rafmacalaba/datause-encoder-data huggingface dataset:
While the KUR program data captures a wealth of information, several shortcomings limit its ability to track whether the program is reaching targeted businesses in an inclusive way. For example, the data system includes an open question on collateral; however, the formatting of the variable makes it difficult to assess the type or value of collateral presented or to cross-check this collateral with any standardized registries. In addition, the data system does not seem to accurately track applicants who are rejected from KUR. Tracking rejection is important to understand the program’s inclusiveness and possible ways to enhance inclusion.<sup>31</sup> Clearer guidelines on how to enter certain data fields, data entry requirements, and training for bank officers could improve the quality and scope of the program’s monitoring and evaluation data.

# **4. Lessons**

Our paper shows that subsidized credit can enable entrepreneurs to enter the formal financial system for the first time. There are two main mechanisms that explain KUR’s impact on financial inclusion. First, the Government of Indonesia sets ambitious targets for the program, which can push financial institutions to focus on lending to MSMEs.<sup>32</sup> On average, the KUR program has disbursed over 90% of its annual targets since 2015, and borrowers describe active outreach from bank officers in qualitative interviews. Second, entrepreneurs who are usually deterred from applying for formal loans due to perceived high interest rates feel more comfortable applying for subsidized loans. Indeed, qualitative interviews with borrowers reveal that in the absence of KUR many would not apply for other types of loans due to the high interest rate. Some report concerns about their ability to repay higher-cost loans, while others simply prefer to use their existing means than to pay more in interest.

By supporting MSMEs in accessing the formal financial system for the first time, subsidized credit programs could have potential longer-term benefits. Our qualitative work shows that the KUR program helps MSMEs formalize their businesses, keep better financial records, and become familiar with bank practices and loan procedures. These changes and the credit history built through KUR loans can improve the bankability of MSMEs. In addition, experience receiving and repaying loans may boost entrepreneurs’ confidence in their ability to manage credit and demonstrate the potential benefits of using commercial finance to expand their business activities. This may increase entrepreneurs’ demand for future credit. The program may also enable banks to learn more about lending to the MSME segment, which could facilitate positive changes in the supply of MSME credit.

> 31  For example, knowing whether women are less likely to apply to KUR than men or are more likely to face rejection from KUR could provide guidance for how to promote women’s access to KUR.

> 32  The program is largely implemented by state-owned banks, which may be particularly sensitive to policy priorities.

**23**
Furthermore, for some important variables, there was no information available on the providers when the youth were 14 years old, which we would have used to measure intergenerational mobility. However, it was possible to estimate the maximum height of the providers using known information - current height and age - adjusted based on the information on height loss reported in the literature.

These results indicate that PROSPERA has achieved effects on health, education and labor income indicators that are consistent with the proposed theory of change. These results could be even greater if there were improvements in the general health and economic well-being of the population. From the public health perspective, a relevant challenge is to understand the ongoing overweight and obesity epidemic: by consuming health capital, this epidemic works counter to PROSPERA ́s goal, reducing the possibility of PROSPERA improving social mobility. As shown in the results, overweight is associated with a reduction in individuals’ income on average, thus reducing their capacity for social mobility.

An additional challenge not analyzed in this paper, but that has been extensively documented, is the need to improve of the quality of the services provided to the population. Evidence indicates that current health services for the population of PROSPERA are of limited quality. The challenge in the quality of health services delivery is not limited to the population of PROSPERA, but is an issue affecting the Mexican population in general.

The intervention analyzed here—that is, one based on incentivizing demand for health and educational services through conditional cash transfers that work to promote the accumulation of human capital and social mobility—showed overall positive results. This intervention could be strengthened by specific actions focused on vulnerable groups, such as the indigenous population, and by generating mechanisms to improve the quality of health and educational services in Mexico.

To the best of our knowledge, this is the first analysis of intergenerational mobility of health in Mexico, and that is based on both anthropometric measurements and information provided directly by individuals from all analyzed generations. A strength of this study is that the analysis uses longitudinal data.

Finally, improving human capital will not suffice to interrupt the intergenerational transmission of poverty in a severely constrained economic context. For example, it helps neither the students nor the economy to train a large cohort of engineers if there are no jobs for new engineers. The improvement in intergenerational mobility, as proposed in the theory of change, rests on assumptions. These assumptions include: the existence of high-quality health and educational services, an economy capable of generating employment opportunities, and equal access to the labor market. With these institutions in place, the improvement in health and human capital estimated here may improve the lives of future generations.

