rafmacalaba/datause-encoder-data huggingface dataset:
4 and stock market development that is independent of other variables associated with economic growth.

This paper builds on Atje and Jovanovic's (1993) study of stock market trading and economic growth in two ways. We use conglomerate indexes of stock market development that combine information on stock market size, trading, and integration. Second, we control for initial conditions and other factors that may affect economic growth in light of evidence that many cross-country regression results are fragile to changes in the conditioning information set [Levine and Renelt (1992)]. Thus, we gauge the robustness of the relationship between overall stock market development and economic growth to changes in the conditioning information set.

We find a strong correlation between overall stock market development and long-run economic growth. After controlling for the initial level of GDP per capita, initial investment in human capital, political stability, and measures monetary, fiscal, and exchange rate policy, stock market development remains positively and significantly correlated with long-run economic growth. The results are consistent with theories that imply a positive relationship between stock market development and long-run economic growth. The results are inconsistent with theories that predict no correlation or a negative association between stock market development and economic performance.

Cross-country growth regressions suffer from measurement, statistical, and conceptual problems. In terms of measurement problems, country officials sometimes define, collect, and measure variables inconsistently across countries. Further, people with detailed country knowledge frequently find discrepancies between published data and what they know happened. In terms of statistical problems, regression analysis assumes that the observations are drawn from the same population. Yet, vastly different countries appear in cross-country regressions. Many countries may be sufficiently different such that they warrant separate analyses. Conceptually, we should interpret the coefficients from cross-country regressions cautiously. When averaging over long periods, many changes are occurring simultaneously: countries change policies; economies experience business cycles; and governments rise and fall. Thus, aggregation may blur important events and differences across countries. Eventually, analysts should extend this research by
# **Table 5.1. GDP Structure, low‐income archetype country in 2015 (percent of GDP)**

|Item|Value|
|---|---|
|Absorption|105.6|
|Private consumption|80.8|
|Government consumption|11.7|
|Fixed investment|19.2|
|Private fixed investment|14.4|
|Government fixed investment|4.8|
|Change in inventories|‐6.2|
|Exports|19.8|
|Imports|25.3|
|GDP at market prices|100.0|
|Net indirect taxes|7.5|
|GDP at factor cost|92.5|

Source: Authors’ calculations based on Table 3.6.

**Table 5.2. Balance of Payments, low‐income archetype country in 2015 (percent of GDP)**

|Item|Value|
|---|---|
|Current account, inflows of foreign exchange||
|Exports|19.8|
|Transfers to non‐government|2.8|
|Transfers to government|0.9|
|Factor income|0.3|
|Foreign savings|4.9|
|Total|28.5|
|Current account, outflows of foreign exchange||
|Imports|25.3|
|Transfers from non‐government|0.5|
|Transfers from government|0.1|
|Factor income|2.6|
|Total|28.5|
|Capital account||
|Net foreign financing to non‐government|0.4|
|Net foreign financing to government|1.5|
|Foreign direct investment|2.9|
|Change in foreign reserves|0.1|
|Total|4.9|

Source: Authors’ calculations based on Table 3.6.

In the government budget (Table 5.3), taxes, at 11.5 percent of GDP, are the main receipt while foreign transfers only amount to 0.9 percent. Government savings (0.8 percent) fall ‐21‐
- 39 -

Money-center banks, most of which are located in New York City, have traditionally dominated US banking. As noted earlier, during the 1970s they focused on asset growth, rather than profitability. Global lending to prestige clients, such as MNCs and developing countries were their prime targets, and innovation and large international branch networks became their hallmarks. Yet, in the US domestic market, deposit taking and retail businesq were severely circumscribed by the McFadden Act of 1927.36/ Until 1985, when the Supreme Court abolished all federal restrictions on interstate retail banking, banks were not permitted to have out-of-state branches or to conduct out of state retail banking, such as deposit taking and consumer credit. To fund their expanding loan portfolios the largest banks therefore had to rely on costly interbank funds from the domestic and eurocurrency markets--hence their name 'money-center banks'. Moreover, the elimination of interest-rate controls, the introduction of new types of interest-bearing checking accounts, and direct competition from nonbank checkable accounts, such as money market accounts, had increased the cost of domestic retail deposits and brought the banks' direct funding costs in line with money-market interest rates.

