rafmacalaba/datause-encoder-data huggingface dataset:
- Brandt, L. H.B. Li. “Bank Discrimination in Transition Economies: Ideology, Information, or Incentives.” _Journal of Comparative Economics_ 31(3), 387-413.

- Brown, J. David, John S. Earl and Scott Gehlbach, 2009. “Helping Hand or Grabbing Hand? State

- Bureaucracy and Privatization Effectiveness,” _American Political Science Review_ , 103: 264-283 Volume 103, Issue 02.

- Byrd, W. and Q. Lin, 1990. _China's rural industry: structure, development and reform_ . Oxford University Press.

- Cai,Hongbin, Hanming Fang, Lixin Colin Xu. 2011. “Eat, Drink, Firms, Government: An Investigation of Corruption from Entertainment and Travel Costs of Chinese Firms.” _Journal of Law and Economics_ 54, 55-78, February.

- Che, Jiahua and Qian, Yingyi, 1998a. “Institutional environment, community government, and corporate governance: understanding China's township-village enterprises”. _Journal of Law, Economics and Organization,_ 14 (1): 1-23.

- Che, Jiahua and Qian, Yingyi, 1998b. “Insecure Property Rights and Government Ownership of Firms.” _Quarterly Journal of Economics._ 113(2):467-96.

- Chung, Chun and Yijiang Wang, 1994. “The nature of the township-village enterprise.” _Journal of Comparative Economics_ , 19(3): 434-52.

- Cull, Robert, Lance E. Davis, Naomi R. Lamoreaux, and Jean-Laurent Rosenthal. 2006. “Historical Financing of Small- and Medium-Size Enterprises.” _Journal of Banking and Finance_ 30: 30173042.

- Cull, Robert, Wei Li, Bo Sun, L. Colin Xu. 2013. “Banks, the Government, and Financial Constraint: Evidence from a Large Sample of Chinese Firms,” World Bank Policy Research Working Paper 6352.

- Cull, Robert, Lixin Colin Xu. 2005. “Institutions, Ownership, and Finance: The Determinants of Investment among Chinese Firms,” _Journal of Financial Economics_ 77, 117-146.

- Cull, Robert, Lixin Colin Xu, Tian Zhu.  2009. "Formal Finance and Trade Credit During China's Transition," _Journal of Financial Intermediation_ 18(2), April, pp. 173-192

Datta-Chaudhuri, Mrinal, 1990. “Market Failure and Government Failure,” _Journal of Economic Perspective_ , 4(3): 25-39.

- Demirguc-Kunt, Asli, and Vojislav Maksimovic. 1998. "Law, Finance, and Firm Growth." _Journal of Finance_ 53, 2107–37.

- Frye, Timothy and Shleifer, Andrei, 1997. “The invisible hand and the helping hand.” _American Economic Review Paper and Proceedings_ 87 (2): 354-358.

- Easterly, William. 2001. The Elusive Quest for Growth: Economists Adventures and Misadventure in the Tropics. The MIT press.

27
where j=0 is in school, j=1 is in school and in labor force, j=2 is in labor force, and j=3 is staying inactive. The relative risk ratios (RRR) are presented.

# **4.2.    Results**

Table 2 presents the results for male youth and female youth activity decisions. Two of the findings are common for male and female youth. First, household heads’ education and employment status play a significant role in determining youth activity decisions. Youth in a family with a better-educated head and/or employed head (in either the agricultural sector or the non-agricultural sector) are more likely to be in school and less likely to be in the labor force or be inactive. Second, the ratio of working individuals to nonworking individuals in a household has a significant and positive effect on youth’s activity decisions, both in statistics and in magnitude. This ratio reflects two specific effects: the income effect and the network effect. The network effect in this context refers to youth who are exposed to greater job opportunities if there are more employed individuals in the family.<sup>10</sup> Since the income effect is the opposite of the network effect (a wealthier family is more likely to keep youth in school), this finding suggests that the network effect outweighs the income effect and the family network can be instrumental in helping youth to find a job and start to work. This finding is consistent with findings from the Pakistan Investment Climate Assessment (PICA) survey that about 50 percent of firms hire employees through a network of family or friends (Hou 2008).

