areas of financial literacy: day-to-day money management; financial planning; choosing appropriate products; and financial knowledge and understanding. One of the central findings from this survey was that financial literacy is to a large extent dependent on educational attainment and income levels (FSB, 2011). For example, the presence of a household budget, indicative of a higher awareness towards financial management, is strongly related to income levels, with 79% of households in the top income quintile having household budgets relative to 36% in the lowest income quintile.

Income differences are also prevalent in the sources of information that influence individuals’ decisions on financial products. While individuals in the richest quintile are far more likely to consult the internet, independent financial advisers or friends in the financial services industry, the poor are more likely to rely on public resources such as newspapers, radio and television. In general, the importance of public media for financial decisions is high across all income groups, with 33% of respondents indicating that TV and radio programs or adverts were the source of information that most influenced their decisions about financial products **,** showing the potential media has to influence financial decisions and behavior in South Africa.

Against this backdrop, financial education through popular media is potentially a powerful tool to stimulate improvements in financial knowledge and behavior. This paper exploits this potential to study the effectiveness of financial messages delivered through a popular soap opera.

# _3.2. Scandal!_

South Africa has a well-developed media industry with popular television shows reaching millions of viewers. Further, educational messages in health and education have previously been incorporated in soap operas such as “Soul City”.

> 7 A total of 3,112 South Africans (individuals aged 16 and above living in South Africa) participated in the survey. The study was undertaken in collaboration with the International Network on Financial Education (INFE) of the Organization for Economic Co-operation and Development (OECD).

11
If the exogenous total number of entrants by region is available, then:

# **c. Data requirements**

The proposed approach may be applied to data on household consumption, typically data from household expenditure or consumption surveys. Three key variables must be drawn from the surveys: (1) a per capita welfare measure to estimate poverty (often calculated as the total consumption or income of a household, divided by the number of household members or adult equivalent members), (2) per capita consumption or expenditure on a good or service of interest, and (3) a relevant poverty line estimated according to household consumption or income.

In addition to microdata from household surveys, a minimum set of parameters defining baseline and reform scenarios of the underlying market structure or a given industry are required, including the number of new firms competing in a market and price elasticities of demand. The model for the extensive margin module that estimates new consumers also requires demographic and spatial variables derived from the household expenditure or consumption survey. The dependent variable for the probabilistic model is a binary dummy indicating whether the household or individual (denoted by h) has any positive or null consumption in the good or service of interest. The (proportional) price change h and the (proportional) welfare change, h, must be included, along with a set of household and individual characteristics and policy variables linked to the use or consumption of the ddpp ddI IIPPIIuu good or service of interest (age, sex, geographical location, literacy, educational attainment, employment status, household assets, and services).<sup>16</sup>

# **3. The mobile telecommunication industry in Ethiopia**

As of 2019, Ethiopia was one of the three countries in the world in which the mobile phone market was a monopoly. In 2018, among countries in Africa, Ethiopia ranked 4th lowest in mobile phone penetration (37.2) per 100 people and 12th lowest in fixed broadband subscriptions (0.05) (figure 1). The government of Ethiopia is undertaking a reform to modernize the economy, including a gradual privatization of key sectors, such as the mobile phone market, which has been dominated by a state-

> 16 The (proportional) welfare change is calculated directly using the WELCOM Stata Tool.

8
related to agriculture or family planning. Of course, townships have responsibility for implementing the policies of higher-level governments and for assisting in the administration of higher-level programs, but as subordinate agents of the counties rather than independent levels of government.

Township finances are largely determined by the “county-township fiscal system”, where the rules are established essentially independently in each county. A “contract” is negotiated between the county and its townships and might differ even among the various townships within the same county.<sup>28</sup> Typical elements of a county-township contract stipulates the “bases” for expenditures, usually made on the basis of the salaries and operational expenses for which the township is responsible; the “bases” for the sharing of local revenues; rules for the sharing of the surplus or deficits between actual revenue collection and the revenue “bases”; “system remittances”’ or “system subsidies”; and rules on how often the contract would be renegotiated. The rules – and indeed all the arrangements – are highly discretionary and often set incentives that are undesirable from a macro point of view.<sup>29</sup> The sources and composition of township revenues in Table 14 shows the ‘hallowing out’ of township finance.

