rafmacalaba/datause-encoder-data huggingface dataset:
We supplement these two follow-up surveys with several other data sources. We used administrative data on program take-up and activities uploaded by firms for describing adoption and compliance in Section 2. Monitors also conducted two visits with all insourcing and outsourcing firms to verify the presence of hired workers or contracted professionals, and also to collect data on these specialists and their activities (used in Section 2.4). A survey of business service providers was used to compare the characteristics of providers in the GEM project to other providers in the marketplace. Finally, in October 2019, we used independent raters to score firms on the quantity and quality of their social media presence (described in Section 5).

# **3.2 Measurement of Main Outcomes**

Appendix 4 details the construction of our main outcome measures: (i) Business Practices; and (ii) Firm Growth.<sup>8</sup> <u>Business Practices.</u> A particular focus of the follow-up surveys was detailed measurement of business practices. We measured 41 different business practices, representing both traditional activities (in finance/accounting, marketing/sales, operations/HR) and also more novel activities (in digital marketing). A concern in collecting such data through surveys, especially after skillbuilding interventions, is the possibility of experimenter demand effects whereby entrepreneurs may say they are using particular practices because they think it is the desirable answer. Our surveys took several precautions to mitigate this possibility. First, the survey was conducted by an independent survey company (Kantar TNS RMS) and not framed as measuring the impacts of the GEM project. Firms were told the purpose was to “help better understand the growth process of enterprises in priority sectors, the challenges your business faces, and your use of different market services and government programs”. Second, enumerators were trained to probe each answer and only record the firm doing the practice if they could provide specific examples or evidence. For example, when asking whether the firm conducted structured research on customers, the firm then needed to provide details of how this was done and what they learned about the needs of current or existing customers based on this research. Third, we identified a subset of practices that could be objectively verified through the use of photographs and physical observation (e.g. observing whether the firm had a balance sheet, used certain marketing materials, recorded maintenance

> 8 Our main outcome measures and hypotheses were predefined in a pre-analysis plan, which was registered in the AEA Social Science Registry prior to beginning the first follow-up survey.

15
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26
currency block (e.g. OECS<sup>26</sup> states) with a common currency and a single central bank is much easier than in a single small state without either the investor base or the fungible borrowing amounts. Below, we attempt to identify some common pre-conditions for market development, which could serve as a useful pointer for other small states planning to access domestic markets.

**_Issuer_ :** The most important requirement is a strong commitment from the government to engage in proactive debt management. A focused issuance program of government securities is central to build large and liquid benchmark bonds. An important step is to announce a borrowing calendar and to then adhere to it.  In most of the sample small states, the decision to borrow and issue Treasury bills (or Treasury bonds) including the auction date, the tenor of issue and the amount is made by the Ministry of Finance or the debt management unit.  The issuance is usually administered by the central bank or the regional central bank for those states that are part of a regional monetary union, for example Guinea Bissau or the Caribbean islands in the East Caribbean Currency Union (ECCU), see  Table 4.

Table 4. Domestic Security Issuance Agency in Small States

|Issuance<br>agency|National Ministry<br>of Finance<br>/Treasury|National central<br>bank|Regional<br>security market|Regional central<br>bank|
|---|---|---|---|---|
|Small states<br>in the<br>sample|ECCU countries<br>for local securities|Bhutan<br>Cape Verde<br>The Gambia<br>Maldives<br>Samoa<br>Solomon Islands<br>Swaziland|ECCU countries<br>for regional<br>securities|Guinea-Bissau|

Source: DeMPA Reports.

**_Investors_** :  The investor base should be well diversified; ranging from long-term players (pension funds with long term liabilities) to broker-dealers, banks with need for shorter term assets.  This has been an area of relative weakness across the small states because of the lack of

> 26The Organization of Eastern Caribbean States

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TABLE 12: TRAINER CHARACTERISTICS ON STUDENT PSYCHOLOGY (LOCUS OF CONTROL)