36/ The McFadden Act that prevented inter-state banking accounts for the uniquely fragmented structure of US banking, with close to 14,000 individual banks. To add to the banks' difficulties, state laws compounded the effects of the &ct by restricting intra-state retail banking. California was the only exception, and allowed state-wide multi-branch retail banking. In most other states local laws restricted retail banking to the point of often allowing only one office per bank. Following the Supreme Court ruling of 1985, state legislatures, not the federal government, can determine the criteria for accepting out-of-state banks. The scene is changing rapidly, as many states are introducing more flexible rules. Only six states still bar all interstate banking. Others have established reciprocal rules to accept banks from other states. only if those states reciprocate privileges for their own banks. Others still have abolished all restrictions or have also passed laws that will relax rules after specific dates. (For a more detailed discussion. (The Economist, 3/26/88; International Banking Survey, pp.21-22.)
Debebe, Zelalem Yilma. 2020. Impact of COVID-19 on human capital. Available online at <u>https://worldbankgroup.sharepoint.com/sites/wbsites/humancapital/SitePages/Detail.aspx/Do cuments/mode=view?_Id=101&SiteURL=/sites/wbsites/humancapital</u> Devercelli, Amanda. 2020. Supporting the youngest learners and their families in the COVID-19 (Coronavirus) response. World Bank. Available online at <u>https://blogs.worldbank.org/education/supporting-youngest-learners-and-their-familiescovid-19-coronavirus-response.</u>

- Dorn, Emma, Bryan Hancock, Jimmy Sarakatsannis, and Ellen Viruleg. 2020. COVID-19 and student learning in the United States: The hurt could last a lifetime. McKinsey & Company Public Sector Practice.

- Dercon, S., & Porter, C. 2014. Live Aid Revisited: Long-Term Impacts of the 1984 Ethiopian Famine on Children. _Journal of the European Economic Association_ , _12_ (4), 927–948. <u>https://doi.org/10.1111/jeea.12088</u>

- Education Endowment Foundation (2019) Digital technology Moderate impact for moderate cost, based on extensive evidence. <u>https://educationendowmentfoundation.org.uk/evidence-</u> - - -

- <u>summaries/teaching learning toolkit/digital technology</u>

- Filmer, Deon, Halsey Rogers, Noam Angrist and Shwetlena Sabarwal. _Forthcoming_ . LearningAdjusted Years of Schooling (LAYS): Defining a New Macro Measure of Education. Economics of Education Review. https://doi.org/10.1016/j.econedurev.2020.101971

- Gold MR, Siegel JE, Russell LB, Weinstein MC. (eds). 1996. Cost-Effectiveness in Health and Medicine. Oxford and New York: Oxford University Press.

- Haacker, M., Hallett, T. B., & Atun, R. 2019. On discount rates for economic evaluations in global health. Health Policy and Planning, czz127. https://doi.org/10.1093/heapol/czz127

- Hall, G., & Patrinos, H. A. (Eds.). 2006. Indigenous peoples, poverty, and human development in Latin America. Palgrave Macmillan.

- Hernandez, D. J. 2011. Double Jeopardy: How Third-Grade Reading Skills and Poverty Influence High School Graduation. Annie E. https://eric.ed.gov/?id=ED518818

- Hodges, Charles,  Stephanie Moore, Barb Lockee, Torrey Trust and Aaron Bond. 2020. The Difference Between Emergency Remote Teaching and Online Learning. Available online at <u>https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teachingand-online-learning%C2%A0%C2%A0</u>

- Jaume, D. and Willén, A. 2019. The Long-Run Effects of Teacher Strikes: Evidence from Argentina," Journal of Labor Economics 37, no. 4: 1097-1139. <u>https://doi.org/10.1086/703134</u>

- Jerrim, John, Luis Alejandro Lopez-Agudo, Oscar D. Marcenaro-Gutierrez, and Nikki Shure. 2017. "What happens when econometrics and psychometrics collide? An example using the PISA data," Economics of Education Review, Elsevier, vol. 61, pages 51-58.