**Table 14.  Structure of Township Revenue, 2000 and 2004**

||**2000**<br>|<br>|**2004**<br>|<br>|
|---|---|---|---|---|
||**RMB/capita**|**percent**|**RMB/capita**|**Percent**|
|**Local Taxation Income**|**95**|**100**|**129**|**100**|
|Value-added Tax|18|19|20|16|
|Business Tax|13|13|42|33|
|Income Tax for Enterprises|6|6|5|4|
|Individual Income Tax|7|7|7|5|
|Agricultural Tax|19|21|26|20|
|Agricultural SpecialtyProducts Tax|13|14|0|0|
|Occupied Land Tax|1|1|3|2|
|Other Income|18|20|26|20|
|**Township-to-County Transfers**|**34**||**93**||

> 28 Important adjustments that determine the sharing of funds (and, hence, power) for several years, such as those on the level of “bases,” are usually the outcome of extensive negotiations between county and township governments. Anecdotal information from our field work suggests that final documents are issued after five or more rounds of discussions.

> 29 For example, the incentives for townships to fulfill their revenue base might be so strong that they borrow funds

> – reportedly even from neighboring townships – in order to be perceived as fulfilling their revenue base.

27
<!-- Start of picture text --> Benno“00-50 x., Oe . 3 reoean a onMaeSe a ny<br>® mms0.1-45.8 = Lee p * x aa> c , eo+ aoer] . TMy: s<br>Rew. aea UF Liwalta | vsy . ao =el so ida—~<br>), eS BP s<br>ee 5<br>ae o__ 250 e000. 13002000<br><!-- End of picture text -->

<!-- Start of picture text --> ieee F eae Pi1sSoaaoe3 eal—F . i: ai EN :<br>Loimps Mmm61-90mmatieo = ; + 4 4aty : : -\t o_ ae‘y 4<br>_ Motee -208 a s“ 4 rs aPee“ : iYeas.ee e rarmaSe<br>“ pple a 5<br>OOS” +<br>+, o 250 <s00___teun, 1.500 2.000<br><!-- End of picture text --> <!-- Start of picture text --> e Major cities am %<br>2019Costet(Siton), mi . . -,pti = allePa: atm tel N<br>04-181 , a 7 aN<br>2802015 ae ee y =a ou " , - ee<br>mm 41.6-59.1 err D5 bs J . F<br>Fees ae, ° ir) poda@ : a *: "ie ieaea tae. +<br>OS oe oy Sie: Rise “Ng<br>: ‘g- o 250 <eo0___ 00. 1,800 2,000<br><!-- End of picture text --> <!-- Start of picture text --> _ ’<br>a7 ; a he ee.3 os. :<br>- “= u . aN<br>Cost (ston), 2019 a ee ‘ =e if L —<br>02-75 err Seger yt, a .* . 4<br>meromm15.1-200: . , rm Poua> . ry Cae. ‘ieoFl ” o a,<br>CS ze “l : 0 2g<br>;: Py o 250 <eo0___ 00. 1,800 2,000<br><!-- End of picture text -->
George, Siddharth, Sarika Gupta, and Yusuf Neggers. 2018. Can We Text Bad Politicians Out of Office? Experimental Evidence from an Indian Election. Working Paper.

Green, E., 2010. Patronage, district creation, and reform in Uganda. Studies in comparative international development, 45(1), pp.83-103.

Grossman, G. and Lewis, J.I., 2014. Administrative unit proliferation. American Political Science Review, 108(1), pp.196-217.

Grossman, Guy and Kristin Michelitch. 2018. Information Dissemination, Competitive Pressure, and Politician Performance between Elections: A Field Experiment in Uganda. American Political Science Review (forthcoming) Gulzar, Saad and Muhammad Yasir Khan. 2017. Motivating Political Candidacy and Performance: Experimental Evidence from Pakistan. Working Paper.

