rafmacalaba/datause-encoder-data huggingface dataset:
potentially informal firms in 574 census blocks. In terms of listed sector, 48% were in commerce, 45% in services, only 1% in manufacturing, and 6% undefined. The large number of firms to be matched in a short timeframe, requiring firms to be on both formal lists, and, possibly the inexperience of the survey firm in doing such a matching exercise, meant that, as we will see, a number of formally registered firms remained in this listed sample.

Since part of our design involves looking at spillovers within blocks we wanted to minimize the risk of spillovers across treatment blocks. To do this, we used the address of each listed firm and, from this, obtained the GPS latitude and longitude, and calculated the number of firms in other census blocks that lay within 100 meters of the listed firm in a straight-line. We then examined in detail the 239 census blocks in which at least 10 percent of the firms were close to at least one firm in another census block. We were most concerned with adjacent census blocks where firms on one side of the street that was a block boundary were in one block, and those on the other side of the street were in a different block. We then used an algorithm to reclassify these into new blocks<sup>8</sup> , giving a total of 662 geographic blocks. Out of these blocks, 57 contained only one potentially informal firm each, and so we dropped these blocks to end up with a sample of 605 geographic blocks containing 7,795 firms.

# **4.2. Randomization into Treatments at the Geographic Block Level**

We randomized these 605 geographic blocks into three groups: control blocks, communication blocks, and inspector blocks. Randomization was stratified by the 9 sub-districts in Belo Horizonte, and by whether the block had above or below the median firm density (measured by the number of listed informal firms divided by the area of the block).<sup>9</sup> This resulted in 201 blocks being chosen as control blocks, 202 inspector blocks, and 202 communication blocks (Table 1).

# **4.3. Baseline Survey of Control and Communication Blocks**

All of the firms listed in the communication and control blocks were then targeted for a baseline survey which took place between May and August 2011 and was carried out by the same firm (Gauss) as had done the listing. Out of these 5,419 firms, 1,455 were found to be formal (through presentation of

> 8 For each street which formed part of more than one block (144 streets in total), we calculated the median street number in each block. We then took the difference in median street numbers across blocks for each street, and for all blocks where this difference was smaller than 250 street numbers, combined the firms that were on the same street but in different blocks into a new block.

> 9 Since firm density (area of the block) was not known for the blocks that we had reclassified to avoid having neighboring firms in different blocks, we had three strata within each subdistrict: above median density, below median density, and reclassified block.

12
to have been tested in the past 12 months (72 percent tested). This partly reflects the introduction of testing through ante-natal surveillance sites in the early years of the epidemic.

In this paper we use the Marriage Transitions in Malawi (MTM) data set, a panel study of initially never-married young women and men in central Malawi, to evaluate how, if at all, VCT influences behaviors of young people. The survey introduced a testing opportunity for a random set of respondents during the second year of the three-year panel. We examine behaviors closely linked with the transition to adulthood and HIV risk. In particular, we estimate the intent-to-treat effect of the VCT intervention on school attendance, marriage, fertility, and reported sexual behavior in the year after the test.

Testing may alter these behaviors in several ways. Consider its potential effect on sexual behavior. If a man discovers he is negative, for example, he could commit to maintaining his status by carefully choosing partners, by choosing monogamy, by using condoms, or perhaps by seeking circumcision. A positive result might lead to a decision to engage in risky sexual behavior as incentives to protect against infection might no longer exist. On the other hand, this same man could be concerned about infecting others and therefore opt for safer behaviors.

Taking a broader view, removing uncertainty about one’s status may affect expectations of young people about their life expectancy as well as opportunities that will be available to them in the future. In turn, it may alter present or near-term decisions with long-term implications, such as investments in human capital through staying in school, selection of partners, and timing of marriage and fertility. Beliefs about own HIV status can also translate into beliefs about the survival of (yet to be born) children, since the virus can be transmitted from mothers to their newborns. Therefore, learning one’s status might affect the desired number and timing of births. Trinitapoli and Yeatman (2011), for example, find that compared to those who express certainty 3
Customer Deposits - Savings Customer Deposits - Term Bonds Issued (Senior Debt and Subordinated Debt) Deposits from Banks Repos and Cash Collateral Liabilities with Remaining Maturity < 1 Year Liabilities with Remaining Maturity > 1 Year and < 3 Years Liabilities with Remaining Maturity > 3 Years and < 5 Years Liabilities with Remaining Maturity > 5 Years Liabilities with Original Maturity < 1 Year Liabilities with Original Maturity > 1 Year and < 3 Years Liabilities with Original Maturity > 3 Years and < 5 Years Liabilities with Original Maturity > 5 Years Total Equity Core Tier 1 Equity Paid-in Capital