The male youth decision model also shows that older and married male youth are more likely to work. Male youth in families with more young children (0-5 or 6-14 years old) or female youth (15-24 years old) are more likely to work. On the contrary, male youth in a family with more adults or more male youth (15-24 years old) are more likely to be in school. This 9
_<mark>Source: FGD report, 2009</mark>_ 72. Narratives collected from FGDs provide further insights into what HE operators perceive as the main contributing factors for their success or failure in business (Appendix B). The entrepreneur from Arusha cited the experience he had accumulated over the years in running his HE and his good cash management skills as the reasons why his business is doing well. He took his first loan to expand his business, and a second loan to have a secure workspace where he could grow it further. His HE enabled him to build up his assets. His is a success story.

73. Similarly, the woman entrepreneur from Mtwara used her savings to start her business of trading cashew nuts. She joined an association of small entrepreneurs and made full use of her membership to avail of loans, entrepreneurship training, and important business and market information. She attributed her success to her constantly seeking and seizing every opportunity to improve her trading enterprise and hone her business skills.

74. The food vendor from Mtwara, however, shows the risks and vulnerability of HEs which operate in difficult, often unfavorable conditions. She started her food-vending business with a small capital from her husband, but suffered an initial loss because of the seasonality of demand for her goods and the demolition of her _banda_ (shed) by thieves. To rebuild, she moved to another rented premise, but her business hardly recovered: “The business was not paying! Unlike in the early days, a big chunk of the cooked food remained unsold and I had to take it home or give it to neighbors.” As a fallback and to reduce the cost of running her business, she left her rented workplace and started to operate from home where her HE continued to struggle to survive.

75. The story of the entrepreneur from Mwanza started out as a success, but ended in failure. He began as a trader in rice, and later opened his own place to sell rice, which proved profitable. Having earned enough capital, he diversified into another more high-risk business, a butchery, which was equally lucrative in the beginning and which helped him build his assets. However, the butchery did not stay profitable for long. He eventually suffered a loss because of lack of demand for his meat products, and was not able to recover: “I could not sell all the meat. For example, when I slaughtered a cow worth TSh 100,000, I could sell about Tsh 70,000 worth on the first day. When meat remains unsold, one has to sell at a lower price the following day. I was losing my capital.” 76. The above narratives show the potentials of household enterprises as a source of livelihood and a pathway out of poverty, but they also reveal some of the risks and vulnerabilities of small HE operators that lock them in it.

36
**Table 4. LEV II Exhaust Emissions Standards- Medium Duty Vehicles (MDVs)**

|Type (Weight<br>(GVWR),lbs.)|Durability<br>Mileage|Emission<br>category|NMOG|CO|NOx|PM|HCHO|
|---|---|---|---|---|---|---|---|
|8,500 - 10,000|12,000|LEV|0.195|6.4|0.2|0.12|0.032|
|||ULEV|0.143|6.4|0.2|0.06|0.016|
|||SULEV|0.1|3.2|0.1|0.06|0.008|
|10,001 - 14,000||||||||
||12,000|LEV|0.23|7.3|0.4|0.12|0.04|
|||ULEV|0.167|7.3|0.4|0.06|0.021|
|||SULEV|0.117|3.7|0.2|0.06|0.01|

Note: Light duty trucks up to 8,500 lbs GVWR, and medium-duty vehicles that are up to 14,000 lbs GVWR fall under the CA LEV-II standards adopted by California. LEV, ULEV and SULEV stand for, respectively, low-emission vehicles, ultra low- emission vehicles and super ultra-low emission vehicles. The LEV II standards indicate the maximum exhaust emission limits for the intermediate and full useful life of LEVs, ULEVs, and SULEVs. It also includes fuel-flexible, bi-fuel, and duel fuel vehicles when operating on the gaseous or alcohol fuels.

