rafmacalaba/datause-encoder-data huggingface dataset:
| POLICY RESEARCH WORKING PAPER 2707

# Summary findings

Beck, Lundberg, and Majnoni extend the recent literature on the link between financial development and economic volatility by focusing on the channels through which the development of financial intermediaries affects economic volatility. Their theoretical model predicts that well-developed financial intermediaries dampen the effect of real sector shocks on the volatility of growth while magnifying the effect of monetary shockssuggesting that, overall, financial intermediaries have no unambiguous effect on growth volatility.

The authors test these predictions in a pane I data set covering 63 countries over the period 1960-97, using the volatility of terms of trade to proxy for real volatility, and the volatility of inflation to proxy for motetary volatility. They find no robust relationship between the development of financial intermediaries and growth volatility, weak evidence that financial intermediaries dampen the effect of terms of trade volatility, and evidence that financial intermediaries magnify the impact of inflation volatility in low- and middle-incom]e countries.

This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to un,derstand the links between the financial system and economic growth. Copies of the paper are available free from the World Bank, 18 18 H Street NW, Washington, DC 20433. Please contactAgnesYaptenco, room MC3-446, telephone 202-473-852 6, fax 202522-1155, email address ayaptencoCaworldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tbeck@worldbank.org, mlundberg@worldbank.org, or gmajnoniCoworldbank.org. November 2001. (48 pages) _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 polishel. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed r this paper are entirely those of the authors. They do not necessarily represen_ **_t_** _he view of the World Bank, its Executive Directors, )r the countPtes they represent._ Produced by the Policy Research Dissemination Center
**Table 1. Comparing the SPI and Other Data and Statistics Indexes**

|||**Assessment Type/**|**Country**|**Time**|**Index**|**No. of**|||**Share of i**<br>|**ndicators in**<br>|**eachpil**<br>|**lar**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**No**|**Index**|<br>**Frequency**|<br>**Coverage**|**Coverage**|**Methodology**|**Indicators**|**Data**<br>**Use**|**Data**<br>**Services**|<br>**Data**<br>**Products**|<br>**Data**<br>**Sources**|**Data**<br>**Infra.**|**Data Sources (Share)**|
|1|Open Data Inventory<br>Indicators & Index (ODIN)|Global/ Annual|192|2016-2022|Nested weight|44|0%|50%|50%|0%|0%|NSO Websites|
|2|Open Data Barometer<br>(ODB)|Global/ Annual|116|2013-2017|Nested weight|46|15%|30%|37%|0%|15%|Expert Survey|
|3|Global Data Barometer<br>(GDB)|Global/ Biennial|109|2019-2021|Nested weight|55|9%|4%|20%|16%|45%|Expert Survey|
|4|Statistical Performance<br>Indicators & Index (SPI)|Global/ Annual|186|2016-2022|Nested weight|51|10%|16%|31%|20%|24%|Public International<br>Databases (86%), NSO<br>Websites (14%)|
|5|Statistical Capacity<br>Indicators & Index (SCI)|Global/ Annual|145|2004-2020|Simple<br>arithmetic<br>average|25|0%|0%|40%|20%|40%|Public International<br>Databases (80%), NSO<br>Websites (20%)|
|6|Ibrahim Index of African<br>Governance Statistical<br>Capacity (IIAG)|Regional/ Annual|54|2010-2022|Simple weight|3<br>statistical<br>capacity<br>indicators|0%|4%|37%|19%|41%|Expert Survey (33%),<br>ODIN (33%), SCI<br>databases (33%)|
|7|EU Snapshot tool|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|131|6%|12%|10%|8%|64%|Expert self-<br>assessment|
|8|UN NQAF self-checklist|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|87|10%|16%|2%|7%|64%|Expert self-<br>assessment|
|9|Paris21 NSDS self-<br>assessment|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|149|14%|23%|1%|1%|61%|Expert self-<br>assessment|

**Note** : N.A. stands for not available. The number of countries shown under the column “Country coverage” are for the latest year for all the indexes. The SPI covers between 167 and 186 countries for 2016-2022. The SCI covers between 115 and 145 countries for 2004-2020. The ODIN covers between 125 and 192 countries for 2015-2022 but offers comparable data starting from 2016. The ODB covers between 30 and 115 countries for 2013-2017. The ODB only covers 30 countries in 2017 and is replaced by the GDB after this year. The GDB covers 109 countries for 2019-2021. The IIAG covers 54 countries for all the years for 2010-2019. All the indexes provide annual data, except for the GDB that provides a single data point for each country for 2019-2021.