35
# **Table 5. Classification of** MICs

|Countries|Institutional<br>Investor Rating<br>(IIR),<br>September 2000|Poverty Headcount<br>(% of population<br>based on $2/day<br>cutoff)|CPIA<br>Rating,<br>2000|Annual<br>Growth in<br>GDP<br>1990-97<br>**()**|Per Capita<br>Income<br>(1999 US $)|No. of<br>countries|
|---|---|---|---|---|---|---|
|||||.<br>**_**<br>|||
|**_Low Capital Access Countries_**|**<35 and unrated**|**41**|**3.1**|**1.2**|**1369**|**32**|
|Algeria, Belarus, Ecuador, Gabon, Grenada, Indonesia,<sup>Jamaica,</sup><br>Kazakhstan, Nigeria, Pakistan, Papua New Guinea,<sup>Paraguay,</sup><br>Romania, Russia, Seychelles, Swaziland,<sup>Turkmenistan, Ukraine</sup><br>and Zimbabwe|_<35_|42|3.0|1.2|1404|19|
|Azerbaijan, Belize, Bosnia and Herzegovina, Dominica,<sup>Equatorial</sup><br>Guinea, Fiji, Macedonia, Marshall Islands, Micronesia,<sup>St. Kitts</sup><br>and Nevis, St. Lucia, St. Vincent & Grenadines<sup>and Uzbekistan</sup>|Unrated|..|3.2|1.1|1193|13|
|**_Middle Capital Access Countries_**<br>Argentina, Brazil, Bulgaria, Colombia, Costa<sup>Rica, Croatia,</sup><br>Dominican Republic, Egypt, El Salvador, Guatemala,<sup>India,</sup><br>Jordan, Latvia, Lebanon, Lithuania, Morocco, Namibia,<sup>Panama,</sup><br>Peru, Philippines, Slovak Republic, Trinidad and Tobago,<sup>Turkey</sup><br>and Venezuela|35-51|30|3.8<br>|3.0<br>|2694<br>|24<br>|
|**_High Capital Access Countries_**<br>Botswana, Chile, China, Czech Republic, Estonia,<sup>Hungary,</sup><br>Korea, Malaysia, Mauritius, Mexico, Poland,<sup>South Africa,</sup><br>Thailand, Tunisia and Uruguay|**>51**|**15**<br>|**4.2**<br>|**5.3**<br>|**3937**<br>|**15**<br>|
|Total||30|3.6|2.8|2603|71|

Notes: Six of the 77 countries are not included in this classification<sup>due to incomplete data.</sup> ".." denotes data not available.

Source: Staff calculations based on World Bank<sup>data.</sup>

**t'**
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Insurance/Reinsurance Premium Structure after Ceding Catastrophe Risk to Pool and Reducing remaining Proportional Reinsurance <!-- Start of picture text --> i O Catastrophe risk portion transferred<br>0<br>_.GO j to pool ($89 = 60% x $149).<br>.0<br>e<br>; Quota / Propor- Net retention<br>^ tional treaty remains at $36 m.<br>*E reinsurance: $24 m or higher by<br>increasing reten-<br>4 Reinsurers provide tion on non-cat<br>Q additional cover risk portion under<br>capacity in pool. quota treaty.<br><!-- End of picture text -->

# **_Inclusion_** _of_ **_Public Sector_** _Assets_ **_and other Uninsured Properties_**

In addition to the restructuring of catastrophe insurance arrangements as per above, primary companies can also augment their non-cat portfolio by insuring public sector assets and currently non-insured households, assuming a supportive regulatory framework for compulsory catastrophe insurance protection. Estimated public sector assets , uninsured households and small businesses in terms of total value, are as follows:

||Government Assets|Uninsured Households/Businesses|
|---|---|---|
|OECS|$2,172 m.|$414 m.|
|Barbados|$836 m.|$298 m.|
|Trinidad & Tobago|$2,161 m.|$350 m.|
|Total|$5,169 m.|$1,062 m.|

In the above figures, government assets include public buildings, government buildings, utilities, ports, airports, roads, schools, hospitals, and other public sector assets. Adding in the above assets totaling almost $6.3 billion and using an average but conservative premium rate of 0.6% of insured value would result in additional premium volume of $37 million of which the catastrophe portion would constitute $22 million and the non-cat portion $15 million, and assuming transfer of all catastrophe risks to the pool, the following premium structure would result in the following:
finalizing the videos. The study team did not produce the health videos; rather, we utilized videos previously used in Gujarat by the United Nations for health education promotion.

Each financial and health session was followed by a group discussion led by a trained instructor who summarized the main points of each video and answered outstanding questions. Appendix I explains the content of both the financial education and health modules further.

# _2.2 Additional Treatments_

In the following sub-sections, we describe three different treatment interventions – pay for performance, individualized financial counseling, and goal setting – that were delivered on a randomized basis at the individual level to test cognitive and behavioral constraints to acquisition and application of financial education. For each treatment, we present the literature supporting these strategies, followed by a discussion of our operationalization of the treatments.