Source: CONCAWE, 2006

# **4.2 Emission Standards in Canada**

The Canadian government, on 12 December 2002, under the Canadian Environmental Protection Act of 1999, published its new On-Road Vehicle and Engine Emission Regulations, which is being applied to vehicles and engines that are manufactured or imported into Canada on or after January 1, 2004. The regulations are similar to established emission standards and test procedures for on-road vehicles in the United States (CONCAWE, 2006).

# **4.3 Vehicle Emission Regulations in Europe**

In Europe, it was the United Nations Economic Commission for Europe (UN-ECE) that formulated emission regulations in the 1970s and early 1980s (CONCAWE, 2006). The motor vehicle emission regulations developed by the ECE were then adopted by individual member states (Faiz et al.1996). Although in the early years the European Union (EU) adopted regulations that were almost identical with the ECE equivalents, EU has since become proactive in formulating vehicle emission standards. Under the provisions of the Treaty of Rome, EU member states are legally obliged to follow EU regulations (CONCAWE, 2006). In order to make the existing regulations for light duty 19
discussion revolves around students who face a language barrier. Incorporating statistics for 2020 and 2021 could also shed light on the interaction of this educational integration process with the effect of the COVID-19 pandemic, which had catastrophic effects on educational systems around the world (UNICEF, 2022) Migrant students face obstacles at every step of the way, and well-formulated, evidence-based policies could help close the widening gap in access to educational opportunities vis-A -vis their native peers.<sup> ̃</sup> This study is the first to analyze the differential effect of student aid on migrants and natives. Future studies should continue providing more and better evidence to improve financial aid policies and increase access to higher education.

26
Table A2: Patient Information Implementation

||(1)<br>Patient Info<br>Mean|(2)<br>N|
|---|---|---|
|Watched any video (constructed)|0.795<br>[0.404]|1002|
|Watched any video: patient survey|0.722<br>[0.448]|1002|
|Watched any video: admin records|0.978<br>[0.146]|686|
|Watched full video: admin records|0.892<br>[0.310]|686|
|Share of video watched: admin records|0.943<br>[0.192]|686|

_Notes:_ Standard deviations in brackets. Information on whether the patient video was watched from two sources. Administrative records were kept by intervention officers and contain information whether the video was partially watched. Patient survey records were collected by surveyors based on observed participation in the intervention. The constructed variable is coded as 1 if at least one record lists the patient as receiving the intervention and the other record agrees or is missing for this patient (but 0 if neither record lists the patient as _yes_ or one record list the patient as _no_ ).

Table A3: Selection into Sample by Treatment

||(1)<br>Full S|(2)<br>ample|(3)<br>Selected:|(4)<br> Home Survey|
|---|---|---|---|---|
||Took<br>Home<br>Survey|Took<br>Home-<br>Based<br>RDT|Took<br>Home<br>Survey|Took<br>Home-<br>Based<br>RDT|
|Extended Training|-0.0260|0.0106|-0.00535|0.0253|
||(0.0278)|(0.0333)|(0.0205)|(0.0358)|
|Patient Information|-0.0204|-0.00419|0.000465|0.0141|
||(0.0186)|(0.0170)|(0.0162)|(0.0192)|
|Mean (Basic Training, No Pat Info)|0.74|0.54|0.87|0.64|
|N|1973|1973|1669|1669|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include survey date fixed effects. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