Jones, S. 2017. Do schools in low income countries really produce so little learning?

- Kaffenberger, M. 2020. Modeling the Long-Run Learning Impact of the Covid-19 Learning Shock: Actions to (More Than) Mitigate Loss. RISE Insight Series. 2020/017. https://doi.org/10.35489/BSG-RISE-RI_2020/017

36
the different actors, the World Food Program (WFP) assumed responsibility for all school nutrition programs (canteens and take home rations) in the Sahel region. Our study covers the region served by the WFP, and all new 46 new schools which were first opened in the academic 20052006. As described in Figure 1, the experiment consisted in randomly assigning these schools to three groups (school canteens, take home rations and control group) after a baseline survey in June 2006. The program was implemented in the following academic year (i.e. 2006-2007) and a follow up survey was fielded in June 2007 at the end of that academic year<sup>3</sup> .

In the two intervention groups, two different programs were implemented: school meals and THR. Under the school meals intervention, lunch was served each school day. The only requirement to have access to the meal is that the pupil be present. Both boys and girls were eligible for the school meals intervention. The THR stipulated that each month, each girl would receive 10 kg of cereal flour, conditional on a 90 percent attendance rate. Attendance records were maintained by the school administration, according to the standard policies applied by the Ministry of Education. In both cases, WFP has developed a quarterly delivery schedule, and the food staples were stored within the school. In keeping with local policy, boys were not eligible for the THR program. The teachers oversaw the administration of the program in collaboration with a representative of WFP. WFP has not reported any issues of concern with the program administration. However, because we did not run random checks on the program administration we cannot completely rule out problems that the WFP itself would not have known about.

We surveyed a random sample of 48 households around each school, making a total of 2208 households, having a total of about 4140 school age children (i.e. aged between 6 and 15). We collected information on household backgrounds, household wealth, school participation for all children, and anthropometric data. In both rounds of the survey, all school age children were asked to solve simple mathematical operations (addition, subtraction, multiplication and division). In the follow up round we administered formal cognitive tests, including the Raven’s Colored Progressive Matrices tests and forward and backward digit span tests in order to measure the program impact on cognitive development and short term memory. In addition hemoglobin levels were taken for

> 3 The trial was originally schedule to last two years but the implementers were reluctant to continue the random assignment into the second year.

8
diversify into new product lines which would shift the demand for labor. Or some operations may spin off from expanding large firms, and the renewed vigor of the spin-offs may shift the demand for labor.<sup>3 8</sup> Some "resident" workers as well as some migrants may decide to go into business for themselves which also shifts demand. Those are some of the dynamics of agglomeration [see box "How Innovation turns 'Growth' into Development" in Appendix 1 for examples of the dynamics of development to which in-migration can be a contributing factor].

On the sending side, there may also be medium-term dynamic effects. Some firms might collapse in the face of rising costs or the loss of key personnel to the North which, in turn, may have a knock-on effect on the viability of related firms (suppliers or customers). In fact, some migrants may have themselves been proprietors of micro-businesses. These effects would shift the labor demand downward in the sending country, part of the dynamics of disagglomeration or ghettoization. Remittances would have an opposing multiplier effect to induce an increase in labor demand (at least where the money is spent). But that demand would collapse if workers returned to take remittance-funded jobs unless the remittances were used in some Jacobs' ladder manner to create jobs not dependent on remittances. Remittances might also have the negative effect of inspiring and funding the migration of additional family members. In the absence of positive developmental changes, one would expect remittances to only mitigate and not reverse the downward shift.