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**Table 3: A hypothetical experiment on the costs and benefits of biotech cotton**

||**_SCENA_**<br>**_Optimal use o_**|**_RIO I_**<br>**_f chemicals_**|**_SCENAR_**<br>**_Sub-optimal use_**|**_IO II_**<br>**_of chemicals_**|
|---|---|---|---|---|
||**_Conventional_**|**_Biotech_**|**_Conventional_**|**_Biotech_**|
|Number of sprays/hectare|10|5|5|5|
|Cost of chemicals, $50/spray|500|250|250|250|
|Cost of biotech seeds, $|0|150|0|150|
|Yield, kgs/hectare|1,000|1,000|500|1,000|
|Revenue, $1.50/kg|1,500|1,500|750|1,500|
|Profit, $|1,000|1,100|500|1,100|
|**Incrementalprofit, $**|**100(=1,100**|**- 1,000)**|**600(=1,10**|**0 – 500)**|

**Source:** Author’s calculations.

**Table 4: The economic effects of biotech cotton**

||**_Insecticide_**<br>**_reduction(%)_**|**_Effective yield_**<br>**_increase(%)_**|**_Gross margin_**<br>**_increase($US/ha)_**|**_Number of surveys_**|
|---|---|---|---|---|
|Argentina|47|33|23|2|
|Australia|48|0|66|1|
|China|65|24|470|1|
|India|41|37|135|2|
|Mexico|77|9|295|1|
|South Africa|33|22|91|2|
|United States|36|10|58|2|
|**AVERAGE/SUM**|**50[53]**|**19[31]**|**163[303]**|**11**|

**Source:** Qaim (2009), p. 672, Table 1.

**Note:** The average reported in the last row has been calculated by the author. Numbers in square brackets show the India/China average.

**Table 5: Changes in yield and insecticide costs from biotech cotton**

||**_Insecticide cost reduction(%)_**|**_Yield change (%)_**|**_Number of surveys _**|
|---|---|---|---|
|Australia|51|-2|2|
|China|65|25|3|
|India|27|15|10|
|Mexico|77|10|2|
|South Africa|38|35|9|
|United States|47|9|1|
|**AVERAGE/SUM**|**41[46]**|**15[20]**|**27**|

**Source:** Tripp (2009), p. 74, Table 4.1.

**Note** : The country averages reported in the last row have been calculated by the author. The original table reports results from individual surveys. Numbers in square brackets show the India/China average.

_— 15 —_
Table 1. Behaviour of small and large firms during recessions and tight money periods (in percent change)

||Sal|es|Inven|tories|Short-te|rm Debt|
|---|---|---|---|---|---|---|
||Large|Small|Large|Small|Large|Small|
|2007-2009 Recession<sup>1/</sup>|-24.62|-20.24|-14.18|-15.36|-38.24|-19.83|
|2001 Recession<sup>2/</sup>|-14.64|-6.92|-12.65|-9.28|-35.95|-12.13|
|All Recessions pre-2001<sup>3/</sup>|-7.59|-10.15|-5.67|-9.11|-24.48|-12.62|
|Source: Kudlyak and Sánchez<br>Note: The table shows the dif<br>quarters following the episode<br>1/The peak of the recession is<br>Tight Money Dates<sup>4/</sup>|(2017).<br>ference betw<br>and the pea<br>Q4 2007.<br>-5.5|een the mini<br>k value of the<br>-9.39|mum value of<br>series.<br>-3.3|the detrended<br>-7.78|series in an i<br>-14.15|nterval of 12<br>-11.45|

Source: Kudlyak and Sánchez (2017). Note: The table shows the difference between the minimum value of the detrended series in an interval of 12 quarters following the episode and the peak value of the series. 1/ The peak of the recession is Q4 2007. 2/ The peak of the recession is Q1 2001. 3/ The peaks of the recessions covered are: Q4 1969, Q4 1973, Q1 1980, Q3 1981 and Q3 1990. 4/ The periods of tight money are Q2 1966, Q4 1968, Q2 1974, Q3 1978, Q4 1979, Q4 1988 and Q2 1994.