A1
||**(1)**<br>**NGO**|**(2)**<br>**NBFI**|**(3)**<br>**Bank**|**(4)**<br>**Credit**<br>**Union/**<br>**Coop-**<br>**erative**|**(5)**<br>**Rural**<br>**Bank**|**(6)**<br>**Total**|
|---|---|---|---|---|---|---|
|1. Borrowers,<br>000|34,458|38,518|38,912|2,762|1,495|118,849|
|2. % Female|87|89|78|57|84|84|
|3. Loans, 000|37,572|42,878|37,121|2,832|2,045|123,156|
|4. % Micro-<br>enterprise|88%|55%|51%|39%|27%|63%|
|5. Loans, USD<br>million|9,977.8|25,946.7|48,815.7|6,642.1|822.8|92,442.9|
|6. % Micro-<br>enterprise|85%|54%|31%|22%|30%|42%|
|7. Average loan<br>size (USD)|289|590|1021|1904|523|674|
|8. Avg Deposit<br>Balance|72|705|542|558|159|411|
|9. Cost Per<br>Borrower|$40|$76|$118|$211|$81|$81|
|10. Operating<br>Expense /<br>Loan<br>Portfolio|14.8%|13.9%|10.6%|9.9%|18.2%|12.0%|
|11. Avg nominal<br>interest|25.4%|25.2%|18.5%|16.7%|28.2%|21%|
|12. PAR > 30<br>days|3.2%|5.1%|5.8%|6.5%|12.1%|5.3%|
|13. PAR > 90<br>days|2.6%|4.0%|4.0%|4.8%|7.7%|3.9%|
|14. Debt-to-<br>equity|1.7x|4.5x|5.9x|4.7x|4.7x|4.6x|
|15. Return on<br>assets (ROA)|5.8%|2.4%|1.9%|1.4%|2.8%|2.4%|
|16. Return on|||||||
|equity (ROE)|16.2%|13.2%|12.7%|8.2%|17.5%|13.2%|
|17. Observations|304|421|114|144|21|1033|

**Table 3. Financial performance of leading microfinance institutions** Adapted from Appendix Table, “Key Operational and Legal Metrics by Legal Status,” p. 35 of MIX Market (2017).

NOTES: Borrowers are active borrowers (in thousands), “Loans, 000” is number of loans outstanding (in thousands). “Loans, USD million” is Gross Loan Portfolio in millions of US 16
<!-- Start of picture text --> 100<br><!-- End of picture text --> <!-- Start of picture text --> Share of Fundamental Rights cases from BIMARU states<br>100<br>80<br>60<br>40<br>20<br>0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year<br>Percentage<br><!-- End of picture text -->

# Figure 4

<!-- Start of picture text --> 25<br><!-- End of picture text --> <!-- Start of picture text --> Fundamental Rights Cases<br>Filed by Cooperative/Group<br>25<br>20<br>15<br>10<br>5<br>0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year<br>Filed by Cooperative/Group Total<br>No. of Cases<br><!-- End of picture text --> 18
indicate that only one country (Finland) produces a conceptual statement with limited actual data and the rest of the responding countries indicate that the sovereign balance sheet is a financial statement with actual quantified data.

Regarding the accounting system used to produce the balance sheet, the survey indicates that the almost half (46 percent) of the respondents producing a sovereign balance sheet are using a full accrual accounting system (including Australia, Canada, New Zealand).<sup>11</sup> Another 18 percent of the respondents, producing a balance sheet, are using semi‐accrual accounting. Only 11 percent mentioned using cash‐based accounting systems, including Norway and Uruguay. Of the remaining 25 percent that indicated “other,” some mentioned that an accrual system is under implementation or is already used but specific transactions linked to the budget execution or tax revenues are still registered on a cash basis (this group includes Bulgaria and Poland which indicated they are not producing a balance sheet). These results are presented in graph 1 (based on the number of countries). Producing a balance sheet based on accrual accounting is important to ensure that policy makers can assess and monitor effectively the mismatches between stocks of assets and liabilities. This requires additional work compared to the traditional budget process that captures cash flows of revenues and expenditures, which explains why some of the respondents still produce a sovereign balance sheets based on semi‐accrual or cash accounting systems.

<!-- Start of picture text --> Graph 1 ‐ Accounting System<br>14 13<br>12<br>10<br>8 7<br>6 5<br>4 3<br>2<br>0<br>Cash Semi‐accrual Fully accrual Other<br><!-- End of picture text --> Source: Responses to SALM survey Concerning publication, in all cases except one, the sovereign balance sheet is published. The sole case of no publication concerns the country producing its sovereign balance sheet on an 11 It is interesting to compare this percentage with the results of a recent IMF report indicating that 29 percent of countries are using either a full or modified‐accrual accounting system. Of course, selection bias related to the typology of respondents to the SALM survey may explain this difference.