# **Income Statement**

Interest Expense Interest Income Net Interest Income Net Income

# **Interest Rates**

Weighted Interest Rate on Gross Loans Weighted Interest Rate on Gross Commercial Loans Weighted Interest Rate on Gross Commercial Loans less than US$100,000 Weighted Interest Rate on Gross Commercial Loans more than US$100,000 Weighted Interest Rate on Gross Mortgages Weighted Interest Rate on Gross Consumer Loans Weighted Interest Rate on Gross Consumer Loans less than US$100,000 Weighted Interest Rate on Gross Consumer Loans more than US$100,000 Weighted Interest Rate on Gross Loans with Remaining Maturity < 1 Year Weighted Interest Rate on Gross Loans with Remaining Maturity > 1 Year and > 3 Years Weighted Interest Rate on Gross Loans with Remaining Maturity > 3 Years and > 5 Years Weighted Interest Rate on Gross Loans with Remaining Maturity > 5 Years Weighted Interest Rate on Gross Loans with Original Maturity < 1 Year Weighted Interest Rate on Gross Loans with Original Maturity > 1 Year and > 3 Years Weighted Interest Rate on Gross Loans with Original Maturity > 3 Years and > 5 Years Weighted Interest Rate on Gross Loans with Original Maturity > 5 Years

# **Ratios**

Return on Assets (End of Period) Return of Equity (End of Period)

# **Bank Information**

Bank Identifier (Can Be Anonymized) Bank Type (Commercial, Development, Mortgage, Cooperative, or Other) <u>Bank Nationality (Foreign or Domestic)</u> 37
Policy Research Working Paper 7963

# **Abstract**

Functioning democracy requires that citizens reward politicians who deliver benefits, yet there is surprisingly little causal evidence of changes in citizen views or behavior in response to specific government programs. This question is examined in Tanzania, which has recently implemented large health programs targeting diseases such as HIV/AIDS and malaria. Tanzania’s 2010–2011 national anti-malaria campaign took place concurrently with a national household survey, which enables a regression discontinuity design based on interview date to estimate the effect of this program on the popularity of local politicians. Bed net distribution results in large, statistically significant improvements in the approval levels of political leaders, especially in malaria endemic areas. Effects are largest shortly after program implementation, but smaller effects persist for up to six months. These findings suggest that citizens update their evaluation of politicians in response to programs, especially when these services address important problems, and that the effects decay over time, but not completely.

This paper is a product of the Impact Evaluation 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 author may be contacted at kcroke@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
**Table 6:  Ownership by Industry Group**

|**Industry**|**Private**|**Public**|**Total**|
|---|---|---|---|
|**Textiles**|94<br>88.7%|12<br>11.3%|106<br>100 %|
|**Apparel**|434<br>98.2%|8<br>1.8 %|442<br>100 %|
|**Shoes and Leather Products**|171|2|173|
||98.8 %|1.2 %|100 %|
|**Electronics**|77<br>97.5 %|2<br>2.5 %|79<br>100 %|
|**At Pt**|123|7|130|
|**uo ars**|94.6 %|5.4 %|100 %|
|**Total**|899|31|930|
||96.7 %|3.3 %|100 %|

**Table 7: Exporters by Industry Group**

||**Textiles**|**Apparel**|**Shoes**<br>**and**<br>**Leather**|**Electronics**|**Auto**<br>**Parts**|**Total**|
|---|---|---|---|---|---|---|
|**Export**|40<br>37.7 %|67<br>15.2 %|58<br>33.5 %|31<br>39.2 %|66<br>50.8 %|262<br>28.2 %|
|**Never**|66|375|115|48|64|668|
|**Export**|62.3%|84.8 %|66.5 %|60.8 %|49.2 %|71.8 %|
||106|442|173|79|130|930|
|**Total**|100 %|100 %|100 %|100 %|100 %|100 %|

10
-8-

reports that women perform 72 percent of the land preparation, 48 percent of the planting, 70 percent of the weeding, and 63 percent of the harvesting. A more recent detailed study in the Ndop Plain region produced similar results.2 In coffee growing areas this means that men have a traditional responsibility for whatever pruning or weeding is done annually and for picking and fermenting the ripe coffee berries.