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# **VIII TABLES**

Table 1. Descriptive Statistics – Remote Sensing Data

||Average|Std. Deviation|Observations|Years Available|
|---|---|---|---|---|
|Night Light Density [0 to 63]|0.732|1.159|71,760|1992-2021|
|Deforestation (Ha/Km2)|0.003|0.017|52,624|2001-2022|
|Land cover of dense-open forests (Km2)|0.035|0.153|47,840|2001-2020|
|Land cover of grassland (Km2)|0.089|0.193|47,840|2001-2022|
|Land cover of croplands (Km2)|0.460|0.441|47,840|2001-2024|

_Notes:_ The table reports the descriptive statistics of the outcomes for which we examine the impacts of the Cox’s Bazar refugee camps. Variables definition: (i) Night light density [0 to 63]: This variable represents the average light density at the grid level, obtained by averaging across pixels at the desired level of aggregation. Values range from 0 (no light) to 63 (maximum light density). (ii) Deforestation _Ha/Km_<sup>2</sup> : This variable corresponds to landsat images from the Hansen Global Forest Change, focusing on identifying forest loss defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. The measure is expressed in hectares per square kilometer. (iii) Dense-open forests refer to areas with dense canopy cover (¿40% tree cover), characterized by high values of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), indicating vigorous and dense vegetation. (iv) Grassland refers to vegetated areas dominated by grasses, herbs, and other non-woody vegetation, typically with sparse to moderate tree cover or no trees present. (v) Croplands refer to areas of land specifically designated or utilized for agricultural cultivation, including the cultivation of crops such as grains, vegetables, fruits, and other agricultural products.

41
Table 4: **Conditional Average Treatment Effect (CATE): Behavior at School**

||“Strong"<br>Group<br>(1)|“Weak"<br>Group<br>(2)|Diff.<br>(3)|MHT.<br>_p_-value<br>(4)|
|---|---|---|---|---|
|ITT behavior at school|0.259|0.212|0.047||
|_Individual Characteristics_|||||
|Female|0.420|0.525|-0.105|0.006|
|Student’s age|13.730|13.475|0.255|0.014|
|Bad or regular behavior at school at baseline|0.904|0.723|0.181|0.000|
|<br>Student is enrolled in the afternoon shift|0.573|0.320|0.253|0.000|
|Student’s course|7.659|7.164|0.495|0.000|
|Travel time (minutes from home to school)|15.181|14.197|0.984|0.349|
|Student has tried to immigrate to USA|0.031|0.015|0.016|0.214|
|Student works|0.099|0.144|-0.045|0.068|
|_Household Characteristics_|||||
|Student’s household composition|||||
|Student lives with both parents|0.519|0.682|-0.163|0.000|
|Student lives only with mother|0.406|0.231|0.175|0.000|
|<br>Student lives only with father|0.007|0.022|-0.015|0.068|
|Student lives only with other relatives|0.075|0.063|0.012|0.572|
|Mother’s education level|||||
|Years of education|8.563|7.116|1.447|0.000|
|_Mechanisms_|||||
|SES Index|-0.069|-0.104|0.035|0.627|
|Fluid Intelligence|-0.207|-0.052|-0.155|0.074|
|Arousal|0.169|-0.012|0.181|0.028|
|Valence|0.114|0.070|0.044|0.622|
|_School and Neighborhood Characteristics_|||||
|School is urban|0.956|0.569|0.387|0.000|
|School is located in a highly violent community|0.853|0.145|0.708|0.000|
|_Country_|||||
|El Salvador|0.372|0.287|0.085|0.021|
|Guatemala|0.191|0.632|-0.441|0.000|
|Honduras|0.437|0.080|0.357|0.000|
|Observations|293|585|||

_Notes: “Strong Group"_ refers to subgroups whose conditional average treatment effect (CATE) is above the 66th percentile of all CATEs when switching from _Clubs_ to the _Mindful_ or _Virtue_ treatment, and equal or below to the 66th percentile for the “Weak group". A positive number in the “Difference” column indicates that the average covariate value for the “Strong" subgroup is higher. _P_ -values for the difference between groups are shown in the fourth column (using Benjamini-Hochberg correction for multiple hypothesis testing).