8
increased profit margins. This expansion seems to occur through channels such as more active employee management, increased marketing, and a movement into the more profitable commerce sector. Neither intervention impacted female entrepreneurs’ profits. Further investigation of gender differences via a lab experiment seems to indicate that this may be because female entrepreneurs are less likely to engage in competitive behavior, and face greater social constraints.

A few other business training programs in Africa also find variable results. Mano et al. (2010) run a randomized experiment to evaluate the effectiveness of management training for metalworker entrepreneurs in an industrial cluster in Kumasi, Ghana. Their baseline survey indicates a low level of familiarity with standard business practices. They find that training improves business practices and performance, but that the extent of improvement varies greatly across the sample. However, one drawback of the study is its small sample size, as there are just over 50 entrepreneurs in the treatment group. Mano et al. (2009) focuses on another metalworking cluster in Nairobi, Kenya, although they do not randomize participation in the training, and the sample size is also fairly small (60 entrepreneurs). Further, they face attrition in the follow-up survey which is correlated with treatment status. Nevertheless, they find that the training leads to improved business practices, and increased value added and producer surplus.

# **_So what might “it” depend on?_**

These studies demonstrate that effects on business outcomes can be quite heterogeneous. Gender, as indicated by several studies seems to play an important role. This finding is also corroborated by Giné and Mansuri (forthcoming), which evaluates a business training program in rural Pakistan. The preliminary results seem to indicate that business training improved business and household outcomes, particularly for men, and that the impact of providing loans is weaker.

It may be the case, as suggested by Klinger and Schündeln (2007), that women are in some ways more cash-constrained than knowledge-constrained. In an RCT focusing solely on women, McKenzie, de Mel, and Woodruff (forthcoming) evaluate an intervention consisting of business training and capital grants for female microentrepreneurs in Sri Lanka. They find that training alone has a significant impact on business practices, but not profits; training and grants together, however, have a significant impact on profits.

37
Policy Research Working Paper 7402

# **Abstract**

Currently 2.5 million Syrians fleeing war have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish - though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.

This paper is a product of the Social Protection and Labor Global Practice Group. 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 xdelcarpio@worldbank.org and mathiswagner@gmail.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
the maximum amount that the average survey respondent would be willing to pay for the proposed improvement in WSS in the context of the existing institutional regime within which households are free to allocate their financial resources. In the context of the case study presented here, WTP is the amount of monthly income that the average household could give up after obtaining the improved and or new WSS and be just as well off as in the situation without an improvement in water supply. By this token WTP is a measure of the average households’ economic value and the basis for its preference for the proposed improvements in WSS.

Maximum WTP for the average household is related to but not equivalent to the future economic demand or to the monthly bill paid by households to the utility for new or improved WSS services. Although maximum WTP is related to both these concepts because it contains similar behavioral information about household preferences, it is different primarily because it is an _ex ante_ measure of consumer surplus. From the perspective of estimating future demand, it does not tell us what level of the service (e.g., how much water) will be consumed. Instead, as a measure of consumer benefits, maximum WTP measures are best used in Kaldor‐Hicks type social cost‐benefit analysis – do the benefits of expanding piped water services exceed the costs of the infrastructure and operational investments?

In the remainder of this short paper, we show that in addition to generating a general measure of economic benefits, WTP surveys often produce two important pieces of operational information. First, because we usually implement a WTP study by using a split‐sample survey experiment (different households are asked to consider different charges say for improved water services), we can trace out the demand in the service area that maps the relationship between charge and the number of people who will take up the service at that particular charge. Such an estimated relationship can depict the ‘uptake rate’ for the service improvements. This interpretation of WTP survey data is common in the health economics and finance literature (see Bala et al., 1999 for an early exposition).

Second, we often collect information on various characteristics of the households and the communities they live in – income, assets, education, attitudes, location, and amenities – that is typically under‐utilized in interpreting and using WTP data. The heterogeneity that exists in the population may not be fully captured in the implementation of the randomized split‐sample 4