Jonas Hjort; Ethnic Divisions and Production in Firms , The Quarterly Journal of Economics, Volume 129, Issue 4, 1 November 2014, Pages 1899–1946, https://doi.org/10.1093/qje/qju028 John, O. P., Srivastava, S. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin; O. P. John (Eds.), Handbook of personality: Theory and research (Vol. 2, pp. 102–138). New York: Guilford Press.

Khemani, S.; Keefer, P. 2014. Mass Media and Public Education: The Effects of Access to Community Radio in Benin. Journal of Development Economics, 109: 57-72.

Khemani, S.; Keefer, P. 2015. The Government Response to Informed Citizens: New Evidence on Media Access and the Distribution of Public Health Benefits in Africa World Bank Economic Review 2016 30:2, 233-267 Laajaj, Rachid; Macours, Karen. 2017. Measuring skills in developing countries (English). Policy Research working paper; no. WPS 8000. Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/775311488980295780/Measuring-skillsin-developing-countries Moore, C., Detert, J.R., Trevino, L.K., Baker, V.L., and Mayer, D.M. (2012). Why employees do bad things: Moral disengagement and unethical organizational behavior. Personnel Psychology, 65, 1-48.

Muralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar. 2016. "Building State 39
In terms of income inequality, on the one hand, ROCs present, on average, higher values in the Gini index than NROCs  a difference of more than 2 percent. On the other hand, LOGCs, on average, have higher total income inequality, while HOGCs present the lowest. There is a substantial difference of 7 percent between the average Gini coefficient of LOGC and HOGC economies.

# **V. SIMULATION**

Our simulation procedure permits to make a dynamic assessment of income distribution by country. The Monte Carlo simulation method combines multiple paths of mean income growth with a variety of inequality trajectories to predict changes in income distribution. The practical exercises described in the sections below use household survey data of incomes and expenditures in the period 1980–2015.<sup>27,28</sup> Thus, given the scarcity of microdata for the study period, per capita consumption growth is initially used to construct synthetic observations of mean income between spells. As an alternative, GDP per capita growth was tested and used instead of per capita consumption growth.

The Monte Carlo simulation procedure consists of deriving simulated paths of the mean income and the Gini coefficient. To recover these paths, at each period , number of random draws are generated to complete the simulated sequences. These sequences tt= {2015, 2016, ... , TT} consist of global mean income growth 2015} } }, global real commodity prices nn nn nn 2015} } }, idiosyncratic shocks kk=1 in TT kk=1the mean-income growth {{gg , ... , {gg nn nn kk=1 TT kk=1 {{pp � , ... , {pp � �, and idiosyncratic shocks in the growth of the Gini coefficient nn nn cc,2015 cc,TT ��εε �kk=1, ... , �εε �kk=1 �. Each draw is generated using mean and variance sample statistics for nn nn cc,2015 cc,TT a specific period. ��uu kk=1, ... , �uu kk=1 Although the simulation exercises are designed to be comprehensive and to project multiple and simultaneous performances of factors affecting the countries’ mean income growth, our model is simple by design and includes some caveats. Four core elements considered when we

> 27 Data were retrieved on February 22, 2019 from PovcalNet (World Bank 2019).

> 28 A priori, given the embedded historical information in the period 1980–2015, country-specific simulations are expected to be much closer to the idea of inequality convergence; this means that inequality tends to decrease in countries with high inequality and increase in countries with low inequality (Benabou 1996, and Ravallion 2003).

Page **17** of **45**
# **Figure 4. Infrastructure Investment in Egypt (1960-2007)**

# **(Percentage of GDP)**

## **(a) Total Investment**

<!-- Start of picture text --> 14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> <!-- Start of picture text --> (b) Transportation (incl. SC) & Communications<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text -->

## **(c) Electricity**

<!-- Start of picture text --> 6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Public Private<br><!-- End of picture text --> 32