<!-- Start of picture text --> W8nch W'nch~~~~lnc< //prcD<br>/ I R W , + \ f S ,,,,~~~~~.,,.,,,,,,,,,,,_,..... .. W I poor<br>Wpoor<br>Migrant Wave| .<br>Lrich 4 + Lpoor<br><!-- End of picture text -->

Figure A5: How North-South Migration Might Worsen the North-South Divide Figure A5 represents three equilibria. When the labor gates are opened, the separate equilibria given by Wrich and **Wpoor** would lead to the first wave of migrants from the poor to the rich country. But the dynamic agglomerative effects of the migration would shift the labor demand curve Drich outward to D'rich in the rich country, and the ghettoizing effect in the poor country would shift the labor demand curve Dpoor downward to **D'Pwr.** This would still leave the wage differential between W'rich and W'poor so there would be a second wave of migrants, and so forth. The size of the shifts is drawn so that the wage in the poor country W'P,,r after the I" wave of migration is still higher than the original wage **Wpo,r.** The ghettoizing effect in the South is shown by the lower level of labor demand at each wage level, i.e., the overall drop in economic

> 38 Nature thrives by having old growth constantly seeding new growth rather than just expanding the old growth.

54
3 through internationally integrated stock markets improves resource allocation and can accelerate the rate of economic growth.

Theoretical disagreement exists, however, about the importance of stock markets for economic growth. Mayer (1988) argues that even large stock markets are unimportant sources of corporate finance. Stiglitz (1985, 1993) says that stock market liquidity will not enhance incentives for acquiring information about firms or exerting corporate governance. Moreover, Devereux and Smith (1994) emphasize that greater risk sharing through internationally integrated stock markets can actually reduce saving rates and slow economic growth. Finally, Shleifer and Summers' (1988) and Morck, Shleifer, and Vishny's (1990a,b) analyses suggest that stock market development can hurt economic growth by easing counterproductive corporate takeovers.

Considering the conflicting theoretical perspectives on the importance of well-functioning stock markets for economic growth, this paper uses cross-country regressions to examine the association between stock market development and economic growth. To conduct this investigation, we need measures of stock market development. Theory, however, does not provide a unique concept or measure of stock market development. Theory suggests that stock market size, liquidity, and integration with world capital markets may affect economic growth. Consequently, this paper uses a conglomerate index of overall stock market development constructed by Demirguc-Kunt and Levine (1996). This index combines information on stock market size, liquidity, and international integration to produce an overall measure of stock market development.

More specifically, we use pooled cross-country, time-series regressions to evaluate the relationship between stock market development and economic growth. Using data on 41 countries over the period 1976-1993, we split the sample period, so that each country has two observations (data permitting) with data averaged over each sub-period. In the tradition of recent work [Barro (1991)], we regress the growth rate of Gross Domestic Product (GDP) per capita on a variety of variables designed to control for initial conditions, political stability, investment in human capital, and macroeconomic conditions. We then include the conglomerate index of stock market development. Thus, we evaluate whether there is a relationship between economic growth
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# **Table S4.14.** ITT effects on other ISFM-related practices.

||Ado<br>ur|pted<br>ea|Ado<br>green m|pted<br>anuring|Ado<br>interc|pted<br>ropping|Ado<br>forag|pted<br>e crops|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|T1 (extension)|0.149***|0.091***|0.055**|0.048***|0.099**|0.072***|0.060**|0.053**|
||(0.043)|(0.024)|(0.022)|(0.014)|(0.041)|(0.028)|(0.028)|(0.023)|
|Unadjusted p-value|0.001|0.000|0.015|0.001|0.016|0.009|0.030|0.021|
|Adjusted p-value|0.003|0.000|0.048|0.003|0.052|0.029|0.097|0.068|
|T2 (extension + video)|0.142***|0.093***|0.053***|0.050***|0.094**|0.063**|0.064*|0.040|
||(0.043)|(0.027)|(0.018)|(0.013)|(0.042)|(0.030)|(0.033)|(0.026)|
|Unadjusted p-value|0.001|0.001|0.003|0.000|0.026|0.035|0.053|0.120|
|Adjusted p-value|0.003|0.003|0.010|0.000|0.083|0.111|0.167|0.348|
|Test T1 >= T2 (unadjusted p-value)|0.562|0.470|0.536|0.437|0.539|0.604|0.453|0.694|
|Test T1 >= T2 (adjusted p-value)|0.921|0.858|0.919|0.847|0.922|0.953|0.867|0.981|
|Endline control mean|0.7|40|0.0|16|0.|204|0.|150|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.034|0.321|0.056|0.330|0.011|0.145|0.007|0.118|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|2,382|2,382|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Average marginal effects (AME) of probit models. Additional control variables identical to those listed in notes of Table 2. Bonferroni-adjusted p-values taking into account correlations between outcomes. One-sided equality tests of T1 and T2 are Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