**Table 2i A.riultur l Setor** in **the** North **West Provinc. Sa Statistie**

|**National**|NW Province<br>--------------------||-----------------|----<br>-|----<br>---|-------------|__|
|---|---|---|---|---|---|---|---|
|Total|Total<br>Pa|t.|Bul<br>D|onga-Mantung|Menchus|**Mesa.**|**Moo**|
|OUSEHOLDS<br>||||||||
|**Farm**<br>1,153,500<br> <br>|131,800<br>|11.4<br>|28,700|25,000|20.300|40,500|17,300|
|Non-farm<br>530.200|21,900|4.1||||||
|Total<br>1,685,700|153,700|9.1||||||
|POPULATION||||||||
|**Farm**<br>6,622.000|953,000|14.4||||||
|non-farm<br>2,376,000|86,000|3.6||||||
|**Total**<br>8,998,000|1,039,000|11.5||||||
|AVERAGE HOUSEHOWLD<br>SIZE||||||||
|Farm<br>5.7|7.2|||||||
|**Non-farm**<br>4.5|3.9|||||||
|**Total**<br>**5.3**|6.8|||||||
|CROP**FARMS**||||||||
|Total<br>Number<br>1,130,200|131,200|11.6|28,700|25,000|20,100|40,100|17,30|
|-Growing<br>Coffee|95,700||24,100|16,500|13,800|31,200|11,10|
|**-percent**of Total<br>**Farms**|72.9||84.0|66.0|68.7|77.8|64.|
|**-Growing**Fooderops|130,600||28,200|25,000|20,000|40,100|17,3-|
|<br>-percent<br>of Total<br>**Farms**|99.5||98.3|100.0|99.5|100.0|100.|
|**Area(hectares)**|1.75||1.90|1.29|1.50|2.30|1.1|
|-Devoted<br>to<br>Coffee|0.39||0.37|0.27|0.36|0.52|0.31|
|-Devoted<br>to<br>Foodcrops|1.36||1.53|1.02|1.14|1.78|0.85|
|**Percentage**<br>**Selling**Produce*|95.0||96.5|95.2|95.1|94.3|93.|
|Gross<br>Income<br>from<br>**Sales**(F CFA)|192,867||139,709|191,055|142,960|291,956|114,23|

Source: 1984 Agricultural Census lfss: * Includes both cash **and** food crops.

1.14 Traditionally men also cleared new farm land, whether for food or cash crops. However, with increased land pressure, there is very little land left to be cleared so women are increasingly left to do any land preparation for food crop plots by themselves. At the same time, women, and indeed the entire household, are called upon to assist with the coffee harvest. Men have also encouraged women to grow food crops -- such as cocoyams, gro-o-undnuts, and even maize -- within stands of coffee so that any weeding the women do on the food crops will benefit the coffee as well.

1.15 Despite women's considerable contribution to cash crop production, men retain complete control over any income realized from the sale of coffee or

2 **Other** descripttons of **the division** of labor **can be** found **in** Bryson (1979, 19 0), **Eybon** (1982), **and NIDENO (1984a).**
Additional examples include an overharvesting of traditional plants and animals for domestic and export markets in Pingbian, China; by-catch losses of larval fishes and shellfish associated with the harvesting of wild prawn seed in the Sunderbans of West Bengal, India; the loss and fragmentation of mangrove habitat in Ca Mau, Vietnam, also partly associated with shrimp exports; and the potential loss of habitat from reduced river flows in the Incomati River Basin of South Africa, should sugar exports increase.

A similar problem arises when common resources can be polluted without penalty or cost. As in the earlier examples, trade does not lie at the root of the pollution problem; however, trade can give rise to incentives that result in increased pollution. Examples include the spread of disease through shared water resources in Ca Mau and the consequences of accumulated waste from caged salmon in Chile.

While trade liberalization can increase incentives for unsustainable exploitation of the resource base, some of the worst examples uncovered in the studies were caused by policies that created incentives against trade, generally in the cause of food self-sufficiency. The severest losses of the Madagascar spiny forest were in the anti-trade period of the 1970s and 1980s and were tied to the government’s support for rice, which also encouraged slash-andburn cultivation.<sup>18</sup> Likewise, in Vietnam, particularly in the south, much of the lost mangrove area was initially cleared for rice cultivation or for charcoal, firewood and building materials in a period when government policy was striving for self-sufficiency. Similarly, in Pingbian, long-standing incentives to grow land-intensive crops on marginal and steeply sloped lands, which created severe erosion problems, were reversed as internal trade and external trade with Vietnam and Laos developed. Instead, farmers shifted their production to supply emerging markets for fruit and vegetables. Protection may also be largely responsible for the overuse of water for sugar production in the Incomati region, since it is doubtful that, without a protected domestic market, sugar would be grown there. It is nonetheless true

> 18 Citing earlier research by the World Bank (2003), Minton et al. (p. 10, 2006) note that “It is estimated that Madagascar lost about 12 million ha of forest between 1960 and 2000, effectively reducing forest cover by 50% in just 40 years.... The severest losses took place during the 1970s and early 1980s during the height of the socialist revolution when the practice of slash-and-burn agriculture was actively encouraged in order to produce more rice to feed the growing urban population. Since the mid 1980s, Madagascar has therefore been the focus of international conservation efforts with international development organizations providing loan and assistance programs explicitly aimed at environmental objectives.”