|||(1)<br>|(2)<br> <br>_y = Locus of contro_|(3)<br> <br>_l (LOC) score of stu_|(4)<br> <br>_dents after training_|(5)<br> <br>|
|---|---|---|---|---|---|---|
|||Entrepreneurial<br>locus of control<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|
|**s**|Age|-0.0111|-0.00283|-0.00562|-0.0124|-0.00378|
|**phic**||(0.00717)|(0.00662)|(0.0114)|(0.0171)|(0.0304)|
|**gra**|Female|0.216|0.137|0.465**|0.329*|0.437|
|**mo**||(0.137)|(0.117)|(0.186)|(0.194)|(0.474)|
|**De**|Years at school|-0.0117|0.0158|-0.0142|0.00958|0.0114|
|||(0.0262)|(0.0291)|(0.0412)|(0.0438)|(0.0453)|
|**preneur**<br>**Model**|Ever been a business||0.427***|0.344**|0.421***|0.392**|
|**Entre**<br>**Role**|owner||(0.128)|(0.133)|(0.102)|(0.177)|
|**e skills**|Digitspan score|||-0.00905<br>(0.0483)|-0.0397<br>(0.0684)|-0.105<br>(0.0873)|
|<br>**itiv**|Raven test score|||-0.0614**|-0.00855|0.0186|
|stics<br>**ogn**||||(0.0246)|(0.0268)|(0.0725)|
|teri<br>**n-c**|Personal intitative|||-0.397|-0.774***|-0.556|
|arac<br>**no**||||(0.267)|(0.190)|(0.494)|
|Ch<br>**and**|Error competence|||0.173|0.666**|1.090***|
|iner<br>**ive**||||(0.243)|(0.324)|(0.377)|
|Tra<br>**Cognit**|Prosocial orientation|||0.695***<br>(0.240)|0.341<br>(0.237)|-0.155<br>(0.636)|
||How satisfied are you||||0.150|0.211|
|**tion**|with your job?||||(0.107)|(0.137)|
|**fac**|Given a choice, would||||-0.175***|-0.208***|
|**atis**|still be a teacher||||(0.0458)|(0.0548)|
|**b s**|Career satisfaction||||0.230***|0.195**|
|**Jo**|||||(0.0719)|(0.0799)|
|**T**|Years as teacher|||||0.0631<br>00375|
|**TVE**<br>**ge**||||||(.)|
|**at**<br>**lle**|Years at TVET Colleges|||||-0.0333|
|**ars**<br>**co**||||||(0.0400)|
|**Ye**|Perceived|||||0137|
||<br>entrepreneurial skills|||||.<br>(0.200)|
||Average score for|3.987|3.987|3.987|3.987|3.987|
||students at baseline|(0.0455)|(0.0455)|(0.0455)|(0.0455)|(0.0455)|
||Observations|557|557|557|557|557|

_Notes:_

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

> (1) OLS regressions sequentially add explanatory variables from columns (1) to (5)
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This latter fact shows that income, as measured by GDP, cannot be the sole determinant of health; if it were, countries that grew richer over time would simply have moved along the curve defined by a given year’s cross-sectional data. On average, countries whose incomes have grown have achieved better health improvements than would have been predicted from the 1975 data.

The concave relationship between income and health suggests the importance of income distribution for a country’s health status: in a country with highly unequal income distribution, the population at large is likely to be less healthy than would be predicted for countries with the same average income. It is commonly argued that this relationship provides a rationale for redistributing a country’s income from rich to poor citizens, so as to raise average health status while keeping average income constant (ignoring the efficiency costs of redistribution). This sounds reasonable if indeed increasing the incomes of the poor will improve their health. However, if one believes that changes in health drive income growth, the same concavity properties imply that redistributing _health_ from the unhealthy to the healthy (that is, in the “wrong” direction) would increase aggregate income, with no effect on average health status. The validity, if not the desirability, of each of these interventions thus depends crucially on the direction of causality between income and health.

**Figure 3. Normalized Cross-Country Standard Deviations of Health and Income, 1960–2004**

<!-- Start of picture text -->
1.4<br>1.2<br>1<br>0.8<br>0.6<br>1960 1970 1980 1990 2000 2004<br>Ln(GDP) Life Expectancy IMR<br><!-- End of picture text -->

_Source:_ Deaton 2006.

_Note:_ The infant mortality rate (IMR) measures the number of children born who die before their first birthday per 1,000 births. The standard deviation of the under-five mortality rate shows a similar evolution and is not presented in this figure. The IMR is a significant factor in life expectancy calculations, because, particularly in countries with high death rates, the majority of a country’s deaths occur in the first year of life.

9
The high interest rate treatment, expressed per $100 of control group deposits, generated Marshallian saver surplus of $1.6, with a standard error of $0.1. This estimate is much more – precise than the estimated demand elasticity the interest rate variation induced by the experiment is sufficiently small that the impacts of the high interest rate treatment on saver welfare are well approximated by the reduced deposits savers can make holding fixed their end-of-year balance. Closely related to this point, bounds on equivalent variation to the high interest rate treatment are not meaningfully different from Marshallian surplus.