21
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Challenges**

Geography poses the greatest challenge to the development of Mali’s ICT sector. ICT services are highly concentrated in the Bamako area, which has only 10 percent of the country’s population. As a result, most of the country’s physical territory remains without coverage. As of 2007, Bamako had 80 percent of the fixed-line capacity and 75 percent of the users in Mali. Consequently, there is a large disparity in access between urban and rural areas. Out of 11,000 villages only 253 have access to telecommunication services.

In 2007, close to 18 percent of Mali’s population was covered by a GSM signal. However, as much as an additional 70 percent of the population living in areas without GSM coverage could be reached on a commercially viable basis (figure 20). The remaining 12 percent of the population (including much of the north of the country, figure 21) can be reached by a GSM signal only under a subsidy scheme. This result is based on the assumption that 4 percent of local income in each area could be captured as revenues for voice telephony services. In the case of the broadband market, access is more challenging. If universal access for broadband services were a target for Mali’s government, subsidies should be granted to more than 20 percent of the population. Other West African countries—such as Ghana, Benin, Côte d’Ivoire, and Senegal—would require little subsidy to reach universal service, as the market would provide coverage on a commercial basis.

**Figure 20. A large part of Mali’s population can be reached by GSM signal on a commercially viable basis**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient Market Gap Coverage gap<br>Percent of population<br>South Africa Nigeria Ghana Benin Côte d’Ivoire Senegal Sudan Malawi Burkina Faso Cameroon Ethiopia Tanzania Lesotho Mali Chad Niger Madagascar Congo-DRC<br><!-- End of picture text --> Source: Mayer and others 2008. Existing Access represents the percentage of the population currently covered by voice infrastructure as of 3rd quarter 2006. Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets. Coverage gap represents the coverage gap—the percentage of the population for whom services are not viable without subsidy.

37
Does not anyone to borrow money from in case of emergency Can borrow money from at least one or multiple persons in case of emergency

|tiple persons in case of emergency|||||0.422*<br>(0.255)|
|---|---|---|---|---|---|
|Did not receive remittances in the past year||||||
|Received remittances in the past year|||||0.901***<br>(0.300)|
|Constant|0.807***|2.404|1.055***|2.079|1.510|
||(0.117)|(1.649)|(0.254)|(1.742)|(1.844)|
|Observations|393|393|393|393|393|
|Prob > F|0.00975|0.0242|0.0103|0.0212|0.00115|

# **Appendix 10 – Risk perceptions and investment behaviors**

_Table 33: Flood risk perception by (1) flood exposure and location (low or high elevation), (2) flood exposure, location and expenditure. Dependent variable: very unlikely, unlikely, indifferent (0) VS likely, very likely (1)._

||(1)|(2)|
|---|---|---|
|Affected: No|||
|Affected: Yes|2.444***|2.469***|
||(0.233)|(0.238)|
|Location: High Elevation|||
|Location: Low Elevation|0.155<br>(0.330)|0.101<br>(0.336)|
|Income||-3.77e-05|
|||(2.51e-05)|
|Constant|-1.717***|-1.466***|
||(0.337)|(0.374)|

_Table 34: Propensity to invest in housing by (1) actual flood exposure in 2015, (2) location, (3) flood exposure and expenditure, (4) flood exposure, expenditure and flood risk perception, (5) flood exposure, expenditures and tenure arrangement._

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|Unaffected||||||
|Affected|1.106***||1.122***|0.747***|1.091***|
||(0.209)||(0.209)|(0.245)|(0.207)|
|High elevation||||||
|Low elevation||0.0367||||

58