20
**Table 6:  India – Distributions of new priority areas and species**

|Priority|Species|Species %|Cum %|% Plants|%Vertebrates|% Arthropods|% Other<br>Species|
|---|---|---|---|---|---|---|---|
|1|117|9.7|9.7|24.8|10.3|62.4|2.6|
|2|93|7.7|17.3|8.6|3.2|28.0|60.2|
|3|86|7.1|24.4|61.6|11.6|24.4|2.3|
|4|46|3.8|28.2|4.3|10.9|52.2|32.6|
|5|41|3.4|31.6|68.3|7.3|14.6|9.8|
|6|41|3.4|35.0|4.9|4.9|90.2|0.0|
|7|41|3.4|38.4|7.3|82.9|7.3|2.4|
|8|37|3.1|41.5|0.0|0.0|100.0|0.0|
|9|36|3.0|44.4|41.7|16.7|36.1|5.6|
|10|35|2.9|47.3|28.6|0.0|40.0|31.4|
|11|31|2.6|49.9|29.0|45.2|19.4|6.5|
|12|24|2.0|51.9|29.2|16.7|45.8|8.3|
|13|23|1.9|53.8|56.5|8.7|17.4|17.4|
|14|20|1.7|55.4|40.0|20.0|35.0|5.0|
|15|19|1.6|57.0|31.6|21.1|31.6|15.8|
|16|19|1.6|58.5|10.5|36.8|42.1|10.5|
|17|17|1.4|60.0|11.8|0.0|76.5|11.8|
|18|16|1.3|61.3|0.0|25.0|75.0|0.0|
|19|16|1.3|62.6|0.0|75.0|25.0|0.0|
|20|16|1.3|63.9|31.2|25.0|31.2|12.5|
|21|15|1.2|65.2|0.0|0.0|0.0|100.0|
|22|14|1.2|66.3|50.0|7.1|14.3|28.6|
|23|12|1.0|67.3|0.0|8.3|83.3|8.3|
|24|12|1.0|68.3|41.7|33.3|16.7|8.3|
|25|12|1.0|69.3|16.7|25.0|41.7|16.7|
|26|12|1.0|70.3|33.3|16.7|41.7|8.3|
|27|11|0.9|71.2|63.6|27.3|9.1|0.0|
|28|9|0.7|71.9|77.8|0.0|11.1|11.1|
|29|9|0.7|72.7|0.0|22.2|11.1|66.7|
|30|9|0.7|73.4|77.8|0.0|0.0|22.2|
|31|8|0.7|74.1|37.5|0.0|62.5|0.0|
|32|8|0.7|74.7|87.5|0.0|12.5|0.0|
|33|8|0.7|75.4|50.0|25.0|25.0|0.0|
|34|8|0.7|76.1|0.0|25.0|50.0|25.0|
|35|7|0.6|76.6|57.1|14.3|14.3|14.3|
|36|7|0.6|77.2|28.6|0.0|71.4|0.0|
|37|7|0.6|77.8|14.3|0.0|71.4|14.3|
|38|6|0.5|78.3|16.7|0.0|83.3|0.0|
|39|5|0.4|78.7|20.0|0.0|80.0|0.0|
|Figure XX: I<br>40|ndia: Spec<br>5|ies Protecto<br>0.4|n vs. Land<br>79.1|in Protected<br>0.0|Areas<br>40.0|40.0|20.0|

37
accessible to researchers, for example through data sharing agreements or by boosting data literacy.

Our results can help countries better understand if their statistical system is supporting the needs of researchers. Given that research is at the heart of evidence-based policy making, a statistical system oriented around making data available and easily accessible for these purposes is important. Countries could use the results to facilitate an understanding of the maturity of their statistical systems in supporting researchers, point to other countries that a particular country could learn from as it seeks to improve, and track progress or assess return on investment for funding related to statistical capacity building.