# _2.2.1 Pay for Performance_

The objective of the pay for performance treatment was to test whether financial learning is constrained by motivational factors in addition to the knowledge barriers examined with classroom-based financial education training. By offering payments for correct responses on a knowledge-based exam, the treatment assessed whether concrete monetary incentives can more effectively induce individuals to lean, retain, and apply financial knowledge.

Monetary incentives have been shown in the literature to advance general education outcomes. In a notable study conducted by Angrist and Lavy (2010) in Israel, a significant increase in high school certification rates was observed for low-performing female students who received experimental cash incentives. In North America, Angrist and Lang (2009) found similar effects produced by a combination of academic support services and financial payments, which led to higher grades for female college freshmen.

In our study, monetary incentives were provided to participants based on their scores in a survey measuring financial or health knowledge acquired in the trainings. Half the participants – selected individually at random and independent of their financial education treatment status – were paid 9
# **Box1:  Russia’s anti-crisis policy response in late 2008 and 2009**

**_The Government has implemented a set of fiscal measures to contain the impact of the crisis._** The total fiscal cost of measures implemented in 2008 and 2009 amount to more than 2.9 trillion rubles or 6.7 percent of GDP (box table 1). These measures were effective in preventing a currency and banking crisis but less so in cushioning the impact on the real economy and the poorest households, although measures targeting the broader middle class (e.g., increases in public sector wages and pensions) that were planned before the crisis were implemented during the crisis, thereby cushioning the overall social impact (Bogetic and Smits 2010).

**Box Table 1: Summary of anti-crisis fiscal measures in 2008 and 2009 (percent of GDP)**

||**2008**|**2009**|**Total**|
|---|---|---|---|
|**Strengthening the financial sector**|**1.9**|**1.4**|**3.3**|
|Recapitalization and other direct support|0.8|0.2|1.0|
|Subordinated loans|1.1|1.2|2.3|
|**Promoting economic growth**|**0.7**|**1.8**|**2.5**|
|Public spendingongoods and services|0.1|0.1|0.2|
|Fiscal stimulus aimed at firms|0.5|1.7|2.2|
|Fiscal stimulus aimed at households|0.1|..|0.1|
|**Protecting socially vulnerable**|**..**|**0.3**|**0.3**|
|**Of which labor market policies (including unemployment)**|**..**|0.3|0.3|
|**Transfers to regions to support anti-crisis measures, introduced**<br>**by sub-national governments (social programs, support to firms).**|**..**|**0.7**|**0.7**|
|**Total **|**2.6**|**4.1 **|**6.7 **|
|_Note_: Excludes quasi-fiscal and monetary measures.<br>_Source_: World Bank estimates; Government of Russia.||||

**_Compared with other G-20 countries, Russia’s “fiscal stimulus” has been large (more than the internationally recommended 2 percent of GDP) (box figure 1)_ .** This seems appropriate in view of (i) the much larger growth deceleration Russia has experienced than many other G-20 countries; (ii) weak macroeconomic stabilizers, and (iii) large fiscal reserves accumulated in the oil Reserve Fund (about 8 percent of GDP).

## **Box figure 1: Estimated size of fiscal stimulus measures in G-20 countries (percent of GDP).**

<!-- Start of picture text --> Turkey<br>Italy<br>Brazil 2008 2009 2010<br>India<br>Argentina<br>France<br>Mexico<br>United Kingdom<br>Indonesia<br>Japan<br>Canada<br>Russia<br>South Africa<br>Germany<br>Australia<br>Spain<br>Korea<br>United States<br>China<br>Saudi Arabia<br>0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%<br><!-- End of picture text --> _Source_ : Data for nonRussia G-20 countries, IMF (based on packages announced through late February). The figures do not include (i) below-the-line operations, (ii) measures that were already planned for, and (iii) banking-sector support measures.

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Policy Research Working Paper 6357

# **Abstract**

Least developed countries rely on preferential market access. Proof of sufficient transformation has to be provided to customs in importing countries by meeting Rules of Origin requirements to benefit from these preferences. These Rules of Origin have turned out to be complicated and burdensome for exporters in the least developed countries. Starting around 2001, under the United States Africa Growth Opportunity Act, 22 African countries exporting apparel to the United States can use fabric from any origin (single transformation) and still meet the criterion for preferential access (the so-called Special Rule), while the European Union continued to require yarn to be woven into fabric and then made into apparel in the same country (double transformation). This paper uses panel estimates over 1996–2004 to exploit this quasi-experimental change in the design of preferences. The paper estimates that this simplification contributed to an increase in export volume of about 168 percent for the top seven beneficiaries or approximately four times as much as the 44 percent growth effect from the initial preference access under the Africa Growth Opportunity Act without the single transformation. This change in design also mattered for diversity in apparel exports, as the number of export varieties grew more rapidly under the Africa Growth Opportunity Act special regime.