For larger price changes, the estimated elasticity plays a much larger role; Marshallian saver surplus is simply _−_ 1 _/ε_ , implying that commitment saving generates saver surplus of $10.3 per $100 of deposits, with a standard error of $8.6. Relatedly, bounds on equivalent variation are also wider, at [$10.3, $10.8]; however, the width of these bounds is small relative to statistical uncertainty in the point estimate for Marshallian saver surplus.

# **4.3 Microfinance (Karlan & Zinman, 2019)**

## **4.3.1 Context and experimental design**

I leverage experimental variation in microfinance interest rates studied by Karlan & Zinman (2019) to estimate the borrower surplus from microfinance. The experiment was implemented with Mexico’s largest microfinance lender, Compartamos Banco, for a single microfinance lending product, Crédito Mujer.

**Microfinance product** I briefly summarize the description of the microfinance credit product from Karlan & Zinman (2019), and incorporate it into the model in Section 2. Each period, borrowing groups of women are evaluated based on observed characteristics, including general creditworthiness and past borrowing and repayment behavior with the microfinance lender. Borrowing groups are offered the opportunity to take a non-collateralized loan up to a threshold based on these characteristics; offered thresholds range from 4,000 MXN to 24,000 MXN. Borrowers are obligated to repay the loan in identical weekly installments over 16 weeks, which are calculated based on three factors: the initial principal, value-added tax on interest payments, and “add-on” interest calculated based on the initial principal rather than the balance of the loan. Monthly add-on interest before the experiment ranged from 4.0% to 5.0%, with the interest determined geographically and on the basis of past borrowing and repayment behavior. I let _bt−_ 1 _∈_ R<sup>_N_</sup> +<sup>denotepastborrowingandrepayment</sup> behavior, _rt_ ( _bt−_ 1) : R<sup>_N_</sup> +<sup>_→{_0</sup><sup>_._040</sup><sup>_,_0</sup><sup>_._045</sup><sup>_,_0</sup><sup>_._050</sup><sup>_}_denotethepre-experimentalinterestrate,</sup> _bt_ ( _bt−_ 1) : R<sup>_N_</sup> +<sup>_→_[4000</sup><sup>_,_24000]denotetheofferedthreshold,and</sup><sup>_bt,_1denotetheprincipalon</sup> new loans taken in period _t_ .

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- **Probable maximum loss** is the largest likely loss to housing and infrastructure in a given region from all perils resulting from a single catastrophic event for a given return period<sup>30</sup> .  This measure is used by insurance professionals as an estimate of loss severity in determining reserves and other forms of claims paying capacity needed to finance a catastrophic loss.

- **Loss exceedance curves** are utilized to calculate 1) the probability that all losses in one year could exceed a certain monetary threshold, and 2) the probability that one event in a particular year could exceed a certain value. Also, these curves are used to calculate the probabilistic estimate of average annual loss, which is a basic input into deriving insurance premiums. An example of a loss exceedance curve in provided in Figure 1.

- **Pure risk premium** is the portion of the insurance premium that is, when aggregated with pure risk premiums from other risks, intended to pay for cumulative losses of an insurer or reinsurer.  The pure risk premium is generally expressed as the average annual loss per 1000 dollars of exposed value.

- **The market value premium** is the cost of transferring the total risk to the private insurance and reinsurance market, which is typically a 3-6 multiple of the pure risk premium. The market value premium adds expenses, underwriting and loss adjustment costs, profit, cost of capital reserves, and inflation to the pure risk premium.<sup>31</sup> A reinsurer will calculate the impact of providing coverage for a specific risk on its overall portfolio of risks to determine the marginal capital costs incurred, as well as the marginal impact of the risk on overall portfolio returns.

Policymakers in developing countries face challenges in employing loss estimate models. The key difficulty is in obtaining reliable scientific data about hazards, such as earthquake fault characteristics and flood mapping data. In addition, data on property types and values may not be available or specific enough to be useful. Finally, there is often little information on the vulnerability of different building types. Each of these limitations introduces uncertainties into the loss estimate methodology and increases the dependency on expert opinion in loss estimation. Finally, the cost of developing and maintaining loss models can be high.<sup>32</sup>

> 30 The inverse of probability of occurrence.

> 31 Walker, George. “Catastrophe Risk Models for Asia from a User Perspective” in Catastrophe Risk and Reinsurance: A Country’s Risk Management Perspective. Risk Books, London (2004). pg. 3.

> 32 Ibid. pg. 6.

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