Our results face some limitations. First, the s2orc database does not currently contain non-English language articles. While the dominance of the English language in academic publications has been well-documented (Altbach 2007), the database omits articles published in other languages. Second, because of computational limitations, only 1 million articles were analyzed out of a sampling frame of around 10 million articles. Future work could bring in these additional articles to improve precision. Third, while the natural language processing model was able to classify whether or not the articles made use of data with 87% accuracy, the model could be extended in ways to extract more information from the articles, including more details about the subject of study and which particular data sources may have been used (surveys, censuses, administrative data, geospatial information etc.).

# **References**

Altbach, Philip G. “The imperial tongue: English as the dominating academic language.” _Economic and political Weekly_ (2007): 3608-3611.

Cameron, Drew B, Anjini Mishra, and Annette N Brown. 2016. “The Growth of Impact Evaluation for International Development: How Much Have We Learned?” _Journal of Development Effectiveness_ 8 (1): 1–21.

Cameron, Grant J., Hai-Anh H. Dang, Mustafa Dinc, James Foster, and Michael M. Lokshin. 2021. “Measuring the Statistical Capacity of Nations.” _Oxford Bulletin of Economics and Statistics_ 83(4): 870-896.

Courtioux, Pierre, François Métivier, and Antoine Rebérioux. 2022. “Nations Ranking in Scientific Competition: Countries Get What They Paid For.” _Economic Modelling_ 116: 105976.

Dang, Hai-Anh H, John Pullinger, Umar Serajuddin, and Brian Stacy. 2023. “Statistical Performance Indicators and Index: A New Tool to Measure Country Statistical Capacity.” _Scientific Data_ 10(1): 146.

Das, Jishnu, Quy-Toan Do, Karen Shaines, and Sowmya Srikant. 2013. “US and Them: The Geography of Academic Research.” _Journal of Development Economics_ 105: 112–30.

24
# **Improving the Resilience of Peru’s Road Network to Climate Events**

Julie Rozenberg, Cecilia Briceno-Garmendia, Xijie Lu, Laura Bonzanigo, Harry Moroz JEL: R40, R42, Q54

**Keywords** : robust decision making, network analysis, transport prioritization, climate change resilience

**Disclaimer** : This paper was originally prepared as a case study and background paper for the Regional Study “Road Networks, Accessibility and Resilience: The Cases of Colombia, Ecuador and Peru” published in 2015 and sponsored by the Chief Economist’s Office in the Latin America and the Caribbean Vice-Presidency of the World Bank. It benefited from funding of the Swiss Corporation for Development (SECO) and the Korean Green Growth Trust Fund (KGGTF).
the various confidence measures for the corruption perceptions question. The estimated effect of corruption is much lower for confidence in the military, at 0.06, than it is for the other three measures, which range from 0.13 to 0.17.

Thus far we have assumed that the slope of the relationship between corruption and confidence in public institutions is the same in all countries, at all income levels, and at all levels of corruption. We now relax this assumption and re-estimate our main specification from Table 3, country-by-country, so that we can investigate how this slope varies across countries. We note first that the means of the country-by-country estimates in Table 5 are slightly smaller than the pooled estimates in Table 3 (at -0.13 and -0.47 for the corruption experiences and perceptions questions, respectively). The sign of the estimated coefficient is also fairly consistently negative across countries, with 67 percent (91 percent) of country estimates being negative for the corruption experiences (perceptions) question. However, and not surprisingly, in many countries the estimated effects are not statistically significant, given the much smaller sample of observations on which to base inference in each country. In fact, the mean number of observations per country for the regressions in Table 5 is just 594, as opposed to 49,019 in the pooled regressions of Table 3.

We next examine how these estimated coefficients vary across regions (using the standard World Bank regional classification). While it is evident that corruption experiences as well as perceptions affect confidence negatively in all regions on average, the magnitude and strength of the relationship varies widely across regions, from -0.06 to -0.32 in the case of corruption experiences, and from -0.10 to -1.00 in the case of corruption perceptions. In the case of corruption experiences, the largest mean estimated effect is for the South Asia region. The relationship between corruption and confidence in institutions is also the strongest in this region with 60 percent of countries reporting a statistically significant negative relationship. At the same time however, while South Asia showed the largest coefficient of corruption experiences, its perceptions coefficient is the smallest among the regions in our sample.

In the remaining panels of Table 5 we document how the estimated correlation between corruption and confidence varies with the average level of corruption, and the 17
# **Figure 19: Insurance Market Indicators - Median Values Over Time (1995-2007)**

<!-- Start of picture text --> 0.025<br>0.020<br>0.015<br>0.010<br>0.005<br>Life Insurance Premium Volume / GDP<br>Non-Life Insurance Premium Volume / GDP<br>1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text -->

**Figure 20: Financial Globalization Indicators – Mean Values for 2003-07, by Income Group**

36