This paper is a product of the Trade and Integration Team, Development Research 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 jaime.demelo@unige.ch and aportugalperez@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
2.2, the data for micro and small enterprises by design covers only informal enterprises. In addition to quantitative and qualitative measures of firm performance, the survey also inquires from establishment owners about problems faced in enterprise operation. To this effect, we examine how these (self-reported) problems affect enterprise performance using the following regression: The unit of observation in (3) is the enterprise _i_ , operating in industry _j_ , and located in district _d_ . Our independent variables of interest are _Problem_<sup>_j_</sup> ; _j ∈{_ 1 _,_ 2 _,_ 3 _,_ 4 _,_ 5 _,_ 6 _}_ , corresponding to each of the 6 problems queried in the survey. These are binary variables, and cover problems faced by the enterprise in terms of a) low demand; b) accessing raw materials; c) accessing credit d) electric power; e) labor; f) recovering financial dues. **X** includes establishment specific covariates, while **Y** contains district specific covariates.<sup>10</sup> We also consider specifications where replace the district covariates with district fixed effects. _μ_ denotes industry (4-digit) fixed effects. As the establishment data is sourced from a single cross-section, the industry fixed effects control for factors such as industry-level demand or industry-specific regulations/government policies which are common to all establishments operating in the industry. All specifications include establishment-specific weights to make the data nationally representative, and standard errors continue to be clustered by district.

Our primary outcomes of interest are logged revenues and logged value addition.<sup>11</sup> _β_<sup>_j_</sup> thereby estimates the effect of each problem on enterprise performance, conditional on the controls and the fixed effects. In addition to quantitative measures of enterprise performance, the survey also provides qualitative measures of enterprise performance, recording whether the establishment is expanding, stagnating, or a new entrant (formed in the past 3 years). Finally, the enterprise data also records outstanding credit, and the source of enterprise credit, permitting us to test whether enterprises in districts with relatively more bank branches or bank credit per capita also have increased access to formal credit.

We begin by analyzing the effect of various problems reported by enterprises and their impact on quantitative measures of enterprise performance. The outcome of interest in

> 10 We include the same set of district covariates as before. The full set of establishment level covariates are: a) logged fixed assets; b) logged wage payments; c) months of operation; d) dummy for female owner; e) dummy if the enterprise is owned by an individual hailing from the SC/ST community; f) a quadratic in establishment age; g) dummy for own account enterprise; h) dummy for whether the enterprise keeps accounts; i) dummy for whether the enterprise operates out of a fixed location; and j) dummy for whether the enterprise is locally registered.

> 11 Value addition is computed as total revenues less total expenses (not including labour costs).

24
those who were contacted by message, the refusal vs. connectivity issues partition was 0.69 and 0.31. On the one hand, this result is expected since completing a digital survey can require more connectivity settings (internet speed, bandwidth, phone data) than a phone-based one (basic cell phone reception). Alternatively, it suggests that lack of response due to refusal to respond can be reduced by implementing phone-based surveys, as documented by Savage and Waldman (2008).<sup>33</sup>

# **5.2 Heterogeneous Impacts on Completion Rates by Respondent’s Characteristics**

There are many reasons to believe the treatment effect is heterogeneous across the population. Although our sample size is relatively small, we explore heterogeneity across key dimensions to assess if the treatment effects estimated on completion rate may differ by respondent characteristics. We measure changes in the likelihood of completing the survey by interacting our treatment status variable with an indicator for some of the relevant baseline categories. We selected these covariates based on their potential availability since researchers can easily get that information before running the survey; this could help them to better target resources toward specific groups.<sup>34</sup> Table 4 and Figure 2 summarize impacts of the phone-based survey on survey completion across some key categories: age, gender, phone exclusivity, and use of electronic-transfer technology (Tigo Money). Column (1) in Table 4 shows heterogeneous results for women relative to men, column (2) presents differences in impacts on individuals sharing the phone relative to those with exclusive use, and column (3) shows results for respondents who use Tigo Money compared to those who do not.

We document three main results. First, women are 15 percentage points more responsive to completing the survey through a phone call, relative to men assigned to the same survey mode. The total treatment

> 33In the context of choice experiments, the authors find that the quality of the online respondents’ answers declines throughout a series of experiments.

> 34Unfortunately, due to the way baseline information was collected for the experiment in which we embedded this project, our database lacks additional baseline characteristics such as educational level, employment status, and other socioeconomic variables that are available only for a partial portion of our sample.

17