49
Equation (2) shows that, where log<sup>_I_</sup> _it_<sup>equals logarithms of the levels of investment for country</sup> i at the period t and _CRit_ , the country risk of each country i also in period t. Most econometric specifications dealing with GDP and Investments suffer from endogeneity. We address this problem by assuming that the investments are being affected by events of the previous year. _GDPit_ − 1 is the Gross Domestic Product PPP in current US millions dollars for the country i in the year t-1. _GROWTH it_ − 1 is the GDP’s growth and both are expected to have a positive impact on Investment levels. _INFLATION it_ − 1 captures the monetary instability for the country i in the year t-1 and is expected to have a negative impact. _OPENNESS it_ − 1 is a proxy of the openness of the country calculated as the sum of exports and imports over the GDP; it is expected to have a positive impact on the investments. Since the participation decisions can be explained also by events of more than one year ago, we estimate the equation with two, three and four years lag.

**Table 1: Descriptive Statistics**

|**Variables**|**Observations**|**Mean**|**Standard Deviation**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|Country Risk|2,566|35.1|13.7|1.2|83.3|
|GDP PPP (MMUS$)|2,646|134,000|512,000|99.8|10,100,000|
|GDP Growth (%)|2,764|3.6|6.9|-51.0|106.3|
|Inflation (%)|2,756|68.6|674.8|-32.8|26762.0|
|Openness(%)|2,606|79.6|39.1|0.2|280.4|

Source: Authors’ calculations

**Table 2: Correlation of the explanatory variables and the Country Risk**

||**Country**<br>**Risk**|**GDP**|**GDP**<br>**Growth**|**GDP per**<br>**cap**|**Inflation**|**Openness**|
|---|---|---|---|---|---|---|
|Country Risk|1||||||
|GDP|0.330|1|||||
|GDP Growth|0.132|0.068|1||||
|GDP per cap|0.470|0.185|0.004|1|||
|Inflation|-0.123|-0.006|-0.210|-0.047|1||
|Openness|-0.002|-0.167|0.034|0.214|0.029|1|

Source: Authors’ calculations Since the model is specified in logarithms, for those countries in which there is no investment in a given year, the observations are dropped. We address this running a fixed effect model as 8
youth from such behaviors; (2) the _micro environment_ includes institutions and individuals with which youth interact personally, i.e., family, school, social networks, and role models. For example, family is the earliest and most enduring influence on the socialization of youth. Family disorganization, lack of family cohesion, and poor parental or adult supervision all present major risk factors for youth. An example of a predictor of risk-taking behavior in connection with school is academic failure. Such failure may be the onset of antisocial behavior and lead to a downward spiral. Social networks and guidance act as protective factors, but clearly established rules and expectations among others are also important in building resiliency and reducing the likelihood of engaging in risky behaviors; (3) the _macro environment,_ which is the economy, inequality, institutions, social norms, sociodemographic differences such as gender and race, and the cultural and historical background, has a critical influence on youth. For example, if this environment is unsafe or uncertain, the likelihood of engaging in risky behaviors increases. The macro environment can also be a protective factor by providing job opportunities and social services.

The framework used in this paper reflects the problematic factors youth may encounter through the transition from childhood to adulthood. Instead of only considering evident signs of direct failures, such as youth committing robbery or using drugs, earlier phases of negative transitions are also identified. Such negative transitions may be observed as school dropout or push out or generally poor financial conditions. Using this framework it is possible to target key remediation points and more importantly key prevention points for youth at risk in Haiti. The following section will outline the general demographic and economic situation in Haiti and sections 4 to 6 will address the more specific situation for youth in order to identify certain groups at risk (the appendix outlines a number of possible intervention and remediation points).

# **3. Lay of the Land**

## **Demographics**

One out of five Haitians are between 15 and 24 years old and thereby categorized as youth for the purpose of this paper (see Figure 3.1). Compared to OECD countries, such as France and USA with respectively 13 and 14 percent of the population being youth, LAC countries have a large proportion of youth, averaging 19 percent in 2005.<sup>7</sup> Haiti is above average for LAC, with slightly over 20 percent of the population at age 15–24.

> 7 US Census Bureau: http://www.census.gov/ipc/www/idbnew.html.

6
- Durlak, J. A., Weissberg, R. P., and Pachan, M. (2010). A meta-analysis of after-school programs that seek to promote personal and social skills in children and adolescents. _American journal of community psychology_ , 45(3):294–309.

- Egana-delSol, P. (2016a). Affective neuroscience meets labor economics: Assessing noncognitive skills on late stage investment on at-risk youth. In _Skills For Sustainable Development: Essays On How Creativity, Entrepreneurship And Emotions Foster Human Development_ , chapter 2. Columbia University Academic Commons, New York.

- Egana-delSol, P. (2016b). Can we trust self-reporting as a measurement of non-cognitive skills? In _Skills For Sustainable Development: Essays On How Creativity, Entrepreneurship And Emotions Foster Human Development_ , chapter 3. Columbia University Academic Commons, New York.

- Egana-delSol, P., Sun, X., and Sajda, P. (2023). Neurophysiological markers of emotion regulation predict efficacy of entrepreneurship education. _Scientific Reports_ , 13(7260):1– 15.

- Ekman, P. and Friesen, W. (1978). Facial action coding system: Manual.

- Fleming, C. B., Catalano, R. F., Mazza, J. J., Brown, E. C., Haggerty, K. P., and Harachi, T. W. (2008). After-school activities, misbehavior in school, and delinquency from the end of elementary school through the beginning of high school: A test of social development model hypotheses. _The Journal of Early Adolescence_ , 28(2):277–303.

- Galasso, E. and Wagstaff, A. (2019). The aggregate income losses from childhood stunting and the returns to a nutrition intervention aimed at reducing stunting. _Economics & Human Biology_ , 34:225–238.

- Galla, B. M., Tsukayama, E., Park, D., Yu, A., and Duckworth, A. L. (2020). The mindful adolescent: Developmental changes in nonreactivity to inner experiences and its association with emotional well-being. _Developmental psychology_ , 56(2):350.

- Ganimian, A. J., Muralidharan, K., and Walters, C. R. (2021). Augmenting state capacity

40
not undertake to provide technical education or training and managerial experience to Indians, thus hampering Indian industrial efficiency.19l Under these circumstances it was obviously difficult for India to establish a modem textile industry. Bairoch suggests that by 1870-80 local industry and artisans provided 25-45 percent of local textile consumption (or a level of de-industrialization equal to 55-75 percent.)<sup>120</sup> De-industrialization was not restricted to the textile industry of India. Other industries (e.g., iron), as well as other colonized countries, suffered the same fate. Bairoch claims that de-industrialization may have been even worse in other Asian countries. He cites China as an exception because its local industry was better able to survive due to a later start to the inflow of manufactured goods from the West, more local autonomy, and the large size of the country itself. In 1890, its local textiles might have supplied around 50-70 percent of local consumption.<sup>12</sup> 1 Table 1.13 below shows Bairoch's estimates of industrialization levels from 1750 to 1990 for the "Third World" (including Africa and Latin America in addition to Asia) and developed countries. The differences are immediately obvious. Until 1860, the "Third World's" level of industrialization exceeds that of developed countries. This, however, is reversed in 1860. Subsequently, the level for the "Third World" begins to decline (until 1928) while that in developed countries accelerates. On a per capita basis, the level was always lower in the Third World (from 1750, at least) than it was in developed countries but the difference widens considerably after 1860.

Table 1.13

||**LevelsofI **<br>**(United**<br>|**ndustrialization,**<br>**Kingdom**In**19**<br>|**1750- 1990**<br>**00**=100)<br>||
|---|---|---|---|---|
||**Tota**|**l**<br>|PerCa|pita<br>|
|||**Developed**||**Developed**|
||**Third World**|<br>**Countries**|**Third World**|**countries**|
|**1750**|93|34|**7**|**8**|
|1800|99|**47**|**6**|8|
|**1830**|112|**73**|**6**|**1i**|
|1860|83|**143**|4|**16**|
|1900|60|481|2|**35**|
|1913|**70**|863|2|**55**|
|1928|98|1,260|3|**71**|
|1938|122|1,560|4|**81**|
|1953|200|2,870|**5**|**135**|
|1973|927|8,430|14|**315**|
|1980|1,320|9,910|19|**347**|
|1990|2,480|12,090|29|**412**|
|Source:Bai|roch(1993:Table8.1,9|1)|||

24